﻿FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Cervantes, J
AF Cervantes, Jorge
TI An Introductory Module of Generative Artificial Intelligence in Medical
   Education
SO MEDICAL SCIENCE EDUCATOR
LA English
DT Article; Early Access
DE Generative artificial intelligence; Medical education; GPT
AB The lack of training in generative AI (GenAI) among most medical educators poses an important challenge. Recognizing this need, an introductory session was created. A pre- and post-survey was distributed to gain insights on the impact of the module and identify concerns about GenAI in medical education. Scores for each statement were higher upon completion of the module session. Feedback showed that the module was well-received and reflected an openness to discuss the benefits and challenges of GenAI. We need to help educators and learners how to use GenAI tools judiciously and understand their benefits and limitations.
C1 [Cervantes, Jorge] Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Dept Med Educ, Ft Lauderdale, FL 33328 USA.
   [Cervantes, Jorge] Nova Southeastern Univ, Dr Kiran C Patel Coll Osteopath Med, Ft Lauderdale, FL 33328 USA.
C3 Nova Southeastern University; Nova Southeastern University
RP Cervantes, J (corresponding author), Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Dept Med Educ, Ft Lauderdale, FL 33328 USA.; Cervantes, J (corresponding author), Nova Southeastern Univ, Dr Kiran C Patel Coll Osteopath Med, Ft Lauderdale, FL 33328 USA.
EM jcervan1@nova.edu
CR Alam F, 2023, FRONT MED-LAUSANNE, V10, DOI 10.3389/fmed.2023.1279707
   Biewer Amanda M, 2024, PLOS Glob Public Health, V4, pe0002031, DOI 10.1371/journal.pgph.0002031
   Boscardin CK, 2024, ACAD MED, V99, P22, DOI 10.1097/ACM.0000000000005439
   Cabral S, 2024, JAMA INTERN MED, V184, P581, DOI 10.1001/jamainternmed.2024.0295
   Cervantes J, 2024, J INVEST MED, V72, P633, DOI 10.1177/10815589241257215
   Cooper A, 2023, NEW ENGL J MED, V389, P385, DOI 10.1056/NEJMp2304993
   Cuff PA, 2023, ART INT HLTH PROF ED
   Emsley R, 2023, SCHIZOPHRENIA-UK, V9, DOI 10.1038/s41537-023-00379-4
   Gordon M, 2024, MED TEACH, V46, P446, DOI 10.1080/0142159X.2024.2314198
   Hatem R, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.44720
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Kohane I., 2023, NEJMAI, V1
   Laupichler MC, 2024, ACAD MED, V99, P508, DOI 10.1097/ACM.0000000000005626
   Lee Su-In, 2024, Lancet, V403, P717, DOI 10.1016/S0140-6736(24)00313-1
   Li R, 2023, JAMA INTERN MED, V183, P596, DOI 10.1001/jamainternmed.2023.1835
   Masters K, 2024, MED TEACH, V46, P1175, DOI 10.1080/0142159X.2023.2298756
   McMurtrie B, 2023, The chronicle of higher education
   Patino GA, 2024, ACAD MED, V99, P477, DOI 10.1097/ACM.0000000000005636
   Roberts LW, 2024, ACAD MED, V99, P471, DOI 10.1097/ACM.0000000000005667
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Templin Tara, 2024, PLOS Digit Health, V3, pe0000503, DOI 10.1371/journal.pdig.0000503
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Tierney AA, 2024, NEJM CATAL INNOV CAR, V5, DOI 10.1056/CAT.23.0404
   van de Ridder JMM, 2023, ACAD MED, V98, P867, DOI 10.1097/ACM.0000000000005254
   Van Veen D, 2024, NAT MED, V30, DOI 10.1038/s41591-024-02855-5
NR 26
TC 0
Z9 0
U1 0
U2 0
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2156-8650
J9 MED SCI EDUC
JI Med. Sci. Educ.
PD 2024 NOV 7
PY 2024
DI 10.1007/s40670-024-02218-2
EA NOV 2024
PG 5
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA L5C5D
UT WOS:001350893300002
DA 2024-12-25
ER

PT J
AU Fleischmann, K
AF Fleischmann, Katja
TI The commodification of creativity: Integrating Generative Artificial
   Intelligence in higher education design curriculum
SO INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL
LA English
DT Article; Early Access
DE Generative Artificial Intelligence; design curriculum; design studio
   pedagogy; design creative process; AI integration; undergraduate design
   education
AB The academic questions posed by the introduction of Generative AI (GenAI) into design higher education are many and reflect educators'uneasiness about its ethical use. Like any new radical technology breakthrough, GenAI straddles a pedagogical fence that divides opinion and focuses on a basic question: How can design educators harness Generative AI's power without compromising the fundamentals of design education? This study contributes to foundational research on how to incorporate GenAI into design education without compromising and commodifying the creative process. This research examines design students' experiences using GenAI in the context of studio practice of a second-year visual communication design course in a Bachelor of Design programme. Reflective comments are analysed and used to inform recommendations for a structured pedagogic approach for implementing GenAI as a building block for the design creative process into the curriculum.
C1 [Fleischmann, Katja] Griffith Univ, Queensland Coll Art & Design, 226 Grey St,South Bank, Brisbane, Qld 4101, Australia.
C3 Griffith University
RP Fleischmann, K (corresponding author), Griffith Univ, Queensland Coll Art & Design, 226 Grey St,South Bank, Brisbane, Qld 4101, Australia.
EM k.fleischmann@griffith.edu.au
CR Auernhammer J., 2020, DRS INT C 2020 BRISB
   Bamford A., 2023, Design week
   Bartlett KA, 2024, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2024.02.006
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Botella M, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.02266
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Cain J, 2023, SHE JI, V9, P197, DOI 10.1016/j.sheji.2023.07.002
   Chaudhry Muhammad Ali, 2022, AI Ethics, V2, P157, DOI 10.1007/s43681-021-00074-z
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Davis M., 2023, The Journal of Design Economics and Innovation, V9, P91
   Davis M, 2023, SHE JI, V9, P97, DOI 10.1016/j.sheji.2023.04.003
   de Rooij A, 2021, INT J DES CREAT INNO, V9, P252, DOI 10.1080/21650349.2021.1951358
   Denzin N. K., 2000, Handbook of qualitative research, DOI DOI 10.1177/1474474013487485
   Department of Education Victoria, 2007, A critical reflection framework
   Ellmers G, 2008, J UNIV TEACH LEARN P, V5
   Fathoni A. F. C. A., 2023, E3S WEB C JAK IND
   Figoli F. A., 2022, AI in design idea development: A workshop on creativity and human-AI collaboration DRS2022
   Fleischmann K., 2022, Journal of Design, Business and Society, V8, P247, DOI [https://doi.org/10.1386/dbs000421, DOI 10.1386/DBS000421]
   Fleischmann K., 2023, Design and Technology Education: An International Journal, V28, P135
   Fleischmann K., 2012, agIdeas research: Design for business, V1, P76
   Fleischmann K, 2013, J LEARN DES, V6, P1
   Fraher R, 2011, DES J, V14, P390, DOI 10.2752/175630611X13091688930372
   Guinness H., 2023, How does ChatGPT work?
   HAHN Christopher., 2008, DOING QUALITATIVE RE
   Harbers M., 2022, Towards a living lab for responsible applied AI DRS2022
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   HOMMES Studio, 2023, Interior design artificial intelligence and its amazing uses
   Huang YC, 2023, INT J DES, V17, P1, DOI 10.57698/v17i2.01
   IBM iX, 2023, IBM iX blog. Focus topic: Generative AI
   Kaiko N., 2023, The rise of artificial intelligence in interior design
   Kiger ME, 2020, MED TEACH, V42, P846, DOI 10.1080/0142159X.2020.1755030
   Kolb D. A., 1984, EXPERIENTIAL LEARNIN, DOI DOI 10.1016/B978-0-7506-7223-8.50017-4
   Lincoln Y. S., 1985, NATURALISTIC INQUIRY, DOI 10.1016/0147-1767(85)90062-8
   Matthews B, 2023, INT J ART DES EDUC, V42, P367, DOI 10.1111/jade.12460
   McLain M, 2022, INT J TECHNOL DES ED, V32, P1629, DOI 10.1007/s10798-021-09667-5
   Meron Y., 2022, Graphic design and artificial intelligence: Interdisciplinary challenges for designers in the search for research collaboration DRS2022
   Morrone M., 2024, AXIOS
   Morse JM., 2009, MIXED METHOD DESIGN
   Offenhuber D, 2023, SHE JI, V9, P264, DOI 10.1016/j.sheji.2023.04.005
   Olsson T., 2021, INTERACTIONS, V28, P62, DOI DOI 10.1145/3467479
   Pilling F, 2019, DES J, V22, P1135, DOI 10.1080/14606925.2019.1594979
   Punch K.F., 2009, INTRO RES METHODS ED
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   SCHON Donald A., 2000, Educando o profissional reflexivo: um novo design para o ensino e a aprendizagem
   Shreeve A., 2011, RES DES ED 1 UBT S D
   Shulman LS, 2005, DAEDALUS-US, V134, P52, DOI 10.1162/0011526054622015
   Solly M., 2019, Smithsonian magazine
   Tang TR, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.767295
   Taylor J., 2023, The Guardian
   Thody A., 2006, WRITING PRESENTING R
   Weingarten E, 2020, SHE JI, V6, P301, DOI 10.1016/j.sheji.2020.07.004
   Wernersson J., 2023, Exploring the potential impact of AI on the role of graphic content creators: Benefits, challenges, and collaborative opportunities
   Yang Q., 2020, Medium
NR 53
TC 0
Z9 0
U1 13
U2 13
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1470-3297
EI 1470-3300
J9 INNOV EDUC TEACH INT
JI Innov. Educ. Teach. Int.
PD 2024 NOV 10
PY 2024
DI 10.1080/14703297.2024.2427039
EA NOV 2024
PG 15
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA L4Y3K
UT WOS:001350783900001
DA 2024-12-25
ER

PT J
AU Piller, FT
   Srour, M
   Marion, TJ
AF Piller, Frank T.
   Srour, Mahdi
   Marion, Tucker J.
TI Generative AI, Innovation, and Trust
SO JOURNAL OF APPLIED BEHAVIORAL SCIENCE
LA English
DT Article; Early Access
DE generative AI; trust; innovation management; organizational capabilities
AB Our paper explores the integration of generative artificial intelligence (GenAI) into organizations' innovation and new product development processes, focusing on when and how to trust AI-generated outcomes in this context. We propose a framework to assess the level of trust required based on task-specific needs and the distinction between general and expert AI models. While inaccuracies in GenAI outputs can foster creativity during ideation, higher accuracy, and trust are essential for tasks requiring domain-specific expertise. The paper concludes by discussing the necessary human capabilities and organizational strategies for effectively deploying GenAI in innovation management.
C1 [Piller, Frank T.] Rhein Westfal TH Aachen, Sch Business & Econ, Templergraben 55, D-52056 Aachen, Germany.
   [Srour, Mahdi; Marion, Tucker J.] Northeastern Univ, Amore McKim Sch Business, Entrepreneurship & Innovat Grp, Boston, MA USA.
C3 RWTH Aachen University; Northeastern University
RP Piller, FT (corresponding author), Rhein Westfal TH Aachen, Sch Business & Econ, Templergraben 55, D-52056 Aachen, Germany.
EM piller@time.rwth-aachen.de
RI Piller, Frank/AAN-3276-2020
OI Piller, Frank/0000-0003-2532-4020
FU National Science Foundation [2050052]; Deutsche Forschungsgemeinschaft
   (DFG) [390621612]
FX The authors disclosed receipt of the following financial support for the
   research and/or authorship of this article: This paper is based on
   research supported by a National Science Foundation grant (#2050052) and
   a grant by Deutsche Forschungsgemeinschaft (DFG) (#390621612).
CR Acar OA, 2023, J MANAGE, DOI 10.1177/01492063231212416
   Bilgram Volker, 2023, IEEE Engineering Management Review, P18, DOI 10.1109/EMR.2023.3272799
   Bouschery S. G., 2024, Artificial Intelligence-augmented brainstorming: how humans and AI beat humans alone
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   Chen WZ, 2024, Arxiv, DOI arXiv:2407.07061
   Clark S., 2023, The Era of AI: End of Year AI Recap
   Cooper RG, 2024, RES TECHNOL MANAGE, V67, P44, DOI 10.1080/08956308.2024.2324241
   Davenport TH, 2023, HARVARD BUS REV, V101, P98
   Fuller J., 2024, Add correct reference to Fuller article in the same JABS issue
   Gama F, 2023, J PROD INNOVAT MANAG, DOI 10.1111/jpim.12698
   Guzik E., 2023, Journal of Creativity, V33, P100065, DOI [DOI 10.1016/J.YJOC.2023.100065, https://doi.org/10.1016/j.yjoc.2023.100065]
   Hagendorff T, 2024, Arxiv, DOI arXiv:2303.13988
   Igna I, 2023, RES POLICY, V52, DOI 10.1016/j.respol.2022.104661
   Ji Z., 2023, FINDINGS ASS COMPUTA, P1827
   Kemp A, 2024, ACAD MANAGE REV, V49, P618, DOI 10.5465/amr.2020.0205
   Lebovitz S, 2023, MIT SLOAN MANAGE REV, V64, P27
   Marion T., 2024, MIT Sloan Management Review, V66
   Marion T. J., 2023, The PDMA handbook of innovation and new product development, V4th ed., P425
   Meincke L, 2024, Arxiv, DOI arXiv:2402.01727
   Piller F T., 2023, The PDMA Handbook of Innovation and New Product Development, P407
   Roberts DL, 2024, TECHNOVATION, V136, DOI 10.1016/j.technovation.2024.103081
   Salvador F., 2022, Harvard Business Review Online
   Xi ZH, 2023, Arxiv, DOI arXiv:2309.07864
   Yuan CX, 2022, J MECH DESIGN, V144, DOI 10.1115/1.4052366
NR 24
TC 0
Z9 0
U1 83
U2 83
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0021-8863
EI 1552-6879
J9 J APPL BEHAV SCI
JI J. Appl. Bahav. Sci.
PD 2024 SEP 30
PY 2024
DI 10.1177/00218863241285033
EA SEP 2024
PG 10
WC Behavioral Sciences; Psychology, Applied; Management; Psychology,
   Experimental
WE Social Science Citation Index (SSCI)
SC Behavioral Sciences; Psychology; Business & Economics
GA I0K9Q
UT WOS:001327245800001
DA 2024-12-25
ER

PT J
AU Campbell, M
   Jovanovic, M
AF Campbell, Mark
   Jovanovic, Mladan
TI Disinfecting AI: Mitigating Generative AI's Top Risks
SO COMPUTER
LA English
DT Article
AB Generative artificial intelligence (GenAI) is poised to become a cornerstone of tomorrow's enterprise architecture, driving innovation and efficiency across industries. But, as organizations embrace this technology, they must mitigate key risks to ensure responsible implementation and thwart AI cyberattacks.
C1 [Campbell, Mark] EVOTEK, San Diego, CA 92121 USA.
   [Jovanovic, Mladan] Singidunum Univ, Comp Sci, Belgrade 11000, Serbia.
RP Campbell, M (corresponding author), EVOTEK, San Diego, CA 92121 USA.
EM mark@evotek.com; mjovanovic@singidunum.ac.rs
RI jovanovic, mladjan/KUF-1892-2024
OI Campbell, Mark/0000-0001-5415-6631; Jovanovic,
   Mladjan/0000-0003-2355-9424
CR AmankwahAmoah J., 2024, INT J INFORM MANAGE, V79, DOI [10.1016/j.ijinfomgt.2024.102759, DOI 10.1016/J.IJINFOMGT.2024.102759]
   [Anonymous], 2023, Patronus AI launches EnterprisePII, the industry's first LLM dataset for detecting business-sensitive information
   Brackney N., 2023, CIO
   Campbell M, 2024, COMPUTER, V57, P116, DOI 10.1109/MC.2023.3339350
   Campbell M, 2021, COMPUTER, V54, P89, DOI 10.1109/MC.2021.3083155
   Carlini N., 2020, P USENIX SEC S, P1
   Clementelli C., 2024, Interviewee to Product Marketing Director
   gartner, Data loss protection
   github, Open LLMs
   Goda V., AI Security: Not your usual security lens
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   htg, 5 consequences of data loss and how to avoid them
   Jiang Zhengbao, 2023, P 2023 C EMPIRICAL M, P7969
   Kannappan A., 2024, Interviewee to Cofounder & CEO
   Lewis P, 2020, ADV NEUR IN, V33
   Lynch S., 2024, Davos 2024: Six takeaways on the AI conversation at WEF
   Maher S., 2023, ForbesOct. 31,
   Marshall M., How enterprises are using open source LLMs: 16 examples
   Ohayon R., 2024, Interviewees to CEO & CTO
   Ovadia O, 2024, Arxiv, DOI arXiv:2312.05934
   Patronus AI, Patronus AI launches out of stealth to help enterprises deploy large language models safely
   Vartan S., 2019, Sci. Amer.Oct. 24,
   Weisz J. D., 2023, P ACM IUI WORKSH SYD, P1
   Wolff J., 2020, How to improve cybersecurity for artificial intelligence
NR 24
TC 0
Z9 0
U1 26
U2 28
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD MAY
PY 2024
VL 57
IS 5
BP 111
EP 116
DI 10.1109/MC.2024.3374433
PG 6
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SL4K7
UT WOS:001234593400016
OA Bronze
DA 2024-12-25
ER

PT J
AU Wang, PC
   Yue, YJ
   Ouyang, MK
   Yin, LP
   Yin, YL
   Li, B
AF Wang, Pengcheng
   Yue, Yongjie
   Ouyang, Mingkun
   Yin, Lipeng
   Yin, Yulong
   Li, Biao
TI Do Narcissistic People Exhibit More Authentic Self-Disclosure to
   Generative AI? The Roles of Short-Form Video Addiction, Loneliness, and
   Usage Intention
SO SOCIAL SCIENCE COMPUTER REVIEW
LA English
DT Article; Early Access
DE narcissism; authentic self-disclosure; generative artificial
   intelligence (GenAI); short-form video addiction; loneliness; intention
   to use GenAI
ID AFFECTIVE PROCESSING SYSTEM; PERSONALITY; INTERNET; TECHNOLOGY;
   ADMIRATION; RIVALRY; MODEL
AB The widespread application of generative artificial intelligence (GenAI) technology has innovated human-AI interactions, making authentic self-disclosure to machines an emerging trend. Drawing on the cognitive-affective personality system theory, this study examined how narcissism, short-form video addiction, and loneliness contribute to the authentic self-disclosure to GenAI, as well as the moderating role of intention to use GenAI. The mediation and moderation analyses of data were collected from 524 college students (357 females, Mage = 21.25) in China. The results indicated that narcissism was positively associated with authentic self-disclosure to GenAI, and short-form video addiction and loneliness sequentially mediated this connection. Intention to use GenAI enhanced the positive association between loneliness and authentic self-disclosure to GenAI. The significance and limitations of the findings were discussed.
C1 [Wang, Pengcheng] Shanghai Jiao Tong Univ, Sch Media & Commun, Shanghai, Peoples R China.
   [Yue, Yongjie] Tsinghua Univ, Sch Journalism & Commun, 30 Shuangqing Rd, Beijing 100084, Peoples R China.
   [Ouyang, Mingkun] Guangxi Minzu Univ, Sch Educ Sci, Nanning, Peoples R China.
   [Yin, Lipeng] Univ Hong Kong, Dept Psychol, Pokfulam, Hong Kong, Peoples R China.
   [Yin, Yulong] Northwest Normal Univ, Sch Psychol, Lanzhou, Peoples R China.
   [Li, Biao] Renmin Univ China, Dept Commun, Beijing, Peoples R China.
C3 Shanghai Jiao Tong University; Tsinghua University; Guangxi Minzu
   University; University of Hong Kong; Northwest Normal University -
   China; Renmin University of China
RP Yue, YJ (corresponding author), Tsinghua Univ, Sch Journalism & Commun, 30 Shuangqing Rd, Beijing 100084, Peoples R China.
EM yueyj2020@163.com
FU Humanities and Social Science Fund of Ministry of Education of China
   [23YJCZH220]; Shanghai Jiao Tong University Humanities Youth Talent
   Cultivation Program [2023QN016]
FX The authors disclosed receipt of the following financial support for the
   research, authorship, and/or publication of this article: Humanities and
   Social Science Fund of Ministry of Education of China (23YJCZH220) and
   Shanghai Jiao Tong University Humanities Youth Talent Cultivation
   Program (2023QN016).
CR Alloway T., 2020, Capitalism and the Enchanted Screen: Myths and Allegories in the Digital Age, V27
   American Psychiatric Association, 2013, DIAGN STAT MAN MENT, DOI DOI 10.1176/APPI.BOOKS.9780890425596
   Amoozadeh M, 2024, Arxiv, DOI [arXiv:2310.04631, 10.48550/arxiv.2310.04631, DOI 10.48550/ARXIV.2310.04631]
   Ampong GOA, 2018, BEHAV SCI-BASEL, V8, DOI 10.3390/bs8060058
   Asad K., 2022, International Journal of Media and Information Literacy, V7, P293, DOI [10.13187/ijmil.2022.2.293, DOI 10.13187/IJMIL.2022.2.293]
   Back MD, 2013, J PERS SOC PSYCHOL, V105, P1013, DOI 10.1037/a0034431
   Barak M, 2018, COMPUT EDUC, V121, P115, DOI 10.1016/j.compedu.2018.01.016
   Barfield JK, 2021, IEEE ROMAN, P67, DOI 10.1109/RO-MAN50785.2021.9515477
   Batool F., 2018, Pakistan Journal of Psychological Research, V33, P35
   Brandtzaeg PB, 2022, HUM COMMUN RES, V48, P404, DOI 10.1093/hcr/hqac008
   Burnay J, 2015, COMPUT HUM BEHAV, V43, P28, DOI 10.1016/j.chb.2014.10.039
   Cao XP, 2022, J HOSP TOUR MANAG, V50, P178, DOI 10.1016/j.jhtm.2022.02.013
   Casale S., 2020, Narcissism and problematic social media use: A systematic literature review. Addictive Behaviors Reports, V11, DOI [10.1016/j.abrep.2020.100252, DOI 10.1016/J.ABREP.2020.100252]
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chen L, 2022, INTERNET RES, V32, P1357, DOI 10.1108/INTR-03-2021-0194
   Choudhury A, 2023, J MED INTERNET RES, V25, DOI 10.2196/47184
   Cohen J., 1975, APPL MULTIPLE REGRES
   Cohen R, 2018, COMPUT HUM BEHAV, V79, P68, DOI 10.1016/j.chb.2017.10.027
   Coutts JJ, 2023, BEHAV RES METHODS, V55, P3772, DOI 10.3758/s13428-022-01988-0
   Dang JN, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2020.106612
   DAVIS MH, 1989, J APPL SOC PSYCHOL, V19, P1100, DOI 10.1111/j.1559-1816.1989.tb01242.x
   Deci E. L., 2012, The Oxford Handbook of Human Motivation, DOI [DOI 10.1093/OXFORDHB/9780195399820.013.0006, 10.1093/oxfordhb/9780195399820.013.0006]
   Drouin M, 2022, COMPUT HUM BEHAV, V128, DOI 10.1016/j.chb.2021.107100
   Eaton NR, 2009, J RES PERS, V43, P208, DOI 10.1016/j.jrp.2009.01.016
   EMMONS RA, 1984, J PERS ASSESS, V48, P291, DOI 10.1207/s15327752jpa4803_11
   Emon M. M. H., 2023, AIUB Journal of Science and Engineering (AJSE), V22, P189, DOI [10.53799/ajse.v22i2.797, DOI 10.53799/AJSE.V22I2.797]
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Erlich H S, 1998, Am J Psychoanal, V58, P135, DOI 10.1023/A:1022160332189
   Eyssel F, 2017, IEEE ROMAN, P922
   Fabris MA, 2020, ADDICT BEHAV, V106, DOI 10.1016/j.addbeh.2020.106364
   Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
   Fu Q, 2022, COMPUT HUM BEHAV, V130, DOI 10.1016/j.chb.2021.107154
   Gasiorowska W, 2021, PERS INDIV DIFFER, V181, DOI 10.1016/j.paid.2021.111002
   Goffman E., 1959, PRESENTATION SELF EV, VAnchor Books ed.
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Grapsas S, 2020, PERSPECT PSYCHOL SCI, V15, P150, DOI 10.1177/1745691619873350
   Grieve R, 2020, COMPUT HUM BEHAV, V102, P144, DOI 10.1016/j.chb.2019.08.020
   Guzman AL, 2020, NEW MEDIA SOC, V22, P70, DOI 10.1177/1461444819858691
   Hancock G.R., 2018, Routledge eBooks, DOI DOI 10.4324/9781315755649
   Hayes A. F., 2017, A regression-based approach, V2nd ed.
   Ho A, 2018, J COMMUN, V68, P712, DOI 10.1093/joc/jqy026
   Hu B, 2023, COMPUT HUM BEHAV, V145, DOI 10.1016/j.chb.2023.107760
   Hughes ME, 2004, RES AGING, V26, P655, DOI 10.1177/0164027504268574
   Jonason PK, 2010, PSYCHOL ASSESSMENT, V22, P420, DOI 10.1037/a0019265
   Kealy D, 2022, SCAND J PSYCHOL, V63, P32, DOI 10.1111/sjop.12773
   Kim J, 2011, CYBERPSYCH BEH SOC N, V14, P359, DOI 10.1089/cyber.2010.0374
   Kim TW, 2022, J SERV RES-US, V25, P649, DOI 10.1177/10946705221120232
   Kim T, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642937
   Kouroupa A, 2022, PLOS ONE, V17, DOI 10.1371/journal.pone.0269800
   Kraut R, 1998, AM PSYCHOL, V53, P1017, DOI 10.1037/0003-066X.53.9.1017
   Lange J, 2016, EUR J PERSONALITY, V30, P168, DOI 10.1002/per.2047
   Lee H, 2021, JAMA-J AM MED ASSOC, V326, P1045, DOI 10.1001/jama.2021.14075
   Lee J, 2023, TELEMAT INFORM, V85, DOI 10.1016/j.tele.2023.102052
   Lee J, 2024, INT J HUM-COMPUT INT, V40, P1620, DOI 10.1080/10447318.2022.2146227
   Lee YC, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376175
   Liu WZ, 2023, COMMUN RES REP, V40, P122, DOI 10.1080/08824096.2023.2212899
   Lu LH, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.893599
   MacDonald KB, 2023, CURR RES BEHAV SCI, V5, DOI 10.1016/j.crbeha.2023.100127
   Maloney D., 2020, Proceedings of the ACM on Human-Computer Interaction, V4, P1, DOI DOI 10.1145/3415246
   Maples Bethanie, 2024, Npj Ment Health Res, V3, P4, DOI 10.1038/s44184-023-00047-6
   McKenna KYA, 2002, J SOC ISSUES, V58, P9, DOI 10.1111/1540-4560.00246
   Mckinsey, 2023, As organizations rapidly deploy generative AI tools, survey respondents expect significant effects on their industries and workforces
   Meng KS, 2021, TELECOMMUN POLICY, V45, DOI 10.1016/j.telpol.2021.102172
   Meyrowitz J., 1986, No sense of place: The impact of electronic media on social behavior
   MISCHEL W, 1995, PSYCHOL REV, V102, P246, DOI 10.1037/0033-295X.102.2.246
   Moon JW, 2001, INFORM MANAGE-AMSTER, V38, P217, DOI 10.1016/S0378-7206(00)00061-6
   Mu HL, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph192114173
   Nielsen YA, 2022, CURR OPIN PSYCHOL, V43, P260, DOI 10.1016/j.copsyc.2021.08.004
   Peng Chen，, 2022, International Journal of Contents, V18, P27
   Peplau L.A., 1979, Love and Attraction, P101, DOI [DOI 10.1016/B978-0-08-022234-9.50020-0, 10.1016/B978-0-08-022234-9.50020-0]
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pham KT, 2022, PSYCHIAT QUART, V93, P249, DOI 10.1007/s11126-022-09973-8
   Piko BF, 2022, J NERV MENT DIS, V210, P818, DOI 10.1097/NMD.0000000000001563
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Prendergast CN, 2019, SCAND J PSYCHOL, V60, P492, DOI 10.1111/sjop.12569
   Punyatoya P, 2019, MARK INTELL PLAN, V37, P80, DOI 10.1108/MIP-02-2018-0058
   RASKIN R, 1988, J PERS SOC PSYCHOL, V54, P890, DOI 10.1037/0022-3514.54.5.890
   Rogier G, 2021, J GAMBL ISSUES, V47, P108, DOI 10.4309/jgi.2021.47.4
   Ryan RM, 2000, AM PSYCHOL, V55, P68, DOI 10.1037/0003-066X.55.1.68
   Sanecka E., 2021, The New Educational Review, V66, P198, DOI [10.15804/tner.21.66.4.16, DOI 10.15804/TNER.21.66.4.16]
   SAS, 2024, Global Market Research: China leads world in GenAI usage while US leads in full implementation
   Schlosser AE, 2020, CURR OPIN PSYCHOL, V31, P1, DOI 10.1016/j.copsyc.2019.06.025
   Schneider S, 2022, EDUC PSYCHOL REV, V34, P1, DOI 10.1007/s10648-021-09626-5
   Shoda Y, 2004, BEHAV THER, V35, P147, DOI 10.1016/S0005-7894(04)80009-1
   Skjuve M, 2021, INT J HUM-COMPUT ST, V149, DOI 10.1016/j.ijhcs.2021.102601
   Sultan AJ, 2023, J ECONOM ADM SCI, V39, P382, DOI 10.1108/JEAS-11-2020-0197
   Ta V, 2020, J MED INTERNET RES, V22, DOI 10.2196/16235
   Turja T, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103220
   Twenge JM, 2008, J PERS, V76, P919, DOI 10.1111/j.1467-6494.2008.00509.x
   Uchida T, 2017, IEEE ROMAN, P207, DOI 10.1109/ROMAN.2017.8172303
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Wang HX, 2022, J RES ADOLESCENCE, V32, P1580, DOI 10.1111/jora.12744
   Wang L, 2017, INT J INFORM MANAGE, V37, P1428, DOI 10.1016/j.ijinfomgt.2016.10.006
   Wang PC, 2019, CURR PSYCHOL, V38, P1512, DOI 10.1007/s12144-017-9711-8
   Wessel JL, 2017, J SOC ISSUES, V73, P240, DOI 10.1111/josi.12214
   WINK P, 1991, J PERS SOC PSYCHOL, V61, P590
   Young K.S., 1998, CYBERPSYCHOL BEHAV, V1, P237, DOI [10.1089/cpb.1998.1.237, DOI 10.1089/CPB.1998.1.237]
   Zhang X, 2019, TELEMAT INFORM, V42, DOI 10.1016/j.tele.2019.101243
   Zheng YH, 2023, INT J HOSP MANAG, V113, DOI 10.1016/j.ijhm.2023.103509
   Zhou T, 2024, UNIVERSAL ACCESS INF, DOI 10.1007/s10209-024-01130-1
NR 100
TC 0
Z9 0
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0894-4393
EI 1552-8286
J9 SOC SCI COMPUT REV
JI Soc. Sci. Comput. Rev.
PD 2024 DEC 18
PY 2024
DI 10.1177/08944393241308511
EA DEC 2024
PG 22
WC Computer Science, Interdisciplinary Applications; Information Science &
   Library Science; Social Sciences, Interdisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science; Social Sciences
   - Other Topics
GA P7E9Z
UT WOS:001379507200001
DA 2024-12-25
ER

PT J
AU Elyoseph, Z
   Gur, T
   Haber, Y
   Simon, T
   Angert, T
   Navon, Y
   Tal, A
   Asman, O
AF Elyoseph, Zohar
   Gur, Tamar
   Haber, Yuval
   Simon, Tomer
   Angert, Tal
   Navon, Yuval
   Tal, Amir
   Asman, Oren
TI An Ethical Perspective on the Democratization of Mental Health With
   Generative AI
SO JMIR MENTAL HEALTH
LA English
DT Article
DE ethics; generative artificial intelligence; generative AI; mental
   health; ChatGPT; large language model; LLM; digital mental health;
   machine learning; AI; technology; accessibility; knowledge; GenA
ID ACCESS; CARE
AB Knowledge has become more open and accessible to a large audience with the "democratization of information" facilitated by technology. This paper provides a sociohistorical perspective for the theme issue "Responsible Design, Integration, and Use of Generative AI in Mental Health." It evaluates ethical considerations in using generative artificial intelligence (GenAI) for the democratization of mental health knowledge and practice. It explores the historical context of democratizing information, transitioning from restricted access to widespread availability due to the internet, open-source movements, and most recently, GenAI technologies such as large language models. The paper highlights why GenAI technologies represent a new phase in the democratization movement, offering unparalleled access to highly advanced technology as well as information. In the realm of mental health, this requires delicate and nuanced ethical deliberation. Including GenAI in mental health may allow, among other things, improved accessibility to mental health care, personalized responses, and conceptual flexibility, and could facilitate a flattening of traditional hierarchies between health care providers and patients. At the same time, it also entails significant risks and challenges that must be carefully addressed. To navigate these complexities, the paper proposes a strategic questionnaire for assessing artificial intelligence-based mental health applications. This tool evaluates both the benefits and the risks, emphasizing the need for a balanced and ethical approach to GenAI integration in mental health. The paper calls for a cautious yet positive approach to GenAI in mental health, advocating for the active engagement of mental health professionals in guiding GenAI development. It emphasizes the importance of ensuring that GenAI advancements are not only technologically sound but also ethically grounded and patient-centered.
C1 [Elyoseph, Zohar] Imperial Coll, Fac Med, Dept Brain Sci, Fulham Palace Rd, London W6 8RF, England.
   [Elyoseph, Zohar] Univ Haifa, Fac Educ, Haifa, Israel.
   [Gur, Tamar] Reichman Univ, Adelson Sch Entrepreneurship, Herzliyya, Israel.
   [Haber, Yuval] Bar Ilan Univ, PhD Program Hermeneut & Cultural Studies, Ramat Gan, Israel.
   [Simon, Tomer] Microsoft Israel R&D Ctr, Tel Aviv, Israel.
   [Angert, Tal; Navon, Yuval] Tel Aviv Univ, Sagol Sch Neurosci, Tel Aviv, Israel.
   [Tal, Amir; Asman, Oren] Tel Aviv Univ, Fac Med & Hlth Sci, Samueli Initiat Responsible AI Med, Tel Aviv, Israel.
   [Asman, Oren] Tel Aviv Univ, Fac Med & Hlth Sci, Dept Nursing, Tel Aviv, Israel.
C3 Imperial College London; University of Haifa; Reichman University; Bar
   Ilan University; Microsoft; Tel Aviv University; Tel Aviv University;
   Tel Aviv University
RP Elyoseph, Z (corresponding author), Imperial Coll, Fac Med, Dept Brain Sci, Fulham Palace Rd, London W6 8RF, England.
EM zohar.j.a@gmail.com
RI ; Asman, Oren/M-2318-2017
OI Tal, PhD, Amir/0000-0002-6099-8904; Haber, Yuval/0000-0003-4933-2113;
   Navon, Yuval/0009-0005-3631-759X; Elyoseph, Zohar/0000-0002-5717-4074;
   Asman, Oren/0000-0003-2439-6997
CR Adhikary PK, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/57306
   [Anonymous], 2018, The burden of mental disorders in the region of the Americas
   [Anonymous], 2013, Mental health action plan 2013-2020
   Araya R, 2018, J AFFECT DISORDERS, V234, P80, DOI 10.1016/j.jad.2018.02.092
   Asman O, 2023, AM J BIOETHICS, V23, P62, DOI 10.1080/15265161.2023.2191046
   Byrow Y, 2020, CLIN PSYCHOL REV, V75, DOI 10.1016/j.cpr.2019.101812
   Castelvecchi D, 2016, NATURE, V538, P21, DOI [10.1038/nature.2016.20491, 10.1038/538020a]
   Coghlan S, 2023, DIGIT HEALTH, V9, DOI 10.1177/20552076231183542
   Cohen IG, 2023, AM J BIOETHICS, V23, P8, DOI 10.1080/15265161.2023.2233357
   Cummings JR, 2017, JAMA PSYCHIAT, V74, P476, DOI 10.1001/jamapsychiatry.2017.0303
   Elyoseph Z, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/53043
   Elyoseph Z, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/54369
   Elyoseph Z, 2024, AM J BIOETHICS, V24, P57, DOI 10.1080/15265161.2023.2278546
   Elyoseph Z, 2024, FAM MED COMMUNITY HE, V12, DOI 10.1136/fmch-2023-002583
   Elyoseph Z, 2023, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1213141
   Elyoseph Z, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1199058
   Ferrario A, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/56569
   Fiske A, 2019, J MED INTERNET RES, V21, DOI 10.2196/13216
   Freeman R, 2023, The NationalLaw Review
   Graham S, 2019, CURR PSYCHIAT REP, V21, DOI 10.1007/s11920-019-1094-0
   Grodniewicz JP, 2023, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1190084
   Haber Y, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/54781
   Hadar-Shoval D, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/55988
   Hadar-Shoval D, 2023, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1234397
   Hartford A, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/53203
   Hatem R, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.44720
   Hodgkinson Stacy, 2017, Pediatrics, V139, DOI 10.1542/peds.2015-1175
   Laestadius L, 2024, NEW MEDIA SOC, V26, P5923, DOI 10.1177/14614448221142007
   Levkovich I, 2023, FAM MED COMMUNITY HE, V11, DOI 10.1136/fmch-2023-002391
   Levkovich I, 2023, JMIR MENT HEALTH, V10, DOI 10.2196/51232
   Munn N, 2023, AI SOC, V38, P1501, DOI 10.1007/s00146-021-01276-z
   Murugesan San, 2007, IT Professional, V9, P34, DOI 10.1109/MITP.2007.78
   Ohtani A, 2015, PSYCHIAT SERV, V66, P798, DOI 10.1176/appi.ps.201400351
   Pilecki Brian, 2015, Psychodyn Psychiatry, V43, P463, DOI 10.1521/pdps.2015.43.3.463
   Reed B, 2015, PHILOS REV, V124, P159, DOI 10.1215/00318108-2812701
   Rubin M, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/56529
   Tal A, 2023, AM J BIOETHICS, V23, P74, DOI 10.1080/15265161.2023.2250297
   Tavory T, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/58493
   Timmons AC, 2023, PERSPECT PSYCHOL SCI, V18, P1062, DOI 10.1177/17456916221134490
   Van Heerden AC, 2023, JAMA PSYCHIAT, V80, P662, DOI 10.1001/jamapsychiatry.2023.1253
   Vigo DV, 2019, LANCET PUBLIC HEALTH, V4, pE89, DOI 10.1016/S2468-2667(18)30203-2
   von Eschenbach WJ., 2021, PHILOS TECHNOLOGY, V34, P1607, DOI [DOI 10.1007/S13347-021-00477-0, 10.1007/S13347-021-00477-0]
   Wallace DP, 2005, REF USER SERV Q, V45, P100
   Whiteford HA, 2013, LANCET, V382, P1575, DOI 10.1016/S0140-6736(13)61611-6
   Zajko M, 2022, SOCIOL COMPASS, V16, DOI 10.1111/soc4.12962
NR 45
TC 1
Z9 1
U1 8
U2 8
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 2368-7959
J9 JMIR MENT HEALTH
JI JMIR Ment. Health
PY 2024
VL 11
AR e58011
DI 10.2196/58011
PG 9
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Psychiatry
GA L0P4O
UT WOS:001347826900001
PM 39417792
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Diebold, P
AF Diebold, Philipp
TI From Backlogs to Bots: Generative AI's Impact on Agile Role Evolution
SO JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS
LA English
DT Article; Early Access
DE agile; agile roles; AI implications; artificial intelligence (AI);
   generative AI; role evolution; scrum
AB This position paper investigates the transformative impact of generative artificial intelligence (GenAI) on Agile roles. Focusing on the product owner, developer, and scrum master, we analyze how GenAI redefines traditional tasks, encouraging a shift towards more strategic and creative functions. Through practical experience, we illustrate AI's role in enhancing Agile processes, its practices and emphasize the need for Agile practitioners to integrate AI understanding. These results highlight the balance between GenAI's efficiencies and Agile's human-centric principles, proposing research directions for AI-enriched Agile practices that promise to drive innovation and maintain the agility in a technologically progressive era.
C1 [Diebold, Philipp] IU Int Univ, Erfurt, Germany.
   [Diebold, Philipp] Bagilstein GmbH, Mainz, Germany.
RP Diebold, P (corresponding author), IU Int Univ, Erfurt, Germany.; Diebold, P (corresponding author), Bagilstein GmbH, Mainz, Germany.
EM philipp.diebold@bagilstein.de
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Ayinde L., 2020, BUS INFORM REV, V37, P142, DOI [10.1177/0266382120968057, DOI 10.1177/0266382120968057]
   Baldauf C., 2018, RetromatRun Great Agile Retrospectives
   Boehm B, 2003, PROCEEDINGS OF THE AGILE DEVELOPMENT CONFERENCE, P32, DOI 10.1109/ADC.2003.1231450
   Davies C., 2022, Precarious Work & The Digital Economy: Next Phase of a New Work Paradigm
   Dornberger R., 2018, Digitalization: Yesterday, today and tomorrow , Business Information Systems and Technology 4.0: New Trends in the Age of Digital Change, P1
   El Khatib M., 2021, Am. J. Ind. Bus. Manag, V11, P251, DOI [DOI 10.4236/AJIBM.2021.113016, 10.4236/AJIBM.2021.113016]
   Fowler M., 2001, Software Development, V9, P28
   Dam HK, 2019, 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: NEW IDEAS AND EMERGING RESULTS (ICSE-NIER 2019), P41, DOI 10.1109/ICSE-NIER.2019.00019
   Koza JR, 2003, IEEE INTELL SYST, V18, P25, DOI 10.1109/MIS.2003.1200724
   Kuchel T, 2023, 38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023, P1018, DOI 10.1145/3555776.3578726
   Lu Q., 2023, Responsible ai Pattern Catalogue: A Collection of Best Practices for ai Governance and Engineering
   Müller R, 2024, PROJ MANAG J, V55, P9, DOI 10.1177/87569728231225198
   Overeem B., 2017, The 8 Stances of a Scrum Master
   Pereira V, 2023, HUM RESOUR MANAGE R, V33, DOI 10.1016/j.hrmr.2021.100857
   Pichler R., 2010, Agile Product Management With Scrum: Creating Products That Customers Love
   Schermuly CC, 2019, COACHING-INT J THEOR, V12, P39, DOI 10.1080/17521882.2018.1528621
   Schwaber K., 2011, Scrum Alliance, V21, P1, DOI DOI 10.1002/9781119203278.APP2
   Siau K, 2020, J DATABASE MANAGE, V31, P74, DOI 10.4018/JDM.2020040105
   Velasquez-Henao Juan David, 2023, DYNA, V90, P9, DOI DOI 10.15446/DYNA.V90N230.111700
NR 19
TC 0
Z9 0
U1 2
U2 2
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 2047-7473
EI 2047-7481
J9 J SOFTW-EVOL PROC
JI J. Softw.-Evol. Proc.
PD 2024 NOV 5
PY 2024
DI 10.1002/smr.2740
EA NOV 2024
PG 5
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA M0I6P
UT WOS:001354472400001
OA hybrid
DA 2024-12-25
ER

PT J
AU Agrawal, KP
AF Agrawal, Kalyan Prasad
TI Organizational Sustainability of Generative AI-Driven Optimization
   Intelligence
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Generative AI; optimization intelligence; normalization process theory
ID CHALLENGES
AB Generative Artificial Intelligence (AI) tools like ChatGPT offer significant potential in the corporate world and organizational leadership. Organizations are actively integrating Generative AI (GenAI) into their operations to leverage its benefits. However, research is yet to explore the factors supporting the continuity of GenAI-powered Optimization Intelligence (GenAI-OI) processes in organizational contexts. This study seeks to address this gap by focusing on the challenges of standardizing such optimization intelligence systems. It utilizes the Normalization Process Theory (NPT) to facilitate a theoretical exploration of this standardization process. From both a research and practical standpoint, it elucidates how NPT can be applied to provide a structured framework for understanding and theorizing the processes involved in embedding and sustaining GenAI-OI systems, which play a pivotal role in shaping the future of organizational agility. This research endeavors to uncover valuable insights for organizations seeking not only to adopt but sustain this technology effectively.
C1 [Agrawal, Kalyan Prasad] Chandragupt Inst Management Patna, Patna 800001, Bihar, India.
RP Agrawal, KP (corresponding author), Chandragupt Inst Management Patna, Patna 800001, Bihar, India.
EM kalyan@cimp.ac.in
CR Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Agrawal KP, 2024, J COMPUT INFORM SYST, V64, P453, DOI 10.1080/08874417.2023.2224262
   Agrawal KP., 2022, ACAD MANAGE P, V2022, DOI [10.5465/AMBPP.2022.13723abstract, DOI 10.5465/AMBPP.2022.13723ABSTRACT]
   Charalampous M, 2019, EUR J WORK ORGAN PSY, V28, P51, DOI 10.1080/1359432X.2018.1541886
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Conboy K, 2019, IEEE SOFTWARE, V36, P44, DOI 10.1109/MS.2018.2884865
   Craig Peter, 2008, BMJ, V337, pa1655, DOI 10.1136/bmj.a1655
   Davis GB, 2002, COMMUN ACM, V45, P67, DOI 10.1145/585597.585617
   De R, 2020, INT J INFORM MANAGE, V55, DOI 10.1016/j.ijinfomgt.2020.102171
   Dikert K, 2016, J SYST SOFTWARE, V119, P87, DOI 10.1016/j.jss.2016.06.013
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elo S, 2008, J ADV NURS, V62, P107, DOI 10.1111/j.1365-2648.2007.04569.x
   Euchner J, 2023, RES TECHNOL MANAGE, V66, P71, DOI 10.1080/08956308.2023.2188861
   Finch TL, 2013, IMPLEMENT SCI, V8, DOI 10.1186/1748-5908-8-43
   Greenberg P.S., 2007, BUS HORIZONS, V50, P325, DOI [DOI 10.1016/J.BUSHOR.2007.02.005, 10.1016/j.bushor.2007.02.005]
   Gursoy D, 2023, J HOSP MARKET MANAG, V32, P579, DOI 10.1080/19368623.2023.2211993
   Hazell CM, 2017, BMC HEALTH SERV RES, V17, DOI 10.1186/s12913-017-2449-z
   Hirsch Peter Buell, 2023, Journal of Business Strategy, P238, DOI 10.1108/JBS-05-2023-0088
   Hodge LM, 2017, J BEHAV HEALTH SER R, V44, P442, DOI 10.1007/s11414-016-9505-z
   James DM, 2011, IMPLEMENT SCI, V6, DOI 10.1186/1748-5908-6-95
   JOSHI K, 1991, MIS QUART, V15, P229, DOI 10.2307/249384
   Karakose T, 2023, ADM SCI, V13, DOI 10.3390/admsci13070157
   Koren M, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0239113
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Krueger R. A., 2014, FOCUS GROUPS PRACTIC
   Lingard Lorelei, 2006, J Interprof Care, V20, P471, DOI 10.1080/13561820600921865
   May C., 2015, Normalization Process Theory On-line Users' Manual, Toolkit and NoMAD instrument
   May C, 2009, SOCIOLOGY, V43, P535, DOI 10.1177/0038038509103208
   May CR, 2009, IMPLEMENT SCI, V4, DOI 10.1186/1748-5908-4-29
   McCall B, 2020, LANCET DIGIT HEALTH, V2, pE293, DOI 10.1016/S2589-7500(20)30103-5
   Miele F, 2020, SOCIOL COMPASS, V14, DOI 10.1111/soc4.12795
   Minelle F., 2023, PM World J, V12, P1
   Moore G., 2014, UK MEDICAL RES COUNC, P1
   Polit-O'Hara D., 2010, Statistics and data analysis for nursing research
   Reeve J, 2016, BMC HEALTH SERV RES, V16, DOI 10.1186/s12913-016-1726-6
   Ritala P., 2023, J. Bus. Strateg, DOI DOI 10.1108/JBS-05-2023-0094
   Sarker S, 2009, IEEE T PROF COMMUN, V52, P75, DOI 10.1109/TPC.2008.2007871
   Walkowiak E, 2023, ECON LETT, V231, DOI 10.1016/j.econlet.2023.111315
NR 38
TC 4
Z9 4
U1 30
U2 74
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD 2023 DEC 15
PY 2023
DI 10.1080/08874417.2023.2286540
EA DEC 2023
PG 15
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA CM9H2
UT WOS:001125779300001
DA 2024-12-25
ER

PT J
AU Dang, A
   Wang, H
AF Dang, Anh
   Wang, Hui
TI Ethical use of generative AI for writing practices: Addressing
   linguistically diverse students in US Universities' AI statements
SO JOURNAL OF SECOND LANGUAGE WRITING
LA English
DT Article
DE Generative AI; ChatGPT; L2 Writing and Generative AI; GenAI Statements;
   University; Policies; Critical AI Literacy
AB Given the rapid development in Generative Artificial Intelligence (GenAI) technologies, conversations regarding how these tools will shape the teaching and learning of writing can be difficult to unpack. Thus, higher-ed institutions across the U.S. are paying more attention to the discussion of GenAI in their own contexts and also establishing guidelines to support instructors and students in this GenAI era. To understand more about the direction of these universities, this research brief examines publicly available statements and resources from 100 U.S. universities on the teaching of writing and GenAI usage, and from there, guide institutions in developing effective strategies for the responsible implementation of these tools. This report also highlights the importance of including L2 students as a focus in the process of crafting these statements, especially when viewing GenAI through the lens of critical pedagogy, social justice and inequalities.
C1 [Dang, Anh; Wang, Hui] Univ Arizona, Language Acquisit & Teaching 2, Tucson, AZ USA.
C3 University of Arizona
RP Dang, A (corresponding author), 1103 E 2nd St, Harvill 241, Tucson, AZ 85721 USA.
EM anhdang@arizona.edu
CR [Anonymous], 2022, US NEWS WORLD REPORT
   Artificial Intelligence at Northwestern, 2024, Use of Generative Artificial Intelligence in. Courses
   Barker LA, 2024, NUTRIENTS, V16, DOI 10.3390/nu16070914
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bonner E., 2023, Teaching English with Technology, V23, P23, DOI DOI 10.56297/BKAM1691/WIEO1749
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Gupta A, 2024, Arxiv, DOI [arXiv:2401.08711, 10.48550/arxiv.2401.08711, DOI 10.48550/ARXIV.2401.08711]
   Liang WX, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100779
   Liu ML, 2024, COMPUT EDUC, V211, DOI 10.1016/j.compedu.2023.104977
   Pack A., 2023, Teaching English with Technology, V23, P4, DOI [10.56297/BUKA4060/VRRO1747, DOI 10.56297/BUKA4060/VRRO1747]
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Skrabut S., 2023, 80 ways to use ChatGPT in the classroom: Using AI to enhance teaching and learning
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Wang H, 2024, Arxiv, DOI arXiv:2312.05235
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
NR 17
TC 0
Z9 0
U1 13
U2 13
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1060-3743
EI 1873-1422
J9 J SECOND LANG WRIT
JI J. Second. Lang. Writ.
PD DEC
PY 2024
VL 66
AR 101157
DI 10.1016/j.jslw.2024.101157
PG 8
WC Linguistics
WE Social Science Citation Index (SSCI)
SC Linguistics
GA M8S4D
UT WOS:001360172400001
DA 2024-12-25
ER

PT J
AU Hilario, E
   Azam, S
   Sundaram, J
   Mohammed, KI
   Shanmugam, B
AF Hilario, Eric
   Azam, Sami
   Sundaram, Jawahar
   Mohammed, Khwaja Imran
   Shanmugam, Bharanidharan
TI Generative AI for pentesting: the good, the bad, the ugly
SO INTERNATIONAL JOURNAL OF INFORMATION SECURITY
LA English
DT Article
DE Cyber security; Generative AI; Large language models; Penetration
   testing; ChatGPT 3.5
AB This paper examines the role of Generative AI (GenAI) and Large Language Models (LLMs) in penetration testing exploring the benefits, challenges, and risks associated with cyber security applications. Through the use of generative artificial intelligence, penetration testing becomes more creative, test environments are customised, and continuous learning and adaptation is achieved. We examined how GenAI (ChatGPT 3.5) helps penetration testers with options and suggestions during the five stages of penetration testing. The effectiveness of the GenAI tool was tested using a publicly available vulnerable machine from VulnHub. It was amazing how quickly they responded at each stage and provided better pentesting report. In this article, we discuss potential risks, unintended consequences, and uncontrolled AI development associated with pentesting.
C1 [Hilario, Eric; Azam, Sami; Mohammed, Khwaja Imran; Shanmugam, Bharanidharan] Charles Darwin Univ, Fac Sci & Technol, Energy & Resources Inst, Darwin, Australia.
   [Sundaram, Jawahar] Christ Acad Inst Adv Studies, Bangalore 560083, India.
C3 Charles Darwin University
RP Shanmugam, B (corresponding author), Charles Darwin Univ, Fac Sci & Technol, Energy & Resources Inst, Darwin, Australia.
EM eric.hilario@students.cdu.edu.au; sami.azam@cdu.edu.au;
   sundaramj@caias.in; khwajaimran.mohammed@cdu.edu.au;
   Bharanidharan.Shanmugam@cdu.edu.au
RI Azam, Sami/AAK-3846-2021; Sundaram, Jawahar/KDO-9782-2024; Shanmugam,
   Bharanidharan/O-7874-2019; Shanmugam, Bharanidharan/C-3611-2011
OI Shanmugam, Bharanidharan/0000-0002-2591-1949; Mohammed, Khwaja
   Imran/0000-0003-1703-5800
FU Charles Darwin University
FX No Statement Available
CR Abu-Dabaseh F., 2018, Computer Science and Information Technology, P121, DOI [10.5121/csit.2018.80610, DOI 10.5121/CSIT.2018.80610]
   Adamovic Sasa, 2019, SINTEZA 2019 INT SCI, P229
   Aggarwal G., 2023, FORBES
   [Anonymous], 2023, EUROPOL CRIMINAL USE
   assets.siemens-energy, 2023, SIEMENS ENERGY DEEPA
   AttackIQ, 2023, ATTACKIQ READY
   Avgerinos T, 2018, IEEE SECUR PRIV, V16, P52, DOI 10.1109/MSP.2018.1870873
   Ben-Moshe S., 2023, CHECK POINT RES
   BlackBerry Ltd, 2023, SAY IT DEC MAK BLACK
   blogs.microsoft, 2023, MICROSOFT MICROSOFT
   Chen JY, 2023, COMPUT SECUR, V126, DOI 10.1016/j.cose.2022.103055
   Cunningham A., 2023, ARS TECHNICA
   CyCraft Technology Corp, 2020, TRAIN MACHINE LEARNI
   CyCraft Technology Corp CyCraft's Fuchikoma at Code Blue, 2019, MODERN DAY GHOST SHE
   Deng G., 2023, PENTESTGPT
   Gal U., 2023, CHATGPT IS DATA PRIV
   github, 2023, THER1D SHELLGPT
   github, 2023, Significant-Gravitas: Auto-GPT
   github, 2023, MORPHEUSLORD GPT VUL
   github, 2023, IMARTINEZ PRIVATEGPT
   Grbic Dijana Vukovic, 2023, 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH), P1, DOI 10.1109/INFOTEH57020.2023.10094141
   Greshake K., 2023, More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Gurman M., 2023, Bloomberg
   help.openai, 2023, OPENAI CHATGPT RELEA
   Hern A., 2023, GUARDIAN
   kali, 2023, OFFENSIVE SECURITY G
   Khan S, 2023, ACM T PRIV SECUR, V26, DOI 10.1145/3546068
   Mansfield-Devine S., 2023, NETW SECUR
   McDaniel L, 2016, P ANN HICSS, P5479, DOI 10.1109/HICSS.2016.677
   Montalbano E., 2023, DARK READING
   openai, 2023, OPENAI USAGE POLICIE
   OpenAI, 2023, OPENAI MICROSOFT EXT
   Papernot N, 2017, PROCEEDINGS OF THE 2017 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (ASIA CCS'17), P506, DOI 10.1145/3052973.3053009
   Petro D., 2017, DEF CON, V25
   Prasad S. Guru, 2023, 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), P107, DOI 10.1109/ICAAIC56838.2023.10141395
   Renaud K., 2023, MIT Sloan Management Review
   Sanjaya I., 2020, INT J COMPUT NETW IN, V12
   Scherb C., 2023, ARXIV
   Shimony E., 2023, CHATTING OUR WAY CRE
   Takaesu I., 2018, BLACKHAT
   Temara S., 2023, RES SQUARE PLATFORM, DOI [10.21203/rs.3.rs-2707376/v1, DOI 10.21203/RS.3.RS-2707376/V1]
   vulnhub, 2019, JAYANTH MISSION PUMP
   Zacharakos A., 2023, SECURITY
   Zhuo Terry Yue, 2023, Red teaming chatgpt via jailbreaking: Bias, robustness, reliability and toxicity
NR 45
TC 3
Z9 3
U1 15
U2 33
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1615-5262
EI 1615-5270
J9 INT J INF SECUR
JI Int. J. Inf. Secur.
PD JUN
PY 2024
VL 23
IS 3
BP 2075
EP 2097
DI 10.1007/s10207-024-00835-x
EA MAR 2024
PG 23
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA SD7Z5
UT WOS:001184206900002
OA hybrid
DA 2024-12-25
ER

PT J
AU Bughin, J
AF Bughin, Jacques
TI The role of firm AI capabilities in generative AI-pair coding
SO JOURNAL OF DECISION SYSTEMS
LA English
DT Article; Early Access
DE Generative AI; productivity; enterprise RBV; capabilities; machine
   learning
ID INFORMATION
AB Generative Artificial Intelligence (genAI) is the latest evidence of the transformative value of AI in organisations. One promising avenue is in software engineering, where genAI can contribute to coding by pairing with developers. Based on a sample of global companies, two key findings emerge from an analysis of the productivity impact of genAI pair coding. Coding quality is negatively correlated with productivity throughput gains, while quality-adjusted productivity gains depend on the extent to which firms have deployed AI capabilities in the form of data, skills upgrading and AI governance. As observed with other digital technologies, the success of using genAI is closely linked to complementary technical skills and organisational resources.
C1 [Bughin, Jacques] Free Univ Brussels, Solvay Brussels Sch Econ & Management, Times2, Av Roosevelt, B-1050 Brussels, Belgium.
C3 Solvay SA; Universite Libre de Bruxelles
RP Bughin, J (corresponding author), Free Univ Brussels, Solvay Brussels Sch Econ & Management, Times2, Av Roosevelt, B-1050 Brussels, Belgium.
EM bughinjacquesrenejean@gmail.com
OI Bughin, Jacques/0000-0002-1973-3656
CR Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Ameye N, 2024, ECON INNOV NEW TECH, DOI 10.1080/10438599.2024.2413940
   Ameye N, 2023, TECHNOVATION, V127, DOI 10.1016/j.technovation.2023.102846
   Baird A, 2020, FRONT BIG DATA, V3, DOI 10.3389/fdata.2020.00025
   Barke S, 2023, P ACM PROGRAM LANG, V7, DOI 10.1145/3586030
   Barney JB, 2001, J MANAGE, V27, P643, DOI 10.1016/S0149-2063(01)00115-5
   Baryannis G, 2019, INT J PROD RES, V57, P2179, DOI 10.1080/00207543.2018.1530476
   Berente N, 2021, MIS QUART, V45, P1433, DOI DOI 10.25300/MISQ/2021/16274
   Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
   Bird Christian, 2022, ACM Queue, P35, DOI 10.1145/3582083
   Björkdahl J, 2020, CALIF MANAGE REV, V62, P17, DOI 10.1177/0008125620920349
   Brynjolfsson E., 2018, The Economics of Artificial Intelligence: An Agenda, P23
   Brynjolfsson E., 2023, GENERATIVE AI WORK N
   Bughin J., 2024, Journal of AI, Robotics Workplace Automation, V3, P1, DOI [https://doi.org/10.69554/WPNS5765, DOI 10.69554/WPNS5765]
   Bughin J., 2022, European Business Review
   Bughin J., 2018, MIT Sloan Management Review, V59, P1
   Bughin J., 2023, European Business Review
   Cette G, 2022, ECON INNOV NEW TECH, V31, P669, DOI 10.1080/10438599.2020.1849967
   Chen XY, 2024, PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON LEARNING@SCALE, L@S 2024, P325, DOI 10.1145/3657604.3664657
   Chowdhury S, 2023, HUM RESOUR MANAGE R, V33, DOI 10.1016/j.hrmr.2022.100899
   Coutinho M, 2024, PROCEEDINGS OF THE 1ST ACM INTERNATIONAL CONFERENCE ON AI-POWERED SOFTWARE, AIWARE 2024, P131, DOI 10.1145/3664646.3664773
   Czarnitzki D, 2023, J ECON BEHAV ORGAN, V211, P188, DOI 10.1016/j.jebo.2023.05.008
   Dakhel AM, 2023, J SYST SOFTWARE, V203, DOI 10.1016/j.jss.2023.111734
   Damioli G, 2023, APPL ECON LETT, V30, P816, DOI 10.1080/13504851.2021.2024129
   Damioli G, 2021, EURASIAN BUS REV, V11, P1, DOI 10.1007/s40821-020-00172-8
   DellAcqua F. E., 2023, Working Paper No. 24-013., DOI [10.2139/ssrn.4573321, DOI 10.2139/SSRN.4573321]
   Desouza KC, 2020, BUS HORIZONS, V63, P205, DOI 10.1016/j.bushor.2019.11.004
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Feng C, 2024, BUS HORIZONS, V67, P537, DOI 10.1016/j.bushor.2024.04.012
   Fountain T, 2019, HARVARD BUS REV, V97, P62
   Gao XY, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15118934
   Hartmann P, 2020, ACAD MANAG DISCOV, V6, P359, DOI 10.5465/amd.2019.0043
   Huang K., 2024, Generative AI security: Theories and practices, P133
   Idrisov B, 2024, ALGORITHMS, V17, DOI 10.3390/a17020062
   Imai S, 2022, PROC IEEE ACM INT C, P319, DOI [10.1109/ICSE-Companion55297.2022.9793778, 10.1145/3510454.3522684]
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kazemitabaar M, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580919
   Khan S, 2018, MECH SYST SIGNAL PR, V107, P241, DOI 10.1016/j.ymssp.2017.11.024
   Laranjo L, 2018, J AM MED INFORM ASSN, V25, P1248, DOI 10.1093/jamia/ocy072
   Lazzeri F, 2023, ROUT STUD INNOV ORG, P33, DOI 10.4324/9781003304616-4
   Lee YS, 2022, TECHNOVATION, V118, DOI 10.1016/j.technovation.2022.102590
   Li M.M., 2024, AMCIS 2024 P
   Liu Y, 2024, ACM T SOFTW ENG METH, V33, DOI 10.1145/3641540
   Lui AKH, 2022, ANN OPER RES, V308, P373, DOI 10.1007/s10479-020-03862-8
   Ma QN, 2023, Arxiv, DOI arXiv:2306.05153
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Milliou C, 2011, INT J IND ORGAN, V29, P513, DOI 10.1016/j.ijindorg.2010.10.003
   Mozannar H, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS, CHI 2024, DOI 10.1145/3613904.3641936
   Murphy-Hill E, 2021, IEEE T SOFTWARE ENG, V47, P582, DOI 10.1109/TSE.2019.2900308
   Noy S., 2023, SSRN Electronic Journal, DOI [https://doi.org/10.2139/ssrn.4375283, DOI 10.2139/SSRN.4375283]
   Papagiannidis E, 2023, INFORM SYST FRONT, V25, P123, DOI 10.1007/s10796-022-10251-y
   Papenmeier A, 2022, ACM T COMPUT-HUM INT, V29, DOI 10.1145/3495013
   Patel V., 2022, Intell Med, V2, P134, DOI [10.1016/j.imed.2021.10.001, DOI 10.1016/J.IMED.2021.10.001]
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Pham H., 2023, Springer handbook of engineering statistics, DOI [https://doi.org/10.1007/978-1-4471-7503-225, DOI 10.1007/978-1-4471-7503-225]
   Renieris E.M., 2023, MIT Sloan Management Review
   Russo D, 2024, ACM T SOFTW ENG METH, V33, DOI 10.1145/3652154
   Sandoval G, 2023, PROCEEDINGS OF THE 32ND USENIX SECURITY SYMPOSIUM, P2205
   Shahid N, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0212356
   Shakina E, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120405
   Shin Y, 2011, IEEE T SOFTWARE ENG, V37, P772, DOI 10.1109/TSE.2010.81
   Simkute A, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2405782
   Steck H, 2021, AI MAG, V42, P7, DOI 10.1609/aaai.12013
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Tian HY, 2023, Arxiv, DOI arXiv:2304.11938
   Vaithilingam P, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519665
   Wamba SF, 2024, INT J PROD ECON, V268, DOI 10.1016/j.ijpe.2023.109131
   Wamba-Taguimdje SL, 2020, BUS PROCESS MANAG J, V26, P1893, DOI 10.1108/BPMJ-10-2019-0411
   Weber Thomas, 2024, Proceedings of the ACM on Human-Computer Interaction, V8, DOI 10.1145/3660247
   Wen JF, 2012, INFORM SOFTWARE TECH, V54, P41, DOI 10.1016/j.infsof.2011.09.002
   Yetistiren B, 2023, Arxiv, DOI arXiv:2304.10778
   Zhu K, 2003, DECISION SCI, V34, P643, DOI 10.1111/j.1540-5414.2003.02460.x
   Ziegler A., 2022, P 6 ACM SIGPLAN INT, V9, DOI [https://doi.org/10.1145/3520312.353486, DOI 10.1145/3520312.353486]
   Zolas N., 2021, no. w28290
NR 74
TC 0
Z9 0
U1 6
U2 6
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1246-0125
EI 2116-7052
J9 J DECIS SYST
JI J. Decis. Syst.
PD 2024 NOV 29
PY 2024
DI 10.1080/12460125.2024.2428187
EA NOV 2024
PG 22
WC Operations Research & Management Science
WE Emerging Sources Citation Index (ESCI)
SC Operations Research & Management Science
GA N5D5F
UT WOS:001364545100001
DA 2024-12-25
ER

PT J
AU Katsamakas, E
   Sanchez-Cartas, JM
AF Katsamakas, Evangelos
   Sanchez-Cartas, J. Manuel
TI Generative Artificial Intelligence, Content Creation, and Platforms
SO JOURNAL OF INDUSTRY COMPETITION & TRADE
LA English
DT Article
DE Artificial Intelligence (AI); Generative Artificial Intelligence
   (GenAI); Platforms; Content creators; Competition; Welfare; L11; L13;
   L82
AB Generative AI (GenAI) is a rapidly growing technology that is expected to transform business and society. An important question is how it will affect platforms and their ecosystems, especially human-generated content upon which many platforms rely. We develop an analytical model to study the economic impact of content platforms that use GenAI, namely GenAI content platforms (GCPs). A GCP connects consumers to content generated by creators or the GenAI and offers APIs and tools to developers who build new applications in a third market. We find that although platforms have incentives to moderate the use of GenAI, this technology increases prices and reduces the quantity and number of content creators. This effect is not homogeneous, as GenAI has less influence on competitive or high entry-cost markets. Consequently, consumer welfare in content markets is reduced, but this decline varies by market. Overall, our analysis contributes to the platform and the AI economics research literature by providing testable hypotheses about the impact of GenAI in platform settings.
C1 [Katsamakas, Evangelos] Fordham Univ, Gabelli Sch Business, 140 W 62nd St, New York, NY 10023 USA.
   [Sanchez-Cartas, J. Manuel] Univ Complutense Madrid, Campus Somosaguas, Madrid 28223, Spain.
C3 Fordham University; Complutense University of Madrid
RP Sanchez-Cartas, JM (corresponding author), Univ Complutense Madrid, Campus Somosaguas, Madrid 28223, Spain.
EM jmscartas@ucm.es
CR Acemoglu D, 2022, ECONOMETRICA, V90, P1973, DOI 10.3982/ECTA19815
   Acemoglu D, 2018, AM ECON REV, V108, P1488, DOI 10.1257/aer.20160696
   Agrawal A., 2019, The economics of artificial intelligence, DOI DOI 10.7208/CHICAGO/9780226613475.001.0001
   Anderson S., 1992, DISCRETE CHOICE THEO
   Bakos Y, 2008, J MANAGE INFORM SYST, V25, P171, DOI 10.2753/MIS0742-1222250208
   Belleflamme P., 2015, Industrial organization: Markets and strategies, V2, DOI DOI 10.1017/CBO9781107707139
   Berente N, 2021, MIS QUART, V45, P1433, DOI DOI 10.25300/MISQ/2021/16274
   Bhargava HK, 2022, MANAGE SCI, V68, P5233, DOI 10.1287/mnsc.2021.4126
   Bloomberg Intelligence, 2023, Bloomberg
   Bresnahan T, 2024, J ECON MANAGE STRAT, V33, P305, DOI 10.1111/jems.12524
   Brynjolfsson Erik, 2016, The second machine age
   Buiten M, 2023, COMPUT LAW SECUR REV, V48, DOI 10.1016/j.clsr.2023.105794
   Burtch G, 2023, PREPRINT, DOI [10.2139/ssrn.4521754, DOI 10.2139/SSRN.4521754]
   Calvano E, 2023, INT J IND ORGAN, V90, DOI 10.1016/j.ijindorg.2023.102973
   Calvano E, 2020, AM ECON REV, V110, P3267, DOI 10.1257/aer20190623
   Cao Y., 2023, A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
   CHANG HF, 1995, RAND J ECON, V26, P34, DOI 10.2307/2556034
   CMA, 2023, AI foundation models: initial report
   De Cremer D., 2023, Harvard Business Review
   DIXIT AK, 1977, AM ECON REV, V67, P297
   Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Evans D.S., 2003, REV NETW ECON, V2, P1, DOI [DOI 10.2202/1446-9022.1026, 10.2202/1446-9022.1026, 10.2139/ssrn.447981, DOI 10.2139/SSRN.447981]
   Federal Trade Commission, 2023, Generative AI raises competition concerns
   Ford M., 2015, Rise of the robots: Technology and the threat of a jobless future
   Fugh-Berman A, 2006, PLOS MED, V3, P762, DOI 10.1371/journal.pmed.0030130
   Hagiu A, 2006, RAND J ECON, V37, P720, DOI 10.1111/j.1756-2171.2006.tb00039.x
   Huang H., 2023, SSRN Electron J, DOI [10.2139/ssrn.4670714, DOI 10.2139/SSRN.4670714]
   Klein T, 2021, RAND J ECON, V52, P538, DOI 10.1111/1756-2171.12383
   Krakowski S, 2023, STRATEGIC MANAGE J, V44, P1425, DOI 10.1002/smj.3387
   Lebovitz S, 2022, ORGAN SCI, V33, P126, DOI 10.1287/orsc.2021.1549
   McKinsey, 2023, EC POTENTIAL GENERAT
   New York Times, 2023, New York Times
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Parker G, 2018, MANAGE SCI, V64, P3015, DOI 10.1287/mnsc.2017.2757
   Parker GG, 2005, MANAGE SCI, V51, P1494, DOI 10.1287/mnsc.1050.0400
   Peukert C, 2024, SSRN Electron J, P1, DOI [10.2139/ssrn.4820846, DOI 10.2139/SSRN.4820846]
   Quinn M, 2023, SSRN Electron J, V40, DOI [10.2139/ssrn.4522386, DOI 10.2139/SSRN.4522386]
   Rahwan I, 2019, NATURE, V568, P477, DOI 10.1038/s41586-019-1138-y
   Rochet JC, 2008, INT J IND ORGAN, V26, P1333, DOI 10.1016/j.ijindorg.2008.01.002
   Rochet JC, 2003, J EUR ECON ASSOC, V1, DOI 10.1162/154247603322493212
   Sanatizadeh A., 2023, SSRN Electron J, DOI [10.2139/ssrn.4459729, DOI 10.2139/SSRN.4459729]
   Sanchez-Cartas JM, 2024, ELECTRON COMMER RES, DOI 10.1007/s10660-024-09821-w
   Sanchez-Cartas JM, 2022, IEEE ACCESS, V10, P10575, DOI 10.1109/ACCESS.2022.3144390
   Shon M, 2021, TELEMAT INFORM, V56, DOI 10.1016/j.tele.2020.101476
   Somers J, 2023, New Yorker
   SPENCE M, 1976, REV ECON STUD, V43, P217, DOI 10.2307/2297319
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Varian H., 2019, EC ARTIFICIAL INTELL, P399, DOI 10.7208/9780226613475-018
   Wang X., 2024, SSRN Electron J, DOI [10.2139/ssrn.4750326, DOI 10.2139/SSRN.4750326]
   Weyl EG, 2010, AM ECON REV, V100, P1642, DOI 10.1257/aer.100.4.1642
   Wired, 2023, The race to build a ChatGPT-powered search engine
   Wired, 2023, Wired
   Xue J., 2023, SSRN Electron J, DOI [10.2139/ssrn.4448938, DOI 10.2139/SSRN.4448938]
   Zhao W., 2024, SSRN Electron J, DOI [10.2139/ssrn.4693181, DOI 10.2139/SSRN.4693181]
NR 57
TC 0
Z9 0
U1 16
U2 16
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1566-1679
EI 1573-7012
J9 J IND COMPET TRADE
JI J. Ind. Compet. Trade
PD DEC
PY 2024
VL 24
IS 1
AR 19
DI 10.1007/s10842-024-00430-9
PG 20
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA H8F2U
UT WOS:001325737300002
DA 2024-12-25
ER

PT J
AU Cherner, T
   Foulger, TS
   Donnelly, M
AF Cherner, Todd
   Foulger, Teresa S.
   Donnelly, Margaret
TI Introducing a Generative AI Decision Tree for Higher Education: A
   Synthesis of Ethical Considerations from Published Frameworks &
   Guidelines
SO TECHTRENDS
LA English
DT Article; Early Access
DE Artificial Intelligence Systems; Generative Artificial Intelligence;
   Ethics of AI; Educational Technology; Higher Education
AB The ethics surrounding the development and deployment of generative artificial intelligence (genAI) is an important topic as institutions of higher education adopt the technology for educational purposes. Concurrently, stakeholders from various organizations have reviewed the literature about the ethics of genAI and proposed frameworks about it. However, there is a dearth of tools available to end users when deciding to adopt a genAI tool. In response, this study's authors analyzed 30 documents - 10 from industry professionals, 10 from governmental organizations, and 10 from academic scholars - that included frameworks, principles, and guidelines that address the ethics of genAI. Using a content analysis, the authors identified key factors and leveraged them to develop a decision tree that end users can employ for making ethical considerations when choosing to adopt different genAI tools.
C1 [Cherner, Todd; Donnelly, Margaret] Univ N Carolina, Chapel Hill, NC 27599 USA.
   [Foulger, Teresa S.] Arizona State Univ, Tempe, AZ USA.
C3 University of North Carolina; University of North Carolina Chapel Hill;
   Arizona State University; Arizona State University-Tempe
RP Cherner, T (corresponding author), Univ N Carolina, Chapel Hill, NC 27599 USA.
EM tcherner@unc.edu; Teresa.Foulger@asu.edu; magadon@unc.edu
RI Foulger, Teresa/AAP-3593-2020
CR Access Now, 2024, About us
   Ai D, 2024, NANO RES, V17, P7746, DOI 10.1007/s12274-024-6765-4
   Amnesty International, 2024, Who we are
   [Anonymous], 2019, Diseno alineado Eticamente una vision para Priorizar el Bienestar Humano con Sistemas Autonomos e Inteligentes, V1
   Buruk B, 2020, MED HEALTH CARE PHIL, V23, P387, DOI 10.1007/s11019-020-09948-1
   Chesterman S, 2024, POLICY SOC, DOI 10.1093/polsoc/puae006
   Duriau VJ, 2007, ORGAN RES METHODS, V10, P5, DOI 10.1177/1094428106289252
   Elo S, 2008, J ADV NURS, V62, P107, DOI 10.1111/j.1365-2648.2007.04569.x
   European Commission's High Level Group on Artificial Intelligence, 2018, A definition of AI: Main capabilities and scientific disciplines
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   Fischer L, 2021, MACH LEARN KNOW EXTR, V3, P56, DOI 10.3390/make3010004
   Floridi L., 2019, Harv Data Sci Rev, V1, P1, DOI DOI 10.1162/99608F92.8CD550D1
   Future of Life Institute, 2024, Asilomar AI Principles
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Hallgren Kevin A, 2012, Tutor Quant Methods Psychol, V8, P23
   HFS Research Ltd, 2023, Go ahead: Ask a question-HFS, first to market with transformative genAI tech
   HolonIQ, 2023, Artificial intelligence in education. 2023 survey insights
   Hsieh HF, 2005, QUAL HEALTH RES, V15, P1277, DOI 10.1177/1049732305276687
   Hu K., 2023, REUTERS         0202
   Krippendorff K, 2012, CONTENT ANAL INTRO I
   Kyngäs H, 2020, APPLICATION OF CONTENT ANALYSIS IN NURSING SCIENCE RESEARCH, P13, DOI 10.1007/978-3-030-30199-6_2
   Lane L, 2023, Artificial intelligence, social harms and human rights, P183, DOI [10.1007/978-3-031-19149-78, DOI 10.1007/978-3-031-19149-78]
   Lombard M, 2002, HUM COMMUN RES, V28, P587, DOI 10.1111/j.1468-2958.2002.tb00826.x
   Marr Bernard, 2023, Forbes. corn
   Mayring P., 2014, Qualitative Content Analysis: Theoretical Foundation, Basic Procedures and Software Solution, P143, DOI DOI 10.4135/9781446282243.N12
   McHugh ML, 2012, BIOCHEM MEDICA, V22, P276, DOI 10.11613/bm.2012.031
   MicroStrategy, 2023, Introducing MicroStrategy ai: Generative ai on trusted data press release
   Mishra P., 2019, Journal of Digital Learning in Teacher Education, V35, P76, DOI DOI 10.1080/21532974.2019.1588611
   Nili A, 2020, INT J INFORM MANAGE, V54, DOI 10.1016/j.ijinfomgt.2020.102154
   Perri L., 2024, Gartner
   Roller M.R., 2019, FORUM QUALITATIVE SO, V20, DOI [10.17169/fqs-20.3.3385, DOI 10.17169/FQS-20.3.3385]
   Soroka L., 2019, Advanced Space Law, V3, P131, DOI [10.29202/asl/2019/3/11, DOI 10.29202/ASL/2019/3/11]
   Toronto Declaration, 2024, Endorsers
   Vrabi Deman D., 2024, AI and Ethics, DOI [10.1007/s43681-024-00474-x, DOI 10.1007/S43681-024-00474-X]
   Zhang Y., 2009, Qualitative analysis of content
NR 35
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD 2024 NOV 28
PY 2024
DI 10.1007/s11528-024-01023-3
EA NOV 2024
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA N9B0J
UT WOS:001367188100001
DA 2024-12-25
ER

PT J
AU Samala, AD
   Rawas, S
   Wang, TC
   Reed, JM
   Kim, J
   Howard, NJ
   Ertz, M
AF Samala, Agariadne Dwinggo
   Rawas, Soha
   Wang, Tianchong
   Reed, Janet Marie
   Kim, Jinhee
   Howard, Natalie-Jane
   Ertz, Myriam
TI Unveiling the landscape of generative artificial intelligence in
   education: a comprehensive taxonomy of applications, challenges, and
   future prospects
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Generative AI; GenAI; Educational technology; ChatGPT; AI applications
   in education; Quality education
ID SYSTEMATIC REVIEWS; CHATGPT; AI
AB The rapid advancement of Generative Artificial Intelligence (GenAI) models, particularly ChatGPT, has sparked widespread discussion among educators and researchers regarding their potential implications for education. This study presents a comprehensive taxonomy of GenAI in academia and education, encompassing a wide range of applications, challenges, ethical considerations, and future prospects. Drawing on a scoping review of 453 articles, including the 50 most cited works throughout 2023, the taxonomy provides a state-of-the-art analysis of the current landscape of GenAI in education. The taxonomy offers a theoretical framework that aligns with the current discourse in GenAI and education, providing a critical evaluation of the existing literature and proposing innovative perspectives and solutions. The practical implications of the taxonomy for educators, researchers, and policymakers are highlighted, emphasizing the need for ethical considerations and informed policies to maximize the benefits of GenAI while minimizing its risks and negative impacts.
C1 [Samala, Agariadne Dwinggo] Univ Negeri Padang, Fac Engn, Padang, West Sumatra, Indonesia.
   [Rawas, Soha] Beirut Arab Univ, Dept Math & Comp Sci, Beirut, Lebanon.
   [Wang, Tianchong] Flinders Univ S Australia, Adelaide, Australia.
   [Reed, Janet Marie] Kent State Univ, Kent, OH USA.
   [Kim, Jinhee] Old Dominion Univ, Norfolk, VA 23529 USA.
   [Howard, Natalie-Jane] Univ Lancaster, Lancaster, Lancs, England.
   [Ertz, Myriam] Univ Quebec Chicoutimi, LaboNFC, Saguenay, PQ, Canada.
C3 Universitas Negeri Padang; Beirut Arab University; Flinders University
   South Australia; University System of Ohio; Kent State University; Kent
   State University Kent; Kent State University Salem; Old Dominion
   University; Lancaster University; University of Quebec; University of
   Quebec Chicoutimi
RP Samala, AD (corresponding author), Univ Negeri Padang, Fac Engn, Padang, West Sumatra, Indonesia.
EM agariadne@ft.unp.ac.id
RI Ertz, Myriam/AAY-3676-2020; Howard, Natalie/IAM-4285-2023; Wang,
   Tianchong/AFS-6958-2022; Reed, Janet/HZH-7451-2023; Samala, Agariadne
   Dwinggo/ADU-7629-2022
OI Howard, Natalie-Jane/0000-0001-7050-6371; Kim,
   Jinhee/0000-0002-3365-7354; Samala, Agariadne
   Dwinggo/0000-0002-4425-0605; Ertz, Myriam/0000-0001-9959-2779; Wang,
   Tianchong/0000-0002-6410-2490; Reed, Janet/0000-0003-3905-4156; Rawas,
   Soha/0000-0001-5128-6529
CR Abd-alrazaq A, 2023, JMIR MED EDUC, V9, DOI 10.2196/48291
   Adarkwah M. A., 2023, Journal of Applied Learning Teaching, V6, P2, DOI [10.37074/jalt.2023.6.2.26, DOI 10.37074/JALT.2023.6.2.26]
   Alabool Hamzeh Mohammad, 2023, 2023 International Conference on Information Technology (ICIT), P184, DOI 10.1109/ICIT58056.2023.10225801
   Alamleh Hosam, 2023, 2023 Systems and Information Engineering Design Symposium (SIEDS), P154, DOI 10.1109/SIEDS58326.2023.10137767
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Alessa A, 2023, PROCEEDINGS OF THE 16TH ACM INTERNATIONAL CONFERENCE ON PERVASIVE TECHNOLOGIES RELATED TO ASSISTIVE ENVIRONMENTS, PETRA 2023, P667, DOI 10.1145/3594806.3596572
   Altmäe S, 2023, REPROD BIOMED ONLINE, V47, P3, DOI 10.1016/j.rbmo.2023.04.009
   Alzubaidi L, 2021, J BIG DATA-GER, V8, DOI 10.1186/s40537-021-00444-8
   Ariyaratne S, 2023, SKELETAL RADIOL, V52, P1755, DOI 10.1007/s00256-023-04340-5
   Arksey H., 2005, INT J SOC RES METHOD, V8, P19, DOI [DOI 10.1080/1364557032000119616, 10.1080/1364557032000119616]
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Bagde H, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e23050
   Bahrini A., 2023, SIEDS, V2023, P274, DOI [10.1109/SIEDS58326.2023.10137850, DOI 10.1109/SIEDS58326.2023.10137850]
   Barrot JS, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100745
   Bender SM., 2023, MEDIA PRACTICE ED, V24, P351, DOI [10.1080/25741136.2023.2204203, DOI 10.1080/25741136.2023.2204203]
   Bitzenbauer P, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13176
   Bond-Taylor S, 2022, IEEE T PATTERN ANAL, V44, P7327, DOI 10.1109/TPAMI.2021.3116668
   Bubeck S., 2023, The Law Teacher, V57, P352
   Burger B, 2023, EUR J INNOV MANAG, V26, P233, DOI 10.1108/EJIM-02-2023-0156
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Castelvecchi Davide, 2022, Nature, DOI 10.1038/d41586-022-04383-z
   Castonguay A, 2023, NURS EDUC TODAY, V129, DOI 10.1016/j.nedt.2023.105916
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chaudhry IS, 2023, COGENT EDUC, V10, DOI 10.1080/2331186X.2023.2210461
   Cheng L, 2023, INT J LEGAL DISCOURS, V8, P31, DOI 10.1515/ijld-2023-2001
   Chiang FK, 2022, COMPUT HUM BEHAV, V129, DOI 10.1016/j.chb.2021.107125
   Choi EPH, 2023, NURS EDUC TODAY, V125, DOI 10.1016/j.nedt.2023.105796
   Chowdhury M. N. U. R., 2023, 2023 3 INT C INT TEC, DOI [10.1109/CONIT59222.2023.10205621, DOI 10.1109/CONIT59222.2023.10205621]
   Chu Chu S T. S T., 2022, Computers and Education: Artificial Intelligence, V3 3, P100091, DOI [10.1016/j.caeai.2022.100091 10.1016/j.caeai.2022.100091, DOI 10.1016/J.CAEAI.2022.100091]
   Ciaccio Edward J., 2023, Informatics in Medicine Unlocked, DOI 10.1016/j.imu.2023.101253
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Currie G, 2023, RADIOGRAPHY, V29, P792, DOI 10.1016/j.radi.2023.05.011
   Currie G, 2023, J NUCL MED TECHNOL, V51, P255, DOI 10.2967/jnmt.123.265864
   Currie G, 2023, J NUCL MED TECHNOL, V51, P247, DOI 10.2967/jnmt.123.265844
   Dai Yun, 2023, Procedia CIRP, P84, DOI 10.1016/j.procir.2023.05.002
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Davenport Thomas, 2019, Future Healthc J, V6, P94, DOI 10.7861/futurehosp.6-2-94
   De Angelis L, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1166120
   Dempere J, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1206936
   Dergaa I, 2023, BIOL SPORT, V40, P615, DOI 10.5114/biolsport.2023.125623
   Desaire H, 2023, CELL REP PHYS SCI, V4, DOI 10.1016/j.xcrp.2023.101426
   Devagiri JS, 2022, EXPERT SYST APPL, V207, DOI 10.1016/j.eswa.2022.118002
   DuBose J., 2023, Public Services Quarterly, V19, P125, DOI [10.1080/15228959.2023.2185338, DOI 10.1080/15228959.2023.2185338]
   Dwinggo S. A., 2023, International Journal of Interactive Mobile Technologies, V17, P4
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eager B, 2023, J UNIV TEACH LEARN P, V20
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Feiyu Xu, 2019, Natural Language Processing and Chinese Computing. 8th CCF International Conference, NLPCC 2019. Proceedings. Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science (LNAI 11839), P563, DOI 10.1007/978-3-030-32236-6_51
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Ferrara E., 2023, First Monday, V28, P1, DOI DOI 10.5210/FM.V28I6.13185
   Fiialka S., 2023, International Journal of Emerging Technologies in Learning, V18, P236, DOI [10.3991/ijet.v18i17.42215, DOI 10.3991/IJET.V18I17.42215]
   Friederichs H, 2023, MED EDUC ONLINE, V28, DOI 10.1080/10872981.2023.2220920
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Garg RK, 2023, HEALTH PROMOT PERSPE, V13, P183, DOI 10.34172/hpp.2023.22
   Ghimire P, 2024, BUILDINGS-BASEL, V14, DOI 10.3390/buildings14010220
   Gill Sukhpal Singh, 2024, Internet of Things and Cyber-Physical Systems, V4, P19, DOI 10.1016/j.iotcps.2023.06.002
   Gill S. S., 2023, Internet of Things and Cyber-Physical Systems, V3, P262, DOI DOI 10.1016/J.IOTCPS.2023.05.004
   Gupta P., 2024, Data Inf. Manag, DOI DOI 10.1016/J.DIM.2024.100066
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Hallsworth JE, 2023, MICROB BIOTECHNOL, V16, P1131, DOI 10.1111/1751-7915.14222
   Han A, 2023, 22ND ANNUAL ACM INTERACTION DESIGN AND CHILDREN CONFERENCE, IDC 2023, P470, DOI 10.1145/3585088.3593867
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   Hsu YC, 2023, TECHTRENDS, V67, P603, DOI 10.1007/s11528-023-00863-9
   Huang JS, 2023, AM J CANCER RES, V13, P1148
   Hussain S., 2017, Lond J Res Comput Sci Technol, V17, P11
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Irwin P, 2023, NURS EDUC TODAY, V127, DOI 10.1016/j.nedt.2023.105835
   Ivanov S, 2021, J TOUR FUTURES, V9, P214, DOI 10.1108/JTF-02-2023-0038
   Jalil S, 2023, IEEE ICST WORKSHOP, P430, DOI 10.1109/ICSTW58534.2023.00078
   Javaid M., 2023, BenchCouncil Trans Benchmarks Standards Eval, V3, P100105, DOI [10.1016/j.tbench.2023.100105, DOI 10.1016/J.TBENCH.2023.100105]
   Johinke R, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.01
   Kamalov F, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151612451
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kaur P., 2023, 6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis, P23, DOI [10.1201/9781003321668-2, DOI 10.1201/9781003321668-2]
   Kazemitabaar M, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580919
   Keiper MC, 2023, J HOSP LEIS SPORT TO, V33, DOI 10.1016/j.jhlste.2023.100456
   Khan RA, 2023, PAK J MED SCI, V39, P605, DOI 10.12669/pjms.39.2.7653
   Khorshidi H., 2023, Inform Med Unlocked, V41, p101314.
   Kirchner GJ, 2023, CLIN ORTHOP RELAT R, V481, P2260, DOI 10.1097/CORR.0000000000002668
   Kooli C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075614
   Levac D, 2010, IMPLEMENT SCI, V5, DOI 10.1186/1748-5908-5-69
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Limna P., 2023, J. Appl. Learn. Teach, V6, P64, DOI [DOI 10.37074/JALT.2023.6.1.32, https://doi.org/10.37074/jalt.2023.6.1.32]
   Livberber T, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e19688
   Lodge JM, 2023, AUSTRALAS J EDUC TEC, V39, P18, DOI 10.14742/ajet.8695
   Lower K, 2023, INDIAN J ORTHOP, V57, P1527, DOI 10.1007/s43465-023-00967-7
   Lyu Q, 2023, VIS COMPUT IND BIOME, V6, DOI 10.1186/s42492-023-00136-5
   Mahendri R. P., 2023, Journal of Hypermedia & Technology-Enhanced Learning (J-HyTEL), V1, P1, DOI [10.58536/j-hytel.v1i1.18, DOI 10.58536/J-HYTEL.V1I1.18]
   Májovsky M, 2023, J MED INTERNET RES, V25, DOI 10.2196/46924
   Markauskaite L., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022.100056, 10.1016/j.caeai.2022.100056]
   Marquez R, 2023, EDUC CHEM ENG, V44, P164, DOI 10.1016/j.ece.2023.05.005
   Martinez-Carranza J, 2023, SENSORS-BASEL, V23, DOI 10.3390/s23218757
   Masters K, 2023, MED TEACH, V45, P574, DOI 10.1080/0142159X.2023.2186203
   Memarian B., 2023, COMPUT HUM BEHAV, V1, P100022, DOI [10.1016/j.chbah.2023.100022, DOI 10.1016/J.CHBAH.2023.100022, https://doi.org/10.1016/j.chbah.2023.100022]
   Meniado JC, 2023, ARAB WORLD ENGL J, V14, P3, DOI 10.24093/awej/vol14no4.1
   Mills A., 2023, Journal of Applied Learning and Teaching, V6, DOI DOI 10.37074/JALT.2023.6.1.34
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Moher D, 2009, ANN INTERN MED, V151, P264, DOI [10.1136/bmj.b2700, 10.1136/bmj.b2535, 10.1371/journal.pmed.1000097, 10.1186/2046-4053-4-1, 10.1016/j.ijsu.2010.07.299, 10.1136/bmj.i4086, 10.1016/j.ijsu.2010.02.007]
   Mondal H, 2023, INDIAN DERMATOL ONL, V14, P482, DOI 10.4103/idoj.idoj_72_23
   Montenegro-Rueda M, 2023, COMPUTERS, V12, DOI 10.3390/computers12080153
   Neumann M, 2023, 2023 IEEE/ACM 5TH INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING EDUCATION FOR THE NEXT GENERATION, SEENG, P29, DOI 10.1109/SEENG59157.2023.00010
   Nikolic S, 2023, EUR J ENG EDUC, V48, P559, DOI 10.1080/03043797.2023.2213169
   Novozhilova E, 2024, MACH LEARN KNOW EXTR, V6, P342, DOI 10.3390/make6010017
   openai, GPT-4V(ision) System Card
   Page MJ, 2021, BMJ-BRIT MED J, V372, DOI [10.1136/bmj.n71, 10.1136/bmj.n160, 10.1016/j.ijsu.2021.105906]
   Patel S, 2023, LANCET DIGIT HEALTH, V5, pE102, DOI 10.1016/S2589-7500(23)00023-7
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   pek Z., 2023, Educ. Process Int. J, V12, P26, DOI [10.22521/edupij.2023.123.2, DOI 10.22521/EDUPIJ.2023.123.2]
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Popenici S., 2023, J. Appl. Learn. Teach, V6, P378, DOI [10.37074/jalt.2023.6.2.4, DOI 10.37074/JALT.2023.6.2.4]
   Prunkl CEA, 2021, NAT MACH INTELL, V3, P104, DOI 10.1038/s42256-021-00298-y
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Raj R., 2023, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, DOI [10.1016/j.benchc.2023.100140, DOI 10.1016/J.BENCHC.2023.100140, 10.1016/j.tbench.2023.100140, DOI 10.1016/J.TBENCH.2023.100140]
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   roboflow, First Impressions with GPT-4V(ision)
   Roumeliotis KI, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15060192
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Rusandi MA, 2023, J PUBLIC HEALTH-UK, V45, pE602, DOI 10.1093/pubmed/fdad049
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   Samala Agariadne Dwinggo, 2023, International Journal of Interactive Mobile Technologies, P22, DOI 10.3991/ijim.v17i05.37067
   Samala Agariadne Dwinggo, 2023, International Journal of Emerging Technologies in Learning, P231, DOI 10.3991/ijet.v18i05.35501
   Samala A D., 2024, International Journal of Interactive Mobile Technologies, V18, P96, DOI [10.3991/ijim.v18i02.46509, DOI 10.3991/IJIM.V18I02.46509]
   Samala A.D., 2024, Int. J. Interact. Mob. Technol, V18, P20, DOI [10.3991/ijim.v18i01.46307, DOI 10.3991/IJIM.V18I01.46307]
   Samala A. D., 2023, International Journal of Interactive Mobile Technologies (IJIM), V17, P33, DOI [10.3991/ijim.v17i18.42161, DOI 10.3991/IJIM.V17I18.42161]
   Samala AD, 2024, INT J ONLINE BIOMED, V20, P174, DOI 10.3991/ijoe.v20i05.45937
   Samala AD, 2023, INT J ENG PEDAGOG, V13, P109, DOI 10.3991/ijep.v13i2.35965
   Sánchez-Ruiz LM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13106039
   Sarker Iqbal H, 2021, SN Comput Sci, V2, P420, DOI 10.1007/s42979-021-00815-1
   Sarker Iqbal H, 2021, SN Comput Sci, V2, P160, DOI 10.1007/s42979-021-00592-x
   Sedaghat S, 2023, CLIN MED, V23, P278, DOI 10.7861/clinmed.2023-0078
   Sheikh H., 2023, Artificial Intelligence: Definition and Background, P15, DOI [10.1007/978-3-031-21448-62, DOI 10.1007/978-3-031-21448-62]
   Siegle D., 2023, A Role for ChatGPT and AI in Gifted Education, V46, P211, DOI [10.1177/10762175231168443, DOI 10.1177/10762175231168443]
   Singh H, 2023, J CHIN ECON BUS STUD, V21, P193, DOI 10.1080/14765284.2023.2210482
   Soori M., 2023, Cogn. Robot, V3, P54, DOI DOI 10.1016/J.COGR.2023.04.001
   Steele J L., 2023, Computers and Education: Artificial Intelligence, V5, P1, DOI [DOI 10.1016/J.CAEAI.2023.100160, 10.1016/j.caeai.2023.100160 10.1016/j.caeai.2023.100160]
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Su YF, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100752
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Swartz MK, 2011, J PEDIATR HEALTH CAR, V25, P1, DOI 10.1016/j.pedhc.2010.09.006
   Tam W, 2023, NURS EDUC TODAY, V129, DOI 10.1016/j.nedt.2023.105917
   Teel Z., 2023, COLL RES LIB NEWS, V84, P205, DOI [DOI 10.5860/CRLN.84.6.205, https://doi.org/10.5860/crln.84.6.205]
   Temsah MH, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11131812
   Timms MJ, 2016, INT J ARTIF INTELL E, V26, P701, DOI 10.1007/s40593-016-0095-y
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Totlis T, 2023, SURG RADIOL ANAT, V45, P1321, DOI 10.1007/s00276-023-03229-1
   Uddin SMJ, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15097121
   unesco, Education in the age of artificial intelligence | The UNESCO Courier
   Wang HY, 2023, INT J MED INFORM, V177, DOI 10.1016/j.ijmedinf.2023.105173
   Wu X., 2024, J INF INTELL, V2, P102, DOI DOI 10.1016/J.JIIXD.2023.10.007
   Xames M. D., 2023, Journal of Applied Learning & Teaching, V6, P390, DOI [10.2139/ssrn.4381803, DOI 10.37074/JALT.2023.6.1.20]
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   Zhang CM, 2021, J IND INF INTEGR, V23, DOI 10.1016/j.jii.2021.100224
   Zhou TQ, 2023, SYSTEM, V118, DOI 10.1016/j.system.2023.103141
   Zhu CJ, 2023, KNOWL MANAG E-LEARN, V15, P133, DOI 10.34105/j.kmel.2023.15.008
NR 162
TC 2
Z9 2
U1 97
U2 97
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 AUG 13
PY 2024
DI 10.1007/s10639-024-12936-0
EA AUG 2024
PG 40
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA D0T4R
UT WOS:001293394800001
DA 2024-12-25
ER

PT J
AU Mehta, N
   Agrawal, A
   Benjamin, J
   Mehta, S
   MacNeill, H
   Masters, K
AF Mehta, Neil
   Agrawal, Anoop
   Benjamin, Jennifer
   Mehta, Seysha
   MacNeill, Heather
   Masters, Ken
TI Pedagogy and generative artificial intelligence: Applying the PICRAT
   model to Google NotebookLM
SO MEDICAL TEACHER
LA English
DT Article; Early Access
DE PICRAT; Google NotebookLM; podcasts; ChatGPT; Generative AI
AB Healthcare educators (HPE) are challenged by rapid developments in Generative Artificial Intelligence (GenAI) tools. They need a standardized model to evaluate these new tools and to guide them in pedagogically-sound integration in the curriculum. PICRAT is an educational model designed specifically to help teachers meet this challenge. NotebookLM is a new multi-featured GenAI tool to help teachers and learners in education and research. Its newest feature allows automatic generation of an engaging podcast (called audio overview) from uploaded education or research content. Using the example of NotebookLM and, specifically, the auto-podcast feature, we illustrate how HPE can use the PICRAT model to evaluate GenAI tools for technology integration. We discuss how this model can be utilized as a standardized approach for evaluation and implementation of GenAI tools in health professions education.
C1 [Mehta, Neil] Case Western Reserve Univ, Cleveland Clin, Lerner Coll Med, Cleveland, OH USA.
   [Agrawal, Anoop] Baylor Coll Med, Dept Med, Houston, TX USA.
   [Benjamin, Jennifer] Baylor Coll Med, Dept Pediat, Houston, TX USA.
   [Mehta, Seysha] Case Western Reserve Univ, Cleveland Clin, Lerner Coll Med, Cleveland, OH USA.
   [MacNeill, Heather] Univ Toronto, Toronto, ON, Canada.
   [MacNeill, Heather] Toronto Metropolitan Univ, Toronto, ON, Canada.
   [Masters, Ken] Sultan Qaboos Univ, Coll Med & Hlth Sci, Muscat, Oman.
C3 Cleveland Clinic Foundation; University System of Ohio; Case Western
   Reserve University; Baylor College of Medicine; Baylor College of
   Medicine; Cleveland Clinic Foundation; University System of Ohio; Case
   Western Reserve University; University of Toronto; Toronto Metropolitan
   University; Sultan Qaboos University
RP Mehta, N (corresponding author), Case Western Reserve Univ, Cleveland Clin, Lerner Coll Med, Sch Med, Cleveland, OH 44106 USA.
EM mehtan@ccf.org
RI MacNeill, Heather/JCN-4866-2023; Mehta, Neil/AAL-9824-2020; Benjamin,
   Jennifer/GLR-1107-2022; Masters, Ken/C-6163-2013
OI Masters, Ken/0000-0003-3425-5020; Benjamin,
   Jennifer/0000-0001-6085-5973; Mehta, Neil/0000-0001-8342-4252; MacNeill,
   Heather/0000-0001-9842-3578
CR Kimmons Royce, 2020, Contemporary Issues in Technology and Teacher Education, V20, P114
   MacNeill H, 2024, MED TEACH, V46, P4, DOI 10.1080/0142159X.2023.2197135
   Masters K, 2024, MED TEACH, V46, P18, DOI 10.1080/0142159X.2023.2259069
   Riddell J, 2020, ACAD MED, V95, P89, DOI 10.1097/ACM.0000000000002984
NR 4
TC 0
Z9 0
U1 6
U2 6
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0142-159X
EI 1466-187X
J9 MED TEACH
JI Med. Teach.
PD 2024 OCT 26
PY 2024
DI 10.1080/0142159X.2024.2418937
EA OCT 2024
PG 3
WC Education, Scientific Disciplines; Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Health Care Sciences & Services
GA K3O9F
UT WOS:001343013900001
PM 39460933
DA 2024-12-25
ER

PT J
AU García-Penalvo, FJ
   Vázquez-Ingelmo, A
AF Garcia-Penalvo, Francisco Jose
   Vazquez-Ingelmo, Andrea
TI What Do We Mean by GenAI? A Systematic Mapping of The Evolution, Trends,
   and Techniques Involved in Generative AI
SO INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL
   INTELLIGENCE
LA English
DT Article
DE Artificial Intelligence; Content Generation; Generative AI; Generative
   Models; Machine Learning; Systematic Literature Mapping
AB Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence' lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".
C1 [Garcia-Penalvo, Francisco Jose; Vazquez-Ingelmo, Andrea] Univ Salamanca, Comp Sci Dept, GRIAL Res Grp, Salamanca, Spain.
C3 University of Salamanca
RP Vázquez-Ingelmo, A (corresponding author), Univ Salamanca, Comp Sci Dept, GRIAL Res Grp, Salamanca, Spain.
EM fgarcia@usal.es; andreavazquez@usal.es
RI Vázquez-Ingelmo, Andrea/A-1133-2019; GARCIA-PENALVO, Francisco
   Jose/D-5445-2013
OI GARCIA-PENALVO, Francisco Jose/0000-0001-9987-5584
FU Ministry of Science and Innovation [PID2020-118345RB-I00]
FX This research was partially funded by the Ministry of Science and
   Innovation through the AvisSA project grant number
   (PID2020-118345RB-I00).
CR Arcas BAY, 2017, ARTS, V6, DOI 10.3390/arts6040018
   Alvarez P, 2023, INT J INTERACT MULTI, V8, P168, DOI 10.9781/ijimai.2022.04.002
   Bozkurt A., 2023, Asian Journal of Distance Education
   Chatterjee A, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.1024449
   Civit M, 2022, EXPERT SYST APPL, V209, DOI 10.1016/j.eswa.2022.118190
   Flores-Vivar JM, 2023, COMUNICAR, V31, P37, DOI 10.3916/C74-2023-03
   Foster D, 2023, Generative Deep Learning, V2nd
   Foster D., 2019, Write, Compose, and Play (Japanese Version), P139
   Garcia-Penalvo F. J., 2023, International Journal of Interactive Multimedia and Artificial Intelligence
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   García-Peñalvo FJ, 2023, EDUC KNOWL SOC, V24, DOI 10.14201/eks.31279
   García-Peñalvo FJ, 2022, EDUC KNOWL SOC, V23, DOI 10.14201/eks.28600
   George A. S., 2023, Partners Universal International Innovation Journal, V1, P9, DOI [DOI 10.5281/ZENODO.7644359, 10.5281/zenodo.7644359]
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Gruetzemacher R, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3505245
   Harshvardhan GM, 2020, COMPUT SCI REV, V38, DOI 10.1016/j.cosrev.2020.100285
   Hazzan Orit, 2023, ChatGPT in Computer Science Education
   Khosravi H., 2022, Computers and Education: Artificial Intelligence, V3, P100074, DOI DOI 10.1016/J.CAEAI.2022.100074
   Kitchenham B., 2007, GUIDELINES PERFORMIN
   Kulkarni RH, 2017, IET SOFTW, V11, P18, DOI 10.1049/iet-sen.2016.0095
   Lies J, 2022, INT J INTERACT MULTI, V7, P115, DOI 10.9781/ijimai.2022.10.001
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Mashkoor A, 2022, COMPUTER, V55, P24, DOI 10.1109/MC.2022.3144805
   Meyer B, 2022, What do ChatGPT and AI-based automatic program generation mean for the future of software
   Mikalef P, 2023, J BUS RES, V164, DOI 10.1016/j.jbusres.2023.113998
   Page MJ, 2021, BMJ-BRIT MED J, V372, pn71, DOI 10.1136/bmj.n71
   Petersen K., 2008, P 12 INT C EV ASS SO, DOI [10.5555/2227115.2227123, DOI 10.14236/EWIC/EASE2008.8]
   Petersen K, 2015, INFORM SOFTWARE TECH, V64, P1, DOI 10.1016/j.infsof.2015.03.007
   Tredinnick L., 2023, The dangers of generative artificial intelligence
   van der Zant T., 2013, Philosophy and Theory of Artificial Intelligence, V5, P107, DOI 10.1007/978-3-642-31674-6
   Vaswani A, 2017, ADV NEUR IN, V30
   Vazquez-Ingelmo A, 2023, Zenodo, DOI 10.5281/ZENODO.8162484
   Yang JF, 2023, Arxiv, DOI [arXiv:2304.13712, DOI 10.48550/ARXIV.2304.13712]
   Zhang C, 2023, INT J INTERACT MULTI, V8, P69, DOI 10.9781/ijimai.2023.01.009
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
   Zhou JM, 2023, INT J INTERACT MULTI, V8, P7, DOI 10.9781/ijimai.2023.04.007
NR 36
TC 32
Z9 33
U1 109
U2 204
PU UNIV INT RIOJA-UNIR
PI LOGRONO
PA RECTORADO, AVENIDA DE LA PAZ, 137, LOGRONO, 26006, SPAIN
SN 1989-1660
J9 INT J INTERACT MULTI
JI Int. J. Interact. Multimed. Artif. Intell.
PD DEC
PY 2023
VL 8
IS 4
DI 10.9781/ijimai.2023.07.006
PG 217
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA DE9W2
UT WOS:001130481400013
OA Green Submitted, gold, Green Published
DA 2024-12-25
ER

PT J
AU Kumar, S
   Gunn, A
AF Kumar, Swapna
   Gunn, Ariel
TI Doctoral students' reflections on generative artificial intelligence
   (GenAI) use in the literature review process
SO INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL
LA English
DT Article; Early Access
DE Generative AI; literature review; doctoral education; dissertation;
   research
AB Amidst discussions about the use of Generative AI (GenAI) for academic research, their potential for doctoral research or literature reviews is an area beginning to be explored. This qualitative study explores doctoral students' perceptions of the value of GenAI tools in the literature review process. The analysis of 26 participants' reflections on their exploration of three GenAI technologies revealed benefits and challenges for the literature review process. Participants highlighted the potential of these tools as complementary to traditional database searches presuming users possess prior information literacy and research skills. They also perceived several challenges such as inaccuracies, usability and privacy and emphasised the importance and indispensability of a human review. This study highlights the need for guidance and strategies for doctoral students on the appropriate and ethical integration of GenAI in literature reviews.
C1 [Kumar, Swapna; Gunn, Ariel] Univ Florida, Coll Educ, Gainesville, FL USA.
C3 State University System of Florida; University of Florida
RP Kumar, S (corresponding author), Univ Florida, Coll Educ, 2821 Norman Hall POB 117048, Gainesville, FL 32611 USA.
EM swapnak@ufl.edu
FU Institutional Review Board (IRB) of the University of Florida, USA
   [IRB202400090]
FX This research was approved [IRB202400090] by the Institutional Review
   Board (IRB) of the University of Florida, USA.
CR Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bjorn GA., 2022, Impacting Education, V7, P47, DOI [https://doi.org/10.5195/ie.2022.237, DOI 10.5195/IE.2022.237]
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Boote D., 2005, Educational Researcher, V34, P3, DOI [DOI 10.3102/0013189X034006003, 10.3102/0013189X034006003]
   Dawson K, 2014, TECHTRENDS, V58, P62, DOI 10.1007/s11528-014-0770-5
   Fan N, 2023, SAGE OPEN, V13, DOI 10.1177/21582440231181296
   GLASER BG, 1965, SOC PROBL, V12, P436, DOI 10.1525/sp.1965.12.4.03a00070
   Godwin-Jones R, 2022, LANG LEARN TECHNOL, V26, P5, DOI 10.10125/73474
   Green R, 2006, J LIBR ADM, V45, P185, DOI 10.1300/J111v45n01_10
   Guerin C, 2021, INNOV EDUC TEACH INT, V58, P624, DOI 10.1080/14703297.2021.1991429
   Kalir RH., 2021, Annotation
   Kumar S, 2022, TECHTRENDS, V66, P721, DOI 10.1007/s11528-022-00739-4
   Kumar S, 2014, TECHTRENDS, V58, P54, DOI 10.1007/s11528-014-0769-y
   Machi LA., 2022, The literature review: 6 steps to success, V4th
   Neumerski C. M., 2023, Impacting Education: Journal on Transforming Professional Practice, V8, P36, DOI [10.5195/ie.2023.361, DOI 10.5195/IE.2023.361]
   Nicholson JM, 2021, QUANT SCI STUD, V2, P882, DOI 10.1162/qss_a_00146
   Schmidt P. G., 2023, Notices of the American Mathematical Society, V71, P1, DOI [10.1090/noti2838, DOI 10.1090/NOTI2838]
   Schryen G., 2024, HICSS 2024 P 57 HAW
   Storey VA, 2023, INT J ADULT EDUC TEC, V14, DOI 10.4018/IJAET.325795
   Tolman S., 2023, Impacting Education: Journal on Transforming Professional Practice, V8, P40, DOI [10.5195/ie.2023.329, DOI 10.5195/IE.2023.329]
   Wisker G, 2015, INNOV EDUC TEACH INT, V52, P64, DOI 10.1080/14703297.2014.981841
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
   Zou M, 2023, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12397-x
   Zou M, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1259531
NR 24
TC 0
Z9 0
U1 9
U2 9
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1470-3297
EI 1470-3300
J9 INNOV EDUC TEACH INT
JI Innov. Educ. Teach. Int.
PD 2024 NOV 16
PY 2024
DI 10.1080/14703297.2024.2427049
EA NOV 2024
PG 14
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M1F8M
UT WOS:001355078300001
DA 2024-12-25
ER

PT J
AU Tan, LN
   Luhrs, M
AF Tan, Linus
   Luhrs, Max
TI Using Generative AI Midjourney to Enhance Divergent and Convergent
   Thinking in an Architect's Creative Design Process
SO DESIGN JOURNAL
LA English
DT Article
DE Generative AI; artificial intelligence; architectural design; convergent
   thinking; divergent thinking
ID ARTIFICIAL-INTELLIGENCE; FIGURAL CREATIVITY
AB Architects use a range of tools, from the traditional pencil to Virtual Reality technologies to prototype and articulate their creative designs. In recent years, Generative Artificial Intelligence (GenAI) software has reached the mainstream and there is an exponential appearance of GenAI images that portray architectural designs. This article documents an architectural design methodology that uses Midjourney, a text-to-image GenAI software, as a design tool that enhances architects' creativity. Prompted by a design brief, we used Midjourney to accelerate (1) the identification and refinement process of developing a prospective user and (2) the ideation process of creating different desirable spaces for the designed user.
C1 [Tan, Linus; Luhrs, Max] Swinburne Univ Technol, Sch Design & Architecture, Melbourne, Vic, Australia.
C3 Swinburne University of Technology
RP Tan, LN (corresponding author), Swinburne Univ Technol, Sch Design & Architecture, Melbourne, Vic, Australia.
EM linustan@swin.edu.au
RI Tan, Linus/KTH-9073-2024
OI Tan, Linus/0000-0002-5705-0493
CR Amabile T. M., 1996, Creativity in context: Update to the social psychology of creativity, DOI DOI 10.4324/9780429501234
   Amershi S, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300233
   As I, 2018, INT J ARCHIT COMPUT, V16, P306, DOI 10.1177/1478077118800982
   Aziz-Zadeh L, 2013, SOC COGN AFFECT NEUR, V8, P475, DOI 10.1093/scan/nss021
   Brown T, 2008, HARVARD BUS REV, V86, P84
   Camburn B, 2020, RES ENG DES, V31, P383, DOI 10.1007/s00163-020-00341-w
   Csikszentmihalyi M., 1996, CREATIVITY FLOW PSYC
   Csikszentmihalyi M., 2016, SYSTEMS MODEL CREATI, DOI DOI 10.1007/978-94-017-9085-7
   Dake D.M., 1991, J VIS LIT, V11, P100
   Dau-Gaspar O, 2013, PROCD SOC BEHV, V78, P662, DOI 10.1016/j.sbspro.2013.04.371
   De Bono Edward., 1985, 6 THINKING HATS ESSE
   del Campo M, 2022, ARCHIT DESIGN, V92, P38, DOI 10.1002/ad.2811
   del Campo Matias, 2019, P 24 CAADRIA C, P15, DOI [10.52842/conf.caadria.2019.2.767, DOI 10.52842/CONF.CAADRIA.2019.2.767]
   Design Council UK, 2019, WHAT IS FRAM INN DES
   Dorst K., 2018, Notes on Design: How Creative Practice Works
   Dorst K, 2019, DESIGN STUD, V65, P60, DOI 10.1016/j.destud.2019.10.005
   Dzieduszynski T, 2022, INT J ARCHIT COMPUT, V20, P196, DOI 10.1177/14780771211066877
   Fink A, 2020, J CREATIVE BEHAV, V54, P676, DOI 10.1002/jocb.402
   Florian M.-C., 2022, ARCHDAILY
   Frankel L., 2010, P DES RES SOC DRS IN, P12
   Frayling C., 2012, MAPPING DESIGN RES, V1, P95
   Gold R, 2012, LATERALITY, V17, P602, DOI 10.1080/1357650X.2011.599936
   Guilford JP, 1950, AM PSYCHOL, V5, P444, DOI 10.1037/h0063487
   GUILFORD JP, 1956, PSYCHOL BULL, V53, P267, DOI 10.1037/h0040755
   Herman LM, 2024, NEW MEDIA SOC, V26, P2721, DOI 10.1177/14614448221089604
   HETRICK SH, 1968, MULTIVAR BEHAV RES, V3, P173, DOI 10.1207/s15327906mbr0302_3
   Iniguez A., 2022, ARCHDAILY
   Kaufman J.C., 2004, CREATIVITY POTENTIAL, P3, DOI DOI 10.1037/10692-001
   Kaufman JC, 2009, REV GEN PSYCHOL, V13, P1, DOI 10.1037/a0013688
   Kaufman JC., 2008, ESSENTIALS CREATIVIT
   Landry R., 1973, The Modern Language Journal, V57, P110, DOI [10.1111/j.1540-4781.1973.tb04676.x, DOI 10.1111/J.1540-4781.1973.TB04676.X]
   Lee K.-F., 2018, AI CAN SAVE OUR HUMA
   Luhrs M., 2022, ARCH MED POL SOC REP
   MACKINNON DW, 1962, AM PSYCHOL, V17, P484, DOI 10.1037/h0046541
   Maher ML., 1996, Advances in Formal Design Methods for CAD: Proceedings of the IFIP WG5.2 Workshop on Formal Design Methods for Computer-Aided Design, P3, DOI DOI 10.1007/978-0-387-34925-1_1
   Midjourney @Midjourney, 2022, Twitter Twitter, We're officially moving to openbeta!
   Miller AI, 2019, ARTIST IN THE MACHINE: THE WORLD OF AI-POWERED CREATIVITY, DOI 10.1080/09540121.2019.1668533
   Mumford MD, 2000, CREATIVITY RES J, V13, P267
   OpenAI, 2022, CHATGPT OPT LANG MOD
   Özerol G, 2023, INT J ARCHIT COMPUT, V21, P23, DOI 10.1177/14780771221100102
   Palmiero M, 2015, FRONT PSYCHOL, V6, DOI 10.3389/fpsyg.2015.01870
   Petsche H, 1996, INT J PSYCHOPHYSIOL, V24, P145, DOI 10.1016/S0167-8760(96)00050-5
   Preiss DD, 2019, FRONT PSYCHOL, V10, DOI 10.3389/fpsyg.2019.01118
   PromptBase, 2023, PROMPT MARK
   Prompti.ai, 2023, PROMPTIAI
   Sch┬u├en, 1983, REFLECTIVE PRACTITIO
   Serra G, 2021, DESIGN STUD, V76, DOI 10.1016/j.destud.2021.101029
   Simeone L., 2022, DRS2022, DOI [https://doi.org/10.21606/drs.2022.197, DOI 10.21606/DRS.2022.197]
   Stability.ai, 2022, Stability AI, Stable diffusion public release
   Stein MI, 1953, J PSYCHOL, V36, P311, DOI 10.1080/00223980.1953.9712897
   Studente S, 2016, THINK SKILLS CREAT, V19, P1, DOI 10.1016/j.tsc.2015.09.001
   Tamke M., 2017, FABRICATE 2017, P98, DOI [https://doi.org/10.2307/j.ctt1n7qkg7, DOI 10.2307/J.CTT1N7QKG7]
   Tamke M, 2018, INT J ARCHIT COMPUT, V16, P123, DOI 10.1177/1478077118778580
   Tan L., P 29 CAADRIA C, P39
   Tan L., 2023, EKSIG 2023, P796
   Tan L., 2023, 7 INT C DES ED RES L, DOI [https://doi.org/10.21606/drslxd.2024.054, DOI 10.21606/DRSLXD.2024.054]
   Torrance E.P., 1966, Torrance tests of creative thinking
   TORRANCE EP, 1962, EDUC LEADERSHIP, V20, P7
   Wertheimer M., 2020, Max Wertheimer Productive Thinking, DOI [DOI 10.1007/978-3-030-36063-4, 10.1007/978-3-030-36063-4]
   Wilkinson S., 2014, P S SIM ARCH URB DES, P143
   Wiltschnig S, 2013, DESIGN STUD, V34, P515, DOI 10.1016/j.destud.2013.01.002
   Wit AJ, 2018, INT J ARCHIT COMPUT, V16, P245, DOI 10.1177/1478077118807266
   Wu ZH, 2021, LECT NOTES COMPUT SC, V12762, P171, DOI 10.1007/978-3-030-78462-1_13
   Yemez N., 2022, Creativity Studies, V15, P25, DOI [10.3846/cs.2022.12603, DOI 10.3846/CS.2022.12603]
   Zhu WF, 2017, HUM BRAIN MAPP, V38, P2094, DOI 10.1002/hbm.23507
NR 65
TC 2
Z9 2
U1 72
U2 85
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1460-6925
EI 1756-3062
J9 DES J
JI Des. J.
PD JUL 3
PY 2024
VL 27
IS 4
BP 677
EP 699
DI 10.1080/14606925.2024.2353479
EA MAY 2024
PG 23
WC Art
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Art
GA ZG3J7
UT WOS:001221039200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Ungureanu, CT
   Amironesei, AE
AF Ungureanu, Carmen Tamara
   Amironesei, Aura Elena
TI Legal issues concerning Generative AI technologies
SO EASTERN JOURNAL OF EUROPEAN STUDIES
LA English
DT Article
DE generative artificial intelligence; training data; civil liability;
   advertising
AB We are witnessing an accelerated technological evolution that has enabled the development of artificial intelligence in various fields, allowing it to gradually infiltrate the entire society. We intend to cover only a small subset of AI technologies in our paper, that of Generative Artificial Intelligence (GenAI). Our objectives are to shed light on the legal issues that GenAI can cause and to find solutions to them. We begin with a definition of GenAI in the much broader context of AI technologies. Answers to a few essential questions are to be found: 'How does GenAI work?', 'What could GenAI be used for?', 'What legal issues could arise from using a GenAI?'. To accomplish our goals, we first conduct a literature review to define artificial intelligence (AI) in general and GenAI in particular. Several lawsuits are chosen to illustrate the magnitude of the legal problems and to test the feasibility of possible solutions in both the national and EU legal systems. Then, we analyse GenAI's output, liability for its contents and for its use, altogether with examples of related contractual clauses.
C1 [Ungureanu, Carmen Tamara; Amironesei, Aura Elena] Alexandru Ioan Cuza Univ, Iasi, Romania.
   [Ungureanu, Carmen Tamara] Alexandru Ioan Cuza Univ, Fac Law, Iasi, Romania.
C3 Alexandru Ioan Cuza University; Alexandru Ioan Cuza University
RP Ungureanu, CT (corresponding author), Alexandru Ioan Cuza Univ, Fac Law, Iasi, Romania.
EM carmen.ungureanu@uaic.ro
FU Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI [PN-
   III-P4-PCE-2021- 1878]; PNCDI III 'Institutions, digitalization and
   regional development in the European Union
FX This work was supported by a grant of the Ministry of Research,
   Innovation and Digitization, CNCS - UEFISCDI, project number PN-
   III-P4-PCE-2021- 1878, within PNCDI III 'Institutions, digitalization
   and regional development in the European Union'.
CR Alemohammad S, 2023, Arxiv, DOI arXiv:2307.01850
   [Anonymous], 2022, Facial recognition: Italian SA fines Clearview AI EUR 20 million
   [Anonymous], 2023, Generative AI is a legal minefield. Axios
   [Anonymous], 2022, Doe v. Github
   [Anonymous], 2013, Terms of Use
   [Anonymous], 2011, FREQ ASK QUEST
   Ariyaratne H, 2023, ChatGPT and Intermediary Liability: Why Section 230 Does Not and Should Not Protect Generative Algorithms, DOI [10.2139/ssrn.4422583, DOI 10.2139/SSRN.4422583]
   Baias F.-A, 2022, Romanian Civil Code
   Banerjee Satya Shankar, 2021, International Journal of Enterprise Network Management, V12, P165, DOI 10.1504/IJENM.2021.116438
   Bastian M., 2023, The DecoderJuly 22
   Bellapu A., 2023, Analytics InsightJanuary 8
   Bertolini A, 2020, Policy Department for Citizens' Rights and Constitutional Affairs, P1
   Business & Human Rights Centre, 2023, OpenAI and Sama hired underpaid Workers in Kenya to filter toxic content for ChatGPT
   California Northern District Court, 2023, Andersen et al. v. Stability AI Ltd. et al. 3:23-CV -00201.
   Carsaniga G., 2022, Open data maturity report 2022
   Cellan-Jones R., 2014, Stephen Hawking warns artificial intelligence could end mankind
   Clifford C., 2018, CNBCMay 13
   Creative Commons CC0, No Rights Reserved
   creativecommons, Attribution 4.0 International (CC BY 4.0)
   Custers B., 2022, Law and Artificial Intelligence Regulating AI and Applying AI in Legal Practice, P3, DOI [10.2139/ssrn.4331754, DOI 10.2139/SSRN.4331754]
   Davis W., 2023, The VergeJuly 9
   Day C., 2016, ColliderMay 5
   Dee Celine Melanie A., 2018, Delphi-Interdisciplinary Review of Emerging Technologies, V1/, P31, DOI DOI 10.21552/DELPHI/2018/1/11
   Demopoulos Alaina, 2023, Guardian
   Dentons, 2023, Part 3: Generative AI-Navigating commercial and civil liability
   Dominte N. R., 2021, Intellectual property law. Legal protection
   EESC, 2018, OJ C 440/51. 51-56
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   El Atillah I., 2023, EuronewsMarch 31
   El Awad, 2021, Journal of Machine Learning Research, V22, P1
   Ericsson, 2022, Ericsson, AI and privacy: Everything you need to know about trust and technology
   EUR-Lex, 2021, PROPOSAL REGULATION
   European Commission, 2022, Proposal for a directive of the European Parliament and of the council on ambient air quality and cleaner air for Europe
   European Commission, 2023, Commission defines high -value datasets to be made available for re -use
   European Parliament, 2023, COM20210206 EUR PARL
   Fallmann D., 2022, ForbesMay 3
   Franceschelli G, 2022, DATA POLICY, V4, DOI 10.1017/dap.2022.10
   Franzen C., 2023, Venture BeatJuly 24
   Garon J. M., 2023, A practical introduction to generative AI, synthetic media, and the messages found in the latest medium, DOI [10.2139/ssrn.4388437, DOI 10.2139/SSRN.4388437]
   Gatto J., 2023, How Generative AI Generates Legal Issues in the Games Industry
   Generative AI, The Power of Generative AI.
   gettyimages.com, 2023, News.room.
   Github.com, Copilot.
   GitHub Docs, GitHub Terms of Service.
   Glover E., 2023, BuiltInOctober 31
   Goldman J., 2023, LexologyJanuary 18
   Gretel.ai, Terms of use.
   Gretel.ai, Gretel.ai's subscription services agreement.
   Hacker P, 2023, Arxiv, DOI [arXiv:2302.02337, 10.48550/arXiv.2302.02337, 10.48550/ARXIV.2302.02337]
   Hauselmann A, 2022, Information technology and law series, V35, P43
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   Hine E, 2023, MIND MACH, V33, P285, DOI 10.1007/s11023-023-09625-1
   Hobbs J., 2023, New York PostMarch 15
   Inaltong N. U, 2020, The Evolution of The Notion of Fair Use in Copyright Law: Where Are We Today, P15, DOI [10.2139/ssrn.3928852, DOI 10.2139/SSRN.3928852]
   Italie H., 2023, AP NewSeptember 21
   JDSUPRA, 2023, The Generative AI Revolution: Key Legal Considerations for the Fashion & Retail Industry
   Jonsson M, 2022, PROCEEDINGS OF THE 14TH CREATIVITY AND COGNITION, C&C 2022, P5, DOI 10.1145/3527927.3532801
   Klein M., 2020, ForbesNovember 17
   Li YL, 2021, PROC VLDB ENDOW, V14, P3182, DOI 10.14778/3476311.3476403
   Lucini F, 2022, MIT SLOAN MANAGE REV, V63, P11
   Mann J., 2023, Ship TechnologyMarch 13
   Margoni Thomas, 2022, GRUR International, V71, P685, DOI DOI 10.1093/GRURINT/IKAC054
   McKinsey & Company, 2023, WHAT IS GENERATIVE A
   Measures for the Management of Generative Artificial Intelligence Services, 2023, Popular Republic of China.
   Merken S., 2023, ReutersJune 9
   Meta, Accelerating MRI Scans with AI.
   Meta.ai, Generating Biographies. Meta AI develops a novel dataset and model to help bring more representation to Wikipedia.
   Meta.ai, Introducing Llama 2.
   Meta.ai, 2023, Llama 2 Responsible Use Guide.
   Mok A., 2021, InsiderSeptember
   Mosqueira-Rey E, 2023, ARTIF INTELL REV, V56, P3005, DOI 10.1007/s10462-022-10246-w
   Muller M, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3503719
   O'Leary T, 2020, ALTA LAW REV, V58, P249
   Open Source Initiative, European Union Public LicenseVersion 1.2
   Open Source Initiative, OSI Approved Licenses (italic).
   OpenAI, Usage Policies
   OpenAI, Terms of Use.
   Park E., 2023, Orange County Lawyer Mag., V65
   Paschen J, 2020, BUS HORIZONS, V63, P403, DOI 10.1016/j.bushor.2020.01.003
   Peter J., 2023, The VergeApril 5
   Rephrase.ai, Terms of Service.
   Rouse M., 2023, TechopediaJune 16
   Russell SJ, 2021, ARTIF INTELL
   Sag M, 2023, Testimony before the U.S. Senate Committee on the Judiciary Subcommittee on Intellectual Property Hearing on 'Artificial Intelligence and Intellectual Property-Part II: Copyright and Artificial Intelligence'
   Sag Matthew, 2023, Houston Law Review, V61, P295
   Sheehan M, 2023, China's AI Regulations and How They Get Made
   Smalley S., 2023, ReutersJanuary 28
   Sobel B., 2020, Artificial Intelligence and Intellectual Property, P1, DOI [10.2139/ssrn.3677548, DOI 10.2139/SSRN.3677548]
   Sobel Benjamin L.W., 2017, Columbia Journal of Law and the Arts, V41, P45
   Surden H., 2019, Georgia State Univ. Law Rev., V35, P19
   Synthesia, Terms of Service.
   Tate R. -M., 2023, MIT Technology ReviewJune 26
   The White House, 2022, Applying the Blueprint for an AI Bill of Rights.
   The White House, 2023, FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
   TikTok, Terms of Service
   U.S. Copyright Office, 2023, Zarya of the Dawn Letter (Registration # VAu001480196).
   UK Department for Education, 2023, Generative artificial intelligence in education. Departmental statement
   VINCENT J., 2023, The VergeJanuary 16
   Weiser B., 2023, The New York TimesJune 8
   Wilson J, 2018, HARVARD BUS REV, V96, P115
   Wu Y., 2023, China BriefingJuly 23
NR 101
TC 0
Z9 0
U1 8
U2 18
PU UNIV ALEXANDRU IOAN CUZA, CENTRUL STUDII EUROPENE
PI IASI
PA BULEVARDUL CAROL I, NR 19, IASI, 700507, ROMANIA
SN 2068-651X
EI 2068-6633
J9 EAST J EUR STUD
JI East. J. Eur. Stud.
PD DEC
PY 2023
VL 14
IS 2
BP 45
EP 75
DI 10.47743/ejes-2023-0203
PG 31
WC Area Studies
WE Emerging Sources Citation Index (ESCI)
SC Area Studies
GA ED6V8
UT WOS:001137028500012
OA gold
DA 2024-12-25
ER

PT J
AU Sandrini, L
   Somogyi, R
AF Sandrini, Luca
   Somogyi, Robert
TI Generative AI and deceptive news consumption
SO ECONOMICS LETTERS
LA English
DT Article
DE Generative AI; News media market; Online advertising; Clickbait; Fake
   news
AB In this paper, we analyze the effects of advancements in generative Artificial Intelligence (GenAI) on the news media market. We model a representative consumer who allocates their time between reading news and deceptive articles. We find that GenAI may induce consumers to inefficiently reallocate their time and increase the consumption of the lower value good, i.e. deceptive content (clickbait articles or fake news). Therefore, early-stage GenAI distorts the incentives of consumers and reduces their welfare. After GenAI technology reaches a certain threshold, however, consumers start benefiting from its advancements. Finally, we find that the negative effects of early-stage GenAI are exacerbated as they induce a lower level of investment in news production.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
C1 [Sandrini, Luca] Budapest Univ Technol & Econ, QSMS Res Ctr, Muegyet rkp 3, H-1111 Budapest, Hungary.
   [Somogyi, Robert] Budapest Univ Technol & Econ, Dept Finance, Muegyet Rkp 3, H-1111 Budapest, Hungary.
   [Somogyi, Robert] Ctr Econ & Reg Studies, Toth Kalman Utca 4, H-1097 Budapest, Hungary.
C3 Budapest University of Technology & Economics; Budapest University of
   Technology & Economics; Hungarian Academy of Sciences; Hungarian
   Research Network; HUN-REN Centre for Economic & Regional Studies
RP Sandrini, L (corresponding author), Budapest Univ Technol & Econ, QSMS Res Ctr, Muegyet rkp 3, H-1111 Budapest, Hungary.
EM sandrini.luca@gtk.bme.hu
RI Sandrini, Luca/LKK-8209-2024; Somogyi, Robert/C-7476-2019
OI Somogyi, Robert/0000-0003-1033-1754
FU National Research Development and Innovation Office (NKFIH) [OTKA
   FK-142492]
FX We thank Aniko Grad-Gyenge, Laszlo A. Koczy, Melika Liporace, Leonardo
   Madio and seminar participants at the QSMS seminar for useful comments.
   Robert Somogyi thanks the support of the National Research Development
   and Innovation Office (NKFIH) under grant number OTKA FK-142492.
CR Acemoglu D., 2021, TASKS AUTOMATION, DOI DOI 10.3386/W28920
   Ahmad W., 2023, SSRN
   Anderson SP, 2020, J ECON THEORY, V186, DOI 10.1016/j.jet.2019.104990
   Anderson SP, 2018, ECON J, V128, P34, DOI 10.1111/ecoj.12428
   Anderson SP, 2005, REV ECON STUD, V72, P947, DOI 10.1111/0034-6527.00357
   Angelucci C, 2019, AM ECON J-MICROECON, V11, P319, DOI 10.1257/mic.20170306
   Athey S., 2021, WORKING PAPER SERIES, V28746
   Calzada J, 2020, MARKET SCI, V39, P134, DOI 10.1287/mksc.2019.1150
   Choné P, 2020, INT J IND ORGAN, V73, DOI 10.1016/j.ijindorg.2020.102663
   D'Annunzio A, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.1435
   Fergusson G., 2023, GENERATING HARMS GEN
   Freimane M., 2023, SUBSTITUTING A UNPUB
   Jones Charles I., 2023, The AI Dilemma: Growth versus Existential Risk
   NAGLER MG, 1993, J PUBLIC ECON, V51, P359, DOI 10.1016/0047-2727(93)90071-Z
   Ngo R, 2022, Arxiv, DOI arXiv:2209.00626
   Piccolo S, 2018, MANAGE SCI, V64, P1291, DOI 10.1287/mnsc.2016.2665
   Sandrini L., 2023, SSRN
   Simonov A., 2023, 17956 CEPR
   SINGH N, 1984, RAND J ECON, V15, P546, DOI 10.2307/2555525
   Zinman J, 2016, AM ECON J-APPL ECON, V8, P177, DOI 10.1257/app.20130346
NR 20
TC 3
Z9 3
U1 38
U2 143
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0165-1765
EI 1873-7374
J9 ECON LETT
JI Econ. Lett.
PD NOV
PY 2023
VL 232
AR 111317
DI 10.1016/j.econlet.2023.111317
EA SEP 2023
PG 4
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA S5JA1
UT WOS:001071515900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Hashmi, N
   Bal, AS
AF Hashmi, Nada
   Bal, Anjali S.
TI Generative AI in higher education and beyond
SO BUSINESS HORIZONS
LA English
DT Article
DE Generative AI; Higher education; Typography; ChatGPT; Artificial
   intelligence
AB Generative artificial intelligence (GenAI) is a method of machine learning that uses algorithms to create new content such as images, text, and video. In the last year, the popularity of GenAI has exploded. Websites like ChatGPT and DALL-E have become ubiquitous in everything from logo and NFT creation to social media content and artistic verse construction. While the popularity of GenAI is undeniable, the adoption of these technological tools has been splintered in higher education. This conceptual study examines the relationship between transparency and responsibility in the usage of GenAI. We go further, examining the relationship between training and application of skills within higher education. Finally, we propose a framework for how higher education can engage with GenAI to better prepare students to use it outside of school. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
C1 [Hashmi, Nada; Bal, Anjali S.] Babson Coll, Babson Pk, MA 02457 USA.
C3 Babson College
RP Bal, AS (corresponding author), Babson Coll, Babson Pk, MA 02457 USA.
EM nhashmi@babson.edu; abal@babson.edu
CR Abdelhalim E., 2024, Business Horizons, V67, P487
   [Anonymous], 2021, MIT NewsMay 18
   [Anonymous], 2017, McKinsey and Company Global Institute
   Arntz M., 2016, OECD Social, Employment and Migration Working Papers, V189, P1, DOI [10.1787/5jlz9h56dvq7-en, DOI 10.1787/1815199X]
   Banks S., 2011, A historical analysis of attitudes toward the use of calculators in junior high and high school math classrooms in the United States since 1975
   Berthon P., 2024, Business Horizons, V67, P461
   Bessen J. E., 2019, Working paper no. 24235
   Brown TB, 2020, ADV NEUR IN, V33
   Brynjolfsson E., 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
   Bughin J., 2018, SKILL SHIFT AUTOMATI
   Castelvecchi D., 2016, Nature News
   Cho KJ, 2020, CRIT CARE MED, V48, pE285, DOI 10.1097/CCM.0000000000004236
   Crawford K, 2016, NATURE, V538, P311, DOI 10.1038/538311a
   Cu M. A., 2023, STANFORD DAILY  0122
   Davenport TH, 2018, HARVARD BUS REV, V96, P108
   DuBois KN, 2019, PERSPECT SCI CHRIST, V71, P199
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Heaven W. D., 2023, MIT Technology ReviewApril 6
   IBM, What are AI hallucinations?
   Kelion L., 2018, BBC News
   Kietzmann J., 2024, Business Horizons, V67, P453
   KPMG, 2023, Despite popularity, six in 10 students consider generative AI cheating
   Marcus G., 2018, PREPRINT, DOI 10.48550/arXiv.1801.00631
   Markoff John., 2015, Machines of Loving Grace: The Quest for Common Ground between Humans and Robots
   McCorduck P, 2004, MACHINES WHO THINK P
   McKinsey, 2023, What's the future of generative AI? An early view in 15 charts
   Taecharungroj V, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7010035
NR 28
TC 15
Z9 15
U1 127
U2 127
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 607
EP 614
DI 10.1016/j.bushor.2024.05.005
EA AUG 2024
PG 8
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2M0Y
UT WOS:001301383700001
DA 2024-12-25
ER

PT J
AU Shin, D
   Koerber, A
   Lim, JS
AF Shin, Donghee
   Koerber, Amy
   Lim, Joon Soo
TI Impact of misinformation from generative AI on user information
   processing: How people understand misinformation from generative AI
SO NEW MEDIA & SOCIETY
LA English
DT Article; Early Access
DE Algorithmic effects on misinformation; algorithmic misinformation;
   ChatGPT; generative AI; heuristic-systematic process;
   misinformation-processing model
ID DIAGNOSTICITY; SERENDIPITY; RESPONSES; NEWS
AB This study examines the impact of artificial intelligence (AI) on the ways in which users process and respond to misinformation in generative artificial intelligence (GenAI) contexts. Drawing on the heuristic-systematic model and the concept of diagnosticity, our approach examines a cognitive model for processing misinformation in GenAI. The study's findings revealed that users with a high-heuristic processing mechanism, which affects positive diagnostic perception, were more likely to proactively discern misinformation than users with low-heuristic processing and low-perceived diagnosticity. When exposed to misinformation from GenAI, users' perceived diagnosticity of misinformation can be accurately predicted by the ways in which they perform heuristic systematic evaluations. With this focus on misinformation processing, this study provides theoretical insights and relevant recommendations for firms to be more resilient in protecting users from the detrimental impacts of misinformation.
C1 [Shin, Donghee] Texas Tech Univ, Dept Chair, Lubbock, TX USA.
   [Koerber, Amy] Texas Tech Univ, Commun Studies, Lubbock, TX USA.
   [Lim, Joon Soo] Syracuse Univ, Newhouse Sch Publ Commun, Publ Relat, Syracuse, NY USA.
   [Shin, Donghee] Texas Tech Univ, Coll Media & Commun, Digital Media & Profess Commun, 3003 15th St, Lubbock, TX 79409 USA.
C3 Texas Tech University System; Texas Tech University; Texas Tech
   University System; Texas Tech University; Syracuse University; Texas
   Tech University System; Texas Tech University
RP Shin, D (corresponding author), Texas Tech Univ, Coll Media & Commun, Digital Media & Profess Commun, 3003 15th St, Lubbock, TX 79409 USA.
EM don.h.shin@ttu.edu
RI Lim, Joon Soo/N-4045-2018; Shin, Don/T-3545-2019
OI Lim, Joon Soo/0000-0003-0519-4169; Shin, Don D.H./0000-0002-5439-4493
CR Ahluwalia R, 2001, J MARKETING RES, V38, P458, DOI 10.1509/jmkr.38.4.458.18903
   Ahmed S, 2022, J MASS COMMUN Q, DOI 10.1177/10776990221110113
   Ali K, 2022, INTERNET RES, V32, P379, DOI 10.1108/INTR-10-2019-0442
   Amoozadeh M, 2024, Arxiv, DOI [arXiv:2310.04631, 10.48550/arxiv.2310.04631, DOI 10.48550/ARXIV.2310.04631]
   Barnoy A, 2022, COMMUN RES, V49, P196, DOI 10.1177/0093650220911814
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Borukhson D., 2021, PROC ANN MEET COGN S, V43, P119, DOI [10.1007/s42113-022-00136-3, DOI 10.1007/S42113-022-00136-3]
   Bryanov K, 2020, PUBLIC OPIN QUART, V84, P216, DOI 10.1093/poq/nfaa011
   Brynjolfsson Erik, 2023, Working Paper No. 31161
   Cappella JN, 2006, J COMMUN, V56, pS265, DOI 10.1111/j.1460-2466.2006.00293.x
   Chaiken Shelly., 1996, PSYCHOL ACTION, P553
   Chen ZFF, 2020, J PROD BRAND MANAG, V29, P188, DOI 10.1108/JPBM-12-2018-2145
   Cho J, 2020, J BROADCAST ELECTRON, V64, P150, DOI 10.1080/08838151.2020.1757365
   Cronbach L.J., 1989, INTELLIGENCE, P147
   De Angelis L, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1166120
   Diakopoulos N, 2017, DIGIT JOURNAL, V5, P809, DOI 10.1080/21670811.2016.1208053
   Ecker UKH, 2022, NAT REV PSYCHOL, V1, P13, DOI 10.1038/s44159-021-00006-y
   Epstein Ziv., Harvard Kennedy School Misinformation Review, P2021, DOI DOI 10.37016/MR-2020-71
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Gran AB, 2021, INFORM COMMUN SOC, V24, P1779, DOI 10.1080/1369118X.2020.1736124
   HAIR JF, 1995, MULTIVARIATE ANAL RE
   Hwang Y, 2023, HEALTH COMMUN, V38, P585, DOI 10.1080/10410236.2021.1964187
   Islam AKMN, 2020, TECHNOL FORECAST SOC, V159, DOI 10.1016/j.techfore.2020.120201
   Joreskog K. G., 1996, LISREL 8 USERS REFER, DOI Chicago
   Kim HK, 2020, SCI COMMUN, V42, P586, DOI 10.1177/1075547020959670
   Kwon Y, 2021, INTERNET RES, V31, P562, DOI 10.1108/INTR-03-2020-0127
   Lazer DMJ, 2018, SCIENCE, V359, P1094, DOI 10.1126/science.aao2998
   Lee J, 2023, HEALTH COMMUN, V38, P1780, DOI 10.1080/10410236.2022.2031452
   Lewandowsky S, 2021, EUR REV SOC PSYCHOL, V32, P348, DOI 10.1080/10463283.2021.1876983
   Margolin DB, 2021, COMMUN THEOR, V31, P714, DOI 10.1093/ct/qtaa002
   Melchior C, 2022, NEW MEDIA SOC, V24, P1500, DOI 10.1177/14614448211038762
   Mhasawade V, 2021, NAT MACH INTELL, V3, P659, DOI 10.1038/s42256-021-00373-4
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Molina M., 2023, New Media Society
   Niu WS, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102362
   Peifer JT, 2021, J MASS COMMUN Q, V98, P828, DOI 10.1177/10776990211012953
   Peng L., 2023, Health Communication, V31, P1
   Pennycook G, 2023, ADV EXP SOC PSYCHOL, V67, P131, DOI 10.1016/bs.aesp.2022.11.003
   Pennycook G, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-30073-5
   Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
   Schrills T, 2023, ACM T INTERACT INTEL, V13, DOI 10.1145/3588594
   Schuetz SW, 2021, EUR J INFORM SYST, V30, P376, DOI 10.1080/0960085X.2021.1895682
   Shin D., 2024, Artificial Misinformation: Exploring Human-Algorithm Interaction Online
   Shin D. D., 2023, Algorithms, humans, and interactions: How do algorithms interact with people? Designing meaningful ai experiences
   Shin D, 2020, J BROADCAST ELECTRON, V64, P541, DOI 10.1080/08838151.2020.1843357
   Shin D, 2019, COMPUT HUM BEHAV, V98, P277, DOI 10.1016/j.chb.2019.04.019
   Shin J, 2020, J HEALTH COMMUN, V25, P394, DOI 10.1080/10810730.2020.1776423
   Siering M, 2022, DECIS SUPPORT SYST, V158, DOI 10.1016/j.dss.2022.113782
   Starke C, 2022, BIG DATA SOC, V9, DOI 10.1177/20539517221115189
   Stecula D.A., 2020, HARVARD KENNEDY SCH, V1, DOI DOI 10.37016/MR-2020-007
   Sundar SS, 2007, J AM SOC INF SCI TEC, V58, P366, DOI 10.1002/asi.20511
   Tamkin A, 2021, Arxiv, DOI [arXiv:2102.02503, 10.48550/arXiv.2102.02503, DOI 10.48550/ARXIV.2102.02503]
   Todorov A., 2002, The persuasion handbook: Developments in theory and practice, V23, P195, DOI 10.4135/9781412976046.N11
   Tully M, 2020, SOC MEDIA SOC, V6, DOI 10.1177/2056305120978377
   TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
   UK National Cyber Security Center, 2023, ChatGPT and large language models
   Varghese J, 2024, J HEPATOL, V80, P977, DOI 10.1016/j.jhep.2023.07.028
   Vraga EK, 2020, POLIT COMMUN, V37, P136, DOI 10.1080/10584609.2020.1716500
   Vu HT, 2024, HEALTH COMMUN, V39, P1113, DOI 10.1080/10410236.2023.2206177
   Walter N, 2021, HEALTH COMMUN, V36, P1776, DOI 10.1080/10410236.2020.1794553
   Walter N, 2020, COMMUN RES, V47, P155, DOI 10.1177/0093650219854600
   Wu SW, 2024, HEALTH COMMUN, V39, P96, DOI 10.1080/10410236.2022.2159143
   Wu YY, 2023, HEALTH COMMUN, V38, P1416, DOI 10.1080/10410236.2021.2010891
   Yi C, 2017, INFORM SYST RES, V28, P413, DOI 10.1287/isre.2017.0695
   Zhao XY, 2024, HEALTH COMMUN, V39, P741, DOI 10.1080/10410236.2023.2184452
   Zrnec A, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102739
NR 66
TC 12
Z9 12
U1 179
U2 282
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1461-4448
EI 1461-7315
J9 NEW MEDIA SOC
JI New Media Soc.
PD 2024 MAR 20
PY 2024
DI 10.1177/14614448241234040
EA MAR 2024
PG 31
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA LV5Z4
UT WOS:001189601300001
DA 2024-12-25
ER

PT J
AU Cheung, KKC
   Pun, J
   Kenneth-Li, W
   Mai, JY
AF Cheung, Kason Ka Ching
   Pun, Jack
   Kenneth-Li, Wangyin
   Mai, Jiayi
TI Exploring Students' Multimodal Representations of Ideas About Epistemic
   Reading of Scientific Texts in Generative AI Tools
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article; Early Access
DE Generative artificial intelligence; Nature of science; Nature of GenAI;
   Multimodality
ID SCIENCE; BELIEFS
AB As students read scientific texts created in generative artificial intelligence (GenAI) tools, they need to draw on their epistemic knowledge of GenAI as well as that of science. However, only a few research discussed multimodality as a methodological approach in characterising students' ideas of GenAI-science epistemic reading. This study qualitatively explored 44 eighth and ninth graders' multimodal representations of ideas about GenAI-science epistemic reading and developed an analytical framework based on Lemke's (1998) typology of representational meaning, namely presentational, organisational, and orientational meanings. Under each representational meaning, several categories were inductively generated while students expressed preferences in using drawn, written, or both drawn and written mode to express certain categories. Findings indicate that a multimodal approach is fruitful in characterising students' semiotic resources in meaning-making of ideas about GenAI-science epistemic reading. We suggested implications regarding future intervention studies on tracking students' ideas about GenAI-science epistemic reading using the analytical framework developed in this study.
C1 [Cheung, Kason Ka Ching] Educ Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
   [Pun, Jack; Kenneth-Li, Wangyin; Mai, Jiayi] City Univ Hong Kong, Dept English, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); City University of Hong Kong
RP Pun, J (corresponding author), City Univ Hong Kong, Dept English, Hong Kong, Peoples R China.
EM cheungkac@eduhk.hk; jack.pun@cityu.edu.hk; wangyili@cityu.edu.hk;
   maggimai@cityu.edu.hk
RI Cheung, Kason Ka Ching/AAR-5020-2021; Pun, Jack/AAY-3953-2021; Pun,
   Jack/M-7557-2015
OI Pun, Jack/0000-0002-8043-7645
FU Quality Education Fund, Hong Kong SAR Government [9420033]
FX Open access publishing enabled by City University of Hong Kong Library's
   agreement with Springer Nature. The study reported in this manuscript is
   based upon work supported by the Quality Education Fund, Hong Kong SAR
   Government (Grant Num-ber: 9420033).
CR Airey J, 2017, MODEL MODEL SCI EDUC, V10, P95, DOI 10.1007/978-3-319-58914-5_5
   Alasadi EA, 2024, J CHEM EDUC, DOI 10.1021/acs.jchemed.4c00138
   Barak M, 2023, RES SCI EDUC, V53, P507, DOI 10.1007/s11165-022-10069-3
   Barelli E, 2024, SCI EDUC-NETHERLANDS, DOI 10.1007/s11191-024-00504-4
   Billingsley B., 2017, School Science Review, V99, P367
   Bråten I, 2011, EDUC PSYCHOL-US, V46, P48, DOI 10.1080/00461520.2011.538647
   Chang HY, 2023, J SCI EDUC TECHNOL, V32, P267, DOI 10.1007/s10956-022-10026-9
   Cheung K. K. C., 2024, Science & Education, P1
   Cheung KKC, 2024, RES SCI EDUC, V54, P957, DOI 10.1007/s11165-024-10177-2
   Cheung KKC, 2023, RES SCI TECHNOL EDUC, V41, P1155, DOI 10.1080/02635143.2021.1993179
   Conley AM, 2004, CONTEMP EDUC PSYCHOL, V29, P186, DOI 10.1016/j.cedpsych.2004.01.004
   Cooper G, 2024, J SCI EDUC TECHNOL, V33, P556, DOI 10.1007/s10956-024-10104-0
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Erduran S, 2024, INT J SCI EDUC, V46, P1982, DOI 10.1080/09500693.2024.2306604
   Erduran Sibel, 2023, Science, V382, peadm9788, DOI 10.1126/science.adm9788
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Ford MJ, 2006, REV RES EDUC, V30, P1, DOI 10.3102/0091732X030001001
   Fui-Hoon Nah F., 2023, Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration, V25, P277, DOI DOI 10.1080/15228053.2023.22338
   Grapin SE, 2023, J RES SCI TEACH, V60, P1998, DOI 10.1002/tea.21850
   Gursesli M. C., 2023, INT C INT DIG STOR
   Jeon J, 2023, COMPUT EDUC, V206, DOI 10.1016/j.compedu.2023.104898
   Johns R., 2010, SURVEY QUESTION BANK, V1, P11
   Kim K, 2023, EDUC INF TECHNOL, V28, P9827, DOI 10.1007/s10639-023-11600-3
   Kim WJ, 2022, ASIA-PAC SCI EDUC, V8, P9, DOI 10.1163/23641177-bja10041
   Konnemann C, 2018, RES SCI EDUC, V48, P1187, DOI 10.1007/s11165-018-9783-y
   Kress G., 2010, Multimodality: A social semiotic approach to contemporary communication
   Lemke J., 1998, READING SCI
   Mellor D, 2014, J PEDIATR PSYCHOL, V39, P369, DOI 10.1093/jpepsy/jst079
   Ng DTK, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13411
   Ng DTK, 2023, IEEE INT CONF ADV LE, P233, DOI 10.1109/ICALT58122.2023.00074
   Nigam A, 2021, EXPERT OPIN DRUG DIS, V16, P1009, DOI 10.1080/17460441.2021.1925247
   OHalloran K.L., 2011, The Bloomsbury handbook of discourse analysis, P249
   Origgi G, 2012, SOC EPISTEMOL, V26, P221, DOI 10.1080/02691728.2011.652213
   Paap KR, 2024, BEHAV RES METHODS, V56, P908, DOI 10.3758/s13428-023-02089-2
   Park J, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00454-3
   Peirce CharlesS., 1950, Philosophical Writings of Peirce, P98
   Prain V, 2012, INT J SCI EDUC, V34, P2751, DOI 10.1080/09500693.2011.626462
   Quinto-Pozos D, 2015, TOP COGN SCI, V7, P12, DOI 10.1111/tops.12120
   Schraw G, 2002, PERSONAL EPISTEMOLOGY: THE PSYCHOLOGY OF BELIEFS ABOUT KNOWLEDGE KNOWING, P261
   Stake R. E., 2013, Multiple case study analysis.
   Subedi B.P., 2016, International Journal of Contemporary Applied Sciences, V3, P36
   Tang K.-S., 2024, Science & Education, P1
   Tang KS, 2022, INT J SCI MATH EDUC, V20, P1, DOI 10.1007/s10763-022-10322-1
   Tang KS, 2022, INT J SCI EDUC, V44, P179, DOI 10.1080/09500693.2021.2021313
   Tang KS, 2019, INT J SCI EDUC, V41, P2296, DOI 10.1080/09500693.2019.1672906
   Tu YF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2286370
   van Leeuwen T., 2005, Introducing social semiotics
   Wanselin H, 2022, RES SCI EDUC, V52, P891, DOI 10.1007/s11165-021-10027-5
   Wilholt T, 2013, BRIT J PHILOS SCI, V64, P233, DOI 10.1093/bjps/axs007
   Yeh HY, 2019, J SCI EDUC TECHNOL, V28, P329, DOI 10.1007/s10956-019-9769-1
NR 50
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD 2024 DEC 5
PY 2024
DI 10.1007/s10956-024-10182-0
EA DEC 2024
PG 14
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA O4F0F
UT WOS:001370693900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Bai, SR
   Gonda, DE
   Hew, KF
AF Bai, Shurui
   Gonda, Donn Emmanuel
   Hew, Khe Foon
TI Write-Curate-Verify: A Case Study of Leveraging Generative AI for
   Scenario Writing in Scenario-Based Learning
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Generative artificial intelligence (GenAI); intrinsic motivation; prompt
   engineering; scenario-based learning (SBL)
ID CLASSICAL TEST THEORY; ITEM RESPONSE THEORY; STRATEGIES; MOTIVATION;
   STUDENTS
AB This case study explored the use of generative artificial intelligence (GenAI), specifically chat generative pretraining transformer (ChatGPT), in writing scenarios for scenario-based learning (SBL). Our research addressed three key questions: 1) how do teachers leverage GenAI to write scenarios for SBL purposes? 2) what is the quality of GenAI-generated SBL scenarios and tasks? and 3) how does GenAI-supported SBL affect students' motivation, learning performance, and learning perceptions? A three-step prompting engineering process (write the prompts, curate the output, and verify the output, WCV) was established during the teacher interaction with GenAI in the scenario writing. Findings revealed that by using the WCV approach, ChatGPT enabled the efficient creation of quality scenarios for SBL purposes in a short timeframe. Moreover, students exhibited increased intrinsic motivation, learning performance, and positive attitudes toward GenAI-supported scenarios. We also suggest guidelines for using the WCV prompt engineering process in scenario writing.
C1 [Bai, Shurui] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Gonda, Donn Emmanuel; Hew, Khe Foon] Univ Hong Kong, Fac Educ, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); University of Hong Kong
RP Bai, SR (corresponding author), Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
RI Bai, Shurui/ABF-9370-2021
OI Bai, Shurui/0000-0003-2004-7810
FU Faculty-Level Teaching Development
FX No Statement Available
CR AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Azevedo JM, 2019, INT J INF LEARN TECH, V36, P322, DOI 10.1108/IJILT-02-2019-0023
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bardach L, 2021, COMPUT EDUC, V169, DOI 10.1016/j.compedu.2021.104194
   Bowman G, 2013, TECHNOL FORECAST SOC, V80, P735, DOI 10.1016/j.techfore.2012.04.009
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Burmark L., 2004, Media and Methods, V40, P4
   Chenail RJ, 2011, QUAL REP, V16, P255
   Cohen SD, 2011, COMMUN TEACH, V25, P197, DOI 10.1080/17404622.2011.601726
   De Champlain AF, 2010, MED EDUC, V44, P109, DOI 10.1111/j.1365-2923.2009.03425.x
   Deshpande A, 2023, Arxiv, DOI arXiv:2304.05335
   Dijkstra R., 2022, P 4 INT WORKSH INT T, P4
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Geerling W., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4356034, DOI 10.2139/SSRN.4356034]
   Gross N, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12080435
   GUBA EG, 1981, ECTJ-EDUC COMMUN TEC, V29, P75
   Haladyna T. M, 2012, DEVELOPING VALIDATIN, V3rd, DOI [10.4324/9780203825945, DOI 10.4324/9780203825945]
   Hu K., 2023, REUTERS         0202
   Klassen J. V., 2021, Teaching Teacher Educ., V104, DOI [10.1016/j.tate.2021.103385.[5]S, DOI 10.1016/J.TATE.2021.103385.[5]S]
   Lambert J., 2018, Digital storytelling: Capturing lives, creating community, V5th, DOI DOI 10.4324/9781351266369
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   MacDonald P, 2002, EDUC PSYCHOL MEAS, V62, P921, DOI 10.1177/0013164402238082
   Mamakli S, 2023, ADV PHYSIOL EDUC, V47, P144, DOI 10.1152/advan.00122.2022
   Mantzarlis A., 2018, Journalism, Fake News Disinformation: Handbook for Journalism Education and Training, P85
   McAlpine M., 2002, PRINCIPLES ASSESSMEN
   Moon J, 2008, NURS EDUC TODAY, V28, P232, DOI 10.1016/j.nedt.2007.05.001
   Murphy H, 2003, EDUC STUD-UK, V29, P243, DOI 10.1080/03055690303278
   Nicholson D., 2009, P HUM FACT ERG SOC A, V53, P1932, DOI [DOI 10.1177/154193120905302611, 10.1177/154193120905302611\n[9]W]
   Ouyang L, 2022, ADV NEUR IN
   Patel N, 2023, J COMPUT ASSIST LEAR, V39, P804, DOI 10.1111/jcal.12776
   PINTRICH PR, 1993, EDUC PSYCHOL MEAS, V53, P801, DOI 10.1177/0013164493053003024
   Qu FY, 2021, 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), P2583
   Robin BR, 2008, THEOR PRACT, V47, P220, DOI 10.1080/00405840802153916
   Rodriguez-Torrealba R, 2022, EXPERT SYST APPL, V208, DOI 10.1016/j.eswa.2022.118258
   RYAN RM, 1982, J PERS SOC PSYCHOL, V43, P450, DOI 10.1037/0022-3514.43.3.450
   Sioni SR, 2017, COMPUT HUM BEHAV, V71, P11, DOI 10.1016/j.chb.2017.01.044
   Smith S. M., 2018, Teaching and learning in higher education, perspectives from UCL, P144
   Smith T, 2017, SIMULAT GAMING, V48, P832, DOI 10.1177/1046878117731888
   Taplay K., 2015, American Journal of Nursing Research, V7, P581, DOI DOI 10.12691/AJNR-7-4-20
   Teubner T, 2023, BUS INFORM SYST ENG+, V65, P95, DOI 10.1007/s12599-023-00795-x
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Unluer S, 2012, QUAL REP, V17
   Wang T.S, 2023, Playing story creation games with large language models: Experiments with GPT-3.5, P297, DOI [10.1007/978-3-031-47658-7_28, DOI 10.1007/978-3-031-47658-728]
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Wing JM, 2006, COMMUN ACM, V49, P33, DOI 10.1145/1118178.1118215
   Yang YTC, 2012, COMPUT EDUC, V59, P339, DOI 10.1016/j.compedu.2011.12.012
   Yin R., 2018, CASE STUDY RES APPL
   Yuan A, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P841, DOI 10.1145/3490099.3511105
   Zitouniatis A, 2023, EDUC INF TECHNOL, V28, P4017, DOI 10.1007/s10639-022-11366-0
NR 49
TC 4
Z9 4
U1 93
U2 129
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PY 2024
VL 17
BP 1313
EP 1324
DI 10.1109/TLT.2024.3378306
PG 12
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA NJ2A1
UT WOS:001200007200002
DA 2024-12-25
ER

PT J
AU Hermann, E
   Puntoni, S
AF Hermann, Erik
   Puntoni, Stefano
TI Artificial intelligence and consumer behavior: From predictive to
   generative AI
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Artificial intelligence; Consumer behavior; Algorithms; Predictive AI;
   Generative AI
ID ALGORITHMS; PEOPLE; CULTURES; SCIENCE; AGE
AB Since the introduction of ChatGPT, the leading example of Generative Artificial Intelligence (GenAI), the research community and the general public have been captivated by GenAI's remarkable advances in performance, and its ability to both imitate and, in some respects, surpass human capabilities. This paper offers a comprehensive analysis of the impact of AI on consumer behavior, focusing on the two pivotal phases of AI development over the past 15 years. We start by reviewing the extensively researched, yet still growing, field of algorithmic predictions and decision-making, alongside the varied positive and negative consumer reactions it elicits. Subsequently, we delve into the just emerging field of GenAI. Here, we differentiate between Convergent Thinking GenAI, which is more domain-specific and geared towards pre-defined task completion, and Divergent Thinking GenAI, which is more domain-general and oriented towards new task fulfillment. For each of these realms, we identify key areas for future investigation.
C1 [Hermann, Erik] ESCP Business Sch, Dept Mkt, Heubnerweg 8-10, D-14093 Berlin, Germany.
   [Puntoni, Stefano] Univ Penn, Wharton Sch, 3730 Walnut St, Philadelphia, PA 19104 USA.
C3 heSam Universite; ESCP Business School; University of Pennsylvania
RP Hermann, E (corresponding author), ESCP Business Sch, Dept Mkt, Heubnerweg 8-10, D-14093 Berlin, Germany.
EM ehermann@escp.eu; puntoni@wharton.upenn.edu
CR Abdi E, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-19501-0
   Agrawal A, 2018, PREDICTION MACHINES
   Ahn J, 2022, J BUS RES, V141, P50, DOI 10.1016/j.jbusres.2021.12.007
   Ameen N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106548
   ANDREONI J, 1989, J POLIT ECON, V97, P1447, DOI 10.1086/261662
   Atari M., 2023, PsyArXiv 2023, DOI [DOI 10.31234/OSF.IO/5B26T, https://doi.org/10.31234/osf.io/5b26t]
   Baek TH, 2023, J CURR ISS RES AD, V44, P249, DOI 10.1080/10641734.2023.2243496
   Banker S, 2019, J PUBLIC POLICY MARK, V38, P500, DOI 10.1177/0743915619858057
   Bauer K, 2024, INFORM SYST RES, V35, DOI 10.1287/isre.2023.1217
   Berger B, 2021, BUS INFORM SYST ENG+, V63, P55, DOI 10.1007/s12599-020-00678-5
   Bigman YE, 2023, J EXP PSYCHOL GEN, V152, P4, DOI 10.1037/xge0001250
   Bigman YE, 2018, COGNITION, V181, P21, DOI 10.1016/j.cognition.2018.08.003
   Binz M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2218523120
   Blut M, 2023, J RETAILING, V99, P440, DOI 10.1016/j.jretai.2023.08.001
   Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
   Boden MA, 2003, The creative mind: myths and mechanisms
   Boyaci T, 2024, MANAGE SCI, V70, DOI 10.1287/mnsc.2023.4744
   Brand James., 2023, Using gpt for market research, DOI DOI 10.2139/SSRN.4395751
   Braun M, 2024, J CONSUM RES, V51, P119, DOI 10.1093/jcr/ucad058
   Bryksina O, 2020, J CONSUM PSYCHOL, V30, P579, DOI 10.1002/jcpy.1175
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Buyalskaya A, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2216115120
   Cadario R, 2021, NAT HUM BEHAV, V5, P1636, DOI 10.1038/s41562-021-01146-0
   Campbell C, 2023, J ADVERTISING RES, V63, P202, DOI 10.2501/JAR-2023-019
   Campbell C, 2022, J ADVERTISING RES, V62, P241, DOI 10.2501/JAR-2022-017
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Castelo N, 2024, J CONSUM PSYCHOL, V34, P326, DOI 10.1002/jcpy.1373
   Castelo N, 2023, J CONSUM RES, V50, P848, DOI 10.1093/jcr/ucad023
   Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
   Castelo Noah., 2019, Journal of the Association for Consumer Research, V4, P217, DOI DOI 10.1086/703462
   Chakrabarty T, 2024, Arxiv, DOI arXiv:2309.14556
   Chang YP, 2023, COMPUT HUM BEHAV, V148, DOI 10.1016/j.chb.2023.107872
   Chen CD, 2024, J BUS RES, V176, DOI 10.1016/j.jbusres.2024.114610
   Chen CD, 2024, J INTERACT MARK, V59, P234, DOI 10.1177/10949968231200221
   Chen Y, 2023, A manager and an AI walk into a bar: does ChatGPT make biased decisions like we do?, DOI DOI 10.2139/SSRN.4380365
   Chen Y., 2023, Proceedings of the National Academy of Science, V20
   Chintalapati S, 2022, INT J MARKET RES, V64, P38, DOI 10.1177/14707853211018428
   Clegg M, 2023, J CONSUM RES, V51, P342, DOI 10.1093/jcr/ucad075
   Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
   De Freitas J, 2024, J CONSUM PSYCHOL, V34, P481, DOI 10.1002/jcpy.1393
   Dietvorst BJ, 2022, J CONSUM PSYCHOL, V32, P406, DOI 10.1002/jcpy.1266
   Dietvorst BJ, 2018, MANAGE SCI, V64, P1155, DOI 10.1287/mnsc.2016.2643
   Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Du SL, 2021, J BUS RES, V129, P961, DOI 10.1016/j.jbusres.2020.08.024
   Duani N, 2024, J ASSOC CONSUM RES, V9, P257, DOI 10.1086/729440
   Duéñez-Guzmán EA, 2023, NAT MACH INTELL, DOI 10.1038/s42256-023-00754-x
   Epstein Z., 2023, PsyArXiv, DOI [10.31234/osf.io/v4mfz, DOI 10.31234/OSF.IO/V4MFZ]
   Epstein Z, 2023, Arxiv, DOI arXiv:2306.04141
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Eshraghian JK, 2020, NAT MACH INTELL, V2, P157, DOI 10.1038/s42256-020-0161-x
   Fan JY, 2023, J APPL PSYCHOL, V108, P1277, DOI 10.1037/apl0001082
   Fei NY, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-30761-2
   Flavián C, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114545
   Fox S, 2019, J CONSUM CULT, V19, P67, DOI 10.1177/1469540517705945
   Fox S, 2018, J CONSUM CULT, V18, P169, DOI 10.1177/1469540516659126
   Franceschelli G, 2024, Arxiv, DOI arXiv:2304.00008
   Garvey AM, 2023, J MARKETING, V87, P10, DOI 10.1177/00222429211066972
   Gelbrich K, 2023, PSYCHOL MARKET, V40, P2291, DOI 10.1002/mar.21893
   Gelfand MJ, 2011, SCIENCE, V332, P1100, DOI 10.1126/science.1197754
   Gigerenzer G, 2011, ANNU REV PSYCHOL, V62, P451, DOI 10.1146/annurev-psych-120709-145346
   Girotra K., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4526071, DOI 10.2139/SSRN.4526071]
   Giroux M, 2022, J BUS ETHICS, V178, P1027, DOI 10.1007/s10551-022-05056-7
   Gorlin M, 2012, J CONSUM PSYCHOL, V22, P320, DOI 10.1016/j.jcps.2012.05.002
   Grewal D, 2021, J BUS RES, V136, P229, DOI 10.1016/j.jbusres.2021.07.043
   Grynbaum M. M., 2023, The NewYork Times27 Dec
   Guha A, 2021, J RETAILING, V97, P28, DOI 10.1016/j.jretai.2021.01.005
   Guilford JP, 1950, AM PSYCHOL, V5, P444, DOI 10.1037/h0063487
   Haase J., 2023, Journal of Creativity, V33, DOI DOI 10.1016/J.YJOC.2023.100066
   Hagendorff T, 2024, Arxiv, DOI arXiv:2307.16513
   Hamilton R, 2021, J MARKETING, V85, P68, DOI 10.1177/0022242920908227
   Hannigan T., 2024, Business Horizons, DOI [10.2139/ssrn.4678265, DOI 10.2139/SSRN.4678265]
   Hartmann J., 2023, The power of generative marketing: Can generative AI reach human-level visual marketing content?, DOI [10.2139/ssrn.4597899, DOI 10.2139/SSRN.4597899]
   Henderson P., 2023, Foundation models and fair use, DOI [10.2139/ssrn.4404340, DOI 10.2139/SSRN.4404340]
   Herak I, 2020, J CONSUM PSYCHOL, V30, P125, DOI 10.1002/jcpy.1128
   Hermann E, 2024, J ACAD MARKET SCI, V52, P1431, DOI 10.1007/s11747-023-00986-8
   Hermann E, 2022, NEW MEDIA SOC, V24, P1258, DOI 10.1177/14614448211022702
   Hermann E, 2022, J BUS ETHICS, V179, P43, DOI 10.1007/s10551-021-04843-y
   Hollebeek LD, 2024, PSYCHOL MARKET, V41, P880, DOI 10.1002/mar.21957
   Holthöwer J, 2023, J ACAD MARKET SCI, V51, P767, DOI 10.1007/s11747-022-00862-x
   Huang MH, 2024, J MARKETING, V88, P1, DOI 10.1177/00222429231224748
   Huang MH, 2022, J RETAILING, V98, P209, DOI 10.1016/j.jretai.2021.03.001
   Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
   Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Hubbard R, 2020, SCI ENG ETHICS, V26, P3217, DOI 10.1007/s11948-020-00266-6
   Huh J, 2023, J ADVERTISING, V52, P477, DOI 10.1080/00913367.2023.2227013
   Jago AS, 2024, J EXP SOC PSYCHOL, V110, DOI 10.1016/j.jesp.2023.104553
   Jago AS, 2024, PERS SOC PSYCHOL B, V50, P793, DOI 10.1177/01461672221149815
   Jago AS, 2022, J EXP PSYCHOL-APPL, V28, P52, DOI 10.1037/xap0000365
   Jago AS, 2019, ACAD MANAG DISCOV, V5, P38, DOI 10.5465/amd.2017.0002
   Jain V, 2024, J CONSUM BEHAV, V23, P676, DOI 10.1002/cb.2233
   Jansen T., 2023, Automated alignment: Guiding visual generative AI for brand building and customer engagement, DOI [10.2139/ssrn.4656622, DOI 10.2139/SSRN.4656622]
   Joosten Jan, 2024, IEEE Engineering Management Review, V52, P153, DOI 10.1109/EMR.2024.3353338
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Karatas M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2218961120
   Kerr N L, 1998, Pers Soc Psychol Rev, V2, P196, DOI 10.1207/s15327957pspr0203_4
   Kim T, 2023, J ACAD MARKET SCI, V51, P785, DOI 10.1007/s11747-021-00832-9
   Kirshner SN, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103580
   Kleinberg J, 2024, PERSPECT PSYCHOL SCI, V19, P827, DOI 10.1177/17456916231212138
   König PD, 2022, BIG DATA SOC, V9, DOI 10.1177/20539517211069632
   Koivisto M, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-40858-3
   Kopalle PK, 2022, INT J RES MARK, V39, P522, DOI 10.1016/j.ijresmar.2021.11.002
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Ladeira W, 2023, J HOSP MARKET MANAG, V32, P694, DOI 10.1080/19368623.2023.2202168
   Lambrecht A, 2019, MANAGE SCI, V65, P2966, DOI 10.1287/mnsc.2018.3093
   Lanz L, 2024, J BUS ETHICS, V189, P625, DOI 10.1007/s10551-023-05393-1
   Le Mens G, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2309350120
   Lee D, 2019, INFORM SYST RES, V30, P239, DOI 10.1287/isre.2018.0800
   Lefkeli D, 2024, INT J RES MARK, V41, P138, DOI 10.1016/j.ijresmar.2023.08.011
   Li MJ, 2022, ELECTRON MARK, V32, P2245, DOI 10.1007/s12525-022-00591-7
   Li P, 2024, INFORM SYST RES, V35, P1257, DOI 10.1287/isre.2021.0053
   Li PY, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.0454
   Li R, 2017, J CONSUM PSYCHOL, V27, P377, DOI 10.1016/j.jcps.2017.04.001
   Lim WM, 2022, PSYCHOL MARKET, V39, P1129, DOI 10.1002/mar.21654
   Liu PJ, 2019, J CONSUM RES, V46, P407, DOI 10.1093/jcr/ucz009
   Logg JM, 2019, ORGAN BEHAV HUM DEC, V151, P90, DOI 10.1016/j.obhdp.2018.12.005
   Longoni C., 2023, PsyArVix, DOI [10.31234/osf.io/na3wb, DOI 10.31234/OSF.IO/NA3WB]
   Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
   Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
   Lv LX, 2024, J ADVERTISING, V53, P36, DOI 10.1080/00913367.2022.2109082
   Ma AY, 2023, J PERS SOC PSYCHOL, V124, P901, DOI 10.1037/pspa0000327
   Mahmud H, 2024, DECIS SUPPORT SYST, V179, DOI 10.1016/j.dss.2024.114168
   Mahmud H, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121390
   Mari A., 2023, Shopping with voice assistants: How empathy affects individual and family decision-making outcomes, DOI [10.2139/ssrn.4352567, DOI 10.2139/SSRN.4352567]
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
   Markowitz D. M., 2023, PsyArXiv, DOI [10.31234/osf.io/hm54g, DOI 10.31234/OSF.IO/HM54G]
   Matz S., 2023, PsyArXiv, DOI [10.31234/osf.io/rn97c, DOI 10.31234/OSF.IO/RN97C]
   Matz SC, 2017, P NATL ACAD SCI USA, V114, P12714, DOI 10.1073/pnas.1710966114
   Matz SC, 2019, J CONSUM PSYCHOL, V29, P370, DOI 10.1002/jcpy.1092
   Matz SC, 2017, CURR OPIN BEHAV SCI, V18, P7, DOI 10.1016/j.cobeha.2017.05.009
   Mehta P, 2022, PSYCHOL MARKET, V39, P2013, DOI 10.1002/mar.21716
   Meincke L., 2024, Prompting Diverse Ideas: Increasing AI Idea Variance
   Miller EJ, 2023, PSYCHOL SCI, V34, P1390, DOI 10.1177/09567976231207095
   Mitchell M, 2023, Arxiv, DOI [arXiv:2311.09247, 10.48550/arXiv.2311.09247, DOI 10.48550/ARXIV.2311.09247]
   Moreau P, 2023, Generative artificial intelligence and design co-creation in luxury new product development: The power of discarded ideas, DOI [10.2139/ssrn.4630856, DOI 10.2139/SSRN.4630856]
   Morewedge CK, 2023, NAT HUM BEHAV, DOI 10.1038/s41562-023-01724-4
   Mukherjee A., 2023, California Management Rev.
   Mustak M, 2021, J BUS RES, V124, P389, DOI 10.1016/j.jbusres.2020.10.044
   Nightingale SJ, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2120481119
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Nussberger AM, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-33417-3
   Ostinelli M, 2024, J CONSUM PSYCHOL, DOI 10.1002/jcpy.1416
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Pentina I, 2023, PSYCHOL MARKET, V40, P1593, DOI 10.1002/mar.21853
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pitardi V, 2022, J SERV MANAGE, V33, P389, DOI 10.1108/JOSM-12-2020-0435
   Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
   Qiu L., 2023, How Much Should We Trust LLM Results for Marketing Research?
   Querci I, 2022, PSYCHOL MARKET, V39, P1888, DOI 10.1002/mar.21705
   Rathee S, 2023, J CONSUM PSYCHOL, V33, P621, DOI 10.1002/jcpy.1351
   Rau PLP, 2009, COMPUT HUM BEHAV, V25, P587, DOI 10.1016/j.chb.2008.12.025
   Raveendhran R, 2021, ORGAN BEHAV HUM DEC, V164, P11, DOI 10.1016/j.obhdp.2021.01.001
   Reich T, 2023, J CONSUM PSYCHOL, V33, P285, DOI 10.1002/jcpy.1313
   Reisenbichler M, 2022, MARKET SCI, V41, P441, DOI 10.1287/mksc.2022.1354
   Ringel D, 2023, Creating synthetic experts with Generative Artificial Intelligence, DOI [10.2139/ssrn.4542949, DOI 10.2139/SSRN.4542949]
   Samuelson P, 2023, SCIENCE, V381, P158, DOI 10.1126/science.adi0656
   Sarstedt M, 2024, PSYCHOL MARKET, V41, P1254, DOI 10.1002/mar.21982
   Schiessl D, 2022, J MARK ANAL, V10, P207, DOI 10.1057/s41270-021-00143-6
   Schmitt M., 2023, Digital deception: Generative Artificial Intelligence in social engineering and phishing, DOI [10.2139/ssrn.4602790, DOI 10.2139/SSRN.4602790]
   Shah AK, 2008, PSYCHOL BULL, V134, P207, DOI 10.1037/0033-2909.134.2.207
   Shankar V, 2018, J RETAILING, V94, pVI, DOI 10.1016/S0022-4359(18)30076-9
   Sourati J, 2023, NAT HUM BEHAV, V7, DOI 10.1038/s41562-023-01648-z
   Srinivasan R, 2021, J MARKETING, V85, P74, DOI 10.1177/0022242921997082
   Stachl C, 2020, P NATL ACAD SCI USA, V117, P17680, DOI 10.1073/pnas.1920484117
   Stella M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2312911120
   Stevenson C, 2022, Arxiv, DOI [arXiv:2206.08932, DOI 10.48550/ARXIV.2206.08932]
   Steyvers M, 2024, PERSPECT PSYCHOL SCI, V19, P722, DOI 10.1177/17456916231181102
   Sullivan YW, 2022, J BUS ETHICS, V178, P917, DOI 10.1007/s10551-022-05053-w
   Suri G, 2024, J EXP PSYCHOL GEN, V153, P1066, DOI 10.1037/xge0001547
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Tan Y., 2023, Unlocking profitability in generative AI: The impact of sequential learning on product versioning strategies
   Triguero I, 2023, Arxiv, DOI [arXiv:2307.14283, 10.48550/arXiv.2307.14283, DOI 10.48550/ARXIV.2307.14283]
   Tully S., 2023, PsyArXiv, DOI [10.31234/osf.io/t9u8g, DOI 10.31234/OSF.IO/T9U8G]
   Turel O, 2023, MIS QUART, V47, P1369, DOI 10.25300/MISQ/2022/17961
   Vaid S, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114110
   Van Noorden R, 2023, NATURE, V621, P672, DOI 10.1038/d41586-023-02980-0
   van Osselaer SMJ, 2020, J RETAILING, V96, P88, DOI 10.1016/j.jretai.2019.12.006
   Vana P., 2024, Generating "accurate"online reviews: Augmenting a transformer-based approach with structured predictions, DOI [10.2139/ssrn.4692101, DOI 10.2139/SSRN.4692101]
   Verma S., 2021, International Journal of Information Management Data Insights, V1, DOI [DOI 10.1016/J.JJIMEI.2020.100002, 10.1016/j.jjimei.2020.100002]
   Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
   Volkmar G, 2022, J BUS RES, V149, P599, DOI 10.1016/j.jbusres.2022.04.007
   von Walter B, 2023, J RETAILING, V99, P280, DOI 10.1016/j.jretai.2023.05.001
   von Walter B, 2022, MARKET LETT, V33, P143, DOI 10.1007/s11002-021-09589-1
   Wang HC, 2023, NATURE, V620, P47, DOI 10.1038/s41586-023-06221-2
   Wang W, 2023, Generative AI and artists: Consumer preferences for style and fair compensation, DOI [10.2139/ssrn.4428509, DOI 10.2139/SSRN.4428509]
   Wang YW, 2024, Arxiv, DOI arXiv:2310.13206
   Wien AH, 2021, J BUS RES, V137, P13, DOI 10.1016/j.jbusres.2021.08.016
   Xie ZH, 2022, PSYCHOL MARKET, V39, P1902, DOI 10.1002/mar.21706
   Yalcin G, 2022, J MARKETING RES, V59, P696, DOI 10.1177/00222437211070016
   Yanxia C, 2024, MARK INTELL PLAN, V42, P1, DOI 10.1108/MIP-03-2023-0103
   Yiu E, 2024, PERSPECT PSYCHOL SCI, V19, P874, DOI 10.1177/17456916231201401
   Yu LZ, 2024, PSYCHOL MARKET, V41, P1133, DOI 10.1002/mar.21975
   Zhang HL, 2023, Arxiv, DOI [arXiv:2311.04378, 10.48550/arXiv.2311.04378]
   Zhang H, 2022, PSYCHOL MARKET, V39, P2171, DOI 10.1002/mar.21721
   Zhou E., 2024, Tech. Rep., P1
   Zhu YM, 2022, J BUS RES, V150, P642, DOI 10.1016/j.jbusres.2022.06.044
NR 198
TC 14
Z9 14
U1 310
U2 317
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD JUL
PY 2024
VL 180
AR 114720
DI 10.1016/j.jbusres.2024.114720
EA MAY 2024
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA TY3I7
UT WOS:001244776600001
DA 2024-12-25
ER

PT J
AU Chen, D
   Liu, Y
   Guo, YT
   Zhang, YL
AF Chen, Dian
   Liu, Ying
   Guo, Yiting
   Zhang, Yulin
TI The revolution of generative artificial intelligence in psychology: The
   interweaving of behavior, consciousness, and ethics
SO ACTA PSYCHOLOGICA
LA English
DT Article
DE Generative artificial intelligence; ChatGPT; Psychology; Natural
   language processing; Ethics
AB In recent years, there have been unparalleled prospects for psychological study due to the swift advancement of generative artificial intelligence (AI) in natural language processing, shown by ChatGPT. This review article looks into the uses and effects of generative artificial intelligence in psychology. We employed a systematic selection process, encompassing papers published between 2015 and 2024 from databases such as Google Scholar, PubMed, and IEEE Xplore, using keywords like "Generative AI in psychology" "ChatGPT and behavior modeling" and "AI in mental health". First, the paper goes over the fundamental ideas of generative AI and lists its uses in data analysis, behavior modeling, and social interaction simulation. A detailed comparison table has been added to contrast conventional research methodologies with GenAI-based approaches in psychology studies. Next, analyzing the theoretical and ethical issues that generative AI raises for psychological research, it highlights how crucial it is to develop a coherent theoretical framework. This study illustrates the benefits of generative AI in handling vast amounts of data and increasing research efficiency by contrasting traditional research methods with AI-driven methodologies. Regarding particular uses, the study explores how generative AI might be used to simulate social interactions, analyze massive amounts of text, and learn about cognitive processes. Section 5 has been expanded to include discussions on political biases, geographic biases, and other biases. In conclusion, the paper looks forward to the future development of generative AI in psychology research and suggests techniques for improving it. We have included methodological solutions such as the Retrieval Augmented Generation (RAG) approach and human-in-the-loop systems, as well as data privacy solutions like open-source local LLMs. In summary, generative AI has the potential to revolutionize psychological research, but in order to maintain the moral and scientific integrity of the field, ethical and theoretical concerns must be carefully considered before applying the technology.
C1 [Chen, Dian; Guo, Yiting; Zhang, Yulin] Southeast Univ, Sch Econ & Management, Nanjing, Peoples R China.
   [Liu, Ying] Peking Union Med Coll, Beijing, Peoples R China.
C3 Southeast University - China; Chinese Academy of Medical Sciences -
   Peking Union Medical College; Peking Union Medical College
RP Guo, YT (corresponding author), Southeast Univ, Sch Econ & Management, Nanjing, Peoples R China.
EM ytguo@seu.edu.cn
CR Acemoglu D, 2019, J ECON PERSPECT, V33, P3, DOI 10.1257/jep.33.2.3
   Atkins G. L., 2024, Public Policy & Aging Report, V34, P74
   Baum B., 2023, Journal of Business and Behavioral Sciences, V35, P3
   Baumeister R. F., 1997, Rev Gen Psychol, V1, P311, DOI [DOI 10.1037/1089-2680.1.3.311, DOI 10.1037//1089-2680.1.3.311]
   Binns R., 2018, P 1 C FAIRN ACC TRAN, VVolume 81, P149, DOI [10.1145/3178876.3186150, DOI 10.1145/3178876.3186150, DOI 10.1145/3287560.3287598]
   Brown TB, 2020, ADV NEUR IN, V33
   Brynjolfsson E., 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
   Caliskan A, 2017, SCIENCE, V356, DOI 10.1126/science.aal4230
   Capraro V, 2024, J ECON LIT, V62, DOI 10.1257/jel.20221613
   Chatterjee S, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100334
   Chheang V., 2024, 2024 IEEE INT C ART
   Das SK, 2022, ADV NUTR, V13, P1, DOI 10.1093/advances/nmab103
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Elyoseph Z, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/54369
   Fahimi F, 2021, IEEE T NEUR NET LEAR, V32, P4039, DOI 10.1109/TNNLS.2020.3016666
   Fiske S.T., 2010, Social beings: Core motives in social psychology
   Floridi L., 2019, Harv Data Sci Rev, V1, P1, DOI DOI 10.1162/99608F92.8CD550D1
   Fujimoto S, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1232003
   Galbusera L, 2024, INT J QUAL STUD HEAL, V19, DOI 10.1080/17482631.2024.2333064
   Gigerenzer G, 2022, ANNU REV ORGAN PSYCH, V9, P171, DOI 10.1146/annurev-orgpsych-012420-090506
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P95
   Gupta P., 2024, Data Inf. Manag, DOI DOI 10.1016/J.DIM.2024.100066
   Hollebeek LD, 2024, PSYCHOL MARKET, V41, P880, DOI 10.1002/mar.21957
   Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
   Johnson W.L., 2000, INT J ARTIFICIAL INT, V11, P47
   Katz A, 2024, Arxiv, DOI arXiv:2410.03721
   Kim J., 2024, P 18 INT C LEARN SCI, P1722
   Kim Y, 2018, IEEE ACCESS, V6, P5308, DOI 10.1109/ACCESS.2018.2791861
   Kusal S, 2023, ARTIF INTELL REV, V56, P15129, DOI 10.1007/s10462-023-10509-0
   Mittelstadt BD, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716679679
   Murray M. D., 2024, Generative artifice: Regulation of Deepfake exploitation and deception under the first amendment
   Palloff R.M., 2001, LESSONS CYBERSPACE C
   Passos L. A., 2022, Expert Systems
   Pataranutaporn P, 2021, NAT MACH INTELL, V3, P1013, DOI 10.1038/s42256-021-00417-9
   Pearl J, 2009, STAT SURV, V3, P96, DOI 10.1214/09-SS057
   Pennebaker J. W., 2015, DEV PSYCHOMETRIC PRO
   Rane N.L., 2023, Int. Res. J. Mod. Eng. Technol. Sci, V5, P427
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   RUSSELL SJ, 2016, ARTIFICIAL INTELLIGE
   Sai S., 2024, IEEE Access.
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Tang YL, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642899
   Tramr F., 2022, P 2022 ACM C FAIRN A
   Usuga-Cadavid JP, 2022, INT J PROD RES, V60, P4548, DOI 10.1080/00207543.2021.1951868
   Uzougbo N. S., 2024, GSC Adv. Res. Rev., V19, P130, DOI [10.30574/gscarr.2024.19.2.0171, DOI 10.30574/GSCARR.2024.19.2.0171]
   Vaswani A, 2017, ADV NEUR IN, V30
   Zhang T, 2022, ELECTRON MARK, V32, P2277, DOI 10.1007/s12525-022-00596-2
   Zlateva P., 2024, Electronics, communications and networks, P110
NR 48
TC 0
Z9 0
U1 10
U2 10
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0001-6918
EI 1873-6297
J9 ACTA PSYCHOL
JI Acta Psychol.
PD NOV
PY 2024
VL 251
AR 104593
DI 10.1016/j.actpsy.2024.104593
PG 6
WC Psychology, Experimental
WE Social Science Citation Index (SSCI)
SC Psychology
GA M2I4R
UT WOS:001355825200001
PM 39522296
OA gold
DA 2024-12-25
ER

PT J
AU Meakin, L
AF Meakin, Lynsey
TI Exploring the Impact of Generative Artificial Intelligence on Higher
   Education Students' Utilization of Library Resources A Critical
   Examination
SO INFORMATION TECHNOLOGY AND LIBRARIES
LA English
DT Article
AB In the field of higher education, generative artificial intelligence (GenAI) has become a revolutionary influence, shaping how students access and use library resources. This study explores the intricate balance of both positive and negative effects that GenAI might have on the academic library experience for higher education (HE) students. The key aspects of enhanced discovery and retrieval, personalization and engagement, streamlined research processes, and digital literacy and information evaluation potentially offered through using generative AI will be considered. These prospective advantages to HE students offered by using GenAI will be examined through will be examined through the theoretical framework of the Technological Acceptance Model (TAM) introduced by Davis et al. in 1986, which suggests that perceived usefulness and perceived ease of use are key factors in determining user acceptance and utilization of technology. The adoption of GenAI by higher education students will be analyzed from this viewpoint before assessing its impact on their use of library resources.
C1 [Meakin, Lynsey] Univ Derby, Inst Educ, Derby, England.
C3 University of Derby
RP Meakin, L (corresponding author), Univ Derby, Inst Educ, Derby, England.
EM l.meakin@derby.ac.uk
OI Meakin, Lynsey/0000-0002-2236-8602
CR Agarwal R, 2000, MIS QUART, V24, P665, DOI 10.2307/3250951
   Al-Adwan AS, 2023, EDUC INF TECHNOL, V28, P15381, DOI 10.1007/s10639-023-11816-3
   Alomary Azza, 2015, 5 INT C 4E LOND UK N, P6
   Anuyahong Bundit, 2023, International Journal of Research and Scientific Innovation (IJRSI), V10, P88, DOI [10.51244/IJRSI.2023.10412, DOI 10.51244/IJRSI.2023.10412]
   Atuase D, 2023, DIGIT LIBR PERSPECT, V39, P111, DOI 10.1108/DLP-03-2022-0025
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bakshy E, 2015, SCIENCE, V348, P1130, DOI 10.1126/science.aaa1160
   Borgesius FJZ, 2016, INTERNET POLICY REV, V5, DOI 10.14763/2016.1.401
   Bourgeois J, 2020, J MED LIBR ASSOC, V108, P618, DOI 10.5195/jmla.2020.691
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chen K, 2024, INFORM LEARN SCI, DOI 10.1108/ILS-10-2023-0160
   Cisek Sabina, 2018, ISIC C, DOI [10.13140/RG.2.2.19536.35842, DOI 10.13140/RG.2.2.19536.35842]
   Cox A, 2023, J ASSOC INF SCI TECH, V74, P367, DOI 10.1002/asi.24635
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Ferrara E., 2023, First Monday, V28, DOI [DOI 10.5210/FM.V28I11.13346, 10.5210/fm.v28i11.13346]
   Fitria T. N., 2023, ELT FORUM, V12, P44, DOI DOI 10.15294/ELT.V12I1.64069
   Garrido A, 2013, IEEE INTELL SYST, V28, P64, DOI 10.1109/MIS.2011.36
   Haim M, 2018, DIGIT JOURNAL, V6, P330, DOI 10.1080/21670811.2017.1338145
   Hepola Janne, 2016, INT C INT SCI DUBL I
   Hess BJ, 2024, MED TEACH, V46, P300, DOI 10.1080/0142159X.2023.2289844
   Hollebeek LD, 2014, J INTERACT MARK, V28, P149, DOI 10.1016/j.intmar.2013.12.002
   Hosseini M, 2023, COLL RES LIBR, V84, P836
   Hussain Abid, 2023, Library Hi Tech News, P15, DOI 10.1108/LHTN-11-2022-0125
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kot FC, 2015, COLL RES LIBR, V76, P566, DOI 10.5860/crl.76.5.566
   Kwak M., 2023, Issues in Information Systems, V24, P222, DOI [10.48009/3iis2023119, DOI 10.48009/3IIS2023119]
   Li K, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15065221
   Liang D, 2014, KNOWL MANAG E-LEARN, V6, P281
   Lou YS, 2021, HUM BEHAV EMERG TECH, V3, P454, DOI 10.1002/hbe2.266
   Lu KL, 2023, EDUC INF TECHNOL, V28, P9747, DOI 10.1007/s10639-023-11591-1
   Malinka K, 2023, PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1, P47, DOI 10.1145/3587102.3588827
   McLaughlin Bryce, 2022, arXiv
   Nahas Kamal, 2024, Nature, DOI 10.1038/d41586-024-00865-4
   Oyelude Adetoun A., 2021, Library Hi Tech News, V38, P1, DOI 10.1108/LHTN-10-2021-0079
   Özkan M, 2023, EUR J THER-ISTANBUL, V29, P996, DOI 10.58600/eurjther1837
   Panda Subhajit, 2022, Library Hi Tech News, P12, DOI 10.1108/LHTN-11-2021-0081
   Parasuraman R, 1997, HUM FACTORS, V39, P230, DOI 10.1518/001872097778543886
   Pariser E., 2011, FILTER BUBBLE WHAT I
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Saúde S, 2024, SOC SCI-BASEL, V13, DOI 10.3390/socsci13080410
   Schemmer M, 2023, PROCEEDINGS OF 2023 28TH ANNUAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2023, P410, DOI 10.1145/3581641.3584066
   Shahzad MF, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-024-12949-9
   Sinha A, 2023, J APPL RES HIGH EDUC, V15, P369, DOI 10.1108/JARHE-06-2021-0233
   Soria KM, 2017, COLL RES LIBR, V78, P812, DOI 10.5860/crl.78.6.812
   Steeleworthy Michael, 2013, Partnership: The Canadian Journal of Library and Information Practice and Research, V8, P1, DOI [10.21083/partnership.v8i1.2220, DOI 10.21083/PARTNERSHIP.V8I1.2220]
   Subaveerapandiyan A., 2023, Library Philosophy and Practice (e-journal), V7828, P14
   Tang AR, 2024, J NURS SCHOLARSHIP, V56, P314, DOI 10.1111/jnu.12938
   United Kingdom Department for Education, Generative Artificial Intelligence (AI) in Education (policy paper),
   Vasconcelos H., 2023, Proceedings of the ACM on Human-Computer Interaction, V7, P1, DOI [https://doi.org/10.1145/3579605, DOI 10.1145/3579605, 10.1145/3579605]
NR 51
TC 0
Z9 0
U1 47
U2 47
PU AMER LIBRARY ASSOC
PI CHICAGO
PA 50 E HURON ST, CHICAGO, IL 60611 USA
SN 0730-9295
EI 2163-5226
J9 INFORM TECHNOL LIBR
JI Inf. Technol. Libr.
PY 2024
VL 43
IS 3
AR 17246
DI 10.5860/ital.v43i3.17246
PG 13
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA H8L0N
UT WOS:001325887700004
OA gold
DA 2024-12-25
ER

PT J
AU Anica-Popa, IF
   Vrîncianu, M
   Anica-Popa, LE
   Cismasu, ID
   Tudor, CG
AF Anica-Popa, Ionut-Florin
   Vrincianu, Marinela
   Anica-Popa, Liana-Elena
   Cismasu, Irina-Daniela
   Tudor, Catalin-Georgel
TI Framework for Integrating Generative AI in Developing Competencies for
   Accounting and Audit Professionals
SO ELECTRONICS
LA English
DT Article
DE Generative Artificial Intelligence (GenAI); competencies; accounting and
   auditing (AA); GenAI risks; Large Language Model (LLM)
ID ARTIFICIAL-INTELLIGENCE; SYSTEMS; ERA
AB The study aims to identify the knowledge, skills and competencies required by accounting and auditing (AA) professionals in the context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies and to develop a framework for integrating GenAI capabilities into organisational systems, harnessing its potential to revolutionise lifelong learning and skills development and to assist day-to-day operations and decision-making. Through a systematic literature review, 103 papers were analysed, to outline, in the current business ecosystem, the competencies' demand generated by AI adoption and, in particular, GenAI and its associated risks, thus contributing to the body of knowledge in underexplored research areas. Positioned at the confluence of accounting, auditing and GenAI, the paper introduces a meaningful overview of knowledge in the areas of effective data analysis, interpretation of findings, risk awareness and risk management. It emphasizes and reshapes the role of required skills for accounting and auditing professionals in discovering the true potential of GenAI and adopting it accordingly. The study introduces a new LLM-based system model that can enhance its GenAI capabilities through collaboration with similar systems and provides an explanatory scenario to illustrate its applicability in the accounting and audit area.
C1 [Anica-Popa, Ionut-Florin; Vrincianu, Marinela; Anica-Popa, Liana-Elena; Tudor, Catalin-Georgel] Bucharest Univ Econ Studies, Dept Management Informat Syst, Bucharest 010374, Romania.
   [Cismasu, Irina-Daniela] Bucharest Univ Econ Studies, Dept Financial & Econ Anal & Valuat, Bucharest 010374, Romania.
C3 Bucharest University of Economic Studies; Bucharest University of
   Economic Studies
RP Anica-Popa, IF (corresponding author), Bucharest Univ Econ Studies, Dept Management Informat Syst, Bucharest 010374, Romania.
EM ionut.anica@ase.ro; marinela.vrincianu@cig.ase.ro;
   liana.anica@cig.ase.ro; irina.cismasu@cig.ase.ro;
   catalin.tudor@cig.ase.ro
RI Anica-Popa, Ionut/B-5737-2011; VRÎNCIANU, MARINELA/GWC-0653-2022; ANICA
   - POPA, Liana - Elena/AAR-5683-2021; CISMASU, IRINA
   DANIELA/ACH-5442-2022
OI ANICA - POPA, Liana - Elena/0000-0001-8837-061X; Anica-Popa,
   Ionut/0000-0002-4804-9159; CISMASU, IRINA DANIELA/0000-0003-1071-2530;
   MARINELA, VRINCIANU/0000-0001-9180-3578
CR ACCA, 2020, ACCA Competency Framework
   AICPA CIMA, 2022, CGMA Competency Framework
   Aksamija A, 2010, AI EDAM, V24, P3, DOI 10.1017/S0890060409990138
   Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Al-Hattami HM, 2021, SAGE OPEN, V11, DOI 10.1177/21582440211007111
   Al-Htaybat K, 2018, ACCOUNT EDUC, V27, P333, DOI 10.1080/09639284.2018.1437758
   Friedrich MPA, 2022, REV GEST ORGAN, V15, P180, DOI 10.22277/rgo.v15i3.6877
   Aldemr C., 2024, Edpacs, V69, P3, DOI [10.1080/07366981.2024.2312001, DOI 10.1080/07366981.2024.2312001]
   Aldredge M, 2021, IND HIGHER EDUC, V35, P83, DOI 10.1177/0950422220954319
   Andiola LM, 2022, ISS ACCOUNT EDUC, V37, P37, DOI 10.2308/ISSUES-2020-037
   Andon P, 2021, ACCOUNT AUDIT ACCOUN, V34, P1769, DOI 10.1108/AAAJ-08-2020-4756
   Andreassen RI, 2020, J MANAG CONTROL, V31, P209, DOI 10.1007/s00187-020-00303-2
   Arachchige ASPM, 2023, EUR J NUCL MED MOL I, V50, P2248, DOI 10.1007/s00259-023-06227-y
   Arnold V, 2023, INT J ACCOUNT INF SY, V50, DOI 10.1016/j.accinf.2023.100638
   Bansal G, 2024, COGN COMPUT, V16, P2487, DOI 10.1007/s12559-023-10236-2
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Borah Asha Rani, 2024, 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), P1527, DOI 10.1109/IDCIoT59759.2024.10467943
   Boyatzis R.E., 1982, COMPETENT MANAGER
   Burns M, 2019, J EMERG TECHNOL ACCO, V16, P81, DOI 10.2308/jeta-52424
   Caliman A., 2024, Today Software Magazine
   Cardon P, 2024, BUS PROF COMMUN Q, V87, P223, DOI 10.1177/23294906231208166
   Cardy RobertL., 2006, BUS HORIZONS, V49, P235, DOI DOI 10.1016/J.BUSHOR.2005.09.004
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chen WR, 2023, J MANAG ANAL, V10, P89, DOI 10.1080/23270012.2023.2180676
   Chunling Zhu, 2022, 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), P358, DOI 10.1109/IWECAI55315.2022.00076
   Cui YY, 2024, BUS HORIZONS, V67, P583, DOI 10.1016/j.bushor.2024.05.003
   Dean S.A., 2019, INT J APPL MANAGEMEN, V18, P17, DOI [DOI 10.5590/IJAMT.2019.18.1.02, 10.5590/IJAMT.2019.18.1.02]
   Dong YH, 2024, IEICE T INF SYST, VE107D, P426, DOI 10.1587/transinf.2023IHP0011
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Duff A, 2020, BRIT ACCOUNT REV, V52, DOI 10.1016/j.bar.2019.03.004
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ferrara E., 2023, Science, V6, P3, DOI [DOI 10.3390/SCI6010003, 10.3390/sci6010003]
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Fotoh LE, 2021, J EMERG TECHNOL ACCO, V18, P77, DOI 10.2308/JETA-2020-063
   Grosu V, 2023, TECHNOL FORECAST SOC, V193, DOI 10.1016/j.techfore.2023.122630
   Han HD, 2023, INT J ACCOUNT INF SY, V48, DOI 10.1016/j.accinf.2022.100598
   Hemachandran K, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/1410448
   Hendriksen C, 2023, J SUPPLY CHAIN MANAG, V59, P65, DOI 10.1111/jscm.12304
   ICAEW, 2018, ACA Qualification Professional Development Ladders
   ICAS, 2023, ICAS Mapping Our New Competencies
   IFAC, 2024, About us
   IMA, 2023, IMA Management Accounting Competency Framework
   Jackson D, 2023, ACCOUNT EDUC, V32, P150, DOI 10.1080/09639284.2022.2057195
   Jemine G, 2024, ACCOUNT AUDIT ACCOUN, V37, P280, DOI 10.1108/AAAJ-08-2022-5981
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Khan MS, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24890
   Kirk H, 2021, Arxiv, DOI [arXiv:2102.04130, DOI arXiv:2102.04130.v1]
   Kommunuri J, 2022, PAC ACCOUNT REV, V34, P585, DOI 10.1108/PAR-06-2021-0107
   Kong AB, 2024, Arxiv, DOI arXiv:2308.07702
   Koreff J, 2023, J INF SYST, V37, P59, DOI 10.2308/ISYS-2022-023
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Kramer LL, 2020, J MED INTERNET RES, V22, DOI 10.2196/14058
   Kroon N., 2021, Journal of Open Innovation: Technology, Market, and Complexity, V7, P163, DOI DOI 10.3390/JOITMC7030163
   Leitner-Hanetseder S, 2021, J APPL ACCOUNT RES, V22, P539, DOI 10.1108/JAAR-10-2020-0201
   Li Q, 2020, J EMERG TECHNOL ACCO, V17, P89, DOI 10.2308/jeta-52665
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lucas HC, 2024, MED EDUC, V58, P1276, DOI 10.1111/medu.15402
   Lv K, 2024, Arxiv, DOI arXiv:2306.09782
   Makridakis S, 2023, FORECASTING-BASEL, V5, P536, DOI 10.3390/forecast5030030
   Mancini D, 2021, MEDITARI ACCOUNT RES, V29, P1041, DOI 10.1108/MEDAR-03-2021-1258
   Masikisiki B, 2023, PROCEEDINGS OF THE 4TH AFRICAN CONFERENCE FOR HUMAN COMPUTER INTERACTION, AFRICHI 2023, P44, DOI 10.1145/3628096.3628747
   Massaro M, 2016, ACCOUNT AUDIT ACCOUN, V29, P767, DOI 10.1108/AAAJ-01-2015-1939
   Mathew R, 2024, TECHTRENDS, V68, P773, DOI 10.1007/s11528-024-00964-z
   Mathisen A, 2012, PEDAGOG CULT SOC, V20, P71, DOI 10.1080/14681366.2012.649416
   Mazzullo E., 2023, Analytics, V2, P877, DOI [10.3390/analytics2040046, DOI 10.3390/ANALYTICS2040046]
   MCCLELLAND DC, 1973, AM PSYCHOL, V28, P1, DOI 10.1037/h0034092
   McKinsey, 2023, EC POTENTIAL GENERAT
   Meredith K, 2020, DECIS SUPPORT SYST, V139, DOI 10.1016/j.dss.2020.113402
   Miao J, 2024, MEDICINA-LITHUANIA, V60, DOI 10.3390/medicina60010148
   Mokander J., 2023, Digital Society, V2, P49, DOI DOI 10.1007/S44206-023-00074-Y
   Moore WB, 2022, J EDUC BUS, V97, P105, DOI 10.1080/08832323.2021.1895045
   Mordor Intelligence AI, 2024, Accounting Market Size & Share Analysis-Growth Trends & Forecasts (2024-2029)
   Munoko I, 2020, J BUS ETHICS, V167, P209, DOI 10.1007/s10551-019-04407-1
   Nazir Anam, 2023, Meta Radiol, V1, DOI 10.1016/j.metrad.2023.100022
   Ndlovu SG, 2024, COGENT BUS MANAG, V11, DOI 10.1080/23311975.2024.2321877
   Ng C, 2023, J EMERG TECHNOL ACCO, V20, P223, DOI 10.2308/JETA-2022-025
   Noordin NA, 2022, J RISK FINANC MANAG, V15, DOI 10.3390/jrfm15080339
   Norzelan NA, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.123022
   OECD Recommendation of the Council on OECD Legal Instruments, 2019, Artificial Intelligence
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Orlova EV, 2023, MATHEMATICS-BASEL, V11, DOI 10.3390/math11183916
   Page MJ, 2021, BMJ-BRIT MED J, V372, DOI [10.1136/bmj.n71, 10.1136/bmj.n160, 10.1016/j.ijsu.2021.105906]
   Piktus A, 2023, NATURE, V618, P465, DOI 10.1038/d41586-023-01411-4
   Plumlee RD, 2015, ACCOUNT REV, V90, P351, DOI 10.2308/accr-50856
   Rawashdeh A, 2024, J APPL ACCOUNT RES, V25, P594, DOI 10.1108/JAAR-10-2022-0273
   Ridzuan NIM, 2022, J RISK FINANC MANAG, V15, DOI 10.3390/jrfm15110536
   Rodgers W, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3269291
   Roumeliotis KI, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15060192
   Sahoo S, 2023, J ENTERP INF MANAG, V36, P221, DOI 10.1108/JEIM-01-2022-0025
   Salijeni G, 2019, ACCOUNT BUS RES, V49, P95, DOI 10.1080/00014788.2018.1459458
   Salinas-Navarro DE, 2024, INTERACT TECHNOL SMA, V21, P708, DOI 10.1108/ITSE-12-2023-0236
   Samiolo R, 2024, CONTEMP ACCOUNT RES, V41, P498, DOI 10.1111/1911-3846.12901
   Schwartz R., 2022, NIST Special Publication, V1270
   Seo J, 2022, KNOWL-BASED SYST, V256, DOI 10.1016/j.knosys.2022.109861
   Shimizu I, 2023, JMIR MED EDUC, V9, DOI 10.2196/53466
   Singh K, 2024, TECHNOVATION, V133, DOI 10.1016/j.technovation.2024.103021
   Sumbal MS, 2024, KYBERNETES, DOI 10.1108/K-06-2023-1126
   Tavares MC, 2023, COGENT BUS MANAG, V10, DOI 10.1080/23311975.2023.2220198
   The Alan Turing Institute, 2023, AI Skills for Business Competency Framework Https://Www.Turing.Ac.Uk/Skills/Collaborate/Ai-Skills-Business-Framework
   Thottoli MM, 2024, ACCOUNT RES J, V37, P134, DOI 10.1108/ARJ-09-2023-0269
   Timpson M, 2021, AUST ACCOUNT REV, V31, P22, DOI 10.1111/auar.12303
   Tiron-Tudor A, 2021, J RISK FINANC MANAG, V14, DOI 10.3390/jrfm14080376
   Trad F, 2024, MACH LEARN KNOW EXTR, V6, P367, DOI 10.3390/make6010018
   Nguyen TM, 2022, INT MARKET REV, V39, P482, DOI 10.1108/IMR-02-2021-0078
   Uscov S., 2022, Curierul Judic, V2, P70
   Varzaru AA, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11142256
   Vasarhelyi MA, 2023, J EMERG TECHNOL ACCO, V20, P1, DOI 10.2308/JETA-2023-047
   Victor BG, 2023, J SOC SOC WORK RES, V14, P563, DOI 10.1086/726021
   Wei JS, 2022, Arxiv, DOI arXiv:2201.11903
   Westermann KD, 2015, CONTEMP ACCOUNT RES, V32, P864, DOI 10.1111/1911-3846.12107
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Woelfel M, 2024, BIG DATA COGN COMPUT, V8, DOI 10.3390/bdcc8010002
   Yu P, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11202776
   Zhang C, 2023, INT J ACCOUNT INF SY, V49, DOI 10.1016/j.accinf.2023.100619
   Zhao JN, 2024, J CORP ACCOUNT FINAN, V35, P269, DOI 10.1002/jcaf.22663
NR 116
TC 2
Z9 2
U1 61
U2 61
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD JUL
PY 2024
VL 13
IS 13
AR 2621
DI 10.3390/electronics13132621
PG 23
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA YD1X0
UT WOS:001266468300001
OA gold
DA 2024-12-25
ER

PT J
AU Cillo, P
   Rubera, G
AF Cillo, Paola
   Rubera, Gaia
TI Generative AI in innovation and marketing processes: A roadmap of
   research opportunities
SO JOURNAL OF THE ACADEMY OF MARKETING SCIENCE
LA English
DT Article; Early Access
DE Generative artificial intelligence; Innovation; Marketing strategy;
   Marketing capabilities; Firm value
ID CONSUMER; PERFORMANCE; PEOPLE; CONSTRAINTS; EXPERIENCES; CREATIVITY;
   THINKING; IDEAS; MODEL
AB Nowadays, we are witnessing the exponential growth of Generative AI (GenAI), a group of AI models designed to produce new content. This technology is poised to revolutionize marketing research and practice. Since the marketing literature about GenAI is still in its infancy, we offer a technical overview of how GenAI models are trained and how they produce content. Following this, we construct a roadmap for future research on GenAI in marketing, divided into two main domains. The first domain focuses on how firms can harness the potential of GenAI throughout the innovation process. We begin by discussing how GenAI changes consumer behavior and propose research questions at the consumer level. We then connect these emerging consumer insights with corresponding firm marketing strategies, presenting research questions at the firm level. The second set of research questions examines the likely consequences of using GenAI to analyze: (1) the relationship between market-based assets and firm value, and (2) consumer skills, preferences, and role in marketing processes.
C1 [Cillo, Paola] Bocconi Univ, Dept Management & Technol, Via Roentgen 1, I-20121 Milan, Italy.
   [Cillo, Paola; Rubera, Gaia] SDA Bocconi Sch Management, DIR Claudio Dematte Res Div, Via Sarfatti 10, I-20136 Milan, Italy.
   [Rubera, Gaia] Bocconi Univ, Dept Mkt, Via Roentgen 1, I-20121 Milan, Italy.
C3 Bocconi University; Bocconi University; Bocconi University
RP Cillo, P (corresponding author), Bocconi Univ, Dept Management & Technol, Via Roentgen 1, I-20121 Milan, Italy.; Cillo, P (corresponding author), SDA Bocconi Sch Management, DIR Claudio Dematte Res Div, Via Sarfatti 10, I-20136 Milan, Italy.
EM paola.cillo@unibocconi.it; gaia.rubera@unibocconi.it
RI RUBERA, GAIA/T-5142-2019; CILLO, PAOLA/KCZ-0688-2024
OI RUBERA, GAIA/0000-0001-6971-0500; CILLO, PAOLA/0000-0002-2496-8900
FU Universit Commerciale Luigi Bocconi
FX No Statement Available
CR Amabile T. M., 1996, Creativity in context: Update to the social psychology of creativity, DOI DOI 10.4324/9780429501234
   Argyle LP, 2023, POLIT ANAL, V31, P337, DOI 10.1017/pan.2023.2
   Bai H., 2023, ARTIFICIAL INTELLIGE
   Bayus BL, 2013, MANAGE SCI, V59, P226, DOI 10.1287/mnsc.1120.1599
   Bell JJ, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.1434
   Binz M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2218523120
   Blut M, 2023, J ACAD MARKET SCI, V51, P941, DOI 10.1007/s11747-023-00925-7
   Bommasani R., 2021, arXiv
   Brand J., 2023, Using gpt for market research
   Brown TJ, 1997, J MARKETING, V61, P68, DOI 10.2307/1252190
   Castaño R, 2008, J MARKETING RES, V45, P320, DOI 10.1509/jmkr.45.3.320
   Castelo N, 2023, J CONSUM RES, V50, P848, DOI 10.1093/jcr/ucad023
   Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
   Chung TS, 2016, J ACAD MARKET SCI, V44, P66, DOI 10.1007/s11747-015-0441-x
   Cillo P, 2021, J BUS RES, V131, P241, DOI 10.1016/j.jbusres.2021.04.001
   Dahl DW, 2002, J MARKETING RES, V39, P47, DOI 10.1509/jmkr.39.1.47.18930
   Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
   Diaz Ruiz C, 2023, J PUBLIC POLICY MARK, V42, P18, DOI 10.1177/07439156221103852
   Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
   Dowling K, 2020, J ACAD MARKET SCI, V48, P449, DOI 10.1007/s11747-019-00699-x
   Eapen TT, 2023, HARVARD BUS REV, V101, P55
   Eisenstein E., 1979, The Printing Press as an Agent of Change, DOI DOI 10.1017/CBO9781107049963
   Else H, 2023, NATURE, V613, P423, DOI 10.1038/d41586-023-00056-7
   Fajnerová I, 2018, BIOMED RES INT, V2018, DOI 10.1155/2018/2716134
   Giebelhausen M, 2014, J MARKETING, V78, P113, DOI 10.1509/jm.13.0056
   Girotra K., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4526071, DOI 10.2139/SSRN.4526071]
   Girotra K, 2010, MANAGE SCI, V56, P591, DOI 10.1287/mnsc.1090.1144
   Hamilton R, 2016, J ACAD MARKET SCI, V44, P281, DOI 10.1007/s11747-016-0476-7
   Han SJ, 2024, COGN SYST RES, V83, DOI 10.1016/j.cogsys.2023.101155
   Harmeling CM, 2017, J ACAD MARKET SCI, V45, P312, DOI 10.1007/s11747-016-0509-2
   Hartmann J., 2023, The power of generative marketing: Can generative AI reach human-level visual marketing content?
   Hoffman DL, 2018, J CONSUM RES, V44, P1178, DOI 10.1093/jcr/ucx105
   Horton J. J, 2023, Large language models as simulated economic agents: What can we learn from homo silicus?
   Huang M H., 2023, Journal of Marketing
   Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
   Hulland J, 2018, J ACAD MARKET SCI, V46, P92, DOI 10.1007/s11747-017-0532-y
   Hutson M., 2023, Nature
   Jakesch M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2208839120
   Jiang DF, 2023, Arxiv, DOI arXiv:2306.02561
   Johar GV, 2022, J CONSUM PSYCHOL, V32, P374, DOI 10.1002/jcpy.1288
   Kahn KB, 2006, J PROD INNOVAT MANAG, V23, P106, DOI 10.1111/j.1540-5885.2006.00186.x
   Karinshak Elise, 2023, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3579592
   KNIGHT KE, 1967, J BUS, V40, P478, DOI 10.1086/295013
   Koivisto M, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-40858-3
   Kojima T, 2022, ADV NEUR IN
   Korinek A, 2023, J ECON LIT, V61, P1281, DOI 10.1257/jel.20231736
   Kornish LJ, 2021, MARKET SCI, V40, P1106, DOI 10.1287/mksc.2021.1300
   Kreps S, 2022, J EXP POLIT SCI, V9, P104, DOI 10.1017/XPS.2020.37
   Lambrecht A, 2019, MANAGE SCI, V65, P2966, DOI 10.1287/mnsc.2018.3093
   Leung E, 2018, J MARKETING RES, V55, P818, DOI 10.1177/0022243718818423
   Li PY, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.0454
   Logg JM, 2019, ORGAN BEHAV HUM DEC, V151, P90, DOI 10.1016/j.obhdp.2018.12.005
   Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
   Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
   Lu KM, 2023, Arxiv, DOI arXiv:2311.08692
   Luo L, 2015, J MARKETING, V79, P100, DOI 10.1509/jm.13.0212
   Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
   Lysyakov M, 2023, INFORM SYST RES, V34, P1191, DOI 10.1287/isre.2022.1184
   Martin KD, 2017, J ACAD MARKET SCI, V45, P135, DOI 10.1007/s11747-016-0495-4
   Martin KD, 2017, J MARKETING, V81, P36, DOI 10.1509/jm.15.0497
   Mason W, 2012, P NATL ACAD SCI USA, V109, P764, DOI 10.1073/pnas.1110069108
   McAlister L, 2023, J ACAD MARKET SCI, V51, P617, DOI 10.1007/s11747-022-00911-5
   McClelland JL, 2020, P NATL ACAD SCI USA, V117, P25966, DOI 10.1073/pnas.1910416117
   McKinlay R, 2016, NATURE, V531, P573, DOI 10.1038/531573a
   Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
   Meyers MA, 2011, HAPPY ACCIDENTS SERE
   Min SW, 2022, Arxiv, DOI [arXiv:2202.12837, 10.48550/arXiv.2202.12837]
   Moreau CP, 2005, J CONSUM RES, V32, P13, DOI 10.1086/429597
   Morewedge CK, 2022, TRENDS COGN SCI, V26, P824, DOI 10.1016/j.tics.2022.07.007
   MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
   Moulard JG, 2021, J ACAD MARKET SCI, V49, P96, DOI 10.1007/s11747-020-00735-1
   Mumford MD, 1997, J CREATIVE BEHAV, V31, P1
   NARVER JC, 1990, J MARKETING, V54, P20, DOI 10.2307/1251757
   Noble SM, 2023, J ACAD MARKET SCI, V51, P747, DOI 10.1007/s11747-023-00948-0
   Paharia N, 2011, J CONSUM RES, V37, P775, DOI 10.1086/656219
   Pansari A, 2017, J ACAD MARKET SCI, V45, P294, DOI 10.1007/s11747-016-0485-6
   Plangger K, 2022, J ACAD MARKET SCI, V50, P1125, DOI 10.1007/s11747-022-00906-2
   Prahalad CK, 2004, J INTERACT MARK, V18, P5, DOI 10.1002/dir.20015
   Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
   Raithel S, 2024, J ACAD MARKET SCI, V52, P716, DOI 10.1007/s11747-023-00967-x
   Reisenbichler M., 2023, MSI Working Paper Series
   Reisenbichler M, 2022, MARKET SCI, V41, P441, DOI 10.1287/mksc.2022.1354
   Ringel Daniel, 2023, Working Paper
   Rubera G, 2016, J ACAD MARKET SCI, V44, P166, DOI 10.1007/s11747-014-0423-4
   Rubera G, 2010, MARKET LETT, V21, P191, DOI 10.1007/s11002-009-9088-z
   Scopelliti I, 2014, J PROD INNOVAT MANAG, V31, P880, DOI 10.1111/jpim.12129
   Slotegraaf RJ, 2003, J MARKETING RES, V40, P295, DOI 10.1509/jmkr.40.3.295.19235
   Sohn A., 2021, The future of disinformation operations and the coming war on brands
   Srivastava RK, 1998, J MARKETING, V62, P2, DOI 10.2307/1251799
   Stephen AT, 2016, J MARKETING RES, V53, P263, DOI 10.1509/jmr.13.0127
   Stevenson CE, 2023, Arxiv, DOI arXiv:2310.20384
   Thomaz F, 2020, J ACAD MARKET SCI, V48, P43, DOI 10.1007/s11747-019-00704-3
   Toubia O, 2017, MARKET SCI, V36, P1, DOI 10.1287/mksc.2016.0994
   Ukanwa K., 2020, Algorithmic discrimination in service
   Vartanian O, 2003, Q J EXP PSYCHOL-A, V56, P641, DOI 10.1080/02724980244000567
   Vaswani A, 2017, ADV NEUR IN, V30
   Vorhies DW, 2005, J MARKETING, V69, P80, DOI 10.1509/jmkg.69.1.80.55505
   Ward TB, 2001, AM PSYCHOL, V56, P350, DOI 10.1037/0003-066X.56.4.350
   Webb T, 2023, NAT HUM BEHAV, V7, P1526, DOI 10.1038/s41562-023-01659-w
   Wei JS, 2022, ADV NEUR IN
   Wilmer HH, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00605
   Winterich KP, 2024, J ACAD MARKET SCI, V52, P1475, DOI 10.1007/s11747-023-00981-z
   Yang Hao, 2023, medRxiv, P2023
   Zeevic M, 2023, Arxiv, DOI arXiv:2308.13067
   Zhang HH, 2022, Arxiv, DOI arXiv:2205.11502
   Zhang YH, 2023, JUDGM DECIS MAK, V18, DOI 10.1017/jdm.2023.37
   Zhao TZ, 2021, PR MACH LEARN RES, V139
NR 107
TC 1
Z9 1
U1 264
U2 264
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0092-0703
EI 1552-7824
J9 J ACAD MARKET SCI
JI J. Acad. Mark. Sci.
PD 2024 AUG 26
PY 2024
DI 10.1007/s11747-024-01044-7
EA AUG 2024
PG 18
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA D7E5L
UT WOS:001297777700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Cui, YY
   van Esch, P
   Phelan, S
AF Cui, Yuanyuan (Gina)
   van Esch, Patrick
   Phelan, Steven
TI How to build a competitive advantage for your brand using generative AI
SO BUSINESS HORIZONS
LA English
DT Article
DE Artificial intelligence; Generative AI; Brand persona; Competitive
   advantage; Large language models; Organizational strategy
ID ARTIFICIAL-INTELLIGENCE; ORGANIZATIONS
AB Generative artificial intelligence-defined as AI-enabled technology that analyzes and learns from existing data and generates novel, humanlike content-has emerged as a revolutionary technology for firms seeking sustainable competitive advantage. We highlight the evolution of generative AI (GenAI) from generic, domain-tailored and collaborative systems, which are democratized and only offer demand-driven insights, to the next frontier of alternative perceptual systems. Managers who integrate current large language models into building their brand personae will empower their firms to experiment along the evolutionary journey. By embedding alternative perceptual systems into GenAI platforms, firms can achieve novel, interactive, and personalized insights that their competitors may find difficult to replicate. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
C1 [Cui, Yuanyuan (Gina)] Clemson Univ, Wilbur O & Ann Powers Coll Business, Clemson, SC USA.
   [Cui, Yuanyuan (Gina)] Clemson Univ Clemson, Media Forens Hub, Clemson, SC USA.
   [van Esch, Patrick] Coastal Carolina Univ, E Craig Wall Sr Coll Business, Conway, SC 29634 USA.
   [Phelan, Steven] Kennesaw State Univ, Michael A Leven Sch Management Entrepreneurship &, Kennesaw, GA USA.
C3 Clemson University; Coastal Carolina University; University System of
   Georgia; Kennesaw State University
RP van Esch, P (corresponding author), Coastal Carolina Univ, E Craig Wall Sr Coll Business, Conway, SC 29634 USA.
EM yuanyuan.cui@aut.ac.nz; pvanesch@coastal.edu; sphelan5@kennesaw.edu
RI Phelan, Steven/KVC-2498-2024; van Esch, Patrick/LMO-2799-2024; Cui,
   Yuanyuan (Gina)/ADD-3909-2022; Phelan, Steven/N-4068-2013
OI Cui, Yuanyuan (Gina)/0000-0001-5148-332X; Phelan,
   Steven/0000-0003-3897-3273
CR Abdelhalim E., 2024, Business Horizons, V67, P487
   Aydin S., Journal of Emerging Economies and Policy, V8, P301
   Bareis J, 2022, SCI TECHNOL HUM VAL, V47, P855, DOI 10.1177/01622439211030007
   Benbya H, 2020, MIS Q EXEC, V19, pIX
   Berthon P., 2024, Business Horizons, V67, P461
   Blümel JH, 2024, J SERV THEOR PRACT, V34, P33, DOI 10.1108/JSTP-03-2023-0098
   Brakus JJ, 2009, J MARKETING, V73, P52, DOI 10.1509/jmkg.73.3.52
   Brewer J, 2024, BUS HORIZONS, V67, P525, DOI 10.1016/j.bushor.2024.04.011
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chui M., 2022, Generative AI is Here: How Tools Like ChatGPT Could Change Your Business
   Chui Michael, 2018, MIS Quarterly
   Cui Y., 2023, ARTIFICIAL INTELLIGE, P73, DOI 10.1007/978-3-031-33898-44
   Davenport T., 2020, Harvard Business Review
   Davenport T. H., 2022, Harvard Business Review
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Garibay OO, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2022.2153320
   Garvey MD, 2021, DECIS SUPPORT SYST, V144, DOI 10.1016/j.dss.2021.113497
   Gill SS, 2022, INTERNET THINGS-NETH, V19, DOI 10.1016/j.iot.2022.100514
   Griskevicius V, 2013, J CONSUM PSYCHOL, V23, P372, DOI 10.1016/j.jcps.2013.03.003
   Hackl C., 2023, ForbesFebruary 20
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Hansen EB, 2021, J MANUF SYST, V58, P362, DOI 10.1016/j.jmsy.2020.08.009
   Hashmi N, 2024, BUS HORIZONS, V67, P607, DOI 10.1016/j.bushor.2024.05.005
   Jarrahi MH., 2019, Business Information Review, V36, P178, DOI [10.1177/0266382119883999, DOI 10.1177/0266382119883999]
   Kamoonpuri SZ, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103258
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Keller KL, 2020, J CONSUM RES, V46, P995, DOI 10.1093/jcr/ucz058
   Kemp A, 2024, ACAD MANAGE REV, V49, P618, DOI 10.5465/amr.2020.0205
   Kietzmann J., 2024, Business Horizons, V67, P453
   Killoran J, 2023, BUS HORIZONS, V66, P585, DOI 10.1016/j.bushor.2023.02.001
   Kim J, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103494
   Klass Y., 2023, ForbesApril 19
   Korzynski P., 2023, International Entrepreneurship Review, V9, P7, DOI DOI 10.15678/IER.2023.0902.01
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   LaGrandeur, 2021, AI ETHICS, V1, P93, DOI DOI 10.1007/S43681-020-00010-7
   Leone D, 2021, J BUS RES, V129, P849, DOI 10.1016/j.jbusres.2020.11.008
   Lim B. Y., 2022, P 2022 CHI C HUM FAC, DOI [10.48550/arXiv.2112.14005, DOI 10.48550/ARXIV.2112.14005]
   Lu Y, 2019, J MANAG ANAL, V6, P1, DOI 10.1080/23270012.2019.1570365
   Ma LY, 2020, INT J RES MARK, V37, P481, DOI 10.1016/j.ijresmar.2020.04.005
   Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Nyatsanga S, 2023, COMPUT GRAPH FORUM, V42, P569, DOI 10.1111/cgf.14776
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Osadchaya E., 2024, Business Horizons, V67, P571
   Park A, 2023, IT PROF, V25, P13, DOI 10.1109/MITP.2023.3340529
   Patil D.D., 2024, Int. J. Intell. Syst. Appl. Eng, V12, P309
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Phelan S., 2023, Creating innovation value from generative AI: A property rights perspective, DOI [10.2139/ssrn.4611557, DOI 10.2139/SSRN.4611557]
   Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
   Ramaul L, 2024, BUS HORIZONS, V67, P615, DOI 10.1016/j.bushor.2024.05.006
   Ransbotham S, 2017, MIT SLOAN MANAGE REV, V59, P20
   Renieris E.M., 2023, MIT Sloan Management Review
   Retkowsky J., 2024, Business Horizons, V67, P525
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Roggeveen AL, 2021, J RETAILING, V97, P81, DOI 10.1016/j.jretai.2020.11.006
   Sathianathan B., 2023, Journal of AI, Robotics, and Workplace Automation, V2, P246
   Solow-Niederman A, 2020, SOUTH CALIF LAW REV, V93, P633
   Sundberg L, 2024, BUS HORIZONS, V67, P561, DOI 10.1016/j.bushor.2024.04.014
   Sundberg L, 2023, BUS HORIZONS, V66, P777, DOI 10.1016/j.bushor.2023.04.003
   Taulli T., 2023, Generative AI: How ChatGPT and Other AI Tools Will Revolutionize Business, P145
   Treccani C, 2021, AI SOC, V36, P1167, DOI 10.1007/s00146-020-01065-0
   van Esch P, 2024, J BUS IND MARK, V39, P673, DOI 10.1108/JBIM-03-2023-0126
   Verma R. K., 2023, International Journal of Responsible Artificial Intelligence, V13, P1
   von Krogh G, 2018, ACAD MANAG DISCOV, V4, P404, DOI 10.5465/amd.2018.0084
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Wilson J, 2018, HARVARD BUS REV, V96, P115
   Yadav A, 2020, ARTIF INTELL REV, V53, P4335, DOI 10.1007/s10462-019-09794-5
   Zhu ML, 2020, APPL PHYS REV, V7, DOI 10.1063/5.0016485
NR 72
TC 12
Z9 12
U1 69
U2 69
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 583
EP 594
DI 10.1016/j.bushor.2024.05.003
EA AUG 2024
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2I6K
UT WOS:001301293900001
DA 2024-12-25
ER

PT J
AU Adarkwah, MA
AF Adarkwah, Michael Agyemang
TI GenAI-Infused Adult Learning in the Digital Era: A Conceptual Framework
   for Higher Education
SO ADULT LEARNING
LA English
DT Article; Early Access
DE artificial intelligence; generative AI; GenAI; adult learning; higher
   education
ID GENERATIVE AI
AB Adult learners are a neglected species in the generative artificial intelligence (GenAI) era. The sweeping changes brought by GenAI in the educational arena have implications for adult learning. GenAI in education will usher in a world of adult learning that will be radically different from its predecessor. However, how adult learners will apply GenAI technologies to achieve their educational and professional goals remains blurred. To address this gap, it is crucial to examine essential principles for integrating GenAI into adult learning. For effective digital transformation of education, GenAI should optimize adult learning and ensure the safety of adult learners. This study proposes a "GenAI adult learning ecology" framework (GenAI-ALE) for higher education institutions in this digital era permeated by GenAI. The GenAI-ALE considers eight (8) essential principles categorized into two main themes; institutional factors (GenAI curriculum design, GenAI divide, GenAI policy, GenAI ethics) and interpersonal factors (GenAI human-centered andragogy, GenAI literacy, GenAI interest, and GenAI virtual learning). Malcolm Knowles' andragogical model is used to provide a context for integrating GenAI into adult learning. Applying the framework in a real-world context follows four iterative systematic steps; pre-perception and perception, GenAI readiness, assessment, and outcome. Reimagining new forms of adult learning in the GenAI revolution calls for higher education institutions to develop education systems where there is a synergy between humans (adult learners) and GenAI.
C1 [Adarkwah, Michael Agyemang] Friedrich Schiller Univ Jena, Inst Educ & Culture, Chair Adult Educ, Planetarium 4, D-07743 Jena, Germany.
C3 Friedrich Schiller University of Jena
RP Adarkwah, MA (corresponding author), Friedrich Schiller Univ Jena, Inst Educ & Culture, Chair Adult Educ, Planetarium 4, D-07743 Jena, Germany.
EM michael.agyemang.adarkwah@uni-jena.de
RI Adarkwah, Michael Agyemang/AAC-8210-2021
OI Adarkwah, Michael Agyemang/0000-0001-8201-8965
CR Adarkwah Michael Agyemang, 2023, Smart Learning for A Sustainable Society: Proceedings of the 7th International Conference on Smart Learning Environments. Lecture Notes in Educational Technology, P117, DOI 10.1007/978-981-99-5961-7_13
   Adarkwah M. A., 2023, Journal of Applied Learning Teaching, V6, P2, DOI [10.37074/jalt.2023.6.2.26, DOI 10.37074/JALT.2023.6.2.26]
   Awidi I. T., 2024, Comput. Educ. Artif. Intell, V6, DOI [10.1016/j.caeai.2024.100226, DOI 10.1016/J.CAEAI.2024.100203]
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Basgen B., 2024, Higher education generative AI readiness assessment
   Bhavya B., 2022, arXiv, DOI DOI 10.48550/ARXIV.2210.04186
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Bridges LM, 2024, J LIBR ADM, V64, P66, DOI 10.1080/01930826.2024.2292484
   Cacicio S., 2023, Adult Literacy Education, V5, P80
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Chen B., 2023, Computers and Education: Artificial Intelligence, V5, P100184, DOI DOI 10.1016/J.CAEAI.2023.100184
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Ciampa K, 2023, J ADOLESC ADULT LIT, V67, P186, DOI 10.1002/jaal.1310
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dave T, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1169595
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elyoseph Z, 2024, AM J BIOETHICS, V24, P57, DOI 10.1080/15265161.2023.2278546
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Forrest SP, 2006, ACAD MANAG LEARN EDU, V5, P113, DOI 10.5465/AMLE.2006.20388390
   Fullan M., 2023, School Leadership Management, P1, DOI [10.1080/13632434.2023.2246856, DOI 10.1080/13632434.2023.2246856]
   Gupta V., 2023, INTERNET REF SERV Q, P1, DOI DOI 10.1080/10875301.2023.2300114
   Healy M., 2023, Using curriculum Theory to inform Approaches to generative AI in schools (SSRN scholarly paper 4564372), DOI [10.2139/ssrn.4564372, DOI 10.2139/SSRN.4564372]
   Hollander J, 2023, DISCOURSE PROCESS, V60, P397, DOI 10.1080/0163853X.2023.2203543
   Hsu YC, 2023, TECHTRENDS, V67, P885, DOI 10.1007/s11528-023-00913-2
   Hsu YC, 2023, TECHTRENDS, V67, P603, DOI 10.1007/s11528-023-00863-9
   Huh S, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.1
   HyScaler, 2023, The Power of AI in Research Hypotheses
   Kang HJ, 2023, HIGH EDUC SKILL WORK, V13, P450, DOI 10.1108/HESWBL-01-2023-0017
   Knowles M.S., 2005, ADULT LEARNER, V6th
   Leiker D, 2023, COMM COM INF SC, V1831, P523, DOI 10.1007/978-3-031-36336-8_81
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lin X, 2024, ADULT LEARN, V35, P156, DOI 10.1177/10451595231184928
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Luo JH, 2024, ASSESS EVAL HIGH EDU, V49, P651, DOI 10.1080/02602938.2024.2309963
   McGrath V., 2009, The Irish Journal of Adult and Community Education, P99
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   OECD, 2023, Generative ai in the classroom: From hype to reality?
   Poquet O, 2021, BRIT J EDUC TECHNOL, V52, P1695, DOI 10.1111/bjet.13123
   Pozdnyakova O, 2017, PROCEDIA ENGINEER, V178, P243, DOI 10.1016/j.proeng.2017.01.105
   Robertson J., 2024, Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction, DOI [10.1016/j.bushor.2024.04.008, DOI 10.1016/J.BUSHOR.2024.04.008]
   Rossman M.H., 2000, New Horizons in Adult Education, V14, P4, DOI [DOI 10.1002/NHA3.10105, 10.1002/nha3.10105]
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Salinas-Navarro DE, 2024, EDUC SCI, V14, DOI 10.3390/educsci14010083
   SHELLEY M, 1984, J AM STAT ASSOC, V79, P240, DOI 10.2307/2288384
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   UNESCO, 2023, Generative AI and the future of education
NR 51
TC 0
Z9 0
U1 33
U2 33
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1045-1595
EI 2162-4070
J9 ADULT LEARN
JI Adult Learn.
PD 2024 AUG 1
PY 2024
DI 10.1177/10451595241271161
EA AUG 2024
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA A4U9A
UT WOS:001282510300001
OA hybrid
DA 2024-12-25
ER

PT J
AU Yang, YM
   Xia, Q
   Liu, CB
   Chiu, TKF
AF Yang, Yiming
   Xia, Qi
   Liu, Chuanbin
   Chiu, Thomas K. F.
TI The impact of TPACK on teachers' willingness to integrate generative
   artificial intelligence (GenAI): The moderating role of negative
   emotions and the buffering effects of need satisfaction
SO TEACHING AND TEACHER EDUCATION
LA English
DT Article
DE TPACK; Negative emotions; Self-determination theory (SDT); Generative
   artificial intelligence (GenAI); Teachers' willingness
ID SELF-DETERMINATION THEORY; PROFESSIONAL-DEVELOPMENT;
   INFORMATION-TECHNOLOGY; ACCEPTANCE; COMPETENCE; EDUCATION; AUTONOMY;
   ADOPTION
AB Understanding teachers' willingness to integrate generative AI (WIAI) is essential in the current dilemma where students' adoption rate is faster than teachers'. Therefore, this study aims to identify factors affecting teachers' WIAI and their interactions from the perspectives of needs satisfaction and emotion. We used regression analyses to analyze data collected from 1348 teachers online. The results supported that TPACK positively influences teachers' WIAI, but this effect is weakened by negative emotions, while needs satisfaction for competence and relatedness buffers the negative effect more effectively than autonomy. These highlight the role of emotional and psychological support in fostering teachers' adoptions.
C1 [Yang, Yiming] Northwest Normal Univ, Fac Educ Sci, Lanzhou, Gansu, Peoples R China.
   [Xia, Qi] Zhejiang Univ, Dept Higher Educ, Hangzhou 310058, Peoples R China.
   [Liu, Chuanbin] Minist Educ, Dev Higher Educ Inst, Ctr Sci Res, Beijing, Peoples R China.
   [Chiu, Thomas K. F.] Chinese Univ Hong Kong, Ctr Univ, Ctr Learning Sci & Technol, Sch Partnership, Hong Kong, Peoples R China.
C3 Northwest Normal University - China; Zhejiang University; Chinese
   University of Hong Kong
RP Xia, Q (corresponding author), Zhejiang Univ, Coll Agr & Biotechnol, Zijingang Campus, Hangzhou 310058, Peoples R China.
EM yimingyang0102@163.com; ixia@zju.edu.cn; liuchuanbingl@163.com;
   Thomas.kf.chiu@gmail.com
RI Liu, Chuanbin/JLL-9341-2023; XIA, Qi/KRO-3142-2024; Chiu, Thomas
   K.F./AAR-4894-2021
OI XIA, Qi/0000-0003-0538-7665; Chiu, Thomas K.F./0000-0003-2887-5477;
   yiming, Yang/0009-0005-9750-8920
FU General Research Project of the Zhejiang Provincial Department of
   Education [Y202455356]
FX This study was supported by the General Research Project of the Zhejiang
   Provincial Department of Education under the Grant No. Y202455356.
CR Abramowitz B, 2023, J RES TECHNOL EDUC, V55, P64, DOI 10.1080/15391523.2022.2119450
   Ahuja Karan, 2019, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, V3, DOI 10.1145/3351229
   An X, 2023, EDUC INF TECHNOL, V28, P5187, DOI 10.1007/s10639-022-11286-z
   Avalos B, 2011, TEACH TEACH EDUC, V27, P10, DOI 10.1016/j.tate.2010.08.007
   BANDURA A, 1986, J SOC CLIN PSYCHOL, V4, P359, DOI 10.1521/jscp.1986.4.3.359
   Baran E, 2023, TECHTRENDS, V67, P945, DOI 10.1007/s11528-023-00905-2
   Beaudry A, 2010, MIS QUART, V34, P689
   Bower M, 2024, EDUC INF TECHNOL, V29, P15403, DOI 10.1007/s10639-023-12405-0
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chen X., 2020, Computers and Education: Artificial Intelligence, V1, P100002, DOI [10.1016/j.caeai.2020.100002 10.1016/j.caeai.2020.100002, DOI 10.1016/J.CAEAI.2020.100002]
   Chen XL, 2022, EDUC TECHNOL SOC, V25, P28
   Chiu TKF, 2024, COMPUT EDUC, V214, DOI 10.1016/j.compedu.2024.105017
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Chiu TKF, 2022, ETR&D-EDUC TECH RES, V70, P931, DOI 10.1007/s11423-022-10096-x
   Chiu TKF, 2022, J RES TECHNOL EDUC, V54, pS14, DOI 10.1080/15391523.2021.1891998
   Chiu TKF, 2017, BRIT J EDUC TECHNOL, V48, P524, DOI 10.1111/bjet.12432
   Clark-Gordon CV, 2019, COMPUT EDUC, V128, P414, DOI 10.1016/j.compedu.2018.10.002
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Derakhshan A, 2024, INT J APPL LINGUIST, V34, P1246, DOI 10.1111/ijal.12561
   Desimone LM, 2009, EDUC RESEARCHER, V38, P181, DOI 10.3102/0013189X08331140
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   ElSayary A, 2024, J COMPUT ASSIST LEAR, V40, P931, DOI 10.1111/jcal.12926
   Farangi MR, 2024, ETHICS BEHAV, DOI 10.1080/10508422.2024.2420133
   Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Giannakos M, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2394886
   Gunasinghe A., 2019, European Journal of Social Sciences Studies
   Guo K, 2023, COMPUT EDUC, V203, DOI 10.1016/j.compedu.2023.104862
   Guskey T.R., 2002, Teachers and Teaching: theory and practice, V8, P381, DOI [https://doi.org/10.1080/135406002100000512, DOI 10.1080/135406002100000512]
   Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
   Han HS, 2024, INT J CONTEMP HOSP M, DOI 10.1108/IJCHM-07-2023-1072
   Hava K, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-024-12939-x
   Hayes A., 2013, PROCESS macro
   Katz I, 2024, TEACH TEACH EDUC, V148, DOI 10.1016/j.tate.2024.104706
   Kay Robin, 2007, Journal of Educational Computing Research, V36, P455, DOI 10.2190/J111-Q132-N166-K249
   Keller MV, 2024, SOC PSYCHOL EDUC, V27, P3119, DOI 10.1007/s11218-024-09888-1
   Kennedy MM, 2016, REV EDUC RES, V86, P945, DOI 10.3102/0034654315626800
   Kline R., 1998, PRINCIPLE PRACTICE S
   Koehler M.J., 2013, HDB RES ED COMMUNICA, V4th, P101, DOI [DOI 10.1007/978-1-4614-3185-59, 10.1007/978-1-4614-3185-5_, DOI 10.1007/978-1-4614-3185-5_9]
   Lan YZ, 2024, TEACH TEACH EDUC, V151, DOI 10.1016/j.tate.2024.104736
   Law L, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100174
   Leung KH, 2024, TEACH TEACH EDUC, V152, DOI 10.1016/j.tate.2024.104792
   Lin YP, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2359222
   Maican CI, 2019, COMPUT EDUC, V128, P113, DOI 10.1016/j.compedu.2018.09.010
   Mao J, 2024, TECHTRENDS, V68, P58, DOI 10.1007/s11528-023-00911-4
   Mercader C, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-0182-x
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Mouratidis A, 2013, LEARN INDIVID DIFFER, V23, P179, DOI 10.1016/j.lindif.2012.09.001
   Niemiec CP, 2009, THEORY RES EDUC, V7, P133, DOI 10.1177/1477878509104318
   Pagán-Garbín I, 2024, TEACH TEACH EDUC, V148, DOI 10.1016/j.tate.2024.104717
   Pallant J., 2010, SPSS SURVIVAL MANUAL, DOI DOI 10.4324/9781003117407
   Pozo-Rico T, 2023, PSYCHOL RES BEHAV MA, V16, P1, DOI 10.2147/PRBM.S382572
   Ramakrishnan A, 2023, IEEE T AFFECT COMPUT, V14, P664, DOI 10.1109/TAFFC.2021.3059209
   Reeve J, 2021, EDUC PSYCHOL-US, V56, P54, DOI 10.1080/00461520.2020.1862657
   Ryan RM, 2017, SELF-DETERMINATION THEORY: BASIC PSYCHOLOGICAL NEEDS IN MOTIVATION, DEVELOPMENT, AND WELLNESS, P272
   Ryan RM, 2000, AM PSYCHOL, V55, P68, DOI 10.1037/0003-066X.55.1.68
   Sahin F, 2022, SOC PSYCHOL EDUC, V25, P567, DOI 10.1007/s11218-022-09702-w
   Sahin F, 2021, EDUC INF TECHNOL, V26, P4795, DOI 10.1007/s10639-021-10497-0
   Samala AD, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-024-12936-0
   Shen XQ, 2024, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2024.2365959
   Shulman LS., 1986, Educational Researcher, V15, P4, DOI [10.1177/002205741319300302, DOI 10.3102/0013189X015002004, 10.30827/profesorado.v23i3.11230, DOI 10.30827/PROFESORADO.V23I3.11230, 10.3102/0013189X015002004]
   Son T, 2024, TEACH TEACH EDUC, V146, DOI 10.1016/j.tate.2024.104640
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Wang K, 2024, BEHAV SCI-BASEL, V14, DOI 10.3390/bs14050373
   Wang MK, 2024, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2024.2365345
   Wang YM, 2021, EDUC TECHNOL SOC, V24, P116
   Wang YY, 2022, INTERACT LEARN ENVIR, V30, P619, DOI 10.1080/10494820.2019.1674887
   Xia Q, 2023, EDUC INF TECHNOL, V28, P8691, DOI 10.1007/s10639-022-11547-x
   Xia Q, 2022, COMPUT EDUC, V189, DOI 10.1016/j.compedu.2022.104582
   Xu WQ, 2022, EDUC INF TECHNOL, V27, P4195, DOI 10.1007/s10639-021-10774-y
   Yang JZ, 2021, INTERACT LEARN ENVIR, V29, P1062, DOI 10.1080/10494820.2019.1627560
   Yang YQ, 2020, BRIT J EDUC TECHNOL, V51, P1960, DOI 10.1111/bjet.13040
   Yin HB, 2013, TEACH TEACH EDUC, V35, P137, DOI 10.1016/j.tate.2013.06.006
   Yin HB, 2024, COMPUT HUM BEHAV, V161, DOI 10.1016/j.chb.2024.108417
NR 78
TC 0
Z9 0
U1 19
U2 19
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0742-051X
EI 1879-2480
J9 TEACH TEACH EDUC
JI Teach. Teach. Educ.
PD FEB
PY 2025
VL 154
AR 104877
DI 10.1016/j.tate.2024.104877
PG 11
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA N9S8N
UT WOS:001367656100001
DA 2024-12-25
ER

PT J
AU Crumbly, J
   Pal, R
   Altay, N
AF Crumbly, Jack
   Pal, Raktim
   Altay, Nezih
TI A classification framework for generative artificial intelligence for
   social good
SO TECHNOVATION
LA English
DT Article
DE Artificial intelligence (AI); Generative artificial intelligence
   (GenAI); Social good; Classification
ID TASK-TECHNOLOGY FIT; AI; ACCEPTANCE; SYSTEMS
AB Many policy makers and corporate leaders are adjusting their strategies to harness the power of GenAI. There are numerous debates on how GenAI would fundamentally change existing business models. However, there is not much discussion on roles of generative AI in the domain of social good. Broader views covering potential opportunities of GenAI to enable diverse initiatives in the social good space are largely missing. We intend to reduce the gap by developing a classification framework that should allow researchers gauge the potential impact of GenAI for social good initiatives. Through case analysis, we assess how value-added abilities of GenAI may influence various social good initiatives. We adopt/develop two loosely connected classification frameworks that are grounded in task-technology fit (TTF) theory. Subsequently, we investigate how our analyses of GenAI initiatives utilizing different dimensions of these two frameworks may be synthesized to provide appropriate explanation for potential success of GenAI for social good. We develop five propositions that will provide guidance to practitioners and researchers. The theoretically grounded analysis of 21 GenAI for social good use cases based on the two classification frameworks, and the resulting propositions are the original contributions of this paper to the AI for social good literature.
C1 [Crumbly, Jack] Tuskegee Univ, Coll Business & Informat Sci, Management Dept, Andrew F Brimmer Hall,Rm 400F, Tuskegee, AL 36088 USA.
   [Pal, Raktim] James Madison Univ, Harrisonburg, VA USA.
   [Altay, Nezih] DePaul Univ, Chicago, IL USA.
C3 Tuskegee University; James Madison University; DePaul University
RP Crumbly, J (corresponding author), Tuskegee Univ, Coll Business & Informat Sci, Management Dept, Andrew F Brimmer Hall,Rm 400F, Tuskegee, AL 36088 USA.
EM jcrumbly@tuskegee.edu; palrx@jmu.edu; naltay@depaul.edu
CR Arsenyan J, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3229821
   Baldassarre M.T., 2023, P 2023 ACM C INF TEC, P363, DOI [10.1145/3582515.3609555, DOI 10.1145/3582515.3609555]
   Ballester O, 2021, PROCEEDINGS OF THE 22ND ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH, DGO 2021, P67, DOI 10.1145/3463677.3463709
   Brynjolfsson E., 2023, Generative AI at Work (No. W31161)
   Cane S, 2009, J COMPUT INFORM SYST, V50, P108
   Chang A.M., 2018, Lean impact: How to innovate for radically greater social good
   Chiarello F, 2024, TECHNOVATION, V133, DOI 10.1016/j.technovation.2024.103002
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Chui Michael, 2018, MIS Quarterly
   Cockburn A, 1997, J OBJECT-ORIENT PROG, V10, P56
   COOPER RB, 1990, MANAGE SCI, V36, P123, DOI 10.1287/mnsc.36.2.123
   Cowls J, 2023, AI SOC, V38, P283, DOI 10.1007/s00146-021-01294-x
   Cowls J, 2021, NAT MACH INTELL, V3, P111, DOI 10.1038/s42256-021-00296-0
   Cyert R., 1963, BEHAV THEORY FIRM
   DAFT RL, 1981, ADMIN SCI QUART, V26, P207, DOI 10.2307/2392469
   DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
   Denyer D., 2008, The Sage Handbook of Organizational Research Methods, DOI DOI 10.1080/03634528709378635
   Dharanikota S., 2021, P 27 AM C INF SYST M
   Dhiman N, 2023, FORESIGHT, V25, P209, DOI 10.1108/FS-10-2021-0207
   Di Vaio A, 2020, J BUS RES, V121, P283, DOI 10.1016/j.jbusres.2020.08.019
   Dicuonzo G, 2023, TECHNOVATION, V120, DOI 10.1016/j.technovation.2022.102510
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Eggers W., 2013, The Solution Revolution: How Business, Government, and Social Enterprise Are Teaming Up to Solve Society's Toughest Problems
   EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
   Ellram L.M., 1996, J BUS LOGIST, V17, P93
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Galbraith J., 1977, ORG DESIGN
   Galbraith J.R., 1973, Designing complex organizations
   Ghasemaghaei M, 2017, DECIS SUPPORT SYST, V101, P95, DOI 10.1016/j.dss.2017.06.004
   Gibbs G., 2007, The SAGE Qualitative Research Kit, DOI DOI 10.4135/9781849208574
   G¢mez-Gonzalez E, 2020, Arxiv, DOI [arXiv:2001.09778, 10.48550/arXiv.2001.09778, DOI 10.48550/ARXIV.2001.09778]
   GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
   Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
   Gurupur V, 2020, MEDICINA-LITHUANIA, V56, DOI 10.3390/medicina56030141
   Haefner N, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120392
   Holland C, 2024, TECHNOVATION, V129, DOI 10.1016/j.technovation.2023.102875
   Holzmeyer C, 2021, INTERDISCIPL SCI REV, V46, P94, DOI 10.1080/03080188.2020.1840221
   Houde S., 2020, arXiv, DOI [10.48550/arXiv.2003.07679, DOI 10.48550/ARXIV.2003.07679]
   Ismail E., 2022, Procedia Computer Science, V198, P530
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Kinkel S, 2022, TECHNOVATION, V110, DOI 10.1016/j.technovation.2021.102375
   Lee YS, 2022, TECHNOVATION, V118, DOI 10.1016/j.technovation.2022.102590
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Mannuru NR, 2023, INFORM DEV, DOI 10.1177/02666669231200628
   Mariani MM, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102623
   Masrom S., 2023, Bulletin of Electrical Engineering and Informatics, V12, P1666
   McKinsey, 2024, McKinsey Chart of the Day
   McKinsey, 2024, Generative AI in operations: capturing the value
   Mhlanga D, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13115788
   Monselise M, 2022, IEEE INT CONF HEALT, P470, DOI 10.1109/ICHI54592.2022.00072
   Nasir O, 2023, TECHNOL SOC, V72, DOI 10.1016/j.techsoc.2022.102171
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Osoba O.A., 2017, INTELLIGENCE OUR IMA
   Owoyemi A, 2020, FRONT DIGIT HEALTH, V2, DOI 10.3389/fdgth.2020.00006
   Pawson R., 2006, Moving beyond effectiveness in evidence synthesis: methodological issues in the synthesis of diverse sources of evidence
   PERROW C, 1967, AM SOCIOL REV, V32, P194, DOI 10.2307/2091811
   Pillai R, 2020, BENCHMARKING, V27, P2599, DOI 10.1108/BIJ-04-2020-0186
   Pundziene A, 2023, TECHNOVATION, V124, DOI 10.1016/j.technovation.2023.102748
   Varshney KR, 2019, Arxiv, DOI [arXiv:1905.11519, 10.48550/arXiv.1905.11519, DOI 10.48550/ARXIV.1905.11519]
   Raman R, 2024, PLOS ONE, V19, DOI 10.1371/journal.pone.0297521
   Robson C., 2002, Real world research
   Shi ZR, 2020, Arxiv, DOI [arXiv:2001.01818, 10.48550/arXiv.2001.01818, DOI 10.48550/ARXIV.2001.01818]
   Singh K, 2024, TECHNOVATION, V133, DOI 10.1016/j.technovation.2024.103021
   Sjödin D, 2023, TECHNOL FORECAST SOC, V197, DOI 10.1016/j.techfore.2023.122903
   Talaei-Khoei A, 2024, TECHNOVATION, V132, DOI 10.1016/j.technovation.2024.102975
   Tan DJ, 2022, GONDWANA RES, V106, P92, DOI 10.1016/j.gr.2021.12.009
   Tangpong CC, 2011, J OPER MANAG, V29, P627, DOI 10.1016/j.jom.2010.08.001
   Tomasev N, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-15871-z
   Truby J, 2020, SUSTAIN DEV, V28, P946, DOI 10.1002/sd.2048
   Umbrello Steven, 2021, AI Ethics, V1, P283, DOI 10.1007/s43681-021-00038-3
   Vernon D, 2019, IEEE ROBOT AUTOM MAG, V26, P131, DOI 10.1109/MRA.2019.2946107
   Vinuesa R, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-019-14108-y
   Wachter RM, 2024, JAMA-J AM MED ASSOC, V331, P65, DOI 10.1001/jama.2023.25054
   Waljee AK, 2022, GUT, V71, P1259, DOI 10.1136/gutjnl-2022-327211
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang R, 2023, IEEE-CAA J AUTOMATIC, V10, P2179, DOI 10.1109/JAS.2023.123999
   Weber R. P., 1990, BASIC CONTENT ANAL, DOI [10.4135/9781412983488, DOI 10.4135/9781412983488]
   Weeks WB, 2023, INT J PUBLIC HEALTH, V68, DOI 10.3389/ijph.2023.1606716
   Wen Jinbo, 2024, IEEE Internet of Things Magazine, V7, P30, DOI 10.1109/IOTM.001.2300255
   Yin R.K., 2009, CASE STUDY RES DESIG, V5
   Yuba Mitsuru, 2023, PLOS Digit Health, V2, pe0000209, DOI 10.1371/journal.pdig.0000209
   Yuce A, 2019, ONLINE INFORM REV, V43, P600, DOI 10.1108/OIR-11-2017-0340
NR 82
TC 0
Z9 0
U1 16
U2 16
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD JAN
PY 2025
VL 139
AR 103129
DI 10.1016/j.technovation.2024.103129
EA NOV 2024
PG 16
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA M5A1D
UT WOS:001357653400001
DA 2024-12-25
ER

PT J
AU Zhao, L
   Rahman, MH
   Yeoh, W
   Wang, S
   Ooi, KB
AF Zhao, Li
   Rahman, Md. Habibur
   Yeoh, William
   Wang, Shan
   Ooi, Keng-Boon
TI Examining factors influencing university students' adoption of
   generative artificial intelligence: a cross-country study
SO STUDIES IN HIGHER EDUCATION
LA English
DT Article; Early Access
DE Generative AI; university students; adoption; survey; UTAUT2
ID INFORMATION-TECHNOLOGY; USER ACCEPTANCE; COMMUNICATION TECHNOLOGY;
   PERSONAL INNOVATIVENESS; EMPIRICAL-EXAMINATION; MODEL; INNOVATIONS;
   INTEGRATION; SERVICES; SYSTEMS
AB The introduction of Generative Artificial Intelligence (GenAI) has transformed the way university students learn. To understand the factors that affect the adoption of GenAI among university students, we proposed a comprehensive research model based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2), along with personal factors customized for GenAI. We conducted a cross-sectional survey to collect data from university students in Malaysia and China through an online questionnaire, yielding a total of 500 valid responses. The data were analyzed using the Partial Least Squares method to assess the influence of various factors on GenAI adoption. Our findings reveal notable differences in the factors affecting GenAI adoption between the two countries, with the Malaysian group showing a more diverse range of influencing factors compared to the Chinese group. This study highlights the importance of considering country-specific differences when devising strategies for the adoption of GenAI. By integrating UTAUT2 with personal factors and conducting a cross-country comparative analysis, this study offers significant insights into how factors influencing GenAI adoption vary between countries. These insights can be valuable for university stakeholders.
C1 [Zhao, Li] Shandong Technol & Business Univ, Coll Int Business, Yantai, Peoples R China.
   [Rahman, Md. Habibur; Yeoh, William] Deakin Univ, Dept Informat Syst & Business Analyt, Geelong, Australia.
   [Rahman, Md. Habibur] Bangladesh Agr Univ, Inst Agribusiness & Dev Studies, Mymensingh, Bangladesh.
   [Yeoh, William] Hong Kong Metropolitan Univ, Lee Shau Kee Sch Business & Adm, Kowloon, Hong Kong, Peoples R China.
   [Wang, Shan] Univ Saskatchewan, Edwards Sch Business, Saskatoon, SK, Canada.
   [Ooi, Keng-Boon] UCSI Univ, Grad Business Sch, Cheras, Malaysia.
C3 Shandong Technology & Business University; Deakin University; Bangladesh
   Agricultural University (BAU); Hong Kong Metropolitan University;
   University of Saskatchewan; UCSI University
RP Rahman, MH (corresponding author), Deakin Univ, Dept Informat Syst & Business Analyt, Geelong, Vic, Australia.
EM habib_du32@yahoo.com
RI Yeoh, William/ABD-6399-2020; Rahman, Md Habibur/LMO-8952-2024
CR Abdullah S. I. N. W., 2024, Contemporary Trends in Innovative Marketing Strategies, P278
   Agarwal R, 1997, DECISION SCI, V28, P557, DOI 10.1111/j.1540-5915.1997.tb01322.x
   Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
   Ahn MJ, 2011, ASIA PAC J MANAG, V28, P257, DOI 10.1007/s10490-009-9147-2
   AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
   Alharbi ST, 2012, INT J CLOUD APPL COM, V2, P1, DOI 10.4018/ijcac.2012040101
   Arenal A, 2020, TELECOMMUN POLICY, V44, DOI 10.1016/j.telpol.2020.101960
   Azhar N. A. Z. M., 2021, Global Business and Management Research, V13, P312
   Ball Christopher, 2023, Proceedings of the Association for Information Science and Technology, P878, DOI 10.1002/pra2.884
   Bandura A., 1986, SOCIAL FDN THOUGHT A
   Brown SA, 2005, MIS QUART, V29, P399
   Budhathoki T, 2024, STUD HIGH EDUC, V49, P831, DOI 10.1080/03075079.2024.2333937
   Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Chatterjee S, 2021, INF COMPUT SECUR, V29, P1, DOI 10.1108/ICS-02-2019-0029
   Chen JY, 2022, COMPUT HUM BEHAV REP, V8, DOI 10.1016/j.chbr.2022.100237
   Chiang ChunFang Chiang ChunFang, 2006, Journal of Hospitality & Leisure Marketing, V15, P49, DOI 10.1300/J150v15n03_04
   Chong AYL, 2012, DECIS SUPPORT SYST, V53, P34, DOI 10.1016/j.dss.2011.12.001
   Choudhury A, 2022, FRONT DIGIT HEALTH, V4, DOI 10.3389/fdgth.2022.920662
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Davis FD, 2004, IEEE T ENG MANAGE, V51, P31, DOI 10.1109/TEM.2003.822468
   DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
   Deci E. L., 2013, Intrinsic motivation and self-determination in human behavior (perspectives in social psychology), DOI DOI 10.1007/978-1-4899-2271-7
   Du SL, 2021, J BUS RES, V129, P961, DOI 10.1016/j.jbusres.2020.08.024
   Du X, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063511
   Dwivedi YK, 2019, INFORM SYST FRONT, V21, P719, DOI 10.1007/s10796-017-9774-y
   Dwivedi YK, 2017, GOV INFORM Q, V34, P211, DOI 10.1016/j.giq.2017.03.001
   Emon M. M. H., 2023, AIUB Journal of Science and Engineering (AJSE), V22, P189, DOI [10.53799/ajse.v22i2.797, DOI 10.53799/AJSE.V22I2.797]
   Fan WJ, 2020, ANN OPER RES, V294, P567, DOI 10.1007/s10479-018-2818-y
   Farooq MS, 2017, INTERACT TECHNOL SMA, V14, P329, DOI 10.1108/ITSE-06-2016-0015
   Fishbein M., 1975, BELIEF ATTITUDE INTE
   Fong LHN, 2013, EUR J TOUR RES, V6, P211, DOI 10.1016/j.lrp.2013.01.002
   Foroughi B, 2024, INT J HUM-COMPUT INT, V40, P4501, DOI 10.1080/10447318.2023.2226495
   Gansser OA, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101535
   Gao LLY, 2024, STUD HIGH EDUC, DOI 10.1080/03075079.2024.2323571
   Gotz O., 2010, Handbook of Partial Least Squares, P691, DOI [DOI 10.1007/978-3-540-32827-830, DOI 10.1007/978-3-540-32827-8_30]
   Gulati A., 2024, Marketing Education Review, V34, P201, DOI [https://doi.org/10.1080/10528008.2023.2300139, DOI 10.1080/10528008.2023.2300139]
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hair JF, 2014, PRIMER PARTIAL LEAST
   HOELTER JW, 1983, SOCIOL METHOD RES, V11, P325, DOI 10.1177/0049124183011003003
   Hussain A, 2019, INT J INFORM MANAGE, V44, P76, DOI 10.1016/j.ijinfomgt.2018.09.016
   Ifinedo P., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), P2937, DOI 10.1109/HICSS.2012.556
   Jang-Jaccard J, 2014, J COMPUT SYST SCI, V80, P973, DOI 10.1016/j.jcss.2014.02.005
   JOE H, 1993, COMPUT STAT DATA AN, V16, P279, DOI 10.1016/0167-9473(93)90130-L
   John Surej P., 2015, Contad. Adm, V60, P230
   Jun J, 2019, TOTAL QUAL MANAG BUS, V30, pS83, DOI 10.1080/14783363.2019.1665820
   Justus M, 2017, QUAL REP, V22, P499
   Kijsanayotin B, 2009, INT J MED INFORM, V78, P404, DOI 10.1016/j.ijmedinf.2008.12.005
   Kim B, 2009, J INF TECHNOL-UK, V24, P35, DOI 10.1057/jit.2008.28
   Kim MK, 2018, ENERGY, V159, P799, DOI 10.1016/j.energy.2018.06.064
   Kulviwat S, 2009, J BUS RES, V62, P706, DOI 10.1016/j.jbusres.2007.04.014
   Lippert SK, 2005, IEEE T ENG MANAGE, V52, P363, DOI 10.1109/TEM.2005.851273
   Lobera J., 2020, Communicating Artificial Intelligence (AI), P80
   Lu J, 2005, J STRATEGIC INF SYST, V14, P245, DOI 10.1016/j.jsis.2005.07.003
   Macedo IM, 2017, COMPUT HUM BEHAV, V75, P935, DOI 10.1016/j.chb.2017.06.013
   Madadi Y, 2011, PROCD SOC BEHV, V15, P3625, DOI 10.1016/j.sbspro.2011.04.346
   Mehta A, 2019, COMPUT EDUC, V141, DOI 10.1016/j.compedu.2019.103617
   Menon D, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e20962
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   O'Dea X, 2024, STUD HIGH EDUC, V49, P811, DOI 10.1080/03075079.2024.2332944
   Orji R. O., 2010, 2010 International Conference on Education and Management Technology (ICEMT 2010), P617, DOI 10.1109/ICEMT.2010.5657581
   Park J, 2022, J PSYCHOL, V156, P68, DOI 10.1080/00223980.2021.2012109
   Phung M. T., 2020, Journal of Promotion Management, V26, P726, DOI [https://doi.org/10.1080/10496491.2020.1729318, DOI 10.1080/10496491.2020.1729318]
   Rana NP, 2016, COMPUT HUM BEHAV, V59, P265, DOI 10.1016/j.chb.2016.02.019
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Ray S., 2023, Forbes
   Razak NA, 2018, STUD EDUC EVAL, V58, P17, DOI 10.1016/j.stueduc.2018.05.003
   Rogers E., 1983, DIFFUSION INNOVATION
   Sheeran P., 2002, EUR REV SOC PSYCHOL, V12, P1, DOI [10.1080/14792772143000003, DOI 10.1080/14792772143000003]
   Solomon M.R., 2014, CONSUMER BEHAV BUYIN
   Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
   Strzelecki A, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13425
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   TAYLOR S, 1995, INFORM SYST RES, V6, P144, DOI 10.1287/isre.6.2.144
   Thatcher JB, 2002, MIS QUART, V26, P381, DOI 10.2307/4132314
   THOMPSON RL, 1991, MIS QUART, V15, P125, DOI 10.2307/249443
   Urban M, 2024, COMPUT EDUC, V215, DOI 10.1016/j.compedu.2024.105031
   van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102644
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang C, 2013, J SERV RES-US, V16, P400, DOI 10.1177/1094670512473200
   Wang CX, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18349
   Woo WS, 2022, J MULTILING MULTICUL, V43, P404, DOI 10.1080/01434632.2020.1742724
   Xie CX, 2024, INT J HUM-COMPUT INT, V40, P613, DOI 10.1080/10447318.2022.2121458
   Yi MY, 2006, DECISION SCI, V37, P393, DOI 10.1111/j.1540-5414.2006.00132.x
   Yousaf O, 2015, PSYCHOL MEN MASCULIN, V16, P234, DOI 10.1037/a0036241
   Zhang CC, 2022, EURASIAN GEOGR ECON, V63, P424, DOI 10.1080/15387216.2022.2039740
NR 88
TC 0
Z9 0
U1 26
U2 26
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0307-5079
EI 1470-174X
J9 STUD HIGH EDUC
JI Stud. High. Educ.
PD 2024 NOV 14
PY 2024
DI 10.1080/03075079.2024.2427786
EA NOV 2024
PG 23
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M1F0W
UT WOS:001355058300001
DA 2024-12-25
ER

PT J
AU Zhu, JJ
   Zhan, LX
   Tan, J
   Cheng, MM
AF Zhu, Jingjie
   Zhan, Lingxue
   Tan, Jie
   Cheng, Mingming
TI Tourism destination stereotypes and generative artificial intelligence
   (GenAI) generated images
SO CURRENT ISSUES IN TOURISM
LA English
DT Article; Early Access
DE Generative artificial intelligence; destination image; image analytics;
   destination stereotype; bias
AB Generative artificial intelligence (GenAI) has started transforming the tourism industry with wide research implications. While recognising its transformative power, tourism literature failed to identify the dark side of GenAI. Using advanced image analytics across 10 tourism destinations, this research investigates how GenAI-generated images reinforce tourism destination stereotypes. Our findings reveal that GenAI tends to generate highly homogenised images, which cannot fully capture the diversity of destinations, leading to stereotypes. This study advances extant tourism literature by providing critical insights into the complex relationships between generative artificial intelligence and tourism.
C1 [Zhu, Jingjie; Zhan, Lingxue; Tan, Jie; Cheng, Mingming] Curtin Univ, Sch Management & Mkt, Social Media Res Lab, Bentley, Australia.
C3 Curtin University
RP Cheng, MM (corresponding author), Curtin Univ, Sch Management & Mkt, Social Media Res Lab, Bentley, Australia.
EM mingming.cheng@curtin.edu.au
RI Cheng, Mingming/ABD-8848-2021
OI Zhu, Jingjie/0000-0002-9113-3161
CR Akter S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102387
   Gonzalo F., 2023, How generative AI is transforming travel marketing
   google, 2023, ABOUT US
   Hsu CHC, 2024, ANN TOURISM RES, V104, DOI 10.1016/j.annals.2023.103723
   Karri VRS, 2023, J HOSP TOUR INSIGHTS, V6, P1290, DOI 10.1108/JHTI-03-2022-0111
   Keogh R, 2014, J VISION, V14, DOI 10.1167/14.12.7
   Kim D, 2019, INFORM SCIENCES, V477, P15, DOI 10.1016/j.ins.2018.10.006
   Lu ZY, 2023, Arxiv, DOI [arXiv:2304.13023, DOI 10.5555/3666122.3667227]
   Lyu YR, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app122211312
   Miao L, 2023, ANN TOURISM RES, V102, DOI 10.1016/j.annals.2023.103642
   Shen XR, 2019, J DESTIN MARK MANAGE, V14, DOI 10.1016/j.jdmm.2019.100375
   Zhan LX, 2024, TOURISM MANAGE, V100, DOI 10.1016/j.tourman.2023.104798
NR 12
TC 1
Z9 1
U1 64
U2 64
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1368-3500
EI 1747-7603
J9 CURR ISSUES TOUR
JI Curr. Issues Tour.
PD 2024 JUL 23
PY 2024
DI 10.1080/13683500.2024.2381250
EA JUL 2024
PG 5
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA ZK7A0
UT WOS:001275245200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Zhai, XM
AF Zhai, Xiaoming
TI Transforming Teachers' Roles and Agencies in the Era of Generative AI:
   Perceptions, Acceptance, Knowledge, and Practices
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article; Early Access
DE Generative artificial intelligence (GenAI); Teacher; Agency; ChatGPT;
   Integration
AB This paper explores the transformative impact of generative artificial intelligence (GenAI) on teachers' roles and agencies in education, presenting a comprehensive framework that addresses teachers' perceptions, knowledge, acceptance, and practices of GenAI. As GenAI technologies, such as ChatGPT, become increasingly integrated into educational settings, both in-service and future teachers are required to adapt to evolving classroom dynamics, where AI plays a significant role in content creation, personalized learning, and student engagement. However, existing literature often treats these factors in isolation, overlooking how they collectively influence teachers' ability to effectively integrate GenAI into their pedagogical practices. This paper fills this gap by proposing a framework that categorizes teachers (including both pre- and in-service teachers, hereafter) into four roles-Observer, Adopter, Collaborator, and Innovator-each representing different levels of GenAI engagement, outlining teachers' agencies in GenAI classrooms. By highlighting the need for quality teacher education programs, continuous professional development and institutional support, we use examples to demonstrate how teachers can evolve from basic GenAI users to co-creators of knowledge alongside GenAI systems. The findings emphasize that for GenAI to reach its full educational potential, teachers must not only accept and understand its capabilities but also integrate it deeply into their teaching practices. This study contributes to the growing literature on GenAI in education, offering practical implications for supporting both in-service and future teachers in navigating the complexities of GenAI adoption.
C1 [Zhai, Xiaoming] Univ Georgia, Educ Ctr AI4STEM, Athens, GA 30666 USA.
   [Zhai, Xiaoming] Univ Georgia, Natl GENIUS Ctr, Athens, GA 30666 USA.
   [Zhai, Xiaoming] Univ Georgia, Dept Math Sci & Social Studies Educ, Athens, GA 30666 USA.
C3 University System of Georgia; University of Georgia; University System
   of Georgia; University of Georgia; University System of Georgia;
   University of Georgia
RP Zhai, XM (corresponding author), Univ Georgia, Educ Ctr AI4STEM, Athens, GA 30666 USA.; Zhai, XM (corresponding author), Univ Georgia, Natl GENIUS Ctr, Athens, GA 30666 USA.; Zhai, XM (corresponding author), Univ Georgia, Dept Math Sci & Social Studies Educ, Athens, GA 30666 USA.
EM Xiaoming.zhai@uga.edu
RI Zhai, Xiaoming/AAB-7129-2021
FU National Science Foundation [2101104]; Institute of Education Sciences
   [R305C240010]
FX This study is supported by the National Science Foundation (#2101104)
   and the Institute of Education Sciences (#R305C240010). Any opinions,
   findings, conclusions, or recommendations expressed in this material are
   those of the author(s) and do not necessarily reflect the views of the
   NSF or IES.
CR Ali F., 2023, Learning: Research and Practice, V9, P135, DOI DOI 10.1080/23735082.2023.2258886
   Bewersdorff A, 2023, Computers and Education: Artificial Intelligence, V4, DOI [10.1016/j.caeai.2023.100143, DOI 10.1016/J.CAEAI.2023.100143]
   Bewersdorff A, 2024, Arxiv, DOI arXiv:2401.00832
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Collie R J., 2024, Computers and Education. Artificial Intelligence, V6, P100224, DOI [10.1016/j.caeai.2024.100224, DOI 10.1016/J.CAEAI.2024.100224]
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
   Feldman-Maggor Y, 2024, J SCI EDUC TECHNOL, DOI 10.1007/s10956-024-10147-3
   Garofalo SG, 2024, SCI EDUC-NETHERLANDS, DOI 10.1007/s11191-024-00534-y
   Goldberg B, 2023, COMM COM INF SC, V1831, P103, DOI 10.1007/978-3-031-36336-8_16
   Guo SC, 2024, Arxiv, DOI arXiv:2406.10506
   Herdliska A., 2024, Uses of artificial intelligence in STEM education, DOI [10.1093/oso/9780198882077.003.0009, DOI 10.1093/OSO/9780198882077.003.0009]
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kong SC, 2024, IEEE T LEARN TECHNOL, V17, P1588, DOI 10.1109/TLT.2024.3392830
   Laak KJ, 2024, COMM COM INF SC, V2150, P502, DOI 10.1007/978-3-031-64315-6_49
   Lan YJ, 2024, EDUC TECHNOL SOC, V27, DOI 10.30191/ETS.202401_27(1).PP01
   Latif E., 2024, IEEE INT C ROB AUT I
   Latif E, 2023, Arxiv, DOI arXiv:2312.10833
   Lee D., 2024, Comput Educ Artif Intell, V6, DOI [10.1016/J.CAEAI.2024.100221, DOI 10.1016/J.CAEAI.2024.100221]
   Lee GG, 2024, IEEE T LEARN TECHNOL, V17, P1683, DOI 10.1109/TLT.2024.3401457
   Lee GG, 2024, Arxiv, DOI arXiv:2405.07163
   Lee GG, 2024, SCI EDUC-NETHERLANDS, DOI 10.1007/s11191-024-00526-y
   Lee GG, 2023, Arxiv, DOI [arXiv:2401.08660, 10.48550/arXiv.2401.08660, DOI 10.48550/ARXIV.2401.08660]
   Lee GG, 2024, Arxiv, DOI [arXiv:2312.03748, 10.1016/j.caeai.2024.100213, DOI 10.1016/J.CAEAI.2024.100213]
   Lee GG, 2023, Arxiv, DOI [arXiv:2311.12990, DOI 10.48550/ARXIV.2311.12990]
   Lee S., 2024, Computers and Education: Artificial Intelligence, V7, P100283
   Martin PP, 2024, J SCI EDUC TECHNOL, V33, P333, DOI 10.1007/s10956-023-10087-4
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2024, TECHTRENDS, V68, P205, DOI 10.1007/s11528-024-00938-1
   Nyaaba M., 2024, Journal of AI, V8, P1, DOI DOI 10.61969/JAI.1385915
   Nyaaba M., 2024, Education Sciences, P1, DOI [10.20944/preprints202409.1276.v2, DOI 10.20944/PREPRINTS202409.1276.V2]
   Nyaaba M, 2024, Arxiv, DOI arXiv:2407.11983
   Pahi K, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100183
   Rosenberg JM, 2015, J RES TECHNOL EDUC, V47, P186, DOI 10.1080/15391523.2015.1052663
   Shi L., 2024, Uses of Artificial Intelligence in STEM Education, P321
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Srinivasan R, 2021, Arxiv, DOI arXiv:2102.11957
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Tang K.-S., 2024, Learning & assessment with generative AI in secondary education, DOI [10.2139/ssrn.4722537, DOI 10.2139/SSRN.4722537]
   Tang KS, 2024, SCI EDUC-NETHERLANDS, DOI 10.1007/s11191-024-00508-0
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Venkatesh V, 2016, J ASSOC INF SYST, V17, P328, DOI 10.17705/1jais.00428
   Yin HB, 2024, COMPUT HUM BEHAV, V161, DOI 10.1016/j.chb.2024.108417
   Zhai X., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4331313, DOI 10.2139/SSRN.4331313]
   Zhai X., 2022, PREPRINT, DOI DOI 10.48550/ARXIV.2210.08141
   Zhai XM, 2024, SCI EDUC-NETHERLANDS, DOI 10.1007/s11191-024-00496-1
   Zhai XM, 2023, J RES SCI TEACH, V60, P1390, DOI 10.1002/tea.21885
   Zhai XM, 2020, STUD SCI EDUC, V56, P111, DOI 10.1080/03057267.2020.1735757
   Zhou M, 2024, Arxiv, DOI [arXiv:2403.02726, DOI 10.48550/ARXIV.2403.02726]
NR 49
TC 0
Z9 0
U1 39
U2 39
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD 2024 NOV 18
PY 2024
DI 10.1007/s10956-024-10174-0
EA NOV 2024
PG 11
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M5B5E
UT WOS:001357690000001
DA 2024-12-25
ER

PT J
AU Liang, ES
   Bai, SR
AF Liang, Echo Shang
   Bai, Shurui
TI Generative AI and the future of connectivist learning in higher
   education
SO JOURNAL OF ASIAN PUBLIC POLICY
LA English
DT Article; Early Access
DE Generative artificial intelligence; ChatGPT; higher education;
   connectivist learning; social entrepreneurship education
ID SOCIAL ENTREPRENEURSHIP; RIGOR
AB The burgeoning field of Generative Artificial Intelligence (GenAI) presents a new avenue for enhancing teaching and learning practices within higher education. While existing research has predominantly focused on GenAI's capabilities to perform specific educational tasks, its potential as an interactive agent engaging in human-like conversations and forming new connections remains underexplored. Drawing upon a connectivist lens that recognizes learning occurs within networks of interactions, we investigate how GenAI tools can contribute to connectivist learning within social entrepreneurship education. Through qualitative interviews with multiple key stakeholder groups, this study reveals three dimensions of new dialogic spaces that can be enabled by GenAI: collaborative learning, knowledge connectivity, and theory-practice integration. This study makes several contributions. First, it expands upon current discussions on AI and higher education, moving beyond tool-based acceptance to actively exploring GenAI as an active learning agent. Second, it contributes to the connectivist learning literature by demonstrating the potential of GenAI tools not only as interaction facilitators but also as interaction agents that create new learning interactions across different levels. Finally, it offers practical insights by bridging the voices and perspectives of different stakeholders to envision a future where GenAI coexists with traditional educational practices and agents.
C1 [Liang, Echo Shang] Educ Univ Hong Kong, Dept Social Sci & Policy Studies, Hong Kong, New Territories, Peoples R China.
   [Bai, Shurui] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, New Territories, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK)
RP Liang, ES (corresponding author), Educ Univ Hong Kong, Dept Social Sci & Policy Studies, Hong Kong, New Territories, Peoples R China.
EM lshang@eduhk.hk
RI Bai, Shurui/ABF-9370-2021
OI Bai, Shurui/0000-0003-2004-7810
FU Education University of Hong Kong [22/2023-2024 R]
FX This work was supported by the Education University of Hong Kong under
   Grant RG [22/2023-2024 R].
CR Ahmed Yazan Al, 2023, 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS), P79, DOI 10.1109/ICCNS58795.2023.10192993
   AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Ayanwale M A., 2022, Computers and Education: Artificial Intelligence, V3, P100099, DOI DOI 10.1016/J.CAEAI.2022.100099
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bulathwela S, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16020781
   Campbell S, 2020, J RES NURS, V25, P652, DOI 10.1177/1744987120927206
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chandra Y, 2017, QUAL MARK RES, V20, P90, DOI 10.1108/QMR-02-2016-0014
   Cheddadi Saoussen, 2021, 2021 16th International Conference on Computer Science & Education (ICCSE), P241, DOI 10.1109/ICCSE51940.2021.9569548
   Chen Y, 2022, DECIS SCI-J INNOV ED, V20, P43, DOI 10.1111/dsji.12253
   Cheng M. W., 2024, Discover Education, V3, P1, DOI [https://doi.org/10.1007/s44217-023-00081-8, DOI 10.1007/S44217-023-00081-8]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Choi JH, 2022, J LEGAL EDUC, V71, P387
   Cingillioglu I, 2023, INT J INF LEARN TECH, V40, P259, DOI 10.1108/IJILT-03-2023-0043
   Clarà M, 2013, DISTANCE EDUC, V34, P129, DOI 10.1080/01587919.2013.770428
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   D'Amato K, 2024, AI SOC, DOI 10.1007/s00146-024-01898-z
   Defourny J, 2010, J SOC ENTREP, V1, P32, DOI 10.1080/19420670903442053
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eisenhardt KM, 2016, ACAD MANAGE J, V59, P1113, DOI 10.5465/amj.2016.4004
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Firat Mehmet, 2023, Journal of Applied Learning and Teaching, V3, P1, DOI DOI 10.37074/JALT.2023.6.1.22
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V329, P637, DOI 10.1001/jama.2023.1344
   Geerling W., 2023, The American Economist, 05694345231169654
   Gioia DA, 2013, ORGAN RES METHODS, V16, P15, DOI 10.1177/1094428112452151
   Goldie JGS, 2016, MED TEACH, V38, P1064, DOI 10.3109/0142159X.2016.1173661
   Han A., 2024, P CHI C HUM FACT COM, P1
   Hashmi N, 2024, BUS HORIZONS, V67, P607, DOI 10.1016/j.bushor.2024.05.005
   Hockerts K, 2018, J SOC ENTREP, V9, P234, DOI 10.1080/19420676.2018.1498377
   Holmes W, 2022, EUR J EDUC, V57, P542, DOI 10.1111/ejed.12533
   Howorth C, 2012, ACAD MANAG LEARN EDU, V11, P371, DOI 10.5465/amle.2011.0022
   Ivanov S, 2023, SERV IND J, V43, P1055, DOI 10.1080/02642069.2023.2258799
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kickul J., 2018, Entrepreneurship Education and Pedagogy, V1, P205, DOI [10.1177/2515127418772177, DOI 10.1177/2515127418772177]
   Kim J, 2020, INT J HUM-COMPUT INT, V36, P1902, DOI 10.1080/10447318.2020.1801227
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Liu JL, 2023, J MED INTERNET RES, V25, DOI 10.2196/48568
   Mair J, 2006, J WORLD BUS, V41, P36, DOI 10.1016/j.jwb.2005.09.002
   Manca S, 2016, J COMPUT ASSIST LEAR, V32, P503, DOI 10.1111/jcal.12154
   Mao J, 2024, TECHTRENDS, V68, P58, DOI 10.1007/s11528-023-00911-4
   Marais N., 2010, Education, Knowledge Economy, V4, P173, DOI DOI 10.1080/17496896.2010.556478
   Mattar J, 2018, RIED-REV IBEROAM EDU, V21, P201, DOI 10.5944/ried.21.2.20055
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mills A., 2023, Journal of Applied Learning and Teaching, V6, DOI DOI 10.37074/JALT.2023.6.1.34
   Neck HM, 2011, J SMALL BUS MANAGE, V49, P55, DOI 10.1111/j.1540-627X.2010.00314.x
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Ong D, 2021, J ASIAN PUBLIC POLIC, V14, P272, DOI 10.1080/17516234.2020.1815274
   OpenAI, 2023, Large language model
   Pache AC, 2012, ACAD MANAG LEARN EDU, V11, P494, DOI 10.5465/amle.2011.0019
   Rasul T., 2023, Journal of Applied Learning & Teaching, V6
   Ravenscroft A, 2011, INT REV RES OPEN DIS, V12, P139, DOI 10.19173/irrodl.v12i3.934
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Shanahan M, 2023, NATURE, V623, P493, DOI 10.1038/s41586-023-06647-8
   Sharma S., 2024, International Journal of System Assurance Engineering Management, P1
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Siemens G., 2005, J INSTRUCTIONAL TECH, V2, P3, DOI DOI 10.3109/0142159X.2016.1173661
   Siemens G., 2020, Journal of Applied Learning and Teaching, V3, P108, DOI DOI 10.37074/JALT.2020.3.1.15
   Smith S, 2024, Arxiv, DOI arXiv:2404.19244
   Speth S., 2023, P 35 INT C SOFTW ENG, P142
   Stathopoulou A, 2019, EUR MANAG J, V37, P421, DOI 10.1016/j.emj.2019.01.008
   Steiner SD., 2018, Journal of Ethics Entrepreneurship, V8, P73
   Strauss A. L., 1990, BASICS QUALITATIVE R
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Tracey P, 2007, ACAD MANAG LEARN EDU, V6, P264, DOI 10.5465/AMLE.2007.25223465
   Utecht J., 2019, CRITICAL QUESTIONE, V10, P107
   Vecchiarini M, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100879
   Wang YT, 2024, STUD HIGH EDUC, DOI 10.1080/03075079.2024.2369202
   Wang ZJ, 2014, INT REV RES OPEN DIS, V15, P121
   Winkler C., 2023, Entrepreneurship Education and Pedagogy, V6, P579
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yusuf A, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00453-6
NR 73
TC 1
Z9 1
U1 45
U2 45
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1751-6234
EI 1751-6242
J9 J ASIAN PUBLIC POLIC
JI J. Asian Public Policy
PD 2024 AUG 29
PY 2024
DI 10.1080/17516234.2024.2386085
EA AUG 2024
PG 23
WC Area Studies
WE Social Science Citation Index (SSCI)
SC Area Studies
GA D9M1L
UT WOS:001299340700001
DA 2024-12-25
ER

PT J
AU Huang, DP
   Huang, YX
   Cummings, JJ
AF Huang, Dongpeng
   Huang, Yixuan
   Cummings, James J.
TI Exploring the integration and utilisation of generative AI in formative
   e-assessments: A case study in higher education
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE individual formative e-assessment; generative artificial intelligence
   (GenAI); college students; usage behaviour; ChatGPT
ID CHATBOT
AB The integration of generative artificial intelligence (GenAI) into web-based individual formative e-assessments in higher education is a nascent field that warrants further exploration. This study investigated the use of GenAI within an 8-week undergraduate-level research methods course at a university in the United States of America, aiming to understand how students leverage GenAI tools during individual formative e-assessments questions. The research revealed that a significant majority of students initially preferred traditional study resources over GenAI. However, a gradual shift towards more balanced use of both resources was observed, particularly in formative e-assessments involving statistical analysis and calculation questions. In their interactions with GenAI, students primarily used it for multiple-choice and true/false questions, often by directly copying and pasting the question prompt into the GenAI interface. Students were able to discern and accept accurate responses generated by GenAI and reject those that were incorrect or contradicted their existing knowledge. Students' reported primary motivations for turning to GenAI were to seek answers to assessment items as well as to corroborate the accuracy of their own responses. This study contributes to the growing body of literature empirically investigating actual usage behaviours with GenAI tools and the motivation behind these behaviours. We discuss the implications and limitations of these findings.
C1 [Huang, Dongpeng; Huang, Yixuan; Cummings, James J.] Boston Univ, Boston, MA 02215 USA.
C3 Boston University
RP Huang, DP (corresponding author), Boston Univ, Boston, MA 02215 USA.
EM dphuang@bu.edu
RI yixuan, huang/GPF-9875-2022
OI Huang, Dongpeng/0000-0002-7593-4612
FU Future of Learning: AI Grant from Boston University Shipley Center
FX This research was supported by a Future of Learning: AI Grant from
   Boston University Shipley Center. We gratefully acknowledge the support
   from our Shipley Center colleagues.
CR Al-Hattami A.A., 2020, International Journal of Advanced Science and Technology, V29, P1537
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Bahati B, 2019, INT J EMERG TECHNOL, V14, P61, DOI [10.3991/ijet.v14i07.9120, 10.3991/IJET.V14I07.9120]
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Brügger R, 2019, LECT NOTES COMPUT SC, V11766, P429, DOI 10.1007/978-3-030-32248-9_48
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chien YC, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.785752
   Cohen D., 2016, J TECHNOLOGY SCI ED, V6, P188, DOI DOI 10.3926/JOTSE.217
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Daly C, 2010, ASSESS EVAL HIGH EDU, V35, P619, DOI 10.1080/02602931003650052
   Darwin A., 2024, COGENT EDUC, V11, P2290342, DOI [10.1080/2331186X.2023.2290342, DOI 10.1080/2331186X.2023.2290342]
   García-Jiménez E, 2015, RELIEVE, V21, DOI 10.7203/relieve.21.2.7546
   George A. S., 2023, Partners Universal International Research Journal, V2, P36, DOI [10.5281/ZENODO.10421475, DOI 10.5281/ZENODO.10421475]
   Haleem A., 2022, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V2, DOI [DOI 10.1016/J.TBENCH.2023.100089, 10.1016/j.tbench.2023.100089]
   Hettiarachchi E, 2015, J UNIVERS COMPUT SCI, V21, P1001
   Hmoud M, 2024, INFORMATION, V15, DOI 10.3390/info15010033
   Hodges C., 2023, EDUCAUSE Review
   Hou I, 2024, PROCEEDINGS OF THE 26TH AUSTRALASIAN COMPUTING EDUCATION CONFERENCE, ACE 2024, P39, DOI 10.1145/3636243.3636248
   Hsieh HF, 2005, QUAL HEALTH RES, V15, P1277, DOI 10.1177/1049732305276687
   Jiao H., 2015, Australasian Journal of Engineering Education, V20, P9, DOI 10.7158/D13-002.2015.20.1
   Joshi A., 2020, Journal of Research in Medical Education & Ethics, V10, P49, DOI [10.5958/2231-6728.2020.00015.3, DOI 10.5958/2231-6728.2020.00015.3]
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Kumar JA, 2021, INT J EDUC TECHNOL H, V18, DOI 10.1186/s41239-021-00302-w
   Lee YF, 2022, ETR&D-EDUC TECH RES, V70, P1843, DOI 10.1007/s11423-022-10142-8
   Lin CJ, 2021, EDUC TECHNOL SOC, V24, P16
   Lin MPC, 2020, EDUC TECHNOL SOC, V23, P78
   Liu CC, 2022, COMPUT EDUC, V189, DOI 10.1016/j.compedu.2022.104576
   Lock Samantha, 2022, The Guardian5 Dec
   Luckin R., 2016, Intelligence unleashed: An argument for AI in education
   Marr B., 2024, Forbes
   McCallum S, 2021, ASSESS EVAL HIGH EDU, V46, P1, DOI 10.1080/02602938.2020.1754761
   McDonald N, 2024, Arxiv, DOI [arXiv:2402.01659, 10.48550/arXiv.2402.01659, DOI 10.48550/ARXIV.2402.01659]
   Mohamed N., 2020, P 2020 3 INT C ED TE, P48, DOI [10.1145/3446590.3446598, DOI 10.1145/3446590.3446598]
   O'Connor C, 2020, INT J QUAL METH, V19, DOI 10.1177/1609406919899220
   Pack A, 2023, TESOL QUART, V57, P1571, DOI 10.1002/tesq.3253
   Patra R., 2022, J AM SOC NEPHROL, P197, DOI [DOI 10.1016/j.ijar.2020.06.001, DOI 10.23919/SOFTCOM.2019.8903763, 10.1007/978-3-030-83553-810]
   Stödberg U, 2012, ASSESS EVAL HIGH EDU, V37, P591, DOI 10.1080/02602938.2011.557496
   TechRepublic. ChatGPT vs Google Gemini, 2024, What are the main differences?
   Voss E, 2023, LANG ASSESS Q, V20, P520, DOI 10.1080/15434303.2023.2288256
   Walker DJ, 2008, LEARN MEDIA TECHNOL, V33, P221, DOI 10.1080/17439880802324178
   Westfall C., 2023, Forbes
   Wu R, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13334
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Yin JQ, 2021, J EDUC COMPUT RES, V59, P154, DOI 10.1177/0735633120952067
NR 45
TC 0
Z9 0
U1 9
U2 9
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2024
VL 40
IS 4
SI SI
BP 7
EP 19
DI 10.14742/ajet.9467
PG 13
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M7L6P
UT WOS:001359314500001
OA gold
DA 2024-12-25
ER

PT J
AU Hsu, YC
   Ching, YH
AF Hsu, Yu-Chang
   Ching, Yu-Hui
TI Generative Artificial Intelligence in Education, Part Two: International
   Perspectives
SO TECHTRENDS
LA English
DT Article
DE ChatGPT; GenAI in education; Generative artificial intelligence; OpenAI;
   Bard; International perspectives
AB Generative artificial intelligence (GenAI) has continued to advance at a stunning pace since our last publication on GenAI in education in spring 2023. In that first article of a two-part series, we discussed the overall dynamic frontier of GenAI, its potential uses and benefits in education, the essential abilities required in the age of GenAI, and the associated issues and concerns surrounding this emerging technology. In this second article, we delve deeper to examine the new developments, stances, policies, and initiatives pertaining to GenAI's roles in education in international contexts.
C1 [Hsu, Yu-Chang; Ching, Yu-Hui] Boise State Univ, Dept Educ Technol, 1910 Univ Dr,MS 1747, Boise, ID 83725 USA.
C3 Boise State University
RP Hsu, YC (corresponding author), Boise State Univ, Dept Educ Technol, 1910 Univ Dr,MS 1747, Boise, ID 83725 USA.
EM hsu@boisestate.edu; yu-huiching@boisestate.edu
CR Abdelrahman H., 2023, 15 BEST FREE CHATGPT
   Adams R., 2022, AI in Africa: Key concerns and policy considerations for the future of the continent
   Anderson N, 2023, BMJ OPEN SPORT EXERC, V9, DOI 10.1136/bmjsem-2023-001568
   [Anonymous], 2023, NBC NEWS
   [Anonymous], 2023, KYODO NEWS
   De Vynck G., 2023, CHATGPT LOSING USERS
   Elias J., 2023, GOOGLE SAYS BARD CAN
   Elimian G., 2023, WHAT IS STATE REGULA
   European Commission, 2023, EUR APPR ART INT
   European Parliament, 2023, Artificial Intelligence Act: deal on comprehensive rules for trustworthy AI. Press release
   European Parliamentary Research Service, 2023, PARL NEG POS ART INT
   Executive Yuan, 2023, CAB APPR DRAFT GUID
   Field H., 2023, CHATGPT CAN NOW SPEA
   Franzen C., 2023, OPENAI GIVES CHATGPT
   Gogo J., 2023, SCH REVERSE CHATGPT
   Government of Canada, 2023, GUID US GEN AI
   He L., 2023, CHINA TAKES MAJOR ST
   HKU, 2023, HKU INTR NEW POL FUL
   Hsu YC, 2023, TECHTRENDS, V67, P603, DOI 10.1007/s11528-023-00863-9
   Ivanov V., 2023, 6 LATEST TRICKS AVOI
   Kang H. M., 2023, S KOREAN U CONSIDER
   Khan Q., 2020, NETEASES PLATFORM RE
   Leung M., 2023, University World News
   Lu H., 2023, NETEASES EDTECH UNIT
   Matsuoe R., 2023, JAPAN DEV GENERATIVE
   National Science Foundation, 2023, NSF ANN 7 NEW NAT AR
   Ocampo Y., 2023, TAIWANS GUIDELINES G
   OECD AI Policy Observatory, 2022, MEX NAT AG
   OHagan C., 2023, UNESCO GOVT MUST QUI
   OpenAI, 2023, About us
   Ortiz S., 2023, 5 BIGGEST RISKS GENE
   Park E., 2023, MOST AM HAVENT USED
   Porter J., 2023, CHINA WANTS HOMEGROW
   Sheehan T., 2023, GENERATIVE AI ED PRE
   Singapore Ministry of Education, 2023, MAN US ART INT AI BO
   Singer N., 2023, DESPITE CHEATING FEA
   Sneider T., 2023, LAND RISING ROBOTS
   Southern M. G., 2023, IS CHATGPT GETTING D
   Thadhagath P. V., 2023, WHY THIS BENGALURU I
   The George Washington University, 2018, ME STRAT
   The Office of the Privacy Commissioner of Canada, 2023, OPC INV CHATGPT JOIN
   The University of Toronto, 2023, GEN ART INT CLASSR
   Tong A., 2023, Exclusive: ChatGPT traffic slips again for third month in a row
   U.S. Department of Education Press Office, 2023, US DEP ED SHAR INS R
   UNESCO, 2023, CHIL MIN SCI TECHN I
   UNESCO, 2023, GUID GEN AI ED RES
   Vermes J., 2023, CBC
   Vogels E. A., 2023, A majority of Americans have heard of ChatGPT, but few have tried it themselves
   Wolber A., 2023, USE GOOGLE BARD GOOG
   Yu Y., 2023, NVIDIAS BOOM GIVES T
NR 50
TC 2
Z9 2
U1 18
U2 79
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD NOV
PY 2023
VL 67
IS 6
BP 885
EP 890
DI 10.1007/s11528-023-00913-2
EA NOV 2023
PG 6
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA AH3R7
UT WOS:001093152600001
DA 2024-12-25
ER

PT J
AU Kim, K
   Hong, GS
   Kim, N
AF Kim, Kiduk
   Hong, Gil-Sun
   Kim, Namkug
TI Primer on Generative Artificial Intelligence and Large Language Models
   in Medical Imaging
SO JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY
LA English
DT Article
DE Generative Artificial Intelligence; Large Language Model; Language
   Vision Model; Image Generative AI; Language Generative AI
AB The recent advent of large language models (LLMs), such as ChatGPT, has drawn attention to generative artificial intelligence (AI) in a number of fields. Generative AI can produce different types of data including text, images, and voice, depending on the training methods and datasets used. Additionally, recent advancements in multimodal techniques, which can simultaneously process multiple data types like text and images, have expanded the potential of using multimodal generative AI in the medical environment where various types of clinical and imaging information are used together. This review summarizes the concepts and types of LLMs, image generative AI, and multimodal AI, and it examines the status and future possibilities of generative AI in the field of radiology.
C1 [Kim, Kiduk; Kim, Namkug] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Convergence Med, Seoul, South Korea.
   [Hong, Gil-Sun; Kim, Namkug] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
   [Hong, Gil-Sun; Kim, Namkug] Univ Ulsan, Res Inst Radiol, Coll Med, Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
C3 University of Ulsan; Asan Medical Center; University of Ulsan;
   University of Ulsan
RP Hong, GS; Kim, N (corresponding author), Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.; Hong, GS; Kim, N (corresponding author), Univ Ulsan, Res Inst Radiol, Coll Med, Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.; Kim, N (corresponding author), Univ Ulsan, Coll Med, Asan Med Ctr, Dept Convergence Med,Dept Radiol, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
EM hgs2013@gmail.com; namkugkim@gmail.com
RI Kim, Namkug/E-3843-2012
OI Kim, Namkug/0000-0002-3438-2217; Kim, Kiduk/0000-0002-9659-897X; Hong,
   Gil-Sun/0000-0002-0068-9413
FU Korea Health Technology R&D Project through the Korea Health Industry
   Development Institute (KHIDI) - Ministry of Health & Welfare, Republic
   of Korea [HI21C1148, HI22C172300]
FX Funding This research was supported by grants from the Korea Health
   Technology R&D Project through the Korea Health Industry Development
   Institute (KHIDI) , funded by the Ministry of Health & Welfare, Republic
   of Korea (HI21C1148 and HI22C172300) .
CR Adams LC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230725
   Alec Radford, 2018, Improving Language Understanding by Generative Pre-Training
   Antol S, 2015, IEEE I CONF COMP VIS, P2425, DOI 10.1109/ICCV.2015.279
   Brown T., 2020, C NEUR INF PROC SYST, P1901
   Cao M, 2022, Arxiv, DOI [arXiv:2203.14713, DOI 10.48550/ARXIV.2203.14713]
   Choi C, 2023, LECT NOTES COMPUT SC, V14229, P344, DOI 10.1007/978-3-031-43999-5_33
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dhariwal P, 2021, ADV NEUR IN, V34
   Gertz Roman Johannes, 2023, Radiology, V307, pe230877, DOI 10.1148/radiol.230877
   Giray L, 2023, ANN BIOMED ENG, DOI 10.1007/s10439-023-03272-4
   Goodfellow IJ., GENERATIVE ADVERSARI
   Ho JAT, 2022, Arxiv, DOI arXiv:2207.12598
   Ho Jonathan, DENOISING DIFFUSION
   Hoang TT, 2020, IEEE IJCNN, DOI 10.1109/ijcnn48605.2020.9207181
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Hong GS, 2023, KOREAN J RADIOL, V24, P1061, DOI 10.3348/kjr.2023.0393
   Hossain MZ, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3295748
   Hu EJ, 2021, Arxiv, DOI arXiv:2106.09685
   Isola P, 2017, PROC CVPR IEEE, P5967, DOI 10.1109/CVPR.2017.632
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jia C, 2021, PR MACH LEARN RES, V139
   Jiang LY, 2023, NATURE, V619, P357, DOI 10.1038/s41586-023-06160-y
   Kaplan J, 2020, Arxiv, DOI [arXiv:2001.08361, 10.48550/arXiv.2001.08361]
   Kim Gwanghyun, 2022, Diffusionclip: Text-guided diffusion models for robust image manipulation
   Kim J, Adaptive latent diffusion model for 3D medical image to image translation: multi-modal magnetic resonance imaging study
   Kim K, 2024, KOREAN J RADIOL, V25, P224, DOI 10.3348/kjr.2023.0818
   Kim S, 2024, KOREAN J RADIOL, V25, P126, DOI 10.3348/kjr.2023.0997
   Kirillov A, 2023, IEEE I CONF COMP VIS, P3992, DOI 10.1109/ICCV51070.2023.00371
   Lee H, 2021, A brief survey of text driven image generation and maniulation.., DOI [10.1109/ICCE-Asia53811.2021.9641929, DOI 10.1109/ICCE-ASIA53811.2021.9641929]
   Lee JS, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0285489
   Lewis P, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
   Lyu Q, 2023, VIS COMPUT IND BIOME, V6, DOI 10.1186/s42492-023-00136-5
   Mazurowski MA, 2023, MED IMAGE ANAL, V89, DOI 10.1016/j.media.2023.102918
   Metz L, 2017, Arxiv, DOI [arXiv:1611.02163, 10.48550/arXiv.1611.02163, DOI 10.48550/ARXIV.1611.02163]
   Mirza M, 2014, Arxiv, DOI arXiv:1411.1784
   Moon HH, 2024, NEURO-ONCOLOGY, V26, P1124, DOI 10.1093/neuonc/noae012
   Mukherjee P, 2023, RADIOLOGY, V309, DOI 10.1148/radiol.231147
   Nagaraja VK, 2016, LECT NOTES COMPUT SC, V9908, P792, DOI 10.1007/978-3-319-46493-0_48
   Nishio M, 2020, COMPUT BIOL MED, V126, DOI 10.1016/j.compbiomed.2020.104032
   Ouyang L, 2022, ADV NEUR IN
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pan Z, 2023, Effective real image editing with accelerated iterative diffusion inversion.
   Preechakul K, 2022, Diffusion autoencoders: toward a meaningful and decodable representation
   Radford A., 2019, OPENAI BLOG
   Radford A, 2021, PR MACH LEARN RES, V139
   Song JM, 2022, Arxiv, DOI [arXiv:2010.02502, 10.48550/arXiv.2010.02502]
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Tu T, 2024, Arxiv, DOI [arXiv:2401.05654, 10.48550/arXiv.2401.05654, DOI 10.48550/ARXIV.2401.05654]
   Uppal S, 2022, INFORM FUSION, V77, P149, DOI 10.1016/j.inffus.2021.07.009
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang ZD, 2023, Arxiv, DOI arXiv:2206.02262
   Wu CY, 2023, Arxiv, DOI arXiv:2308.02463
   Xia WH, 2023, IEEE T PATTERN ANAL, V45, P3121, DOI 10.1109/TPAMI.2022.3181070
   Yin Y, 2023, DiffGAR: model-agnostic restoration from generative artifacts using image-toimage diffusion models, DOI [10.1145/3577530.3577539, DOI 10.1145/3577530.3577539]
   Zaremba W, 2015, Arxiv, DOI arXiv:1409.2329
   Zellers R, 2019, From recognition to cognition: visual commonsense reasoning.
   Zhu JY, 2017, IEEE I CONF COMP VIS, P2242, DOI 10.1109/ICCV.2017.244
NR 57
TC 0
Z9 0
U1 8
U2 8
PU KOREAN SOCIETY OF RADIOLOGY
PI SEOUL
PA 71, YANGJAECHEON-RO, SEOCHO-GU, SEOUL, SOUTH KOREA
EI 2951-0805
J9 J KOREAN SOC RADIOL
JI J. Korean Soc. Radiol.
PD SEP
PY 2024
VL 85
IS 5
BP 848
EP 860
DI 10.3348/jksr.2024.0066
PG 13
WC Radiology, Nuclear Medicine & Medical Imaging
WE Emerging Sources Citation Index (ESCI)
SC Radiology, Nuclear Medicine & Medical Imaging
GA K8W8M
UT WOS:001346654900003
PM 39416320
OA gold
DA 2024-12-25
ER

PT J
AU Guan, LH
   Zhang, EY
   Gu, MM
AF Guan, Lihang
   Zhang, Ellen Yue
   Gu, Michelle Mingyue
TI Examining generative AI-mediated informal digital learning of English
   practices with social cognitive theory: a mixed-methods study
SO RECALL
LA English
DT Article; Early Access
DE generative AI; oral proficiency; social cognitive theory; technological
   pedagogical and content knowledge; TPACK; informal digital learning of
   English; IDLE; holistic learning ecology
ID CONTENT KNOWLEDGE TPACK; SELF-EFFICACY; LANGUAGE; TECHNOLOGY
AB This study explores the integration of generative artificial intelligence (GenAI) in informal digital learning of English (IDLE) practices, focusing on its potential to enhance language learning outcomes and addressing the technological challenges language teachers face in utilising AI-based tools to facilitate second language acquisition. Based on the research context of IDLE and holistic learning ecology and drawing on the theoretical frameworks of technological pedagogical and content knowledge and social cognitive theory, we performed a mixed-methods investigation with an empirical experiment to assess the effectiveness of GenAI followed by semi-structured interviews. The results suggest that the GenAImediated IDLE practices effectively improve college students' oral proficiency in English from both technological and humanistic perspectives. However, results also indicate that the GenAI conversational partner alone is not adequate to provoke continuous extramural GenAI-mediated IDLE practices. We discuss the theoretical and pragmatic significance of GenAI-mediated IDLE in educational equity and reformation.
C1 [Guan, Lihang; Gu, Michelle Mingyue] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Zhang, Ellen Yue] Educ Univ Hong Kong, Grad Sch, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK)
RP Guan, LH; Gu, MM (corresponding author), Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
EM s1136696@s.eduhk.hk; yuezhang@eduhk.hk; mygu@eduhk.hk
OI Guan, Lihang/0000-0001-6835-0830
CR Adipat S, 2021, EDUC INF TECHNOL, V26, P6461, DOI 10.1007/s10639-021-10648-3
   Ahn HS, 2017, CONTEMP EDUC PSYCHOL, V48, P149, DOI 10.1016/j.cedpsych.2016.08.002
   Ai HY, 2017, RECALL, V29, P313, DOI 10.1017/S095834401700012X
   Alghamdi J, 2020, EDUC INF TECHNOL, V25, P4721, DOI 10.1007/s10639-020-10169-5
   Ates H, 2022, EDUC INF TECHNOL, V27, P2521, DOI 10.1007/s10639-021-10671-4
   Bandura A., 2014, SOCIAL COGNITIVE THE, P341
   Bandura A., 1986, SOCIAL FDN THOUGHT A
   Belda-Medina J, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12178427
   BENSON P., 2011, Beyond the language classroom, P7, DOI 10.1057/9780230306790
   Botero GG, 2021, COMPUT ASSIST LANG L, V34, P1013, DOI 10.1080/09588221.2019.1650780
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Brinkmann S., 2020, The Oxford handbook of qualitative research, V2nd, DOI DOI 10.1093/OXFORDHB/9780190847388.013.22
   Brown J.S., 2000, Change, V32, P10, DOI [10.1080/00091380009601719, DOI 10.1080/00091380009601719]
   Calvo LCS, 2024, EDUC INF TECHNOL, V29, P5169, DOI 10.1007/s10639-023-12000-3
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Chang CY, 2022, BRIT J EDUC TECHNOL, V53, P171, DOI 10.1111/bjet.13158
   Chen YC, 2024, COMPUT ASSIST LANG L, V37, P789, DOI 10.1080/09588221.2022.2055083
   Chen YC, 2014, COMPUT HUM BEHAV, V31, P159, DOI 10.1016/j.chb.2013.10.040
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Copur-Gencturk Y, 2020, EDUC RESEARCHER, V49, P30, DOI 10.3102/0013189X19890577
   Deci E.L., 2012, Handbook of Theories of Social Psychology, P416, DOI [DOI 10.4135/9781446249215.N21, 10.4135/9781446249215.n21, 10.4135/9781446201022]
   Denessen E, 2022, LEARN INSTR, V78, DOI 10.1016/j.learninstruc.2020.101437
   Deng XJ, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.976330
   Dian M, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0245972
   Dong Y, 2015, EDUC TECHNOL SOC, V18, P158
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Godwin-Jones R, 2023, LANG LEARN TECHNOL, V27, P6
   Godwin-Jones R, 2022, LANG LEARN TECHNOL, V26, P5, DOI 10.10125/73474
   Goldenthal E, 2021, COMPUT HUM BEHAV, V125, DOI 10.1016/j.chb.2021.106975
   Green J.L., 2012, Handbook of complementary methods in education research
   Greene MD, 2020, EDUC TECHNOL SOC, V23, P75
   Haleem A., 2022, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V2, DOI [DOI 10.1016/J.TBENCH.2023.100089, 10.1016/j.tbench.2023.100089]
   Ibrahim A, 2020, STUD EDUC EVAL, V64, DOI 10.1016/j.stueduc.2020.100836
   Jiang MYC, 2021, AUSTRALAS J EDUC TEC, V37, P110, DOI 10.14742/ajet.6798
   Kim A, 2024, SYSTEM, V122, DOI 10.1016/j.system.2024.103256
   Koehler Matthew J., 2009, Contemporary Issues in Technology and Teacher Education, V9, P60
   Koh JHL, 2014, COMPUT EDUC, V70, P222, DOI 10.1016/j.compedu.2013.08.017
   Krashen S., 1992, Linguistics and language pedagogy: The state of the art, P409
   Labadze L, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00426-1
   Lai C, 2022, LANG LEARN TECHNOL, V26, P1
   Lai C, 2021, COMPUT EDUC, V175, DOI 10.1016/j.compedu.2021.104314
   Lai C, 2015, TESOL QUART, V49, P278, DOI 10.1002/tesq.171
   LaScotte D, 2021, RELC J, V52, P144, DOI 10.1177/0033688220953910
   Lee JS, 2019, ELT J, V73, P419, DOI 10.1093/elt/ccz018
   Lee JS, 2019, BRIT J EDUC TECHNOL, V50, P767, DOI 10.1111/bjet.12599
   Lee JS, 2019, LANG LEARN TECHNOL, V23, P114, DOI 10.10125/44675
   Lee JS, 2018, TESOL QUART, V52, P435, DOI 10.1002/tesq.422
   Li L, 2024, AUSTRALAS J EDUC TEC, V40, P1, DOI 10.14742/ajet.8821
   Li P, 2022, BILING-LANG COGN, V25, P361, DOI 10.1017/S1366728921000353
   Li Y, 2019, LANG TEACH RES, V23, P352, DOI 10.1177/1362168817730420
   Liu CC, 2023, INTERACT LEARN ENVIR, V31, P5614, DOI 10.1080/10494820.2021.2012812
   Liu GX, 2024, INNOV LANG LEARN TEA, V18, P125, DOI 10.1080/17501229.2023.2240316
   Liu GL, 2024, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2024.2310288
   Liu GL, 2024, RECALL, V36, P72, DOI 10.1017/S0958344023000204
   Liu PL, 2023, EDUC TECHNOL SOC, V26, P5, DOI 10.30191/ETS.202307_26(3).0002
   Liu SQ, 2014, PSYCHOL BULL, V140, P1009, DOI 10.1037/a0035923
   Luckin R, 2008, COMPUT EDUC, V50, P449, DOI 10.1016/j.compedu.2007.09.018
   Meniado JC, 2023, RELC J, V54, P461, DOI 10.1177/00336882231160610
   Miguel-Revilla D, 2020, AUSTRALAS J EDUC TEC, V36, DOI 10.14742/ajet.5281
   Nakatsuhara F, 2021, LANG ASSESS Q, V18, P83, DOI 10.1080/15434303.2020.1799222
   Ng DTK, 2023, EDUC INF TECHNOL, V28, P8445, DOI 10.1007/s10639-022-11491-w
   Ng W, 2012, COMPUT EDUC, V59, P1065, DOI 10.1016/j.compedu.2012.04.016
   Niloy AC, 2024, J COMPUT ASSIST LEAR, V40, P919, DOI 10.1111/jcal.12929
   Nowell LS, 2017, INT J QUAL METH, V16, DOI 10.1177/1609406917733847
   Ong QKL, 2024, EDUC INF TECHNOL, V29, P1939, DOI 10.1007/s10639-023-11852-z
   Ou AW, 2024, SYSTEM, V121, DOI 10.1016/j.system.2024.103225
   Ping WJ, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.992038
   Robinson K., 2019, You, your child, and school: Navigate your way to the best education
   Saadati Z, 2023, INTERACT LEARN ENVIR, V31, P3148, DOI 10.1080/10494820.2021.1920429
   Sasaki A, 2010, RECALL, V22, P70, DOI 10.1017/S0958344009990206
   Saubern R, 2020, AUSTRALAS J EDUC TEC, V36, DOI 10.14742/ajet.6378
   Shadiev R, 2024, EDUC INF TECHNOL, V29, P7759, DOI 10.1007/s10639-023-12143-3
   Shadiev R, 2019, BRIT J EDUC TECHNOL, V50, P1415, DOI 10.1111/bjet.12648
   Soyoof A, 2023, COMPUT ASSIST LANG L, V36, P608, DOI 10.1080/09588221.2021.1936562
   Starck JG, 2020, EDUC RESEARCHER, V49, P273, DOI 10.3102/0013189X20912758
   Sun JM, 2023, EDUC INF TECHNOL, V28, P1509, DOI 10.1007/s10639-022-11256-5
   Swain M, 2005, HANDBOOK OF RESEARCH IN SECOND LANGUAGE TEACHING AND LEARNING, P471
   Tai TY, 2024, COMPUT EDUC, V210, DOI 10.1016/j.compedu.2023.104965
   Tai TY, 2023, INTERACT LEARN ENVIR, V31, P1485, DOI 10.1080/10494820.2020.1841801
   Tai TY, 2024, COMPUT ASSIST LANG L, V37, P1281, DOI 10.1080/09588221.2022.2075013
   Tencent Education, 2021, Women tongjile bai yu suo gaoxiao 2021 xinsheng dashuju, faxian zhexie xuexiao nannv bili chaju da We compiled big data on first-year students from over a hundred universities in 2021 and found that there is a significant gender ratio disparity in these schools
   Tondeur J, 2017, AUSTRALAS J EDUC TEC, V33, P46, DOI 10.14742/ajet.3504
   Tseng YC, 2023, EDUC TECHNOL SOC, V26, P1, DOI 10.30191/ETS.202310_26(4).0001
   Umansky IM, 2021, AM EDUC RES J, V58, P993, DOI 10.3102/0002831221997571
   Wagner L, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.01324
   Wang DY, 2020, IEEE ACCESS, V8, P46335, DOI 10.1109/ACCESS.2020.2974101
   Yang H, 2022, RECALL, V34, P327, DOI 10.1017/S0958344022000039
   Zhang RF, 2022, EDUC INF TECHNOL, V27, P8041, DOI 10.1007/s10639-022-10941-9
   Zhang Y, 2023, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2023.2267627
   Zhang Y, 2024, COMPUT ASSIST LANG L, V37, P1904, DOI 10.1080/09588221.2022.2134424
   Zhou SQ, 2023, CURR PSYCHOL, V42, P31536, DOI 10.1007/s12144-022-04110-x
NR 91
TC 1
Z9 1
U1 43
U2 43
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 0958-3440
EI 1474-0109
J9 RECALL
JI ReCALL
PD 2024 OCT 25
PY 2024
DI 10.1017/S0958344024000259
EA OCT 2024
PG 17
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA K2H5K
UT WOS:001342140100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Ferraro, C
   Demsar, V
   Sands, S
   Restrepo, M
   Campbell, C
AF Ferraro, Carla
   Demsar, Vlad
   Sands, Sean
   Restrepo, Mariluz
   Campbell, Colin
TI The paradoxes of generative AI-enabled customer service: A guide for
   managers
SO BUSINESS HORIZONS
LA English
DT Article
DE Artificial intelligence; Generative AI; AI chatbots; Customer service;
   Customer support
ID CHATBOTS
AB Generative artificial intelligence (GenAI) presents a disruptive innovation for brands and society, and the power of which is still yet to be realized. In the context of customer service, gen AI affords companies new possibilities to communicate, connect, and engage customers. This article draws on scholarly research and consultation with customer service leaders to present and discuss the possibilities for GenAI in the context of customer service, specifically GenAI chatbots. Importantly, this article presents potential paradoxes of GenAI-enabled customer service, adding to the debate about the role and impact of GenAI for brands. Specifically, we present six paradoxes of GenAI customer service: (1) connected yet isolated, (2) lower cost yet higher price, (3) higher quality yet less empathy, (4) satisfied yet frustrated, (5) personalized yet intrusive, and (6) powerful yet vulnerable. For each paradox, we suggest brand response strategies to mitigate downside and manage potential upside. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
C1 [Ferraro, Carla; Demsar, Vlad; Sands, Sean] Swinburne Univ Technol, Dept Management & Mkt, Hawthorn 3122, Australia.
   [Restrepo, Mariluz] CPM Australia, Wantirna South, Vic 3152, Australia.
   [Campbell, Colin] Univ San Diego, San Diego, CA USA.
C3 Swinburne University of Technology; University of San Diego
RP Sands, S (corresponding author), Swinburne Univ Technol, Dept Management & Mkt, Hawthorn 3122, Australia.
EM cferraro@swin.edu.au; vdemsar@swin.edu.au; ssands@swin.edu.au;
   mariluz.restrepo@cpm-aus.com.au; colincampbell@sandiego.edu
RI Sands, Sean/KQT-9307-2024
OI Demsar, Vlad/0000-0002-3161-3729; Sands, Sean/0000-0001-9192-3676
FX The authors would like to acknowledge the postgraduate student cohort of
   the Semester 1, 2023 Services Marketing and Customer Man-agement unit at
   Swinburne University of Tech-nology for the discussions and project work
   which provided foundational insight into the possibilities and paradoxes
   of employing GenAI chatbots for customer service. We would also like to
   thank Paul Crummy, Managing Director of Direct Sales at CPM Australia
   for his assistance gathering the views of customer service leaders and
   valuable input in the development of this manuscript.
CR Adamopoulou E, 2020, MACH LEARN APPL, V2, DOI 10.1016/j.mlwa.2020.100006
   Agrawal A., 2022, HARVARD BUSINESS REV
   Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
   Arsel Z, 2017, J CONSUM RES, V44, P939, DOI 10.1093/jcr/ucx096
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Bamberger S., 2023, How generative AI is already transforming customer service
   Behera RK, 2024, INFORM SYST FRONT, V26, P899, DOI 10.1007/s10796-021-10168-y
   Ben Mimoun MS, 2017, INFORM MANAGE-AMSTER, V54, P545, DOI 10.1016/j.im.2016.11.008
   Bernazzani S., 2022, HubspotSeptember 20
   Bibler VS, 2020, RUSS STUD PHILOS, V58, P338, DOI 10.1080/10611967.2020.1863720
   Biddle S., 2022, The Intercept
   Bock DE, 2020, J SERV MARK, V34, P317, DOI 10.1108/JSM-01-2019-0047
   Boyes C., 2021, The New DailyOctober 12
   Brachten F, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102375
   Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Campbell C, 2021, J INTERACT MARK, V56, P96, DOI 10.1016/j.intmar.2021.06.001
   Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
   Cantor B., 2023, Customer Contact WeeklyMay 31
   Castillo D, 2021, SERV IND J, V41, P900, DOI 10.1080/02642069.2020.1787993
   Cherney M. A., 2023, ReutersOctober 4
   Chui M., 2021, Global survey: The state of AI in 2021 | McKinsey
   Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
   CPM, 2023, The state of customer experience in Australia-Unlocking consumer trends for CX success
   Cui YY, 2024, BUS HORIZONS, V67, P583, DOI 10.1016/j.bushor.2024.05.003
   Ferraro C, 2023, BUS HORIZONS, V66, P667, DOI 10.1016/j.bushor.2023.01.007
   Grand View Research, 2023, Chatbot Market Size Worth $3.99 Billion By 2030
   Grewal D, 2017, J RETAILING, V93, P1, DOI 10.1016/j.jretai.2016.12.008
   Grieve P., 2024, ZendeskMarch 11
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Haugeland IKF, 2022, INT J HUM-COMPUT ST, V161, DOI 10.1016/j.ijhcs.2022.102788
   Hoang H., 2023, BiPlusMarch 9
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Huang YS, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103044
   Hulick K., 2023, Science NewsApril 12
   Hyken S., 2017, ForbesJuly 15
   Javaid S., 2023, AI MultipleOctober 12
   Jenneboer L, 2022, J THEOR APPL EL COMM, V17, P212, DOI 10.3390/jtaer17010011
   Juniper Research, 2019, Bank cost savings via chatbots to reach $7.3 billion by 2023, as automated customer experience evolves
   Khogali HO, 2023, TECHNOL SOC, V73, DOI 10.1016/j.techsoc.2023.102232
   Khurana D, 2023, MULTIMED TOOLS APPL, V82, P3713, DOI 10.1007/s11042-022-13428-4
   Kietzmann J., 2024, Business Horizons, V67, P453
   Kvale K., 2020, INT WORKSH CHATB RES, P205
   Lin XL, 2022, IND MARKET MANAG, V101, P45, DOI 10.1016/j.indmarman.2021.11.016
   Marietto MDB, 2013, Arxiv, DOI [arXiv:1307.3091, DOI 10.5121/IJCSES.2013.4301]
   Mason Paul, 2016, The GuardianMarch 29
   Metz C., 2023, New York TimesMarch 29
   Mick DG, 1998, J CONSUM RES, V25, P123, DOI 10.1086/209531
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Murtarelli G, 2021, J BUS RES, V129, P927, DOI 10.1016/j.jbusres.2020.09.018
   Nicolescu L, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11101579
   Osadchaya E., 2024, Business Horizons, V67, P571
   Pallant JI, 2022, J RETAIL CONSUM SERV, V64, DOI 10.1016/j.jretconser.2021.102774
   Poireault K., 2023, Infosecurity MagazineJanuary 27
   Ramaul L, 2024, BUS HORIZONS, V67, P615, DOI 10.1016/j.bushor.2024.05.006
   Ramesh K, 2017, COMM COM INF SC, V750, P336, DOI 10.1007/978-981-10-6544-6_31
   Rosen P., 2023, Business InsiderMarch 1
   Sands S, 2022, PSYCHOL MARKET, V39, P2039, DOI 10.1002/mar.21723
   Sands S, 2021, J SERV MANAGE, V32, P246, DOI 10.1108/JOSM-06-2019-0203
   Schulz M., 2023, Vogue BusinessMarch 15
   Siggelkow N., 2023, Harvard Business ReviewApril 4
   Suthar S., 2023, The Next ScoopJuly 29
   Unbabel, 2021, Unbabel's 2021 Global Multilingual CX Survey reveals 68% of consumers prefer to speak with brands in their native language
   Vincent J, 2022, AI-generated answers temporarily banned on coding Q&A site Stack Overflow
   Wirtz J, 2020, ORGAN DYN, V49, DOI 10.1016/j.orgdyn.2019.04.005
   Xu AB, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P3506, DOI 10.1145/3025453.3025496
   Yoon N, 2021, J THEOR APPL EL COMM, V16, P1912, DOI 10.3390/jtaer16050107
   Zarouali B, 2018, CYBERPSYCH BEH SOC N, V21, P491, DOI 10.1089/cyber.2017.0518
   Zhang JJY, 2023, J INTERNET COMMER, V22, P122, DOI 10.1080/15332861.2021.1966723
NR 69
TC 17
Z9 17
U1 77
U2 77
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 549
EP 559
DI 10.1016/j.bushor.2024.04.013
EA AUG 2024
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2L0A
UT WOS:001301355300001
OA hybrid
DA 2024-12-25
ER

PT J
AU Vartiainen, H
   Tedre, M
AF Vartiainen, Henriikka
   Tedre, Matti
TI How Text-to-Image Generative AI Is Transforming Mediated Action
SO IEEE COMPUTER GRAPHICS AND APPLICATIONS
LA English
DT Article
DE Generative AI; Cultural differences; Art; Global communication; Training
   data; Psychology; Neural networks; Text-to-image; Artificial
   intelligence
AB This article examines the intricate relationship between humans and text-to-image generative models (generative artificial intelligence/genAI) in the realm of art. The article frames that relationship in the theory of mediated action-a well-established theory that conceptualizes how tools shape human thoughts and actions. The article describes genAI systems as learning, cocreating, and communicating, multimodally capable hybrid systems that distill and rely on the wisdom and creativity of massive crowds of people and can sometimes surpass them. Those systems elude the theoretical description of the role of tools and locus of control in mediated action. The article asks how well the theory can accommodate both the transformative potential of genAI tools in creative fields and art, and the ethics of the emergent social dynamics it generates. The article concludes by discussing the fundamental changes and broader implications that genAI brings to the realm of mediated action and, ultimately, to the very fabric of our daily lives.
C1 [Vartiainen, Henriikka] Univ Eastern Finland, Sch Appl Educ Sci & Teacher Educ, FI-80101 Joensuu, Finland.
   [Tedre, Matti] Univ Eastern Finland, Sch Comp, Joensuu, Finland.
C3 University of Eastern Finland; University of Eastern Finland
RP Vartiainen, H (corresponding author), Univ Eastern Finland, Sch Appl Educ Sci & Teacher Educ, FI-80101 Joensuu, Finland.
EM henriikka.vartiainen@uef.fi; mmeri@cs.joensuu.fi
FU Strategic Research Council
FX No Statement Available
CR Benjamin R., 2019, Race After Technology: Abolitionist Tools for the New Jim Code
   Bommasani R., 2021, arXiv
   Bowker G. C., 1999, Sorting Things Out: Classification and its Consequences
   Cole M., 1993, Distributed Cognitions: Psychological and Educational Considerations, P1
   Crawford K., 2021, Atlas ofAI: Power, Politics, and the Planetary Costs of Artificial Intelligence
   Darwiche A, 2018, COMMUN ACM, V61, P56, DOI 10.1145/3271625
   Engestrom Y., 1999, PERSPECTIVES ACTIVIT, P377, DOI [DOI 10.1017/CBO9780511812774.025, 10.1017/CBO9780511812774.025]
   Eubanks V, 2018, AUTOMATING INEQUALIT
   Hakkarainen K, 2013, SEMPRE STUD PSYCHOL, P13
   Heikkila M., 2022, MIT Tech. Rev., V125, P9
   Jenkins H, 2007, NORD J DIGIT LIT, V2, P23
   Jo ES, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P306, DOI 10.1145/3351095.3372829
   Levy P., 1999, Collective intelligence: Mankind's emerging world in cyberspace
   Pea R., 1993, Distributed cognitions: Psychological and educational considerations, P47
   Rahwan I, 2019, NATURE, V568, P477, DOI 10.1038/s41586-019-1138-y
   Vartiainen H., 2023, G5FB8, DOI [10.35542/osf.io/g5fb8, DOI 10.35542/OSF.IO/G5FB8]
   Vincent J., 2022, TheVerge
   Vygotsky L.S., 1978, MIND SOC DEV HIGHER, DOI 10.2307/j.ctvjf9vz4
   Wertsch J., 1997, MIND ACTION
   Wertsch J.V., 1991, VOICES MIND SOCIOCUL
   Wertsch JV, 2007, CAMBRIDGE COMPANION TO VYGOTSKY, P178
NR 21
TC 3
Z9 3
U1 10
U2 17
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0272-1716
EI 1558-1756
J9 IEEE COMPUT GRAPH
JI IEEE Comput. Graph. Appl.
PD MAR-APR
PY 2024
VL 44
IS 2
BP 12
EP 22
DI 10.1109/MCG.2024.3355808
PG 11
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA MJ6S8
UT WOS:001193299600008
PM 38285567
OA hybrid, Green Submitted
DA 2024-12-25
ER

PT J
AU Bannister, P
AF Bannister, Peter
TI English Medium Instruction Educator Language Assessment Literacy and the
   Test of Generative AI in Online Higher Education
SO JOURNAL OF RESEARCH IN APPLIED LINGUISTICS
LA English
DT Article
DE Generative Artificial Intelligence; English as a Medium of Instruction;
   Language Assessment Literacy; Academic Integrity; Online Higher
   Education
ID TEACHERS; CHALLENGES
AB Generative Artificial Intelligence (GenAI) is often portrayed as disruptive. Higher Education (HE) assessment is not exempt from this, although the implications for multilingual settings remain an area of limited exploration. Drawing on the scarce literature in English as a Medium of Instruction (EMI) assessment and EMI educator language assessment literacy (LAL), this study sought to explore EMI online HE educator LAL and awareness of GenAI's potential impact on established language assessment praxis. A sequential explanatory mixed-methods approach was used, comprising a survey on LAL self-perceptions (n=174) and semi-structured interviews (n=12). Findings illustrate general tendencies of low levels of LAL and practitioner unease towards GenAI-assisted academic misconduct. A heightened lack of confidence in GenAI tool usage detection efficacy and HE institutional capacity to respond to their evolving capacities in a timely manner was also found. It is therefore suggested that, given the complexity and continuing swift development of GenAI tools, the implementation of continuous professional development programmes focused on enhancing EMI educators' language assessment literacy and competence in using GenAI technologies is prioritised. These findings underscore the need for initiatives that not only improve technical skills but also address ethical considerations and strategies to uphold academic integrity in the face of emerging GenAI capabilities.
C1 [Bannister, Peter] Univ Int La Rioja, Doctoral Sch, Vice Rectorate Res, La Rioja, Spain.
C3 Universidad Internacional de La Rioja (UNIR)
RP Bannister, P (corresponding author), Univ Int La Rioja, Doctoral Sch, Vice Rectorate Res, La Rioja, Spain.
EM peter.bannister@unir.net
RI Bannister, Peter/HDO-4393-2022
OI Bannister, Peter/0000-0002-7216-3912
FU Project of Analysis and Development for the Optimization of Assessment
   and Regulation of Generative Artificial Intelligence (PANDORA) Research
   Project of Universidad Internacional de La Rioja [PP-2023-02]
FX This research has been financed by the Project of Analysis and
   Development for the Optimization of Assessment and Regulation of
   Generative Artificial Intelligence (PANDORA) Research Project with
   reference PP-2023-02 granted in the 2023 UNIR Research Projects Call of
   Universidad Internacional de La Rioja.
CR Afshar HS, 2021, STUD EDUC EVAL, V70, DOI 10.1016/j.stueduc.2021.101042
   Amani S, 2023, Arxiv, DOI [arXiv:2304.14415, 10.48550/arXiv.2304.14415, DOI 10.48550/ARXIV.2304.14415]
   Andujar A, 2023, J RES APPL LINGUIST, V14, P7, DOI 10.22055/RALS.2023.45267.3177
   Bañeres D, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-022-00371-5
   Bannister P, 2023, AULA ABIERTA, V52, P401, DOI 10.17811/rifie.52.4.2023.401-409
   Bannister P, 2023, IRAN J LANG TEACH RE, V11, P53, DOI 10.30466/ijltr.2023.121406
   Battersby M., 2019, Studies in critical thinking. Windsor in argumentation, V8, P289
   Bearman M, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13337
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Birhane A, 2023, NAT REV PHYS, V5, P277, DOI 10.1038/s42254-023-00581-4
   Boden M. A., 2004, The Creative Mind: Myths and Mechanisms
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P1, DOI DOI 10.5281/ZENODO.7755273
   Braun V, 2023, INT J TRANSGEND HEAL, V24, P1, DOI 10.1080/26895269.2022.2129597
   Brew M., 2023, Asian Journal of Distance Education, V18, P1, DOI [10.5281/zenodo.8032387, DOI 10.5281/ZENODO.8032387]
   Carrigan M, 2023, Impact of Social SciencesApril 27
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Chen ZQ, 2022, MOB INF SYST, V2022, DOI 10.1155/2022/3201004
   Clarke V., 2014, SAGE HDB QUALITATIVE, P1947, DOI [DOI 10.1037/13620-004, 10.1007/978-1-4614-5583-7_311, 10.1007/978-981-10-5251-4_103, DOI 10.4135/9781526405555]
   Cresswell J. W., 2011, Designing and Conducting Mixed Methods Research, V2nd
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   Dawson P., 2020, Re-imagining university assessment in a digital world, P37, DOI DOI 10.1007/978-3-030-41956-14
   Dawson P., 2021, Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education
   Dearden J., 2014, ENGLISH MEDIUM INSTR
   Deng CY, 2020, ISCIENCE, V23, DOI 10.1016/j.isci.2020.101656
   Derakhshan A, 2024, J RES APPL LINGUIST, V15, P17, DOI 10.22055/RALS.2023.44418.3111
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eager B, 2023, J UNIV TEACH LEARN P, V20
   Eaton S.E., 2021, Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00137-0
   Farazouli A, 2024, ASSESS EVAL HIGH EDU, V49, P363, DOI 10.1080/02602938.2023.2241676
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Fido D., 2023, The Interdisciplinary Journal of Student Success
   Fulcher G, 2012, LANG ASSESS Q, V9, P113, DOI 10.1080/15434303.2011.642041
   Gan L, 2022, LANG ASSESS Q, V19, P503, DOI 10.1080/15434303.2022.2128802
   Harding L., 2022, The handbook of language assessment across modalities, P373, DOI [10.1093/oso/9780190885052.003.0032, DOI 10.1093/OSO/9780190885052.003.0032]
   Hultgren A. K., 2022, Journal of English-Medium Instruction, V1, P105, DOI [10.1075/jemi.21019.hul, DOI 10.1075/JEMI.21019.HUL]
   Johinke R, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.01
   Koszowy M, 2019, PRAGMAT SOC, V10, P287, DOI 10.1075/ps.16051.kos
   Kremmel B, 2020, LANG ASSESS Q, V17, P100, DOI 10.1080/15434303.2019.1674855
   Lasagabaster D., 2022, Journal of English-Medium Instruction, V1, P48, DOI [DOI 10.1075/JEMI.21011.LAS, 10.1075/jemi.21011.las]
   Liang WX, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100779
   Liu JE, 2023, TEACH TEACH EDUC, V129, DOI 10.1016/j.tate.2023.104150
   Macaro E., 2018, English medium instruction content and language in policy and practice
   Macaro E, 2022, LANG TEACHING, V55, P533, DOI 10.1017/S0261444822000052
   Mancho-Bares G., 2022, Journal of English-Medium Instruction, V1, P232, DOI [10.1075/jemi.21008.man, DOI 10.1075/JEMI.21008.MAN]
   Mckinley J., 2022, J ENGLISH MEDIUM INS, V1, P85
   Newcomer KE., 2015, Handbook of practical program evaluation, V492, P492, DOI [10.1002/9781119171386.CH19, DOI 10.1002/9781119171386.CH19]
   Nieminen JH, 2023, HIGH EDUC, V85, P1381, DOI 10.1007/s10734-022-00895-9
   Nikolic S, 2023, EUR J ENG EDUC, V48, P559, DOI 10.1080/03043797.2023.2213169
   Pack A, 2023, TESOL QUART, V57, P1571, DOI 10.1002/tesq.3253
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ruslin R., 2022, IOSR Journal of Research Method in Education, V12, P22, DOI [10.9790/7388-1201052229, DOI 10.9790/7388-1201052229]
   Russell S, 2019, Artificial intelligence and the problem of human control
   Sadasivan VS, 2024, Arxiv, DOI [arXiv:2303.11156, DOI 10.48550/ARXIV.2303.11156]
   Sah P. K., 2022, Journal of English-Medium Instruction, V1, P124, DOI [10.1075/jemi.21022.sah, DOI 10.1075/JEMI.21022.SAH]
   Scarino A, 2013, LANG TEST, V30, P309, DOI 10.1177/0265532213480128
   Shahzadi A, 2022, STUD LANG ASSESS, V11, P92
   Sharples M., 2023, Learn. Res. Pract, V9, P159, DOI [DOI 10.1080/23735082.2023.2261131, 10.1080/23735082.2023.2261131]
   Sharples M, 2022, INT J ARTIF INTELL E, V32, P1119, DOI 10.1007/s40593-022-00300-7
   Sharples Mike., 2022, Story Machines: How Computers Have Become Creative Writers
   Stobart G., 2008, TESTING TIMES USES A
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Sudajit-apa M., Journal of Research in Applied Linguistics, DOI [10.22055/rals.2024.46100.3232, DOI 10.22055/RALS.2024.46100.3232]
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Taylor L, 2013, LANG TEST, V30, P403, DOI 10.1177/0265532213480338
   Touvron H, 2023, arXiv, DOI [DOI 10.48550/ARXIV, 10.48550/arXiv]
   Tsagari D, 2021, PERSPECTIVES ON LANGUAGE ASSESSMENT LITERACY, P13
   Tsagari D, 2017, PAP LANG TEST ASSESS, V6, P41
   Vogt K, 2014, LANG ASSESS Q, V11, P374, DOI 10.1080/15434303.2014.960046
   Wang T, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13116716
   Yildirim-Erbasli SN, 2023, Computers and Education: Artificial Intelligence, V4, DOI [10.1016/j.caeai.2023.100135, DOI 10.1016/J.CAEAI.2023.100135]
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   Yufik YM, 2022, FRONT SYST NEUROSCI, V16, DOI 10.3389/fnsys.2022.1081112
   Zajko M, 2022, SOCIOL COMPASS, V16, DOI 10.1111/soc4.12962
NR 78
TC 2
Z9 2
U1 6
U2 6
PU SHAHID CHAMRAN UNIV AHVAZ, IRAN
PI AHVAZ
PA Faculty Letters & Humanity, AHVAZ, 00000, IRAN
SN 2345-3303
EI 2588-3887
J9 J RES APPL LINGUIST
JI J. Res. Appl. Linguist.
PY 2024
VL 15
IS 2
BP 55
EP 72
DI 10.22055/rals.2024.45862.3214
PG 18
WC Linguistics
WE Emerging Sources Citation Index (ESCI)
SC Linguistics
GA L1R3L
UT WOS:001348559300005
DA 2024-12-25
ER

PT J
AU Chan, CKY
   Zhou, WX
AF Chan, Cecilia Ka Yuk
   Zhou, Wenxin
TI An expectancy value theory (EVT) based instrument for measuring student
   perceptions of generative AI
SO SMART LEARNING ENVIRONMENTS
LA English
DT Article
DE Expectancy-value theory (EVT); Validated instrument; Generative AI;
   ChatGPT; Unified theory of acceptance and use of technology (UTAUT);
   Technology acceptance model (TAM); Theory of planned behavior (TPB)
ID ARTIFICIAL-INTELLIGENCE; TECHNOLOGY ACCEPTANCE; USER ACCEPTANCE;
   EDUCATION
AB This study examines the relationship between student perceptions and their intention to use generative artificial intelligence (GenAI) in higher education. With a sample of 405 students participating in the study, their knowledge, perceived value, and perceived cost of using the technology were measured by an Expectancy-Value Theory (EVT) instrument. The scales were first validated and the correlations between the different components were subsequently estimated. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of GenAI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption.
C1 [Chan, Cecilia Ka Yuk; Zhou, Wenxin] Univ Hong Kong, Fac Educ, Teaching & Learning Innovat Ctr TAL, Pokfulam, Hong Kong, Peoples R China.
   [Chan, Cecilia Ka Yuk] Univ Hong Kong, Teaching & Learning Innovat Ctr, Pokfulam, Room CPD-1-81,Centennial Campus, Hong Kong, Peoples R China.
C3 University of Hong Kong; University of Hong Kong
RP Chan, CKY (corresponding author), Univ Hong Kong, Fac Educ, Teaching & Learning Innovat Ctr TAL, Pokfulam, Hong Kong, Peoples R China.; Chan, CKY (corresponding author), Univ Hong Kong, Teaching & Learning Innovat Ctr, Pokfulam, Room CPD-1-81,Centennial Campus, Hong Kong, Peoples R China.
EM Cecilia.Chan@cetl.hku.hk
OI ZHOU, Wenxin/0009-0001-7260-5602
CR Abdelwahab HR, 2023, IND HIGHER EDUC, V37, P22, DOI 10.1177/09504222221087614
   Abdullah F, 2016, COMPUT HUM BEHAV, V56, P238, DOI 10.1016/j.chb.2015.11.036
   Agrawal A., 2022, Harvard Business Review
   Ajzen I, 2002, J APPL SOC PSYCHOL, V32, P665, DOI 10.1111/j.1559-1816.2002.tb00236.x
   Back I, 2021, TEACH TEACH EDUC, V104, DOI 10.1016/j.tate.2021.103390
   Backfisch I, 2021, COMPUT EDUC, V166, DOI 10.1016/j.compedu.2021.104159
   Bai L., 2023, Brain-X, V1, DOI [10.1002/brx2.30, DOI 10.1002/BRX2.30]
   Ball C, 2018, INFORM COMMUN SOC, V22, P105, DOI 10.1080/1369118X.2017.1355403
   Bonsu E., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4387107, DOI 10.2139/SSRN.4387107]
   BROWN T. A., 2006, CONFIRMATORY FACTOR
   Chai CS, 2021, EDUC TECHNOL SOC, V24, P89
   Chan C.K.Y., 2022, ASSESSMENT EXPERIENT, DOI DOI 10.4324/9781003018391
   Chan C. K. Y., 2023, Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of 'AI-giarism'
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chassignol M, 2018, PROCEDIA COMPUT SCI, V136, P16, DOI 10.1016/j.procs.2018.08.233
   Chen JL, 2011, COMPUT EDUC, V57, P1501, DOI 10.1016/j.compedu.2011.02.009
   Chen MY, 2022, FRONT MED-LAUSANNE, V9, DOI 10.3389/fmed.2022.990604
   Cheng SL, 2020, TEACH TEACH EDUC, V91, DOI 10.1016/j.tate.2020.103062
   Chui M., 2022, GENERATIVE IS HERE T
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Dahlkemper MN, 2023, Arxiv, DOI arXiv:2304.05906
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Fabrigar LR, 1999, PSYCHOL METHODS, V4, P272, DOI 10.1037/1082-989X.4.3.272
   Flake JK, 2015, CONTEMP EDUC PSYCHOL, V41, P232, DOI 10.1016/j.cedpsych.2015.03.002
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Fu SX, 2020, BRIT J EDUC TECHNOL, V51, P1674, DOI 10.1111/bjet.12995
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gado S, 2022, PSYCHOL LEARN TEACH-, V21, P37, DOI 10.1177/14757257211037149
   Gaskin James., 2023, MASTER VALIDITY TOOL
   Haensch AC, 2023, Arxiv, DOI [arXiv:2303.05349, DOI 10.1109/BIGSURV59479.2023.10486710]
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Hu YH, 2022, EDUC INF TECHNOL, V27, P2013, DOI 10.1007/s10639-021-10664-3
   HyScaler, 2023, The Power of AI in Research Hypotheses
   Ifinedo P, 2018, INT J EDUC TECHNOL H, V15, DOI 10.1186/s41239-018-0092-3
   Iyer LS., 2021, Transportation Engineering, V5, P100083, DOI [DOI 10.1016/J.TRENG.2021.100083, 10.1016/j.treng.2021.100083]
   Jeffrey T., 2020, Journal of Systemics, Cybernetics and Informatics, V18, P8
   Kim J, 2020, INT J HUM-COMPUT INT, V36, P1902, DOI 10.1080/10447318.2020.1801227
   Königstorfer F, 2020, J BEHAV EXP FINANC, V27, DOI 10.1016/j.jbef.2020.100352
   Kumar V R., 2022, 2022 IEEE Integrated STEM Education Conference (ISEC), P450, DOI [10.1109/ISEC54952.2022.10025165, DOI 10.1109/ISEC54952.2022.10025165]
   Maheshwari G, 2021, EDUC INF TECHNOL, V26, P6629, DOI 10.1007/s10639-021-10465-8
   Malechwanzi JM, 2016, COGENT EDUC, V3, DOI 10.1080/2331186X.2016.1201990
   Mok L., 2023, Hong Kong Free Press HKFP
   Mucharraz Y., 2023, ChatGPT and AI text generators: Should academia adapt or resist?
   Pimentel J.L., 2010, USM RD J, V18, P109
   Raffaghelli JE, 2022, COMPUT EDUC, V182, DOI 10.1016/j.compedu.2022.104468
   Raman R., 2023, PREPRINT, DOI [10.21203/rs.3.rs-2734142/v1, DOI 10.21203/RS.3.RS-2734142/V1]
   Ranellucci J, 2020, J COMPUT ASSIST LEAR, V36, P810, DOI 10.1111/jcal.12459
   Regmi K, 2020, BMC MED EDUC, V20, DOI 10.1186/s12909-020-02007-6
   RUSSELL SJ, 2016, ARTIFICIAL INTELLIGE
   SchermellehEngel K., 2003, METHODS PSYCHOL RES, V8, P23, DOI DOI 10.1002/0470010940
   Schulman J., 2022, Introducing ChatGPT
   Sin HX, 2022, PSYCHOL MUSIC, V50, P976, DOI 10.1177/03057356211024344
   Steiger JH, 2007, PERS INDIV DIFFER, V42, P893, DOI 10.1016/j.paid.2006.09.017
   Stuber J., 2018, Barriers of digital technologies in higher education: A teachers perspective from a Swedish university
   Teo T, 2009, COMPUT EDUC, V52, P302, DOI 10.1016/j.compedu.2008.08.006
   Topol EJ, 2019, NAT MED, V25, P44, DOI 10.1038/s41591-018-0300-7
   Tsz Kit Ng Davy, 2021, Proceedings of the Association for Information Science and Technology, V58, P504, DOI 10.1002/pra2.487
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Wang FM, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00417-2
   WIGFIELD A, 1992, DEV REV, V12, P265, DOI 10.1016/0273-2297(92)90011-P
   Wigfield A, 2000, CONTEMP EDUC PSYCHOL, V25, P68, DOI 10.1006/ceps.1999.1015
   Wigfield A, 1994, EDUC PSYCHOL REV, V6, P49, DOI 10.1007/BF02209024
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhai XS, 2021, COMPLEXITY, V2021, DOI 10.1155/2021/8812542
   Zou B., 2020, Technology and the Psychology of Second Language Learners and Users. New Language Learning and Teaching Environments, DOI [10.1007/978-3-030-34212, DOI 10.1007/978-3-030-34212]
NR 70
TC 14
Z9 14
U1 69
U2 138
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
EI 2196-7091
J9 SMART LEARN ENVIRON
JI Smart Learn. Env.
PD DEC 7
PY 2023
VL 10
IS 1
AR 64
DI 10.1186/s40561-023-00284-4
PG 22
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA AD1B8
UT WOS:001116420600001
OA gold
DA 2024-12-25
ER

PT J
AU Yang, YY
   Luo, JW
   Yang, MY
   Yang, RD
   Chen, JY
AF Yang, Yunying
   Luo, Jinwen
   Yang, Miaoyan
   Yang, Runde
   Chen, Jiayin
TI From surface to deep learning approaches with Generative AI in higher
   education: an analytical framework of student agency
SO STUDIES IN HIGHER EDUCATION
LA English
DT Article
DE Generative AI; GPT; student agency; learning approaches; higher
   education
ID PROFESSIONAL AGENCY; TEACHERS
AB Recent emergence of generative artificial intelligence (GenAI) technology has stimulated interests as well as concerns in their potential in teaching and learning. Situated in the new and transforming context, this study provides an avenue for students to introspectively explore their use of GenAI in a postgraduate course. Seventy-four students from three Chinese universities participated in this study. By analyzing student interviews conducted pre- and post-course, alongside their chat logs with GenAI and reflective journal entries detailing their learning approaches, the research uncovers a spectrum of student perspectives on GenAI's impact, ranging from beneficial optimism, to cautious skepticism and adaptable pragmatism. Notably, student agency is identified as a crucial element in relation to these themes. This was articulated in four types of learning activities: receptive, resistive, resourceful, and reflective. The research underscores the importance of supporting and empowering student agency in the learning approaches aided by GenAI in education, highlighting its role in optimizing its use and enhancing autonomous, lifelong learning skills amidst the evolving technologically advanced learning landscape.
C1 [Yang, Yunying] South China Normal Univ, Sch Informat Technol Educ, Guangzhou, Peoples R China.
   [Luo, Jinwen] Univ Calif Los Angeles, Sch Educ & Informat Studies, Los Angeles, CA USA.
   [Yang, Miaoyan] Xiamen Univ, Sch Sociol & Anthropol, Sociol Dept, Xiamen, Peoples R China.
   [Yang, Runde] Block Art Online, New York City, NJ USA.
   [Chen, Jiayin] Guangzhou Univ, Ctr Human Geog & Urban Dev, Sch Geog & Remote Sensing, Guangzhou, Peoples R China.
   [Yang, Miaoyan] Xiamen Univ, Sch Sociol & Anthropol, Sociol Dept, Siming South Rd 422, Xiamen, Fujian, Peoples R China.
C3 South China Normal University; University of California System;
   University of California Los Angeles; Xiamen University; Guangzhou
   University; Xiamen University
RP Yang, MY (corresponding author), Xiamen Univ, Sch Sociol & Anthropol, Sociol Dept, Siming South Rd 422, Xiamen, Fujian, Peoples R China.
EM miaoyanyang@163.com
RI Luo, Jinwen/HNI-0681-2023
OI Luo, Jinwen/0000-0002-8511-7165
FU National Social Science Fund of China
FX No Statement Available
CR Ali M., 2021, FOSTERING COMMUNICAT, P36, DOI DOI 10.4018/978-1-7998-4846-2.CH003
   Bandura A, 2001, ANNU REV PSYCHOL, V52, P1, DOI 10.1146/annurev.psych.52.1.1
   Biesta G., 2007, Studies in the Education of Adults, V39, P132, DOI DOI 10.1080/02660830.2007.11661545
   Bommasani R., 2021, ARXIV
   Chan C. K. Y., 2023, INT J EDUC TECHNOL H, V20, P1, DOI DOI 10.1186/S41239-022-00368-0
   Damsa CI, 2010, J LEARN SCI, V19, P143, DOI 10.1080/10508401003708381
   Darvishi A, 2024, COMPUT EDUC, V210, DOI 10.1016/j.compedu.2023.104967
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Eteläpelto A, 2013, EDUC RES REV-NETH, V10, P45, DOI 10.1016/j.edurev.2013.05.001
   Gimpel Henner, 2023, Tech. Rep.
   Haleem A., 2022, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V2, DOI [DOI 10.1016/J.TBENCH.2023.100089, 10.1016/j.tbench.2023.100089]
   Hamilton ML, 2008, STUD TEACH EDUC, V4, P17, DOI 10.1080/17425960801976321
   Hökkä P, 2017, TEACH TEACH EDUC, V63, P36, DOI 10.1016/j.tate.2016.12.001
   Lipponen L, 2011, TEACH TEACH EDUC, V27, P812, DOI 10.1016/j.tate.2011.01.001
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   OECD, 2018, STUDENT AGENCY 2030
   Paavola S., 2005, Science Education, V14, P535, DOI DOI 10.1007/S11191-004-5157-0
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Perera P., 2023, INT J RES INNOVATION, V7, P306, DOI DOI 10.47772/IJRISS.2023.7623
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ruohotie-Lyhty M, 2016, TEACH TEACH EDUC, V55, P318, DOI 10.1016/j.tate.2016.01.022
   Shidiq M., 2023, PROCEEDING INT C ED, V1, P353
   Stenalt H., 2020, ASCILITES 1 VIRTUA, P273
   Wambsganss T, 2022, COMPUT EDUC, V191, DOI 10.1016/j.compedu.2022.104644
   Wang YM, 2021, EDUC TECHNOL SOC, V24, P116
   Yu H, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1181712
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 27
TC 2
Z9 2
U1 97
U2 167
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0307-5079
EI 1470-174X
J9 STUD HIGH EDUC
JI Stud. High. Educ.
PD MAY 3
PY 2024
VL 49
IS 5
SI SI
BP 817
EP 830
DI 10.1080/03075079.2024.2327003
EA MAR 2024
PG 14
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA SX4A9
UT WOS:001180409900001
DA 2024-12-25
ER

PT J
AU Bozkurt, A
AF Bozkurt, Aras
TI GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and
   Integrity in a Time of Generative AI
SO OPEN PRAXIS
LA English
DT Article
DE Generative AI; GenAI; artificial intelligence; AI; AIEd; chatbots;
   conversational agents; education; teaching; learning; higher education;
   educational technology; GPT; generative pre-trained transformer;
   ChatGPT; large language models; LLMs; natural language processing;
   collaboration; cocreating; academic writing; transparency in research;
   authorship; ownership; ethics; academic integrity
ID CHATGPT
AB This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI -generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI -inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various informationprocessing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI -driven change.
C1 [Bozkurt, Aras] Tripura Univ, Agartala, Tripura, India.
C3 Tripura University
RP Bozkurt, A (corresponding author), Tripura Univ, Agartala, Tripura, India.
EM arasbozkurt@gmail.com
RI Bozkurt, Aras/O-3654-2017
OI Bozkurt, Aras/0000-0002-4520-642X
FU Anadolu University [SBA -2023-1852]
FX This paper is funded by Anadolu University with grant number SBA
   -2023-1852.
CR Ali MJ, 2023, SEMIN OPHTHALMOL, V38, P403, DOI 10.1080/08820538.2023.2193444
   [Anonymous], 2023, NATURE, V613, P612, DOI 10.1038/d41586-023-00191-1
   [Anonymous], 2021, The fundamental values of academic integrity
   Ansari AN, 2024, EDUC INF TECHNOL, V29, P11281, DOI 10.1007/s10639-023-12223-4
   Arachchige APM, 2023, NUCL MED MOLEC IMAG, V57, P213, DOI 10.1007/s13139-023-00816-3
   Bakla A., 2023, Transforming the Language Teaching Experience in the Age of AI, P89, DOI 10.4018/978-1-6684-9893-4.ch005
   Bhatia G, 2023, ASIAN J PSYCHIATR, V84, DOI 10.1016/j.ajp.2023.103564
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Bozkurt A., 2023, Research, writing, and creative process in open and distance education: Tales from the field, P101, DOI [10.11647/OBP.0356, DOI 10.11647/OBP.0356]
   Bozkurt A., 2023, ASIAN J DISTANCE ED, V18, pi, DOI [DOI 10.5281/ZENODO.8174941, 10.4018/979-8-3693-1351-0]
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Bozkurt A, 2023, OPEN PRAX, V15, P178, DOI 10.55982/openpraxis.15.3.579
   Bozkurt A, 2023, OPEN PRAX, V15, P261, DOI 10.55982/openpraxis.15.4.609
   Concannon F., 2023, Irish Journal of Technology Enhanced Learning, V7, DOI [10.22554/ijtel.v7i1.116, DOI 10.22554/IJTEL.V7I1.116]
   COPE (Committee on Publication Ethics), 2023, Authorship and contributorship
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   CSE, 2022, Recommendations for promoting integrity in scientific journal publications
   Currie GM, 2023, SEMIN NUCL MED, V53, P719, DOI 10.1053/j.semnuclmed.2023.04.008
   da Silva JAT, 2023, LEARN PUBL, V36, P453, DOI 10.1002/leap.1547
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Dempere J, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1206936
   Dien J, 2023, BIOL PSYCHOL, V181, DOI 10.1016/j.biopsycho.2023.108621
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Emsley R, 2023, SCHIZOPHRENIA-UK, V9, DOI 10.1038/s41537-023-00379-4
   Farrelly T, 2023, EDUC SCI, V13, DOI 10.3390/educsci13111109
   Gates B., 2023, Gates Notes
   Harari Y. N., 2023, The Economist
   Herbold Steffen, 2023, Sci Rep, V13, P18617, DOI 10.1038/s41598-023-45644-9
   ICMJE, 2024, ICMJE-Recommendations-defining the role of authors and contributors
   ICMJE (International Committee of Medical Journal Editors), 2022, Recommendations
   Ide K, 2023, J EPIDEMIOL, V33, P381, DOI 10.2188/jea.JE20230030
   Imran M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13605
   Jarrah AM, 2023, ONLINE J COMMUN MEDI, V13, DOI 10.30935/ojcmt/13572
   Kirwan A, 2024, IRISH EDUC STUD, V43, P1389, DOI 10.1080/03323315.2023.2284901
   Kitamura FC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230171
   Lee JY, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.6
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   McGuire A., 2023, Irish Journal of Technology Enhanced Learning, V7, P21, DOI [10.22554/ijtel.v7i2.131, DOI 10.22554/IJTEL.V7I2.131]
   Nagarkar Shubhada, 2023, Indian J Med Ethics, VVIII, P93, DOI 10.20529/IJME.2023.029
   O'Connor S, 2023, NURSE EDUC PRACT, V67, DOI 10.1016/j.nepr.2023.103572
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   OpenAI, 2022, INTR CHATGPT
   Rahimi F, 2023, ANN BIOMED ENG, V51, P2340, DOI 10.1007/s10439-023-03260-8
   Schroeder R., 2023, Inside Higher Ed
   Semrl N, 2023, HUM REPROD, V38, P2281, DOI 10.1093/humrep/dead207
   Sharma R. C., 2024, Transforming education with generative AI: Prompt engineering and synthetic content creation, DOI 10.4018/979-8-3693-1351-0
   Siegerink B, 2023, NURSE EDUC PRACT, V68, DOI 10.1016/j.nepr.2023.103599
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   Stokel-Walker Chris, 2022, Nature, DOI 10.1038/d41586-022-04397-7
   Tang GY, 2023, IRISH J MED SCI, V192, P3195, DOI 10.1007/s11845-023-03374-x
   Tang GY, 2024, ACCOUNT RES, V31, P1242, DOI 10.1080/08989621.2023.2180359
   Teixeira da Silva JA, 2023, NURSE EDUC PRACT, V68, DOI 10.1016/j.nepr.2023.103600
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Weissman J., 2023, Inside higher ed
   Zielinski C., 2023, Chatbots, ChatGPT, and scholarly manuscripts: WAME recommendations on ChatGPT and Chatbots in relation to scholarly publications, DOI [10.25100/cm.v54i3.5868, DOI 10.25100/CM.V54I3.5868]
NR 59
TC 11
Z9 11
U1 93
U2 120
PU INT COUNCIL OPEN & DISTANCE EDUCATION
PI OSLO
PA LILLEAKERVEIEN 23, OSLO, 0283, NORWAY
SN 2304-070X
J9 OPEN PRAX
JI Open Prax.
PY 2024
VL 16
IS 1
BP 1
EP 10
DI 10.55982/openpraxis.16.1.654
PG 10
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA NZ8G5
UT WOS:001204361200009
OA gold
DA 2024-12-25
ER

PT J
AU Ferrara, E
AF Ferrara, Emilio
TI GenAI against humanity: nefarious applications of generative artificial
   intelligence and large language models
SO JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE
LA English
DT Article
DE AI; Generative AI; Large Language Models; Risks; Social media
AB Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are marvels of technology; celebrated for their prowess in natural language processing and multimodal content generation, they promise a transformative future. But as with all powerful tools, they come with their shadows. Picture living in a world where deepfakes are indistinguishable from reality, where synthetic identities orchestrate malicious campaigns, and where targeted misinformation or scams are crafted with unparalleled precision. Welcome to the darker side of GenAI applications. This article is not just a journey through the meanders of potential misuse of GenAI and LLMs, but also a call to recognize the urgency of the challenges ahead. As we navigate the seas of misinformation campaigns, malicious content generation, and the eerie creation of sophisticated malware, we'll uncover the societal implications that ripple through the GenAI revolution we are witnessing. From AI-powered botnets on social media platforms to the unnerving potential of AI to generate fabricated identities, or alibis made of synthetic realities, the stakes have never been higher. The lines between the virtual and the real worlds are blurring, and the consequences of potential GenAI's nefarious applications impact us all. This article serves both as a synthesis of rigorous research presented on the risks of GenAI and misuse of LLMs and as a thought-provoking vision of the different types of harmful GenAI applications we might encounter in the near future, and some ways we can prepare for them.
C1 [Ferrara, Emilio] Univ Southern Calif, Thomas Lord Dept Comp Sci, Los Angeles, CA 90007 USA.
C3 University of Southern California
RP Ferrara, E (corresponding author), Univ Southern Calif, Thomas Lord Dept Comp Sci, Los Angeles, CA 90007 USA.
EM emiliofe@usc.edu
RI Ferrara, Emilio/F-6136-2012
OI Ferrara, Emilio/0000-0002-1942-2831
FU University of Southern California [HR001121C0169]; DARPA
FX Work supported in part by DARPA (contract #HR001121C0169).
CR Baeza-Yates R, 2018, COMMUN ACM, V61, P54, DOI 10.1145/3209581
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Caliskan A, 2017, SCIENCE, V356, DOI 10.1126/science.aal4230
   Cao Yunkang, 2023, arXiv
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Ferrara E., 2023, First Monday, V28
   Ferrara E, 2024, MACH LEARN APPL, V15, DOI 10.1016/j.mlwa.2024.100525
   Ferrara E, 2019, COMMUN ACM, V62, P82, DOI 10.1145/3299768
   Floridi L, 2019, NAT MACH INTELL, V1, P261, DOI 10.1038/s42256-019-0055-y
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
   Köbis N, 2021, NAT HUM BEHAV, V5, P679, DOI 10.1038/s41562-021-01128-2
   Kshetri N, 2022, COMPUTER, V55, P60, DOI 10.1109/MC.2022.3144763
   Mazurczyk W., 2024, ARXIV
   Menczer F., 2023, NAT MACH INTELL, V2023, P1
   Mozes M., 2023, arXiv, P1
   Lara MAR, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-32186-3
   Schramowski P, 2022, NAT MACH INTELL, V4, P258, DOI 10.1038/s42256-022-00458-8
   Seymour M, 2023, COMMUN ACM, V66, P56, DOI 10.1145/3584973
   Shaw A, 2023, SCI ADV, V9, DOI 10.1126/sciadv.adk2031
   Sison AJG, 2024, INT J HUM-COMPUT INT, V40, P4853, DOI 10.1080/10447318.2023.2225931
   Treleaven P., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4507244, DOI 10.2139/SSRN.4507244]
   von Ahn L, 2004, COMMUN ACM, V47, P57, DOI 10.1145/966389.966390
   Vosoughi S, 2018, SCIENCE, V359, P1146, DOI 10.1126/science.aap9559
   Yang Kai, 2023, ARXIV
   Ziems C, 2023, ARXIV
NR 29
TC 14
Z9 14
U1 17
U2 32
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
SN 2432-2717
EI 2432-2725
J9 J COMPUT SOC SCI
JI J. Comput. Soc. Sci.
PD APR
PY 2024
VL 7
IS 1
BP 549
EP 569
DI 10.1007/s42001-024-00250-1
EA FEB 2024
PG 21
WC Social Sciences, Mathematical Methods
WE Emerging Sources Citation Index (ESCI)
SC Mathematical Methods In Social Sciences
GA YI4U2
UT WOS:001169032300001
OA Green Submitted, hybrid
DA 2024-12-25
ER

PT J
AU Hsu, YC
   Ching, YH
AF Hsu, Yu-Chang
   Ching, Yu-Hui
TI Generative Artificial Intelligence in Education, Part One: the Dynamic
   Frontier
SO TECHTRENDS
LA English
DT Article
DE ChatGPT; GenAI in education; Generative artificial intelligence; OpenAI
AB Generative artificial intelligence (GenAI), such as ChatGPT, has taken the world by storm. ChatGPT attracted 1 million users in 5 days and 100 million users in 2 months since its launch in November 2022. In this first article of a two-part series, we discuss the overall dynamic frontier of GenAI, its potential uses and benefits in education, essential abilities in the age of GenAI, and the corresponding issues and concerns of this new technology. In the next article of this series, we will expand upon the discussion of the dynamic frontier of GenAI to examine various aspects related to GenAI in education in international contexts.
C1 [Hsu, Yu-Chang; Ching, Yu-Hui] Boise State Univ, Dept Educ Technol, 1910 Univ Dr,MS 1747, Boise, ID 83725 USA.
C3 Boise State University
RP Hsu, YC (corresponding author), Boise State Univ, Dept Educ Technol, 1910 Univ Dr,MS 1747, Boise, ID 83725 USA.
EM hsu@boisestate.edu
CR Altair, 2022, US
   Alves R., 2023, LEONARDO AIS GAME AS
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bozkurt A., 2023, Asian Journal of Distance Education, V18
   Chartr, 2022, CHATGPT BOT TAK TECH
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dai W, 2023, IEEE INT CONF ADV LE, P323, DOI 10.1109/ICALT58122.2023.00100
   Etherington D., 2019, MICROSOFT INVESTS 1
   Firat M., 2023, retim Teknolojisi Ve Hayat Boyu renme Dergisi-Instructional Technology and Lifelong Learning, V4, P1, DOI [10.52911/itall.1244453, DOI 10.52911/ITALL.1244453]
   Fitria T. N., 2023, ELT FORUM, V12, P44, DOI DOI 10.15294/ELT.V12I1.64069
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Koraishi O., 2023, Language Education and Technology, V3
   Kuhail MA, 2023, EDUC INF TECHNOL, V28, P973, DOI 10.1007/s10639-022-11177-3
   Lawton G., 2023, ENTERPRISE AI
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Lomas N., 2023, How to ask OpenAI for your personal data to be deleted or not used to train its AIs
   McFarland A., 2023, 8 BEST VIDEO GENERAT
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   OpenAI, 2021, DALL E CREAT IM TEXT
   Pathak A., 2023, SYNTHESIA GAME CHANG
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   SALLAM M, 2023, HEALTHCARE-BASEL, V11, DOI DOI 10.3390/HEALTHCARE11060887
   Simpson J., 2023, 5 BEST MUSIC GENERAT
   Sorribas J. E., 2018, GENERATIVE DISCRIMIN
   Surameery NMS., 2023, INT J INFORM TECHNOL, V3, P17, DOI DOI 10.55529/IJITC.31.17.22
   Temsah O, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37281
   Teubner T, 2023, BUS INFORM SYST ENG+, V65, P95, DOI 10.1007/s12599-023-00795-x
   Trust T., 2023, Contemporary Issues in Technology and Teacher Education, V23
   Warren Tom, 2023, Microsoft's Bing chatbot gets smarter with restaurant bookings, image results, and more
   Yalalov D., 2022, MIDJOURNEY DALL E AR
   Ye Y., 2023, ARXIV
NR 33
TC 25
Z9 25
U1 21
U2 153
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD JUL
PY 2023
VL 67
IS 4
BP 603
EP 607
DI 10.1007/s11528-023-00863-9
EA JUN 2023
PG 5
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA O9IH6
UT WOS:001003127400001
DA 2024-12-25
ER

PT J
AU Huynh, MT
AF Huynh, Minh-Tay
TI Using generative AI as decision-support tools: unraveling users' trust
   and AI appreciation
SO JOURNAL OF DECISION SYSTEMS
LA English
DT Article; Early Access
DE AI appreciation; generative AI; theory of consumption values; privacy
   calculus theory; privacy risks; decision-making
ID INFORMATION PRIVACY CONCERNS; CONSUMPTION VALUES;
   ARTIFICIAL-INTELLIGENCE; CALCULUS; INTENTION; TECHNOLOGY; ADOPTION;
   PERSPECTIVE; DISCLOSURE; INTERNET
AB This study examines how organisational users accept recommendations when collaborating with Generative Artificial Intelligence (GenAI) to inform decisions, balancing perceived benefits and privacy concerns. Combining the theory of consumption values and privacy calculus theory, this work develops a research model capturing the key factors driving users' trust in GenAI and AI appreciation. Structural equation modelling analysis (N = 211) reveals that functional, social, emotional, and epistemic values positively impact the perceived benefits of disclosing information for advice. Information sensitivity increases perceived privacy risks, while information control reduces this perception. Perceived benefits positively influence users' trust, while perceived risks negatively affect it. Trust in GenAI is a significant predictor of AI appreciation. This study contributes to human-AI collaboration research by illuminating a mechanism leading to users' trust and AI appreciation while addressing privacy concerns. The findings offer actionable insights for managers and organisations seeking to adopt GenAI for their decision support system.
C1 [Huynh, Minh-Tay] Free Univ, Fac Econ & Management, Piazza Univ 1, I-39100 Bolzano, Bolzano, Italy.
C3 Free University of Bozen-Bolzano
RP Huynh, MT (corresponding author), Free Univ, Fac Econ & Management, Piazza Univ 1, I-39100 Bolzano, Bolzano, Italy.
EM mhuynh@unibz.it
CR Abumalloh RA, 2024, COMPUT IND, V161, DOI 10.1016/j.compind.2024.104128
   Akbasli IT, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04447-0
   Alessandro G, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2333933
   Ali O, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102402
   Amin MAS, 2024, IND MANAGE DATA SYST, V124, P1677, DOI 10.1108/IMDS-09-2023-0608
   ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
   Araujo T, 2020, AI SOC, V35, P611, DOI 10.1007/s00146-019-00931-w
   Ayinde L., 2023, BUS INFORM REV, V40, P137, DOI [https://doi.org/10.1177/02663821231187991, DOI 10.1177/02663821231187991]
   Banihani I, 2024, J DECIS SYST, DOI 10.1080/12460125.2024.2349425
   Blau P. M., 1964, EXCHANGE POWER SOC
   Bonaccio S, 2006, ORGAN BEHAV HUM DEC, V101, P127, DOI 10.1016/j.obhdp.2006.07.001
   Boo HC, 2022, INT J CONTEMP HOSP M, V34, P4052, DOI 10.1108/IJCHM-12-2021-1471
   Burton JW, 2020, J BEHAV DECIS MAKING, V33, P220, DOI 10.1002/bdm.2155
   Chakraborty D, 2023, TECHNOVATION, V120, DOI 10.1016/j.technovation.2022.102481
   Charfeddine M, 2024, IEEE ACCESS, V12, P30263, DOI 10.1109/ACCESS.2024.3367792
   Chatterjee J, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2022.100676
   Chu MN, 2023, IEEE ACCESS, V11, P76427, DOI 10.1109/ACCESS.2023.3297447
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Chui M., 2023, QuantumBlack, AI by McKinsey
   Cong-Lem N, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2344142
   Corlatescu DG, 2024, COMPUT HUM BEHAV, V154, DOI 10.1016/j.chb.2024.108154
   Culnan MJ, 1999, ORGAN SCI, V10, P104, DOI 10.1287/orsc.10.1.104
   DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
   Diakopoulos N, 2016, COMMUN ACM, V59, P56, DOI 10.1145/2844110
   Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
   Dijkstra JJ, 1998, BEHAV INFORM TECHNOL, V17, P155, DOI 10.1080/014492998119526
   Dinev T, 2006, EUR J INFORM SYST, V15, P389, DOI 10.1057/palgrave.ejis.3000590
   Dinev T, 2013, EUR J INFORM SYST, V22, P295, DOI 10.1057/ejis.2012.23
   Duan SXX, 2022, INTERNET RES, V32, P1725, DOI 10.1108/INTR-03-2021-0160
   Duan SX, 2021, IND MANAGE DATA SYST, V121, P1599, DOI 10.1108/IMDS-12-2020-0697
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elkefi S, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2354428
   Featherman MS, 2003, INT J HUM-COMPUT ST, V59, P451, DOI 10.1016/S1071-5819(03)00111-3
   Fernandes T, 2023, J CONSUM MARK, V40, P181, DOI 10.1108/JCM-03-2021-4510
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Fox G, 2021, COMPUT HUM BEHAV, V121, DOI 10.1016/j.chb.2021.106806
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gefen D, 2005, COMMUN ASSOC INF SYS, V16, P91, DOI 10.17705/1CAIS.01605
   Gonçalves HM, 2016, J BUS RES, V69, P1484, DOI 10.1016/j.jbusres.2015.10.129
   GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Gupta P, 2024, J ENTERP INF MANAG, V37, P959, DOI 10.1108/JEIM-09-2022-0356
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
   Harborth D, 2023, COMPUT SECUR, V132, DOI 10.1016/j.cose.2023.103338
   Harman H. H., 1976, MODERN FACTOR ANAL
   HAVLENA WJ, 1986, J CONSUM RES, V13, P394, DOI 10.1086/209078
   Hegarty J, 2024, J DECIS SYST, DOI 10.1080/12460125.2024.2359163
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hermann E, 2024, J BUS RES, V180, DOI 10.1016/j.jbusres.2024.114720
   Holmstrm J., 2024, Business Horizons, DOI [10.1016/j.bushor.2024.02.010, DOI 10.1016/J.BUSHOR.2024.02.010]
   Hong WY, 2013, MIS QUART, V37, P275
   Huang K., 2024, Future of business and finance. beyond AI: ChatGPT, Web3, P297
   Humphreys D., 2024, AI and Ethics, DOI [10.1007/s43681-024-00443-4, DOI 10.1007/S43681-024-00443-4]
   Jabbar A, 2023, RES INT BUS FINANC, V64, DOI 10.1016/j.ribaf.2022.101826
   Jarrahi MH, 2018, BUS HORIZONS, V61, P577, DOI 10.1016/j.bushor.2018.03.007
   Jeong YO, 2019, COMPUT HUM BEHAV, V97, P231, DOI 10.1016/j.chb.2019.02.021
   Jozani M, 2020, COMPUT HUM BEHAV, V107, DOI 10.1016/j.chb.2020.106260
   Kang JW, 2019, INT J CONTEMP HOSP M, V31, P734, DOI [10.1108/ijchm-12-2017-0783, 10.1108/IJCHM-12-2017-0783]
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kaur P, 2021, INT J CONTEMP HOSP M, V33, P1129, DOI 10.1108/IJCHM-05-2020-0477
   Kharitonova K, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2321959
   Kim D, 2019, COMPUT HUM BEHAV, V92, P273, DOI 10.1016/j.chb.2018.11.022
   Kim JS, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2311971
   Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
   Kock N, 2012, J ASSOC INF SYST, V13, P546, DOI 10.17705/1jais.00302
   Koh L, 2023, TECHNOL SOC, V72, DOI 10.1016/j.techsoc.2023.102203
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Lamarre E., 2024, The McKinsey Quarterly
   LAUFER RS, 1977, J SOC ISSUES, V33, P22, DOI 10.1111/j.1540-4560.1977.tb01880.x
   Lee DH, 2008, PSYCHOL MARKET, V25, P692, DOI 10.1002/mar.20232
   Li H, 2016, INT J MED INFORM, V88, P8, DOI 10.1016/j.ijmedinf.2015.12.010
   Liao ZQ, 2024, ANN BIOMED ENG, V52, P125, DOI 10.1007/s10439-023-03288-w
   Lin JB, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.11.001
   Liu HF, 2021, J BUS RES, V137, P69, DOI 10.1016/j.jbusres.2021.08.018
   Liu SR, 2023, J AM MED INFORM ASSN, V30, P1237, DOI 10.1093/jamia/ocad072
   Löloff J, 2024, BEHAV INFORM TECHNOL, V43, P3407, DOI 10.1080/0144929X.2023.2276812
   Logg JM, 2019, ORGAN BEHAV HUM DEC, V151, P90, DOI 10.1016/j.obhdp.2018.12.005
   Lu CH, 2024, ELECTRON COMMER R A, V64, DOI 10.1016/j.elerap.2024.101374
   Ma XY, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102362
   Maddison Lewis., 2023, TechRadar
   Mahmud H, 2023, TECHNOL FORECAST SOC, V193, DOI 10.1016/j.techfore.2023.122641
   Mahmud H, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121390
   Malhotra NK, 2004, INFORM SYST RES, V15, P336, DOI 10.1287/isre.1040.0032
   Malodia S, 2024, IEEE T ENG MANAGE, V71, P491, DOI 10.1109/TEM.2021.3117884
   Meade AW, 2012, PSYCHOL METHODS, V17, P437, DOI 10.1037/a0028085
   Meah MR, 2024, J COMPUT INFORM SYST, DOI 10.1080/08874417.2024.2372036
   Nguyen T, 2021, J INTERNET COMMER, V20, P215, DOI 10.1080/15332861.2021.1875764
   Olson Christi, 2019, Voice report. From answers to action: customer adoption of voice technology and digital assistants
   Pal D., 2020, SN Computer Science, V1, P1, DOI DOI 10.1007/S42979-020-00287-9
   Palan S, 2018, J BEHAV EXP FINANC, V17, P22, DOI 10.1016/j.jbef.2017.12.004
   Papagiannidis S, 2022, INFORM SYST FRONT, V24, P1189, DOI 10.1007/s10796-021-10227-4
   Patel SB, 2023, LANCET DIGIT HEALTH, V5, pE107, DOI 10.1016/S2589-7500(23)00021-3
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Peer E, 2017, J EXP SOC PSYCHOL, V70, P153, DOI 10.1016/j.jesp.2017.01.006
   Picco E, 2024, COGN TECHNOL WORK, V26, P139, DOI 10.1007/s10111-023-00742-6
   Plangger K, 2020, J INTERACT MARK, V50, P32, DOI 10.1016/j.intmar.2019.10.004
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Polyportis A, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2317364
   Power DJ, 2019, J DECIS SYST, V28, P120, DOI 10.1080/12460125.2019.1623534
   Ram S., 1989, Journal of Consumer Marketing, V6, P5, DOI [10.1108/EUM0000000002542, DOI 10.1108/EUM0000000002542]
   Rauschnabel PA, 2018, J BUS RES, V92, P374, DOI 10.1016/j.jbusres.2018.08.008
   Rice S, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2023.102426
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Rohm AJ, 2004, J BUS RES, V57, P1000, DOI 10.1016/S0148-2963(02)00345-4
   Sah J, 2024, INT J HUM-COMPUT INT, V40, P3173, DOI 10.1080/10447318.2023.2184102
   Sai S, 2024, IEEE ACCESS, V12, P53497, DOI 10.1109/ACCESS.2024.3385107
   Saif N, 2024, COMPUT HUM BEHAV, V154, DOI 10.1016/j.chb.2023.108097
   Schomakers EM, 2022, INT J HUM-COMPUT INT, V38, P1276, DOI 10.1080/10447318.2021.1994211
   SCHWARTZ SH, 1992, ADV EXP SOC PSYCHOL, V25, P1, DOI 10.1016/s0065-2601(08)60281-6
   Shankar A, 2024, BUS STRATEG ENVIRON, V33, P3749, DOI 10.1002/bse.3679
   SHETH JN, 1991, J BUS RES, V22, P159, DOI 10.1016/0148-2963(91)90050-8
   Shin D, 2022, INT J INFORM MANAGE, V65, DOI 10.1016/j.ijinfomgt.2022.102494
   Shin D, 2023, J INF SCI, V49, P18, DOI 10.1177/0165551520985495
   Shin D, 2021, INT J HUM-COMPUT ST, V146, DOI 10.1016/j.ijhcs.2020.102551
   Shin D, 2020, COMPUT HUM BEHAV, V109, DOI 10.1016/j.chb.2020.106344
   Shin D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102061
   Simon H.A., 1957, Models of man: Social and rational
   SIMON HA, 1987, INTERFACES, V17, P11, DOI 10.1287/inte.17.5.11
   Singla A., 2024, The state of AI in early 2024
   Sison AJG, 2024, INT J HUM-COMPUT INT, V40, P4853, DOI 10.1080/10447318.2023.2225931
   Smith HJ, 2011, MIS QUART, V35, P989
   Sonkor MS, 2024, INT J CONSTR MANAG, DOI 10.1080/15623599.2024.2355782
   Sun YQ, 2015, COMPUT HUM BEHAV, V52, P278, DOI 10.1016/j.chb.2015.06.006
   SYAM SS, 1994, EUR J OPER RES, V73, P450, DOI 10.1016/0377-2217(94)90238-0
   Tanrikulu C, 2021, INT J CONSUM STUD, V45, P1176, DOI 10.1111/ijcs.12687
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Turel O, 2010, INFORM MANAGE-AMSTER, V47, P53, DOI 10.1016/j.im.2009.10.002
   Ullah R, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2024.102454
   Vandecasteele B, 2010, INT J RES MARK, V27, P308, DOI 10.1016/j.ijresmar.2010.08.004
   Venkatesh V, 2012, MIS QUART, V36, P157
   Vimalkumar M, 2021, COMPUT HUM BEHAV, V120, DOI 10.1016/j.chb.2021.106763
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang WS, 2023, INFORM TECHNOL PEOPL, V36, P2211, DOI 10.1108/ITP-04-2021-0293
   Wanner J, 2022, ELECTRON MARK, V32, P2079, DOI 10.1007/s12525-022-00593-5
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Wu M, 2024, J RETAIL CONSUM SERV, V81, DOI 10.1016/j.jretconser.2024.104030
   Xu CY, 2015, DECIS SUPPORT SYST, V79, P171, DOI 10.1016/j.dss.2015.08.008
   Xu H, 2012, INFORM SYST RES, V23, P1342, DOI 10.1287/isre.1120.0416
   Xu TH, 2024, J MED INTERNET RES, V26, DOI 10.2196/57896
   Yang HL, 2017, COMPUT HUM BEHAV, V73, P583, DOI 10.1016/j.chb.2017.04.018
   Yang QW, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102065
   Yilmaz FGK, 2024, INT J HUM-COMPUT INT, V40, P8703, DOI 10.1080/10447318.2023.2288730
   You S, 2022, J MANAGE INFORM SYST, V39, P336, DOI 10.1080/07421222.2022.2063553
   Zhang Q, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103302
   Zhang YF, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P295, DOI 10.1145/3351095.3372852
   Zhou T, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2376370
   Zhu YH, 2023, INTERNET RES, V33, P280, DOI 10.1108/INTR-07-2021-0454
   Zimmer JC, 2010, INFORM MANAGE-AMSTER, V47, P115, DOI 10.1016/j.im.2009.12.003
NR 151
TC 0
Z9 0
U1 8
U2 8
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1246-0125
EI 2116-7052
J9 J DECIS SYST
JI J. Decis. Syst.
PD 2024 NOV 28
PY 2024
DI 10.1080/12460125.2024.2428166
EA NOV 2024
PG 32
WC Operations Research & Management Science
WE Emerging Sources Citation Index (ESCI)
SC Operations Research & Management Science
GA N5I6E
UT WOS:001364677800001
DA 2024-12-25
ER

PT J
AU Noroozi, O
   Soleimani, S
   Farrokhnia, M
   Banihashem, SK
AF Noroozi, Omid
   Soleimani, Saba
   Farrokhnia, Mohammadreza
   Banihashem, Seyyed Kazem
TI Generative AI in Education: Pedagogical, Theoretical, and Methodological
   Perspectives
SO INTERNATIONAL JOURNAL OF TECHNOLOGY IN EDUCATION
LA English
DT Article
DE Artificial Intelligence; AI; ChatGPT; Education; Generative AI; GenAI
   Learning
ID ARTIFICIAL-INTELLIGENCE; CHATGPT; INTEGRATION; ENGLISH; IMPACT
AB Recently, ChatGPT, a cutting-edge large language model, has emerged as a powerful Generative Artificial Intelligence (GenAI) tool with the capacity to influence education. ChatGPT provides ample opportunities for learners, researchers, educators, and practitioners to achieve the intended learning outcomes in various disciplines. This special issue examines the diverse applications and implications of GenAI tools including ChatGPT in education, highlighting their potential to enhance teaching and learning across various contexts. Key findings from seventeen studies collected in this special issue demonstrate that GenAI tools can significantly improve educational outcomes by providing personalized feedback, facilitating language learning, and supporting both qualitative and quantitative research methodologies. The findings emphasize GenAI's capacity to increase learner engagement and motivation, yet also underscore the need for robust ethical guidelines and human oversight due to potential issues with privacy, bias, and accuracy. This special issue also highlights the challenges GenAI faces, such as limitations in contextual understanding and its impact on critical thinking skills. In addition, it provides a foundational framework for exploring effective and responsible GenAI integration, aiming to enrich educational experiences. We conclude that future research should focus on the longitudinal effects of GenAI tools on learning outcomes, developing ethical frameworks for their use, and ensuring their adaptability to diverse learner populations to promote inclusive educational practices.
C1 [Noroozi, Omid] Wageningen Univ & Res, Wageningen, Netherlands.
   [Soleimani, Saba; Farrokhnia, Mohammadreza] Univ Twente, Enschede, Netherlands.
   [Banihashem, Seyyed Kazem] Open Univ, Heerlen, Netherlands.
C3 Wageningen University & Research; University of Twente; Open University
   Netherlands
RP Noroozi, O (corresponding author), Wageningen Univ & Res, Wageningen, Netherlands.
EM omid.noroozi@wur.nl
RI Farrokhnia, Mohammadreza/AAP-5863-2020; Nouroozi, Omid/AAO-3416-2021
OI Farrokhnia, Mohammadreza/0000-0002-0150-5372; Noroozi,
   Omid/0000-0002-0622-289X; Banihashem, Seyyed Kazem/0000-0002-9978-3783;
   Soleimani, Saba/0009-0001-9272-8327
CR Adeyele VO, 2024, INT J TECHNOL EDUC, V7, P200, DOI 10.46328/ijte.638
   Akgun Selin, 2022, AI Ethics, V2, P431, DOI 10.1007/s43681-021-00096-7
   Banihashem SK, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00455-4
   Banihashem SK, 2022, EDUC RES REV-NETH, V37, DOI 10.1016/j.edurev.2022.100489
   Bayat M, 2022, J EDUC RES, DOI 10.1080/00220671.2022.2155602
   Bond M, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-023-00436-z
   Das SR, 2024, INT J TECHNOL EDUC, V7, P86, DOI 10.46328/ijte.583
   Famaye T, 2024, INT J TECHNOL EDUC, V7, P174, DOI 10.46328/ijte.651
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   García-Peñalvo FJ, 2023, EDUC KNOWL SOC, V24, DOI 10.14201/eks.31279
   Giray L, 2024, INT J TECHNOL EDUC, V7, P40, DOI 10.46328/ijte.618
   Gökcearslan S, 2024, INT J TECHNOL EDUC, V7, P19, DOI 10.46328/ijte.600
   González-Calatayud V, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125467
   Hammoda B, 2024, INT J TECHNOL EDUC, V7, P154, DOI 10.46328/ijte.530
   Haro AV, 2022, J CONSTR PSYCHOL, V35, P123, DOI 10.1080/10720537.2020.1734995
   Haro AV, 2019, TECHNOL PEDAGOG EDUC, V28, P329, DOI 10.1080/1475939X.2019.1612772
   Harry A., 2023, Interdiciplinary Journal and Humanity (INJURITY), V2, P260, DOI [10.58631/injurity.v2i3.52, DOI 10.58631/INJURITY.V2I3.52]
   Holmes W, 2022, INT J ARTIF INTELL E, V32, P504, DOI 10.1007/s40593-021-00239-1
   Huang XD, 2021, EDUC INF TECHNOL, V26, P5127, DOI 10.1007/s10639-021-10530-2
   Jones P., 2023, Journal of Artificial Intelligence, Machine Learning and Data Science, V1, P50
   Kadaruddin K., 2023, International Journal of Business, Law, and Education, V4, P618, DOI DOI 10.56442/IJBLE.V4I2.215
   Karaman MR, 2024, INT J TECHNOL EDUC, V7, P107, DOI 10.46328/ijte.607
   Kim D, 2024, INT J TECHNOL EDUC, V7, P259, DOI 10.46328/ijte.691
   Kurni M., 2023, A Beginner's Guide to Introduce Artificial Intelligence in Teaching and Learning, P213, DOI [10.1007/978-3-031-32653-012, DOI 10.1007/978-3-031-32653-012]
   Mabuan RA, 2024, INT J TECHNOL EDUC, V7, P128, DOI 10.46328/ijte.523
   Maghsudi S, 2021, IEEE SIGNAL PROC MAG, V38, P37, DOI 10.1109/MSP.2021.3055032
   McGuire A, 2024, INT J TECHNOL EDUC, V7, P326, DOI 10.46328/ijte.639
   Mutammimah H, 2024, INT J TECHNOL EDUC, V7, P290, DOI 10.46328/ijte.656
   Noroozi O, 2019, COMPUT HUM BEHAV, V100, P298, DOI 10.1016/j.chb.2018.12.019
   Noroozi O, 2018, EDUC PSYCHOL REV, V30, P153, DOI 10.1007/s10648-017-9400-z
   Noroozi O, 2012, EDUC RES REV-NETH, V7, P79, DOI 10.1016/j.edurev.2011.11.006
   Polat H, 2024, INT J TECHNOL EDUC, V7, P59, DOI 10.46328/ijte.606
   Putjorn Tat, 2023, 15 INT C INF TECHN E, P353, DOI [10.1109/icitee59582.2023.10317680, DOI 10.1109/ICITEE59582.2023.10317680]
   Rahimi M, 2024, INT J TECHNOL EDUC, V7, P239, DOI 10.46328/ijte.741
   Sapkota B, 2024, INT J TECHNOL EDUC, V7, P218, DOI 10.46328/ijte.677
   Solak E, 2024, INT J TECHNOL EDUC, V7, P353, DOI 10.46328/ijte.732
   Steiss J, 2024, LEARN INSTR, V91, DOI 10.1016/j.learninstruc.2024.101894
   Tapalova O, 2022, ELECTRON J E-LEARN, V20, P639
   Theelen H, 2024, INT J TECHNOL EDUC, V7, P1, DOI 10.46328/ijte.537
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Yurt E, 2024, INT J TECHNOL EDUC, V7, P308, DOI 10.46328/ijte.725
NR 41
TC 5
Z9 5
U1 82
U2 82
PU INT SOC TECHNOLOGY EDUCATION & SCIENCE-ISTES
PI MONUMENT
PA 19723 LINDENMERE DR, MONUMENT, COLORADO, UNITED STATES
EI 2689-2758
J9 INT J TECHNOL EDUC
JI Int. J. Technol. Educ.
PY 2024
VL 7
IS 3
BP 373
EP 385
DI 10.46328/ijte.845
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA XE3F0
UT WOS:001259960900001
OA gold
DA 2024-12-25
ER

PT J
AU Bi, XM
   Su, XY
   Liu, XY
AF Bi, Xiaomei
   Su, Xingyuan
   Liu, Xiaoyan
TI An Ellulian analysis of propaganda in the context of generative AI
SO ETHICS AND INFORMATION TECHNOLOGY
LA English
DT Article
DE Jacques Ellul; Propaganda; Generative artificial intelligence;
   Technique; Ethics
AB The application of generative artificial intelligence (GenAI) technologies in the field of propaganda influences information creation, dissemination, and reception, and introduces new ethical challenges. This paper revisits the philosophical discourses of Jacques Ellul on technology and propaganda, placing them within the context of the rise of today's generative AI technologies. Ellul identified the First Industrial Revolution as the initial juncture in the history of human technology that formed technique as a social phenomenon, which subsequently shaped the nature of propaganda as a technique. Subsequent developments in computer technology in the latter half of the 20th century enabled the formation of a technological system. This raises the question: Could generative AI represent another pivotal moment in the evolution of the technological system, and what are the ethical implications of propaganda technology in this context? This article seeks to illuminate current discussions on GenAI technology and propaganda ethics with Ellul's insightful theoretical insights. In terms of research methodology, this study relies on textual interpretation and classical hermeneutics, including three processes: syntactic text interpretation, historical background, and situational application. Ellul's research delves into the intrinsic links and inherent ethical dimensions between propaganda and technology, examining their comprehensive and enduring impacts. This normative perspective is crucial for a deep understanding of contemporary propaganda within the framework of emerging technologies, helping us to transcend the escalating spiral of propaganda technology and counter-propaganda techniques. By incorporating propaganda facilitated by generative AI technologies into the overall development logic of the technological society, this approach explores its ethical implications from a more macroscopic and holistic perspective.
C1 [Bi, Xiaomei] East China Univ Polit Sci & Law, Sch Commun, Shanghai 201620, Peoples R China.
   [Su, Xingyuan; Liu, Xiaoyan] Beijing Jiaotong Univ, Sch Languages & Commun Studies, Beijing 100044, Peoples R China.
C3 East China University Political Science & Law; Beijing Jiaotong
   University
RP Liu, XY (corresponding author), Beijing Jiaotong Univ, Sch Languages & Commun Studies, Beijing 100044, Peoples R China.
EM 2908@ecupl.edu.cn; 23121643@bjtu.edu.cn; lxystella@126.com
FU Shanghai Planning Office of Philosophy and Social Scienc
FX No Statement Available
CR Altheide D.L., 1980, BUREAUCRATIC PROPAGA
   Amodei D, 2016, Arxiv, DOI arXiv:1606.06565
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Bergman S, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-56648-4
   Bogart L., 1976, Free, DOI [10.1086/268346, DOI 10.1086/268346]
   Borau S, 2021, PSYCHOL MARKET, V38, P1052, DOI 10.1002/mar.21480
   CASIA, 2023, Launch of the Wise Goodness - Observe and Act AI Big Model E-thical Safety Observatory
   CDRF, 2022, How do we address the ethical challenges posed by AI
   Cunningham Stanley B., 1992, Communication Studies, V43, P233, DOI DOI 10.1080/10510979209368375
   Dhariwal P, 2020, Arxiv, DOI arXiv:2005.00341
   ELLUL J, 1962, TECHNOL CULT, V3, P394, DOI 10.2307/3100993
   ELLUL J, 1981, COMMUNICATION, V6, P159
   Ellul J., 2004, PERSPECTIVES OUR AGE
   Ellul J., 1965, Propaganda: The format-ion of Men's Attitudes
   Ellul J., 1980, TECHNOLOGICAL SYSTEM
   Ellul J., 1967, POLITICAL ILLUSION
   Ellul Jacques., 1989, PRESENCE KINGDOM
   Ellul Jacques., 1964, TECHNOLOGICAL SOC
   Ellul Jacques., 1990, The Technological Bluff
   Ellul Jacques., 1973, Propaganda: The Formation of Men's Attitudes
   Feldstein S, 2023, SURVIVAL, V65, P117, DOI 10.1080/00396338.2023.2261260
   Ferkiss V., 1970, Technological man: The myth and the reality, P37
   Francois Camille, 2020, Algorithms
   Funk Alice, 2023, FREEDOM NET 2023 REP
   Gabriel I, 2020, MIND MACH, V30, P411, DOI 10.1007/s11023-020-09539-2
   Goldstein J. A., 2023, PREPRINT, DOI DOI 10.48550/ARXIV.2301.04246
   Hui Y., 2013, Jacques Ellul and the Technological Society in the 21st Century, DOI [10.1007/978-94-007-6658-76, DOI 10.1007/978-94-007-6658-76]
   Kierkegaard Soren., 1962, THE PRESENT AGE
   Lasswell H. D., 1948, COMMUNICATION IDEAS, P37
   Lee A.M., 1939, FINE ART PROPAGANDA
   Lovekin D., 1991, TECHNIQUE DISCOURSE
   Lv Z., 2023, Cogn. Robot., V3, P208, DOI [10.1016/j.cogr.2023.06.001, DOI 10.1016/J.COGR.2023.06.001]
   Matthias A., 2004, Ethics and Information Technology, V6, P175, DOI 10.1007/s10676-004-3422-1
   Maynard A., 2022, Conduc-ting Socially Responsible and Ethical Counter Influence Operations Research
   MENNINGER D, 1981, POLITY, V14, P110, DOI 10.2307/3234498
   Mitcham C., 2013, Jacques Ellul and the Technological Society in the 21st Century, V13, P17, DOI [10.1007/978-94-007-6658-72, DOI 10.1007/978-94-007-6658-72]
   Moor JH, 2006, IEEE INTELL SYST, V21, P18, DOI 10.1109/MIS.2006.80
   Ng V., 2023, P AAAI C ART INT, V37, P15368, DOI [10.1609/aaai.v37i13.26792, DOI 10.1609/AAAI.V37I13.26792]
   ODonnell, 1992, Propaganda and persu-asion, P116
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Park A, 2023, IT PROF, V25, P13, DOI 10.1109/MITP.2023.3340529
   Radford A, 2021, PR MACH LEARN RES, V139
   Rosenberg L., 2023, P 17 INT MULT SOC CY, DOI [10.54808/IMSCI2023.01.165, DOI 10.54808/IMSCI2023.01.165]
   Smith S., 2022, arXiv, DOI 10.48550/arXiv.2201.11990
   Soufi D., 2024, El Pais
   Sun Y, 2021, Arxiv, DOI arXiv:2107.02137
   Sutrop M., 2020, Acta Baltica Historiae Et Philosophiae Scientiarum, V8, P54, DOI [https://doi.org/10.11590/abhps.2020.2.04, DOI 10.11590/ABHPS.2020.2.04]
   TAYLOR W, 1964, J HIGH EDUC, V35, P294, DOI 10.2307/1978782
   Van Vleet J.E., 2014, DIALECTICAL THEOLOGY
   van Wynsberghe A., 2021, AI Ethics, V1, P213, DOI [10.1007/s43681-021-00043-6, DOI 10.1007/S43681-021-00043-6, DOI 10.1007/S43681-021-00043]
   Rae JW, 2022, Arxiv, DOI [arXiv:2112.11446, DOI 10.48550/ARXIV.2112.11446]
   Wallach W., 2008, MORAL MACHINES TEACH
   Wang J., 2017, eflux Journal, V87
   Weidinger L, 2021, Arxiv, DOI arXiv:2112.04359
   [严昊 Yan Hao], 2023, [中国图象图形学报, Journal of Image and Graphics], V28, P2749
   Zlateva P., 2024, Electronics, communications and networks, P110
NR 56
TC 0
Z9 0
U1 11
U2 11
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1388-1957
EI 1572-8439
J9 ETHICS INF TECHNOL
JI Ethics Inf. Technol.
PD SEP
PY 2024
VL 26
IS 3
AR 60
DI 10.1007/s10676-024-09776-4
PG 11
WC Ethics; Information Science & Library Science; Philosophy
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Social Sciences - Other Topics; Information Science & Library Science;
   Philosophy
GA E6I1P
UT WOS:001304011800001
DA 2024-12-25
ER

PT J
AU Rice, M
   DePascal, N
   de Jesús, JTA
   McFeely, H
   Traylor, A
   Heaviland, L
AF Rice, Mary
   DePascal, Nicholas
   de Jesus, Joaquin T. Argueello
   McFeely, Helen
   Traylor, Amy
   Heaviland, Lehman
TI <i>What</i> is the machine? Teachers' professional learning about
   generative artificial intelligence as tutors for children
SO PROFESSIONAL DEVELOPMENT IN EDUCATION
LA English
DT Article; Early Access
DE Critical posthumanism; discourse analysis; Freirean dialogue; generative
   AI; Illich's conviviality; professional learning for technologies
ID EDUCATION; AI
AB With the introduction of artificial intelligence (AI), particularly Generative AI (GenAI) to school settings, teachers are likely to be drawn into professional learning scenarios where they will be expected to learn how to use programs and applications for remediation and tutoring of children. Previous research highlights how professional learning for teachers about GenAI has been focused on gaining their acquiescence to using GenAI by arguing that it is both superior and inescapable. This critical posthuman work uses a professional learning scenario to ask questions about the historically present challenges of asking teachers to use GenAI as a universal good when this discourse has been used to colonise and imperialise in schools, particularly with technologies. Our critical posthuman diffractive reading revealed alternative encounters that teachers and students could have with GenAI and reasonable pathways for avoiding it. Implications of this work include a need to draw on the diffractive encounters presented here to understand and address who and what the machine is when it comes to adopting advanced technologies for various purposes in schools.
C1 [Rice, Mary; DePascal, Nicholas; de Jesus, Joaquin T. Argueello; McFeely, Helen; Traylor, Amy; Heaviland, Lehman] Univ New Mexico, Language Literacy & Sociocultural Studies, 222 Hokona Hall MSC05 3040, Albuquerque, NM 87131 USA.
C3 University of New Mexico
RP Rice, M (corresponding author), Univ New Mexico, Language Literacy & Sociocultural Studies, 222 Hokona Hall MSC05 3040, Albuquerque, NM 87131 USA.
EM maryrice@unm.edu
OI Traylor, Amy/0009-0004-5390-4385; Rice, Mary F./0000-0002-8138-512X
CR Arnesen K.T., 2019, J ONLINE LEARNING RE, V5, P251
   Arvin Maile., 2013, FEMINIST FORMATIONS, V25, P8, DOI [DOI 10.1353/FF.2013.0006, 10.1353/ff.2013.0006]
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Barad K., 2007, M UNIVERSE HALFWAY
   Barad Karen., 1996, FEMINISM SCI PHILOS, P161
   BARR M, 1987, WOMEN STUD, V14, P187, DOI 10.1080/00497878.1987.9978697
   Benjamin R, 2019, CAPTIVATING TECHNOLOGY, P1
   Blain L., 2024, GPT-4 is 82% more persuasive than humans, and AIs can now read emotions
   Bonilla-Silva E., 2021, RACISM RACISTS COLOR
   Boninger F., 2019, National education policy center
   Bower M, 2024, EDUC INF TECHNOL, V29, P15403, DOI 10.1007/s10639-023-12405-0
   Braidotti R., 2002, Between feminism and psychoanalysis (pp. 89-105)
   Braidotti R, 2019, THEOR CULT SOC, V36, P31, DOI 10.1177/0263276418771486
   Brassington L., 2024, Revolutionizing English education: the power of AI in the classroom, P125
   Brayboy B.M. J., 2005, The Urban Review, V37, P425, DOI [10.1007/s11256-005-0018-y, https://doi.org/10.1007/s11256-005-0018-y, DOI 10.1007/S11256-005-0018-Y]
   Broussard M, 2018, ARTIFICIAL UNINTELLIGENCE: HOW COMPUTERS MISUNDERSTAND THE WORLD
   Bullough R.V., 2001, ED RES, V30, P13, DOI [10.3102/0013189X030003013, DOI 10.3102/0013189X030003013]
   Caine V, 2017, QUAL INQ, V23, P215, DOI 10.1177/1077800416643997
   Cajete G., 2000, NATIVE SCI NATURAL L
   Calderón D, 2012, HARVARD EDUC REV, V82, P513, DOI 10.17763/haer.82.4.l518621577461p68
   Carr LeslieG., 1997, Colorblind Racism
   Chambers DW, 2019, J PHILOS EDUC, V53, P21, DOI 10.1111/1467-9752.12340
   CLANDININ DJ, 1989, CURRICULUM INQ, V19, P121, DOI 10.2307/1179405
   Clandinin J., 1986, TEACH TEACH EDUC, V2, P377, DOI DOI 10.1016/0742-051X(86)90030-2
   Coffey D., 2024, Teen
   Crawford K., 2022, Atlas of AI
   Creed Barbara., 1993, The Monstrous Feminine: Film, Feminism, Psychoanalysis
   DAVIES B, 1990, J THEOR SOC BEHAV, V20, P43, DOI 10.1111/j.1468-5914.1990.tb00174.x
   DiMaggio PJ, 2000, ADV STRATEG MANAGE, V 17, P143, DOI 10.2307/2095101
   Evans W, 2021, EDUC SCI, V11, DOI 10.3390/educsci11060281
   Fiebrink Rebecca., 2019, Wekinator
   Forbes JackD., 2008, COLUMBUS OTHER CANNI
   Fox L., 2014, Effects of technology on literacy skills and motivation to read and write
   Fox N.J., 2017, Sociology and the New Materialism
   Freire P., 2008, Education for critical consciousness
   Freire P., 2021, Pedagogy in Process: The Letters to Guinea-Bissau
   Freire P., 2021, Pedagogy of hope: Reliving pedagogy of the oppressed
   Freire P., 1972, PEDAGOGY OPPRESSED
   Gadotti M, 2009, DEV CHANGE, V40, P1255, DOI 10.1111/j.1467-7660.2009.01606.x
   Goldweber M., 2015, ACM SIGCAS computers and society, V45, P29, DOI [https://doi.org/10.1145/2809957.2809963, DOI 10.1145/2809957.2809963]
   Gonzalez-Barahona JM, 2021, COMPUTER, V54, P75, DOI 10.1109/MC.2020.3041887
   Haaland D., 2022, Interior secretary Haaland remarks on abuse of native American children
   Harari YN  ..., 2024, Nexus: A brief history of information networks for the stone age to AI
   Haraway D.J., 1992, Science as Culture, V3, P64, DOI DOI 10.1080/09505439209526336
   Haraway Donna, 2016, A/B: Auto/Biography studies, DOI DOI 10.1080/08989575.2019.1664163
   Hodges CB, 2024, TECHTRENDS, V68, P195, DOI 10.1007/s11528-023-00926-x
   Illich Ivan., 1975, Tools for Conviviality
   Jackson ZI, 2020, Sex Cul, P1, DOI 10.18574/nyu/9781479890040.001.0001
   Jandri P., 2021, Freirean echoes: scholars and practitioners dialogue on critical ideas in education, V1st, P91
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kimmerer R. W., 2020, BRAIDING SWEETGRASS
   Knox J, 2013, OPEN PRAX, V5, P21, DOI 10.5944/openpraxis.5.1.36
   Kristeva Julia., 1980, DESIRE LANGUAGE
   Lee E, 2022, J MED HUMANIT, V43, P523, DOI 10.1007/s10912-021-09722-1
   Lim A, 2024, REFLECT PRACT, V25, P391, DOI 10.1080/14623943.2024.2321227
   Lu JJ, 2024, IEEE T LEARN TECHNOL, V17, P1279, DOI 10.1109/TLT.2024.3369690
   Merten Kai., 2021, Diffractive Reading. New Materialism, Theory
   Mila, FLAIR initiative
   Mills C., 1997, RACIAL CONTRACT
   MISHLER EG, 1990, HARVARD EDUC REV, V60, P415, DOI 10.17763/haer.60.4.n4405243p6635752
   Mohanty CT., 2003, Feminism without Borders, DOI 10.1515/9780822384649
   Morris MR, 2020, COMMUN ACM, V63, P35, DOI 10.1145/3356727
   Mulcahy D, 2011, EDUC PHILOS THEORY, V43, P94, DOI 10.1111/j.1469-5812.2009.00617.x
   Nazaretsky T, 2022, BRIT J EDUC TECHNOL, V53, P914, DOI 10.1111/bjet.13232
   Powell A., 2024, AI is expensive
   Ricaurte P, 2024, BIG DATA SOC, V11, DOI 10.1177/20539517241229697
   Rice M, 2024, LEARN MEDIA TECHNOL, V49, P755, DOI 10.1080/17439884.2024.2394471
   Rillig MC, 2023, ENVIRON SCI TECHNOL, DOI 10.1021/acs.est.3c01106
   Smith A., 2012, Racial Formation in the Twenty-First Century, P66, DOI DOI 10.1525/9780520953765
   Strom K, 2021, PROF DEV EDUC, V47, P199, DOI 10.1080/19415257.2021.1901005
   The White House, 2023, President Biden issues executive order on safe, secure, and trustworthy artificial intelligence
   Tsing A.Et Al., 2017, Arts of Living on a Damaged Planet
   United Nations System Chief Executives Board for Coordination, 2022, Addendum. principles of the ethical use of artificial intelligence in the united nations system
   Van Langenhove L., 2017, FRONT SOCIOL, DOI 10.3389/fsoc.2017.00009
   Velander J, 2024, EDUC INF TECHNOL, V29, P4085, DOI 10.1007/s10639-023-11990-4
   Wilson E.O., 1975, P1
   Ziarek EP, 2022, POSTMOD CULT, V32, DOI 10.1353/pmc.2022.0002
NR 77
TC 2
Z9 2
U1 13
U2 13
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1941-5257
EI 1941-5265
J9 PROF DEV EDUC
JI Prof. Dev. Educ.
PD 2024 SEP 29
PY 2024
DI 10.1080/19415257.2024.2407413
EA SEP 2024
PG 16
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA H6R9V
UT WOS:001324705900001
DA 2024-12-25
ER

PT J
AU Singh, K
   Chatterjee, S
   Mariani, M
AF Singh, Kuldeep
   Chatterjee, Sheshadri
   Mariani, Marcello
TI Applications of generative AI and future organizational performance: The
   mediating role of explorative and exploitative innovation and the
   moderating role of ethical dilemmas and environmental dynamism
SO TECHNOVATION
LA English
DT Article
DE Generative AI; Organization future performance; Exploratory and
   exploitative innovation; Environmental dynamism; Ethics
ID PLS-SEM; ARTIFICIAL-INTELLIGENCE; SYSTEMS; DESIGN; BIAS
AB Generative Artificial Intelligence (GenAI) is one of the popular AI technologies which can produce multiple kinds of contents including music, text, image, as well as synthetic data. As GenAI technology can produce various forms of contents, organizations must face ethical dilemmas as to where this technology is likely to be used. Organizations do not want to compromise their ethical standards and compliance policies. Against this backdrop, the aim of this study is to examine if GenAI technology could improve the future performance of the organizations. This study deployed ethical dilemmas and environmental dynamism as two moderators acting on different linkages between adoption of GenAI and organizational future performance. With the help of literature review and theories, a theoretical model has been developed conceptually which was validated using PLS-SEM technique with the feedback of 326 responses from different types of organizations. This study found that the adoption of GenAI could improve exploratory and exploitative innovation under the moderating effects of environmental dynamism and ethical dilemmas. Moreover, it highlighted that the application of GenAI could improve organizational performance.
C1 [Singh, Kuldeep] Gati Shakti Vishwavidyalaya, Sch Management, Vadodara 390004, Gujarat, India.
   [Chatterjee, Sheshadri] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur, W Bengal, India.
   [Mariani, Marcello] Univ Reading, Henley Business Sch, Reading, England.
   [Mariani, Marcello] Univ Bologna, Bologna, Italy.
C3 Indian Institute of Technology System (IIT System); Indian Institute of
   Technology (IIT) - Kharagpur; University of Reading; University of
   Bologna
RP Mariani, M (corresponding author), Univ Reading, Henley Business Sch, Reading, England.; Mariani, M (corresponding author), Univ Bologna, Bologna, Italy.
EM kuldeepsinghcsr@gmail.com; sheshadri.academic@gmail.com;
   m.mariani@henley.ac.uk
RI Chatterjee, Sheshadri/AAN-9917-2020; Mariani, Marcello/ABW-5250-2022;
   Singh, Kuldeep/K-7517-2018
OI Singh, Kuldeep/0000-0002-8180-4646; Chatterjee,
   Sheshadri/0000-0003-1075-5549; MARIANI, MARCELLO/0000-0002-7916-2576
CR Abrokwah-Larbi K, 2023, Industrial Artificial Intelligence, V1, P11
   Agnese J, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1345
   Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   AL-khatib AW, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102403
   Al-Surmi A, 2022, INT J PROD RES, V60, P4464, DOI 10.1080/00207543.2021.1966540
   ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
   Baabdullah AM, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122951
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   Birkinshaw J.M., 2020, the future of management in an AI world, P23, DOI DOI 10.1007/978-3-030-20680-22
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Chatterjee S, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100334
   Chatterjee S, 2022, J BUS RES, V153, P46, DOI 10.1016/j.jbusres.2022.08.019
   Chatterjee S, 2022, J BUS RES, V150, P437, DOI 10.1016/j.jbusres.2022.06.033
   Chatterjee S, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2021.101621
   Chatterjee S, 2024, IEEE T ENG MANAGE, V71, P10373, DOI 10.1109/TEM.2021.3134188
   Chatterjee S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132112134
   Chatterjee S, 2024, INFORM SYST FRONT, V26, P121, DOI 10.1007/s10796-021-10197-7
   Chatterjee S, 2022, INT J ORGAN ANAL, V30, P1595, DOI 10.1108/IJOA-02-2021-2627
   Chatterjee S, 2021, INFORM TECHNOL PEOPL, V34, P1800, DOI 10.1108/ITP-05-2020-0267
   Chatterjee S, 2019, INT J ELECTRON GOV R, V15, P19, DOI 10.4018/IJEGR.2019040102
   Chaudhuri R, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141912760
   Chaudhuri R, 2023, J INTELLECT CAP, V24, P283, DOI 10.1108/JIC-07-2021-0177
   Chaudhuri R, 2023, EUROMED J BUS, V18, P467, DOI 10.1108/EMJB-11-2021-0181
   Chaudhuri R, 2023, J FAM BUS MANAG, V13, P46, DOI 10.1108/JFBM-12-2021-0153
   Chen LQ, 2019, J VIS COMMUN IMAGE R, V61, P10, DOI 10.1016/j.jvcir.2019.02.009
   Chen LJ, 2022, J BUS IND MARK, V37, P1025, DOI 10.1108/JBIM-09-2020-0448
   Chen YS, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102232
   Chen YJ, 2021, J PROD INNOVAT MANAG, V38, P574, DOI 10.1111/jpim.12595
   Cooper GA, 1998, TECHNOL CULT, V39, P161, DOI 10.2307/3107028
   Cordasco C, 2021, TECHNOVATION, V107, DOI 10.1016/j.technovation.2021.102272
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Deja M, 2013, J INTELL MANUF, V24, P831, DOI 10.1007/s10845-012-0633-x
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Dubey R, 2020, INT J PROD ECON, V226, DOI 10.1016/j.ijpe.2019.107599
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Enkel E, 2017, TECHNOVATION, V60-61, P29, DOI 10.1016/j.technovation.2016.08.002
   Ford J, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114124
   Garbuio M, 2021, J PROD INNOVAT MANAG, V38, P701, DOI 10.1111/jpim.12602
   Gonzalez-Rodriguez D, 2018, FUTURES, V103, P27, DOI 10.1016/j.futures.2018.03.003
   Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Hsu YC, 2023, TECHTRENDS, V67, P603, DOI 10.1007/s11528-023-00863-9
   Huang SL, 2014, CREAT INNOV MANAG, V23, P453, DOI 10.1111/caim.12085
   Jansen JJP, 2006, MANAGE SCI, V52, P1661, DOI 10.1287/mnsc.1060.0576
   Jansen JJP, 2009, LEADERSHIP QUART, V20, P5, DOI 10.1016/j.leaqua.2008.11.008
   Jiang JF, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095648
   Just J, 2024, TECHNOVATION, V129, DOI 10.1016/j.technovation.2023.102883
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kemp A, 2024, ACAD MANAGE REV, V49, P618, DOI 10.5465/amr.2020.0205
   Ketokivi MA, 2004, J OPER MANAG, V22, P247, DOI 10.1016/j.jom.2002.07.001
   Kock Florian, 2021, Tourism Management, V86, DOI 10.1016/j.tourman.2021.104330
   Kock N., 2015, WarpPLS 5.0 user manual
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Lewis SC, 2019, DIGIT JOURNAL, V7, P409, DOI 10.1080/21670811.2019.1577147
   Li SY, 2021, IND MARKET MANAG, V98, P105, DOI 10.1016/j.indmarman.2021.07.015
   Limaj E, 2019, J BUS RES, V94, P137, DOI 10.1016/j.jbusres.2017.10.052
   Lindell MK, 2001, J APPL PSYCHOL, V86, P114, DOI 10.1037//0021-9010.86.1.114
   Long Y, 2021, KYBERNETES, V50, P3246, DOI 10.1108/K-07-2020-0479
   Macdonald C, 2023, J GLOB HEALTH, V13, DOI 10.7189/jogh.13.01003
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Mariani M, 2023, INT J CONTEMP HOSP M, V35, P2929, DOI 10.1108/IJCHM-08-2022-1006
   Mariani MM, 2023, J BUS RES, V161, DOI 10.1016/j.jbusres.2023.113838
   Mariani MM, 2024, INT J PROD RES, V62, P5400, DOI 10.1080/00207543.2022.2160027
   Mariani MM, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113364
   Mariani MM, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102623
   Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Nayak B, 2023, J BUS IND MARK, V38, P656, DOI 10.1108/JBIM-06-2021-0306
   O'Connor GC, 2006, J PROD INNOVAT MANAG, V23, P475, DOI 10.1111/j.1540-5885.2006.00219.x
   Olan F, 2022, J BUS RES, V145, P605, DOI 10.1016/j.jbusres.2022.03.008
   Oniani D, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00965-x
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Ringle CM, 2023, DATA BRIEF, V48, DOI 10.1016/j.dib.2023.109074
   Rogers E., 1995, Diffusion of innovations
   Sarstedt M, 2020, INT J MARKET RES, V62, P288, DOI 10.1177/1470785320915686
   Saw S.K., 2022, Int. J. Sustain. Strat. Manag., V10, P78
   Segers S, 2024, INT J ETHICS EDUC, V9, P57, DOI 10.1007/s40889-023-00179-5
   Singh N, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122967
   Srivastava RK, 2001, J MANAGE, V27, P777, DOI 10.1016/S0149-2063(01)00123-4
   van Dun C, 2023, DECIS SUPPORT SYST, V165, DOI 10.1016/j.dss.2022.113880
   Vartiainen H, 2023, DIGIT CREAT, V34, P1, DOI 10.1080/14626268.2023.2174557
   Verma S, 2024, J SCI TECHNOL POLICY, V15, P1294, DOI 10.1108/JSTPM-05-2022-0090
   Vrontis D, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14137949
   Vrontis D, 2022, EUR MANAG J, V40, P151, DOI 10.1016/j.emj.2022.01.007
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wamba SF, 2020, INT J PROD ECON, V222, DOI 10.1016/j.ijpe.2019.09.019
   Wamba-Taguimdje SL, 2020, BUS PROCESS MANAG J, V26, P1893, DOI 10.1108/BPMJ-10-2019-0411
   [王程 Wang Cheng], 2020, [中国图象图形学报, Journal of Image and Graphics], V25, P19
   Wang HC, 2023, NATURE, V620, P47, DOI 10.1038/s41586-023-06221-2
   Wank AA, 2021, MEM COGNITION, V49, P422, DOI 10.3758/s13421-020-01098-2
   Xie XM, 2021, J BUS RES, V124, P299, DOI 10.1016/j.jbusres.2020.11.058
   Yan H, 2023, IEEE T MULTIMEDIA, V25, P2323, DOI [10.1109/TCSS.2022.3161996, 10.1109/TMM.2022.3146010]
   Zhang ZG, 2020, TECHNOL ANAL STRATEG, V32, P666, DOI 10.1080/09537325.2019.1693534
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 100
TC 8
Z9 8
U1 190
U2 230
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD MAY
PY 2024
VL 133
AR 103021
DI 10.1016/j.technovation.2024.103021
EA APR 2024
PG 13
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA QM2Z5
UT WOS:001221238600001
OA hybrid
DA 2024-12-25
ER

PT J
AU Orhan, A
   Yildiz, TA
   Yagci, SÇ
AF Orhan, Ali
   Yildiz, Tugba Aydin
   Yagci, Sule Cinar
TI Assessing EFL learners' attitudes on Generative Artificial Intelligence:
   Development and validation of Generative Artificial Intelligence
   attitude scale for EFL learners (GenAIAS)
SO JOURNAL OF RESEARCH ON TECHNOLOGY IN EDUCATION
LA English
DT Article; Early Access
DE Generative artificial intelligence; EFL; scale development; attitude;
   psychometric qualities
ID FIT INDEXES; ACCEPTANCE; INTERNET; GENDER
AB Generative Artificial Intelligence (GenAI) has emerged as a transformative force in education, particularly in the context of English as a Foreign Language (EFL) instruction. This study aims to develop and validate the Generative Artificial Intelligence Attitude Scale (GenAIAS) to assess EFL learners' attitudes toward GenAI. The research involved two independent samples of university students in T & uuml;rkiye, with data collected through scales and analyzed using exploratory and confirmatory factor analyses. The final scale, comprising 33 items, was categorized into four factors: Learning/Utility, Enjoyment, Usefulness, and Interest. The scale demonstrated good psychometric properties, including satisfactory convergent and discriminant validity, internal consistency, and measurement invariance across genders. The findings indicate no significant gender differences in attitudes toward GenAI, suggesting a general acceptance of GenAI technologies among EFL learners. Additionally, a positive correlation was observed between daily internet usage and attitudes toward GenAI. This study contributes to the growing body of knowledge on GenAI in language education and offers practical implications for integrating GenAI into language learning, highlighting the importance of fostering positive attitudes for successful technology adoption. Future research should further validate the scale with diverse populations to enhance its reliability and applicability.
C1 [Orhan, Ali; Yagci, Sule Cinar] Zonguldak Bulent Ecevit Univ, Sch Foreign Languages, Zonguldak, Turkiye.
   [Yildiz, Tugba Aydin] Zonguldak Bulent Ecevit Univ, Fac Educ, Zonguldak, Turkiye.
C3 Zonguldak Bulent Ecevit University; Zonguldak Bulent Ecevit University
RP Orhan, A (corresponding author), Zonguldak Bulent Ecevit Univ, Sch Foreign Languages, Zonguldak, Turkiye.
EM ali_orh_an@hotmail.com
RI Orhan, Ali/GYA-2384-2022
CR Aggarwal D., 2023, Journal of Artificial Intelligence, Machine Learning and Neural Network, V3, P23, DOI [https://doi.org/10.55529/jaimlnn.36.23.28, DOI 10.55529/JAIMLNN.36.23.28]
   Alfadda HA, 2021, J PSYCHOLINGUIST RES, V50, P883, DOI 10.1007/s10936-020-09752-1
   Alzahrani L., 2023, International Journal of Recent Technology and Engineering (IJRTE), V11, P65, DOI DOI 10.35940/IJRTE.F7475.0311623
   Aydin Yildiz T., 2023, International Journal of Language Academy, V11, P277, DOI [10.29228/ijla.70698, DOI 10.29228/IJLA.70698]
   Aydin Yildiz T., 2023, Participatory Educational Research, V10, P111, DOI [10.17275/per.23.62.10.4, DOI 10.17275/PER.23.62.10.4]
   Aydin Yildiz T., 2023, Journal of Teacher Education and Lifelong Learning, V5, P582, DOI [10.51535/tell.1314355, DOI 10.51535/TELL.1314355]
   Bialosiewicz S., 2013, An introduction to measurement invariance testing: Resource packet for participants
   Brown TB., 2020, ADV NEURAL INFORM PR, V2020, P1877, DOI [10.48550/ARXIV.2005.14165, DOI 10.48550/ARXIV.2005.14165]
   Buyukozturk S, 2010, Sosyal bilimler iin veri analizi el kitab: statistik, aratrma deseni SPSS uygulamalar ve yorum, V12th
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan D., 2011, APA handbook of industrial and organization psychology, Vol 1: Building and developing the organization, V1, P85, DOI [10.1037/12169-004, DOI 10.1037/12169-004]
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Coniam D, 2014, TEXT TALK, V34, P545, DOI 10.1515/text-2014-0018
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   DiPaola D, 2023, PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, P305, DOI 10.1145/3545945.3569743
   Donat Elisabeth, 2009, Informing Science, V12, P37
   Draxler F, 2023, DESIGNING INTERACTIVE SYSTEMS CONFERENCE, DIS 2023, P2249, DOI 10.1145/3563657.3596112
   Elbanna S., 2023, MANAGEMENT SUSTAINAB, DOI DOI 10.1108/MSAR-03-2023-0016
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Foster D., 2022, Generative deep learning
   Fryer LK, 2017, COMPUT HUM BEHAV, V75, P461, DOI 10.1016/j.chb.2017.05.045
   Gilreath H, 2024, J PRINT MEDIA TECHNO, V13, P35, DOI 10.14622/JPMTR-2401
   Grassini S, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1191628
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Haristiani Nuria, 2019, Journal of Physics: Conference Series, V1387, DOI 10.1088/1742-6596/1387/1/012020
   Hockly N, 2023, RELC J, V54, P445, DOI 10.1177/00336882231168504
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Huang AYQ, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104684
   Jang Y, 2022, EDUC INF TECHNOL, V27, P11635, DOI 10.1007/s10639-022-11086-5
   Kadaruddin K., 2023, International Journal of Business, Law, and Education, V4, P618, DOI DOI 10.56442/IJBLE.V4I2.215
   Kim S. W., 2020, Journal of the Korea Academia-Industrial Cooperation Society, V25, P251, DOI [https://doi.org/10.5762/KAIS.2024.25.3.251, DOI 10.5762/KAIS.2024.25.3.251]
   Kim Seong-Won, 2020, [The Journal of Korean Association of Computer Education, 컴퓨터교육학회 논문지], V23, P17
   Kizilcec RF, 2024, INT J ARTIF INTELL E, V34, P12, DOI 10.1007/s40593-023-00351-4
   Kline R. B., 2005, PRINCIPLES AND PRACT
   Latikka R, 2023, POETICS, V101, DOI 10.1016/j.poetic.2023.101839
   Law L, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100174
   Lin P, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445377
   Liu GX, 2024, INNOV LANG LEARN TEA, V18, P125, DOI 10.1080/17501229.2023.2240316
   Luckin R., 2022, COMPUTERS ED ARTIFIC, V3, DOI [DOI 10.1016/J.CAEAI.2022.100076, 10.1016/J.CAEAI.2022.100076]
   Luckin R., 2024, Development and Learning in Organizations, DOI [https://doi.org/10.1108/DLO-04-2024-0108, DOI 10.1108/DLO-04-2024-0108]
   Marsh HW, 2004, STRUCT EQU MODELING, V11, P320, DOI 10.1207/s15328007sem1103_2
   McArthur D., 2005, J. Educ. Technol., V1, P42, DOI DOI 10.26634/JET.1.4.972
   Mukherjee M. S., 2024, International Journal of Indian Psychology, V12, P1727, DOI [https://doi.org/10.25215/1201.160, DOI 10.25215/1201.160]
   Muniandy J, 2024, TEACH PUBLIC ADMIN, DOI 10.1177/01447394241230152
   Muthn L. K., 1998, Mplus users guide, V8th ed.
   Nazaretsky T, 2022, BRIT J EDUC TECHNOL, V53, P914, DOI 10.1111/bjet.13232
   Nugroho A, 2023, REGIST J, V16, P224, DOI 10.18326/rgt.v16i2.224-247
   Nunnally JC., 1994, PSYCHOMETRIC THEORY
   Ong CS, 2006, COMPUT HUM BEHAV, V22, P816, DOI 10.1016/j.chb.2004.03.006
   Owan Owan V J. V J., 2023, Eurasia Journal of Mathematics, Science and Technology Education, V19 19, DOI [DOI 10.29333/EJMSTE/13428, 10.29333/ejmste/13428]
   Pallant J., 2010, SPSS SURVIVAL MANUAL, DOI DOI 10.4324/9781003117407
   Park J, 2024, J OCCUP ORGAN PSYCH, V97, P920, DOI 10.1111/joop.12502
   Perera P., 2023, Journal of Advances in Education and Philosophy, V7, P246, DOI [10.36348/jaep.2023.v07i08.001, DOI 10.36348/JAEP.2023.V07I08.001, https://doi.org/10.36348/jaep.2023.v07i08.001]
   Porter CE, 2006, J BUS RES, V59, P999, DOI 10.1016/j.jbusres.2006.06.003
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Ruwe T., 2023, Computers and Education, V5, P100189, DOI [https://doi.org/10.1016/j.caeai.2023.100189, DOI 10.1016/J.CAEAI.2023.100189]
   Santamaria Tricia, 2017, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V61, P853, DOI 10.1177/1541931213601686
   Schepman A, 2020, COMPUT HUM BEHAV REP, V1, DOI 10.1016/j.chbr.2020.100014
   Schumacher P, 2001, COMPUT HUM BEHAV, V17, P95, DOI 10.1016/S0747-5632(00)00032-7
   Schweinberger SR, 2020, COMPUT HUM BEHAV, V106, DOI 10.1016/j.chb.2020.106256
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Suh W, 2022, SAGE OPEN, V12, DOI 10.1177/21582440221100463
   TABACHNICK BG, 2001, USING MULTIVARIATE S
   Tan S., 2023, Learning intelligence: Innovative and digital transformative learning strategies, P335
   Tang J., 2023, Advances in Educational Technology and Psychology, V7, P1, DOI [10.23977/aetp.2023.070401, DOI 10.23977/AETP.2023.070401]
   Terzi R., 2020, International Online Journal of Education and Teaching, V7, P1501
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Wang YY, 2022, INTERACT LEARN ENVIR, V30, P619, DOI 10.1080/10494820.2019.1674887
   Winkler R., 2018, Academy of Management Proceedings, V2018, P15903, DOI [DOI 10.5465/AMBPP.2018.15903ABSTRACT, 10.5465/ambpp.2018.15903abstract]
   Woods S, 2005, IEEE-RAS INT C HUMAN, P375
   Xia Q, 2022, COMPUT EDUC, V189, DOI 10.1016/j.compedu.2022.104582
   Yilmaz FGK, 2024, INT J HUM-COMPUT INT, V40, P8703, DOI 10.1080/10447318.2023.2288730
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhu CJ, 2023, KNOWL MANAG E-LEARN, V15, P133, DOI 10.34105/j.kmel.2023.15.008
   Ziefle M., 2010, 4th International ICST Conference on Pervasive Computing Technologies for Healthcare, P1, DOI [https://doi.org/10.4108/ICST.PERVASIVEHEALTH2010.8859, DOI 10.4108/ICST.PERVASIVEHEALTH2010.8859]
   Zulkarnain N. S., 2023, International Journal of Academic Research in Progressive Education and Development, V12, P861, DOI [https://doi.org/10.6007/IJARPED/v12-i2/17119, DOI 10.6007/IJARPED/V12-I2/17119]
NR 78
TC 0
Z9 0
U1 8
U2 8
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1539-1523
EI 1945-0818
J9 J RES TECHNOL EDUC
JI J. Res. Technol. Educ.
PD 2024 DEC 2
PY 2024
DI 10.1080/15391523.2024.2437744
EA DEC 2024
PG 21
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA O5A0F
UT WOS:001371239900001
DA 2024-12-25
ER

PT J
AU Chiu, TKF
AF Chiu, Thomas K. F.
TI The impact of Generative AI (GenAI) on practices, policies and research
   direction in education: a case of ChatGPT and Midjourney
SO INTERACTIVE LEARNING ENVIRONMENTS
LA English
DT Article; Early Access
DE artificial intelligence; Generative AI in education; teacher education;
   learning; teaching; assessment; administration
ID ARTIFICIAL-INTELLIGENCE; EVALUATION SYSTEM; DESIGN; IMPLEMENTATION;
   COMPETENCE; STUDENT
AB Generative artificial intelligence (GenAI) tools have become increasingly accessible and have impacted school education in numerous ways. However, most of the discussions occur in higher education. In schools, teachers' perspectives are crucial for making sense of innovative technologies. Accordingly, this qualitative study aims to investigate how GenAI changes our school education from the perspectives of teachers and leaders. It used four domains - learning, teaching, assessment, and administration - as the initial framework suggested in a systematic literature review study on AI in education. The participants were 88 school teachers and leaders of different backgrounds. They completed a survey and joined a focus group to share how ChatGPT and Midjounery had a GenAI effect on school education. Thematic analysis identified four main themes and 12 subthemes. The findings provide three suggestions for practices: know-it-all attitude, new prerequisite knowledge, interdisciplinary teaching, and three implications for policy: new assessment, AI education, and professional standards. They also further suggest six future research directions for GenAI in education.
C1 [Chiu, Thomas K. F.] Chinese Univ Hong Kong, Ctr Learning Sci & Technol, Dept Curriculum & Instruct, Ctr Univ & Sch Partnership,Shatin, Hong Kong, Peoples R China.
C3 Chinese University of Hong Kong
RP Chiu, TKF (corresponding author), Chinese Univ Hong Kong, Ctr Learning Sci & Technol, Dept Curriculum & Instruct, Ctr Univ & Sch Partnership,Shatin, Hong Kong, Peoples R China.
EM tchiu@cuhk.edu.hk
RI Chiu, Thomas K.F./AAR-4894-2021
OI Chiu, Thomas K.F./0000-0003-2887-5477
FU University Grants Committee (Hong Kong) General Research Fund [2180090]
FX This study was supported by a University Grants Committee (Hong Kong)
   General Research Fund [grant number 2180090].
CR Ando H., 2014, COMPREHENSIVE PSYCHO, V3, DOI [10.2466/03.CP.3.4, DOI 10.2466/03.CP.3.4]
   Rodríguez JA, 2021, PIXEL-BIT, P107, DOI 10.12795/pixelbit.86171
   Bennett RE, 2011, ASSESS EDUC, V18, P5, DOI 10.1080/0969594X.2010.513678
   Black P, 2011, ASSESS EDUC, V18, P451, DOI 10.1080/0969594X.2011.557020
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Buck GA, 2009, J SCI TEACH EDUC, V20, P475, DOI 10.1007/s10972-009-9142-y
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Chiu TKF, 2022, IEEE T EDUC, V65, P30, DOI 10.1109/TE.2021.3085878
   Chiu TKF, 2021, COMPUT HUM BEHAV, V124, DOI 10.1016/j.chb.2021.106909
   Chiu TKF, 2022, J RES TECHNOL EDUC, V54, pS14, DOI 10.1080/15391523.2021.1891998
   Chiu TKF, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12145568
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Costa-Mendes R, 2021, EDUC INF TECHNOL, V26, P1527, DOI 10.1007/s10639-020-10316-y
   Cukurova M, 2020, INT J ARTIF INTELL E, V30, P205, DOI 10.1007/s40593-019-00188-w
   Dillion D.R., 2019, Journal of Technology and Teacher Education, V27, P527
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Francis DC, 2019, SCHOOL SCI MATH, V119, P382, DOI 10.1111/ssm.12364
   Fu SX, 2020, BRIT J EDUC TECHNOL, V51, P1674, DOI 10.1111/bjet.12995
   Fung D, 2014, INT J EDUC RES, V66, P45, DOI 10.1016/j.ijer.2014.02.002
   Gunawan KDH, 2021, J PENELIT PEMBELAJAR, V7, P55, DOI 10.30870/jppi.v7i1.8655
   Hartnett M, 2015, AUSTRALAS J EDUC TEC, V31, P86
   Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
   Hu JJ, 2021, INT J EMERG TECHNOL, V16, P87, DOI 10.3991/ijet.v16i05.20299
   Huang SP., 2018, EURASIA Journal of Mathematics, Science and Technology Education, V14, P3277, DOI [10.29333/ejmste/91248, DOI 10.29333/EJMSTE/91248]
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Khan ZF, 2020, INT J EMERG TECHNOL, V15, P4, DOI 10.3991/ijet.v15i09.12387
   Kim HS, 2021, J ASIA TEFL, V18, P161, DOI 10.18823/asiatefl.2021.18.1.10.161
   Koc-Januchta MM, 2020, J EDUC COMPUT RES, V58, P1190, DOI 10.1177/0735633120921581
   Kwok Percy, 2004, Asia Pacific Education Review, V5, P64
   Lampos V, 2021, NPJ SCI LEARN, V6, DOI 10.1038/s41539-021-00102-x
   Li Q, 2021, INT J EMERG TECHNOL, V16, P32, DOI 10.3991/ijet.v16i05.20309
   Lin YT, 2019, ASIA-PAC EDUC RES, V28, P77, DOI 10.1007/s40299-018-0415-0
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Miao Hongyu, 2023, Asian Pac Isl Nurs J, V7, pe48136, DOI 10.2196/48136
   Mokmin N. A. M., 2020, International Journal of Human Movement and Sports Sciences, V8, P258, DOI [10.13189/saj.2020.080514, DOI 10.13189/SAJ.2020.080514]
   Perry NE, 2002, EDUC PSYCHOL, V37, P5, DOI 10.1207/S15326985EP3701_2
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Ruipérez-Valiente JA, 2019, IEEE T LEARN TECHNOL, V12, P112, DOI 10.1109/TLT.2017.2784420
   Ryan RM, 2020, CONTEMP EDUC PSYCHOL, V61, DOI 10.1016/j.cedpsych.2020.101860
   Samarakou M, 2015, INT J EMERG TECHNOL, V10, P22, DOI 10.3991/ijet.v10i3.4484
   Aldeman NLS, 2021, BMC MED EDUC, V21, DOI 10.1186/s12909-021-02680-1
   Seymour E, 2004, SCI EDUC, V88, P493, DOI 10.1002/sce.10131
   Sun Y, 2021, INT J EMERG TECHNOL, V16, P221, DOI 10.3991/ijet.v16i08.22131
   Tang J, 2021, INT J EMERG TECHNOL, V16, P17, DOI 10.3991/ijet.v16i05.20293
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tongco M. D. C., 2007, Ethnobotany Research and Applications, V5, P147
   Trenshaw KF, 2016, INT J ENG EDUC, V32, P1194
   Tsai SC, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-00186-2
   Vázquez-Cano E, 2021, INT J EDUC TECHNOL H, V18, DOI 10.1186/s41239-021-00269-8
   Villegas-Ch W, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041500
   Xia Q, 2022, COMPUT EDUC, V189, DOI 10.1016/j.compedu.2022.104582
   Yang CB, 2020, INT J EMERG TECHNOL, V15, P195, DOI 10.3991/ijet.v15i17.16737
   Yang YY, 2019, J EDUC EVAL HEALTH P, V16, DOI 10.3352/jeehp.2019.16.7
   Yau KW, 2023, EDUC INF TECHNOL, V28, P1041, DOI 10.1007/s10639-022-11161-x
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 56
TC 102
Z9 103
U1 505
U2 1140
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1049-4820
EI 1744-5191
J9 INTERACT LEARN ENVIR
JI Interact. Learn. Environ.
PD 2023 SEP 2
PY 2023
DI 10.1080/10494820.2023.2253861
EA SEP 2023
PG 17
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA Q5WA1
UT WOS:001058212500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Moon, J
   Lee, U
   Koh, J
   Jeong, Y
   Lee, Y
   Byun, G
   Lim, J
AF Moon, Jewoong
   Lee, Unggi
   Koh, Junbo
   Jeong, Yeil
   Lee, Yunseo
   Byun, Gyuri
   Lim, Jieun
TI Generative Artificial Intelligence in Educational Game Design: Nuanced
   Challenges, Design Implications, and Future Research
SO TECHNOLOGY KNOWLEDGE AND LEARNING
LA English
DT Article; Early Access
DE Generative artificial intelligence; Educational game design; Digital
   game-based learning; Emerging technology
AB This paper reviews the role of Generative Artificial Intelligence (GenAI) in transforming the landscape of educational game design. The recent rise and development of GenAI have expanded its applications in creating dynamic and interactive game systems. This review explores the potential of GenAI to craft personalized educational game designs that can adaptively support real-time student interactions. We discuss GenAI's capabilities in educational game design and their significance in educational research and practices. In addition, we highlight current design challenges, suggest future research avenues, and discuss the implications of GenAI integration for educational game design and digital game-based learning.
C1 [Moon, Jewoong] Univ Alabama, Dept Educ Leadership Policy & Technol Studies, Tuscaloosa, AL 35487 USA.
   [Lee, Unggi] Korea Univ, Seoul, South Korea.
   [Koh, Junbo; Jeong, Yeil] Seoul Natl Univ, Seoul, South Korea.
   [Lee, Yunseo] Pungnap Elementary Sch, Seoul, South Korea.
   [Byun, Gyuri] Youlhyun Elementary Sch, Tunnel, South Korea.
   [Lim, Jieun] Daegu Natl Univ Educ, Daegu, South Korea.
C3 University of Alabama System; University of Alabama Tuscaloosa; Korea
   University; Seoul National University (SNU)
RP Moon, J (corresponding author), Univ Alabama, Dept Educ Leadership Policy & Technol Studies, Tuscaloosa, AL 35487 USA.
EM jmoon19@ua.edu
RI Moon, Jewoong/AFN-2663-2022; LEE, UNGGI/KVY-9008-2024
OI Moon, Jewoong/0000-0001-6311-3019
CR Anjum A, 2024, Arxiv, DOI arXiv:2403.02454
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Barthet M., 2022, P 17 INT C FDN DIG G, P1
   Blattmann A, 2023, PROC CVPR IEEE, P22563, DOI 10.1109/CVPR52729.2023.02161
   Bommasani R., 2021, arXiv
   Brown TB, 2020, ADV NEUR IN, V33
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Connolly TM, 2012, COMPUT EDUC, V59, P661, DOI 10.1016/j.compedu.2012.03.004
   Cozar-Gutierrez R., 2016, International Journal of Educational Technology in Higher Education, V13, P1, DOI DOI 10.1186/S41239-016-0003-4
   D'Mello SK, 2024, AI MAG, V45, P61, DOI 10.1002/aaai.12158
   del Blanco A, 2013, IEEE GLOB ENG EDUC C, P1255, DOI 10.1109/EduCon.2013.6530268
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dong QX, 2024, Arxiv, DOI [arXiv:2301.00234, DOI 10.48550/ARXIV.2301.00234]
   Dosovitskiy A, 2021, Arxiv, DOI arXiv:2010.11929
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Ferrara E, 2023, Arxiv, DOI [arXiv:2304.03738, DOI 10.48550/ARXIV.2304.03738]
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Fotaris P., 2023, P 17 EUR C GAM BAS L
   French F, 2023, MULTIMODAL TECHNOLOG, V7, DOI 10.3390/mti7080081
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Gozalo-Brizuela R, 2023, Arxiv, DOI [arXiv:2306.02781, DOI 10.3844/JCSSP.2024.801.818]
   Habgood MPJ, 2011, J LEARN SCI, V20, P169, DOI 10.1080/10508406.2010.508029
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Ho JAT, 2022, Arxiv, DOI arXiv:2210.02303
   Hsu YC, 2023, TECHTRENDS, V67, P603, DOI 10.1007/s11528-023-00863-9
   Inie Nanna, 2023, 2023 CHI C HUMAN FAC, DOI DOI 10.1145/3544549.3585657
   Jeong Y., 2024, Immersive Learn Res Pract, V1, P16, DOI 10.56198/5M1RHT9ZJ
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Ke FF, 2016, BRIT J EDUC TECHNOL, V47, P1183, DOI 10.1111/bjet.12314
   Kiili K., 2005, Internet and Higher Education, V8, P13, DOI 10.1016/j.iheduc.2004.12.001
   Kumaran Vikram, 2023, P AAAI C ARTIFICIAL, P86
   Lee U., 2023, Education and Information Technologies, P1
   Lee U., 2023, NEURIPS 23 WORKSH GE
   Lin ZY, 2023, Arxiv, DOI [arXiv:2305.07465, 10.48550/arXiv.2305.07465]
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Liu ZC, 2020, ETR&D-EDUC TECH RES, V68, P1931, DOI 10.1007/s11423-020-09791-4
   Lv Z., 2023, Cognitive Robotics
   Moon J, 2024, Computers Education: X Reality, DOI [10.1016/j.cexr.2024.100063, DOI 10.1016/J.CEXR.2024.100063]
   Moon J, 2020, BRIT J EDUC TECHNOL, V51, P1766, DOI 10.1111/bjet.13005
   Netland T., 2023, INFORMS Trans. Educ., DOI [10.1016/j.cogr.2023.06.001, DOI 10.1287/ITED.2022.0067]
   Nigam A, 2021, EDUC INF TECHNOL, V26, P6421, DOI 10.1007/s10639-021-10597-x
   Noroozi M, 2018, PROC CVPR IEEE, P9359, DOI 10.1109/CVPR.2018.00975
   Park JS, 2023, Arxiv, DOI [arXiv:2304.03442, DOI 10.48550/ARXIV.2304.03442]
   Pataranutaporn P, 2021, NAT MACH INTELL, V3, P1013, DOI 10.1038/s42256-021-00417-9
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pothukuchi A. S., 2023, SSRN, V11
   Radford A., 2019, OPENAI BLOG
   Ratican J., 2023, J. Intell. Learn. Syst. Appl, V15, DOI [10.4236/jilsa.2023.151002, DOI 10.4236/JILSA.2023.151002]
   Santiago  JMS, 2023, Arxiv, DOI arXiv:2304.01860
   Shemshack A, 2020, SMART LEARN ENVIRON, V7, DOI 10.1186/s40561-020-00140-9
   Soflano M, 2015, COMPUT EDUC, V86, P192, DOI 10.1016/j.compedu.2015.03.015
   Streicher A, 2016, LECT NOTES COMPUT SC, V9970, P332, DOI 10.1007/978-3-319-46152-6_14
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Sung HY, 2018, INTERACT LEARN ENVIR, V26, P1053, DOI 10.1080/10494820.2018.1437049
   Treanor M., 2015, P 10 INT C FDN DIG G
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang L, 2024, Arxiv, DOI arXiv:2308.11432
   Werning S., 2024, Creative tools and the softwarization of cultural production, DOI [10.1007/978-3-031-45693-04, DOI 10.1007/978-3-031-45693-04]
   Wu WH, 2012, J COMPUT ASSIST LEAR, V28, P265, DOI 10.1111/j.1365-2729.2011.00437.x
   Xu YZ, 2024, Arxiv, DOI arXiv:2309.04658
   Yang DJ, 2024, Arxiv, DOI arXiv:2404.17794
NR 64
TC 1
Z9 1
U1 19
U2 19
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 2211-1662
EI 2211-1670
J9 TECHNOL KNOWL LEARN
JI Technol. Knowl. Learn.
PD 2024 JUL 23
PY 2024
DI 10.1007/s10758-024-09756-z
EA JUL 2024
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA ZJ0H1
UT WOS:001274805000001
DA 2024-12-25
ER

PT J
AU Matias, CF
AF Matias, Celia F.
TI Generative AI, Copyright and Emancipation: The Case of Digital Art
SO LAW TECHNOLOGY AND HUMANS
LA English
DT Article
DE Copyright; digital art; fair use; generative AI; emancipation
AB The emancipatory effect of copyright on the lives of creators has long been hindered by the concentration of rights by powerful entities that can hold creativity hostage through exclusive rights over countless cultural references. Exceptions to copyright have played an important counter-hegemonic role, supporting what we might call a public domain counterprinciple. The recent explosion of generative artificial intelligence (GenAI) upends this scenario, with creators bringing copyright infringement claims to the courts to determine, inter alia, the existence and relevance of copying in the training process and whether AI outputs qualify as derivative works. Using digital art as an example, this article assesses these dynamics from the perspective of emancipation, considering the interplay of copyright rules, exceptions, principles and counterprinciples, and seeks to devise pathways, within and outside copyright, to address the challenges posed to creators by GenAI.
C1 [Matias, Celia F.] Univ Macau, Fac Law, Macau, Peoples R China.
C3 University of Macau
RP Matias, CF (corresponding author), Univ Macau, Fac Law, Macau, Peoples R China.
RI Matias, Celia/LNQ-0405-2024
CR Agnello R, 2010, J BLACK STUD, V41, P56, DOI 10.1177/0021934708328444
   Allen Amy., 2016, END PROGR DECOLONIZI
   Allen Greg, 2005, New York TimesMay 1
   [Anonymous], 1999, GUIDELINES COMMUNITY
   [Anonymous], 2023, Harvey Weinstein timeline: How the scandal has unfolded
   [Anonymous], Obvious Art, Portrait of Edmond de Bellamy
   Arcangel Cory, Super Mario Clouds. Things I Made
   Aufderheide Patricia, 2011, Reclaiming Fair Use: How to Put Balance Back in Copyright
   Benkler Y., 2006, WEALTH NETWORKS SOCI
   Bently L., 2014, Intellectual Property Law
   Blühdorn I, 2022, EUR J SOC THEORY, V25, P3, DOI 10.1177/13684310211048116
   Brandon John, 2023, ForbesDecember 12
   BYSTRYN M, 1978, SOC RES, V45, P390
   Claburn Thomas, 2023, The RegisterFebruary 2
   Currah A, 2006, J ECON GEOGR, V6, P439, DOI 10.1093/jeg/lbl006
   Drahos Peter., 1996, PHILOS INTELLECTUAL
   Dusollier S, 2013, CAM INTELLECT PROP, P258
   Feygin Yakov, 2021, A Data Dividend That Works: Steps Toward Building an Equitable Data Economy
   Fleming Olivia, 2014, VogueMay 13
   Fuller LonL., 1969, MORALITY LAW
   GARFIELD E, 1986, CURR CONTENTS, P3
   GARFIELD E, 1978, CURR CONTENTS, P5
   Geiger C, 2017, WHAT IF WE COULD REIMAGINE COPYRIGHT?, P73
   Geiger Christophe, 2021, The Cambridge Handbook of Copyright Limitations and Exceptions, P174
   Gerlieb A, 2021, ARTS, V10, DOI 10.3390/arts10030052
   Goold Patrick R., 2017, American University International Law Review, V33
   Gottardis Andreas, 2014, Reason and Utopia: Reconsidering the Concept of Emancipation in Critical Theory
   Greenleaf G., 2018, Public Rights: Copyright's Public Domains Cambridge University Press
   Handley Lucy, 2024, Part Scary, Part Exciting: How Artists are Using AI in Their Work
   Hardy I. Trotter, 1988, Columbia-VLA Journal of Law & the Arts, V12, P181
   Harris Gareth, 2023, The Art Newspaper
   Henderson Peter, 2023, Stanford Law and Economics Olin Working Paper No. 584, DOI [10.2139/ssrn.4404340, DOI 10.2139/SSRN.4404340]
   Horkheimer Max., 2002, CRITICAL THEORY SELE
   Horkheimer Max., 2002, Critical Theory: Selected Essays, P47
   Hughes J, 2017, COPYRIGHT LAW IN AN AGE OF LIMITATIONS AND EXCEPTIONS, P234
   Jackson Gita, 2021, ViceDecember 14
   Jenkins H., 2006, FANS BLOGGERS GAMERS
   Kang X, 2019, INFORMATICS-BASEL, V6, DOI 10.3390/informatics6040052
   Karapapa S., 2020, Defences to Copyright Infringement: Creativity, Innovation and Freedom on the Internet
   Katz Ariel, 2021, The Cambridge Handbook of Copyright Limitations and Exceptions, P111
   Keller Paul, 2023, Kluwer Copyright Blog (blog)
   Knight Lucy, 2023, The Guardian
   Lemley Mark A., 2021, Texas Law Review, V99, P743
   Lemley Mark A., 2024, Science and Technology Law Review, V25, P21, DOI [10.2139/ssrn.4517702, DOI 10.2139/SSRN.4517702]
   Lessig L., 2006, CODE OTHER LAWS CYBE
   Lessig L., 2004, Free Culture: How Big Media Uses Technology and the Law to Lock Down Culture and Control Creativity
   Marx K., 1975, Early writings
   McKendrick Joe, 2024, ForbesJune 23
   Migliorini S, 2024, EUR J RISK REGUL, V15, P719, DOI 10.1017/err.2024.4
   Murray Andrew, 2016, Information Technology Law: The Law and Society, V4th
   Murray Michael D., 2023, SMU Science & Technology Law Review, V26, P259
   Quintais JP, 2023, COMPUT LAW SECUR REV, V48, DOI 10.1016/j.clsr.2023.105792
   Quintais Joao Pedro, 2022, reCreating Europe Report, P110
   Rachum-Twaig Omri, 2019, Copyright Law and Derivative Works: Regulating Creativity
   Raz J., 1979, The Rule of Law and Its Virtue. The Authority of Law: Essays on Law and Morality, P210
   Rendas T, 2023, IIC-INT REV INTELL P, V54, P1, DOI 10.1007/s40319-022-01260-0
   Robertson Adi, 2022, The VergeNovember 16
   Roose Kevin, 2024, New York Times
   Rosati E, 2018, J INTELLET PROP LAW, V13, P525, DOI 10.1093/jiplp/jpy067
   Samuelson P, 2023, SCIENCE, V381, P158, DOI 10.1126/science.adi0656
   Samuelson P, 2017, COPYRIGHT LAW IN AN AGE OF LIMITATIONS AND EXCEPTIONS, P12
   Schulze Elizabeth, 2019, Thousands Protest Against Controversial EU Internet Law Claiming It Will Enable Online Censorship
   Shapiro AM, 1998, GROUND WATER, V36, P37, DOI 10.1111/j.1745-6584.1998.tb01063.x
   Smyrnaios Nikos., 2018, Internet Oligopoly: The Corporate Takeover of Our Digital World
   Solaiman I, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P111, DOI 10.1145/3593013.3593981
   Tang XY, 2021, FORDHAM LAW REV, V90, P1151
   Thevenin B., 2019, The International Encyclopedia of Media Literacy, DOI DOI 10.1002/9781118978238.IEML0014
   Thomas HS, 2019, BRIT J SURG, V106, pE103, DOI 10.1002/bjs.11051
   Trosow SamuelE., 2003, The Canadian Journal of Law and Jurisprudence, V16, P217
   Tufekci Zeynep., 2017, Twitter and Tear Gas: The Power and Fragility of Networked Protest
   Unger R.M., 1996, What Should Legal Analysis Become?
   UNGER RM, 1983, HARVARD LAW REV, V96, P561, DOI 10.2307/1341032
   Veitch Scott, 2018, Jurisprudence: Themes and Concepts, V3rd
   Visone Alexio, 2016, Master's thesis
   WARTENBERG TE, 1982, HUM STUD, V5, P77, DOI 10.1007/BF02127669
   Wright Erik Olin., 1993, Sociological Theory, V11, P39
   Xiang Chloe, 2022, Artists are Revolting
NR 77
TC 0
Z9 0
U1 0
U2 0
PU QUEENSLAND UNIV TECHNOLOGY
PI BRISBANE
PA GPO  BOX 2434, BRISBANE, QLD 4001, AUSTRALIA
EI 2652-4074
J9 LAW TECHNOL HUMANS
JI Law Technol. Humans
PY 2024
VL 6
IS 3
BP 123
EP 138
DI 10.5204/lthj.3567
PG 16
WC Law; Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Government & Law; Social Sciences - Other Topics
GA O0L9B
UT WOS:001368155500001
OA gold
DA 2024-12-25
ER

PT J
AU Yudhistyra, WI
   Srinuan, C
AF Yudhistyra, Wecka Imam
   Srinuan, Chalita
TI Exploring the Acceptance of Technological Innovation Among Employees in
   the Mining Industry: A Study on Generative Artificial Intelligence
SO IEEE ACCESS
LA English
DT Article
DE Mining industry; Companies; Industries; Generative AI; Developing
   countries; Productivity; Mathematical models; Analytical models; Vehicle
   dynamics; Acceptance; employees; generative artificial intelligence;
   innovation; mining industry
ID INFORMATION; ADOPTION; MODEL; DETERMINANTS
AB Generative Artificial Intelligence (GenAI) technology innovation holds promise for revolutionizing the mining industry. However, the sector's traditionally conservative stance toward innovation has led to limited research. Thus, this manuscript aims to address this gap by exploring factors influencing the acceptance of GenAI technology innovation among mining industry employees from a developing country perspective. Based on literature reviews seven factors were identified to influence employees' acceptance of GenAI technological innovation. These factors were examined in a data sample of 286 mining industry employees collected via an office-intercept survey and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using the SmartPLS software. Through thorough data analysis, models have been constructed that exhibit robustness, evidenced by their strong validity, reliability, and excellent fit with the data. The findings reveal that attitude, perceived usefulness, perceived ease of use, compatibility, company size, industrial competitiveness, and top management support significantly influence the acceptance of GenAI technology innovation. Notably, attitude was identified as the most influential factor, significantly shaping the likelihood of adoption. Conversely, company size had a comparatively minor impact on acceptance, and the regulatory framework showed no significant effect on the adoption of GenAI technology innovation. These results have important theoretical and managerial implications. Theoretically, they provide a nuanced understanding of the key factors driving technology acceptance in the mining industry, challenging the traditional emphasis on regulatory frameworks. Managerially, they underscore the importance of focusing on factors when developing strategies to foster technological adoption.
C1 [Yudhistyra, Wecka Imam; Srinuan, Chalita] King Mongkuts Inst Technol Ladkrabang KMITL, Business Sch, Bangkok 10520, Thailand.
C3 King Mongkuts Institute of Technology Ladkrabang
RP Yudhistyra, WI (corresponding author), King Mongkuts Inst Technol Ladkrabang KMITL, Business Sch, Bangkok 10520, Thailand.
EM wecka.yu@kmitl.ac.th
RI Yudhistyra, Wecka/JCE-6321-2023
FU King Mongkut's Institute of Technology Ladkrabang [KDS 2022/030];
   University of Gadjah Mada [KE/UGM/015/EC/2024]; University of
   Muhammadiyah Malang [E.6.m/209/KE-UMM/IX/2024]
FX This work was supported by King Mongkut's Institute of Technology
   Ladkrabang, under Grant KDS 2022/030.This work involved human subjects
   in its research. Approval of all ethical and experimental procedures and
   protocols was granted by the University of Gadjah Mada under Application
   No. KE/UGM/015/EC/2024 and the University of Muhammadiyah Malang under
   Application No. E.6.m/209/KE-UMM/IX/2024.
CR Allen I.E., 2007, Quality Progress, V40, P64
   Arbulu I., 2018, Reinvigorating ASEAN manufacturing for the future, P26
   Aznar-Sánchez JA, 2019, J CLEAN PROD, V221, P38, DOI 10.1016/j.jclepro.2019.02.243
   Baig MI, 2023, EDUC INF TECHNOL, V28, P16457, DOI 10.1007/s10639-023-11875-6
   Bartos PJ, 2007, RESOUR POLICY, V32, P149, DOI 10.1016/j.resourpol.2007.07.001
   Becker JM, 2015, MARKET LETT, V26, P643, DOI 10.1007/s11002-014-9299-9
   Bellamy D, 2011, RESOUR POLICY, V36, P149, DOI 10.1016/j.resourpol.2010.09.002
   Chatterjee S, 2023, INFORM SYST FRONT, V25, P1299, DOI 10.1007/s10796-021-10181-1
   Chatterjee S, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120880
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier, P68
   Creswell John W., 2023, Research Design, V6th
   DAMANPOUR F, 1992, ORGAN STUD, V13, P375, DOI 10.1177/017084069201300304
   Dasgupta S., 1999, Journal of Global Information Management, V7, P30
   DAVIS FD, 1993, INT J MAN MACH STUD, V38, P475, DOI 10.1006/imms.1993.1022
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Debasa F, 2023, EUR RES MANAG BUS EC, V29, DOI 10.1016/j.iedeen.2022.100205
   Drucker P. F., 2013, HBR's 10 Must Reads on Innovation, Featuring
   Drucker P.F., 2007, Management: Tasks, responsibilities, practices
   Ediriweera A, 2021, RESOUR POLICY, V73, DOI 10.1016/j.resourpol.2021.102188
   Euchner J, 2023, RES TECHNOL MANAGE, V66, P71, DOI 10.1080/08956308.2023.2188861
   Frambach RT, 2002, J BUS RES, V55, P163, DOI 10.1016/S0148-2963(00)00152-1
   GEISSER S, 1975, J AM STAT ASSOC, V70, P320, DOI 10.2307/2285815
   Gruenhagen JH, 2020, RESOUR POLICY, V65, DOI 10.1016/j.resourpol.2019.101540
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hsu HY, 2019, J BUS IND MARK, V34, P575, DOI 10.1108/JBIM-03-2017-0068
   Kim D, 2014, INFORM MANAGE-AMSTER, V51, P451, DOI 10.1016/j.im.2014.02.011
   Lath V., 2020, How digital innovation will transform indonesia's mining industry
   Lian JW, 2014, INT J INFORM MANAGE, V34, P28, DOI 10.1016/j.ijinfomgt.2013.09.004
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu L, 2024, IEEE ACCESS, V12, P122735, DOI 10.1109/ACCESS.2024.3437418
   Malhotra N.K., 2004, MARKETING RES APPL O, V4th
   Orlikowski WJ, 1991, INFORM SYST RES, V2, P1, DOI 10.1287/isre.2.1.1
   PORTER ME, 1985, HARVARD BUS REV, V63, P149
   Qatawneh N, 2024, IEEE ACCESS, V12, P132996, DOI 10.1109/ACCESS.2024.3432493
   Rogers E. M., 2003, DIFFUSION INNOVATION
   Saif N, 2024, COMPUT HUM BEHAV, V154, DOI 10.1016/j.chb.2023.108097
   Sánchez F, 2020, MINING METALL EXPLOR, V37, P1385, DOI 10.1007/s42461-020-00262-1
   Sharma PN, 2021, DECISION SCI, V52, P567, DOI 10.1111/deci.12329
   Sharma PN, 2019, J ASSOC INF SYST, V20, P346, DOI 10.17005/1.jais.00538
   Sharma S, 2024, IEEE T ENG MANAGE, V71, P1773, DOI 10.1109/TEM.2022.3203469
   STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x
   Sun SW, 2020, IND MARKET MANAG, V86, DOI 10.1016/j.indmarman.2019.09.003
   Swani K, 2021, IND MARKET MANAG, V93, P389, DOI 10.1016/j.indmarman.2020.05.033
   To ML, 2006, IND MANAGE DATA SYST, V106, P1133, DOI 10.1108/02635570610710791
   Ursavas O. F., 2022, Conducting technology acceptance research in education: Theory, models, implementation, and analysis, DOI [10.1007/978-3-031-10846-4, DOI 10.1007/978-3-031-10846-4]
   Wang WS, 2018, RESOUR POLICY, V58, P144, DOI 10.1016/j.resourpol.2018.04.008
   Wang YM, 2010, TECHNOL FORECAST SOC, V77, P803, DOI 10.1016/j.techfore.2010.03.006
NR 51
TC 0
Z9 0
U1 8
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 165797
EP 165809
DI 10.1109/ACCESS.2024.3493242
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA M0L5Q
UT WOS:001354548300001
DA 2024-12-25
ER

PT J
AU Zhang, Y
   Dong, CQ
AF Zhang, Yang
   Dong, Changqi
TI Unveiling the Dynamic Mechanisms of Generative AI in English Language
   Learning: A Hybrid Study Based on fsQCA and System Dynamics
SO BEHAVIORAL SCIENCES
LA English
DT Article
DE English language learning; generative artificial intelligence; higher
   education; multi-dimensional mechanisms; qualitative comparative
   analysis; system dynamics
ID QUALITATIVE COMPARATIVE-ANALYSIS; TECHNOLOGY ACCEPTANCE MODEL;
   EDUCATIONAL-TECHNOLOGY; MEDIUM INSTRUCTION; USER ACCEPTANCE
AB The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human-computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.
C1 [Zhang, Yang] Harbin Inst Technol, Fac Humanities & Social Sci, Harbin 150001, Peoples R China.
   [Dong, Changqi] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
C3 Harbin Institute of Technology; Harbin Institute of Technology
RP Dong, CQ (corresponding author), Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China.
EM zhangyanghit@hit.edu.cn; 21b910041@stu.hit.edu.cn
FU Special Funds for the Basic Research Operating Costs of the Central
   Universities [HIT.HSS.202131]; Special Funds for the Basic Research
   Operating Costs of the Central Universities for the Study of "David
   Lodge's Spatial Writing and Spatial Consciousness"; Higher Education
   Teaching Reform Research Project of Heilongjiang Province
FX This research was supported by the Special Funds for the Basic Research
   Operating Costs of the Central Universities for the Study of "David
   Lodge's Spatial Writing and Spatial Consciousness", grant number,
   HIT.HSS.202131, and the Higher Education Teaching Reform Research
   Project of Heilongjiang Province "Research on the Digital Transformation
   of Generative Artificial Intelligence-Assisted Foreign Language
   Teaching".
CR Adeoye-Olatunde OA, 2021, J AM COLL CLIN PHARM, V4, P1358, DOI 10.1002/jac5.1441
   Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Aiken R.M., 2000, International Journal of Artificial Intelligence in Education, V11, P163
   [Anonymous], 2019, JEDM J ED DATA MININ, DOI [DOI 10.1257/POL.20170603, 10.5281/ZENODO.3554745, DOI 10.5281/ZENODO.3554745]
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Bannister P, 2023, AULA ABIERTA, V52, P401, DOI 10.17811/rifie.52.4.2023.401-409
   Barlas Y, 1996, SYST DYNAM REV, V12, P183, DOI 10.1002/(SICI)1099-1727(199623)12:3<183::AID-SDR103>3.0.CO;2-4
   Berendt B, 2020, LEARN MEDIA TECHNOL, V45, P312, DOI 10.1080/17439884.2020.1786399
   Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
   Chen CM, 2008, COMPUT EDUC, V51, P787, DOI 10.1016/j.compedu.2007.08.004
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   de Bot K, 2007, BILING-LANG COGN, V10, P7, DOI 10.1017/S1366728906002732
   Dong CQ, 2023, SYSTEMS-BASEL, V11, DOI 10.3390/systems11040212
   Dörnyei Z, 2009, LANG LEARN, V59, P230
   Farrelly T, 2023, EDUC SCI, V13, DOI 10.3390/educsci13111109
   Forrester JW, 2007, SYST DYNAM REV, V23, P345, DOI 10.1002/sdr.382
   Fredricks JA, 2004, REV EDUC RES, V74, P59, DOI 10.3102/00346543074001059
   Furnari S, 2021, ACAD MANAGE REV, V46, P778, DOI 10.5465/amr.2019.0298
   Galloway N, 2021, ELT J, V75, P33, DOI 10.1093/elt/ccaa063
   García-Peñalvo FJ, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13042023
   GARDNER RC, 1985, LANG LEARN, V35, P207, DOI 10.1111/j.1467-1770.1985.tb01025.x
   Granic A, 2019, BRIT J EDUC TECHNOL, V50, P2572, DOI 10.1111/bjet.12864
   Greckhamer T, 2018, STRATEG ORGAN, V16, P482, DOI 10.1177/1476127018786487
   Grgurovic M, 2013, RECALL, V25, P165, DOI 10.1017/S0958344013000013
   Hew KF, 2019, BRIT J EDUC TECHNOL, V50, P956, DOI 10.1111/bjet.12770
   Hillman T, 2020, LEARN MEDIA TECHNOL, V45, P7, DOI 10.1080/17439884.2020.1683748
   Holmes W, 2022, INT J ARTIF INTELL E, V32, P504, DOI 10.1007/s40593-021-00239-1
   Hooshyar D, 2020, ENTROPY-SWITZ, V22, DOI 10.3390/e22010012
   Hou CB, 2021, MOBILE NETW APPL, V26, P2164, DOI 10.1007/s11036-021-01773-x
   Hsu YC, 2023, TECHTRENDS, V67, P603, DOI 10.1007/s11528-023-00863-9
   Jacobson MJ, 2019, EDUC RESEARCHER, V48, P112, DOI 10.3102/0013189X19826958
   Jin L, 2018, COMPUT ASSIST LANG L, V31, P27, DOI 10.1080/09588221.2017.1376687
   Johnson RB., 2004, ED RES, V33, P14, DOI DOI 10.3102/0013189X033007014
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Kohnke L., 2024, Comput. Educ. Artif. Intell, V7, P100279, DOI [10.1016/j.caeai.2024.100279, DOI 10.1016/J.CAEAI.2024.100279]
   Law L, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100174
   Lee D, 2020, INT REV RES OPEN DIS, V21, P23
   Liang JC, 2023, INTERACT LEARN ENVIR, V31, P4270, DOI 10.1080/10494820.2021.1958348
   Liu CY, 2019, J TEACH ENGL SPECIF, V7, P517, DOI 10.22190/JTESAP1904517C
   Liu CC, 2023, INTERACT LEARN ENVIR, V31, P5614, DOI 10.1080/10494820.2021.2012812
   McDonald N, 2024, Arxiv, DOI [arXiv:2402.01659, 10.48550/arXiv.2402.01659, DOI 10.48550/ARXIV.2402.01659]
   Misangyi VF, 2017, J MANAGE, V43, P255, DOI 10.1177/0149206316679252
   Naeem K, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15086826
   Nasrullah N., 2024, Futur. Educ, V4, P217, DOI [10.57125/FED.2024.09.25.13, DOI 10.57125/FED.2024.09.25.13]
   Nye BD, 2015, INT J ARTIF INTELL E, V25, P177, DOI 10.1007/s40593-014-0028-6
   Ogunleye B, 2024, EDUC SCI, V14, DOI 10.3390/educsci14060636
   Palinkas LA, 2019, ANNU REV PUBL HEALTH, V40, P423, DOI 10.1146/annurev-publhealth-040218-044215
   Pappas IO, 2019, COMPUT HUM BEHAV, V92, P646, DOI 10.1016/j.chb.2017.10.010
   Ragin C.C., 2009, Redesigning Social Inquiry: Fuzzy Sets and Beyond, P106
   Ragin C.C., 2000, Fuzzy-Set Social Science, P34
   Rihoux B, 2006, INT SOCIOL, V21, P679, DOI 10.1177/0268580906067836
   Sadan V., 2014, International Journal of Nursing Education, V6, P254, DOI [10.5958/j.0974-9357.6.1.052, DOI 10.5958/J.0974-9357.6.1.052]
   Schaufeli WB, 2004, J ORGAN BEHAV, V25, P293, DOI 10.1002/job.248
   Shin D, 2021, INT J HUM-COMPUT ST, V146, DOI 10.1016/j.ijhcs.2020.102551
   Shu X, 2022, MATH PROBL ENG, V2022, DOI 10.1155/2022/2776823
   Sterman JD, 2001, CALIF MANAGE REV, V43, P8, DOI 10.2307/41166098
   Strauss LM, 2015, HIGH EDUC, V69, P375, DOI 10.1007/s10734-014-9781-6
   Sweller J, 2019, EDUC PSYCHOL REV, V31, P261, DOI 10.1007/s10648-019-09465-5
   Tafazoli D., 2024, Comput. Educ. Artif. Intell, V7, P100275, DOI [10.1016/j.caeai.2024.100275, DOI 10.1016/J.CAEAI.2024.100275]
   Tchounikine P, 2019, INT J COMP-SUPP COLL, V14, P237, DOI 10.1007/s11412-019-09302-5
   Thurlings M, 2017, EDUC REV, V69, P554, DOI 10.1080/00131911.2017.1281226
   Vaishnav P.B., 2024, Asian J. Educ. Soc. Stud, V50, P1, DOI [10.9734/ajess/2024/v50i71438, DOI 10.9734/AJESS/2024/V50I71438]
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Vis B, 2012, SOCIOL METHOD RES, V41, P168, DOI 10.1177/0049124112442142
   Vlachogianni P, 2022, J RES TECHNOL EDUC, V54, P392, DOI 10.1080/15391523.2020.1867938
   Wang K, 2024, BEHAV SCI-BASEL, V14, DOI 10.3390/bs14050373
   Weng X., 2023, Computers and Education: Artificial Intelligence, DOI [10.1016/j.caeai.2022.100117, DOI 10.1016/J.CAEAI.2022.100117]
   Werang BR, 2022, QUAL REP, V27, P555, DOI 10.46743/2160-3715/2022.5165
   Wilson J, 2018, HARVARD BUS REV, V96, P115
   Xie H, 2019, COMPUT EDUC, V140, DOI 10.1016/j.compedu.2019.103599
   Xu W, 2023, INT J HUM-COMPUT INT, V39, P494, DOI [10.1080/10447318.2022.2041900, 10.1109/IECON49645.2022.9968424]
   Yan LX, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13370
   Yu ZG, 2019, COMPUT ASSIST LANG L, V32, P323, DOI 10.1080/09588221.2018.1517093
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zein S, 2020, LANG TEACHING, V53, P491, DOI 10.1017/S0261444820000208
   Zhao Y, 2021, J EDUC CHANG, V22, P3, DOI 10.1007/s10833-021-09417-3
NR 79
TC 0
Z9 0
U1 7
U2 7
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-328X
J9 BEHAV SCI-BASEL
JI Behav. Sci.
PD NOV
PY 2024
VL 14
IS 11
AR 1015
DI 10.3390/bs14111015
PG 37
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA N4G8F
UT WOS:001363951300001
PM 39594315
OA gold
DA 2024-12-25
ER

PT J
AU Decardi-Nelson, B
   Alshehri, AS
   Ajagekar, A
   You, FQ
AF Decardi-Nelson, Benjamin
   Alshehri, Abdulelah S.
   Ajagekar, Akshay
   You, Fengqi
TI Generative AI and process systems engineering: The next frontier
SO COMPUTERS & CHEMICAL ENGINEERING
LA English
DT Article
DE Generative AI; Process systems engineering; Large language models;
   Multiscale
ID MATHEMATICAL-PROGRAMMING TECHNIQUES; MIXED-INTEGER OPTIMIZATION; AIDED
   MOLECULAR DESIGN; OF-THE-ART; DATA-DRIVEN; GLOBAL OPTIMIZATION;
   SURROGATE MODELS; NEURAL-NETWORKS; VARIATIONAL AUTOENCODER;
   CHEMICAL-PRODUCT
AB This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, generalpurpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
C1 [Decardi-Nelson, Benjamin; Ajagekar, Akshay; You, Fengqi] Cornell Univ, Syst Engn, Ithaca, NY 14853 USA.
   [Alshehri, Abdulelah S.] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA.
   [Alshehri, Abdulelah S.] King Saud Univ, Coll Engn, Dept Chem Engn, Riyadh 11421, Saudi Arabia.
C3 Cornell University; Cornell University; King Saud University
RP You, FQ (corresponding author), Cornell Univ, Syst Engn, Ithaca, NY 14853 USA.
EM fengqi.you@cornell.edu
RI Decardi-Nelson, Benjamin/JQW-0212-2023; Alshehri,
   Abdulelah/AAT-6066-2020; Ajagekar, Akshay/HLH-2467-2023; You,
   Fengqi/B-5040-2011
OI You, Fengqi/0000-0001-9609-4299; Alshehri, Abdulelah/0000-0001-5213-3575
FU Schmidt Futures via an Eric and Wendy Schmidt AI in Science Postdoctoral
   Fellowship
FX B.D.-N. acknowledges the partial support from Schmidt Futures via an
   Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship to Cornell
   University. We also acknowledge the use of generative AI to polish the
   language in the manuscript. Figures are created with Bio- Render.com .
CR Ajagekar A, 2023, NPJ COMPUT MATER, V9, DOI 10.1038/s41524-023-01099-0
   Ajagekar A, 2021, APPL ENERG, V303, DOI 10.1016/j.apenergy.2021.117628
   Ajagekar A, 2020, COMPUT CHEM ENG, V143, DOI 10.1016/j.compchemeng.2020.107119
   Akay B, 2022, ARTIF INTELL REV, V55, P829, DOI 10.1007/s10462-021-09992-0
   Alayrac JB, 2022, ADV NEUR IN
   Alcántara A, 2023, APPL MATH MODEL, V121, P445, DOI 10.1016/j.apm.2023.04.032
   Alhamoud K, 2024, COMPUT CHEM ENG, V183, DOI 10.1016/j.compchemeng.2024.108622
   Alizadeh R, 2020, RES ENG DES, V31, P275, DOI 10.1007/s00163-020-00336-7
   Alshehri A.S., 2021, Computer Aided Chemical Engineering, V50, P227
   Alshehri AS, 2023, Arxiv, DOI arXiv:2309.12460
   Alshehri AS, 2022, CHEM ENG J, V444, DOI 10.1016/j.cej.2022.136669
   Alshehri AS, 2022, CURR OPIN CHEM ENG, V36, DOI 10.1016/j.coche.2021.100752
   Alshehri AS, 2021, FRONT CHEM ENG, V3, DOI 10.3389/fceng.2021.700717
   Alshehri AS, 2022, AICHE J, V68, DOI 10.1002/aic.17469
   Alshehri AS, 2020, COMPUT CHEM ENG, V141, DOI 10.1016/j.compchemeng.2020.107005
   Anderson R, 2020, MATH PROGRAM, V183, P3, DOI 10.1007/s10107-020-01474-5
   Angeli D, 2012, IEEE T AUTOMAT CONTR, V57, P1615, DOI 10.1109/TAC.2011.2179349
   [Anonymous], 2018, MolGAN: an implicit generative model for small molecular graphs.
   Arjovsky M, 2017, PR MACH LEARN RES, V70
   Attari V, 2023, ACTA MATER, V259, DOI 10.1016/j.actamat.2023.119204
   Austin ND, 2016, CHEM ENG RES DES, V116, P2, DOI 10.1016/j.cherd.2016.10.014
   Azerbayev Z, 2024, Arxiv, DOI arXiv:2310.10631
   Badini S, 2023, ADV IND ENG POLY RES, V6, P278, DOI 10.1016/j.aiepr.2023.03.003
   Balaji S, 2023, Arxiv, DOI [arXiv:2310.03030, DOI 10.48550/ARXIV.2310.03030]
   Balhorn LS, 2023, Arxiv, DOI arXiv:2312.02873
   Baltean-Lugojan R., 2018, Selecting cutting planes for quadratic semidefinite outer-approximation via trained neural networks
   Bang RS, 2023, AICHE J, V69, DOI 10.1002/aic.18020
   Bank D., 2023, Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, P353, DOI [10.1007/978-3-031-24628-9_16, DOI 10.1007/978-3-031-24628-9_16, 10.1007/978-3-031-24628-916]
   Bao H., 2021, arXiv
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   BenTal A, 2009, PRINC SER APPL MATH, P1
   Berthold T., 2015, Heuristic Algorithms in Global MINLP Solvers
   Bertsekas D. P., 1997, J. Oper. Res. Soc., V48, P334, DOI 10.1057/palgrave.jors.2600425
   Beyer HG, 2007, COMPUT METHOD APPL M, V196, P3190, DOI 10.1016/j.cma.2007.03.003
   Bhaskar V, 2000, AICHE J, V46, P1046, DOI 10.1002/aic.690460516
   Bhosekar A, 2018, COMPUT CHEM ENG, V108, P250, DOI 10.1016/j.compchemeng.2017.09.017
   Biegler L.T., 1997, Aperspectiveonnonlinearmodelpredictivecontrol
   BIEGLER LT, 1984, COMPUT CHEM ENG, V8, P243, DOI 10.1016/0098-1354(84)87012-X
   Biegler LT, 2004, COMPUT CHEM ENG, V28, P1169, DOI 10.1016/j.compchemeng.2003.11.003
   Billings SA, 2013, NONLINEAR SYSTEM IDENTIFICATION: NARMAX METHODS IN THE TIME, FREQUENCY, AND SPATIO-TEMPORAL DOMAINS, P1, DOI 10.1002/9781118535561
   Bińkowski M, 2021, Arxiv, DOI [arXiv:1801.01401, DOI 10.48550/ARXIV.1801.01401]
   Birge J. R., 1997, INFORMS Journal on Computing, V9, P111, DOI 10.1287/ijoc.9.2.111
   Boiko DA, 2023, NATURE, V624, P570, DOI 10.1038/s41586-023-06792-0
   Bonassi F, 2022, J PROCESS CONTR, V114, P92, DOI 10.1016/j.jprocont.2022.04.011
   Bond-Taylor S, 2022, IEEE T PATTERN ANAL, V44, P7327, DOI 10.1109/TPAMI.2021.3116668
   Brown TB, 2020, ADV NEUR IN, V33
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Burer S., 2012, SURVEYS OPERATIONS R, V17, P97, DOI DOI 10.1016/J.SORMS.2012.08.001
   Caballero JA, 2008, AICHE J, V54, P2633, DOI 10.1002/aic.11579
   Cang RJ, 2018, COMP MATER SCI, V150, P212, DOI 10.1016/j.commatsci.2018.03.074
   Cappart Q, 2023, J MACH LEARN RES, V24
   Carlucho I, 2020, ISA T, V102, P280, DOI 10.1016/j.isatra.2020.02.017
   CHARNES A, 1959, MANAGE SCI, V6, P73, DOI 10.1287/mnsc.6.1.73
   Chen C, 2024, Arxiv, DOI [arXiv:2401.04070, 10.48550/arXiv.2401.04070, DOI 10.48550/ARXIV.2401.04070]
   Chen JH, 2014, SEP PURIF TECHNOL, V122, P149, DOI 10.1016/j.seppur.2013.10.023
   Chen Ricky TQ, 2019, Advances in Neural Information Processing Systems, V32
   Chen W, 2021, J MECH DESIGN, V143, DOI 10.1115/1.4048626
   Chen W, 2019, J MECH DESIGN, V141, DOI 10.1115/1.4044076
   Chen Y, 2018, CHINESE J CHEM ENG, V26, P1700, DOI 10.1016/j.cjche.2017.09.010
   Chen YZ, 2018, 2018 52ND ANNUAL CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS (CISS)
   Chen YZ, 2018, IEEE T POWER SYST, V33, P3265, DOI 10.1109/TPWRS.2018.2794541
   Chen ZS, 2021, APPL SOFT COMPUT, V101, DOI 10.1016/j.asoc.2020.107070
   Chenreddy A, 2022, ADV NEUR IN
   Cho KYHY, 2014, Arxiv, DOI arXiv:1406.1078
   Chong HY, 2021, SOFT COMPUT, V25, P11209, DOI 10.1007/s00500-021-05886-z
   Chowdhery A, 2023, J MACH LEARN RES, V24
   Coelho IM, 2017, APPL ENERG, V201, P412, DOI 10.1016/j.apenergy.2017.01.003
   Coley CW, 2018, ACCOUNTS CHEM RES, V51, P1281, DOI 10.1021/acs.accounts.8b00087
   Cozad A, 2014, AICHE J, V60, P2211, DOI 10.1002/aic.14418
   Dai HJ, 2017, ADV NEUR IN, V30
   dAnterroches L, 2005, Process Flowsheet Generation & Design through a Group Contribution Approach
   Daoutidis P, 2024, COMPUT CHEM ENG, V181, DOI 10.1016/j.compchemeng.2023.108523
   Davies A, 2021, NATURE, V600, P70, DOI 10.1038/s41586-021-04086-x
   Decardi-Nelson B, 2022, CHEM ENG RES DES, V177, P502, DOI 10.1016/j.cherd.2021.11.003
   del Rio-Chanona EA, 2019, AICHE J, V65, P915, DOI 10.1002/aic.16473
   Demirel SE, 2019, IND ENG CHEM RES, V58, P5950, DOI 10.1021/acs.iecr.8b05961
   Devlin J., 2018, ARXIV
   Dinh L, 2017, Arxiv, DOI arXiv:1605.08803
   Djeumou Franck, 2022, P MACHINE LEARNING R, V168
   Dogru O, 2022, COMPUT CHEM ENG, V161, DOI 10.1016/j.compchemeng.2022.107760
   Dong W, 2022, APPL ENERG, V308, DOI 10.1016/j.apenergy.2021.118387
   Dubourg V, 2011, ADAPTIVE SURROGATE M
   Dunn A.G., 2023, J. Commun. Healthc, P1
   DURAN MA, 1986, MATH PROGRAM, V36, P307, DOI 10.1007/BF02592064
   Esche E, 2022, CHEM ENG RES DES, V177, P184, DOI 10.1016/j.cherd.2021.10.042
   Fuentes-Cortés LF, 2022, IND ENG CHEM RES, V61, P8932, DOI 10.1021/acs.iecr.2c00335
   Fan DC, 2023, COMPUT CHEM ENG, V176, DOI 10.1016/j.compchemeng.2023.108293
   de Canete JF, 2012, COMPUT CHEM ENG, V40, P157, DOI 10.1016/j.compchemeng.2012.01.003
   Fifty C, 2023, Arxiv, DOI [arXiv:2310.08863, 10.48550/arXiv.2310.08863, DOI 10.48550/ARXIV.2310.08863]
   Flamm C, 2022, IEEE ACM T COMPUT BI, V19, P429, DOI 10.1109/TCBB.2020.2998948
   FONSECA CM, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P416
   Forster T, 2023, AICHE J, V69, DOI 10.1002/aic.18110
   Franzoni Valentina, 2023, Computational Science and Its Applications - ICCSA 2023 Workshops: Proceedings. Lecture Notes in Computer Science (14107), P118, DOI 10.1007/978-3-031-37114-1_9
   Ganea OE, 2021, ADV NEUR IN, V34
   Gani R, 2019, CURR OPIN CHEM ENG, V23, P184, DOI 10.1016/j.coche.2019.04.007
   Gao P, 2023, Arxiv, DOI arXiv:2304.15010
   Gao Q., 2023, arXiv
   Gao QH, 2024, CURR OPIN CHEM ENG, V44, DOI 10.1016/j.coche.2024.101012
   Gao QH, 2023, Arxiv, DOI arXiv:2308.07822
   Gasse M, 2019, ADV NEUR IN, V32
   Geng Z., 2024, Adv. Neural Inf. Process. Syst, V36
   Ghojogh B, 2021, Arxiv, DOI arXiv:2111.13282
   Girin L., 2020, arXiv
   Goerigk M, 2023, COMPUT OPER RES, V151, DOI 10.1016/j.cor.2022.106087
   Goerigk M, 2021, Arxiv, DOI arXiv:2011.09769
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Goodfellow I., 2014, NeurIPS, V27
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Göttl Q, 2021, CHEM-ING-TECH, V93, P2010, DOI 10.1002/cite.202100086
   Grimstad B, 2019, COMPUT CHEM ENG, V131, DOI 10.1016/j.compchemeng.2019.106580
   Grossmann IE, 2017, THEOR FOUND CHEM EN+, V51, P893, DOI 10.1134/S0040579517060057
   Grossmann I.E., Challenges in the new millennium: product discovery and design, enterprise and supply chain optimization, global life cycle assessment
   Grossmann I.E., 1998, AIChE Symposium Series, 2002, P150
   Grossmann IE, 2004, COMPUT CHEM ENG, V28, P1193, DOI 10.1016/j.compchemeng.2003.11.006
   GROSSMANN IE, 1995, COMPUT CHEM ENG, V19, pS189, DOI 10.1016/0098-1354(95)00072-A
   Grossmann IE, 2000, LATIN AM APPL RES, V30, P263
   Grossmann IE, 2000, AICHE J, V46, P1700, DOI 10.1002/aic.690460902
   Grossmann IE, 2016, COMPUT CHEM ENG, V91, P3, DOI 10.1016/j.compchemeng.2016.03.002
   Guo F, 2020, CHEMOMETR INTELL LAB, V197, DOI 10.1016/j.chemolab.2019.103922
   Guo Z., 2023, Nat. Rev. Bioeng, DOI [10.1038/s44222-023-00114-9,2023/10/27, DOI 10.1038/S44222-023-00114-9,2023/10/27]
   Gustafsson J., 2023, Scenario generation for stress testing using generative adversarial networks: deep learning approach to generate extreme but plausible scenarios
   He YL, 2022, J PROCESS CONTR, V113, P18, DOI 10.1016/j.jprocont.2022.03.008
   Henao CA, 2011, AICHE J, V57, P1216, DOI 10.1002/aic.12341
   Hensel M, 2017, ADV NEUR IN, V30
   Hinton G.E., 1993, Advances in Neural Information Processing Systems, V6, P3
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Hoogeboom E, 2022, PR MACH LEARN RES
   Hoogeboom Emiel, 2021, INT C LEARNING REPRE, DOI 10.48550/arXiv.2110.02037
   Horan D, 2021, ADV NEUR IN
   Horst R., 2013, Global Optimization: Deterministic Approaches
   Hsu YC, 2022, APL MATER, V10, DOI 10.1063/5.0082338
   Hu C, 2023, AICHE J, V69, DOI 10.1002/aic.18210
   Hu GQ, 2023, APPL ENERG, V348, DOI 10.1016/j.apenergy.2023.121450
   Hua CQ, 2024, Arxiv, DOI [arXiv:2304.14621, DOI 10.48550/ARXIV.2304.14621]
   Huang B, 2008, LECT NOTES CONTR INF, V374, P1
   Huang CH, 2022, ACS OMEGA, V7, P2996, DOI 10.1021/acsomega.1c06033
   Huang L, 2023, AAAI CONF ARTIF INTE, P5105
   Huang N, 2020, CSEE J POWER ENERGY
   Huang NT, 2021, J ENERGY STORAGE, V33, DOI 10.1016/j.est.2020.102081
   Huang ZR, 2022, PATTERN RECOGN, V123, DOI 10.1016/j.patcog.2021.108353
   Hughes M, 2020, COMPUT OPER RES, V122, DOI 10.1016/j.cor.2020.104998
   Midgley LI, 2020, Arxiv, DOI [arXiv:2009.13265, 10.48550/arxiv.2009.13265, DOI 10.48550/ARXIV.2009.13265]
   Istadi, 2006, IND ENG CHEM RES, V45, P6655, DOI 10.1021/ie060562c
   Jablonka KM, 2023, DIGIT DISCOV, V2, P1233, DOI 10.1039/d3dd00113j
   Jensen Z, 2021, ACS CENTRAL SCI, V7, P858, DOI 10.1021/acscentsci.1c00024
   Ji XF, 2021, IEEE T EVOLUT COMPUT, V25, P794, DOI 10.1109/TEVC.2021.3064835
   Jiang CM, 2018, IEEE ACCESS, V6, P62193, DOI 10.1109/ACCESS.2018.2875936
   Jiang YJ, 2023, ENGINEERING-PRC, V25, P32, DOI 10.1016/j.eng.2022.04.021
   Jiang YC, 2021, IEEE SENS J, V21, P12868, DOI 10.1109/JSEN.2020.3033153
   Jiang ZMJ, 2022, ADV ENG INFORM, V54, DOI 10.1016/j.aei.2022.101786
   Jin W., Junction tree variational autoencoder for molecular graph generation.
   Jin YC, 2019, IEEE T EVOLUT COMPUT, V23, P442, DOI 10.1109/TEVC.2018.2869001
   Jin YC, 2002, IEEE T EVOLUT COMPUT, V6, P481, DOI 10.1109/TEVC.2002.800884
   Jumper J, 2021, NATURE, V596, P583, DOI 10.1038/s41586-021-03819-2
   Kadlec P, 2011, COMPUT CHEM ENG, V35, P1, DOI 10.1016/j.compchemeng.2010.07.034
   Kadlec P, 2009, COMPUT CHEM ENG, V33, P795, DOI 10.1016/j.compchemeng.2008.12.012
   Kaelbling LP, 1996, J ARTIF INTELL RES, V4, P237, DOI 10.1613/jair.301
   Kajino H, 2019, PR MACH LEARN RES, V97
   Kallrath J, 2000, CHEM ENG RES DES, V78, P809, DOI 10.1205/026387600528012
   Karalias N., 2020, Advances in Neural Information Processing Systems, V33, P6659
   Karras Tero, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P8107, DOI 10.1109/CVPR42600.2020.00813
   Karras T, 2019, PROC CVPR IEEE, P4396, DOI 10.1109/CVPR.2019.00453
   Kaveh M, 2023, NEURAL PROCESS LETT, V55, P4519, DOI 10.1007/s11063-022-11055-6
   Kazda K., 2022, High-Dimensional Optimization and Probability: With a View Towards Data Science, P341
   Kenthapadi K, 2023, PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, P5805, DOI 10.1145/3580305.3599557
   Kim S, 2023, APPL THERM ENG, V223, DOI [10.1016/j.applthermaleng.2023.120038, 10.1021/acsenergylett.3c02307]
   Kim SH, 2020, COMPUT CHEM ENG, V140, DOI 10.1016/j.compchemeng.2020.106847
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   Kirillov A, 2023, Arxiv, DOI arXiv:2304.02643
   Klatt KU, 2009, COMPUT CHEM ENG, V33, P536, DOI 10.1016/j.compchemeng.2008.09.002
   Kobyzev I, 2021, IEEE T PATTERN ANAL, V43, P3964, DOI 10.1109/TPAMI.2020.2992934
   Koh J.Y., 2023, INT C MACHINE LEARNI, P17283
   KOSMATOPOULOS EB, 1995, IEEE T NEURAL NETWOR, V6, P422, DOI 10.1109/72.363477
   Krenn M, 2020, MACH LEARN-SCI TECHN, V1, DOI 10.1088/2632-2153/aba947
   Kumar M, 2007, IEEE SENS J, V7, P723, DOI 10.1109/JSEN.2007.894905
   Larochelle H., JMLR WORKSHOP C P, P29
   Latifi SE, 2017, COMPUT CHEM ENG, V106, P224, DOI 10.1016/j.compchemeng.2017.05.022
   Lawrence NP, 2022, CONTROL ENG PRACT, V121, DOI 10.1016/j.conengprac.2021.105046
   Lazzara M, 2022, AEROSP SCI TECHNOL, V126, DOI 10.1016/j.ast.2022.107629
   Lee JH, 2018, COMPUT CHEM ENG, V114, P111, DOI 10.1016/j.compchemeng.2017.10.008
   Lee YS, 2020, COMPUT CHEM ENG, V136, DOI 10.1016/j.compchemeng.2020.106802
   Lew AJ, 2023, MATER TODAY, V64, P10, DOI 10.1016/j.mattod.2023.03.007
   Li BH, 2023, Arxiv, DOI arXiv:2307.16125
   Li BW, 2022, COMPUT CHEM ENG, V157, DOI 10.1016/j.compchemeng.2021.107599
   Li CY, 2023, Arxiv, DOI arXiv:2309.10020
   Li JN, 2023, Arxiv, DOI arXiv:2301.12597
   Li KW, 2022, IEEE T CYBERNETICS, V52, P13142, DOI 10.1109/TCYB.2021.3103811
   Li P, 2008, COMPUT CHEM ENG, V32, P25, DOI 10.1016/j.compchemeng.2007.05.009
   Li RZ, 2021, AIAA J, V59, P3988, DOI 10.2514/1.J060189
   Li XN, 2004, CHEM ENG PROCESS, V43, P583, DOI 10.1016/j.cep.2003.05.002
   Li ZW, 2018, ADV NEUR IN, V31
   Liang JK, 2020, IEEE J SEL AREA COMM, V38, P110, DOI 10.1109/JSAC.2019.2952182
   Lin YH, 2022, ADV NEUR IN
   Liu CY, 2023, Arxiv, DOI [arXiv:2302.02591, 10.48550/arXiv.2302.02591]
   Liu H, 2023, Arxiv, DOI arXiv:2302.00902
   Liu HT, 2023, Arxiv, DOI [arXiv:2304.08485, 10.48550/arXiv.2304.08485]
   Liu QL, 2019, COMPUT CHEM ENG, V124, P285, DOI 10.1016/j.compchemeng.2019.01.006
   Liu S, 2019, IEEE T AUTOMAT CONTR, V64, P4698, DOI 10.1109/TAC.2019.2902041
   Liu ZJ, 2023, Arxiv, DOI arXiv:2310.02170
   Livne M, 2024, Arxiv, DOI arXiv:2311.12410
   Longadge R, 2013, Arxiv, DOI arXiv:1305.1707
   Ma Chao, 2020, Advances in Neural Information Processing Systems, V33
   MACGREGOR JF, 1995, CONTROL ENG PRACT, V3, P403, DOI 10.1016/0967-0661(95)00014-L
   Makhzani A, 2016, Arxiv, DOI [arXiv:1511.05644, 10.48550/arXiv.1511.05644, DOI 10.48550/ARXIV.1511.05644]
   Mangasarian OL., 1994, Nonlinear Programming, DOI DOI 10.1137/1.9781611971255
   Mann V, 2024, COMPUT CHEM ENG, V181, DOI 10.1016/j.compchemeng.2023.108505
   Mann V, 2021, COMPUT CHEM ENG, V155, DOI 10.1016/j.compchemeng.2021.107533
   Mao YW, 2020, SCI ADV, V6, DOI 10.1126/sciadv.aaz4169
   Margraf JT, 2023, NAT CATAL, V6, P112, DOI 10.1038/s41929-022-00896-y
   Maziarka L, 2020, J CHEMINFORMATICS, V12, DOI 10.1186/s13321-019-0404-1
   McBride K, 2019, CHEM-ING-TECH, V91, P228, DOI 10.1002/cite.201800091
   Mencarelli L, 2020, COMPUT CHEM ENG, V136, DOI 10.1016/j.compchemeng.2020.106808
   Meyers J, 2021, DRUG DISCOV TODAY, V26, P2707, DOI 10.1016/j.drudis.2021.05.019
   Misener R, 2023, COMPUT CHEM ENG, V179, DOI 10.1016/j.compchemeng.2023.108411
   Mitsioni I, 2023, IEEE T ROBOT, V39, P3242, DOI 10.1109/TRO.2023.3266995
   Nian R, 2020, COMPUT CHEM ENG, V139, DOI 10.1016/j.compchemeng.2020.106886
   Ning C, 2022, IEEE T POWER SYST, V37, P191, DOI 10.1109/TPWRS.2021.3096144
   Ning C, 2019, COMPUT CHEM ENG, V125, P434, DOI 10.1016/j.compchemeng.2019.03.034
   Ning C, 2017, AICHE J, V63, P4343, DOI 10.1002/aic.15792
   Niu ZY, 2021, NEUROCOMPUTING, V452, P48, DOI 10.1016/j.neucom.2021.03.091
   Nowozin S, 2016, ADV NEUR IN, V29
   Oeing J, 2022, DIGIT CHEM ENG, V4, DOI 10.1016/j.dche.2022.100038
   Ojha VK, 2017, ENG APPL ARTIF INTEL, V60, P97, DOI 10.1016/j.engappai.2017.01.013
   Olya MH, 2022, EXPERT SYST APPL, V187, DOI 10.1016/j.eswa.2021.115924
   Ouyang L, 2022, ADV NEUR IN
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Palmer K, 2002, CHEM ENG RES DES, V80, P760, DOI 10.1205/026387602320776830
   Pan ZQ, 2019, IEEE ACCESS, V7, P36322, DOI 10.1109/ACCESS.2019.2905015
   Paulus Max B., 2022, P MACHINE LEARNING R
   Pholdee N, 2015, J MECH SCI TECHNOL, V29, P3427, DOI 10.1007/s12206-015-0741-6
   Pistikopoulos EN, 2021, COMPUT CHEM ENG, V147, DOI 10.1016/j.compchemeng.2021.107252
   Prekopa A., 2013, STOCHASTIC PROGRAMMI
   Preuss R, 2018, ENTROPY-SWITZ, V20, DOI 10.3390/e20030201
   Pun FW, 2023, Trends in Pharmacological Sciences
   Qin SJ, 2012, ANNU REV CONTROL, V36, P220, DOI 10.1016/j.arcontrol.2012.09.004
   Radaideh MI, 2020, RELIAB ENG SYST SAFE, V195, DOI 10.1016/j.ress.2019.106731
   Radford A, 2021, PR MACH LEARN RES, V139
   Raina A, 2022, J MECH DESIGN, V144, DOI 10.1115/1.4052566
   Ramesh A, 2022, ARXIV PREPRINT ARXIV, DOI DOI 10.48550/ARXIV.2204.06125
   Rawlings JB, 2012, IEEE DECIS CONTR P, P3851, DOI 10.1109/CDC.2012.6425822
   Rawlings JB, 2000, IEEE CONTR SYST MAG, V20, P38, DOI 10.1109/37.845037
   Rawte V, 2023, Arxiv, DOI [arXiv:2309.05922, 10.48550/arXiv.2309.05922, DOI 10.48550/ARXIV.2309.05922]
   Reed S, 2022, Arxiv, DOI arXiv:2205.06175
   Regenwetter L, 2022, J MECH DESIGN, V144, DOI 10.1115/1.4053859
   Reis M.S., 2008, Statist. Pract. Bus. Industry, P337
   Reiter E, 2018, COMPUT LINGUIST, V44, P393, DOI [10.1162/coli_a_00322, 10.1162/COLI.a.00322]
   Ren YM, 2022, COMPUT CHEM ENG, V165, DOI 10.1016/j.compchemeng.2022.107956
   Rittig JG, 2023, AICHE J, V69, DOI 10.1002/aic.17971
   Rodriguez S, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-21330-0
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Rosen AS, 2021, MATTER-US, V4, P1578, DOI 10.1016/j.matt.2021.02.015
   Rosenfeld R, 2000, P IEEE, V88, P1270, DOI 10.1109/5.880083
   RYOO HS, 1995, COMPUT CHEM ENG, V19, P551, DOI 10.1016/0098-1354(94)00097-8
   Sachio S, 2022, CHEM ENG RES DES, V183, P160, DOI 10.1016/j.cherd.2021.10.032
   Saharia C, 2022, ADV NEUR IN
   Sahinidis NV, 2004, COMPUT CHEM ENG, V28, P971, DOI 10.1016/j.compchemeng.2003.09.017
   Sahinidis NV, 2000, COMPUT CHEM ENG, V24, P2157, DOI 10.1016/S0098-1354(00)00583-4
   Salimans T, 2016, ADV NEUR IN, V29
   Samek W., 2019, Towards explainable artificial intelligence, P5, DOI DOI 10.1007/978-3-030-28954-6_1
   Saptoro A, 2014, PROCEDIA CHEM, V9, P226, DOI 10.1016/j.proche.2014.05.027
   Sargent RWH, 2004, COMPUT CHEM ENG, V28, P437, DOI 10.1016/j.compchemeng.2003.09.032
   SCHMIDHUBER J, 1991, IEEE IJCNN, P1458, DOI 10.1109/IJCNN.1991.170605
   Schreck JS, 2019, ACS CENTRAL SCI, V5, P970, DOI 10.1021/acscentsci.9b00055
   Schrittwieser J, 2020, NATURE, V588, P604, DOI 10.1038/s41586-020-03051-4
   Schweidtmann AM, 2023, COMPUT CHEM ENG, V172, DOI 10.1016/j.compchemeng.2023.108202
   Schweidtmann AM, 2021, MATH PROGRAM COMPUT, V13, P553, DOI 10.1007/s12532-021-00204-y
   Seborg D.E., 2016, Process dynamics and control
   Secinaro S, 2021, BMC MED INFORM DECIS, V21, DOI 10.1186/s12911-021-01488-9
   Seider W.D., 2017, Product and process design principles
   Senties OB, 2009, IND ENG CHEM RES, V48, P9546, DOI 10.1021/ie8018577
   Shao SY, 2019, COMPUT IND, V106, P85, DOI 10.1016/j.compind.2019.01.001
   Sharifnia SME, 2021, COMPUT IND ENG, V162, DOI 10.1016/j.cie.2021.107693
   Shi CC, 2020, Arxiv, DOI [arXiv:2001.09382, 10.48550/arXiv.2001.09382, DOI 10.48550/ARXIV.2001.09382]
   Siirola JD, 2003, COMPUT CHEM ENG, V27, P1801, DOI 10.1016/S0098-1354(03)00152-2
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Singh A, 2014, IND ENG CHEM RES, V53, P15111, DOI 10.1021/ie5020519
   Sitapure N, 2023, COMPUT CHEM ENG, V177, DOI 10.1016/j.compchemeng.2023.108339
   Sitapure N, 2023, CHEM ENG RES DES, V194, P461, DOI 10.1016/j.cherd.2023.04.028
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Song BY, 2024, J COMPUT INF SCI ENG, V24, DOI 10.1115/1.4063954
   Song SZ, 2023, Arxiv, DOI arXiv:2311.07594
   Staerk H, 2024, Arxiv, DOI arXiv:2310.05764
   Stephanopoulos G, 1996, COMPUT CHEM ENG, V20, P743, DOI 10.1016/0098-1354(95)00194-8
   Stops L, 2023, AICHE J, V69, DOI 10.1002/aic.17938
   Sun LN, 2020, COMPUT METHOD APPL M, V361, DOI 10.1016/j.cma.2019.112732
   Susto GA, 2015, COMPUT OPER RES, V53, P328, DOI 10.1016/j.cor.2014.05.008
   Swanson K, 2024, NAT MACH INTELL, V6, DOI 10.1038/s42256-024-00809-7
   Taifouris M, 2020, CURR OPIN CHEM ENG, V27, P1, DOI 10.1016/j.coche.2019.10.001
   Tan CQ, 2018, LECT NOTES COMPUT SC, V11141, P270, DOI 10.1007/978-3-030-01424-7_27
   Tang P, 2021, ISA T, V114, P444, DOI 10.1016/j.isatra.2021.01.002
   Tang Y., 2020, P MACHINE LEARNING R
   Tawarmalani M, 2005, MATH PROGRAM, V103, P225, DOI 10.1007/s10107-005-0581-8
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Tripathy RK, 2018, J COMPUT PHYS, V375, P565, DOI 10.1016/j.jcp.2018.08.036
   Trischler AP, 2016, NEURAL NETWORKS, V80, P67, DOI 10.1016/j.neunet.2016.04.001
   Ullah Z, 2022, PROCESS SAF ENVIRON, V162, P337, DOI 10.1016/j.psep.2022.04.013
   Valadi J., 2014, APPL METAHEURISTICS, DOI DOI 10.1007/978-3-319-06508-3
   Vân AHT, 2012, BIOINFORMATICS, V28, P1766, DOI 10.1093/bioinformatics/bts238
   van Kalmthout S.C., 2022, arXiv, DOI DOI 10.48550/ARXIV.2211.04327
   VANOVERSCHEE P, 1994, AUTOMATICA, V30, P75, DOI 10.1016/0005-1098(94)90230-5
   Vaswani A, 2017, ADV NEUR IN, V30
   VENKATASUBRAMANIAN V, 1994, COMPUT CHEM ENG, V18, P833, DOI 10.1016/0098-1354(93)E0023-3
   Venkatasubramanian V, 2022, CURR OPIN CHEM ENG, V36, DOI 10.1016/j.coche.2021.100749
   Venkatsubramanian V, 2003, COMPUT CHEM ENG, V27, P293, DOI 10.1016/S0098-1354(02)00160-6
   Viberg M, 1995, AUTOMATICA, V31, P1835, DOI 10.1016/0005-1098(95)00107-5
   Vincent P, 2010, J MACH LEARN RES, V11, P3371
   Vogel G, 2023, OPTIM ENG, V24, P2911, DOI 10.1007/s11081-023-09798-9
   Vogel G, 2023, COMPUT CHEM ENG, V171, DOI 10.1016/j.compchemeng.2023.108162
   Wang C, 2021, COMPUT CHEM ENG, V155, DOI 10.1016/j.compchemeng.2021.107495
   Wang H, 2018, IEEE T GEOSCI REMOTE, V56, P6956, DOI 10.1109/TGRS.2018.2846199
   Wang J, 2022, INFOR, V60, P133, DOI 10.1080/03155986.2021.2015825
   Wang Q, 2021, KNOWL-BASED SYST, V233, DOI 10.1016/j.knosys.2021.107526
   Wang S., 2023, EMNLP DEMOS, P346
   Wang WH, 2023, Arxiv, DOI arXiv:2305.11175
   Wang X, 2020, J PROCESS CONTR, V85, P91, DOI 10.1016/j.jprocont.2019.11.004
   Wang XL, 2021, KNOWL-BASED SYST, V227, DOI 10.1016/j.knosys.2021.107190
   Wang X, 2023, LANGMUIR, V39, P1793, DOI 10.1021/acs.langmuir.2c02622
   Wang YQ, 2020, ACM COMPUT SURV, V53, DOI 10.1145/3386252
   Wang ZD, 2023, Arxiv, DOI arXiv:2206.02262
   Wang ZH, 2023, Arxiv, DOI arXiv:2302.00244
   Wang ZS, 2021, ADV ENG INFORM, V49, DOI 10.1016/j.aei.2021.101315
   WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
   Wu JY, 2023, Arxiv, DOI arXiv:2311.13165
   Wu Z, 2019, AICHE J, V65, DOI 10.1002/aic.16734
   Wu Z, 2019, AICHE J, V65, DOI 10.1002/aic.16729
   Xia YF, 2017, EXPERT SYST APPL, V78, P225, DOI 10.1016/j.eswa.2017.02.017
   Xie JY, 2023, COMPUT CHEM ENG, V173, DOI 10.1016/j.compchemeng.2023.108209
   Xie RM, 2020, IEEE T IND INFORM, V16, P2820, DOI 10.1109/TII.2019.2951622
   Xu LK, 2023, CHEM ENG SCI, V282, DOI 10.1016/j.ces.2023.119188
   Xu M., 2022, arXiv
   Yan K, 2020, BUILD ENVIRON, V172, DOI 10.1016/j.buildenv.2020.106698
   Yang CR, 2024, Arxiv, DOI arXiv:2309.03409
   Yang D, 2022, COMPUT OPTIM APPL, V83, P759, DOI 10.1007/s10589-022-00404-9
   Yang LY, 2024, ACM COMPUT SURV, V56, DOI [10.1145/3626235, 10.1145/3648469]
   Yang X.-S., 2010, Engineering Optimization. An Introduction with Metaheuristic Applications
   Yang XY, 2022, ALGORITHMS, V15, DOI 10.3390/a15060205
   Yang ZY, 2022, AAAI CONF ARTIF INTE, P3081
   Yang ZJ, 2018, J MECH DESIGN, V140, DOI 10.1115/1.4041371
   Yao ZP, 2021, NAT MACH INTELL, V3, P76, DOI 10.1038/s42256-020-00271-1
   Yim J, 2023, Arxiv, DOI [arXiv:2302.02277, 10.48550/arXiv.2302.02277]
   Yu HB, 2018, INFORM SCIENCES, V454, P59, DOI 10.1016/j.ins.2018.04.062
   Yu JH, 2022, Arxiv, DOI [arXiv:2206.10789, 10.48550/arXiv.2206.10789]
   Yu LL, 2023, Arxiv, DOI arXiv:2309.02591
   Yu Z, 2023, Arxiv, DOI arXiv:2303.01903
   Yule GU, 1927, PHILOS T R SOC LOND, V226, P267, DOI 10.1098/rsta.1927.0007
   Zavala VM, 2023, IND ENG CHEM RES, V62, P8995, DOI 10.1021/acs.iecr.3c01565
   Zhang CH, 2023, REACT CHEM ENG, V8, P2491, DOI 10.1039/d2re00406b
   Zhang H, 2022, 36 C NEURAL INFORM P
   Zhang J, 2023, COMPUT CHEM ENG, V177, DOI 10.1016/j.compchemeng.2023.108335
   Zhang L, 2020, CURR OPIN CHEM ENG, V27, P22, DOI 10.1016/j.coche.2019.10.005
   Zhang L, 2018, COMPUT CHEM ENG, V115, P295, DOI 10.1016/j.compchemeng.2018.04.018
   Zhang L, 2016, ANNU REV CHEM BIOMOL, V7, P557, DOI 10.1146/annurev-chembioeng-080615-034439
   Zhang LM, 2023, IEEE I CONF COMP VIS, P3813, DOI 10.1109/ICCV51070.2023.00355
   Zhang Q., 2021, Advances in Neural Information Processing Systems, V34, P16280
   Zhang RR, 2024, Arxiv, DOI [arXiv:2303.16199, 10.48550/arXiv.2303.16199]
   Zhang SF, 2018, IEEE SYS MAN CYBERN, P415, DOI 10.1109/SMC.2018.00080
   Zhang TY, 2022, INT C PATT RECOG, P3105, DOI 10.1109/ICPR56361.2022.9956256
   Zhang W., 2023, NEURIPS 2023 WORKSH
   Zhao SP, 2020, AICHE J, V66, DOI 10.1002/aic.16963
   Zhu DY, 2023, Arxiv, DOI [arXiv:2304.10592, 10.48550/arXiv.2304.10592]
   Zhu QX, 2021, ENG APPL ARTIF INTEL, V106, DOI 10.1016/j.engappai.2021.104497
   Zhu XL, 2022, IEEE T IND INFORM, V18, P5190, DOI 10.1109/TII.2021.3110197
   Zuo K, 2000, COMPUT CHEM ENG, V24, P1105, DOI 10.1016/S0098-1354(00)00490-7
NR 364
TC 9
Z9 9
U1 46
U2 48
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0098-1354
EI 1873-4375
J9 COMPUT CHEM ENG
JI Comput. Chem. Eng.
PD AUG
PY 2024
VL 187
AR 108723
DI 10.1016/j.compchemeng.2024.108723
EA MAY 2024
PG 23
WC Computer Science, Interdisciplinary Applications; Engineering, Chemical
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA TK2Z3
UT WOS:001241104200001
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Teo, ZL
   Quek, CWN
   Wong, JLY
   Ting, DSW
AF Teo, Zhen Ling
   Quek, Chrystie Wan Ning
   Wong, Joy Le Yi
   Ting, Daniel Shu Wei
TI Cybersecurity in the generative artificial intelligence era
SO ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY
LA English
DT Article
DE Generative Artificial Intelligence; ChatGPT; Cybersecurity; Privacy
   risks; Large language model
AB Generative Artificial Intelligence (GenAI) are algorithms capable of generating original content. The ability of GenAI to learn and generate novel outputs alike human cognition has taken the world by storm and ushered in a new era. In this review, we explore the role of GenAI in healthcare, including clinical, operational, and research applications, and delve into the cybersecurity risks of this technology. We discuss risks such as data privacy risks, data poisoning attacks, the propagation of bias, and hallucinations. In this review, we recommend risk mitigation strategies to enhance cybersecurity in GenAI technologies and further explore the use of GenAI as a tool in itself to enhance cybersecurity across the various AI algorithms. GenAI is emerging as a pivotal catalyst across various industries including the healthcare domain. Comprehending the intricacies of this technology and its potential risks will be imperative for us to fully capitalise on the benefits that GenAI can bring.
C1 [Teo, Zhen Ling; Quek, Chrystie Wan Ning; Wong, Joy Le Yi; Ting, Daniel Shu Wei] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.
   [Quek, Chrystie Wan Ning; Wong, Joy Le Yi; Ting, Daniel Shu Wei] Duke NUS Med Sch, Singapore, Singapore.
C3 National University of Singapore; Singapore National Eye Center;
   National University of Singapore
RP Teo, ZL; Ting, DSW (corresponding author), Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.
EM zhenling.teo@mohh.com.sg; daniel.ting@duke-nus.edu.sg
RI Teo, Zhen Ling/HLH-2188-2023
FU National Medical Research Council Singapore
   [MOH-000655-00/MOH-001014-00]; Duke-NUS Medical School
   [Duke-NUS/RSF/2021/001805/FY2020/EX/15-A58]; Agency for Science
   Technology and Research [A20H4g2141, H20C6a0032]
FX This research was funded by National Medical Research Council Singapore,
   MOH-000655-00/MOH-001014-00, Duke-NUS Medical School,
   Duke-NUS/RSF/2021/001805/FY2020/EX/15-A58 and Agency for Science
   Technology and Research, A20H4g2141, H20C6a0032.
CR Adnan M, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-05539-7
   Ali S, 2024, AAAI CONF ARTIF INTE, P23268
   Arya V, 2019, Arxiv, DOI arXiv:1909.03012
   Baid G, 2023, NAT BIOTECHNOL, V41, P232, DOI 10.1038/s41587-022-01435-7
   Betzler BK, 2023, LANCET DIGIT HEALTH, V5, pE917, DOI 10.1016/S2589-7500(23)00201-7
   Casper S, 2023, Arxiv, DOI [arXiv:2307.15217, 10.48550/arXiv.2307.15217]
   Chung HW, 2024, J MACH LEARN RES, V25
   Cuttitta A, 2021, JAMA OPHTHALMOL, V139, P1309, DOI 10.1001/jamaophthalmol.2021.4393
   D'Amico S, 2023, JCO CLIN CANCER INFO, V7, DOI 10.1200/CCI.23.00021
   Das A., 2024, PREPRINT
   de Raffele D, 2024, CHEM COMMUN, V60, P632, DOI 10.1039/d3cc04630c
   Eppright C, 2021, Oracle
   Froelicher D, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-25972-y
   Gu B, 2023, ASIA-PAC J OPHTHALMO, V12, P392, DOI 10.1097/APO.0000000000000619
   Gunasekeran DV, 2022, FRONT MED-LAUSANNE, V9, DOI 10.3389/fmed.2022.875242
   Habashi AG, 2023, J NEUROENG REHABIL, V20, DOI 10.1186/s12984-023-01169-w
   Habli I, 2020, B WORLD HEALTH ORGAN, V98, P251, DOI 10.2471/BLT.19.237487
   Helen D., 2024, Revolut. Healthc. Sec. AI, P79
   Hsia Y, 2023, ASIA-PAC J OPHTHALMO, V12, P21, DOI 10.1097/APO.0000000000000576
   Huang SK, 2020, AD HOC NETW, V105, DOI 10.1016/j.adhoc.2020.102177
   Hueso M, 2023, REV INVEST CLIN, V75, P309, DOI [10.24875/ric.23000162, 10.24875/RIC.23000162]
   Jin K, 2022, ADV OPHTHALMOL PRACT, V2, DOI 10.1016/j.aopr.2022.100078
   Jin LC, 2020, COMPUT INTEL NEUROSC, V2020, DOI 10.1155/2020/1459107
   Jindal JA, 2024, J AM MED INFORM ASSN, V31, DOI 10.1093/jamia/ocae043
   Kanjee Z, 2023, JAMA-J AM MED ASSOC, V330, P78, DOI 10.1001/jama.2023.8288
   Larson HJ, 2024, BMJ-BRIT MED J, V384, DOI 10.1136/bmj.q69
   Li LH, 2023, ASIA-PAC J OPHTHALMO, V12, P486, DOI 10.1097/APO.0000000000000583
   Li ZW, 2023, CELL REP MED, V4, DOI 10.1016/j.xcrm.2023.101095
   Lin SPN, 2022, Arxiv, DOI [arXiv:2109.07958, 10.48550/arXiv.2109.07958, DOI 10.48550/ARXIV.2109.07958]
   Luo RQ, 2022, BRIEF BIOINFORM, V23, DOI 10.1093/bib/bbac409
   Mosqueira-Rey E, 2023, ARTIF INTELL REV, V56, P3005, DOI 10.1007/s10462-022-10246-w
   Naik N, 2022, FRONT SURG, V9, DOI 10.3389/fsurg.2022.862322
   Nan Y, 2022, INFORM FUSION, V82, P99, DOI 10.1016/j.inffus.2022.01.001
   Nazer Lama H, 2023, PLOS Digit Health, V2, pe0000278, DOI 10.1371/journal.pdig.0000278
   Nicoletti L., 2023, Humans are biased: Generative AI is even worse
   OpenAI, 2023, March 20 ChatGPT outage: Here's what happened
   OpenAI, 2024, CHATGPT OUR LANGUAGE
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Pruneski JA, 2023, KNEE SURG SPORT TR A, V31, P1203, DOI 10.1007/s00167-022-07272-0
   Rabie OBJ, 2024, INT J INF SECUR, V23, P51, DOI 10.1007/s10207-023-00748-1
   Radclyffe C, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1020592
   Raman R, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24727
   Reddy S, 2024, IMPLEMENT SCI, V19, DOI 10.1186/s13012-024-01357-9
   Rehman A, 2024, BIOMED SIGNAL PROCES, V89, DOI 10.1016/j.bspc.2023.105893
   Rodler S, 2024, SURGERY, V175, P1496, DOI 10.1016/j.surg.2024.02.019
   Saeed H, 2022, PLOS ONE, V17, DOI 10.1371/journal.pone.0266462
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   Selvarajan S, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-34354-x
   Sharma N, 2024, P CHI C HUM FACT COM, DOI [10.1145/3613904.3642459, DOI 10.1145/3613904.3642459]
   Shoja MM, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.40883
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Singhal K, 2023, Arxiv, DOI [arXiv:2305.09617, DOI 10.48550/ARXIV.2305.09617]
   SOMMERSPERGER M, 2022, BIOMED OPT EXPRESS, V13, P2414, DOI 10.1364/BOE.454286
   Stryker Cole, 2024, What Is Generative AI?'
   Subbanna N, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21113874
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
   Vert JP, 2023, NAT BIOTECHNOL, V41, P750, DOI 10.1038/s41587-023-01789-6
   Waisberg E, 2024, Surv Ophthalmol, VS0039-6257, P00044
   Yap A, 2022, ASIA-PAC J OPHTHALMO, V11, P287, DOI 10.1097/APO.0000000000000525
   Yigit Y, 2024, Arxiv, DOI arXiv:2403.08701
   Yiu A, 2023, Ann R Coll Surg Engl
   Yoo TK, 2020, COMPUT BIOL MED, V118, DOI 10.1016/j.compbiomed.2020.103628
   Yu P, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11202776
   Zack Travis, 2024, Lancet Digit Health, V6, pe12, DOI 10.1016/S2589-7500(23)00225-X
   Zhang R, 2022, J COMPUT BIOL, V29, P1198, DOI 10.1089/cmb.2022.0264
NR 66
TC 0
Z9 0
U1 33
U2 33
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
EI 2162-0989
J9 ASIA-PAC J OPHTHALMO
JI Asia-Pac. J. Ophthalmol.
PD JUL-AUG
PY 2024
VL 13
IS 4
AR 100091
DI 10.1016/j.apjo.2024.100091
PG 6
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA F3R1L
UT WOS:001309019100001
PM 39209217
OA gold
DA 2024-12-25
ER

PT J
AU Liu, YH
   Park, J
   Mcminn, S
AF Liu, Yanhua
   Park, Jaeuk
   Mcminn, Sean
TI Using generative artificial intelligence/ChatGPT for academic
   communication: Students' perspectives
SO INTERNATIONAL JOURNAL OF APPLIED LINGUISTICS
LA English
DT Article
DE generative AI; higher education; students' perception; teaching and
   learning; ChatGPT
AB Generative artificial intelligence (GenAI) tools such as ChatGPT with their human-like intelligence and language processing capabilities are significantly impacting the way we live, work, and communicate with each other. While scholars have increasingly focused on the use of GenAI in higher education since its inception, little is known about how key higher education stakeholders, particularly students, perceive its impact on teaching and learning within the context of academic communication, an area central to students' development of transferable skills and literacy competencies yet heavily influenced by the technology. This empirical study addresses the gap by investigating students' experiences and attitudes toward GenAI tools for English academic communication, focusing on their overall perceptions, perceived benefits, limitations, and challenges. Drawing on data from a questionnaire survey with 475 students and interviews with 12 at two universities in China, our findings indicate that students generally view GenAI positively, considering them useful for learning academic communication skills, particularly in writing, grammar, vocabulary, and reading. However, limitations are recognized in terms of giving feedback on critical thinking, creativity, and speaking skills. In addition, information reliability, ethical issues, and impact on assessment and academic integrity also emerged as important concerns. Our study argues that universities should embrace and capitalize on the affordances of GenAI and address its challenges to better support students' learning of critical academic literacy.
C1 [Liu, Yanhua; Park, Jaeuk] Hong Kong Univ Sci & Technol, Pillar Language Educ, 1 Duxue Rd, Guangzhou 511453, Peoples R China.
   [Mcminn, Sean] Hong Kong Univ Sci & Technol, Ctr Educ Innovat, Hong Kong, Peoples R China.
C3 Hong Kong University of Science & Technology; Hong Kong University of
   Science & Technology
RP Park, J (corresponding author), Hong Kong Univ Sci & Technol, Pillar Language Educ, 1 Duxue Rd, Guangzhou 511453, Peoples R China.
EM jaeukpark@hkust-gz.edu.cn
RI Liu, Yanhua/HSD-6801-2023; Park, Jaeuk/KOC-8935-2024
OI Park, Jaeuk/0000-0002-6957-3208; Mcminn, Sean William
   John/0000-0001-9392-4680; Liu, Yanhua/0000-0003-0017-5575
FU Center for Education Innovation
FX We would like to thank the participants in the study as well as the
   anonymous reviewers for their insightful comments. We would also like to
   deliver gratitude to the Center for Education Innovation for supporting
   this scholarship activity.
CR Abdelwahab HR, 2023, IND HIGHER EDUC, V37, P22, DOI 10.1177/09504222221087614
   Ahmad H., 2023, INT J DATA NETWORK S, V7, P35, DOI DOI 10.5267/J.IJDNS.2022.12.009
   Alphoso G., 2023, FORBES
   Alshater M., 2022, EXPLORING ROLE ARTIF, DOI [10.2139/ssrn.4312358, DOI 10.2139/SSRN.4312358]
   Athaluri SA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37432
   Bailey D, 2021, INTERACT TECHNOL SMA, V18, P85, DOI 10.1108/ITSE-08-2020-0170
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Biggs J., 2011, EBOOK TEACHING QUALI
   Brown TB, 2020, ADV NEUR IN, V33
   Cassidy C., 2023, AUSTR U RETURN PEN P
   Chakraborty U., 2023, Rise of generative AI and ChatGPT: Understand how generative AI and ChatGPT are transforming and reshaping the business world
   Chan C. K. Y., 2023, STUDENTSVOICES GENER
   Chan C. K. Y., 2023, AI GENERATION GAP AR
   Chan C. K. Y., 2023, DECONSTRUCTING STUDE
   Chomsky N, 2023, NEW YORK TIMES
   Chuah KM, 2021, INT J EMERG TECHNOL, V16, P223, DOI 10.3991/ijet.v16i20.24917
   Clarizia Fabio, 2018, Cyberspace Safety and Security. 10th International Symposium, CSS 2018. Proceedings: Lecture Notes in Computer Science (LNCS 11161), P291, DOI 10.1007/978-3-030-01689-0_23
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Drnyei Z., 2022, QUESTIONNAIRES 2 LAN
   Elkhodr M., 2023, STEM Educ, V3, P70, DOI [10.3934/steme.2023006, DOI 10.3934/STEME.2023006]
   Essel HB, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00362-6
   Barcelos AMF, 2011, SYSTEM, V39, P281, DOI 10.1016/j.system.2011.07.001
   Gayed JM., 2022, COMPUTERS ED ARTIFIC, V3, P100055, DOI DOI 10.1016/J.CAEAI.2022.100055
   Gilson A, 2022, medRxiv
   Godwin-Jones R, 2023, LANG LEARN TECHNOL, V27, P6
   Han Daeun, 2021, Robotics & AI Ethics, V6, P1
   Hong, 2023, J ED TECHNOLOGY INNO, V5, pArticl, DOI [10.61414/jeti.v5i1.103, DOI 10.61414/JETI.V5I1.103]
   Huang WJ, 2022, J COMPUT ASSIST LEAR, V38, P237, DOI 10.1111/jcal.12610
   Hyland K., 2009, ACAD DISCOURSE ENGLI
   Hyland K, 2021, J ENGL ACAD PURP, V49, DOI 10.1016/j.jeap.2020.100929
   Ilkka T., 2018, IMPACT ARTIFICIAL IN
   Kashefi A., 2023, J Mach Learn Modeling Comput, DOI DOI 10.1615/JMACHLEARNMODELCOMPUT.2023048492
   Kim J, 2020, INT J HUM-COMPUT INT, V36, P1902, DOI 10.1080/10447318.2020.1801227
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kung T. H, 2023, PLOS Digit Health, V2, DOI DOI 10.1371/JOURNAL.PDIG.0000198.PDIG-D-22-00371
   Lee YF, 2022, ETR&D-EDUC TECH RES, V70, P1843, DOI 10.1007/s11423-022-10142-8
   Liu Y., 2023, Meta-Radiology, DOI [DOI 10.1016/J.METRAD.2023.100017, DOI 10.1016/J.METRAD.2023.1000172]
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Morandini Sofia, 2023, International Journal of an Emerging Transdiscipline, P39, DOI 10.28945/5078
   Neuendorf KA., 2017, CONTENT ANAL GUIDEBO, DOI DOI 10.4135/9781071802878
   Okonkwo C. W., 2021, Comput. Educ. Artif. Intell., V2, DOI [10.1016/j.caeai.2021.100033, DOI 10.1016/J.CAEAI.2021.100033]
   Pack A, 2023, TESOL QUART, V57, P1571, DOI 10.1002/tesq.3253
   Peacock M., 1999, International Journal of Applied Linguistics, V9, P247, DOI DOI 10.1111/J.1473-4192.1999.TB00175.X
   Plata S., 2023, ASIAN J UNI EDU, V19, P743, DOI [DOI 10.24191/AJUE.V19I4.24697, 10.24191/ajue.v19i4.24697]
   Pokrivcakova S., 2022, TEACHER TRAINEES ATT, DOI [10.21125/inted.2022.2108, DOI 10.21125/INTED.2022.2108]
   Qadir J., 2023, 2023 IEEE GLOB ENG E, P19
   Raman R., 2023, U STUDENTS EARLY ADO, DOI [10.21203/rs.3.rs-2734142/v1, DOI 10.21203/RS.3.RS-2734142/V1]
   Sharadgah TA, 2022, J INF TECHNOL EDUC-R, V21, P337, DOI 10.28945/4999
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Sumakul D. T. Y., 2022, P 67 TEFLIN INT VIRT, DOI [10.2991/assehr.k.220201.009, DOI 10.2991/ASSEHR.K.220201.009]
   Swales JM, 2019, J ENGL ACAD PURP, V38, P75, DOI 10.1016/j.jeap.2019.01.003
   Wen XH, 2020, SYSTEM, V90, DOI 10.1016/j.system.2020.102216
   Yang HZ, 2022, AUSTRALAS J EDUC TEC, V38, P180, DOI 10.14742/ajet.7492
   Zou B, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15042872
NR 54
TC 2
Z9 2
U1 181
U2 181
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0802-6106
EI 1473-4192
J9 INT J APPL LINGUIST
JI Int. J. Appl. Linguist.
PD NOV
PY 2024
VL 34
IS 4
BP 1437
EP 1461
DI 10.1111/ijal.12574
EA JUN 2024
PG 25
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA L1R0Z
UT WOS:001257874500001
DA 2024-12-25
ER

PT J
AU Giannakos, M
   Azevedo, R
   Brusilovsky, P
   Cukurova, M
   Dimitriadis, Y
   Hernandez-Leo, D
   Jaervelae, S
   Mavrikis, M
   Rienties, B
AF Giannakos, Michail
   Azevedo, Roger
   Brusilovsky, Peter
   Cukurova, Mutlu
   Dimitriadis, Yannis
   Hernandez-Leo, Davinia
   Jaervelae, Sanna
   Mavrikis, Manolis
   Rienties, Bart
TI The promise and challenges of generative AI in education
SO BEHAVIOUR & INFORMATION TECHNOLOGY
LA English
DT Article; Early Access
DE Generative AI in education; AI in education; large language models;
   commentary
ID LEARNING DESIGN; ARTIFICIAL-INTELLIGENCE; FRAMEWORK; TEACHERS
AB Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI's capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.
C1 [Giannakos, Michail] Norwegian Univ Sci & Technol NTNU, Dept Comp Sci, Trondheim, Norway.
   [Giannakos, Michail] Univ Agder, Dept Informat Syst, Kristiansand, Norway.
   [Azevedo, Roger] Univ Cent Florida, Sch Modeling Simulat & Training, Orlando, FL USA.
   [Brusilovsky, Peter] Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA USA.
   [Cukurova, Mutlu; Mavrikis, Manolis] UCL, Inst Educ, UCL Knowledge Lab, London, England.
   [Dimitriadis, Yannis] Univ Valladolid, Sch Telecommun Engn, Valladolid, Spain.
   [Hernandez-Leo, Davinia] Univ Pompeu Fabra UPF, Dept Informat & Commun Technol, Barcelona, Spain.
   [Jaervelae, Sanna] Univ Oulu, Learning & Educ Technol LET Res Lab, Oulu, Finland.
   [Rienties, Bart] Open Univ, Inst Educ Technol, Milton Keynes, England.
C3 Norwegian University of Science & Technology (NTNU); University of
   Agder; State University System of Florida; University of Central
   Florida; Pennsylvania Commonwealth System of Higher Education (PCSHE);
   University of Pittsburgh; University of London; University College
   London; UCL Institute of Education; Universidad de Valladolid; Pompeu
   Fabra University; University of Oulu; Open University - UK
RP Giannakos, M (corresponding author), Norwegian Univ Sci & Technol NTNU, Dept Comp Sci, Trondheim, Norway.; Giannakos, M (corresponding author), Univ Agder, Dept Informat Syst, Kristiansand, Norway.
EM michailg@ntnu.no
RI Rienties, Bart/AAH-2397-2019; Cukurova, Mutlu/AAE-9382-2019;
   Dimitriadis, Yannis/K-6846-2014; Hernández-Leo, Davinia/C-2929-2011;
   Brusilovsky, Peter/AAJ-2440-2020
OI Jarvela, Sanna/0000-0001-6223-3668; Cukurova, Mutlu/0000-0001-5843-4854
CR Achiam J., 2023, GPT-4 Technical Report
   Akata Z, 2020, COMPUTER, V53, P18, DOI 10.1109/MC.2020.2996587
   Albó L, 2022, INT J ARTIF INTELL E, V32, P4, DOI 10.1007/s40593-021-00253-3
   Albuquerque J., 2024, Unpicking the DNA of Learning Design Decisions
   Aleven V., 2013, Design Recommendations for Adaptive Intelligent Tutoring Systems, V1, P165
   Aleven V., 2023, Handbook of Artificial Intelligence in Education, P184, DOI [https://doi.org/10.4337/9781800375413.00019, DOI 10.4337/9781800375413.00019]
   Aleven V, 2017, EDUC PSYCHOL HANDB, P522
   Amarasinghe I, 2023, LECT NOTES COMPUT SC, V14200, P32, DOI 10.1007/978-3-031-42682-7_3
   Amarasinghe I, 2022, J COMPUT ASSIST LEAR, DOI 10.1111/jcal.12711
   Asensio-Pérez JI, 2017, COMPUT EDUC, V114, P92, DOI 10.1016/j.compedu.2017.06.011
   Azevedo R., 2023, Handbook of Artificial Intelligence in Education, P141
   Azevedo R, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.813632
   Balaban I, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app132212318
   Becker BA, 2023, PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, P500, DOI 10.1145/3545945.3569759
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bhandari S., 2023, WORKSH GEN AI ED GAI
   Blum W., 2007, MATH MODELING, P222, DOI [10.1533/9780857099419.5.221, DOI 10.1533/9780857099419.5.221]
   Bodily R, 2018, PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE (LAK'18): TOWARDS USER-CENTRED LEARNING ANALYTICS, P41, DOI 10.1145/3170358.3170409
   Bond M, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-023-00436-z
   Brin D, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-43436-9
   Bulathwela S, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16020781
   Bulathwela S, 2023, LECT NOTES ARTIF INT, V13916, P327, DOI 10.1007/978-3-031-36272-9_27
   Bull J., 2022, Computers and Education: Artificial Intelligence, V3, P1000069
   Bull S, 2010, STUD COMPUT INTELL, V308, P301
   Chen Mark, 2021, arXiv
   Conati C, 2018, Arxiv, DOI arXiv:1807.00154
   Cukurova M., 2024, LEARN AN KNOWL C
   Cukurova M, 2020, INT J ARTIF INTELL E, V30, P205, DOI 10.1007/s40593-019-00188-w
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   Demetriadis Stavros, 2023, Augmented Intelligence and Intelligent Tutoring Systems: 19th International Conference, ITS 2023, Proceedings. Lecture Notes in Computer Science (13891), P691, DOI 10.1007/978-3-031-32883-1_60
   Deng GL, 2024, Arxiv, DOI arXiv:2308.06782
   Department for Education, 2023, Generative Artificial Intelligence (AI) in Education
   Drugova E, 2024, J COMPUT ASSIST LEAR, V40, P510, DOI 10.1111/jcal.12894
   du Boulay B., 2023, Handbook of Artificial Intelligence in Education, DOI [https://doi.org/10.4337/9781800375413.00038, DOI 10.4337/9781800375413.00038]
   Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102542
   Ebert C, 2023, IEEE SOFTWARE, V40, P30, DOI 10.1109/MS.2023.3265877
   ERICSSON KA, 1993, PSYCHOL REV, V100, P363, DOI 10.1037/0033-295X.100.3.363
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Finnie-Ansley James, 2023, ACE '23: Australasian Computing Education Conference, P97, DOI 10.1145/3576123.3576134
   Finnie-Ansley J, 2022, PROCEEDINGS OF THE 24TH AUSTRALASIAN COMPUTING EDUCATION CONFERENCE, ACE 2022, P10, DOI 10.1145/3511861.3511863
   Gobert Janice D., 2023, XRDS: Crossroads, The ACM Magazine for Students, P36, DOI 10.1145/3589647
   Goodyear P, 2021, ETR&D-EDUC TECH RES, V69, P445, DOI 10.1007/s11423-020-09926-7
   Gutiérrez-Páez NF, 2023, COMPUT EDUC, V201, DOI 10.1016/j.compedu.2023.104829
   Gutierrez-Santos Sergio, 2012, Journal of Research and Practice in Information Technology, V44, P347
   Hamilton A. D., 2023, The Future of AI in Education: 13 Things we can do to Minimize the Damage, DOI [10.35542/osf.io/372vr, DOI 10.35542/OSF.IO/372VR]
   Hannafin M. J., 1995, Automating Instructional Design: Computer-Based Development and Delivery Tools. Proceedings of the NATO Advanced Study Institute, P101
   Hassany M, 2023, Arxiv, DOI arXiv:2312.02105
   Heffernan N. T., 2008, International Journal of Artificial Intelligence in Education, V18, P153
   Hellas A, 2023, PROCEEDINGS OF THE 2023 ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH V.1, ICER 2023 V1, P93, DOI 10.1145/3568813.3600139
   Hernández-Leo D, 2019, BRIT J EDUC TECHNOL, V50, P139, DOI 10.1111/bjet.12645
   Hernández-Leo D, 2017, EDULEARN PROC, P5681
   Hernndez-Leo D., 2022, Artificial Intelligence and the Rights of the Child: Towards an Integrated Agenda for Research and Policy. EUR 31048 EN, P73
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Holstein K., 2019, Grantee Submission
   Holstein K, 2020, LECT NOTES ARTIF INT, V12163, P240, DOI 10.1007/978-3-030-52237-7_20
   Holstein K, 2017, SEVENTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE (LAK'17), P257, DOI 10.1145/3027385.3027451
   Jarvela S., 2024, LAK 2024 C
   Jarvela S., 2021, International handbook of computer-supported collaborative learning, P281, DOI [DOI 10.1007/978-3-030-65291-315, 10.1007/978-3-030-65291-3_15, DOI 10.1007/978-3-030-65291-3_15]
   Järvelä S, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13325
   Jury B, 2024, PROCEEDINGS OF THE 26TH AUSTRALASIAN COMPUTING EDUCATION CONFERENCE, ACE 2024, P77, DOI 10.1145/3636243.3636252
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Katz D. M., 2023, SSRN, DOI [10.2139/SSRN.4389233, 10.2139/ssrn.4389233, DOI 10.2139/SSRN.4389233]
   Kiesler N., 2023, IEEE Frontiers in Education Conference (FIE)
   Kirschner PA, 2013, EDUC PSYCHOL-US, V48, P169, DOI 10.1080/00461520.2013.804395
   Kistner S, 2010, METACOGN LEARN, V5, P157, DOI 10.1007/s11409-010-9055-3
   Koedinger K. R., 1997, International Journal of Artificial Intelligence in Education, V8, P30
   Koutcheme Charles, 2023, Artificial Intelligence in Education: 24th International Conference, AIED 2023, Proceedings. Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence (13916), P798, DOI 10.1007/978-3-031-36272-9_74
   Krippendorff K, 2012, CONTENT ANAL INTRO I
   Leiker D, 2023, Arxiv, DOI [arXiv:2306.01815, 10.48550/arXiv.2306.01815, DOI 10.48550/ARXIV.2306.01815]
   Leiker D, 2023, Arxiv, DOI arXiv:2304.03784
   Leinonen J, 2023, PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, P563, DOI 10.1145/3545945.3569770
   Li YJ, 2022, SCIENCE, V378, P1092, DOI 10.1126/science.abq1158
   Ljubojevic D., 2011, Arts and Science of Learning Design 2011 (ASLD 2011) Workshop
   MacNeil S, 2023, PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, P931, DOI 10.1145/3545945.3569785
   Malmberg J, 2022, J LEARN ANAL, V9, P77, DOI 10.18608/jla.2022.7429
   Mangaroska K, 2019, IEEE T LEARN TECHNOL, V12, P516, DOI 10.1109/TLT.2018.2868673
   Dagnino FM, 2018, BRIT J EDUC TECHNOL, V49, P998, DOI 10.1111/bjet.12695
   Mavrikis M., 2024, Learning and Teaching Mathematical Modelling with AI and Computational Tools: Insights from a Pilot with Wolfram notebooks
   Mavrikis M., 2021, Bildung Und Erziehung, V74, P249
   Mavrikis M, 2022, ETR&D-EDUC TECH RES, V70, P691, DOI 10.1007/s11423-022-10104-0
   Mavrikis M, 2019, BRIT J EDUC TECHNOL, V50, P2920, DOI 10.1111/bjet.12876
   Mavrikis M, 2013, PERS UBIQUIT COMPUT, V17, P1605, DOI 10.1007/s00779-012-0524-3
   McCalla G, 2023, Elgar Handb Educ, V5, P10
   Melis E., 2001, Artificial Intelligence in Education, P580
   Meyer J., 2023, Computers and Education: Artificial Intelligence, V6, P100199
   Michos K, 2020, COMPUT EDUC, V143, DOI 10.1016/j.compedu.2019.103679
   Misiejuk K, 2023, FRONT EDUC, V7, DOI 10.3389/feduc.2022.996006
   Molenaar I., 2019, Designing Dashboards to Support Learners' Self-Regulated Learning
   Mor Y, 2012, RES LEARN TECHNOL, V20, P85, DOI 10.3402/rlt.v20i0.19196
   Nazaretsky T., 2021, CEUR WORKSHOP P
   Nazaretsky T, 2022, BRIT J EDUC TECHNOL, V53, P914, DOI 10.1111/bjet.13232
   Neumann AT, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.668220
   Newell A., 1959, Report on a general problem-solving program
   Nguyen Huy A., 2023, Responsive and Sustainable Educational Futures: 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Proceedings. Lecture Notes in Computer Science (14200), P278, DOI 10.1007/978-3-031-42682-7_19
   Nguyen Q., 2022, Open World Learning, P189
   Nguyen N, 2022, IEEE WORK CONF MIN S, P1, DOI 10.1145/3524842.3528470
   Nirala KK, 2022, MULTIMED TOOLS APPL, V81, P22215, DOI 10.1007/s11042-021-11458-y
   Ohlsson S., 1992, Journal of Artificial Intelligence in Education, V3, P429
   Ohlsson S., 1999, International Journal of Artificial Intelligence in Education, V10, P238
   Oli P., 2023, Proceedings of Proceedings of the Workshop on Generative AI for Education (GAIED) at the Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, December 2023
   OpenAI, 2023, INTRO CHATGPT
   Ouyang F., 2021, COMPUTERS ED ARTIFIC, V2, P100020, DOI [DOI 10.1016/J.CAEAI.2021.100020, 10.1016/j.caeai.2021.100020 10.1016/j.caeai.2021.100020]
   Pane JF, 2014, EDUC EVAL POLICY AN, V36, P127, DOI 10.3102/0162373713507480
   Pankiewicz M., 2023, Proceedings of 31st International Conference on Computers in Education (ICCE2023), Matsue, Shimane, Japan, December 48, 2023
   Phung T., 2023, Proceedings of the 16th International Conference on Educational Data Mining (EDM 2023), P482
   Porter L., 2024, Learn AI-Assisted Python Programming: With Github Copilot and ChatGPT
   Pozdniakov S, 2022, LAK22 CONFERENCE PROCEEDINGS: THE TWELFTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, P175, DOI 10.1145/3506860.3506885
   Prather J, 2023, PROCEEDINGS OF THE 2023 WORKING GROUP REPORTS ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE-WGR 2023, DOI 10.1145/3623762.3633499
   Pressey SL, 1926, SCHOOL SOC, V23, P373
   Prieto LP, 2013, IEEE T LEARN TECHNOL, V6, P324, DOI 10.1109/TLT.2013.22
   Nguyen Q, 2020, ASSESS EVAL HIGH EDU, V45, P594, DOI 10.1080/02602938.2019.1679088
   Rizvi S, 2022, COMPUT HUM BEHAV, V126, DOI 10.1016/j.chb.2021.106973
   Sabzalieva E., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Santos E. A., 2023, P ACM C GLOB COMP ED, V1, DOI [10.1145/3576882.3617909, DOI 10.1145/3576882.3617909]
   Sarsa Sami, 2022, ICER 2022 V1: Proceedings of the 2022 ACM Conference on International Computing Education Research V.1, P27, DOI 10.1145/3501385.3543957
   Savelka J, 2023, PROCEEDINGS OF THE 2023 ACM CONFERENCE ON INTERNATIONAL COMPUTING EDUCATION RESEARCH V.1, ICER 2023 V1, P78, DOI 10.1145/3568813.3600142
   Sharples M., 2023, Learn. Res. Pract, V9, P159, DOI [DOI 10.1080/23735082.2023.2261131, 10.1080/23735082.2023.2261131]
   Shneiderman Ben., AIS Transactions on Human-Computer Interaction, V12, P109, DOI [10.17705/1thci.00131, DOI 10.17705/1THCI.00131, https://doi.org/10.17705/1thci.00131]
   Smetana LK, 2012, INT J SCI EDUC, V34, P1337, DOI 10.1080/09500693.2011.605182
   Stephanidis C, 2019, INT J HUM-COMPUT INT, V35, P1229, DOI 10.1080/10447318.2019.1619259
   Suraworachet W., 2024, The 14th Learning Analytics and Knowledge Conference (LAK 24), Kyoto, Japan. ACM, New York, NY, DOI [https://doi.org/10.1145/3636555, DOI 10.1145/3636555]
   Susnjak T, 2024, INT J ARTIF INTELL E, V34, P452, DOI 10.1007/s40593-023-00336-3
   Tankelevitch L, 2024, Arxiv, DOI arXiv:2312.10893
   Taub M., 2023, New Directions for Teaching and Learning, V2023, P25, DOI [https://doi.org/10.1002/tl.20545, DOI 10.1002/TL.20545]
   Toetenel L, 2016, BRIT J EDUC TECHNOL, V47, P981, DOI 10.1111/bjet.12423
   Tran A., 2023, P FRONT ED C FIE 202, V18
   U.K. Government, 2023, BLETCHLEY DECLARATIO
   Wang ZC, 2022, LECT NOTES COMPUT SC, V13355, P153, DOI 10.1007/978-3-031-11644-5_13
   Wang ZC, 2021, 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), P5986
   Winne P.H., 2017, Teachers College Record., V119, P1, DOI DOI 10.1177/016146811711901312
   Winne P. H., HDB SELF REGULATION, P36, DOI [10.4324/9781315697048-2, DOI 10.4324/9781315697048-2, 10.4324/9781315697048-3, DOI 10.4324/9781315697048-3]
   Winne PH., 2022, The Cambridge handbook of learning sciences, V3, P93, DOI [10.1017/9781108888295.007, DOI 10.1017/9781108888295.007]
   Yan LX, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13370
   Yang HW, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app131910737
   Yeh YF, 2021, COMPUT EDUC, V171, DOI 10.1016/j.compedu.2021.104238
NR 137
TC 2
Z9 2
U1 102
U2 102
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0144-929X
EI 1362-3001
J9 BEHAV INFORM TECHNOL
JI Behav. Inf. Technol.
PD 2024 AUG 28
PY 2024
DI 10.1080/0144929X.2024.2394886
EA AUG 2024
PG 27
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA E4R7A
UT WOS:001302897500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Whalen, J
   Grube, W
   Xu, CY
   Trust, T
AF Whalen, Jeromie
   Grube, William
   Xu, Chenyang
   Trust, Torrey
TI K-12 Educators' Reactions and Responses to ChatGPT and GenAI During the
   2022-2023 School Year
SO TECHTRENDS
LA English
DT Article; Early Access
DE Artificial intelligence; AI detection; AI tools; ChatGPT; Education;
   Educator response; Educator reactions; GenAI; Generative AI; K-12
AB Launched in November of 2022, the generative artificial intelligence tool ChatGPT garnered immediate societal interest and adoption as its advanced large language modeling proved capable of producing sophisticated, human-like responses to user-generated prompts. In this preliminary study, K-12 teachers in the United States were surveyed on their initial perceptions of-and responses to-the use and misuse of ChatGPT during the 2022-2023 school year. The results show that most teachers moderately understood the tool, but adoption was rare. Most educators did not use AI detection tools and had a neutral stance on the difficulty of detecting when a student used generative AI on an assignment without their aid. Response to the unethical use of AI by students was met in various ways, including discussion, reassessment, revision and disciplinary measures. Findings contribute to understanding educators' reactions and responses to genAI and help inform responsible AI integration in educational settings.
C1 [Whalen, Jeromie] Univ Massachusetts, 413-727-2201,28 Sunrise Circle, Amherst, MA 01075 USA.
   [Grube, William] North Dakota State Univ, Fargo, ND USA.
   [Xu, Chenyang; Trust, Torrey] Univ Massachusetts, Amherst, MA USA.
C3 University of Massachusetts System; University of Massachusetts Amherst;
   North Dakota State University Fargo; University of Massachusetts System;
   University of Massachusetts Amherst
RP Whalen, J (corresponding author), Univ Massachusetts, 413-727-2201,28 Sunrise Circle, Amherst, MA 01075 USA.
EM jpwhalen@umass.edu; william.grube@ndsu.edu; chenyangxu@umass.edu;
   torrey@umass.edu
RI Trust, Torrey/AAE-9012-2020
OI Whalen, Jeromie/0000-0003-0721-2818
CR Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Common Sense Media, 2023, New poll finds parents lag behind kids on AI and want rules and reliable information to help them
   Dillman DA., 2014, Internet, mail, and mixed-mode surveys: The tailored design method, V4, DOI [10.1002/9781394260645, DOI 10.1002/9781394260645]
   Erickson F., 1986, Handbook of research on teaching, V3rd ed., P119
   Fowler G., 2023, Washington Post
   Goulding G., 2023, NBC2 News
   Hays L, 2024, TECHTRENDS, V68, P281, DOI 10.1007/s11528-023-00924-z
   Hu K., 2023, REUTERS         0202
   Jimenez K., 2023, USA Today
   Johnson A., 2023, Forbes
   Khalil M., 2023, Rethinking of Plagiarism Detection, DOI [10.35542/osf.io/fnh48, DOI 10.35542/OSF.IO/FNH48]
   Klee M., 2023, Rolling Stone
   Laird E., 2023, Off-task: EdTech threats to student privacy and equity in the age of AI
   Lee VR., 2024, COMPUTERS ED ARTIFIC, V7, P100253, DOI 10.1016/j.caeai.2024.100253
   Matzinger K., 2023, Junior Achievement USA
   MCCARTHY J, 1956, P NATL ACAD SCI USA, V42, P654, DOI 10.1073/pnas.42.9.654
   McGehee N., 2023, Michigan Virtual
   Nazaretsky T, 2022, LAK22 CONFERENCE PROCEEDINGS: THE TWELFTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, P56, DOI 10.1145/3506860.3506866
   Nowell LS, 2017, INT J QUAL METH, V16, DOI 10.1177/1609406917733847
   Ogurlu U., 2023, Research in Social Sciences and Technology, V8, P196, DOI DOI 10.46303/RESSAT.2023.39
   Pew Research Center, 2023, The American trends panel wave 123 internet Topline
   Poole DL., 2010, ARTIF INTELL, DOI DOI 10.1017/CBO9780511794797
   Rogers MP, 2024, PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1, P1147, DOI 10.1145/3626252.3630784
   Sidoti O., 2023, About 1 in 5 U.S. teens who've heard of ChatGPT have used it for schoolwork
   Turing AM., 1950, MIND, VLIX, P433, DOI [DOI 10.1093/MIND/LIX.236.433, 10.1093/mind/LIX.236.433]
   Twining P, 2017, COMPUT EDUC, V106, pA1, DOI 10.1016/j.compedu.2016.12.002
   Vogels EA, 2023, Pew Research Center
   Walton Family Foundation, 2023, CHATGPT used by teachers more than students, new survey from Walton Family Foundation finds
   Westfall C., 2023, Forbes
   Woodruff K., 2023, REIMAGINING ED ROLE, DOI [https://doi.org/10.5772/intechopen.1002741, DOI 10.5772/INTECHOPEN.1002741]
   Zhang P, 2024, EUR J EDUC, V59, DOI 10.1111/ejed.12599
NR 31
TC 0
Z9 0
U1 7
U2 7
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD 2024 NOV 21
PY 2024
DI 10.1007/s11528-024-01028-y
EA NOV 2024
PG 13
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA M8X2U
UT WOS:001360301100001
DA 2024-12-25
ER

PT J
AU Berlinski, E
   Morales, J
   Sponem, S
AF Berlinski, Elise
   Morales, Jeremy
   Sponem, Samuel
TI Artificial imaginaries: Generative AIs as an advanced form of capitalism
SO CRITICAL PERSPECTIVES ON ACCOUNTING
LA English
DT Article
DE Generative AI; ChatGPT; Social imaginaries; Standardization; Domination
AB In this essay, we characterize three paradoxical imaginaries that structure the development of generative artificial intelligence (genAI). At the institutional level, these technologies develop in a context that celebrates openness and liberality. Yet, both in the US and in Europe, they serve to centralize power and resources. At the organizational level, while the imaginary is that these technologies make work more interesting, we show that they rather produce anxiety and a new class of precarious workers. At the epistemic level, generative artificial intelligence promises access to unlimited knowledge. This knowledge may appear robust, as these technologies become performative. However, the knowledge they produce is doubtful. Overall, these technologies centralize power and exclude, they standardize knowledge, and they produce, reproduce, amplify and extend various structures of domination.
C1 [Berlinski, Elise] Neoma Business Sch, Paris, France.
   [Morales, Jeremy] Univ Bristol, Bristol, England.
   [Sponem, Samuel] HEC Montreal, Montreal, PQ, Canada.
C3 University of Bristol; Universite de Montreal; HEC Montreal
RP Morales, J (corresponding author), Univ Bristol, Bristol, England.
EM elise.berlinski@neoma-bs.fr; jeremy.morales@bristol.ac.uk;
   samuel.sponem@hec.ca
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Adams-Prassl Jeremias., 2019, Comparative Labor Law  Policy Journal, V41, P123
   Alombert A., 2023, Appareil, V26, DOI [10.4000/appareil.7134, DOI 10.4000/APPAREIL.7134]
   Anderson C, 2008, WIRED, V16
   Anderson C., 2009, FREE FUTURE RADICAL
   [Anonymous], 2014, Who Invented Backpropagation?
   Bergvall-Kåreborn B, 2014, NEW TECH WORK EMPLOY, V29, P213, DOI 10.1111/ntwe.12038
   Berlinski E., 2020, AOC18 September
   Berlinski E, 2024, CRIT PERSPECT ACCOUN, V98, DOI 10.1016/j.cpa.2023.102697
   Bolukbasi T, 2016, ADV NEUR IN, V29
   Burrell J, 2021, ANNU REV SOCIOL, V47, P213, DOI 10.1146/annurev-soc-090820-020800
   Caliskan A, 2017, SCIENCE, V356, DOI 10.1126/science.aal4230
   Casilli A. A., 2023, Artificial intelligence doesn't destroy jobs, it precarizes them (op-ed Domani
   Casilli AntonioA., 2019, En attendant les robots: enquete sur le travail du clic
   Castoriadis C., 1975, LINSTITUTION IMAGINA
   Clark S., 2023, The Era of AI: End of Year AI Recap
   Cooper C, 2015, ACCOUNT ORG SOC, V47, P14, DOI 10.1016/j.aos.2015.10.004
   Covington P, 2016, PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), P191, DOI 10.1145/2959100.2959190
   Crevier D., 1993, AI TUMULTUOUS HIST S
   Delfanti A, 2021, NEW MEDIA SOC, V23, P39, DOI 10.1177/1461444819891613
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Durocher S, 2014, ACCOUNT BUS RES, V44, P630, DOI 10.1080/00014788.2014.938012
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   European Commission, 2019, Towards European media sovereignty
   European Commission, 2019, Ethics guidelines for trustworthy AI
   European Commission, 2018, COMM ART INT EUR
   European Parliament, 2019, Artificial intelligence act (P9_TA(2023)0236)
   Future of Life Institute, 2023, PAUS GIANT AI EXP OP
   Gendron Y, 2022, CRIT PERSPECT ACCOUN, V87, DOI 10.1016/j.cpa.2021.102411
   Ghio A, 2024, CRIT PERSPECT ACCOUN, V98, DOI 10.1016/j.cpa.2023.102687
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Gonzalez RobertoJ., 2022, War Virtually: The Quest to Automate Conflict, Militarize Data, and Predict the Future
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Hagey K., 2021, The Wall Street Journal
   Harvey David, 2005, BRIEF HIST NEOLIBERA, DOI [10.1093/oso/9780199283262.001.0001, DOI 10.1093/OSO/9780199283262.001.0001]
   Hastie TJ., 2009, ELEMENTS STAT LEARNI, DOI 10.1007/978-0-387-84858-7
   Hawkins A. J., 2017, The VergeFebruary 13
   Huneman P, 2023, Les societes du profilage. Evaluer, optimiser, predire
   James G, 2013, SPRINGER TEXTS STAT, V103, P1, DOI [10.1007/978-1-4614-7138-7, 10.1007/978-1-4614-7138-7_1]
   Jasanoff S., 2015, Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power, DOI [10.7208/chicago/9780226276663.001.0001, DOI 10.7208/CHICAGO/9780226276663.001.0001]
   Kellogg KC, 2020, ACAD MANAG ANN, V14, P366, DOI 10.5465/annals.2018.0174
   Kleinberg J, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2018340118
   Kotliar DM, 2021, SCI TECHNOL HUM VAL, V46, P346, DOI 10.1177/0162243920925147
   Lange AC, 2019, ORGANIZATION, V26, P598, DOI 10.1177/1350508418808230
   Lauscher A, 2020, Arxiv, DOI arXiv:2005.00633
   LeCun Y., 2016, Les Enjeux de la Recherche en Intelligence Artificielle
   Light Jennifer., 2003, WARFARE WELFARE DEFE
   Macintosh NB, 2000, ACCOUNT ORG SOC, V25, P13, DOI 10.1016/S0361-3682(99)00010-0
   MacKenzie D, 2019, THEOR CULT SOC, V36, P39, DOI 10.1177/0263276419829541
   Morozov E., 2014, To Save Everything, Click Here: Technology, solutionism and the urge to fix problems that don't exist, V1st
   Morozov Evgeny., 2012, The Net Delusion: The Dark Side of Internet Freedom
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   ONeill C, 2016, Weapons of Math Destruction. How big Data increases Inequality and threatens Democracy
   Pasquale D, 2016, The Black Box Society: The Secret Algorithms That Control Money and Information
   Perrigo B, 2023, TimeJanuary 18
   Piquart A., 2022, Le MondeJanuary 20
   Power M, 2022, ORGAN THEOR, V3, DOI 10.1177/26317877211052296
   Sarker Iqbal H, 2021, SN Comput Sci, V2, P420, DOI 10.1007/s42979-021-00815-1
   Saxe A, 2021, NAT REV NEUROSCI, V22, P55, DOI 10.1038/s41583-020-00395-8
   Seaver N, 2019, J MAT CULT, V24, P421, DOI 10.1177/1359183518820366
   Senn-Kalb L., 2023, Artificial Intelligence: In-depth market analysis (50485)
   Silver D, 2016, NATURE, V529, P484, DOI 10.1038/nature16961
   Stiegler B., 2019, Il faut s'adapter": sur un nouvel imperatif politique
   Turner Fred., 2006, COUNTERCULTURE CYBER
   Vargha Z, 2018, LONG RANGE PLANN, V51, P480, DOI 10.1016/j.lrp.2017.03.003
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
   Villani C, 2018, DONNER SENS INTELLIG
   Wood AJ, 2019, SOCIOLOGY, V53, P931, DOI 10.1177/0038038519828906
   Zuboff S, 2015, J INF TECHNOL-UK, V30, P75, DOI 10.1057/jit.2015.5
NR 69
TC 2
Z9 2
U1 13
U2 24
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1045-2354
EI 1095-9955
J9 CRIT PERSPECT ACCOUN
JI Crit. Perspect. Account.
PD MAR
PY 2024
VL 99
AR 102723
DI 10.1016/j.cpa.2024.102723
EA FEB 2024
PG 9
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LP0S3
UT WOS:001187893700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Peebles, AL
   Snyder, MN
AF Peebles, Alanna L.
   Snyder, Maura N.
TI Are you smarter than ChatGPT? Challenging our understanding of media
   literacy through generative AI
SO COMMUNICATION TEACHER
LA English
DT Article; Early Access
AB Higher education has witnessed a paradigm shift from the rapid rise of generative artificial intelligence (genAI). Basing its name on the television show, Are You Smarter Than a 5th Grader?, this single-class activity was designed to foster media literacy students' understanding of popular genAI tools. To compare and contrast the capabilities of different tools, students played an interactive media literacy quiz against OpenAI's ChatGPT and Microsoft's Copilot. The follow-up discussion focused on genAI use, including issues of credibility, accuracy, and ethical issues related to genAI output. This activity is highly adaptable and allows instructors to customize the provided content as genAI tools continue to change. Courses: Media Literacy, Introduction to Mediated Communication, Introduction to Media Studies, Introduction to Communication, Media Effects, Public Speaking, Research Methods. Objectives: Students will (a) test their media literacy knowledge, (b) compare and contrast the capabilities and limits of GPT-3.5 and GPT-4, (c) judge the credibility and accuracy of information provided by genAI, (d) discuss appropriate uses and applications of genAI, and (e) discuss pitfalls and ethical considerations when discussing the use of genAI.
C1 [Peebles, Alanna L.] San Diego State Univ, Sch Journalism & Media Studies, San Diego, CA 92182 USA.
   [Snyder, Maura N.] Canisius Univ, Dept Commun, Buffalo, NY USA.
C3 California State University System; San Diego State University
RP Peebles, AL (corresponding author), San Diego State Univ, Sch Journalism & Media Studies, San Diego, CA 92182 USA.
EM apeebles@sdsu.edu
RI Peebles, Alanna/KFS-2088-2024; Snyder, Maura/JOK-8603-2023
OI Snyder, Maura/0009-0005-8572-0086; Peebles, Alanna/0000-0002-0391-2385
CR Dempere J, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1206936
   Fong J., 2023, Vox
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Goldberg D., 2024, P SOC INF TECHN TEAC, P757
   Hobbs R., 2021, Media literacy in action: Questioning the media, P59
   Hua SY, 2024, DATA INTELLIGENCE, V6, P201, DOI 10.1162/dint_a_00243
   Hughes, 2023, BBC,September 25
   ibm, 2023, What is generative AI?
   Kruger L., 2023, Risks and ethical considerations of generative AI
   Leaver T., 2023, M/C J, DOI [10.5204/mcj.3004, DOI 10.5204/MCJ.3004]
   Microsoft, Learn about Copilot prompting
   Microsoft, 2024, Frequently asked questions about Copilot
   Microsoft, The art and science of working with AI
   OpenAI, 2023, YouTube
   OpenAI, 2024, Hello GPT-4O
   OpenAI, Prompt Engineering
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Wang AI, 2020, COMPUT EDUC, V149, DOI 10.1016/j.compedu.2020.103818
NR 18
TC 0
Z9 0
U1 4
U2 4
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1740-4622
EI 1740-4630
J9 COMMUN TEACH
JI Commun. Teach.
PD 2024 OCT 19
PY 2024
DI 10.1080/17404622.2024.2414032
EA OCT 2024
PG 6
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA J6U1T
UT WOS:001338388900001
DA 2024-12-25
ER

PT J
AU Tang, KS
   Cooper, G
   Rappa, N
   Cooper, M
   Sims, C
   Nonis, K
AF Tang, Kok-Sing
   Cooper, Grant
   Rappa, Natasha
   Cooper, Martin
   Sims, Craig
   Nonis, Karen
TI A dialogic approach to transform teaching, learning & assessment with
   generative AI in secondary education: a proof of concept
SO PEDAGOGIES
LA English
DT Article
DE Action research; ChatGPT; dialogic education; generative artificial
   intelligence; secondary teaching
AB This paper explores the pedagogical potential of Generative Artificial Intelligence (GenAI) in secondary education through a dialogic approach to teaching, learning and assessment. It presents an ongoing action research project in collaboration with a high school in Western Australia, involving four teachers to integrate GenAI in their classrooms. The study aims to develop and evaluate innovative pedagogies for leveraging GenAI to enhance educational practices and student learning outcomes across three action research teams focusing on critical questioning, assessment and differentiation. Drawing on Bakhtin's concept of heteroglossia, the study conceptualizes GenAI not as a definitive knowledge provider but as a dialogic agent that facilitates collaborative dialogue and co-construction of knowledge among students. This perspective aims to encourage students to critically engage with AI-generated content and integrate multiple viewpoints into their learning, thus fostering key epistemic skills. Initial findings demonstrate active student engagement in dialogues with GenAI, highlighting the use of follow-up questions that indicate critical thinking and creativity. These findings underscore the significance of integrating multiple perspectives and fostering epistemic skills among students, promoting a comprehensive and ethical approach to AI use in education. The research calls for further exploration of GenAI's pedagogic potential and its broader implications for educational practices, suggesting a promising avenue for pedagogical innovation and the development of critical thinking skills in the digital age.
C1 [Tang, Kok-Sing; Cooper, Grant; Cooper, Martin; Sims, Craig; Nonis, Karen] Curtin Univ, Sch Educ, Perth, Australia.
   [Rappa, Natasha] Murdoch Univ, Sch Educ, Perth, Australia.
C3 Curtin University; Murdoch University
RP Tang, KS (corresponding author), Curtin Univ, Sch Educ, Perth, Australia.
EM kok-sing.tang@curtin.edu.au
RI Tang, Kok-Sing/I-3245-2019; Rappa, Natasha Anne/N-3436-2018
OI Rappa, Natasha Anne/0000-0002-3217-0296
FU Innovation & Excellence Award from Curtin University School of Education
FX The work was supported by the Innovation & Excellence Award from Curtin
   University School of Education.
CR Bakhtin M., 1981, DIALOGIC IMAGINATION
   Ferrance E., 2000, Action research
   Fox E, 2008, EDUC PSYCHOL REV, V20, P373, DOI 10.1007/s10648-008-9079-2
   Gibbs K, 2021, AUST J TEACH EDUC, V46, P97
   Kulkarni T., 2022, Artificial intelligence in higher education, P95, DOI [https://doi.org/10.1201/9781003184157, DOI 10.1201/9781003184157]
   Maini V., 2017, Machine Learning for Humans
   Mayweg-Paus E, 2016, INT J EDUC RES, V79, P195, DOI 10.1016/j.ijer.2016.05.017
   McAteer M., 2013, Action research in education
   Palieraki S., 2021, European Journal of Educational Research, V10, P1487, DOI [https://doi.org/10.12973/eu-jer.10.3.1487, DOI 10.12973/EU-JER.10.3.1487]
   Settlage J, 2019, SCI EDUC, V103, P1112, DOI 10.1002/sce.21510
   Tang KS, 2024, SCI EDUC, V108, P1329, DOI 10.1002/sce.21875
NR 11
TC 2
Z9 2
U1 45
U2 45
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1554-480X
EI 1554-4818
J9 PEDAGOGIES
JI Pedagogies
PD JUL 2
PY 2024
VL 19
IS 3
SI SI
BP 493
EP 503
DI 10.1080/1554480X.2024.2379774
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA F5X4Q
UT WOS:001310545500011
OA hybrid
DA 2024-12-25
ER

PT J
AU Bridges, LM
   McElroy, K
   Welhouse, Z
AF Bridges, Laurie M.
   McElroy, Kelly
   Welhouse, Zach
TI Generative Artificial Intelligence: 8 Critical Questions for Libraries
SO JOURNAL OF LIBRARY ADMINISTRATION
LA English
DT Article
DE Generative artificial intelligence; AI literacy; critical information
   literacy; information literacy; chatGPT; libraries; librarians;
   education
ID AI; CHATGPT
AB In this article, we provide a brief overview of generative artificial intelligence (GenAI) and large language models (LLMs). We then propose eight critical questions that libraries should ask when exploring this technology and its implications for their communities. We argue that libraries have a unique role in facilitating informed and responsible use of GenAI, as well as safeguarding and promoting the values of access, privacy, and intellectual freedom.
C1 [Bridges, Laurie M.; McElroy, Kelly; Welhouse, Zach] Oregon State Univ, Corvallis, OR 97331 USA.
C3 Oregon State University
RP Bridges, LM (corresponding author), Oregon State Univ, Corvallis, OR 97331 USA.
EM Laurie.Bridges@oregonstate.edu
RI Bridges, Laurie/AAI-2672-2021
OI Welhouse, Zach/0009-0002-4538-9024; McElroy, Kelly/0000-0002-1521-9182;
   Bridges, Laurie/0000-0002-2765-5440
CR American Library Association, 2017, COD ETH
   Anderson A., 2023, AI K STATE LIB
   [Anonymous], 2023, NEW YORK CITY ARTIFI
   Barcelona City Council Communications Office, 2021, PRESS REL AJ BARC IM
   Bauld A., 2023, SCH LIBR J
   Belot H., 2023, GUARDIAN        1102
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bertuzzi L., 2023, WWW EURACTIV    1109
   Biden J.R., 2023, Executive order on the safe, secure, and trustworthy development and use of artificial intelligence
   Blanc R. M., 2023, OPEN DATA AI BLACK B
   Bockting CL, 2023, NATURE, V622, P693, DOI 10.1038/d41586-023-03266-1
   Bommasani R., 2022, ARXIV
   Bradley F, 2022, J AUST LIB INF ASSOC, V71, P189, DOI 10.1080/24750158.2022.2101911
   Brake J., 2023, ABSENT MINDED P 1017
   Bridges L., 2023, EXPLORING CHATGPT UN
   Brown A.M., 2017, EMERGENT STRATEGY SH
   Buolamwini J., 2023, Unmasking AI
   Cardona MA., 2023, Artificial intelligence and the future of teaching and learning
   Carras C., 2023, LOS ANGELES TIM 1116
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Cave S., 2023, Imagining AI, DOI [10.1093/oso/9780192865366.003.0001, DOI 10.1093/OSO/9780192865366.003.0001]
   Center for Research on Foundation Models, FDN MOD TRANSP IND
   Chesterman S., 2023, 4590006 SSRN, DOI [10.2139/ssrn.4590006, DOI 10.2139/SSRN.4590006]
   Coffey L., 2023, Inside Higher Ed
   College of Southern Idaho Generative AI Committee, TECHN GEN ART INT
   Conroy G, 2023, NATURE, V622, P234, DOI 10.1038/d41586-023-03144-w
   Electronic Frontier Foundation, 2023, FRAM LEG ART INT
   Elicit, 2023, ELICIT BLOG     0925
   Fargo Public Library, 2023, FARG LIB HOST PAN DI
   Fowler G., 2023, Wash. Post
   Friedland Alex., 2023, What Are Generative AI, Large Language Models, and Foundation Models?
   Garon Jon M., 2022, Northern Kentucky Law Review, V49, P181
   Generative AI in Higher Education [MOOC], 2023, ACT 1 3 WHAT IS GEN
   Ghaffary S., 2023, BLOOMBERG       0921
   Groft L. D., 2023, SCH LIBR J
   Guild John Grisham, 2023, 13 OTHER AUTHORS FIL
   Heikkila M, 2022, MIT TECHNOL REV
   Hervieux S., 2023, RISE AI IMPLICATIONS
   Hicks M., 2023, CHRON HIGHER EDUC
   Hwang Y, 2021, CYBERPSYCH BEH SOC N, V24, P188, DOI 10.1089/cyber.2020.0174
   International Federation of Library Association and Institutions [IFLA] Artificial Intelligence Special Interest Group, 2023, DEV LIB STRAT RESP A
   Jones E, 2023, EXPLAINER WHAT IS FD
   Kelly S. M., 2023, CNN BUSINESS    0819
   Leffer L., 2023, SCI AM          1019
   Lenz M., 2023, 2023 STATE AI LEGISL
   Li F. F., 2023, ATLANTIC
   Li F. F., 2023, WORLDS I SEE
   Lo LS, 2023, IFLA J-INT FED LIBR, V49, P645, DOI 10.1177/03400352231196172
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Mearian L., 2023, Computerworld
   Merritt R., 2023, NVIDIA Blog
   Michalak R, 2023, J LIBR ADM, V63, P928, DOI 10.1080/01930826.2023.2262367
   Murgia M., 2023, OPENAI CHIEF SEEKS N
   Nadelman J., 2023, USE GENERATIVE AI LO
   New York Public Library, 2023, BUILD WORLD WE WANT
   Noble S. U., 2018, ALGORITHMS OPPRESSIO, DOI DOI 10.2307/J.CTT1PWT9W5
   Nordling L, 2023, NATURE, V622, P655, DOI 10.1038/d41586-023-03235-8
   OECD A. I., 2021, DAT NAT AI POL
   OECD A. I. Policy Observatory, OECD AI PRINC OV
   OpenAI, 2023, OPENAI DAT PARTN
   Oregon State University Higher Education AI Task Force, OSU HIGH ED AI TASK
   Perrigo B, 2023, TIME
   Research Solutions Inc., 2023, RES SOL ANN ACQ SCIT
   Ridley M, 2021, INFORM TECHNOL LIBR, V40, DOI 10.6017/ital.v40i2.12963
   Rowe N., 2023, WIRED
   Schmidt A., 2023, INT COMMUN GAZ
   Stanford HAI, 2021, WORKSH FDN MOD DAY 1
   Stanford HAI & Center for Research on Model Foundations, 2023, WHAT IS FDN MOD EXPL
   Terry Owen Kichizo, 2023, CHRON HIGHER EDUC
   The Conversation US B., 2023, WHY WE NEED SEE INSI
   The GovLab, AI LOC
   Timsit A., 2023, WASH POST
   Van Noorden R, 2023, NATURE, V621, P672, DOI 10.1038/d41586-023-02980-0
   Velasquez S., 2023, UNM NEWSROOM    0706
   Verma P., 2023, WASH. POSTMay 7
   Walters WH, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-41032-5
   Wilkerson L., 2021, MISSOURI LAW REV, V86, P407
NR 77
TC 2
Z9 2
U1 84
U2 185
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0193-0826
EI 1540-3564
J9 J LIBR ADM
JI J. Libr. Adm.
PD JAN 2
PY 2024
VL 64
IS 1
BP 66
EP 79
DI 10.1080/01930826.2024.2292484
EA JAN 2024
PG 14
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA HC4Q0
UT WOS:001142901900001
DA 2024-12-25
ER

PT J
AU Bull, C
   Kharrufa, A
AF Bull, Christopher
   Kharrufa, Ahmed
TI Generative Artificial Intelligence Assistants in Software Development
   Education: A Vision for Integrating Generative Artificial Intelligence
   Into Educational Practice, Not Instinctively Defending Against It
SO IEEE SOFTWARE
LA English
DT Article
DE Codes; Chatbots; Software; Software development management; Programming
   profession; Industries; Artificial intelligence
AB The use of Generative AI in software development is gaining traction. But what are the potentials and implications on software development education? We gathered insights on the use of Generative AI from professional software developers and make some pedagogical recommendations.
C1 [Bull, Christopher; Kharrufa, Ahmed] Newcastle Univ, Sch Comp, Newcastle Upon Tyne NE4 5TG, England.
C3 Newcastle University - UK
RP Bull, C (corresponding author), Newcastle Univ, Sch Comp, Newcastle Upon Tyne NE4 5TG, England.
EM christopher.bull@newcastle.ac.uk; ahmed.kharrufa@newcastle.ac.uk
RI Bull, Christopher/W-5764-2019
OI Bull, Christopher/0000-0002-9811-4190; Kharrufa,
   Ahmed/0000-0002-3461-4161
CR Boyle T., 1997, DESIGN MULTIMEDIA LE
   Brandt J, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P513
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Finnie-Ansley J, 2022, PROCEEDINGS OF THE 24TH AUSTRALASIAN COMPUTING EDUCATION CONFERENCE, ACE 2022, P10, DOI 10.1145/3511861.3511863
   Freitag C, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2021.100340
   Grams C., 2019, New StackOct.
   Mashkoor A, 2022, COMPUTER, V55, P24, DOI 10.1109/MC.2022.3144805
   Puryear G., 2022, J. Comput. Sci. College, V38, P6
   Sparrow B, 2011, SCIENCE, V333, P776, DOI 10.1126/science.1207745
   Sweller J., 2011, Cognitive load theory, P99
   Vygotsky M., 1978, Mind in Society: Development of Higher Psychological Processes, V13
   Xu Frank F., 2022, MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, P1, DOI 10.1145/3520312.3534862
NR 12
TC 8
Z9 8
U1 69
U2 122
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0740-7459
EI 1937-4194
J9 IEEE SOFTWARE
JI IEEE Softw.
PD MAR-APR
PY 2024
VL 41
IS 2
BP 52
EP 59
DI 10.1109/MS.2023.3300574
PG 8
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA LA2C0
UT WOS:001183977400002
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Richmond, JL
   Nicholls, K
AF Richmond, Jenny L.
   Nicholls, Kate
TI Using Generative AI to Promote Psychological, Feedback, and Artificial
   Intelligence Literacies in Undergraduate Psychology
SO TEACHING OF PSYCHOLOGY
LA English
DT Article; Early Access
DE generative AI; psychological literacy; feedback literacy; AI literacy
AB Background With the arrival of generative artificial intelligence (genAI) tools, psychology educators are rethinking their assessment practices.Objective This paper describes one approach to integrating genAI into an assessment designed to promote psychological literacy.Method Students used ChatGPT to generate a media release about a published article and then wrote a critique. We evaluated whether students were able to use the marking rubric to assess the ChatGPT output, and whether working with the rubric early in the assessment process had benefits for their grades on subsequent tasks.Results The results show that students accurately assessed the ChatGPT output against the marking rubric, judging the output to be stylistically good but lacking in accurate coverage of the aims, methods, and results of the research. Working with genAI and the marking rubric early in the assessment process had benefits for performance, relative to cohorts that had engaged in peer review.Conclusion By allowing students to use genAI and scaffolding the process of critiquing and revising, students gained competencies in psychological, feedback, and AI literacies.Teaching implications Integrating genAI presents opportunities for learning, if educators can think beyond the artifact and design assessment that allows our students to showcase their learning process.
C1 [Richmond, Jenny L.; Nicholls, Kate] Univ New South Wales, Sch Psychol, Sydney, NSW 2052, Australia.
C3 University of New South Wales Sydney
RP Richmond, JL (corresponding author), Univ New South Wales, Sch Psychol, Sydney, NSW 2052, Australia.
EM j.richmond@unsw.edu.au
CR Anderson L. W., 2001, A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objective
   Bloom B. S., 1956, TAXONOMY ED OBJECTIV
   Boud D., 2020, Assessment 2020: Seven propositions for assessment reform in higher education
   Carless D, 2018, ASSESS EVAL HIGH EDU, V43, P1315, DOI 10.1080/02602938.2018.1463354
   Cranney J, 2022, FRONT EDUC, V7, DOI 10.3389/feduc.2022.790600
   Gado S, 2022, PSYCHOL LEARN TEACH-, V21, P37, DOI 10.1177/14757257211037149
   Halupa C., 2017, Handbook of research on creative problem-solving skill development in higher education, P429, DOI DOI 10.4018/978-1-5225-0643-0
   Laupichler M.C., 2022, Comput. Educ. Artif. Intell, V3, P100101, DOI DOI 10.1016/J.CAEAI.2022.100101
   Lodge JM, 2023, Assessment reform for the age of Artificial Intelligence
   McGovern T.V., 2010, Undergraduate education in psychology: A blueprint for the future of the discipline, P9
   Nolan S. A., Beta.R1 Version: International Undergraduate Foundational Psychology Competences
   Nolan SA, 2023, PSYCHOL LEARN TEACH-, V22, P256, DOI 10.1177/14757257231195344
   Richmond J., 2024, Using generative AI to promote psychological, feedback, and artificial intelligence literacies in undergraduate psychology
   Rivers C., 2023, Times Higher Education
   Wright S., 2012, Mind Shift
NR 15
TC 0
Z9 0
U1 34
U2 34
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0098-6283
EI 1532-8023
J9 TEACH PSYCHOL
JI Teach. Psychol.
PD 2024 OCT 14
PY 2024
DI 10.1177/00986283241287203
EA OCT 2024
PG 7
WC Education & Educational Research; Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Psychology
GA I8I1I
UT WOS:001332628200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Cardoso, AG
   Chan, E
   Quintao, L
   Pereira, C
AF Cardoso, Andre Guskow
   Chan, Elizabeth
   Quintao, Luisa
   Pereira, Cesar
TI Generative Artificial Intelligence and Legal Decision-making
SO GLOBAL TRADE AND CUSTOMS JOURNAL
LA English
DT Article
DE Generative AI; Legal decision-making; AI-assisted arbitration;
   Disclosure obligations; Moot court; Legal technology; Ethical AI
AB This article explores the transformative impact of generative artificial intelligence (GenAI) on legal decision-making processes. It also examines disclosure obligations and the challenges AI-assisted decisions pose in litigation and arbitration. A case study using ChatGPT as arbitrators in the Willem C. Vis International Commercial Arbitration Moot provides practical insights into AI's ' s potential and limitations in arbitral decision-making. The experiment highlights key learnings about technology constraints, due process concerns, and the enforceability of AI-assisted decisions. The article concludes with reflections on integrating AI into legal frameworks, emphasizing the need for updated regulations and best practices to ensure transparency, fairness, and accountability in AI applications within the legal domain.
C1 [Cardoso, Andre Guskow] Justen Pereira Oliveira & Talamini, Curitiba, Brazil.
   [Chan, Elizabeth] Stevenson Wong & Co, Hong Kong, Peoples R China.
   [Quintao, Luisa; Pereira, Cesar] Justen Pereira Oliveira & Talamini, Sao Paulo, Brazil.
   [Quintao, Luisa] Chartered Inst Arbitrators FCiarb, London, England.
RP Cardoso, AG (corresponding author), Justen Pereira Oliveira & Talamini, Curitiba, Brazil.
EM andre@justen.com.br; elizabeth.chan@sw-hk.com;
   luisa.quintao@justen.com.br; cesar@justen.com.br
CR AAA & ICDR, 2023, Principles Supporting the Use of AI in Alternative Dispute Resolution
   [Anonymous], 2023, EUROPEAN COMMISSION
   [Anonymous], 2023, Co -Counsel
   [Anonymous], 2023, Muhammad Iqbal s/o Muhammad Din, Caste Choghatta, r/o Hussain Park Lahore v. Zayad (deceased) son of Muhammad Aalim through legal heirs
   [Anonymous], 2024, Snell v. United Specialty Insurance Company (22-12581, USCA11)
   Cave Bryan, 2023, Annual Arbitration Survey 2023: The Rise of Machine Learning, P20
   Chan Elizabeth, served on the nine-member drafting committee
   chatgpt, Full chat with Neo
   Chojecki Sarah Elisabeth, 2021, BCDR Int'l Arb. Rev., V8, P121
   Collenette J, 2023, ARTIF INTELL-AMST, V317, DOI 10.1016/j.artint.2023.103861
   conjur, Decision in the process
   Courts of New Zealand, 2023, New Zealand Guidelines for Use of Generative AI in Courts and Tribunals: Judges, Judicial Officers, Tribunal Members and Judicial Support Staff'
   EU, 2019, EU Ethical Guidelines for Trustworthy AI
   Gomez Katia Fach, 2023, The Technological Competence of Arbitrators: A Comparative and International Legal Study
   Gray Kevin W., 2024, Danubia Files 2: Lessons from the Vis Moot, P473
   Greenwood Dazza, 2023, MIT Computational Law Report 5
   harvard, ABOUT US
   HSF, 2023, HSF Notes27 Sep.
   ICC Commission on Arbitration and ADR, 2023, Effective Conflict Management
   Jus Mundi, About us
   Knight Sian, AIming High. First Impressions of a ChatGPT Artificial Intelligence (AI) Co -arbitrator from the Human Observer
   Lexis+AI, About us
   Magesh V., 2024, Hallucination -Free? Assessing the Reliability of Leading AI Legal Research Tools, V24
   OECD, 2019, RECOMMENDATION COUNC
   Pereira Cesar, 2023, International Commercial Arbitration Practice: 21st Century Perspectives
   Powers T M., 2020, The Oxford Handbook of Ethics of AI, P25, DOI [DOI 10.1093/OXFORDHB/9780190067397.013.2, 10.1093/oxfordhb/9780190067397.013.2]
   Quanlin Qiu, 2023, China Daily1 Sep
   Resnik P, 2024, Arxiv, DOI arXiv:2406.13138
   sali, Currently, there are initiatives as SALI-Standards Advancement for the Legal Industry, that intend to define broad standards for legal data
   Sapna Shangiani KC, 2024, Kluwer Arbitration Blog
   Silicon Valley Arbitration and Mediation Center, 2024, SVAMC Publishes Guidelines on the Use of Artificial Intelligence in Arbitration
   Simson Caroline, 2024, Law36010 Apr.
   Souza-McMurtrie Leonardo, 2023, Kluwer Arbitration Blog26 Feb.
   svamc, 2024, Silicon Valley Arbitration and Mediation Center Guidelines on the Use of Artificial Intelligence in Arbitration
   Tribunal de Contas da Uniao, 2023, Noticias TCU3 Oct.
   U.S. Federal Government, 2022, The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People
   U.S. Federal Government, 2023, Fact Sheet: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence'
   UK Courts and Tribunals Judiciary, 2023, Artificial Intelligence (AI): Guidance for Judicial Office Holders, P2
   Unikowsky Adam, 2024, In AI we Trust, part II, Adam's Legal Newsletter
   United Nations, 2024, Document A/78/L.49
   US Federal Government, 2024, Memorandum for the Heads of Executive Departments and Agencies
   Wang N., 2022, Artificial Intelligence and Its Discontents. Social and Cultural Studies of Robots and AI, DOI DOI 10.1007/978-3-030-88615-8_10
   Winter -Barker Sam, 2024, Kluwer Arbitration Blog27 Jun.
   youtube, The recording of the experiment is available at: What if the Arbitrator is Replaced by AI?
   Youtube, About Us
NR 45
TC 0
Z9 0
U1 3
U2 3
PU KLUWER LAW INT
PI ALPHEN AAN DEN RIJN
PA ZUIDPOOLSINGEL 2, PO BOX 316, 2400 AH ALPHEN AAN DEN RIJN, NETHERLANDS
SN 1569-755X
EI 1875-6468
J9 GLOB TRADE CUST J
JI Glob. Trade Cust. J.
PD NOV
PY 2024
VL 19
IS 11-12
BP 710
EP 730
PG 21
WC International Relations
WE Emerging Sources Citation Index (ESCI)
SC International Relations
GA J9V2Z
UT WOS:001340462500008
DA 2024-12-25
ER

PT J
AU Mannuru, NR
   Shahriar, S
   Teel, ZA
   Wang, T
   Lund, BD
   Tijani, S
   Pohboon, CO
   Agbaji, D
   Alhassan, J
   Galley, J
   Kousari, R
   Ogbadu-Oladapo, L
   Saurav, SK
   Srivastava, A
   Tummuru, SP
   Uppala, S
   Vaidya, P
AF Mannuru, Nishith Reddy
   Shahriar, Sakib
   Teel, Zoe A.
   Wang, Ting
   Lund, Brady D.
   Tijani, Solomon
   Pohboon, Chalermchai Oak
   Agbaji, Daniel
   Alhassan, Joy
   Galley, Jaklyn
   Kousari, Raana
   Ogbadu-Oladapo, Lydia
   Saurav, Shubham Kumar
   Srivastava, Aishwarya
   Tummuru, Sai Priya
   Uppala, Sravya
   Vaidya, Praveenkumar
TI Artificial intelligence in developing countries: The impact of
   generative artificial intelligence (AI) technologies for development
SO INFORMATION DEVELOPMENT
LA English
DT Article; Early Access
DE generative AI; artificial intelligence; fourth industrial revolution;
   developing countries; technological change
ID HEALTH-CARE; INDUSTRIAL-REVOLUTION; INNOVATION; POLICY
AB This paper explores the potential impact of Generative Artificial Intelligence (Generative AI) on developing countries, considering both positive and negative effects across various domains of information, culture, and industry. Generative Artificial Intelligence refers to artificial intelligence (AI) systems that generate content, such as text, audio, or video, aiming to produce novel and creative outputs based on training data. Compared to conversational artificial intelligence, generative artificial intelligence systems have the unique capability of not only providing replies but also generating the content of those responses. Recent advancements in Artificial Intelligence during the Fourth Industrial Revolution, exemplified by tools like ChatGPT, have gained popularity and reshaped content production and creation. However, the benefits of generative artificial intelligence are not equally accessible to all, especially in developing countries, where limited access to cutting-edge technologies and inadequate infrastructure pose challenges. This paper seeks to understand the potential impact of generative AI technologies on developing countries, considering economic growth, access to technology, and the potential paradigm shift in education, healthcare, and the environment. The findings emphasize the importance of providing the necessary support and infrastructure to ensure that generative AI contributes to inclusive development rather than deepening existing inequalities. The study highlights the significance of integrating Generative AI into the context of the Fourth Industrial Revolution in developing countries, where technological change is a crucial determinant of progress and equitable growth.
C1 [Mannuru, Nishith Reddy; Teel, Zoe A.; Lund, Brady D.; Pohboon, Chalermchai Oak; Agbaji, Daniel; Ogbadu-Oladapo, Lydia; Tummuru, Sai Priya; Uppala, Sravya] Univ North Texas, Dept Informat Sci, Denton, TX 76205 USA.
   [Shahriar, Sakib] Univ Guelph, Sch Comp Sci, Guelph, ON, Canada.
   [Wang, Ting; Galley, Jaklyn] Emporia State Univ, Sch Lib & Informat Management, Emporia, KS USA.
   [Tijani, Solomon] Nigerian Inst Social & Econ Res, Ibadan, Nigeria.
   [Kousari, Raana] Iran Univ Med Sci, Sch Hlth Management & Informat Sci, Dept Med Lib & Informat Sci, Tehran, Iran.
   [Saurav, Shubham Kumar] Indian Stat Inst, Documentat Res & Training Ctr, Bangalore, India.
   [Srivastava, Aishwarya] Seth MR Jaipuria Sch, Rajajipuram Campus, Lucknow, India.
   [Vaidya, Praveenkumar] CHRIST Univ Pune Lavasa India, Pune, Maharashtra, India.
C3 University of North Texas System; University of North Texas Denton;
   University of Guelph; Iran University of Medical Sciences; Indian
   Statistical Institute; Indian Statistical Institute Bangalore
RP Lund, BD (corresponding author), Univ North Texas, Dept Informat Sci, Denton, TX 76205 USA.
EM Brady.Lund@unt.edu
RI Saurav, Shubham/LVR-0355-2024; Teel, Zoe/KFS-1378-2024; Mannuru, Nishith
   Reddy/GRJ-1450-2022; Vaidya, Praveen/HOH-1733-2023; Shahriar,
   Sakib/HNS-7528-2023; kousari, raana/AAF-1520-2021; Wang,
   Ting/ABG-3298-2022; Agbaji, Daniel/GYU-3641-2022; Lund,
   Brady/GLV-4793-2022; Agbaji, Daniel/Y-3950-2018
OI Ogbadu-Oladapo, Lydia/0000-0003-2973-9805; Saurav, Shubham
   Kumar/0009-0006-2003-0014; Agbaji, Daniel/0000-0001-6904-1829; Vaidya,
   Praveenkumar/0000-0002-5678-5682; Lund, Brady/0000-0002-4819-8162;
   Tijani, Solomon/0000-0001-8535-8527
CR Abbas F., 2021, Humanities and Social Sciences Review, V9, P1071, DOI [10.18510/hssr.2021.93106, DOI 10.18510/HSSR.2021.93106]
   Acemoglu D, 2022, J LABOR ECON, V40, pS293, DOI 10.1086/718327
   Alam A., 2022, 2022 INT C SUST COMP, P69, DOI [DOI 10.1109/ICSCDS53736.2022.9760932, 10.1109/ICSCDS53736.2022.9760932]
   Allen G. C., 2019, UNDERSTANDING CHINAS
   Aly H, 2022, REV ECON POLIT SCI-R, V7, P238, DOI 10.1108/REPS-11-2019-0145
   Anctil D., 2023, Pedagogie collegiale, V36
   [Anonymous], 2016, Artificial Intelligence, Automation, and the Economy
   [Anonymous], 2022, TIMES
   [Anonymous], 2022, FINANC TIMES
   Aronson SJ, 2015, NATURE, V526, P336, DOI 10.1038/nature15816
   Arun C., 2020, The Oxford Handbook of Ethics of AI, P588, DOI [10.1093/oxfordhb/9780190067397.013.38, DOI 10.1093/OXFORDHB/9780190067397.013.38]
   Ashkenazi S., 2023, The environmental pollution behind the boom in Artificial Intelligence
   Aydin O., 2023, Is ChatGPT leading generative AI? What is beyond expectations?
   Bang Y, 2023, Arxiv, DOI arXiv:2302.04023
   Bartekova E., 2022, OECD Environ. Work. Pap., DOI [10.1787/19970900, DOI 10.1787/19970900]
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Beerepoot N, 2015, GLOBAL NETW, V15, P236, DOI 10.1111/glob.12051
   Bejakovic P, 2020, EMPL RELAT, V42, P921, DOI 10.1108/ER-07-2019-0274
   Blaas Q., 2023, Co-designing with artificial intelligence
   Boulanger-Lewandowski N., 2012, arXiv
   Bouton CE, 2016, NATURE, V533, P247, DOI 10.1038/nature17435
   Brasil S, 2019, GENES-BASEL, V10, DOI 10.3390/genes10120978
   Cadario R, 2021, NAT HUM BEHAV, V5, P1636, DOI 10.1038/s41562-021-01146-0
   Chang CH, 2023, INT RES GEOGR ENVIRO, V32, P85, DOI 10.1080/10382046.2023.2194036
   Charlesworth A., 2009, The digital revolution
   Chen Mei, 2020, Healthc Manage Forum, V33, P10, DOI 10.1177/0840470419873123
   Chin A, 2006, REV ECON STAT, V88, P572, DOI 10.1162/rest.88.3.572
   Choi J., 2020, The Future of Work in Africa: Harnessing the Potential of Digital Technologies for All
   CostanzaChock S, 2020, INFORM POL, P1
   Crawford K., 2021, Atlas of AL
   Damioli G, 2023, APPL ECON LETT, V30, P816, DOI 10.1080/13504851.2021.2024129
   Davenport Thomas, 2019, Future Healthc J, V6, P94, DOI 10.7861/futurehosp.6-2-94
   Dawson D., 2019, Artificial intelligence: Australia's ethics framework-A discussion paper
   DEDOMBAL FT, 1972, BMJ-BRIT MED J, V2, P9, DOI 10.1136/bmj.2.5804.9
   Dergaa I., 2023, From human writing to artificial
   DIgnazio C, 2020, STRONG IDEAS SERIES, P1
   Dilsizian SE, 2014, CURR CARDIOL REP, V16, DOI 10.1007/s11886-013-0441-8
   Ding J., 2018, Deciphering Chinas AI dream
   Durga M. V. S. S., 2018, Journal for Research Scholars and Professionals of The English language Learning, V7, P1
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Ernst E, 2019, IZA J LABOR POLICY, V9, DOI 10.2478/izajolp-2019-0004
   Foksinska A, 2022, FRONT ARTIF INTELL, V5, DOI 10.3389/frai.2022.910216
   Garbarine R., 2023, How is AI Changing How We Write and Create?
   Genz S., 2021, ZEW-Centre for European Economic Research Discussion Paper, P21
   Ghosh M, 2024, J SCI TECHNOL POLICY, V15, P67, DOI 10.1108/JSTPM-02-2021-0031
   Google Cloud, 2022, Incident Report
   Goralski MA, 2020, INT J MANAG EDUC-OXF, V18, DOI 10.1016/j.ijme.2019.100330
   Grassmann C, 2021, HUM RESOUR DEV REV, V20, P106, DOI 10.1177/1534484320982891
   Guo WS, 2020, IEEE COMMUN MAG, V58, P39, DOI 10.1109/MCOM.001.2000050
   Hagerty A., 2019, arXiv, DOI 10.48550/ARXIV.1907.07892
   Heng SMD, 2022, INT J INFORM MANAGE, V64, DOI 10.1016/j.ijinfomgt.2021.102454
   Houde S., 2020, arXiv, DOI [10.48550/arXiv.2003.07679, DOI 10.48550/ARXIV.2003.07679]
   Huntingford C, 2019, ENVIRON RES LETT, V14, DOI 10.1088/1748-9326/ab4e55
   Hurvitz N, 2021, EUR J HUM GENET, V29, P1485, DOI 10.1038/s41431-021-00928-4
   Jalil S, 2023, Arxiv, DOI arXiv:2302.03287
   Jiang F, 2017, STROKE VASC NEUROL, V2, P230, DOI 10.1136/svn-2017-000101
   Jiao WX, 2023, Arxiv, DOI [arXiv:2301.08745, DOI 10.48550/ARXIV.2301.08745, 10.48550/ARXIV.2301.08745]
   Kakaniet V., 2020, Journal of Agriculture and Food Research, V2
   Kässi O, 2018, TECHNOL FORECAST SOC, V137, P241, DOI 10.1016/j.techfore.2018.07.056
   Keding C, 2021, TECHNOL FORECAST SOC, V171, DOI 10.1016/j.techfore.2021.120970
   Kiely DG, 2019, PULM CIRC, V9, DOI 10.1177/2045894019841990
   Kok J. N., 2009, ARTIF INTELL, V1, P270
   Kolbjornsrud Vegard, 2017, Strategy & Leadership, V45, P37, DOI 10.1108/SL-12-2016-0085
   Korinek A., 2021, NBER Working Paper No. 28453
   Kumar S, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101737
   Kundu M, 2000, PATTERN RECOGN, V33, P351, DOI 10.1016/S0031-3203(99)00065-5
   Leoste J, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9222876
   Liaw W, 2020, FAM MED, V52, P8, DOI 10.22454/FamMed.2020.881454
   Lim W.M., 2023, INT J MANAG EDUC-OXF, V21
   Lim WM, 2022, PSYCHOL MARKET, V39, P1129, DOI 10.1002/mar.21654
   Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
   Lund B., 2023, INFORM LITERACY DATA, DOI [10.2139/ssrn.4324580, DOI 10.2139/SSRN.4324580]
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lund B, 2021, INFORM TECHNOL LIBR, V40, DOI 10.6017/ital.v40i1.13193
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Lund BD, 2023, INFORM DEV, V39, P376, DOI 10.1177/02666669211052522
   Lund BD, 2022, ACCOUNT RES, V29, P224, DOI 10.1080/08989621.2021.1913124
   Lutz C, 2019, HUM BEHAV EMERG TECH, V1, P141, DOI 10.1002/hbe2.140
   Luxton David D, 2019, AMA J Ethics, V21, pE131, DOI 10.1001/amajethics.2019.131
   MacWilliams J, 2020, J COMMERCIAL BIOTECH, V25, P42
   Maharaj S., 2023, Medium. Medium
   Mahomed S, 2018, S AFR J BIOETH LAW, V11, P93, DOI 10.7196/SAJBL.2018.v11i2.664
   Makridakis S, 2017, FUTURES, V90, P46, DOI 10.1016/j.futures.2017.03.006
   Malhotra A., 2021, Smart Computing, P519
   Meltzer JP, 2018, The impact of artificial intelligence on international trade
   Mhlanga D, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13115788
   Michelle LK, 2023, Risks and ethical considerations of Generative AI
   MILLER RA, 1994, J AM MED INFORM ASSN, V1, P8, DOI 10.1136/jamia.1994.95236141
   Mogaji E, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101711
   Mohajan Haradhan., 2019, Journal of Social Sciences and Humanities, V5, P377
   Murphy P., 2023, The benefits of Generative AI in Sustainable Home Building and Architecture
   Mytton D, 2021, NPJ CLEAN WATER, V4, DOI 10.1038/s41545-021-00101-w
   Nadeem M, 2024, J KNOWL ECON, V15, P4730, DOI 10.1007/s13132-023-01158-3
   Neill DB, 2013, IEEE INTELL SYST, V28, P92, DOI 10.1109/MIS.2013.51
   Noble S. U., 2018, ALGORITHMS OPPRESSIO, DOI DOI 10.2307/J.CTT1PWT9W5
   OECD, 2022, OECD Digital Economy Papers, V341
   Panch T, 2019, NPJ DIGIT MED, V2, DOI 10.1038/s41746-019-0155-4
   Panesar SS, 2020, NEUROSURGERY, V87, P33, DOI 10.1093/neuros/nyz471
   Pascual MG., 2023, EL PAIS English
   Pataranutaporn P, 2021, NAT MACH INTELL, V3, P1013, DOI 10.1038/s42256-021-00417-9
   PWC, 2023, Sizing the prize
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Ralston S.J., 2021, Postdigital Science and Education, V3, P83, DOI [DOI 10.1007/S42438-020-00121-8, 10.1007/s42438-020-00121-8]
   Rampersad G, 2020, J BUS RES, V116, P68, DOI 10.1016/j.jbusres.2020.05.019
   Reddy S, 2020, J AM MED INFORM ASSN, V27, P491, DOI 10.1093/jamia/ocz192
   Reddy S, 2019, J ROY SOC MED, V112, P22, DOI 10.1177/0141076818815510
   Reed JC, 2017, Chest radiology: patterns and differential diagnoses
   Roberts H, 2021, AI SOC, V36, P59, DOI 10.1007/s00146-020-00992-2
   Rosenbaum E., 2023, CNBC
   Roshid M M., 2013, Mevlana International Journal of Education, V3, P68
   Rudolph J urgen, 2023, Journal of Applied Learning and Teaching, V6
   Rusk N, 2016, NAT METHODS, V13, P35, DOI 10.1038/nmeth.3707
   Schiff Daniel, 2021, IEEE Transactions on Technology and Society, V2, P31, DOI 10.1109/TTS.2021.3052127
   Schiff D, 2022, INT J ARTIF INTELL E, V32, P527, DOI 10.1007/s40593-021-00270-2
   Shadlen KC, 2012, EUR J DEV RES, V24, P300, DOI 10.1057/ejdr.2012.9
   Shaheen MY, PREPRINT, DOI [10.14293/S2199-1006.1.SOR-.PPVRY8K.v1, DOI 10.14293/S2199-1006.1.SOR-.PPVRY8K.V1]
   Shahriar S, 2022, DISPLAYS, V73, DOI 10.1016/j.displa.2022.102237
   Sharma H., 2022, RE IMAGINING ED FUTU, P159
   Siddik MA, 2021, ENVIRON RES LETT, V16, DOI 10.1088/1748-9326/abfba1
   Singh G., 2021, arXiv
   Sobaih A. E. E., 2015, Journal of Human Resources in Hospitality & Tourism, V14, P221, DOI 10.1080/15332845.2014.904167
   Sobania D, 2023, Arxiv, DOI [arXiv:2301.08653, 10.48550/ARXIV.2301.08653, 10.48550/arXiv.2301.08653]
   Somashekhar SP, 2017, CANCER RES, V77, DOI 10.1158/1538-7445.SABCS16-S6-07
   Soto DA, 2020, TECHNOLOGY FUTURE WO, V236, DOI [10.1787/55354f8f-en, DOI 10.1787/55354F8F-EN]
   Stearns P.N., 2020, The industrial revolution in world history
   Strusani D., 2019, The role of artificial intelligence in supporting development in emerging markets
   Surameery NMS., 2023, International Journal of Information Technology Computer Engineering (IJITC), V3
   Tacheva Z., 2023, Advance, DOI [10.31124/advance.22012724.v1, DOI 10.31124/ADVANCE.22012724.V1]
   Teel ZA., 2023, The Serials Librarian, DOI [10.1080/0361526X.2023.2173357, DOI 10.1080/0361526X.2023.2173357]
   Tolan Songul, 2021, Journal of Artificial Intelligence Research, V71, P191
   Vayena E, 2018, PLOS MED, V15, DOI 10.1371/journal.pmed.1002689
   Wakunuma K., 2020, Journal of Responsible Technology, V4
   Wang T, 2024, HEALTH COMMUN, V39, P1628, DOI 10.1080/10410236.2023.2228010
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Whittlestone J, 2019, ETHICAL SOC IMPLICAT
   Wiljer D, 2019, J MED IMAGING RADIAT, V50, pS8, DOI 10.1016/j.jmir.2019.09.010
   World Trade Organization, 2023, Who are the developing countries in the WTO?
   Wu B, 2022, WIREL COMMUN MOB COM, V2022, DOI 10.1155/2022/5295801
   Yu KH, 2018, NAT BIOMED ENG, V2, P719, DOI 10.1038/s41551-018-0305-z
   Yu P., 2020, Florida Law Review, V72
   Zhang Y, 2023, CARBON ENERGY, V5, DOI 10.1002/cey2.341
   Zhao X, 2023, RELC J, V54, P890, DOI 10.1177/00336882221094089
NR 142
TC 37
Z9 37
U1 147
U2 499
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0266-6669
EI 1741-6469
J9 INFORM DEV
JI Inf. Dev.
PD 2023 SEP 14
PY 2023
DI 10.1177/02666669231200628
EA SEP 2023
PG 19
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA R8BL1
UT WOS:001066552100001
DA 2024-12-25
ER

PT J
AU Gao, WY
   Mei, YH
   Duh, H
   Zhou, ZB
AF Gao, Weiyue
   Mei, Yihan
   Duh, Henry
   Zhou, Zhibin
TI Envisioning the incorporation of Generative Artificial Intelligence into
   future product design education: Insights from practitioners, educators,
   and students
SO DESIGN JOURNAL
LA English
DT Article; Early Access
DE Product design; design education; Generative Artificial Intelligence
   (GenAI); AI-generated content; large language models
AB The advancement of Generative Artificial Intelligence (GenAI) has introduced diverse opportunities and challenges in product design, demanding a corresponding evolution in design education. This study investigated GenAI's roles in product design and its implications for design education through semi-structured interviews with 24 participants, comprising practitioners, educators, and students, who had hands-on experience leveraging GenAI in design. Through thematic analysis, we identified critical functions of GenAI in product design, including generating inspiration, exploring potential solutions, conducting prototyping, performing evaluations, finalizing detailed outputs, and serving as design materials, presenting both opportunities and challenges. Moreover, fully incorporating GenAI in product design requires adaptation in educational initiatives, including capability development, curriculum content, and assessment methods. This study provides insights from a multi-stakeholder perspective, revealing the roles of GenAI in product design and advancing educational curricula to prepare the next-generation designers to be proficient in effectively leveraging GenAI for innovation.
C1 [Gao, Weiyue; Mei, Yihan; Duh, Henry; Zhou, Zhibin] Hong Kong Polytech Univ, Sch Design, Hong Kong, Peoples R China.
   [Gao, Weiyue; Mei, Yihan; Duh, Henry; Zhou, Zhibin] Hong Kong Polytech Univ, PolyU NVIDIA Joint Res Ctr, Hong Kong, Peoples R China.
C3 Hong Kong Polytechnic University; Hong Kong Polytechnic University
RP Zhou, ZB (corresponding author), Hong Kong Polytech Univ, Sch Design, Hong Kong, Peoples R China.
EM zhibin.zhou@polyu.edu.hk
FU Hong Kong Polytechnic university [P0045910, P0045406, P0046516,
   P0042736, P0046486]
FX The research presented in this paper was funded by grants from the Hong
   Kong Polytechnic university [Project No. P0045910, No. P0045406, No.
   P0046516, No. P0042736, No. P0046486].
CR Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Cagan Jonathan., 2013, Creating Breakthrough Products: Revealing the Secrets That Drive Global Innovation
   Chen XJ, 2024, EDUC INF TECHNOL, V29, P17485, DOI 10.1007/s10639-024-12549-7
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cormio L, 2024, DESIGN STUD, V91-92, DOI 10.1016/j.destud.2024.101253
   Cross N., 2011, DESIGN THINKING UNDE, DOI DOI 10.5040/9781474293884
   Cross N., 1982, DESIGN STUD, V3, P221, DOI [10.1016/0142-694X(82)90040-0, DOI 10.1016/0142-694X(82)90040-0, 10.1007/1846283019, DOI 10.1007/1846283019]
   Design Council, 2024, The Double Diamond: A Universally Accepted Depiction of the Design Process
   Ding L, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00434-1
   Fukumoto Kenji, 2021, iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence, P223, DOI 10.1145/3487664.3487696
   Goldman A, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519863
   Ha J, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642472
   Hämäläinen P, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580688
   Han A, 2023, 22ND ANNUAL ACM INTERACTION DESIGN AND CHILDREN CONFERENCE, IDC 2023, P470, DOI 10.1145/3585088.3593867
   Ho J. C. F., 2024, DRS2024, P1
   Huang KL, 2024, LEARN INSTR, V92, DOI 10.1016/j.learninstruc.2024.101929
   Kim TS, 2022, P 2022 CHI C HUM FAC, DOI [DOI 10.1145/3491102.3501931, 10.1109/ICCE53296.2022.9730306]
   Lanzi PL, 2023, PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023, P1383, DOI 10.1145/3583131.3590351
   Lee UG, 2024, EDUC INF TECHNOL, V29, P9575, DOI 10.1007/s10639-023-12150-4
   Liu V, 2023, DESIGNING INTERACTIVE SYSTEMS CONFERENCE, DIS 2023, P1955, DOI 10.1145/3563657.3596098
   Lively J., 2023, DS Journal of Artificial Intelligence and Robotics, V1, P23, DOI [https://doi.org/10.59232/AIR-V1I1P103, DOI 10.59232/AIR-V1I1P103]
   Mesa D, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142114358
   Meyer MW, 2020, SHE JI, V6, P13, DOI 10.1016/j.sheji.2019.12.002
   Motlagh NY, 2023, Arxiv, DOI [arXiv:2309.02029, DOI 10.48550/ARXIV.2309.02029]
   Paay J, 2021, DESIGN STUD, V76, DOI 10.1016/j.destud.2021.101031
   Petridis S., 2023, 2023 CHI C HUM FACT, P1, DOI DOI 10.1145/3544549.3585628
   Petridis S, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580907
   Schmidt A, 2023, COMPANION OF THE 2023 ACM SIGCHI SYMPOSIUM ON ENGINEERING INTERACTIVE COMPUTING SYSTEMS, EICS 2023, P7, DOI 10.1145/3596454.3597176
   Shin D, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS, CHI 2024, DOI 10.1145/3613904.3642266
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Sun LY, 2022, AI EDAM, V36, DOI 10.1017/S0890060421000391
   Tholander J, 2023, DESIGNING INTERACTIVE SYSTEMS CONFERENCE, DIS 2023, P1930, DOI 10.1145/3563657.3596014
   Wu JY, 2023, Arxiv, DOI arXiv:2304.06632
   Xu Frank F., 2022, MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, P1, DOI 10.1145/3520312.3534862
   Yan C, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3502075
   Zhou Z., 2019, 2016 CHI C HUM FACT, P1
   Zhou Z., 2023, IASDR 2023 LIFE CHAN, DOI [https://doi.org/10.21606/iasdr.2023.168, DOI 10.21606/IASDR.2023.168]
   Zhou ZB, 2024, HUM-COMPUT INTER-US, DOI 10.1080/07370024.2024.2420991
   Zhou ZB, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2370635
   Zhu QH, 2022, Arxiv, DOI [arXiv:2204.09658, 10.48550/ARXIV.2204.09658, DOI 10.48550/ARXIV.2204.09658]
NR 40
TC 0
Z9 0
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1460-6925
EI 1756-3062
J9 DES J
JI Des. J.
PD 2024 DEC 7
PY 2024
DI 10.1080/14606925.2024.2435703
EA DEC 2024
PG 21
WC Art
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Art
GA P6K0Z
UT WOS:001378963100001
DA 2024-12-25
ER

PT J
AU Sidaoui, K
   Mahr, D
   Odekerken-Schröder, G
AF Sidaoui, Karim
   Mahr, Dominik
   Odekerken-Schroder, Gaby
TI Generative AI in Responsible Conversational Agent Integration:
   Guidelines for Service Managers
SO ORGANIZATIONAL DYNAMICS
LA English
DT Article
DE Conversational agents; Software development life cycle; Inclusive
   design; Ethics; Generative artificial intelligence; Corporate digital
   responsibility; Organizational sensemaking; European Union Artificial
   Intelligence Act
AB Responsible integration of conversational agents (CAs) like chatbots is crucial for service firms to mitigate risks and foster positive outcomes. This article provides managerial guidelines through a Corporate Digital Responsibility (CDR) lens, focusing on CDR Culture, Management Structure, and Digital Governance across the service firm, software provider, and customers/society. It examines how organizational sensemaking processes of creation, interpretation, and enactment are triggered by CA-related issues and events. The research highlights the role of generative AI (GenAI) in implementing CDR factors and responsible CA software development lifecycle phases during development and integration. Guidelines are provided for leveraging GenAI to enhance CDR Culture, incorporate ethical considerations into CDR Management Structure, and enable robust Digital Governance mechanisms to prioritize customer/societal well-being. A multilevel framework illustrates reinforcing the guidelines through organizational sensemaking processes, and fostering responsible CA integration aligned with ethical principles and societal values.
C1 [Sidaoui, Karim] Radboud Univ Nijmegen, Inst Management Res, Dept Mkt, Nijmegen, Netherlands.
   [Mahr, Dominik; Odekerken-Schroder, Gaby] Maastricht Univ, Dept Mkt & Supply Chain Management, Maastricht, Netherlands.
C3 Radboud University Nijmegen; Maastricht University
RP Sidaoui, K (corresponding author), Radboud Univ Nijmegen, Inst Management Res, Dept Mkt, Nijmegen, Netherlands.
EM karim.sidaoui@ru.nl
OI Mahr, Dominik/0000-0003-1804-9622
CR Brown AD, 2015, ORGAN STUD, V36, P265, DOI 10.1177/0170840614559259
   Kunz WH, 2024, J RES INTERACT MARK, V18, P31, DOI 10.1108/JRIM-06-2023-0176
   Pothukuchi A. S., 2023, SSRN, V11
   Wirtz J, 2023, J SERV RES-US, V26, P173, DOI 10.1177/10946705221130467
   Worsdorfer M, 2023, AI ETHICS
NR 5
TC 2
Z9 2
U1 44
U2 44
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0090-2616
EI 1873-3530
J9 ORGAN DYN
JI Organ. Dyn.
PD APR-JUN
PY 2024
VL 53
IS 2
AR 101045
DI 10.1016/j.orgdyn.2024.101045
EA MAY 2024
PG 7
WC Business; Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA WA1S7
UT WOS:001252062900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Luo, T
   Muljana, PS
   Ren, XY
   Young, D
AF Luo, Tian
   Muljana, Pauline S.
   Ren, Xinyue
   Young, Dara
TI Exploring instructional designers' utilization and perspectives on
   generative AI tools: A mixed methods study
SO ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT
LA English
DT Article; Early Access
DE GenAI; ChatGPT; Instructional design; Instructional designers
AB The emergence of generative artificial intelligence (GenAI) has caused significant disruptions on a global scale in various workplace settings, including the field of instructional design (ID). Given the paucity of research investigating the impact of GenAI on ID work, we conducted a mixed methods study to understand instructional designers (IDs)' perceptions and experiences of utilizing GenAI across a spectrum of ID tasks. A total of 70 IDs completed an online survey, and 13 of them participated in the semi-structured interviews. The survey results indicated IDs' familiarity with and perceived usability of GenAI tools in performing various ID responsibilities in their specific contexts. Qualitative findings further explained that IDs often utilized GenAI tools in (1) brainstorming ideas, (2) handling low-stake tasks, (3) streamlining design process, and (4) enhancing collaborations. Participants also expressed their concerns and challenges while using GenAI in ID, including (1) quality concerns, (2) data security and privacy concerns, (3) concerns over authorship, ownership and plagiarism, amongst others. Implications and recommendations are also discussed to inform future ID practices and research.
C1 [Luo, Tian; Ren, Xinyue; Young, Dara] Old Dominion Univ, Dept STEM Educ & Profess Studies, Instructional Design & Technol, Norfolk, VA 23529 USA.
   [Muljana, Pauline S.] Univ Tennessee, Theory & Practice Teacher Educ, Knoxville, TN USA.
C3 Old Dominion University; University of Tennessee System; University of
   Tennessee Knoxville
RP Luo, T (corresponding author), Old Dominion Univ, Dept STEM Educ & Profess Studies, Instructional Design & Technol, Norfolk, VA 23529 USA.
EM tluo@odu.edu; pmuljana@utk.edu; xren@odu.edu; dyoun028@odu.edu
RI Ren, Xinyue/AAZ-4197-2021
OI Ren, Xinyue/0000-0002-1042-0100
CR Aaij R, 2013, J HIGH ENERGY PHYS, DOI 10.1007/JHEP04(2013)001
   Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Ansari AN, 2024, EDUC INF TECHNOL, V29, P11281, DOI 10.1007/s10639-023-12223-4
   Bagde H, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e23050
   Bakla A., 2023, Transforming the Language Teaching Experience in the Age of AI, P89, DOI 10.4018/978-1-6684-9893-4.ch005
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bolick AD., 2024, TechTrends, V68, P91, DOI [10.1007/s11528-023-00894-2, DOI 10.1007/S11528-023-00894-2]
   Bond J., 2020, The Journal of Applied Instructional Design, V9, DOI DOI 10.51869/92JBKD
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Campbell K, 2009, ETR&D-EDUC TECH RES, V57, P645, DOI 10.1007/s11423-007-9061-6
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chang DH, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712921
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Chng LK., 2023, Asian Journal of Distance Education, V18, P32, DOI [10.5281/zenodo.8188576, DOI 10.5281/ZENODO.8188576]
   Choi GW, 2024, TECHTRENDS, V68, P832, DOI 10.1007/s11528-024-00967-w
   Chu T, 2024, AAAI CONF ARTIF INTE, P17871
   Claude 3, Anthropic. Introducing the next generation of Claude
   Creswell John, 2011, DESIGNING CONDUCTING
   Crompton H, 2024, TECHTRENDS, V68, P380, DOI 10.1007/s11528-024-00939-0
   Deike M, 2024, J BUS FINANC LIBR, V29, P125, DOI 10.1080/08963568.2024.2317534
   Dousay T. A., 2023, Leary Foundations of Learning and Instructional Design Technology: Historical Roots and Current Trends
   Drysdale J, 2019, ONLINE LEARN, V23, P56, DOI 10.24059/olj.v23i3.2058
   Ebert C, 2023, IEEE SOFTWARE, V40, P30, DOI 10.1109/MS.2023.3265877
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Eloundou T., 2023, Technical Report
   Evmenova AS, 2024, TECHTRENDS, V68, P820, DOI 10.1007/s11528-024-00966-x
   Farina A., 2024, Exploring the Ethical Implications of Generative AI, P63, DOI [10.4018/979-8-3693-1565-1.ch005, DOI 10.4018/979-8-3693-1565-1.CH005]
   Fink A., 2009, How to conduct surveys: A step-by-step guide
   Foung D., 2024, Computers and Education: Artificial Intelligence, DOI [10.1016/j.caeai.2024.100250, DOI 10.1016/J.CAEAI.2024.100250]
   Hargis J., 2024, Global and Lokal Distance EducationGlokalde, V10
   Heston T. F., 2023, International Medical Education, V2, P198, DOI [DOI 10.3390/IME2030019, https://doi.org/10.3390/ime2030019]
   Hicks MT, 2024, ETHICS INF TECHNOL, V26, DOI 10.1007/s10676-024-09775-5
   Hodges CB, 2024, TECHTRENDS, V68, P195, DOI 10.1007/s11528-023-00926-x
   Humphreys D., 2024, AI and Ethics, DOI [10.1007/s43681-024-00443-4, DOI 10.1007/S43681-024-00443-4]
   Imran M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13605
   Jonassen David H., 2008, Educational Technology, V48, P21
   Kearns K. D., 2016, Coll. Teach, V65, P17, DOI DOI 10.1080/87567555.2016.1222575
   Keener CP, 2017, HORIZON, V25, P235, DOI 10.1108/OTH-10-2016-0051
   Kemp J.E., 1977, Instructional design: A plan for unit and course development
   Koraishi O., 2023, Language Education Technology, V3, P55
   Koszalka TA, 2013, IBSTPI BK SER, P1
   Krushinskaia K, 2024, COMM COM INF SC, V2151, P395, DOI 10.1007/978-3-031-64312-5_49
   Kumar S., 2017, INT J E LEARNING, V16, P371
   Kumar S, 2024, ONLINE LEARN, V28, P207, DOI 10.24059/olj.v28i3.4501
   Larson M, 2009, ETR&D-EDUC TECH RES, V57, P1, DOI 10.1007/s11423-006-9031-4
   Law L, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100174
   Levin G, 2024, BJOG-INT J OBSTET GY, V131, P378, DOI 10.1111/1471-0528.17641
   Li B, 2023, INT J COMPUT-ASSIST, V13, DOI 10.4018/IJCALLT.326135
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lowe D. A., 2023, The Journal of Applied Instructional Design, V12
   Lowell VL, 2018, J COMPUT HIGH EDUC, V30, P72, DOI 10.1007/s12528-018-9170-8
   Mai DTT, 2024, FRONT EDUC, V9, DOI 10.3389/feduc.2024.1328769
   Mao J, 2024, TECHTRENDS, V68, P58, DOI 10.1007/s11528-023-00911-4
   McHugh ML, 2012, BIOCHEM MEDICA, V22, P276, DOI 10.11613/bm.2012.031
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Montenegro-Rueda M, 2023, COMPUTERS, V12, DOI 10.3390/computers12080153
   Morrison GR., 2019, DESIGNING EFFECTIVE
   Muljana PS, 2023, RES LEARN TECHNOL, V31, DOI 10.25304/rlt.v31.2934
   Muljana PS, 2021, J COMPUT HIGH EDUC, V33, P206, DOI 10.1007/s12528-020-09262-y
   OpenAI, 2024, Hello GPT-4O
   Parsons B, 2024, TECHTRENDS, V68, P67, DOI 10.1007/s11528-023-00912-3
   Pasick A., 2023, The New York Times
   Patricio Maria Raquel, 2024, Information Technology and Systems: ICITS 2024. Lecture Notes in Networks and Systems (933), P339, DOI 10.1007/978-3-031-54256-5_32
   Pichai S., 2023, Introducing Gemini: our largest and most capable AI model
   Reiser RA, 2001, ETR&D-EDUC TECH RES, V49, P53, DOI 10.1007/BF02504506
   Ren XY, 2019, EDUC INF TECHNOL, V24, P3483, DOI 10.1007/s10639-019-09940-0
   Ritzhaupt AD, 2018, J COMPUT HIGH EDUC, V30, P3, DOI 10.1007/s12528-017-9163-z
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Sharif A, 2015, RUSC-UNIV KNOWL SOC, V12, P72, DOI 10.7238/rusc.v12i3.2176
   Singleton K, 2019, ONLINE LEARN, V23, P206, DOI 10.24059/olj.v23i1.1407
   Song NY, 2024, J CONTING CRISIS MAN, V32, DOI 10.1111/1468-5973.12532
   Sora, Creating video from text
   Stefaniak JE, 2024, ONLINE LEARN, V28, DOI 10.24059/olj.v28i3.4458
   Toner H., 2023, What are generative AI, large language models, and foundation models
   Trust T., 2023, Contemp. Issues Technol. Teach. Educ, V23, P1
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Villachica SW, 2010, PERFORM IMPROV Q, V22, P33, DOI 10.1002/piq.20067
   Volkan H., 2024, Assessment of readability, reliability, and quality of ChatGPT, DOI [10.1097/MD.0000000000039305, DOI 10.1097/MD.0000000000039305]
   Waisberg E, 2024, EYE, V38, P2502, DOI 10.1038/s41433-024-03098-x
   Wang XM, 2021, INT J TRAIN DEV, V25, P95, DOI 10.1111/ijtd.12209
   West R., 2018, Foundations of learning and instructional design technology: The past, present, andfuture of learning and instructional design technology
   Zhang P, 2024, EUR J EDUC, V59, DOI 10.1111/ejed.12599
NR 84
TC 0
Z9 0
U1 8
U2 8
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1042-1629
EI 1556-6501
J9 ETR&D-EDUC TECH RES
JI ETR&D-Educ. Tech. Res. Dev.
PD 2024 NOV 25
PY 2024
DI 10.1007/s11423-024-10437-y
EA NOV 2024
PG 26
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA N2D1E
UT WOS:001362491300001
OA hybrid
DA 2024-12-25
ER

PT J
AU Liu, XH
   Xiao, YY
AF Liu, Xiaohua
   Xiao, Yangyu
TI Chinese university teachers' engagement with generative AI in different
   stages of foreign language teaching: A qualitative enquiry through the
   prism of ADDIE
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Generative artificial intelligence; ChatGPT; Language teaching; Higher
   education; Interview; ADDIE
AB The bulk of recent research on generative AI (GenAI) in education mainly focused on its potential pedagogical uses as well as how students actually exploit such tools during learning, while little has been done to systematically investigate how teachers integrate those tools in different parts of their teaching process. To bridge this gap, the present study conducted in-depth interviews with 17 university English as a foreign language (EFL) teachers in mainland China, and scrutinized their different phases of teaching for any GenAI involvement. Through the lens of the ADDIE model (i.e., analysis, design, development, implementation, and evaluation), it was found that GenAI tools were mainly involved in the development stage (e.g., devising teaching activities and developing teaching materials). During the implementation stage, teachers merely introduced GenAI tools to their students, through means such as demonstrations and workshops, indicating a lack of organic integration of those tools into curriculum-based activities. GenAI involvement in the other three stages of instruction was reported to be none to minimal. The participants also shared both positive and negative experiences with GenAI in their EFL instruction. These findings highlight the urgent need for providing stage-specific professional training on how to integrate GenAI into different instructional stages and developing specialized educational GenAI programs that can produce accurate and high-quality outputs. Other implications for incorporating GenAI into education are also discussed.
C1 [Liu, Xiaohua; Xiao, Yangyu] Chinese Univ Hong Kong, Sch Humanities & Social Sci, Shenzhen, Guangdong Provi, Peoples R China.
C3 The Chinese University of Hong Kong, Shenzhen
RP Xiao, YY (corresponding author), Chinese Univ Hong Kong, Sch Humanities & Social Sci, Shenzhen, Guangdong Provi, Peoples R China.
EM liuxiaohua@cuhk.edu.cn; shirleyxiao@cuhk.edu.cn
RI Xiao, Yangyu/ABE-3355-2021; Liu, Xiaohua/D-8966-2013
OI Liu, Xiaohua/0000-0002-5781-5775
FU Guangdong Province Philosophy and Social Science Planning Project
   [GD24CWY04]; Shenzhen Municipality Peacock Talent Fund [2024TC0129];
   Shenzhen Educational Sciences Planning Project (The 14th Five-Year Plan)
   [YBZZ21019]; Teaching Innovation Grant from The Chinese University of
   Hong Kong, Shenzhen [I10120230444]
FX This study was supported by the following grants: Guangdong Province
   Philosophy and Social Science Planning Project (Grant number:
   GD24CWY04); Shenzhen Municipality Peacock Talent Fund (Grant number:
   2024TC0129); Shenzhen Educational Sciences Planning Project (The 14th
   Five-Year Plan) (Grant number: YBZZ21019); Teaching Innovation Grant
   from The Chinese University of Hong Kong, Shenzhen (Grant number:
   I10120230444).
CR Adams D, 2023, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12427-8
   Alghamdi R, 2024, EDUC INF TECHNOL, V29, P18901, DOI 10.1007/s10639-024-12594-2
   Bower M, 2024, EDUC INF TECHNOL, V29, P15403, DOI 10.1007/s10639-023-12405-0
   Dahri NA, 2024, EDUC INF TECHNOL, V29, P18695, DOI 10.1007/s10639-024-12599-x
   Dornyei Z., 2007, Research Methods in Applied Linguistics
   Essien A, 2024, STUD HIGH EDUC, V49, P865, DOI 10.1080/03075079.2024.2316881
   Gao LLY, 2024, STUD HIGH EDUC, DOI 10.1080/03075079.2024.2323571
   Hockly N, 2023, RELC J, V54, P445, DOI 10.1177/00336882231168504
   HyScaler, 2023, The Power of AI in Research Hypotheses
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Ji H, 2023, J RES TECHNOL EDUC, V55, P48, DOI 10.1080/15391523.2022.2142873
   Karatas F, 2024, EDUC INF TECHNOL, V29, P19343, DOI 10.1007/s10639-024-12574-6
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kim A, 2024, SYSTEM, V122, DOI 10.1016/j.system.2024.103256
   King N., 2010, Interviews in qualitative research
   Kiryakova G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13101056
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Lee J, 2017, ETR&D-EDUC TECH RES, V65, P427, DOI 10.1007/s11423-016-9502-1
   Li HX, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.737746
   Merriam S.B., 2015, QUALITATIVE RES GUID
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Mohamed AM, 2024, EDUC INF TECHNOL, V29, P3195, DOI 10.1007/s10639-023-11917-z
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Nguyen A, 2024, STUD HIGH EDUC, V49, P847, DOI 10.1080/03075079.2024.2323593
   Paik M. C., 2003, Statistical methods for rates and proportions, DOI DOI 10.1002/0471445428
   Tan S.C., 2023, Learning: Research and Practice, V9, P125, DOI [10.1080/23735082.2023.2258895, DOI 10.1080/23735082.2023.2258895]
   Ulla MB., 2023, LEARNING RES PRACTIC, V0, P1, DOI [10.1080/23735082.2023.2257252, DOI 10.1080/23735082.2023.2257252]
   Urban M, 2024, COMPUT EDUC, V215, DOI 10.1016/j.compedu.2024.105031
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yang YY, 2024, STUD HIGH EDUC, V49, P817, DOI 10.1080/03075079.2024.2327003
   Yeh HC, 2019, EDUC TECHNOL SOC, V22, P88
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
   Zou D, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2277747
NR 34
TC 0
Z9 0
U1 51
U2 51
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 OCT 28
PY 2024
DI 10.1007/s10639-024-13117-9
EA OCT 2024
PG 24
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA K5T8U
UT WOS:001344506100002
DA 2024-12-25
ER

PT J
AU Spennemann, DHR
AF Spennemann, Dirk H. R.
TI Generative Artificial Intelligence, Human Agency and the Future of
   Cultural Heritage
SO HERITAGE
LA English
DT Article
DE authorship; ChatGPT; cultural heritage; futurist perspectives;
   generative artificial intelligence; virtual heritage
ID DIFFERENCE
AB The first half of 2023 was dominated by a public discussion of the nature and implications of generative artificial intelligence (genAI) models that are poised to become the most significant cross-cultural global disruptor since the invention of the World-Wide Web. It can be predicted that genAI will affect how cultural heritage is being managed and practiced, primarily by providing analysis and decision-making tools, but also by genAI generated texts and images, in particular reconstructions of objects and sites. The more speculative interpretations of contexts and alternative interpretations generated by genAI models may constitute manifestations of cultural heritage in their own right. But do these constitute human cultural heritage, or are they AI cultural heritage? This paper is a deliberation of the realities and future(s) of cultural heritage in a genAI and post-genAI world.
C1 [Spennemann, Dirk H. R.] Charles Sturt Univ, Gulbali Inst, POB 789, Albury, NSW 2640, Australia.
C3 Charles Sturt University
RP Spennemann, DHR (corresponding author), Charles Sturt Univ, Gulbali Inst, POB 789, Albury, NSW 2640, Australia.
EM dspennemann@csu.edu.au
RI Spennemann, Dirk/J-4199-2016
OI Spennemann, Dirk/0000-0003-2639-7950
CR Alcántara-Pilar JM, 2018, J DESTIN MARK MANAGE, V8, P301, DOI 10.1016/j.jdmm.2017.06.002
   [Anonymous], 2013, The Burra Charter: The Australia ICOMOS Charter for Places of Cultural Significance - Article 12. Participation [em linha]
   Aslan S, 2020, PATTERN RECOGN LETT, V131, P158, DOI 10.1016/j.patrec.2019.12.007
   Beets B, 2023, J MED INTERNET RES, V25, DOI 10.2196/40337
   Bickford Anne., 1981, Australian Archaeology, V13, P1, DOI [10.1080/03122417.1981.12092815, DOI 10.1080/03122417.1981.12092815]
   Biswas S., Importance of chat GPT in Agriculture: According to Chat GPT
   Boesch C, 1998, CURR ANTHROPOL, V39, P591, DOI 10.1086/204785
   Borji A, 2022, Arxiv, DOI [arXiv:2210.00586, 10.48550/arXiv.2210.00586, DOI 10.48550/ARXIV.2210.00586]
   Boyd Robert, 1996, Proceedings of the British Academy, V88, P77
   Bronfman Z., 2021, Journal of Artificial Intelligence and Consciousness, V8, P183, DOI DOI 10.1142/S2705078521500168
   Cardarelli L, 2022, J ARCHAEOL SCI, V144, DOI 10.1016/j.jas.2022.105640
   Chang KK, 2023, Arxiv, DOI arXiv:2305.00118
   Chatterjee A, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.1024449
   Cobb PJ, 2023, ADV ARCHAEOL PRACT, V11, P363, DOI 10.1017/aap.2023.20
   Coghlan S., 2023, Philos. Technol, V36, P25, DOI DOI 10.1007/S13347-023-00627-6
   Collins E., LaMDA Our Breakthrough Conversation Technology
   Committee on Publication, Ethics Authorship and AI Tools
   Currie G, 2023, RADIOGRAPHY, V29, P792, DOI 10.1016/j.radi.2023.05.011
   De Smet P, 2008, FORENSIC SCI INT, V176, P124, DOI 10.1016/j.forsciint.2007.07.013
   Diaz-Andreu M., 2017, Journal of Community Archaeology and Heritage, V4, P2, DOI [10.1080/20518196.2016.1228213, DOI 10.1080/20518196.2016.1228213]
   Elsevier, USE AI AI ASSISTED W
   Elsevier, Publishing Ethics
   Emerald Publishing's, Emerald Publishing's Stance on AI Tools and Authorship
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V329, P637, DOI 10.1001/jama.2023.1344
   Frackiewicz M., ChatGPT-4 for Digital Archaeology: AI-Powered Artifact Discovery and Analysis
   Fredheim LH, 2016, INT J HERIT STUD, V22, P466, DOI 10.1080/13527258.2016.1171247
   Gibert M, 2022, AI SOC, V37, P319, DOI 10.1007/s00146-021-01179-z
   Ginsburg J., 2002, DePaul Law Review, V52, P1063, DOI DOI 10.2139/SSRN.368481
   Griffin G, 2024, AI SOC, V39, P2359, DOI 10.1007/s00146-023-01689-y
   Grünebaum A, 2023, AM J OBSTET GYNECOL, V228, P696, DOI 10.1016/j.ajog.2023.03.009
   Hekman S, 1997, SIGNS, V22, P341, DOI 10.1086/495159
   Hines Andy., 2006, Thinking About the Future: Guidelines for Strategic Foresight
   Howard K., 2016, Music as intangible cultural heritage: Policy, ideology, and practice in the preservation of East Asian traditions
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Kelley PG, 2021, AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, P627, DOI 10.1145/3461702.3462605
   Kim B., 2007, Handbook of Asian American psychology, V2nd, P141
   King MR, 2023, CELL MOL BIOENG, V16, P1, DOI 10.1007/s12195-022-00754-8
   Korsten B., The Next Rembrandt
   Krützen M, 2005, P NATL ACAD SCI USA, V102, P8939, DOI 10.1073/pnas.0500232102
   Lambers K., 2019, Journal of Computer Applications in Archaeology, V2, P31, DOI DOI 10.5334/JCAA.32
   Lenzerini F, 2011, EUR J INT LAW, V22, P101, DOI 10.1093/ejil/chr006
   Leshkevich T, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12052712
   Levene A., Artificial Intelligence and Authorship
   Lowenthal David., 1985, The Past is a Foreign Country
   Makhortykh M., 2023, Disc. Artif. Intell., V3, P28
   Marcus G., 2022, arXiv, DOI 10.48550/arXiv.2204.13807
   Marie I, 2005, J ARCHAEOL SCI, V32, P1527, DOI 10.1016/j.jas.2005.04.011
   Markov T., New and Improved Content Moderation Tooling
   Munjeri D, 2004, MUSEUM INT, V56, P12, DOI 10.1111/j.1350-0775.2004.00453.x
   Murtagh WilliamJ., 1997, Keeping Time: The History and Theory of Preservation in American
   Nascimento CMC, 2023, J CHEM INF MODEL, V63, P1649, DOI 10.1021/acs.jcim.3c00285
   Ncube C.B., 2018, Potchefstroom Electron. Law J, V21, P2, DOI [10.17159/1727-3781/2018/v21i0a4979, DOI 10.17159/1727-3781/2018/V21I0A4979]
   Neves P.S., 2022, AIS-Archit. Image Stud, V3, P58
   Nguyen P., 2019, Pub. Int. LJNZ, V6, P121
   Nihei Y., 2001, Tohoku Psychologica Folia, V60, P93
   OConnor R., How DALL-E 2 Actually Works
   Ostertag C, 2020, PATTERN RECOGN LETT, V131, P336, DOI 10.1016/j.patrec.2020.01.012
   Parker M, 2022, ACOUST AUST, V50, P23, DOI 10.1007/s40857-021-00257-y
   Qi X, 2023, AGING HEALTH RES, V3, DOI 10.1016/j.ahr.2023.100136
   Rao ARY, 2023, medRxiv, DOI [10.1101/2023.02.21.23285886, 10.1101/2023.02.21.23285886v1, DOI 10.1101/2023.02.21.23285886V1, 10.1101/2023.02.21.23285886, DOI 10.1101/2023.02.21.23285886]
   Romanengo C, 2020, PATTERN RECOGN LETT, V131, P405, DOI 10.1016/j.patrec.2020.01.025
   Rosati E, 2017, J INTELLET PROP LAW, V12, P973, DOI 10.1093/jiplp/jpx199
   Rose D.V., 2007, CONFLICTS TENSIONS, P102, DOI DOI 10.4135/9781446214671
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ruskov M, 2023, Arxiv, DOI arXiv:2302.08961
   Sage, ChatGPT and Generative AI
   Schwitzgebel E, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100818
   Shah C., 2022, Inf. Matters, V2, P1, DOI [10.2139/ssrn.4213593, DOI 10.2139/SSRN.4213593]
   Silverman H, 2011, CONTESTED CULTURAL HERITAGE: RELIGION, NATIONALISM, ERASURE, AND EXCLUSION IN A GLOBAL WORLD, P1, DOI 10.1007/978-1-4419-7305-4_1
   Smith G.S., 2017, Heritage values in contemporary society
   Smith L., 2006, USES HERITAGE
   Sng GGR, 2023, DIABETES CARE, V46, pE103, DOI 10.2337/dc23-0197
   Sok S., 2023, SSRN ELECT J, V3, P110, DOI DOI 10.2139/SSRN.4378735
   Spennemann D. H. R., 2007, Int. Rev. Public Non Profit Mark, V4, P91, DOI DOI 10.1007/BF03180757
   Spennemann D.H.R., 2011, Alaska Park Science, V10, P16
   Spennemann D.H.R., 2007, Int. J. Herit. Stud, V13, P4, DOI DOI 10.1080/13527250601010828
   Spennemann D.H.R., 2011, CRM J. Herit. Steward, V8, P7
   Spennemann D.H.R., 2023, KNOWLEDGE, V3, P480, DOI [10.3390/knowledge3030032, DOI 10.3390/KNOWLEDGE3030032]
   Spennemann D.H.R., 2020, arXiv
   Spennemann DHR, 2024, INTERACT TECHNOL SMA, V21, P690, DOI 10.1108/ITSE-10-2023-0195
   Spennemann DHR, 2023, Arxiv, DOI arXiv:2308.03301
   Spennemann DHR, 2023, HERITAGE-BASEL, V6, P5732, DOI 10.3390/heritage6080302
   Spennemann DHR, 2023, HERITAGE-BASEL, V6, P3864, DOI 10.3390/heritage6050205
   Spennemann DHR, 2007, FUTURES, V39, P861, DOI 10.1016/j.futures.2006.12.008
   Spennemann DHR, 2023, HERITAGE-BASEL, V6, P548, DOI 10.3390/heritage6010029
   Spennemann DHR, 2022, HERITAGE-BASEL, V5, P2007, DOI 10.3390/heritage5030105
   Spennemann R, 2022, AAA-ARB ANGLIST AM, V47, P225, DOI 10.24053/AAA-2022-0013
   Subaveerapandiyan A, 2023, IFLA J-INT FED LIBR, V49, P503, DOI 10.1177/03400352231180230
   Surameery NMS., 2023, INT J INFORM TECHNOL, V3, P17, DOI DOI 10.55529/IJITC.31.17.22
   Taylor Francis, Taylor Francis clarifies the responsible use of AI tools in academic content creation
   Tenzer M., 2023, SocArXiv
   Trichopoulos G, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12183829
   Trichopoulos G, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7030148
   Tunbridge J.E., 1996, DISSONANT HERITAGE
   UNESCO, 2001, Records of the General Conference, 31st Session Paris, France, October 15 November 3, 2001 Volume 1 Resolutions, V1, P62
   UNESCO, 2020, BASIC TEXTS 2003 CON
   US Copyright Office, 2021, COMPENDIUM OF US COPYRIGHT OFFICE PRACTICES, V3
   US Copyright Office, 2023, Copyright registration guidance: works containing material generated by artificial intelligence
   van Duijne F., 2018, Introduction to Strategic Foresight, VVolume 1, P67
   van Schaik Carel P., 2009, P299
   Vecco M, 2010, J CULT HERIT, V11, P321, DOI 10.1016/j.culher.2010.01.006
   Walter Y., 2022, arXiv
   Wells J.C., 2018, Human-centered built environment heritage preservation: Theory and evidence-based best practice
   Whiten A, 1999, NATURE, V399, P682, DOI 10.1038/21415
   Wiley, Best Practice Guidelines on Research Integrity and Publication Ethics
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Zhu Y, 2023, Preprints, DOI [10.20944/preprints202302.0324.v1, 10.20944/preprints202302.0324.v1, DOI 10.20944/PREPRINTS202302.0324.V1]
   Zielinski C., 2023, Chatbots, generative AI, and scholarly manuscripts: WAME recommendations on chatbots and generative artificial intelligence in relation to scholarly publications
NR 108
TC 1
Z9 1
U1 10
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2571-9408
J9 HERITAGE-BASEL
JI Heritage
PD JUL
PY 2024
VL 7
IS 7
BP 3597
EP 3609
DI 10.3390/heritage7070170
PG 13
WC Humanities, Multidisciplinary; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Arts & Humanities - Other Topics; Science & Technology - Other Topics
GA ZT6J8
UT WOS:001277577800001
OA gold
DA 2024-12-25
ER

PT J
AU Layman, L
   Vetter, R
AF Layman, Lucas
   Vetter, Ron
TI Generative Artificial Intelligence and the Future of Software Testing
SO COMPUTER
LA English
DT Article
DE Software testing; Privacy; Quality assurance; Artificial intelligence
AB This virtual roundtable focuses on applications of generative artificial intelligence (GenAI) to software testing with four leading experts from the field. Our experts reflect on transforming the work of software testing with GenAI, its impact on quality assurance engineers, and privacy concerns.
C1 [Layman, Lucas] Univ North Carolina Wilmington, Comp Sci, Wilmington, NC 28403 USA.
   [Vetter, Ron] Univ North Carolina Wilmington, Coll Sci & Engn, Wilmington, NC 28403 USA.
C3 University of North Carolina; University of North Carolina Wilmington;
   University of North Carolina; University of North Carolina Wilmington
RP Layman, L (corresponding author), Univ North Carolina Wilmington, Comp Sci, Wilmington, NC 28403 USA.
EM laymanl@uncw.edu; vetterr@uncw.edu
OI Layman, Lucas/0000-0002-2534-8762
CR Akata Z, 2020, COMPUTER, V53, P18, DOI 10.1109/MC.2020.2996587
   blogs.microsoft.com, Microsoft
   github.com, GitHub Copilot
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Lin HY, 2022, COMPUTER, V55, P76, DOI 10.1109/MC.2022.3151419
   Murali V, 2024, Arxiv, DOI arXiv:2305.12050
   Ozkaya I, 2023, IEEE SOFTWARE, V40, P4, DOI 10.1109/MS.2023.3248401
NR 7
TC 3
Z9 3
U1 14
U2 45
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD JAN
PY 2024
VL 57
IS 1
BP 27
EP 32
DI 10.1109/MC.2023.3306998
PG 6
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EC7P2
UT WOS:001136783900007
OA Bronze
DA 2024-12-25
ER

PT J
AU Goldkind, L
   Ming, JY
   Fink, A
AF Goldkind, Lauri
   Ming, Joy
   Fink, Alex
TI AI in the Nonprofit Human Services: Distinguishing Between Hype, Harm,
   and Hope
SO HUMAN SERVICE ORGANIZATIONS MANAGEMENT LEADERSHIP & GOVERNANCE
LA English
DT Article; Early Access
DE AI; artificial intelligencer; GenAI; generative artificial intelligence;
   responsible ai; responsble artificial intelligence
AB Human service organizations (HS) are important for serving historically underserved communities, faces challenges adopting digital technologies. The emergence of generative AI (GenAI), such as OpenAI's ChatGPT, presents unprecedented opportunities and risks for the sector. This paper explores how GenAI, with its easy to use interface and ability to automate tasks, could enhance nonprofit operations, from fundraising to program evaluation. However, its adoption is not without significant concerns, including data privacy, algorithmic bias, and misinformation risks. The sector must navigate the "hype cycle," balancing optimism with cautious, responsible implementation. By understanding GenAI's capabilities and limitations, leaders can craft policies that align with nonprofit values, ensure ethical deployment, and protect marginalized populations. Encouraging staff experimentation, developing AI literacy, and fostering collaboration are essential steps to integrate AI responsibly. We offer a roadmap for nonprofits to engage GenAI's potential while safeguarding against harm, fostering innovation, and supporting mission-driven outcomes.
C1 [Goldkind, Lauri] Fordham Univ, Grad Sch Social Serv, 113 West 60th St, New York, NY 10023 USA.
   [Ming, Joy] Cornell Univ, Informat Sci, Ithaca, NY USA.
   [Fink, Alex] Augsburg Univ, Dept Social Work, Minneapolis, MN USA.
C3 Fordham University; Cornell University; Augsburg University
RP Goldkind, L (corresponding author), Fordham Univ, Grad Sch Social Serv, 113 West 60th St, New York, NY 10023 USA.
EM lgoldkind@gmail.com
CR Abebe R, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P252, DOI 10.1145/3351095.3372871
   Ananya, 2024, AI image generators often give racist and sexist results: Can they be fixed?
   Andrason S. P., 2020, Doctoral dissertation .
   Asakura K, 2020, J TEACH SOC WORK, V40, P501, DOI 10.1080/08841233.2020.1813234
   Brachman M, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3650841
   Bright J., 2024, Digital Government: Research and Practice, DOI [https://doi.org/10.1145/3700140, DOI 10.1145/3700140]
   Callahan M., 2023, Algorithms were supposed to reduce bias in criminal justicedo they?
   Cariceo O., 2018, Methodological Innovations, V11, P1, DOI [https://doi.org/10.1177/2059799118814392, DOI 10.1177/2059799118814392]
   Chien C.V., 2024, Loyola of Los Angeles Law Review
   Citron DK, 2022, BOSTON U LAW REV, V102, P793
   Collier K., 2023, A researcher tried to buy mental health data. It was surprisingly easy
   Dimas GL, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517231171051
   Dixit P., 2023, BuzzFeed News31 January
   Fenn J., 2011, gartner inc. (G00214001)
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Friedman B., 1996, INTERACTIONS, V3, P16, DOI 10.1145/242485.242493
   Ghoshal Sucheta, 2020, Proceedings of the ACM on Human-Computer Interaction, V4, DOI 10.1145/3392862
   Goldkind L., 2018, Leave no org behind: Exploring the digital life of community action agencies
   Goldkind L, 2021, J COMMUNITY PRACT, V29, P237, DOI 10.1080/10705422.2021.1984354
   Goldkind L, 2021, SOC WORK, V66, P372, DOI 10.1093/sw/swab028
   Grant N, 2023, NEW YORK TIMES
   Hu K., 2023, REUTERS         0202
   incidentdatabase.ai, Welcome to the Artificial Intelligence Incident Database
   Jindal JA, 2024, J AM MED INFORM ASSN, V31, DOI 10.1093/jamia/ocae043
   Johnson N, 2024, PROCEEDINGS OF THE 2024 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, ACM FACCT 2024, P337, DOI 10.1145/3630106.3658910
   Kawakami A, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642849
   Keddell E, 2019, SOC SCI-BASEL, V8, DOI 10.3390/socsci8100281
   Kelly-Lyth A, 2021, OXFORD J LEGAL STUD, V41, P899, DOI 10.1093/ojls/gqab006
   Lane J, 2014, PRIVACY, BIG DATA, AND THE PUBLIC GOOD: FRAMEWORKS FOR ENGAGEMENT, pXI
   Magalhaes JC, 2021, INT J COMMUN-US, V15, P343
   Marchal N, 2024, Arxiv, DOI [arXiv:2406.13843, 10.48550/arXiv.2406.13843, DOI 10.48550/ARXIV.2406.13843]
   McCarthy L., 2023, The New York Times8 June
   McIlwain Charlton D., 2019, Black software: The Internet and racial justice, from the AfroNet to black lives matter
   Mir D. J., 2021, Designing for the privacy commons. Governing privacy in knowledge commons
   Mollick E., 2023, Detecting the secret cyborgs. One useful thing
   Noble S., 2018, ALGORITHMS OPPRESSIO, DOI [10.18574/nyu/9781479833641.001.0001, DOI 10.2307/J.CTT1PWT9W5, DOI 10.18574/NYU/9781479833641.001.0001]
   Offenhartz J., 2024, Nycs AI chatbot was caught telling businesses to break the law. The city isnt taking it down
   Perkovi G., 2024, P 2024 47 MIPRO ICT, P2084
   Pinho JC, 2006, J NONPROFIT PUBLIC S, V16, P171, DOI 10.1300/J054v16n01_09
   Raftree L., 2024, MERL Tech
   Rodriguez MY, 2024, J SOC WORK EDUC, V60, P174, DOI 10.1080/10437797.2024.2340931
   Shelby R, 2023, Arxiv, DOI arXiv:2210.05791
   Smith M. L., 2024, University of New Hampshire Law Review, V22, P9
   Smuha NA, 2021, INTERNET POLICY REV, V10, DOI 10.14763/2021.3.1574
   Southerland V. M., 2021, Maryland Law Review, V80, P487
   Suresh H, 2024, PROCEEDINGS OF THE 2024 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, ACM FACCT 2024, P1609, DOI 10.1145/3630106.3658992
   Toyama K, 2015, GEEK HERESY RESCUING
   Tsesis A., 2014, Wake Forest Law Review, V49, P433
   USAID, 2022, Cybersecurity: The next frontier in global development and humanitarian response
   Valentine S., 2019, Fordham Urban Law Journal, V46
   Victor B. G., 2024, The International Journal of Social Work Values and Ethics, V200, P200, DOI [https://doi.org/10.55521/10-021-112, DOI 10.55521/10-021-112]
   Victor BG, 2021, J SOC SOC WORK RES, V12, P631, DOI 10.1086/712734
   Vigil J., 2019, Journal of Biology and Todays World, DOI [https://doi.org/10.35248/2322-3308.20.9.002, DOI 10.35248/2322-3308.20.9.002]
   Xu ZW, 2024, Arxiv, DOI [arXiv:2401.11817, 10.48550/ARXIV.2401.11817, 10.48550/arXiv.2401.11817]
   Yang JF, 2024, ACM T KNOWL DISCOV D, V18, DOI 10.1145/3649506
NR 55
TC 0
Z9 0
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 2330-3131
EI 2330-314X
J9 HUM SERV ORG MANAGE
JI Hum. Serv. Organ. Manag. Leadersh. Gov.
PD 2024 NOV 27
PY 2024
DI 10.1080/23303131.2024.2427459
EA NOV 2024
PG 12
WC Public Administration; Social Work
WE Social Science Citation Index (SSCI)
SC Public Administration; Social Work
GA O2Y7N
UT WOS:001369853300001
DA 2024-12-25
ER

PT J
AU Brüns, JD
   Meissner, M
AF Bruens, Jasper David
   Meissner, Martin
TI Do you create your content yourself? Using generative artificial
   intelligence for social media content creation diminishes perceived
   brand authenticity
SO JOURNAL OF RETAILING AND CONSUMER SERVICES
LA English
DT Article
DE Generative artificial intelligence; Content creation; Brand
   authenticity; Algorithm aversion; Social media
ID ANTECEDENTS; DISCLOSURE; PASSION
AB Recent studies have demonstrated the potential of generative artificial intelligence (GenAI) in enhancing marketing content. However, its impact on consumer behavior has remained empirically untested. In response to social media platforms mandating the disclosure of GenAI content, we investigate how followers perceive brands that use GenAI for content creation. Drawing from literature on algorithm aversion and brand authenticity, the results of three experimental studies indicate that brands' GenAI adoption induces negative attitudinal and behavioral follower reactions. These effects are mediated by followers' perceptions of brand authenticity and can be triggered by GenAI disclosure. Negative reactions are attenuated if GenAI is used to assist humans in content creation rather than to replace them through automation. Our findings underscore the need for nuance in brands' GenAI adoption to unlock economic benefits without compromising on relationships with consumers.
C1 [Bruens, Jasper David; Meissner, Martin] Tech Univ Munich, Ctr Digital Transformat, Bildungs Campus 9, D-74076 Heilbronn, Germany.
C3 Technical University of Munich
RP Brüns, JD (corresponding author), Tech Univ Munich, Ctr Digital Transformat, Bildungs Campus 9, D-74076 Heilbronn, Germany.
EM Jasper.bruens@tum.de; Martin.meissner@tum.de
RI ; Meissner, Martin/O-3209-2019
OI Bruns, Jasper David/0000-0001-6567-0070; Meissner,
   Martin/0000-0002-3574-4283
CR [Anonymous], 2023, Amazon
   ASHFORTH BE, 1989, ACAD MANAGE REV, V14, P20, DOI 10.2307/258189
   Ashley C, 2015, PSYCHOL MARKET, V32, P15, DOI 10.1002/mar.20761
   Audrezet A, 2020, J BUS RES, V117, P557, DOI 10.1016/j.jbusres.2018.07.008
   Beverland MB, 2005, J MANAGE STUD, V42, P1003, DOI 10.1111/j.1467-6486.2005.00530.x
   Beverland MB, 2010, J CONSUM RES, V36, P838, DOI 10.1086/615047
   Bigman YE, 2018, COGNITION, V181, P21, DOI 10.1016/j.cognition.2018.08.003
   Boerman SC, 2017, J INTERACT MARK, V38, P82, DOI 10.1016/j.intmar.2016.12.002
   Burton JW, 2020, J BEHAV DECIS MAKING, V33, P220, DOI 10.1002/bdm.2155
   Campagna CL, 2023, J MARKET THEORY PRAC, V31, P129, DOI 10.1080/10696679.2021.2018937
   Campbell M.C., 1995, Journal of Consumer Psychology, V4, P225, DOI [DOI 10.1207/S15327663JCP0403_02, 10.1207/s15327663jcp0403_02]
   Carroll GR, 2009, RES ORGAN BEHAV, V29, P255, DOI 10.1016/j.riob.2009.06.003
   Castelo N, 2023, J CONSUM RES, V50, P848, DOI 10.1093/jcr/ucad023
   Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
   Cheung ML, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2022.102940
   Ki CW, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102133
   Clark M., 2023, Levi's Addresses Backlash after Using AI Models to 'increase Diversity' in Online Shopping
   de Kerviler G, 2022, J BUS ETHICS, V179, P89, DOI 10.1007/s10551-021-04779-3
   Eisend M, 2020, J ADVERTISING, V49, P344, DOI 10.1080/00913367.2020.1765909
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Gartner, 2023, GARTNER EXPERTS ANSW
   Gensler S, 2013, J INTERACT MARK, V27, P242, DOI 10.1016/j.intmar.2013.09.004
   Granulo A, 2021, J CONSUM PSYCHOL, V31, P72, DOI 10.1002/jcpy.1181
   Gray HM, 2007, SCIENCE, V315, P619, DOI 10.1126/science.1134475
   Guha A, 2021, J RETAILING, V97, P28, DOI 10.1016/j.jretai.2021.01.005
   Harman DM, 2021, J RETAIL CONSUM SERV, V62, DOI 10.1016/j.jretconser.2021.102603
   Haslam N, 2008, SOC COGNITION, V26, P248, DOI 10.1521/soco.2008.26.2.248
   Hollebeek LD, 2019, J INTERACT MARK, V45, P27, DOI 10.1016/j.intmar.2018.07.003
   Jago AS, 2019, ACAD MANAG DISCOV, V5, P38, DOI 10.5465/amd.2017.0002
   Jakesch M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2208839120
   KATZ E, 1973, PUBLIC OPIN QUART, V37, P508
   Kim H, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103581
   Kim J, 2016, COMPUT HUM BEHAV, V62, P570, DOI 10.1016/j.chb.2016.03.083
   Kim T, 2020, TELEMAT INFORM, V51, DOI 10.1016/j.tele.2020.101406
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Kumar V, 2021, J RETAIL CONSUM SERV, V61, DOI 10.1016/j.jretconser.2021.102579
   Lee SS, 2022, INT J ADVERT, V41, P30, DOI 10.1080/02650487.2021.1986257
   Leung E, 2018, J MARKETING RES, V55, P818, DOI 10.1177/0022243718818423
   Leung FF, 2022, J ACAD MARKET SCI, V50, P226, DOI 10.1007/s11747-021-00829-4
   Levi Strauss & Co, 2023, LS&Co. partners with Lalaland.ai
   Li SX, 2023, J RETAIL CONSUM SERV, V70, DOI 10.1016/j.jretconser.2022.103139
   Liu NTY, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103259
   Longoni C, 2022, J MARKETING, V86, P91, DOI 10.1177/0022242920957347
   Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
   Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
   Lv XY, 2022, J RETAIL CONSUM SERV, V68, DOI 10.1016/j.jretconser.2022.103078
   Magni F, 2024, J BUS PSYCHOL, V39, P643, DOI 10.1007/s10869-023-09910-x
   Mahmud H, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121390
   Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
   Matz S., 2023, The Potential of Generative AI for Personalized Persuasion at Scale, DOI [10.31234/osf.io/rn97c, DOI 10.31234/OSF.IO/RN97C]
   Morhart F, 2015, J CONSUM PSYCHOL, V25, P200, DOI 10.1016/j.jcps.2014.11.006
   Moulard JG, 2021, J ACAD MARKET SCI, V49, P96, DOI 10.1007/s11747-020-00735-1
   Moulard JG, 2016, PSYCHOL MARKET, V33, P421, DOI 10.1002/mar.20888
   Moulard JG, 2015, PSYCHOL MARKET, V32, P173, DOI 10.1002/mar.20771
   Moulard JG, 2014, PSYCHOL MARKET, V31, P576, DOI 10.1002/mar.20719
   Prado PHM, 2014, REV BRASIL MARK, V13, P4, DOI 10.5585/remark.v13i4.2739
   Muntinga DG, 2011, INT J ADVERT, V30, P13, DOI 10.2501/IJA-30-1-013-046
   NapoleonCat, 2023, Instagram Users in United States of America
   Napoli J, 2014, J BUS RES, V67, P1090, DOI 10.1016/j.jbusres.2013.06.001
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   OpenAI, 2023, INTRO GPTS
   Overgoor G, 2019, CALIF MANAGE REV, V61, P156, DOI 10.1177/0008125619859318
   Palan S, 2018, J BEHAV EXP FINANC, V17, P22, DOI 10.1016/j.jbef.2017.12.004
   Park J, 2023, J RETAIL CONSUM SERV, V74, DOI 10.1016/j.jretconser.2023.103426
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Reisenbichler M, 2022, MARKET SCI, V41, P441, DOI 10.1287/mksc.2022.1354
   Rogan J., 2022, The Joe Rogan Experience
   Roggeveen AL, 2021, J RETAILING, V97, P81, DOI 10.1016/j.jretai.2020.11.006
   Silver Ike., 2021, Consumer Psychology Review, V4, P70, DOI [10.1002/arcp.1064, DOI 10.1002/ARCP.1064]
   Spiggle S, 2012, J MARKETING RES, V49, P967, DOI 10.1509/jmr.11.0015
   The White House, 2023, FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
   Thomson M, 2006, J MARKETING, V70, P104, DOI 10.1509/jmkg.70.3.104
   TikTok, 2023, New Labels for Disclosing AI-Generated Content
   TikTok, 2023, Community guidelines
   Vaid S, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114110
   von Armin Iris, 2023, We imagined How Our V-Neck Sweater Theo would Travel and Who Would Wear it where and How. Then the
   Wahid R, 2023, ASIA PAC J MARKET LO, V35, P1813, DOI 10.1108/APJML-10-2023-994
   Wang W., 2023, SSRN Electron. J., DOI [10.2139/ssrn.4428509, DOI 10.2139/SSRN.4428509]
   Yalcin G, 2022, J MARKETING RES, V59, P696, DOI 10.1177/00222437211070016
   Zhang LX, 2021, J SERV MARK, V35, P628, DOI 10.1108/JSM-05-2020-0162
NR 80
TC 10
Z9 11
U1 140
U2 166
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0969-6989
EI 1873-1384
J9 J RETAIL CONSUM SERV
JI J. Retail. Consum. Serv.
PD JUL
PY 2024
VL 79
AR 103790
DI 10.1016/j.jretconser.2024.103790
EA MAR 2024
PG 15
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA QD1T7
UT WOS:001218856500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Hamerman, EJ
   Aggarwal, A
   Martins, C
AF Hamerman, Eric J.
   Aggarwal, Anubhav
   Martins, Chrissy
TI An investigation of generative AI in the classroom and its implications
   for university policy
SO QUALITY ASSURANCE IN EDUCATION
LA English
DT Article; Early Access
DE Generative AI; ChatGPT; Social norms; Cheating; Instructional technology
ID SOCIAL NORMS; STUDENTS
AB Purpose - The emergence of widely available Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, presents both opportunities and threats for higher education. This study aims to investigate the factors that influence students' current use of GenAI and students' perceptions of how GenAI can facilitate learning, as well as informs recommendations for institutional policies related to GenAI. Design/methodology/approach - A mixed-method approach was used. A survey of undergraduate business students was followed by a case study that required students to use GenAI as part of a homework assignment and then reflect on their learning experience. Findings - Students used GenAI more frequently when they perceived that it helped their learning outcomes and when it was perceived as a social norm. Conversely, the perception that GenAI was cheating reduced its usage. Male (vs female) students used GenAI more frequently. Students preferred institutional policies that allowed the use of GenAI but also set clear boundaries for its use. They reported that the assignment that required the use of GenAI enhanced their learning experience. Practical implications - Results from the survey and case study imply that institutions should set policies establishing clear boundaries for the use of GenAI while encouraging and training faculty to incorporate GenAI into classroom assignments. Doing so can facilitate student learning and train students on an important technology that prepares them for the workforce. Originality/value - This study provides insight into students' usage of GenAI, explores factors that predict its usage, provides policy recommendations for educational institutions and offers a template for incorporating GenAI into classroom assignments.
C1 [Hamerman, Eric J.; Aggarwal, Anubhav; Martins, Chrissy] Iona Univ, LaPenta Sch Business, Dept Mkt, New Rochelle, NY 10804 USA.
RP Hamerman, EJ (corresponding author), Iona Univ, LaPenta Sch Business, Dept Mkt, New Rochelle, NY 10804 USA.
EM ehamerman@iona.edu
CR Abdaljaleel M, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-52549-8
   Aldhaen E, 2024, COMPET REV, V34, P51, DOI 10.1108/CR-01-2023-0008
   Anthenien AM, 2018, SOC PSYCHOL EDUC, V21, P303, DOI 10.1007/s11218-017-9412-z
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Berkowitz Leonard., 1972, Advances in Experimental Social Psychology, V6, P63, DOI [10.1016/S0065-2601(08)60025-8, DOI 10.1016/S0065-2601(08)60025-8]
   Choi JH, 2022, J LEGAL EDUC, V71, P387
   Costa M., 2023, BBC News
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Dervenis C, 2022, QUAL ASSUR EDUC, V30, P199, DOI 10.1108/QAE-08-2021-0126
   Eapen TT, 2023, HARVARD BUS REV, V101, P55
   Ellis L., 2024, Wall Street Journal
   Farhi F., 2023, COMPUTERS ED ARTIFIC, V100180, DOI [10.1016/j.caeai.2023.100180, DOI 10.1016/J.CAEAI.2023.100180, https://doi.org/10.1016/j.caeai.2023.100180]
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   Glynn CJ, 2009, POLIT COMMUN, V26, P48, DOI 10.1080/10584600802622860
   Gouday A., 2024, Future-proofing higher education
   Gross N, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12080435
   Guha A, 2024, J MARKET EDUC, V46, P6, DOI 10.1177/02734753231215436
   Hosny M., 2014, J APPL SCI, V14, P748, DOI [DOI 10.3923/jas.2014.748.757, 10.3923/jas.2014.748.757]
   Hu K., 2023, REUTERS         0202
   Janahi Yusuf Mohamed, 2023, Development and Learning in Organizations: An International Journal, V37, P29, DOI 10.1108/DLO-09-2022-0183
   Kleebayoon A, 2023, CELL MOL BIOENG, V16, P173, DOI 10.1007/s12195-023-00759-x
   Lambert J., 2023, Computers in the Schools, V41, pe1
   Lang O, 2024, EBIOMEDICINE, V102, DOI 10.1016/j.ebiom.2024.105075
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   McKinsey, 2023, What is artificial general intelligence (AGI)?
   Mumtaz S, 2023, QUAL ASSUR EDUC, V31, P4, DOI 10.1108/QAE-12-2021-0197
   Neighbors C, 2007, J STUD ALCOHOL DRUGS, V68, P556, DOI 10.15288/jsad.2007.68.556
   Quinn R., 2024, Inside Higher Ed
   Rospigliosi PA, 2023, INTERACT LEARN ENVIR, V31, P1, DOI 10.1080/10494820.2023.2180191
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Spennemann DHR, 2024, INTERACT TECHNOL SMA, V21, P690, DOI 10.1108/ITSE-10-2023-0195
   Stokel-Walker C., 2024, Nature, V614, P214
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Takefuji Y., 2024, Journal of Diabetes and Metabolic Disorders, V23, pe1419
   Terwiesch C., 2023, Would chatgpt get a wharton MBA? a prediction based on its performance in the operations management course. mack institute for innovation management at the wharton school
   Thurzo A, 2023, EDUC SCI, V13, DOI 10.3390/educsci13020150
   Tindle R., 2023, PsyArxiv Preprints, V13
   Tossavainen T., 2020, LUMAT: International Journal on Math, Science and Technology Education, V8, P252
   United Nations, 2024, Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning Opportunities for All
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2000, ORGAN BEHAV HUM DEC, V83, P33, DOI 10.1006/obhd.2000.2896
   Welding L., 2023, BestColleges
   Yu H, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1181712
   zelik P., 2024, Smart Learning Environments, V11, pe10
NR 46
TC 0
Z9 0
U1 24
U2 24
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0968-4883
EI 1758-7662
J9 QUAL ASSUR EDUC
JI QUALITY ASSURANCE EDUCATION
PD 2024 AUG 26
PY 2024
DI 10.1108/QAE-08-2024-0149
EA AUG 2024
PG 14
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA E9T2H
UT WOS:001306345800001
DA 2024-12-25
ER

PT J
AU Bughin, J
AF Bughin, Jacques
TI What drives the corporate payoffs of using generative artificial
   intelligence?
SO STRUCTURAL CHANGE AND ECONOMIC DYNAMICS
LA English
DT Article
DE AI; Generative AI; Productivity impact; Capabilities; Entropy
ID INFORMATION-TECHNOLOGY; COMPETITIVE ADVANTAGE; DYNAMIC CAPABILITIES;
   FIRM PERFORMANCE; PRODUCTIVITY; AI; TRANSFORMATION; DIFFUSION; RESOURCES
AB Artificial Intelligence, a set of technologies that aim to replicate human cognitive functions, has seen remarkable improvements over the last decade. In particular, generative AI (GenAI), a subset of AI able to generate content tasks based on Large Language Models (LLM), has recently gained momentum. Based on an extensive analysis of generative AI use cases in large enterprises, we find that Gen AI shows strong labor productivity improvements across metrics such as throughput time, unit cost, and task effectiveness. However, the distribution of gains is asymmetric in favor of a few companies. While the current distribution of gains does not provide evidence of a power law effect, the current asymmetry reflects differences in AI resources/capabilities across companies mainly data access, AI talent, or AI governance.
C1 [Bughin, Jacques] Free Univ Brussels, Solvay Brussels Sch Econ & Management, Brussels, Belgium.
C3 Universite Libre de Bruxelles; Solvay SA
RP Bughin, J (corresponding author), Free Univ Brussels, Solvay Brussels Sch Econ & Management, Brussels, Belgium.
EM jacques.bughin@fortinocapital.com
OI Bughin, Jacques/0000-0002-1973-3656
CR Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Ameye N, 2023, TECHNOVATION, V127, DOI 10.1016/j.technovation.2023.102846
   Anderton B., 2023, ECB Working Paper
   Andriani P, 2009, ORGAN SCI, V20, P1053, DOI 10.1287/orsc.1090.0481
   Antony J, 2022, TQM J, V34, P2069, DOI 10.1108/TQM-08-2021-0242
   Ashrafi R, 2015, INFORM SYST MANAGE, V32, P15, DOI 10.1080/10580530.2015.983016
   Babina T, 2024, J FINANC ECON, V151, DOI 10.1016/j.jfineco.2023.103745
   Barney JB, 2001, J MANAGE, V27, P643, DOI 10.1016/S0149-2063(01)00115-5
   Baumgartner H, 2021, J ACAD MARKET SCI, V49, P221, DOI 10.1007/s11747-020-00766-8
   Berente N, 2021, MIS QUART, V45, P1433, DOI DOI 10.25300/MISQ/2021/16274
   Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
   Bilgram H., 2023, IEEE Eng. Manag. Rev., DOI [10.1109/EMR, DOI 10.1109/EMR]
   Bird Christian, 2022, ACM Queue, P35, DOI 10.1145/3582083
   Björkdahl J, 2020, CALIF MANAGE REV, V62, P17, DOI 10.1177/0008125620920349
   Bottazzi G, 2008, IND CORP CHANGE, V17, P711, DOI 10.1093/icc/dtn027
   Brand J., 2023, Using chatGPT for market research
   Brynjolfsson E, 2000, J ECON PERSPECT, V14, P23, DOI 10.1257/jep.14.4.23
   Brynjolfsson E., 2023, GENERATIVE AI WORK N
   Bughin Jacques, 2016, Journal of Big Data, V3, DOI 10.1186/s40537-015-0014-3
   Bughin J., 2024, J. AI, Robot. Workplace Automat, P1
   Bughin J., 2023, Appl. Market. Anal.: Peer-Rev. J., V9, P110
   Bughin J, 2024, AI SOC, DOI 10.1007/s00146-024-02014-x
   Bughin J, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1239466
   Calvino F., 2023, A Portrait of AI Adopters Across Countries: Firm Characteristics, Assets' Complementarities and Productivity
   Cao B, 2024, IEEE T MULTIMEDIA, V26, P995, DOI 10.1109/TMM.2023.3274990
   Chen Q, 2023, INTERNET RES, V33, P2205, DOI 10.1108/INTR-09-2021-0686
   Cirillo Valeria, 2023, Structural Change and Economic Dynamics, P89, DOI 10.1016/j.strueco.2023.04.011
   Clauset A, 2009, SIAM REV, V51, P661, DOI 10.1137/070710111
   Cooney Haynes, 2021, Strategy & Leadership, P25, DOI 10.1108/SL-06-2021-0055
   Corrado C, 2021, OXFORD REV ECON POL, V37, P435, DOI 10.1093/oxrep/grab018
   Corrocher N, 2011, TECHNOL FORECAST SOC, V78, P547, DOI 10.1016/j.techfore.2010.10.006
   Crafts N, 2021, OXFORD REV ECON POL, V37, P521, DOI 10.1093/oxrep/grab012
   Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
   Cubric M, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101257
   Czarnitzki D, 2023, J ECON BEHAV ORGAN, V211, P188, DOI 10.1016/j.jebo.2023.05.008
   Da Silva S, 2013, PHYSICA A, V392, P5376, DOI 10.1016/j.physa.2013.07.008
   Damioli G, 2023, APPL ECON LETT, V30, P816, DOI 10.1080/13504851.2021.2024129
   Davenport TH, 2023, HARVARD BUS REV, V101, P116
   DAVIDSON R, 1982, REV ECON STUD, V49, P551, DOI 10.2307/2297286
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Falk M, 2005, TECHNOVATION, V25, P1229, DOI 10.1016/j.technovation.2004.07.004
   Felipe CM, 2020, DECISION SCI, V51, P575, DOI 10.1111/deci.12379
   Feng C.M., 2024, Business Horizons
   Ferràs-Hernández X, 2023, CALIF MANAGE REV, V65, P73, DOI 10.1177/00081256231164362
   Fuller A, 2020, IEEE ACCESS, V8, P108952, DOI 10.1109/ACCESS.2020.2998358
   Golab-Andrzejak Edyta, 2023, Procedia Computer Science, P397, DOI 10.1016/j.procs.2023.01.305
   Goldfarb A, 2023, RES POLICY, V52, DOI 10.1016/j.respol.2022.104653
   Gómez-Bengoechea G, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2023.102423
   Hanelt A, 2021, J MANAGE STUD, V58, P1159, DOI 10.1111/joms.12639
   Hollenstein H, 2008, RES POLICY, V37, P545, DOI 10.1016/j.respol.2007.12.006
   Huang M.H., 2023, J Mark
   Hulkko H, 2005, PROC INT CONF SOFTW, P495
   KARSHENAS M, 1993, RAND J ECON, V24, P503, DOI 10.2307/2555742
   Kazemitabaar M, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580919
   Kreitmeir D, 2024, Arxiv, DOI arXiv:2403.01964
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Kumar V., 2024, Int. J. Inf. Manag.
   Laranjo L, 2018, J AM MED INFORM ASSN, V25, P1248, DOI 10.1093/jamia/ocy072
   Li N, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.1071986
   Libai B, 2020, J INTERACT MARK, V51, P44, DOI 10.1016/j.intmar.2020.04.002
   Liu HC, 2023, FRONT ENG MANAG, V10, P191, DOI 10.1007/s42524-022-0243-z
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   NEDERHOF AJ, 1985, EUR J SOC PSYCHOL, V15, P263, DOI 10.1002/ejsp.2420150303
   Nicolescu L, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11101579
   Nicoletti G, 2020, EUR ECON REV, V128, DOI 10.1016/j.euroecorev.2020.103513
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Nucci F, 2023, ECON MODEL, V128, DOI 10.1016/j.econmod.2023.106524
   Papenmeier A, 2022, ACM T COMPUT-HUM INT, V29, DOI 10.1145/3495013
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Rasheed Z, 2024, Arxiv, DOI arXiv:2404.18496
   Rigney D., 2010, The Matthew Effect: How One Advantage Begets Another
   Russo D, 2024, ACM T SOFTW ENG METH, V33, DOI 10.1145/3652154
   Sabherwal R, 2015, MIS QUART, V39, P809, DOI 10.25300/MISQ/2015/39.4.4
   Schryen G, 2013, EUR J INFORM SYST, V22, P139, DOI 10.1057/ejis.2012.45
   Shahid N, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0212356
   Simkute A, 2024, Arxiv, DOI arXiv:2402.11364
   Sjödin D, 2021, J BUS RES, V134, P574, DOI 10.1016/j.jbusres.2021.05.009
   Smith G., 2023, IEEE Spectrummarch 13
   Tambe P., 2020, 28285 NBER
   Tambe P, 2014, MANAGE SCI, V60, P1452, DOI 10.1287/mnsc.2014.1899
   Taulli T., 2023, The future. Generative AI
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Van Reenen J., 2017, Harv. Bus. Rev.
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
   Vial G, 2021, MIT SLOAN MANAGE REV, V62
   Wamba SF, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2022.102544
   Wamba-Taguimdje S.-L., 2020, Digital Business Transformation. Lecture Notes in Information Systems and Organisation, P3, DOI [10.1007/978-3-030-47355-6_1, DOI 10.1007/978-3-030-47355-6_1]
   Wang LY, 2023, PHARMACEUTICALS-BASE, V16, DOI 10.3390/ph16020253
   Warner KSR, 2019, LONG RANGE PLANN, V52, P326, DOI 10.1016/j.lrp.2018.12.001
   Wen XT, 2012, J NEUROSCI, V32, P1284, DOI 10.1523/JNEUROSCI.2817-11.2012
   Yang CH, 2022, RES POLICY, V51, DOI 10.1016/j.respol.2022.104536
   Yeniyurt S, 2019, IND MARKET MANAG, V79, P46, DOI 10.1016/j.indmarman.2019.03.008
   Yetistiren B, 2023, Arxiv, DOI arXiv:2304.10778
   Zhou E, 2024, PNAS NEXUS, V3, DOI 10.1093/pnasnexus/pgae052
   Ziegler Albert, 2022, MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, P21, DOI 10.1145/3520312.3534864
   Zolas N., 2021, No. w28290
NR 98
TC 0
Z9 0
U1 31
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0954-349X
EI 1873-6017
J9 STRUCT CHANGE ECON D
JI Struct. Change and Econ. Dyn.
PD DEC
PY 2024
VL 71
BP 658
EP 668
DI 10.1016/j.strueco.2024.09.011
EA SEP 2024
PG 11
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA I1W0H
UT WOS:001328216800001
DA 2024-12-25
ER

PT J
AU Mariani, M
   Dwivedi, YK
AF Mariani, Marcello
   Dwivedi, Yogesh K.
TI Generative artificial intelligence in innovation management: A preview
   of future research developments
SO JOURNAL OF BUSINESS RESEARCH
LA English
DT Article
DE Generative artificial intelligence; Delphi study; Management; Innovation
ID PRODUCT DEVELOPMENT; DELPHI METHOD; THINKING; ENTREPRENEURSHIP;
   EXPERIMENTATION; COMMUNICATION; CAPABILITIES; RATIONALITY; INFORMATION;
   ANALYTICS
AB This study outlines the future research opportunities related to Generative Artificial Intelligence (GenAI) in innovation management. To this end, it combines a review of the academic literature with the results of a Delphi study involving leading innovation management scholars. Ten major research themes emerged that can guide future research developments at the intersection of GenAI and innovation management: 1) Gen AI and innovation types; 2) GenAI, dominant designs and technology evolution; 3) Scientific and artistic creativity and GenAI-enabled innovations; 4) GenAI-enabled innovations and intellectual property; 5) GenAI and new product development; 6) Multimodal/unimodal GenAI and innovation outcomes; 7) GenAI, agency and ecosystems; 8) Policymakers, lawmakers and anti-trust authorities in the regulation of GenAI-enabled innovation; 9) Misuse and unethical use of GenAI leading to biased innovation; and 10) Organizational design and boundaries for GenAIenabled innovation. The paper concludes by discussing how these themes can inform theoretical development in innovation management studies.
C1 [Mariani, Marcello] Univ Reading, Henley Business Sch, Henley On Thames RG9 3AU, Oxon, England.
   [Mariani, Marcello] Univ Bologna, Dept Management, Bologna, Italy.
   [Dwivedi, Yogesh K.] Swansea Univ, Sch Management, Emerging Markets Res Ctr EMaRC, Bay Campus, Swansea SA1 8EN, Wales.
   [Dwivedi, Yogesh K.] Symbiosis Inst Business Management, Dept Management, Pune, India.
   [Dwivedi, Yogesh K.] Symbiosis Int Deemed Univ, Pune, Maharashtra, India.
C3 University of Reading; University of Bologna; Swansea University;
   Symbiosis International University; Symbiosis Institute of Business
   Management (SIBM) Pune; Symbiosis International University
RP Mariani, M (corresponding author), Univ Reading, Henley Business Sch, Henley On Thames RG9 3AU, Oxon, England.
EM m.mariani@henley.ac.uk; y.k.dwivedi@swansea.ac.uk
RI Mariani, Marcello/ABW-5250-2022; Dwivedi, Yogesh/A-5362-2008
OI MARIANI, MARCELLO/0000-0002-7916-2576
CR Aimar A, 2019, J HEALTHC ENG, V2019, DOI 10.1155/2019/5340616
   Akter S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102387
   Amabile T. M., 1996, Creativity in context: Update to the social psychology of creativity, DOI DOI 10.4324/9780429501234
   Amabile TM, 2020, ACAD MANAG DISCOV, V6, P351, DOI 10.5465/amd.2019.0075
   ANDERSON P, 1990, ADMIN SCI QUART, V35, P604, DOI 10.2307/2393511
   Bamberger PA, 2018, ACAD MANAG DISCOV, V4, P1, DOI 10.5465/amd.2018.0003
   Banerjee A, 2019, ANNU REV ECON, V11, P959, DOI 10.1146/annurev-economics-080218-030229
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   Benbya H, 2020, MIS Q EXEC, V19, pIX
   Bengio Y, 2001, ADV NEUR IN, V13, P932
   Bilgram V., 2023, IEEE Engineering Management Review
   Bjarnason T, 2005, SUBST USE MISUSE, V40, P1733, DOI 10.1080/10826080500224707
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   Bove T., 2023, Fortunem
   Brittain B., 2023, Reuters
   Brossard M., 2020, How generative design could reshape the future of product development
   Brynjolfsson E., 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
   Burger B, 2023, EUR J INNOV MANAG, V26, P233, DOI 10.1108/EJIM-02-2023-0156
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Carnevalli JA, 2008, INT J PROD ECON, V114, P737, DOI 10.1016/j.ijpe.2008.03.006
   Chatterjee S, 2022, INT J LAW MANAG, V64, P110, DOI 10.1108/IJLMA-02-2021-0049
   Chatterjee S, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120783
   Chesbrough H. W., 2003, OPEN INNOVATION THE
   Chesbrough HW, 2007, CALIF MANAGE REV, V50, P57, DOI 10.2307/41166416
   CHISUM DS, 1986, U PITT LAW REV, V47, P959
   Cho HJ, 2008, J CONSUM PSYCHOL, V18, P205, DOI 10.1016/j.jcps.2008.04.009
   Chowdhury M., 2022, Why Patenting Machine Learning Algorithm is Nearly Impossible?
   CHURCHILL GA, 1982, J MARKETING RES, V19, P491, DOI 10.2307/3151722
   Clarysse B, 2014, RES POLICY, V43, P1164, DOI 10.1016/j.respol.2014.04.014
   Cockburn I.M., 2018, The economics of artificial intelligence: An agenda, P115, DOI DOI 10.3386/W24449
   COOPER RG, 1991, IND MARKET MANAG, V20, P137, DOI 10.1016/0019-8501(91)90032-B
   Croitoru FA, 2023, IEEE T PATTERN ANAL, V45, P10850, DOI 10.1109/TPAMI.2023.3261988
   Csikszentmihalyi M., 1997, Flow and the psychology of discovery and invention, V39
   Csikzentimihalyi M., 1975, BOREDOM ANXIETY EXPE
   Cui AS, 2017, J PROD INNOVAT MANAG, V34, P60, DOI 10.1111/jpim.12326
   DALKEY N, 1963, MANAGE SCI, V9, P458, DOI 10.1287/mnsc.9.3.458
   Daugherty P. R., 2018, Human+ machine: Reimagining work in the age of AI
   Davenport TH, 2018, HARVARD BUS REV, V96, P108
   De Massis A., 2018, ACAD MANAGEMENT P, V2018, P16625
   Delbecq A.L., 1975, GROUP TECHNIQUES PRO
   Deng J., 2022, FRONTIERS COMPUTING, V2, P81, DOI [DOI 10.54097/FCIS.V2I2.4465, 10.54097/fcis.v2i2.4465]
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   DEWAR RD, 1986, MANAGE SCI, V32, P1422, DOI 10.1287/mnsc.32.11.1422
   Dörr KN, 2016, DIGIT JOURNAL, V4, P700, DOI 10.1080/21670811.2015.1096748
   Duchesneau T. D., 1979, Determinants, Processes, and Methodological Issues, V1
   Durkheim E., 1982, RULES SOCIOLOGICAL M
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102542
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Ebers M, 2021, J, V4, P589, DOI [10.3390/j4040043, DOI 10.3390/J4040043]
   Emmert-Streib F, 2020, FRONT ARTIF INTELL, V3, DOI 10.3389/frai.2020.524339
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   ETTLIE JE, 1983, ACAD MANAGE J, V26, P27, DOI 10.5465/256133
   Etzioni A., 2017, Issues in Science and Technology
   Farish K, 2020, J INTELLET PROP LAW, V15, P40, DOI 10.1093/jiplp/jpz139
   Ferraris A, 2021, J BUS RES, V128, P711, DOI 10.1016/j.jbusres.2019.11.003
   Ferràs-Hernández X, 2023, CALIF MANAGE REV, V65, P73, DOI 10.1177/00081256231164362
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Floridi L., 2013, PHILOS INFORM
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Foley J, 2022, Creative Bloq
   Forest J, 2011, CREAT INNOV MANAG, V20, P207, DOI 10.1111/j.1467-8691.2011.00603.x
   Fuller J., 2019, MIT Sloan Manag. Rev, V60, P1
   Gault F, 2018, RES POLICY, V47, P617, DOI 10.1016/j.respol.2018.01.007
   Gaur A, 2018, J WORLD BUS, V53, P280, DOI 10.1016/j.jwb.2017.11.003
   GETZELS JW, 1961, AM SOCIOL REV, V26, P351, DOI 10.2307/2090662
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gopalakrishnan S, 1997, OMEGA-INT J MANAGE S, V25, P15, DOI 10.1016/S0305-0483(96)00043-6
   Gordon T.J., 1994, The Delphi method
   Gragousian D., 2022, How businesses should respond to the EU's Artificial Intelligence Act
   Graham N., 2021, Business Going Digital
   Graves A, 2014, PR MACH LEARN RES, V32, P1764
   GRIFFIN A, 1992, J PROD INNOVAT MANAG, V9, P171, DOI 10.1111/1540-5885.930171
   Grisoni F, 2021, SCI ADV, V7, DOI 10.1126/sciadv.abg3338
   Gursoy D, 2023, J HOSP MARKET MANAG, V32, P579, DOI 10.1080/19368623.2023.2211993
   Haefner N, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120392
   Hallowell MR, 2010, J CONSTR ENG M, V136, P99, DOI 10.1061/(ASCE)CO.1943-7862.0000137
   Harrmann LK, 2023, EUR J MARKETING, V57, P834, DOI 10.1108/EJM-11-2021-0914
   HAUSER JR, 1988, HARVARD BUS REV, V66, P63
   Helfat CE, 2002, IND CORP CHANGE, V11, P725, DOI 10.1093/icc/11.4.725
   Hendriksen C, 2023, J SUPPLY CHAIN MANAG, V59, P65, DOI 10.1111/jscm.12304
   HERSTATT C, 1992, J PROD INNOVAT MANAG, V9, P213, DOI 10.1111/1540-5885.930213
   Hickman E, 2021, EUR BUS ORGAN LAW RE, V22, P593, DOI 10.1007/s40804-021-00224-0
   Hine E, 2022, NAT MACH INTELL, V4, P608, DOI 10.1038/s42256-022-00513-4
   HIRSCH PM, 1972, AM J SOCIOL, V77, P639, DOI 10.1086/225192
   Hu K., 2023, REUTERS         0202
   Huang MH, 2021, J SERV RES-US, V24, P30, DOI 10.1177/1094670520902266
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Iansiti M, 2004, HARVARD BUS REV, V82, P68
   IBM, 2022, How to use AI to discover new drugs and materials with limited data
   Johnson CD, 2021, ACAD MANAGE PERSPECT, V35, P292, DOI 10.5465/amp.2017.0159
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Kapoor P., 1987, Systems approach to documentary maritime fraud
   Keil M, 2002, INFORM SYST J, V12, P103, DOI 10.1046/j.1365-2575.2002.00121.x
   Kelly J., 2021, Forbes
   Kietzmann J, 2020, BUS HORIZONS, V63, P135, DOI 10.1016/j.bushor.2019.11.006
   Koivisto M, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-40858-3
   Kruger J, 2004, J EXP SOC PSYCHOL, V40, P91, DOI 10.1016/S0022-1031(03)00065-9
   La Roche J., 2017, Yahoo! Finance
   Lehman DW, 2019, ACAD MANAG ANN, V13, P1, DOI 10.5465/annals.2017.0047
   Leone D, 2021, J BUS RES, V129, P849, DOI 10.1016/j.jbusres.2020.11.008
   LEVY NJ, 1961, PSYCHIAT QUART, V35, P66, DOI 10.1007/BF01572558
   Lobschat L, 2021, J BUS RES, V122, P875, DOI 10.1016/j.jbusres.2019.10.006
   Longoni Chiara, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P97, DOI 10.1145/3531146.3533077
   Macdonald N., 1954, Comput. Autom., V3, P6
   MACKINNON DW, 1965, AM PSYCHOL, V20, P273, DOI 10.1037/h0022403
   Marconi F, 2020, NEWSMAKERS, P1, DOI 10.7312/marc19136
   Marcus G., 2022, Scientific American
   Mariani Marcello M., 2021, Technological Forecasting and Social Change, V172, DOI 10.1016/j.techfore.2021.121009
   Mariani MM, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102623
   Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
   Mariani MM, 2020, J BUS RES, V121, P338, DOI 10.1016/j.jbusres.2020.09.012
   Martineau K., 2023, What is Generative AI? April 20 IBM Research
   MEDNICK SA, 1962, PSYCHOL REV, V69, P220, DOI 10.1037/h0048850
   Merk D, 2018, COMMUN CHEM, V1, DOI 10.1038/s42004-018-0068-1
   Mikolov T, 2010, 11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, P1045
   Miller L.E., 2006, P 2006 ANN M MIDWEST
   Mittelstadt BD, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716679679
   MOORE JF, 1993, HARVARD BUS REV, V71, P75
   Morley J, 2020, SCI ENG ETHICS, V26, P2141, DOI 10.1007/s11948-019-00165-5
   Nagendran M, 2020, BMJ-BRIT MED J, V368, DOI 10.1136/bmj.m689
   Nambisan S, 2017, ENTREP THEORY PRACT, V41, P1029, DOI 10.1111/etap.12254
   NELSON RR, 1977, RES POLICY, V6, P36, DOI 10.1016/0048-7333(77)90029-4
   NIJSSEN EJ, 1995, J PROD INNOVAT MANAG, V12, P99, DOI 10.1016/0737-6782(94)00032-B
   Nilsson N. J., 1971, Problem-solving methods in artificial intelligence
   Nilsson N. J, 2010, QUEST ARTIFICIAL INT, DOI 10.1017/CBO9780511819346
   Paschen J, 2020, BUS HORIZONS, V63, P403, DOI 10.1016/j.bushor.2020.01.003
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perez-Vega R, 2021, J BUS RES, V129, P902, DOI 10.1016/j.jbusres.2020.11.002
   Peterson JC, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2115228119
   Radford A., 2018, Technical Reports
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Saura JR, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2022.101679
   Ray A, 2021, U NSW LAW J, V44, P983
   RHODES M, 1961, PHI DELTA KAPPAN, V42, P305
   Samuelson P, 2023, SCIENCE, V381, P158, DOI 10.1126/science.adi0656
   Schank Roger C., 1975, IJCAI, V1, P151
   Schilling M.A., 2023, Strategic Management of Technological Innovation, V7th
   Schilling M.A., 2008, Strategic Management of Technological Innovation
   Schilling M. A., 2018, PublicAffairs
   SCHMENNER RW, 1988, SLOAN MANAGE REV, V30, P11
   Schneider J, 2023, INFORM SYST MANAGE, V40, P229, DOI 10.1080/10580530.2022.2085825
   Serrano J.A.R., 2023, Forbes
   Simon HA, 1991, ORGAN SCI, V2, P125, DOI 10.1287/orsc.2.1.125
   SIMON Herbert A., 1982, Models of bounded rationality
   Skinner R, 2015, COMMUN ASSOC INF SYS, V37, P31
   Smuha N.A., 2021, How the EU Can Achieve Legally Trustworthy AI: A Response to the European Commission's Proposal for an Artificial Intelligence Act, DOI [10.2139/ssrn, DOI 10.2139/SSRN]
   Snyder H, 2019, J BUS RES, V104, P333, DOI 10.1016/j.jbusres.2019.07.039
   Staw B.M., 1990, Innovation and creativity at work: Psychological and organizational strategies, P287
   STRAUS MA, 1968, AM J SOCIOL, V73, P417, DOI 10.1086/224503
   SULER JR, 1980, PSYCHOL BULL, V88, P144, DOI 10.1037/0033-2909.88.1.144
   Suominen A, 2023, TECHNOVATION, V123, DOI 10.1016/j.technovation.2023.102719
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Thomke S, 2020, HARVARD BUS REV, V98, P40
   Toner H., 2023, What are generative AI, large language models, and foundation models
   Tsamados A, 2022, AI SOC, V37, P215, DOI 10.1007/s00146-021-01154-8
   UTTERBACK JM, 1975, OMEGA-INT J MANAGE S, V3, P639, DOI 10.1016/0305-0483(75)90068-7
   Vaswani A, 2017, ADV NEUR IN, V30
   Verganti R, 2020, J PROD INNOVAT MANAG, V37, P212, DOI 10.1111/jpim.12523
   Vieira ES, 2009, SCIENTOMETRICS, V81, P587, DOI 10.1007/s11192-009-2178-0
   Wamba SF, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2022.102544
   Wamba SF, 2023, INFORM SYST FRONT, V25, P2123, DOI 10.1007/s10796-021-10142-8
   Wamba-Taguimdje SL, 2020, BUS PROCESS MANAG J, V26, P1893, DOI 10.1108/BPMJ-10-2019-0411
   Wang Y, 2017, ADV MANUF, V5, P311, DOI 10.1007/s40436-017-0204-7
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/357980.357991
   Westerlund M, 2019, TECHNOL INNOV MANAG, V9, P39, DOI 10.22215/timreview/1282
   Wiles J., 2023, CHATGPT FUTURE GENER
   Wirtz J, 2023, J SERV RES-US, V26, P173, DOI 10.1177/10946705221130467
   WOODMAN RW, 1993, ACAD MANAGE REV, V18, P293, DOI 10.2307/258761
   Wu BH, 2021, PROC CVPR IEEE, P2318, DOI 10.1109/CVPR46437.2021.00235
   Zahra SA, 2012, BUS HORIZONS, V55, P219, DOI 10.1016/j.bushor.2011.12.004
NR 172
TC 25
Z9 25
U1 336
U2 457
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0148-2963
EI 1873-7978
J9 J BUS RES
JI J. Bus. Res.
PD MAR
PY 2024
VL 175
AR 114542
DI 10.1016/j.jbusres.2024.114542
EA FEB 2024
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA LH3R0
UT WOS:001185863800001
OA Green Accepted, hybrid, Green Published
DA 2024-12-25
ER

PT J
AU Khoury, CJ
   Enver, N
   Paderno, A
   Ratti, E
   Rameau, A
AF Khoury, Carolyn Jane
   Enver, Necati
   Paderno, Alberto
   Ratti, Emanuele
   Rameau, Anais
TI Using Generative Artificial Intelligence in the Production and
   Dissemination of Innovation in Otolaryngology-Ethical Considerations
SO OTOLARYNGOLOGY-HEAD AND NECK SURGERY
LA English
DT Article
DE generative AI; otolaryngology
C1 [Khoury, Carolyn Jane] Weill Cornell Med, Sean Parker Inst Voice, New York, NY USA.
   [Enver, Necati] Marmara Univ, Fac Med, Dept Otorhinolaryngol Head & Neck Surg, Istanbul, Turkiye.
   [Paderno, Alberto] IRCCS Humanitas Res Hosp, Otorhinolaryngol Unit, Milan, Italy.
   [Ratti, Emanuele] Univ Bristol, Dept Philosophy, Bristol, England.
   [Rameau, Anais] Weill Cornell Med Coll, Dept Otolaryngol Head & Neck Surg, Sean Parker Inst Voice, New York, NY 10065 USA.
C3 Cornell University; Weill Cornell Medicine; Marmara University;
   University of Bristol; Cornell University; Weill Cornell Medicine
RP Rameau, A (corresponding author), Weill Cornell Med Coll, Dept Otolaryngol Head & Neck Surg, Sean Parker Inst Voice, New York, NY 10065 USA.
EM anr2783@med.cornell.edu
RI Paderno, Alberto/AAB-8008-2019; Ratti, Emanuele/AAA-5417-2020; Enver,
   Necati/G-8704-2012
OI RATTI, EMANUELE/0000-0003-1409-8240; Enver, Necati/0000-0002-3161-8810;
   Khoury, Carolyn/0009-0007-4558-3907; Paderno,
   Alberto/0000-0002-1621-2142
FU Paul B. Beeson Emerging Leaders Career Development Award in Aging from
   the National Institute on Aging [K76 AG079040]; Bridge2AI award from the
   NIH Common Fund [OT2 OD032720]; Notre Dame-IBM Tech Ethics Lab
   [262812UB]; Innovate UK; Horizon Europe [10063119]; Horizon Europe
   Guarantee [10063119] Funding Source: Horizon Europe Guarantee
FX Anais Rameau was supported by a Paul B. Beeson Emerging Leaders Career
   Development Award in Aging (K76 AG079040) from the National Institute on
   Aging and by the Bridge2AI award (OT2 OD032720) from the NIH Common
   Fund. Emanuele Ratti was supported in part by an award from the Notre
   Dame-IBM Tech Ethics Lab (number 262812UB) and Innovate UK and Horizon
   Europe (project id 10063119). Such supports do not constitute
   endorsement by the sponsor of the views expressed in this publication.
CR Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bommasani R., 2021, ARXIV PREPRINT
   Eapen TT., 2023, HARVARD BUS REV
   Jin C., 2020, COMPUT INTEL NEUROSC, V2020, P1
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Link K., 2023, STANFORD U ARTIFICIA
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   SALLAM M, 2023, HEALTHCARE-BASEL, V11, DOI DOI 10.3390/HEALTHCARE11060887
   Suresh K, 2023, JAMA OTOLARYNGOL, V149, P555, DOI 10.1001/jamaoto.2023.0218
   Xu L, 2021, JMIR CANCER, V7, DOI 10.2196/27850
NR 10
TC 0
Z9 0
U1 2
U2 23
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0194-5998
EI 1097-6817
J9 OTOLARYNG HEAD NECK
JI Otolaryngol. Head Neck Surg.
PD JUN
PY 2024
VL 170
IS 6
SI SI
BP 1607
EP 1610
DI 10.1002/ohn.601
EA DEC 2023
PG 4
WC Otorhinolaryngology; Surgery
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Otorhinolaryngology; Surgery
GA SO7F2
UT WOS:001113018400001
PM 38044483
DA 2024-12-25
ER

PT J
AU Mustapha, KB
   Yap, EH
   Abakr, YA
AF Mustapha, Khameel B.
   Yap, Eng Hwa
   Abakr, Yousif Abdalla
TI Bard, ChatGPT and 3DGPT: a scientometric analysis of generative AI tools
   and assessment of implications for mechanical engineering education
SO INTERACTIVE TECHNOLOGY AND SMART EDUCATION
LA English
DT Article
DE Generative AI; ChatGPT; Bard; 3DGPT; Mechanical engineering; Engineering
   education
ID ARTIFICIAL-INTELLIGENCE; COMMUNICATION; MATHEMATICS; PERCEPTIONS;
   CHALLENGES; CURRICULUM; MODELS
AB PurposeFollowing the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.Design/methodology/approachAs part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.FindingsThe study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).Originality/valueTo the best of the authors' knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.
C1 [Mustapha, Khameel B.; Abakr, Yousif Abdalla] Univ Nottingham Malaysia, Dept Mech Mat & Mfg Engn, Semenyih, Malaysia.
   [Yap, Eng Hwa] Xian Jiaotong Liverpool Univ, XJTLU Entrepreneur Coll Taicang, Sch Robot, Taicang, Greater Suzhou, Peoples R China.
C3 University of Nottingham Malaysia; Xi'an Jiaotong-Liverpool University
RP Mustapha, KB (corresponding author), Univ Nottingham Malaysia, Dept Mech Mat & Mfg Engn, Semenyih, Malaysia.
EM Khameelb.Mustapha@nottingham.edu.my; Eng.Hwa@xjtlu.edu.cn;
   Yousif.Ab@nottingham.edu.my
RI Yap, Eng/N-1470-2019; Mustapha, Khameel/AAU-8335-2020
CR Abid A, 2021, NAT MACH INTELL, V3, P461, DOI 10.1038/s42256-021-00359-2
   Akin F, 2016, MATER DESIGN, V90, P1207, DOI 10.1016/j.matdes.2015.04.045
   Albort-Morant G, 2017, SUSTAINABILITY-BASEL, V9, DOI 10.3390/su9061011
   ALLEN TJ, 1980, IEEE T ENG MANAGE, V27, P2, DOI 10.1109/TEM.1980.6447372
   Almazrouei E., 2023, Findings of the Association for Computational Linguistics: ACL, P10755
   Anil R, 2023, ARXIV
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Authentise, 2023, 3DGPT
   Badini S, 2023, ADV IND ENG POLY RES, V6, P278, DOI 10.1016/j.aiepr.2023.03.003
   Baechle-Clayton M, 2022, J COMPOS SCI, V6, DOI 10.3390/jcs6070202
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Bannour N, 2021, P 2 WORKSH SIMPL EFF, P11, DOI [10.18653/v1/2021.sustainlp-1.2, DOI 10.18653/V1/2021.SUSTAINLP-1.2]
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Berdanier CGP, 2023, J ENG EDUC, V112, P583, DOI 10.1002/jee.20541
   Bird J., 2014, Mechanical engineering principles, V3
   Birhane A, 2023, NAT REV PHYS, V5, P277, DOI 10.1038/s42254-023-00581-4
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Black P., 1998, Educational Assessment Principles, Policy and Practice, V5, P7, DOI [DOI 10.1080/0969595980050102, 10.1080/0969595980050102]
   Blanco-González A, 2023, PHARMACEUTICALS-BASE, V16, DOI 10.3390/ph16060891
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bommasani R., 2021, ARXIV
   Borji A., 2023, arXiv
   Brainard S, 2017, SPACE POLICY, V42, P70, DOI 10.1016/j.spacepol.2017.07.001
   Brown TB., 2020, ADV NEURAL INFORM PR, V2020, P1877, DOI [10.48550/ARXIV.2005.14165, DOI 10.48550/ARXIV.2005.14165]
   Care C, 2010, HIST COMPUT-SPRINGER, P1, DOI 10.1007/978-1-84882-948-0
   Carr D.F., 2023, CHATGPT GREW ANOTHER
   Carvallo Juan Pablo, 2023, Proceedings of the 18th Latin American Conference on Learning Technologies (LACLO 2023). Lecture Notes in Educational Technology, P328, DOI 10.1007/978-981-99-7353-8_25
   Chan AKC, 2016, ENGINEERING-PRC, V2, P10, DOI 10.1016/J.ENG.2016.01.003
   Chen H., 2023, ARXIV
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Chong S, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10113960
   Chu Z., 2023, ARXIV
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dang H., 2022, ARXIV
   Deeley SJ, 2018, ASSESS EVAL HIGH EDU, V43, P439, DOI 10.1080/02602938.2017.1356906
   Dhillon B.S., 2006, Creativity for engineers
   Duong CD, 2024, INTERACT TECHNOL SMA, V21, P356, DOI 10.1108/ITSE-05-2023-0096
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eager B, 2023, J UNIV TEACH LEARN P, V20
   Edström K, 2014, EUR J ENG EDUC, V39, P539, DOI 10.1080/03043797.2014.895703
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Ellegaard O, 2015, SCIENTOMETRICS, V105, P1809, DOI 10.1007/s11192-015-1645-z
   Fan Lijie, 2023, ARXIV
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Flegg J, 2012, INT J MATH EDUC SCI, V43, P717, DOI 10.1080/0020739X.2011.644333
   Frenkel M., 2023, ARXIV
   Frieder S., 2023, ARXIV
   Gabajiwala E., 2022, FUTURISTIC TRENDS NE, P523
   Gill Sukhpal Singh, 2024, Internet of Things and Cyber-Physical Systems, V4, P19, DOI 10.1016/j.iotcps.2023.06.002
   González-Pérez LI, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14031493
   Gozalo-Brizuela R., 2023, ARXIV
   Gravel J., 2023, Mayo Clinic Proceedings: Digital Health, V1, P226
   Guersenzvaig A., 2023, NOBODY WRITING NOBOD
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hariri W, 2023, ARXIV
   Hazell J., 2023, ARXIV
   Henderson M, 2019, ASSESS EVAL HIGH EDU, V44, P1237, DOI 10.1080/02602938.2019.1599815
   Hodge B., 2002, Journal of Engineering Education, V91, P415, DOI DOI 10.1002/J.2168-9830.2002.TB00726.X
   Hopfenbeck TN, 2023, ASSESS EDUC, V30, P99, DOI 10.1080/0969594X.2023.2212192
   Ibrahim D, 2011, PROCEDIA COMPUT SCI, V3, DOI 10.1016/j.procs.2010.12.140
   Iqbal S, 2014, 2014 IEEE INTERNATIONAL CONFERENCE ON MOOC, INNOVATION AND TECHNOLOGY IN EDUCATION (MITE), P101, DOI 10.1109/MITE.2014.7020249
   Jablonka KM, 2023, DIGIT DISCOV, V2, P1233, DOI 10.1039/d3dd00113j
   Javaid M., 2023, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V3, DOI [10.1016/j.tbench.2023.100115, DOI 10.1016/J.TBENCH.2023.100115]
   John Schulman B.Z., 2023, INTRO CHATGPT
   Johri A, 2023, J ENG EDUC, V112, P572, DOI 10.1002/jee.20537
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Kang D., 2023, ARXIV
   Kasirzadeh A, 2022, PHILOS TECHNOL, DOI DOI 10.1007/S13347-023-00606-X
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Katona Z, 2011, J MARKETING RES, V48, P425, DOI 10.1509/jmkr.48.3.425
   Klahn C., 2020, P AMPA2020
   Koukis Nikolaos, 2019, Interactive Technology and Smart Education, V16, P74, DOI 10.1108/ITSE-10-2018-0081
   Kwan C.C.L., 2023, INT C TECHN ED, P275, DOI [10.1007/978-981-99-8255-424, DOI 10.1007/978-981-99-8255-424]
   Lahat A, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-31412-2
   Leonarcl D.A., 2011, CREATIVITY INNOVATIO, V325
   Li Junyi, 2022, ARXIV
   Lin TY, 2022, AI OPEN, V3, P111, DOI 10.1016/j.aiopen.2022.10.001
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Luccioni A. S., 2022, arXiv
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Martinez P, 2019, AUTOMAT CONSTR, V107, DOI 10.1016/j.autcon.2019.102947
   Menekse M, 2023, J ENG EDUC, V112, P578, DOI 10.1002/jee.20539
   Meta A, 2023, Introducing LLaMA: A foundational, 65-billion-parameter large language model
   Michko G.M., 2008, 2008 38 ANN FRONTIER, pS1A
   Milano S, 2023, NAT MACH INTELL, V5, P333, DOI 10.1038/s42256-023-00644-2
   Mishra Swaroop, 2022, ARXIV
   Mitchell M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2215907120
   Mollick E.R., 2022, New modes of learning enabled by AI chatbots: Three methods and assignments
   Mollick E. R., 2023, The Wharton School Research Paper, DOI DOI 10.2139/SSRN.4391243
   Mollick E, 2023, RES TECHNOL MANAGE, V66, P11, DOI 10.1080/08956308.2023.2213102
   Moore S, 2022, LECT NOTES COMPUT SC, V13450, P243, DOI 10.1007/978-3-031-16290-9_18
   Moral-Muñoz JA, 2020, PROF INFORM, V29, DOI 10.3145/epi.2020.ene.03
   Mozes M., 2023, arXiv, P1
   Narayan A., 2022, ARXIV
   Necesal P, 2012, LECT NOTES ENG COMP, P271
   Neto DDES, 2016, J CONSTR ENG M, V142, DOI 10.1061/(ASCE)CO.1943-7862.0001163
   Nikolic S, 2023, EUR J ENG EDUC, V48, P559, DOI 10.1080/03043797.2023.2213169
   Norton R.L., 2010, Machine Design
   Oduro S, 2022, EUR J INNOV MANAG, V25, P567, DOI 10.1108/EJIM-10-2020-0425
   Ouyang L, 2022, ADV NEUR IN
   Ozkan A, 2016, EURASIAN J EDUC RES, P239
   Palmer S, 2011, EUR J ENG EDUC, V36, P357, DOI 10.1080/03043797.2011.593095
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Peng Y., 2023, NATURE NANOTECHNOL, V29, P1, DOI DOI 10.1038/S41565-023-01483-3
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Punie Y., 2017, European Framework for the Digital Competence of Educators: DigCompEdu
   Pursnani V., 2023, ARXIV
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Ramalhete PS, 2010, MATER DESIGN, V31, P2275, DOI 10.1016/j.matdes.2009.12.013
   Rillig MC, 2023, ENVIRON SCI TECHNOL, DOI 10.1021/acs.est.3c01106
   Salah M., 2023, Computers in Human Behavior: Artificial Humans, DOI [10.1016/j.chbah.2023.100006, DOI 10.1016/J.CHBAH.2023.100006]
   Sánchez-Ruiz LM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13106039
   Sazhin SS, 1998, INT J ENG EDUC, V14, P145
   Scao TL, 2022, ARXIV
   Sezgin S, 2021, COMPUT APPL ENG EDUC, V29, P950, DOI 10.1002/cae.22350
   Shanahan M., 2022, arXiv
   SHANNON CE, 1948, BELL SYST TECH J, V27, P379, DOI DOI 10.1002/J.1538-7305.1948.TB01338.X
   Solaiman I, 2021, ADV NEUR IN, V34
   Sorby SA, 1999, COMPUT APPL ENG EDUC, V7, P252, DOI 10.1002/(SICI)1099-0542(1999)7:4<252::AID-CAE7>3.0.CO;2-Z
   Sosa ME, 2002, IEEE T ENG MANAGE, V49, P45, DOI 10.1109/17.985747
   SprutCAM Tech, 2023, MEET SPRUTCAM X AI A
   Stiene Riemer M.S., 2023, GENERATIVE AI ROADMA
   Tan H, 2021, J ENVIRON MANAGE, V297, DOI 10.1016/j.jenvman.2021.113382
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Thurzo A, 2023, EDUC SCI, V13, DOI 10.3390/educsci13020150
   Tiwari CK, 2023, INTERACT TECHNOL SMA, DOI 10.1108/ITSE-04-2023-0061
   Tolouei-Rad M., 2006, J ACHIEVEMENTS MAT M, V18, P31
   Tshai KY., 2014, Engineering Education, V9, P74, DOI DOI 10.11120/ENED.2014.00020
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   van Kesteren IEH, 2008, MATER DESIGN, V29, P133, DOI 10.1016/j.matdes.2006.11.008
   Vaswani A., 2017, ADV NEURAL INFORM PR, V2017, P5999
   Wang HF, 2023, ENGINEERING-PRC, V25, P51, DOI 10.1016/j.eng.2022.04.024
   Watkins R., 2023, AI ETHICS, P1
   Wei J., 2022, ARXIV
   Wiggins WF, 2022, RADIOL-ARTIF INTELL, V4, DOI 10.1148/ryai.220119
   World Health Organization, 2021, ETHICS GOVERNANCE AR
   Wos, 2023, WEB SCI
   Wu C.-J., 2022, Proceedings of Machine Learning and Systems, V4, P795
   Yang J., 2023, ARXIV
   Zhai X., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4331313, DOI 10.2139/SSRN.4331313]
   Zhao W., 2023, ARXIV
   Zhavoronkov A, 2023, NAT MED, V29, P532, DOI 10.1038/d41591-023-00014-w
   Zhu P., 2023, ARXIV
NR 146
TC 1
Z9 1
U1 12
U2 32
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-5659
EI 1758-8510
J9 INTERACT TECHNOL SMA
JI Interact. Technol. Smart Educ.
PD OCT 30
PY 2024
VL 21
IS 4
SI SI
BP 588
EP 624
DI 10.1108/ITSE-10-2023-0198
EA FEB 2024
PG 37
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA K4N9Y
UT WOS:001161398500001
DA 2024-12-25
ER

PT J
AU Cogo, A
   Patsko, L
   Szoke, J
AF Cogo, Alessia
   Patsko, Laura
   Szoke, Joanna
TI Generative artificial intelligence and ELT
SO ELT JOURNAL
LA English
DT Article
DE Generative artificial intelligence (GenAI); ELT; Special Issue;
   technology; critical thinking
AB There is undoubtedly, and understandably, a growing interest in incorporating generative artificial intelligence (GenAI) technologies into ELT. While advanced AI models have the potential to support language education, offering new tools and resources to enhance learning, their use also raises important questions regarding ethics and responsibility. As we follow the emergence of GenAI as another ELT tool, it is crucial to strike a balance between leveraging the benefits of these technologies and maintaining the core values of effective pedagogy. Educators must develop clear guidelines and best practices for the responsible integration of AI in the classroom, ensuring that it enhances rather than replaces human interaction and critical thinking. In this Special Issue on GenAI and ELT we explore some of the applications, their potential, and the challenges of incorporating GenAI in ELT.
C1 [Szoke, Joanna] Karoli Gaspar Univ, Budapest, Hungary.
   [Szoke, Joanna] Cambridge Univ Press & Assessment, Cambridge, England.
RP Szoke, J (corresponding author), Karoli Gaspar Univ, Budapest, Hungary.
EM elt.journal.editor@gmail.com; laura@laurapatsko.com;
   joanna.szoke@icloud.com
OI Szoke, Joanna/0000-0002-5291-7043
FU Oxford University Press
FX We would like to thank Oxford University Press for funding the ELT
   Journal Debate at the IATEFL Conference in Brighton in 2024 where we
   started working together on our Special Issue.
CR Edmett A., 2023, ARTIFICIAL INTELLIGE
   Martineau K., 2024, IBM RES
   Novawan A., 2024, J ENGLISH ACAD PROFE, V10, P1, DOI DOI 10.25047/JEAPCO.V9I1.3754
   Rahimi A. R., 2024, Computers & Education: Artificial Intelligence, V7, DOI [10.1016/j.caeai.2024.100258, DOI 10.1016/J.CAEAI.2024.100258]
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Warner B., 2022, AI LANGUAGE LEARNING
   Warschauer M., 2024, LANG LEARN TECHNOL, V28
NR 7
TC 0
Z9 0
U1 38
U2 38
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0951-0893
EI 1477-4526
J9 ELT J
JI ELT J.
PD OCT 7
PY 2024
VL 78
IS 4
BP 373
EP 377
DI 10.1093/elt/ccae051
EA OCT 2024
PG 5
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA L1Q4A
UT WOS:001327266200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Wang, CH
   Zou, B
   Du, YR
   Wang, ZX
AF Wang, Chenghao
   Zou, Bin
   Du, Yiran
   Wang, Zixun
TI The impact of different conversational generative AI chatbots on EFL
   learners: An analysis of willingness to communicate, foreign language
   speaking anxiety, and self-perceived communicative competence
SO SYSTEM
LA English
DT Article
DE Generative artificial intelligence (GenAI); GenAI chatbot; Avatar;
   Willingness to communicate (WTC); Foreign language speaking anxiety
   (FLSA); Self-perceived communicative competence; (SPCC); English
   Speaking
ID ENGLISH; L2; MOTIVATION; CONFIDENCE; MODEL
AB Based on the Interaction Hypothesis, the study investigates the impact of different conversational Generative Artificial Intelligence (GenAI) chatbots on English as a Foreign Language (EFL) learners' willingness to communicate (WTC), foreign language speaking anxiety (FLSA), selfperceived communicative competence (SPCC) and speaking performance. Three groups of Chinese undergraduate students were recruited: a control group (CG, N = 33) and two experimental groups (EG1, N = 33; EG2, N = 33). The CG interacted with the teacher and classmates during the speaking class. In contrast, EG1 interacted with a text- and voice-based conversational GenAI chatbot called Typebot, while EG2 engaged with a conversational GenAI chatbot that featured both text and voice interaction along with human-like avatars named D-ID Agent. Quantitative analysis using multilevel modelling revealed that EG2 showed significant improvements in WTC and SPCC and a notable reduction in FLSA levels compared to CG. However, the pre- and postspeaking test results showed no significant differences in speaking performance across the groups. Qualitative data from semi-structured interviews supported these findings, highlighting the immersive learning experience and emotional support provided by the human-like avatars. These results suggest that visually embodied GenAI chatbots can effectively enhance the emotional experience during the language learning. The study provides practical insights for language educators on integrating GenAI technologies in language teaching, emphasising the benefits of human-like avatars in fostering a more engaging and supportive learning environment.
C1 [Wang, Chenghao; Zou, Bin] Xian Jiaotong Liverpool Univ, Dept Appl Linguist, Suzhou, Peoples R China.
   [Du, Yiran] Harvard Univ, Human Dev & Educ, Cambridge, MA USA.
   [Wang, Zixun] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China.
C3 Xi'an Jiaotong-Liverpool University; Harvard University; Zhejiang
   University
RP Zou, B (corresponding author), Xian Jiaotong Liverpool Univ, Dept Appl Linguist, Suzhou, Peoples R China.; Wang, ZX (corresponding author), Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China.
EM dancerluo@outlook.com; bin.zou@xjtlu.edu.cn; yid164@harvard.edu;
   wangzixun1211@gmail.com
RI Du, Yiran/JBS-8109-2023; Zou, Bin/AAP-9618-2020
OI Du, Yiran/0000-0002-6576-0073; Zou, Bin/0000-0002-4863-0998; Wang,
   Chenghao/0009-0009-5655-3740; wang, zixun/0009-0007-7947-0708
FU University Research Centre for Culture, Communication and Society (CCCS)
   at Xi'an Jiaotong-Liverpool University; ILABI lab at ZheJiang
   Univerisity
FX This study is funded by the University Research Centre for Culture,
   Communication and Society (CCCS) at Xi'an Jiaotong-Liverpool University
   and ILABI lab at ZheJiang Univerisity.
CR Adi Waloyo A., 2021, English Learning Innovation, V2, P62, DOI [10.22219/englie.v2i2.17736, DOI 10.22219/ENGLIE.V2I2.17736]
   Alawida M, 2023, INFORMATION, V14, DOI 10.3390/info14080462
   Baharun H, 2016, SOCIOINT16: 3RD INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES AND HUMANITIES, P666
   Barrios E, 2021, LANG TEACH RES, DOI 10.1177/13621688211054046
   Bishop D., 2023, Evaluating what works: An intuitive guide to intervention research for practitioners
   Cao YQ, 2014, TESOL QUART, V48, P789, DOI 10.1002/tesq.155
   Chapelle C. A., 2024, Exploring AI in applied linguistics
   Chen ZW, 2024, SAGE OPEN, V14, DOI 10.1177/21582440231219312
   Cheng YH, 2024, Arxiv, DOI [arXiv:2401.03428, 10.48550/arXiv.2401.03428]
   Crompton H, 2024, BRIT J EDUC TECHNOL, V55, P2503, DOI 10.1111/bjet.13460
   D-ID, 2024, Creative reality studio
   de Saint Leger D, 2009, SYSTEM, V37, P269, DOI 10.1016/j.system.2009.01.001
   Dewaele JM, 2021, INNOV LANG LEARN TEA, V15, P66, DOI 10.1080/17501229.2019.1675667
   Dewaele JM, 2019, J LANG SOC PSYCHOL, V38, P523, DOI 10.1177/0261927X19864996
   Dewaele JM, 2007, INT J MULTILING, V4, P169, DOI 10.2167/ijm080.0
   Ducker N, 2021, SYSTEM, V103, DOI 10.1016/j.system.2021.102634
   Endler NS, 2001, J ANXIETY DISORD, V15, P231, DOI 10.1016/S0887-6185(01)00060-3
   Ericsson E, 2023, COGENT EDUC, V10, DOI 10.1080/2331186X.2023.2170088
   Eysenck Michael.W., 1992, Anxiety: The Cognitive Perspective
   Fallah N, 2014, LEARN INDIVID DIFFER, V30, P140, DOI 10.1016/j.lindif.2013.12.006
   Fathi J, 2024, SYSTEM, V121, DOI 10.1016/j.system.2024.103254
   Fryer L, 2006, LANG LEARN TECHNOL, V10, P8
   Gan ZD, 2013, J MULTILING MULTICUL, V34, P231, DOI 10.1080/01434632.2013.768622
   Gass S.M., 2003, HDB 2 LANGUAGE ACQUI, P224, DOI DOI 10.1002/9780470756492.CH9
   Hashimoto Y., 2002, Second Language Studies, V20, P29, DOI DOI 10.12691/education-2-11-8
   Hew KF, 2023, J COMPUT HIGH EDUC, V35, P40, DOI 10.1007/s12528-022-09338-x
   HORWITZ EK, 1986, MOD LANG J, V70, P125, DOI 10.2307/327317
   Horwitz EK., 1991, LANGUAGE ANXIETY THE
   Huang WJ, 2022, J COMPUT ASSIST LEAR, V38, P237, DOI 10.1111/jcal.12610
   Jelinková J, 2023, J TEACH ENGL SPECIF, V11, P663, DOI 10.22190/JTESAP230911051J
   Jia W, 2024, LANG LEARN TECHNOL, V28, P1
   Jin S, 2024, BRIT J EDUC TECHNOL, V55, P586, DOI 10.1111/bjet.13381
   Kim A, 2024, SYSTEM, V122, DOI 10.1016/j.system.2024.103256
   King N., 2018, INTERVIEWS QUALITATI
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kok J. N., 2009, ARTIF INTELL, V1, P270
   Lam W., 2000, ELT J, V54, P245, DOI [10.1093/elt/54.3.245, DOI 10.1093/ELT/54.3.245]
   Lee JH, 2020, ELT J, V74, P338, DOI 10.1093/elt/ccaa035
   Li S., 2018, The TESOL encyclopedia of English language teaching, P1, DOI [10.1002/9781118784235.eelt0247, DOI 10.1002/9781118784235.EELT0247]
   Lin YT, 2019, ASIA-PAC EDUC RES, V28, P101, DOI 10.1007/s40299-018-0417-y
   Liu H., 2024, Modern Foreign Languages, V44, P439, DOI [10.20071/j.cnki.xdwy.20240523.001, DOI 10.20071/J.CNKI.XDWY.20240523.001]
   Long M.H., 1996, HDB 2 LANGUAGE ACQUI, P413, DOI [DOI 10.1016/B978-012589042-7/50015-3, 10.1016/b978-012589042-7/50015-3]
   LONG MH, 1983, APPL LINGUIST, V4, P126, DOI 10.1093/applin/4.2.126
   MacIntyre PD, 2002, LANG LEARN, V52, P537, DOI 10.1111/1467-9922.00194
   Macintyre PD, 1998, MOD LANG J, V82, P545, DOI 10.2307/330224
   Maclntyre P.D., 1994, COMMUN RES REP, V11, P135, DOI [10.1080/08824099409359951, DOI 10.1080/08824099409359951]
   Maier U., 2022, Comput. Educ. Artif. Intell., V3, DOI DOI 10.1016/J.CAEAI.2022.100080
   Mayer RE, 2002, LEARN INSTR, V12, P107, DOI 10.1016/S0959-4752(01)00018-4
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Mohamed AM, 2024, EDUC INF TECHNOL, V29, P3195, DOI 10.1007/s10639-023-11917-z
   Okonkwo C. W., 2021, Comput. Educ. Artif. Intell., V2, DOI [10.1016/j.caeai.2021.100033, DOI 10.1016/J.CAEAI.2021.100033]
   Öz H, 2015, LEARN INDIVID DIFFER, V37, P269, DOI 10.1016/j.lindif.2014.12.009
   Ozdemir E, 2022, INNOV LANG LEARN TEA, V16, P234, DOI 10.1080/17501229.2021.1907750
   Ozturk G., 2014, Journal of Language and Linguistic Studies, V10, P1
   Pawlak M., 2011, Multilingual matters
   Peng JE, 2019, SYSTEM, V82, P161, DOI 10.1016/j.system.2019.04.006
   Peng JE, 2010, LANG LEARN, V60, P834, DOI 10.1111/j.1467-9922.2010.00576.x
   Rad HS, 2024, INNOV LANG LEARN TEA, V18, P364, DOI 10.1080/17501229.2024.2309539
   Rahimi M, 2024, COMPUT ASSIST LANG L, V37, P924, DOI 10.1080/09588221.2022.2064512
   Richmond V.P., 1992, J APPL COMMUN RES, V20, P95, DOI DOI 10.1080/00909889209365321
   Rogers P, 2005, BEHAV INFORM TECHNOL, V24, P151, DOI 10.1080/01449290410001723472
   Sha GQ, 2009, COMPUT ASSIST LANG L, V22, P269, DOI [10.1080/0958822090292084, 10.1080/09588220902920284]
   Shadiev R, 2023, IEEE T LEARN TECHNOL, V16, P664, DOI 10.1109/TLT.2023.3243721
   Shao KQ, 2019, SYSTEM, V86, DOI 10.1016/j.system.2019.102121
   Shirvan ME, 2019, J PSYCHOLINGUIST RES, V48, P1241, DOI 10.1007/s10936-019-09656-9
   Song CP, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1260843
   Spielberger C.D., 1980, TEST ANXIETY INVENTO
   Sun H., 2020, Waiyu Yanjiu, V182, P57
   Swain M., 2000, SOCIOCULTURAL THEORY, P97
   Tai TY, 2024, COMPUT EDUC, V220, DOI 10.1016/j.compedu.2024.105112
   Tai TY, 2023, INTERACT LEARN ENVIR, V31, P1485, DOI 10.1080/10494820.2020.1841801
   Tudini V., 2003, ALSIC Apprentissage des Langues et Systemes d'Information et de Communication, V6, P83
   Wan YW, 2024, RELC J, DOI 10.1177/00336882231224813
   Wang C., 2023, Journal of China Computer-Assisted Language Learning, DOI [10.1515/jccall-2023-0019, DOI 10.1515/JCCALL-2023-0019]
   Wang C., 2024, Journal for Language Teaching, V4, P1, DOI [10.54475/jlt.2024.004, DOI 10.54475/JLT.2024.004]
   Wang XH, 2024, COMPUT ASSIST LANG L, V37, P814, DOI 10.1080/09588221.2022.2056203
   Wang YF, 2013, INT J ADV COMPUT SC, V4, P124
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/357980.357991
   Xiao YH, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e37238
   Yang H, 2022, RECALL, V34, P327, DOI 10.1017/S0958344022000039
   Yang YF, 2024, COMPUT ASSIST LANG L, V37, P410, DOI 10.1080/09588221.2022.2039203
   Yuan Q, 2024, INT J HUM-COMPUT INT, V40, P3313, DOI 10.1080/10447318.2023.2189818
   Zhang C, 2024, SYSTEM, V121, DOI 10.1016/j.system.2024.103259
   Zhang JY, 2018, SYSTEM, V72, P226, DOI 10.1016/j.system.2018.01.003
   Zou B., 2024, English for academic purposes in the EMI context in Asia, P287, DOI [10.1007/978-3-031-63638-7_12, DOI 10.1007/978-3-031-63638-7_12]
   Zou B, 2023, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2023.2278608
   Zulkepli N., 2021, Journal of Language and Linguistic Studies, V17, P160
NR 87
TC 1
Z9 1
U1 46
U2 46
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0346-251X
EI 1879-3282
J9 SYSTEM
JI System
PD DEC
PY 2024
VL 127
AR 103533
DI 10.1016/j.system.2024.103533
EA NOV 2024
PG 16
WC Education & Educational Research; Linguistics
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Linguistics
GA L8B0U
UT WOS:001352910000001
DA 2024-12-25
ER

PT J
AU Schneider, J
AF Schneider, Johannes
TI Explainable Generative AI (GenXAI): a survey, conceptualization, and
   research agenda
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Generative artificial intelligence; Explainability; Conceptualization;
   Survey; Explainable artificial intelligence; Research agenda
ID ARTIFICIAL-INTELLIGENCE
AB Generative AI (GenAI) represents a shift from AI's ability to "recognize" to its ability to "generate" solutions for a wide range of tasks. As generated solutions and applications grow more complex and multi-faceted, new needs, objectives, and possibilities for explainability (XAI) have emerged. This work elaborates on why XAI has gained importance with the rise of GenAI and the challenges it poses for explainability research. We also highlight new and emerging criteria that explanations should meet, such as verifiability, interactivity, security, and cost considerations. To achieve this, we focus on surveying existing literature. Additionally, we provide a taxonomy of relevant dimensions to better characterize existing XAI mechanisms and methods for GenAI. We explore various approaches to ensure XAI, ranging from training data to prompting. Our paper provides a concise technical background of GenAI for non-technical readers, focusing on text and images to help them understand new or adapted XAI techniques for GenAI. However, due to the extensive body of work on GenAI, we chose not to delve into detailed aspects of XAI related to the evaluation and usage of explanations. Consequently, the manuscript appeals to both technical experts and professionals from other fields, such as social scientists and information systems researchers. Our research roadmap outlines over ten directions for future investigation.
C1 [Schneider, Johannes] Univ Liechtenstein, Vaduz, Liechtenstein.
C3 University of Liechtenstein
RP Schneider, J (corresponding author), Univ Liechtenstein, Vaduz, Liechtenstein.
EM johannes.schneider@uni.li
FU University of Liechtenstein
FX Two very humble and reputable researchers also contributed to this
   manuscript but felt that their contribution did not deserve
   co-authorship or explicit mention in the acknowledgments, which is
   remarkable in the competitive field of research. I want to thank both of
   them cordially.
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Adadi A, 2018, IEEE ACCESS, V6, P52138, DOI 10.1109/ACCESS.2018.2870052
   Adebayo J, 2018, ADV NEUR IN, V31
   Ali A, 2022, PR MACH LEARN RES, P435
   Amershi S, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300233
   [Anonymous], 2023, GUARDIAN
   Askell A, 2021, Arxiv, DOI arXiv:2112.00861
   Augustin Maximilian, 2022, ADV NEUR IN
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Barkan O, 2021, PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021, P2882, DOI 10.1145/3459637.3482126
   Beaudouin V, 2020, Arxiv, DOI arXiv:2003.07703
   Betker J., 2023, Comput. Sci, V2, P8
   Bodria F, 2023, DATA MIN KNOWL DISC, V37, P1719, DOI 10.1007/s10618-023-00933-9
   Brooks T., 2024, VIDEO GENERATION MOD
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Carroll JM, 1988, Handbook of HumanComputer Interaction, P45, DOI DOI 10.1016/B978-0-444-70536-5.50007-5
   Chen BL, 2021, Arxiv, DOI arXiv:2104.03869
   Chen LQ, 2023, J COMPUT INF SCI ENG, V23, DOI 10.1115/1.4062232
   Chen SY, 2020, PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), P7870
   Chen Xi, 2016, ADV NEURAL INFORM PR, V29
   Chen XY, 2023, Arxiv, DOI [arXiv:2304.05128, 10.48550/arXiv.2304.05128]
   Chen ZC, 2023, Arxiv, DOI [arXiv:2303.16537, DOI 10.48550/ARXIV.2303.16537]
   Choi JH, 2022, J LEGAL EDUC, V71, P387
   Chuang YN, 2024, Arxiv, DOI [arXiv:2402.04678, DOI 10.48550/ARXIV.2402.04678]
   Common Crawl Foundation, 2024, Common crawl
   Conmy A., 2024, Adv Neural Inf Process Syst, V36, P16318
   Correia AD, 2022, ARTIF INTELL REV, V55, P6037, DOI 10.1007/s10462-022-10148-x
   Creswell A, 2022, Arxiv, DOI arXiv:2208.14271
   Dai DM, 2022, PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS), P8493
   Deldjoo Y, 2023, Arxiv, DOI arXiv:2307.11761
   Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
   Dufter P, 2022, COMPUT LINGUIST, V48, P733, DOI 10.1162/coli_a_00445
   Dwivedi R, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3561048
   El Zini J, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3529755
   Elhage N, 2022, Transf Circ Thread
   Elyoseph Z, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1199058
   Enguehard J, 2023, Arxiv, DOI arXiv:2305.15853
   Ethayarajh K, 2021, Arxiv, DOI arXiv:2105.14652
   European Union, 2023, Eu AI act
   Faubel L, 2023, Arxiv, DOI arXiv:2309.12756
   Fok R, 2024, Arxiv, DOI arXiv:2305.07722
   Foote A, 2023, Arxiv, DOI arXiv:2305.19911
   Gao YF, 2023, Arxiv, DOI arXiv:2303.14524
   Gawlikowski J, 2023, ARTIF INTELL REV, V56, P1513, DOI 10.1007/s10462-023-10562-9
   Geiger Atticus, 2021, Advances in Neural Information Processing Systems, V34, P9574
   Geva M, 2022, Arxiv, DOI arXiv:2203.14680
   Ghorbani A, 2019, AAAI CONF ARTIF INTE, P3681
   Goodfellow I.J., 2015, 3 INT C LEARN REPR I
   Goyal T, 2022, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), P2061
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Grisold T, 2023, P INT C INF SYST
   Grosse R, 2023, Arxiv, DOI arXiv:2308.03296
   Grynbaum MM, 2023, The Times Sues OpenAI and Microsoft Over A.I. Use of Copyrighted Work
   Guidotti R, 2024, DATA MIN KNOWL DISC, V38, P2770, DOI 10.1007/s10618-022-00831-6
   Guidotti R, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3236009
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Gurrapu S, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1225093
   He HF, 2022, Arxiv, DOI arXiv:2301.00303
   Hernandez E, 2024, Arxiv, DOI arXiv:2304.00740
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Huang J, 2023, P 2023 C EMPIRICAL M, P1051, DOI DOI 10.18653/V1
   Huang J, 2024, Arxiv, DOI [arXiv:2310.01798, DOI 10.48550/ARXIV.2310.01798]
   Huang YH, 2023, Arxiv, DOI [arXiv:2307.10236, DOI 10.48550/ARXIV.2307.10236]
   Jain S, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P3543
   Jeanneret G, 2022, P AS C COMP VIS, P219
   Jin D, 2020, AAAI CONF ARTIF INTE, V34, P8018
   Johny L, 2024, EUR C INF SYST ECIS, P17
   Kadavath S, 2022, Arxiv, DOI arXiv:2207.05221
   Katz DM, 2024, PHILOS T R SOC A, V382, DOI 10.1098/rsta.2023.0254
   Katz S, 2023, arXiv
   Kiciman E, 2023, Arxiv, DOI [arXiv:2305.00050, 10.48550/arXiv.2305.00050, DOI 10.48550/ARXIV.2305.00050]
   Kim B, 2018, PR MACH LEARN RES, V80
   King WR, 2005, COMMUN ASSOC INF SYS, V16, P665
   Kirillov A, 2023, Arxiv, DOI arXiv:2304.02643
   Kokalj E., 2021, P EACL HACK NEWS MED, P16
   Kwon M, 2022, Arxiv, DOI [arXiv:2210.10960, 10.48550/arXiv:2210.10960]
   Lewis P, 2020, ADV NEUR IN, V33
   Li J., 2016, P THE 016 C N AM CHA, P681, DOI [10.18653/v1/N16-1082, DOI 10.18653/V1/N16-1082]
   Li L, 2023, ACM T INFORM SYST, V41, DOI 10.1145/3580488
   Li M, 2023, INT C MACHINE LEARNI, P20452
   Li PZ, 2023, APPL SOFT COMPUT, V138, DOI 10.1016/j.asoc.2023.110176
   Liao QV, 2023, Arxiv, DOI [arXiv:2306.01941, 10.48550/arXiv.2306.01941, DOI 10.48550/ARXIV.2306.01941]
   Lin CH, 2023, PROC CVPR IEEE, P300, DOI 10.1109/CVPR52729.2023.00037
   Lin TY, 2022, AI OPEN, V3, P111, DOI 10.1016/j.aiopen.2022.10.001
   Ling C, 2023, Arxiv, DOI [arXiv:2305.18703, 10.48550/arXiv.2305.18703, DOI 10.48550/ARXIV.2305.18703]
   Liu LZ, 2021, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2021, P820
   Liu T, 2022, arXiv
   Longo L, 2024, INFORM FUSION, V106, DOI 10.1016/j.inffus.2024.102301
   Longpre S., 2023, INT C MACHINE LEARNI, P22631
   Lou RZ, 2024, Arxiv, DOI arXiv:2303.10475
   Lundberg SM, 2017, ADV NEUR IN, V30
   Lundstrom D, 2022, PR MACH LEARN RES
   Luo HY, 2024, Arxiv, DOI [arXiv:2401.12874, 10.48550/arXiv.2401.12874, DOI 10.48550/ARXIV.2401.12874.ARXIV]
   Lyu Q, 2024, COMPUT LINGUIST, V50, P657, DOI 10.1162/coli_a_00511
   Ma YW, 2023, Arxiv, DOI [arXiv:2309.16298, DOI 10.48550/ARXIV.2309.16298]
   MacKenzie IS., 2024, Human-Computer Interaction: An Empirical Research Perspective, V2
   Mao JG, 2024, Arxiv, DOI [arXiv:2311.10813, 10.48550/arXiv.2311.10813]
   Marvin R, 2018, Arxiv, DOI arXiv:1808.09031
   Maynez J, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1906
   McKinsey & Company, 2023, The economic potential of generative AI
   Meng K, 2022, ADV NEUR IN
   Menick Jacob, 2022, arXiv
   Meronen L, 2024, P IEEE CVF WINT C AP, P2680
   Meske C, 2022, INFORM SYST MANAGE, V39, P53, DOI 10.1080/10580530.2020.1849465
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   Minh D, 2022, ARTIF INTELL REV, V55, P3503, DOI 10.1007/s10462-021-10088-y
   Mishra A, 2023, Arxiv, DOI arXiv:2304.01964
   Modarressi A, 2023, Arxiv, DOI arXiv:2306.02873
   Mohebbi H, 2021, Arxiv, DOI arXiv:2104.01477
   Molnar C., 2020, Lulu
   Montavon Gregoire, 2019, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, P193, DOI DOI 10.1007/978-3-030-28954-610/FIGURES/5
   Nichol Alexander Quinn, 2022, P 39 INT C MACHINE L, P16784
   Nickerson RC, 2013, EUR J INFORM SYST, V22, P336, DOI 10.1057/ejis.2012.26
   Olah C., 2020, DISTILL, V5, DOI [10.23915/distill.00024.001, DOI 10.23915/DISTILL.00024.001, 10.2 3915/distill.00024.001]
   Olah Chris, 2022, Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases
   Olsson Catherine., 2022, arXiv, DOI 10.48550/arXiv.2209.11895
   OpenAI, 2023, Language models can explain neurons in language models
   OpenAI, 2023, Introducing the GPT store
   Ouyang L, 2022, ADV NEUR IN
   Pan LM, 2023, Arxiv, DOI [arXiv:2308.03188, 10.48550/arXiv.2308.03188]
   Park DH, 2018, PROC CVPR IEEE, P8779, DOI 10.1109/CVPR.2018.00915
   Petko Georgiev, 2024, arXiv, DOI [10.48550/arXiv.2403.05530, DOI 10.48550/ARXIV.2403.05530]
   Poli M, 2023, Arxiv, DOI arXiv:2302.10866
   Porter J., 2023, Chatgpt continues to be one of the fastest-growing services ever
   Radford A., 2019, OPENAI BLOG
   Rago A, 2021, ARTIF INTELL-AMST, V296, DOI 10.1016/j.artint.2021.103506
   Ram O, 2023, Arxiv, DOI arXiv:2212.10380
   Ramesh A., 2022, arXiv
   Rauker T, 2023, 2023 IEEE CONFERENCE ON SECURE AND TRUSTWORTHY MACHINE LEARNING, SATML, P464, DOI 10.1109/SaTML54575.2023.00039
   Reed S, 2022, Arxiv, DOI arXiv:2205.06175
   Ribeiro MT, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1135, DOI 10.1145/2939672.2939778
   Sabour S., 2017, P 31 INT C NEUR INF, V30, P1
   Saeed W, 2023, KNOWL-BASED SYST, V263, DOI 10.1016/j.knosys.2023.110273
   Saha S., 2022, P C EMP METH NAT LAN, P2121
   Saharia C, 2022, ADV NEUR IN
   Schick T., 2024, Adv. Neural Inform. Process. Syst, V36, P68539
   Schneider J, 2019, P EUR C INF SYST ECI
   Schneider J, 2024, P INT C COMP SUPP ED
   Schneider J., 2023, SN Comput Sci, V5, P81
   Schneider J, 2024, Arxiv, DOI arXiv:2403.08802
   Schneider J, 2024, BUS INFORM SYST ENG+, V66, P221, DOI 10.1007/s12599-024-00851-0
   Schneider J, 2023, Arxiv, DOI arXiv:2312.03720
   Schneider J, 2023, J INF SECUR APPL, V76, DOI 10.1016/j.jisa.2023.103517
   Schneider J, 2023, J INF SECUR APPL, V75, DOI 10.1016/j.jisa.2023.103502
   Schneider J, 2024, DATA MIN KNOWL DISC, V38, P2975, DOI 10.1007/s10618-023-00920-0
   Schneider J, 2022, AI COMMUN, V35, P153, DOI 10.3233/AIC-210081
   Schneider J, 2023, INFORM SYST MANAGE, V40, P229, DOI 10.1080/10580530.2022.2085825
   Schneider J, 2023, MACH LEARN, V112, P4167, DOI 10.1007/s10994-022-06157-0
   Schramowski P, 2020, NAT MACH INTELL, V2, P476, DOI 10.1038/s42256-020-0212-3
   Schwalbe G, 2024, DATA MIN KNOWL DISC, V38, P3043, DOI 10.1007/s10618-022-00867-8
   Selva J, 2023, IEEE T PATTERN ANAL, V45, P12922, DOI 10.1109/TPAMI.2023.3243465
   Selvaraju RR, 2017, IEEE I CONF COMP VIS, P618, DOI 10.1109/ICCV.2017.74
   Serrano S, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P2931
   Shahsavar Y, 2023, JMIR HUM FACTORS, V10, DOI 10.2196/47564
   Shen TH, 2023, Arxiv, DOI arXiv:2309.15025
   Sikdar S, 2021, 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, VOL 1 (ACL-IJCNLP 2021), P865
   Silva A, 2023, INT J HUM-COMPUT INT, V39, P1390, DOI 10.1080/10447318.2022.2101698
   Singh C, 2024, Arxiv, DOI [arXiv:2402.01761, DOI 10.48550/ARXIV.2402.01761]
   Singh C, 2023, Arxiv, DOI arXiv:2305.09863
   Singh C, 2023, Arxiv, DOI arXiv:2210.01848
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Slack D, 2023, NAT MACH INTELL, V5, P873, DOI 10.1038/s42256-023-00692-8
   Ross AS, 2017, Arxiv, DOI arXiv:1703.03717
   Sobania D, 2023, Arxiv, DOI [arXiv:2301.08653, 10.48550/ARXIV.2301.08653, 10.48550/arXiv.2301.08653]
   Sottana A., 2023, EMNLP, P8776
   Speith Timo, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P2239, DOI 10.1145/3531146.3534639
   Sreedharan S., 2022, Explainable human-AI interaction: a planning perspective
   Stremmel J, 2022, PR MACH LEARN RES, V193, P218
   Sun A., 2024, Adv Neural Inf Process Syst, V36, P21826
   Müller ST, 2021, Arxiv, DOI arXiv:2102.04972
   Taori R., 2023, Stanford Alpaca: An instruction-following llama model
   Teehan R, 2022, PROCEEDINGS OF WORKSHOP ON CHALLENGES & PERSPECTIVES IN CREATING LARGE LANGUAGE MODELS (BIGSCIENCE EPISODE #5), P146
   Tenney I, 2019, Arxiv, DOI arXiv:1905.06316
   Theis S, 2023, LECT NOTES ARTIF INT, V14050, P355, DOI 10.1007/978-3-031-35891-3_22
   Theissler A, 2022, IEEE ACCESS, V10, P100700, DOI 10.1109/ACCESS.2022.3207765
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Touvron H, 2023, Arxiv, DOI [arXiv:2307.09288, 10.48550/arXiv.2307.09288]
   Turpin M., 2024, Adv Neural Inf Process Syst, V36, P74952
   Vaswani A, 2017, ADV NEUR IN, V30
   Vedula Nikhita, 2023, P ACM INT C WEB SEAR, P949
   Vig J, 2019, PROCEEDINGS OF THE 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: SYSTEM DEMONSTRATIONS, (ACL 2019), P37
   Walke F, 2023, P WIRTSCHAFTSINFORMA
   Wang BX, 2022, Arxiv, DOI arXiv:2205.01287
   Wang HR, 2023, Arxiv, DOI arXiv:2310.05253
   Wang Kevin, 2022, arXiv
   Wang ZH, 2024, Arxiv, DOI arXiv:2302.01560
   Webster J, 2002, MIS QUART, V26, pXIII
   Wei JS, 2022, ADV NEUR IN
   Weidinger Laura, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P214, DOI 10.1145/3531146.3533088
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Wu T., 2021, arXiv
   Wu ZF, 2023, Arxiv, DOI [arXiv:2307.02477, DOI 10.48550/ARXIV.2307.02477, 10.48550/arXiv.2307.02477]
   Wu ZY, 2021, Arxiv, DOI arXiv:2004.14786
   Xing Z, 2023, Arxiv, DOI arXiv:2310.10647
   Xu P, 2023, IEEE T PATTERN ANAL, V45, P12113, DOI 10.1109/TPAMI.2023.3275156
   Yang K, 2023, C EMP METH NAT LANG, P6056
   Yang LY, 2024, ACM COMPUT SURV, V56, DOI [10.1145/3626235, 10.1145/3648469]
   Yang S, 2023, PROCEEDINGS OF THE 61ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2023): LONG PAPERS, VOL 1, P10270
   Ye X., 2022, Adv. Neural. Inf. Process. Syst, V35, P30378
   Yin KY, 2022, Arxiv, DOI arXiv:2202.10419
   Yordanov Y, 2022, Arxiv, DOI arXiv:2112.06204
   Zaidan Omar, 2007, HUMAN LANGUAGE TECHN, P260
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
   Zhang C, 2023, Arxiv, DOI arXiv:2303.13336
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
   Zhang NY, 2024, Arxiv, DOI arXiv:2401.01286
   Zhang SY, 2024, Arxiv, DOI [arXiv:2308.10792, DOI 10.48550/ARXIV.2308.10792]
   Zhao H., 2023, ACM Transactions on Intelligent Systems and Technology, V15, P1
   Zhao RC, 2023, Arxiv, DOI arXiv:2305.02160
   Zhong ZX, 2021, Arxiv, DOI arXiv:2104.05240
   Zhou YQY, 2023, Arxiv, DOI arXiv:2306.08042
   Ziems C, 2024, Arxiv, DOI arXiv:2305.03514
NR 212
TC 1
Z9 1
U1 15
U2 15
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD SEP 15
PY 2024
VL 57
IS 11
AR 289
DI 10.1007/s10462-024-10916-x
PG 38
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA F8G4E
UT WOS:001312133800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Tsao, J
   Nogues, C
AF Tsao, Jack
   Nogues, Collier
TI Beyond the author: Artificial intelligence, creative writing and
   intellectual emancipation
SO POETICS
LA English
DT Article
DE Generative AI (GenAI); Artificial Intelligence (AI) literacies;
   Creativity; Intellectual emancipation; Creative writing;
   JacquesRancie`re
AB This study explores university students' engagement with Generative Artificial Intelligence (GenAI) tools for creative writing and graphic storytelling, drawing on Jacques Rancie`re's philosophy of intellectual equality and emancipation. Qualitative data analysis from a co-curricular creative writing programme, including reflections, surveys, and focus-group interviews, reveals emerging artificial intelligence literacies and students' improvisational aptitudes for interpreting, subverting, and transforming notions of authorship. Students decentred authorial attribution through the pragmatic adoption of the technology as a creative catalyst, negotiated creative conventions by adopting non-conventional communication strategies, and reconceptualised creativity as distributed across human and non-human agents. Our approach of student-driven learning for autonomous exploration, sense-making, and criticality with GenAI indicates the potential for promoting conditions for students to exercise intellectual equality and emancipation. The findings contribute to the understanding of authorship and creativity; begin to contour emerging GenAI literacies and competencies; and suggest that creative collaborations with GenAI may be a promising way to foster emancipatory practices in the classroom, while nurturing creative and critical skills.
C1 [Tsao, Jack] Univ Hong Kong, Common Core Off, Off 139 1-F Main Bldg, Hong Kong, Peoples R China.
   [Nogues, Collier] Chinese Univ Hong Kong, Dept English, Hong Kong, Peoples R China.
C3 University of Hong Kong; Chinese University of Hong Kong
RP Tsao, J (corresponding author), Univ Hong Kong, Common Core Off, Off 139 1-F Main Bldg, Hong Kong, Peoples R China.
EM jtsao@hku.hk
RI Tsao, Jack/IAM-2382-2023
OI Nogues, Collier/0000-0002-2321-2028
FU Hong Kong University Grant Committee Teaching Development and Language
   Enhancement Grant; University of Hong Kong [012530415]
FX Funding details This study was funded by the Hong Kong University Grant
   Committee Teaching Development and Language Enhancement Grant and
   allocated by the University of Hong Kong Vice-President and
   Pro-Vice-Chancellor (Teaching and Learning) , ID 012530415.
CR Abdel-Hack E.M., 2014, Educational Research, V5, P8, DOI [10.14303/er.2014, DOI 10.14303/ER.2014.011]
   Bardzell J., 2007, Human Technology: An Interdisciplinary Journal on Humans in ICT Environments
   Biesta G.J.J., 2013, BEAUTIFUL RISK ED, DOI [10.4324/9781315635866, DOI 10.4324/9781315635866]
   Bingham C., 2010, Jacques Ranciere: Education, Truth, Emancipation, DOI [10.5040/9781472546975, DOI 10.5040/9781472546975]
   Bumgarner B. L., 2012, Doctoral Thesis
   Burrell J, 2021, ANNU REV SOCIOL, V47, P213, DOI 10.1146/annurev-soc-090820-020800
   Clark E, 2018, IUI 2018: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P329, DOI 10.1145/3172944.3172983
   Corcoran S., 2010, Dissensus: On politics and aesthetics, P1
   Dahlström H, 2019, EDUC INF TECHNOL, V24, P1563, DOI 10.1007/s10639-018-9844-x
   Davis O, 2010, FR STUD, V64, P178, DOI 10.1093/fs/knq001
   Ewalt JP, 2016, PHILOS RHETORIC, V49, P26, DOI 10.5325/philrhet.49.1.0026
   Fryer N, 2015, RIDE-J APPL THEATRE, V20, P331, DOI 10.1080/13569783.2015.1059258
   Galloway S, 2012, EDUC THEORY, V62, P163, DOI 10.1111/j.1741-5446.2012.00441.x
   Godart F, 2020, ANNU REV SOCIOL, V46, P489, DOI 10.1146/annurev-soc-121919-054833
   Heath, 1977, IMAGE MUSIC TEXT
   Hjulström E, 2022, ETHICS EDUC, V17, P421, DOI 10.1080/17449642.2022.2153470
   Horner B, 1997, COLL ENGL, V59, P505, DOI 10.2307/378664
   Kangasharju A., 2022, Comp. Educ. Artif. Intell, V3, P100048, DOI DOI 10.1016/J.CAEAI.2022.100048
   Karlsson M, 2022, CULT ORGAN, V28, P194, DOI 10.1080/14759551.2022.2026947
   Klerfelt A., 2007, Barns multimediala berattande: En lank mellan mediakultur och pedagogisk praktik
   Kogut M, 2021, MANAGE LEARN, V52, P165, DOI 10.1177/1350507620969549
   Lev-Aladgem S, 2015, RIDE-J APPL THEATRE, V20, P511, DOI 10.1080/13569783.2015.1068108
   McKinsey & Company, 2023, The economic potenial of generative AI: The next productivity frontier
   Novak-Leonard JL, 2022, POETICS, V90, DOI 10.1016/j.poetic.2021.101599
   Ouyang F., 2021, COMPUTERS ED ARTIFIC, V2, P100020, DOI [DOI 10.1016/J.CAEAI.2021.100020, 10.1016/j.caeai.2021.100020 10.1016/j.caeai.2021.100020]
   Plate D., 2022, Art and Design Review, V10, P376, DOI [10.4236/adr.2022.103029, DOI 10.4236/ADR.2022.103029]
   Ranciere J, 2013, BLOOMSB REVELAT, P1
   Rancière J, 2007, ARTFORUM INT, V45, P270
   Ranciere Jacques., 1991, The Ignorant Schoolmaster: Five Lessons in Intellectual Emancipation
   Roemmele M., 2018, P 1 WORKSHOP STORYTE
   Sartre J. P., 2022, Being and nothingness: An essay in phenomenological ontology
   Simons M, 2011, EDUC PHIL THEOR SPEC, V17, P1
   Simpson P. W., 2021, Doctoral Thesis
   Sinner A, 2015, EDUC PHILOS THEORY, V47, P502, DOI 10.1080/00131857.2014.883962
   Trafí-Prats L, 2012, STUD ART EDUC, V53, P125, DOI 10.1080/00393541.2012.11518857
   Sgourev SV, 2021, POETICS, V89, DOI 10.1016/j.poetic.2021.101581
   Vlieghe J, 2018, EDUC PHILOS THEORY, V50, P917, DOI 10.1080/00131857.2016.1200002
   Williams Raymond., 1977, MARXISM LIT
NR 38
TC 1
Z9 1
U1 58
U2 97
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0304-422X
EI 1872-7514
J9 POETICS
JI Poetics
PD FEB
PY 2024
VL 102
AR 101865
DI 10.1016/j.poetic.2024.101865
EA JAN 2024
PG 12
WC Literature; Sociology
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Literature; Sociology
GA IP6M3
UT WOS:001167572400001
DA 2024-12-25
ER

PT J
AU Feng, C
   Botha, E
   Pitt, L
AF Feng, Cai (Mitsu)
   Botha, Elsamari
   Pitt, Leyland
TI From HAL to GenAI: Optimizing chatbot impacts with CARE
SO BUSINESS HORIZONS
LA English
DT Article
DE Generative AI; Chatbots; AI adoption; AI risks; AI ethics; Large
   language models
ID ARTIFICIAL-INTELLIGENCE; CHALLENGES
AB This article explores the evolution and prospects of conversational chatbots, specifically the latest generation referred to as generative artificial intelligence (GenAI) chatbots. This article comprehensively examines GenAI chatbots' business applications and impact across macro, meso, and micro levels of organizations. At the macro level, this article explores how GenAI chatbots reshape industry dynamics. The meso perspective delves into organizational changes, while the micro lens focuses on enhancing individual productivity, learning, and creativity. Gen- AI chatbots' immense potential is accompanied by risks in four areas: matching, ethics, technology, and adaptability (META). In response to these challenges, the article introduces a human-centric CARE framework-standing for collaboration, accountability, responsiveness, and empowerment-to mitigate the risks and to optimize the effects of GenAI chatbots. This work provides practical guidelines for navigating the complex landscape of GenAI implementation. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
C1 [Feng, Cai (Mitsu); Pitt, Leyland] Simon Fraser Univ, Beedie Sch Business, Vancouver, BC, Canada.
   [Botha, Elsamari] Univ Canterbury, UC Business Sch, Christchurch, New Zealand.
C3 Simon Fraser University; University of Canterbury
RP Feng, C (corresponding author), Simon Fraser Univ, Beedie Sch Business, Vancouver, BC, Canada.
EM caif@sfu.ca; elsamari.botha@canterbury.ac.nz; lpitt@sfu.ca
OI Feng, Cai/0000-0002-8130-7092; Pitt, Leyland/0000-0002-3099-9164
CR Ajao E., 2023, TechTargetOctober 25
   Amazon, 2024, Generative AI powered assistant - Amazon Q - AWS
   Amazon Staff, 2023, A new study reveals 5 ways AI will transform the workplace as we know it
   [Anonymous], 2023, Generative AI Could Raise Global GDP by 7%
   [Anonymous], 2015, What is prior art?
   Bankins S, 2024, J ORGAN BEHAV, V45, P159, DOI 10.1002/job.2735
   Beatoven A. I., 2024, Royalty free AI music generator
   Bertics A., 2023, The EconomistNovember 13
   Brynjolfsson Erik, 2023, Working Paper No. 31161
   Canhoto AI, 2020, BUS HORIZONS, V63, P183, DOI 10.1016/j.bushor.2019.11.003
   Canhoto AI, 2015, J MARKET MANAG-UK, V31, P1141, DOI 10.1080/0267257X.2015.1047466
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chan-Olmsted SM, 2019, JMM-INT J MEDIA MANA, V21, P193, DOI 10.1080/14241277.2019.1695619
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
   Cui YY, 2024, BUS HORIZONS, V67, P583, DOI 10.1016/j.bushor.2024.05.003
   de Bellefonds N., 2023, BCG Global
   Dooley J., 2023, AI Search BlogAugust 8
   Ellie, 2024, Your AI email assistant
   Expert I, 2023, Health IT Answers
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Fowler G. A., 2023, MediumNovember 10
   Gartner, 2023, Generative AI: What it is, tools, models, applications, and use cases
   Gartner, What generative AI means for business
   Ghimire P, 2023, Arxiv, DOI arXiv:2310.04427
   Google, 2024, Vertex AI
   Gordon C., 2023, ForbesFebruary 2
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   GSPANN Technologies, 2023, Medium
   Habib S., 2024, J. Creat, V34, DOI [10.1016/j.yjoc.2023.100072, DOI 10.1016/J.YJOC.2023.100072]
   Hamilton RH, 2020, BUS HORIZONS, V63, P85, DOI 10.1016/j.bushor.2019.10.001
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Hartley JL, 2019, BUS HORIZONS, V62, P707, DOI 10.1016/j.bushor.2019.07.006
   Hironde J.-B., 2023, ForbesAugust 31
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Hwang AHC, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445270
   Gómez-Caicedo MI, 2022, FRONT ARTIF INTELL, V5, DOI 10.3389/frai.2022.974180
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Kalliamvakou E., 2022, The GitHub BlogSeptember 7
   Kelly J., 2024, ForbesJanuary 18
   Kietzmann J., 2024, Business Horizons, V67, P453
   Kietzmann J, 2020, BUS HORIZONS, V63, P131, DOI 10.1016/j.bushor.2019.11.005
   Kiryakova G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13101056
   Lee I, 2020, BUS HORIZONS, V63, P157, DOI 10.1016/j.bushor.2019.10.005
   Lu QH, 2024, ACM COMPUT SURV, V56, DOI 10.1145/3626234
   Makarius EE, 2020, J BUS RES, V120, P262, DOI 10.1016/j.jbusres.2020.07.045
   Malik A, 2023, HUM RESOUR MANAGE R, V33, DOI 10.1016/j.hrmr.2022.100940
   Mallick S., 2023, The Economic TimesDecember 17
   Maruf R., 2023, CNN
   McKendrick J., 2022, ForbesDecember 21
   Megahed FM, 2024, QUAL ENG, V36, P287, DOI 10.1080/08982112.2023.2206479
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Moore J., 2023, TechTargetApril 27
   Neill C., 2023, History HitMarch 13
   Neubert MJ, 2020, BUS HORIZONS, V63, P195, DOI 10.1016/j.bushor.2019.11.001
   Ngai EWT, 2021, ELECTRON COMMER R A, V50, DOI 10.1016/j.elerap.2021.101098
   Nolan B., 2023, Business InsiderJune 19
   Notion A. I., 2024, Notion AI: Now with Q&A
   Novak M., 2023, ForbesFebruary 18
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   OpenAI, 2022, ChatGPT: Optimizing language models for dialogue
   OpenAI, 2024, Dall E 3
   OpenAI, 2024, ChatGPT-Release notes
   Osadchaya E, 2024, BUS HORIZONS, V67, P571, DOI 10.1016/j.bushor.2024.05.002
   Otter A. I., 2024, AI M NOT TAK REAL TI
   Parikh NA, 2023, Arxiv, DOI arXiv:2306.04605
   Perkins C., 2023, First lawsuits arrive addressing generative AI
   Perrigo B., 2023, TimeFebruary 17
   Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Quach K., 2023, The RegisterFebruary 8
   Ray S., 2023, ForbesOctober 5
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Ryan-Mosley T., 2023, MIT Technology ReviewJune 26
   Salesforce, 2023, More than half of generative AI adopters use unapproved tools at work
   Savarese S., 2023, SalesforceMarch 7
   Sayer P., 2023, CIONovember 16
   Singh H., 2023, Analytics VidhyaDecember 16
   Sivasubramanian S., 2023, commitment. AmazonNovember 20
   Sundberg L, 2024, BUS HORIZONS, V67, P561, DOI 10.1016/j.bushor.2024.04.014
   Supercreator A. I., 2024, Create videos 10x faster with AI
   Thorbecke C., 2022, CNN
   Tong SL, 2021, STRATEGIC MANAGE J, V42, P1600, DOI 10.1002/smj.3322
   Tredinnick L., 2023, Business Information Review, V40, P98
   Varshney N., 2023, MediumAugust 20
   Webber SS, 2019, BUS HORIZONS, V62, P741, DOI 10.1016/j.bushor.2019.07.007
   Wright SA, 2018, BUS HORIZONS, V61, P823, DOI 10.1016/j.bushor.2018.07.001
NR 87
TC 9
Z9 9
U1 47
U2 47
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 537
EP 548
DI 10.1016/j.bushor.2024.04.012
EA AUG 2024
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2K8K
UT WOS:001301351100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Liu, AL
   Wang, SF
AF Liu, Ailing
   Wang, Shaofeng
TI Generative artificial intelligence (GenAI) and entrepreneurial
   performance: implications for entrepreneurs
SO JOURNAL OF TECHNOLOGY TRANSFER
LA English
DT Article
DE Generative artificial intelligence; Entrepreneurial performance;
   Resource-based theory; Internal integration; External collaboration;
   Chinese university student entrepreneurs; O33; L26; M13; O32; M15
AB This study examines the impact of Generative Artificial Intelligence (GenAI) resources on entrepreneurial performance in China, focusing on internal integration and external collaboration mediating roles. Drawing upon Resource-Based Theory (RBT), this study proposes a theoretical model that outlines how tangible, intangible, and human resources related to GenAI affect entrepreneurial performance. GenAI internal integration and external collaboration serve as mediators. A purposive sampling technique was employed to collect data from Chinese university students who have initiated startups utilizing GenAI technologies. The Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was applied to analyze data from 491 respondents. Findings reveal that GenAI's tangible, intangible, and human resources significantly foster both internal integration and external collaboration, which, in turn, positively influence entrepreneurial performance. This study contributes to the entrepreneurship and management literature by elucidating the mechanism through which GenAI resources enhance entrepreneurial outcomes, and offers practical insights for entrepreneurs on leveraging GenAI resources to bolster internal and external collaborative efforts for improved performance.
C1 [Liu, Ailing] Ningbo Univ Technol, Coll Mech Engn, Ningbo 315211, Peoples R China.
   [Wang, Shaofeng] Fuzhou Univ Int Studies & Trade, Int Business Sch, Fuzhou 350202, Peoples R China.
   [Liu, Ailing] Hangzhou Normal Univ, Jinghengyi Sch Educ, Hangzhou, Peoples R China.
C3 Ningbo University of Technology; Hangzhou Normal University
RP Wang, SF (corresponding author), Fuzhou Univ Int Studies & Trade, Int Business Sch, Fuzhou 350202, Peoples R China.
EM liuailing913@outlook.com; vipwhsl@hotmail.com
RI Wang, Shaofeng/N-6103-2017
OI Wang, Shaofeng/0000-0002-0300-2453
FU Major Humanities and Social Sciences Research Projects in Zhejiang
   higher education institutions
FX Thank all respondents for participating in this survey. Thanks to our
   colleagues and Digital Tools for their help with the article's language.
CR Abaddi S, 2024, J ENTREP EMERG ECON, V16, P1903, DOI 10.1108/JEEE-07-2023-0260
   Amani D, 2024, INT J MANAG EDUC-OXF, V22, DOI 10.1016/j.ijme.2023.100915
   ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
   Audretsch DB, 2023, J TECHNOL TRANSFER, V48, P1535, DOI 10.1007/s10961-023-10034-w
   Baabdullah AM, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122951
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   Ben Youssef A, 2021, TECHNOL FORECAST SOC, V164, DOI 10.1016/j.techfore.2020.120043
   Chen DH, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.884830
   Colombelli A, 2023, J TECHNOL TRANSFER, V48, P1599, DOI 10.1007/s10961-023-10029-7
   Eisenhardt KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Ge BS, 2022, SYST RES BEHAV SCI, V39, P440, DOI 10.1002/sres.2850
   Giuggioli G, 2023, INT J ENTREP BEHAV R, V29, P816, DOI 10.1108/IJEBR-05-2021-0426
   Goel RK, 2024, J TECHNOL TRANSFER, DOI 10.1007/s10961-024-10089-3
   Gupta V., 2023, INTERNET REF SERV Q, P1, DOI DOI 10.1080/10875301.2023.2300114
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hilkenmeier F, 2021, J TECHNOL TRANSFER, DOI 10.1007/s10961-021-09913-x
   Huang M, 2022, CHINA WORLD ECON, V30, P135, DOI 10.1111/cwe.12421
   KANBACH DK, 2023, REV MANAG SCI, P1
   Khalid N, 2020, J INTELL FUZZY SYST, V39, P5417, DOI 10.3233/JIFS-189026
   Li DY, 2023, ASIA PAC BUS REV, V29, P967, DOI 10.1080/13602381.2023.2188764
   Lin HF, 2008, TECHNOVATION, V28, P135, DOI 10.1016/j.technovation.2007.10.003
   Lin HF, 2022, TECHNOL SOC, V68, DOI 10.1016/j.techsoc.2021.101833
   Link AN, 2023, FOUND TRENDS ENTREP, V19, P590, DOI 10.1561/0300000127
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Marques CS, 2024, J TECHNOL TRANSFER, DOI 10.1007/s10961-024-10084-8
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Obschonka M, 2020, SMALL BUS ECON, V55, P529, DOI 10.1007/s11187-019-00202-4
   PETERAF MA, 1993, STRATEGIC MANAGE J, V14, P179, DOI 10.1002/smj.4250140303
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Rajaram K., 2024, BUS HORIZONS
   Rana NP, 2024, TECHNOVATION, V135, DOI 10.1016/j.technovation.2024.103064
   Ringle CM, 2016, IND MANAGE DATA SYST, V116, P1865, DOI 10.1108/IMDS-10-2015-0449
   Shepherd DA, 2022, J BUS VENTURING, V37, DOI 10.1016/j.jbusvent.2022.106227
   Singh K, 2024, TECHNOVATION, V133, DOI 10.1016/j.technovation.2024.103021
   Tran H, 2023, J SMALL BUS ENTERP D, V30, P853, DOI 10.1108/JSBED-09-2023-508
   Upadhyay N, 2023, INT J ENTREP BEHAV R, V29, P80, DOI 10.1108/IJEBR-02-2022-0154
   Upadhyay N, 2022, INT J ENTREP BEHAV R, V28, P1138, DOI 10.1108/IJEBR-01-2021-0052
   Vecchiarini M, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100879
   Wales WJ, 2023, J TECHNOL TRANSFER, V48, P1752, DOI 10.1007/s10961-023-10021-1
   Wamba SF, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2022.102544
   Wang S., 2024, J CLEAN PROD
   Wang SF, 2023, J CLEAN PROD, V419, DOI 10.1016/j.jclepro.2023.137980
   Wang SW, 2023, J BIOPHARM STAT, DOI 10.1080/10543406.2023.2210684
   Winkler C., 2023, Entrepreneurship Education and Pedagogy, V6, P579
   Woollacott E., 2024, BBC NEWS
   Xu CJ, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.731713
   Yeh CH, 2021, INT J MANAG EDUC-OXF, V19, DOI 10.1016/j.ijme.2021.100565
NR 49
TC 2
Z9 2
U1 51
U2 51
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0892-9912
EI 1573-7047
J9 J TECHNOL TRANSFER
JI J. Technol. Transf.
PD DEC
PY 2024
VL 49
IS 6
SI SI
BP 2389
EP 2412
DI 10.1007/s10961-024-10132-3
EA SEP 2024
PG 24
WC Engineering, Industrial; Management
WE Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA P1O8S
UT WOS:001313590500001
DA 2024-12-25
ER

PT J
AU Park, HE
AF Park, Ha Eun (Grace)
TI The double-edged sword of generative artificial intelligence in
   digitalization: An affordances and constraints perspective
SO PSYCHOLOGY & MARKETING
LA English
DT Article
DE AI-generated content; ChatGPT; digitalization; generative AI; generative
   artificial intelligence; netnography; technology affordance theory;
   technology affordances and constraints
ID FUTURE; TECHNOLOGIES; INTERNET; THINGS; FIELD
AB Generative artificial intelligence (AI) has gained prominence across various industries and domains, offering capabilities to generate human-like text, creative ideas, and solutions. This paper explores customers' responses to the use of generative AI in digitalizing content production and consumption processes. Drawing on technology affordance theory, this article examines how are the affordances of generative AI leveraged to contribute to the gradual digitalization of individuals. This netnographic study is based on over 9 months naturalistic observations of the AI Community online, culminating in 1572 pages of data. The findings identify different types of affordances that foster digitalization: automated content creation, automated data analysis, and AI-generated content dissemination. This study also identifies the constraints of generative AI and discusses potential interventions to address these constraints and prevent unintended consequences. This research provides insights for scholars, professionals, and educators to better understand the dynamics of leveraging generative AI.
C1 [Park, Ha Eun (Grace)] Auckland Univ Technol, AUT Business Sch, 42 Wakefield St, Auckland 1010, New Zealand.
C3 Auckland University of Technology
RP Park, HE (corresponding author), Auckland Univ Technol, AUT Business Sch, 42 Wakefield St, Auckland 1010, New Zealand.
EM grace.park@aut.ac.nz
RI Park, Ha Eun/AGV-9932-2022
OI Park, Ha Eun/0000-0002-5418-5931
FX No Statement Available
CR Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Alsaleh DA, 2019, J RES INTERACT MARK, V13, P119, DOI 10.1108/JRIM-10-2017-0092
   Anderson C, 2017, INFORM ORGAN-UK, V27, P100, DOI 10.1016/j.infoandorg.2017.03.002
   Bozkurt V, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2297114
   Bygstad B, 2016, J INF TECHNOL-UK, V31, P83, DOI 10.1057/jit.2015.13
   Ciuchita R, 2022, J SERV MANAGE, V33, P688, DOI 10.1108/JOSM-10-2021-0407
   Cortez RM, 2017, IND MARKET MANAG, V66, P90, DOI 10.1016/j.indmarman.2017.07.017
   Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
   Davenport TH, 2018, HARVARD BUS REV, V96, P108
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   El Amri D, 2019, PSYCHOL MARKET, V36, P444, DOI 10.1002/mar.21189
   Faik I, 2020, MIS QUART, V44, P1359, DOI 10.25300/MISQ/2020/14193
   Guha A, 2021, J RETAILING, V97, P28, DOI 10.1016/j.jretai.2021.01.005
   Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Jäger AK, 2020, INT J RETAIL DISTRIB, V48, P803, DOI 10.1108/IJRDM-02-2019-0044
   Javornik A, 2016, J RETAIL CONSUM SERV, V30, P252, DOI 10.1016/j.jretconser.2016.02.004
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Kim JS, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2311971
   Klein HK, 1999, MIS QUART, V23, P67, DOI 10.2307/249410
   Kozinets R., 2020, Netnography: The Essential Guide to Qualitative Social Media Research, V3rd
   Kozinets RV, 2021, J CONSUM RES, V48, P428, DOI 10.1093/jcr/ucab014
   Kozinets RV, 2002, J MARKETING RES, V39, P61, DOI 10.1509/jmkr.39.1.61.18935
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Langley A, 1999, ACAD MANAGE REV, V24, P691, DOI 10.2307/259349
   Leminen S, 2018, J BUS IND MARK, V33, P749, DOI 10.1108/JBIM-10-2015-0206
   Leonardi PM, 2013, MIS QUART, V37, P749, DOI 10.25300/MISQ/2013/37.3.04
   Leonardi PM, 2011, MIS QUART, V35, P147
   Liu PJ, 2022, J ASSOC CONSUM RES, V7, P198, DOI 10.1086/718457
   Lo FY, 2018, TECHNOL FORECAST SOC, V137, P10, DOI 10.1016/j.techfore.2018.09.029
   Mahr D, 2022, J SERV MANAGE, V33, P648, DOI 10.1108/JOSM-03-2022-0075
   Majchrzak A., 2016, MIS Quarterly, V40, P267, DOI DOI 10.25300/MISQ/2016/40:2.03
   Majchrzak A., 2012, ENCY MANAGEMENT THEO
   Mardon R, 2023, J CONSUM RES, V50, P255, DOI 10.1093/jcr/ucac057
   Mariani MM, 2022, PSYCHOL MARKET, V39, P755, DOI 10.1002/mar.21619
   Martín-Peña ML, 2019, J BUS IND MARK, V35, P564, DOI 10.1108/JBIM-12-2018-0400
   Mustak M, 2021, J BUS RES, V124, P389, DOI 10.1016/j.jbusres.2020.10.044
   Naik P, 2020, IND MARKET MANAG, V89, P232, DOI 10.1016/j.indmarman.2020.03.010
   Ng ICL, 2017, INT J RES MARK, V34, P3, DOI 10.1016/j.ijresmar.2016.11.003
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Ozolins U, 2020, EXPERT REV PHARM OUT, V20, P69, DOI 10.1080/14737167.2020.1734453
   Parekh P, 2020, VIS COMPUT IND BIOME, V3, DOI 10.1186/s42492-020-00057-7
   Park M, 2020, J RETAIL CONSUM SERV, V52, DOI 10.1016/j.jretconser.2019.101912
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Petit O, 2019, J INTERACT MARK, V45, P42, DOI 10.1016/j.intmar.2018.07.004
   Piepponen A, 2022, J BUS RES, V150, P311, DOI 10.1016/j.jbusres.2022.05.017
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Castillo-Villar FR, 2023, MARK INTELL PLAN, V41, P124, DOI 10.1108/MIP-01-2022-0045
   Ritter T, 2020, IND MARKET MANAG, V86, DOI 10.1016/j.indmarman.2019.11.019
   Strauss A. L., 1990, BASICS QUALITATIVE R
   Strong DM, 2014, J ASSOC INF SYST, V15, P53
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Ballestar MT, 2019, REV MANAG SCI, V13, P589, DOI 10.1007/s11846-018-0316-x
   Vaast E, 2013, J COMPUT-MEDIAT COMM, V19, P78, DOI 10.1111/jcc4.12032
   Vahdat A, 2021, AUSTRALAS MARK J, V29, P187, DOI 10.1016/j.ausmj.2020.01.002
   van Esch P, 2021, PSYCHOL MARKET, V38, P1081, DOI 10.1002/mar.21494
   Volkoff O, 2013, MIS QUART, V37, P819, DOI 10.25300/MISQ/2013/37.3.07
   Wu Y, 2022, J ACAD MARKET SCI, V50, P429, DOI 10.1007/s11747-022-00837-y
   Yilmaz FGK, 2024, INT J HUM-COMPUT INT, V40, P8703, DOI 10.1080/10447318.2023.2288730
   Zhang H, 2018, ELECTRON COMMER RES, V18, P3, DOI 10.1007/s10660-017-9279-2
NR 61
TC 1
Z9 1
U1 155
U2 155
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0742-6046
EI 1520-6793
J9 PSYCHOL MARKET
JI Psychol. Mark.
PD NOV
PY 2024
VL 41
IS 11
BP 2924
EP 2941
DI 10.1002/mar.22094
EA AUG 2024
PG 18
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA I1Y9B
UT WOS:001287809800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Bearman, M
   Tai, J
   Dawson, P
   Boud, D
   Ajjawi, R
AF Bearman, Margaret
   Tai, Joanna
   Dawson, Phillip
   Boud, David
   Ajjawi, Rola
TI Developing evaluative judgement for a time of generative artificial
   intelligence
SO ASSESSMENT & EVALUATION IN HIGHER EDUCATION
LA English
DT Article
DE Generative artificial intelligence; evaluative judgement; assessment for
   learning; higher education
ID FEEDBACK
AB Generative artificial intelligence (AI) has rapidly increased capacity for producing textual, visual and auditory outputs, yet there are ongoing concerns regarding the quality of those outputs. There is an urgent need to develop students' evaluative judgement - the capability to judge the quality of work of self and others - in recognition of this new reality. In this conceptual paper, we describe the intersection between evaluative judgement and generative AI with a view to articulating how assessment practices can help students learn to work productively with generative AI. We propose three foci: (1) developing evaluative judgement of generative AI outputs; (2) developing evaluative judgement of generative AI processes; and (3) generative AI assessment of student evaluative judgements. We argue for developing students' capabilities to identify and calibrate quality of work - uniquely human capabilities at a time of technological acceleration - through existing formative assessment strategies. These approaches circumvent and interrupt students' uncritical usage of generative AI. The relationship between evaluative judgement and generative AI is more than just the application of human judgement to machine outputs. We have a collective responsibility, as educators and learners, to ensure that humans do not relinquish their roles as arbiters of quality.
C1 [Bearman, Margaret; Tai, Joanna; Dawson, Phillip; Boud, David; Ajjawi, Rola] Deakin Univ, Ctr Res Assessment & Digital Learning CRADLE, Melbourne, Australia.
   [Boud, David] Univ Technol Sydney, Fac Arts & Social Sci, Sydney, Australia.
   [Boud, David] Middlesex Univ, Work & Learning Res Ctr, London, England.
C3 Deakin University; University of Technology Sydney; Middlesex University
RP Bearman, M (corresponding author), Deakin Univ, Ctr Res Assessment & Digital Learning CRADLE, Melbourne, Australia.
EM margaret.bearman@deakin.edu.au
RI Tai, Joanna/AAV-9790-2020; Dawson, Phillip/F-6438-2010; Ajjawi,
   Rola/HRC-6132-2023; Bearman, Margaret/R-1191-2019; Boud,
   David/R-7498-2019
OI Boud, David/0000-0002-6883-2722; Tai, Joanna/0000-0002-8984-2671;
   Dawson, Phillip/0000-0002-4513-8287; Ajjawi, Rola/0000-0003-0651-3870;
   Bearman, Margaret/0000-0002-6862-9871
CR Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Aoun JE, 2017, ROBOT-PROOF: HIGHER EDUCATION IN THE AGE OF ARTIFICIAL INTELLIGENCE, P1
   Autor DH, 2003, Q J ECON, V118, P1279, DOI 10.1162/003355303322552801
   Barnett R, 2017, EDUC SCI, V7, DOI 10.3390/educsci7010038
   Bearman M., 2020, RE IMAGINING U ASSES, P49, DOI DOI 10.1007/978-3-030-41956-1_5
   Bearman M., 2018, DEVELOPING EVALUATIV, P147
   Bearman M, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13337
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Bearman M, 2023, ASSESS EVAL HIGH EDU, V48, P291, DOI 10.1080/02602938.2022.2069674
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Boud D., 2000, STUDIES INCONTINUING, V22, P151167, DOI [https://doi.org/10.1080/713695728, DOI 10.1080/713695728]
   Chen LW, 2022, ASSESS EVAL HIGH EDU, V47, P493, DOI 10.1080/02602938.2021.1933378
   Chong SW, 2021, ASIAN-PAC J SEC FOR, V6, DOI 10.1186/s40862-021-00115-4
   Dawson P., 2020, Re-imagining university assessment in a digital world, P37, DOI DOI 10.1007/978-3-030-41956-14
   De Mello Heredia J., 2023, EV JUDG SOC INF TECH
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Freeman J., 2024, PROVIDE PUNISH STUDE
   Guo Y, 2023, J CHEM EDUC, V100, P4876, DOI 10.1021/acs.jchemed.3c00505
   Gyamfi G, 2022, ASSESS EVAL HIGH EDU, V47, P126, DOI 10.1080/02602938.2021.1887081
   Kuhn D, 2000, COGNITIVE DEV, V15, P309, DOI 10.1016/S0885-2014(00)00030-7
   Lipnevich AA, 2009, EDUC ASSESS EVAL ACC, V21, P347, DOI 10.1007/s11092-009-9082-2
   Lipnevich AA, 2009, J EXP PSYCHOL-APPL, V15, P319, DOI 10.1037/a0017841
   Luo JH, 2023, ASSESS EVAL HIGH EDU, V48, P513, DOI 10.1080/02602938.2022.2088690
   Malecka B, 2023, TEACH HIGH EDUC, V28, P1761, DOI 10.1080/13562517.2021.1928061
   Markauskaite L., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022.100056, 10.1016/j.caeai.2022.100056]
   McIver S, 2023, ACT LEARN HIGH EDUC, V24, P207, DOI 10.1177/14697874211054755
   Molloy E, 2019, MED EDUC, V53, P32, DOI 10.1111/medu.13649
   Nam J., 2023, BESTCOLLEGES    1122
   Nicol D, 2021, ASSESS EVAL HIGH EDU, V46, P756, DOI 10.1080/02602938.2020.1823314
   Prather J, 2023, PROCEEDINGS OF THE 2023 WORKING GROUP REPORTS ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE-WGR 2023, DOI 10.1145/3623762.3633499
   SADLER DR, 1989, INSTR SCI, V18, P119, DOI 10.1007/BF00117714
   Siiman L. A., 2023, INT C INN TECHN LEAR
   Skeat N., 2023, SEMESTER, V1
   Tai J, 2018, HIGH EDUC, V76, P467, DOI 10.1007/s10734-017-0220-3
   Telio S, 2016, MED EDUC, V50, P933, DOI 10.1111/medu.13063
   Watling C, 2012, MED EDUC, V46, P192, DOI 10.1111/j.1365-2923.2011.04126.x
   Welding L., 2023, BESTCOLLEGES    0323
NR 37
TC 5
Z9 5
U1 58
U2 91
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0260-2938
EI 1469-297X
J9 ASSESS EVAL HIGH EDU
JI Assess. Eval. High. Educ.
PD AUG 17
PY 2024
VL 49
IS 6
BP 893
EP 905
DI 10.1080/02602938.2024.2335321
EA MAR 2024
PG 13
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA E4M7Z
UT WOS:001199939900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Tang, KS
AF Tang, Kok-Sing
TI Informing research on generative artificial intelligence from a language
   and literacy perspective: A meta-synthesis of studies in science
   education
SO SCIENCE EDUCATION
LA English
DT Article
DE generative AI; language and literacy of science; multilingualism;
   multimodality; reading and writing
ID MEANING-MAKING; SCHOOL SCIENCE; STUDENTS; FRAMEWORK; REPRESENTATIONS;
   PARTICIPATION; CONSTRUCTION; INSTRUCTION; AWARENESS; EVERYDAY
AB Research in languages and literacies in science education (LLSE) has developed substantial theoretical and pedagogical insights into how students learn science through language, discourse, and multimodal representations. At the same time, language is central to the functioning of generative artificial intelligence (GenAI). On this common basis concerning the role of language, this paper explores how foundational ideas from LLSE studies can inform the use of GenAI in science education. A bibliometric analysis of 412 journal articles from Web of Science provided the initial step to identify major themes and relationships in the LLSE literature. The analysis revealed four clusters of research in LLSE: reading and writing scientific text, science discourse and interaction, multilingual science classroom, and multimodality and representations. Each cluster was further analyzed through close reading of selected articles to identify and connect key constructs to the potential use of GenAI. These constructs include the interactive-constructive reading model, text genre, reading-writing integration, dialogic interaction, critical questioning, argumentation, translanguaging, hybridity, thematic pattern, modal affordance, and transduction. From these ideas and connections, the paper recommends several pedagogical principles for science educators to guide the use of GenAI. It concludes that LLSE research offers valuable insights for researchers and teachers to investigate and design the use of GenAI in science education. In turn, the impending use of GenAI also calls for a rethinking of literacy that will shape future research in LLSE.
C1 [Tang, Kok-Sing] Curtin Univ, Sch Educ, GPO Box U1987, Perth, WA 6845, Australia.
C3 Curtin University
RP Tang, KS (corresponding author), Curtin Univ, Sch Educ, GPO Box U1987, Perth, WA 6845, Australia.
EM kok-sing.tang@curtin.edu.au
RI Tang, Kok-Sing/I-3245-2019
CR Ahmad N, 2023, COMPUTER, V56, P72, DOI 10.1109/MC.2023.3263576
   Anderson C.W., 2007, Handbook of research on science education, P3
   Bakhtin M., 1986, SPEECH GENRES OTHER, P60, DOI DOI 10.1080/02699206.2018.1498541
   Bakhtin M., 1981, DIALOGIC IMAGINATION
   Bezerner J, 2008, WRIT COMMUN, V25, P166, DOI 10.1177/0741088307313177
   Botpress, 2023, LIST LANG SUPP CHATG
   Braun I, 2014, SCI EDUC, V98, P867, DOI 10.1002/sce.21117
   Brown BA, 2008, SCI EDUC, V92, P708, DOI 10.1002/sce.20251
   Bruna KR, 2009, LANG CULT TEACH, P167
   Buxton C A., 2023, Handbook of research on science education, V1st, P291, DOI [DOI 10.4324/9780367855758-13, 10.4324/9780367855758]
   Byhring AK, 2016, RES SCI EDUC, V46, P1, DOI 10.1007/s11165-014-9454-6
   Charamba E, 2020, INT J SCI EDUC, V42, P1779, DOI 10.1080/09500693.2020.1783019
   Cheng Maurice M. W., 2020, Learning: Research and Practice, V6, P70, DOI [https://doi.org/10.1080/23735082.2020.1750675, DOI 10.1080/23735082.2020.1750675]
   Cheuk T, 2021, SCI EDUC, V105, P825, DOI 10.1002/sce.21671
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Council of Chief State School Officers, 2010, COUNCIL CHIEF STATE
   Danielsson K, 2023, SCI EDUC, V107, P1561, DOI 10.1002/sce.21814
   Deng Y, 2019, INT J SCI EDUC, V41, P1408, DOI 10.1080/09500693.2019.1610811
   Deutscher M., 2023, MICROSOFT COPILOT RE
   diSessa AA, 2004, COGNITION INSTRUCT, V22, P293, DOI 10.1207/s1532690xci2203_2
   Dori YJ, 2023, J SCI EDUC TECHNOL, V32, P962, DOI 10.1007/s10956-022-10015-y
   Enghag M, 2007, RES SCI EDUC, V37, P449, DOI 10.1007/s11165-006-9035-4
   Erwin EJ, 2011, J EARLY INTERVENTION, V33, P186, DOI 10.1177/1053815111425493
   ESERA, 2023, ESERA SIG 6 LANG LIT
   Fang ZH, 2005, SCI EDUC, V89, P335, DOI 10.1002/sce.20050
   Felton M, 2022, SCI EDUC, V106, P1354, DOI 10.1002/sce.21740
   Flavell J.H., 2002, COGNITIVE DEV
   Garcia Ofelia., 2009, BILINGUAL ED 21 CENT, DOI DOI 10.1093/ACPROF:OSO/9780198237907.001.0001
   Gee J.P., 2005, Situated literacies: Reading and writing in context, V2, P177
   González-Howard M, 2020, SCI EDUC, V104, P953, DOI 10.1002/sce.21592
   Gulen K., 2023, TRACING EVOLUTION RE
   Haglund J, 2013, STUD SCI EDUC, V49, P35, DOI 10.1080/03057267.2013.801119
   Halliday M.A.K., 1993, Writing Science: Literacy and Discursive Power
   Halliday M.A. K., 1993, Writing science: Literacy and discursive power, P54
   Halliday M. A. K., 1978, LANGUAGE SOCIAL SEMI
   Hand B, 1999, INT J SCI EDUC, V21, P1021, DOI 10.1080/095006999290165
   Hand B., 2008, Science inquiry, argument and language: A case for the science writing heuristic
   Hand BM, 2003, J RES SCI TEACH, V40, P607, DOI 10.1002/tea.10101
   Hand B, 2018, SCI EDUC, V102, P693, DOI 10.1002/sce.21341
   Hoettecke D, 2020, SCI EDUC, V104, P641, DOI 10.1002/sce.21575
   HOLLIDAY WG, 1994, J RES SCI TEACH, V31, P877, DOI 10.1002/tea.3660310905
   Karlsson A, 2020, CULT STUD SCI EDUCAT, V15, P1, DOI 10.1007/s11422-019-09933-y
   Karlsson A, 2019, INT J SCI EDUC, V41, P2049, DOI 10.1080/09500693.2018.1477261
   Kelly GJ, 2021, RES SCI EDUC, V51, P225, DOI 10.1007/s11165-020-09984-0
   Kelly GJ, 2008, ENCYCLOPEDIA OF LANGUAGE AND EDUCATION, VOL 3: DISCOURSE AND EDUCATION, P329
   Kloser M, 2023, INT J SCI EDUC, V45, P895, DOI 10.1080/09500693.2023.2177126
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kress G., 2006, READING IMAGES GRAMM
   Kress G., 2001, MULTIMODAL TEACHING
   Lammers A, 2019, INT J SCI EDUC, V41, P2323, DOI 10.1080/09500693.2019.1675197
   Latour B., 1979, Laboratory Life: The Construction of Scientific Facts
   Leary H, 2018, TECHTRENDS, V62, P525, DOI 10.1007/s11528-018-0312-7
   Lee O, 2008, SCI EDUC, V92, P733, DOI 10.1002/sce.20255
   Lehesvuori S, 2019, INT J SCI EDUC, V41, P2557, DOI 10.1080/09500693.2019.1689586
   Lemke J., 1998, Teaching all the languages of science: Words, symbols, images, and actions
   Lemke J., 1990, Talking Science: Language, Learning and Values
   Lemke J., 1998, Reading Science: Critical and Functional Perspectives on the Discourses of Science, P87, DOI DOI 10.4324/9780203982327
   Lemke JL, 2001, J RES SCI TEACH, V38, P296, DOI 10.1002/1098-2736(200103)38:3<296::AID-TEA1007>3.0.CO;2-R
   Li TT, 2023, J RES SCI TEACH, V60, P1385, DOI 10.1002/tea.21867
   Licona PR, 2020, CULT STUD SCI EDUCAT, V15, P485, DOI 10.1007/s11422-019-09946-7
   Lin VSY, 1997, SCIENCE, V278, P840, DOI 10.1126/science.278.5339.840
   Linderoth C, 2024, POLICY FUTURES EDUC, V22, P1662, DOI 10.1177/14782103241228900
   Lo YY, 2019, J IMMERS CONTENT-BAS, V7, P151, DOI 10.1075/jicb.00006.lo
   Martínez-Alvarez P, 2018, LINGUIST EDUC, V47, P68, DOI 10.1016/j.linged.2018.08.003
   Martins M, 2022, SCI EDUC, V106, P573, DOI 10.1002/sce.21708
   Mayweg-Paus E, 2016, INT J EDUC RES, V79, P195, DOI 10.1016/j.ijer.2016.05.017
   McNeill KL, 2017, SCI EDUC, V101, P426, DOI 10.1002/sce.21274
   Mejia Cristian, 2021, Front Res Metr Anal, V6, P742311, DOI 10.3389/frma.2021.742311
   Memarian B., 2023, COMPUT HUM BEHAV, V1, P100022, DOI [10.1016/j.chbah.2023.100022, DOI 10.1016/J.CHBAH.2023.100022, https://doi.org/10.1016/j.chbah.2023.100022]
   Milne C., 2019, Material practice and materiality. Too long ignored in science education
   Mody CCM, 2015, SCI EDUC, V99, P1026, DOI 10.1002/sce.21190
   Moje EB, 2007, REV RES EDUC, V31, P1, DOI 10.3102/0091732X07300046001
   Mortimer EF., 2003, Meaning making in secondary science classrooms
   National Research Council, 2014, LIT SCI EXPL INT NEX
   Nielsen W, 2022, RES SCI EDUC, V52, P871, DOI 10.1007/s11165-021-10038-2
   Nigro RG, 2022, INT J SCI EDUC, V44, P1792, DOI 10.1080/09500693.2022.2095681
   Norris SP, 2003, SCI EDUC, V87, P224, DOI 10.1002/sce.10066
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Park J, 2021, SCI EDUC, V105, P1013, DOI 10.1002/sce.21668
   Porayska-Pomsta K, 2016, INT J ARTIF INTELL E, V26, P679, DOI 10.1007/s40593-016-0101-4
   Pozzer L, 2020, CULT STUD SCI EDUCAT, V15, P31, DOI 10.1007/s11422-019-09910-5
   Prain V, 2022, RES SCI EDUC, V52, P805, DOI 10.1007/s11165-021-10025-7
   Prain V, 2012, INT J SCI EDUC, V34, P2751, DOI 10.1080/09500693.2011.626462
   Pun JKH, 2023, RES SCI TECHNOL EDUC, V41, P271, DOI 10.1080/02635143.2021.1895101
   Quílez J, 2021, INT J SCI EDUC, V43, P1459, DOI 10.1080/09500693.2021.1918794
   Roth WM, 1997, INT J SCI EDUC, V19, P1075, DOI 10.1080/0950069970190906
   Rudsberg K, 2017, CULT STUD SCI EDUCAT, V12, P709, DOI 10.1007/s11422-015-9721-5
   Ryu M, 2019, INT J SCI EDUC, V41, P1303, DOI 10.1080/09500693.2019.1605229
   Saul W., 2004, CROSSING BORDERS LIT
   Sidar C., 2023, FORBES
   Singh G, 2019, CULT STUD SCI EDUCAT, V14, P643, DOI 10.1007/s11422-018-9866-0
   Siry C, 2020, ASIA-PAC SCI EDUC, V6, P346, DOI 10.1163/23641177-bja10017
   Sjoberg M, 2023, SCI EDUC, V107, P124, DOI 10.1002/sce.21765
   Solli A, 2019, RES SCI EDUC, V49, P1595, DOI 10.1007/s11165-017-9668-5
   Southgate E., 2018, ART INTELLIGENCE EME
   Stina CK, 2023, J RES SCI TEACH, V60, P2395, DOI 10.1002/tea.21913
   Stone P., 2016, One hundred year study on artificial intelligence: Report of the 2015-2016 study panel
   Tang K.-S., 2020, DISCOURSE STRATEGIES, DOI [10.4324/9780429352171a, DOI 10.4324/9780429352171A]
   Tang K.-S., 2024, AUSTR SCI ED ASS C
   Tang KS, 2024, SCI EDUC-NETHERLANDS, DOI 10.1007/s11191-024-00508-0
   Tang KS, 2022, J RES SCI TEACH, V59, P969, DOI 10.1002/tea.21749
   Tang KS, 2021, INT J SCI MATH EDUC, V19, P1311, DOI 10.1007/s10763-020-10121-6
   Tang KS, 2020, COGNITION INSTRUCT, V38, P474, DOI 10.1080/07370008.2020.1745803
   Tang KS, 2019, REV EDUC-US, V7, P675, DOI 10.1002/rev3.3162
   Tang KS, 2019, J IMMERS CONTENT-BAS, V7, P315, DOI 10.1075/jicb.00007.tan
   Tang KS, 2017, CLASSR DISCOURSE, V8, P19, DOI 10.1080/19463014.2016.1263576
   Tang KS, 2016, INT J SCI EDUC, V38, P1415, DOI 10.1080/09500693.2016.1192309
   Tang KS, 2014, SCI EDUC, V98, P305, DOI 10.1002/sce.21099
   Tang KS, 2011, INT J SCI EDUC, V33, P1775, DOI 10.1080/09500693.2010.508899
   Tang KS, 2010, RES SCI EDUC, V40, P81, DOI 10.1007/s11165-009-9158-5
   Tytler R, 2020, J RES SCI TEACH, V57, P209, DOI 10.1002/tea.21590
   Ünsal Z, 2018, RES SCI EDUC, V48, P1027, DOI 10.1007/s11165-016-9597-8
   Vygotsky L S., 1986, Thought and language ,translated and
   Wang JR, 2014, J RES SCI TEACH, V51, P175, DOI 10.1002/tea.21131
   Wellington J., 2001, Language and literacy in science education
   Wilmes SED, 2024, RES SCI TECHNOL EDUC, V42, P1, DOI 10.1080/02635143.2024.2309058
   Wilmes SED, 2018, SCI EDUC, V102, P1107, DOI 10.1002/sce.21462
   Wilson RE, 2021, INT J SCI EDUC, V43, P1341, DOI 10.1080/09500693.2021.1909774
   Xu LH, 2021, INT J SCI MATH EDUC, V19, P1167, DOI 10.1007/s10763-020-10102-9
   Yarden A, 2001, J BIOL EDUC, V35, P190, DOI 10.1080/00219266.2001.9655776
   Yore L., 2018, Global developments in literacy research for science education
   Yore L., 2011, PACIFIC CRYSTAL CTR
   Yore LD, 2022, INT J SCI MATH EDUC, V20, P237, DOI 10.1007/s10763-022-10321-2
   Yore LD, 2003, INT J SCI EDUC, V25, P689, DOI 10.1080/09500690305018
   Yore LD, 1998, J RES SCI TEACH, V35, P27, DOI 10.1002/(SICI)1098-2736(199801)35:1<27::AID-TEA3>3.0.CO;2-P
   Zhai X., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4331313, DOI 10.2139/SSRN.4331313]
   Zhai XM, 2023, J RES SCI TEACH, V60, P1390, DOI 10.1002/tea.21885
   Zohar A, 2013, STUD SCI EDUC, V49, P121, DOI 10.1080/03057267.2013.847261
NR 129
TC 5
Z9 5
U1 71
U2 102
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0036-8326
EI 1098-237X
J9 SCI EDUC
JI Sci. Educ.
PD SEP
PY 2024
VL 108
IS 5
BP 1329
EP 1355
DI 10.1002/sce.21875
EA APR 2024
PG 27
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA A7T9D
UT WOS:001209422500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Kumar, S
   Gunn, A
   Rose, R
   Pollard, R
   Johnson, M
   Ritzhaupt, AD
AF Kumar, Swapna
   Gunn, Ariel
   Rose, Robert
   Pollard, Rhiannon
   Johnson, Margeaux
   Ritzhaupt, Albert D.
TI The Role of Instructional Designers in the Integration of Generative
   Artificial Intelligence in Online and Blended Learning in Higher
   Education
SO ONLINE LEARNING
LA English
DT Article
DE Instructional designers; generative artificial intelligence; higher
   education; qualitative; online education
AB The purpose of this exploratory research study was to examine the roles instructional designers (IDs) play in the integration of generative Artificial Intelligence (GenAI) into their higher education institutions, and how they use GenAI technologies in their own professional practices. Data were collected from 15 participants in the United States (U.S.) in an ID role or with similar job titles (e.g., educational technologist). Using a general qualitative approach, semi- structured interviews were conducted in Zoom about IDs' use and integration of GenAI. Our analysis resulted in three primary themes related to IDs' inte gration of GenAI in online and blended education: (a) the use of GenAI for instructional design; (b) collaborative guidance for faculty integration of GenAI; and (c) training, resources, and guidelines on the integration of GenAI. A common thread through a ll the themes was IDs' conscientious and cautious approach and ethical concerns about GenAI integration. We unpack these themes and discuss the implications of IDs in higher education integrating GenAI to meet organizational, faculty, and student needs.
C1 [Kumar, Swapna; Gunn, Ariel; Rose, Robert; Pollard, Rhiannon; Johnson, Margeaux; Ritzhaupt, Albert D.] Univ Florida, Gainesville, FL 32611 USA.
C3 State University System of Florida; University of Florida
RP Kumar, S (corresponding author), Univ Florida, Gainesville, FL 32611 USA.
OI Kumar, Swapna/0000-0003-1151-7593
CR Alkhatlan A, 2018, Arxiv, DOI [arXiv:1812.09628, DOI 10.5120/IJCA2019918451]
   Anderson MC, 2019, MED SCI EDUC, V29, P507, DOI 10.1007/s40670-019-00720-6
   Baranwal D., 2022, PEDAGOGICAL RES, V7, DOI [10.29333/pr/11553, DOI 10.29333/PR/11553]
   Bolick AD., 2024, TechTrends, V68, P91, DOI [10.1007/s11528-023-00894-2, DOI 10.1007/S11528-023-00894-2]
   Bond A., 2023, Educause Review
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P1, DOI DOI 10.5281/ZENODO.7755273
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Brito F., 2017, International Journal of Educational and Pedagogical Sciences, V11, P1752, DOI DOI 10.5281/ZENODO.1340296
   Chng LK., 2023, Asian Journal of Distance Education, V18, P32, DOI [10.5281/zenodo.8188576, DOI 10.5281/ZENODO.8188576]
   Conijn R, 2023, J LEARN ANAL, V10, DOI 10.18608/jla.2023.7801
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Damiano Amanda D., 2024, Journal of Educational Technology Systems, V52, P346, DOI 10.1177/00472395241233290
   Goksel N., 2019, Handbook of research on learning in the age of transhumanism, P224, DOI [10.4018/978-1-5225-8431-5.ch014, DOI 10.4018/978-1-5225-8431-5.CH014]
   González-Calatayud V, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125467
   Green B. P., 2020, Artificial Intelligence and Ethics: Sixteen Challenges and Opportunities
   Lincoln Y.S., 1986, NATURALISTIC EVALUAT, V30, P73, DOI [DOI 10.1002/EV.142, https://doi.org/10.1002/ev.1427, DOI 10.1002/EV.1427]
   Ji H, 2023, J RES TECHNOL EDUC, V55, P48, DOI 10.1080/15391523.2022.2142873
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kenny R.F., 2005, CAN J LEARN TECHNOL, V31
   Kim M, 2024, TECHTRENDS, V68, P37, DOI 10.1007/s11528-023-00899-x
   Kln S., 2023, Asian J. Distance Educ, V18, P205, DOI [10.5281/zenodo.7857396, DOI 10.5281/ZENODO.7857396]
   Kumar S., 2017, INT J E LEARNING, V16, P371
   Mao J, 2024, TECHTRENDS, V68, P58, DOI 10.1007/s11528-023-00911-4
   Marino MT, 2023, J SPEC EDUC TECHNOL, V38, P404, DOI 10.1177/01626434231165977
   McDonald J., 2007, Instructional design case studies in communities of practice, P170, DOI [10.4018/978-1-59904-322-7.ch009, DOI 10.4018/978-1-59904-322-7.CH009]
   Merriam S.B., 2015, QUALITATIVE RES GUID
   Moore S, 2024, TECHTRENDS, V68, P27, DOI 10.1007/s11528-023-00895-1
   Park J.Y., 2017, INT ED STUDIES, V10, P87, DOI DOI 10.5539/IES.V10N9P87
   Park Y, 2024, INT REV RES OPEN DIS, V25, P164
   Patton M. Q., 2002, QUALITATIVE RES EVAL, DOI 10.1002/nur.4770140111
   Pelletier K., 2021, 2021 EDUCAUSE Horizon Report
   Pollard R., 2022, The Journal of Applied Instructional Design, V11, P7, DOI DOI 10.59668/354.5896
   Rahman MR, 2024, EDUC INF TECHNOL, V29, P7361, DOI 10.1007/s10639-023-11866-7
   Ritzhaupt AD., 2021, A practitioner's guide to instructional design in higher education
   Ritzhaupt AD, 2015, PERFORM IMPROV Q, V28, P51, DOI 10.1002/piq.21196
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Samala A D., 2024, International Journal of Interactive Mobile Technologies, V18, P96, DOI [10.3991/ijim.v18i02.46509, DOI 10.3991/IJIM.V18I02.46509]
   Schwier R.A., 2007, Instructional design: Case studies in communities of practice, P1, DOI DOI 10.4018/978-1-59904-322-7.CH001
   Terwiesch C., 2023, Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
   Wang M., 2023, ADV ED TECHNOLOGY PS, V7, P128, DOI DOI 10.23977/AETP.2023.070219
   Wang XM, 2021, INT J TRAIN DEV, V25, P95, DOI 10.1111/ijtd.12209
   Xie JR, 2021, DISTANCE EDUC, V42, P70, DOI 10.1080/01587919.2020.1869526
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhang K., 2021, COMPUTERS ED ARTIFIC, V2, P100025, DOI [DOI 10.1016/J.CAEAI.2021.100025, https://doi.org/10.1016/j.caeai.2021.100025, 10.1016/j.caeai.2021.100025]
NR 48
TC 1
Z9 1
U1 24
U2 24
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD SEP
PY 2024
VL 28
IS 3
BP 207
EP 231
DI 10.24059/olj.v28i3.4501
PG 25
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA F3Z1S
UT WOS:001309228000009
OA gold
DA 2024-12-25
ER

PT J
AU Almassaad, A
   Alajlan, H
   Alebaikan, R
AF Almassaad, Ahmad
   Alajlan, Hayat
   Alebaikan, Reem
TI Student Perceptions of Generative Artificial Intelligence: Investigating
   Utilization, Benefits, and Challenges in Higher Education
SO SYSTEMS
LA English
DT Article
DE generative AI; AI ethics; perceptions; higher education
ID FIT
AB This research explores the use of Generative Artificial Intelligence (GenAI) tools among higher education students in Saudi Arabia, aiming to understand their current perceptions of these technologies. This study utilizes the Technology Acceptance Model (TAM) and the theory of Task-Technology Fit (TTF) to examine students' utilization, perceived benefits, and challenges associated with these tools. A cross-sectional survey was conducted, yielding 859 responses. The findings indicate that 78.7% of students frequently use GenAI tools, while 21.3% do not, often due to a lack of knowledge or interest. ChatGPT emerged as the most widely used GenAI tool, utilized by 86.2% of respondents, followed by other tools like Gemini, Socratic, and CoPilot. Students primarily use these tools for defining or clarifying concepts, translation, generating ideas in writing, and summarizing academic literature. They cite benefits such as ease of access, time-saving, and instant feedback. However, they express concerns about the challenges, including subscription fees, unreliable information, plagiarism, reduced human-to-human interaction, and impacts on learning autonomy. This study underscores the need for increased awareness, ethical guidelines, and robust academic integrity measures to ensure the responsible use of GenAI tools in educational settings. These findings highlight the need for a balanced utilization of GenAI tools in higher education that maximizes benefits while addressing potential challenges and guides the development of policies, curricula, and support systems.
C1 [Almassaad, Ahmad; Alajlan, Hayat; Alebaikan, Reem] King Saud Univ, Curriculum & Instruct Dept, Comp Educ Sect, Riyadh 11451, Saudi Arabia.
C3 King Saud University
RP Alajlan, H (corresponding author), King Saud Univ, Curriculum & Instruct Dept, Comp Educ Sect, Riyadh 11451, Saudi Arabia.
EM aalmassaad@ksu.edu.sa; hayatajlan@ksu.edu.sa; ebaikan@ksu.edu.sa
RI Almassaad, Ahmad/KHY-7272-2024; Alebaikan, Reem/E-5032-2018
OI Almassaad, Ahmad/0000-0002-8152-7490; ALEBAIKAN,
   REEM/0000-0002-4563-685X; Alajlan, Hayat/0000-0002-5668-8348
FU RESEARCHERS SUPPORTING PROGRAM, King Saud University, Riyadh, Saudi
   Arabia [RSPD2024R874]; RESEARCHERS SUPPORTING PROGRAM; King Saud
   University, Riyadh, Saudi Arabia
FX This research was funded by the RESEARCHERS SUPPORTING PROGRAM, Project
   number (RSPD2024R874), King Saud University, Riyadh, Saudi Arabia.
CR Alier M, 2024, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2024.02.011
   anthology, Anthology AI in Higher Ed: Hype, Harm, or Help|Anthology
   Basit T.N., 2010, Conducting research in educational contexts
   Bates T, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-00218-x
   Chan C.K.Y., 2024, GENERATIVE AI HIGHER
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   chegg, Chegg.org Over Half (55%) of Undergraduate Students Worldwide Want Involvement of Human Expertise in GenAI, According to New Global Survey
   Chen JJ, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su152416928
   Creswell, 2012, ED RES PLANNING COND
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Emkan Education, 2024, AI In Education: Navigating from Policy to Practice for a Future-Ready KSA
   Farhi F., 2023, COMPUTERS ED ARTIFIC, V100180, DOI [10.1016/j.caeai.2023.100180, DOI 10.1016/J.CAEAI.2023.100180, https://doi.org/10.1016/j.caeai.2023.100180]
   Fischer I., 2023, Transforming Higher Education How We Can Harness AI in Teaching and Assessments and Uphold Academic Rigour and Integrity
   García-Peñalvo FJ, 2023, EDUC KNOWL SOC, V24, DOI 10.14201/eks.31279
   GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
   Grájeda A, 2024, COGENT EDUC, V11, DOI 10.1080/2331186X.2023.2287917
   Harry A., 2023, Interdiciplinary Journal and Humanity (INJURITY), V2, P260, DOI [10.58631/injurity.v2i3.52, DOI 10.58631/INJURITY.V2I3.52]
   Hosseini M, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0292216
   Ismail F., 2023, Journal of Applied Learning and Teaching, V6, P56, DOI [10.37074/jalt.2023.6.2.34, DOI 10.37074/JALT.2023.6.2.34]
   JISC, 2023, National Centre for AI in Tertiary Education: Student Perceptions of Generative AI
   Johnston H, 2024, INT J EDUC INTEGR, V20, DOI 10.1007/s40979-024-00149-4
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   ksu, KSU Open Data Data of Enrolled Students
   KSU College of Computer and Information Science, Department of Computer Science: About the Department
   KSU (King Saud University), History
   KSU (King Saud University), Colleges
   Mai DTT, 2024, FRONT EDUC, V9, DOI 10.3389/feduc.2024.1328769
   Malmstrm H., 2023, Chatbots and Other AI for Learning: A Survey of Use and Views among University Students in Sweden, DOI [10.17196/CLS.CSCLHE/2023/01, DOI 10.17196/CLS.CSCLHE/2023/01]
   Marikyan D., 2023, TECHNOLOGY ACCEPTANC
   2023, Arxiv, DOI [arXiv:2310.03715, DOI 10.48550/ARXIV.2310.03715, 10.48550/arXiv.2310.03715]
   Ngo T. T. A., 2023, Int. J. Emerg. Technol. Learn., V18, P4, DOI [DOI 10.3991/IJET.V18I17.39019, https://doi.org/10.3991/ijet.v18i17.39019, 10.3991/ijet.v18i14.39903, DOI 10.3991/IJET.V18I14.39903]
   Pan ZL, 2024, SYSTEMS-BASEL, V12, DOI 10.3390/systems12050176
   Popenici Stefan A D, 2017, Res Pract Technol Enhanc Learn, V12, P22, DOI 10.1186/s41039-017-0062-8
   Saudi Data & AI Authority (SDAIA), The Saudi Academic Framework for AI Qualifications (Education Intelligence)
   Saudi Data & AI Authority (SDAIA), Generative AI in Education
   Stohr C., 2024, Comput. Educ. Artif. Intell., V7, DOI [10.1016/j.caeai.2024.100259, DOI 10.1016/J.CAEAI.2024.100259]
   Strzelecki A, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13425
   The Open Innovation Team and Department for Education, 2024, Generative AI in Education Educator and Expert Views
   UNESCO, Guidance for Generative AI in Education and Research
   UNESCO, ChatGPT and Artificial Intelligence in Higher Education: Quick Start Guide
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   von Garrel J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02304-7
   Western Canadian Deans of Graduate Studies (WCDGS), 2023, Working Group Report
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhou X, 2024, J UNIV TEACH LEARN P, V21
   Zhu CJ, 2023, KNOWL MANAG E-LEARN, V15, P133, DOI 10.34105/j.kmel.2023.15.008
   Zigurs I, 1998, MIS QUART, V22, P313, DOI 10.2307/249668
NR 47
TC 0
Z9 0
U1 36
U2 36
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-8954
J9 SYSTEMS-BASEL
JI Systems-Basel
PD OCT
PY 2024
VL 12
IS 10
AR 385
DI 10.3390/systems12100385
PG 16
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA K2C2N
UT WOS:001342002500001
OA gold
DA 2024-12-25
ER

PT J
AU Bordas, A
   Le Masson, P
   Thomas, M
   Weil, B
AF Bordas, Antoine
   Le Masson, Pascal
   Thomas, Maxime
   Weil, Benoit
TI What is generative in generative artificial intelligence? A design-based
   perspective
SO RESEARCH IN ENGINEERING DESIGN
LA English
DT Article
DE Generative artificial intelligence; Generativity; Design theory;
   Artificial intelligence
AB Generative artificial intelligence (GenAI) models have attracted tremendous interest since the advent of ChatGPT, raising numerous opportunities and challenges. However, their generative power has not yet been studied, leaving open the question of what is truly generated by these tools. This paper addresses this question and precisely characterizes the generativity behind GenAI models. Owing to the latest advancements in engineering design, we first propose a framework for uncovering the various types of generativity. Then, we consider the main families of GenAI models and systematically analyze them to characterize their generativity within this framework. By doing so, we highlight the existence of two distinct generative levels in GenAI: one leading to the generation of new artifacts and the other leading to the generation of GenAI models themselves. We are also able to characterize the generativity of both of these levels, thus specifically confirming the generative power of GenAI and opening research avenues toward human-GenAI collaboration.
C1 [Bordas, Antoine; Le Masson, Pascal; Thomas, Maxime; Weil, Benoit] Mines Paris PSL Univ, Ctr Gest Sci CGS, i3 UMR, CNRS, 60 Bd St Michel, F-75272 Paris, France.
   [Thomas, Maxime] EPF Engn Sch, 55 President Wilson, F-94230 Cachan, France.
C3 Universite PSL; MINES ParisTech; Centre National de la Recherche
   Scientifique (CNRS)
RP Bordas, A (corresponding author), Mines Paris PSL Univ, Ctr Gest Sci CGS, i3 UMR, CNRS, 60 Bd St Michel, F-75272 Paris, France.
EM antoine.bordas@minesparis.psl.eu
FU Mines Paris - PSL
FX Open access funding provided by Mines Paris- PSL.
CR Alec Radford, 2018, Improving Language Understanding by Generative Pre-Training
   [Anonymous], 2001, Axiomatic design: Advances and Applications, DOI DOI 10.1093/OSO/9780195178760.003.0002
   [Anonymous], 1998, The republic
   Arora S., 2017, Proceedings of the 34th International Conference on Machine Learning-Volume
   Arora Sanjeev, 2018, P INT C LEARN REPR, DOI 10.48550/arXiv.1706.08224
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bar-Tal O, 2024, ARXIV, DOI DOI 10.48550/ARXIV.2401.12945
   Barros M., 2014, Design Computing and Cognition, V12, P285, DOI [10.1007/978-94-017-9112-016, DOI 10.1007/978-94-017-9112-0_16]
   Bauer P, 2015, NATURE, V525, P47, DOI 10.1038/nature14956
   Bounoua-Lahouari TNK., 1995, INTRO NUMERICAL WEAT, DOI [10.1201/9781315137285, DOI 10.1201/9781315137285]
   Brown TB, 2020, ADV NEUR IN, V33
   Cao H, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2209.02646
   Cao Y., 2023, J. ACM, V37, P1
   Carvajal Perez D, 2018, 15 INT DES C DUBR CR
   Chang Z, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2306.04542
   Creswell A, 2018, IEEE SIGNAL PROC MAG, V35, P53, DOI 10.1109/MSP.2017.2765202
   Dehghani M, 2019, 7 INT C LEARN REPR, DOI DOI 10.48550/ARXIV.1807.03819
   Doersch C, 2021, ARXIV, DOI [DOI 10.48550/ARXIV.1606.05908, 10.48550/arXiv.1606.05908]
   Doshi A. R., 2023, Generative artificial intelligence enhances creativity, DOI DOI 10.2139/SSRN.4535536
   Eris O., 2003, DS 31: Proceedings of ICED 03, the 14th International Conference on Engineering Design, V03, P587
   Evbuomwan NFO, 1996, P I MECH ENG B-J ENG, V210, P301, DOI 10.1243/PIME_PROC_1996_210_123_02
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Garcia-Penalvo F, 2023, WHAT WE MEAN GENAI S, DOI [10.9781/ijimai.2023.07.006, DOI 10.9781/IJIMAI.2023.07.006]
   Geng D, 2022, MEDIUM
   Ghahramani Z, 2015, NATURE, V521, P452, DOI 10.1038/nature14541
   Ghasemi P., 2023, P DESIGN SOC, V3, P633, DOI [10.1017/pds.2023.64, DOI 10.1017/PDS.2023.64]
   Goodfellow I J, 2014, EXPLAINING HARNESSIN
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gozalo-Brizuela R, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2301.04655
   Gui J, 2023, IEEE T KNOWL DATA EN, V35, P3313, DOI 10.1109/TKDE.2021.3130191
   Gullichsen E., 1985, VISUAL COMPUT, V1, P161, DOI [10.1007/BF01910018, DOI 10.1007/BF01910018]
   Harrison Robert L, 2010, AIP Conf Proc, V1204, P17
   Hastie TJ., 2009, ELEMENTS STAT LEARNI, DOI 10.1007/978-0-387-84858-7
   Hatchuel A, 2004, Design 2004: Proceedings of the 8th International Design Conference, Vols 1-3, P245
   Hatchuel A., 2011, EXPERTS ORG KNOWLEDG, DOI [10.1093/actrade/9780199584536.001.0001, DOI 10.1093/ACTRADE/9780199584536.001.0001]
   Hatchuel A., 2021, PROC SOC, V1, P3419, DOI [10.1017/pds.2021.603, DOI 10.1017/PDS.2021.603]
   Hatchuel A, 2018, RES ENG DES, V29, P5, DOI 10.1007/s00163-017-0275-2
   Hatchuel A, 2011, INT CONF ENG DES, V2, P86
   Hatchuel A, 2009, RES ENG DES, V19, P181, DOI 10.1007/s00163-008-0043-4
   Jang S, 2015, REDOX BIOL, V6, P552, DOI 10.1016/j.redox.2015.09.040
   Jech T., 2007, SET THEORY 3 MILLENN
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   KOFFMAN EB, 1973, IEEE T EDUC, VE 16, P182, DOI 10.1109/TE.1973.4320845
   Lam R, 2023, SCIENCE, V382, P1416, DOI 10.1126/science.adi2336
   Le Masson P, 2016, RES ENG DES, V27, P91, DOI 10.1007/s00163-015-0206-z
   Le Masson P, 2013, RES ENG DES, V24, P105, DOI 10.1007/s00163-012-0140-2
   Lin T., 2022, AI Open, V3, P111, DOI DOI 10.1016/J.AIOPEN.2022.10.001
   Nichol Alexander Quinn, 2022, PMLR, P16784, DOI DOI 10.48550/ARXIV.2112.10741
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Paladugu PS, 2023, ANN BIOMED ENG, V51, P2130, DOI 10.1007/s10439-023-03304-z
   Pinaya WHL, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2307.15208
   Rafner J, 2023, NAT HUM BEHAV, DOI 10.1038/s41562-023-01751-1
   Rane N., 2024, Roles and challenges of ChatGPT and similar generative artificial intelligence for achieving the sustainable development goals (SDGs), DOI [10.2139/ssrn.4603244, DOI 10.2139/SSRN.4603244]
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Redtenbacher FJ, 1868, RESULTATS SCIENTIFIQ
   REICH Y, 1995, RES ENG DES, V7, P1, DOI 10.1007/BF01681909
   Reich Y, 2009, PROCEEDINGS OF THE 9TH BIENNIAL CONFERENCE ON ENGINEERING SYSTEMS DESIGN AND ANALYSIS - 2008, VOL 3, P223
   Rogers PC, 2005, 5th IEEE International Conference on Advanced Learning Technologies, Proceedings, P809, DOI 10.1109/ICALT.2005.271
   Saadi J., 2023, PROC SOC, V3, P2805, DOI [10.1017/pds.2023.281, DOI 10.1017/PDS.2023.281]
   Samvelyan M, 2024, ARXIV, DOI DOI 10.48550/ARXIV.2402.16822
   Sanders EBN, 2000, COLLABORATIVE DESIGN, P3
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Stokes JM, 2020, CELL, V180, P688, DOI 10.1016/j.cell.2020.01.021
   Szegedy Christian, 2014, P INT C LEARNING REP
   Thomas M., 2023, PROC SOC, V3, P827, DOI [10.1017/pds.2023.83, DOI 10.1017/PDS.2023.83]
   Trinh TH, 2024, NATURE, V625, DOI 10.1038/s41586-023-06747-5
   Truong T., 2023, ADV NEURAL INF PROCE, V36, P77379
   van der Zant T., 2013, Philosophy and Theory of Artificial Intelligence, V5, P107, DOI 10.1007/978-3-642-31674-6
   Vaswani A, 2017, ADV NEUR IN, V30
   Verganti R, 2020, J PROD INNOVAT MANAG, V37, P212, DOI 10.1111/jpim.12523
   Villani MJ, 2024, ARXIV, DOI DOI 10.48550/ARXIV.2403.18415
   Wamba SF, 2024, INT J PROD RES, V62, P5676, DOI 10.1080/00207543.2023.2294116
   Wang KF, 2017, IEEE-CAA J AUTOMATIC, V4, P588, DOI 10.1109/JAS.2017.7510583
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Xiao C, 2019, ARXIV, DOI DOI 10.48550/ARXIV.1801.02610
   Yang LY, 2024, ACM COMPUT SURV, V56, DOI [10.1145/3626235, 10.1145/3648469]
   Yang Y, 2018, ARXIV, DOI DOI 10.48550/ARXIV.1812.03511
   Yenduri G, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2305.10435
   Zeng Z., 2019, DESIGN USER EXPERIEN, P400, DOI DOI 10.1007/978-3-030-23541-329
   Zhang C., Text-to-image Diffusion Models in Generative AI: A Survey, P2023, DOI [DOI 10.48550/ARXIV.2303.07909, 10.48550/ARXIV.2303.07909]
   Zhu Q, 2022, P DESIGN SOC, V2, P1825, DOI DOI 10.1017/PDS.2022.185
   Zhu QH, 2023, J COMPUT INF SCI ENG, V23, DOI 10.1115/1.4056220
   Zhu QH, 2023, J MECH DESIGN, V145, DOI 10.1115/1.4056598
   Zhuang FZ, 2021, P IEEE, V109, P43, DOI 10.1109/JPROC.2020.3004555
NR 84
TC 0
Z9 0
U1 26
U2 26
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0934-9839
EI 1435-6066
J9 RES ENG DES
JI Res. Eng. Design
PD OCT
PY 2024
VL 35
IS 4
BP 427
EP 443
DI 10.1007/s00163-024-00441-x
EA OCT 2024
PG 17
WC Engineering, Multidisciplinary; Engineering, Industrial; Engineering,
   Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA J2T6E
UT WOS:001329068700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Nutsugah, N
   Senanu, B
AF Nutsugah, Noel
   Senanu, Bright
TI On the Tech Trek and Industrial Revolutions: Unravelling the Impact of
   Generative AI on Public Relations Praxis in Africa
SO JOURNAL OF PUBLIC RELATIONS RESEARCH
LA English
DT Article
DE Africa; generative artificial intelligence; industrial revolutions;
   public relations
ID TECHNOLOGY ACCEPTANCE MODEL; USER ACCEPTANCE; WEB
AB This study holds substantial significance as it represents a pioneering continent-wide empirical endeavor to comprehend the extent to which public relations (PR) professionals value and engage with Generative Artificial Intelligence (GenAI) technologies and the consequential impact they exert on the praxis of the profession in Africa. The study assumes a qualitative approach with data collected from in-house and agency PR professionals across the major economic blocks in Africa. Beyond unearthing the benefits and threats, we also found practical, socio-cultural, and ethical implications of the influx of GenAI technologies, based upon which we proffered valuable recommendations for both practice and scholarly pursuits. We make a central argument that even though there is a high adoption and usage of GenAIs among PR professionals in Africa, there are currently no ethical policies guiding its usage, and this threatens the professions' quest to be transparent and accountable to their clients and publics.
C1 [Nutsugah, Noel] Univ Media Arts & Commun UniMAC, Dept Publ Relat, 5 Alboran St,South Legon, Accra, Ghana.
   [Senanu, Bright] Univ Media Arts & Commun, Mkt Div, Accra, Ghana.
RP Nutsugah, N (corresponding author), Univ Media Arts & Commun UniMAC, Dept Publ Relat, 5 Alboran St,South Legon, Accra, Ghana.
EM noel.nutsugah@gij.edu.gh
RI Senanu, Bright/AAC-9547-2022
OI Nutsugah, Noel/0000-0003-0792-6650
CR Aduhene DT, 2021, INT J SOC ECON, V48, P543, DOI 10.1108/IJSE-08-2020-0582
   Botan CH, 2004, J COMMUN, V54, P645, DOI 10.1093/joc/54.4.645
   Bourne C, 2019, PUBLIC RELAT INQ, V8, P109, DOI 10.1177/2046147X19835250
   BROOM GM, 1979, PUBLIC RELAT REV, V5, P47, DOI 10.1016/S0363-8111(79)80027-2
   Bryan A., 2015, Social research methods, V5th
   Collins CS, 2018, INT J QUAL METH, V17, DOI 10.1177/1609406918797475
   Creswell J. W., 2008, RES DESIGN QUALITATI
   Croucher SM., 2019, UNDERSTANDING COMMUN, VSecond
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Dwivedi YK, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122579
   Fu JS, 2024, J COMMUN, V74, P36, DOI 10.1093/joc/jqad031
   Galloway C, 2018, PUBLIC RELAT REV, V44, P734, DOI 10.1016/j.pubrev.2018.10.008
   Gesualdi M, 2019, PUBLIC RELAT REV, V45, P372, DOI 10.1016/j.pubrev.2018.12.002
   Goralski MA, 2020, INT J MANAG EDUC-OXF, V18, DOI 10.1016/j.ijme.2019.100330
   Hagendorff T, 2020, MIND MACH, V30, P99, DOI 10.1007/s11023-020-09517-8
   Hayes RA, 2023, PUBLIC RELAT REV, V49, DOI 10.1016/j.pubrev.2022.102273
   Hill LN, 2000, PUBLIC RELAT REV, V26, P31, DOI 10.1016/S0363-8111(00)00029-1
   Hu GW, 2024, ACCOUNT RES, V31, P978, DOI 10.1080/08989621.2023.2184262
   Huang GX, 2023, J COMMUN, V73, P552, DOI 10.1093/joc/jqad024
   Johnson M.A., 1997, J PUBLIC RELAT RES, V9, P213, DOI DOI 10.1207/S1532754XJPRR0903_
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Keppler SM, 2023, J COMPUT-MEDIAT COMM, V28, DOI 10.1093/jcmc/zmad020
   Lattimore D. L., 2016, PUBLIC RELATIONS PRO
   Leonardi PM, 2009, HUM COMMUN RES, V35, P407, DOI 10.1111/j.1468-2958.2009.01357.x
   Lim JS, 2022, TECHNOL SOC, V69, DOI 10.1016/j.techsoc.2022.101965
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Logan N, 2024, J PUBLIC RELAT RES, V36, P283, DOI 10.1080/1062726X.2024.2308868
   Marangunic N, 2015, UNIVERSAL ACCESS INF, V14, P81, DOI 10.1007/s10209-014-0348-1
   Martinez B., 2023, TELEMATICS INFORMATI, V11, P100076, DOI https://doi.org/10.1016/j.teler.2023.100076
   Men LR, 2022, J PUBLIC RELAT RES, V34, P20, DOI 10.1080/1062726X.2022.2068553
   Meng JB, 2021, J COMPUT-MEDIAT COMM, V26, P207, DOI 10.1093/jcmc/zmab005
   Mensah K., 2023, VISUAL POLITICS GLOB
   Nutsugah N, 2023, PUBLIC RELAT REV, V49, DOI 10.1016/j.pubrev.2023.102348
   Panda G, 2019, J CREAT COMMUN, V14, P196, DOI 10.1177/0973258619866585
   Pantano E, 2022, J SERV RES-US, V25, P583, DOI 10.1177/10946705221103538
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pichai K., 2023, J STUDENT RES, V12, P1, DOI [https://doi.org/10.47611/jsrhs.v12i4.6213, DOI 10.47611/JSRHS.V12I4.6213]
   Pratt CB, 2008, J PUBLIC RELAT RES, V20, P20, DOI 10.1080/10627260701727002
   Priporas CV, 2017, COMPUT HUM BEHAV, V77, P374, DOI 10.1016/j.chb.2017.01.058
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Sallot LM, 2004, PUBLIC RELAT REV, V30, P269, DOI 10.1016/j.pubrev.2004.05.002
   Sandberg J, 2021, J MANAGE STUD, V58, P487, DOI 10.1111/joms.12587
   Shehab M, 2020, INT J COMPUT INTEG M, V33, P1129, DOI 10.1080/0951192X.2020.1780320
   Soriano AS, 2021, PUBLIC RELAT REV, V47, DOI 10.1016/j.pubrev.2021.102035
   Tam L, 2022, J MARK COMMUN, V28, P183, DOI 10.1080/13527266.2020.1851286
   Valin J., 2018, AI PR CONSULTATIONS
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Verma S., 2021, International Journal of Information Management Data Insights, V1, DOI [DOI 10.1016/J.JJIMEI.2020.100002, 10.1016/j.jjimei.2020.100002]
   Waddington S., 2023, ANAL IMPACT AI SKILL
   Zerfass A, 2020, J COMMUN MANAG, V24, P377, DOI 10.1108/JCOM-10-2019-0137
NR 53
TC 0
Z9 0
U1 22
U2 22
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1062-726X
EI 1532-754X
J9 J PUBLIC RELAT RES
JI J. Public Relat. Res.
PD JUL 3
PY 2024
VL 36
IS 4
BP 341
EP 359
DI 10.1080/1062726X.2024.2368485
EA JUN 2024
PG 19
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA YS1H3
UT WOS:001261767000001
DA 2024-12-25
ER

PT J
AU Kim, J
   Yu, S
   Detrick, R
   Li, N
AF Kim, Jinhee
   Yu, Seongryeong
   Detrick, Rita
   Li, Na
TI Exploring students' perspectives on Generative AI-assisted academic
   writing
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE AI-assisted writing; AI literacy; academic writing; AI in education;
   generative Artificial Intelligence; student-AI Interaction
ID ARTIFICIAL-INTELLIGENCE; TIME CONSTRAINTS; CHALLENGES; DISCOURSE;
   ENGLISH; SKILLS
AB The rapid development of generative artificial intelligence (GenAI), including large language models (LLM), has merged to support students in their academic writing process. Keeping pace with the technical and educational landscape requires careful consideration of the opportunities and challenges that GenAI-assisted systems create within education. This serves as a useful and necessary starting point for fully leveraging its potential for learning and teaching. Hence, it is crucial to gather insights from diverse perspectives and use cases from actual users, particularly the unique voices and needs of student-users. Therefore, this study explored and examined students' perceptions and experiences about GenAI-assisted academic writing by conducting in-depth interviews with 20 Chinese students in higher education after completing academic writing tasks using a ChatGPT4-embedded writing system developed by the research team. The study found that students expected AI to serve multiple roles, including multi-tasking writing assistant, virtual tutor, and digital peer to support multifaceted writing processes and performance. Students perceived that GenAI-assisted writing could benefit them in three areas including the writing process, performance, and their affective domain. Meanwhile, they also identified AI-related, student-related, and task-related challenges that were experienced during the GenAI-assisted writing activity. These findings contribute to a more nuanced understanding of GenAI's impact on academic writing that is inclusive of student perspectives, offering implications for educational AI design and instructional design.
C1 [Kim, Jinhee; Detrick, Rita] Old Dominion Univ, Dept STEM Educ & Profess Studies, Norfolk, VA 23529 USA.
   [Yu, Seongryeong] Old Dominion Univ, Dept Teaching & Learning, Norfolk, VA USA.
   [Li, Na] Univ Liverpool, Dept Psychol, Liverpool, England.
   [Li, Na] Xian Jiaotong Liverpool Univ, Acad Future Educ, Dept Educ Studies, Suzhou, Peoples R China.
C3 Old Dominion University; Old Dominion University; University of
   Liverpool; Xi'an Jiaotong-Liverpool University
RP Kim, J (corresponding author), Old Dominion Univ, Dept STEM Educ & Profess Studies, Norfolk, VA 23529 USA.; Li, N (corresponding author), Univ Liverpool, Dept Psychol, Liverpool, England.; Li, N (corresponding author), Xian Jiaotong Liverpool Univ, Acad Future Educ, Dept Educ Studies, Suzhou, Peoples R China.
EM jhkim@odu.edu; na.li@xjtlu.edu.cn
RI Li, Na/GQP-4933-2022
OI Detrick, Rita/0009-0000-5610-2864; Li, Na/0000-0003-2395-3499; Kim,
   Jinhee/0000-0002-3365-7354; Yu, Seongryeong/0000-0003-1087-3713
FU Xi'an Jiaotong-Liverpool University
FX No Statement Available
CR Aldabbus S., 2022, BRIT J ENGLISH LINGU, V10, P1, DOI [10.37745/bjel.2013/vol10n3111, DOI 10.37745/BJEL.2013/VOL10N3111]
   Alemi M, 2015, INT J SOC ROBOT, V7, P523, DOI 10.1007/s12369-015-0286-y
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Allen L. K., 2017, Reading comprehension in educational settings, P125, DOI [10.1075/swll.16.05all, DOI 10.1075/SWLL.16.05ALL]
   Alqaraawi A, 2020, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2020, P275, DOI 10.1145/3377325.3377519
   Anderson L. W., 2001, A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objective
   Behrooz H, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15129701
   Benson L, 1996, ORGAN BEHAV HUM DEC, V67, P222, DOI 10.1006/obhd.1996.0075
   Biswas G, 2005, APPL ARTIF INTELL, V19, P363, DOI 10.1080/08839510590910200
   Bloom B., 1976, Human characteristics and school learning
   Bloom B. S., 1956, TAXONOMY ED OBJECTIV
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Chase CC, 2009, J SCI EDUC TECHNOL, V18, P334, DOI 10.1007/s10956-009-9180-4
   Chichekian T, 2022, FRONT ARTIF INTELL, V5, DOI 10.3389/frai.2022.903051
   Choudhuri R., 2023, P INT C SOFTW ENG, P1, DOI [10.1145/3597503.3639201, DOI 10.1145/3597503.3639201]
   Clarke V, 2015, QUALITATIVE PSYCHOL, P222, DOI DOI 10.4135/9781526405555.N2
   Crcek N, 2023, J LANG EDUC, V9, P128, DOI 10.17323/jle.2023.17379
   Dale R, 2021, NAT LANG ENG, V27, P511, DOI 10.1017/S1351324921000164
   Dornyei Z., 2007, Research Methods in Applied Linguistics
   El Shazly R, 2021, EXPERT SYST, V38, DOI 10.1111/exsy.12667
   Emery F.E., 1959, CHARACTERISTICS SOCI
   Endsley MR, 2000, SITUATION AWARENESS ANALYSIS AND MEASUREMENT, P3
   Epting LK, 2018, PERSPECT BEHAV SCI, V41, P561, DOI 10.1007/s40614-018-0175-4
   Feak C., 2012, Academic writing for graduate students: Essential tasks and skills, V3rd, DOI 10.3998/mpub.2173936
   Fengchun M., 2023, Guidance for generative AI in education and research, DOI [10.54675/EWZM9535, DOI 10.54675/EWZM9535]
   Fitria TN., 2021, Metathesis: Journal of English Language, Literature, and Teaching, V5, P65, DOI [10.31002/metathesis.v5i1.3519, DOI 10.31002/METATHESIS.V5I1.3519]
   Flower L., 1981, College Composition and Communication, V32, P365, DOI [DOI 10.2307/356600, 10.2307/356600]
   Gayed JM., 2022, COMPUTERS ED ARTIFIC, V3, P100055, DOI DOI 10.1016/J.CAEAI.2022.100055
   Glesne C., 2006, Becoming qualitative researchers, V3rd
   Gonzalez C, 2004, HUM FACTORS, V46, P449, DOI 10.1518/hfes.46.3.449.50395
   Gupta S, 2022, FRONT EDUC, V7, DOI 10.3389/feduc.2022.891534
   Han J., 2023, P 10 ACM C LEARN SCA
   Hattie J, 2007, REV EDUC RES, V77, P81, DOI 10.3102/003465430298487
   Heintz K, 2022, SCI EDIT, V9, P37, DOI 10.6087/kcse.261
   Holmes W., 2022, Artificial intelligence and education
   Huang XY, 2023, EDUC TECHNOL SOC, V26, P112, DOI 10.30191/ETS.202301_26(1).0009
   Hyland K, 2014, ROUTLEDGE COMPANION TO ENGLISH STUDIES, P392
   Imran M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13605
   Jiang JL, 2023, INT J HUM-COMPUT INT, V39, P1789, DOI 10.1080/10447318.2022.2093863
   JINHEE KIM, 2020, [Journal of Education & Culture, 교육문화연구], V26, P71
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Ketelhut DJ, 2010, BRIT J EDUC TECHNOL, V41, P56, DOI 10.1111/j.1467-8535.2009.01036.x
   Kim J, 2024, AUSTRALAS J EDUC TEC, V40, P19, DOI 10.14742/ajet.8859
   Kim J, 2023, ASIA PAC J EDUC, DOI 10.1080/02188791.2023.2286206
   Kim J, 2024, EDUC INF TECHNOL, V29, P8693, DOI 10.1007/s10639-023-12109-5
   Kim J, 2022, EDUC INF TECHNOL, V27, P6069, DOI 10.1007/s10639-021-10831-6
   Kusters R, 2020, FRONT BIG DATA, V3, DOI 10.3389/fdata.2020.577974
   Latour B., 2005, REASSEMBLING SOCIAL, DOI [10.1093/oso/9780199256044.001.0001, DOI 10.1093/OSO/9780199256044.001.0001]
   Lee SS, 2023, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12371-7
   Lehne M, 2019, NPJ DIGIT MED, V2, DOI 10.1038/s41746-019-0158-1
   Liao QV, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376590
   Lin MPC, 2020, EDUC TECHNOL SOC, V23, P78
   Liu ML, 2024, COMPUT EDUC, V211, DOI 10.1016/j.compedu.2023.104977
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lustig M.W., 2010, INTERCULTURAL COMPET
   Malik A. R., 2023, International Journal of Educational Research Open, V5, DOI [10.1016/j.ijedro.2023.100296, DOI 10.1016/J.IJEDRO.2023.100296]
   Marzano R.J., 2004, BUILDING BACKGROUND
   Marzuki, 2023, COGENT EDUC, V10, DOI 10.1080/2331186X.2023.2236469
   Mason R., 2013, Using communications media in open and flexible learning
   McKinley J, 2018, J SECOND LANG WRIT, V42, P1, DOI 10.1016/j.jslw.2018.07.003
   McKinnon G., 2023, ChatGPT: The future of artificial intelligence and natural language
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   Molenaar I, 2022, EUR J EDUC, V57, P632, DOI 10.1111/ejed.12527
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Parodi G, 2007, READ WRIT, V20, P225, DOI 10.1007/s11145-006-9029-7
   Pineteh E.A., 2014, International Journal of Higher Education, V3, P12
   Prentice FM, 2018, INT J EDUC INTEGR, V14, DOI 10.1007/s40979-018-0036-7
   Price M, 2010, ASSESS EVAL HIGH EDU, V35, P277, DOI 10.1080/02602930903541007
   Rastegary H., 1993, Time pressure and stress in human judgment and decision making, P217, DOI DOI 10.1007/978-1-4757-6846-6_15
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Renz A, 2021, TECHNOL INNOV MANAG, V11, P5, DOI 10.22215/timreview/1438
   Rogerson AM, 2017, INT J EDUC INTEGR, V13, DOI 10.1007/s40979-016-0013-y
   Rosé CP, 2019, BRIT J EDUC TECHNOL, V50, P2943, DOI 10.1111/bjet.12858
   Rowland DR, 2023, J ACAD LANG LEARN, V17, pT31
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   Sandoval WA, 2005, SCI EDUC, V89, P634, DOI 10.1002/sce.20065
   Schiefele U, 1996, LEARN INDIVID DIFFER, V8, P141, DOI 10.1016/S1041-6080(96)90030-8
   Sekaran K., 2020, Telecommunication Computing Electronics and Control, V18, P1268, DOI [DOI 10.12928/TELKOMNIKA.V18I3.14028, 10.12928/telkomnika.v18i3.14028]
   Singh H, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090924
   Spector JM, 2019, SMART LEARN ENVIRON, V6, DOI 10.1186/s40561-019-0088-z
   Srinivasan R, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4812
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   van den Berghe R, 2019, REV EDUC RES, V89, P259, DOI 10.3102/0034654318821286
   Wale BD, 2021, ASIAN-PAC J SEC FOR, V6, DOI 10.1186/s40862-020-00108-9
   Yang FM, 2020, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2020, P189, DOI 10.1145/3377325.3377480
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhang LM, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1118261
   Zhu XJ, 2015, AAAI CONF ARTIF INTE, P4083
NR 91
TC 3
Z9 3
U1 255
U2 255
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 JUL 31
PY 2024
DI 10.1007/s10639-024-12878-7
EA JUL 2024
PG 36
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA A3C0L
UT WOS:001281329100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Reisman, S
AF Reisman, Sorel
TI Practical Classroom Use of Generative Artificial Intelligence-A Case
   Study
SO COMPUTER
LA English
DT Article
DE Generative AI; Performance evaluation; Educational technology;
   Educational courses; Costs; Quality assessment; Software tools
AB This article describes the author's unsuccessful attempt to use current generative artificial intelligence tools to reduce instructors' workloads by creating course syllabi that reference no cost resources.
C1 [Reisman, Sorel] Calif State Univ Fullerton, Dept Informat Syst & Decis Sci, informat Syst, Fullerton, CA 92831 USA.
C3 California State University System; California State University
   Fullerton
RP Reisman, S (corresponding author), Calif State Univ Fullerton, Dept Informat Syst & Decis Sci, informat Syst, Fullerton, CA 92831 USA.
EM sreisman@computer.org
OI Reisman, Sorel/0000-0002-7222-4084
CR [Anonymous], 2024, AI Impacts Wiki
   [Anonymous], WIKIPEDIA
   [Anonymous], Share your work
   Butler S., How To Geek
   chtbl.com, Freakonomics Radio Podcast
   Fortinet, 2024, What Is an Attack Surface? Definition and How to Reduce It. Fortinet
   Lohr S., 2021, NY Times
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Multimedia Educational Resource for Learning and Online Teaching, About us
   Pear J.J., 2011, New directions for teaching and learning. Vol. 128: Evidence-based teaching, V128, P85, DOI DOI 10.1002/TL.471
   Rumore MM, 2016, AM J PHARM EDUC, V80, DOI 10.5688/ajpe8010177
   Stahl B.C., 2021, Artificial Intelligence for human flourishing-Beyong priciples for machine learning
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/357980.357991
NR 13
TC 0
Z9 0
U1 44
U2 44
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD JUN
PY 2024
VL 57
IS 6
BP 110
EP 114
DI 10.1109/MC.2024.3382453
PG 5
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TG5B0
UT WOS:001240114700001
DA 2024-12-25
ER

PT J
AU Chen, AJ
   Liu, L
   Zhu, TY
AF Chen, Anjun
   Liu, Lei
   Zhu, Tongyu
TI Advancing the democratization of generative artificial intelligence in
   healthcare: a narrative review
SO JOURNAL OF HOSPITAL MANAGEMENT AND HEALTH POLICY
LA English
DT Article
DE Generative AI (GenAI); ChatGPT; AI democratization; healthcare; learning
   health system (LHS)
ID MACHINE; MODELS
AB Background and Objective: The emergence of ChatGPT-like generative artificial intelligence (GenAI) has dramatically transformed the healthcare landscape, bringing new hope for the democratization of artificial intelligence (AI) in healthcare-a topic that has not been comprehensively reviewed. This review aims to analyze the reasons propelling the democratization of healthcare GenAI, outline the initial evidence in the literature, and propose future directions to advance GenAI democratization. Methods: We conducted a deep literature search for GenAI studies using Google Scholar, PubMed, ChatGPT, Journal of the American Medical Association ( JAMA ), Nature, , Springer Link, and Journal of Medical Internet Research (JMIR). JMIR ). We performed an abstraction analysis on the nature of GenAI versus traditional AI and the applications of GenAI in medical education and clinical care. Key Content and Findings: (I) A detailed comparison of traditional and GenAI in healthcare reveals that large language model (LLM)-based GenAI's unprecedent general-purpose capabilities and natural language interaction ability, coupled with its free public availability, make GenAI ideal for democratization in healthcare. (II) We have identified plenty of initial evidence for GenAI democratization in medical education and clinical care, marking the start of the emerging trend of GenAI democratization in a host of impactful applications. Applications in medical education include medical exam preparation, medical teaching and training, and simulation. Applications in clinical care include diagnosis assistance, disease risk prediction, new generalist chatbots, treatment decision support, surgery support, medical image analysis, patient communication, physician communication, documentation automation, clinical trial automation, informatics tasks automation, and specialized or custom LLMs. (III) Responsible AI is essential for the future of healthcare GenAI. National initiatives and regulatory efforts are working to ensure safety, efficacy, accountability, equity, security and privacy are built into healthcare GenAI. Responsible GenAI requires a human-machine collaboration approach, where AI augments human expertise rather than replaces it. Conclusions: The democratization of GenAI in healthcare has just begun, driven by the nature of GenAI and guided by the principle of human-machine collaboration. To further advance GenAI democratization, we propose three key future directions: integrating GenAI in medical education curricula, democratizing GenAI clinical evaluation research, and building learning health systems (LHS) with GenAI for system- level enforcement of democratization. Democratizing GenAI in healthcare will revolutionize medicine and significantly impact care delivery and health policies.
C1 [Chen, Anjun] ELHS Inst, Hlth Syst Sci, 748 Matadero Ave, Palo Alto, CA 94306 USA.
   [Chen, Anjun; Liu, Lei; Zhu, Tongyu] Fudan Univ, Healthcare AI Inst, Med Sch, Shanghai, Peoples R China.
C3 Fudan University
RP Chen, AJ (corresponding author), ELHS Inst, Hlth Syst Sci, 748 Matadero Ave, Palo Alto, CA 94306 USA.
EM aj@elhsi.org
OI Chen, Anjun/0000-0003-4209-8301
FX The authors thank Professor Zhigang Pan of Fudan University Affiliated
   Zhongshan Hospital for discussion of AI-enabled primary care and its
   democratization. Funding: None.
CR Abdullahi T, 2024, JMIR MED EDUC, V10, DOI 10.2196/51391
   Abràmoff MD, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00913-9
   Arasteh ST, 2024, NAT COMMUN, V15, DOI 10.1038/s41467-024-45879-8
   Ayers JW, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.17517
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Ayoub NF, 2023, JAMA OTOLARYNGOL, V149, P556, DOI 10.1001/jamaoto.2023.0704
   Badal K, 2023, COMMUN MED-LONDON, V3, DOI 10.1038/s43856-023-00279-9
   Balogh EP, 2015, IMPROVING DIAGNOSIS IN HEALTH CARE, P1, DOI 10.17226/21794
   Barile J, 2024, JAMA PEDIATR, V178, P313, DOI 10.1001/jamapediatrics.2023.5750
   Benary M, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.43689
   Bernstein IA, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.30320
   Boscardin CK, 2024, ACAD MED, V99, P22, DOI 10.1097/ACM.0000000000005439
   Chang BS, 2023, JAMA-J AM MED ASSOC, V330, P1521, DOI 10.1001/jama.2023.16943
   Chen A, 2023, J Am Med Inform Assoc
   Chen AJ, 2024, JMIR AI, V3, DOI 10.2196/56590
   Chen AJ, 2024, ACAD MED, V99, P589, DOI 10.1097/ACM.0000000000005672
   Chen A, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.27647
   Chen AJ, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-23011-4
   Chen PHC, 2019, NAT MATER, V18, P410, DOI 10.1038/s41563-019-0345-0
   Chen S, 2023, JAMA ONCOL, V9, P1459, DOI 10.1001/jamaoncol.2023.2954
   Clusmann J, 2023, COMMUN MED-LONDON, V3, DOI 10.1038/s43856-023-00370-1
   Cohen JP, 2021, CAN MED ASSOC J, V193, pE1391, DOI 10.1503/cmaj.202066
   Decker H, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.36997
   Deo RC, 2015, CIRCULATION, V132, P1920, DOI 10.1161/CIRCULATIONAHA.115.001593
   Dorr DA, 2023, JAMA-J AM MED ASSOC, V329, P1347, DOI 10.1001/jama.2023.2771
   Dzau VJ, 2023, PNAS NEXUS, V2, DOI 10.1093/pnasnexus/pgad410
   Egger J, 2022, COMPUT METH PROG BIO, V221, DOI 10.1016/j.cmpb.2022.106874
   Eriksen A.V., 2023, NEJM AI, P1, DOI DOI 10.1056/AIP2300031
   Ferreira Alana L, 2023, JMIR Dermatol, V6, pe49280, DOI 10.2196/49280
   Ferruz N, 2024, NAT MACH INTELL, V6, P6, DOI 10.1038/s42256-023-00784-5
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Goldberg CB, 2024, NAT MED, V30, P623, DOI 10.1038/s41591-024-02853-7
   Goodman KE, 2024, JAMA-J AM MED ASSOC, V331, P637, DOI 10.1001/jama.2024.0555
   Goodman RS, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.36483
   Gottlieb S, 2023, JAMA-HEALTH FORUM, V4, DOI 10.1001/jamahealthforum.2023.3909
   Grigorian A, 2023, JAMA SURG, V158, P1220, DOI 10.1001/jamasurg.2023.3875
   Han C, 2024, ISCIENCE, V27, DOI 10.1016/j.isci.2024.109022
   Harris E, 2023, JAMA-J AM MED ASSOC, V330, P792, DOI 10.1001/jama.2023.14311
   Haug CJ, 2023, NEW ENGL J MED, V388, P1201, DOI 10.1056/NEJMra2302038
   Haupt CE, 2023, JAMA-J AM MED ASSOC, V329, P1349, DOI 10.1001/jama.2023.5321
   Holderried F, 2024, JMIR MED EDUC, V10, DOI 10.2196/53961
   Howell Michael D, 2024, JAMA, V331, P242, DOI 10.1001/jama.2023.25057
   Hswen Y, 2023, JAMA-J AM MED ASSOC, V330, P1604, DOI 10.1001/jama.2023.19293
   Hu Y, 2024, J AM MED INFORM ASSN, V31, DOI 10.1093/jamia/ocad259
   Huang AS, 2024, JAMA OPHTHALMOL, V142, P371, DOI 10.1001/jamaophthalmol.2023.6917
   Huang JAT, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.36100
   Huang RST, 2023, JMIR MED EDUC, V9, DOI 10.2196/50514
   Iannantuono GM, 2023, FRONT ONCOL, V13, DOI 10.3389/fonc.2023.1268915
   Ito N, 2023, JMIR MED EDUC, V9, DOI 10.2196/47532
   Jiang LY, 2023, NATURE, V619, P357, DOI 10.1038/s41586-023-06160-y
   Jindal JA, 2024, J AM MED INFORM ASSN, V31, DOI 10.1093/jamia/ocae043
   Kanjee Z, 2023, JAMA-J AM MED ASSOC, V330, P78, DOI 10.1001/jama.2023.8288
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Kelly CJ, 2019, BMC MED, V17, DOI 10.1186/s12916-019-1426-2
   Khera R, 2023, JAMA-J AM MED ASSOC, V330, P818, DOI 10.1001/jama.2023.15481
   Kim HK, 2024, DISCOV APPL SCI, V6, DOI 10.1007/s42452-024-05871-9
   Kim J, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.38050
   Koohi-Moghadam M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01987-4
   Kottlors J, 2023, RADIOLOGY, V308, DOI 10.1148/radiol.231167
   Kresevic S, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-024-01091-y
   Ktena I, 2024, NAT MED, V30, DOI 10.1038/s41591-024-02838-6
   Kulkarni PA, 2023, JAMA-J AM MED ASSOC, V330, P317, DOI 10.1001/jama.2023.11440
   Kung T. H, 2023, PLOS Digit Health, V2, DOI DOI 10.1371/JOURNAL.PDIG.0000198.PDIG-D-22-00371
   Lee P, 2023, NEW ENGL J MED, V388, P1233, DOI 10.1056/NEJMsr2214184
   Liu JL, 2023, J MED INTERNET RES, V25, DOI 10.2196/48568
   Liu SR, 2023, J AM MED INFORM ASSN, V30, P1237, DOI 10.1093/jamia/ocad072
   Liu XN, 2023, J TRANSL MED, V21, DOI 10.1186/s12967-023-04314-0
   Matheny ME, 2020, JAMA-J AM MED ASSOC, V323, P509, DOI 10.1001/jama.2019.21579
   McDuff D., 2023, PREPRINT, DOI DOI 10.48550/ARXIV.2312.00164
   Mehandru N, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-024-01083-y
   Mello MM, 2024, JAMA-J AM MED ASSOC, V331, P17, DOI 10.1001/jama.2023.25051
   Mello MM, 2023, JAMA-HEALTH FORUM, V4, DOI 10.1001/jamahealthforum.2023.1938
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Mihalache A, 2024, JAMA OPHTHALMOL, V142, P321, DOI 10.1001/jamaophthalmol.2024.0017
   Mihalache A, 2023, JAMA OPHTHALMOL, V141, P589, DOI 10.1001/jamaophthalmol.2023.1144
   Moor M, 2023, NATURE, V616, P259, DOI 10.1038/s41586-023-05881-4
   National Academies of Sciences Engineering and Medicine, 2023, ART INT HLTH PROF ED, DOI [10.17226/27174, DOI 10.17226/27174]
   Nayak A, 2023, JAMA INTERN MED, V183, P1026, DOI 10.1001/jamainternmed.2023.2561
   Omiye Jesutofunmi A, 2023, NPJ Digit Med, V6, P195, DOI 10.1038/s41746-023-00939-z
   Oniani D, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00965-x
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Rajkomar A, 2019, NEW ENGL J MED, V380, P1347, DOI 10.1056/NEJMra1814259
   Rao A, 2023, J MED INTERNET RES, V25, DOI 10.2196/48659
   Rengers TA, 2024, JAMA SURG, V159, P445, DOI 10.1001/jamasurg.2023.6496
   Sandmann S, 2024, NAT COMMUN, V15, DOI 10.1038/s41467-024-46411-8
   Sarraju A, 2023, JAMA-J AM MED ASSOC, V329, P842, DOI 10.1001/jama.2023.1044
   Savage T, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-024-01010-1
   Schubert MC, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.46721
   Seastedt Kenneth P, 2022, PLOS Digit Health, V1, pe0000102, DOI 10.1371/journal.pdig.0000102
   Shah NH, 2024, JAMA-J AM MED ASSOC, V331, P245, DOI 10.1001/jama.2023.26930
   Shah NH, 2023, JAMA-J AM MED ASSOC, V330, P866, DOI 10.1001/jama.2023.14217
   Shea YF, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.25000
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Singhal K, 2023, Arxiv, DOI [arXiv:2305.09617, DOI 10.48550/ARXIV.2305.09617]
   Smith MW, 2013, BEATING THE ODDS: THE LIFE AND TIMES OF E. A. MILNE, P1, DOI 10.1142/p856
   Sorin V, 2023, NPJ BREAST CANCER, V9, DOI 10.1038/s41523-023-00557-8
   Stade Elizabeth C, 2024, Npj Ment Health Res, V3, P12, DOI 10.1038/s44184-024-00056-z
   Strong E, 2023, JAMA INTERN MED, V183, P1028, DOI 10.1001/jamainternmed.2023.2909
   Tang LY, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00896-7
   Tu T, 2024, Arxiv, DOI [arXiv:2401.05654, 10.48550/arXiv.2401.05654, DOI 10.48550/ARXIV.2401.05654]
   Wachter RM, 2024, JAMA-J AM MED ASSOC, V331, P65, DOI 10.1001/jama.2023.25054
   Wang HY, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-023-00989-3
   Wang L, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-024-01029-4
   Wilkinson J, 2020, LANCET DIGIT HEALTH, V2, pE677, DOI 10.1016/S2589-7500(20)30200-4
   Wornow M, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00879-8
   Yang X, 2022, NPJ DIGIT MED, V5, DOI 10.1038/s41746-022-00742-2
   Zhou JX, 2023, medRxiv, DOI [10.1101/2023.06.10.23291127, 10.1101/2023.06.10.23291127, DOI 10.1101/2023.06.10.23291127]
   Zhu LX, 2023, J TRANSL MED, V21, DOI 10.1186/s12967-023-04123-5
NR 108
TC 1
Z9 1
U1 12
U2 12
PU AME PUBLISHING COMPANY
PI SHATIN
PA FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG
   00000, PEOPLES R CHINA
SN 2523-2533
J9 J HOSP MANAG HLTH P
JI J. Hosp. Manag. Health Policy
PD JUN 30
PY 2024
VL 8
AR 12
DI 10.21037/jhmhp-24-54
PG 18
WC Health Care Sciences & Services
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services
GA A3N6J
UT WOS:001281635300002
DA 2024-12-25
ER

PT J
AU Fern, J
AF Fern, James
TI A More-than-Human Ecology: Evolving Generative Artificial Intelligence
   in Higher Education
SO EDUCATION SCIENCES
LA English
DT Article
DE generative artificial intelligence; higher education; assessment and
   feedback
AB The significant improvements in generative artificial intelligence (GenAI) observed in recent years present higher education with both an opportunity and a significant challenge. Its successful integration will require careful planning and sound pedagogical underpinnings, both in regard to learning and teaching as well as assessment and feedback. Drawing upon theories from the more-than-human world, as well as concepts such as originality, equality, and sustainability, it is possible to develop a dialogue around GenAI that places the students' learning journey at the heart of the discussion.
C1 [Fern, James] Univ Bath, Dept Hlth, Bath BA2 7AY, England.
C3 University of Bath
RP Fern, J (corresponding author), Univ Bath, Dept Hlth, Bath BA2 7AY, England.
EM j.fern@bath.ac.uk
CR Abram D., 2024, More than human rights: An ecology of law, thought and narrative for earthly flourishing, P341
   Ali SH, 2014, RESOURCES-BASEL, V3, P123, DOI 10.3390/resources3010123
   Amani S, 2023, Arxiv, DOI [arXiv:2304.14415, 10.48550/arXiv.2304.14415, DOI 10.48550/ARXIV.2304.14415]
   [Anonymous], 2008, SCIENCEDAILY
   assets.publishing.service.gov.uk, Generative AI in Education Department for Education
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Bridle J., 2023, Ways of being: animals, plants, machines: the search for a planetary intelligence
   Caldwell M., 2023, Houst. Law Rev, V411, P61
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Crawford K., 2021, Atlas ofAI: Power, Politics, and the Planetary Costs of Artificial Intelligence
   DAVIES D, 1975, NATURE, V258, P286, DOI 10.1038/258286a0
   de Vries A, 2023, JOULE, V7, P2191, DOI 10.1016/j.joule.2023.09.004
   Eaton SE, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00144-1
   Feng SB, 2023, PROCEEDINGS OF THE 61ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2023): LONG PAPERS, VOL 1, P11737
   Forman N., 2023, International Journal of Advanced Natural Sciences and Engineering Researches, V7, P95, DOI DOI 10.59287/IJANSER.562
   Freeman J., New HEPI Policy Note Finds More Than Half of Students Have Used Generative AI for Help on Assessments-But only 5% Likely to be Using AI to Cheat
   Gagliano M, 2014, OECOLOGIA, V175, P63, DOI 10.1007/s00442-013-2873-7
   Ibrahim H, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-38964-3
   Jishnu D., 2023, J. Vis. Perform. Arts, V4, P65
   Karthikeyan V., 2024, Applications, Challenges, and the Future of ChatGPT, P30, DOI [10.4018/979-8-3693-6824-4.ch002, DOI 10.4018/979-8-3693-6824-4.CH002]
   Khan S., 2024, Brave new words-how AI will revolutionize education (and why that's a good thing)
   Klein N., 2023, Doppelganger: A Trip into the Mirror World
   Lee K.F., 2021, AI 2041: Ten visions for our future
   Luccioni AS, 2024, Arxiv, DOI [arXiv:2311.16863, DOI 10.48550/ARXIV.2311.16863]
   Luccioni Alexandra Sasha, 2022, arXiv, DOI [10.48550/arXiv.2211.02001, DOI 10.48550/ARXIV.2211.02001]
   Mollick E.R., 2024, arXiv, DOI [10.2139/ssrn.4802463, DOI 10.2139/SSRN.4802463]
   Mortensen O., Statistics Facts
   Mytton D, 2021, NPJ CLEAN WATER, V4, DOI 10.1038/s41545-021-00101-w
   Nicoletti L., Humans and Biased Generative AI is Even Worse
   Pearson J, 2021, EDUC ACTION RES, V29, P259, DOI 10.1080/09650792.2020.1829496
   PebblePad PebblePad, Survey Finds UK Students Using AI Tools to Support Studies, Not Outsource Writing
   Pereira D, 2016, ASSESS EVAL HIGH EDU, V41, P1008, DOI 10.1080/02602938.2015.1055233
   Powell S., 2024, Using Generative AI Effectively in Higher Education Sustainable and Ethical Practices for Learning, Teaching and Assessment, P97
   RM Technology, A Staggering Two Thirds of Secondary School Students Use AI to do Their School Work
   Seymour, 2019, The Twittering Machine: How Capitalism Stole Our Social Life
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Smith Zadie., 2019, NEW YORK REV
   Strubell E, 2019, Arxiv, DOI arXiv:1906.02243
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   Wikelski M, 2020, ETHOLOGY, V126, P931, DOI 10.1111/eth.13078
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
NR 42
TC 0
Z9 0
U1 5
U2 5
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD OCT
PY 2024
VL 14
IS 10
AR 1102
DI 10.3390/educsci14101102
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA K2C2Z
UT WOS:001342003700001
OA gold
DA 2024-12-25
ER

PT J
AU Xing, Y
AF Xing, Yu
TI The Influence of Responsible Innovation on Ideological Education in
   Universities Under Generative Artificial Intelligence
SO IEEE ACCESS
LA English
DT Article
DE Empirical analysis; generative artificial intelligence; ideological and
   political education; responsible innovation; Empirical analysis;
   generative artificial intelligence; ideological and political education;
   responsible innovation
AB With the rapid development of generative AI technology, it has been widely used in the field of higher education, especially in ideological and political education in universities (hereinafter referred to as IPE). From the perspective of responsible innovation, this study deeply discusses the influence of generative artificial intelligence (AI) on IPE in universities. This study empirically analyzes the practical application and effect of generative AI in university IPE through questionnaires, interviews and classroom observation. The results show that the introduction of generative AI improves students' acceptance and classroom participation, which has a positive impact on teaching effect. At the same time, the research also reveals the social, environmental and ethical responsibilities that need to be paid attention to during the application of generative AI. By constructing structural equation model (SEM), this study further discusses the relationship between the use frequency of generative AI, students' acceptance and the diversity of teachers' teaching methods and the teaching effect, which provides useful reference and enlightenment for future educational reform and technology integration. In addition, this study also discusses the challenges and limitations of the application of generative AI in university IPE, and puts forward corresponding suggestions to promote its effective application and development in the field of education.
C1 [Xing, Yu] Shenyang Polytech Coll, Shenyang 110045, Peoples R China.
RP Xing, Y (corresponding author), Shenyang Polytech Coll, Shenyang 110045, Peoples R China.
EM 199089854@qq.com
CR Adams B, 2020, IEEE SOFTWARE, V37, P104, DOI 10.1109/MS.2020.2975075
   Daghigh AJ, 2022, JOURNALISM, V23, P1530, DOI 10.1177/14648849221074493
   Fengqin R., 2023, J. Chongqing Univ. Posts Telecommun. (Social Sci. Ed.), V35, P80
   Furong G., 2023, Library Inf. Knowl., V40, P97
   Guoming Y., 2023, J. Xinjiang Normal Univ., Philosophy Social Sci. Ed., V44, P81
   Guoqing X., 2023, J. East China Normal Univ., Educ. Sci. Ed., V41, P64
   Hanwei T., 2023, Res. Educ. Develop., V43, P1
   Huan W., 2023, U.S.-China Educ. Rev., A, V13, P6
   Jun X., 2023, Res. Audio-Vis. Educ., V44, P57
   Kiess J, 2022, POLITICS-OXFORD, V42, P75, DOI 10.1177/0263395721990287
   Li Guo-qing, 2022, Journal of Landscape Research, V14, P99, DOI 10.16785/j.issn1943-989x.2022.6.025
   Li H., 2023, Open Educ. Res., V29, P31
   Li Z., 2023, J. East China Normal Univ., Educ. Sci. Ed., V41, P47
   [刘佳玮 Liu Jiawei], 2022, [电子测量与仪器学报, Journal of Electronic Measurement and Instrument], V36, P1
   Minjing N., 2023, J. East China Normal Univ., Educ. Sci. Ed., V41, P151
   Nanping Y., 2023, J. East China Normal Univ., Educ. Sci. Ed., V41, P15
   Weili W., 2023, President Eur. Soc. Commun. Res. Educ., V45, P152
   Weiran W., 2023, J. Educ. Sci. Hum. Normal Univ., V22, P35
   Wu B., 2023, Res. Educ. Develop., V43, P40
   Xiaozhe Y., 2023, Global Educ. Outlook, V52, P3
   Yan Y., 2022, J. Northeastern Univ. (Social Sci.), V24, P137
   Zhai X., 2023, Open Educ. Res., V29, P26
   Zhu YG, 2023, J OCEANOL LIMNOL, V41, P1504, DOI 10.1007/s00343-022-2068-3
   Zongkai Y., 2023, J. East China Normal Univ., Educ. Sci. Ed., V41, P26
NR 24
TC 0
Z9 0
U1 60
U2 60
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 133008
EP 133017
DI 10.1109/ACCESS.2024.3459469
PG 10
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA I0P0P
UT WOS:001327352400001
OA gold
DA 2024-12-25
ER

PT J
AU Ma, MQ
AF Ma, Muqing
TI Exploring the acceptance of generative artificial intelligence for
   language learning among EFL postgraduate students: An extended TAM
   approach
SO INTERNATIONAL JOURNAL OF APPLIED LINGUISTICS
LA English
DT Article; Early Access
DE generative artificial intelligence (GenAI); personal innovativeness;
   postgraduate students; technology acceptance model (TAM); trust
ID PERSONAL INNOVATIVENESS; USER ACCEPTANCE
AB This study delves into the acceptance of generative artificial intelligence (GenAI) for English language learning among Chinese postgraduate students, examining how individual, social, and technological factors influence this process. Utilizing an extended technology acceptance model, the research collected data from 497 students via a survey, analyzed through partial least square-structural equation modeling. Key findings underscore personal innovativeness, subjective norms, and trust as significant predictors of GenAI adoption, with an intricate interplay between perceived ease of use and usefulness affecting behavioral intentions. The insights offer theoretical and practical implications for enhancing GenAI's educational integration, emphasizing the importance of fostering innovation, peer influence, trust, and support infrastructure. This contribution enriches the understanding of GenAI's educational potential, particularly in non-native English contexts, paving the way for further exploration in this evolving domain.
C1 [Ma, Muqing] Shanghai Polytech Univ, Sch Foreign Languages & Cultural Commun, Shanghai, Peoples R China.
C3 Shanghai Polytechnic University
RP Ma, MQ (corresponding author), Shanghai Polytech Univ, Sch Foreign Languages & Cultural Commun, Shanghai, Peoples R China.
EM mmqsisu@163.com
RI Ma, Muqing/JFS-5640-2023
OI Ma, Muqing/0000-0001-7467-1542
FU National Social Science Fund of China [20BYY105]
FX National Social Science Fund of China, Grant/Award Number: 20BYY105
CR Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
   AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
   Al-Adwan AS, 2023, EDUC INF TECHNOL, V28, P15381, DOI 10.1007/s10639-023-11816-3
   Alwahaishi S, 2021, INT J DIST EDUC TECH, V19, DOI 10.4018/IJDET.286742
   An X, 2023, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2023.2246519
   An X, 2023, EDUC INF TECHNOL, V28, P5187, DOI 10.1007/s10639-022-11286-z
   ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
   Annamalai N., 2023, Computers and Education: Artificial Intelligence, V5, P100153, DOI [https://doi.org/10.1016/j.caeai.2023.100153, DOI 10.1016/J.CAEAI.2023.100153]
   Ansari AN, 2024, EDUC INF TECHNOL, V29, P11281, DOI 10.1007/s10639-023-12223-4
   Bibauw S, 2022, LANG LEARN TECHNOL, V26
   Cai QQ, 2024, INT J HUM-COMPUT INT, V40, P7112, DOI 10.1080/10447318.2023.2261725
   Cargill M, 2018, ENGL SPECIF PURP, V52, P13, DOI 10.1016/j.esp.2018.05.002
   Chahal J, 2022, J COMPUT HIGH EDUC, V34, P844, DOI 10.1007/s12528-022-09327-0
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chavoshi A, 2019, TELEMAT INFORM, V38, P133, DOI 10.1016/j.tele.2018.09.007
   Chen SY, 2023, EDUC INF TECHNOL, V28, P11631, DOI 10.1007/s10639-023-11601-2
   Chen YL, 2022, EDUC SCI, V12, DOI 10.3390/educsci12070437
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Ebadi S, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00250-0
   Foroughi B, 2024, INT J HUM-COMPUT INT, V40, P4501, DOI 10.1080/10447318.2023.2226495
   Godwin-Jones R, 2022, LANG LEARN TECHNOL, V26, P5, DOI 10.10125/73474
   Goli M, 2023, INT J TECHNOL HUM IN, V19, P1, DOI 10.4018/IJTHI.318481
   Graves BC, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100754
   Guo K, 2024, EDUC INF TECHNOL, V29, P8435, DOI 10.1007/s10639-023-12146-0
   Hair J., 2022, Research Methods in Applied Linguistics, V1, DOI [DOI 10.1016/J.RMAL.2022.100027, 10.1016/j.rmal.2022.100027]
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hao S, 2017, ETR&D-EDUC TECH RES, V65, P101, DOI 10.1007/s11423-016-9465-2
   Huang XY, 2023, EDUC TECHNOL SOC, V26, P112, DOI 10.30191/ETS.202301_26(1).0009
   Ironsi CS, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00272-8
   Jo H, 2023, TELEMAT INFORM, V85, DOI 10.1016/j.tele.2023.102067
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Labadze L, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00426-1
   Li B, 2023, INT J COMPUT-ASSIST, V13, DOI 10.4018/IJCALLT.326135
   Li C, 2021, ENERG SOURCE PART A, DOI [10.1080/15567036.2021.1903620, 10.1007/s10639-021-10462-x]
   Lin HC, 2022, INT J MOB LEARN ORG, V16, P74, DOI 10.1504/IJMLO.2022.10043753
   Liu GX, 2024, INNOV LANG LEARN TEA, V18, P125, DOI 10.1080/17501229.2023.2240316
   Mohamed AM, 2024, EDUC INF TECHNOL, V29, P3195, DOI 10.1007/s10639-023-11917-z
   Ni AH, 2023, EDUC INF TECHNOL, V28, P3191, DOI 10.1007/s10639-022-11305-z
   Ofosu-Ampong K., 2023, Information and Knowledge Management, V13, DOI DOI 10.7176/IKM/13-4-03
   Pack A, 2023, TESOL QUART, V57, P1571, DOI 10.1002/tesq.3253
   Pillai R, 2024, INFORM TECHNOL PEOPL, V37, P328, DOI 10.1108/ITP-02-2021-0152
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Song YJ, 2017, J EDUC COMPUT RES, V55, P865, DOI 10.1177/0735633116688320
   Tafazoli D, 2019, INT J INF COMMUN TEC, V15, P60, DOI 10.4018/IJICTE.2019070105
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Wang WT, 2021, EDUC TECHNOL SOC, V24, P14
   Wang XH, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104703
   Wang Y., 2014, Theory and Practice in Language Studies, V4, P160, DOI [10.4304/tpls.4.1.160-166, DOI 10.4304/TPLS.4.1.160-166]
   Yan LX, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13370
   Zhang DY, 2021, COMPUT ASSIST LANG L, V34, P1128, DOI 10.1080/09588221.2019.1662455
   Zhang RF, 2023, INNOV LANG LEARN TEA, V17, P932, DOI 10.1080/17501229.2023.2197417
   Zhang SA, 2023, EDUC INF TECHNOL, V28, P15223, DOI 10.1007/s10639-023-11805-6
   Zhang Y, 2024, COMPUT ASSIST LANG L, V37, P1904, DOI 10.1080/09588221.2022.2134424
   Zhao Y, 2021, BRIT J EDUC TECHNOL, V52, P20, DOI 10.1111/bjet.13002
NR 56
TC 0
Z9 0
U1 75
U2 75
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0802-6106
EI 1473-4192
J9 INT J APPL LINGUIST
JI Int. J. Appl. Linguist.
PD 2024 AUG 9
PY 2024
DI 10.1111/ijal.12603
EA AUG 2024
PG 18
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA C0V2Y
UT WOS:001286621300001
DA 2024-12-25
ER

PT J
AU Bergin, D
   Oppegaard, B
AF Bergin, Daniel
   Oppegaard, Brett
TI Automating Media Accessibility: An Approach for Analyzing Audio
   Description Across Generative Artificial Intelligence Algorithms
SO TECHNICAL COMMUNICATION QUARTERLY
LA English
DT Article; Early Access
DE Audio description; alt-text; blind; bias; disability studies; ethics;
   generative artificial intelligence; low vision; media accessibility;
   Kamala Harris
ID TECHNICAL COMMUNICATION; IDENTITY THEORY; FUTURE
AB A surge in public availability of emerging GenAI-AD has brought back the promises of automated accessibility for people who cannot see or see well. This article tests those promises through a double-rendering method that asks GenAI-AD engines to describe a simple portrait of a person and then returns these generated texts into GenAI-AD engines for visualizations of what they earlier had described, revealing insights about GenAI efficacies, ethics, and biases.
C1 [Bergin, Daniel; Oppegaard, Brett] Univ Hawaii, Honolulu, HI USA.
C3 University of Hawaii System
RP Oppegaard, B (corresponding author), Univ Hawaii, Sch Commun & Informat, 2550 Campus Rd,Crawford 310, Honolulu, HI 96822 USA.
EM brett.oppegaard@hawaii.edu
RI Oppegaard, Brett/J-1653-2015
OI Oppegaard, Brett/0000-0002-5778-1464
FU U.S. National Endowment for the Humanities [276851-21]; U.S. National
   Park Service [P20AC01084-01]; Google; UH Foundation [127-7390-4]
FX This work was supported by the U.S. National Endowment for the
   Humanities under Grant 276851-21; the U.S. National Park Service under
   Grant P20AC01084-01; and Google, under a grant administered via the UH
   Foundation, 127-7390-4.
CR Avnoon N, 2024, NEW MEDIA SOC, V26, P5962, DOI 10.1177/14614448221145728
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Benjamin Ruha, 2019, RACE TECHNOLOGY ABOL
   BURKE PJ, 1980, SOC PSYCHOL QUART, V43, P18, DOI 10.2307/3033745
   Conway M., 2023, Describing people and portraits through audio description: Preferences of people who are blind, low vision, and deafblind
   Conway M, 2020, TECH COMMUN-STC, V67, P68
   CostanzaChock S, 2020, INFORM POL, P1
   Davenport T. H., 2022, Harvard Business Review
   De Cosmo L, 2022, The Scientific American
   Dilmegani C., 2023, AI Multiple
   Duncan M, 2022, TECH COMMUN Q, V31, P207, DOI 10.1080/10572252.2021.1977850
   Farhi Paul., 2023, WASHINGTON POST
   Fresno N., 2015, Audio describing characters: What features do audiences remember?
   Graham SS, 2022, TECH COMMUN Q, V31, P89, DOI 10.1080/10572252.2021.1955151
   Guzman AL, 2020, NEW MEDIA SOC, V22, P70, DOI 10.1177/1461444819858691
   Heath A., 2024, The Verge
   Hogg MA, 1995, SOC PSYCHOL QUART, V58, P255, DOI 10.2307/2787127
   Huang K., 2023, The New York Times
   Iyer A., 2023, Analytics India Mag
   Jones NN, 2016, TECH COMMUN Q, V25, P211, DOI 10.1080/10572252.2016.1224655
   Kendall L, 2011, J POP CULT, V44, P505, DOI 10.1111/j.1540-5931.2011.00846.x
   Koirala S, 2022, J VISUAL IMPAIR BLIN, V116, P461, DOI 10.1177/0145482X221116903
   Krippendorff K, 2012, CONTENT ANAL INTRO I
   Levitt H.M., 2021, Essentials of critical-constructivist grounded theory research, DOI DOI 10.1037/0000231-000
   Maszerowska Anna., 2014, Audio Description: New Perspectives Illustrated, DOI 10.1075/btl.112
   Matamala Anna, 2016, RES AUDIO DESCRIPTIO
   McPherson P., 2020, Reuters
   Melonon L., 2013, Rhetorical accessability: At the intersection of technical communication and disability studies, DOI [https://doi.org/10.4324/9781315231501, DOI 10.4324/9781315231501]
   Oppegaard B., 2022, Technical Communication, V69, P27, DOI DOI 10.55177/TC124312
   Oppegaard B., 2024, Amplifying voices in UX: Balancing design and user needs in technical communication
   Oppegaard B, 2024, PERSPECT STUD TRANSL, V32, P43, DOI 10.1080/0907676X.2022.2116990
   Osborne AC, 2013, SPORT SOC, V16, P672, DOI 10.1080/17430437.2012.753523
   Owens TJ, 2010, ANNU REV SOCIOL, V36, P477, DOI 10.1146/annurev.soc.34.040507.134725
   Ploin A., 2022, AI ARTS MACHINE LEAR
   Rennie DL, 2000, THEOR PSYCHOL, V10, P481, DOI 10.1177/0959354300104003
   Roose K., 2022, The New York Times
   Ross DG, 2019, IEEE T PROF COMMUN, V62, P4, DOI 10.1109/TPC.2018.2867179
   Rouan R., 2022, USA Today27 July
   Schwartz, 2019, IEEE SPECTRUM
   Simonite T., 2018, WIRED
   Stets JE, 2000, SOC PSYCHOL QUART, V63, P224, DOI 10.2307/2695870
   Suleyman M., 2022, Wired
   Walch K., 2021, Cognilytica
   Walton R., 2019, TECHNICAL COMMUNICAT
   WebAIM, 2024, Screen Reader User Survey No. 10
   Wenyan Tu, 2021, Journal of Physics: Conference Series, V1883, DOI 10.1088/1742-6596/1883/1/012165
   Wood Graeme., 2022, The Atlantic
   Wright D, 2011, TECH COMMUN Q, V20, P443, DOI 10.1080/10572252.2011.596716
   Zdenek S., 2015, Reading sounds: Closed-captioned media and popular culture, DOI [https://doi.org/10.7208/9780226312811, DOI 10.7208/9780226312811]
NR 49
TC 0
Z9 0
U1 2
U2 2
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1057-2252
EI 1542-7625
J9 TECH COMMUN Q
JI Tech. Commun. Q.
PD 2024 JUL 6
PY 2024
DI 10.1080/10572252.2024.2372771
EA JUL 2024
PG 16
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA XS1P2
UT WOS:001263577800001
DA 2024-12-25
ER

PT J
AU Jochim, J
   Lenz-Kesekamp, VK
AF Jochim, Julia
   Lenz-Kesekamp, Vera Kristina
TI Teaching and testing in the era of text-generative AI: exploring the
   needs of students and teachers
SO INFORMATION AND LEARNING SCIENCES
LA English
DT Article; Early Access
DE AI; Generative artificial intelligence; GenAI; ChatGPT; Large language
   model; Higher education; Exams; Domestication Theory
ID DOMESTICATION
AB Purpose - Large language models such as ChatGPT are a challenge to academic principles, calling into question well-established practices, teaching and exam formats. This study aims to explore the adaptation process regarding text-generative artificial intelligence (AI) of students and teachers in higher education and to identify needs for change. Design/methodology/approach - The issue is explored in a mixed-methods approach based on Domestication Theory (Silverstone et al., 1992; Silverstone, 1994), incorporating views of both teaching staff and students. Both statistical and content analyses were carried out. Findings - The results show that both students and teachers are conflicted about generative AI and its usage. Trepidation and fear stand against a general feeling that AI is an integral part of the future and needs to be embraced. Both groups show marked needs for training and rules and offer a variety of ideas for new exam formats. Originality/value - This study provides a unique insight by exploring the attitudes and usage intentions regarding generative AI of two stakeholder groups: students and teachers. Its results can be of significant use to institutions deciding on their strategy regarding AI. It illustrates attitudes and usage intentions as well as needs of both groups. In addition, ideas for new assessment and teaching formats were generated.
C1 [Jochim, Julia] Europa Fernhochschule Hamburg, Dept Digital Media, Hamburg, Germany.
   [Lenz-Kesekamp, Vera Kristina] Europa Fernhochschule Hamburg, Dept Business Digitalisat & Management, Hamburg, Germany.
RP Jochim, J (corresponding author), Europa Fernhochschule Hamburg, Dept Digital Media, Hamburg, Germany.
EM julia.jochim@euro-fh.de; vera.kristina.lenz-kesekamp@euro-fh.de
OI Jochim, Julia/0009-0006-8350-5240
CR [Anonymous], 1989, Multimethod Research: A Synthesis of Styles, DOI 10.1002/nur.4770140212
   Baron N. S., 2023, CONVERSATION    0119
   Baume M., 2024, INTED2024 P, P3980, DOI [10.21125/inted.2024.1026, DOI 10.21125/INTED.2024.1026]
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Braun D.M., 2023, EINFLUSS DER KUNSTLICHEN INTELLIGENZ AUF ARBEITSTATIGKEITEN UND BERUFSBILDER. Kurzstudie fur das Handelsblatt
   Buck Isabella., 2023, die hochschullehre 9 (1), P70
   Chen L, 2023, Arxiv, DOI arXiv:2304.09823
   Choi JH, 2022, J LEGAL EDUC, V71, P387
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Ehlers U.D., 2024, Forschungsbericht 1: Theorie, Methodisches Design Und Ergebnisse Der KIKompetenzstudie 2024, P110
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Evans O., 2023, ChatGPT impacts on access-efficiency, employment, education and ethics: the socio-economics of an AI language model
   Finnie-Ansley J, 2022, PROCEEDINGS OF THE 24TH AUSTRALASIAN COMPUTING EDUCATION CONFERENCE, ACE 2022, P10, DOI 10.1145/3511861.3511863
   Friedrich J.D., 2023, Zur Bedeutung von ChatGPT & der Notwendigkeit eines progressiven Umgangs mit neuen KI-Technologien im Hochschulbereich. Ein Zwischenstand in 6 Thesen
   George A. S., 2023, Partners Universal International Innovation Journal (PUIIJ), V1, P154, DOI DOI 10.5281/ZENODO.8076921
   Gimpel Henner, 2023, Tech. Rep.
   Grassini S, 2023, EDUC SCI, V13, DOI 10.3390/educsci13070692
   Harwood SA, 2011, INFORM ORGAN-UK, V21, P84, DOI 10.1016/j.infoandorg.2011.03.002
   Hodges CB, 2024, TECHTRENDS, V68, P195, DOI 10.1007/s11528-023-00926-x
   Huang K., 2023, New York TimesJan. 16
   Ifenthaler D., 2023, KUNSTL INTELL, P71, DOI 10
   Johnson B., 2003, Handbook of Mixed Methods in Social Behavioral Research, P297
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Kunschke D., 2022, FinTech: Digitalisierung, Knstliche Intelligenz und aufsichtsrechtliche Regulierung von Finanzdienstleistungen, P91, DOI [10.37307/b.978-3-503-20689-6.03, DOI 10.37307/B.978-3-503-20689-6.03]
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Malinka K, 2023, PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1, P47, DOI 10.1145/3587102.3588827
   Mayring P., 2019, HDB METHODEN EMPIRIS, DOI [DOI 10.1007/978-3-531-18939-0_38, 10.1007/978-3-658-21308-4_42, DOI 10.1007/978-3-658-21308-4_42]
   Meyer JG, 2023, BIODATA MIN, V16, DOI 10.1186/s13040-023-00339-9
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mital M, 2018, TECHNOL FORECAST SOC, V136, P339, DOI 10.1016/j.techfore.2017.03.001
   Ng DTK, 2023, ETR&D-EDUC TECH RES, V71, P137, DOI 10.1007/s11423-023-10203-6
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Reinmann G., 2023, Impact Free
   Scheerder AJ, 2019, NEW MEDIA SOC, V21, P2099, DOI 10.1177/1461444819844299
   Silverstone R., 1994, TELEVISION EVERYDAY
   Silverstone Roger., 1992, Consuming Technologies: Media and information in domestic spaces
   Snchez-Torres J.A., 2022, Advances in Digital Marketing and eCommerce, P233, DOI [10.1007/978-3-031-05728-125, DOI 10.1007/978-3-031-05728-125]
   Southworth J., 2023, COMPUTERS ED ARTIFIC, V4, pPG, DOI [10.1016/j.caeai.2023.100127 10.1016/j.caeai.2023.100127, DOI 10.1016/J.CAEAI.2023.100127, 10.1016/j.caeai.2023.100127]
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Susnjak T., 2022, PREPRINT, DOI [DOI 10.48550/ARXIV.2212.09292, 10.48550/arXiv.2212.09292]
   Tashakkori A., 1998, Mixed methodology: Combining qualitative and quantitative approaches, V46
   von Garrel J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02304-7
   Wessels D., 2022, Hochschulforum Digitalisierung-Hochschulbildung im digitalen Zeitalter
   Yu H, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1181712
   Zarifhonarvar A., 2023, Journal of Electronic Business & Digital Economics, DOI [DOI 10.1108/JEBDE-10-2023-0021/FULL/PDF, 10.1108/JEBDE-10-2023-0021, DOI 10.1108/JEBDE-10-2023-0021, DOI 10.2139/SSRN.4350925]
NR 47
TC 2
Z9 2
U1 41
U2 41
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2398-5348
EI 2398-5356
J9 INFORM LEARN SCI
JI Inf. Learn. Sci.
PD 2024 JUL 2
PY 2024
DI 10.1108/ILS-10-2023-0165
EA JUL 2024
PG 21
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA WX7C7
UT WOS:001258225900001
DA 2024-12-25
ER

PT J
AU Wilderbeek, FLLD
AF Wilderbeek, Francisco Leslie Lopez del Castillo
TI Generative Artificial Intelligence: Technological Determinism or
   Socially Constructed Artifact
SO PALABRA CLAVE
LA English
DT Article
DE Social construction of technology; technological determinism; GenAI; ge-
   nerative artificial intelligence; SCOT
ID AI
AB This article discusses generative artificial intelligence (GenAI) using the social construction of technology (SCOT) model, observing the social actors affected by this technology with influence to decide their future. The results indicate that the success of GenAI does not have a neutral origin but is conditioned by the interests of various social actors. However, it also suggests that this technology is in a phase of interpretive flexibility; that is, the affected groups are still deciding their position on GenAI and what it could be like.
C1 [Wilderbeek, Francisco Leslie Lopez del Castillo] Univ Pompeu Fabra, Fabra, Spain.
C3 Pompeu Fabra University
RP Wilderbeek, FLLD (corresponding author), Univ Pompeu Fabra, Fabra, Spain.
EM franciscoleslie@alumni.upf.edu
CR Abbring J., 2002, Losing work, moving on: international perspectives on worker displacement, DOI [10.17848/9781417505333.Ch2, DOI 10.17848/9781417505333.CH2]
   Altman Sam, 2021, Moore's law for everything
   [Anonymous], 1964, Understanding Media: The Extensions of Man
   Bengio Y., 2023, Pause giant ai experiments: An open letter
   Bessen J., 2020, Automation: A guide for policymakers
   Black Julia, 2019, European Journal of Law and Technology, V10, P67
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Chohan U. W., 2023, Generative AI, ChatGPT, and the Future of Jobs, DOI [10.2139/ssrn.4411068, DOI 10.2139/SSRN.4411068]
   De Clement Z. D, 2022, Revista Estudios Juridicos, V22, DOI [10.17561/rej.n22.7524, DOI 10.17561/REJ.N22.7524]
   Dedehayir O, 2016, TECHNOL FORECAST SOC, V108, P28, DOI 10.1016/j.techfore.2016.04.005
   Del Castillo AP, 2024, AI SOC, V39, P2601, DOI 10.1007/s00146-023-01719-9
   Delipetrev B., 2020, Historical evolution of Artificial Intelligence, DOI DOI 10.2760/801580
   Edwards P., 1996, The Closed World: Computers and the Politics of Discourse in Cold War America
   Ellingrud W, 2023, Generative AI and the future of work in America
   Erdélyi OJ, 2018, PROCEEDINGS OF THE 2018 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY (AIES'18), P95, DOI 10.1145/3278721.3278731
   European Parliament, 2020, Artificial Intelligence: Threats and Opportunities | News | European Parliament
   Floridi L., 2020, Philos Technol, V33, P1, DOI [10.1007/s13347-020-00396-6, DOI 10.1007/S13347-020-00396-6]
   Floridi L., 2019, Philos Technol, V32, P1, DOI DOI 10.1007/S13347-019-00345-Y
   Ghoshal A., 2023, Computer World
   Global Data, 2023, GenAI startups rewrite unicorn playbook: Race to billion-dollar valuations in record time, reveals Global Data
   Goldfarb B., 2019, Bubbles and crashes: The boom and bust of technological innovation, DOI [10.1515/9781503607934, DOI 10.1515/9781503607934]
   Goldman Sachs, 2023, GEN AI COULD RAIS GL
   Gunkel D.J., 2012, COMMUNICATION 1, V1, P1, DOI [DOI 10.7275/R5QJ7F7R, https://doi.org/10.7275/R5QJ7F7R]
   Hacker P., 2023, Sustainable AI Regulation, DOI [10.2139/ssrn.4467684, DOI 10.2139/SSRN.4467684]
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   Hirsch-Kreinsen H, 2024, AI SOC, V39, P1641, DOI 10.1007/s00146-023-01629-w
   Hoffmann-Riem W, 2020, REGULATING ARTIFICIAL INTELLIGENCE, P1, DOI 10.1007/978-3-030-32361-5_1
   Jiang Y., 2022, Discov. Artif. Intell, V2, P4, DOI DOI 10.1007/S44163-022-00022-8
   Kalla D., 2023, International Journal of Innovative Science and Research Technology, V8
   Klein HK, 2002, SCI TECHNOL HUM VAL, V27, P28, DOI 10.1177/016224390202700102
   Lerner J, 2020, J ECON PERSPECT, V34, P237, DOI 10.1257/jep.34.3.237
   Liu Z. Z., 2022, INT C PUBL ORG IC 20, P97
   Lyonnet V., 2022, P EUR ESSEC PAR DEC, DOI [10.2139/ssrn.4260882, DOI 10.2139/SSRN.4260882]
   McKinsey, 2023, Report
   Nield T, 2019, Towards Data Science
   Nissim G, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101732
   Niyazov S., 2019, Towards Data Science
   Orchard T, 2023, ECON BUS REV-POL, V9, P9, DOI 10.18559/ebr.2023.2.732
   Oxford Analytica, 2023, Oxford Analytica Daily Brief
   Parlamento Europeo, 2023, Ley de IA de la UE: primera normativa sobre inteligencia artificial
   PINCH TJ, 1984, SOC STUD SCI, V14, P399, DOI 10.1177/030631284014003004
   Postman N., 2011, Technopoly: The surrender of culture to technology
   Rikap C, 2023, Same end by different means: Google, Amazon, Microsoft and Facebook's strategies to dominate artificial intelligence, DOI [10.2139/ssrn.4472222, DOI 10.2139/SSRN.4472222]
   Schuchmann S., 2019, Towards Data Science
   Schwab K., 2020, The future of jobs report 2020
   Shin R., 2023, Fortune 2 de junio
   Slowinski S., 2023, BNP Paribas
   Strate L, 1999, WESTERN J COMM, V63, P382, DOI 10.1080/10570319909374648
   Sundberg L, 2023, BUS HORIZONS, V66, P777, DOI 10.1016/j.bushor.2023.04.003
   Sweney M., 2023, The Guardian 18 de mayo
   Tasioulas J, 2019, J PRACT ETHICS, V7, P61
   Tasioulas John., 2023, Vienna Lectures on Legal Philosophy, DOI [10.2139/ssrn.4319969, DOI 10.2139/SSRN.4319969]
   Toosi A, 2021, PET CLIN, V16, P449, DOI 10.1016/j.cpet.2021.07.001
   Ulnicane I., 2022, The routledge handbook of european integrations, DOI [10.4324/9780429262081-19, DOI 10.4324/9780429262081-19]
   Vinsel L, 2023, MIT SLOAN MANAGE REV, V64, P8
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Walch K., 2019, FORBES
   Winner Langdon., 1993, SCI CULT-UK, V3, P427, DOI DOI 10.1080/09505439309526358
NR 59
TC 0
Z9 0
U1 8
U2 16
PU UNIV SABANA, FAC COMUNICACION
PI CUNDINAMARCA
PA CAMPUS PUENTE COMUN, KM 7, AUTOPISTA NORTE BOGOTA CHIA, CUNDINAMARCA,
   00000, COLOMBIA
SN 0122-8285
EI 2027-534X
J9 PALABRA CLAVE
JI Palabra Clave
PD MAR
PY 2024
VL 27
IS 1
AR e2719
DI 10.5294/pacla.2024.27.1.9
PG 23
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA LU0P8
UT WOS:001189199900001
OA gold
DA 2024-12-25
ER

PT J
AU Ooi, KB
   Tan, GWH
   Al-Emran, M
   Al-Sharafi, MA
   Capatina, A
   Chakraborty, A
   Dwivedi, YK
   Huang, TL
   Kar, AK
   Lee, VH
   Loh, XM
   Micu, A
   Mikalef, P
   Mogaji, E
   Pandey, N
   Raman, R
   Rana, NP
   Sarker, P
   Sharma, A
   Teng, C
   Wamba, SF
   Wong, LW
AF Ooi, Keng-Boon
   Tan, Garry Wei-Han
   Al-Emran, Mostafa
   Al-Sharafi, Mohammed A.
   Capatina, Alexandru
   Chakraborty, Amrita
   Dwivedi, Yogesh K.
   Huang, Tzu-Ling
   Kar, Arpan Kumar
   Lee, Voon-Hsien
   Loh, Xiu-Ming
   Micu, Adrian
   Mikalef, Patrick
   Mogaji, Emmanuel
   Pandey, Neeraj
   Raman, Ramakrishnan
   Rana, Nripendra P.
   Sarker, Prianka
   Sharma, Anshuman
   Teng, Ching-, I
   Wamba, Samuel Fosso
   Wong, Lai-Wan
TI The Potential of Generative Artificial Intelligence Across Disciplines:
   Perspectives and Future Directions
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Generative artificial intelligence; machine learning; large language
   model; ChatGPT; Bard
ID MANAGEMENT
AB In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).
C1 [Ooi, Keng-Boon; Tan, Garry Wei-Han] UCSI Univ, UCSI Grad Business Sch, Kuala Lumpur, Malaysia.
   [Ooi, Keng-Boon] FORE Sch Management, New Delhi, India.
   [Ooi, Keng-Boon; Tan, Garry Wei-Han] Swinburne Univ Technol, Fac Business Design & Arts, Sarawak Campus, Kuching, Malaysia.
   [Tan, Garry Wei-Han] Adamson Univ, Coll Business Adm, Manila, Philippines.
   [Al-Emran, Mostafa] British Univ Dubai, Fac Engn & IT, Dubai, U Arab Emirates.
   [Al-Emran, Mostafa] Dijlah Univ Coll, Dept Comp Tech Engn, Baghdad, Iraq.
   [Al-Sharafi, Mohammed A.] Univ Tenaga Nas, Inst Informat & Comp Energy, Kajang, Selangor, Malaysia.
   [Capatina, Alexandru; Micu, Adrian] Dunarea de Jos Univ Galati, Dept Business Adm, Galati, Romania.
   [Chakraborty, Amrita] Tech Talk, New Delhi, India.
   [Dwivedi, Yogesh K.] Swansea Univ Bay Campus, Sch Management, Digital Futures Sustainable Business & Soc Res Gr, Swansea, Wales.
   [Dwivedi, Yogesh K.; Raman, Ramakrishnan] Symbiosis Int, Dept Management, Pune, India.
   [Huang, Tzu-Ling] Natl Cent Univ, Dept Informat Management, Taoyuan, Taiwan.
   [Kar, Arpan Kumar] Indian Inst Technol Delhi, Dept Management Studies, New Delhi, India.
   [Lee, Voon-Hsien; Loh, Xiu-Ming] Univ Tunku Abdul Rahman, Fac Business & Finance, Kampar, Malaysia.
   [Mikalef, Patrick] Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Trondheim, Norway.
   [Mikalef, Patrick] SINTEF Digital, Dept Technol Management, Trondheim, Norway.
   [Mogaji, Emmanuel] Keele Univ, Keele Business Sch, Keele, England.
   [Pandey, Neeraj] Indian Inst Management Mumbai, Mumbai, India.
   [Rana, Nripendra P.] Qatar Univ, Coll Business & Econ, Doha, Qatar.
   [Sarker, Prianka] Manchester Metropolitan Univ, Business Sch, Manchester, England.
   [Sharma, Anshuman] Ajman Univ, Coll Business Adm, Dept Mkt, Ajman, U Arab Emirates.
   [Teng, Ching-, I] Chang Gung Univ, Grad Inst Management, Taoyuan, Taiwan.
   [Wamba, Samuel Fosso] TBS Business Sch, Informat Operat & Management Sci, Toulouse, France.
   [Wong, Lai-Wan] Xiamen Univ Malaysia, Sch Elect & Comp Engn, Sepang, Malaysia.
   [Ooi, Keng-Boon] UCSI Univ, UCSI Grad Business Sch, 1 Jalan Menara Gading,UCSI Hts, Kuala Lumpur 56000, Malaysia.
C3 UCSI University; FORE School of Management; Swinburne University of
   Technology Sarawak; Swinburne University of Technology; Adamson
   University; Dijlah University College; Universiti Tenaga Nasional;
   Dunarea De Jos University Galati; Symbiosis International University;
   National Central University; Indian Institute of Technology System (IIT
   System); Indian Institute of Technology (IIT) - Delhi; Universiti Tunku
   Abdul Rahman (UTAR); Norwegian University of Science & Technology
   (NTNU); SINTEF; Keele University; Indian Institute of Management (IIM
   System); Indian Institute of Management Mumbai; Qatar University;
   Manchester Metropolitan University; Ajman University; Chang Gung
   University; Xiamen University Malaysia Campus; UCSI University
RP Ooi, KB (corresponding author), UCSI Univ, UCSI Grad Business Sch, 1 Jalan Menara Gading,UCSI Hts, Kuala Lumpur 56000, Malaysia.
EM ooikengboon@gmail.com
RI OOI, Keng-Boon/I-4143-2019; Lee, Voon-Hsien/S-6123-2017; Ming,
   Loh/AAD-6957-2021; sarker, prianka/KBA-0097-2024; Mogaji,
   Emmanuel/B-8900-2014; Raman, Ramakrishnan/U-2170-2017; Dwivedi,
   Yogesh/A-5362-2008; Tan Wei Han, Garry/C-6565-2011; WONG,
   LaiWan/AAV-8498-2020; Fosso Wamba, Samuel/AAB-4953-2019; Micu,
   Adrian/AAJ-9641-2020; Al-Emran, Mostafa/W-4466-2018; Rana,
   Nripendra/ABA-4719-2020; Mikalef, Patrick/GVU-5020-2022; Al-Sharafi,
   Mohammed A./E-1530-2017; Pandey, Neeraj/D-1968-2013; Kar, Arpan
   Kumar/B-9999-2009
OI Al-Sharafi, Mohammed A./0000-0003-0726-6031; Dwivedi,
   Yogesh/0000-0002-5547-9990; Ooi, Keng-Boon/0000-0002-3384-1207; Micu,
   Adrian/0000-0003-3161-5748; Pandey, Neeraj/0000-0002-6238-6397; Kar,
   Arpan Kumar/0000-0003-4186-4887
CR Abdulquadri A, 2021, J ENTERP COMMUNITIES, V15, P258, DOI 10.1108/JEC-06-2020-0126
   Achiam J., 2023, GPT-4 Technical Report
   Akter S, 2022, J BUS RES, V144, P201, DOI 10.1016/j.jbusres.2022.01.083
   Al-Emran M, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3237789
   [Anonymous], 2023, Reuters
   [Anonymous], 2023, GUARDIAN
   [Anonymous], 2023, Bloomberg
   Baduge SK, 2022, AUTOMAT CONSTR, V141, DOI 10.1016/j.autcon.2022.104440
   Bing Team, 2023, NEW BING EDG INCR LI
   Bogue R, 2019, IND ROBOT, V46, P461, DOI 10.1108/IR-03-2019-0053
   Borden B., 2023, ERA GENERATIVE DRIVI
   Boston Consulting Group, 2023, GEN AI
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P1, DOI DOI 10.5281/ZENODO.7755273
   Buhalis D, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102253
   Cao LL, 2021, INT J RETAIL DISTRIB, V49, P958, DOI 10.1108/IJRDM-09-2020-0350
   Chakraborty A, 2021, INT J INF LEARN TECH, V38, P273, DOI 10.1108/IJILT-06-2020-0125
   Chawla Y, 2022, DIGIT POLICY REGUL G, V24, P17, DOI 10.1108/DPRG-05-2021-0062
   Chen XL, 2023, INFORM PROCESS MANAG, V60, DOI 10.1016/j.ipm.2022.103113
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Chi OH, 2023, INT J INFORM MANAGE, V70, DOI 10.1016/j.ijinfomgt.2023.102623
   Chiu YT, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102379
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Cukka P., 2023, GENERATIVE MISSING P
   Czarnecka B, 2020, INT J BANK MARK, V38, P756, DOI 10.1108/IJBM-07-2019-0249
   Deloitte, 2018, DEL SKILLS GAP FUT W
   Denodo, 2019, DAT LANDSC IS FRAGM
   Dickson B., 2020, COULD SAVE WORLD IT
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2023, PSYCHOL MARKET, V40, P750, DOI 10.1002/mar.21767
   Dwivedi YK, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102542
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Edelman DC., 2023, GENERATIVE WILL CHAN
   Edwards B., 2023, GPT 4 WILL HUNT TREN
   El Hana N, 2023, TECHNOL FORECAST SOC, V188, DOI 10.1016/j.techfore.2022.122297
   Elegant NX, 2019, INTERNET CLOUD HAS D
   Enberg J., 2023, CHATGPT GENERATIVE C
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fügener A, 2021, MIS QUART, V45, P1527, DOI 10.25300/MISQ/2021/16553
   Füller J, 2022, TECHNOL FORECAST SOC, V178, DOI 10.1016/j.techfore.2022.121598
   Gao CA, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00819-6
   Garg S, 2022, INT J PRODUCT PERFOR, V71, P1590, DOI 10.1108/IJPPM-08-2020-0427
   Goldman Sachs, 2023, GEN COULD RAIS GLOB
   Guha A, 2021, J RETAILING, V97, P28, DOI 10.1016/j.jretai.2021.01.005
   Gurman M., 2023, Samsung Bans Staff's AI Use After Spotting ChatGPT Data Leak
   Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
   Hatzius J., 2023, POTENTIALLY LARGE EF
   Heins C, 2023, FORESIGHT, V25, P264, DOI 10.1108/FS-10-2021-0210
   Herm LV, 2023, INF SYST E-BUS MANAG, V21, P1, DOI 10.1007/s10257-022-00553-8
   Hu K., 2023, REUTERS         0202
   Huang SS, 2021, J FINANC REGUL COMPL, V29, P336, DOI 10.1108/JFRC-06-2020-0062
   Isabel, 2023, DRAGGAN AI POW IM ED
   Jalil S, 2023, IEEE ICST WORKSHOP, P430, DOI 10.1109/ICSTW58534.2023.00078
   Jeon J, 2023, EDUC INF TECHNOL, V28, P11963, DOI 10.1007/s10639-023-11656-1
   Jesuthasan R., 2023, NAVIGATING IMPACT GE
   Jussupow E, 2021, INFORM SYST RES, V32, P713, DOI 10.1287/isre.2020.0980
   Kalwar, 2023, THINGS YOU NEED KNOW
   Kamoonpuri SZ, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103258
   Kar Sudatta, 2021, IEEE Engineering Management Review, V49, P76, DOI 10.1109/EMR.2021.3107344
   Kar S, 2021, IEEE ACCESS, V9, P30017, DOI 10.1109/ACCESS.2021.3059407
   Kaur J., 2023, GENERATIVE AI TELECO
   Kelly C., 2023, COKE ASKS CONSUMERS
   Khan RA, 2023, PAK J MED SCI, V39, P605, DOI 10.12669/pjms.39.2.7653
   Kokemuller N., 2023, DIFFERENCES FRONT EN
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Kumar P, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102598
   Kumar P, 2023, INFORM SYST FRONT, V25, P2197, DOI [10.1007/s10796-021-10136-6, 10.1109/TNSE.2021.3089435]
   Kumar S., 2021, International Journal of Information Management Data Insights, Elsevier Ltd, V1, P100008, DOI [10.1016/j.jjimei.2021.100008, DOI 10.1016/J.JJIMEI.2021.100008]
   Lancaster A., 2023, Beyond chatbots: the rise of large language models
   Langer M, 2021, COMPUT HUM BEHAV, V123, DOI 10.1016/j.chb.2021.106878
   Larsen B., 2023, Generative AI: A game‐changer society needs to be ready for
   Lee JC, 2022, INT J BANK MARK, V40, P631, DOI 10.1108/IJBM-08-2021-0394
   Levy A., 2023, 2 CO ARE USING GENER
   Leyer M, 2021, BUS HORIZONS, V64, P711, DOI 10.1016/j.bushor.2021.02.026
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu KF, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107026
   Loh XM, 2022, IND MANAGE DATA SYST, V122, P1645, DOI 10.1108/IMDS-09-2021-0558
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Mahmoud A B., 2020, Retail Futures, P165, DOI DOI 10.1108/978-1-83867-663-620201019
   Mamaghani M., 2021, DETECTING FINANCIAL
   Manyika J., 2018, AI, automation, and the future of work: Ten things to solve for (Tech4Good)
   Mauran C., 2023, BING CHATBOT NOW LET
   McGee-Smith S., 2023, SALESFORCES EINSTEIN
   Mehdi Yusuf, 2023, Reinventing search with a new AI-powered Microsoft Bing and Edge, your copilot for the web
   Mello G., 2023, WALL STREET BANKS AR
   Mikalef P, 2022, EUR J INFORM SYST, V31, P257, DOI 10.1080/0960085X.2022.2026621
   Mogaji E, 2022, INT J BANK MARK, V40, P1272, DOI 10.1108/IJBM-09-2021-0440
   Mogaji E, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101711
   Mogaji E, 2021, AUSTRALAS MARK J, V29, P235, DOI 10.1016/j.ausmj.2020.05.003
   Morgan B., 2019, 50 STATS PROVE VALUE
   Morgan Blake., 2019, The 20 Best Examples Of Using Artificial Intelligence For Retail Experiences
   Morra J., 2023, SYSTEM LEVEL PCB DES
   Mukherjee S., 2023, EU PROPOSES NEW COPY
   Nanath K., 2023, INT J INF MANAG DATA, V3, P100167, DOI [10.1016/j.jjimei.2023.100167, DOI 10.1016/J.JJIMEI.2023.100167]
   Neto JAR, 2023, CHATGTP GENERATIVE H
   Nicholls J, 2021, IEEE ACCESS, V9, P163965, DOI 10.1109/ACCESS.2021.3134076
   Niet I, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.690237
   Nishant R, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102104
   Oosthuizen K, 2021, AUSTRALAS MARK J, V29, P264, DOI 10.1016/j.ausmj.2020.07.007
   Pandey N, 2020, J STRATEG MARK, V28, P522, DOI 10.1080/0965254X.2019.1569109
   Papagiannidis E, 2023, INFORM SYST FRONT, V25, P123, DOI 10.1007/s10796-022-10251-y
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pichai S., 2023, An important next step on our AI journey
   Piktus A, 2023, NATURE, V618, P465, DOI 10.1038/d41586-023-01411-4
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Qin X, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107041
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Ramasundaram A, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102599
   Rathore AK, 2017, DECIS ANAL, V14, P229, DOI 10.1287/deca.2017.0355
   Reddington C., 2023, CO ARE BOOSTING PROD
   Salesforce, 2023, SAL ANN EINST GPT WO
   Schwartz EH., 2023, DUOLINGO OPENAI WILL
   Sharma A, 2023, NAT MACH INTELL, V5, P46, DOI 10.1038/s42256-022-00593-2
   Siggelkow N., 2023, CREATE WINNING CUSTO
   Singh M, 2020, INT J CLOTH SCI TECH, V32, P177, DOI 10.1108/IJCST-12-2018-0148
   Sinha P., 2023, GENERATIVE WILL CHAN
   Soetan TO, 2021, J SERV MARK, V35, P947, DOI 10.1108/JSM-07-2020-0280
   Sohn K, 2021, INT J RETAIL DISTRIB, V49, P61, DOI 10.1108/IJRDM-03-2020-0091
   Srinivasan R, 2021, COMMUN ACM, V64, P44, DOI 10.1145/3464903
   Stone M., 2023, SHOPIFY JUST MADE IT
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Suen HY, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107713
   Syam N, 2018, IND MARKET MANAG, V69, P135, DOI 10.1016/j.indmarman.2017.12.019
   Tambe P, 2019, CALIF MANAGE REV, V61, P15, DOI 10.1177/0008125619867910
   Tellez A., 2023, THESE MAJOR CO SNAP
   Thormundsson B., 2023, GENERATIVE ARTIFICIA
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Toribio A., 2023, GOOGLE INCORPORATES
   Trocin C, 2023, INFORM SYST FRONT, V25, P2139, DOI 10.1007/s10796-021-10146-4
   Tschang FT, 2021, ACAD MANAGE PERSPECT, V35, P642, DOI 10.5465/amp.2019.0062
   Tutun S, 2023, INFORM SYST FRONT, V25, P1261, DOI 10.1007/s10796-022-10282-5
   van Wynsberghe A., 2021, AI Ethics, V1, P213, DOI [10.1007/s43681-021-00043-6, DOI 10.1007/S43681-021-00043-6, DOI 10.1007/S43681-021-00043]
   Vlahou A, 2021, HYPERTENSION, V77, P1029, DOI 10.1161/HYPERTENSIONAHA.120.16340
   Votto A.M., 2021, International Journal of Information Management Data Insights, V1, DOI [DOI 10.1016/J.JJIMEI.2021.100047, 10.1016/j.jjimei.2021.100047]
   Weber FD, 2019, DIGIT POLICY REGUL G, V21, P264, DOI 10.1108/DPRG-09-2018-0050
   Wei J., 2022, P ADV NEUR INF PROC, V35, P24824, DOI DOI 10.48550/ARXIV.2201.11903
   Wei YH, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102838
   Weightman G., 2015, HIST BAR CODE
   Wertz J., 2022, DIGITIZATION IS IMPA
   Whitmore G., 2023, EXPEDIA APP INTEGRAT
   Wong LW, 2023, IEEE T ENG MANAGE, V70, P67, DOI 10.1109/TEM.2021.3053359
   Yi-no kang Enoch, 2023, Computers in Human Behavior, DOI 10.1016/j.chb.2022.107529
   Yusuf K., 2023, INTRO WATSONX FUTURE
   Zao-Sanders M., 2023, FRAMEWORK PICKING RI
NR 146
TC 101
Z9 102
U1 157
U2 427
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD 2023 OCT 5
PY 2023
DI 10.1080/08874417.2023.2261010
EA OCT 2023
PG 32
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA T8CC2
UT WOS:001080196300001
DA 2024-12-25
ER

PT J
AU Banh, L
   Strobel, G
AF Banh, Leonardo
   Strobel, Gero
TI Generative artificial intelligence
SO ELECTRONIC MARKETS
LA English
DT Article
DE Generative AI; Artificial intelligence; Deep learning; Deep generative
   models; Large language models
ID INFORMATION-SYSTEMS; ARTIFIICIAL INTELLIGENCE; AI
AB Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.
C1 [Banh, Leonardo; Strobel, Gero] Univ Duisburg Essen, Univ Str 2, D-45141 Essen, Germany.
C3 University of Duisburg Essen
RP Banh, L (corresponding author), Univ Duisburg Essen, Univ Str 2, D-45141 Essen, Germany.
EM leonardo.banh@uni-due.de; gero.strobel@uni-due.de
RI Strobel, Gero/LCE-0506-2024
OI Banh, Leonardo/0000-0001-9185-0243
FU Projekt DEAL
FX Open Access funding enabled and organized by Projekt DEAL.
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Ågerfalk PJ, 2022, COMMUN ASSOC INF SYS, V50, P420, DOI 10.17705/1CAIS.05017
   Aggarwal A., 2021, INT J INF MANAG DATA, V1, P100004, DOI DOI 10.1016/J.JJIMEI.2020.100004
   Agostinelli A., 2015, arXiv
   Ali Hazrat, 2023, Artificial Intelligence and Cognitive Science: 30th Irish Conference, AICS 2022, Revised Selected Papers. Communications in Computer and Information Science (1662), P32, DOI 10.1007/978-3-031-26438-2_3
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   [Anonymous], 2022, Washington Post
   Baeza-Yates R, 2018, COMMUN ACM, V61, P54, DOI 10.1145/3209581
   Bakpayev M, 2022, AUSTRALAS MARK J, V30, P90, DOI 10.1016/j.ausmj.2020.04.002
   BBC, 2023, Fake Trump arrest photos: How to spot an AI-generated image
   Berente N, 2021, MIS QUART, V45, P1433, DOI DOI 10.25300/MISQ/2021/16274
   Bhayana R, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230582
   Borsos Z, 2022, Arxiv, DOI arXiv:2209.03143
   Brand J, 2023, HARVARD BUSINESS SCH, DOI DOI 10.2139/SSRN.4395751
   Brasse J, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00644-5
   Brown TB, 2020, ADV NEUR IN, V33
   Brynjolfsson E., 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
   Brynjolfsson E., 2023, NBER Working Paper No. 31161, V31161, DOI DOI 10.3386/W31161
   Brynjolfsson E, 2017, SCIENCE, V358, P1530, DOI 10.1126/science.aap8062
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Burger B, 2023, EUR J INNOV MANAG, V26, P233, DOI 10.1108/EJIM-02-2023-0156
   Burström T, 2021, J BUS RES, V127, P85, DOI 10.1016/j.jbusres.2021.01.016
   Castelvecchi D, 2016, NATURE, V538, P21, DOI [10.1038/nature.2016.20491, 10.1038/538020a]
   Choi H, 2022, PROCEEDINGS OF THE THIRTY-FIRST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2022, P2888
   Christiano PF, 2017, ADV NEUR IN, V30
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dang H., 2022, ARXIV, DOI [10.48550/arXiv.2209.01390, DOI 10.48550/ARXIV.2209.01390]
   Danks D, 2017, PROCEEDINGS OF THE TWENTY-SIXTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4691
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Dziri N, 2022, NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES, P5271
   Einola K, 2023, HUM RESOUR MANAGE-US, V62, P117, DOI 10.1002/hrm.22147
   Elasri M, 2022, NEURAL PROCESS LETT, V54, P4609, DOI 10.1007/s11063-022-10777-x
   Elicit, 2022, Frequently asked questions: What is elicit?
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Esser P., 2023, arXiv
   Feng ZY, 2020, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2020, P1536
   Ferrara E, 2023, Arxiv, DOI [arXiv:2304.03738, DOI 10.48550/ARXIV.2304.03738]
   Ferreira KJ, 2016, M&SOM-MANUF SERV OP, V18, P69, DOI 10.1287/msom.2015.0561
   Fügener A, 2021, MIS QUART, V45, P1527, DOI 10.25300/MISQ/2021/16553
   Gao J, 2022, Advances In Neural Information Processing Systems, V35
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Griffith S., 2013, Advances in neural information processing systems, V26
   Gui J, 2023, IEEE T KNOWL DATA EN, V35, P3313, DOI 10.1109/TKDE.2021.3130191
   Guo D., 2021, 9 INT C LEARN REPR 2
   Haase J., 2023, AMCIS 2023 P
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hamm P, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00640-9
   Hamon R., 2020, EUR, V30040
   Harmon P., 1985, EXPERT SYSTEMS ARTIF
   Harshvardhan GM, 2020, COMPUT SCI REV, V38, DOI 10.1016/j.cosrev.2020.100285
   Hartmann J, 2023, Arxiv, DOI [arXiv:2301.01768, DOI 10.48550/ARXIV.2301.01768, 10.48550/ARXIV.2301.01768]
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hooker S, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2021.100241
   Horneber D, 2023, BUS INFORM SYST ENG+, V65, P723, DOI 10.1007/s12599-023-00817-8
   Houde S., 2020, ARXIV, DOI [10.48550/arXiv.2003.07679, DOI 10.48550/ARXIV.2003.07679]
   Hu K., 2023, REUTERS         0202
   Huang S., 2022, Generative AI: A Creative New World
   Hughes A., 2023, BBC Science Focus
   Jakesch M., 2023, P 2023 CHI C HUM FAC, P1
   Jakesch M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2208839120
   Janiesch C, 2021, ELECTRON MARK, V31, P685, DOI 10.1007/s12525-021-00475-2
   Jasper, 2022, ChatGPT vs. Jasper: How it's different from Jasper chat
   Jebara Tony, 2004, KLUWER INT SER ENG C
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jin Y, 2023, PROCEEDINGS OF THE 61ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL 2023, VOL 1, P7515
   Johnson DG, 2017, J ASSOC INF SCI TECH, V68, P2267, DOI 10.1002/asi.23867
   Jumper J, 2021, NATURE, V596, P583, DOI 10.1038/s41586-021-03819-2
   Kingma D. P., 2014, INT C LEARN REPR 202
   Kingma DP, 2014, ADV NEUR IN, V27
   Kodali N, 2017, Arxiv, DOI arXiv:1705.07215
   Kowalczyk P., 2023, ECIS 2023 Research-in-Progress Papers
   Kreps S, 2022, J EXP POLIT SCI, V9, P104, DOI 10.1017/XPS.2020.37
   Kühl N, 2022, ELECTRON MARK, V32, P2235, DOI 10.1007/s12525-022-00598-0
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Lehmann Florian, 2020, i-com: Journal of Interactive Media, V19, P251, DOI 10.1515/icom-2020-0025
   Leiker D., 2023, 24 INT C ART INT ED
   Li H, 2022, COMMUN ACM, V65, P56, DOI 10.1145/3490443
   Li JY, 2021, MIS QUART, V45, P1603, DOI 10.25300/MISQ/2021/16523
   Li M, 2022, EXPERT SYST APPL, V192, DOI 10.1016/j.eswa.2021.116383
   Lingyun Qiu, 2005, ACM Transactions on Computer-Human Interaction, V12, P329, DOI 10.1145/1121112.1121113
   Lins S, 2021, BUS INFORM SYST ENG+, V63, P441, DOI 10.1007/s12599-021-00708-w
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Longoni Chiara, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P97, DOI 10.1145/3531146.3533077
   Lukyanenko R, 2022, ELECTRON MARK, V32, P1993, DOI 10.1007/s12525-022-00605-4
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Lysyakov M, 2023, INFORM SYST RES, V34, P1191, DOI 10.1287/isre.2022.1184
   Mayahi S., 2022, arXiv
   Mehrabi N, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3457607
   Meske C, 2022, ELECTRON MARK, V32, P2103, DOI 10.1007/s12525-022-00607-2
   Microsoft, 2023, Microsoft and OpenAI extend partnership
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   Mirbabaie M, 2022, ELECTRON MARK, V32, P73, DOI 10.1007/s12525-021-00496-x
   Mirsky Y, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3425780
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Moussawi S, 2021, ELECTRON MARK, V31, P343, DOI 10.1007/s12525-020-00411-w
   Murphy C, 2023, J SPINAL CORD MED, V46, P341, DOI 10.1080/10790268.2023.2198926
   Nichol A, 2022, Arxiv, DOI arXiv:2212.08751
   Ntoutsi E, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1356
   Oppenlaender Jonas, 2022, Academic Mindtrek 2022: 25th International Academic Mindtrek conference, P192, DOI 10.1145/3569219.3569352
   Ouyang L, 2022, ADV NEUR IN
   Pan ZQ, 2019, IEEE ACCESS, V7, P36322, DOI 10.1109/ACCESS.2019.2905015
   Patterson DW., 1990, Introduction to Artificial Intelligence and Expert Systems
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Pentina I, 2023, COMPUT HUM BEHAV, V140, DOI 10.1016/j.chb.2022.107600
   Perez F., 2022, 36 C NEUR INF PROC S
   Piccialli F, 2021, INFORM SYST FRONT, V23, P1467, DOI 10.1007/s10796-021-10131-x
   Poole Ben, 2023, 11 INT C LEARN REPR
   Raj M, 2023, Arxiv, DOI [arXiv:2303.06217, 10.48550/arXiv.2303.06217, DOI 10.48550/ARXIV.2303.06217]
   Ray Susmita, 2019, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), P35, DOI 10.1109/COMITCon.2019.8862451
   Riedl R, 2022, ELECTRON MARK, V32, P2021, DOI 10.1007/s12525-022-00594-4
   Rix J., 2023, AMCIS 2023 P
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Ruthotto Lars, 2021, GAMM - Mitteilungen, V44, P1, DOI 10.1002/gamm.202100008
   Samtani S, 2023, J MANAGE INFORM SYST, V40, P271, DOI 10.1080/07421222.2023.2172772
   Schneider J, 2023, BUS INFORM SYST ENG+, V65, P65, DOI 10.1007/s12599-022-00780-w
   Schoormann T, 2023, COMMUN ASSOC INF SYS, V52
   Schramowski P, 2022, NAT MACH INTELL, V4, P258, DOI 10.1038/s42256-022-00458-8
   Schuhmann C., 2022, Advances in Neural Information Processing Systems, V35
   Selz D, 2020, ELECTRON MARK, V30, P57, DOI 10.1007/s12525-019-00393-4
   Smits J., 2022, LAW ARTIFICIAL INTEL, V35, P323, DOI DOI 10.1007/978-94-6265-523-2_17
   Stability.ai, 2023, Stability AI launches the first of its StableLM suite of language models-stability AI
   Strobel G., 2024, HAW INT C SYST SCI 2
   Strobel G, 2023, COMMUN ASSOC INF SYS, V53, P42, DOI 10.17705/1CAIS.05303
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Synthesia, 2023, Synthesia-#1 AI Video Generator
   Teubner T, 2023, BUS INFORM SYST ENG+, V65, P95, DOI 10.1007/s12599-023-00795-x
   Tomczak J. M., 2022, Deep generative modeling, DOI DOI 10.1007/978-3-030-93158-2
   Tomitza C., 2023, WIRTSCH 2023 P
   van den Broek E, 2021, MIS QUART, V45, P1557, DOI 10.25300/MISQ/2021/16559
   van Dun C, 2023, DECIS SUPPORT SYST, V165, DOI 10.1016/j.dss.2022.113880
   Van Slyke C, 2023, COMMUN ASSOC INF SYS, V53, P1, DOI 10.17705/1CAIS.05301
   Vasist PN, 2022, COMMUN ASSOC INF SYS, V51, P590, DOI 10.17705/1CAIS.05126
   Vaswani A, 2017, ADV NEUR IN, V30
   Walters WP, 2020, NAT BIOTECHNOL, V38, P143, DOI 10.1038/s41587-020-0418-2
   Wang CY, 2023, Arxiv, DOI arXiv:2301.02111
   Wanner J, 2022, ELECTRON MARK, V32, P2079, DOI 10.1007/s12525-022-00593-5
   Wei RQ, 2021, IEEE ACCESS, V9, P4939, DOI 10.1109/ACCESS.2020.3048309
   Weidinger Laura, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P214, DOI 10.1145/3531146.3533088
   Weisz J., 2023, 4 WORKSH HUM AI COCR
   Weng SS, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12020625
   Wessel M., 2023, Journal of Management Information Systems
   Willcocks L, 2020, J INF TECHNOL-UK, V35, P286, DOI 10.1177/0268396220925830
   Winston Patrick Henry., 1993, Artificial Intelligence
   Yang RB, 2022, ELECTRON MARK, V32, P2053, DOI 10.1007/s12525-022-00592-6
   Zhan FN, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence, V45, DOI [arXiv:2112.13592, 10.48550/arXiv:2112.13592, DOI 10.48550/ARXIV:2112.13592]
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
   Zhang D, 2023, DECIS SUPPORT SYST, V166, DOI 10.1016/j.dss.2022.113911
   Zhou JW, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581318
NR 152
TC 24
Z9 25
U1 204
U2 206
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1019-6781
EI 1422-8890
J9 ELECTRON MARK
JI Electron. Mark.
PD DEC
PY 2023
VL 33
IS 1
AR 63
DI 10.1007/s12525-023-00680-1
PG 17
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA A5J8E
UT WOS:001282899500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Cervantes, J
   Smith, B
   Ramadoss, T
   D'Amario, V
   Shoja, MM
   Rajput, V
AF Cervantes, Jorge
   Smith, Blake
   Ramadoss, Tanya
   D'Amario, Vanessa
   Shoja, Mohammadali M.
   Rajput, Vijay
TI Decoding medical educators' perceptions on generative artificial
   intelligence in medical education
SO JOURNAL OF INVESTIGATIVE MEDICINE
LA English
DT Article
DE Generative artificial intelligence; medical education; GPT
AB Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.
   The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.
C1 [Cervantes, Jorge; Ramadoss, Tanya; Shoja, Mohammadali M.; Rajput, Vijay] Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Ft Lauderdale, FL 33328 USA.
   [Smith, Blake; D'Amario, Vanessa] Nova Southeastern Univ, Dr Kiran C Patel Coll Osteopath Med, Ft Lauderdale, FL 33314 USA.
C3 Nova Southeastern University; Nova Southeastern University
RP Cervantes, J (corresponding author), Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Ft Lauderdale, FL 33328 USA.
EM jcervan1@nova.edu
CR Boscardin CK, 2024, ACAD MED, V99, P22, DOI 10.1097/ACM.0000000000005439
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Emsley R, 2023, SCHIZOPHRENIA-UK, V9, DOI 10.1038/s41537-023-00379-4
   Ibrahim H, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-38964-3
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Laupichler MC, 2024, ACAD MED, V99, P508, DOI 10.1097/ACM.0000000000005626
   Lee P., 2023, The AI revolution in medicine: GPT-4 and beyond
   Li R, 2023, JAMA INTERN MED, V183, P596, DOI 10.1001/jamainternmed.2023.1835
   McMurtrie B, 2023, The Chronicle of Higher Education
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Shoja MM, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.40883
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Tangadulrat P, 2023, JMIR MED EDUC, V9, DOI 10.2196/50658
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   van de Ridder JMM, 2023, ACAD MED, V98, P867, DOI 10.1097/ACM.0000000000005254
NR 16
TC 0
Z9 0
U1 0
U2 0
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1081-5589
EI 1708-8267
J9 J INVEST MED
JI J. Investigative Med.
PD OCT
PY 2024
VL 72
IS 7
BP 633
EP 639
DI 10.1177/10815589241257215
PG 7
WC Medicine, General & Internal; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine; Research & Experimental Medicine
GA K7W7U
UT WOS:001345955900122
DA 2024-12-25
ER

PT J
AU Berghel, H
AF Berghel, Hal
TI Generative Artificial Intelligence, Semantic Entropy, and the Big Sort
SO COMPUTER
LA English
DT Article
AB While artificial intelligence (AI) presents several threats, the threat generative AI poses is immediate and existential.
C1 [Berghel, Hal] Univ Nevada, Comp Sci, Las Vegas, NV 89154 USA.
C3 Nevada System of Higher Education (NSHE); University of Nevada Las Vegas
RP Berghel, H (corresponding author), Univ Nevada, Comp Sci, Las Vegas, NV 89154 USA.
EM hlb@computer.org
CR ANDERSEN Kurt, 2018, Fantasyland: How America Went Haywire: A 500-Year History
   [Anonymous], 1950, General Education in a Free Society: A Report of the Harvard Committee
   Benkler Y., 2018, Network propaganda: Manipulation, disinformation, and radicalization in American politics, DOI DOI 10.1093/OSO/9780190923624.003.0011
   Berghel H, 2023, COMPUTER, V56, P130, DOI 10.1109/MC.2023.3252379
   Berghel H, 2018, COMPUTER, V51, P89, DOI 10.1109/MC.2018.1151023
   Berghel H, 2017, COMPUTER, V50, P80, DOI 10.1109/MC.2017.56
   Berghel H, 2015, COMPUTER, V48, P72, DOI 10.1109/MC.2015.304
   Berghel H, 2015, COMPUTER, V48, P75, DOI 10.1109/MC.2015.256
   Bishop Bill., 2009, BIG SORT WHY CLUSTER
   Blake A., 2017, The Washington Post
   Bush V., 1945, ATLANTIC, V176, P101, DOI DOI 10.1145/227181.227186
   Davies D., 2016, NPR
   Kessler Glenn., 2016, Washington Post
   Lakoff George., 2004, Dont Think of an Elephant!: Know Your Values and Frame the Debate: the Essential Guide for Progressives
   Limbaugh Rush., 2013, The Rush Limbaugh Show
   Mencken H. L., 1956, Gamalielese again
   Mervis J., 2016, Sci. Mag.
   Rheingold H, 2012, NET SMART: HOW TO THRIVE ONLINE, P77
   Roberts David, 2017, VOX             0519
   Rosenfeld S, 2018, Democracy and Truth: A Short History
   Schneier Bruce, 2023, NEW YORK TIMES
   Walters M., 2023, MSN.COM
   Wylie C., 2019, MINDF CK CAMBRIDGE A
   Zakrzewsky C., 2023, The Washington Post
NR 24
TC 1
Z9 1
U1 8
U2 26
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD JAN
PY 2024
VL 57
IS 1
BP 130
EP 135
DI 10.1109/MC.2023.3331594
PG 6
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA EC7P2
UT WOS:001136783900016
OA Bronze
DA 2024-12-25
ER

PT J
AU Wang, Y
   Zhao, LB
AF Wang, Yu
   Zhao, Liangbin
TI Toward the Transparent Use of Generative Artificial Intelligence in
   Academic Articles
SO JOURNAL OF SCHOLARLY PUBLISHING
LA English
DT Article
DE generative AI; AI assisting academic writing; research transparency
AB With the breakthrough development of generative artificial intelligence (AI), its usage in academic articles is rapidly increasing, and the risk of the lack of research transparency arises with that use. To address this risk, the sources, mechanisms, and quality of AI-generated scholarly content are studied to calibrate our expectations for this technology. The authors find that generative AI has great potential to improve the efficiency of researchers and to enhance research articles but also has significant inherent limitations. Then, they examine the use of generative AI in academic articles from three perspectives: AI-assisted research issue development, AI-assisted addressing of research questions, and AI-assisted research findings communication. On this basis, the authors propose a tiered disclosure strategy based on the generative AI usage context for researchers to transparently use generative AI.
C1 [Wang, Yu] Sichuan Univ, Coll Literature & Journalism, Chengdu, Peoples R China.
   [Zhao, Liangbin] ASTAR, Inst High Performance Comp, Singapore, Singapore.
C3 Sichuan University; Agency for Science Technology & Research (A*STAR);
   A*STAR - Institute of High Performance Computing (IHPC)
RP Wang, Y (corresponding author), Sichuan Univ, Coll Literature & Journalism, Chengdu, Peoples R China.
FU National Social Science Fund [22CXW028]
FX Generative AI tools were not used in this article. The authors are
   sincerely grateful to the reviewers for their time and effort in
   reviewing the manuscript, which provided insightful and valuable
   comments that helped improve the final manuscript's quality. This work
   was supported by the National Social Science Fund (grant no. 22CXW028).
CR Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223
   Bilodeau C, 2022, WIRES COMPUT MOL SCI, V12, DOI 10.1002/wcms.1608
   Chandio AA, 2023, ENVIRON DEV SUSTAIN, V25, P1614, DOI 10.1007/s10668-022-02111-1
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   du Terrail JO, 2023, NAT MED, DOI 10.1038/s41591-022-02155-w
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Else H, 2023, NATURE, V613, P423, DOI 10.1038/d41586-023-00056-7
   Fazackerley A., 2023, The Guardian19 March
   Feldman Richard., 2003, Epistemology
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Gao CA, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00819-6
   Guo BY, 2023, Arxiv, DOI [arXiv:2301.07597, DOI 10.48550/ARXIV.2301.07597]
   icml, INT C MACH LEARN CLA
   KHALIFA AA, 2024, ARAB GULF J SCI 0104, pNIL1, DOI DOI 10.1108/AGJSR-09-2023-0423
   Liu YH, 2023, Arxiv, DOI [arXiv:2304.01852, DOI 10.1016/J.METRAD.2023.100017]
   Lund BD, 2024, LEARN PUBL, V37, P13, DOI 10.1002/leap.1582
   Mollaki V, 2024, RES ETHICS-UK, V20, P239, DOI 10.1177/17470161231224552
   Nan Y, 2022, INFORM FUSION, V82, P99, DOI 10.1016/j.inffus.2022.01.001
   Niedenthal PM, 2005, PERS SOC PSYCHOL REV, V9, P184, DOI 10.1207/s15327957pspr0903_1
   Ouyang L., 2022, arXiv, DOI DOI 10.48550/ARXIV.2203.02155
   Roberts Siobhan, 2023, The New York Times, 10 July
   Santurkar S, 2023, Arxiv, DOI [arXiv:2303.17548, 10.48550/arXiv.2303.17548, DOI 10.48550/ARXIV.2303.17548]
   Short C. E., 2023, J Bus Ventur Insights, V19, DOI DOI 10.1016/J.JBVI.2023.E00388
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Vert JP, 2023, NAT BIOTECHNOL, V41, P750, DOI 10.1038/s41587-023-01789-6
   Wolfram S., 2023, WHAT IS CHATGPT DOIN
   Zhang WY, 2023, J ENGL ACAD PURP, V63, DOI 10.1016/j.jeap.2023.101231
NR 29
TC 0
Z9 0
U1 24
U2 24
PU UNIV TORONTO PRESS INC
PI TORONTO
PA JOURNALS DIVISION, 5201 DUFFERIN ST, DOWNSVIEW, TORONTO, ON M3H 5T8,
   CANADA
SN 1198-9742
EI 1710-1166
J9 J SCHOLARLY PUBL
JI J. Sch. Publ.
PD OCT 1
PY 2024
VL 55
IS 4
BP 467
EP 484
DI 10.3138/jsp-2023-0053
PG 18
WC Humanities, Multidisciplinary; Information Science & Library Science
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics; Information Science & Library Science
GA J7Q0T
UT WOS:001338960200004
DA 2024-12-25
ER

PT J
AU Rodriguez, MY
   Goldkind, L
   Victor, BG
   Hiltz, B
   Perron, BE
AF Rodriguez, Maria Y.
   Goldkind, Lauri
   Victor, Bryan G.
   Hiltz, Barbara
   Perron, Brian E.
TI Introducing Generative Artificial Intelligence Into the MSW Curriculum:
   A Proposal for the 2029 Educational Policy and Accreditation Standards
SO JOURNAL OF SOCIAL WORK EDUCATION
LA English
DT Article
ID SOCIAL-WORK
AB The most recent Council on Social Work Education's Educational Policy and Accreditation Standards (EPAS) demands that social workers develop competence in the ethical and professional deployment of technology. Arguably, artificial intelligence has become a critical element in the technological landscape, most recently with the advent of Generative Artificial Intelligence (GenAI). Beginning in late 2022, there has been an explosion of interest in GenAI, along with a massive and ongoing rollout of GenAI tools for personal and professional use, such as ChatGPT. While GenAI will undoubtedly affect social work practice, scholars and ethicists have raised crucial concerns about GenAI, its potential abuses, and misuses, making it critical that social workers are trained in the proper usage of these technologies. Accordingly, we call here for a 10th competency to be added to the 2029 EPAS: Competency Ten: Social Workers demonstrate the knowledge, skills, and understanding to responsibly and effectively use Generative Artificial Intelligence tools. The current article discusses the recent developments in GenAI and offers social work educators guidance for including GenAI content in social work curricula to meet this proposed standard.
C1 [Rodriguez, Maria Y.; Goldkind, Lauri; Victor, Bryan G.; Hiltz, Barbara; Perron, Brian E.] Univ Buffalo, Sch Social Work, 685 Baldy Hall, Buffalo, NY 14260 USA.
C3 State University of New York (SUNY) System; University at Buffalo, SUNY
RP Rodriguez, MY (corresponding author), Univ Buffalo, Sch Social Work, 685 Baldy Hall, Buffalo, NY 14260 USA.
EM myr2@buffalo.edu
RI Goldkind, Lauri/JAC-6492-2023; Rodriguez, Maria/IXN-1478-2023; Victor,
   Bryan/T-8349-2019
OI Goldkind, PhD, LMSW, Lauri/0000-0002-0967-3960; Victor,
   Bryan/0000-0002-2092-912X; Rodriguez, Maria Y./0000-0003-1401-2099
CR Andrason S. P., 2020, Doctoral dissertation .
   [Anonymous], 2022, 2022 educational policy and accreditation standards for baccalaureate and masters social work programs
   [Anonymous], 2021, CODE ETHICS
   Asakura K, 2020, J TEACH SOC WORK, V40, P501, DOI 10.1080/08841233.2020.1813234
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bearman M, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13337
   Beutel G, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04425-6
   Cariceo O., 2018, Methodological Innovations, V11, P1, DOI [https://doi.org/10.1177/2059799118814392, DOI 10.1177/2059799118814392]
   Chakravorti B., 2023, FOREIGN POLICY
   Danneels E, 2004, J PROD INNOVAT MANAG, V21, P246, DOI 10.1111/j.0737-6782.2004.00076.x
   de Jong E., 2019, NULL, V15, P331, DOI DOI 10.1080/1547688X.2019.1663331
   de Rosnay M. D., 2016, J PEER PRODUCTION
   Dhingra H., 2023, QUEER PEOPLE ARE PEO
   Felten Ed, 2023, ARXIV
   Flemotomos N, 2022, BEHAV RES METHODS, V54, P690, DOI 10.3758/s13428-021-01623-4
   Gao Y., 2023, RETRIEVAL AUGMENTED
   Goldkind L, 2021, SOC WORK, V66, P372, DOI 10.1093/sw/swab028
   Goldkind L, 2020, FAM SOC, V101, P6, DOI 10.1177/1044389419872125
   Golensky M, 2006, ADMIN SOC WORK, V30, P5, DOI 10.1300/J147v30n03_02
   Heikkil M., TECHNOLOGY REV
   Jin BH, 2011, INNOV EDUC TEACH INT, V48, P171, DOI 10.1080/14703297.2011.564012
   Lewis P, 2020, ADV NEUR IN, V33
   Li Yingcong, 2023, ARXIV
   Matthews D, 2023, VoxAug. 1
   McBeath B, 2016, FAM SOC, V97, P5, DOI 10.1606/1044-3894.2016.97.9
   Microsoft, 2023, MICROSOFT LEARN
   Omiye Jesutofunmi A, 2023, NPJ Digit Med, V6, P195, DOI 10.1038/s41746-023-00939-z
   Patton DU, 2023, J SOC SOC WORK RES, V14, P553, DOI 10.1086/726042
   Perron BE, 2019, CHILD ABUSE NEGLECT, V98, DOI 10.1016/j.chiabu.2019.104180
   Silva DE, 2024, NEW MEDIA SOC, V26, P2992, DOI 10.1177/14614448221098042
   Singer JB, 2023, J SOC WORK EDUC, V59, P294, DOI 10.1080/10437797.2023.2189878
   Victor BG, 2021, J SOC SOC WORK RES, V12, P631, DOI 10.1086/712734
   Wang Jianyi, 2023, ARXIV
   Wang K., 2023, ARXIV
   Wei J., 2022, ARXIV
   White J., 2023, ARXIV
   Whittaker Meredith, 2019, AI Now Institute, V8
   Wing JM, 2006, COMMUN ACM, V49, P33, DOI 10.1145/1118178.1118215
   Wu Robin T, 2023, Am J Otolaryngol, V44, P103980, DOI 10.1016/j.amjoto.2023.103980
NR 39
TC 3
Z9 3
U1 28
U2 34
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1043-7797
EI 2163-5811
J9 J SOC WORK EDUC
JI J. Soc. Work Educ.
PD APR 2
PY 2024
VL 60
IS 2
BP 174
EP 182
DI 10.1080/10437797.2024.2340931
EA JUN 2024
PG 9
WC Education & Educational Research; Social Work
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Social Work
GA YT3C2
UT WOS:001235403500001
DA 2024-12-25
ER

PT J
AU Kshetri, N
AF Kshetri, Nir
TI Generative Artificial Intelligence in the Financial Services Industry
SO COMPUTER
LA English
DT Article
DE Generative AI; Financial industry; Artificial intelligence; Chatbots;
   Investment; Financial services; Customer services; Banking; Synthetic
   data
AB I examine the factors driving generative artificial intelligence (GAI) adoption in the financial services industry, addressing associated challenges, diverse development and deployment strategies, and various GAI applications, including customer service enhancements and risk management.
C1 [Kshetri, Nir] Univ North Carolina Greensboro, Bryan Sch Business & Econ, Greensboro, NC 27412 USA.
C3 University of North Carolina; University of North Carolina Greensboro
RP Kshetri, N (corresponding author), Univ North Carolina Greensboro, Bryan Sch Business & Econ, Greensboro, NC 27412 USA.
EM nbkshetr@uncg.edu
CR Abrego M., Business Insider
   Anderson R, 2001, 17TH ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, PROCEEDINGS, P358, DOI 10.1109/ACSAC.2001.991552
   [Anonymous], Capturing the full value of generative AI in banking
   [Anonymous], Insurers can enhance customer experience and competitive edge with generative AI
   [Anonymous], 2023, MIT Technol. Rev.
   [Anonymous], The economic potential of generative AI: The next productivity frontier
   [Anonymous], 2023, The Economist
   Bant A., Gartner Newsroom
   businessinsider, Business Insider
   channel newsasia, CNA
   Ebert L., BNP Paribas Capital Introduction
   Fauscette M., Arion Research
   Gopalakrishnan S., Unleashing a new era of productivity in investment banking through the power of generative AI
   Hrushka A., Banking Dive
   Kreger A., Finextra
   Levitt K., NVIDIA
   Li Y., 2022, CNBC
   Mahidhar V., 2018, Harvard Business Review
   Masters B., 2023, Financial Times
   Mittal A., Unite.AI
   Nadig D., ETF Trends
   Nichols C., South State Correspondent
   Oi R., 2024, Fintech News Singapore
   Potluru VK, 2024, Arxiv, DOI arXiv:2401.00081
   pymnts, PYMNTS
   Ramesh R., BankInfoSecurity
   Riemer S., A generative AI roadmap for financial institutions
   Ruth J.-P. S., InformationWeek
   security today, Security Today
   Slivka A., Mastercard Perspectives
   Strange A., Andreessen Horowitz
   Wang S., Andreessen Horowitz
   Wolfe Daniel., 2007, American Banker
NR 33
TC 1
Z9 1
U1 72
U2 72
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD JUN
PY 2024
VL 57
IS 6
BP 102
EP 108
DI 10.1109/MC.2024.3382452
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA TG5B0
UT WOS:001240114700019
DA 2024-12-25
ER

PT J
AU Yoon, H
   Hwang, J
   Lee, K
   Roh, KH
   Kwon, ON
AF Yoon, Hyunkyoung
   Hwang, Jihye
   Lee, Kyungwon
   Roh, Kyeong Hah
   Kwon, Oh Nam
TI Students' use of generative artificial intelligence for proving
   mathematical statements
SO ZDM-MATHEMATICS EDUCATION
LA English
DT Article
DE Artificial intelligence (AI); Undergraduate students; Proving; ChatGPT
ID PROOF
AB In this exploratory study, we investigate undergraduate students' engagement with generative Artificial Intelligence (genAI) in proving mathematical statements. We selected six mathematical statements to conduct interviews with three students. We present the emergent framework, Students' Interactive Proving Experience with AI (SIPE-AI), which explains the processes of students' use of genAI in their proving and the factors influencing these processes. Our findings identify three factors that shape students' use of genAI: conceptions of proof, conceptions of genAI, and ethical considerations. The results suggest a need to guide undergraduate students in critically engaging with genAI tools, rather than passively accepting their outputs. We also discuss the implications of these findings for enhancing undergraduate mathematics education by fostering informed and critical use of genAI in mathematical proving.
C1 [Yoon, Hyunkyoung] Calif State Polytech Univ Pomona, Pomona, CA USA.
   [Hwang, Jihye; Roh, Kyeong Hah] Arizona State Univ, Tempe, AZ USA.
   [Lee, Kyungwon; Kwon, Oh Nam] Seoul Natl Univ, Seoul, South Korea.
C3 California State University System; California State Polytechnic
   University Pomona; Arizona State University; Arizona State
   University-Tempe; Seoul National University (SNU)
RP Kwon, ON (corresponding author), Seoul Natl Univ, Seoul, South Korea.
EM hkyoon@cpp.edu; jihye.hwang@asu.edu; kyungwon.lee.snu@gmail.com;
   khroh@asu.edu; onkwon@snu.ac.kr
RI Hwang, Jihye/LJK-4682-2024
OI Hwang, Jihye/0000-0002-7075-4508; Lee, Kyungwon/0009-0004-0448-0838;
   Kwon, Oh Nam/0000-0002-6989-5469
FU Seoul National University
FX Open Access funding enabled and organized by Seoul National University.
CR Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Castle S. D., 2022, P 24 ANN C RES UND M
   Firat Mehmet, 2023, Journal of Applied Learning and Teaching, V3, P1, DOI DOI 10.37074/JALT.2023.6.1.22
   Frieder S., 2023, ARXIV
   Ginsburg H.P., 1997, Entering the child's mind: The clinical interview in psychological research and practice, DOI DOI 10.1017/CBO9780511527777
   Goldin G.A., 1997, J RES MATH ED MONOGR, V9, P40, DOI DOI 10.2307/749946
   Harel G., 1998, RES COLLEGIATE MATH, V3, P234, DOI DOI 10.1090/CBMATH/007/07
   Hwang J., 2024, P 26 ANN C RES UND M
   Inglis M, 2009, COGNITION INSTRUCT, V27, P25, DOI 10.1080/07370000802584513
   Ko YY, 2009, J MATH BEHAV, V28, P68, DOI 10.1016/j.jmathb.2009.04.005
   Levin M., 2020, P 23 ANN C RES UND M
   Levin M., 2024, P 26 ANN C RES UND M
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Ngo T. T. A., 2023, Int. J. Emerg. Technol. Learn., V18, P4, DOI [DOI 10.3991/IJET.V18I17.39019, https://doi.org/10.3991/ijet.v18i17.39019, 10.3991/ijet.v18i14.39903, DOI 10.3991/IJET.V18I14.39903]
   OpenAI, 2023, ChatGPT: Optimizing Language Models for Dialogue
   Powers RA, 2010, INT J MATH EDUC SCI, V41, P501, DOI 10.1080/00207390903564603
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Selden A, 2003, J RES MATH EDUC, V34, P4, DOI 10.2307/30034698
   Strauss A. L., 1990, BASICS QUALITATIVE R
   Stylianides AJ, 2007, J RES MATH EDUC, V38, P289, DOI 10.2307/30034869
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Thompson A.G., 1992, HDB RES MATH TEACHIN, P127
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Weber K, 2014, INT J MATH EDUC SCI, V45, P89, DOI 10.1080/0020739X.2013.790514
   Weber K, 2010, MATH THINK LEARN, V12, P306, DOI 10.1080/10986065.2010.495468
NR 25
TC 3
Z9 3
U1 41
U2 41
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1863-9690
EI 1863-9704
J9 ZDM-MATH EDUC
JI ZDM-Math. Educ.
PD DEC
PY 2024
VL 56
IS 7
SI SI
BP 1531
EP 1551
DI 10.1007/s11858-024-01629-0
EA AUG 2024
PG 21
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA N4Q5W
UT WOS:001297791100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Oswick, C
AF Oswick, Cliff
TI Generative Artificial Intelligence and Generative Conversations:
   Contrasting Futures for Organizational Change?
SO JOURNAL OF APPLIED BEHAVIORAL SCIENCE
LA English
DT Article
DE organizational change; OD intervention strategies; OD practice;
   decision-making
AB To what extent are "generative AI" (as a machine-based form of decision-making) and "generative dialogue" (as a human-based form of decision-making) complimentary or competing? What takes precedence in generative change processes? More fundamentally, should generative artificial intelligence (AI) processes assist human decision-making or should human processes assist generative AI decision-making? The possible implications of these questions are explored. Moreover, it is posited that as generative AI evolves and gains more traction in organizational change initiatives, it is important that it is deployed in ways that support generative conversations rather than ways that undermine or replace them.
C1 [Oswick, Cliff] City Univ London, Bayes Business Sch, London, England.
   [Oswick, Cliff] City Univ London, Bayes Business Sch, 106 Bunhill Row, London, England.
C3 City St Georges, University of London; City, University of London; City
   St Georges, University of London; City, University of London
RP Oswick, C (corresponding author), City Univ London, Bayes Business Sch, 106 Bunhill Row, London, England.
EM cliff.oswick.1@city.ac.uk
CR Averbuch T., 2021, INITIATING INVITING
   Bailey DE, 2022, ORGAN SCI, V33, P1, DOI 10.1287/orsc.2021.1562
   Bartunek JM., 2021, OXFORD HDB ORG CHANG, P50
   Beckhard R., 1969, ORG DEV STRATEGIES M
   Bratt B.H., 2020, TEAM DISCOVERED DIAL
   Bushe G.R., 2013, OD Practitioner, V45, P10
   Bushe G.R., 2020, DYNAMICS GENERATIVE
   Bushe GR, 2021, J APPL BEHAV SCI, V57, P530, DOI 10.1177/00218863211038119
   Bushe GR, 2009, J APPL BEHAV SCI, V45, P348, DOI 10.1177/0021886309335070
   Cooperrider D.L., 1998, Appreciative Inquiry
   Cooperrider D.L., 1987, RES ORG CHANGE DEV, V1, P129
   HACKMAN JR, 1978, ORGAN DYN, V7, P3, DOI 10.1016/0090-2616(78)90031-1
   Hastings BJ, 2022, J APPL BEHAV SCI, V58, P120, DOI 10.1177/00218863211019561
   Hastings BJ, 2021, J APPL BEHAV SCI, V57, P259, DOI 10.1177/0021886320978427
   Kanitz R, 2023, J APPL BEHAV SCI, V59, P345, DOI 10.1177/00218863231168974
   Lewis, 2021, COCREATING PLANNING
   Marshak R., 2018, OD Practitioner, V50, P9
   Marshak R.J., 2020, DIALOGIC PROCESS CON
   Marshak RJ, 2022, J APPL BEHAV SCI, V58, P149, DOI 10.1177/00218863211060884
   McKergow, 2020, HOSTING GENERATIVE C
   Oswick C., 2023, Research in organizational change and development, P155
   Oswick C, 2022, J APPL BEHAV SCI, V58, P153, DOI 10.1177/00218863211060880
   Stirling-Wilkie G., 2021, PHYS SPACE VIRTUAL S
   Tsoukas H, 2005, J ORGAN CHANGE MANAG, V18, P96, DOI 10.1108/09534810510579878
   von Krogh G, 2023, ACAD MANAGE J, V66, P367, DOI 10.5465/amj.2023.4002
NR 25
TC 3
Z9 3
U1 63
U2 112
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0021-8863
EI 1552-6879
J9 J APPL BEHAV SCI
JI J. Appl. Bahav. Sci.
PD JUN
PY 2024
VL 60
IS 2
BP 225
EP 229
DI 10.1177/00218863241232412
EA FEB 2024
PG 5
WC Behavioral Sciences; Psychology, Applied; Management; Psychology,
   Experimental
WE Social Science Citation Index (SSCI)
SC Behavioral Sciences; Psychology; Business & Economics
GA OY5U7
UT WOS:001170073000001
OA Green Accepted
DA 2024-12-25
ER

PT J
AU Blonder, R
   Feldman-Maggor, Y
   Rap, S
AF Blonder, Ron
   Feldman-Maggor, Yael
   Rap, Shelley
TI Are They Ready to Teach? Generative AI as a Means to Uncover Pre-Service
   Science Teachers' PCK and Enhance Their Preparation Program
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article; Early Access
DE Artificial intelligence (AI); Teacher preparation; Initial teacher
   education; PCK; Science education
ID PEDAGOGICAL CONTENT KNOWLEDGE; EDUCATION; CHALLENGES; STUDENTS
AB Integrating generative artificial intelligence (GenAI) in pre-service teachers' education programs offers a transformative opportunity to enhance the pedagogical development of future science educators. This conceptual paper suggests applying the GenAI tool to evaluate pedagogical content knowledge (PCK) among pre-service science teachers. By holding interactive dialogues with GenAI, pre-service teachers engage in lesson planning in a way that reveals their understanding of content, pedagogy, and PCK while facilitating the practical application of theoretical knowledge. Interpretation of these interactions provides insights into teachers-to-be knowledge and skills, enabling personalized learning experiences and targeted program adjustments. The paper underscores the need to equip pre-service teachers with the necessary competencies to utilize GenAI effectively in their future teaching practices. It contributes to the ongoing discourse on technology's role in teacher preparation programs, highlighting the potential of addressing existing challenges in evaluating and developing teacher knowledge via GenAI. The suggested future research directions aim to further investigate the GenAI usage implications in educational contexts.
C1 [Blonder, Ron; Rap, Shelley] Weizmann Inst Sci, Dept Sci Teaching, Rehovot, Israel.
   [Feldman-Maggor, Yael] KTH Royal Inst Technol, EECS Sch Elect Engn & Comp Sci Technol & Interact, Stockholm, Sweden.
   [Feldman-Maggor, Yael] Digital Futures, Stockholm, Sweden.
C3 Weizmann Institute of Science; Royal Institute of Technology
RP Blonder, R (corresponding author), Weizmann Inst Sci, Dept Sci Teaching, Rehovot, Israel.
EM ron.blonder@weizmann.ac.il; yaelfm@kth.se; shelley.rap@weizmann.ac.il
FU Weizmann Institute of Science
FX Open access funding provided by Weizmann Institute of Science.
CR Abbitt JT, 2011, J RES TECHNOL EDUC, V43, P281, DOI 10.1080/15391523.2011.10782573
   Abell SK, 2009, J SCI TEACH EDUC, V20, P77, DOI 10.1007/s10972-008-9115-6
   Akaygun S., 2024, 16 EUROPEAN C RES CH
   Tacoshi MMA, 2014, PROBL EDUC 21ST CENT, V62, P124
   Angeli C, 2009, COMPUT EDUC, V52, P154, DOI 10.1016/j.compedu.2008.07.006
   [Anonymous], 2003, Standards for science teacher preparation, P86
   Antink-Meyer A, 2017, INT J SCI EDUC, V39, P1511, DOI 10.1080/09500693.2017.1338787
   Araújo JL, 2024, J CHEM EDUC, V101, P1858, DOI 10.1021/acs.jchemed.3c00745
   Avargil S, 2012, J SCI EDUC TECHNOL, V21, P207, DOI 10.1007/s10956-011-9302-7
   Barendsen E, 2019, RES SCI EDUC, V49, P1141, DOI 10.1007/s11165-017-9637-z
   Barnard L, 2009, INTERNET HIGH EDUC, V12, P1, DOI 10.1016/j.iheduc.2008.10.005
   Baumert J, 2010, AM EDUC RES J, V47, P133, DOI 10.3102/0002831209345157
   Blonder R., 2022, Handbook of Research on Science Teacher Education, P300, DOI [10.4324/9781003098478-26, DOI 10.4324/9781003098478-26]
   Blonder R, 2024, CHEM TEACH INT, DOI 10.1515/cti-2024-0014
   Blonder R, 2017, EDUC INF TECHNOL, V22, P697, DOI 10.1007/s10639-015-9384-6
   Bravo P, 2016, INT J SCI EDUC, V38, P2500, DOI 10.1080/09500693.2016.1249983
   Bryan LA, 2015, NANOTECHNOL REV, V4, P7, DOI 10.1515/ntrev-2014-0029
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Cetin-Dindar A, 2018, CHEM EDUC RES PRACT, V19, P167, DOI 10.1039/c7rp00175d
   Chan KKH, 2022, INT J SCI EDUC, V44, P487, DOI 10.1080/09500693.2022.2035011
   Chen BH, 2024, Arxiv, DOI arXiv:2310.14735
   Cohen G., 2023, Ubiquity Proceedings, V3, P289, DOI [10.5334/uproc.99, DOI 10.5334/UPROC.99]
   Cooper G, 2024, J SCI EDUC TECHNOL, V33, P556, DOI 10.1007/s10956-024-10104-0
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cun A., 2024, Exploring New Horizons: Generative Artificial Intelligence and Teacher Education, V62
   Daher W, 2023, INFORMATION, V14, DOI 10.3390/info14070409
   De jongO., 2002, SCI TECHNOL EDUC LIB, P369
   Easa E, 2022, CHEM TEACH INT, V4, P71, DOI 10.1515/cti-2021-0022
   Feldman-Maggor Y., 2024, Lecture Notes in Computer Science, P15159
   Feldman-Maggor Y, 2024, J SCI EDUC TECHNOL, DOI 10.1007/s10956-024-10147-3
   Feldman-Maggor Y, 2024, COMPUT EDUC, V219, DOI 10.1016/j.compedu.2024.105074
   Feldman-Maggor Y, 2016, CHEM EDUC RES PRACT, V17, P283, DOI 10.1039/c5rp00184f
   Gess-Newsome J., 2015, Re-examining Pedagogical Content Knowledge in Science Education, P28, DOI DOI 10.4324/9781315735665
   Gess-Newsome J, 2019, INT J SCI EDUC, V41, P944, DOI 10.1080/09500693.2016.1265158
   Guler-Nalbantoglu F., 2021, International Journal of Research in Education and Science (IJRES), V74, P1263, DOI [10.46328/ijres.2451, DOI 10.46328/IJRES.2451]
   Gurin-Schleifer A., 2024, P 19 WORKSHOP INNOVA
   Hadas B., 2023, Chemistry Teacher International, V5, P189, DOI [10.1515/cti-2022-0037, DOI 10.1515/CTI-2022-0037]
   Hagevik R, 2010, J SCI TEACH EDUC, V21, P7, DOI 10.1007/s10972-009-9155-6
   Hill HC, 2018, AM EDUC RES J, V55, P1076, DOI 10.3102/0002831218769614
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   HUBERMAN M, 1989, TEACH COLL REC, V91, P31
   Kind V, 2019, INT J SCI EDUC, V41, P911, DOI 10.1080/09500693.2017.1311049
   Kind V, 2009, STUD SCI EDUC, V45, P169, DOI 10.1080/03057260903142285
   Kirschner S, 2016, INT J SCI EDUC, V38, P1343, DOI 10.1080/09500693.2016.1190479
   Kuhn C, 2016, EMPIR RES VOCAT EDUC, V8, DOI 10.1186/s40461-016-0031-2
   Lee E, 2007, SCHOOL SCI MATH, V107, P52, DOI 10.1111/j.1949-8594.2007.tb17768.x
   Lee S., 2024, Computers and Education: Artificial Intelligence
   Lehane Louise, 2016, Educ. quím, V27, P52
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Lorenz U, 2023, LECT NOTES COMPUT SC, V14296, P13, DOI 10.1007/978-3-031-44900-0_2
   Loughran J, 2004, J RES SCI TEACH, V41, P370, DOI 10.1002/tea.20007
   Loughran J., 2001, Research in Science Education, V31, P289, DOI [10.1023/A:1013124409567, DOI 10.1023/A:1013124409567]
   Loughran J, 2008, INT J SCI EDUC, V30, P1301, DOI 10.1080/09500690802187009
   Lu J., 2024, IEEE Transactions on Learning Technologies
   Magnusson S., 1999, Examining pedagogical content knowledge, P95
   Mazibe EN, 2020, RES SCI EDUC, V50, P941, DOI 10.1007/s11165-018-9718-7
   Mejeh M, 2022, VOCAT LEARN, V15, P531, DOI 10.1007/s12186-022-09298-4
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Nilsson P, 2008, INT J SCI EDUC, V30, P1281, DOI 10.1080/09500690802186993
   Nilsson P, 2012, J SCI TEACH EDUC, V23, P699, DOI 10.1007/s10972-011-9239-y
   Ning YM, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16030978
   Njiku J., 2020, Journal of Digital Learning in Teacher Education, V36, P150, DOI [10.1080/21532974.2020.1724840, DOI 10.1080/21532974.2020.1724840]
   Nyaaba M., 2024, Journal of AI, V8, P1, DOI DOI 10.61969/JAI.1385915
   Nyaaba M, 2024, Arxiv, DOI arXiv:2407.11983
   Ozdengelen E., 2024, 16 EUROPEAN C RES CH
   Ruff E. F., 2024, Journal of Chemical Education
   Schmidt DA, 2009, J RES TECHNOL EDUC, V42, P123, DOI 10.1080/15391523.2009.10782544
   Shulman L.S., 2015, Re-examining pedagogical content knowledge in science education, P3, DOI DOI 10.4324/9781315735665
   SHULMAN LS, 1987, HARVARD EDUC REV, V57, P1, DOI 10.17763/haer.57.1.j463w79r56455411
   Shulman LS., 1986, Educational Researcher, V15, P4, DOI [10.1177/002205741319300302, DOI 10.3102/0013189X015002004, 10.30827/profesorado.v23i3.11230, DOI 10.30827/PROFESORADO.V23I3.11230, 10.3102/0013189X015002004]
   Shwartz G., 2019, Eurasia Journal of Mathematics, Science and Technology Education, V16, P1, DOI [DOI 10.29333/EJMSTE/8502, https://doi.org/10.29333/ejmste/8502]
   Sloan K., 2018, Pedagogical Content Knowledge in STEM, P157, DOI [10.1007/978-3-319-97475, DOI 10.1007/978-3-319-97475]
   Smith P. S., 2015, Re-examining Pedagogical Content Knowledge in Science Education, DOI [10.4324/9781315735665, DOI 10.4324/9781315735665]
   Tal M, 2021, CHEM EDUC RES PRACT, V22, P1003, DOI 10.1039/d0rp00359j
   Tang KS, 2024, PEDAGOGIES, V19, P493, DOI 10.1080/1554480X.2024.2379774
   Tao Y, 2024, PNAS NEXUS, V3, DOI 10.1093/pnasnexus/pgae346
   Tigchelaar A, 2010, EDUC RES REV-NETH, V5, P164, DOI 10.1016/j.edurev.2009.11.002
   Traube T, 2023, J CHEM EDUC, V100, P4360, DOI 10.1021/acs.jchemed.3c00645
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Verma G, 2023, J SCI TEACH EDUC, V34, P793, DOI 10.1080/1046560X.2023.2263251
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
   Zhai X., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4331313, DOI 10.2139/SSRN.4331313]
   Zhai X., 2023, ChatGPT and AI: The game changer for education, P16
   Zhai X., 2023, Shanghai Education, P16
NR 85
TC 0
Z9 0
U1 22
U2 22
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD 2024 NOV 18
PY 2024
DI 10.1007/s10956-024-10180-2
EA NOV 2024
PG 10
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M3G2N
UT WOS:001356453100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Zhao, HR
   Yuan, BC
   Song, Y
AF Zhao, Hairong
   Yuan, Bocong
   Song, Yang
TI Employees' perception of generative artificial intelligence and the dark
   side of work outcomes
SO JOURNAL OF HOSPITALITY AND TOURISM MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Threat of job intelligence to employment;
   Technical fear towards AI
ID TECHNOLOGY; AUTOMATION; WORKPLACE; ROBOT
AB Artificial intelligence (as well as generative AI) has been increasingly applied in the tourism and hospitality industry and has an important impact on the work behavior of practitioners. Drawing from the transactional theory of stress and coping, this study is to clarify the mechanism of potential negative impact of AI on the work outcomes of tourism and hospitality practitioners who use generative AI (GenAI) to assist their work. This study conducts in-depth interviews and thematic analysis to explore how the use of GenAI affects negative work behaviors among tourism and hospitality practitioners. The results show that employees' technical fear towards AI is negatively associated with their sense of realism, self-investment, and habitual perception, but positively associated with the perceived threat of job intelligence to employment. Moreover, the technical fear towards AI can be positively associated with their transgression behavior. The findings of this study can be illuminating for helping tourism and hospitality organizations develop sustainable and healthy workplace guidelines.
C1 [Zhao, Hairong] Sun Yat Sen Univ, Int Sch Business & Finance, Zhuhai, Peoples R China.
   [Yuan, Bocong; Song, Yang] Sun Yat Sen Univ, Sch Tourism Management, Zhuhai, Peoples R China.
C3 Sun Yat Sen University; Sun Yat Sen University
RP Yuan, BC (corresponding author), Sun Yat Sen Univ, Sch Tourism Management, Zhuhai, Peoples R China.
EM yuanbc@mail.sysu.edu.cn
CR Abdelhakim AS, 2023, TOUR MANAG PERSPECT, V45, DOI 10.1016/j.tmp.2022.101049
   Brougham D, 2018, J MANAGE ORGAN, V24, P239, DOI 10.1017/jmo.2016.55
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chan ESW, 2018, J HOSP TOUR RES, V42, P829, DOI 10.1177/1096348015614959
   Chen AH, 2023, INFORM TECHNOL PEOPL, V36, P2826, DOI 10.1108/ITP-01-2022-0059
   Clarke V, 2017, J POSIT PSYCHOL, V12, P297, DOI 10.1080/17439760.2016.1262613
   Dabbous A, 2022, J ASIA BUS STUD, V16, P245, DOI 10.1108/JABS-09-2020-0372
   Delgosha MS, 2021, COMPUT HUM BEHAV, V117, DOI 10.1016/j.chb.2020.106660
   Hamarat H, 2024, WORLDW HOSP TOUR THE, V16, P127, DOI 10.1108/WHATT-03-2024-0061
   Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
   Huang YY, 2024, J RETAIL CONSUM SERV, V77, DOI 10.1016/j.jretconser.2023.103700
   Kalemci RA, 2019, EUR J MANAG BUS ECON, V28, P126, DOI 10.1108/EJMBE-11-2018-0125
   Kang J, 2024, J HOSP TOUR TECHNOL, V15, P916, DOI 10.1108/JHTT-12-2023-0411
   Karthikeyan K., 2010, International Journal of Business and Management, V5, P77
   Khasawneh OY, 2018, COMPUT HUM BEHAV, V88, P210, DOI 10.1016/j.chb.2018.07.007
   Kim KJ, 2013, COMPUT HUM BEHAV, V29, P1799, DOI 10.1016/j.chb.2013.02.009
   Kong HY, 2021, INT J CONTEMP HOSP M, V33, P717, DOI 10.1108/IJCHM-07-2020-0789
   Lazarus R. S., 1984, STRESS APPRAISAL COP
   Lazarus R. S., 1966, PSYCHOL STRESS COPIN
   McClure PK, 2018, SOC SCI COMPUT REV, V36, P139, DOI 10.1177/0894439317698637
   Osiceanu ME, 2015, PROCD SOC BEHV, V180, P1137, DOI 10.1016/j.sbspro.2015.02.229
   Parvez MO, 2022, INT J HOSP MANAG, V107, DOI 10.1016/j.ijhm.2022.103336
   Rydzik A, 2022, J SUSTAIN TOUR, V30, P2860, DOI 10.1080/09669582.2021.1928680
   Salamzadeh Y., 2013, AWERProcedia Information Technology Computer Science, V3, P186
   Shin H, 2023, J HOSP TOUR MANAG, V57, P40, DOI 10.1016/j.jhtm.2023.09.001
   Sigala M, 2024, J HOSP TOUR MANAG, V60, P384, DOI 10.1016/j.jhtm.2024.08.004
   Tuomi A, 2020, ANN TOURISM RES, V84, DOI 10.1016/j.annals.2020.102978
   Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
   Wingreen SC, 2019, ELECTRON COMMER RES, V19, P339, DOI 10.1007/s10660-018-9305-z
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Zhang XT, 2023, J HOSP MARKET MANAG, V32, P264, DOI 10.1080/19368623.2023.2166186
   Zhu MF, 2020, TOURISM MANAGE, V81, DOI 10.1016/j.tourman.2020.104167
   Zirar A, 2023, TECHNOVATION, V124, DOI 10.1016/j.technovation.2023.102747
NR 33
TC 1
Z9 1
U1 49
U2 49
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1447-6770
EI 1839-5260
J9 J HOSP TOUR MANAG
JI J. Hosp. Tour. Manag.
PD DEC
PY 2024
VL 61
BP 191
EP 199
DI 10.1016/j.jhtm.2024.10.007
EA OCT 2024
PG 9
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA K8Y5T
UT WOS:001346699900001
DA 2024-12-25
ER

PT J
AU Dwivedi, R
   Elluri, L
AF Dwivedi, Rahul
   Elluri, Lavanya
TI Exploring Generative Artificial Intelligence Research: A Bibliometric
   Analysis Approach
SO IEEE ACCESS
LA English
DT Article
DE Bibliometrics; Generative AI; Artificial intelligence; Databases; Market
   research; Indexes; Internet; Generative artificial intelligence;
   bibliometric analysis
ID CO-AUTHORSHIP NETWORKS; INTELLECTUAL STRUCTURE; INFORMATION-SCIENCE;
   BRADFORD LAW; COCITATION; PUBLICATIONS; SCIENTOMETRICS; SCATTERING;
   EVOLUTION
AB Artificial Intelligence (AI) and its many applications are changing our lives in ways we could not have imagined a decade ago. Generative artificial intelligence is an artificial intelligence system capable of generating texts, images, and other media based on the input training data. Although still in their early stages, numerous examples of such systems in different domains have gained widespread attention from the public, media, policymakers, and researchers. This study aims to explore the generative AI academic research in the past decade using bibliometrics, text analysis, and social network analysis. Specifically, research themes and their relationships, the evolution of research themes over time, and prominent authors, articles, journals, institutions, and countries publishing in generative AI are identified. The data was further found to partially support the classical bibliometrics laws of Zipf, and Bradford's. The two overarching research themes identified using knowledge synthesis from most cited articles and journals are technical advancements and developments in generative AI systems; and their applications to image processing, pattern recognition, and computer vision. ChatGPT, large language models, and the application of generative AI to healthcare and education are emerging research topics. Additionally, generative AI's usefulness to geoscience, remote sensing, Internet of Things (IoT), and cybersecurity are discussed.
C1 [Dwivedi, Rahul; Elluri, Lavanya] Texas A&M Univ Cent Texas, Subhani Dept Comp Informat Syst, Killeen, TX 76549 USA.
C3 Texas A&M University System; Texas A&M University Central Texas
RP Dwivedi, R (corresponding author), Texas A&M Univ Cent Texas, Subhani Dept Comp Informat Syst, Killeen, TX 76549 USA.
EM rahul.dwivedi@tamuct.edu
RI Dwivedi, Rahul/LGZ-6424-2024; , Lavanya Elluri/ACG-4312-2022
OI Dwivedi, Rahul/0000-0001-9994-1991; , Lavanya Elluri/0000-0002-8881-3369
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Chadegani AA, 2013, Arxiv, DOI [arXiv:1305.0377, 10.5539/ass.v9n5p18, DOI 10.5539/ASS.V9N5P18]
   Agostinelli A., 2015, arXiv
   Ahmed T, 2017, PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON COMMUNICATION, COMPUTING AND DIGITAL SYSTEMS (C-CODE), P54, DOI 10.1109/C-CODE.2017.7918901
   Akhavan P, 2016, SCIENTOMETRICS, V107, P1249, DOI 10.1007/s11192-016-1938-x
   Alonso JM, 2018, COMM COM INF SC, V853, P3, DOI 10.1007/978-3-319-91473-2_1
   [Anonymous], 2024, AI
   [Anonymous], 2024, CNNJan.
   [Anonymous], 2024, TimeJan.
   [Anonymous], 2024, Introducing ChatGPT
   [Anonymous], 2024, Trustworthy
   Appel G., 2023, Harvard Business Review
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Arjovsky M, 2017, PR MACH LEARN RES, V70
   Astrom F., 2009, Celebrating Scholarly Communication Studies: A Festschrift for Olle Persson at his 60th Birthday (International Society for Scientometrics and Informetrics), V5, P7
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Belter CW, 2015, J MED LIBR ASSOC, V103, P219, DOI 10.3163/1536-5050.103.4.014
   Birkle C, 2020, QUANT SCI STUD, V1, P363, DOI 10.1162/qss_a_00018
   Borgatti SP., 1998, Connections, V21, P27
   Borgohain DJ, 2021, COLLNET J SCIENTOMET, V15, P197, DOI 10.1080/09737766.2021.1943041
   Bradford SC., 1934, Engineering, V137, P85, DOI [DOI 10.1177/016555158501000407, 10.1177/016555158501000, DOI 10.1177/016555158501000]
   Camps-Valls G., 2021, Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences, DOI DOI 10.1002/9781119646181
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Chang CH, 2023, INT RES GEOGR ENVIRO, V32, P85, DOI 10.1080/10382046.2023.2194036
   Chaturbhuj S., 2020, Library Philosophy Pract., V4524, P1
   Chen C., 2014, Coll Comput Inf, V1, P1, DOI DOI 10.1007/S11192-015-1576-8
   Chen Mark, 2021, arXiv
   Cintas-Canto A, 2023, Arxiv, DOI [arXiv:2306.08178, DOI 10.48550/ARXIV.2306.08178]
   Cocchia A, 2014, PROGR IS, P13, DOI 10.1007/978-3-319-06160-3_2
   Colton S, 2012, FRONT ARTIF INTEL AP, V242, P21, DOI 10.3233/978-1-61499-098-7-21
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Coulter N, 1998, J AM SOC INFORM SCI, V49, P1206, DOI 10.1002/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.0.CO;2-F
   CULNAN MJ, 1987, MIS QUART, V11, P341, DOI 10.2307/248680
   Dahesh MB, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101399
   Desai N, 2018, J SURG RES, V229, P90, DOI 10.1016/j.jss.2018.03.062
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Donthu N, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102307
   Donthu N, 2020, J BUS RES, V109, P1, DOI 10.1016/j.jbusres.2019.10.039
   Douzas G, 2018, EXPERT SYST APPL, V91, P464, DOI 10.1016/j.eswa.2017.09.030
   DROTT MC, 1981, LIBR TRENDS, V30, P41
   Ductor L, 2015, OXFORD B ECON STAT, V77, P385, DOI 10.1111/obes.12070
   Dwivedi R., 2023, International Journal of Information Management Data Insights, V3, DOI [10.1016/j.jjimei.2023.100185, DOI 10.1016/J.JJIMEI.2023.100185]
   Dwivedi R, 2024, INT J INF SECUR, V23, P2159, DOI 10.1007/s10207-024-00840-0
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elluri L, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2270457
   Elluri L, 2019, 2019 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2019), P198, DOI 10.1109/SMARTCOMP.2019.00053
   FEDOROWICZ J, 1982, J AM SOC INFORM SCI, V33, P285, DOI 10.1002/asi.4630330507
   Gao F, 2021, MICROSYST TECHNOL, V27, P1545, DOI 10.1007/s00542-019-04426-y
   GARFIELD E, 1964, SCIENCE, V144, P649, DOI 10.1126/science.144.3619.649
   Gartner, 2024, aBOUT US
   GLANZEL W, 1985, SCIENTOMETRICS, V7, P211, DOI 10.1007/BF02017147
   GM H., 2020, Comput. Sci. Rev., V38
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Guo YQ, 2020, J MED INTERNET RES, V22, DOI 10.2196/18228
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Haase J., 2023, Journal of Creativity, V33, DOI DOI 10.1016/J.YJOC.2023.100066
   Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
   He KM, 2016, PROC CVPR IEEE, P770, DOI 10.1109/CVPR.2016.90
   He Q, 1999, LIBR TRENDS, V48, P133
   Ho YS, 2020, COLLNET J SCIENTOMET, V14, P369, DOI 10.1080/09737766.2021.1918032
   Hood WW, 2001, SCIENTOMETRICS, V52, P291, DOI 10.1023/A:1017919924342
   Hou JH, 2018, SCIENTOMETRICS, V115, P869, DOI 10.1007/s11192-018-2695-9
   Hwang GJ, 2023, EDUC TECHNOL SOC, V26, DOI 10.30191/ETS.202304_26(2).0014
   Iqbal W, 2019, SCIENTOMETRICS, V119, P1121, DOI 10.1007/s11192-019-03086-z
   Isola P, 2017, PROC CVPR IEEE, P5967, DOI 10.1109/CVPR.2017.632
   Jiahui Gong, 2023, MobiSys '23: Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services, P610, DOI 10.1145/3581791.3597297
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Khalili A., 1994, Journal of Research on Computing in Education, V27, P48
   Kingma DP., 2014, PREPRINT
   Kitchenham B, 2009, INFORM SOFTWARE TECH, V51, P7, DOI 10.1016/j.infsof.2008.09.009
   Knani M, 2022, INT J HOSP MANAG, V107, DOI 10.1016/j.ijhm.2022.103317
   Kuefler A, 2017, IEEE INT VEH SYM, P204, DOI 10.1109/IVS.2017.7995721
   Kumar S, 2015, ASLIB J INFORM MANAG, V67, P55, DOI 10.1108/AJIM-09-2014-0116
   Ledig C, 2017, PROC CVPR IEEE, P105, DOI 10.1109/CVPR.2017.19
   Li EY, 2013, RES POLICY, V42, P1515, DOI 10.1016/j.respol.2013.06.012
   Li W., 2002, Glottometrics, V5, P21
   Li YJ, 2022, SCIENCE, V378, P1092, DOI 10.1126/science.abq1158
   Liao ZQ, 2024, ANN BIOMED ENG, V52, P125, DOI 10.1007/s10439-023-03288-w
   Liu P, 2022, IEEE GEOSC REM SEN M, V10, P295, DOI 10.1109/MGRS.2022.3165967
   Liu SR, 2023, J AM MED INFORM ASSN, V30, P1237, DOI 10.1093/jamia/ocad072
   Luo LY, 2022, FRONT ROBOT AI, V9, DOI 10.3389/frobt.2022.728776
   Lv Z., 2023, Cogn. Robot., V3, P217
   Mangalaraj G, 2023, INFORM SYST MANAGE, V40, P302, DOI 10.1080/10580530.2022.2140368
   Martín-Noguerol T, 2023, EUR J RADIOL, V161, DOI 10.1016/j.ejrad.2023.110726
   Martinelli DD, 2022, COMPUT BIOL MED, V145, DOI 10.1016/j.compbiomed.2022.105403
   Martínez-López FJ, 2018, EUR J MARKETING, V52, P439, DOI 10.1108/EJM-11-2017-0853
   McCarthy J., 2007, Tech. Rep.)
   McGee R. W., 2023, Empirical Study
   McNulty K., 2022, Handbook of Graphs and Networks in People Analytics: With Examples in R and Python
   Merigó JM, 2017, OMEGA-INT J MANAGE S, V73, P37, DOI 10.1016/j.omega.2016.12.004
   Mirza M, 2014, Arxiv, DOI arXiv:1411.1784
   Moral-Muñoz JA, 2020, PROF INFORM, V29, DOI 10.3145/epi.2020.ene.03
   Mosqueira-Rey E, 2023, ARTIF INTELL REV, V56, P3005, DOI 10.1007/s10462-022-10246-w
   Muhuri PK, 2018, APPL SOFT COMPUT, V69, P381, DOI 10.1016/j.asoc.2018.03.041
   Nerur SP, 2008, STRATEG MANAGE J, V29, P319, DOI 10.1002/smj.659
   Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898
   NICHOLLS PT, 1988, INFORM PROCESS MANAG, V24, P469, DOI 10.1016/0306-4573(88)90049-0
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   OpenAI, 2024, About us
   Pepe A, 2011, J AM SOC INF SCI TEC, V62, P2121, DOI 10.1002/asi.21629
   Piantadosi ST, 2014, PSYCHON B REV, V21, P1112, DOI 10.3758/s13423-014-0585-6
   Pilkington A, 2009, J OPER MANAG, V27, P185, DOI 10.1016/j.jom.2008.08.001
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Price D.J.D.S., 1963, Little Science, Big Science
   Qadir J., 2023, P IEEE GLOB ENG ED C, P9
   Raman R, 2022, IEEE ACCESS, V10, P35561, DOI 10.1109/ACCESS.2022.3161639
   Ramesh Aditya, 2021, DALL-E: Creating Images from Text
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   Ravikumar S, 2015, SCIENTOMETRICS, V102, P929, DOI 10.1007/s11192-014-1402-8
   Renaud K., 2023, MIT Sloan Manage. Rev., V64, P1
   Riahi Y, 2021, EXPERT SYST APPL, V173, DOI 10.1016/j.eswa.2021.114702
   Ronneberger O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4_28
   Roose K., 2022, Artists aren't happy
   Rose ME, 2019, SOFTWAREX, V10, DOI 10.1016/j.softx.2019.100263
   Santana E, 2016, Arxiv, DOI arXiv:1608.01230
   Science of Science (Sci2), 2009, Tool
   Shera J. H., 1953, P SC BRADTF DOC, P45
   Singer U, 2022, Arxiv, DOI arXiv:2209.14792
   SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406
   Song P, 2020, ASIA PAC EDUC REV, V21, P473, DOI 10.1007/s12564-020-09640-2
   Summerfield C., 2022, NATURAL GEN INTELLIG
   Thoppilan R., 2022, arXiv
   University of Washington Computer Science, 2024, About Us
   van Bussel MJP, 2022, BMC HEALTH SERV RES, V22, DOI 10.1186/s12913-022-08189-7
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vogel J. J., 2006, Journal of Educational Computing Research, V34, P229, DOI 10.2190/FLHV-K4WA-WPVQ-H0YM
   Walid R., 2024, P INT C MATH COMP SI, P26
   Wang GX, 2019, INT GEOSCI REMOTE SE, P10027, DOI [10.1109/IGARSS.2019.8898915, 10.1109/igarss.2019.8898915]
   Wang Z, 2004, IEEE T IMAGE PROCESS, V13, P600, DOI 10.1109/TIP.2003.819861
   WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302
   White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4
   Yan EJ, 2009, J AM SOC INF SCI TEC, V60, P2107, DOI 10.1002/asi.21128
   Yinka-Banjo C, 2020, ARTIF INTELL REV, V53, P1721, DOI 10.1007/s10462-019-09717-4
   Zakaria R, 2021, J APICULT RES, V60, P359, DOI 10.1080/00218839.2021.1898789
   Zhang LF, 2022, IEEE GEOSC REM SEN M, V10, P270, DOI 10.1109/MGRS.2022.3145854
   Zhao DZ, 2008, J AM SOC INF SCI TEC, V59, P2070, DOI 10.1002/asi.20910
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
   Zhu JY, 2017, IEEE I CONF COMP VIS, P2242, DOI 10.1109/ICCV.2017.244
   Zhu JW, 2020, SCIENTOMETRICS, V123, P321, DOI 10.1007/s11192-020-03387-8
   Zipf George Kingsley, 2016, Human behavior and the principle of least effort: An introduction to human ecology
NR 142
TC 0
Z9 0
U1 59
U2 59
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 119884
EP 119902
DI 10.1109/ACCESS.2024.3450629
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA F2K7D
UT WOS:001308166500001
OA gold
DA 2024-12-25
ER

PT J
AU Benbya, H
   Strich, F
   Tamm, T
AF Benbya, Hind
   Strich, Franz
   Tamm, Toomas
TI Navigating Generative Artificial Intelligence Promises and Perils for
   Knowledge and Creative Work
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS
LA English
DT Article
DE Generative Artificial Intelligence; Creativity; Knowledge workers;
   knowledge management; Large Language Models; Research Agenda; Image
   Generation Models
ID SUPPORT-SYSTEMS; AI SYSTEMS; QUALITY
AB Generative artificial intelligence (GenAI) is rapidly becoming a viable tool to enhance productivity and act as a catalyst for innovation across various sectors. Its ability to perform tasks that have traditionally required human judgment and creativity is transforming knowledge and creative work. Yet it also raises concerns and implications that could reshape the very landscape of knowledge and creative work. In this editorial, we undertake an in-depth examination of both the opportunities and challenges presented by GenAI for future IS research.
C1 [Benbya, Hind; Strich, Franz; Tamm, Toomas] Deakin Univ, Ctr Artificial Intelligence & Future Business, Geelong, Vic, Australia.
C3 Deakin University
RP Benbya, H (corresponding author), Deakin Univ, Ctr Artificial Intelligence & Future Business, Geelong, Vic, Australia.
EM h.benbya@deakin.edu.au; f.strich@deakin.edu.au;
   toomas.tamm@deakin.edu.au
RI Strich, Franz/T-8925-2019; Tamm, Toomas/L-1195-2013
OI Strich, Franz/0000-0002-0170-9025
CR Ali S., 2021, arXiv
   Avital M, 2009, INFORM SYST J, V19, P345, DOI 10.1111/j.1365-2575.2007.00291.x
   Avrahami O, 2021, Arxiv, DOI [arXiv:2112.01516, DOI 10.48550/ARXIV.2112.01516]
   Benbya H, 2008, CHANDOS KNOWL MANAGE, P1
   Benbya H, 2021, J ASSOC INF SYST, V22, P281, DOI 10.17705/1jais.00662
   Benbya H, 2020, MIS Q EXEC, V19, pIX
   Boden MA, 1998, ARTIF INTELL, V103, P347, DOI 10.1016/S0004-3702(98)00055-1
   Bown O., 2012, Computers and creativity, P361
   Brea E, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102643
   Brynjolfsson E, 2023, 31161 NBER
   Chemla-Romeu-Santos A, 2022, Arxiv, DOI arXiv:2211.08856
   Chiarella SG, 2022, COMPUT HUM BEHAV, V137, DOI 10.1016/j.chb.2022.107406
   Davenport T., 2005, THINKING LIVING
   Davis JU, 2021, C&C'21: PROCEEDINGS OF THE 13TH CONFERENCE ON CREATIVITY AND COGNITION, DOI 10.1145/3450741.3465260
   Dean DL, 2006, J ASSOC INF SYST, V7, P646, DOI 10.17705/1jais.00106
   Deng J., 2022, FRONTIERS COMPUTING, V2, P81, DOI [DOI 10.54097/FCIS.V2I2.4465, 10.54097/fcis.v2i2.4465]
   DiPaola S, 2018, PROCEDIA COMPUT SCI, V145, P158, DOI 10.1016/j.procs.2018.11.024
   ELAM JJ, 1987, INFORM MANAGE, V13, P215, DOI 10.1016/0378-7206(87)90045-0
   Elgammal A, 2019, AM SCI, V107, P18
   Epstein Z, 2022, Arxiv, DOI [arXiv:2206.00533, 10.48550/arXiv.2206.00533, DOI 10.48550/ARXIV.2206.00533]
   Eshraghian JK, 2020, NAT MACH INTELL, V2, P157, DOI 10.1038/s42256-020-0161-x
   Forrest R., 2023, ComputerWeekly
   Fügener A, 2021, MIS QUART, V45, P1527, DOI 10.25300/MISQ/2021/16553
   Giermindl LM, 2022, EUR J INFORM SYST, V31, P410, DOI 10.1080/0960085X.2021.1927213
   Grierson J., 2023, GUARDIAN
   Gurman M., 2023, Bloomberg
   Haase J, 2023, Arxiv, DOI [arXiv:2303.12003, DOI 10.48550/ARXIV.2303.12003, 10.48550/arXiv.2303.12003]
   Hacker P, 2023, Arxiv, DOI [arXiv:2302.02337, 10.48550/arXiv.2302.02337, 10.48550/ARXIV.2302.02337]
   Haefner N, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120392
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hong JW, 2019, ACM T MULTIM COMPUT, V15, DOI 10.1145/3326337
   Jarrahi MH., 2023, MIT Sloan Management Review, V64, P1
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Jia N, 2024, ACAD MANAGE J, V67, P5, DOI 10.5465/amj.2022.0426
   Kahn S., 2023, TED
   Koster R, 2022, NAT HUM BEHAV, V6, P1398, DOI 10.1038/s41562-022-01383-x
   Lamb C, 2017, J MATH ARTS, V11, P159, DOI 10.1080/17513472.2017.1373561
   Lee JS, 2023, NAT COMPUT SCI, V3, P382, DOI 10.1038/s43588-023-00440-3
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lukpat A., 2023, The Wall Street Journal
   Massetti B, 1996, MIS QUART, V20, P83, DOI 10.2307/249543
   Mayer AS, 2020, MIS Q EXEC, V19, P239, DOI 10.17705/2msqe.00036
   Mello G., 2023, Bloomberg
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Miller AI, 2020, AM SCI, V108, P244
   Mohr J., 2023, Knowledge management, I'd like to introduce my new friend, generative AI
   Morrow E, 2023, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.971044
   Ni B, 2023, CHEM-US, V9, P1828, DOI 10.1016/j.chempr.2023.03.020
   Oppenlaender Jonas, 2022, Academic Mindtrek 2022: 25th International Academic Mindtrek conference, P192, DOI 10.1145/3569219.3569352
   Oppenlaender J, 2023, Arxiv, DOI [arXiv:2303.13530, DOI 10.48550/ARXIV.2303.13530, 10.48550/arXiv.2303.13530]
   Oppenlaender J, 2023, Arxiv, DOI arXiv:2204.13988
   Oppermann L, 2019, HALFWAY TO THE FUTURE SYMPOSIUM (HTTF 2019), DOI 10.1145/3363384.3363481
   Oracle, 2020, As uncertainty remains, anxiety and stress reach a tipping point at work (AI@Work Study)
   Pedota M, 2022, CREAT INNOV MANAG, V31, P109, DOI 10.1111/caim.12468
   Pilcicki R., 2022, P PAC AS C INF SYST
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Renaud K., 2023, MIT Sloan Manage. Rev., V64, P1
   Roose K., 2022, The New York Times
   Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
   Shneiderman Ben., AIS Transactions on Human-Computer Interaction, V12, P109, DOI [10.17705/1thci.00131, DOI 10.17705/1THCI.00131, https://doi.org/10.17705/1thci.00131]
   Siau K, 2020, J DATABASE MANAGE, V31, P74, DOI 10.4018/JDM.2020040105
   Siemon D, 2022, COMMUN ASSOC INF SYS, V50, P241, DOI 10.17705/1CAIS.05009
   Smits J., 2022, LAW ARTIFICIAL INTEL, V35, P323, DOI DOI 10.1007/978-94-6265-523-2_17
   Strich F, 2021, J ASSOC INF SYST, V22, P304, DOI 10.17705/1jais.00663
   Sun LY, 2019, FRONT INFORM TECH EL, V20, P1644, DOI 10.1631/FITEE.1900386
   Tarafdar M, 2023, INFORM SYST J, V33, P232, DOI 10.1111/isj.12389
   Taylor Josh, 2023, The Guardian
   Vasist PN, 2022, COMMUN ASSOC INF SYS, V51, P590, DOI 10.17705/1CAIS.05126
   Vrontis D, 2022, INT J HUM RESOUR MAN, V33, P1237, DOI 10.1080/09585192.2020.1871398
   Wierenga B, 1998, MIS QUART, V22, P81, DOI 10.2307/249679
   Zhou L., 2021, AIS Trans. Human-Comput. Interact., V13, P243, DOI DOI 10.17705/1THCI.00149
   Ziegler Albert, 2022, MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming, P21, DOI 10.1145/3520312.3534864
NR 72
TC 5
Z9 5
U1 140
U2 202
PU ASSOC INFORMATION SYSTEMS
PI ATLANTA
PA GEORGIA STATE UNIV, 35 BROAD STREET, STE 916-917, ATLANTA, GA 30303 USA
SN 1536-9323
EI 1558-3457
J9 J ASSOC INF SYST
JI J. Assoc. Inf. Syst.
PY 2024
VL 25
IS 1
SI SI
DI 10.17705/1jais.00861
PG 15
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA LB5N1
UT WOS:001184331000006
OA Bronze
DA 2024-12-25
ER

PT J
AU Tian, YQ
   Zhang, ZY
   Yang, YZ
   Chen, ZR
   Yang, ZH
   Jin, RC
   Quek, TQS
   Wong, KK
AF Tian, Yuqing
   Zhang, Zhaoyang
   Yang, Yuzhi
   Chen, Zirui
   Yang, Zhaohui
   Jin, Richeng
   Quek, Tony Q. S.
   Wong, Kai-Kit
TI An Edge-Cloud Collaboration Framework for Generative AI Service
   Provision With Synergetic Big Cloud Model and Small Edge Models
SO IEEE NETWORK
LA English
DT Article
DE Computational modeling; Data models; Training; Adaptation models; Task
   analysis; Artificial intelligence; Cloud computing; Generative AI; big
   AI model; edge-cloud collaboration
AB Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future networks. However, training and deploying big artificial intelligence models (BAIMs) introduces substantial computational and communication overhead. This poses a critical challenge to centralized approaches, due to the need of high-performance computing infrastructure and the reliability, secrecy and timeliness issues in long-distance access of cloud services. Therefore, there is an urging need to decentralize the services, partly moving them from the cloud to the edge and establishing native GenAI services to enable private, timely, and personalized experiences. In this paper, we propose a brand-new bottom-up BAIM architecture with synergetic big cloud model and small edge models, and design a distributed training framework and a task-oriented deployment scheme for efficient provision of native GenAI services. The proposed framework can facilitate collaborative intelligence, enhance adaptability, gather edge knowledge and alleviate edge-cloud burden. The effectiveness of the proposed framework is demonstrated through an image generation use case. Finally, we outline fundamental research directions to fully exploit the collaborative potential of edge and cloud for native GenAI and BAIM applications.
C1 [Tian, Yuqing; Zhang, Zhaoyang; Yang, Yuzhi; Chen, Zirui; Yang, Zhaohui; Jin, Richeng] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China.
   [Tian, Yuqing; Zhang, Zhaoyang; Yang, Yuzhi; Chen, Zirui; Yang, Zhaohui; Jin, Richeng] Zhejiang Prov Key Lab Informat Proc Commun & Netwo, Hangzhou 310027, Peoples R China.
   [Quek, Tony Q. S.] Singapore Univ Technol & Design SUTD, ISTD Pillar, Tampines 487372, Singapore.
   [Quek, Tony Q. S.] SUTD ZJU IDEA Ctr Network Intelligence, Singapore 487372, Singapore.
   [Wong, Kai-Kit] UCL, Dept Elect & Elect Engn, London WC1E 6BT, England.
C3 Zhejiang University; University of London; University College London
RP Zhang, ZY (corresponding author), Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China.; Zhang, ZY (corresponding author), Zhejiang Prov Key Lab Informat Proc Commun & Netwo, Hangzhou 310027, Peoples R China.
EM tianyq@zju.edu.cn; ning_ming@zju.edu.cn; yuzhi_yang@zju.edu.cn;
   ziruichen@zju.edu.cn; yang_zhaohui@zju.edu.cn; richengjin@zju.edu.cn;
   tonyquek@sutd.edu.sg; kai-kit.wong@ucl.ac.uk
RI Zhang, Zhaoyang/HJO-8920-2023; Yang, Yuzhi/GZK-4997-2022; Jin,
   Richeng/HNQ-8413-2023; Zhang, Zhaoyang/C-1446-2015
OI Zhang, Zhaoyang/0000-0003-2346-6228; Jin, Richeng/0000-0002-1480-585X;
   Yang, Yuzhi/0000-0003-2454-1904; Yang, Zhaohui/0000-0002-4475-589X
FU National Natural Science Foundation of China [U20A20158, 62394292];
   Ministry of Industry and Information Technology [TC220H07E]; National
   Key Research and Development Program of China [2020YFB1807101]; Zhejiang
   Provincial Key Research and Development Program [2023C01021];
   Fundamental Research Funds for the Central Universities [226-2024-00069]
FX This work was supported in part by the National Natural Science
   Foundation of China under Grant U20A20158 and Grant 62394292, in part by
   the Ministry of Industry and Information Technology under Grant
   TC220H07E, in part by the National Key Research and Development Program
   of China under Grant 2020YFB1807101, in part by the Zhejiang Provincial
   Key Research and Development Program under Grant 2023C01021, and in part
   by the Fundamental Research Funds for the Central Universities under
   Grant 226-2024-00069.
CR Abdelsadek MY, 2021, IEEE INT CONF COMM, DOI 10.1109/ICCWorkshops50388.2021.9473753
   Chen HH, 2006, IEEE WIREL COMMUN, V13, P68, DOI 10.1109/MWC.2006.1593527
   Jayaprakash A, 2020, IEEE ICC, DOI 10.1109/icc40277.2020.9149274
   Liu SC, 2021, IEEE COMMUN MAG, V59, P30, DOI 10.1109/MCOM.001.2001081
   Meng ET, 2020, IEEE ACCESS, V8, P148203, DOI 10.1109/ACCESS.2020.3015754
   Shi JL, 2024, IEEE T MOBILE COMPUT, V23, P1785, DOI [10.1109/TMC.2023.3240763, 10.1109/MNET.129.2200458]
   Tang QQ, 2021, IEEE INTERNET THINGS, V8, P9164, DOI 10.1109/JIOT.2021.3056569
   Wang CX, 2023, IEEE COMMUN SURV TUT, V25, P905, DOI 10.1109/COMST.2023.3249835
   Wang M, 2022, IEEE GLOB COMM CONF, P747, DOI 10.1109/GLOBECOM48099.2022.10000629
   Wang X, 2023, IEEE T WIREL COMMUN, V22, P9076, DOI 10.1109/TWC.2023.3268097
   Zhang JM, 2021, IEEE GLOB COMM CONF, DOI 10.1109/GLOBECOM46510.2021.9685288
   Zhao L, 2022, IEEE T AERO ELEC SYS, V58, P3746, DOI 10.1109/TAES.2022.3169130
   Zheng BX, 2022, IEEE J SEL AREA COMM, V40, P3057, DOI 10.1109/JSAC.2022.3196119
   Zhou CH, 2021, IEEE T WIREL COMMUN, V20, P911, DOI 10.1109/TWC.2020.3029143
   Zhou XY, 2023, IEEE T WIREL COMMUN, V22, P2847, DOI 10.1109/TWC.2022.3214862
NR 15
TC 0
Z9 0
U1 4
U2 4
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0890-8044
EI 1558-156X
J9 IEEE NETWORK
JI IEEE Netw.
PD SEP
PY 2024
VL 38
IS 5
BP 37
EP 46
DI 10.1109/MNET.2024.3420755
PG 10
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA H3M7E
UT WOS:001322517900033
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Wang, YC
   Xue, JT
   Wei, CW
   Kuo, CCJ
AF Wang, Yun-Cheng
   Xue, Jintang
   Wei, Chengwei
   Kuo, C. -C. Jay
TI An Overview on Generative AI at Scale With Edge–Cloud Computing
SO IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY
LA English
DT Article
DE Computational modeling; Artificial intelligence; Cloud computing;
   Servers; Chatbots; Biological system modeling; Training; AI-generated
   content; edge-cloud computing; distributed system; lightweight models;
   metaverse; artificial intelligence of things
ID EDGE; CLOUD; ALGORITHMS; NETWORK; FOG
AB As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.
C1 [Wang, Yun-Cheng; Xue, Jintang; Wei, Chengwei; Kuo, C. -C. Jay] Univ Southern Calif, Dept Elect & Comp Engn, Los Angeles, CA 90089 USA.
C3 University of Southern California
RP Wang, YC (corresponding author), Univ Southern Calif, Dept Elect & Comp Engn, Los Angeles, CA 90089 USA.
EM yunchenw@usc.edu
RI Kuo, C.-C. Jay/A-7110-2011
OI Kuo, C.-C. Jay/0000-0001-9474-5035; Wang, Yun Cheng/0000-0001-9778-4806;
   Xue, Jintang/0009-0004-3531-8147
CR Adamopoulou E, 2020, Artificial intelligence applications and innovations 2020, DOI [10.1007/978-3-030-49186-4_31, DOI 10.1007/978-3-030-49186-4_31]
   Aoki N, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101490
   Arora S, 2013, Arxiv, DOI arXiv:1306.4134
   Arslan E., 2020, PROC IEEE GLOBAL COM, P1
   Asperti A., 2021, SN Computer Science, V2, P301, DOI DOI 10.1007/S42979-021-00702-9
   Ayad A, 2021, IEEE GLOB COMM CONF, DOI 10.1109/GLOBECOM46510.2021.9685493
   Azizi Z, 2022, Arxiv, DOI arXiv:2206.00162
   Barrett C, 2023, Arxiv, DOI arXiv:2308.14840
   Brock A, 2019, Arxiv, DOI arXiv:1809.11096
   Brown TB, 2020, ADV NEUR IN, V33
   Cao KY, 2020, IEEE ACCESS, V8, P85714, DOI 10.1109/ACCESS.2020.2991734
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Celikyilmaz A, 2021, Arxiv, DOI arXiv:2006.14799
   Chen H.-S., 2021, P IEEE INT C MULT EX, P1
   Cheng P, 2022, P IEEE, V110, P476, DOI 10.1109/JPROC.2022.3153167
   Cho KYHY, 2014, Arxiv, DOI arXiv:1406.1078
   Chowdhery A, 2022, Arxiv, DOI [arXiv:2204.02311, DOI 10.48550/ARXIV.2204.02311]
   Chun BG, 2011, EUROSYS 11: PROCEEDINGS OF THE EUROSYS 2011 CONFERENCE, P301
   Clark K, 2020, Arxiv, DOI arXiv:2003.10555
   Creswell A, 2018, IEEE SIGNAL PROC MAG, V35, P53, DOI 10.1109/MSP.2017.2765202
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   Deng JR, 2021, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2021, P3600
   Ding AY, 2022, ACM SIGCOMM COMP COM, V52, P28
   Donahue C, 2019, Arxiv, DOI arXiv:1802.04208
   Du H, 2023, arXiv
   Du HY, 2023, Arxiv, DOI arXiv:2303.13052
   Du YF, 2022, Arxiv, DOI arXiv:2202.10936
   Duan SJ, 2023, IEEE COMMUN SURV TUT, V25, P591, DOI 10.1109/COMST.2022.3218527
   Elgendy I. A., 2022, Security and Privacy Preserving for IoT and 5G Networks: Techniques, Challenges, and New Directions, V95, P117, DOI [10.1007/978-3-030-85428-76, DOI 10.1007/978-3-030-85428-76]
   Engel J, 2019, Arxiv, DOI arXiv:1902.08710
   Erol-Kantarci M, 2018, L N INST COMP SCI SO, V223, P169, DOI 10.1007/978-3-319-74439-1_15
   Fedus W, 2018, Arxiv, DOI arXiv:1801.07736
   Feng C, 2022, J NETW COMPUT APPL, V202, DOI 10.1016/j.jnca.2022.103366
   Firouzi F, 2022, INFORM SYST, V107, DOI 10.1016/j.is.2021.101840
   Foster D., 2019, Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play
   Gao R, 2020, IEEE T IMAGE PROCESS, V29, P3665, DOI 10.1109/TIP.2020.2964429
   Garfinkel S., 1999, ARCHITECTS INFORM SO
   Girin L, 2022, Arxiv, DOI arXiv:2008.12595
   Goodfellow I., 2020, Generative adversarial networks, V63
   Gou JP, 2021, INT J COMPUT VISION, V129, P1789, DOI 10.1007/s11263-021-01453-z
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Gregor K, 2015, PR MACH LEARN RES, V37, P1462
   Gui J, 2023, IEEE T KNOWL DATA EN, V35, P3313, DOI 10.1109/TKDE.2021.3130191
   Han K, 2023, IEEE T PATTERN ANAL, V45, P87, DOI 10.1109/TPAMI.2022.3152247
   Harshvardhan GM, 2020, COMPUT SCI REV, V38, DOI 10.1016/j.cosrev.2020.100285
   Hazra A, 2022, IEEE SENS J, V22, P8663, DOI 10.1109/JSEN.2022.3157863
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Hong Y, 2019, ACM COMPUT SURV, V52, DOI 10.1145/3301282
   Hua HC, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3555802
   Huda SMA, 2022, J NETW COMPUT APPL, V201, DOI 10.1016/j.jnca.2022.103341
   Hung V, 2009, INTERNATIONAL CONFERENCE ON INFORMATION, PROCESS, AND KNOWLEDGE MANAGEMENT: EKNOW 2009, PROCEEDINGS, P60, DOI 10.1109/eKNOW.2009.10
   Jabbar A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3463475
   Ji SX, 2022, IEEE T NEUR NET LEAR, V33, P494, DOI 10.1109/TNNLS.2021.3070843
   Jiang YA, 2023, IEEE T NEUR NET LEAR, V34, P10374, DOI 10.1109/TNNLS.2022.3166101
   Kalyan K.S., 2021, arXiv
   Kaplan J, 2020, Arxiv, DOI [arXiv:2001.08361, 10.48550/arXiv.2001.08361]
   Khan LU, 2021, IEEE COMMUN SURV TUT, V23, P1759, DOI 10.1109/COMST.2021.3090430
   Khan S, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3505244
   Kim S, 2019, Arxiv, DOI [arXiv:1811.02155, DOI 10.48550/ARXIV.1811.02155]
   King MR, 2023, ANN BIOMED ENG, V51, P291, DOI 10.1007/s10439-022-03121-w
   Ko H, 2020, IEEE T IMAGE PROCESS, V29, P5964, DOI 10.1109/TIP.2020.2987180
   Ko SW, 2022, IEEE T WIREL COMMUN, V21, P6568, DOI 10.1109/TWC.2022.3151131
   Kumar Y, 2023, MULTIMED TOOLS APPL, V82, P15171, DOI 10.1007/s11042-022-13943-4
   Kuo CCJ, 2023, J VIS COMMUN IMAGE R, V90, DOI 10.1016/j.jvcir.2022.103685
   Larsen ABL, 2016, PR MACH LEARN RES, V48
   Lee H., 2021, PROC IEEE INT C CONS, P1
   Lei XJ, 2022, IEEE INT SYMP CIRC S, P3314, DOI 10.1109/ISCAS48785.2022.9937959
   Lei XJ, 2021, APSIPA TRANS SIGNAL, V10, DOI 10.1017/ATSIP.2021.15
   Lei XJ, 2020, ASIAPAC SIGN INFO PR, P1698
   Li FY, 2020, IEEE T CYBERNETICS, V50, P4146, DOI 10.1109/TCYB.2019.2921475
   Lim WYB, 2023, IEEE WIREL COMMUN, V30, P64, DOI 10.1109/MWC.018.2100716
   Lin KY, 2018, PROC CVPR IEEE, P732, DOI 10.1109/CVPR.2018.00083
   Lin T., 2022, AI Open, V3, P111, DOI DOI 10.1016/J.AIOPEN.2022.10.001
   Lin YJ, 2023, IEEE OPEN J COMP SOC, V4, P72, DOI 10.1109/OJCS.2023.3260732
   Liu DY, 2020, IEEE OPEN J COMM SOC, V1, P634, DOI 10.1109/OJCOMS.2020.2994737
   Liu Y, 2024, IEEE T NEUR NET LEAR, V35, P7478, DOI 10.1109/TNNLS.2022.3227717
   Luo B., 2022, Wiley Interdiscipl. Rev. Data Min. Knowl. Discov., V12, P1434
   Mao YY, 2017, IEEE COMMUN SURV TUT, V19, P2322, DOI 10.1109/COMST.2017.2745201
   Martin D, 2022, IEEE T VIS COMPUT GR, V28, P2003, DOI 10.1109/TVCG.2022.3150502
   McEnroe P, 2022, IEEE INTERNET THINGS, V9, P15435, DOI 10.1109/JIOT.2022.3176400
   Mirka M, 2022, DES AUT TEST EUROPE, P1135, DOI 10.23919/DATE54114.2022.9774721
   Murshed MGS, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3469029
   Nema P, 2018, Arxiv, DOI arXiv:1704.08300
   Nguyen DC, 2021, IEEE COMMUN SURV TUT, V23, P1622, DOI 10.1109/COMST.2021.3075439
   Nichol A, 2022, Arxiv, DOI arXiv:2212.08751
   Oussidi A, 2018, 2018 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND COMPUTER VISION (ISCV2018)
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pan ZQ, 2019, IEEE ACCESS, V7, P36322, DOI 10.1109/ACCESS.2019.2905015
   Parikh RB, 2019, JAMA-J AM MED ASSOC, V322, P2377, DOI 10.1001/jama.2019.18058
   Parikh S, 2019, PROCEDIA COMPUT SCI, V160, P734, DOI 10.1016/j.procs.2019.11.018
   Patterson D, 2021, Arxiv, DOI [arXiv:2104.10350, DOI 10.48550/ARXIV.2104.10350]
   Qi X, 2018, 2018 THIRD IEEE/ACM SYMPOSIUM ON EDGE COMPUTING (SEC), P367, DOI 10.1109/SEC.2018.00047
   Qiao TT, 2019, PROC CVPR IEEE, P1505, DOI 10.1109/CVPR.2019.00160
   Radford A., 2019, OPENAI BLOG
   Radford A, 2021, PR MACH LEARN RES, V139
   Raffel C, 2020, J MACH LEARN RES, V21
   Ratican J., 2023, J. Intell. Learn. Syst. Appl., V15, P24
   Ren J, 2018, IEEE NETWORK, V32, P137, DOI 10.1109/MNET.2018.1700415
   Ren Y, 2019, ADV NEUR IN, V32
   Ren Y, 2021, Arxiv, DOI [arXiv:2006.04558, DOI 10.48550/ARXIV.2006.04558]
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Roselli D, 2019, COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2019 ), P539, DOI 10.1145/3308560.3317590
   Rui LL, 2022, IEEE T NETW SERV MAN, V19, P4165, DOI 10.1109/TNSM.2022.3202796
   Safavi T, 2019, IEEE DATA MINING, P528, DOI 10.1109/ICDM.2019.00063
   Samikwa E, 2022, COMPUT NETW, V218, DOI 10.1016/j.comnet.2022.109380
   Selva J, 2023, IEEE T PATTERN ANAL, V45, P12922, DOI 10.1109/TPAMI.2023.3243465
   Shi JW, 2023, Arxiv, DOI arXiv:2304.12298
   Shi WS, 2016, COMPUTER, V49, P78, DOI 10.1109/MC.2016.145
   Shi YM, 2020, IEEE COMMUN SURV TUT, V22, P2167, DOI 10.1109/COMST.2020.3007787
   Shuyang Gu, 2020, Computer Vision - ECCV 2020 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12356), P369, DOI 10.1007/978-3-030-58621-8_22
   Smith S., 2022, arXiv, DOI 10.48550/arXiv.2201.11990
   Sohn K, 2015, ADV NEUR IN, V28
   Surbiryala J, 2019, 2019 3RD IEEE INTERNATIONAL CONFERENCE ON CLOUD AND FOG COMPUTING TECHNOLOGIES AND APPLICATIONS (IEEE CLOUD SUMMIT 2019), P1, DOI 10.1109/CloudSummit47114.2019.00007
   Sutskever I, 2014, ADV NEUR IN, V27
   Suzuki M, 2022, ADV ROBOTICS, V36, P261, DOI 10.1080/01691864.2022.2035253
   Tan YX, 2023, IEEE ACCESS, V11, P88099, DOI 10.1109/ACCESS.2023.3306422
   Taori R., 2023, A strong, replicable instruction-following model
   Tay Y, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3530811
   Thoppilan R., 2022, arXiv
   Tolstikhin I., 2017, arXiv
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Tuli Shreshth, 2019, 2019 4th International Conference on Information Systems and Computer Networks (ISCON), P496, DOI 10.1109/ISCON47742.2019.9036216
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang B, 2022, Arxiv, DOI arXiv:2209.11910
   Wang CG, 2020, Arxiv, DOI [arXiv:2010.11967, 10.48550/arXiv.2010.11967]
   Wang L, 2022, IEEE T PATTERN ANAL, V44, P3048, DOI 10.1109/TPAMI.2021.3055564
   Wang Y., 2022, SPIE, V12226, P70
   Wang YC, 2022, Arxiv, DOI arXiv:2208.09137
   Wang ZW, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3439723
   Wei CW, 2023, Arxiv, DOI arXiv:2303.05759
   Wei RQ, 2020, IEEE ACCESS, V8, P153651, DOI 10.1109/ACCESS.2020.3018151
   Wu H, 2019, IEEE INTERNET THINGS, V6, P9880, DOI 10.1109/JIOT.2019.2932995
   Wu JY, 2023, Arxiv, DOI arXiv:2304.06632
   Xiao ZJ, 2021, 2021 ACM/IEEE 6TH SYMPOSIUM ON EDGE COMPUTING (SEC 2021), P148, DOI 10.1145/3453142.3491272
   Xu H., 2019, PROC IEEEACM INT C C, P1
   Xu MR, 2023, IEEE J-STSP, V17, P1064, DOI 10.1109/JSTSP.2023.3293650
   Xu MR, 2023, Arxiv, DOI arXiv:2303.16129
   Yang ZL, 2019, ADV NEUR IN, V32
   Yao JC, 2023, IEEE T KNOWL DATA EN, V35, P6866, DOI 10.1109/TKDE.2022.3178211
   Yasunaga M, 2021, Arxiv, DOI arXiv:2104.06378
   Zhang CN, 2023, Arxiv, DOI arXiv:2303.11717
   Zhang C, 2023, Arxiv, DOI arXiv:2303.13336
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
   Zhang HQ, 2023, Arxiv, DOI arXiv:2201.05337
   Zhang J, 2021, IEEE INTERNET THINGS, V8, P7789, DOI 10.1109/JIOT.2020.3039359
   Zhang SF, 2018, IEEE SYS MAN CYBERN, P415, DOI 10.1109/SMC.2018.00080
   Zhang W., 2017, PROC 2 ACMIEEE S EDG, P1
   Zhang Zongshun, 2023, IEEE Transactions on Big Data, P1380, DOI 10.1109/TBDATA.2023.3280405
   Zhang ZC, 2023, Arxiv, DOI arXiv:2303.12618
   Zhao M, 2022, IEEE T NETW SCI ENG, V9, P2330, DOI 10.1109/TNSE.2022.3163193
NR 150
TC 5
Z9 5
U1 15
U2 28
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
EI 2644-125X
J9 IEEE OPEN J COMM SOC
JI IEEE Open J. Commun. Soc.
PY 2023
VL 4
BP 2952
EP 2971
DI 10.1109/OJCOMS.2023.3320646
PG 20
WC Engineering, Electrical & Electronic; Telecommunications
WE Emerging Sources Citation Index (ESCI)
SC Engineering; Telecommunications
GA AX6R7
UT WOS:001121792100004
OA Green Submitted, gold, Green Published
DA 2024-12-25
ER

PT J
AU Qu, Y
   Tan, MXY
   Wang, J
AF Qu, Yao
   Tan, Michelle Xin Yi
   Wang, Jue
TI Disciplinary differences in undergraduate students' engagement with
   generative artificial intelligence
SO SMART LEARNING ENVIRONMENTS
LA English
DT Article
DE Generative artificial intelligence; Undergraduate students; Academic
   disciplines; Technology adoption; Task engagement
ID TECHNOLOGY ACCEPTANCE MODEL; UNIVERSITY; FUTURE; TAM
AB The rapid development of generative artificial intelligence (GenAI) technologies has sparked widespread discussions about their potential applications in higher education. However, little is known about how students from various disciplines engage with GenAI tools. This study explores undergraduate students' GenAI knowledge, usage intentions, and task-specific engagement across academic disciplines. Using a disciplinary categorization framework, we examine how the hard/soft and pure/applied dimensions relate to students' interactions with GenAI. We surveyed 193 undergraduates from diverse disciplines at a university in Singapore. The questionnaire assessed students' GenAI knowledge, usage intentions, and engagement with GenAI for cognitive and routine tasks against their disciplinary background. The results indicate substantial disciplinary disparities in the level of engagement of students with GenAI. Compared to pure fields, applied fields (both hard and soft) consistently exhibit higher levels of GenAI knowledge and utilization intentions. Furthermore, the engagement of GenAI in routine tasks is relatively consistent across disciplines; however, there are substantial disparities in cognitive tasks, with applied fields exhibiting higher engagement. These results suggest that the practical orientation of applied fields drives GenAI adoption and utilization in academic settings. The study emphasizes considering disciplinary differences to better integrate GenAI into higher education and calls for tailored approaches that align with each field's unique epistemological and methodological traditions to balance GenAI's practical benefits with the preservation of core disciplinary knowledge and skills.
C1 [Qu, Yao; Tan, Michelle Xin Yi; Wang, Jue] Nanyang Technol Univ, Sch Social Sci, Singapore, Singapore.
C3 Nanyang Technological University
RP Wang, J (corresponding author), Nanyang Technol Univ, Sch Social Sci, Singapore, Singapore.
EM wangjue@ntu.edu.sg
RI Wang, Jue/P-6168-2014
OI Wang, Jue/0000-0002-3401-713X
FU Institute for Pedagogical Innovation, Research & Excellence (InsPIRE) at
   Nanyang Technological University
FX The study received financial support from the Institute for Pedagogical
   Innovation, Research & Excellence (InsPIRE) at Nanyang Technological
   University. We also appreciate the valuable comments provided by the
   InsPIRE team. We would like to thank Anna-Lena Ruland and Yong in Choi
   for their invaluable inspiration in idea generation. The opinions
   expressed and any errors contained herein are solely the responsibility
   of the authors.
CR Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Andersen J. P., 2024, Generative artificial intelligence (GenAI) in the research process-a survey of researchers' practices and perceptions, DOI [10.31235/osf.io/83whe, DOI 10.31235/OSF.IO/83WHE]
   Autor DH, 2003, Q J ECON, V118, P1279, DOI 10.1162/003355303322552801
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   BECHER T, 1994, STUD HIGH EDUC, V19, P151, DOI 10.1080/03075079412331382007
   Becher T., 2001, ACAD TRIBES TERRITOR
   Berg C., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4407587, DOI 10.2139/SSRN.4407587]
   Bhattacharya K, 2023, INDIAN J SURG, V85, P1346, DOI 10.1007/s12262-023-03727-x
   BIGLAN A, 1973, J APPL PSYCHOL, V57, P204, DOI 10.1037/h0034699
   Bisdas S, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.795284
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Clark B.R., 1986, The higher education system: Academic organization in crossnational perspective
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Eaton SE, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00144-1
   Eggmann F, 2023, J ESTHET RESTOR DENT, V35, P1098, DOI 10.1111/jerd.13046
   Elshaer IA, 2024, EUR J INVEST HEALTH, V14, P1981, DOI 10.3390/ejihpe14070132
   Etzkowitz H, 2000, RES POLICY, V29, P313, DOI 10.1016/S0048-7333(99)00069-4
   Frey CB, 2017, TECHNOL FORECAST SOC, V114, P254, DOI 10.1016/j.techfore.2016.08.019
   Gasevic D, 2014, INT REV RES OPEN DIS, V15, P134
   Gray A., 2024, arXiv, DOI [10.48550/arXiv.2403.16887, DOI 10.48550/ARXIV.2403.16887]
   Harrer S, 2023, EBIOMEDICINE, V90, DOI 10.1016/j.ebiom.2023.104512
   Healey M., 2000, Higher Education Research Development, V19, P169, DOI [10.1080/072943600445637, DOI 10.1080/072943600445637]
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Kember D, 2011, RES HIGH EDUC, V52, P278, DOI 10.1007/s11162-010-9194-z
   Kitamura FC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230171
   Koehler M., 2023, Academy of Management Proceedings, V2023, P15327, DOI [10.5465/AMPROC.2023.15327abstract, DOI 10.5465/AMPROC.2023.15327ABSTRACT]
   Koehler M, 2024, RES POLICY, V53, DOI 10.1016/j.respol.2024.104985
   Liang YC, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00273-7
   Margaryan A, 2011, COMPUT EDUC, V56, P429, DOI 10.1016/j.compedu.2010.09.004
   McChesney K, 2019, INT J RES METHOD EDU, V42, P225, DOI 10.1080/1743727X.2019.1590811
   Meyer JanH.F., THRESHOLD CONCEPTS T
   Neumann R, 2001, STUD HIGH EDUC, V26, P135, DOI 10.1080/03075070120052071
   Popenici Stefan A D, 2017, Res Pract Technol Enhanc Learn, V12, P22, DOI 10.1186/s41039-017-0062-8
   Raman R, 2024, HUM BEHAV EMERG TECH, V2024, DOI 10.1155/2024/3085910
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Scherer R, 2019, COMPUT EDUC, V128, P13, DOI 10.1016/j.compedu.2018.09.009
   Skulmowski A, 2024, EDUC PSYCHOL REV, V36, DOI 10.1007/s10648-024-09894-x
   Starkey L, 2023, ETR&D-EDUC TECH RES, V71, P117, DOI 10.1007/s11423-023-10196-2
   Stohr C., 2024, Comput. Educ. Artif. Intell., V7, DOI [10.1016/j.caeai.2024.100259, DOI 10.1016/J.CAEAI.2024.100259]
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tourangeau R, 2007, PSYCHOL BULL, V133, P859, DOI 10.1037/0033-2909.133.5.859
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Walsh JP, 1996, SOC STUD SCI, V26, P661, DOI 10.1177/030631296026003006
   Warschauer M, 2023, J SECOND LANG WRIT, V62, DOI 10.1016/j.jslw.2023.101071
   Wu B, 2017, COMPUT HUM BEHAV, V67, P221, DOI 10.1016/j.chb.2016.10.028
NR 47
TC 0
Z9 0
U1 9
U2 9
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
EI 2196-7091
J9 SMART LEARN ENVIRON
JI Smart Learn. Env.
PD NOV 11
PY 2024
VL 11
IS 1
AR 51
DI 10.1186/s40561-024-00341-6
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA L7H1R
UT WOS:001352381600001
OA gold
DA 2024-12-25
ER

PT J
AU Hostetter, AB
   Call, N
   Frazier, G
   James, T
   Linnertz, C
   Nestle, E
   Tucci, M
AF Hostetter, Autumn B.
   Call, Natalie
   Frazier, Grace
   James, Tristan
   Linnertz, Cassandra
   Nestle, Elizabeth
   Tucci, Miaflora
TI Student and Faculty Perceptions of Generative Artificial Intelligence in
   Student Writing
SO TEACHING OF PSYCHOLOGY
LA English
DT Article; Early Access
DE student writing, generative artificial intelligence; plagiarism
ID TO-LEARN ASSIGNMENTS; PSYCHOLOGY; RETENTION
AB Background Psychology instructors frequently assign writing-to-learn exercises that include personal reflection. Generative Artificial Intelligence (GenAI) can write text that passes for humans in other domains.Objective Do students and faculty rate a reflection written by GenAI differently than reflections written by students? Do students and faculty agree about the appropriateness of using GenAI for college-level writing?Method Eighty-three students and 82 faculty read four reflections (three written by undergraduate students and one by GenAI). After rating the quality of each, they chose which one they thought was AI-generated. Participants then rated the ethicality of nine potential ways to use GenAI in college-level writing and the potential of each to compromise learning.Results Participants rated the AI-generated reflection similarly to the student-generated reflections and failed to reliably detect AI-generated writing. Faculty and students agreed that using GenAI to produce the final text for a student likely compromises learning more than using it to generate ideas.Conclusion AI-generated reflections blend in with student-written reflections, and students and faculty agree about the potential detriments to learning.Teaching Implications GenAI can be hard to detect in the psychology classroom. Rather than implementing one-size-fits-all policies, instructors might focus classroom conversations on how GenAI could compromise learning.
C1 [Hostetter, Autumn B.; Call, Natalie; Frazier, Grace; James, Tristan; Linnertz, Cassandra; Nestle, Elizabeth; Tucci, Miaflora] Kalamazoo Coll, Dept Psychol, 1200 Acad St, Kalamazoo, MI 49006 USA.
C3 Kalamazoo College
RP Hostetter, AB (corresponding author), Kalamazoo Coll, Dept Psychol, 1200 Acad St, Kalamazoo, MI 49006 USA.
EM autumn.hostetter@kzoo.edu
OI Hostetter, Autumn/0000-0002-9610-8619
CR American Psychological Association, 2023, APA Guidelines for the Undergraduate Psychology Major
   Anderson L. W., 2001, A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objective
   Burton JW, 2020, J BEHAV DECIS MAKING, V33, P220, DOI 10.1002/bdm.2155
   Chang TS, 2021, 2021 2ND INTERNATIONAL CONFERENCE ON EDUCATION DEVELOPMENT AND STUDIES, ICEDS 2021, P31, DOI 10.1145/3459043.3459065
   Clerwall C, 2014, JOURNAL PRACT, V8, P519, DOI 10.1080/17512786.2014.883116
   Colman AndrewM., 2009, DICT PSYCHOL, DOI [10.1093/acref/9780199534067.001.0001, DOI 10.1093/ACREF/9780199534067.001.0001]
   Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
   Dunn DS, 2013, AUST J PSYCHOL, V65, P5, DOI 10.1111/ajpy.12004
   Fahmi M. A., 2021, JEES (Journal of English Educators Society), V6, P18, DOI [10.21070/jees.v6i1.849, DOI 10.21070/JEES.V6I1.849]
   Figueredo L, 2005, READ PSYCHOL, V26, P441, DOI 10.1080/02702710500400495
   Gingerich KJ, 2014, TEACH PSYCHOL, V41, P303, DOI 10.1177/0098628314549701
   Graefe A, 2018, JOURNALISM, V19, P595, DOI 10.1177/1464884916641269
   Hostetter A. B., 2024, Student and faculty perceptions of generative artificial intelligence in student writing, DOI [10.17605/OSF.IO/PHFTJ, DOI 10.17605/OSF.IO/PHFTJ]
   Kellogg RT, 2007, PSYCHON B REV, V14, P237, DOI 10.3758/BF03194058
   Kim NJ, 2022, FRONT EDUC, V7, DOI 10.3389/feduc.2022.755914
   Köbis N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106553
   Marche S, 2022, ATLANTIC
   McGuire L., 2009, J SCHOLARSHIP TEACHI, V9, P93
   National Association of Colleges and Employers, 2022, The attributes employers want to see on college students' resumes
   Nevid JS, 2012, TEACH PSYCHOL, V39, P272, DOI 10.1177/0098628312456622
   OpenAI, 2023, CHATGPT JAN 15 VERSI
   SERPninja, 2024, Flesch Kincaid calculator
   Spirgel AS, 2016, EDUC PSYCHOL REV, V28, P171, DOI 10.1007/s10648-014-9290-2
   Stewart TL, 2010, TEACH PSYCHOL, V37, P46, DOI 10.1080/00986280903425813
   Tian E., 2023, Towards Detection of AI-Generated Text Using Zero-Shot and Supervised Methods
   Undetectable LLC, 2024, Undetectable AI
   Writer, 2024, AI content detector
   Zimmerman J., 2006, NATL ASS SECONDARY S, V90, P238, DOI DOI 10.1177/0192636506291521
NR 28
TC 1
Z9 1
U1 34
U2 34
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0098-6283
EI 1532-8023
J9 TEACH PSYCHOL
JI Teach. Psychol.
PD 2024 SEP 10
PY 2024
DI 10.1177/00986283241279401
EA SEP 2024
PG 11
WC Education & Educational Research; Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Psychology
GA F3L7Y
UT WOS:001308879900001
DA 2024-12-25
ER

PT J
AU Jose, EMK
   Prasanna, A
   Kushwaha, BP
   Das, M
AF Jose, Emily Maria K.
   Prasanna, Akshara
   Kushwaha, Bijay Prasad
   Das, Madhumita
TI Can generative AI motivate management students? The role of perceived
   value and information literacy
SO INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION
LA English
DT Article
DE Artificial intelligence; GenAI; ChatGPT; Management education;
   Disruptive technology; Student perception
ID ARTIFICIAL-INTELLIGENCE; HIGHER-EDUCATION; TRENDS
AB Generative Artificial Intelligence (GenAI) is a disruptive technology that has started to be used among students in management education. However, the question is whether the utilisation of GenAI in educational settings stimulates students to engage in learning activities and broaden their knowledge base. Hence, this study investigates student motivation and perception of using GenAI (ChatGPT) technology in management education. A random sampling technique was employed to survey 478 students from various educational institutions in the southern region of India. The outcomes revealed that GenAI presents both prospects and hurdles in the domain of management education. The disruptive nature of this technology brings forth numerous opportunities for acquiring knowledge and augmenting one's cognitive capacity. Nonetheless, a cautious and accountable approach is imperative for the successful integration of GenAI into the of management education. Consequently, this study provides pragmatic implications for students, educators, and educational institutions. The effectiveness of GenAI in practical settings can be heightened by arranging interactive training sessions led by AI experts, devising easily accessible online educational modules, embedding AI proficiencies into educators through collaborative endeavors or specialized training programs, and establishing systematic assessment protocols to ensure continual improvement.
C1 [Jose, Emily Maria K.] Xavier Inst Management & Entrepreneurship, Bangalore, India.
   [Prasanna, Akshara; Kushwaha, Bijay Prasad] Vellore Inst Technol, VIT Business Sch, Vellore, India.
   [Das, Madhumita] Indian Inst Management, Bodh Gaya, India.
C3 Vellore Institute of Technology (VIT); VIT Vellore; Indian Institute of
   Management (IIM System); Indian Institute of Management Bodh Gaya
RP Prasanna, A; Kushwaha, BP (corresponding author), Vellore Inst Technol, VIT Business Sch, Vellore, India.; Kushwaha, BP (corresponding author), VIT Univ, Gandhi Block, Vellore 632007, India.
EM emily@xime.org; aksharap23@gmail.com; bijayrsm@gmail.com;
   madhumita.d@iimbg.ac.in
RI Kushwaha, Bijay/ADC-7017-2022; das, madhumita/ABH-2793-2020
OI Das, Madhumita/0000-0002-6374-9399; Kushwaha, Bijay
   Prasad/0000-0001-5086-3400; K Jose, Emily Maria/0000-0003-4673-4045
CR Abdi ES, 2024, J LIBR INF SCI, V56, P950, DOI 10.1177/09610006231180320
   Ajibade Patrick, 2023, Library Hi Tech News, P12, DOI 10.1108/LHTN-09-2022-0109
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Aljanazrah A, 2022, FRONT EDUC, V7, DOI 10.3389/feduc.2022.1047035
   An JF, 2023, NATURE, V615, P586, DOI 10.1038/d41586-023-00843-2
   Aronson S., 2023, NEJM Catalyst Innovations in Care Delivery, V321, P2281, DOI [10.1056/CAT.23.0063, DOI 10.1056/CAT.23.0063]
   Atasever A., 2023, PARTICIPATORY ED RES, V10, P17, DOI [10.17275/per.23.2.10.1, DOI 10.17275/PER.23.2.10.1]
   Baek TH, 2023, TELEMAT INFORM, V83, DOI 10.1016/j.tele.2023.102030
   Barton T. D., 2018, Liquid Legal. Law for Professionals., DOI [10.1007/978-3-030-48266-4_4, DOI 10.1007/978-3-030-48266-4_4]
   Bowles DC, 2023, PEDAGOGY HEAL PROMOT, V9, P75, DOI 10.1177/23733799231175171
   Bridges E, 2023, J BUS FINANC LIBR, V28, P153, DOI 10.1080/08963568.2023.2213510
   Buckley A., 2023, Applied Marketing Analytics, V9, P145
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Chaka C., 2023, INT J LEARNING TEACH, V22, P1, DOI DOI 10.26803/IJLTER.22.6.1
   Cham TH, 2016, VINE J INF KNOWL MAN, V46, P2, DOI 10.1108/VJIKMS-03-2014-0021
   Chan C. K. Y., 2023, DECONSTRUCTING STUDE
   Chan CKY, 2024, STUD EDUC EVAL, V83, DOI 10.1016/j.stueduc.2024.101395
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chyne RC, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102771
   Clune J, 2020, Arxiv, DOI arXiv:1905.10985
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dai Yun, 2023, Procedia CIRP, P84, DOI 10.1016/j.procir.2023.05.002
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eapen TT, 2023, HARVARD BUS REV, V101, P55
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Fischer JE, 2023, PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON CONVERSATIONAL USER INTERFACES, CUI 2023, DOI 10.1145/3571884.3603756
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Gill Sukhpal Singh, 2024, Internet of Things and Cyber-Physical Systems, V4, P19, DOI 10.1016/j.iotcps.2023.06.002
   Griesbaum J, 2023, COMMUN INF LIT, V17, P260, DOI 10.15760/comminfolit.2023.17.1.4
   Guo B, 2015, ACM COMPUT SURV, V48, DOI 10.1145/2794400
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Han A, 2023, 22ND ANNUAL ACM INTERACTION DESIGN AND CHILDREN CONFERENCE, IDC 2023, P470, DOI 10.1145/3585088.3593867
   Hanson J, 2023, J MUSIC TEACH EDUC, DOI 10.1177/10570837231177988
   Hashim MAM, 2022, EDUC INF TECHNOL, V27, P3171, DOI 10.1007/s10639-021-10739-1
   Hicks A, 2023, J LIBR INF SCI, V55, P548, DOI 10.1177/09610006221090677
   Hicks A, 2023, J LIBR INF SCI, V55, P282, DOI 10.1177/09610006211067216
   Hulleman CS, 2010, PSYCHOL BULL, V136, P422, DOI 10.1037/a0018947
   Khatri P, 2024, INT J MANAG EDUC-OXF, V22, DOI 10.1016/j.ijme.2024.100933
   Kim B, 2023, Computers in Libraries, V43, P41
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Koohi-Moghadam M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01987-4
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Kothari AN, 2023, ANN SURG ONCOL, V30, P3174, DOI 10.1245/s10434-023-13442-2
   Kumar S, 2023, INFORM SYST FRONT, V25, P871, DOI 10.1007/s10796-022-10279-0
   Lancaster T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00131-6
   Li YJ, 2022, SCIENCE, V378, P1092, DOI 10.1126/science.abq1158
   Lim W M., 2023, Global Business and Organizational Excellence, DOI DOI 10.1002/JOE.22218
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Limna P., 2023, J. Appl. Learn. Teach, V6, P64, DOI [DOI 10.37074/JALT.2023.6.1.32, https://doi.org/10.37074/jalt.2023.6.1.32]
   Lubis A. H, 2017, The impact of ICT usage towards learning process quality among lecturers on selected private universities in medan, Indonesia
   Lund B., 2023, SSRN Scholarly Paper, DOI [10.2139/ssrn.4324580, DOI 10.2139/SSRN.4324580]
   Mahajan R, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114104
   Mills A., 2023, Journal of Applied Learning and Teaching, V6, DOI DOI 10.37074/JALT.2023.6.1.34
   Miura Y., 2017, Unifying text, metadata, and user network representations with a neural network for geolocation prediction, V1, P1260
   Nakaziba S, 2023, LIBR MANAGE, V44, P97, DOI 10.1108/LM-08-2022-0071
   Orben A, 2020, SOC PSYCH PSYCH EPID, V55, P407, DOI 10.1007/s00127-019-01825-4
   Orchard T, 2023, ECON BUS REV-POL, V9, P9, DOI 10.18559/ebr.2023.2.732
   Otterbacher J, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100796
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   Saadia H, 2024, ONLINE INFORM REV, V48, P257, DOI 10.1108/OIR-06-2022-0345
   Saúde S, 2024, SOC SCI-BASEL, V13, DOI 10.3390/socsci13080410
   Schechter C, 2011, J EDUC ADMIN, V49, P143, DOI 10.1108/09578231111116707
   Shahzad MF, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e29523
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Sobhanmanesh F, 2023, ALGORITHMS, V16, DOI 10.3390/a16030155
   Steinfeld K, 2023, INT J ARCHIT COMPUT, V21, P211, DOI 10.1177/14780771231168230
   Strowel A, 2023, IIC-INT REV INTELL P, DOI 10.1007/s40319-023-01321-y
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   SWELLER J, 1988, COGNITIVE SCI, V12, P257, DOI 10.1207/s15516709cog1202_4
   Taylor C., 2023, Journal of Information Literacy, V17, P89, DOI [10.11645/17.1.3258, DOI 10.11645/17.1.3258]
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tzafilkou K, 2023, EDUC INF TECHNOL, V28, P16017, DOI 10.1007/s10639-023-11848-9
   Walczak K, 2023, ECON BUS REV-POL, V9, P71, DOI 10.18559/ebr.2023.2.743
   Wang M, 2023, J INF SCI, DOI 10.1177/01655515231191210
   Wedgwood T, 2009, INT J HERIT STUD, V15, P277, DOI 10.1080/13527250902933611
   Wickstrom K., 2017, International Journal of Innovation Studies, V1, P193, DOI [DOI 10.1016/j.ijis.2017.11.002, 10.1016/j.ijis.2017.11.002, DOI 10.1016/J.IJIS.2017.11.002]
   Wigfield A, 2000, CONTEMP EDUC PSYCHOL, V25, P68, DOI 10.1006/ceps.1999.1015
   Yesmin S, 2023, REV EDUC-US, V11, DOI 10.1002/rev3.3389
   Yilmaz H., 2023, International Educational Review, V1, P57, DOI [10.58693/ier.114, DOI 10.58693/IER.114]
   Yilmaz R., 2023, COMPUT HUM BEHAV, V1, DOI DOI 10.1016/J.CHBAH.2023.100005
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Yilmaz Ramazan, 2022, Computers and Education: Artificial Intelligence, V3, DOI [10.1016/j.caeai, DOI 10.1016/J.CAEAI.2022.100092]
NR 88
TC 1
Z9 1
U1 28
U2 28
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 1472-8117
EI 2352-3565
J9 INT J MANAG EDUC-OXF
JI Int. J. Manag. Educ.
PD NOV
PY 2024
VL 22
IS 3
AR 101082
DI 10.1016/j.ijme.2024.101082
PG 15
WC Business; Education & Educational Research; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Education & Educational Research
GA M2L3A
UT WOS:001355899800001
DA 2024-12-25
ER

PT J
AU Ng, W
   Hao, F
   Zhang, C
AF Ng, Wailing
   Hao, Fei
   Zhang, Chen
TI From Function to Relation: Exploring the Dual Influences of Warmth and
   Competence on Generative Artificial Intelligence Services in the
   Hospitality Industry
SO JOURNAL OF HOSPITALITY & TOURISM RESEARCH
LA English
DT Article; Early Access
DE generative artificial intelligence; technology acceptance;
   customer-brand identification; customer-brand attachment; technology
   readiness index
ID CUSTOMER SATISFACTION; BRAND ATTACHMENT; UNIFIED THEORY; ACCEPTANCE;
   CONSUMERS; TECHNOLOGIES; IDENTIFICATION; READINESS; LOYALTY; IMPACT
AB Generative artificial intelligence (GenAI) has emerged as a transformative force in the rapidly-evolving hospitality industry. This study examined the correlation of acceptance of GenAI services with perceived warmth and competence, and the consequent correlations with customer outcomes in the hospitality industry. Employing a Hospitality Service Dialogue System prototype, this study collected responses from 1,306 hotel guests. Perceptions of warmth and competence correlated with increased customer intention to use GenAI services and satisfaction. Satisfaction correlated significantly with customer-brand identification and attachment. The Technology Readiness Index moderated the relationships of warmth and competence with the acceptance of GenAI services. This investigation extends the stereotype content model and the behaviors from intergroup affect and stereotypes map framework by integrating them with the technology acceptance literature, unveiling service innovations enabled by technology in the hospitality industry. This study provides hotel managers, technology developers, and marketers with strategic directions for implementing GenAI.
C1 [Ng, Wailing; Zhang, Chen] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China.
   [Hao, Fei; Zhang, Chen] Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, 17 Sci Museum Rd,TST East, Hong Kong, Peoples R China.
C3 Hong Kong Polytechnic University; Hong Kong Polytechnic University
RP Hao, F (corresponding author), Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, 17 Sci Museum Rd,TST East, Hong Kong, Peoples R China.
EM ffaye.hao@polyu.edu.hk
RI Hao, Fei/AAO-3459-2020
OI Hao, Fei/0000-0002-2295-7255
FU Hong Kong Polytechnic University (UGC) [P0043849]
FX The authors disclosed receipt of the following financial support for the
   research, authorship, and/or publication of this article: This project
   received funding from The Hong Kong Polytechnic University (UGC),
   [P0043849]; The Hong Kong Polytechnic University (UGC) under VP(RI)'s
   Special Allocation, [P0045695]; the University Grants Committee (Hong
   Kong) under the Mr and Mrs Chan Chak Fu Research Assistantship,
   [P0045911]; the Research Grants Council under the RGC Early Career
   Scheme, [P0047204, 25504823]; the Innovation and Technology Commission,
   [P0043294, ITS/028/22FP]; and the Research Grants Council under the RGC
   General Research Fund, [15505324].
CR Aggarwal P, 2004, J CONSUM RES, V31, P87, DOI 10.1086/383426
   Arici HE, 2024, J HOSP TOUR RES, V48, P1081, DOI 10.1177/10963480231168609
   Bettencourt LA, 1997, J RETAILING, V73, P383, DOI 10.1016/S0022-4359(97)90024-5
   Bhattacharya CB, 2003, J MARKETING, V67, P76, DOI 10.1509/jmkg.67.2.76.18609
   Cao LM, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231182965
   Cuddy AJC, 2007, J PERS SOC PSYCHOL, V92, P631, DOI 10.1037/0022-3514.92.4.631
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Fernandes T, 2021, J BUS RES, V122, P180, DOI 10.1016/j.jbusres.2020.08.058
   Fiske ST, 2007, TRENDS COGN SCI, V11, P77, DOI 10.1016/j.tics.2006.11.005
   Ghorbanzadeh D, 2020, COGENT PSYCHOL, V7, DOI 10.1080/23311908.2020.1782098
   Güntürkün P, 2020, J SERV RES-US, V23, P476, DOI 10.1177/1094670520920354
   Guo Q, 2024, J HOSP TOUR RES, V48, P450, DOI 10.1177/10963480221108906
   Gursoy D, 2024, INT J CONTEMP HOSP M, DOI 10.1108/IJCHM-03-2024-0322
   Gursoy D, 2023, J HOSP MARKET MANAG, V32, P579, DOI 10.1080/19368623.2023.2211993
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Hao F, 2021, ASIA PAC J TOUR RES, V26, P1386, DOI 10.1080/10941665.2021.1984264
   Hwang J, 2021, INT J HOSP MANAG, V99, DOI 10.1016/j.ijhm.2021.103050
   Kim H, 2024, J HOSP TOUR RES, DOI 10.1177/10963480241229235
   Kim J, 2012, J HOSP TOUR RES, V36, P85, DOI 10.1177/1096348011407311
   Kim S, 2020, INT J HOSP MANAG, V88, DOI 10.1016/j.ijhm.2020.102533
   Kolbl I, 2020, J BUS RES, V118, P346, DOI 10.1016/j.jbusres.2020.06.048
   Lewis P, 2020, ADV NEUR IN, V33
   Li MW, 2020, J HOSP TOUR MANAG, V44, P184, DOI 10.1016/j.jhtm.2020.06.015
   Li YQ, 2021, TOUR REV, DOI 10.1108/TR-05-2020-0241
   Liang HG, 2007, MIS QUART, V31, P59
   Liu X, 2022, ANN TOURISM RES, V92, DOI 10.1016/j.annals.2021.103324
   Parasuraman A., 2000, Journal of Service Research, V2, P307, DOI [DOI 10.1177/109467050024001, 10.1177/109467050024001]
   Park CW, 2010, J MARKETING, V74, P1, DOI 10.1509/jmkg.74.6.1
   Peng CM, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102533
   Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
   Pizam A, 2016, INT J CONTEMP HOSP M, V28, P2, DOI 10.1108/IJCHM-04-2015-0167
   Quan LJ, 2022, INT J HOSP MANAG, V101, DOI 10.1016/j.ijhm.2021.103123
   Radojevic T, 2015, TOURISM MANAGE, V51, P13, DOI 10.1016/j.tourman.2015.04.002
   Rather RA, 2019, INT J CONTEMP HOSP M, V31, P1432, DOI 10.1108/IJCHM-10-2017-0627
   Rather RA, 2018, J HOSP MARKET MANAG, V27, P487, DOI 10.1080/19368623.2018.1404539
   Shams G, 2024, J HOSP TOUR RES, DOI 10.1177/10963480241280991
   Shimul AS, 2022, J BRAND MANAG, V29, P400, DOI 10.1057/s41262-022-00279-5
   Shin M, 2020, INT J CONTEMP HOSP M, V13, P3991, DOI 10.1108/IJCHM-06-2020-0550
   Song XX, 2024, TOUR REV, V79, P505, DOI 10.1108/TR-04-2023-0265
   Sun S, 2020, INT J HOSP MANAG, V90, DOI 10.1016/j.ijhm.2020.102633
   Sun S, 2019, INT J HOSP MANAG, V77, P89, DOI 10.1016/j.ijhm.2018.06.017
   Tse S, 2022, J TRAVEL RES, V61, P565, DOI 10.1177/0047287521997576
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang PQ, 2024, CURR ISSUES TOUR, DOI 10.1080/13683500.2023.2300030
   Wang Y, 2017, J TRAVEL RES, V56, P563, DOI 10.1177/0047287516657891
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Xiong L, 2023, J HOSP TOUR MANAG, V56, P376, DOI 10.1016/j.jhtm.2023.07.008
   Zhang Y, 2024, J HOSP TOUR RES, V48, P991, DOI 10.1177/10963480231186656
NR 50
TC 0
Z9 0
U1 18
U2 18
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1096-3480
EI 1557-7554
J9 J HOSP TOUR RES
JI J. Hosp. Tour. Res.
PD 2024 NOV 7
PY 2024
DI 10.1177/10963480241292016
EA NOV 2024
PG 14
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA L3X5D
UT WOS:001350085100001
DA 2024-12-25
ER

PT J
AU Chenneville, T
   Duncan, B
   Silva, G
AF Chenneville, Tiffany
   Duncan, Brianna
   Silva, Gabriella
TI More Questions Than Answers: Ethical Considerations at the Intersection
   of Psychology and Generative Artificial Intelligence
SO TRANSLATIONAL ISSUES IN PSYCHOLOGICAL SCIENCE
LA English
DT Article
DE ethics; psychology; technology; artificial intelligence; generative AI
AB Psychology has changed considerably over the past several decades in response to technological advances. Changes to the profession accelerated during COVID-19, a time during which there was a rapid increase in the use of cloud-based virtual collaboration platforms (e.g., Zoom, Microsoft Teams) for telehealth services as well as virtual teaching and research activities. Technology continues to advance swiftly, and the introduction of artificial intelligence (AI), particularly generative AI (genAI), is creating profound changes in all aspects of society, including the psychology profession. With these changes come questions about how to practice ethically as a clinician, as a teacher, and as a researcher. In this article, we explore the ethical principles and standards of most relevance to genAI use in the activities of psychologists (clinical, teaching, research) across settings (private practice, hospitals, colleges/universities, research centers). Ethical issues and questions are presented within the context of the current American Psychological Association's Ethical Principles of Psychologists and Code of Conduct (2017), which is under revision. Recommendations are provided for approaching ethical concerns amidst the rapid technological advances that are changing the way psychologists do their work.
C1 [Chenneville, Tiffany; Duncan, Brianna; Silva, Gabriella] Univ S Florida, Dept Psychol, Davis Hall 117,140 7th Ave South, St Petersburg, FL 33701 USA.
   [Chenneville, Tiffany] Univ S Florida, Dept Pediat, St Petersburg, FL USA.
   [Chenneville, Tiffany] Univ Witwatersrand, Fac Hlth Sci, Sch Clin Med, Perinatal HIV Res Unit PHRU, Johannesburg, South Africa.
C3 State University System of Florida; University of South Florida; State
   University System of Florida; University of South Florida; University of
   Witwatersrand
RP Chenneville, T (corresponding author), Univ S Florida, Dept Psychol, Davis Hall 117,140 7th Ave South, St Petersburg, FL 33701 USA.
EM chennevi@usf.edu
OI Chenneville, Tiffany/0000-0001-5598-9387
NR 0
TC 1
Z9 1
U1 9
U2 9
PU EDUCATIONAL PUBLISHING FOUNDATION-AMERICAN PSYCHOLOGICAL ASSOC
PI WASHINGTON
PA 750 FIRST ST, NE, WASHINGTON, DC 20002-4242 USA
SN 2332-2136
EI 2332-2179
J9 TRANSL ISS PSYCH SCI
JI Transl. Iss. Psychol. Sci.
PD JUN
PY 2024
VL 10
IS 2
SI SI
BP 162
EP 178
DI 10.1037/tps0000400
EA JUN 2024
PG 17
WC Psychology; Psychology, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Psychology
GA I1H7N
UT WOS:001300816200001
DA 2024-12-25
ER

PT J
AU Perkins, M
   Roe, J
   Vu, BH
   Postma, D
   Hickerson, D
   McGaughran, J
   Khuat, HQ
AF Perkins, Mike
   Roe, Jasper
   Vu, Binh H.
   Postma, Darius
   Hickerson, Don
   McGaughran, James
   Khuat, Huy Q.
TI Simple techniques to bypass GenAI text detectors: implications for
   inclusive education
SO INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
LA English
DT Article
DE Generative artificial intelligence; Adversarial techniques; Academic
   integrity; Higher education; AI text detectors
AB This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content modified to evade detection (n = 805). We compare these detectors to assess their reliability in identifying AI-generated text in educational settings, where they are increasingly used to address academic integrity concerns. Results show significant reductions in detector accuracy (17.4%) when faced with simple techniques to manipulate the AI generated content. The varying performances of GenAI tools and detectors indicate they cannot currently be recommended for determining academic integrity violations due to accuracy limitations and the potential for false accusation which undermines inclusive and fair assessment practices. However, these tools may support learning and academic integrity when used non-punitively. This study aims to guide educators and institutions in the critical implementation of AI text detectors in higher education, highlighting the importance of exploring alternatives to maintain inclusivity in the face of emerging technologies.
C1 [Perkins, Mike; Vu, Binh H.; Postma, Darius; Hickerson, Don; McGaughran, James; Khuat, Huy Q.] British Univ Vietnam, Hanoi, Vietnam.
   [Roe, Jasper] James Cook Univ, Singapore, Singapore.
C3 James Cook University
RP Perkins, M (corresponding author), British Univ Vietnam, Hanoi, Vietnam.
EM Mike.p@buv.edu.vn
RI Roe, Jasper/JOK-3723-2023
CR Abd-Elaal ES, 2022, EUR J ENG EDUC, V47, P725, DOI 10.1080/03043797.2022.2046709
   Adamson D., 2023, New research: Turnitin's AI detector shows no statistically significant bias against English Language Learners
   Alsmadi I, 2021, Arxiv, DOI arXiv:2110.13980
   Amano T, 2023, PLOS BIOL, V21, DOI 10.1371/journal.pbio.3002184
   Anderson N, 2023, BMJ OPEN SPORT EXERC, V9, DOI 10.1136/bmjsem-2023-001568
   Bedington Andelyn, 2024, Computers and Composition, V71, DOI 10.1016/j.compcom.2024.102833
   Bissessar C., 2023, Equity in Education & Society, DOI [10.1177/27526461231215083, DOI 10.1177/27526461231215083]
   Borji A., 2023, GPT-4, Claude, and Bard, DOI [10.2139/ssrn.4476855, DOI 10.2139/SSRN.4476855]
   Chaka C., 2023, J. Appl. Learn. Teach., V6, DOI DOI 10.37074/JALT.2023.6.2.12
   Chaka C., 2023, International Journal of Learning, Teaching and Educational Research, V22, P6
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chang YP, 2023, Arxiv, DOI arXiv:2307.03109
   Copyleaks.com, AI-Based Plagiarism & AI Content Detection
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crossplag.com, The only cross-lingual plagiarism checker-Crossplag
   Curtis GJ., 2024, Second handbook of academic integrity, P681, DOI [10.1007/978-3-031-54144-5110, DOI 10.1007/978-3-031-54144-5110]
   Daly TM, 2024, INT J EDUC INTEGR, V20, DOI 10.1007/s40979-023-00148-x
   Elali FR, 2023, PATTERNS, V4, P1, DOI 10.1016/j.patter.2023.100706
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00140-5
   Fleckenstein J., 2024, Computers and Education: Artificial Intelligence, V6, DOI [10.1016/j.caeai.2024.100209, DOI 10.1016/J.CAEAI.2024.100209]
   Foltynek T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00133-4
   Fröhling L, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.443
   Fu QK, 2024, COMPUT ASSIST LANG L, V37, P179, DOI 10.1080/09588221.2022.2033787
   Furze L., 2024, arXiv
   Gao CA, 2022, bioRxiv, DOI [10.1101/2022.12.23.521610, 10.1101/2022.12.23.521610, DOI 10.1101/2022.12.23.521610V1, DOI 10.1101/2022.12.23.521610]
   GPTKit.com, GPTKit-AI Generated Text Detector Tool for Chat GPT. GPTKit-Highly Accurate Detection of GPT Generated Text
   GPTZero, GPTZero FAQ
   Hilliard LP, 2019, INTERNET HIGH EDUC, V41, P11, DOI 10.1016/j.iheduc.2018.11.002
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Ippolito D, 2020, Arxiv, DOI [arXiv:1911.00650, 10.48550/arXiv.1911.00650, DOI 10.48550/ARXIV.1911.00650]
   Krishna K, 2023, Arxiv, DOI arXiv:2303.13408
   Lancaster T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00131-6
   Liang WX, 2023, Arxiv, DOI arXiv:2304.02819
   Lin YT, 2023, Arxiv, DOI arXiv:2305.13711
   Lozic E, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15100336
   Lutz C, 2019, HUM BEHAV EMERG TECH, V1, P141, DOI 10.1002/hbe2.140
   Manyika J., 2019, The coming of AI Spring
   Mitchell E, 2023, Arxiv, DOI [arXiv:2301.11305, 10.48550/arXiv.2301.11305.CROSSREF]
   Morris C, 2019, HIGH EDUC PEDAGOG, V4, P435, DOI 10.1080/23752696.2019.1669479
   OpenAI, 2023, New ai classifier for indicating ai-written text
   OpenAI, GPT-2 Output Detector
   Orenstrakh MS, 2023, Arxiv, DOI arXiv:2307.07411
   Originality.AI, 2023, AI Content Detector Accuracy Review + Open Source Dataset and Research Tool - Originality
   Paraphrasing Tool-QuillBot AI, About us
   Perkins M., 2024, JALT, V7, DOI [10.37074/jalt.2024.7.1.22, DOI 10.37074/JALT.2024.7.1.22]
   Perkins Mike, 2024, Mendeley Data, V3, DOI 10.17632/XV6FK2MMH9.3
   Perkins M, 2024, J UNIV TEACH LEARN P, V21, DOI 10.53761/q3azde36
   Perkins Mike, 2023, F1000Res, V12, P1398, DOI 10.12688/f1000research.142411.2
   Perkins M, 2024, J ACAD ETHICS, V22, P89, DOI 10.1007/s10805-023-09492-6
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Qiu SL, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9050909
   Radia P, 2009, INTERNET HIGH EDUC, V12, P156, DOI 10.1016/j.iheduc.2009.05.002
   Roe J., 2024, AI and the Anthropological Imagination: Rethinking Education in the Digital Age
   Roe J, 2024, Arxiv, DOI [arXiv:2408.01075, 10.48550/arXiv.2408.01075, DOI 10.48550/ARXIV.2408.01075]
   Roe J, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00138-z
   Roe J, 2022, INT J EDUC INTEGR, V18, DOI 10.1007/s40979-022-00109-w
   Rogerson AM, 2017, INT J EDUC INTEGR, V13, DOI 10.1007/s40979-016-0013-y
   Sadasivan VS, 2024, Arxiv, DOI [arXiv:2303.11156, DOI 10.48550/ARXIV.2303.11156]
   Sobaih A. E., 2024, Journal of Applied Learning Teaching, V7, DOI [10.37074/jalt.2024.7.1.21, DOI 10.37074/JALT.2024.7.1.21]
   Solaiman I, 2019, Arxiv, DOI arXiv:1908.09203
   STM, 2023, Generative AI in scholarly publications. Ethical and practical guidelines for the use of generative AI in the publication process
   Sweenor D., 2023, The CIOs Guide to Adopting Generative AI: Five Keys to Success
   Tian E., 2023, ESL Bias in AI Detection is an Outdated Narrative
   Tian E., 2023, GPTZero | The Trusted AI Detector for ChatGPT, GPT-4, & More
   Turnitin, 2023, AI Writing Detection
   Turnitin, 2023, The launch of Turnitin's AI writing detector and the road ahead
   Walters W H., 2023, Open Inf. Sci, V7, DOI [10.1515/opis-2022-0158, DOI 10.1515/OPIS-2022-0158]
   Wang WQ, 2023, IEEE T KNOWL DATA EN, V35, P3159, DOI 10.1109/TKDE.2021.3117608
   Weber-Wulff D, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00146-z
   Wu TS, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519729
   Wu ZF, 2023, Arxiv, DOI [arXiv:2307.02477, DOI 10.48550/ARXIV.2307.02477, 10.48550/arXiv.2307.02477]
   ZeroGPT.com, AI Detector-Trusted AI Checker for ChatGPT
NR 72
TC 1
Z9 1
U1 20
U2 20
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2365-9440
J9 INT J EDUC TECHNOL H
JI Int. J. Educ. Technol. High. Educ.
PD SEP 9
PY 2024
VL 21
IS 1
AR 53
DI 10.1186/s41239-024-00487-w
PG 25
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA F1C2A
UT WOS:001307257000001
OA gold
DA 2024-12-25
ER

PT J
AU Moorhouse, BL
AF Moorhouse, Benjamin Luke
TI Generative artificial intelligence and ELT
SO ELT JOURNAL
LA English
DT Article
DE generative artificial intelligence (GenAI); ChatGPT; large language
   models (LLMs); conversational AI
AB In this series, we explore technology-related themes and topics. The series aims to discuss and demystify what may be new areas for some readers and to consider their relevance for English language teachers.
C1 [Moorhouse, Benjamin Luke] Hong Kong Baptist Univ HKBU, Dept Educ Studies, Hong Kong, Peoples R China.
RP Moorhouse, BL (corresponding author), Hong Kong Baptist Univ HKBU, Dept Educ Studies, Hong Kong, Peoples R China.
EM blmoorhouse@hkbu.edu.hk
RI Moorhouse, Benjamin/U-2683-2019
OI Moorhouse, Benjamin Luke/0000-0002-3913-5194
CR Ali O, 2024, TECHNOL FORECAST SOC, V199, DOI 10.1016/j.techfore.2023.123076
   Barrot JS, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100745
   Cain W, 2024, TECHTRENDS, V68, P47, DOI 10.1007/s11528-023-00896-0
   Crawford K, 2024, NATURE, V626, P693, DOI 10.1038/d41586-024-00478-x
   Crompton H, 2024, TECHTRENDS, V68, P380, DOI 10.1007/s11528-024-00939-0
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Hockly N, 2023, RELC J, V54, P445, DOI 10.1177/00336882231168504
   Hwang GJ, 2023, EDUC TECHNOL SOC, V26, DOI 10.30191/ETS.202304_26(2).0014
   Javier DRC, 2024, TESOL J, V15, DOI 10.1002/tesj.755
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Lee JH, 2020, ELT J, V74, P338, DOI 10.1093/elt/ccaa035
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Meniado JC, 2023, RELC J, V54, P461, DOI 10.1177/00336882231160610
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   OpenAI, 2024, ChatGPT: A large language model for natural language processing (used for scientific editing). Version GPT-4
   OpenAI, 2024, DALL E DALL E 3 IMAG
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
NR 17
TC 2
Z9 2
U1 44
U2 44
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0951-0893
EI 1477-4526
J9 ELT J
JI ELT J.
PD JUL 18
PY 2024
VL 78
IS 4
BP 378
EP 392
DI 10.1093/elt/ccae032
EA JUL 2024
PG 15
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA L1Q3W
UT WOS:001272195900001
DA 2024-12-25
ER

PT J
AU Alavi, M
   Leidner, DE
   Mousavi, R
AF Alavi, Maryam
   Leidner, Dorothy E.
   Mousavi, Reza
TI A Knowledge Management Perspective of Generative Artificial Intelligence
SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS
LA English
DT Article
DE Generative Artificial Intelligence (GenAI); Knowledge Management;
   Knowledge Workers; Organizational Learning; AI-Driven Innovation;
   Ethical Implications of AI; GenAI Integration in Organizations
AB In this editorial, revisiting Alavi and Leidner (2001) as a conceptual lens, we consider the organizational implications of generative artificial intelligence (GenAI) from a knowledge management (KM) perspective. We examine how GenAI impacts the processes of knowledge creation, storage, transfer, and application, highlighting both the opportunities and challenges this technology presents. In knowledge creation, GenAI enhances information processing and cognitive functions, fostering individual and organizational learning. However, it also introduces risks like AI bias and reduced human socialization, potentially marginalizing junior knowledge workers. For knowledge storage and retrieval, GenAI's ability to quickly access vast knowledge bases significantly changes employee interactions with KM systems. This raises questions about balancing human-derived tacit knowledge with AI-generated explicit knowledge. The paper also explores GenAI's role in knowledge transfer, particularly in training and cultivating a learning culture. Challenges include an overreliance on AI and risks in disseminating sensitive information. In terms of knowledge application, GenAI is seen as a tool to boost productivity and innovation, but issues like knowledge misapplication, intellectual property, and ethical considerations are critical. Conclusively, the paper argues for a balanced approach to integrating GenAI into KM processes. It advocates for harmonizing GenAI's capabilities with human insights to effectively manage knowledge in contemporary organizations, ensuring both technological advances and ethical responsibility.
C1 [Alavi, Maryam] Georgia Inst Technol, Scheller Coll Business, Atlanta, GA 30308 USA.
   [Leidner, Dorothy E.; Mousavi, Reza] Univ Virginia, McIntire Sch Commerce, Charlottesville, VA USA.
C3 University System of Georgia; Georgia Institute of Technology;
   University of Virginia
RP Alavi, M (corresponding author), Georgia Inst Technol, Scheller Coll Business, Atlanta, GA 30308 USA.
EM maryam.alavi@scheller.gatech.edu; dorothy@virginia.edu;
   mousavi@virginia.edu
CR Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961
   Alavi M, 2011, HANDBOOK OF ORGANIZATIONAL LEARNING AND KNOWLEDGE MANAGEMENT, 2ND EDITION, P105
   [Anonymous], 2023, McKinsey BlogAugust 16
   [Anonymous], 2023, Generative AI Could Raise Global GDP by 7%
   Brittain B, 2023, REUTERS
   Brynjolfsson E., 2023, Generative AI at Work NBER Working Paper No. w31161
   Davenport T., 1998, WORKING KNOWLEDGE OR
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Hie BL, 2024, NAT BIOTECHNOL, V42, DOI 10.1038/s41587-023-01763-2
   JP Morgan, 2023, WIRED special feature: Smart Money
   Liu Y, 2024, Arxiv, DOI arXiv:2305.13860
   Meta Platforms Inc. [Meta, 2023, Llama 2: open source, free for research and commercial use
   Moret M, 2023, NAT COMMUN, V14, DOI 10.1038/s41467-022-35692-6
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Reid F., 2023, The Intercom BlogJune 15
   Saunders W, 2017, Arxiv, DOI arXiv:1707.05173
   Scholz J., 2013, P 26 INT C NEURAL IN
   SIMON HA, 1956, PSYCHOL REV, V63, P129, DOI 10.1037/h0042769
   Son Hugh, 2023, CNBC
   Spisak B., 2023, HBR
NR 21
TC 12
Z9 12
U1 253
U2 355
PU ASSOC INFORMATION SYSTEMS
PI ATLANTA
PA GEORGIA STATE UNIV, 35 BROAD STREET, STE 916-917, ATLANTA, GA 30303 USA
SN 1536-9323
EI 1558-3457
J9 J ASSOC INF SYST
JI J. Assoc. Inf. Syst.
PY 2024
VL 25
IS 1
SI SI
BP 1
EP 12
DI 10.17705/1jais.00859
PG 12
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA LB5N1
UT WOS:001184331000001
OA Bronze
DA 2024-12-25
ER

PT J
AU Alier, M
   García-Peñalvo, FJ
   Camba, JD
AF Alier, Marc
   Garcia-Penalvo, Francisco Jose
   Camba, Jorge D.
TI Generative Artificial Intelligence in Education: From Deceptive to
   Disruptive
SO INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL
   INTELLIGENCE
LA English
DT Article
DE Artificial Intelligence; Ethical Implications; Ethical Principles;
   Generative Artificial Intelligence; Large Language Model
AB Generative Artificial Intelligence (GenAI) has emerged as a promising technology that can create original content, such as text, images, and sound. The use of GenAI in educational settings is becoming increasingly popular and offers a range of opportunities and challenges. This special issue explores the management and integration of GenAI in educational settings, including the ethical considerations, best practices, and opportunities. The potential of GenAI in education is vast. By using algorithms and data, GenAI can create original content that can be used to augment traditional teaching methods, creating a more interactive and personalized learning experience. In addition, GenAI can be utilized as an assessment tool and for providing feedback to students using generated content. For instance, it can be used to create custom quizzes, generate essay prompts, or even grade essays. The use of GenAI as an assessment tool can reduce the workload of teachers and help students receive prompt feedback on their work. Incorporating GenAI in educational settings also poses challenges related to academic integrity. With availability of GenAI models, students can use them to study or complete their homework assignments, which can raise concerns about the authenticity and authorship of the delivered work. Therefore, it is important to ensure that academic standards are maintained, and the originality of the student's work is preserved. This issue highlights the need for implementing ethical practices in the use of GenAI models and ensuring that the technology is used to support and not replace the student's learning experience.
C1 [Alier, Marc] Univ Politecn Cataluna, Barcelona, Spain.
   [Garcia-Penalvo, Francisco Jose] Univ Salamanca, Res Inst Educ Sci, Salamanca, Spain.
   [Camba, Jorge D.] Purdue Univ, Purdue, IN 47907 USA.
C3 Universitat Politecnica de Catalunya; University of Salamanca; Purdue
   University System; Purdue University
RP Camba, JD (corresponding author), Purdue Univ, Purdue, IN 47907 USA.
EM marc.alier@upc.edu; fgarcia@usal.es; jdorribo@purdue.edu
RI GARCÍA-PEÑALVO, Francisco/D-5445-2013
OI Dorribo Camba, Jorge/0000-0001-5384-3253
FU Ministry of Science and Innovation [PID2020-118345RB-I00]; Departament
   de Recerca i Universitats de la Generalitat de Catalunya [2021 SGR
   01412]
FX The Ministry of Science and Innovation partially funded this monograph
   through the AvisSA project grant number (PID2020-118345RB-I00) . The
   Departament de Recerca i Universitats de la Generalitat de Catalunya
   partially funded this monograph through the 2021 SGR 01412 research
   groups award.
CR Abdou Mostafa, 2021, P 25 C COMP NAT LANG, P109, DOI DOI 10.18653/V1/2021.CONLL-1.9
   Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Alier M., 2023, P TEEM 2023 11 INT C
   Alier M, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13169206
   Alier-Forment M., 2023, EP-31 Las Alucinaciones de ChatGPT con Faraon Llorens
   Altman S., 2023, OpenAI
   Amo-Filva Daniel, 2023, Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality. Lecture Notes in Educational Technology, P1199, DOI 10.1007/978-981-99-0942-1_126
   Anand A., 2018, Annals: Computer Science Series, V16, P185
   Andreas J, 2022, Findings of the Association for Computational Linguistics: EMNLP 2022, P5769
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Bahrini A, 2023, Arxiv, DOI [arXiv:2304.09103, 10.48550/arXiv.2304.09103, DOI 10.48550/ARXIV.2304.09103, 10.48550/arxiv.2304.09103]
   Bartolomé A, 2018, INT J EDUC TECHNOL H, V15, DOI 10.1186/s41239-018-0095-0
   Bowman SR, 2023, Arxiv, DOI [arXiv:2304.00612, DOI 10.48550/ARXIV.2304.00612]
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Castañeda L, 2018, INT J EDUC TECHNOL H, V15, DOI 10.1186/s41239-018-0109-y
   Cifuentes SC, 2016, IEEE INT CONF ADV LE, P431, DOI 10.1109/ICALT.2016.23
   Chang YP, 2024, ACM T INTEL SYST TEC, V15, DOI 10.1145/3641289
   Choi EPH, 2023, NURS EDUC TODAY, V125, DOI 10.1016/j.nedt.2023.105796
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Croitoru FA, 2023, IEEE T PATTERN ANAL, V45, P10850, DOI 10.1109/TPAMI.2023.3261988
   Diamandis P. H., 2012, Exponential Technology Series
   Diamandis P. H., 2020, The Future is Closer than it Seems. How Technologies Change Business, Industry and our Life
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elliott V., 2023, WiredDecember 28
   Fernandez Enguita M., 2024, Cuadernos de Pedagogia
   Flores-Vivar JM, 2023, COMUNICAR, V31, P37, DOI 10.3916/C74-2023-03
   Fourrier C., 2023, Hugging Face
   García FJ, 2005, COMPUT EDUC, V44, P301, DOI 10.1016/j.compedu.2004.02.004
   García-Holgado A, 2020, INT J INTERACT MULTI, V6, P136, DOI 10.9781/ijimai.2020.05.005
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   García-Peñalvo FJ, 2023, EDUC KNOWL SOC, V24, DOI 10.14201/eks.31279
   García-Peñalvo FJ, 2022, EDUC KNOWL SOC, V23, DOI 10.14201/eks.28600
   Garcia-Perialvo F. J., 2024, Cuadernos de Pedagogia
   Gasevic D., 2023, Computers and Education: Artificial Intelligence, V4, P100130, DOI [10.1016/j.caeai.2023.100130 10.1016/j.caeai.2023.100130, DOI 10.1016/J.CAEAI.2023.100130, 10.1016/j.caeai.2023.100130]
   Harris T., 2023, AI and The Future of Life
   Henrickson L, 2024, AI SOC, V39, P2647, DOI 10.1007/s00146-023-01752-8
   Hoffmann J, 2022, Arxiv, DOI [arXiv:2203.15556, 10.48550/arXiv.2203.15556]
   Hu EJ, 2021, Arxiv, DOI arXiv:2106.09685
   Hu YP, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3487890
   Iskender A, 2023, EUR J TOUR RES, V34, DOI 10.54055/ejtr.v34i.3169
   García-Peñalvo FJ, 2015, EDUC KNOWL SOC, V16, P119, DOI 10.14201/eks2015161119144
   Kojima T, 2022, Arxiv, DOI arXiv:2205.11916
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Lee H, 2024, ANAT SCI EDUC, V17, P926, DOI 10.1002/ase.2270
   Levesque H., 2012, 13 INT C PRINC KNOWL
   Li Belinda Z, 2021, arXiv
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Llorens-Largo F., 2023, Universidad
   Llorens-Largo F., 2023, Aula Magna 2.0
   Lopez C., 2005, RED. Revista de Educacion a Distancia, VIV
   Mahajan V., 2023, 100+ Incredible ChatGPT Statistics Facts in 2023. notta.ai
   Maia JDZ, 2023, WORLD-BASEL, V4, P288, DOI 10.3390/world4020019
   Nazir Anam, 2023, Meta Radiol, V1, DOI 10.1016/j.metrad.2023.100022
   Nye Maxwell, 2021, arXiv
   OpenAI, 2023, GPT-4 technical report
   OpenAI, 2023, GPT-4V(ision) System Card
   Papert S., 1987, EDUC RESEARCHER, V16, P22, DOI DOI 10.3102/0013189X016001022
   Patel Dylan, 2023, GPT-4 architecture, infrastructure, training dataset, costs, vision, MoE
   Peral-García D, 2024, COMPUT SCI REV, V51, DOI 10.1016/j.cosrev.2024.100619
   Pichai S., 2024, AI
   Radford A., 2018, Technical Reports
   Radford A., 2019, OPENAI BLOG
   ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519
   Sabzalieva E., 2023, ED/HE/IESALC/ IP/2023/12
   Sadasivan VS, 2024, Arxiv, DOI [arXiv:2303.11156, DOI 10.48550/ARXIV.2303.11156]
   Schaller RR, 1997, IEEE SPECTRUM, V34, P52, DOI 10.1109/6.591665
   Selwyn N, 2016, LEARN MEDIA TECHNOL, V41, P437, DOI 10.1080/17439884.2015.1012523
   Sevilla J, 2022, IEEE IJCNN, DOI 10.1109/IJCNN55064.2022.9891914
   Sharples M, 2009, TECHNOLOGY-ENHANCED LEARNING: PRINCIPLES AND PRODUCTS, P233, DOI 10.1007/978-1-4020-9827-7_14
   Shin D, 2023, J INF SCI, V49, P18, DOI 10.1177/0165551520985495
   Srinivasan B., 2022, The network state: How to start a new country
   Srivastava A., 2022, arXiv, DOI DOI 10.48550/ARXIV.2206.04615
   Stephenson N., 1995, DIAMOND AGE YOUNG LA
   Stephenson N., 1992, Snowcrash
   Taori R., 2023, Alpaca: a strong, replicable instruction-following model
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Topinka R., The GuardianFebruary 13th
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   UNESCO, 2022, Recommendation on the Ethics of Artificial Intelligence
   Urdan T. A., 2000, Corporate e -learning: Exploring a new frontier
   Vaswani A, 2017, ADV NEUR IN, V30
   Villagrá-Arnedo CJ, 2020, INT J INTERACT MULTI, V6, P112, DOI 10.9781/ijimai.2020.05.006
   Wang T., 2021, COMPUTERS ED ARTIFIC, V2, P100031, DOI DOI 10.1016/J.CAEAI.2021.100031
   Wang T, 2024, Arxiv, DOI arXiv:2309.09435
   Wei JS, 2022, Arxiv, DOI [arXiv:2206.07682, DOI 10.48550/ARXIV.2206.07682]
   Wei JS, 2022, Arxiv, DOI arXiv:2201.11903
   Willinson S., 2023, Simon Willinson's Blog
   Yang ZY, 2023, Arxiv, DOI [arXiv:2309.17421, DOI 10.48550/ARXIV.2309.17421]
   Yin SK, 2023, Arxiv, DOI [arXiv:2306.13549, DOI 10.48550/ARXIV.2306.13549]
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
   Zheng LM, 2023, Arxiv, DOI [arXiv:2306.05685, 10.48550/arXiv.2306.05685]
   Zhou Jeffrey, 2023, arXiv
NR 94
TC 12
Z9 12
U1 86
U2 136
PU UNIV INT RIOJA-UNIR
PI LOGRONO
PA RECTORADO, AVENIDA DE LA PAZ, 137, LOGRONO, 26006, SPAIN
SN 1989-1660
J9 INT J INTERACT MULTI
JI Int. J. Interact. Multimed. Artif. Intell.
PD MAR
PY 2024
VL 8
IS 5
DI 10.9781/ijimai.2024.02.011
PG 83
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KO8Q8
UT WOS:001181004000002
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Adiasto, K
AF Adiasto, Krisna
TI SustAInable Employability: Sustainable Employability in the Age of
   Generative Artificial Intelligence
SO GROUP & ORGANIZATION MANAGEMENT
LA English
DT Article
DE employment; sustainability; information technology; human resource
   management; discourse
AB Since the release of ChatGPT's research preview in late 2022, generative artificial intelligence systems have become increasingly more prominent in public awareness due their potential transformative consequences for work. The present GOMusing explores the possible implications of implementing generative AI systems in the workplace for sustainable employability.
C1 [Adiasto, Krisna] Radboud Univ Nijmegen, Behav Sci Inst, Nijmegen, Netherlands.
   [Adiasto, Krisna] Radboud Univ Nijmegen, Behav Sci Inst, Dept Work Hlth & Performance, Thomas Aquinostr 4, NL-6525 GD Nijmegen, Netherlands.
C3 Radboud University Nijmegen; Radboud University Nijmegen
RP Adiasto, K (corresponding author), Radboud Univ Nijmegen, Behav Sci Inst, Dept Work Hlth & Performance, Thomas Aquinostr 4, NL-6525 GD Nijmegen, Netherlands.
EM krisna.adiasto@ru.nl
OI Adiasto, Krisna/0000-0001-6557-4900
CR Agrawal A, 2019, INF ECON POLICY, V47, P1, DOI 10.1016/j.infoecopol.2019.05.001
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bowles DC, 2023, PEDAGOGY HEAL PROMOT, V9, P75, DOI 10.1177/23733799231175171
   Brouwers LAM, 2015, BMC PUBLIC HEALTH, V15, DOI 10.1186/s12889-015-1894-z
   Brynjolfsson E., 2023, W31161 NBER, pw31161, DOI [10.3386/w31161, DOI 10.3386/W31161]
   Cacciamani G. E., 2023, DEV CHATGPT GENERATI, DOI DOI 10.48550/ARXIV.2307.08974
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Choudhury P, 2020, STRATEGIC MANAGE J, V41, P1381, DOI 10.1002/smj.3152
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eloundou T., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2303.10130
   Else H, 2023, NATURE, V613, P423, DOI 10.1038/d41586-023-00056-7
   EU AI Act: First regulation on artificial intelligence, 2023, EUROPEAN PARLIAMENT
   Frey CB, 2017, TECHNOL FORECAST SOC, V114, P254, DOI 10.1016/j.techfore.2016.08.019
   Gozalo-Brizuela R., 2023, CHATGPT IS NOT ALL Y, DOI DOI 10.48550/ARXIV.2301.04655
   Hacker P., 2023, REGULATING CHATGPT O, DOI DOI 10.48550/ARXIV.2302.02337
   Mok Aaron., 2023, Business Insider
   Mollman S., 2023, Fortune
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Stokel-Walker Chris, 2022, Nature, DOI 10.1038/d41586-022-04397-7
   Tabrizi B., 2023, HARVARD BUSINESS REV
   van der Klink JJL, 2016, SCAND J WORK ENV HEA, V42, P71, DOI 10.5271/sjweh.3531
   Yilmaz ED, 2023, SSRN ELECT J, DOI [10.2139/ssrn.4400516, DOI 10.2139/SSRN.4400516]
NR 22
TC 3
Z9 3
U1 73
U2 108
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1059-6011
EI 1552-3993
J9 GROUP ORGAN MANAGE
JI Group Organ. Manage.
PD DEC
PY 2024
VL 49
IS 6
SI SI
BP 1338
EP 1348
DI 10.1177/10596011241238792
EA MAR 2024
PG 11
WC Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Psychology; Business & Economics
GA L0Y3V
UT WOS:001176154700001
OA hybrid, Green Published
DA 2024-12-25
ER

PT J
AU Salinas-Navarro, DE
   Vilalta-Perdomo, E
   Michel-Villarreal, R
   Montesinos, L
AF Salinas-Navarro, David Ernesto
   Vilalta-Perdomo, Eliseo
   Michel-Villarreal, Rosario
   Montesinos, Luis
TI Using Generative Artificial Intelligence Tools to Explain and Enhance
   Experiential Learning for Authentic Assessment
SO EDUCATION SCIENCES
LA English
DT Article
DE experiential learning; authentic assessment; constructive alignment;
   generative artificial intelligence; educational innovation; higher
   education
ID ONLINE
AB The emergence of generative artificial intelligence (GenAI) requires innovative educational environments to leverage this technology effectively to address concerns like academic integrity, plagiarism, and others. Additionally, higher education needs effective pedagogies to achieve intended learning outcomes. This emphasizes the need to redesign active learning experiences in the GenAI era. Authentic assessment and experiential learning are two possible meaningful alternatives in this context. Accordingly, this article investigates how GenAI can enhance teaching and learning by constructively addressing study situations beyond conventional learning approaches and cultivating high-order skills and knowledge acquisition. This study employs thing ethnography to examine GenAI tools' integration with authentic assessment and experiential learning and explore implementation alternatives. The results reveal insights into creating human-centered and GenAI-enhanced learning experiences within a constructive alignment. Specific examples are also provided to guide their implementation. Our contributions extend beyond the traditional use of GenAI tools as mere agents-to-write or agents-to-answer questions to become agents-to-support experiential learning for authentic assessment. These findings underscore the transformative role of GenAI tools in enhancing teaching and learning efficacy and effectiveness. The limitations in treating GenAI tools as subjects in thing ethnography are acknowledged, with potential for future implementation evaluation.
C1 [Salinas-Navarro, David Ernesto; Vilalta-Perdomo, Eliseo] Aston Univ, Community Resilience & Sustainabil Educ Lab CoRSEL, Birmingham B4 7ET, England.
   [Michel-Villarreal, Rosario] Univ Leeds, Sustainabil Res Inst, Sch Earth & Environm, Leeds LS2 9JT, England.
   [Montesinos, Luis] Tecnol Monterrey, Inst Adv Mat Sustainable Mfg, Mexico City 14380, Mexico.
C3 Aston University; University of Leeds; Tecnologico de Monterrey
RP Salinas-Navarro, DE (corresponding author), Aston Univ, Community Resilience & Sustainabil Educ Lab CoRSEL, Birmingham B4 7ET, England.; Montesinos, L (corresponding author), Tecnol Monterrey, Inst Adv Mat Sustainable Mfg, Mexico City 14380, Mexico.
EM d.salinas-navarro@aston.ac.uk; e.vilaltaperdomo@aston.ac.uk;
   r.michel-villarreal@leeds.ac.uk; lmontesinos@tec.mx
RI Vilalta-Perdomo, Eliseo/AAK-4541-2021; SALINAS-NAVARRO, DAVID
   ERNESTO/ABG-4204-2020; Vilalta-Perdomo, Eliseo/N-9549-2014; Montesinos,
   Luis/J-4255-2018
OI Michel-Villarreal, Rosario/0000-0002-1158-924X; SALINAS-NAVARRO, DAVID
   ERNESTO/0000-0002-7919-4885; Vilalta-Perdomo,
   Eliseo/0000-0002-4551-8327; Montesinos, Luis/0000-0003-3976-4190
FU Writing Lab, Institute for the Future of Education, Tecnologico de
   Monterrey, Mexico
FX No Statement Available
CR Albert D., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4516801, DOI 10.2139/SSRN.4516801]
   Asch DA., 2023, CATALYST NONISSUE CO, DOI [DOI 10.1056/CAT.23.0043, 10.1056/cat.23.0043]
   Hernández-Lara AB, 2018, BEHAV INFORM TECHNOL, V37, P419, DOI 10.1080/0144929X.2018.1441326
   Bell E., 2022, BUSINESS RES METHODS
   Benkert C., Experiential Learning: What's Missing in Most Change Programs
   Biggs J, 1996, HIGH EDUC, V32, P347, DOI 10.1007/BF00138871
   Biggs J., 2003, P TEACHING LEARNING
   Biggs J., 2011, Teaching for quality learning at University
   Bradberry LA, 2019, J POLITICAL SCI EDUC, V15, P94, DOI 10.1080/15512169.2018.1485571
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Chang W.-W., 2017, P 2017 C DESIGNING I
   Cila N., 2015, P 3 SEMINAR RES NETW
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   de Zeeuw G., 1996, Three Phases of Science: A Methodological Exploration. Working Paper 7, VVolume 7
   dos Santos R. P., 2023, SSRN J, DOI [10.2139/ssrn.4447416, DOI 10.2139/SSRN.4447416]
   EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Eriksson P., 2015, Qualitative Methods in Business Research: A Practical Guide to Social Research
   Euchner J, 2023, RES TECHNOL MANAGE, V66, P71, DOI 10.1080/08956308.2023.2188861
   Freeman S, 2014, P NATL ACAD SCI USA, V111, P8410, DOI 10.1073/pnas.1319030111
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   Geerling W, 2023, 68 AM. ECONOMIST, V68
   Giaccardi E, 2016, DESIGN ANTHROPOLOGICAL FUTURES, P235
   Giaccardi E, 2016, DIS 2016: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON DESIGNING INTERACTIVE SYSTEMS, P377, DOI 10.1145/2901790.2901905
   Healey M, 2000, J GEOGR, V99, P185, DOI 10.1080/00221340008978967
   Holmes W., 2023, Guidance for generative AI in education and research
   Iskender A, 2023, EUR J TOUR RES, V34, DOI 10.54055/ejtr.v34i.3169
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Karakose T., 2023, Educ. Process Int. J, V12, DOI [DOI 10.22521/EDUPIJ.2023.122.1, 10.22521/edupij.2023.122.1]
   King N., 2018, INTERVIEWS QUALITATI
   Koh K.H., 2017, Oxford Research Encyclopedia of Education, DOI DOI 10.1093/ACREFORE/9780190264093.013.22
   Kokoç M, 2021, BEHAV INFORM TECHNOL, V40, P161, DOI 10.1080/0144929X.2019.1680731
   Kolb AY, 2005, ACAD MANAG LEARN EDU, V4, P193, DOI 10.5465/AMLE.2005.17268566
   Kolb D. A., 1984, EXPERIENTIAL LEARNIN, DOI DOI 10.1016/B978-0-7506-7223-8.50017-4
   Lalley J.P., 2007, Education, V128, P64
   Lawrie G, 2023, CHEM EDUC RES PRACT, V24, P392, DOI 10.1039/d3rp90003g
   LECOMPTE MD, 1982, REV EDUC RES, V52, P31, DOI 10.2307/1170272
   Leung Lawrence, 2015, J Family Med Prim Care, V4, P324, DOI 10.4103/2249-4863.161306
   Martínez-Cerdá JF, 2018, BEHAV INFORM TECHNOL, V37, P1055, DOI 10.1080/0144929X.2018.1476919
   Merrett C.G., 2023, P ASEE ANN C EXPOSIT
   Merriam S.B., 1998, QUALITATIVE RES CASE
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mollick E, How to Use AI to Do Practical Stuff: A New Guide
   Opara E., 2023, GLOBAL ACAD J HUMANI, V5, P33, DOI DOI 10.36348/GAJHSS.2023.V05I02.001
   Popper K., 1935, LOGIC SCI DISCOVERY
   Radcliffe D., 2019, Learning Spaces in Higher Education: Positive Outcomes by Design, P9
   Reeves S, 2008, BMJ-BRIT MED J, V337, DOI 10.1136/bmj.a1020
   Rinaldo R, 2022, SOCIOL METHOD RES, V51, P34, DOI 10.1177/0049124119882471
   Rudolph J., 2023, JALT, V6, P37074, DOI DOI 10.37074/JALT.2023.6.1.23
   Salinas-Navarro D.E., 2019, P 2019 IEEE FRONTIER, P1
   Saunders M., 2016, Research Methods for Business Students, V7th
   Smith A, 2023, INT J SOC PSYCHIATR, V69, P1882, DOI 10.1177/00207640231178451
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Tsai CW, 2011, BEHAV INFORM TECHNOL, V30, P261, DOI 10.1080/0144929X.2010.514359
   Tulubas T., 2023, Educational Process International Journal, V12, P93, DOI [10.22521/edupij.2023.122.6, DOI 10.22521/EDUPIJ.2023.122.6]
   Vahl M, 1997, SYSTEMS FOR SUSTAINABILITY, P147
   van der Zant T., 2013, Philosophy and Theory of Artificial Intelligence, V5, P107, DOI 10.1007/978-3-642-31674-6
   Villarroel V, 2020, INNOV EDUC TEACH INT, V57, P38, DOI 10.1080/14703297.2018.1564882
   Villarroel V, 2018, ASSESS EVAL HIGH EDU, V43, P840, DOI 10.1080/02602938.2017.1412396
   WIGGINS G, 1989, PHI DELTA KAPPAN, V70, P703, DOI 10.1177/003172171109200721
   Wiggins G., 1990, Practical Assessment, Research Evaluation, V2, P2, DOI [10.7275/ffb1-mm19, DOI 10.7275/FFB1-MM19]
   Willis EM, 2010, QUAL HEALTH RES, V20, P556, DOI 10.1177/1049732310361243
   Yi-Ching Huang, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3479866
   Yurman P, 2022, PROCEEDINGS OF THE 14TH CREATIVITY AND COGNITION, C&C 2022, P56, DOI 10.1145/3527927.3531448
NR 64
TC 15
Z9 15
U1 34
U2 92
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD JAN
PY 2024
VL 14
IS 1
AR 83
DI 10.3390/educsci14010083
PG 24
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA FX0B2
UT WOS:001149022400001
OA gold, Green Accepted
DA 2024-12-25
ER

PT J
AU Jang, S
   Lee, H
   Kim, Y
   Lee, D
   Shin, J
   Nam, J
AF Jang, Soobin
   Lee, Haeyoon
   Kim, Yujin
   Lee, Daeho
   Shin, Jungwoo
   Nam, Jungwoo
TI When, What, and how should generative artificial intelligence explain to
   Users?
SO TELEMATICS AND INFORMATICS
LA English
DT Article
DE Generative AI; Conversational user interface; Explainable AI; Conjoint
   analysis
ID SERVICES
AB With the commercialization of ChatGPT, , generative artificial intelligence (AI) has been applied almost everywhere in our lives. However, even though generative AI has become a daily technology that anyone can use, most non-majors need to know the process and reason for the results because it can be misused due to lack of sufficient knowledge and misunderstanding. Therefore, this study investigated users' preferences for when, what, and how generative AI should provide explanations about the process of generating and the reasoning behind the results, using conjoint method and mixed logit analysis. The results show that users are most sensitive to the timing of providing eXplainable AI (XAI), and that users want additional information only when they ask for explanations during the process of using generative AI. The results of this study will help shape the XAI design of future generative AI from a user perspective and improve usability.
C1 [Jang, Soobin; Lee, Daeho] Sungkyunkwan Univ, Dept Appl Artificial Intelligence, Seoul, South Korea.
   [Lee, Haeyoon; Lee, Daeho] Sungkyunkwan Univ, Dept Interact Sci, Seoul, South Korea.
   [Kim, Yujin; Lee, Daeho] Sungkyunkwan Univ, Sch Convergence, Seoul, South Korea.
   [Shin, Jungwoo] Kyung Hee Univ, Dept Ind & Management Syst Engn, Yongin, South Korea.
   [Shin, Jungwoo] Kyung Hee Univ, Dept Big Data Analyt, Seoul, South Korea.
   [Nam, Jungwoo] Sungkyunkwan Univ, Dept Human Artificial Intelligence Interact, Seoul, South Korea.
C3 Sungkyunkwan University (SKKU); Sungkyunkwan University (SKKU);
   Sungkyunkwan University (SKKU); Kyung Hee University; Kyung Hee
   University; Sungkyunkwan University (SKKU)
RP Nam, J (corresponding author), Sungkyunkwan Univ, Dept Human Artificial Intelligence Interact, Seoul, South Korea.
EM redjungw@g.skku.edu
OI Nam, Jungwoo/0000-0001-5908-0752; Lee, Haeyoon/0009-0004-5732-8821;
   Jang, Soobin/0009-0002-3511-1133; Shin, Jungwoo/0000-0002-7772-8636
FU MSIT (Ministry of Science and ICT) of Korea, under the Graduate School
   of Metaverse Convergence support program [IITP-2024-RS-2023-00254129];
   Ministry of Education of the Republic of Korea; National Research
   Foundation of Korea [NRF-2023S1A5A2A21086671]
FX This research was supported by the MSIT (Ministry of Science and ICT) of
   Korea, under the Graduate School of Metaverse Convergence support
   program (IITP-2024-RS-2023-00254129) supervised by the IITP (Institute
   for Information & Communications Techonlogy Planning & Evaluation) and
   by the Ministry of Education of the Republic of Korea and the National
   Research Foundation of Korea (NRF-2023S1A5A2A21086671) .
CR Abdellatif A, 2020, IEEE WORK CONF MIN S, P174, DOI 10.1145/3379597.3387472
   Adadi A, 2018, IEEE ACCESS, V6, P52138, DOI 10.1109/ACCESS.2018.2870052
   Adamopoulou E, 2020, MACH LEARN APPL, V2, DOI 10.1016/j.mlwa.2020.100006
   Allenby GM, 1999, J ECONOMETRICS, V89, P57
   Huang CZA, 2020, Arxiv, DOI arXiv:2010.05388
   Arioua A, 2015, LECT NOTES ARTIF INT, V9310, P282, DOI 10.1007/978-3-319-23540-0_19
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Batish R., 2018, Voicebot and Chatbot Design: Flexible Conversational Interfaces with Amazon Alexa, Google Home, and Facebook Messenger
   Castelvecchi D, 2016, NATURE, V538, P21, DOI [10.1038/nature.2016.20491, 10.1038/538020a]
   Chaves AP, 2022, ACM T COMPUT-HUM INT, V29, DOI 10.1145/3487193
   Chung WY, 2022, TELEMAT INFORM, V70, DOI 10.1016/j.tele.2022.101799
   Cocarascu O, 2019, AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, P1261
   Colton S., 2021, P INT C COMP CREAT
   Cramer H, 2008, USER MODEL USER-ADAP, V18, P455, DOI 10.1007/s11257-008-9051-3
   Das Arun, 2020, arXiv, DOI [10.48550/arXiv.2006.11371, DOI 10.48550/ARXIV.2006.11371]
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ebbers F, 2021, ELECTRON MARK, V31, P411, DOI 10.1007/s12525-020-00447-y
   Edwards YD, 2003, J MARKETING RES, V40, P321, DOI 10.1509/jmkr.40.3.321.19233
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Folstad A., 2017, Interactions, V24, P38, DOI [DOI 10.1145/3085558, 10.1145/3085558]
   Folstad A, 2018, LECT NOTES COMPUT SC, V11193, P194, DOI 10.1007/978-3-030-01437-7_16
   Gunning D, 2019, SCI ROBOT, V4, DOI 10.1126/scirobotics.aay7120
   Gupta A, 2022, PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), P3531, DOI 10.1145/3485447.3512248
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   HENSHER DA, 1994, TRANSPORTATION, V21, P107, DOI 10.1007/BF01098788
   Hernandez-Bocanegra DC, 2021, LECT NOTES COMPUT SC, V12933, P597, DOI 10.1007/978-3-030-85616-8_35
   Hill J, 2015, COMPUT HUM BEHAV, V49, P245, DOI 10.1016/j.chb.2015.02.026
   Hohman F, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300809
   Hughes RT, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.604234
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Khurana A, 2021, S VIS LANG HUM CEN C, DOI 10.1109/VL/HCC51201.2021.9576440
   Kim D, 2023, TECHNOL FORECAST SOC, V188, DOI 10.1016/j.techfore.2023.122343
   Kim N, 2019, TECHNOL FORECAST SOC, V139, P277, DOI 10.1016/j.techfore.2018.11.014
   Kizilcec RF, 2016, 34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, P2390, DOI 10.1145/2858036.2858402
   Klein A, 2014, TELEMAT INFORM, V31, P410, DOI 10.1016/j.tele.2013.11.006
   Kocon J, 2023, INFORM FUSION, V99, DOI 10.1016/j.inffus.2023.101861
   Kulesza T, 2013, Ways Explanations Impact End Users' Mental Models, P3
   Kung T. H, 2023, PLOS Digit Health, V2, DOI DOI 10.1371/JOURNAL.PDIG.0000198.PDIG-D-22-00371
   Laato S, 2022, INTERNET RES, V32, P1, DOI 10.1108/INTR-08-2021-0600
   Lamy JB, 2019, ARTIF INTELL MED, V94, P42, DOI 10.1016/j.artmed.2019.01.001
   Liao QV, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376590
   Lim BY, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P2119
   Lim S, 2019, ENERG ECON, V81, P1167, DOI 10.1016/j.eneco.2015.01.018
   Lister K, 2020, 17TH INTERNATIONAL WEB FOR ALL CONFERENCE (WEB4ALL), DOI 10.1145/3371300.3383343
   Louie R, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376739
   Maeng K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101327
   Mandel DR, 2021, JUDGM DECIS MAK, V16, P363
   Martens D, 2014, MIS QUART, V38, P73, DOI 10.25300/MISQ/2014/38.1.04
   Martin K, 2019, LECT NOTES ARTIF INT, V11927, P309, DOI 10.1007/978-3-030-34885-4_24
   Massie S., 2004, P 7 ECCBR, P135
   McTear M., 2018, STUDIENTEXTE SPRACHK, P175
   McTear M., 2020, History of Human Computer Interaction
   McTear M.F., 2017, Future and emerging trends in language technology: Machine learning and big data, P38, DOI [DOI 10.1007/978-3-319-69365-1_3, 10.1007/978-3-319-69365-1_3]
   McTear Michael., 2016, The Conversational Interface, V6, P102
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   Mitrovic S., 2023, arXiv
   Muhammad K, 2017, LECT NOTES ARTIF INT, V10339, P227, DOI 10.1007/978-3-319-61030-6_16
   Muller M, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3517644
   Namoun A, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11010237
   Nordin I, 2000, Med Health Care Philos, V3, P297
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   OpenAI, 2022, CHATGPT OPT LANG MOD
   Paikari E, 2018, 2018 IEEE/ACM 11TH INTERNATIONAL WORKSHOP ON COOPERATIVE AND HUMAN ASPECTS OF SOFTWARE ENGINEERING (CHASE), P13, DOI 10.1145/3195836.3195859
   Petrin A., 2003, OMITTED PRODUCT ATTR
   Rago A, 2020, KR2020: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P805
   Ramon Y, 2021, Arxiv, DOI arXiv:2107.02624
   Rao Vithala R., 2014, Applied Conjoint Analysis, DOI [10.1007/978-3-540-87753-0, DOI 10.1007/978-3-540-87753-0]
   Ray P.P., 2023, Internet of Things and Cyber-Physical Systems
   Shin D, 2021, INT J HUM-COMPUT ST, V146, DOI 10.1016/j.ijhcs.2020.102551
   Shin J, 2016, TECHNOL FORECAST SOC, V112, P329, DOI 10.1016/j.techfore.2016.08.004
   Shin J, 2014, ENERG ECON, V42, P17, DOI 10.1016/j.eneco.2013.11.014
   Shneiderman B., 2010, Designing the user interface: Strategies for effective human-computer interaction
   Shon M, 2021, TELEMAT INFORM, V60, DOI 10.1016/j.tele.2021.101581
   Sokol K, 2020, KUNSTL INTELL, V34, P235, DOI 10.1007/s13218-020-00637-y
   Sugisaki K, 2020, MUC 2020: PROCEEDINGS OF MENSCH UND COMPUTER 2020, P309, DOI 10.1145/3404983.3405505
   Suh M, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445219
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Sundar SS, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300768
   Susnjak T, 2024, INT J ARTIF INTELL E, V34, P452, DOI 10.1007/s40593-023-00336-3
   TELLIS GJ, 1990, J MARKETING, V54, P34, DOI 10.2307/1251868
   Train K., 2005, Applications of Simulation Methods in Environmental Resource Economics, P117
   Train K. E., 2001, A comparison of hierarchical Bayes and maximum simulated likelihood for mixed logit, P1
   Train KE, 2009, DISCRETE CHOICE METHODS WITH SIMULATION, 2ND EDITION, P1
   Valtolina S., 2018, CEUR Workshop Proceedings, V2101, P62
   Van der Zant T., 2013, Generative artificial intelligence, P107
   Wachter S., 2017, HARV JL TECH, V31, DOI DOI 10.2139/SSRN.3063289
   Walton D, 2000, SYNTHESE, V123, P327, DOI 10.1023/A:1005237527730
   Wang WQ, 2007, J MANAGE INFORM SYST, V23, P217, DOI 10.2753/MIS0742-1222230410
   Weisz J.D., 2023, arXiv, DOI DOI 10.48550/ARXIV.2301.05578
   Weisz JD, 2021, IUI '21 - 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P402, DOI 10.1145/3397481.3450656
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/357980.357991
   Windschitl PD, 1996, J EXP PSYCHOL-APPL, V2, P343, DOI 10.1037/1076-898X.2.4.343
   Yue T, 2023, Democratizing financial knowledge with ChatGPT by OpenAI: Unleashing the Power of Technology
   Zakos J, 2008, LECT NOTES ARTIF INT, V5138, P437, DOI 10.1007/978-3-540-87881-0_46
   Zhou L, 2020, COMPUT LINGUIST, V46, P53, DOI [10.1162/COLI_a_00368, 10.1162/coli_a_00368]
NR 95
TC 0
Z9 0
U1 61
U2 61
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0736-5853
J9 TELEMAT INFORM
JI Telemat. Inform.
PD SEP
PY 2024
VL 93
AR 102175
DI 10.1016/j.tele.2024.102175
EA AUG 2024
PG 14
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA D1Q3Y
UT WOS:001293999100001
DA 2024-12-25
ER

PT J
AU Moorhouse, BL
   Wan, YW
   Wu, CZ
   Kohnke, L
   Ho, TY
   Kwong, T
AF Moorhouse, Benjamin Luke
   Wan, Yuwei
   Wu, Chenze
   Kohnke, Lucas
   Ho, Tsz Ying
   Kwong, Theresa
TI Developing language teachers' professional generative AI competence: An
   intervention study in an initial language teacher education course
SO SYSTEM
LA English
DT Article
ID DIGITAL COMPETENCE
AB Generative Artificial Intelligence (GenAI) tools have been argued to have transformative potential in education; yet existing literature suggests that language teachers generally lack the abilities to leverage these tools effectively and critically. Conducted in an initial language teacher education programme at a Hong Kong university, this mixed-method intervention study aims to explore the effects of explicit training for using GenAI tools for language teaching in rising pre-service language teachers' professional GenAI competence (P-GenAI-C). 54 M.Ed students took part in an 11-week course intervention aiming to enhance the five aspects in the P-GenAI-C framework. Analysis of pre- and post-intervention questionnaires, which encompassed a mix of open and closed items to gather participants' knowledge and perceptions of utilising GenAI tools, as well as the follow-up interviews, revealed that the intervention was effective in stretching all aspects of pre-service teachers' P-GenAI-C. While there was greater evidence of improvement in participants' pedagogical competence and critical awareness of GenAI tools deployment, there was less evidence of development in other aspects, such as teachers' capacity to guide their students to use GenAI tools effectively and responsibly. This discrepancy might be attributed to the lack of such content in the course intervention. Implications for incorporating elements of P-GenAI-C into teacher preparation courses and programmes are discussed.
C1 [Moorhouse, Benjamin Luke; Wan, Yuwei; Wu, Chenze; Ho, Tsz Ying] Hong Kong Baptist Univ, Dept Educ Studies, Hong Kong, Peoples R China.
   [Kohnke, Lucas] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Kwong, Theresa] Hong Kong Baptist Univ, Ctr Holist Teaching & Learning, Hong Kong, Peoples R China.
C3 Hong Kong Baptist University; Education University of Hong Kong (EdUHK);
   Hong Kong Baptist University
RP Wan, YW (corresponding author), Hong Kong Baptist Univ, Dept Educ Studies, Hong Kong, Peoples R China.
EM blmoorhouse@hkbu.edu.hk; yuweiwan@life.hkbu.edu.hk;
   23481536@life.hkbu.edu.hk; lmakohnke@eduhk.hk; joey0909@hkbu.edu.hk;
   theresa@hkbu.edu.hk
RI Wan, Yuwei/LCD-5710-2024; Moorhouse, Benjamin/U-2683-2019; Kwong,
   Theresa/D-3705-2014; Kohnke, Lucas/R-5263-2019
OI Kohnke, Lucas/0000-0001-6717-5719; Wu, Chenze/0009-0007-1290-513X;
   Kwong, Theresa/0000-0001-8294-8580; Moorhouse, Benjamin
   Luke/0000-0002-3913-5194
CR Ali J. K. M., 2023, J. Engl. Stud. Arab. Felix, V2, P41, DOI [DOI 10.56540/JESAF.V2I1.51, https://doi.org/10.56540/jesaf.v2i1.51]
   Barrot JS, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100745
   Braun V, 2021, INT J SOC RES METHOD, V24, P641, DOI 10.1080/13645579.2020.1805550
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Cain W, 2024, TECHTRENDS, V68, P47, DOI 10.1007/s11528-023-00896-0
   Cao L., 2023, Navigating a world of generative AI: suggestions for educators
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Clark S, 2019, ASIA-PAC J TEACH EDU, V47, P32, DOI 10.1080/1359866X.2018.1497772
   Cohen J., 1988, STAT POWER ANAL BEHA
   Cohen L., 2011, RES METHODS ED, V7th
   Dakakni D., 2023, COMPUTERS ED ARTIFIC, V5, P100179, DOI 10.1016/j.caeai.2023.100179
   Falloon G, 2020, ETR&D-EDUC TECH RES, V68, P2449, DOI 10.1007/s11423-020-09767-4
   Cervera MG, 2022, EUR J TEACH EDUC, V45, P451, DOI 10.1080/02619768.2022.2135855
   Gudmundsdottir GB, 2018, EUR J TEACH EDUC, V41, P214, DOI 10.1080/02619768.2017.1416085
   Harfitt GJ, 2015, ASIA-PAC J TEACH EDU, V43, P22, DOI 10.1080/1359866X.2014.932333
   Hockly N, 2023, RELC J, V54, P445, DOI 10.1177/00336882231168504
   Hwang GJ, 2023, EDUC TECHNOL SOC, V26, DOI 10.30191/ETS.202304_26(2).0014
   Instefjord EJ, 2017, TEACH TEACH EDUC, V67, P37, DOI 10.1016/j.tate.2017.05.016
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Kallio H, 2016, J ADV NURS, V72, P2954, DOI 10.1111/jan.13031
   König J, 2020, EUR J TEACH EDUC, V43, P608, DOI 10.1080/02619768.2020.1809650
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Korthagen F, 2017, TEACH TEACH, V23, P387, DOI 10.1080/13540602.2016.1211523
   la Velle L, 2022, J EDUC TEACHING, V48, P271, DOI 10.1080/02607476.2022.2075189
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lindfors M, 2021, EDUC INQ, V12, P390, DOI 10.1080/20004508.2021.1890936
   Lo Chung Kwan, 2017, Res Pract Technol Enhanc Learn, V12, P4, DOI 10.1186/s41039-016-0044-2
   Maloy R. W., 2024, EdTechnica: The open Encyclopedia of educational technology
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Moorhouse BL, 2024, SYSTEM, V122, DOI 10.1016/j.system.2024.103290
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Moorhouse BL, 2024, ASIA-PAC EDUC RES, V33, P1105, DOI 10.1007/s40299-023-00778-2
   Moorhouse BL., 2024, Computers and Education: Artificial Intelligence, DOI DOI 10.1016/J.CAEAI.2024.100201
   Ng DTK, 2023, ETR&D-EDUC TECH RES, V71, P137, DOI 10.1007/s11423-023-10203-6
   Ngao AI, 2022, EDUC SCI, V12, DOI 10.3390/educsci12080549
   Park M, 2022, ASIA PAC J EDUC, V42, P320, DOI 10.1080/02188791.2020.1815649
   Starkey L, 2022, EUR J TEACH EDUC, V45, P476, DOI 10.1080/02619768.2021.1975109
   Starkey L, 2020, CAMB J EDUC, V50, P37, DOI 10.1080/0305764X.2019.1625867
   Ulla MB., 2023, LEARNING RES PRACTIC, V0, P1, DOI [10.1080/23735082.2023.2257252, DOI 10.1080/23735082.2023.2257252]
   Wan YW, 2024, RELC J, DOI 10.1177/00336882231224813
   Wang W., 2023, THESIS U OXFORD
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yang Z, 2024, ASIA PAC J EDUC, DOI 10.1080/02188791.2023.2300137
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
   Zucchet E., 2023, Berlitz Blog
NR 46
TC 0
Z9 0
U1 82
U2 82
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0346-251X
EI 1879-3282
J9 SYSTEM
JI System
PD OCT
PY 2024
VL 125
AR 103399
DI 10.1016/j.system.2024.103399
EA JUL 2024
PG 13
WC Education & Educational Research; Linguistics
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Linguistics
GA ZK7D9
UT WOS:001275249200001
DA 2024-12-25
ER

PT J
AU Hui, X
   Reshef, O
   Zhou, LF
AF Hui, Xiang
   Reshef, Oren
   Zhou, Luofeng
TI The Short-Term Effects of Generative Artificial Intelligence on
   Employment: Evidence from an Online Labor Market
SO ORGANIZATION SCIENCE
LA English
DT Article
DE artificial intelligence; online labor markets; large language model;
   generative AI
ID PRODUCTIVITY; TECHNOLOGY; AUTOMATION
AB Generative artificial intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them. We examine the short-term effects of the recently released generative AI models (ChatGPT, DALL-E 2, and Midjourney) on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based generative AI models. Exploring the heterogeneity by freelancers' employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that generative AI may transform the role of human capital in the organization and reduce overall demand for workers.
C1 [Hui, Xiang; Reshef, Oren] Washington Univ St Louis, Olin Business Sch, St Louis, MO 63130 USA.
   [Zhou, Luofeng] NYU, Stern Sch Business, New York, NY 10012 USA.
C3 Washington University (WUSTL); New York University
RP Reshef, O (corresponding author), Washington Univ St Louis, Olin Business Sch, St Louis, MO 63130 USA.
EM hui@wustl.edu; oren@wustl.edu; lz2198@stern.nyu.edu
RI Reshef, Oren/GXN-4080-2022; Hui, Xiang/KGM-0588-2024
OI Reshef, Oren/0000-0002-9266-700X; Hui, Xiang/0000-0001-5697-5016
CR Abadie A, 2022, Q J ECON, V138, P1, DOI 10.1093/qje/qjac038
   Acemoglu D., 2018, NBER Working Paper, No. 24196, DOI [10.3386/w24196, DOI 10.3386/W24196]
   Acemoglu D, 2022, J LABOR ECON, V40, pS293, DOI 10.1086/718327
   Acemoglu D, 2020, J POLIT ECON, V128, P2188, DOI 10.1086/705716
   Acemoglu D, 2011, HBK ECON, V4, P1043, DOI 10.1016/S0169-7218(11)02410-5
   Agrawal A, 2019, INF ECON POLICY, V47, P1, DOI 10.1016/j.infoecopol.2019.05.001
   Agrawal A, 2019, J ECON PERSPECT, V33, P31, DOI 10.1257/jep.33.2.31
   Allen RT, 2022, ORGAN SCI, V33, P149, DOI 10.1287/orsc.2021.1554
   Argyres NS, 2012, ORGAN SCI, V23, P1643, DOI 10.1287/orsc.1110.0736
   Autor DH, 2003, Q J ECON, V118, P1279, DOI 10.1162/003355303322552801
   Bailey DE, 2022, ORGAN SCI, V33, P1, DOI 10.1287/orsc.2021.1562
   Bailey DE, 2022, ILR REV, V75, P527, DOI 10.1177/00197939221076747
   Balasubramanian N, 2022, ACAD MANAGE REV, V47, P448, DOI 10.5465/amr.2019.0470
   Barach MA, 2021, J LABOR ECON, V39, P193, DOI 10.1086/709277
   Berg JM, 2023, ACAD MANAG DISCOV, V9, P424, DOI 10.5465/amd.2023.0106
   Bertrand M, 2004, Q J ECON, V119, P249, DOI 10.1162/003355304772839588
   Brynjolfsson E, 2023, 31161 NBER
   Brynjolfsson E., 2018, The Economics of Artificial Intelligence: An Agenda, P23
   Brynjolfsson E, 2019, MANAGE SCI, V65, P5449, DOI 10.1287/mnsc.2019.3388
   Brynjolfsson E, 2018, AEA PAP P, V108, P43, DOI 10.1257/pandp.20181019
   Burtch G, 2023, PREPRINT, DOI [10.2139/ssrn.4521754, DOI 10.2139/SSRN.4521754]
   Cameron AC, 2008, REV ECON STAT, V90, P414, DOI 10.1162/rest.90.3.414
   Cao X, 2020, IMPACT FORCED INTERV, DOI [10.2139/ssrn.3640862, DOI 10.2139/SSRN.3640862]
   Cengiz D, 2019, Q J ECON, V134, P1405, DOI 10.1093/qje/qjz014
   Cheng Z, 2022, PREPRINT, DOI [10.2139/ssrn.3868599, DOI 10.2139/SSRN.3868599]
   Choudhury P, 2020, STRATEGIC MANAGE J, V41, P1381, DOI 10.1002/smj.3152
   Cockburn I.M., 2018, The economics of artificial intelligence: An agenda, P115, DOI DOI 10.3386/W24449
   Cowgill B., 2020, Proceedings of the 21st ACM Conference on Economics and Computation, EC'20, page, P679, DOI DOI 10.1145/3391403.3399545
   DellAcqua F, 2023, NAVIGATING JAGGED TE
   Eloundou T., 2023, PREPRINT
   Faraj S, 2018, INFORM ORGAN-UK, V28, P62, DOI 10.1016/j.infoandorg.2018.02.005
   Farber HS, 2021, Q J ECON, V136, P1325, DOI 10.1093/qje/qjab012
   Feigenbaum J, 2024, Q J ECON, V139, P1879, DOI 10.1093/qje/qjae005
   Felten EW, 2018, AEA PAP P, V108, P54, DOI 10.1257/pandp.20181021
   Felten EW, 2023, SSRN WORKING PAPER, DOI [10.2139/ssrn.4414065, DOI 10.2139/SSRN.4414065]
   Furman J., 2019, Innov. Policy Econ, V19, P161, DOI [10.1086/699936, DOI 10.1086/699936]
   Furman JL, 2020, ORGAN SCI, V31, P330, DOI 10.1287/orsc.2019.1308
   Goldberg SG, 2024, AM ECON J-ECON POLIC, V16, P325, DOI 10.1257/pol.20210309
   Goldfarb A, 2018, EC ARTIFICIAL INTELL, P463, DOI DOI 10.7208/CHICAGO/9780226613475.003.0015
   Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110
   Horton JJ, 2017, PREPRINT, DOI [10.2139/ssrn.2898827, DOI 10.2139/SSRN.2898827]
   Horton JJ, 2010, LECT NOTES COMPUT SC, V6484, P515, DOI 10.1007/978-3-642-17572-5_45
   Jin GZ, 2018, EC ARTIFICIAL INTELL, P439
   Kellogg KC, 2020, ACAD MANAG ANN, V14, P366, DOI 10.5465/annals.2018.0174
   Krakowski S, 2023, STRATEGIC MANAGE J, V44, P1425, DOI 10.1002/smj.3387
   Lebovitz S, 2022, ORGAN SCI, V33, P126, DOI 10.1287/orsc.2021.1549
   Lifshitz-Assaf, 2019, USING TECHNOLOGY AUG
   Lipsitz M, 2022, MANAGE SCI, V68, P143, DOI 10.1287/mnsc.2020.3918
   Liu J., 2023, PREPRINT
   Luo XM, 2021, J MARKETING, V85, P14, DOI 10.1177/0022242920956676
   Luo XM, 2019, MARKET SCI, V38, P937, DOI 10.1287/mksc.2019.1192
   Masclans Roger, 2023, The commercial potential of science and its realization: Evidence from a measure using a large language model
   MINTZBERG H, 1980, MANAGE SCI, V26, P322, DOI 10.1287/mnsc.26.3.322
   Mokyr J, 2015, J ECON PERSPECT, V29, P31, DOI 10.1257/jep.29.3.31
   Mollick E, 2023, PREPRINT
   Murray A, 2021, ACAD MANAGE REV, V46, P552, DOI 10.5465/amr.2019.0186
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   OpenAI, 2023, PREPRINT
   Overby E, 2010, INFORM SYST RES, V21, P700, DOI 10.1287/isre.1100.0319
   Peng S, 2023, PREPRINT
   Pfeffer J., 2015, Organizational Behavior 2, P373, DOI DOI 10.4324/9781315702001-32/EXTERNAL-CONTROL-ORGANIZATIONS%E2%80%94RESOURCE-DEPENDENCE-PERSPECTIVEJEFFREY-PFEFFER-GERALD-SALANCIK
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Reshef O, 2023, AM ECON J-MICROECON, V15, P183, DOI 10.1257/mic.20220055
   Schanke S, 2021, INFORM SYST RES, V32, P736, DOI 10.1287/isre.2021.1015
   Sun C, 2019, PREPRINT, DOI [10.2139/ssrn.3480877, DOI 10.2139/SSRN.3480877]
   Syverson C, 2004, J POLIT ECON, V112, P1181, DOI 10.1086/424743
   Tambe P, 2019, CALIF MANAGE REV, V61, P15, DOI 10.1177/0008125619867910
   Troncoso I, 2023, PREPRINT, DOI [10.2139/ssrn.4520172, DOI 10.2139/SSRN.4520172]
   Tschang FT, 2021, ACAD MANAGE PERSPECT, V35, P642, DOI 10.5465/amp.2019.0062
   Tucker Tucker Catherine. Catherine., 2018, The economics of artificial intelligence: An agenda, P423, DOI DOI 10.7208/CHICAGO/9780226613475.003.0017
   van Inwegen, 2023, 30886 NBER
   Webb M., 2020, Working Paper
   Yilmaz ED, 2023, SSRN ELECT J, DOI [10.2139/ssrn.4400516, DOI 10.2139/SSRN.4400516]
   Zammuto RF, 2007, ORGAN SCI, V18, P749, DOI 10.1287/orsc.1070.0307
   Zhang, 2021, CONSUMER AI COCREATI
NR 75
TC 1
Z9 1
U1 275
U2 275
PU INFORMS
PI CATONSVILLE
PA 5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA
SN 1047-7039
J9 ORGAN SCI
JI Organ Sci.
PD NOV-DEC
PY 2024
VL 35
IS 6
BP 1977
EP 1989
DI 10.1287/orsc.2023.18441
EA SEP 2024
PG 13
WC Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA M9X0Z
UT WOS:001321191300001
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Fleischmann, K
AF Fleischmann, Katja
TI Making the case for introducing generative artificial intelligence (AI)
   into design curricula
SO ART DESIGN & COMMUNICATION IN HIGHER EDUCATION
LA English
DT Article
DE generative AI; design education; AI literacy; professional design
   practice; prompt engineering; design studio pedagogy; AI design training
ID EDUCATION; TECHNOLOGY
AB The use of generative artificial intelligence (AI) in higher education design programmes is expanding, yet there is little formalized approach to its integration. Professionally, generative AI is starting to become an indispensable tool for ideation and prototyping, two fundamental skills taught in design's studio pedagogy. Yet this digital leap into the future risks leaving design educators behind unless they take a proactive approach to its implementation and present its strengths and weaknesses. This study surveyed 74 design students from an Australian university, exploring their current utilization of generative AI and their projections for its future application in design practice. Findings confirm that generative AI is being used in an ad hoc way by students to speed up the ideation process tempered by a sceptical view of its creative output. A list of generative AI training for integration into the design curricula based on current research and survey results is proposed.
C1 [Fleischmann, Katja] Griffith Univ, Queensland Coll Art & Design, Gold Coast Campus,Parklands Dr, Southport, Qld 4222, Australia.
C3 Griffith University; Griffith University - Gold Coast Campus
RP Fleischmann, K (corresponding author), Griffith Univ, Queensland Coll Art & Design, Gold Coast Campus,Parklands Dr, Southport, Qld 4222, Australia.
EM k.fleischmann@griffith.edu.au
OI Fleischmann, Katja/0000-0002-0246-260X
CR AIContentfy Team, 2023, AIContentfy, 10 May
   Appel G., 2023, Harvard Business Review
   Auernhammer J., 2020, SYN DRS INT C 2020 1, V1, P11, DOI DOI 10.21606/DRS.2020.282
   Bamford A., 2023, Design Week, 7 July
   Cain J, 2023, SHE JI, V9, P197, DOI 10.1016/j.sheji.2023.07.002
   Coursera, 2023, Prompt engineering for ChatGPT
   Creswell J. W., 2008, RES DESIGN QUALITATI
   Davis M, 2023, SHE JI, V9, P97, DOI 10.1016/j.sheji.2023.04.003
   Debrusk C., 2018, MIT Sloan Management Review, 26 March
   DesignGuru, 2023, Medium: Visual Design
   Drucker J., 2012, Graphic design history: A critical guide
   Edberg E., 2020, Adoption of AI in digital design: A qualitative study about the effects on the profession'
   Fatima I., 2023, master's thesis
   Fielding NG, 2012, J MIX METHOD RES, V6, P124, DOI 10.1177/1558689812437101
   Fleischmann K., 2023, Design and Technology Education: An International Journal, V28, P135
   Fleischmann K., 2014, Studies in Material Thinking, V11, P1
   Fleischmann K, 2022, DES J, V25, P25, DOI 10.1080/14606925.2021.2004717
   Fleischmann K, 2010, ART DES COMMUN HIGH, V9, P57, DOI 10.1386/adch.9.1.57_1
   Fleischmann K, 2013, J LEARN DES, V6, P1
   Friedman K., 2012, VISIBLE LANG, V46, P132
   Gilbert T., 2023, AI revolution"means design studios could look very different in three years'
   Grierson J., 2023, The Guardian
   Harbers M., 2022, DRS2022, P1, DOI DOI 10.21606/DRS.2022.422
   Hardesty L., 2018, MIT NEWS
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   HOMM$S Studio, 2023, Interiors Special Projects
   Huang YC, 2023, INT J DES, V17, P1, DOI 10.57698/v17i2.01
   Kaiko N., 2023, The rise of artificial intelligence in interior design
   Katja Fleischmann., 2015, INT J ARTS SCI, V8, P101
   Kaushik V, 2019, SOC SCI-BASEL, V8, DOI 10.3390/socsci8090255
   Lawton G., 2023, Tech Target, 7 November
   Lim J.-S., 2018, Design as a Catalyst for Change, P1208
   Lindebaum D, 2024, BRIT J MANAGE, V35, P566, DOI 10.1111/1467-8551.12781
   Lorenz P., 2023, OECD Artificial Intelligence Papers, V1, DOI [10.1787/fae2d1-6-en, DOI 10.1787/FAE2D1-6-EN]
   Marr B., 2023, Forbes, 24 July,
   Matthews B, 2023, INT J ART DES EDUC, V42, P367, DOI 10.1111/jade.12460
   Mattioli F., 2022, DRS2022, P2
   Meron Y., 2022, DRS2022, P2
   Milmo D., 2023, The Guardian, 2 November
   Morrone M., 2024, Copyright law is AI's 2024 battlefield'
   Naveed H, 2024, Arxiv, DOI [arXiv:2307.06435, 10.48550/arXiv.2307.06435, DOI 10.48550/ARXIV.2307.06435]
   Noguera J. C., 2022, Industrial design takes on AI
   Olsson T., 2021, INTERACTIONS, V28, P62, DOI DOI 10.1145/3467479
   Pennefather P. P., 2023, Creative Prototyping with Generative AI: Augmenting Creative Workflows with Generative AI
   Philips M., 2023, The present and future of AI in design
   Porcedda MG, 2023, Information Law and, P261
   Punch K.F., 2009, INTRO RES METHODS ED
   ROSSMAN GB, 1985, EVALUATION REV, V9, P627, DOI 10.1177/0193841X8500900505
   Simeone L., 2022, DRS2022, P1
   Sless D., 2012, Visible Language, V46, P54
   Solly M., 2019, Smithsonian Magazine, 24 September
   Tashakkori A., 2009, SAGE HDB APPL SOCIAL, P283, DOI [10.4135/9781483348858.n9, DOI 10.4135/9781483348858.N9]
   Taylor J., 2023, The Guardian
   Verganti R, 2020, J PROD INNOVAT MANAG, V37, P212, DOI 10.1111/jpim.12523
   Vynck G. D., 2024, Washington Post
   Weingarten E, 2020, SHE JI, V6, P301, DOI 10.1016/j.sheji.2020.07.004
   Wernersson J., 2023, master's thesis
   Wright KB, 2005, J COMPUT-MEDIAT COMM, V10
   Zhou JY, 2024, Arxiv, DOI arXiv:2401.07312
NR 59
TC 0
Z9 0
U1 22
U2 22
PU INTELLECT LTD
PI BRISTOL
PA THE MILL, PARNALL RD, BRISTOL, BS16 3JG, ENGLAND
SN 1474-273X
EI 2040-0896
J9 ART DES COMMUN HIGH
JI Art Des. Commun. High. Educ.
PD OCT 1
PY 2024
VL 23
IS 2
BP 187
EP 207
DI 10.1386/adch_00088_1
PG 21
WC Art
WE Emerging Sources Citation Index (ESCI)
SC Art
GA K1W9N
UT WOS:001341864700007
DA 2024-12-25
ER

PT J
AU Rajaram, K
   Tinguely, PN
AF Rajaram, Kumaran
   Tinguely, Patrick Nicolas
TI Generative artificial intelligence in small and medium enterprises:
   Navigating its promises and challenges
SO BUSINESS HORIZONS
LA English
DT Article
DE Generative artificial intelligence; Small and medium enterprises; AI
   management; Competitiveness; Digital innovation
ID CORPORATE CULTURE
AB The latest technological developments in generative artificial intelligence (GenAI) offer powerful capabilities to small and medium enterprises (SMEs) as they facilitate the democratization of scalability and creativity. With little technical expertise or financial resources, SMEs can leverage this technology to streamline work processes and unleash innovation, improving their product offerings and long-term competitiveness. In this article, we discuss how SMEs can navigate both the promises and challenges of GenAI and offer a roadmap for deploying the technology. We then introduce a sailing metaphor that reveals key strategic dimensions for GenAI deployment: competency of employees, effective leadership and work values, organizational culture, collaboration and cooperation, and relationships with third parties. We conclude with practical recommendations for successfully deploying GenAI in SMEs. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
C1 [Rajaram, Kumaran] Nanyang Technol Univ, Nanyang Business Sch, 91 Nanyang Ave,Gaia ABS-05-003, Singapore 639956, Singapore.
   [Tinguely, Patrick Nicolas] Swiss Fed Inst Technol, Dept Management Technol & Econ, Weinbergstr 56-58, CH-8092 Zurich, Switzerland.
C3 Nanyang Technological University; Swiss Federal Institutes of Technology
   Domain; ETH Zurich
RP Rajaram, K (corresponding author), Nanyang Technol Univ, Nanyang Business Sch, 91 Nanyang Ave,Gaia ABS-05-003, Singapore 639956, Singapore.
EM rkumaran@ntu.edu.sg; ptinguely@ethz.ch
RI Rajaram, Kumaran/AGW-8838-2022
OI Tinguely, Patrick/0000-0001-8997-1708
CR Acar O. A., 2024, Harvard Business Review February 1
   AI TechPark, 2023, Democratizing creativity: GenAI for SMBs and SMEs
   Akter S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102387
   Askeland H., 2020, Understanding Values Work: Institutional Perspectives in Organizations and Leadership, DOI DOI 10.1007/978-3-030-37748-98
   Audretsch DB, 2020, EUR ECON REV, V123, DOI 10.1016/j.euroecorev.2020.103391
   Bailey DE, 2022, ORGAN SCI, V33, P1, DOI 10.1287/orsc.2021.1562
   Bluebox Content Team, 2024, 7 essential risk management strategies every Singapore SME should adopt
   Bughin J., 2023, Leveraging AI: What makes superstar firms better than the average corporation?, DOI [10.2139/ssrn.4490396, DOI 10.2139/SSRN.4490396]
   Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
   Canhoto AI, 2020, BUS HORIZONS, V63, P183, DOI 10.1016/j.bushor.2019.11.003
   Chui M., 2022, GenAI is here: How tools like ChatGPT could change your business
   Chui M., 2018, MCKINSEY Q
   Cowgill B, 2019, Bias and productivity in humans and machines, DOI [10.2139/ssrn.3433737, DOI 10.2139/SSRN.3433737]
   Daniels L., 2023, Tastewise March 9
   Davenport TH, 2018, HARVARD BUS REV, V96, P108
   DESHPANDE R, 1993, J MARKETING, V57, P23, DOI 10.2307/1252055
   Desouza KC, 2020, BUS HORIZONS, V63, P205, DOI 10.1016/j.bushor.2019.11.004
   Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
   Eisfeldt A L., 2023, Generative AI and Firm Values
   Felten E W., 2023, How will Language Modelers like ChatGPT Affect Occupations and Industries? (SSRN Scholarly Paper 4375268), DOI DOI 10.2139/SSRN.4375268
   Felten E. W., 2023, Occupational heterogeneity in exposure to generative AI, DOI [10.2139/ssrn.4414065, DOI 10.2139/SSRN.4414065]
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Forbes, 2023, 19 effective ways small businesses can leverage GenAI
   Fountain T, 2019, HARVARD BUS REV, V97, P62
   Frissora M., 2021, Entrepreneur November 23
   Gates B., 2023, The Age of AI has Begun: Artificial Intelligence is as Revolutionary as Mobile Phones and the Internet
   GoDaddy Inc, 2023, Small business owners beginning to turn to AI for help with everyday tasks: GoDaddy study
   González-Varona JM, 2021, IFIP ADV INF COMM TE, V598, P237, DOI 10.1007/978-3-030-62412-5_20
   Greentech Innovators, 2024, We give waste a value
   Haryanto A.T., 2017, International Review of Management and Marketing, V7, P484
   Heaven W. D., 2023, MIT Technology Review May 12
   Holmstrom J, 2022, BUS HORIZONS, V65, P329, DOI 10.1016/j.bushor.2021.03.006
   Hoppner T., 2023, ChatGPT, Bard, & Co.: An introduction to AI for competition and regulatory lawyers, DOI [10.2139/ssrn.4371681, DOI 10.2139/SSRN.4371681]
   Hu K., 2023, REUTERS         0202
   Jain A., 2023, LatentView May 30
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Jebara Tony, 2004, KLUWER INT SER ENG C
   Kaplan A, 2020, BUS HORIZONS, V63, P37, DOI 10.1016/j.bushor.2019.09.003
   Karadakal NV., 2015, Journal of Global Entrepreneurship Research, V5, P1, DOI DOI 10.1186/S40497-015-0019-6
   Kietzmann J., 2024, Business Horizons, V67, P453
   Kietzmann J, 2018, J ADVERTISING RES, V58, P263, DOI 10.2501/JAR-2018-035
   Kojima T, 2022, ADV NEUR IN
   Kshetri N., 2023, International Journal of Information Management, V75
   Kuzlu M., 2023, P 12 MED C EMB COMP
   Lebovitz S, 2022, ORGAN SCI, V33, P126, DOI 10.1287/orsc.2021.1549
   Lee I, 2020, BUS HORIZONS, V63, P157, DOI 10.1016/j.bushor.2019.10.005
   Leyer M, 2021, BUS HORIZONS, V64, P711, DOI 10.1016/j.bushor.2021.02.026
   Major G., 2023, Cavedale Advisory September 10
   Martínez-Del-Río J, 2023, INT J MANPOWER, V44, P618, DOI 10.1108/IJM-12-2021-0707
   McAfee A, 2023, HARVARD BUS REV, V101, P42
   McKinsey, 2023, The economic potential of GenAI: The next productivity frontier
   Meister W., 2006, P PMI GLOB C 2006 AS
   Menkhoff T., 2023, Asian Academy of Management Journal, V8, P49
   Milmo D., 2023, The Guardian
   Mohiuddin Z.A., 2017, J MARKETING MANAGEME, V8, P18
   Mukherjee A., 2023, California Management Review July 24
   Ortega-Parra A, 2013, MANAGE DECIS, V51, P1071, DOI 10.1108/MD-08-2012-0599
   Paschen U, 2020, BUS HORIZONS, V63, P147, DOI 10.1016/j.bushor.2019.10.004
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Rajaram K., 2023, Leading and transforming organizations - navigating the future
   Ransbotham S., 2019, MIT SLOAN MANAGE REV, V61180
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Shrestha YR, 2023, NAT COMPUT SCI, V3, P908, DOI 10.1038/s43588-023-00540-0
   Simoneaux S., 2014, Journal of Pension Benefits, V22, P51
   Textio, 2024, Why Textio
   Tinguely PN, 2023, J ORGAN DES, V12, P263, DOI 10.1007/s41469-023-00153-x
   Valve Corporation, 2024, About
   Veiseh S., 2014, J BUSINESS STUDIES Q, V5, P113
   Walt L. J. V. D., 2015, South African Journal of Economic and Management Sciences, V7, P1373
   Walter A.T., 2021, MANAGEMENT REV Q, V71, P343, DOI [10.1007/s11301-020-00186-6, DOI 10.1007/S11301-020-00186-6]
   Weiser B., 2023, New York Times,May 27
   Wishart M., 2018, BUSINESS RESILIENCE
   World Bank, 2023, SMALL MEDIUM ENTERPR
   Zavo L., 2022, Forbes October 20
NR 74
TC 2
Z9 2
U1 62
U2 62
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 629
EP 648
DI 10.1016/j.bushor.2024.05.008
EA AUG 2024
PG 20
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2K9B
UT WOS:001301352800001
DA 2024-12-25
ER

PT J
AU Decardi-Nelson, B
   Alshehri, AS
   You, FQ
AF Decardi-Nelson, Benjamin
   Alshehri, Abdulelah S.
   You, Fengqi
TI Generative artificial intelligence in chemical engineering spans
   multiple scales
SO FRONTIERS IN CHEMICAL ENGINEERING
LA English
DT Article
DE artificial intelligence; AI; generative learning; quantum-chemical
   calculations; materials; process engineering
AB Recent advances in generative artificial intelligence (GenAI), particularly large language models (LLMs), are profoundly impacting many fields. In chemical engineering, GenAI plays a pivotal role in the design, scale-up, and optimization of chemical and biochemical processes. The natural language understanding capabilities of LLMs enable the interpretation of complex chemical and biological data. Given the rapid developments of GenAI, this paper explores the extensive applications of GenAI in multiscale chemical engineering, spanning from quantum mechanics to macro-level optimization. At quantum and molecular levels, GenAI accelerates the discovery of novel products and enhances the understanding of fundamental phenomena. At larger scales, GenAI improves process design and operational efficiency, contributing to sustainable practices. We present several examples to demonstrate the role of GenAI, including its impact on nanomaterial hardness enhancement, novel catalyst generation, protein design, and the development of autonomous experimental platforms. This multiscale integration demonstrates the potential of GenAI to address complex challenges, drive innovation, and foster advancements in chemical engineering.
C1 [Decardi-Nelson, Benjamin; You, Fengqi] Cornell Univ, Syst Engn, Ithaca, NY 14850 USA.
   [Decardi-Nelson, Benjamin; You, Fengqi] Cornell Univ, Cornell Univ Sci Inst, Ithaca, NY 14850 USA.
   [Alshehri, Abdulelah S.; You, Fengqi] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14850 USA.
   [Alshehri, Abdulelah S.] King Saud Univ, Coll Engn, Dept Chem Engn, Riyadh, Saudi Arabia.
C3 Cornell University; Cornell University; Cornell University; King Saud
   University
RP You, FQ (corresponding author), Cornell Univ, Syst Engn, Ithaca, NY 14850 USA.; You, FQ (corresponding author), Cornell Univ, Cornell Univ Sci Inst, Ithaca, NY 14850 USA.; You, FQ (corresponding author), Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14850 USA.
EM fengqi.you@cornell.edu
RI Decardi-Nelson, Benjamin/JQW-0212-2023; You, Fengqi/B-5040-2011;
   Alshehri, Abdulelah/AAT-6066-2020
OI Alshehri, Abdulelah/0000-0001-5213-3575
FU Schmidt Futures via an Eric and Wendy Schmidt AI in Science Postdoctoral
   Fellowship
FX BD-N. acknowledges the partial support from Schmidt Futures via an Eric
   and Wendy Schmidt AI in Science Postdoctoral Fellowship to Cornell
   University.
CR Achiam J., 2023, GPT-4 Technical Report
   Alshehri AS, 2022, CHEM ENG J, V444, DOI 10.1016/j.cej.2022.136669
   Alshehri AS, 2021, FRONT CHEM ENG, V3, DOI 10.3389/fceng.2021.700717
   Chen PY, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2308698120
   Chen R. T., 2019, Adv. Neural Inf. Process. Syst., P32
   Chiang L, 2017, ANNU REV CHEM BIOMOL, V8, P63, DOI 10.1146/annurev-chembioeng-060816-101555
   Dan JD, 2023, SCI ADV, V9, DOI 10.1126/sciadv.adj0904
   Decardi-Nelson B, 2024, COMPUT CHEM ENG, V187, DOI 10.1016/j.compchemeng.2024.108723
   Duan CR, 2023, NAT COMPUT SCI, V3, P1045, DOI 10.1038/s43588-023-00563-7
   Gangwal A, 2024, DRUG DISCOV TODAY, V29, DOI 10.1016/j.drudis.2024.103992
   Gartner T. E., 2024, Nat. Chem. Eng, V1, P6, DOI [10.1038/s44286-023-00010-4, DOI 10.1038/S44286-023-00010-4]
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Grossmann I, 2005, AICHE J, V51, P1846, DOI 10.1002/aic.10617
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Huang K., 2024, GENERATIVE AI SECURI, P31
   Ingraham JB, 2023, NATURE, V623, P1070, DOI 10.1038/s41586-023-06728-8
   Langer R., 2024, Nat. Chem. Eng., V1, P10, DOI [10.1038/s44286-023-00016-y, DOI 10.1038/S44286-023-00016-Y]
   Lew AJ, 2023, MATTER-US, V6, P1975, DOI 10.1016/j.matt.2023.03.031
   Liu DF, 2023, J CHEM INF MODEL, V63, P7669, DOI 10.1021/acs.jcim.3c01572
   Luo B., 2023, AutoPCF: a novel automatic product carbon footprint estimation framework based on large language models, V2, P102, DOI [10.1609/aaaiss.v2i1.27656, DOI 10.1609/AAAISS.V2I1.27656]
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Preuss N, 2024, J CLEAN PROD, V466, DOI 10.1016/j.jclepro.2024.142824
   Qin XK, 2023, Arxiv, DOI arXiv:2309.16721
   Rawte V., 2003, arXiv
   Ross A, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445296
   Schilter O, 2023, DIGIT DISCOV, V2, P728, DOI 10.1039/d2dd00125j
   Schweidtmann A.M., 2024, Nature Chemical Engineering, V1, P193, DOI [10.1038/s44286-024-00041-5, DOI 10.1038/S44286-024-00041-5]
   Subramanian A., 2024, CLOSING EXECUTION GA
   Takeishi N., 2021, Physics-integrated variational autoencoders for robust and interpretable generative modeling
   TorrenteMurciano L., 2024, Nature Chemical Engineering, V1, P18, DOI [10.1038/s44286-023-00017-x, DOI 10.1038/S44286-023-00017-X]
   Vaswani A, 2017, ADV NEUR IN, V30
   Vogel G, 2023, COMPUT CHEM ENG, V171, DOI 10.1016/j.compchemeng.2023.108162
   Wang YH, 2024, ACS OMEGA, V9, P5954, DOI 10.1021/acsomega.3c09762
   Wang ZZ, 2022, PHYS FLUIDS, V34, DOI 10.1063/5.0133054
   Whang SE, 2023, VLDB J, V32, P791, DOI 10.1007/s00778-022-00775-9
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Yao ZP, 2021, NAT MACH INTELL, V3, P76, DOI 10.1038/s42256-020-00271-1
   Zhang JY, 2024, IEEE T PATTERN ANAL, V46, P5625, DOI 10.1109/TPAMI.2024.3369699
   Zhang WN, 2023, PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, P7062
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
NR 40
TC 0
Z9 0
U1 11
U2 11
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2673-2718
J9 FRONT CHEM ENG
JI Front. Chem. Eng.
PD AUG 29
PY 2024
VL 6
AR 1458156
DI 10.3389/fceng.2024.1458156
PG 5
WC Biotechnology & Applied Microbiology; Engineering, Chemical
WE Emerging Sources Citation Index (ESCI)
SC Biotechnology & Applied Microbiology; Engineering
GA F5M2Q
UT WOS:001310252000001
OA gold
DA 2024-12-25
ER

PT J
AU Doshi, AR
   Bell, JJ
   Mirzayev, E
   Vanneste, BS
AF Doshi, Anil R.
   Bell, J. Jason
   Mirzayev, Emil
   Vanneste, Bart S.
TI Generative artificial intelligence and evaluating strategic decisions
SO STRATEGIC MANAGEMENT JOURNAL
LA English
DT Article; Early Access
DE business models; generative artificial intelligence (AI); large language
   models (LLMs); strategic decision making; strategic decisions
ID WISDOM; CROWDS; MODEL; BIAS
AB Research SummaryStrategic decisions are uncertain and often irreversible. Hence, predicting the value of alternatives is important for strategic decision making. We investigate the use of generative artificial intelligence (AI) in evaluating strategic alternatives using business models generated by AI (study 1) or submitted to a competition (study 2). Each study uses a sample of 60 business models and examines agreement in business model rankings made by large language models (LLMs) and those by human experts. We consider multiple LLMs, assumed LLM roles, and prompts. We find that generative AI often produces evaluations that are inconsistent and biased. However, when aggregating evaluations, AI rankings tend to resemble those of human experts. This study highlights the value of generative AI in strategic decision making by providing predictions.Managerial SummaryManagers are seeking to create value by integrating generative AI into their organizations. We show how managers can use generative AI to help evaluate strategic decisions. Generative AI's single evaluations are often inconsistent or biased. However, if managers aggregate many evaluations across LLMs, prompts, or roles, the results show that the resulting evaluations tend to resemble those of human experts. This approach allows managers to obtain insight on strategic decisions across a variety of domains with relatively low investments in time or resources, which can be combined with human inputs.
C1 [Doshi, Anil R.; Mirzayev, Emil; Vanneste, Bart S.] UCL, UCL Sch Management, London, England.
   [Bell, J. Jason] Univ Oxford, Said Business Sch, Oxford, England.
C3 University of London; University College London; University of Oxford
RP Doshi, AR (corresponding author), UCL, UCL Sch Management, London, England.
EM anil.doshi@ucl.ac.uk
RI Bell, J/IQS-9420-2023; Doshi, Anil/R-9052-2018; Vanneste,
   Bart/B-3644-2010
OI Mirzayev, Emil/0009-0007-5376-8469
CR Almaatouq A, 2024, TOP COGN SCI, V16, P302, DOI 10.1111/tops.12706
   Arend R. J., 2024, Uncertainty in strategic decision making-analysis, categorization, causation and resolution
   Arslan HA, 2023, MANAGE SCI, V69, P4435, DOI 10.1287/mnsc.2022.4588
   Balasubramanian N, 2022, ACAD MANAGE REV, V47, P448, DOI 10.5465/amr.2019.0470
   Bardolet D, 2011, STRATEGIC MANAGE J, V32, P1465, DOI 10.1002/smj.966
   BARNEY JB, 1986, MANAGE SCI, V32, P1230
   BATCHELOR R, 1995, MANAGE SCI, V41, P68, DOI 10.1287/mnsc.41.1.68
   Boussioux L, 2024, ORGAN SCI, V35, P1589, DOI 10.1287/orsc.2023.18430
   Brown TB, 2020, ADV NEUR IN, V33
   Casadesus-Masanell R, 2010, LONG RANGE PLANN, V43, P195, DOI 10.1016/j.lrp.2010.01.004
   Chang YP, 2023, Arxiv, DOI arXiv:2307.03109
   Chen KY, 2004, MANAGE SCI, V50, P983, DOI 10.1287/mnsc.1040.0247
   Chen YT, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2316205120
   Choi J, 2023, ORGAN SCI, V34, P1073, DOI 10.1287/orsc.2022.1609
   Choudhary V, 2023, J MANAGE, DOI 10.1177/01492063231194968
   Csaszar FA, 2024, Strategy Sci., V9, P322
   Csaszar FA, 2018, STRATEG SCI, V3, P513, DOI 10.1287/stsc.2018.0063
   Csaszar FA, 2013, MANAGE SCI, V59, P2257, DOI 10.1287/mnsc.1120.1698
   Cuypers IRP, 2017, STRATEGIC MANAGE J, V38, P609, DOI 10.1002/smj.2502
   Davis-Stober CP., 2014, Decision, V1, P79, DOI [10.1037/dec0000004, DOI 10.1037/DEC0000004]
   de Oliveira S, 2018, P NATL ACAD SCI USA, V115, P2066, DOI 10.1073/pnas.1717632115
   Deshpande A, 2023, Arxiv, DOI arXiv:2304.05335
   Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1, DOI 10.1007/3-540-45014-9_1
   Doshi AR, 2024, SCI ADV, V10, DOI 10.1126/sciadv.adn5290
   EISENHARDT KM, 1992, STRATEGIC MANAGE J, V13, P17, DOI 10.1002/smj.4250130904
   Elbanna S, 2007, STRATEGIC MANAGE J, V28, P431, DOI 10.1002/smj.597
   Gaessler F, 2023, STRATEGIC MANAGE J, V44, P2724, DOI 10.1002/smj.3512
   Galton F, 1907, NATURE, V75, P450, DOI 10.1038/075450a0
   Gans JS, 2019, STRATEGIC MANAGE J, V40, P736, DOI 10.1002/smj.3010
   Gary MS, 2011, STRATEGIC MANAGE J, V32, P569, DOI 10.1002/smj.899
   Gavetti G, 2000, ADMIN SCI QUART, V45, P113, DOI 10.2307/2666981
   Gavetti G, 2016, STRATEG SCI, V1, P207, DOI 10.1287/stsc.2016.0018
   GEMAN S, 1992, NEURAL COMPUT, V4, P1, DOI 10.1162/neco.1992.4.1.1
   Ghemawat P, 1991, COMMITMENT DYNAMIC S
   Guzman J, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2213627120
   Guzman J, 2020, MANAGE SCI, V66, P4808, DOI 10.1287/mnsc.2020.3612
   HANSEN LK, 1990, IEEE T PATTERN ANAL, V12, P993, DOI 10.1109/34.58871
   He LS, 2022, MANAGE SCI, V68, P3635, DOI 10.1287/mnsc.2021.4090
   Helfat CE, 2015, STRATEGIC MANAGE J, V36, P831, DOI 10.1002/smj.2247
   Hendrycks D, 2021, Arxiv, DOI [arXiv:2009.03300, 10.48550/arXiv.2009.03300]
   Jiang Z., 2017, Generalized ambiguity decompositions for classification with applications in active learning and unsupervised ensemble pruning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31
   Joseph J, 2020, ACAD MANAG ANN, V14, P267, DOI 10.5465/annals.2017.0103
   Kapoor R, 2023, STRATEGIC MANAGE J, V44, P704, DOI 10.1002/smj.3450
   Keuschnigg M, 2017, MANAGE SCI, V63, P818, DOI 10.1287/mnsc.2015.2364
   Knudsen T, 2007, ORGAN SCI, V18, P39, DOI 10.1287/orsc.1060.0216
   Kojima T, 2022, ADV NEUR IN
   Kotha R, 2023, STRATEGIC MANAGE J, V44, P549, DOI 10.1002/smj.3438
   Krogh A., 1995, Advances in Neural Information Processing Systems 7, P231
   Larrick RP, 2006, MANAGE SCI, V52, P111, DOI 10.1287/mnsc.1050.0459
   Leiblein MJ, 2018, STRATEG SCI, V3, P558, DOI 10.1287/stsc.2018.0074
   Levinthal DA, 2011, STRATEGIC MANAGE J, V32, P1517, DOI 10.1002/smj.963
   Li JT, 2015, STRATEGIC MANAGE J, V36, P918, DOI 10.1002/smj.2250
   Lichtendahl KC, 2013, MANAGE SCI, V59, P1594, DOI 10.1287/mnsc.1120.1667
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Malenko A., 2024, Catching outliers: Committee voting and the limits of consensus when financing innovation
   Markides C.M., 2000, ALL RIGHT MOVES GUID
   Massa L, 2017, ACAD MANAG ANN, V11, P73, DOI 10.5465/annals.2014.0072
   McFadden D, 1986, MARKET SCI, V5, P275, DOI DOI 10.1287/MKSC.5.4.275
   McLean I., 1995, Classics of Social Choice, P91
   MINTZBERG H, 1976, ADMIN SCI QUART, V21, P246, DOI 10.2307/2392045
   Mollick E, 2016, MANAGE SCI, V62, P1533, DOI 10.1287/mnsc.2015.2207
   Murphy KP., 2023, Probabilistic Machine Learning: Advanced Topics
   Naveed H, 2024, Arxiv, DOI [arXiv:2307.06435, 10.48550/arXiv.2307.06435, DOI 10.48550/ARXIV.2307.06435]
   Page SE, 2007, DIFFERENCE: HOW THE POWER OF DIVERSITY CREATES BETTER GROUPS, FIRMS, SCHOOLS, AND SOCIETIES, P1
   Peterson A, 2021, STRATEGIC MANAGE J, V42, P2357, DOI 10.1002/smj.3327
   Piezunka H, 2023, ORGAN SCI, V34, DOI 10.1287/orsc.2023.1653
   Porter, 1980, COMPETITIVE STRATEGY
   QUENOUILLE MH, 1956, BIOMETRIKA, V43, P353
   Salewski L, 2023, Arxiv, DOI [arXiv:2305.14930, DOI 10.48550/ARXIV.2305.14930]
   Surowiecki J., 2005, WISDOM CROWDS, DOI DOI 10.5555/1095645
   Terwiesch C., 2023, The innovation tournament handbook: A stepbystep guide to finding exceptional solutions to any challenge
   Tsay CJ, 2021, ACAD MANAG DISCOV, V7, P343, DOI 10.5465/amd.2019.0234
   Ueda N., 1996, Generalization error of ensemble estimators. Proceedings of International Conference on Neural Networks (ICNN'96), 1
   Van den Steen E, 2018, MANAGE SCI, V64, P4533, DOI 10.1287/mnsc.2017.2857
   Vanneste BS., 2024, Academy of Management Review, DOI [10.5465/amr.2022.0041, DOI 10.5465/AMR.2022.0041]
   Vaswani A, 2017, ADV NEUR IN, V30
   Wei J., 2022, Transactions on Machine Learning Research
   Wei JS, 2022, ADV NEUR IN
   Wood D, 2023, J MACH LEARN RES, V24
   Xu BF, 2023, Arxiv, DOI arXiv:2305.14688
   Zheng LM, 2023, Arxiv, DOI [arXiv:2306.05685, 10.48550/arXiv.2306.05685]
   Zohrehvand A, 2024, LONG RANGE PLANN, V57, DOI 10.1016/j.lrp.2023.102392
NR 82
TC 0
Z9 0
U1 101
U2 101
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0143-2095
EI 1097-0266
J9 STRATEGIC MANAGE J
JI Strateg. Manage. J.
PD 2024 NOV 14
PY 2024
DI 10.1002/smj.3677
EA NOV 2024
PG 28
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA M3X8L
UT WOS:001356917200001
OA Green Submitted, hybrid
DA 2024-12-25
ER

PT J
AU Chen, K
   Tallant, AC
   Selig, I
AF Chen, Kong
   Tallant, April C.
   Selig, Ian
TI Exploring generative AI literacy in higher education: student adoption,
   interaction, evaluation and ethical perceptions
SO INFORMATION AND LEARNING SCIENCES
LA English
DT Article; Early Access
DE Generative artificial intelligence; AI in higher education; AI literacy;
   Educational technologies; AI learning and teaching; Student interaction
   with AI; Using AI in academic assignments
AB PurposeCurrent knowledge and research on students' utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students' engagement with these tools.Design/methodology/approachThis research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%).FindingsMost respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI's ethical and appropriate use.Originality/valueLiterature related to generative AI use in higher education specific to ChatGPT is predominantly from educators' viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.
C1 [Chen, Kong; Tallant, April C.; Selig, Ian] Western Carolina Univ, Coulter Fac Commons, Cullowhee, NC 28723 USA.
C3 University of North Carolina; Western Carolina University
RP Chen, K (corresponding author), Western Carolina Univ, Coulter Fac Commons, Cullowhee, NC 28723 USA.
EM woshick@hotmail.com
CR [Anonymous], 2023, Future of jobs report
   [Anonymous], 2023, IntelligentSeptember
   Bailey J., 2023, Educ. Next, V23, P28
   Borenstein Jason, 2021, AI Ethics, V1, P61, DOI 10.1007/s43681-020-00002-7
   Brewer J., 2020, EDUCAUSE Review Special Report, P40
   Davies RS, 2011, TECHTRENDS, V55, P45, DOI 10.1007/s11528-011-0527-3
   Dijkstra R., 2022, P 4 INT WORKSH INT T, P4
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Giannini S., 2023, Generative AI and the future of education
   Google Trends, 2023, ChatGPT
   Greitemeyer T, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e19909
   Haque MU., 2022, arXiv, DOI [DOI 10.48550/ARXIV.2212.05856, 10.48550/arXiv.2212, 10.48550/arxiv.2212.05856]
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   King Michael, 2017, EDUCAUSE Review, V52, P10
   Kong SC., 2021, COMPUTERS ED ARTIFIC, V2, P100026, DOI [DOI 10.1016/J.CAEAI.2021.100026, 10.1016/j.caeai.2021.100026]
   Kumar V R., 2022, 2022 IEEE Integrated STEM Education Conference (ISEC), P450, DOI [10.1109/ISEC54952.2022.10025165, DOI 10.1109/ISEC54952.2022.10025165]
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Malinka K, 2023, PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1, P47, DOI 10.1145/3587102.3588827
   McMurtrie B., 2023, The Chronicle of Higher Education
   Mehta I., 2023, Stack overflow cuts 28% of its staff
   Meyer JG, 2023, BIODATA MIN, V16, DOI 10.1186/s13040-023-00339-9
   Mogavi RH, 2023, Arxiv, DOI arXiv:2305.13114
   Neumann M, 2023, 2023 IEEE/ACM 5TH INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING EDUCATION FOR THE NEXT GENERATION, SEENG, P29, DOI 10.1109/SEENG59157.2023.00010
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   ODea X., 2023, THE Campus Learn, Share, Connect
   Pelletier K., 2023, 2023 EDUCAUSE Horizon Report, Teaching and Learning Edition
   Posit Team, 2023, RStudio: Integrated Development Environment for R
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sabzalieva E., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Scott-Branch J., 2023, The Intersection of AI, Information and Digital Literacy: Harnessing ChatGPT and Other Generative Tools to Enhance Teaching and Learning
   Sheikh H, 2023, Mission AI: The New System Technology, DOI [10.1007/978-3-031-21448-6, DOI 10.1007/978-3-031-21448-6]
   Southworth J., 2023, COMPUTERS ED ARTIFIC, V4, pPG, DOI [10.1016/j.caeai.2023.100127 10.1016/j.caeai.2023.100127, DOI 10.1016/J.CAEAI.2023.100127, 10.1016/j.caeai.2023.100127]
   U.S. Department of Education, 2023, Artificial intelligence and future of teaching and learning: Insights and recommendations
   Wang BC, 2023, BEHAV INFORM TECHNOL, V42, P1324, DOI 10.1080/0144929X.2022.2072768
   Welding L., 2023, BESTCOLLEGES    0327
NR 37
TC 1
Z9 1
U1 175
U2 175
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2398-5348
EI 2398-5356
J9 INFORM LEARN SCI
JI Inf. Learn. Sci.
PD 2024 JUL 5
PY 2024
DI 10.1108/ILS-10-2023-0160
EA JUL 2024
PG 17
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA XH8O6
UT WOS:001260885300001
DA 2024-12-25
ER

PT J
AU Maphoto, KB
   Sevnarayan, K
   Mohale, NE
   Suliman, Z
   Ntsopi, TJ
   Mokoena, D
AF Maphoto, Kgabo bridget
   Sevnarayan, Kershnee
   Mohale, Ntshimane elphas
   Suliman, Zuleika
   Ntsopi, Tumelo jacquiline
   Mokoena, Douglas
TI Advancing Students' Academic Excellence in Distance Education: Exploring
   the Potential of Generative AI Integration to Improve Academic Writing
   Skills
SO OPEN PRAXIS
LA English
DT Article
DE ChatGPT; distance learning; generative artificial intelligence; Human-AI
   collaboration framework; academic writing
ID SUPPORT
AB This qualitative study explores the potential of generative artificial intelligence (AI) to improve the academic writing skills of a large student cohort within the context of a distance learning institution. Utilising qualitative methods, the research explores diverse approaches and applications of generative AI to elevate teaching and learning experiences. Grounded in socio-cultural theory and a human -AI collaboration framework, the study highlights the synergistic interplay between human intelligence and generative AI capabilities. Email interviews with lecturers, focus group discussions with students, and informal discussions with markers on a WhatsApp group helped researchers to (1) understand lecturers' perceptions of generative AI integration in the Academic Writing module, (2) explore students' perspectives on the potential of generative AI as a guide in the Academic Writing module, and (3) examine the potential of generative AI on students' motivation to enhance their academic writing skills. Findings from the study reveal that the potential of generative AI has a positive impact on teaching and learning experiences, providing innovative opportunities for academics. This research contributes to the discourse on the intersection of generative AI and education, reiterating the innovative potential of generative AI in redefining pedagogical strategies and shaping the future of distance learning.
C1 [Maphoto, Kgabo bridget; Sevnarayan, Kershnee; Mohale, Ntshimane elphas; Suliman, Zuleika; Ntsopi, Tumelo jacquiline; Mokoena, Douglas] Univ South Africa, Pretoria, South Africa.
C3 University of South Africa
RP Maphoto, KB (corresponding author), Univ South Africa, Pretoria, South Africa.
EM maphokb@unisa.ac.za
OI Suliman, Zuleika/0009-0009-6038-9621; Mohale, Dr Ntshimane
   Elphas/0000-0002-9609-1845; Sevnarayan, Kershnee/0000-0002-2446-8885
FU National Research Foundation of South Africa
   [SRUG2204285127-2023-03-22-CUGR]
FX <BOLD>Acknowledgements</BOLD> This work is based on the research
   supported in part by the National Research Foundation of South Africa
   (Ref Number SRUG2204285127-2023-03-22-CUGR) .
CR Adedoyin Olasile Babatunde, 2023, Interactive Learning Environments, P863, DOI 10.1080/10494820.2020.1813180
   Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Alam A., 2021, 2021 International Conference on Computational Intelligence and Computing Applications, P1, DOI DOI 10.1109/ICCICA52458.2021.9697272
   Alam A., 2022, COMMUNICATION, P252, DOI [10.1007/978-3-031-43145-6_21, DOI 10.1007/978-3-031-43145-6_21]
   Aljohani R.A., 2021, J APPL LINGUIST LANG, V8, P36
   Allal L, 2000, LEARN INSTR, V10, P137, DOI 10.1016/S0959-4752(99)00025-0
   Altinmakas D, 2019, J ENGL ACAD PURP, V37, P88, DOI 10.1016/j.jeap.2018.11.006
   Asiamah N, 2017, QUAL REP, V22, P1607
   Ausat A. M. A., 2023, Journal on Education, V5, P16100
   Bansal G, 2019, AAAI CONF ARTIF INTE, P2429
   Baskara R., 2023, IJELTAL (Indonesian Journal of English Language Teaching and Applied Linguistics), V7
   Bell S, 2023, BMC MED, V21, DOI 10.1186/s12916-023-03039-7
   Bennani S, 2022, COMPUT APPL ENG EDUC, V30, P628, DOI 10.1002/cae.22477
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bozkurt A, 2024, OPEN PRAX, V16, P283, DOI [10.55982/openpraxis.16.3.739, 10.55982/openpraxis.16.1.654]
   Bygstad B, 2022, COMPUT EDUC, V182, DOI 10.1016/j.compedu.2022.104463
   Cai QQ, 2024, INT J HUM-COMPUT INT, V40, P7112, DOI 10.1080/10447318.2023.2261725
   Campbell S, 2020, J RES NURS, V25, P652, DOI 10.1177/1744987120927206
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chang DH, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712921
   Chang WL, 2009, COMPUT ASSIST LANG L, V22, P283, DOI 10.1080/09588220903184518
   Chassignol M, 2018, PROCEDIA COMPUT SCI, V136, P16, DOI 10.1016/j.procs.2018.08.233
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dahlin E, 2021, INT J QUAL METH, V20, DOI 10.1177/16094069211025453
   Duarte RD, 2023, FRONT ARTIF INTEL AP, V368, P470, DOI 10.3233/FAIA230126
   Dehouche N., 2021, Ethic in Science and Environmental Politics, V21, P17, DOI [DOI 10.3354/ESEP00195, 10.3354/esep00195]
   Dergaa I, 2023, BIOL SPORT, V40, P615, DOI 10.5114/biolsport.2023.125623
   Dey S., 2021, REPORTS CHEATING COL
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eaton S. E., 2021, ARTIFICIAL INTELLIGE, DOI [10.11575/PRISM/38967, DOI 10.11575/PRISM/38967]
   Fitria T. N., 2023, ELT FORUM, V12, P44, DOI DOI 10.15294/ELT.V12I1.64069
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Golan R, 2023, NAT REV UROL, V20, P327, DOI 10.1038/s41585-023-00746-x
   Griffin, 2016, FACES ANONYMITY ANON
   Hall E, 2016, AM J HEALTH EDUC, V47, P136, DOI 10.1080/19325037.2016.1157532
   Hassan I, 2021, ARAB WORLD ENGL J, P377, DOI 10.24093/awej/call7.26
   Hibert AI, 2019, LECT NOTES COMPUT SC, V11722, P199, DOI 10.1007/978-3-030-29736-7_15
   HUANG F, 2022, SUSTAINABILITY SWITZ, V14, P1, DOI DOI 10.3390/SU14148774
   Hunt N, 2007, QUAL HEALTH RES, V17, P1415, DOI 10.1177/1049732307308761
   Ilham I., 2020, ENGLISH REV J ENGLIS, V8, P31, DOI [10.25134/erjee.v8i2.2988, DOI 10.25134/ERJEE.V8I2.2988]
   Iqbal N., 2023, Glob J Manag Adm Sci, V3, P171, DOI DOI 10.46568/GJMAS.V3I4.163
   Jain R, 2023, KYBERNETES, V52, P5017, DOI 10.1108/K-04-2022-0548
   Jeon J, 2022, EDUC INF TECHNOL, V27, P5767, DOI 10.1007/s10639-021-10839-y
   Khabib S., 2022, Teach. English Foreign Lang. J., V1, P114, DOI [10.12928/tefl.v1i2.249, DOI 10.12928/TEFL.V1I2.249]
   Khalil M, 2023, LECT NOTES COMPUT SC, V14040, P475, DOI 10.1007/978-3-031-34411-4_32
   Khalo, 2021, READING WRITING CTR
   Kiryakova G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13101056
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Koka N. A., 2023, J SW JIAOTONG U, V58, DOI [10.35741/issn.0258-2724.58.5.6, DOI 10.35741/ISSN.0258-2724.58.5.6]
   Koltovskaia S, 2020, ASSESS WRIT, V44, DOI 10.1016/j.asw.2020.100450
   Lantolf J.P., 1993, GEORGETOWN U ROUND T, P220
   Lentz G. S., 2020, THESIS CAPE PENINSUL
   Lin, 2023, SUPERCHARGING ACAD W, DOI [10.31234/osf.io/9yhwz, DOI 10.31234/OSF.IO/9YHWZ]
   Mahmud H, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121390
   Malik A. R., 2023, International Journal of Educational Research Open, V5, DOI [10.1016/j.ijedro.2023.100296, DOI 10.1016/J.IJEDRO.2023.100296]
   McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
   McLean S, 2023, J EXP THEOR ARTIF IN, V35, P649, DOI 10.1080/0952813X.2021.1964003
   Miles, 2017, WORKSH BLACK DOCT NE
   Mohale N. E., 2023, THESIS UNISA S AFRIC
   Mphahlele A, 2019, ASSESS EVAL HIGH EDU, V44, P1079, DOI 10.1080/02602938.2019.1573971
   Naidu K, 2023, ONLINE J COMMUN MEDI, V13, DOI 10.30935/ojcmt/13291
   Nazari N, 2021, HELIYON, V7, DOI 10.1016/j.heliyon.2021.e07014
   Nguyen Q H., 2023, Proceedings of the AsiaCALL International Conference, V4, P75, DOI DOI 10.54855/PAIC.2346
   Palermo C, 2020, J WRIT RES, V12, P63, DOI 10.17239/jowr-2020.12.01.04
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Ponelis S. R., 2015, International Journal of Doctoral Studies, V10, P535, DOI DOI 10.28945/2339
   Qiao HL, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1255594
   Roe J., 2023, Journal of English and Applied Linguistics, V2, P3, DOI 10.59588/2961-3094.1035
   Saglamel H., 2022, ACUITY J ENGL LANG P, V7, DOI [10.35974/acuity.v7i2.2541, DOI 10.35974/ACUITY.V7I2.2541]
   SALLAM M, 2023, HEALTHCARE-BASEL, V11, DOI DOI 10.3390/HEALTHCARE11060887
   Schmohl T., 2020, C P FUT ED 2020
   Scott S., 2013, Sociocultural theory
   Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
   Sevnarayan K, 2024, J ACAD ETHICS, V22, P51, DOI 10.1007/s10805-023-09501-8
   Shabani K., 2010, English language teaching, V3, P237, DOI DOI 10.5539/ELT.V3N4P237
   Strobl C, 2019, COMPUT EDUC, V131, P33, DOI 10.1016/j.compedu.2018.12.005
   Tao B., 2019, Arctic Journal, V72, P30
   Tasker TJ, 2019, CURRICULUM TEACH DIA, V21, P119
   Walsham G, 2006, EUR J INFORM SYST, V15, P320, DOI 10.1057/palgrave.ejis.3000589
   Weiwei Zhou, 2020, Journal of Physics: Conference Series, V1646, DOI 10.1088/1742-6596/1646/1/012142
   Wilder N., 2021, EUROPEAN C ACAD INTE, P179
   Woithe J., 2023, UNDERSTANDING ADOPTI
   Wright-St Clair., 2015, Qualitative research methodologies for occupational science and therapy, P53, DOI DOI 10.4324/9780203383216-14
   Xu W, 2023, INT J HUM-COMPUT INT, V39, P494, DOI [10.1080/10447318.2022.2041900, 10.1109/IECON49645.2022.9968424]
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yao D, 2021, J ASIA TEFL, V18, P949, DOI 10.18823/asiatefl.2021.18.3.14.949
   Zulfa S., 2023, INT C ED, P47
NR 87
TC 0
Z9 0
U1 90
U2 94
PU INT COUNCIL OPEN & DISTANCE EDUCATION
PI OSLO
PA LILLEAKERVEIEN 23, OSLO, 0283, NORWAY
SN 2304-070X
J9 OPEN PRAX
JI Open Prax.
PY 2024
VL 16
IS 2
BP 142
EP 159
DI 10.55982/openpraxis.16.2.649
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA TO9Z7
UT WOS:001242333800004
OA gold
DA 2024-12-25
ER

PT J
AU Vrontis, D
   Chaudhuri, R
   Chatterjee, S
AF Vrontis, Demetris
   Chaudhuri, Ranjan
   Chatterjee, Sheshadri
TI Reassessing the Constructs Related to Technology Adoption Theories in
   the Global Marketing Domain in Light of the Emergence of GenAI
SO JOURNAL OF GLOBAL MARKETING
LA English
DT Article
DE Global marketing; constructs; technology adoption; GenAI, TOE, diffusion
   of innovations
ID SOCIAL COGNITIVE THEORY; SELF-DETERMINATION THEORY; ACCEPTANCE MODEL;
   ARTIFICIAL-INTELLIGENCE; PLANNED BEHAVIOR; USER ACCEPTANCE; UNIFIED
   THEORY; VALIDATION; DIFFUSION; UTAUT
AB The purpose of this study is to understand the opportunities and challenges for the global marketing field due to emergence of generative artificial intelligence and the need to reassess the constructs related to technology adoption theories. Given that this technology is so recent, there is a need to investigate this aspect. This study explores the influence of generative AI on various constructs from ten technology adoption theories, models, and approaches as they relate to the global marketing domain. This research study excludes other psychological and cross-cultural theories and constructs related to the domain. This study is unique for its wide range of implications toward the adoption of technology in global marketing initiatives because GenAI can assess consumer sentiment, which helps global marketers make accurate decisions.
C1 [Vrontis, Demetris] Univ Nicosia, Gnosis Mediterranean Inst Management Sci, Sch Business, Nicosia, Cyprus.
   [Vrontis, Demetris] Lebanese Amer Univ, Adnan Kassar Sch Business, Dept Management Studies, Beirut, Lebanon.
   [Vrontis, Demetris] SP Jain Sch Global Management, Singapore, Singapore.
   [Chaudhuri, Ranjan] Leonard de Vinci Pole Univ, Res Ctr, Paris, France.
   [Chatterjee, Sheshadri] Indian Inst Technol Kharagpur, Kharagpur, India.
C3 University of Nicosia; Lebanese American University; Indian Institute of
   Technology System (IIT System); Indian Institute of Technology (IIT) -
   Kharagpur
RP Chatterjee, S (corresponding author), Indian Inst Technol Kharagpur, Kharagpur, India.
EM sheshadri.academic@gmail.com
RI Chatterjee, Sheshadri/AAN-9917-2020
OI Chatterjee, Sheshadri/0000-0003-1075-5549
CR Abed SS, 2020, INT J INFORM MANAGE, V53, DOI 10.1016/j.ijinfomgt.2020.102118
   Agrawal KP, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2286540
   Al Amin M, 2022, J GLOB MARK, V35, P384, DOI 10.1080/08911762.2022.2051157
   Al Amin M, 2022, J GLOB MARK, V35, P228, DOI 10.1080/08911762.2021.1980640
   Al-Hujran O, 2018, INT J E-BUS RES, V14, P77, DOI 10.4018/IJEBR.2018070105
   Ammenwerth Elske, 2006, BMC Med Inform Decis Mak, V6, P3, DOI 10.1186/1472-6947-6-3
   Awa HO, 2016, INFORM TECHNOL PEOPL, V29, P901, DOI 10.1108/ITP-03-2015-0068
   Baabdullah AM, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122951
   BANDURA A, 1986, ORGAN BEHAV HUM DEC, V38, P92, DOI 10.1016/0749-5978(86)90028-2
   BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
   Bandura A, 2001, ANNU REV PSYCHOL, V52, P1, DOI 10.1146/annurev.psych.52.1.1
   BANDURA A, 1989, AM PSYCHOL, V44, P1175, DOI 10.1037/0003-066X.44.9.1175
   BECKER TE, 1995, J MANAGE, V21, P617, DOI 10.1016/0149-2063(95)90002-0
   Buckley A., 2023, Applied Marketing Analytics, V9, P145
   Burhanudddin, 2019, POL J MANAG STUD, V20, P119, DOI 10.17512/pjms.2019.20.1.10
   Chang A., 2012, Journal The WINNERS, V13, P106, DOI DOI 10.21512/TW.V13I2.656
   Chatterjee Sheshadri, 2015, 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), P105, DOI 10.1109/ICSCTI.2015.7489547
   Chatterjee S, 2022, J BUS RES, V153, P46, DOI 10.1016/j.jbusres.2022.08.019
   Chatterjee S, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2021.101621
   Chatterjee S, 2022, TECHNOL FORECAST SOC, V176, DOI 10.1016/j.techfore.2021.121446
   Chatterjee S, 2024, INFORM SYST FRONT, V26, P121, DOI 10.1007/s10796-021-10197-7
   Chatterjee S, 2021, INFORM TECHNOL PEOPL, V34, P1800, DOI 10.1108/ITP-05-2020-0267
   Chatterjee S, 2015, IEEE INT ADV COMPUT, P393, DOI 10.1109/IADCC.2015.7154737
   Chaudhuri R, 2023, J FAM BUS MANAG, V13, P46, DOI 10.1108/JFBM-12-2021-0153
   Compeau D. R., 1995, Management Information Systems Quarterly, V19, P189, DOI 10.2307/249688
   Concannon F., 2023, Irish Journal of Technology Enhanced Learning, V7, DOI [10.22554/ijtel.v7i1.116, DOI 10.22554/IJTEL.V7I1.116]
   Cui YY, 2024, BUS HORIZONS, V67, P583, DOI 10.1016/j.bushor.2024.05.003
   Das S, 2024, TECHNOL FORECAST SOC, V201, DOI 10.1016/j.techfore.2024.123241
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   de Groot J, 2007, J APPL SOC PSYCHOL, V37, P1817, DOI 10.1111/j.1559-1816.2007.00239.x
   Deci E. L., 2013, Intrinsic motivation and self-determination in human behavior (perspectives in social psychology), DOI DOI 10.1007/978-1-4899-2271-7
   Deci E.L., 2002, HDB SELF DETERMINATI, P431, DOI DOI 10.1111/BJHP.12054
   Deci EL, 2017, ANNU REV ORGAN PSYCH, V4, P19, DOI 10.1146/annurev-orgpsych-032516-113108
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Dwivedi YK, 2017, GOV INFORM Q, V34, P211, DOI 10.1016/j.giq.2017.03.001
   El-Gohary Hatem, 2010, International Journal of Customer Relationship Marketing and Management, V1, P56, DOI 10.4018/jcrmm.2010070105
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   FISHBEIN M, 1975, BELIEF ATTITUDE INTE
   Furneaux B, 2010, INTEGR SER INFORM SY, V28, P87, DOI 10.1007/978-1-4419-6108-2_5
   Gao LL, 2014, ASIA PAC J MARKET LO, V26, P211, DOI 10.1108/APJML-06-2013-0061
   Giordano V, 2024, TECHNOL FORECAST SOC, V203, DOI 10.1016/j.techfore.2024.123389
   Golab-Andrzejak E, 2023, CYBERNET SYST, DOI 10.1080/01969722.2023.2296253
   GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
   Greenhalgh T, 2004, MILBANK Q, V82, P581, DOI 10.1111/j.0887-378X.2004.00325.x
   Gupta R., 2024, Int J Inf Manage Data Insights, V4, P100232, DOI [10.1016/j.jjimei.2024.100232, DOI 10.1016/J.JJIMEI.2024.100232]
   Ha C.L., 1998, J PROD BRAND MANAG, V7, P51, DOI DOI 10.1108/10610429810209737
   Hale J.L., 2002, Developments in Theory and Practice, V14, P259, DOI 10.4135/9781412976046
   Hassan LM, 2016, J CONSUM BEHAV, V15, P72, DOI 10.1002/cb.1536
   Hemmati A, 2022, INTERNET THINGS-NETH, V20, DOI 10.1016/j.iot.2022.100635
   Hermann E, 2024, J BUS RES, V180, DOI 10.1016/j.jbusres.2024.114720
   Hong SJ, 2006, DECIS SUPPORT SYST, V42, P1819, DOI 10.1016/j.dss.2006.03.009
   Hu PJ, 1999, J MANAGE INFORM SYST, V16, P91, DOI 10.1080/07421222.1999.11518247
   Huang K., 2024, GENERATIVE AI SECURI, P31
   Humphreys D., 2024, AI and Ethics, DOI [10.1007/s43681-024-00443-4, DOI 10.1007/S43681-024-00443-4]
   Ismail AR, 2012, J GLOB MARK, V25, P226
   Israflzade K., 2023, EURASIA P ED SOCIAL, V32, P132
   Iyer P, 2024, J BUS RES, V180, DOI 10.1016/j.jbusres.2024.114699
   Jain V, 2024, J CONSUM BEHAV, V23, P676, DOI 10.1002/cb.2233
   Khajehpour M, 2011, PROCD SOC BEHV, V15, P1188, DOI 10.1016/j.sbspro.2011.03.261
   Klenk M, 2024, ETHICS INF TECHNOL, V26, DOI 10.1007/s10676-024-09745-x
   Kunz WH, 2024, J RES INTERACT MARK, V18, P31, DOI 10.1108/JRIM-06-2023-0176
   Lai ZJ, 2024, ASIA PAC J MARKET LO, DOI 10.1108/APJML-03-2024-0386
   Li Y, 2024, INT J CONTEMP HOSP M, DOI 10.1108/IJCHM-11-2023-1767
   Liao CC, 2007, COMPUT HUM BEHAV, V23, P2804, DOI 10.1016/j.chb.2006.05.006
   Madigan R, 2017, TRANSPORT RES F-TRAF, V50, P55, DOI 10.1016/j.trf.2017.07.007
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Montano D.E., 2015, Health behavior: Theory, research and practice, DOI DOI 10.5771/9783845288277
   Nadeem M., 2024, BRIT J MARKETING STU, V12, P1, DOI [10.37745/bjms.2013/vol12n1115, DOI 10.37745/BJMS.2013/VOL12N1115]
   Ng JYY, 2012, PERSPECT PSYCHOL SCI, V7, P325, DOI 10.1177/1745691612447309
   Obrenovic B, 2024, AI SOC, DOI 10.1007/s00146-024-01889-0
   Oldenburg B., 2008, Health behavior and health education: Theory, research, and practice, V4th, P313
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pérez-Núñez A, 2023, HISPANIA-J DEV INTER, V106, P355, DOI 10.1353/hpn.2023.a906568
   Perri C, 2020, TECHNOL FORECAST SOC, V155, DOI 10.1016/j.techfore.2020.119991
   Polonsky MJ, 2023, AUSTRALAS MARK J, V31, P91, DOI 10.1177/14413582231167882
   Rajaretnam J, 2018, J GLOB MARK, V31, P60
   Reeve J, 2012, HANDBOOK OF RESEARCH ON STUDENT ENGAGEMENT, P149, DOI 10.1007/978-1-4614-2018-7_7
   Rogers E.M., 1962, DIFFUSION INNOVATION
   ROGERS EM, 1995, JOINT COMM J QUAL IM, V21, P324, DOI 10.1016/S1070-3241(16)30155-9
   Rogers EM, 2009, COMMUN SER, P418
   Ryan R M., 2023, The Oxford Handbook of Self-Determination Theory, V1st, DOI [DOI 10.1093/OXFORDHB/9780197600047.013.2, 10.1093/oxfordhb/9780197600047.001.0001, DOI 10.1093/OXFORDHB/9780197600047.001.0001]
   Ryan RM, 2000, AM PSYCHOL, V55, P68, DOI 10.1037/0003-066X.55.1.68
   Saleh RA, 2023, PERTANIKA J SCI TECH, V31, P2531, DOI 10.47836/pjst.31.5.26
   Salinas-Navarro DE, 2024, INTERACT TECHNOL SMA, V21, P708, DOI 10.1108/ITSE-12-2023-0236
   Sánchez-Ruiz LM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13106039
   Schlagwein D, 2023, J INF TECHNOL-UK, V38, P232, DOI 10.1177/02683962231200411
   Sharples M., 2023, Learn. Res. Pract, V9, P159, DOI [DOI 10.1080/23735082.2023.2261131, 10.1080/23735082.2023.2261131]
   Singh B., 2024, INT J RES ENG SCI MA, V7, P83
   Singh S, 2023, J GLOB MARK, V36, P93, DOI 10.1080/08911762.2022.2141167
   Tan GWH, 2014, COMPUT HUM BEHAV, V36, P198, DOI 10.1016/j.chb.2014.03.052
   Thukral V., 2023, APPL MARKETING ANALY, V9, P281, DOI [https://doi.org/10.69554/DMIV5161, DOI 10.69554/DMIV5161]
   Tornatzky L., 1990, PROCESS TECHNOLOGY I
   Ullah F, 2021, TECHNOL FORECAST SOC, V167, DOI 10.1016/j.techfore.2021.120743
   Vaid S, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114110
   Vallerand RJ, 1997, ADV EXP SOC PSYCHOL, V29, P271, DOI 10.1016/S0065-2601(08)60019-2
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Verma S., 2021, International Journal of Information Management Data Insights, V1, DOI [DOI 10.1016/J.JJIMEI.2020.100002, 10.1016/j.jjimei.2020.100002]
   Wang X, 2024, J GLOB MARK, V37, P159, DOI 10.1080/08911762.2024.2319608
   Wang YS, 2008, GOV INFORM Q, V25, P717, DOI 10.1016/j.giq.2007.06.002
   Wang YS, 2009, GOV INFORM Q, V26, P158, DOI 10.1016/j.giq.2008.07.001
   Wejnert B, 2002, ANNU REV SOCIOL, V28, P297, DOI 10.1146/annurev.soc.28.110601.141051
   Williams MD, 2015, J ENTERP INF MANAG, V28, P443, DOI 10.1108/JEIM-09-2014-0088
   WOOD R, 1989, ACAD MANAGE REV, V14, P361, DOI 10.2307/258173
   Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
   Zigurs I, 1998, MIS QUART, V22, P313, DOI 10.2307/249668
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 110
TC 0
Z9 0
U1 12
U2 12
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0891-1762
EI 1528-6975
J9 J GLOB MARK
JI J. Glob. Mark.
PD OCT 19
PY 2024
VL 37
IS 5
BP 357
EP 378
DI 10.1080/08911762.2024.2412756
EA OCT 2024
PG 22
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA K7N7G
UT WOS:001338457400001
DA 2024-12-25
ER

PT J
AU Mao, J
   Chen, BY
   Liu, JC
AF Mao, Jin
   Chen, Baiyun
   Liu, Juhong Christie
TI Generative Artificial Intelligence in Education and Its Implications for
   Assessment
SO TECHTRENDS
LA English
DT Article
DE Generative Artificial Intelligence; Assessment; Systems Thinking
ID PRIVACY
AB The abrupt emergence and rapid advancement of generative artificial intelligence (AI) technologies, transitioning from research labs to potentially all aspects of social life, has brought a profound impact on education, science, arts, journalism, and every facet of human life and communication. The purpose of this paper is to recapitulate the use of AI in education and examine potential opportunities and challenges of employing generative AI for educational assessment, with systems thinking in mind. Following a review of the opportunities and challenges, we discuss key issues and dilemmas associated with using generative AI for assessment and for education in general. We hope that the opportunities, challenges, and issues discussed in this paper could serve as a foundation for educators to harness the power of AI within the digital learning ecosystem.
C1 [Mao, Jin] Wilkes Univ, Wilkes Barre, PA 18766 USA.
   [Chen, Baiyun] Univ Cent Florida, Orlando, FL USA.
   [Liu, Juhong Christie] James Madison Univ, Harrisonburg, VA USA.
C3 Wilkes University; State University System of Florida; University of
   Central Florida; James Madison University
RP Mao, J (corresponding author), Wilkes Univ, Wilkes Barre, PA 18766 USA.
EM jinjoy.mao@wilkes.edu; baiyun.chen@ucf.edu; liujc@jmu.edu
RI Chen, Baiyun/AAF-5141-2020
OI Mao, Jin/0000-0001-8498-3523; Liu, Juhong Christie/0000-0002-3384-4379;
   Chen, Baiyun/0000-0002-4010-9890
CR Archibald A, 2023, TECHTRENDS, V67, P285, DOI 10.1007/s11528-022-00825-7
   Armstrong K., 2023, BBC News
   Baker M. J., 2000, International Journal of Artificial Intelligence in Education, V11, P122
   Bowen J., 2012, Teaching naked: How moving technology out of your classromm will improve student learning, VFirst
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Celik I, 2022, TECHTRENDS, V66, P616, DOI 10.1007/s11528-022-00715-y
   Chrisinger B., 2023, CHRONICLE HIGHER ED
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
   Darling-Hammond L., 2017, DEVELOPING MEASURING
   Dhirani LL, 2023, SENSORS-BASEL, V23, DOI 10.3390/s23031151
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Gamage KAA, 2020, EDUC SCI, V10, DOI 10.3390/educsci10110301
   Gewirtz D., 2023, CAN DETECTORS SAVE U
   Hu K., 2023, REUTERS         0202
   Humble N., 2022, DISCOVER ARTIFICIAL, V2, DOI DOI 10.1007/S44163-022-00039-Z
   Ifenthaler D, 2016, ETR&D-EDUC TECH RES, V64, P923, DOI 10.1007/s11423-016-9477-y
   Ivarsson J, 2023, COMPUT SUPP COOP W J, V32, P545, DOI 10.1007/s10606-023-09465-8
   Jones-Rooy A., 2019, QUARTZ
   Kaipa RM, 2021, J APPL RES HIGH EDUC, V13, P16, DOI 10.1108/JARHE-01-2020-0011
   Kan M., 2023, CHATGPT MAY BE FASTE
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kelley K. J., 2023, Inside Higher Ed
   Kennedy Brian., 2023, Public awareness of artificial intelligence in everyday activities
   Kowch EG., 2018, PRINCIPLES TEACHING, DOI [10.1109/ICIME.2018.00075, DOI 10.1109/ICIME.2018.00075]
   Lamb R., 2022, COMPUTERS ED ARTIFIC, V3, DOI 10.1016/j.caeai.2022.100078
   Lee V. R., 2023, CONVERSATION
   Markauskaite L., 2022, Comput. Educ. Artif. Intell., V3, DOI [10.1016/j.caeai.2022.100056, DOI 10.1016/J.CAEAI.2022.100056]
   Martinez D., 2019, ARTIF INTELL
   McAdoo T., 2023, APA Style
   McKinsey & Company, 2023, WHAT IS GENERATIVE A
   Metz C., 2023, New York Times
   Mislevy R., 2012, Journal of Educational Data Mining, V2, P11, DOI DOI 10.5281/ZENODO.3554641
   Mutimukwe C, 2022, BRIT J EDUC TECHNOL, V53, P932, DOI 10.1111/bjet.13234
   Nelson J., 2023, DECRYPT
   Ntoutsi E, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1356
   OpenAI, 2022, CHATGPT OPT LANG MOD
   Oppy G., 2021, The Stanford encyclopedia of philosophy
   Ouyang F, 2022, EDUC INF TECHNOL, V27, P7893, DOI 10.1007/s10639-022-10925-9
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Pham YK, 2022, PSYCHOL SCHOOLS, V59, P413, DOI 10.1002/pits.22617
   Popenici Stefan A D, 2017, Res Pract Technol Enhanc Learn, V12, P22, DOI 10.1186/s41039-017-0062-8
   Pressey SL, 1950, J PSYCHOL, V29, P417, DOI 10.1080/00223980.1950.9916043
   Price WN, 2019, NAT MED, V25, P37, DOI 10.1038/s41591-018-0272-7
   Satariano A., 2023, NY TIMES
   Selwyn N, 2022, EUR J EDUC, V57, P620, DOI 10.1111/ejed.12532
   Senge P. M., 1990, 5 DISCIPLINE ART PRA
   Shepard L., 2000, EDUC RESEARCHER, V29, P4, DOI [DOI 10.3102/0013189X029007004, https://doi.org/10.3102/0013189X029007004]
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   SURDEN Harry., 2019, Georgia State University Law Review, V35
   Swiecki Z, 2022, Computers and Education: Artificial Intelligence, V3, DOI DOI 10.1016/J.CAEAI.2022.100075
   Terry O. K., 2023, CHRONICLE HIGHER ED
   The National Assessment of Educational Progress (NAEP), 2021, TECHNOLOGY BASED ASS
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   U.S. DOE Office of Educational Technology, 2023, ARTIF INTELL
   U.S. DOE Office of Educational Technology, 2017, REIMAGINING ROLE TEC
   von Davier M., 2019, ARXIV, DOI DOI 10.48550/ARXIV.1908.08594
   Waller C., 2023, STATE GLOBAL DIGITAL
   Wang P., 2020, J ARTIFICIAL GEN INT, V11, P73, DOI DOI 10.1016/J.CAEAI.2022.100061
   Wiggins G., 1998, Educative assessment. Designing assessments to inform and improve student performance, V1
   Xu WQ, 2022, EDUC INF TECHNOL, V27, P4195, DOI 10.1007/s10639-021-10774-y
   Yu E., 2023, J ED ONLINE, V20, P235, DOI [10.9743/jeo.2023.20.1.16, DOI 10.9743/JEO.2023.20.1.16]
   Zhou H., 2023, SCHOLARLY KITCHEN
NR 64
TC 28
Z9 28
U1 63
U2 178
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD JAN
PY 2024
VL 68
IS 1
SI SI
BP 58
EP 66
DI 10.1007/s11528-023-00911-4
EA NOV 2023
PG 9
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA EH5R3
UT WOS:001100085100001
DA 2024-12-25
ER

PT J
AU Lannelongue, L
AF Lannelongue, Loic
TI Modeling the increase of electronic waste due to generative AI
SO NATURE COMPUTATIONAL SCIENCE
LA English
DT Article
AB A recent study has modeled and quantified the expected rise in electronic waste due to the increasing deployment of generative artificial intelligence.
C1 [Lannelongue, Loic] Univ Cambridge, Dept Publ Hlth & Primary Care, Cambridge Baker Syst Genom Initiat, Cambridge, England.
   [Lannelongue, Loic] Univ Cambridge, British Heart Fdn Cardiovasc Epidemiol Unit, Dept Publ Hlth & Primary Care, Cambridge, England.
   [Lannelongue, Loic] Univ Cambridge, Victor Phillip Dahdaleh Heart & Lung Res Inst, Cambridge, England.
   [Lannelongue, Loic] Wellcome Genome Campus, Hlth Data Res UK Cambridge, Cambridge, England.
   [Lannelongue, Loic] Univ Cambridge, Cambridge, England.
C3 University of Cambridge; University of Cambridge; University of
   Cambridge; University of Cambridge
RP Lannelongue, L (corresponding author), Univ Cambridge, Dept Publ Hlth & Primary Care, Cambridge Baker Syst Genom Initiat, Cambridge, England.; Lannelongue, L (corresponding author), Univ Cambridge, British Heart Fdn Cardiovasc Epidemiol Unit, Dept Publ Hlth & Primary Care, Cambridge, England.; Lannelongue, L (corresponding author), Univ Cambridge, Victor Phillip Dahdaleh Heart & Lung Res Inst, Cambridge, England.; Lannelongue, L (corresponding author), Wellcome Genome Campus, Hlth Data Res UK Cambridge, Cambridge, England.; Lannelongue, L (corresponding author), Univ Cambridge, Cambridge, England.
EM LL582@medschl.cam.ac.uk
CR Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Children and Digital Dumpsites, 2021, E WASTE EXPOSURE CHI
   Ficher M, 2024, INT J LIFE CYCLE ASS, DOI 10.1007/s11367-024-02367-x
   Hodgson C., 2024, FINANCIAL TIMES 0515
   Lannelongue L, 2023, NAT COMPUT SCI, V3, P514, DOI 10.1038/s43588-023-00461-y
   Rahman-Jones I., 2024, BBC NEWS
   Strubell E, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3645
   Wang P, 2024, NAT COMPUT SCI, V4, P818, DOI 10.1038/s43588-024-00712-6
NR 8
TC 0
Z9 0
U1 2
U2 2
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2662-8457
J9 NAT COMPUT SCI
JI Nat. Comput. Sci.
PD NOV
PY 2024
VL 4
IS 11
BP 805
EP 806
DI 10.1038/s43588-024-00726-0
EA NOV 2024
PG 2
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods; Multidisciplinary Sciences
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Science & Technology - Other Topics
GA M8E6G
UT WOS:001350163600003
PM 39516374
DA 2024-12-25
ER

PT J
AU Duckett, J
   Westrick, NM
AF Duckett, Jana
   Westrick, Nicole M.
TI Exploring the use, adoption, and ethics of generative artificial
   intelligence in the public relations and communication professions
SO COMMUNICATION TEACHER
LA English
DT Article; Early Access
AB The integration of generative artificial intelligence (genAI) into public relations and communication education presents new challenges and ethical considerations for educators. This paper presents a series of activities to explore implications of genAI adoption in public relations and communication professions, focusing on the need to equip future professionals with the knowledge and skills to navigate genAI technologies effectively and ethically. This unit can be integrated throughout the semester or used as individual class assignments. Recommended courses: Strategic Communication: Theory and Practice, Media Literacy. Courses: Introductory courses in Strategic Communication, Public Relations, and Journalism; may also be adapted for Media Literacy and general education communication courses. Objectives: Students will be able to recognize and apply appropriate citations for genAI-related academic and professional work, evaluate the societal and cultural impacts of genAI, utilize genAI tools for graphic content creation, analyze professional ethics related to the use of genAI, and devise ethical genAI usage recommendations for practitioners.
C1 [Duckett, Jana; Westrick, Nicole M.] Morgan State Univ, Sch Global Journalism & Commun, Strateg Commun, Baltimore, MD 21251 USA.
C3 Morgan State University
RP Duckett, J (corresponding author), Morgan State Univ, Sch Global Journalism & Commun, Strateg Commun, Baltimore, MD 21251 USA.
EM jana.duckett@morgan.edu
OI Westrick, Nicole/0000-0002-4378-8390; Duckett, Jana/0000-0003-3159-6145
CR Fitzpatrick D., 2023, The AI classroom: The ultimate guide to artificial intelligence in education
   Galloway C, 2018, PUBLIC RELAT REV, V44, P734, DOI 10.1016/j.pubrev.2018.10.008
   George B, 2023, ADM SCI, V13, DOI 10.3390/admsci13090196
   Kothari A., 2020, Media Practice and Education, V21, P212, DOI [https://doi.org/10.1080/25741136.2020.1787724, DOI 10.1080/25741136.2020.1787724]
   Lee JD, 2004, HUM FACTORS, V46, P50, DOI 10.1518/hfes.46.1.50.30392
   Lee KF, 2018, AI SUPERPOWERS CHINA
   Luttrell R., 2020, Journalism  Mass Communication Educator, V75, P470, DOI DOI 10.1177/1077695820925286
   midjourney, Midjourney Documentation and User Guide
   Opdahl AL, 2023, DATA KNOWL ENG, V146, DOI 10.1016/j.datak.2023.102182
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Public Relations Society of America, 1989, Code of ethics for public relations society of America
   Public Relations Society of America, 2023, Promise and pitfalls: The ethical use of AI for public relations practitioners. Guidance from the PRSA board of ethics and professional standards
   Zerfass A, 2020, J COMMUN MANAG, V24, P377, DOI 10.1108/JCOM-10-2019-0137
NR 13
TC 1
Z9 1
U1 7
U2 7
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1740-4622
EI 1740-4630
J9 COMMUN TEACH
JI Commun. Teach.
PD 2024 SEP 25
PY 2024
DI 10.1080/17404622.2024.2395312
EA SEP 2024
PG 9
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA G9O1A
UT WOS:001319836100001
DA 2024-12-25
ER

PT J
AU Melnyk, O
   Ismail, A
   Ghorashi, NS
   Heekin, M
   Javan, R
AF Melnyk, Oleksiy
   Ismail, Ahmed
   Ghorashi, Nima S.
   Heekin, Mary
   Javan, Ramin
TI Generative Artificial Intelligence Terminology: A Primer for Clinicians
   and Medical Researchers
SO CUREUS JOURNAL OF MEDICAL SCIENCE
LA English
DT Article
DE generative artificial intelligence; convoluted neural network; deep
   learning (dl); large multimodal model; large language model; natural
   language processing; artificial intelligence; chatgpt; gpt-4
ID RADIOLOGY
AB Generative artificial intelligence (AI) is rapidly transforming the medical field, as advanced tools powered by large language models (LLMs) make their way into clinical practice, research, and education. Chatbots, which can generate human-like responses, have gained attention for their potential applications. Therefore, familiarity with LLMs and other promising generative AI tools is crucial to harness their potential safely and effectively. As these AI-based technologies continue to evolve, medical professionals must develop a strong understanding of AI terminologies and concepts, particularly generative AI, to effectively tackle real-world challenges and create solutions. This knowledge will enable healthcare professionals to utilize AI-driven innovations for improved patient care and increased productivity in the future. In this brief technical report, we explore 20 of the most relevant terminology associated with the underlying technology behind LLMs and generative AI as they relate to the medical field and provide some examples of how these topics relate to healthcare applications to help in their understanding.
C1 [Melnyk, Oleksiy; Ismail, Ahmed; Ghorashi, Nima S.; Heekin, Mary; Javan, Ramin] George Washington Univ, Sch Med & Hlth Sci, Dept Radiol, Washington, DC 20052 USA.
C3 George Washington University
RP Javan, R (corresponding author), George Washington Univ, Sch Med & Hlth Sci, Dept Radiol, Washington, DC 20052 USA.
EM rjavan@mfa.gwu.edu
RI Javan, Ramin/AAN-8503-2020
OI Ghorashi, Nima/0000-0001-8485-7121
CR [Anonymous], 2023, All you need to know about ChatGPT, the A.I. chatbot that's got the world talking and tech giants clashing
   [Anonymous], 2023, GPT-4 Technical Report
   [Anonymous], 2023, Understanding reinforcement learning from human feedback (RLHF): part 1
   Bashath S, 2022, INFORM SCIENCES, V585, P498, DOI 10.1016/j.ins.2021.11.061
   Biesialska M, 2021, J INTELL INF SYST, V57, P601, DOI 10.1007/s10844-021-00664-7
   Chen PH, 2020, ACAD RADIOL, V27, P6, DOI 10.1016/j.acra.2019.08.010
   Cheng DN, 2021, IEEE T PARALL DISTR, V32, P1702, DOI 10.1109/TPDS.2020.3048836
   Erickson BJ, 2018, J AM COLL RADIOL, V15, P521, DOI 10.1016/j.jacr.2017.12.027
   Fei NY, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-30761-2
   Giger ML, 2018, J AM COLL RADIOL, V15, P512, DOI 10.1016/j.jacr.2017.12.028
   Han J, 2012, MOR KAUF D, P1
   Henighan T, 2020, Arxiv, DOI [arXiv:2010.14701, 10.48550/arXiv.2010.14701]
   Lakhani P, 2018, J AM COLL RADIOL, V15, P350, DOI 10.1016/j.jacr.2017.09.044
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   RUSSELL SJ, 2016, ARTIFICIAL INTELLIGE
   Salehinejad H, 2018, Arxiv, DOI arXiv:1801.01078
   Sarker Iqbal H, 2021, SN Comput Sci, V2, P160, DOI 10.1007/s42979-021-00592-x
   Sorin V, 2020, J AM COLL RADIOL, V17, P639, DOI 10.1016/j.jacr.2019.12.026
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
NR 20
TC 2
Z9 2
U1 7
U2 28
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2168-8184
J9 CUREUS J MED SCIENCE
JI Cureus J Med Sci
PD DEC 4
PY 2023
VL 15
IS 12
AR e49890
DI 10.7759/cureus.49890
PG 8
WC Medicine, General & Internal
WE Emerging Sources Citation Index (ESCI)
SC General & Internal Medicine
GA CB1M2
UT WOS:001122699000011
PM 38174178
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Zyda, M
AF Zyda, Michael
TI Generative AI Changes the World, Maybe
SO COMPUTER
LA English
DT Article
AB Generative artificial intelligence is starting a new industrial revolution that is global in scope. The global desire for that technology maybe has started Cold War 2.0.
C1 [Zyda, Michael] Univ Southern Calif, Comp Sci Games Program, Los Angeles, CA 90089 USA.
   [Zyda, Michael] Univ Southern Calif, Dept Comp Sci, Engn Practice, Los Angeles, CA 90089 USA.
C3 University of Southern California; University of Southern California
RP Zyda, M (corresponding author), Univ Southern Calif, Comp Sci Games Program, Los Angeles, CA 90089 USA.; Zyda, M (corresponding author), Univ Southern Calif, Dept Comp Sci, Engn Practice, Los Angeles, CA 90089 USA.
EM zyda@mikezyda.com
RI Zyda, Mike/LFU-7145-2024
OI Zyda, Mike/0000-0002-7154-9231
CR [Anonymous], What is a cold war?
   [Anonymous], 2024, Learn ANY Language Easily with These ChatGPT Prompts
   [Anonymous], The definition of industrial revolution
   Naomi O., 2014, Science and Technology in the Global Cold War
   Sweet J., 2024, Generative AI: Steam engine of the fourth industrial revolution?
   Zyda M, 2023, COMPUTER, V56, P145, DOI 10.1109/MC.2023.3247239
NR 6
TC 1
Z9 1
U1 10
U2 10
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD SEP
PY 2024
VL 57
IS 9
BP 118
EP 123
DI 10.1109/MC.2024.3416231
PG 6
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA E8Z2P
UT WOS:001305820000013
DA 2024-12-25
ER

PT J
AU Melnyk, M
   Malynoshevska, A
   Androsovych, K
AF Melnyk, Maryna
   Malynoshevska, Alona
   Androsovych, Kseniia
TI GENERATIVE ARTIFICIAL INTELLIGENCE IN PSYCHOLOGY: IMPLICATIONS AND
   RECOMMENDATIONS FOR SCIENCE AND PRACTICE
SO INFORMATION TECHNOLOGIES AND LEARNING TOOLS
LA English
DT Article
DE generative artificial intelligence; psychology; psychodiagnostics;
   psychotherapy
ID AI
AB Generative artificial intelligence (AI) is becoming increasingly prevalent across various fields, particularly in psychology, where it has the potential to significantly transform approaches to diagnosis, therapy, and research. This paper summarizes current research on the use of generative AI in psychology and its impact on the theory and practice of psychological science. One of the primary applications of generative AI is in psychodiagnostics, where it can be used to automate the creation of diagnostic tools and interpret test results, analyze large volumes of data, and provide more accurate diagnostic conclusions. This significantly reduces the workload on psychologists while simultaneously increasing the efficiency of diagnostic processes. In the field of psychotherapy, generative AI can be used to create individualized therapeutic programs that provide continuous support to users, which is particularly important when access to qualified specialists is limited. Another important aspect is the use of generative AI in psychological research: AI can help in creating behavior models, predicting mental disorders, developing new research methodologies, reducing routine administrative burdens, and more. While generative AI revolutionizes the work of psychologists, it simultaneously creates complex issues related to ethics, confidentiality, accuracy of diagnostic and therapeutic methods, and more. To ensure that generative AI is effective and ethical, clear standards and regulatory frameworks must be developed for its use. Therefore, the authors propose recommendations for the implementation of AI in psychological practice, emphasizing the need to develop specific guidelines to address these issues. The role of psychologists in ensuring the ethical use of AI, the necessity of continuous monitoring, and the assessment of its impact on users are also discussed. Overall, generative AI holds great potential for psychological research and practice, but its implementation requires careful planning and consideration of ethical aspects to ensure safety and effectiveness.
C1 [Melnyk, Maryna] NAES Ukraine, Inst Gifted Child, Giftedness Diag Dept, Kyiv, Ukraine.
   [Malynoshevska, Alona] NAES Ukraine, Inst Gifted Child, Kyiv, Ukraine.
   [Androsovych, Kseniia] Academician Yuriy Bugay Int Sci & Tech Univ, Higher Educ Inst, Dept Psychol, Kyiv, Ukraine.
RP Melnyk, M (corresponding author), NAES Ukraine, Inst Gifted Child, Giftedness Diag Dept, Kyiv, Ukraine.
EM maryna.melnyk@ukr.net; jakovyna@ukr.net; ksn@ukr.net
CR Abrams Z., 2023, Monitor on Psychology, V54
   Agbavor Felix, 2022, PLOS Digit Health, V1, pe0000168, DOI 10.1371/journal.pdig.0000168
   Aher G, 2023, PR MACH LEARN RES, V202, P337
   Andersson G, 2019, CAN J PSYCHIAT, V64, P465, DOI 10.1177/0706743719839381
   [Anonymous], 2021, Views about AI's impact on society in the next 20 years
   [Anonymous], 2024, APA Journals
   Bigman YE, 2023, J EXP PSYCHOL GEN, V152, P4, DOI 10.1037/xge0001250
   Bird T, 2018, BEHAV COGN PSYCHOTH, V46, P570, DOI 10.1017/S1352465817000820
   Cho CH, 2020, JMIR MENT HEALTH, V7, DOI 10.2196/21283
   Demetriou EA, 2020, FRONT PSYCHIATRY, V11, DOI 10.3389/fpsyt.2020.00545
   Dillion D, 2023, TRENDS COGN SCI, V27, P597, DOI 10.1016/j.tics.2023.04.008
   European Union Agency for Cybersecurity, Cybersecurity Threats Fast-Forward 2030: Fasten your Security-Belt Before the Ride!
   Farella Mariella, 2023, Higher Education Learning Methodologies and Technologies Online: 4th International Conference, HELMeTO 2022, Revised Selected Papers. Communications in Computer and Information Science (1779), P760, DOI 10.1007/978-3-031-29800-4_57
   Fast E, 2017, AAAI CONF ARTIF INTE, P963
   Ganjavi C, 2024, BMJ-BRIT MED J, V384, DOI 10.1136/bmj-2023-077192
   GARFIELD E, 1987, CURR CONTENTS, P3
   Ghassemi M, 2023, NATURE, V624, P39, DOI 10.1038/d41586-023-03798-6
   Gierl MJ, 2018, APPL PSYCH MEAS, V42, P42, DOI 10.1177/0146621617726788
   Glomann L, 2019, ADV INTELL SYST, V787, P264, DOI 10.1007/978-3-319-94229-2_25
   Green EP, 2020, JMIR FORM RES, V4, DOI 10.2196/17895
   Gual-Montolio P, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19137737
   Hall Russell P 3rd, 2024, JID Innov, V4, P100256, DOI 10.1016/j.xjidi.2024.100256
   Horn RL, 2020, ADM POLICY MENT HLTH, V47, P852, DOI 10.1007/s10488-020-01056-9
   Inkster B, 2018, JMIR MHEALTH UHEALTH, V6, DOI 10.2196/12106
   Kelly S, 2023, ERGONOMICS, V66, P1782, DOI 10.1080/00140139.2023.2289864
   Kuzminska O, 2024, INF TECHNOL LEARN TO, V100, P92, DOI 10.33407/itlt.v100i2.5602
   Li J, 2019, TOURISM MANAGE, V73, P172, DOI 10.1016/j.tourman.2019.02.006
   Liu H, 2022, INTERNET INTERV, V27, DOI 10.1016/j.invent.2022.100495
   Luan H, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.580820
   Májovsky M, 2023, J MED INTERNET RES, V25, DOI 10.2196/46924
   Mentis AFA, 2023, MOL PSYCHIATR, DOI 10.1038/s41380-023-02047-6
   Morales S, 2017, FRONT PSYCHIATRY, V8, DOI 10.3389/fpsyt.2017.00007
   Pinchuk O, 2024, INF TECHNOL LEARN TO, V100, P180, DOI 10.33407/itlt.v100i2.5676
   Schramowski P, 2022, NAT MACH INTELL, V4, P258, DOI 10.1038/s42256-022-00458-8
   Shahzad MF, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e21818
   Stower R, 2024, COMPUT HUM BEHAV, V157, DOI 10.1016/j.chb.2024.108229
   Van Noorden R, 2023, NATURE, V621, P672, DOI 10.1038/d41586-023-02980-0
   Walker L., 2024, The Brussels Times
NR 38
TC 0
Z9 0
U1 4
U2 4
PU NATL ACAD EDUCATIONAL SCIENCES UKRAINE, INST DIGITALISATION EDUCATION
PI KYIV
PA VUL M BERLYNSKOHO 9, KYIV, 04060, UKRAINE
SN 2076-8184
J9 INF TECHNOL LEARN TO
JI Inf. Technol. Learn. Tools
PY 2024
VL 103
IS 5
BP 188
EP 206
DI 10.33407/itlt.v103i5.5748
PG 19
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA N3D6H
UT WOS:001363187800012
OA gold
DA 2024-12-25
ER

PT J
AU Kong, SC
   Yang, Y
AF Kong, Siu-Cheung
   Yang, Yin
TI A Human-Centered Learning and Teaching Framework Using Generative
   Artificial Intelligence for Self-Regulated Learning Development Through
   Domain Knowledge Learning in K-12 Settings
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Generative AI; Education; Artificial intelligence; Learning (artificial
   intelligence); Guidelines; Task analysis; Transformers; Generative
   artificial intelligence; human-centered; learning and teaching
   framework; pedagogical design; self-regulated learning (SRL)
ID AI
AB The advent of generative artificial intelligence (AI) has ignited an increase in discussions about generative AI tools in education. In this study, a human-centered learning and teaching framework that uses generative AI tools for self-regulated learning development through domain knowledge learning was proposed to catalyze changes in educational practices. The framework illustrates how generative AI tools can revolutionize educational practices and transform the processes of teaching and learning to become human-centered. It emphasizes the evolving roles of teachers, who increasingly become skillful facilitators and humanistic storytellers who craft differentiated instructions and attempt to develop students' individualized learning. Drawing upon insights from neuroscience, the framework guides students to employ generative AI tools to augment their attentiveness, stimulate active engagement in learning, receive immediate feedback, and encourage self-reflection. The pedagogical approach is also reimagined; teachers equipped with generative AI tools and AI literacy can refine their teaching strategies to better equip students to meet future challenges. The practical application of the framework is demonstrated in a case study involving the development of Chinese language writing ability among primary students within a K-12 educational context. This article also reports the results of a 60-h development programme for teachers. Specifically, providing in-service teachers with cases involving uses of the proposed framework helped them to better understand the generative AI concepts and integrate them into their teaching and learning and increased their perceived ability to design AI-integrated courses that would enhance students' attention, engagement, confidence, and satisfaction.
C1 [Kong, Siu-Cheung; Yang, Yin] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong 999077, Peoples R China.
   [Kong, Siu-Cheung] Educ Univ Hong Kong, Artificial Intelligence & Digital Competency Educ, Hong Kong 999077, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK)
RP Kong, SC (corresponding author), Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong 999077, Peoples R China.
EM sckong@eduhk.hk; yyin@eduhk.hk
RI YANG, YIN/IUN-3736-2023
OI Kong, Siu-Cheung/0000-0002-8691-3016; YANG, YIN/0000-0002-9966-248X
FU Research Grants Council, University Grants Committee of the Hong Kong
   Special Administrative Region, China
FX No Statement Available
CR Abdullah Z., 2023, INNOV TEACHING LEARN, V7, P84
   Australian Government Department of Education, 2023, Australian framework for generative artificial intelligence in schools
   Bower M, 2024, EDUC INF TECHNOL, V29, P15403, DOI 10.1007/s10639-023-12405-0
   Bozkurt A., 2023, ASIAN J DISTANCE ED, V18, pi, DOI [DOI 10.5281/ZENODO.8174941, 10.4018/979-8-3693-1351-0]
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Chiu TKF, 2022, IEEE T EDUC, V65, P30, DOI 10.1109/TE.2021.3085878
   Chiu TKF, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12145568
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Diwan C., 2023, Computers and Education: Artificial Intelligence, V4, P100, DOI DOI 10.1016/J.CAEAI.2022.100110
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Hadi Mogavi Reza, 2024, Computers in Human Behavior: Artificial Humans, V2, DOI 10.1016/j.chbah.2023.100027
   Hirankerd Kongkiat, 2020, International Journal of Information and Education Technology, V10, P259, DOI 10.18178/ijiet.2020.10.4.1373
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Keller JM., 1987, J INSTRUCTIONAL DEV, V10, P2, DOI 10.1007/BF02905780
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kong SC, 2023, 31ST INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION, ICCE 2023, VOL II, P394
   Kong SC, 2024, RES PRACT TECH ENHAN, V19, DOI 10.58459/rptel.2024.19030
   Kong SC, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104707
   Kong SC, 2020, COMPUT EDUC, V151, DOI 10.1016/j.compedu.2020.103872
   Larsen-Freeman D, 2006, APPL LINGUIST, V27, P590, DOI 10.1093/applin/aml029
   Li K, 2018, COMPUT EDUC, V122, P54, DOI 10.1016/j.compedu.2018.03.019
   Liu XX, 2021, THINK SKILLS CREAT, V39, DOI 10.1016/j.tsc.2020.100752
   Lu JJ, 2024, IEEE T LEARN TECHNOL, V17, P1279, DOI 10.1109/TLT.2024.3369690
   Luckin R., 2022, COMPUTERS ED ARTIFIC, V3, DOI [DOI 10.1016/J.CAEAI.2022.100076, 10.1016/J.CAEAI.2022.100076]
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Molenaar I., 2022, Computers and Education: Artificial Intelligence, V3, DOI DOI 10.1016/J.CAEAI.2022.100070
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Rowland DR, 2023, J ACAD LANG LEARN, V17, pT31
   Schaper MM, 2024, INT J TECHNOL DES ED, V34, P1187, DOI 10.1007/s10798-023-09857-3
   Sharma B., 2016, ASIAN PACIFIC J HLTH, V3, P271, DOI [10.21276/apjhs.2016.3.4.43, DOI 10.21276/APJHS.2016.3.4.43]
   Shulman LS, 2019, PROFESORADO, V23, P269, DOI 10.30827/profesorado.v23i3.11230
   Shyr WJ, 2018, J COMPUT ASSIST LEAR, V34, P53, DOI 10.1111/jcal.12213
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Sulla F, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1208012
   Sun T, 2020, SYSTEM, V90, DOI 10.1016/j.system.2020.102221
   Teke H., 2023, Int. J. Educ. Lit. Stud., V11, P13, DOI [10.7575/aiac.ijels.v.11n.2p.13, DOI 10.7575/AIAC.IJELS.V.11N.2P.13]
   Tossell CC, 2024, IEEE T LEARN TECHNOL, V17, P1069, DOI 10.1109/TLT.2024.3355015
   Tseng JJ, 2022, COMPUT ASSIST LANG L, V35, P948, DOI 10.1080/09588221.2020.1868531
   Wang M, 2024, IEEE T LEARN TECHNOL, V17, P629, DOI 10.1109/TLT.2023.3324714
   Wu TT, 2024, J EDUC COMPUT RES, V61, P3, DOI 10.1177/07356331231191125
   Xu MR, 2024, IEEE COMMUN SURV TUT, V26, P1127, DOI 10.1109/COMST.2024.3353265
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   ZAFARI M, 2022, IEEE ACCESS, V10, P61905, DOI 10.1109/ACCESS.2022.3179356
   Zimmerman BJ, 2002, THEOR PRACT, V41, P64, DOI 10.1207/s15430421tip4102_2
   Zou B, 2023, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2023.2278608
NR 50
TC 6
Z9 6
U1 247
U2 302
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PY 2024
VL 17
BP 1588
EP 1599
DI 10.1109/TLT.2024.3392830
PG 12
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA PT4J0
UT WOS:001216318700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Cooper, G
   Tang, KS
AF Cooper, Grant
   Tang, Kok-Sing
TI Pixels and Pedagogy: Examining Science Education Imagery by Generative
   Artificial Intelligence
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article
DE Generative artificial intelligence and science education; DALL-E 3;
   ChatGPT; GPT4; Digital technologies; Cultural capital
AB The proliferation of generative artificial intelligence (GenAI) means we are witnessing transformative change in education. While GenAI offers exciting possibilities for personalised learning and innovative teaching methodologies, its potential for reinforcing biases and perpetuating stereotypes poses ethical and pedagogical concerns. This article aims to critically examine the images produced by the integration of DALL-E 3 and ChatGPT, focusing on representations of science classrooms and educators. Applying a capital lens, we analyse how these images portray forms of culture (embodied, objectified and institutionalised) and explore if these depictions align with, or contest, stereotypical representations of science education. The science classroom imagery showcased a variety of settings, from what the GenAI described as vintage to contemporary. Our findings reveal the presence of stereotypical elements associated with science educators, including white-lab coats, goggles and beakers. While the images often align with stereotypical views, they also introduce elements of diversity. This article highlights the importance for ongoing vigilance about issues of equity, representation, bias and transparency in GenAI artefacts. This study contributes to broader discourses about the impact of GenAI in reinforcing or dismantling stereotypes associated with science education.
C1 [Cooper, Grant; Tang, Kok-Sing] Curtin Univ, Kent St, Bentley 6102, Australia.
C3 Curtin University
RP Cooper, G (corresponding author), Curtin Univ, Kent St, Bentley 6102, Australia.
EM grant.cooper@curtin.edu.au
FU Curtin University
FX No Statement Available
CR Abbott R., 2022, FLA LAW REV
   Aikenhead G., 1996, STUD SCI EDUC, V27, P1
   Aikenhead G.S., 2006, SCI ED EVERYDAY LIFE
   Archambault L, 2013, J RES TECHNOL EDUC, V46, P1, DOI 10.1080/15391523.2013.10782611
   Archer L, 2015, J RES SCI TEACH, V52, P922, DOI 10.1002/tea.21227
   Bodzin A. M., 2001, Science and Children, V38, P36
   Bourdieu P., 1973, POWER IDEOLOGY ED, P71, DOI [DOI 10.4324/9781351018142-3, DOI 10.4324/9781351018142]
   Bourdieu P., 2010, DISTINCTION SOCIAL C
   Chan Colleen, 2023, arXiv
   Cooper G., 2024, IN PRESS
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cooper G, 2020, RES SCI EDUC, V50, P361, DOI 10.1007/s11165-018-9692-0
   Cooper G, 2020, INT J SCI EDUC, V42, P151, DOI 10.1080/09500693.2019.1708510
   Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
   DeWitt J, 2016, INT J SCI EDUC, V38, P2431, DOI 10.1080/09500693.2016.1248520
   Dobrin Sidney., 2023, Talking about generative AI: A guide for instructors
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ferguson SL, 2020, SCHOOL SCI MATH, V120, P55, DOI 10.1111/ssm.12382
   Fullarton S., 2003, Patterns of participation in Year 12 (Longitudinal Surveys of Australian Youth Research Report No.33)
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   Gorard S, 2009, STUD SCI EDUC, V45, P93, DOI 10.1080/03057260802681821
   Gormally C, 2021, J MICROBIOL BIOL EDU, V22, DOI 10.1128/jmbe.v22i1.2273
   Gough A, 2011, AUST J ENVIRON EDUC, V27, P9, DOI 10.1017/S0814062600000045
   Harding S., 1993, The racial economy of science: Toward a democratic future, P1
   Leavy A, 2023, INT J SCI EDUC, DOI 10.1080/09500693.2023.2193302
   Mello R. F., 2023, ARXIV
   OpenAI, 2023, EDUCATOR FAQ
   Page J, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SAFETY FOR ROBOTICS (ISR), P1, DOI 10.1109/IISR.2018.8535903
   Pei Wang, 2019, Journal of Artificial General Intelligence, V10, P1, DOI 10.2478/jagi-2019-0002
   Perera P., 2023, JOURNEY EFFECTIVE PO
   Schlegel D., 2021, PACIS p, P44
   Schubhendu S., 2013, Applicability of Artificial Intelligence in Different Fields of Life
   Tranter D., 2003, REFEREED PAPER PRESE
   Zhai X, 2023, COMPLEX INTELL SYST, V9, P2865, DOI 10.1007/s40747-021-00617-1
NR 34
TC 7
Z9 7
U1 68
U2 110
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD AUG
PY 2024
VL 33
IS 4
BP 556
EP 568
DI 10.1007/s10956-024-10104-0
EA MAR 2024
PG 13
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA XK5L5
UT WOS:001183200200001
OA Green Published, hybrid
DA 2024-12-25
ER

PT J
AU Huang, ZL
   Zhang, XD
   Tang, ZQ
   Xu, F
   Datcu, M
   Han, JW
AF Huang, Zhongling
   Zhang, Xidan
   Tang, Zuqian
   Xu, Feng
   Datcu, Mihai
   Han, Junwei
TI Generative Artificial Intelligence Meets Synthetic Aperture Radar: A
   survey
SO IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
LA English
DT Article; Early Access
ID IMAGE QUALITY ASSESSMENT; POLARIMETRIC SAR; NEURAL-NETWORK; SHIP
   DETECTION; SIMULATION; GAN; TRANSLATION; EXTRACTION; MODEL
AB SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the data. The challenges can be addressed using generative AI technologies. Generative AI, often known as GenAI, is a very advanced and powerful technology in the field of artificial intelligence that has gained significant attention. The advancement has created possibilities for the creation of texts, photorealistic pictures, videos, and material in various modalities. This paper aims to comprehensively investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field and compare them with computer vision tasks, analyzing the similarity, difference, and general challenges of them. Then, an overview of the latest GenAI models is systematically reviewed, including various basic models and their variations targeting the general challenges. Additionally, the corresponding applications in SAR domain are also included. Specifically, we propose to summarize the physical model based simulation approaches for SAR, and analyze the hybrid modeling methods that combine the GenAI and interpretable models. The evaluation methods that have been or could be applied to SAR, are also explored. Finally, the potential challenges and future prospects are discussed. To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images.
C1 [Huang, Zhongling; Zhang, Xidan; Tang, Zuqian; Han, Junwei] Northwestern Polytech Univ, Sch Automat, Brain & Artificial Intelligence Lab, Xian 710072, Peoples R China.
   [Xu, Feng] NOAA, Ctr Satellite Applicat & Res, College Pk, MD USA.
   [Datcu, Mihai] Natl Sci & Technol Univ POLITEHN Bucharest, RO-060042 Bucharest, Romania.
   [Datcu, Mihai] Swiss Fed Inst Technol, Inst Commun Technol, Swiss Fed Inst Technol, Zurich, Switzerland.
   [Datcu, Mihai] German Aerosp Ctr DLR, Cologne, Germany.
   [Datcu, Mihai] Paris Inst Technol, DLR CNES Chair, ParisTech, Paris, France.
   [Datcu, Mihai] ESA Working Grp Big Data Space, Paris, France.
C3 Northwestern Polytechnical University; National Oceanic Atmospheric
   Admin (NOAA) - USA; Swiss Federal Institutes of Technology Domain; ETH
   Zurich; Helmholtz Association; German Aerospace Centre (DLR)
RP Huang, ZL (corresponding author), Northwestern Polytech Univ, Sch Automat, Brain & Artificial Intelligence Lab, Xian 710072, Peoples R China.
EM huangzhongling@nwpu.edu.cn; zhangxidan@mail.nwpu.edu.cn;
   heutzq@hrbeu.edu.cn; fengxu@fudan.edu.cn; mihai.datcu@upb.ro;
   jhan@nwpu.edu.cn
RI Huang, Zhongling/J-3430-2019
OI Datcu, Mihai/0000-0002-3477-9687
FU National Natural Science Foundation of China [62101459]
FX This work was supported by the National Natural Science Foundation of
   China under Grant 62101459.
CR Abdollahzadeh M., 2023, arXiv
   Acharya S, 2024, J HIGH ENERGY PHYS, DOI 10.1007/JHEP03(2024)092
   Aghababaei H, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3299419
   Alanov A, 2023, IEEE I CONF COMP VIS, P2184, DOI 10.1109/ICCV51070.2023.00208
   [Anonymous], 2016, The Air Force Moving and Stationary Target Recognition Database
   Ao DY, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10101597
   Arshad T, 2024, IEEE GEOSCI REMOTE S, V21, DOI 10.1109/LGRS.2024.3379509
   Asiyabi RM, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3267185
   ASVESTAS JS, 1980, J MATH PHYS, V21, P290, DOI 10.1063/1.524413
   Auer S, 2016, INT GEOSCI REMOTE SE, P6730, DOI 10.1109/IGARSS.2016.7730757
   Auer S, 2010, IEEE T GEOSCI REMOTE, V48, P1445, DOI 10.1109/TGRS.2009.2029339
   Baghirli O, 2023, Arxiv, DOI arXiv:2309.16812
   Bai XY, 2024, IEEE GEOSCI REMOTE S, V21, DOI 10.1109/LGRS.2023.3337143
   Balz T, 2015, ISPRS J PHOTOGRAMM, V101, P102, DOI 10.1016/j.isprsjprs.2014.12.008
   Balz T, 2009, IEEE T GEOSCI REMOTE, V47, P3519, DOI 10.1109/TGRS.2009.2022326
   Betker J., 2023, Comput. Sci, V2, P8
   Borts D, 2024, Arxiv, DOI arXiv:2405.04662
   Cao CJ, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2021.3134674
   Cao CJ, 2020, IEEE T GEOSCI REMOTE, V58, P3495, DOI 10.1109/TGRS.2019.2957453
   Chang YL, 2011, PROG ELECTROMAGN RES, V119, P35, DOI 10.2528/PIER11061507
   Chen JL, 2024, IEEE T GEOSCI REMOTE, V62, DOI 10.1109/TGRS.2024.3411392
   Chen JL, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3323947
   Chen JW, 2018, PROC CVPR IEEE, P3155, DOI 10.1109/CVPR.2018.00333
   Chen X, 2016, ADV NEUR IN, V29
   Chen X, 2021, IEEE T IMAGE PROCESS, V30, P3041, DOI 10.1109/TIP.2021.3055936
   Chen Y, 2018, PROC CVPR IEEE, P9465, DOI 10.1109/CVPR.2018.00986
   Choi H, 2018, IEEE SENS J, V18, P3131, DOI 10.1109/JSEN.2018.2794550
   Cochin C., 2008, P 7 EUR C SYNTH AP R, P1
   Collins MJ, 2009, IEEE T GEOSCI REMOTE, V47, P3530, DOI 10.1109/TGRS.2009.2021260
   Cui KW, 2022, AAAI CONF ARTIF INTE, P499
   Dai QJ, 2023, REMOTE SENS LETT, V14, P620, DOI 10.1080/2150704X.2023.2221795
   Dar SUH, 2020, IEEE J-STSP, V14, P1072, DOI 10.1109/JSTSP.2020.3001737
   Datcu M, 2023, IEEE GEOSC REM SEN M, V11, P8, DOI 10.1109/MGRS.2023.3237465
   Deng JW, 2024, IEEE GEOSCI REMOTE S, V21, DOI 10.1109/LGRS.2024.3381210
   Deng JW, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15174182
   Deng JY, 2024, IEEE SENS J, V24, P11705, DOI 10.1109/JSEN.2024.3360981
   Deng KL, 2022, PROC CVPR IEEE, P12872, DOI 10.1109/CVPR52688.2022.01254
   Deng XP, 2017, REMOTE SENS-BASEL, V9, DOI 10.3390/rs9040348
   Deng XP, 2016, IEEE T GEOSCI REMOTE, V54, P3035, DOI 10.1109/TGRS.2015.2510399
   Dhariwal P, 2021, ADV NEUR IN, V34
   Ding GQ, 2022, PROC CVPR IEEE, P11184, DOI 10.1109/CVPR52688.2022.01091
   Doi K, 2020, INT GEOSCI REMOTE SE, P2069, DOI 10.1109/IGARSS39084.2020.9323085
   Du SY, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2021.3065682
   Ehret Thibaud, 2024, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), P564, DOI 10.1109/CVPRW63382.2024.00061
   Fang TT, 2022, ADV NEUR IN
   Feng J, 2024, IEEE T GEOSCI REMOTE, V62, DOI 10.1109/TGRS.2024.3403868
   Feng ZP, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15051254
   Fracastoro G, 2021, IEEE GEOSC REM SEN M, V9, P29, DOI 10.1109/MGRS.2021.3070956
   Franceschetti G, 2003, IEEE T GEOSCI REMOTE, V41, P1986, DOI 10.1109/TGRS.2003.814626
   FRANCESCHETTI G, 1992, IEEE T GEOSCI REMOTE, V30, P110, DOI 10.1109/36.124221
   Frery AC, 1997, IEEE T GEOSCI REMOTE, V35, P648, DOI 10.1109/36.581981
   Fu MH, 2021, IEEE COMPUT SOC CONF, P203, DOI 10.1109/CVPRW53098.2021.00029
   Fu S., 2021, P INT APPL COMP EL S, P1, DOI [10.23919/ACES-China52398.2021.9581838, DOI 10.23919/ACES-CHINA52398.2021.9581838]
   Fu SL, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3320515
   Fu SL, 2022, IEEE T IMAGE PROCESS, V31, P6679, DOI 10.1109/TIP.2022.3215069
   Fu SL, 2021, SCI CHINA INFORM SCI, V64, DOI 10.1007/s11432-020-3077-5
   Ou FZ, 2021, PROC CVPR IEEE, P7666, DOI 10.1109/CVPR46437.2021.00758
   Gao F, 2018, REMOTE SENS-BASEL, V10, DOI 10.3390/rs10060846
   Gao G, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3336300
   Gao G, 2023, ISPRS J PHOTOGRAMM, V202, P663, DOI 10.1016/j.isprsjprs.2023.07.006
   Gao G, 2023, IEEE GEOSC REM SEN M, V11, P40, DOI 10.1109/MGRS.2023.3274301
   Gao G, 2018, IEEE T GEOSCI REMOTE, V56, P5636, DOI 10.1109/TGRS.2018.2822759
   Gao G, 2017, IEEE T GEOSCI REMOTE, V55, P4811, DOI 10.1109/TGRS.2017.2701813
   Gao G, 2017, IEEE T GEOSCI REMOTE, V55, P1812, DOI 10.1109/TGRS.2016.2634862
   Ge Yixiao, 2020, ICLR
   Gelautz M, 1998, ISPRS J PHOTOGRAMM, V53, P17, DOI 10.1016/S0924-2716(97)00028-2
   Ghozatlou O, 2023, INT GEOSCI REMOTE SE, P4056, DOI 10.1109/IGARSS52108.2023.10283353
   Giry-Fouquet Y., 2022, P EUSAR 2022 14 EUR, P1
   Gleich D, 2009, IEEE T IMAGE PROCESS, V18, P2167, DOI 10.1109/TIP.2009.2023729
   Goodfellow I. J., 2014, arXiv, DOI 10.48550/arXiv.1406.2661
   Greivenkamp J. E., 2004, Field guide to geometrical optics
   Gu F, 2019, INT GEOSCI REMOTE SE, P2575, DOI [10.1109/igarss.2019.8899202, 10.1109/IGARSS.2019.8899202]
   Guha S, 2023, Arxiv, DOI arXiv:2311.10868
   Guo JY, 2017, IEEE GEOSCI REMOTE S, V14, P1111, DOI 10.1109/LGRS.2017.2699196
   Guo J, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13183575
   Guo Q, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3330478
   Guo QL, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2021.3131993
   Guo R, 2023, IEEE SIGNAL PROC MAG, V40, P18, DOI 10.1109/MSP.2022.3198805
   Guo YS, 2022, INT GEOSCI REMOTE SE, P639, DOI 10.1109/IGARSS46834.2022.9884798
   Guo Z, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14153740
   Hammer H., 2009, Proc.SPIE, V7477, P406, DOI 10.1117/12.830380
   Han FZ, 2024, IEEE T GEOSCI REMOTE, V62, DOI 10.1109/TGRS.2024.3360470
   Härkönen E, 2020, ADV NEUR IN, V33
   He C, 2012, IEEE J-STARS, V5, P1272, DOI 10.1109/JSTARS.2012.2189555
   He QS, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2021.3116707
   Hensel M, 2017, ADV NEUR IN, V30
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Ho JAT, 2022, Arxiv, DOI arXiv:2207.12598
   Hong Y, 2022, LECT NOTES COMPUT SC, V13676, P259, DOI 10.1007/978-3-031-19787-1_15
   Hou XY, 2020, SCI CHINA INFORM SCI, V63, DOI 10.1007/s11432-019-2772-5
   Hu XW, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13183554
   Hu XR, 2024, Arxiv, DOI arXiv:2401.03122
   Huang HH, 2019, INT GEOSCI REMOTE SE, P2782, DOI [10.1109/IGARSS.2019.8900494, 10.1109/igarss.2019.8900494]
   Huang HB, 2018, ADV NEUR IN, V31
   Huang M., 2021, arXiv
   Huang SY, 2023, IEEE I CONF COMP VIS, P18190, DOI 10.1109/ICCV51070.2023.01672
   Huang T., 2024, P IEEE CVF C COMP VI, P129, DOI [10.1109/CVPR52733.2024.02277, DOI 10.1109/CVPR52733.2024.02277]
   Huang Y, 2022, INT GEOSCI REMOTE SE, P1576, DOI 10.1109/IGARSS46834.2022.9884284
   Huang ZL, 2024, Arxiv, DOI arXiv:2407.19436
   Huang ZL, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3247898
   Huang ZL, 2022, ISPRS J PHOTOGRAMM, V190, P25, DOI 10.1016/j.isprsjprs.2022.05.008
   [黄钟泠 Huang Zhongling], 2022, [雷达学报, Journal of Radars], V11, P107
   Huang ZL, 2021, IEEE T GEOSCI REMOTE, V59, P3054, DOI 10.1109/TGRS.2020.3014335
   Huang ZL, 2021, IEEE GEOSCI REMOTE S, V18, P107, DOI 10.1109/LGRS.2020.2965558
   Huang ZL, 2020, ISPRS J PHOTOGRAMM, V161, P179, DOI 10.1016/j.isprsjprs.2020.01.016
   Hwang J, 2020, I C INF COMM TECH CO, P191, DOI [10.1109/ictc49870.2020.9289381, 10.1109/ICTC49870.2020.9289381]
   Inkawhich N, 2021, IEEE J-STARS, V14, P2942, DOI 10.1109/JSTARS.2021.3059991
   Gulrajani I, 2017, ADV NEUR IN, V30
   Isola P, 2017, PROC CVPR IEEE, P5967, DOI 10.1109/CVPR.2017.632
   Jain A, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P5865, DOI 10.1109/ICCV48922.2021.00583
   JAKEMAN E, 1980, J PHYS A-MATH GEN, V13, P31, DOI 10.1088/0305-4470/13/1/006
   Jia HC, 2023, INT GEOSCI REMOTE SE, P2057, DOI 10.1109/IGARSS52108.2023.10283368
   Jiang LM, 2021, ADV NEUR IN, V34
   Jing R, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15092217
   Johari MM, 2022, PROC CVPR IEEE, P18344, DOI 10.1109/CVPR52688.2022.01782
   Jozdani S, 2022, INT J APPL EARTH OBS, V108, DOI 10.1016/j.jag.2022.102734
   Ju MR, 2023, IEEE SENS J, V23, P28500, DOI 10.1109/JSEN.2023.3323322
   Ju MR, 2024, SIGNAL IMAGE VIDEO P, V18, P589, DOI 10.1007/s11760-023-02793-8
   Kang JM, 2024, GUID NAVIG CONTROL, V04, DOI 10.1142/S2737480724410012
   Karnewar A, 2020, PROC CVPR IEEE, P7796, DOI 10.1109/CVPR42600.2020.00782
   Karras T, 2020, ADV NEUR IN, V33
   Karras T, 2018, Arxiv, DOI arXiv:1710.10196
   Karras T, 2021, ADV NEUR IN, V34
   Karras T, 2020, PROC CVPR IEEE, P8107, DOI 10.1109/CVPR42600.2020.00813
   Karras T, 2019, PROC CVPR IEEE, P4396, DOI 10.1109/CVPR.2019.00453
   Kasdorf S, 2021, IEEE T ANTENN PROPAG, V69, P4808, DOI 10.1109/TAP.2021.3060051
   KELLER JB, 1962, J OPT SOC AM, V52, P116, DOI 10.1364/JOSA.52.000116
   Khanna S., 2024, P 12 INT C LEARN REP, P1
   Kim M, 2022, PROC CVPR IEEE, P12902, DOI 10.1109/CVPR52688.2022.01257
   Ko J, 2022, IEEE J-STARS, V15, P3, DOI 10.1109/JSTARS.2021.3132027
   Kodali N, 2017, Arxiv, DOI arXiv:1705.07215
   Kong J., 2021, P 13 IEEE INT C ADV, P215
   Kong YY, 2024, REMOTE SENS-BASEL, V16, DOI 10.3390/rs16010050
   Kuang Y, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15245654
   Kuruoglu EE, 2004, IEEE T IMAGE PROCESS, V13, P527, DOI 10.1109/TIP.2003.818017
   Lahiri A, 2020, PROC CVPR IEEE, P13693, DOI 10.1109/CVPR42600.2020.01371
   Laine S, 2017, Arxiv, DOI arXiv:1610.02242
   Landy JC, 2019, IEEE T GEOSCI REMOTE, V57, P4164, DOI 10.1109/TGRS.2018.2889763
   Lattari F, 2023, IEEE IJCNN, DOI 10.1109/IJCNN54540.2023.10191089
   Lee J, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3318980
   Lewis B, 2019, PROC SPIE, V10987, DOI 10.1117/12.2523460
   Li HY, 2022, NEUROCOMPUTING, V479, P47, DOI 10.1016/j.neucom.2022.01.029
   Li HH, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3211415
   Li Q, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14030439
   Li XH, 2021, ISPRS J PHOTOGRAMM, V179, P14, DOI 10.1016/j.isprsjprs.2021.07.007
   Li Y, 2020, IEEE ACCESS, V8, P60338, DOI 10.1109/ACCESS.2020.2977103
   Lingzhi Zhang, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12358), P566, DOI 10.1007/978-3-030-58601-0_34
   Liu A, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3298067
   Liu B., 2020, P INT C LEARN REPR A, P1
   Liu L, 2018, INT GEOSCI REMOTE SE, P4411, DOI 10.1109/IGARSS.2018.8517866
   Liu LQ, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3173532
   Liu R., 2020, P INT JOINT C NEUR N, P1, DOI DOI 10.1109/IJCNN48605.2020.9206847
   Liu SC, 2019, IEEE I CONF COMP VIS, P7707, DOI 10.1109/ICCV.2019.00780
   Liu YX, 2024, Arxiv, DOI [arXiv:2402.17177, 10.48550/arXiv.2402.17177, DOI 10.48550/ARXIV.2402.17177]
   Liu Y, 2022, PROC CVPR IEEE, P7814, DOI 10.1109/CVPR52688.2022.00767
   Liu Z, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3217063
   Luan JW, 2024, IEEE J-STARS, V17, P3211, DOI 10.1109/JSTARS.2024.3351206
   Luo ZM, 2020, INT GEOSCI REMOTE SE, P2459, DOI 10.1109/IGARSS39084.2020.9323439
   Lv JW, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15163920
   Lv XL, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3305094
   Makhzani A, 2016, Arxiv, DOI [arXiv:1511.05644, 10.48550/arXiv.1511.05644, DOI 10.48550/ARXIV.1511.05644]
   Makitalo M., 2010, P INT C MATH METH EL, P1, DOI [10.1109/JSEN.2018.2794550, DOI 10.1109/JSEN.2018.2794550]
   Mao XD, 2017, IEEE I CONF COMP VIS, P2813, DOI 10.1109/ICCV.2017.304
   Mari Roger, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), P2035, DOI 10.1109/CVPRW59228.2023.00197
   Mari R, 2022, IEEE COMPUT SOC CONF, P1310, DOI 10.1109/CVPRW56347.2022.00137
   Marmanis D., 2017, arXiv
   Meng YP, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3222360
   Mezghanni M, 2021, PROC CVPR IEEE, P9326, DOI 10.1109/CVPR46437.2021.00921
   Midjourney, Twitter
   Mildenhall B, 2022, COMMUN ACM, V65, P99, DOI 10.1145/3503250
   Mingyu Yin, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12373), P87, DOI 10.1007/978-3-030-58604-1_6
   Mirza M, 2014, Arxiv, DOI arXiv:1411.1784
   Mittal A, 2013, IEEE SIGNAL PROC LET, V20, P209, DOI 10.1109/LSP.2012.2227726
   Mittal A, 2012, IEEE T IMAGE PROCESS, V21, P4695, DOI 10.1109/TIP.2012.2214050
   Miyato T, 2018, Arxiv, DOI [arXiv:1802.05637, DOI 10.48550/ARXIV.1802.05637]
   Miyato T, 2018, Arxiv, DOI arXiv:1802.05957
   Müller N, 2022, PROC CVPR IEEE, P3961, DOI 10.1109/CVPR52688.2022.00394
   Nazeri Kamyar, 2018, Articulated Motion and Deformable Objects. 10th International Conference, AMDO 2018. Proceedings: LNCS 10945, P85, DOI 10.1007/978-3-319-94544-6_9
   Niemeyer M, 2022, PROC CVPR IEEE, P5470, DOI 10.1109/CVPR52688.2022.00540
   Niu L, 2024, Arxiv, DOI arXiv:2106.14490
   Niu SR, 2021, IEEE J-STARS, V14, P2593, DOI 10.1109/JSTARS.2021.3056920
   Niu SR, 2020, IEEE T GEOSCI REMOTE, V58, P4901, DOI 10.1109/TGRS.2020.2968493
   Niu X, 2021, INT J REMOTE SENS, V42, P4762, DOI 10.1080/01431161.2020.1836426
   Odena A, 2017, PR MACH LEARN RES, V70
   Oh J, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13193939
   Ojha U, 2021, PROC CVPR IEEE, P10738, DOI 10.1109/CVPR46437.2021.01060
   Oliver C., 2004, Understanding Synthetic Aperture Radar Images
   Oyedare T, 2022, IEEE ACCESS, V10, P66238, DOI 10.1109/ACCESS.2022.3185124
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Peng G., 2022, P IEEE INT C SIGN PR, P1, DOI [10.1109/ICSPCC55723.2022.9984374, DOI 10.1109/ICSPCC55723.2022.9984374]
   Perera MV, 2023, IEEE GEOSCI REMOTE S, V20, DOI 10.1109/LGRS.2023.3270799
   Phaphuangwittayakul A, 2022, IEEE T MULTIMEDIA, V24, P2205, DOI 10.1109/TMM.2021.3077729
   Prinster D, 2022, ADV NEUR IN
   Qin JK, 2022, IEEE J-STARS, V15, P7153, DOI 10.1109/JSTARS.2022.3199091
   Radford A., 2018, Technical Reports
   Radford A, 2016, Arxiv, DOI [arXiv:1511.06434, 10.48550/arXiv.1511.06434, DOI 10.48550/ARXIV.1511.06434]
   Rangzan M, 2024, Arxiv, DOI arXiv:2401.00440
   Reed S, 2016, PR MACH LEARN RES, V48
   Research and Technology Organization, 1998, S RTO SYST CONC INT
   Reyes MF, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11172067
   Rizaev IG, 2022, ISPRS J PHOTOGRAMM, V187, P120, DOI 10.1016/j.isprsjprs.2022.02.017
   Roessle B, 2022, PROC CVPR IEEE, P12882, DOI 10.1109/CVPR52688.2022.01255
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Rumelhart D. E., 1985, Technical report, DOI 10.1016/b978-1-4832-1446-7.50035-2
   Sajjadi M, 2016, ADV NEUR IN, V29
   Saxena D, 2023, PROC CVPR IEEE, P16230, DOI 10.1109/CVPR52729.2023.01557
   Schmitt M., 2018, INT ARCH PHOTOGRAMME, V42, P1045, DOI [DOI 10.5194/ISPRS-ARCHIVES-XLII-2-1045-2018, 10.5194/ isprsarchives-XLII-2-1045-2018]
   Schmitt M, 2018, Arxiv, DOI arXiv:1807.01569
   Schmitt M, 2019, Arxiv, DOI [arXiv:1906.07789, DOI 10.5194/ISPRS-ANNALS-IV-2-W7-153-2019]
   Shen HF, 2020, ISPRS J PHOTOGRAMM, V161, P90, DOI 10.1016/j.isprsjprs.2020.01.006
   Shen KQ, 2024, NEURAL NETWORKS, V169, P698, DOI 10.1016/j.neunet.2023.10.058
   Shermeyer J, 2020, IEEE COMPUT SOC CONF, P768, DOI 10.1109/CVPRW50498.2020.00106
   Shi H, 2024, FRONT NEUROSCI-SWITZ, V18, DOI 10.3389/fnins.2024.1352841
   Shi H, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2022.3168391
   Shi Xin, 2022, 2022 3rd China International SAR Symposium (CISS), P1, DOI 10.1109/CISS57580.2022.9971215
   Shi Y, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3185298
   Shoshan A, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P14063, DOI 10.1109/ICCV48922.2021.01382
   Snyder William, 2023, Proceedings of SPIE, DOI 10.1117/12.2666925
   Song Q., 2021, P 34 GEN ASS SCI S I, P1, DOI [10.23919/URSIGASS51995.2021.9560559, DOI 10.23919/URSIGASS51995.2021.9560559]
   Song Q, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2021.3086817
   Song Q, 2019, INT GEOSCI REMOTE SE, P9498, DOI [10.1109/IGARSS.2019.8898922, 10.1109/igarss.2019.8898922]
   Song Q, 2018, IEEE ACCESS, V6, P1647, DOI 10.1109/ACCESS.2017.2779875
   Song YZ, 2023, PROC CVPR IEEE, P18310, DOI 10.1109/CVPR52729.2023.01756
   Sun K, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13142743
   Sun YS, 2023, IEEE J-STARS, V16, P1785, DOI 10.1109/JSTARS.2023.3239633
   Sun YS, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15030708
   Sun YC, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14081793
   Talebi H, 2018, IEEE T IMAGE PROCESS, V27, P3998, DOI 10.1109/TIP.2018.2831899
   Thoppilan R., 2022, arXiv
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Trevithick A, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P15162, DOI 10.1109/ICCV48922.2021.01490
   Truong P, 2023, PROC CVPR IEEE, P4190, DOI 10.1109/CVPR52729.2023.00408
   Tseng HY, 2021, PROC CVPR IEEE, P7917, DOI 10.1109/CVPR46437.2021.00783
   Tuia D, 2024, Arxiv, DOI [arXiv:2305.08413, DOI 10.48550/ARXIV.2305.08413]
   Turnes JN, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2020.3031199
   Vitale S, 2021, IEEE T GEOSCI REMOTE, V59, P9336, DOI 10.1109/TGRS.2020.3034852
   Wang C, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14040931
   Wang CD, 2023, IEEE T GEOSCI REMOTE, V61, DOI 10.1109/TGRS.2023.3279663
   Wang CW, 2022, IEEE J-STARS, V15, P9381, DOI 10.1109/JSTARS.2022.3218369
   Wang HX, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3200996
   Wang HC, 2023, INT GEOSCI REMOTE SE, P7046, DOI 10.1109/IGARSS52108.2023.10282825
   Wang K, 2019, IEEE GEOSCI REMOTE S, V16, P912, DOI 10.1109/LGRS.2018.2884898
   Wang L, 2019, IEEE ACCESS, V7, P129136, DOI 10.1109/ACCESS.2019.2939649
   Wang P, 2018, IEEE RAD CONF, P570, DOI 10.1109/RADAR.2018.8378622
   Wang SY, 2024, ACM COMPUT SURV, V56, DOI 10.1145/3648609
   Wang X, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15205062
   Wang YN, 2024, Arxiv, DOI arXiv:2401.01165
   Wang Y, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14143302
   Wang Z., 2006, Modern image quality assessment
   Wang ZB, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3212208
   Wei J, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15102547
   Wei PC, 2024, ISPRS J PHOTOGRAMM, V208, P296, DOI 10.1016/j.isprsjprs.2024.01.017
   Wei SJ, 2022, INT J APPL EARTH OBS, V114, DOI 10.1016/j.jag.2022.103019
   Woollard M, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14112561
   Wu Y., 2024, P 37 INT C NEUR INF, V36, P57099
   Wu YZ, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P14357, DOI 10.1109/ICCV48922.2021.01411
   Wu YC, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3197409
   Wu ZZ, 2021, PROC CVPR IEEE, P12858, DOI 10.1109/CVPR46437.2021.01267
   Xia WH, 2023, IEEE T PATTERN ANAL, V45, P3121, DOI 10.1109/TPAMI.2022.3181070
   Xiao JY, 2022, PROC CVPR IEEE, P11194, DOI 10.1109/CVPR52688.2022.01092
   Xiao SY, 2023, INT GEOSCI REMOTE SE, P810, DOI 10.1109/IGARSS52108.2023.10282914
   Xie Y, 2022, PROC CVPR IEEE, P9120, DOI 10.1109/CVPR52688.2022.00892
   Xu F, 2006, IEEE T GEOSCI REMOTE, V44, P3219, DOI 10.1109/TGRS.2006.879544
   Xu Y., 2023, CHINESE C PATTERN RE, P418
   Xu YY, 2021, IEEE T IMAGE PROCESS, V30, P6024, DOI 10.1109/TIP.2021.3090658
   Yang CY, 2023, IEEE I CONF COMP VIS, P7699, DOI 10.1109/ICCV51070.2023.00711
   Yang JW, 2023, PROC CVPR IEEE, P8254, DOI 10.1109/CVPR52729.2023.00798
   Yang MP, 2023, Arxiv, DOI arXiv:2307.16879
   Yang MY, 2021, PROC CVPR IEEE, P9588, DOI 10.1109/CVPR46437.2021.00947
   Yang T, 2021, PROC CVPR IEEE, P672, DOI 10.1109/CVPR46437.2021.00073
   Yang X, 2022, IEEE T GEOSCI REMOTE, V60, DOI 10.1109/TGRS.2022.3165371
   Yang X, 2022, PATTERN RECOGN, V121, DOI 10.1016/j.patcog.2021.108208
   Yoo J, 2021, IEEE ACCESS, V9, P27003, DOI 10.1109/ACCESS.2021.3057455
   Yu A, 2021, PROC CVPR IEEE, P4576, DOI 10.1109/CVPR46437.2021.00455
   Yu ZP, 2024, Arxiv, DOI arXiv:2405.13570
   Yu ZY, 2022, INT GEOSCI REMOTE SE, P2626, DOI 10.1109/IGARSS46834.2022.9884006
   Yue DX, 2021, IEEE GEOSC REM SEN M, V9, P115, DOI 10.1109/MGRS.2020.3027609
   Yue DX, 2021, IEEE GEOSC REM SEN M, V9, P82, DOI 10.1109/MGRS.2020.3004508
   Yue DX, 2020, IEEE T GEOSCI REMOTE, V58, P2947, DOI 10.1109/TGRS.2019.2958125
   Zeng ZQ, 2024, IEEE J-STARS, V17, P6290, DOI 10.1109/JSTARS.2024.3370185
   Zhan FN, 2023, IEEE T PATTERN ANAL, V45, P15098, DOI 10.1109/TPAMI.2023.3305243
   Zhang C, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2022.3141389
   Zhang C, 2024, IEEE GEOSC REM SEN M, V12, P12, DOI 10.1109/MGRS.2024.3450681
   Zhang F, 2022, IEEE J-STARS, V15, P901, DOI 10.1109/JSTARS.2021.3138781
   Zhang H, 2020, Arxiv, DOI arXiv:1910.12027
   Zhang HQ, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15133322
   Zhang JW, 2023, ARTIF INTELL REV, V56, P11905, DOI 10.1007/s10462-023-10469-5
   Zhang JX, 2020, IEEE ACCESS, V8, P70925, DOI 10.1109/ACCESS.2020.2987105
   Zhang LL, 2022, IEEE GEOSC REM SEN M, V10, P115, DOI 10.1109/MGRS.2022.3186904
   Zhang LM, 2023, IEEE I CONF COMP VIS, P3813, DOI 10.1109/ICCV51070.2023.00355
   Zhang ML, 2021, IEEE T MULTIMEDIA, V23, P1938, DOI 10.1109/TMM.2020.3006414
   Zhang MJ, 2024, REMOTE SENS-BASEL, V16, DOI 10.3390/rs16020242
   Zhang Q, 2021, REMOTE SENS-BASEL, V13, DOI 10.3390/rs13010128
   Zhang R, 2018, PROC CVPR IEEE, P586, DOI 10.1109/CVPR.2018.00068
   Zhang SX, 2011, IEEE T GEOSCI REMOTE, V49, P3765, DOI 10.1109/TGRS.2011.2164409
   Zhang WX, 2020, IEEE T CIRC SYST VID, V30, P36, DOI 10.1109/TCSVT.2018.2886771
   Zhang XS, 2022, PROC CVPR IEEE, P5439, DOI 10.1109/CVPR52688.2022.00537
   Zhang X, 2024, ISPRS J PHOTOGRAMM, V207, P203, DOI 10.1016/j.isprsjprs.2023.12.002
   Zhang Y., 2022, P 36 INT C NEUR INF, V35, P37297
   Zhang YP, 2023, IEEE T GEOSCI REMOTE, V61, DOI [10.1109/TGRS.2023.3304684, 10.1109/TGRS.2023.3235881]
   Zhang YX, 2023, PROC CVPR IEEE, P10146, DOI 10.1109/CVPR52729.2023.00978
   Zhang ZC, 2022, AAAI CONF ARTIF INTE, P3408
   Zhao B, 2019, PROC CVPR IEEE, P8576, DOI 10.1109/CVPR.2019.00878
   Zhao Shengyu, 2020, Advances in Neural Information Processing Systems, V33, P7559
   Zhao YT, 2022, IEEE GEOSCI REMOTE S, V19, DOI 10.1109/LGRS.2022.3177001
   Zhao YQ, 2023, PROC CVPR IEEE, P7380, DOI 10.1109/CVPR52729.2023.00713
   Zhao YQ, 2022, PROC CVPR IEEE, P9130, DOI 10.1109/CVPR52688.2022.00893
   Zhao Yunqing, 2022, Advances in Neural Information Processing Systems
   Zhao ZL, 2021, AAAI CONF ARTIF INTE, V35, P11033
   Zheng CX, 2023, PROC CVPR IEEE, P3272, DOI 10.1109/CVPR52729.2023.00319
   Zhu JP, 2023, IEEE I CONF COMP VIS, P7622, DOI 10.1109/ICCV51070.2023.00704
   Zhu JY, 2017, IEEE I CONF COMP VIS, P2242, DOI 10.1109/ICCV.2017.244
   Zhu MZ, 2022, REMOTE SENS-BASEL, V14, DOI 10.3390/rs14010204
   Zhuang YH, 2024, LECT NOTES COMPUT SC, V14428, P442, DOI 10.1007/978-981-99-8462-6_36
   Zou LC, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20226673
   Zou XC, 2024, IEEE T GEOSCI REMOTE, V62, DOI 10.1109/TGRS.2024.3365806
   Zuo Z., 2021, P IEEE INT GEOSC REM, P3026, DOI [10.1109/IGARSS47720.2021.9555111, DOI 10.1109/IGARSS47720.2021.9555111]
NR 317
TC 0
Z9 0
U1 7
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2473-2397
EI 2168-6831
J9 IEEE GEOSC REM SEN M
JI IEEE Geosci. Remote Sens. Mag.
PD 2024 NOV 13
PY 2024
DI 10.1109/MGRS.2024.3483459
EA NOV 2024
PG 43
WC Geochemistry & Geophysics; Remote Sensing; Imaging Science &
   Photographic Technology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geochemistry & Geophysics; Remote Sensing; Imaging Science &
   Photographic Technology
GA M5X9F
UT WOS:001358274600001
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Bajenaru, L
   Tomescu, M
   Grigorovici-Toganel, I
AF Bajenaru, Lidia
   Tomescu, Mihaela
   Grigorovici-Toganel, Iulia
TI Leveraging generative Artificial Intelligence for advanced healthcare
   solutions
SO ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA
   ROMANA DE INFORMATICA SI AUTOMATICA
LA English
DT Article
DE Artificial Intelligence (AI); Generative Artificial Intelligence
   (GenAI); Healthcare; Medical Diagnostics; Generative Adversarial Network
   (GAN)
ID DEEP; AI
AB The research aimed to explore the potential of advanced machine learning (ML) algorithms in clinical and biomedical research. The significance of frameworks like generative adversarial networks (GANs), autoencoders, and autoregressive models in tackling the issues of representation learning and the quality of generated content is highlighted. This paper also presents a proposed system architecture for integrating generative artificial intelligence (GenAI) into healthcare processes. This architecture encompasses components for data ingestion, preprocessing, model training, image enhancement, diagnostic analysis, and user interfaces for healthcare providers and patients, utilizing advanced artificial intelligence (AI) models. The paper underscores the necessity of robust data governance frameworks, ethical guidelines, and secure infrastructures to mitigate the associated risks. By fostering collaborative AI-human systems and continuously assessing ethical implications, the healthcare industry can fully exploit GenAI's potential to improve patient outcomes and operational efficiency.
C1 [Bajenaru, Lidia; Tomescu, Mihaela; Grigorovici-Toganel, Iulia] Natl Inst Res & Dev Informat ICI Bucharest, Bucharest, Romania.
   [Bajenaru, Lidia] Natl Univ Sci & Technol Politehn Bucharest, Bucharest, Romania.
C3 National Institute R&D Informatics Bucharest
RP Bajenaru, L (corresponding author), Natl Inst Res & Dev Informat ICI Bucharest, Bucharest, Romania.; Bajenaru, L (corresponding author), Natl Univ Sci & Technol Politehn Bucharest, Bucharest, Romania.
EM lidia.bajenaru@ici.ro; mihaela.tomescu@ici.ro;
   iulia.grigorovici@gmail.com
RI Lidia, Bajenaru/AEL-9680-2022; Tomescu, Mihaela/JGE-2359-2023
CR Abrahams E., 2024, The New Era of Precision Medicine, P1
   Ali F, 2020, FRONT NEUROL, V11, DOI 10.3389/fneur.2020.559322
   Bhimavarapu U, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11010097
   Bracci F, 2012, 2012 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), P812, DOI 10.1109/ISCC.2012.6249401
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Danek B, 2024, bioRxiv, DOI [10.1101/2023.10.04.560604, 10.1101/2023.10.04.560604, DOI 10.1101/2023.10.04.560604]
   Floroiu I, 2023, ROM J INF TECH AUT C, V33, P99, DOI 10.33436/v33i3y202308
   GDPR.EU, 2020, Complete guide to GDPR compliance
   Gesk TS, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2022.101704
   Gheorghe-Moisii M, 2024, ROM J INF TECH AUT C, V34, P97, DOI 10.33436/v34i1y202409
   Gulshan V, 2016, JAMA-J AM MED ASSOC, V316, P2402, DOI 10.1001/jama.2016.17216
   Hernandez M, 2022, NEUROCOMPUTING, V493, P28, DOI 10.1016/j.neucom.2022.04.053
   Jänes J, 2024, MOL SYST BIOL, V20, P162, DOI 10.1038/s44320-024-00016-x
   Jin CB, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19102361
   Kang E, 2017, MED PHYS, V44, pe360, DOI 10.1002/mp.12344
   Kasirzadeh A, 2022, PHILOS TECHNOL, DOI DOI 10.1007/S13347-023-00606-X
   Kaylor A., 2024, AI advancements in healthcare boost diagnostic accuracy, operational efficiency
   Kellogg M, 2020, IEEE INT CONF AUTOM, P511, DOI 10.1145/3324884.3416593
   Kormilitzin A, 2021, ARTIF INTELL MED, V118, DOI 10.1016/j.artmed.2021.102086
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Li T, 2020, IEEE SIGNAL PROC MAG, V37, P50, DOI 10.1109/MSP.2020.2975749
   Li X., 2021, Discov Artif Intell, V1, P1, DOI DOI 10.1007/S44163-021-00006-0
   Luo RQ, 2022, BRIEF BIOINFORM, V23, DOI 10.1093/bib/bbac409
   Maleki Varnosfaderani S., 2024, Bioengineering, V11, DOI [10.3390/bioengineering11040337.337, DOI 10.3390/BIOENGINEERING11040337.337]
   Manolio Teri A, 2020, Am J Hum Genet, V107, P1007, DOI 10.1016/j.ajhg.2020.11.005
   Martín-Noguerol T, 2023, EUR J RADIOL, V161, DOI 10.1016/j.ejrad.2023.110726
   Mehrabi N, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3457607
   Meyer AND, 2020, J MED INTERNET RES, V22, DOI 10.2196/14679
   Miotto R, 2018, BRIEF BIOINFORM, V19, P1236, DOI 10.1093/bib/bbx044
   Murtaza H, 2023, COMPUT SCI REV, V48, DOI 10.1016/j.cosrev.2023.100546
   NVIDIA, 2021, NVIDIA Clara: AI-Powered Solutions for Healthcare
   Ooi K.- B., 2023, Journal of Computer Information Systems, DOI [10.1080/08874417.2023.2261010.1-32, DOI 10.1080/08874417.2023.2261010.1-32]
   Pantelopoulos A, 2010, IEEE T SYST MAN CY C, V40, P1, DOI 10.1109/TSMCC.2009.2032660
   Petcu I, 2022, ROM J INF TECH AUT C, V32, P21, DOI 10.33436/v32i2y202202
   Rajpurkar P, 2022, NAT MED, V28, P31, DOI 10.1038/s41591-021-01614-0
   Randall JE., 2022, INTELL BASED MED, V6, P100068, DOI [DOI 10.1016/J.IBMED.2022.100068, 10.1016/j.ibmed.2022.100068]
   Reddy S, 2024, IMPLEMENT SCI, V19, DOI 10.1186/s13012-024-01357-9
   Rieke N, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-00323-1
   Sai S, 2024, IEEE ACCESS, V12, P31078, DOI 10.1109/ACCESS.2024.3367715
   Sakirin T., 2023, Babylonian Journal of Artificial Intelligence, V2023, P10, DOI DOI 10.58496/BJAI/2023/003
   Salapura V, 2017, CLOSER: PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, P568, DOI 10.5220/0006356705960602
   Sarre-Lazcano C, 2017, J CLIN ONCOL, V35, DOI 10.1200/JCO.2017.35.15_suppl.e18166
   Shokrollahi Y, 2023, Arxiv, DOI arXiv:2310.00795
   Sufi F, 2024, INFORMATION, V15, DOI 10.3390/info15020099
   Summerfield C., 2022, NATURAL GEN INTELLIG
   Topol E, 2019, The Topol Review: Preparing the Healthcare Workforce to Deliver the Digital Future
   Topol Eric J, 2023, Science, V381, padk6139, DOI 10.1126/science.adk6139
   Tsimberidou AM, 2020, CANCER TREAT REV, V86, DOI 10.1016/j.ctrv.2020.102019
   van Bussel MJP, 2022, BMC HEALTH SERV RES, V22, DOI 10.1186/s12913-022-08189-7
   Wang Y, 2019, EXPERT SYST APPL, V137, P167, DOI 10.1016/j.eswa.2019.04.057
   Wang Z., 2020, P IEEE CVF C COMP VI, DOI [10.48550/arXiv.1911.11834, DOI 10.48550/ARXIV.1911.11834]
   Xu L, 2021, JMIR CANCER, V7, DOI 10.2196/27850
   Yimam D, 2016, J INTERNET SERV APPL, V7, DOI 10.1186/s13174-016-0046-8
   Zaman R., 2022, Second Machine Age what, why, how, and next?
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
   Zhou S.K, 2023, Deep Learning for Medical Image Analysis
   Ziller A, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-93030-0
NR 57
TC 0
Z9 0
U1 4
U2 4
PU INST NATL CERCETARE-DEZVOLTARE INFORMATICA-ICI
PI BUCHAREST
PA 8-10 MARESAL A AVERESCU AV, SECTOR 1, BUCHAREST, 011455, ROMANIA
SN 1220-1758
EI 1841-4303
J9 ROM J INF TECH AUT C
JI Rom. J. Infor. Tech. Autom. Control
PY 2024
VL 34
IS 3
SI SI
BP 149
EP 164
DI 10.33436/v34i3y202411
PG 16
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA I5C6T
UT WOS:001330437700010
OA gold
DA 2024-12-25
ER

PT J
AU Cervantes, J
   Smith, B
   Ramadoss, T
   D'Amario, V
   Shoja, MM
   Rajput, V
AF Cervantes, Jorge
   Smith, Blake
   Ramadoss, Tanya
   D'Amario, Vanessa
   Shoja, Mohammadali M.
   Rajput, Vijay
TI Decoding medical educators' perceptions on generative artificial
   intelligence in medical education
SO JOURNAL OF INVESTIGATIVE MEDICINE
LA English
DT Article
DE Generative artificial intelligence; medical education; GPT
AB Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.
C1 [Cervantes, Jorge; Ramadoss, Tanya; Shoja, Mohammadali M.; Rajput, Vijay] Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Ft Lauderdale, FL 33328 USA.
   [Smith, Blake; D'Amario, Vanessa] Nova Southeastern Univ, Dr Kiran C Patel Coll Osteopath Med, Ft Lauderdale, FL 33328 USA.
C3 Nova Southeastern University; Nova Southeastern University
RP Cervantes, J (corresponding author), Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Ft Lauderdale, FL 33328 USA.
EM jcervan1@nova.edu
CR Boscardin CK, 2024, ACAD MED, V99, P22, DOI 10.1097/ACM.0000000000005439
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Emsley R, 2023, SCHIZOPHRENIA-UK, V9, DOI 10.1038/s41537-023-00379-4
   Ibrahim H, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-38964-3
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Laupichler MC, 2024, ACAD MED, V99, P508, DOI 10.1097/ACM.0000000000005626
   Lee P., 2023, The AI revolution in medicine: GPT-4 and beyond
   Li R, 2023, JAMA INTERN MED, V183, P596, DOI 10.1001/jamainternmed.2023.1835
   McMurtrie B, 2023, The Chronicle of Higher Education
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Shoja MM, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.40883
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Tangadulrat P, 2023, JMIR MED EDUC, V9, DOI 10.2196/50658
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   van de Ridder JMM, 2023, ACAD MED, V98, P867, DOI 10.1097/ACM.0000000000005254
NR 16
TC 1
Z9 1
U1 0
U2 0
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1081-5589
EI 1708-8267
J9 J INVEST MED
JI J. Investigative Med.
PD OCT
PY 2024
VL 72
IS 7
BP 633
EP 639
DI 10.1177/10815589241257215
PG 7
WC Medicine, General & Internal; Medicine, Research & Experimental
WE Science Citation Index Expanded (SCI-EXPANDED)
SC General & Internal Medicine; Research & Experimental Medicine
GA M0F9T
UT WOS:001354401200005
DA 2024-12-25
ER

PT J
AU Solaiman, B
AF Solaiman, Barry
TI Generative artificial intelligence (GenAI) and decision-making: Legal &
   ethical hurdles for implementation in mental health
SO INTERNATIONAL JOURNAL OF LAW AND PSYCHIATRY
LA English
DT Article
DE Generative artificial intelligence (GenAI); Mental health; Psychiatry;
   Ethics; Law; Healthcare
AB This article argues that significant risks are being taken with using GenAI in mental health that should be assessed urgently. It recommends that guidelines for using generative artificial intelligence (GenAI) in mental health care must be established promptly. Currently, clinicians using chatbots without appropriate approval risk undermining legal protections for patients. This could harm the patient and undermine the standards of the profession, undermining trust in an area where human involvement in decision-making is critical. To explore these concerns, this paper is divided into three parts. First, it examines the needs of patients in mental health. Second, it explores the potential benefits of GenAI in mental health and highlights the risks of its use as it pertains to patient needs. Third, it notes the ethical and legal concerns around data use and medical liability that require careful attention. The impact of the European Union's (EU) Artificial Intelligence Act (AI-Act) is also considered. It will be seen that these laws are insufficient in the context of mental health. As such, the paper recommends that guidelines should be developed to help resolve the existing legal gaps until codified rules are established.
C1 [Solaiman, Barry] HBKU Law, Law, Doha, Qatar.
   [Solaiman, Barry] Weill Cornell Med, Med Eth Clincal Med, Ar Rayyan, Qatar.
C3 Qatar Foundation (QF); Weill Cornell Medical College Qatar
RP Solaiman, B (corresponding author), HBKU Law, Doha, Qatar.
EM barrysol@cantab.net
FU Qatar National Library (QNL)
FX Open Access funding provided by the Qatar National Library (QNL) .
CR Alanezi F, 2024, J MULTIDISCIP HEALTH, V17, P461, DOI 10.2147/JMDH.S447368
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Andreou A., 2023, Psychology TodayMarch 9
   Bair H., 2023, MedPage TodayAugust 28
   Banerjee Sri, 2024, Int J Environ Res Public Health, V21, DOI 10.3390/ijerph21070910
   Bellier-Teichmann T, 2016, FRONT PUBLIC HEALTH, V4, DOI 10.3389/fpubh.2016.00022
   Biringer E, 2017, BMC HEALTH SERV RES, V17, DOI 10.1186/s12913-017-2719-9
   Blease C, 2024, PSYCHIAT RES, V333, DOI 10.1016/j.psychres.2024.115724
   Blease C, 2023, BMJ MENTAL HEALTH, V26, DOI 10.1136/bmjment-2023-300884
   Block VJ, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.832520
   Brown C, 2021, BRIT J PSYCHIAT, V218, P131, DOI 10.1192/bjp.2019.245
   Chen T, 2022, Artificial intelligence in healthcare
   Chien WT, 2001, J ADV NURS, V34, P304, DOI 10.1046/j.1365-2648.2001.01759.x
   Cohen, 2023, HARVARD BUSINESS REV
   Cohen I. G., 2024, Research Handbook On Health, AI and The Law, P167, DOI [10.4337/9781802205657.00017, DOI 10.4337/9781802205657.00017]
   Cohen IG, 2023, AM J BIOETHICS, V23, P8, DOI 10.1080/15265161.2023.2233357
   Cohen IG, 2014, HEALTH AFFAIR, V33, P1139, DOI 10.1377/hlthaff.2014.0048
   Dave T, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1169595
   DeAngelis T., 2019, MONITOR PSYCHOL, V50
   Dergaa I, 2024, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1277756
   Diaz N., 2023, Becker's hospital Review
   Elyoseph Z, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/53043
   European Parliament, 2024, Provisional agreement resulting from interinstitutional negotiations, 'proposal for a regulation of the European Parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts' (AI-act) COM (2021) 0106 (COM)
   Froomkin AM, 2019, Ariz Law Rev, V61, P33
   Gerke S., 2020, ARTIF INTELL, P295, DOI [10.1016/B978-0-12-818438-7.00012-5, DOI 10.1016/B978-0-12-818438-7.00012-5, 10.1016/b978-0-12-818438-7.00012-5]
   Hanna JJ, 2023, medRxiv, DOI [10.1101/2023.08.28.23294730, DOI 10.1101/2023.08.28.23294730]
   Hui A., 2023, Verywell HealthMay 24
   Jorgensen Kim, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20031965
   Kanjee Z, 2023, JAMA-J AM MED ASSOC, V330, P78, DOI 10.1001/jama.2023.8288
   King M, 2022, LANCET PSYCHIAT, V9, pE48, DOI 10.1016/S2215-0366(22)00312-1
   Loaiza-Bonilla A., 2023, Medscape Oncology
   Malik A., 2024, Research handbook on health, AI and the Law
   Manderius C, 2023, BMC NURS, V22, DOI 10.1186/s12912-023-01186-z
   Minssen T., 2024, Research handbook on health, AI and the law
   Minssen T, 2023, JAMA-J AM MED ASSOC, V330, P315, DOI 10.1001/jama.2023.9651
   Moodie C., 2023, ABC News, may 27
   Pandya A, 2024, FRONT HUM DYNAM, V5, DOI 10.3389/fhumd.2023.1289255
   Price W. N., 2024, Research handbook on health, AI and the Law
   Radez J, 2021, EUR CHILD ADOLES PSY, V30, P183, DOI 10.1007/s00787-019-01469-4
   Reader R., 2024, Politico, feb 18
   Saraceno B., 2017, International encyclopedia of public health, Vsecond
   Schmidt M, 2020, ISSUES MENT HEALTH N, V41, P182, DOI 10.1080/01612840.2019.1663565
   Sezgin Emre, 2024, Npj Ment Health Res, V3, P25, DOI 10.1038/s44184-024-00067-w
   Sharma A, 2023, NAT MACH INTELL, V5, P46, DOI 10.1038/s42256-022-00593-2
   Solaiman B., 2024, Law, Innovation and Technology, DOI [10.31235/osf.io/5ynem, DOI 10.31235/OSF.IO/5YNEM]
   Solaiman B., 2021, University of Memphis Law Review, P1103
   Solaiman B., 2023, Privacy and medical confidentiality in healthcare, P115, DOI [10.4337/9781035309436.0001, DOI 10.4337/9781035309436.0001]
   Solaiman B., 2024, Research handbook on health. AI and the law, DOI [10.4337/9781802205657.00015, DOI 10.4337/9781802205657.00015]
   Solaiman B., 2024, Research handbook on health, AI and the Law, DOI [10.4337/9781802205657, DOI 10.4337/9781802205657]
   Solaiman B, 2024, MED LAW REV, DOI 10.1093/medlaw/fwae033
   Solaiman B, 2023, AM J LAW MED, V49, P250, DOI 10.1017/amj.2023.30
   Solaiman B, 2022, BJPSYCH INT, V19, P15, DOI 10.1192/bji.2021.21
   Tambuyzer E, 2014, HEALTH EXPECT, V17, P138, DOI 10.1111/j.1369-7625.2011.00743.x
   Thomas KC, 2018, ADM POLICY MENT HLTH, V45, P611, DOI 10.1007/s10488-018-0849-y
   Tortora L, 2024, FRONT PSYCHIATRY, V15, DOI 10.3389/fpsyt.2024.1346059
   van Dusseldorp L, 2023, J AM ASSOC NURSE PRA, V35, P281, DOI 10.1097/JXX.0000000000000867
   Victoria State Government Department of Health, 2023, Health service use of unregulated Artificial Intelligence (AI)
   Wachter RM, 2024, JAMA-J AM MED ASSOC, V331, P65, DOI 10.1001/jama.2023.25054
   Wei YH, 2023, ASIAN J PSYCHIATR, V90, DOI 10.1016/j.ajp.2023.103808
   Wennström E, 2004, BRIT J PSYCHIAT, V185, P505, DOI 10.1192/bjp.185.6.505
NR 60
TC 0
Z9 0
U1 9
U2 9
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0160-2527
EI 1873-6386
J9 INT J LAW PSYCHIAT
JI Int. J. Law Psychiatr.
PD NOV-DEC
PY 2024
VL 97
AR 102028
DI 10.1016/j.ijlp.2024.102028
EA OCT 2024
PG 7
WC Law; Psychiatry
WE Social Science Citation Index (SSCI)
SC Government & Law; Psychiatry
GA J9F7M
UT WOS:001340054500001
PM 39426042
OA hybrid
DA 2024-12-25
ER

PT J
AU Tang, AR
   Li, KK
   Kwok, KO
   Cao, LJ
   Luong, S
   Tam, W
AF Tang, Arthur
   Li, Kin-Kit
   Kwok, Kin On
   Cao, Liujiao
   Luong, Stanley
   Tam, Wilson
TI The importance of transparency: Declaring the use of generative
   artificial intelligence (AI) in academic writing
SO JOURNAL OF NURSING SCHOLARSHIP
LA English
DT Article
ID REPORTING GUIDELINES; CHATGPT
AB The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing.
C1 [Tang, Arthur; Luong, Stanley] RMIT Univ, Sch Sci Engn & Technol, Ho Chi Minh City, Vietnam.
   [Li, Kin-Kit] City Univ Hong Kong, Dept Social & Behav Sci, Hong Kong, Peoples R China.
   [Kwok, Kin On] Chinese Univ Hong Kong, JC Sch Publ Hlth & Primary Care, Hong Kong, Peoples R China.
   [Kwok, Kin On] Chinese Univ Hong Kong, Stanley Ho Ctr Emerging Infect Dis, Hong Kong, Peoples R China.
   [Kwok, Kin On] Chinese Univ Hong Kong, Hong Kong Inst Asia Pacific Studies, Hong Kong, Peoples R China.
   [Kwok, Kin On] Imperial Coll London, Sch Publ Hlth, Dept Infect Dis Epidemiol, London, England.
   [Cao, Liujiao] Sichuan Univ, West China Hosp, West China Sch Nursing, Chengdu, Peoples R China.
   [Tam, Wilson] Natl Univ Singapore, Alice Lee Ctr Nursing Studies, Singapore, Singapore.
C3 Royal Melbourne Institute of Technology (RMIT); City University of Hong
   Kong; Chinese University of Hong Kong; Chinese University of Hong Kong;
   Chinese University of Hong Kong; Imperial College London; Sichuan
   University; National University of Singapore
RP Kwok, KO (corresponding author), Chinese Univ Hong Kong, JC Sch Publ Hlth & Primary Care, Hong Kong, Peoples R China.
EM kkokwok@cuhk.edu.hk
RI Tang, Arthur/C-8784-2009; Tam, Wilson/H-5890-2019; Kwok, Kin
   On/A-5074-2018; Tang, Arthur/D-3144-2019
OI Cao, Liujiao/0009-0001-4928-277X; Tam, Wilson/0000-0003-0641-3060; Kwok,
   Kin On/0000-0002-2804-5433; Luong, Stanley/0000-0002-3303-2979; Tang,
   Arthur/0000-0002-6655-6883
CR Amdur RJ, 1997, JAMA-J AM MED ASSOC, V277, P909, DOI 10.1001/jama.277.11.909
   Cacciamani GE, 2023, NATURE, V618, P238, DOI 10.1038/d41586-023-01853-w
   Clarivate, 2022, 2011 J CIT REP SCI E
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V329, P637, DOI 10.1001/jama.2023.1344
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Liu XX, 2020, LANCET DIGIT HEALTH, V2, pE537, DOI [10.1016/S2589-7500(20)30219-3, 10.1136/bmj.m3164, 10.1016/S2589-7500(20)30218-1]
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   O'Connor S, 2023, NURSE EDUC PRACT, V67, DOI 10.1016/j.nepr.2023.103572
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   Republic of Namibia Office of the Prime Minister, 2023, Namibia livelihood vulnerability assessment and analysis (VAA)
   Rivera SC, 2020, LANCET DIGIT HEALTH, V2, pE549, DOI [10.1038/s41591-020-1037-7, 10.1136/bmj.m3210, 10.1016/S2589-7500(20)30219-3]
   Singh Chawla Dalmeet, 2020, Nature, DOI 10.1038/d41586-020-00031-6
   StokelWalker C., 2023, NATURE, V613, p620 621, DOI [10.1038/d4158602300107z, DOI 10.1038/D4158602300107Z]
   Tam W, 2023, NURS EDUC TODAY, V129, DOI 10.1016/j.nedt.2023.105917
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
NR 15
TC 16
Z9 16
U1 56
U2 147
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1527-6546
EI 1547-5069
J9 J NURS SCHOLARSHIP
JI J. Nurs. Scholarsh.
PD MAR
PY 2024
VL 56
IS 2
BP 314
EP 318
DI 10.1111/jnu.12938
EA OCT 2023
PG 5
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA JS9J1
UT WOS:001099765700001
PM 37904646
OA hybrid
DA 2024-12-25
ER

PT J
AU Mateus, JC
   Lugo, N
   Cappello, G
   Guerrero-Pico, M
AF Mateus, Julio-Cesar
   Lugo, Nohemi
   Cappello, Giancarlo
   Guerrero-Pico, Mar
TI Communication Educators Facing the Arrival of Generative Artificial
   Intelligence: Exploration in Mexico, Peru, and Spain
SO DIGITAL EDUCATION REVIEW
LA English
DT Article
DE Generative Artificial Intelligence; ChatGPT; Training for Communicators;
   University Teachers; Higher Education
AB This research explores university educators' perspectives on the opportunities, concerns, and considerations associated with Generative Artificial Intelligence (GenAI) in the training of professional communicators. Positioned at the early stages of ChatGPT's integration into educational settings, the study examines teachers' assignment instructions, assessments of ChatGPT's responses, and reflections on these outcomes. Employing a cross-sectional, qualitative methodology, the research involves a sample of 22 teachers from communication faculties in Mexico, Peru, and Spain. Utilizing Bloom's taxonomy and an inductive approach for data analysis, the findings unveil nuanced views on GenAI's role in teaching practice. Teachers perceive ChatGPT as a tool with varying impacts depending on its application. They articulate distinct roles for ChatGPT, viewing it as either an ally or a rival, prompting discussions on anthropomorphizing technologies and emphasizing the need to empower students in GenAI tool usage, establish ethical protocols, and reconsider assessment methods, among other key considerations.
C1 [Mateus, Julio-Cesar; Cappello, Giancarlo] Univ Lima, Lima, Peru.
   [Lugo, Nohemi] Tecnol Monterrey, Monterrey, Mexico.
   [Guerrero-Pico, Mar] Univ Pompeu Fabra, Barcelona, Spain.
C3 Universidad de Lima; Tecnologico de Monterrey; Pompeu Fabra University
RP Mateus, JC (corresponding author), Univ Lima, Lima, Peru.
EM jmateus@ulima.edu.pe; nlugo@tec.mx; gcappell@ulima.edu.pe;
   mariadelmar.guerrero@upf.edu
RI Guerrero, Mar/AAM-7879-2021; Mateus, Julio-César/U-1475-2019
OI Mateus, Julio Cesar/0000-0001-5161-3737; Cappello,
   Giancarlo/0000-0003-2908-6429
FX The authors thank Cesar Guarniz, research assistant at the Scientific
   Research Institute of the University of Lima, for his support in
   organizing the fieldwork.
CR [Anonymous], International handbook of research on teacher beliefs, P403, DOI [10.4324/9780203108437, DOI 10.4324/9780203108437]
   [Anonymous], Education and Information Technologies, V25, P3443, DOI [10.22555/joeed.v8i1.308, DOI 10.22555/JOEED.V8I1.308]
   Ball B., 2023, Advertising: ChatGPT and the Role of AI
   Bloom B. S., 1956, TAXONOMY ED OBJECTIV
   Bongiovanni P., 2023, Inteligencia Artificial para Educadores. Guia Basica en Espanol Enero 2023
   Bouckaert M., 2023, OECD Education Working Papers, V293, DOI [10.1787/35dbd439-en, DOI 10.1787/35DBD439-EN]
   Busquet J, 2012, Lo sublime y lo vulgar. La cultura de masas o la pervivencia de un mito
   Chandio M T., 2021, Journal of Education Educational Development, V8, P109, DOI [10.22555/joeed.v8i1.308, DOI 10.22555/JOEED.V8I1.308]
   ChatGPT, 2023, Mesopotamian J. CyberSecur., P16, DOI [DOI 10.58496/MJCS/2023/003, 10.58496/mjcs/2023/003]
   Chatterjee J, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2022.100676
   Chatterjee S, 2020, EDUC INF TECHNOL, V25, P3443, DOI 10.1007/s10639-020-10159-7
   Chomsky Noam, 2023, The New York Times 8 March
   Codina L., 2023, Como utilizar ChatGPT en el aula con perspectiva etica y pensamiento critico: una proposicion para docentes y educadores
   Craig D., 2023, Inteligencia Artificial en Educacion (Guia Basica)
   de Vicente-Yagüe-Jara MI, 2023, COMUNICAR, V31, P47, DOI 10.3916/C77-2023-04
   Deng J., 2022, FRONTIERS COMPUTING, V2, P81, DOI [DOI 10.54097/FCIS.V2I2.4465, 10.54097/fcis.v2i2.4465]
   Elkins Katherine, 2020, J. Cult. Anal., V5, P17212, DOI [DOI 10.22148/001C.17212, DOI 10.31235/OSF.IO/UJVKN]
   Ertmer P. A., 2015, Teachers' beliefs and uses of technology to support 21st-century teaching and learning
   Ferrante E., 2023, Aprendizaje automatico? Un viaje al corazon de la inteligencia artificial contemporanea
   Flores-Vivar JM, 2023, COMUNICAR, V31, P37, DOI 10.3916/C74-2023-03
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   García-Marín D, 2021, VIVAT ACAD, DOI 10.15178/va.2021.154.e1324
   Giordano A, 2021, J ASSOC CONSUM RES, V6, P503, DOI 10.1086/716072
   Granic A, 2019, BRIT J EDUC TECHNOL, V50, P2572, DOI 10.1111/bjet.12864
   Henry G.T., 2009, Applied social research methods, V2nd, P77, DOI DOI 10.4135/9781412985451
   Herft A, 2023, A teacher's prompt guide to ChatGPT aligned with 'What Works Best' guide
   Hirsh-Pasek K., 2023, ChatGPT: Educational friend or foe?.
   Idroes G. M., 2023, Journal of Educational Management and Learning, V1, P8, DOI 10.60084
   Ipsos, 2023, Ipsos Update: Una seleccion de los estudios y reflexiones mas recientes de los equipos de Ipsos en todo el mundo
   Jimaa S, 2011, PROCD SOC BEHV, V28, DOI 10.1016/j.sbspro.2011.11.133
   Loo N, 2023, NewsNation
   Lopezosa C, 2023, PROF INFORM, V32, DOI 10.3145/epi.2023.jul.08
   Matthews B, 2023, INT J ART DES EDUC, V42, P367, DOI 10.1111/jade.12460
   MINGZHU CUI, 2022, [Archives of Design Research, 디자인학연구], V35, P285, DOI 10.15187/adr.2022.11.35.4.285
   Mucharaz y Cano Y, 2023, Chatgpt and AI text generators: Should academia adapt or resist
   Olguin M., 2023, RoastBrief
   Ong WJ, 2012, NEW ACCENT, P1
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Rios Hernandez I. N., 2024, Austral Comunicacion, V13, DOI [10.26422/aucom.2024.1301.rio, DOI 10.26422/AUCOM.2024.1301.RIO]
   Roger-Monzo V., 2024, INTED2024 P, P2631, DOI [10.21125/inted.2024.0732, DOI 10.21125/INTED.2024.0732]
   Sanchez M., 2013, The Tulane Hullabaloo
   Sanchez-Vera M. D. M., 2023, EDUCAR, V60, P33, DOI [10.5565/rev/educar.1810, DOI 10.5565/REV/EDUCAR.1810]
   Sibagatulina A, 2023, Hipertext.net, V26, P11, DOI [10.31009/HIPERTEXT.NET.2023.I26.02, DOI 10.31009/HIPERTEXT.NET.2023.I26.02]
   Sloterdijk P., 2002, El desprecio de las masas. Ensayo sobre las luchas culturales en la sociedad moderna
   Susnjak T., 2022, PREPRINT, DOI [DOI 10.48550/ARXIV.2212.09292, 10.48550/arXiv.2212.09292]
   UNESCO, 2023, ChatGPT and Artificial Intelligence in Higher Education: Quick Start Guide.
   Valenzuela C., 2023, ComputerHoy
   Zhai X., 2022, CHATGPT NEXT GENERAT, DOI [10.2139/ssrn.4331313, DOI 10.2139/SSRN.4331313]
NR 48
TC 0
Z9 0
U1 18
U2 18
PU UNIV BARCELONA, RES GROUP EDUC & VIRTUAL LEARNING, DIGITAL EDUC
   OBSERVATORY
PI BARCELONA
PA PASSEIG DE LA VALL D HEBRON, 171, BARCELONA, 08035, SPAIN
SN 2013-9144
J9 DIGIT EDUC REV
JI Digit. Educ. Rev.
PD JUN
PY 2024
IS 45
BP 106
EP 115
DI 10.1344/der.2024.45.106-114
PG 10
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA YS4X5
UT WOS:001270470900014
OA Green Submitted, gold
DA 2024-12-25
ER

PT J
AU Sáez-Velasco, S
   Alaguero-Rodríguez, M
   Delgado-Benito, V
   Rodríguez-Cano, S
AF Saez-Velasco, Sara
   Alaguero-Rodriguez, Mario
   Delgado-Benito, Vanesa
   Rodriguez-Cano, Sonia
TI Analysing the Impact of Generative AI in Arts Education: A
   Cross-Disciplinary Perspective of Educators and Students in Higher
   Education
SO INFORMATICS-BASEL
LA English
DT Article
DE generative artificial intelligence; artificial intelligence in
   education; educational technology; arts education; higher education
ID ARTIFICIAL-INTELLIGENCE
AB Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators and students of arts education is with regard to generative Artificial Intelligence. It is a qualitative research study using focus groups as a data collection technique in order to obtain an overview of the participating subjects. The research design consists of two phases: (1) generation of illustrations from prompts by students, professionals and a generative AI tool; and (2) focus groups with students (N = 5) and educators (N = 5) of artistic education. In general, the perception of educators and students coincides in the usefulness of generative AI as a tool to support the generation of illustrations. However, they agree that the human factor cannot be replaced by generative AI. The results obtained allow us to conclude that generative AI can be used as a motivating educational strategy for arts education.
C1 [Saez-Velasco, Sara; Delgado-Benito, Vanesa; Rodriguez-Cano, Sonia] Univ Burgos, Fac Educ, Burgos 09001, Spain.
   [Alaguero-Rodriguez, Mario] Univ Burgos, Fac Humanities, Burgos, Spain.
C3 Universidad de Burgos; Universidad de Burgos
RP Delgado-Benito, V (corresponding author), Univ Burgos, Fac Educ, Burgos 09001, Spain.
EM ssvelasco@ubu.es; malaguero@ubu.es; vdelgado@ubu.es; srcano@ubu.es
RI Rodríguez-Cano, Sonia/ABA-5725-2021; Sáez-Velasco, Sara/JTU-5299-2023;
   Delgado Benito, Vanesa/H-7517-2015
OI Rodriguez Cano, Sonia/0000-0002-4242-6865; Delgado Benito,
   Vanesa/0000-0001-8168-7120
CR Al Darayseh A., 2023, COMPUTERS ED ARTIFIC, V4, P100132, DOI DOI 10.1016/J.CAEAI.2023.100132
   Albar Mansoa P.J, 2024, Encuentros, V20, P145, DOI [10.5281/zenodo.10052355, DOI 10.5281/ZENODO.10052355]
   Aldoseri A, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16051790
   Algerafi MAM, 2023, IEEE ACCESS, V11, P99752, DOI 10.1109/ACCESS.2023.3314499
   Balcombe L, 2023, INFORMATICS-BASEL, V10, DOI 10.3390/informatics10040082
   Bates EA, 2017, QUAL RES PSYCHOL, V14, P459, DOI 10.1080/14780887.2017.1359352
   beta.dreamstudio, DreamStudio Londres: Stability.ai
   Cascales R., 2023, The Polish Journal of Aesthetics, V71, P17
   Castañeda L, 2018, INT J EDUC TECHNOL H, V15, DOI 10.1186/s41239-018-0109-y
   Chassignol M, 2018, PROCEDIA COMPUT SCI, V136, P16, DOI 10.1016/j.procs.2018.08.233
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chen XW, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su152215830
   Coppin B., 2004, ARTIF INTELL
   Dathathri S, 2020, Arxiv, DOI arXiv:1912.02164
   de Winter JCF, 2023, INFORMATICS-BASEL, V10, DOI 10.3390/informatics10040087
   Demartini CG, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031347
   Epstein Z., 2023, arXiv, DOI [10.1126/science.adh4451, DOI 10.1126/SCIENCE.ADH4451]
   Fawns T., 2022, Postdigital Science and Education, V4, P711, DOI [10.1007/s42438-022-00302-7, DOI 10.1007/S42438-022-00302-7]
   Gong CW, 2023, FRONT NEUROSCI-SWITZ, V17, DOI 10.3389/fnins.2023.1203104
   Greenwood N., 2020, Work. Older People, V24, P95, DOI [10.1108/WWOP-10-2019-0027, DOI 10.1108/WWOP-10-2019-0027]
   Hamal O, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14052862
   Homes W, 2024, INT J ARTIF INTELL E, V34, P1, DOI 10.1007/s40593-023-00352-3
   Hurworth Rosalind., 2005, Evaluation Journal of Australasia, V2, P52, DOI [DOI 10.1177/1035719X05004001-208, https://doi.org/10.1177/1035719X05004001-208]
   Ivanova M, 2024, INFORMATICS-BASEL, V11, DOI 10.3390/informatics11010010
   KITZINGER J, 1995, BRIT MED J, V311, P299, DOI 10.1136/bmj.311.7000.299
   Leonard N., 2020, Art Education (Reston), V73, P22, DOI [10.1080/00043125.2020.1746163, DOI 10.1080/00043125.2020.1746163]
   MAYS N, 1995, BRIT MED J, V311, P109, DOI 10.1136/bmj.311.6997.109
   Morales-Chan M. A., 2023, Explorando el Potencial de Chat GPT: Una Clasificacin de Prompts Efectivos para la Enseanza
   Niccio R.T., 2018, Computer Supported Qualitative ResearchProceedings of the Second International Symposium on Qualitative Research, East Hanover, NJ, USA, 27 June29 July 2018, P393
   Ning YM, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16030978
   Ouyang F., 2021, COMPUTERS ED ARTIFIC, V2, P100020, DOI [DOI 10.1016/J.CAEAI.2021.100020, 10.1016/j.caeai.2021.100020 10.1016/j.caeai.2021.100020]
   Relmasira SC, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151813595
   Rodrigues OS, 2023, TEXTO LIVRE, V16, DOI 10.1590/1983-3652.2023.45997
   Roll I, 2016, INT J ARTIF INTELL E, V26, P582, DOI 10.1007/s40593-016-0110-3
   Sabzalieva E., 2023, ChatGPT e inteligencia artificial en la educacion superior: guia de inicio rapido
   Santaella L., 2023, A inteligencia artificial e inteligente?
   Selwyn N., 2016, Is Technology Good for Education?
   Sharma Ramesh C., 2019, Asian Journal of Distance Education, V14, P1
   Sun S., 2019, IEEE Access, V7, P49918
   Timms MJ, 2016, INT J ARTIF INTELL E, V26, P701, DOI 10.1007/s40593-016-0095-y
   Wang YY, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16041524
   Whitby B., 2008, Artificial Intelligence: A Beginner's Guide
   Williamson B, 2020, TEACH HIGH EDUC, V25, P351, DOI 10.1080/13562517.2020.1748811
NR 43
TC 1
Z9 1
U1 70
U2 70
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-9709
J9 INFORMATICS-BASEL
JI Informatics-Basel
PD JUN
PY 2024
VL 11
IS 2
AR 37
DI 10.3390/informatics11020037
PG 14
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA WP0S2
UT WOS:001255966600001
OA gold
DA 2024-12-25
ER

PT J
AU Li, JL
   Zhang, MY
   Li, NY
   Weyns, D
   Jin, Z
   Tei, K
AF Li, Jialong
   Zhang, Mingyue
   Li, Nianyu
   Weyns, Danny
   Jin, Zhi
   Tei, Kenji
TI Generative AI for Self-Adaptive Systems: State of the Art and Research
   Roadmap
SO ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS
LA English
DT Article
ID IDENTIFICATION; ADAPTATION; VISION
AB Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI's within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies.
C1 [Li, Jialong] Waseda Univ, Tokyo, Japan.
   [Zhang, Mingyue] Southwest Univ, Chongqing, Peoples R China.
   [Li, Nianyu] Zhongguancun Lab, Beijing, Peoples R China.
   [Weyns, Danny] Linnaeus Univ, Vaxjo, Sweden.
   [Weyns, Danny] Katholieke Univ Leuven, Leuven, Belgium.
   [Jin, Zhi] Peking Univ, Beijing, Peoples R China.
   [Tei, Kenji] Tokyo Inst Technol, Tokyo, Japan.
C3 Waseda University; Southwest University - China; Zhongguancun
   Laboratory; Linnaeus University; KU Leuven; Peking University; Institute
   of Science Tokyo; Tokyo Institute of Technology
RP Li, NY (corresponding author), Zhongguancun Lab, Beijing, Peoples R China.
EM lijialong@fuji.waseda.jp; myzhangswu@swu.edu.cn; li_nianyu@pku.edu.cn;
   danny.weyns@lnu.se; zhijin@pku.edu.cn; tei@c.titech.ac.jp
RI Weyns, Danny/J-1267-2018; Jin, Zhi/E-1288-2013; Zhang,
   Mingyue/KQU-0432-2024
OI Jin, Zhi/0000-0003-1087-226X
CR Ahmad Wasi, 2020, P 58 ANN M ASS COMP, P4998, DOI 10.18653/v1/2020.acl-main.449
   Ahmed T, 2022, IEEE INT CONF AUTOM, DOI 10.1145/3551349.3559555
   Ahmed Toufique, 2024, P IEEEACM 46 INT C S, DOI [10.1145/3597503.3639183, DOI 10.1145/3597503.3639183]
   Ajagbe M, 2022, 2022 30TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE (RE 2022), P309, DOI 10.1109/RE54965.2022.00046
   Alsayed AS, 2024, IEEE WORK CONF MIN S, P419, DOI 10.1145/3643991.3644916
   Andersson Jesper, 2009, Modeling Dimensions of Self-Adaptive Software Systems, P27, DOI [10.1007/978-3-642-02161-9_2, DOI 10.1007/978-3-642-02161-9_2]
   Arawjo I, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS, CHI 2024, DOI 10.1145/3613904.3642016
   Arora Chetan, 2024, P 32 IEEE INT REQ EN
   Astekin Merve, 2024, P 2 INT WORKSH INT R
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Barnes Michael, 2010, Human-Robot Interactions in Future Military Operations (Human Factors in Defence)
   Bengio Y, 2001, ADV NEUR IN, V13, P932
   Berabi B, 2021, PR MACH LEARN RES, V139
   Bernstein MS, 2023, ADJUNCT PROCEEDINGS OF THE 36TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE & TECHNOLOGY, UIST 2023 ADJUNCT, DOI 10.1145/3586182.3617431
   Bertolino A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3447240
   Bertolino A, 2012, COMPUTER, V45, P66, DOI 10.1109/MC.2011.227
   Besta M, 2024, AAAI CONF ARTIF INTE, P17682
   Bhatia Shreya, 2024, P 1 INT WORKSH LARG
   Blair G, 2009, COMPUTER, V42, P22, DOI 10.1109/MC.2009.326
   Bosselut A, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P4762
   Bozhinoski Darko, 2024, P 19 C SOFTW ENG AD
   Brohan A, 2023, Arxiv, DOI arXiv:2307.15818
   Cachay Salva Ruhling, 2023, ADV NEUR IN
   Cai Jinyu, 2024, P GEN EV COMP C GECC
   Cai ZF, 2023, Arxiv, DOI [arXiv:2306.07932, 10.48550/arXiv.2306.07932, DOI 10.48550/ARXIV.2306.07932]
   Calinescu R, 2018, IEEE T SOFTWARE ENG, V44, P1039, DOI 10.1109/TSE.2017.2738640
   Calinescu R, 2011, IEEE T SOFTWARE ENG, V37, P387, DOI 10.1109/TSE.2010.92
   Camara Javier, 2015, 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), P146, DOI 10.1109/SEAMS.2015.14
   Campbell C, 2019, SIGN SYST STUD, V47, P352, DOI 10.12697/SSS.2019.47.3-4.01
   Cao Defu, 2024, P 12 INT C LEARN REP
   Cao HZ, 2023, AAAI CONF ARTIF INTE, P6906
   Carta T, 2023, PR MACH LEARN RES, V202
   Chan Chi-Min, 2024, P 12 INT C LEARN REP
   Chan Kenneth, 2024, P 19 C SOFTW ENG AD
   Chebotar Yevgen, 2023, P 7 ANN C ROB LEARN
   Chefer H, 2021, PROC CVPR IEEE, P782, DOI 10.1109/CVPR46437.2021.00084
   Chen Chang, 2024, P 12 INT C LEARN REP
   Chen Huayu, 2023, P 11 INT C LEARN REP
   Chen JJ, 2024, Arxiv, DOI arXiv:2404.18231
   Chen JJ, 2023, Arxiv, DOI arXiv:2305.05976
   Chen LL, 2021, ADV NEUR IN, V34
   Chen Mark, 2021, arXiv
   Chen Peng, 2024, P 12 INT C LEARN REP
   Chen Q, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642480
   Chen Weize, 2024, P 12 INT C LEARN REP
   Chen Xiaolei, 2024, 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), P354, DOI 10.1145/3639478.3643112
   Chen Yang, 2024, 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), P441, DOI 10.1145/3639478.3641227
   Chen YC, 2024, Arxiv, DOI arXiv:2309.15943
   Cheng BHC, 2009, LECT NOTES COMPUT SC, V5525, P1, DOI 10.1007/978-3-642-02161-9_1
   Christiano P, 2017, Arxiv, DOI arXiv:1706.03741
   Chu Simon, 2024, P 19 C SOFTW ENG AD
   Chung JJY, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519873
   Ciborowska A, 2022, PROC INT CONF SOFTW, P946, DOI 10.1145/3510003.3510042
   Cui C, 2023, Arxiv, DOI arXiv:2311.12320
   Czajka L, 2018, J AUTOM REASONING, V61, P423, DOI 10.1007/s10817-018-9458-4
   da Silva Carlos Eduardo, 2011, P 6 INT S SOFTW ENG, P148, DOI [10.1145/1988008.1988029, DOI 10.1145/1988008.1988029]
   Dai Zhirui, 2024, P IEEE INT C ROB AUT
   Dalal Murtaza, 2024, P 12 INT C LEARN REP
   Das Abhimanyu, 2024, 41 INT C MACH LEARN
   Das BC, 2024, Arxiv, DOI arXiv:2402.00888
   de Lemos Rogerio, 2013, Software Engineering for Self- Adaptive Systems: A Second Research Roadmap, P1, DOI [DOI 10.1007/978-3-642-35813-5_1, 10.1007/978-3-642-35813-5_1]
   de Zarzà I, 2023, SENSORS-BASEL, V23, DOI 10.3390/s23135899
   Deng GL, 2024, Arxiv, DOI arXiv:2308.06782
   Deng Mingkai, 2022, P C EMP METH NLP AB, P3369, DOI DOI 10.18653/V1/2022.EMNLP-MAIN.222
   Deng Yinlin, 2024, P 46 IEEEACM INT C S, DOI 10.1145/3597503.3623343
   Deshpande G, 2021, 29TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE WORKSHOPS (REW 2021), P136, DOI 10.1109/REW53955.2021.00025
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Ding Bosheng, 2022, P ANN M ASS COMP LIN
   Dobson S, 2006, ACM T AUTON ADAP SYS, V1, P223, DOI 10.1145/1186778.1186782
   Dong Yihan, 2024, P 23 INT C AUTONOMOU, P2731
   Dosovitskiy A., 2020, ARXIV201011929
   Driess Danny, 2023, P 40 INT C MACHINE L
   Du L, 2022, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), P2628
   Du Y., 2023, P INT C MACH LEARN I, DOI 10.48550/arXiv.2302.06692
   Ehsan U, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3636311
   Fan A, 2023, 2023 IEEE/ACM INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: FUTURE OF SOFTWARE ENGINEERING, ICSE-FOSE, P31, DOI 10.1109/ICSE-FoSE59343.2023.00008
   Fan CY, 2024, AAAI CONF ARTIF INTE, P17960
   Fan Xinyao, 2024, P 12 INT C LEARN REP
   Fan ZY, 2023, PROC INT CONF SOFTW, P1469, DOI 10.1109/ICSE48619.2023.00128
   Fantechi Alessandro, 2023, 2023 IEEE 31 INT REQ, P335, DOI 10.1109/RE57278.2023.00045
   Fedorenko E, 2024, NATURE, V630, P575, DOI 10.1038/s41586-024-07522-w
   Feng N, 2024, INT REQUIR ENG CONF, P129, DOI 10.1109/RE59067.2024.00022
   Ferreira S, 2024, Arxiv, DOI arXiv:2405.03825
   Filieri A, 2014, 36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2014), P299, DOI 10.1145/2568225.2568272
   First E, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P1229, DOI 10.1145/3611643.3616243
   Fredericks EM, 2014, 9TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2014), P17, DOI 10.1145/2593929.2593937
   Fredericks EM, 2013, PROCEEDINGS OF THE 8TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2013), P169, DOI 10.1109/SEAMS.2013.6595504
   Fu M, 2022, PROCEEDINGS OF THE 30TH ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2022, P935, DOI 10.1145/3540250.3549098
   Furuta Hiroki, 2022, P INT C LEARN REPR
   Gallici Matteo, 2023, P 2023 INT C AUT AG, P1679
   Gao Jensen, 2024, P IEEE INT C ROB AUT
   Garlan D, 2004, COMPUTER, V37, P46, DOI 10.1109/MC.2004.175
   Geng Mingyang, 2024, P IEEE ACM 46 INT C, DOI DOI 10.1145/3597503.3608134
   Gheibi O, 2024, ACM T AUTON ADAP SYS, V19, DOI 10.1145/3636428
   Gheibi O, 2021, 2021 INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2021), P104, DOI 10.1109/SEAMS51251.2021.00023
   Gheibi O, 2021, ACM T AUTON ADAP SYS, V15, DOI 10.1145/3469440
   Ghosh A, 2022, Arxiv, DOI [arXiv:2209.07667, 10.48550/arXiv.2209.07667, DOI 10.48550/ARXIV.2209.07667]
   Gil M, 2019, INT J HUM-COMPUT ST, V130, P21, DOI 10.1016/j.ijhcs.2019.04.006
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Google Deepmind, 2024, Project Astra
   Graule Moritz A., 2024, P IEEE INT C ROB AUT
   Gruver Nate, 2023, P ADV NEURAL INFORM
   Guan L, 2023, ADV NEUR IN
   Guo TC, 2024, Arxiv, DOI arXiv:2402.01680
   Guo Xiaoyu, 2024, P 5 INT WORKSH QUANT
   Guo XD, 2024, Arxiv, DOI arXiv:2403.12482
   Gupta Agrim, 2022, P INT C LEARN REPR
   Gupta P, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P1483, DOI 10.1145/3611643.3616253
   Hamman ST, 2017, IEEE T EDUC, V60, P205, DOI 10.1109/TE.2016.2636125
   Han Jesse Michael, 2022, P INT C LEARN REPR
   Hao Shibo, 2023, FINDINGS ASS COMPUTA, P5000, DOI 10.18653/v1/2023.findings-acl.309
   Happe A, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P2082, DOI 10.1145/3611643.3613083
   Harman M, 2012, ACM COMPUT SURV, V45, DOI 10.1145/2379776.2379787
   Hassani Shabnam, 2024, P 32 IEEE INT REQ EN
   Hazra R, 2024, AAAI CONF ARTIF INTE, P20123
   He Haoran, 2023, P ADV NEURAL INFORM, V36, P64896
   Hickmann M, 2000, LINGUISTICS, V38, P409, DOI 10.1515/ling.38.2.409
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Hoffmann J, 2024, INT WORKSH AUTOMAT, P76, DOI 10.1145/1234567890
   Hollmann N, 2023, ADV NEUR IN
   Hong Junyuan, 2024, P 12 INT C LEARN REP
   Hong Sirui, P 12 INT C LEARN REP
   Hou Y, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3650839
   Howard J, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P328
   Hu A, 2023, Arxiv, DOI arXiv:2309.17080
   Hu RH, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P1419, DOI 10.1109/ICCV48922.2021.00147
   Hu Siyi, 2021, P INT C LEARN REPR
   Hu X, 2023, IEEE INT CONF AUTOM, P1111, DOI 10.1109/ASE56229.2023.00165
   Huang K, 2023, IEEE INT CONF AUTOM, P1162, DOI 10.1109/ASE56229.2023.00181
   Huang T, 2022, IEEE INT CONF AUTOM, DOI 10.1145/3551349.3560414
   Huang Wenlong, 2022, P 6 ANN C ROB LEARN
   Huang YT, 2024, Arxiv, DOI arXiv:2404.05442
   Hubara I, 2018, J MACH LEARN RES, V18
   Human Compatible AI, 2023, overcooked_ai: A Cooperative Multi-Agent Environment Based on the Overcooked Game
   Hunt William, 2024, P 23 INT C AUT AG MU, P2809
   Iftikhar M. Usman, 2017, 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Proceedings, P76, DOI 10.1109/SEAMS.2017.21
   Iftikhar MU, 2014, 9TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2014), P125, DOI 10.1145/2593929.2593944
   Inaba T, 2023, 61ST CONFERENCE OF THE THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL 2023, VOL 2, P1522
   Inala Jeevana Priya, 2020, P ADV NEURAL INFORM, V33, P13597
   Izquierdo S., 2024, P IEEE INT C ROB AUT
   Jamshidi P, 2019, 2019 IEEE/ACM 14TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2019), P39, DOI 10.1109/SEAMS.2019.00015
   Janner M., 2022, PR MACH LEARN RES
   Jha P, 2024, AAAI CONF ARTIF INTE, P23521
   Jiang Albert Qiaochu, 2022, P ADV NEURAL INFORM
   Jiang JW, 2023, AAAI CONF ARTIF INTE, P4365
   Jiang N, 2023, PROC INT CONF SOFTW, P1430, DOI 10.1109/ICSE48619.2023.00125
   Jiang Peiling, 2023, P 36 ANN ACM S US IN, DOI DOI 10.1145/3586183.3606737
   Jiang Shengbei, 2024, P 1 INT WORKSH LARG
   Jin Peng, 2023, P 37 C NEUR INF PROC
   Kachris C, 2024, Arxiv, DOI arXiv:2401.09890
   Kamburjan Eduard, 2024, P 19 C SOFTW ENG AD
   Kang Bingyi, 2023, P 37 C NEUR INF PROC
   Kang Qitong, 2024, P 23 INT C AUT AG MU, P2321
   Kephart JO, 2003, COMPUTER, V36, P41, DOI 10.1109/MC.2003.1160055
   Khan JY, 2022, IEEE INT CONF AUTOM, DOI 10.1145/3551349.3559548
   Kim D, 2009, 2009 ICSE WORKSHOP ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, P76, DOI 10.1109/SEAMS.2009.5069076
   Kim J, 2022, PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, P762, DOI 10.1145/3534678.3539454
   Kim J, 2018, LECT NOTES COMPUT SC, V11206, P577, DOI 10.1007/978-3-030-01216-8_35
   Knill K, 1997, TEXT SPEECH LANG TEC, V2, P27
   Ko HK, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642943
   Kojima T, 2022, ADV NEUR IN
   Kollovieh Marcel, 2023, ADV NEUR IN
   Koo R, 2024, Arxiv, DOI arXiv:2309.17012
   Kwon Minae, 2023, P 11 INT C LEARN REP
   Lahami M, 2021, SOFTWARE QUAL J, V29, P555, DOI 10.1007/s11219-021-09558-x
   Lajko Mark, 2024, P IEEE ACM INT WORKS
   Le VH, 2023, IEEE INT CONF AUTOM, P1699, DOI 10.1109/ASE56229.2023.00206
   Lee C, 2023, IEEE INT CONF AUTOM, P116, DOI 10.1109/ASE56229.2023.00082
   Lee Namyeong, 2023, P INT C AUT AG MULT, P2815
   Lewis P, 2020, ADV NEUR IN, V33
   Li J, 2024, ACM T AUTON ADAP SYS, V19, DOI 10.1145/3616496
   Li JL, 2024, PROCEEDINGS OF THE 2024 IEEE/ACM 19TH SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, SEAMS 2024, P77, DOI 10.1145/3643915.3644088
   Li JL, 2022, 2022 29TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE, APSEC, P189, DOI 10.1109/APSEC57359.2022.00031
   Li Jinyang, 2023, P 37 C NEUR INF PROC
   Li L, 2021, 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (ACL-IJCNLP 2021), VOL 1, P4947
   Li NY, 2024, ACM T AUTON ADAP SYS, V19, DOI 10.1145/3652949
   Li NY, 2023, I W S E ADAP SM SYS, P133, DOI 10.1109/SEAMS59076.2023.00027
   Li NY, 2020, I W S E ADAP SM SYS, P181, DOI 10.1145/3387939.3391592
   Li NY, 2021, 2021 INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2021), P48, DOI 10.1109/SEAMS51251.2021.00017
   Li NY, 2020, 2020 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS (ACSOS 2020), P195, DOI 10.1109/ACSOS49614.2020.00042
   Li Ruikun, 2023, Advanced Data Mining and Applications: 19th International Conference, ADMA 2023, Proceedings. Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence (14176), P661, DOI 10.1007/978-3-031-46661-8_44
   Li SK, 2022, ADV NEUR IN
   Li W., 2023, PMLR, P20035
   Li Yucheng, 2023, P 2023 C EMPIRICAL M, P6342, DOI [10.18653/v1/2023.emnlp-main.391, DOI 10.18653/V1/2023.EMNLP-MAIN.391.URL]
   Lin BY, 2023, ADV NEUR IN
   Lin CH, 2023, PROC CVPR IEEE, P300, DOI 10.1109/CVPR52729.2023.00037
   Lin JF, 2021, PROC INT CONF SOFTW, P324, DOI 10.1109/ICSE43902.2021.00040
   Lin YT, 2023, Arxiv, DOI arXiv:2305.13711
   Lin Y, 2024, Arxiv, DOI arXiv:2309.06256
   Liu Jianwei, 2024, P IEEE INT C ROB AUT
   Liu Jijia, 2024, 23 INT C AUTONOMOUS, P1219
   Liu MJ, 2024, Arxiv, DOI arXiv:2311.00176
   Liu Shengcai, 2024, P 12 INT C LEARN REP
   Liu Tennison, 2024, P 12 INT C LEARN REP
   Liu X, 2023, ADJUNCT PROCEEDINGS OF THE 36TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE & TECHNOLOGY, UIST 2023 ADJUNCT, DOI 10.1145/3586182.3615978
   Liu Y, 2023, Arxiv, DOI [arXiv:2308.05374, DOI 10.48550/ARXIV.2308.053742308.05374]
   Liu YL, 2024, INT C PROGRAM COMPRE, P35, DOI 10.1145/3643916.3644408
   Liu Yong, 2022, P ADV NEURAL INFORM
   Liu Z, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P9992, DOI 10.1109/ICCV48922.2021.00986
   Liu Zuxin, 2023, P 40 INT C MACH LEAR
   López-Ruiz S, 2022, PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22), P395, DOI 10.1145/3512290.3528740
   Lou Xingzhou, 2024, P 23 INT C AUT AG MU, P1274
   Lu Jack, 2024, IEEE INT C ROBOTICS
   Luo Haotian, 2024, P 12 INT C LEARN REP
   Luo XC, 2022, IEEE INT CONF AUTOM, DOI 10.1145/3551349.3560417
   Ma Lipeng, 2024, P 46 IEEE ACM INT C, DOI [10.1145/3597503.3623304, DOI 10.1145/3597503.3623304]
   Ma Xiao, 2024, P IEEE INT C ROB AUT
   Ma Yecheng Jason, 2024, P 12 INT C LEARN REP
   Ma Zeyang, 2024, P IEEEACM 46 INT C S, DOI [10.1145/3597503.3639150, DOI 10.1145/3597503.3639150]
   Madaan Aman, 2022, P ACL 2022 WORKSH CO
   Madugalla Anuradha, 2024, arXiv
   Mamede C, 2022, IEEE INT CONF AUTOM, DOI 10.1145/3551349.3559534
   Mandi Zhao, 2024, P IEEE INT C ROB AUT
   Mastropaolo Antonio, 2024, P 32 IEEE ACM INT C
   Mavrogiannis Angelos, 2024, P IEEE INT C ROBOT A
   Mc Donnell N, 2023, ACM T AUTON ADAP SYS, V18, DOI 10.1145/3584731
   McDermott MBA, 2023, ADV NEUR IN
   Mehder S, 2022, INT REQUIR ENG CONF, P176, DOI 10.1109/REW56159.2022.00040
   Melo LC, 2022, PR MACH LEARN RES
   Microsoft, 2024, GraphRAG: Graph Retrieval-Augmented Generation
   Mikolov T, 2010, 11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, P1045
   Mishra Utkarsh Aashu, 2023, P RSS 2023 WORKSH LE
   Mishra Utkarsh Aashu, 2023, P 7 ANN C ROB LEARN
   Moreno G, 2019, 2019 IEEE/ACM 14TH INTERNATIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS (SEAMS 2019), P181, DOI 10.1109/SEAMS.2019.00031
   Moreno GA, 2016, PR INT CONF AUTONOM, P147, DOI 10.1109/ICAC.2016.59
   Moreno GA, 2015, 2015 10TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE 2015) PROCEEDINGS, P1, DOI 10.1145/2786805.2786853
   Murphy C, 2009, SECOND INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION, AND VALIDATION, PROCEEDINGS, P111, DOI 10.1109/ICST.2009.18
   Nakagawa H, 2023, Intern Req Engg Work, P247, DOI 10.1109/REW57809.2023.00050
   Nam Daye, 2024, P IEEEACM 46 INT C S, DOI DOI 10.1145/3597503.3639187
   Nandu Digital Economy Governance Research Center, 2023, Observation Report
   Nascimento Nathalia, 2023, 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), P104, DOI 10.1109/ACSOS-C58168.2023.00048
   Ni Fei, 2023, P 40 INT C MACH LEAR
   Nottingham Kolby, 2023, P 40 INT C MACH LEAR
   Nunes JPKS, 2024, PROCEEDINGS OF THE 2024 IEEE/ACM 19TH SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, SEAMS 2024, P168, DOI 10.1145/3643915.3644094
   OpenAI, 2024, Hello GPT-4O
   OpenAI, 2023, Generative Models
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pan JB, 2024, Arxiv, DOI arXiv:2407.01009
   Pandey A, 2016, INT CONF SELF SELF, P130, DOI 10.1109/SASO.2016.19
   Pandya Ravi, 2024, P IEEE INT C ROB AUT
   Parisotto E., 2020, INT C MACHINE LEARNI, P7487
   Park JS, 2023, Arxiv, DOI [arXiv:2304.03442, DOI 10.48550/ARXIV.2304.03442]
   Parra-Ullauri J, 2022, ACM/IEEE 25TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS, MODELS 2022 COMPANION, P286, DOI 10.1145/3550356.3561538
   Pearce Tim, 2023, P 11 INT C LEARN REP
   Plein Laura, 2024, 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), P360, DOI 10.1145/3639478.3643119
   Pluhacek M, 2023, PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, P1812, DOI 10.1145/3583133.3596401
   Potyka Nico, 2024, P 23 INT C AUT AG MU, P1593
   Prasad A, 2023, 17TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EACL 2023, P3845
   Preda AR, 2024, IEEE WORK CONF MIN S, P242, DOI 10.1145/3643991.3644922
   Pronovost E, 2023, ADV NEUR IN
   Pternea M, 2024, Arxiv, DOI arXiv:2402.01874
   Qin Yujia, 2024, P 12 INT C LEARN REP
   Radford Alec, 2021, arXiv
   Raffel C, 2023, Arxiv, DOI [arXiv:1910.10683, DOI 10.48550/ARXIV.1910.10683]
   Ramesh A., 2022, arXiv
   Rana Krishan, 2023, P 7 ANN C ROB LEARN
   Ranz F, 2017, PROCEDIA MANUF, V9, P182, DOI 10.1016/j.promfg.2017.04.011
   Rao N, 2023, IEEE INT CONF AUTOM, P409, DOI 10.1109/ASE56229.2023.00193
   Rasul K, 2021, PR MACH LEARN RES, V139, DOI DOI 10.48550/ARXIV.2101.12072
   Reif E, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3650798
   Ren Kan, 2024, EACL, V1, P2931
   REYNOLDS DA, 1995, IEEE T SPEECH AUDI P, V3, P72, DOI 10.1109/89.365379
   Ribeiro F, 2023, 2023 IEEE/ACM INTERNATIONAL WORKSHOP ON AUTOMATED PROGRAM REPAIR, APR, P21, DOI 10.1109/APR59189.2023.00011
   Ringwald C, 2024, AAAI CONF ARTIF INTE, P23411
   Rocamonde Juan, 2024, P 12 INT C LEARN REP
   Roose Kevin, 2022, Artists Aren't Happy
   Saccon Enrico, 2024, P IEEE INT C ROB AUT
   Sahoo P, 2024, Arxiv, DOI [arXiv:2402.07927, 10.48550/arxiv.2402.07927, DOI 10.48550/ARXIV.2402.07927]
   Sakib Md Sadman, 2024, P IEEE INT C ROB AUT
   Sanchez Raquel, 2024, P 19 C SOFTW ENG AD
   Santoni de Sio F., 2021, Philosophy & Technology, V34, P1057, DOI [DOI 10.1007/S13347-021-00450, DOI 10.1007/S13347-021-00450-X]
   Santos Sofia, 2024, P IEEE ACM INT WORKS
   Sarda K., 2023, 2023 IEEE INT C AUTO, P16, DOI 10.1109/ACSOS-C58168.2023.00025
   Sawyer P., 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), P95, DOI 10.1109/RE.2010.21
   Schick Timo, 2023, arXiv
   Schuller A, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3650860
   Sera Rie, 2024, P 17 INT C COOP HUM, P1
   Shah Dhruv, 2022, P 6 ANN C ROB LEARN
   Shah Dhruv, 2023, C ROBOT LEARNING, V229, P2683
   Shen Lifeng, 2024, P 12 INT C LEARN REP
   Shen YL, 2023, Arxiv, DOI arXiv:2303.17580
   Shevtsov S, 2018, IEEE T SOFTWARE ENG, V44, P784, DOI 10.1109/TSE.2017.2704579
   Shi Haochen, 2024, P 23 INT C AUT AG MU, P2465
   Shi JY, 2024, Arxiv, DOI arXiv:2310.07127
   Shi Xiaoming, 2023, P ADV NEURAL INFORM
   Shou Xiao, 2023, P ADV NEURAL INFORM, V36, P46520
   Shriram J, 2023, ADJUNCT PROCEEDINGS OF THE 36TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE & TECHNOLOGY, UIST 2023 ADJUNCT, DOI 10.1145/3586182.3625118
   Shukla Yash, 2024, P 23 INT C AUT AG MU, P1736
   Silva S, 2024, ACM T AUTON ADAP SYS, V19, DOI 10.1145/3627163
   Silver Daniel L., 2013, P LIFEL MACH LEARN 2
   Sobania D, 2023, 2023 IEEE/ACM INTERNATIONAL WORKSHOP ON AUTOMATED PROGRAM REPAIR, APR, P23, DOI 10.1109/APR59189.2023.00012
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Song Kaitao, 2024, P 12 INT C LEARN REP
   Song Y, 2019, ADV NEUR IN, V32
   Song Yang, 2021, P INT C LEAR REPR
   Souza VitorE Silva., 2011, P 6 INT S SOFTWARE E, P60, DOI 10.1145/1988008.1988018
   Sui Y, 2024, PROCEEDINGS OF THE 17TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, WSDM 2024, P645, DOI 10.1145/3616855.3635752
   Sun Hao, 2024, P 12 INT C LEARN REP
   Sun Haotian, 2023, P 37 C NEUR INF PROC
   Sun Jiankai, 2023, P ADV NEUR INF PROC, V36, P80324
   Sun LC, 2024, Arxiv, DOI arXiv:2401.05561
   Sun Yuqiang, 2024, P 46 IEEEACM INT C S, DOI 10.1145/3597503.3639117
   Sundararajan M, 2017, PR MACH LEARN RES, V70
   Sykes Daniel, 2008, P 2008 INT WORKSH SO, P1
   Szot Andrew, 2024, P 12 INT C LEARN REP
   Takagi S, 2022, ADV NEUR IN
   Tan Weihao, 2024, P 12 INT C LEARN REP
   Tang Binh, 2021, Adv. Neural Inf. Process. Syst, V34, P23592
   Tang Peiwang, 2023, P 2023 INT C AUT AG, P1670
   Tang Z, 2021, 2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021, P1193, DOI 10.1109/ASE51524.2021.9678882
   Tanneberg Daniel, 2024, P IEEE INT C ROB AUT
   Tao ZW, 2024, Arxiv, DOI arXiv:2404.14387
   Tashiro Y, 2021, ADV NEUR IN, V34
   Tian XY, 2023, Arxiv, DOI arXiv:2310.18075
   Todorov E, 2012, IEEE INT C INT ROBOT, P5026, DOI 10.1109/IROS.2012.6386109
   Tsigkanos Christos, 2023, Computational Science - ICCS 2023: 23rd International Conference, Proceedings. Lecture Notes in Computer Science (14073), P321, DOI 10.1007/978-3-031-35995-8_23
   Tsigkanos C, 2023, Soft Anal Evol Reeng, P678, DOI 10.1109/SANER56733.2023.00070
   Tufano M, 2022, 3RD ACM/IEEE INTERNATIONAL CONFERENCE ON AUTOMATION OF SOFTWARE TEST (AST 2022), P54, DOI 10.1145/3524481.3527220
   U.S. Department of Defense, 2023, DoD directive 3000.09, autonomy in weapon systems
   Le VH, 2021, 2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021, P492, DOI 10.1109/ASE51524.2021.9678773
   Varenov V, 2021, 29TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE WORKSHOPS (REW 2021), P444, DOI [10.1109/REW53955.2021.9714713, 10.1109/REW53955.2021.00081]
   Vaswani A, 2017, ADV NEUR IN, V30
   Villegas N.M., 2013, DYNAMICO: A Reference Model for Governing Control Objectives and Context Relevance in Self-Adaptive Software Systems, P265, DOI [DOI 10.1007/978-3-642-35813-511, 10.1007/978-3-642-35813-5_11, DOI 10.1007/978-3-642-35813-5_11]
   Walker J, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3650844
   Wan XC, 2023, Arxiv, DOI arXiv:2305.14926
   Wan Xingchen, 2023, FINDINGS ASS COMPUTA, P3493
   Wang Bailin, 2023, P 37 C NEUR INF PROC
   Wang BY, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580895
   Wang JD, 2023, Arxiv, DOI arXiv:2302.12095
   Wang L, 2024, FRONT COMPUT SCI-CHI, V18, DOI 10.1007/s11704-024-40231-1
   Wang Letian, 2022, P 6 ANN C ROB LEARN
   Wang WS, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P146, DOI 10.1145/3611643.3616256
   Wang Xuezhi, 2024, P 12 INT C LEARN REP
   Wang Xuezhi, 2023, P 11 INT C LEARN REP
   Wang Yidong, 2024, P 12 INT C LEARN REP
   Wang Z., 2023, P 11 INT C LEARN REP
   Wang Zihao, 2023, P 37 C NEUR INF PROC
   Wei JS, 2022, Arxiv, DOI arXiv:2201.11903
   Wei YX, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P172, DOI 10.1145/3611643.3616271
   Welleck S, 2022, ADV NEUR IN
   Wen HM, 2023, 31ST ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS, ACM SIGSPATIAL GIS 2023, P328, DOI 10.1145/3589132.3625614
   Wen QS, 2023, PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, P6778
   Weyns Danny, 2022, ACM SIGSOFT Software Engineering Notes, P18, DOI 10.1145/3561846.3561852
   Weyns D., 2012, 2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, P89, DOI 10.1109/SEAMS.2012.6224395
   Weyns D, 2022, J INTEGR DES PROCESS, V26, P351, DOI 10.3233/JID-220003
   Weyns D, 2023, I W S E ADAP SM SYS, P90, DOI 10.1109/SEAMS59076.2023.00022
   Weyns D, 2023, ACM T AUTON ADAP SYS, V18, DOI 10.1145/3589227
   Weyns D, 2023, ACM T SOFTW ENG METH, V32, DOI 10.1145/3522585
   Weyns D, 2012, ACM T AUTON ADAP SYS, V7, DOI 10.1145/2168260.2168268
   Weyns Danny, 2020, An Introduction to Self-Adaptive Systems: A Contemporary Software Engineering Perspective
   Weyns Danny, 2013, SOFTWARE ENG SELF AD, P76
   Whittle J, 2009, PROCEEDINGS OF THE 2009 17TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, P79, DOI 10.1109/RE.2009.36
   Wood NG, 2023, ETHICS INF TECHNOL, V25, DOI 10.1007/s10676-023-09690-1
   Wu Haoze, 2023, P 3 WORKSH MATH REAS
   Wu SF, 2020, ADV NEUR IN, V33
   Wu TS, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3517582
   Wu Yuhuai, 2022, P ADV NEURAL INFORM
   Xi ZH, 2023, Arxiv, DOI arXiv:2309.07864
   Xia CS, 2023, IEEE INT CONF AUTOM, P522, DOI 10.1109/ASE56229.2023.00047
   Xia CS, 2023, PROC INT CONF SOFTW, P1482, DOI 10.1109/ICSE48619.2023.00129
   Xia Chunqiu Steven., P IEEEACM 46 INT C S, P2024, DOI DOI 10.1145/3597503.3639121
   Xiao Ziyang, 2024, 12 INT C LEARNING RE
   Xie Tian, 2022, P INT C LEARN REPR
   Xu Hanwei, 2022, P 2022 C EMPIRICAL M, P8162, DOI 10.18653/v1/2022.emnlp-main.559
   Xu Jiehui, 2022, P INT C LEARN REPR
   Xu Mengdi, 2023, P 11 INT C LEARN REP
   Xu XH, 2024, Arxiv, DOI arXiv:2402.13116
   Xu ZL, 2024, Arxiv, DOI arXiv:2310.18940
   Xue Zhiyi, 2024, 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), P314, DOI 10.1145/3639478.3643087
   Yamagata Taku, 2023, P 40 INT C MACH LEAR
   Yan H, 2024, Arxiv, DOI arXiv:2312.08248
   Yang Aidan Z. H., 2024, P IEEE ACM 46 INT C, DOI DOI 10.1145/3597503.3623342
   Yang FK, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P2050, DOI 10.1145/3611643.3613866
   Yang Heng, 2023, FIND ASS COMP LING E, P13593, DOI [10.18653/v1/2023.findings-emnlp.907, DOI 10.18653/V1/2023.FINDINGS-EMNLP.907]
   Yang JD, 2024, IEEE ACCESS, V12, P27858, DOI 10.1109/ACCESS.2024.3366803
   Yang LY, 2024, ACM COMPUT SURV, V56, DOI [10.1145/3626235, 10.1145/3648469]
   Yang Yaodong, 2022, P ADV NEURAL INFORM
   Yang ZJ, 2024, Arxiv, DOI [arXiv:2311.01043, DOI 10.48550/ARXIV.2311.01043]
   Yang Zhun, 2023, P 11 INT C LEARN REP
   Yang Zhutian, 2023, P 7 ANN C ROB LEARN
   Yang Ziyi, 2024, P IEEE INT C ROB AUT
   Yao JA, 2023, Arxiv, DOI arXiv:2311.03739
   Yao SY, 2023, ADV NEUR IN
   Yao SY, 2022, ADV NEUR IN
   Yao YF, 2024, HIGH-CONFID COMPUT, V4, DOI 10.1016/j.hcc.2024.100211
   Yoneda Takuma, 2024, P IEEE INT C ROB AUT
   Yu CN, 2023, IEEE INT CONF ROBOT, P3432, DOI 10.1109/ICRA48891.2023.10161018
   Yu Tao, 2024, 12 INT C LEARN REPR
   Yu Tianhe, 2019, P C ROB LEARN COPL
   Yu WH, 2023, Arxiv, DOI arXiv:2306.08647
   Yuan W, 2022, PROCEEDINGS OF THE 31ST ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS, ISSTA 2022, P678, DOI 10.1145/3533767.3534219
   Ze Yanjie, 2024, P WORKSH 3D VIS REPR
   Zeng A, 2023, AAAI CONF ARTIF INTE, P11121
   Zeng FL, 2023, Arxiv, DOI arXiv:2311.07226
   Zhang B, 2024, Arxiv, DOI [arXiv:2311.13884, 10.48550/arxiv.2311.13884, DOI 10.48550/ARXIV.2311.13884]
   Zhang Chenyuan, 2024, 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), P276, DOI 10.1145/3639478.3643065
   Zhang CY, 2024, AAAI CONF ARTIF INTE, P17591
   Zhang CR, 2024, AAAI CONF ARTIF INTE, P19525
   Zhang H, 2023, IEEE ROBOT AUTOM LET, V8, P4831, DOI 10.1109/LRA.2023.3290511
   Zhang James Y., 2024, P 12 INT C LEARN REP
   Zhang Jesse, 2023, P 7 ANN C ROB LEARN
   Zhang MY, 2021, 2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS (ACSOS 2021), P1, DOI 10.1109/ACSOS52086.2021.00024
   Zhang Mingyue, 2024, P ACM INT C HUM FACT
   Zhang Qiang, 2024, P IEEE INT C ROB AUT
   Zhang QJ, 2024, Arxiv, DOI arXiv:2312.15223
   Zhang SJ, 2023, Arxiv, DOI arXiv:2305.02499
   Zhang Tianjun, 2023, P 11 INT C LEARN REP
   Zhang Weinan, 2022, P ADV NEURAL INFORM
   Zhang Xiaohan, 2024, P IEEE INT C ROB AUT
   Zhang Yunhao, 2023, P 11 INT C LEARN REP
   Zhang ZY, 2024, Arxiv, DOI [arXiv:2311.07989, DOI 10.48550/ARXIV.2311.07989]
   Zhao A, 2024, AAAI CONF ARTIF INTE, P19632
   Zhao HY, 2023, Arxiv, DOI [arXiv:2309.01029, DOI 10.48550/ARXIV.2309.01029]
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
   Zheng Qinqing, 2022, INT C MACH LEARN ICM, P27042
   Zhong ZY, 2023, IEEE INT CONF ROBOT, P3560, DOI 10.1109/ICRA48891.2023.10161463
   Zhong ZY, 2023, Arxiv, DOI arXiv:2306.06344
   Zhou Denny, 2024, P 12 INT C LEARN REP
   Zhou Haotian, 2024, P IEEE INT C ROB AUT
   Zhou Jin Peng, 2024, P 12 INT C LEARN REP
   Zhou Siyuan, 2023, P 37 C NEUR INF PROC
   Zhou T, 2023, ADV NEUR IN
   Zhou T, 2022, PR MACH LEARN RES
   Zhou X, 2024, 2024 IEEE/ACM 46TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: NEW IDEAS AND EMERGING RESULTS, ICSE-NIER 2024, P47, DOI 10.1145/3639476.3639762
   Zhou Zhehua, 2024, P IEEE INT C ROB AUT
   Zhu Fangqi, 2023, P 27 AAAI C ART INT, DOI [10.1609/aaai.v37i11.26645, DOI 10.1609/AAAI.V37I11.26645]
   Zhu TC, 2023, PROCEEDINGS OF THE THIRTY-SECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2023, P4693
   Zhu XZ, 2023, Arxiv, DOI arXiv:2305.17144
   Zhu ZB, 2024, Arxiv, DOI arXiv:2311.01223
   Zhuang Yuchen, 2024, P 12 INT C LEARN REP
   Zohar O, 2023, Arxiv, DOI arXiv:2306.08893
   Zou H, 2023, Arxiv, DOI arXiv:2305.14671
NR 434
TC 0
Z9 0
U1 1
U2 1
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 1556-4665
EI 1556-4703
J9 ACM T AUTON ADAP SYS
JI ACM Trans. Auton. Adapt. Syst.
PD SEP
PY 2024
VL 19
IS 3
AR 13
DI 10.1145/3686803
PG 60
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA K9C8Z
UT WOS:001346813900006
OA Bronze
DA 2024-12-25
ER

PT J
AU Nan, D
   Sun, S
   Zhang, S
   Zhao, X
   Kim, JH
AF Nan, Dongyan
   Sun, Seungjong
   Zhang, Shunan
   Zhao, Xiangying
   Kim, Jang Hyun
TI Analyzing behavioral intentions toward Generative Artificial
   Intelligence: the case of ChatGPT
SO UNIVERSAL ACCESS IN THE INFORMATION SOCIETY
LA English
DT Article; Early Access
DE Generative AI; ChatGPT; Expectation confirmation model; Continuance
   intention to use; Intention to recommend
ID INFORMATION PRIVACY CONCERNS; EXPECTATION-CONFIRMATION MODEL;
   CONTINUANCE INTENTION; USER ACCEPTANCE; PERCEIVED INNOVATIVENESS;
   PRODUCT INNOVATIVENESS; SYSTEMS CONTINUANCE; IMPACT; ADOPTION;
   DETERMINANTS
AB Generative artificial intelligence (AI) is an innovative AI technology that has garnered considerable attention worldwide. This study aimed to facilitate the development of such technologies by examining the factors affecting individuals' intentions toward generative AI (e.g., ChatGPT). Concretely, we developed a causal model by extending the expectation confirmation model with information system success theory, privacy concerns, and perceived innovativeness. Then, we tested the model by analyzing survey-based data from 252 Korean ChatGPT users. As a result, we found that antecedent variables -information quality, system quality, privacy concerns, and perceived innovativeness- play notable roles in affecting users' intentions to continually use and recommend generative AI ChatGPT. Overall, the current research is one of the first attempts to track the variables influencing individuals' intentions to continually use and recommend in the context of generative AI ChatGPT.
C1 [Nan, Dongyan; Zhang, Shunan; Zhao, Xiangying; Kim, Jang Hyun] Sungkyunkwan Univ, Dept Human Artificial Intelligence Interact, Seoul, South Korea.
   [Nan, Dongyan; Zhang, Shunan; Zhao, Xiangying; Kim, Jang Hyun] Sungkyunkwan Univ, Dept Interact Sci, Seoul, South Korea.
   [Sun, Seungjong; Kim, Jang Hyun] Sungkyunkwan Univ, Dept Appl Artificial Intelligence, Seoul, South Korea.
C3 Sungkyunkwan University (SKKU); Sungkyunkwan University (SKKU);
   Sungkyunkwan University (SKKU)
RP Kim, JH (corresponding author), Sungkyunkwan Univ, Dept Human Artificial Intelligence Interact, Seoul, South Korea.; Kim, JH (corresponding author), Sungkyunkwan Univ, Dept Interact Sci, Seoul, South Korea.; Kim, JH (corresponding author), Sungkyunkwan Univ, Dept Appl Artificial Intelligence, Seoul, South Korea.
EM alohakim@skku.edu
OI ZHANG, Shunan/0000-0003-3875-1849
FU National Research Foundation of Korea
FX No Statement Available
CR Abdul S., 2023, How to Fix the Network Error in ChatGPT
   Ahlstrom D, 2010, ACAD MANAGE PERSPECT, V24, P11, DOI 10.5465/AMP.2010.52842948
   Al-Sharafi MA, 2022, INT J BANK MARK, V40, P1071, DOI 10.1108/IJBM-07-2021-0291
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Avancha S, 2012, ACM COMPUT SURV, V45, DOI 10.1145/2379776.2379779
   Baek TH, 2023, TELEMAT INFORM, V83, DOI 10.1016/j.tele.2023.102030
   Bhattacherjee A, 2004, MIS QUART, V28, P229
   Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
   Bölen MC, 2020, TECHNOL SOC, V60, DOI 10.1016/j.techsoc.2019.101209
   Brown I., 2008, Electron. J. Inform. Syst. Evaluation, V11, P109
   Byrne B, 2010, INTERNATIONAL HANDBOOK OF PSYCHOLOGY IN EDUCATION, P3
   Cheng YM, 2023, LIBR HI TECH, V41, P1748, DOI 10.1108/LHT-11-2021-0391
   Cheng YM, 2014, INFORM TECHNOL PEOPL, V27, P230, DOI 10.1108/ITP-01-2013-0024
   Chin WW, 1998, QUANT METH SER, P295
   Cho H, 2023, UNIVERSAL ACCESS INF, V22, P1325, DOI 10.1007/s10209-022-00913-8
   Choi HS, 2017, COMPUT SECUR, V67, P244, DOI 10.1016/j.cose.2017.03.007
   Choudhury A, 2023, J MED INTERNET RES, V25, DOI 10.2196/47184
   Daghan G, 2016, COMPUT HUM BEHAV, V60, P198, DOI 10.1016/j.chb.2016.02.066
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   DeLone WH, 1992, INFORM SYST RES, V3, P60, DOI 10.1287/isre.3.1.60
   Dhagarra D, 2020, INT J MED INFORM, V141, DOI 10.1016/j.ijmedinf.2020.104164
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Falak Z., 2023, How to Fix the ChatGPT Login Error
   Falk RF., 1992, PRIMER SOFT MODELING
   Falkenreck C, 2011, J MARKET MANAG-UK, V27, P225, DOI 10.1080/0267257X.2011.545672
   Fang YH, 2011, INTERNET RES, V21, P479, DOI 10.1108/10662241111158335
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Foroughi B, 2024, INT J HUM-COMPUT INT, V40, P4501, DOI 10.1080/10447318.2023.2226495
   Fu FQ, 2013, J MARKET THEORY PRAC, V21, P257, DOI 10.2753/MTP1069-6679210302
   Hann IH, 2007, J MANAGE INFORM SYST, V24, P13, DOI 10.2753/MIS0742-1222240202
   Hsiao KL, 2022, LIBR HI TECH, V40, P929, DOI 10.1108/LHT-08-2021-0274
   Hu K., 2023, REUTERS         0202
   Hussain K, 2025, KYBERNETES, V54, P371, DOI 10.1108/K-05-2023-0779
   Hwang J, 2019, INT J HOSP MANAG, V81, P94, DOI 10.1016/j.ijhm.2019.03.002
   Inman JJ, 2017, J RETAILING, V93, P7, DOI 10.1016/j.jretai.2016.12.006
   Jasmine Hashana A. M., 2023, 2023 7 INT C TRENDS, P1001, DOI DOI 10.1109/ICOEI56765.2023.10125852
   Jo H, 2023, TELEMAT INFORM, V85, DOI 10.1016/j.tele.2023.102067
   Kalinic Z, 2019, J RETAIL CONSUM SERV, V49, P143, DOI 10.1016/j.jretconser.2019.03.016
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Langerak F, 2006, J PROD INNOVAT MANAG, V23, P203, DOI 10.1111/j.1540-5885.2006.00194.x
   Lee KY, 2021, INTERNET RES, V31, P1899, DOI 10.1108/INTR-06-2020-0327
   Li J, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101410
   Li L, 2021, ELECTRON MARK, V31, P575, DOI 10.1007/s12525-020-00454-z
   Li Y, 2014, DECIS SUPPORT SYST, V57, P343, DOI 10.1016/j.dss.2013.09.018
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Ling ESW, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02353-y
   Liu KF, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107026
   Liu Y, 2011, COMPUT HUM BEHAV, V27, P890, DOI 10.1016/j.chb.2010.11.014
   Liu YL, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107716
   Lu YF, 2019, J ELECTRON COMMER RE, V20, P105
   Malhotra NK, 2004, INFORM SYST RES, V15, P336, DOI 10.1287/isre.1040.0032
   McKinney V, 2002, INFORM SYST RES, V13, P296, DOI 10.1287/isre.13.3.296.76
   Menon D, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e20962
   Miltgen CL, 2013, DECIS SUPPORT SYST, V56, P103, DOI 10.1016/j.dss.2013.05.010
   Molinari Lori K., 2008, Journal of Services Marketing, V22, P363, DOI 10.1108/08876040810889139
   Mouakket S, 2015, COMPUT HUM BEHAV, V53, P102, DOI 10.1016/j.chb.2015.06.045
   Nan DY, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2278941
   Nan DY, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.103108
   Nan D, 2022, TECHNOL FORECAST SOC, V176, DOI 10.1016/j.techfore.2021.121451
   Nelson RR, 2005, J MANAGE INFORM SYST, V21, P199, DOI 10.1080/07421222.2005.11045823
   Oghuma AP, 2016, TELEMAT INFORM, V33, P34, DOI 10.1016/j.tele.2015.05.006
   Oliveira T, 2016, COMPUT HUM BEHAV, V61, P404, DOI 10.1016/j.chb.2016.03.030
   Ottenbacher MC, 2009, INT J CONTEMP HOSP M, V21, P523, DOI 10.1108/09596110910967782
   Ouyang L, 2022, ADV NEUR IN
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Prayag G, 2017, J TRAVEL RES, V56, P41, DOI 10.1177/0047287515620567
   Rajaobelina L, 2021, PSYCHOL MARKET, V38, P2339, DOI 10.1002/mar.21548
   Ray PP., 2023, CHATGPT COMPREHENSIV
   Rudolph J., 2023, J. Appl. Learn. Teach., V6
   Seddon PB, 1997, INFORM SYST RES, V8, P240, DOI 10.1287/isre.8.3.240
   Shahsavar Y, 2023, JMIR HUM FACTORS, V10, DOI 10.2196/47564
   Short C. E., 2023, Journal of Business Venturing Insights, V19
   Shukla P, 2014, INFORM MANAGE-AMSTER, V51, P113, DOI 10.1016/j.im.2013.11.003
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Sun SW, 2022, TECHNOL FORECAST SOC, V178, DOI 10.1016/j.techfore.2022.121596
   Tan X, 2015, J ENTERP INF MANAG, V28, P423, DOI 10.1108/JEIM-04-2014-0039
   Teo TSH, 2008, J MANAGE INFORM SYST, V25, P99, DOI 10.2753/MIS0742-1222250303
   Venkatesh V, 2011, INFORM SYST J, V21, P527, DOI [10.1111/j.1365-2575.2011.00373.x, 10.1111/J.1365-2575.2011.00373.x]
   VENKATRAMAN MP, 1991, J RETAILING, V67, P51
   Walters WP, 2020, NAT BIOTECHNOL, V38, P143, DOI 10.1038/s41587-020-0418-2
   Watchravesringkan K, 2010, J FASH MARK MANAG, V14, P263, DOI 10.1108/13612021011046101
   Xia Q, 2022, COMPUT EDUC, V189, DOI 10.1016/j.compedu.2022.104582
   Xu H, 2011, J ASSOC INF SYST, V12, P798
   Xu XW, 2020, J TRAVEL TOUR MARK, V37, P429, DOI 10.1080/10548408.2020.1784365
   Yin FS, 2015, TECHNOL FORECAST SOC, V99, P267, DOI 10.1016/j.techfore.2015.07.019
   Yuen KF, 2019, J RETAIL CONSUM SERV, V49, P316, DOI 10.1016/j.jretconser.2019.03.022
   Zheng YM, 2013, DECIS SUPPORT SYST, V56, P513, DOI 10.1016/j.dss.2012.11.008
NR 87
TC 2
Z9 2
U1 73
U2 98
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1615-5289
EI 1615-5297
J9 UNIVERSAL ACCESS INF
JI Univers. Access Inf. Soc.
PD 2024 APR 25
PY 2024
DI 10.1007/s10209-024-01116-z
EA APR 2024
PG 11
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA OM1S8
UT WOS:001207604900001
DA 2024-12-25
ER

PT J
AU Al-Abdullatif, AM
AF Al-Abdullatif, Ahlam Mohammed
TI Modeling Teachers' Acceptance of Generative Artificial Intelligence Use
   in Higher Education: The Role of AI Literacy, Intelligent TPACK, and
   Perceived Trust
SO EDUCATION SCIENCES
LA English
DT Article
DE generative artificial intelligence; GenAI; AI literacy; intelligent
   TPACK; acceptance; teachers
ID PEDAGOGICAL CONTENT KNOWLEDGE; ADOPTION; TECHNOLOGY
AB This study delves into the factors that drive teachers' adoption of generative artificial intelligence (GenAI) technologies in higher education. Anchored by the technology acceptance model (TAM), the research expands its inquiry by integrating the constructs of intelligent technological pedagogical content knowledge (TPACK), AI literacy, and perceived trust. Data were gathered from a sample of 237 university teachers through a structured questionnaire. The study employed structural equation modeling (SEM) to determine the relationships among the constructs. The results revealed that both AI literacy and perceived ease were the most influential factors affecting teachers' acceptance of GenAI. Notably, intelligent TPACK and perceived trust were found to be pivotal mediators in this relationship. The findings underscore the importance of fostering AI literacy and adapting intelligent TPACK frameworks to better equip educators in the age of AI. Furthermore, there is a clear need for targeted professional development initiatives focusing on practical training that enhances AI literacy. These programs should provide hands-on experience with GenAI tools, boosting educators' confidence and ability to integrate them into their teaching practices.
C1 [Al-Abdullatif, Ahlam Mohammed] King Faisal Univ, Coll Educ, Curriculum & Instruct Dept, POB 400, Al Hufuf 31982, Al Ahsa, Saudi Arabia.
C3 King Faisal University
RP Al-Abdullatif, AM (corresponding author), King Faisal Univ, Coll Educ, Curriculum & Instruct Dept, POB 400, Al Hufuf 31982, Al Ahsa, Saudi Arabia.
EM aalabdullateef@kfu.edu.sa
RI Al-Abdullatif, Ahlam/P-9441-2016
OI Al-Abdullatif, Ahlam/0000-0003-2815-1137
FU Deanship of Scientific Research, Vice Presidency of Graduate Studies and
   scientific research at King Faisal University;  [A374]
FX The Deanship of Scientific Research, Vice Presidency of Graduate Studies
   and scientific research at King Faisal University (grant A374).
CR Al Darayseh A., 2023, COMPUTERS ED ARTIFIC, V4, P100132, DOI DOI 10.1016/J.CAEAI.2023.100132
   Al-Abdullatif AM, 2024, BEHAV SCI-BASEL, V14, DOI 10.3390/bs14090845
   Al-Abdullatif AM, 2023, EDUC SCI, V13, DOI 10.3390/educsci13111151
   Al-Abdullatif AM, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1129070
   Al-Amri NA, 2024, EDUC SCI, V14, DOI 10.3390/educsci14091034
   Amoozadeh Matin, 2024, SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, P67, DOI 10.1145/3626252.3630842
   Baek TH, 2023, TELEMAT INFORM, V83, DOI 10.1016/j.tele.2023.102030
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chatterjee S, 2020, EDUC INF TECHNOL, V25, P3443, DOI 10.1007/s10639-020-10159-7
   Chen X., 2020, Computers and Education: Artificial Intelligence, V1, P100002, DOI [10.1016/j.caeai.2020.100002 10.1016/j.caeai.2020.100002, DOI 10.1016/J.CAEAI.2020.100002]
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Davis F. D., 1986, THESIS
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Elmohandes N, 2024, EUR J TOUR RES, V36, DOI 10.54055/ejtr.v36i.3286
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   Ge J, 2023, HEPATOL COMMUN, V7, DOI 10.1097/HC9.0000000000000097
   Gill Sukhpal Singh, 2024, Internet of Things and Cyber-Physical Systems, V4, P19, DOI 10.1016/j.iotcps.2023.06.002
   Gillath O, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2020.106607
   Grassini S, 2023, EDUC SCI, V13, DOI 10.3390/educsci13070692
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hsu LW, 2016, COMPUT ASSIST LANG L, V29, P1287, DOI 10.1080/09588221.2016.1278024
   Jang J, 2021, IEEE ACCESS, V9, P6798, DOI 10.1109/ACCESS.2020.3048708
   Joo YJ, 2018, EDUC TECHNOL SOC, V21, P48
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kim S, 2021, KUNSTL INTELL, V35, P139, DOI 10.1007/s13218-021-00731-9
   Kline R. B., 2015, PRINCIPLES PRACTICE
   Kong SC., 2021, COMPUTERS ED ARTIFIC, V2, P100026, DOI [DOI 10.1016/J.CAEAI.2021.100026, 10.1016/j.caeai.2021.100026]
   Kong SC, 2022, COMPUT HUM BEHAV REP, V7, DOI 10.1016/j.chbr.2022.100223
   Laupichler M.C., 2022, Comput. Educ. Artif. Intell, V3, P100101, DOI DOI 10.1016/J.CAEAI.2022.100101
   Lee T, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2020.102098
   Liao YK, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14020815
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Lodge JM, 2023, AUSTRALAS J EDUC TEC, V39, P18, DOI 10.14742/ajet.8695
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Mailizar M., 2021, Journal of Digital Learning in Teacher Education, V37, P196, DOI DOI 10.1080/21532974.2021.1934613
   Malik R, 2021, INT J EMERG TECHNOL, V16, P200, DOI 10.3991/ijet.v16i18.24315
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Mou J, 2017, BEHAV INFORM TECHNOL, V36, P125, DOI 10.1080/0144929X.2016.1203024
   Mujiono M., 2023, Jurnal Kependidikan: Jurnal Hasil Penelitian Dan Kajian Kepustakaan Di Bidang Pendidikan, Pengajaran Dan Pembelajaran, V9, P618, DOI [10.33394/jk.v9i2.7801, DOI 10.33394/JK.V9I2.7801]
   Nazaretsky T, 2022, LAK22 CONFERENCE PROCEEDINGS: THE TWELFTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, P56, DOI 10.1145/3506860.3506866
   Nazaretsky T, 2022, BRIT J EDUC TECHNOL, V53, P914, DOI 10.1111/bjet.13232
   Nazzal A., 2022, Webology, V19, P2414
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Rane N., 2023, Future Prospects, and Ethical Considerations in Education
   Rosli MS, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141811389
   Scherer R, 2019, COMPUT EDUC, V128, P13, DOI 10.1016/j.compedu.2018.09.009
   Su JH, 2024, INTERACT LEARN ENVIR, V32, P5494, DOI 10.1080/10494820.2023.2217864
   Thohir MA, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/12918
   Tossell CC, 2024, IEEE T LEARN TECHNOL, V17, P1069, DOI 10.1109/TLT.2024.3355015
   Uygun D., 2024, Advances in Mobile Learning Educational Research, V4, P931, DOI [10.25082/amler.2024.01.005, DOI 10.25082/AMLER.2024.01.005]
   Velander J, 2024, EDUC INF TECHNOL, V29, P4085, DOI 10.1007/s10639-023-11990-4
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Wang BC, 2023, BEHAV INFORM TECHNOL, V42, P1324, DOI 10.1080/0144929X.2022.2072768
   Yang JZ, 2021, INTERACT LEARN ENVIR, V29, P1062, DOI 10.1080/10494820.2019.1627560
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   Yue M, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142315620
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhang K., 2021, COMPUTERS ED ARTIFIC, V2, P100025, DOI [DOI 10.1016/J.CAEAI.2021.100025, https://doi.org/10.1016/j.caeai.2021.100025, 10.1016/j.caeai.2021.100025]
   Zhao LL, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142114549
NR 64
TC 0
Z9 0
U1 14
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD NOV
PY 2024
VL 14
IS 11
AR 1209
DI 10.3390/educsci14111209
PG 20
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA N5K9O
UT WOS:001364738600001
OA gold
DA 2024-12-25
ER

PT J
AU Sekli, GFM
   Godo, A
   Veliz, JC
AF Sekli, Giulio F. Marchena
   Godo, Amy
   Veliz, Jose Carlos
TI GENERATIVE AI SOLUTIONS FOR FACULTY AND STUDENTS: A REVIEW OF LITERATURE
   AND ROADMAP FOR FUTURE RESEARCH
SO JOURNAL OF INFORMATION TECHNOLOGY EDUCATION-RESEARCH
LA English
DT Article
DE generative AI; education; systematic literature review; teaching
   materials; skill development; academic performance
ID CHATGPT
AB Aim/Purpose This paper aims to address the gap in comprehensive, real -world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness and real -world impact. Background This study addresses this gap by conducting a systematic literature review to collate and analyze real -life instances of GenAI applications in higher education, thus providing a nuanced understanding of its practical implementations and measurable outcomes. Methodology The paper utilizes a systematic literature review methodology, adopting the PRISMA approach complemented by a thematic analysis procedure to ensure a comprehensive and in-depth evaluation of the literature. It synthesizes information from relevant articles from 2022 to 2024, focusing on the applications of GenAI in higher education. This analysis covers various aspects, including research settings, analysis scales, data types, collection tools, and analytical methods. Contribution The paper contributes to the academic community by offering a comprehensive review of GenAI applications in education, highlighting the current precision level of these tools, and providing strategic recommendations for their effective use in academia. Furthermore, the research defines seven specific cases where Gen AI can be utilized as a reference for educational institutions in their adoption strategies. Findings Key findings include the versatility of GenAI in generating teaching materials, enhancing skill development, supporting student tasks, academic performance evaluation, feedback delivery, and its role as a virtual assistant and in research support. Recommendations for Practitioners Recommendations for Researchers Practitioners are advised to explore the integration of GenAI for diverse educational purposes, from content creation to student assessment, while being cognizant of its limitations and ethical considerations. Future research should focus on addressing the gaps identified, such as the implications of GenAI in research roles, its application in various disciplines, and the exploration of newly developed AI tools tailored to specific educational needs. Impact on Society The findings of this paper highlight the potential of GenAI in revolutionizing the educational sector, offering personalized learning experiences, and significantly influencing teaching methodologies and student engagement, but it also reveals significant deficiencies of Generative AI, known as hallucinations, which can impact the expected results. Future Research Subsequent research should explore the evolving capabilities of GenAI models, their impact on various academic disciplines, and the development of pedagogical strategies to optimize their use in education.
C1 [Sekli, Giulio F. Marchena; Godo, Amy; Veliz, Jose Carlos] Peru Pontificia Univ Catol Peru, Ctr Catol Grad Business Sch, Lima, Peru.
RP Sekli, GFM (corresponding author), Peru Pontificia Univ Catol Peru, Ctr Catol Grad Business Sch, Lima, Peru.
EM gmarchena@pucp.pe; a20184887@pucp.edu.pe; jcveliz@pucp.edu.pe
RI MARCHENA SEKLI, GIULIO FRANZ/IST-0279-2023
OI Veliz Palomino, Jose Carlos/0000-0002-1157-0653; Marchena Sekli,
   Giulio/0000-0003-3854-2879
CR Alneyadi S, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13417
   Alvarez-alvarez C, 2023, ETR&D-EDUC TECH RES, V71, P1709, DOI 10.1007/s11423-023-10239-8
   Ansari AN, 2024, EDUC INF TECHNOL, V29, P11281, DOI 10.1007/s10639-023-12223-4
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Banks J., 2024, Gemma: Introducing new state-of-the-art open models
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Barrett JS, 2024, CTS-CLIN TRANSL SCI, V17, DOI 10.1111/cts.13723
   Bonnet W., 2023, Using Quiz Wizard for language courses
   Boschetti F, 2019, AGE ACCESS GRUNDFRAG, V10, P321, DOI 10.1515/9783110599572-018
   Boyd E., 2024, Microsoft and Mistral AI announce new partnership to accelerate AI innovation and introduce Mistral Large first on Azure
   Cardoso V., 2024, Can Devin, the new AI, replace human software engineers?
   Carrasco Rodríguez A, 2023, STUD HIST-HIST MOD, V45, P101, DOI 10.14201/shhmo2023451101146
   Chaudhry IS, 2023, COGENT EDUC, V10, DOI 10.1080/2331186X.2023.2210461
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dai Y, 2023, AUSTRALAS J EDUC TEC, V39, P74, DOI 10.14742/ajet.8843
   Dasari D, 2024, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1295413
   Deike M, 2024, J BUS FINANC LIBR, V29, P125, DOI 10.1080/08963568.2024.2317534
   Dinh H, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app132212446
   Galli G., 2024, Peeling back the curtain to unmask the wizard of AI: Considering the collaborative relationship between non-technical subject matter experts and artificial intelligence, DOI [10.2139/ssrn.4694869, DOI 10.2139/SSRN.4694869]
   Grassini S, 2023, EDUC SCI, V13, DOI 10.3390/educsci13070692
   Guo K, 2024, EDUC INF TECHNOL, V29, P8435, DOI 10.1007/s10639-023-12146-0
   Guo Y, 2023, J CHEM EDUC, V100, P4876, DOI 10.1021/acs.jchemed.3c00505
   Ha Y J., 2023, Exploring the impacts of generative AI on the future of teaching and learning
   Heinrichs J., 2024, Unite.AI
   Hijriyah L, 2024, OPEN HOUSE INT, V49, P63, DOI 10.1108/OHI-02-2023-0031
   Hsu MH, 2024, HEALTH EDUC J, V83, P352, DOI 10.1177/00178969231197371
   Ibrahimov V., 2023, Medium
   Ismail SN, 2021, RESOUR POLICY, V74, DOI 10.1016/j.resourpol.2021.102250
   Ismail SN, 2021, SAFETY SCI, V143, DOI 10.1016/j.ssci.2021.105438
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Karakose T, 2023, ADM SCI, V13, DOI 10.3390/admsci13070157
   Kirwan A, 2024, IRISH EDUC STUD, V43, P1389, DOI 10.1080/03323315.2023.2284901
   Lesage J, 2024, INT J MECH ENG EDUC, V52, P88, DOI 10.1177/03064190231166665
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Lopez-Garcia G, 2021, IEEE ACCESS, V9, P72387, DOI 10.1109/ACCESS.2021.3080085
   Masters K, 2024, MED TEACH, V46, P752, DOI 10.1080/0142159X.2024.2305365
   McCallum L, 2024, J MULTICULT EDUC, V18, P153, DOI 10.1108/JME-06-2023-0043
   McHugh-Johnson M., 2024, 100 things we announced at I/O 2024
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Michalon B, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1251163
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Moher D, 2009, ANN INTERN MED, V151, P264, DOI [10.1136/bmj.b2700, 10.1136/bmj.b2535, 10.1371/journal.pmed.1000097, 10.1186/2046-4053-4-1, 10.1016/j.ijsu.2010.07.299, 10.1136/bmj.i4086, 10.1016/j.ijsu.2010.02.007]
   Mollick E., 2024, What OpenAI did
   Mondal H, 2023, INDIAN J VASCULAR EN, V10, P200, DOI 10.4103/ijves.ijves_37_23
   Ngo A, 2024, ACAD PATHOL, V11, DOI 10.1016/j.acpath.2023.100099
   Nowell LS, 2017, INT J QUAL METH, V16, DOI 10.1177/1609406917733847
   OpenAI, 2024, Hello GPT-4O
   Oppenlaender J, 2024, Arxiv, DOI arXiv:2303.13534
   Ossa C, 2023, EUR J EDUC PSYCHOL, V16, DOI 10.32457/ejep.v16i2.2412
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Popovici MD, 2024, INT J HUM-COMPUT INT, V40, P7743, DOI 10.1080/10447318.2023.2269006
   Porter L., 2024, Learn AI-Assisted Python Programming: With Github Copilot and ChatGPT
   Psiropoulos D, 2016, EDUC INF TECHNOL, V21, P209, DOI 10.1007/s10639-014-9316-x
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Rose R., 2023, ChatGPT in higher education: Artificial Intelligence and its pedagogical value.
   Ross EAS, 2023, J CLASS TEACH, DOI 10.1017/S2058631023000430
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Sharadgah TA, 2022, J INF TECHNOL EDUC-R, V21, P337, DOI 10.28945/4999
   Smith A, 2023, INT J SOC PSYCHIATR, V69, P1882, DOI 10.1177/00207640231178451
   Tobler S, 2024, METHODSX, V12, DOI 10.1016/j.mex.2023.102531
   Urrutia F, 2024, J EDUC COMPUT RES, V61, P187, DOI 10.1177/07356331231191174
   Vaismoradi M, 2013, NURS HEALTH SCI, V15, P398, DOI 10.1111/nhs.12048
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Vartiainen H, 2023, INT J EDUC ART, V19, P405, DOI 10.1386/eta_00143_1
   Walters WH, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-41032-5
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Yang XZ, 2024, ECNU REV EDUC, V7, P699, DOI 10.1177/20965311231210006
   Zadorozhnyy A, 2024, LANGUAGES-BASEL, V9, DOI 10.3390/languages9010005
   Zupic I, 2015, ORGAN RES METHODS, V18, P429, DOI 10.1177/1094428114562629
NR 71
TC 0
Z9 0
U1 64
U2 64
PU INFORMING SCIENCE INST
PI SANTA ROSA
PA 131 BROOKHILL CT, SANTA ROSA, CA 95409 USA
SN 1547-9714
EI 1539-3585
J9 J INF TECHNOL EDUC-R
JI J. Inf. Technol. Educ.-Res.
PY 2024
VL 23
AR 5304
DI 10.28945/5304
PG 23
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA UC0S6
UT WOS:001245751400001
OA gold
DA 2024-12-25
ER

PT J
AU Walker, DOH
   Larson, M
AF Walker, Dayna O. H.
   Larson, Milan
TI Leveraging Generative Artificial Intelligence (AI) for Human Resource
   Management: The AI Job Description Assignment
SO JOURNAL OF MANAGEMENT EDUCATION
LA English
DT Article; Early Access
DE generative AI; human resource management; job description; job analysis
AB The introduction of generative artificial intelligence (AI) technology is rapidly changing the field of human resource management (HRM). Management educators are called to prepare students for the opportunities and challenges this new technology will bring. Thus, our aim is to provide management educators with an assignment to introduce students to the opportunities and limitations of using generative AI for writing job descriptions. The AI Job Description Assignment invites students to leverage generative AI to write a first draft job description and then refine that initial draft using information obtained from a mini-job analysis conducted with real people. The assignment exposes students to a new technology while simultaneously reinforcing the age-old importance of including job incumbents in the process through job analysis interviews or surveys. Students learn both the benefits and limitations of generative AI while re-imagining a traditional HRM method. A how-to guide is provided for instructors along with quantitative and qualitative evidence of effectiveness.
C1 [Walker, Dayna O. H.] Univ Colorado, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918 USA.
   [Larson, Milan] Univ Northern Colorado, Greeley, CO USA.
C3 University of Colorado System; University of Colorado at Colorado
   Springs; University of Northern Colorado
RP Walker, DOH (corresponding author), Univ Colorado, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918 USA.
EM dherber2@uccs.edu
CR Acar O.A., 2023, Harvard Business Review
   Allen DB, 2022, J MANAG EDUC, V46, P178, DOI 10.1177/1052562920983839
   Allen SJ, 2020, J MANAG EDUC, V44, P362, DOI 10.1177/1052562920903077
   Blackman R, 2022, HARVARD BUS REV, V100, P118
   Budhwar P, 2022, INT J HUM RESOUR MAN, V33, P1065, DOI 10.1080/09585192.2022.2035161
   Campion MA, 2011, PERS PSYCHOL, V64, P225, DOI 10.1111/j.1744-6570.2010.01207.x
   Cascio W. F., 2022, Managing human resources: Productivity, quality of work life, profits, V12th ed.
   Chauhan R. S., 2019, Management Teaching Review, V4, P79, DOI [10.1177/2379298118806548, DOI 10.1177/2379298118806548]
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Dastin Jeffrey, 2018, REUTERS         1010
   Dessler G., 2020, Human Resource Management
   Driscoll J., 1994, Senior Nurse, V14, P47
   Evans R., 2023, The future of HR: Your mission, should you choose to accept it . Audio podcast episode . The Future of HR
   Fan YF, 2024, J MANAG EDUC, V48, P231, DOI 10.1177/10525629231205604
   Fischer I, 2024, J MANAG EDUC, V48, P829, DOI 10.1177/10525629231201843
   Gartner, AI in HR: A guide to implementing AI in your HR organization
   Hancock B., 2023, McKinsey Talks Talent
   Herbold Steffen, 2023, Sci Rep, V13, P18617, DOI 10.1038/s41598-023-45644-9
   Hill C, 2014, EXPERIENCE-DRIVEN LEADER DEVELOPMENT: MODELS, TOOLS, BEST PRACTICES, AND ADVICE FOR ON-THE-JOB DEVELOPMENT, P229
   Hyde SJ, 2024, J MANAG EDUC, V48, P708, DOI 10.1177/10525629241230357
   Judge T., 2022, Staffing organizations
   Kaddoura M., 2013, Educational Research Quarterly, V36, P3
   Keeler JB, 2022, IND ORGAN PSYCHOL-US, V15, P65, DOI 10.1017/iop.2021.128
   Lakhani K., 2023, Harvard Business Review
   Levine EL, 1997, PERS PSYCHOL, V50, P1009, DOI 10.1111/j.1744-6570.1997.tb01493.x
   Marr Bernard, 2023, Forbes. corn
   Maurer R., 2024, Society for Human Resource Management
   MCCORMICK EJ, 1979, J APPL PSYCHOL, V64, P51, DOI 10.1037/0021-9010.64.1.51
   Metz C., 2023, The New York Times Company
   Morgan R. B., 2011, Journal of Human Resources Education, V5, P9
   Morgeson F. P., 2019, Job and work analysis, V3rd ed.
   Morgeson FP, 2000, J ORGAN BEHAV, V21, P819, DOI 10.1002/1099-1379(200011)21:7<819::AID-JOB29>3.0.CO;2-I
   Nufer S., 2023, Artificial intelligence will help us revolutionize education InstructureCon 2023 session
   Phillips J., 2023, Strategic staffing, V5th
   Primoff E. S., 1975, How to prepare and conduct job element examinations, V75
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   Sackett P.R., 2003, HDB IND ORG PSYCHOL, V12, P21
   Sanchez JI, 2012, ANNU REV PSYCHOL, V63, P397, DOI 10.1146/annurev-psych-120710-100401
   Sanders N. R., 2023, Harvard Business Review
   Schraagen Jan Maarten, 2000, Cognitive task analysis
   Segal J. A., 2023, Viewpoint: AI tools cant replace authentic thinking when crafting job descriptions
   Shippmann JS, 2000, PERS PSYCHOL, V53, P703, DOI 10.1111/j.1744-6570.2000.tb00220.x
   Singh P, 2008, HUM RESOUR MANAGE R, V18, P87, DOI 10.1016/j.hrmr.2008.03.004
   Smith-Jentsch KA, 2020, ORGAN DYN, V49, DOI 10.1016/j.orgdyn.2019.06.001
   Society for Human Resource Management, Job analysis 101: Essential steps to define and evaluate roles
   Society for Human Resource Management, Using artificial intelligence for employment purposes
   Sollosy M, 2022, INT J MANAG EDUC-OXF, V20, DOI 10.1016/j.ijme.2022.100720
   Strah N, 2022, IND ORGAN PSYCHOL-US, V15, P1, DOI 10.1017/iop.2021.94
   Weekley JA, 2019, J OCCUP ORGAN PSYCH, V92, P764, DOI 10.1111/joop.12272
   Xu JJ, 2021, INT J MANAG EDUC-OXF, V19, DOI 10.1016/j.ijme.2021.100550
NR 50
TC 0
Z9 0
U1 16
U2 16
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1052-5629
EI 1552-6658
J9 J MANAG EDUC
JI J. Manag. Educ.
PD 2024 NOV 18
PY 2024
DI 10.1177/10525629241294075
EA NOV 2024
PG 29
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA M3Q2Z
UT WOS:001356717800001
DA 2024-12-25
ER

PT J
AU Birks, S
   Gray, J
   Darling-Pomranz, C
AF Birks, Samuel
   Gray, James
   Darling-Pomranz, Claire
TI Using artificial intelligence to provide a 'flipped assessment' approach
   to medical education learning opportunities
SO MEDICAL TEACHER
LA English
DT Article; Early Access
DE Generative artificial intelligence; AI; GenAI; formative learning;
   multiple choice questions
AB Purpose of the article: Generative AI can potentially streamline the creation of practice exam questions. This study sought to evaluate medical students' confidence using generative AI for this purpose, and overall attitudes towards its use. Materials and methods: The study used a mixed-methods approach with a pre-post intervention design. 68 medical and physician associate students were recruited to attend a workshop where they were shown how to use Google Bard (now Gemini) to write exam questions before being encouraged to do this themselves with guidance. A survey was completed before and after. Seven students also participated in a follow-up focus group. Results: The results showed an increase in participants' confidence in using AI to write practice exam questions (p < 0.001) after the workshop. Qualitative feedback highlighted pros and cons of using generative AI to write exam questions, alongside some concerns about its implementation. Students noted other positive uses in the curriculum and expressed a desire for institutional clarity on appropriate AI use. Conclusions: While increased confidence is positive, rigorous evaluation of AI-generated question quality is needed to confirm accuracy. Teaching students to use generative AI to create and critique practice questions represents a means of encouraging appropriate AI use.
C1 [Birks, Samuel; Gray, James; Darling-Pomranz, Claire] Univ Sheffield, Sch Med & Populat Hlth, Sheffield, England.
C3 University of Sheffield
RP Birks, S (corresponding author), Univ Sheffield, Sheffield, England.
EM s.birks@sheffield.ac.uk
OI Darling-Pomranz, Claire/0000-0002-4898-4649
CR Bozkurt A., 2023, Asian J Distance Educ, V18, P1
   Chada Bharadwaj V, 2022, Future Healthc J, V9, P313, DOI 10.7861/fhj.2022-0068
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Clarke V., 2013, SUCCESSFUL QUALITATI
   Coughlin PA, 2017, EUR J VASC ENDOVASC, V54, P654, DOI 10.1016/j.ejvs.2017.07.012
   Fisher J, 2024, BMC MED EDUC, V24, DOI 10.1186/s12909-024-05517-9
   Google, 2024, Gemini API
   Google, What are AI hallucinations? Google Cloud)
   Gordon M, 2024, MED TEACH, V46, P446, DOI 10.1080/0142159X.2024.2314198
   Hurtubise L, 2015, J MED EDUC CURRIC DE, V2, P35, DOI [10.4137/JMECDECDECD.S23895, 10.4137/JMECD.S23895]
   Indran IR, 2024, MED TEACH, V46, P1021, DOI 10.1080/0142159X.2023.2294703
   Knoth N., 2024, Comput. Educ, V6, P100225, DOI [10.1016/j.caeai.2024.100225, DOI 10.1016/J.CAEAI.2024.100225]
   Korzynski P, 2023, ENTREPR BUS ECON REV, V11, P25, DOI 10.15678/EBER.2023.110302
   Lage MJ, 2000, J ECON EDUC, V31, P30, DOI 10.2307/1183338
   Medical Schools Council (MSC), 2018, Writing Questions for Undergraduate Exams-a resource pack for MSCAA
   Mohammed PS, 2019, PERSP RETHINK REFORM, P17, DOI 10.1007/978-981-13-8161-4_2
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Stadler M, 2024, GMS J MED EDU, V41, DOI 10.3205/zma001675
   Tan Tan S C. S C., 2022, Computers and Education: Artificial Intelligence, V3 3, P100097, DOI DOI 10.1016/J.CAEAI.2022.100097
   Tetzlaff L, 2021, EDUC PSYCHOL REV, V33, P863, DOI 10.1007/s10648-020-09570-w
   Weidinger Laura, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P214, DOI 10.1145/3531146.3533088
NR 21
TC 0
Z9 0
U1 1
U2 1
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0142-159X
EI 1466-187X
J9 MED TEACH
JI Med. Teach.
PD 2024 NOV 29
PY 2024
DI 10.1080/0142159X.2024.2434101
EA NOV 2024
PG 8
WC Education, Scientific Disciplines; Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Health Care Sciences & Services
GA O0L6S
UT WOS:001368149300001
PM 39616548
DA 2024-12-25
ER

PT J
AU Woo, DJ
   Guo, K
   Susanto, H
AF Woo, David James
   Guo, Kai
   Susanto, Hengky
TI Exploring EFL students' prompt engineering in human-AI story writing: an
   activity theory perspective
SO INTERACTIVE LEARNING ENVIRONMENTS
LA English
DT Article; Early Access
DE Generative artificial intelligence; prompt engineering; EFL students;
   story writing; human-machine collaboration
AB This study applies Activity Theory to investigate how English as a foreign language (EFL) students prompt generative artificial intelligence (AI) tools during short story writing. Sixty-seven Hong Kong secondary school students created their own generative-AI tools using open-source language models and wrote short stories with them. The study collected and analyzed the students' generative-AI tools, short stories, and written reflections on their conditions or purposes for prompting. The research identified three main themes regarding the purposes for which students' prompt generative-AI tools during short story writing: a lack of awareness of purposes, overcoming writer's block, and developing, expanding, and improving the story. The study also identified common characteristics of students' activity systems, including the sophistication of their generative-AI tools, the quality of their stories, and their school's overall academic achievement level, for their prompting of generative-AI tools for the three purposes during short story writing. The study's findings suggest that teachers should be aware of students' purposes for prompting generative-AI tools to provide tailored instructions and scaffolded guidance. The findings may also help designers provide differentiated instructions for users at various levels of story development when using a generative-AI tool.
C1 [Woo, David James] Precious Blood Secondary Sch, Dept English, Hong Kong, Peoples R China.
   [Guo, Kai] Univ Hong Kong, Fac Educ, Hong Kong, Peoples R China.
   [Susanto, Hengky] Educ Univ Hong Kong, Dept Sci & Environm Studies, Hong Kong, Peoples R China.
C3 University of Hong Kong; Education University of Hong Kong (EdUHK)
RP Woo, DJ (corresponding author), Precious Blood Secondary Sch, Dept English, Hong Kong, Peoples R China.
EM net_david@pbss.hk
RI Guo, Kai/AAD-2448-2022; Woo, David/KGL-1423-2024
OI Guo, Kai/0000-0001-9699-7527; Woo, David James/0000-0003-4417-3686
CR Adams AM, 2019, READ WRIT, V32, P235, DOI 10.1007/s11145-018-9859-0
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Chen XL, 2021, IEEE INT CONF ADV LE, P241, DOI 10.1109/ICALT52272.2021.00079
   Clark E, 2021, 2021 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL-HLT 2021), P3566
   Dai F, 2010, WORLD ENGLISH, V29, P546, DOI 10.1111/j.1467-971X.2010.01681.x
   Dang H, 2022, Arxiv, DOI [arXiv:2209.01390, DOI 10.48550/ARXIV.2209.01390, 10.48550/ARXIV.2209.01390]
   De Wilde V, 2023, J SECOND LANG WRIT, V59, DOI 10.1016/j.jslw.2022.100960
   Engestrom Y., 1999, LEARNING EXPANDING A
   Engestrom Y., 2001, J ED WORK, V14, P133, DOI DOI 10.1080/13639080020028747
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fortunati L, 2014, TELEMAT INFORM, V31, P39, DOI 10.1016/j.tele.2013.02.005
   Gayed JM., 2022, COMPUTERS ED ARTIFIC, V3, P100055, DOI DOI 10.1016/J.CAEAI.2022.100055
   Guo K, 2024, EDUC INF TECHNOL, V29, P1, DOI [10.1007/s10639-023-12230-5, 10.1080/14740338.2024.2327502]
   Guo K, 2022, ASSESS WRIT, V54, DOI 10.1016/j.asw.2022.100666
   Haristiani Nuria, 2019, Journal of Physics: Conference Series, V1387, DOI 10.1088/1742-6596/1387/1/012020
   Hyland K., 2019, Second language writing, V2nd
   Ippolito D., 2022, arXiv, DOI DOI 10.48550/ARXIV.2211.05030
   Jeon J, 2024, COMPUT ASSIST LANG L, V37, P1, DOI 10.1080/09588221.2021.2021241
   Kuiken F, 2021, INT J BILING EDUC BI, V24, P1474, DOI 10.1080/13670050.2020.1726280
   Latifi S, 2023, INTERACT LEARN ENVIR, V31, P655, DOI 10.1080/10494820.2020.1799032
   Latifi S, 2021, BRIT J EDUC TECHNOL, V52, P768, DOI 10.1111/bjet.13054
   Latifi S, 2021, INNOV EDUC TEACH INT, V58, P197, DOI 10.1080/14703297.2019.1687005
   Lee M, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3502030
   Leontiev AN., 1981, PROBLEMS DEV MIND
   Liu P., 2021, arXiv
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Oppenlaender J, 2024, Arxiv, DOI arXiv:2303.13534
   Reynolds L, 2021, Arxiv, DOI [arXiv:2102.07350, 10.48550/arXiv.2102.07350, DOI 10.48550/ARXIV.2102.07350]
   Rospigliosi PA, 2023, INTERACT LEARN ENVIR, V31, P1, DOI 10.1080/10494820.2023.2180191
   Saldaa J., 2012, The Coding Manual for Qualitative Researchers
   Strobelt H., 2022, arXiv, DOI [DOI 10.48550/ARXIV.2208.07852, 10.48550/arxiv.2208.07852]
   Tai TY, 2023, INTERACT LEARN ENVIR, V31, P1485, DOI 10.1080/10494820.2020.1841801
   Vygotsky L.S., 1978, MIND SOC DEV HIGHER, DOI 10.2307/j.ctvjf9vz4
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Woo DJ, 2023, J EDUC COMPUT RES, V61, P1464, DOI 10.1177/07356331231175999
   Yang D., 2022, JOINT P ACM IUI WORK, P10
   Zhou YC, 2023, Arxiv, DOI arXiv:2211.01910
   Zotzmann K, 2021, J SECOND LANG WRIT, V52, DOI 10.1016/j.jslw.2021.100810
NR 38
TC 0
Z9 0
U1 131
U2 131
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1049-4820
EI 1744-5191
J9 INTERACT LEARN ENVIR
JI Interact. Learn. Environ.
PD 2024 JUN 29
PY 2024
DI 10.1080/10494820.2024.2361381
EA JUN 2024
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA XB7Y4
UT WOS:001259300700001
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Zhang, Y
   Zhang, JR
   Yue, S
   Lu, W
   Ren, J
   Shen, XM
AF Zhang, Ye
   Zhang, Jinrui
   Yue, Sheng
   Lu, Wei
   Ren, Ju
   Shen, Xuemin
TI Mobile Generative AI: Opportunities and Challenges
SO IEEE WIRELESS COMMUNICATIONS
LA English
DT Article
DE Privacy; Costs; Generative AI; Memory management; Chatbots; Mobile
   handsets; Explosions
AB Recently, generative artificial intelligence (GenAI) has gained significant interest on a global scale, particularly with the explosion of some killer GenAl applications, like ChatGPT. However, due to the excessively large sizes of generative models, most current GenAl applications are deployed in the cloud, easily causing high cost, long delay, and potential risk of privacy leakage, thereby greatly impeding GenAl's further expansion and development. In this article, we explore mobile GenAl - deploying large generative models on mobile devices, aiming to bring the GenAl capability to the physical proximity to users. First, we analyze the benefits and opportunities of mobile GenAl in terms of cost, delay, privacy, personalization, and application. Then, we test various large generative models on the mobile testbed, and reveal mobile GenAl's key bottlenecks in inference latency and memory consumption. Accordingly, we propose a weight occupancy strategy for model compression during inference, and discuss the pros and cons thereof. Finally future directions are pointed out to foster continued research efforts.
C1 [Zhang, Ye; Lu, Wei] Beijing Jiaotong Univ, Sch Software Engn, Beijing, Peoples R China.
   [Zhang, Jinrui; Yue, Sheng; Ren, Ju] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.
   [Shen, Xuemin] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON, Canada.
C3 Beijing Jiaotong University; Tsinghua University; University of Waterloo
RP Zhang, JR; Yue, S (corresponding author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China.
EM sshen@uwaterloo.ca
RI Ren, Ju/ABD-5251-2021; Yue, Sheng/GXH-1274-2022; Shen,
   Xuemin/AAH-2564-2020
FU National Key R&D Program of China [2022YFF0604502]
FX This research was supported by the National Key R&D Program of China
   under Grant No. 2022YFF0604502.
CR apps.apple, Draw Things: Al Generation
   Brown TB, 2020, ADV NEUR IN, V33
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Dettmers T., 2022, Advances in Neural Information Processing Systems, V35, P30318
   Dettmers T, 2022, Arxiv, DOI arXiv:2208.07339
   Dice D., 2021, arXiv
   Frantar E, 2023, Arxiv, DOI [arXiv:2210.17323, 10.48550/arxiv.2210.17323]
   github, NVIDIA/FasterTransformer: Transformer Related Optimi- zation, Including BERT, GPT
   Kwon W, 2023, PROCEEDINGS OF THE TWENTY-NINTH ACM SYMPOSIUM ON OPERATING SYSTEMS PRINCIPLES, SOSP 2023, P611, DOI 10.1145/3600006.3613165
   Lv CF, 2022, PROCEEDINGS OF THE 16TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, OSDI 2022, P249
   reuters, Focus: For Tech Giants, Al Like Bing and Bard Poses Bil- lion-Dollar Search Problem
   Xu DL, 2023, Arxiv, DOI arXiv:2309.04255
   Zhang C, 2023, Arxiv, DOI arXiv:2304.06488
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
   Zhao Y, 2024, Arxiv, DOI arXiv:2311.16567
NR 15
TC 0
Z9 0
U1 15
U2 15
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1536-1284
EI 1558-0687
J9 IEEE WIREL COMMUN
JI IEEE Wirel. Commun.
PD AUG
PY 2024
VL 31
IS 4
BP 58
EP 64
DI 10.1109/MWC.006.2300576
PG 7
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA D0Z5F
UT WOS:001293556000037
DA 2024-12-25
ER

PT J
AU Blanke, JM
AF Blanke, Jordan M.
TI ChatGPT: The Sky is Not Falling
SO JOURNAL OF LEGAL STUDIES EDUCATION
LA English
DT Article; Early Access
AB Generative artificial intelligence (generative AI) applications such as ChatGPT and its brethren erupted onto the scene last year and have been quite a disruptor throughout higher education. Like much new technology, generative AI brings with it benefits and challenges. This note focuses on early experiences with ChatGPT, attempts to learn how to use it, and possible changes needed for our assessment methodologies. I provide some suggested modifications to traditional assignments to adapt to generative AI's ubiquity.
C1 [Blanke, Jordan M.] Mercer Univ, Stetson Hatcher Sch Business, Comp Sci & Law, Atlanta, GA USA.
   [Blanke, Jordan M.] Mercer Univ, Stetson Hatcher Sch Business, Atlanta, GA 30341 USA.
C3 Mercer University; Mercer University
RP Blanke, JM (corresponding author), Mercer Univ, Stetson Hatcher Sch Business, Atlanta, GA 30341 USA.
EM blanke_j@mercer.edu
CR Blanke J.M., 2018, CASE W RES J L TECH, V9, P1
   Blanke Jordan M., 2021, IDAHO L. REV, V55, P281
   Brittain Blake, 2023, REUTERSJune 29
   Brittain Blake, 2023, REUTERSSeptember 11
   celt.iastate, about us
   Chechitelli Annie, 2023, TURNITINJune 14
   Collins Barry, 2022, FORBESDecember 22
   Daher Rafif Srour, 2023, WORLD.EDUMarch 14
   De Vynck Gerrit, 2023, WASH POSTSeptember 20
   Degnan Bill, 2012, Tic Tac Toe Computer 1956
   examind, about us
   Fowler Geoffrey A., 2023, WASH. POSTAugust 14
   gptzero, about us
   Knight Will, 2020, WIREDFebruary 21
   Metz Cade, 2023, N.Y. TIMESFebruary 26
   Roose K., 2023, N.Y. TIMESAugust 24
   Roose Kevin, 2023, New York Times
   Rovella Davis, 2023, BLOOMBERGSeptember 21
   scribbr, about us
   Throbecke Catherine, 2023, CNN BUSINESSAugust 29
   turnitin, about us
   Walters Alexandra, 2023, N.Y. TIMESSeptember 20
   Waters Richard, 2023, FINANCIAL TIMESJanuary 16
   Weiser Benjamin, 2023, N.Y. TIMESJune 22
NR 24
TC 0
Z9 0
U1 7
U2 17
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0896-5811
EI 1744-1722
J9 J LEG STUD EDUC
JI J. Leg. Stud. Educ.
PD 2024 JAN 30
PY 2024
DI 10.1111/jlse.12145
EA JAN 2024
PG 9
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA IZ5J9
UT WOS:001170176400001
DA 2024-12-25
ER

PT J
AU Aksoy, DA
   Kursun, E
AF Aksoy, Dilara Arzugul
   Kursun, Engin
TI Behind the Scenes: A Critical Perspective on GenAI and Open Educational
   Practices
SO OPEN PRAXIS
LA English
DT Article
DE Artificial intelligence; AI; generative artificial intelligence; GenAI;
   open educational resources; OER; open educational practices; OEP; AI in
   Education (AIEd); openness in education
ID ARTIFICIAL-INTELLIGENCE; ETHICAL CHALLENGES; RESOURCES OER;
   OPPORTUNITIES; BARRIERS; LESSONS; AI
AB Artificial Intelligence (AI) is a rapidly evolving field that is influencing every aspect of life. Generative AI (GenAI) as a sub-branch of AI is used to create content in various formats such as text, images, video, and audio. This paper discusses the implications of GenAI for Open Educational Practices (OEP), highlighting the potential affordances and challenges. GenAI can address the challenges within the OEP by leveraging openness and ethical use. GenAI's "generative" nature and human-like language capability can provide resources such as course material, activities, examples, questions, assessment, and learning outcomes in the context of OEP. With machine learning and deep learning infrastructure, it can support the discoverability and accessibility of open resources by increasing the metadata quality. GenAI can automatically score student assignments, answer their questions, and provide instant feedback to address the lack of interaction and feedback that arises due to the large number of students, especially in massive open online courses (MOOC). On the other hand, GenAI brings challenges such as data privacy and security, copyright, biased outputs, and the generation of false information. The conclusions emphasize the importance of a nuanced approach that considers not only the advantages but also the risks associated with adopting GenAI in the OEP world. Researching and developing how to apply these technologies to education is important to shape the future of education.
C1 [Aksoy, Dilara Arzugul] Bayburt Univ, Bayburt, Turkiye.
   [Kursun, Engin] Carl von Ossietzky Univ Oldenburg, Erzurum, Turkiye.
   [Kursun, Engin] Carl von Ossietzky Univ Oldenburg, Oldenburg, Germany.
C3 Bayburt University; Carl von Ossietzky Universitat Oldenburg
RP Aksoy, DA (corresponding author), Bayburt Univ, Bayburt, Turkiye.
EM dilaraaksoy@bayburt.edu.tr
RI AKSOY, Dilara Arzugül/IAN-5162-2023
OI Kursun, Engin/0000-0002-5649-8595
FX We acknowledge the timeless insight of the Anatolian philosopher Yunus
   Emre, who profoundly stated, 'Whatever you share is truly yours, not the
   one you save for you.' This sentiment captures the essence of our
   commitment to the philosophy of openness-emphasizing that it is through
   the act of sharing that knowledge truly becomes a collective treasure.
CR Ahmet E., 2022, Yeni Medya, V2022, P247
   Akgun Selin, 2022, AI Ethics, V2, P431, DOI 10.1007/s43681-021-00096-7
   Aksela M, 2016, EDUC SCI, V6, DOI 10.3390/educsci6040040
   Allotey P, 2021, OPEN PRAX, V13, P127, DOI 10.5944/openpraxis.13.1.1172
   Alseddiqi M., 2023, European Journal of Education and Pedagogy, V4, P1, DOI DOI 10.24018/EJEDU.2023.4.4.686
   [Anonymous], 2019, Artificial intelligence in education: Challenges and opportunities for sustainable development
   [Anonymous], 2023, Fed. Reg., V88
   Athaluri SA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37432
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Beaven T, 2013, J INTERACT MEDIA EDU
   Belikov OM, 2016, OPEN PRAX, V8, P235, DOI 10.5944/openpraxis.8.3.308
   Bozkurt A., 2023, ASIAN J DISTANCE ED, V18, pi, DOI [DOI 10.5281/ZENODO.8174941, 10.4018/979-8-3693-1351-0]
   Bozkurt A, 2024, OPEN PRAX, V16, P283, DOI [10.55982/openpraxis.16.3.739, 10.55982/openpraxis.16.1.654]
   Bozkurt A, 2023, OPEN PRAX, V15, P178, DOI 10.55982/openpraxis.15.3.579
   Bozkurt A, 2023, OPEN PRAX, V15, P261, DOI 10.55982/openpraxis.15.4.609
   Brent I, 2012, J INTERACT MEDIA EDU
   Carter L., 2020, AIS Transactions on Human-Computer Interaction, V12, P253, DOI DOI 10.17705/1THCI.00138
   Cechinel C, 2011, COMPUT EDUC, V57, P1255, DOI 10.1016/j.compedu.2011.01.012
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chimbo B., 2021, IEEE 5 EC TECHN CHAP, P1, DOI [10.1109/ETCM53643.2021.9590799, DOI 10.1109/ETCM53643.2021.9590799]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cox G., 2024, Mousaion: South African Journal of Information Studies, V42, DOI [10.25159/2663-659X/15331, DOI 10.25159/2663-659X/15331]
   Croom A., 2023, Updating an OER Textbook via AI and ChatGPT
   Dholakia U.M., 2006, WHAT MAKES OPEN ED P
   Downes S., 2019, The International Journal of Open Educational Resources, V1, DOI [10.18278/ijoer.1.2.4, DOI 10.18278/IJOER.1.2.4]
   Du SL, 2021, J BUS RES, V129, P961, DOI 10.1016/j.jbusres.2020.08.024
   Duan Y., 2023, OPENCON VIRT C OHIO
   Duval Erik., 2002, D Lib Mag, V8, P1, DOI [10.1045/april2002-weibel, DOI 10.1045/APRIL2002-WEIBEL]
   Edelsbrunner S., 2023, GMW JAHR NOV 2 3 202
   Ehlers U-D., 2010, Open educational practices: Unleashing the power of OER
   ENCORE+, 2023, Challenges and opportunities using AI machines in the open education field
   Farooqi A., 2022, Global Political Review, V7, P7, DOI [10.31703/gpr.2022(VII-IV).02, DOI 10.31703/GPR.2022(VII-IV).02]
   Farrow R., 2015, European Journal of Open, Distance and E-Learning, V18, P49, DOI DOI 10.1515/EURODL-2015-0013
   Firat M., 2023, How chat GPT can transform autodidactic experiences and open education? preprint, DOI DOI 10.31219/OSF.IO/9GE8M
   Ganapathi J, 2018, INT REV RES OPEN DIS, V19, P114
   Geser G., 2007, Open educational practices and resources: OLCOS roadmap 2012, DOI [10.7238/rusc.v4i1.295, DOI 10.7238/RUSC.V4I1.295]
   Gillani N, 2023, EDUC TECHNOL SOC, V26, P99, DOI 10.30191/ETS.202301_26(1).0008
   Giray L, 2023, J PRACT CARDIOVASC S, V9, P161, DOI 10.4103/jpcs.jpcs_45_23
   Glazko K, 2023, PROCEEDINGS OF THE 25TH INTERNATIONAL ACM SIGACCESS CONFERENCE ON COMPUTERS AND ACCESSIBILITY, ASSETS 2023, DOI 10.1145/3597638.3614548
   Green BP, 2018, SCI FIDES, V6, P9, DOI 10.12775/SetF.2018.015
   Huang J., 2021, Academic Journal of Interdisciplinary Studies, V10, P206, DOI DOI 10.36941/AJIS-2021-0077
   Huttner N., 2018, Seeking a sustainable OER ecosystem
   Ingavelez-Guerra P, 2022, IEEE ACCESS, V10, P9703, DOI 10.1109/ACCESS.2021.3139537
   Iniesto F, 2021, J INTERACT MEDIA EDU, DOI 10.5334/jime.679
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Kaabi K., 2020, INT MULT ORG KNOWL A, DOI [10.1109/OCTA49274.2020.9151847, DOI 10.1109/OCTA49274.2020.9151847]
   Kaplan A, 2020, BUS HORIZONS, V63, P37, DOI 10.1016/j.bushor.2019.09.003
   Khan academy, About us
   Khan AU, 2021, TURK ONLINE J DISTAN, V22, P82
   Khanmigo, About Us
   Khosravi H., 2022, Computers and Education: Artificial Intelligence, V3, P100074, DOI DOI 10.1016/J.CAEAI.2022.100074
   Kimmons R, 2016, TEACH COLL REC, V118
   Kopp M., 2023, INNOVATING HIGHER ED, P115
   Koseoglu S, 2018, DISTANCE EDUC, V39, P441, DOI 10.1080/01587919.2018.1520042
   Lalonde C., 2023, ChatGPT and open education
   Lantrip J, 2021, COMMUNITY COLL J RES, V45, P896, DOI 10.1080/10668926.2020.1838967
   Lim ZW, 2023, EBIOMEDICINE, V95, DOI 10.1016/j.ebiom.2023.104770
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Luo T, 2020, OPEN LEARN, V35, P140, DOI 10.1080/02680513.2019.1677222
   Magomadov V. S., 2020, Journal of Physics: Conference Series, V1691, DOI 10.1088/1742-6596/1691/1/012169
   Mayende G, 2017, INT J ENG PEDAGOG, V7, P109, DOI 10.3991/ijep.v7i2.6925
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Minudri T., 2023, Coursera announces new AI content and innovations to help HR and learning leaders drive organizational agility amid relentless disruption
   Norris ME, 2023, FRONT EDUC, V7, DOI 10.3389/feduc.2022.1069388
   OpenLearn, 2022, Accessibility statement for OpenLearn
   Osoba O., 2017, An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence, DOI [DOI 10.7249/RR1744, 10.7249/RR1744]
   Pachigolla V. S., 2019, Integration of artificial intelligence based technologies in development of OER
   Park JH, 2009, EDUC TECHNOL SOC, V12, P207
   Pounds A, 2019, AQUACULT INT, V27, P695, DOI 10.1007/s10499-019-00355-9
   Rawte V, 2023, PREPRINT, DOI DOI 10.48550/ARXIV.2309.11064
   Recalde L, 2021, INT CONF EDEMOC EGOV, P182, DOI 10.1109/ICEDEG52154.2021.9530872
   Regan PM, 2019, ETHICS INF TECHNOL, V21, P167, DOI 10.1007/s10676-018-9492-2
   Rennie F, 2011, INT REV RES OPEN DIS, V12, P88, DOI 10.19173/irrodl.v12i4.871
   Romero-Pelaez A, 2018, PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY AND COMPUTERS (ICETC 2018), P292, DOI 10.1145/3290511.3290540
   Sanders CK, 2021, J HUM RIGHTS SOC WOR, V6, P130, DOI 10.1007/s41134-020-00147-9
   Santos-Hermosa G, 2020, PROF INFORM, V29, DOI 10.3145/epi.2020.nov.37
   Sharma R. C., 2021, Education Matters@ETMA
   Tang HT, 2021, DISTANCE EDUC, V42, P582, DOI 10.1080/01587919.2021.1986371
   Tang HT, 2020, INT REV RES OPEN DIS, V21, P211
   Tapalova O, 2022, ELECTRON J E-LEARN, V20, P639
   Tlili A, 2022, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2022.2101595
   Tlili A, 2021, ETR&D-EDUC TECH RES, V69, P515, DOI 10.1007/s11423-021-09993-4
   Trust T., 2023, Contemp. Issues Technol. Teach. Educ, V23, P1
   U.S. District Court, 2023, About Us
   UNESCO, 2021, AI and education: Guidance for policy-makers, DOI [10.54675/PCSP7350, DOI 10.54675/PCSP7350]
   UNESCO, 2022, Recommendation on the Ethics of Artificial Intelligence
   Verma A., 2023, SSRN Electronic Journal, DOI [10.2139/ssrn.4537389, DOI 10.2139/SSRN.4537389]
   Walsh K., 2023, Understanding CC licenses and Generative AI
   Wang W, 2023, LIBR HI TECH, V41, P432, DOI 10.1108/LHT-06-2022-0306
   Wiley D., 2023, AI, instructional design, and OER
   Wu Y., 2022, 3 INT C MENT HLTH ED, DOI [10.2991/assehr.k.220704.002, DOI 10.2991/ASSEHR.K.220704.002]
   Xu ZZ, 2023, Arxiv, DOI arXiv:2305.11186
   Zhang ZC, 2010, INT REV RES OPEN DIS, V11, P17, DOI 10.19173/irrodl.v11i1.775
NR 93
TC 0
Z9 0
U1 7
U2 7
PU INT COUNCIL OPEN & DISTANCE EDUCATION
PI OSLO
PA LILLEAKERVEIEN 23, OSLO, 0283, NORWAY
SN 2304-070X
J9 OPEN PRAX
JI Open Prax.
PY 2024
VL 16
IS 3
BP 457
EP 470
DI 10.55982/openpraxis.16.3.674
PG 14
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA H0B4W
UT WOS:001320186000012
OA gold
DA 2024-12-25
ER

PT J
AU Feng, XR
   Xu, KZ
   Luo, MJ
   Chen, HC
   Yang, YF
   He, Q
   Song, CX
   Li, RY
   Wu, Y
   Wang, HB
   Tham, YC
   Ting, DSW
   Lin, HT
   Wong, TY
   Lam, DSC
AF Feng, Xiaoru
   Xu, Kezheng
   Luo, Ming-Jie
   Chen, Haichao
   Yang, Yangfan
   He, Qi
   Song, Chenxin
   Li, Ruiyao
   Wu, You
   Wang, Haibo
   Tham, Yih Chung
   Ting, Daniel Shu Wei
   Lin, Haotian
   Wong, Tien Yin
   Lam, Dennis Shun-chiu
TI Latest developments of generative artificial intelligence and
   applications in ophthalmology
SO ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY
LA English
DT Article
DE Generative artificial intelligence; Ophthalmology; Risk management;
   Clinical workflow; AI in medical research
ID ADVERSARIAL NETWORK; MODEL; IMPLEMENTATION; PREDICTION; CHATGPT
AB The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.
C1 [Feng, Xiaoru] Tsinghua Univ, Tsinghua Med, Sch Biomed Engn, Beijing, Peoples R China.
   [Feng, Xiaoru; Wu, You] Tsinghua Univ, Tsinghua Med, Inst Hosp Management, Beijing, Peoples R China.
   [Xu, Kezheng; Luo, Ming-Jie; Yang, Yangfan; Lin, Haotian] Sun Yat Sen Univ, Guangdong Prov Clin Res Ctr Ocular Dis, Guangdong Prov Key Lab Ophthalmol & Visual Sci, State Key Lab Ophthalmol,Zhongshan Ophthalm Ctr, Guangzhou, Peoples R China.
   [Chen, Haichao; Wong, Tien Yin] Tsinghua Univ, Beijing Tsinghua Changgung Hosp, Sch Clin Med, Tsinghua Med, Beijing, Peoples R China.
   [He, Qi; Song, Chenxin; Li, Ruiyao; Wang, Haibo] Sun Yat Sen Univ, Affiliated Hosp 1, Res Ctr Big Data & Artificial Res Med, Guangzhou, Peoples R China.
   [Wu, You] Tsinghua Univ, Sch Basic Med Sci, Tsinghua Med, Beijing, Peoples R China.
   [Wu, You] Johns Hopkins Univ, Bloomberg Sch Publ Hlth, Dept Hlth Policy & Management, Baltimore, MD USA.
   [Tham, Yih Chung] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Ophthalmol, Singapore, Singapore.
   [Tham, Yih Chung] Natl Univ Singapore, Ctr Innovat & Precis Eye Hlth, Yong Loo Lin Sch Med, Singapore, Singapore.
   [Tham, Yih Chung; Ting, Daniel Shu Wei] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore.
   [Tham, Yih Chung; Ting, Daniel Shu Wei; Wong, Tien Yin] Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Program, Singapore, Singapore.
   [Ting, Daniel Shu Wei] Stanford Univ, Byers Eye Inst, Palo Alto, CA USA.
   [Lin, Haotian] Sun Yat Sen Univ, Ctr Precis Med, Zhongshan Sch Med, Guangzhou, Peoples R China.
   [Lin, Haotian] Sun Yat Sen Univ, Zhongshan Sch Med, Dept Genet & Biomed Informat, Guangzhou, Peoples R China.
   [Lin, Haotian] Sun Yat Sen Univ, Hainan Eye Hosp, Haikou, Peoples R China.
   [Lin, Haotian] Sun Yat Sen Univ, Zhongshan Ophthalm Ctr, Key Lab Ophthalmol, Haikou, Peoples R China.
   [Wong, Tien Yin] Tsinghua Univ, Tsinghua Med, Beijing, Peoples R China.
   [Lam, Dennis Shun-chiu] Chinese Univ Hong Kong Shenzhen, Int Eye Res Inst, Shenzhen, Peoples R China.
   [Lam, Dennis Shun-chiu] C MER Int Eye Care Grp, Hong Kong, Peoples R China.
C3 Tsinghua University; Tsinghua University; Sun Yat Sen University;
   Tsinghua University; Sun Yat Sen University; Tsinghua University; Johns
   Hopkins University; Johns Hopkins Bloomberg School of Public Health;
   National University of Singapore; National University of Singapore;
   National University of Singapore; Singapore National Eye Center;
   National University of Singapore; Stanford University; Sun Yat Sen
   University; Sun Yat Sen University; Sun Yat Sen University; Sun Yat Sen
   University; Tsinghua University; The Chinese University of Hong Kong,
   Shenzhen
RP Wu, Y (corresponding author), Tsinghua Univ, Tsinghua Med, Inst Hosp Management, Beijing, Peoples R China.; Wang, HB (corresponding author), Sun Yat Sen Univ, Affiliated Hosp 1, Guangzhou, Peoples R China.
EM youwu@tsinghua.edu.cn; haibo@mail.harvard.edu
RI you, wu/KBB-7094-2024; Luo, Mingjie/LVS-3830-2024; Tham, Yih
   Chung/IUP-0091-2023; Wong, Tien/AAC-9724-2020; li, ruiyao/KHW-1440-2024;
   CHEN, HAICHAO/HRC-8252-2023; wang, chenxi/HMD-9902-2023
OI Wu, You/0000-0001-9672-6129; Xu, Kezheng/0009-0006-3003-3374; Feng,
   Xiaoru/0000-0001-5674-9043
FU Tsinghua University Start-up Fund [53335000124]
FX This work was supported by Tsinghua University Start-up Fund
   #53335000124.
CR Abdullah YI, 2021, ASIA-PAC J OPHTHALMO, V10, P289, DOI 10.1097/APO.0000000000000397
   Acosta JN, 2022, NAT MED, V28, P1773, DOI 10.1038/s41591-022-01981-2
   Akram MU, 2020, DATA BRIEF, V33, DOI 10.1016/j.dib.2020.106433
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Altair, 2023, Frictionless AI Global Survey Report
   American Journal of Ophthalmology, Declaration of generative AI in scientific writing
   Anton N, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12126061
   ASPE-Office of the Assistant Secretary for Planning and Evaluation, Health Insurance Portability and Accountability Act of 1996
   Bajwa Junaid, 2021, Future Healthc J, V8, pe188, DOI 10.7861/fhj.2021-0095
   Balas M., 2023, JFO Open Ophthalmology, V1, P100005, DOI DOI 10.1016/J.JFOP.2023.100005
   Betzler BK, 2023, LANCET DIGIT HEALTH, V5, pE917, DOI 10.1016/S2589-7500(23)00201-7
   Brown TB, 2020, ADV NEUR IN, V33
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Burlina P, 2022, JAMA OPHTHALMOL, V140, P185, DOI 10.1001/jamaophthalmol.2021.5557
   Burlina PM, 2019, JAMA OPHTHALMOL, V137, P258, DOI 10.1001/jamaophthalmol.2018.6156
   Cabitza F, 2017, JAMA-J AM MED ASSOC, V318, P517, DOI 10.1001/jama.2017.7797
   Cai LZ, 2023, AM J OPHTHALMOL, V254, P141, DOI 10.1016/j.ajo.2023.05.024
   Cai ZY, 2022, LECT NOTES COMPUT SC, V13438, P88, DOI 10.1007/978-3-031-16452-1_9
   Channa R, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00785-z
   Chen JS, 2021, OPHTHALMOL SCI, V1, DOI 10.1016/j.xops.2021.100079
   Combs C Donald, 2019, AMA J Ethics, V21, pE153, DOI 10.1001/amajethics.2019.153
   Cyberspace Administration of China, 2023, Interim measures for the management of generative artificial intelligence services
   Daye D, 2022, RADIOLOGY, V305, P555, DOI 10.1148/radiol.212151
   De Angelis L, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1166120
   Delsoz M, 2023, OPHTHALMOL THER, V12, P3121, DOI 10.1007/s40123-023-00805-x
   Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
   Ellaway RH, 2023, ADV HEALTH SCI EDUC, V28, P659, DOI 10.1007/s10459-023-10257-4
   elsevier, The Use of Generative AI and AIassisted Technologies in Writing for Elsevier
   European Union, General Data Protection Regulation
   Evans NG, 2022, OPHTHALMOL SCI, V2, DOI 10.1016/j.xops.2022.100141
   Extance A, 2018, NATURE, V561, P273, DOI 10.1038/d41586-018-06617-5
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Floridi L., 2021, Ethics, governance, and policies in artificial intelligence, V144, P19, DOI [10.1007/s11023-018-9482-5, DOI 10.1007/S11023-018-9482-5, 10.1007/978-3-030-81907-1_3]
   Froomkin AM, 2019, Ariz Law Rev, V61, P33
   Ghalibafan S, 2022, Retina, V10, P1097
   Gutwirth S, 2016, Data Protection on the Move: Current Developments in ICT, Privacy and Data Protection
   Habicht J, 2024, PsyArXiv Prepr
   Hager P, 2024, NAT MED, DOI 10.1038/s41591-024-03097-1
   Hall MA, 2009, JAMA-J AM MED ASSOC, V301, P1282, DOI 10.1001/jama.2009.389
   He JX, 2019, NAT MED, V25, P30, DOI 10.1038/s41591-018-0307-0
   He KM, 2022, PROC CVPR IEEE, P15979, DOI 10.1109/CVPR52688.2022.01553
   Heisler M, 2020, BIOMED OPT EXPRESS, V11, P3843, DOI 10.1364/BOE.392648
   Hendrycks D, 2019, PR MACH LEARN RES, V97
   Hervella AS, 2022, COMPUT BIOL MED, V143, DOI 10.1016/j.compbiomed.2022.105302
   Holmberg OG, 2020, NAT MACH INTELL, V2, P719, DOI 10.1038/s42256-020-00247-1
   Honavar S, 2023, INDIAN J OPHTHALMOL, V71, P2328, DOI 10.4103/IJO.IJO_1478_23
   Honavar SG, 2022, INDIAN J OPHTHALMOL, V70, P1075, DOI 10.4103/ijo.IJO_644_22
   Hu XY, 2023, OPHTHALMOL THER, V12, P3395, DOI 10.1007/s40123-023-00789-8
   Hudson DL, 2010, ANN TELECOMMUN, V65, P593, DOI 10.1007/s12243-010-0170-6
   JAMA Ophthalmology, Authorship Criteria and Contributions
   Jin K, 2022, ADV OPHTHALMOL PRACT, V2, DOI 10.1016/j.aopr.2022.100078
   Kapoor R, 2019, SURV OPHTHALMOL, V64, P233, DOI 10.1016/j.survophthal.2018.09.002
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Khanna RK, 2023, J FR OPHTALMOL, V46, P697, DOI 10.1016/j.jfo.2023.07.001
   Kocaballi AB, 2020, J AM MED INFORM ASSN, V27, P1695, DOI 10.1093/jamia/ocaa131
   Kostkova P, 2016, FRONT PUBLIC HEALTH, V4, DOI 10.3389/fpubh.2016.00007
   Lee H, 2021, RETINA-J RET VIT DIS, V41, P572, DOI 10.1097/IAE.0000000000002898
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Leong YY, 2022, ASIA-PAC J OPHTHALMO, V11, P111, DOI 10.1097/APO.0000000000000512
   Li J., 2024, Nat. Med., P1
   Li JPO, 2021, PROG RETIN EYE RES, V82, DOI 10.1016/j.preteyeres.2020.100900
   Li LH, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00943-3
   Li WT, 2020, NAT BIOMED ENG, V4, P767, DOI 10.1038/s41551-020-0577-y
   Li ZW, 2023, CELL REP MED, V4, DOI 10.1016/j.xcrm.2023.101095
   Lin DR, 2021, LANCET DIGIT HEALTH, V3, pE486, DOI 10.1016/S2589-7500(21)00086-8
   Liu XX, 2022, LANCET DIGIT HEALTH, V4, pE384, DOI 10.1016/S2589-7500(22)00003-6
   Liu YT, 2020, BRIT J OPHTHALMOL, V104, P1735, DOI 10.1136/bjophthalmol-2019-315338
   Liu ZL, 2023, Arxiv, DOI arXiv:2312.05256
   Lu AQ, 2022, J CATARACT REFR SURG, V48, P1242, DOI 10.1097/j.jcrs.0000000000000963
   Luo YH, 2020, IEEE J BIOMED HEALTH, V24, P3374, DOI 10.1109/JBHI.2020.2999077
   Luxton DD, 2014, PROF PSYCHOL-RES PR, V45, P332, DOI 10.1037/a0034559
   Lyons RJ, 2024, CAN J OPHTHALMOL, V59, pe301, DOI 10.1016/j.jcjo.2023.07.016
   Mintz Y, 2019, MINIM INVASIV THER, V28, P73, DOI 10.1080/13645706.2019.1575882
   Moshirfar M, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.40822
   Nakamoto K, 2023, SURG ENDOSC, V37, P3136, DOI 10.1007/s00464-022-09520-3
   Nakayama LF, 2022, FRONT OPHTHALMOL-SWI, V2, DOI 10.3389/fopht.2022.898181
   Nanji K, 2024, CAN J OPHTHALMOL, V59, pe69, DOI 10.1016/j.jcjo.2023.10.001
   Nova K., 2023, J. Adv. Anal. Healthc. Manag, V7, P115
   Omiye JA, 2024, ANN INTERN MED, V177, DOI 10.7326/M23-2772
   Ong J, 2023, OSLI RETINA, V54, P557, DOI 10.3928/23258160-20230926-01
   Ophthalmology, Declaration of generative AI in scientific writing
   Papalois ZA, 2022, EUR SURG RES, V63, P40, DOI 10.1159/000520386
   Park SJ, 2016, JAMA OPHTHALMOL, V134, P778, DOI 10.1001/jamaophthalmol.2016.1158
   Patel SB, 2023, LANCET DIGIT HEALTH, V5, pE107, DOI 10.1016/S2589-7500(23)00021-3
   Peng QS, 2022, ASIA-PAC J OPHTHALMO, V11, P126, DOI 10.1097/APO.0000000000000515
   Potapenko I, 2023, ACTA OPHTHALMOL, V101, P829, DOI 10.1111/aos.15661
   Radford A, 2018, Open Prepr, P1
   Raghavendra C, 2017, 2017 INTERNATIONAL CONFERENCE ON ALGORITHMS, METHODOLOGY, MODELS AND APPLICATIONS IN EMERGING TECHNOLOGIES (ICAMMAET)
   Rahmat MH, 2013, 2013 IEEE BUSINESS ENGINEERING AND INDUSTRIAL APPLICATIONS COLLOQUIUM (BEIAC 2013), P219
   Rampton V, 2022, BMJ-BRIT MED J, V379, DOI 10.1136/bmj.o2853
   Ruamviboonsuk P, 2021, ASIA-PAC J OPHTHALMO, V10, P307, DOI 10.1097/APO.0000000000000403
   Sear RF, 2021, ADV ARTIF INTELL MAC, V1, P191
   Sendak MP, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-0253-3
   Sevilla J, 2022, IEEE IJCNN, DOI 10.1109/IJCNN55064.2022.9891914
   Shi M, 2023, TRANSL VIS SCI TECHN, V12, DOI 10.1167/tvst.12.11.12
   Singh S, 2023, SEMIN OPHTHALMOL, V38, P503, DOI 10.1080/08820538.2023.2209166
   Singhal K, 2023, Arxiv, DOI [arXiv:2305.09617, DOI 10.48550/ARXIV.2305.09617]
   Sorin V., 2023, medRxiv
   Su YH, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21155163
   Suthar AC, 2022, Handbook of Research on Lifestyle Sustainability and Management Solutions Using AI, Big Data Analytics, and Visualization, P37
   Ta AWA, 2022, HEALTH CARE SCI, V1, P41, DOI 10.1002/hcs2.10
   Taloni A, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-45837-2
   Taloni A, 2023, EYE, DOI 10.1038/s41433-023-02678-7
   Tan Z, 2019, ASIA-PAC J OPHTHALMO, V8, P197, DOI 10.22608/APO.2019122
   Taribagil P, 2023, EXPERT REV OPHTHALMO, V18, P45, DOI 10.1080/17469899.2023.2175672
   Teo ZL, 2022, ASIA-PAC J OPHTHALMO, V11, P500, DOI 10.1097/APO.0000000000000582
   Ting DSW, 2019, OPHTHALMOLOGY, V126, P1475, DOI 10.1016/j.ophtha.2019.09.014
   Ting DSW, 2019, PROG RETIN EYE RES, V72, DOI 10.1016/j.preteyeres.2019.04.003
   Ting DSW, 2019, BRIT J OPHTHALMOL, V103, P167, DOI 10.1136/bjophthalmol-2018-313173
   Vaswani A, 2017, ADV NEUR IN, V30
   Wachter RM, 2020, JAMA-J AM MED ASSOC, V323, P507, DOI 10.1001/jama.2019.21215
   Waisberg E, 2024, Surv Ophthalmol, VS0039-6257, P00044
   Waisberg E, 2024, EYE, V38, P2502, DOI 10.1038/s41433-024-03098-x
   Waisberg E, 2023, EYE, V37, P3874, DOI 10.1038/s41433-023-02595-9
   Wallach W, 2008, AI SOC, V22, P463, DOI 10.1007/s00146-007-0093-6
   Wang BS, 2023, Arxiv, DOI [arXiv:2212.10001, DOI 10.48550/ARXIV.2212.10001]
   Williams AM, 2018, PHYSIOL GENOMICS, V50, P237, DOI 10.1152/physiolgenomics.00119.2017
   World Health Organization, 2023, Regulatory considerations on artificial intelligence for health
   Xi ZH, 2023, Arxiv, DOI arXiv:2309.07864
   Xinhua News Agency, Personal Information Protection Law of the People's Republic of China
   Xu P, 2023, medRxiv
   Yang R, 2023, HEALTH CARE SCI, V2, P255, DOI 10.1002/hcs2.61
   Yang YH, 2023, INTEL MED, V3, P144, DOI 10.1016/j.imed.2021.11.002
   Yang ZY, 2023, Arxiv, DOI [arXiv:2309.17421, DOI 10.48550/ARXIV.2309.17421]
   Yanqing Ji, 2012, Proceedings of the 2012 Ninth International Conference on Information Technology: New Generations (ITNG), P490, DOI 10.1109/ITNG.2012.111
   Ye QY, 2024, Arxiv, DOI arXiv:2311.05661
   Yoo TK, 2021, COMPUT METH PROG BIO, V205, DOI 10.1016/j.cmpb.2021.106086
   Yoo TK, 2021, MED BIOL ENG COMPUT, V59, P401, DOI 10.1007/s11517-021-02321-1
   Yoo TK, 2020, COMPUT BIOL MED, V118, DOI 10.1016/j.compbiomed.2020.103628
   Zhou Y, 2022, IEEE J BIOMED HEALTH, V26, P56, DOI 10.1109/JBHI.2020.3045475
   Zhou YK, 2023, NATURE, V622, P156, DOI 10.1038/s41586-023-06555-x
   Zou J, 2021, EBIOMEDICINE, V67, DOI 10.1016/j.ebiom.2021.103358
NR 132
TC 1
Z9 1
U1 34
U2 34
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
EI 2162-0989
J9 ASIA-PAC J OPHTHALMO
JI Asia-Pac. J. Ophthalmol.
PD JUL-AUG
PY 2024
VL 13
IS 4
AR 100090
DI 10.1016/j.apjo.2024.100090
PG 11
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA F3R1E
UT WOS:001309018400001
PM 39128549
OA gold
DA 2024-12-25
ER

PT J
AU Cantens, T
AF Cantens, Thomas
TI How will the state think with ChatGPT? The challenges of generative
   artificial intelligence for public administrations
SO AI & SOCIETY
LA English
DT Article; Early Access
DE Generative artificial intelligence; Public administrations
AB This article explores the challenges surrounding generative artificial intelligence (GenAI) in public administrations and its impact on human-machine interactions within the public sector. First, it aims to deconstruct the reasons for distrust in GenAI in public administrations. The risks currently linked to GenAI in the public sector are often similar to those of conventional AI. However, while some risks remain pertinent, others are less so because GenAI has limited explainability, which, in return, limits its uses in public administrations. Confidentiality, marking of GenAI outputs and errors are specific matters for which responses should be technical as well as cultural, as they are pushing the boundaries of our instrumental conceptions of machines. Second, this article proposes some paradigm shifts in the perspective of using GenAI in public administrations due to the radical change caused by its language-based nature. GenAI represents a profound break from the "numerical" nature of AI systems implemented in public administrations to date. The transformative impact of GenAI on the intellectual production of the state raises fears of the replacement, or rather enslavement, of civil servants to machines. The article argues for the development of critical thinking as a specific skill for civil servants who have become highly specialized and will have to think with a machine that is eclectic by nature. It anticipates a transformation in the political nature of public administrations, which should lead to more considerations for the strategic stake related to training corpus and for our conceptualization of the neutrality of AI.
C1 [Cantens, Thomas] Off Secretary Gen World Customs Org WCO, Res & Policy Unit, Brussels, Belgium.
   [Cantens, Thomas] Auvergne Univ, Ctr Etud & Rech Dev Int, Clermont Ferrand, France.
C3 Universite Clermont Auvergne (UCA)
RP Cantens, T (corresponding author), Off Secretary Gen World Customs Org WCO, Res & Policy Unit, Brussels, Belgium.; Cantens, T (corresponding author), Auvergne Univ, Ctr Etud & Rech Dev Int, Clermont Ferrand, France.
EM thomascantens@hotmail.com
RI Cantens, Thomas/G-6451-2015
OI Cantens, Thomas/0009-0001-9750-7769
CR Alon-Barkat S, 2023, J PUBL ADM RES THEOR, V33, P153, DOI 10.1093/jopart/muac007
   Babl FE, 2023, EMERG MED AUSTRALAS, V35, P809, DOI 10.1111/1742-6723.14233
   Rodrigues FB, 2024, IEEE T COMPUT SOC SY, V11, P4727, DOI 10.1109/TCSS.2022.3159677
   Beheshti A, 2023, Arxiv, DOI arXiv:2306.01771
   Bezes P., 2016, Revue francaise de science politique, V66, P407, DOI [10.3917/rfsp.663.0407, DOI 10.3917/RFSP.663.0407]
   Chen M, 2024, AI SOC, V39, P2307, DOI 10.1007/s00146-023-01681-6
   Chen ZH, 2023, Arxiv, DOI arXiv:2306.03763
   Cheng LY, 2023, Arxiv, DOI [arXiv:2305.15038, 10.48550/arXiv.2305.15038, DOI 10.48550/ARXIV.2305.15038]
   Chomsky N., 2023, N Y Times, P8
   Coeckelbergh M, 2024, AI SOC, V39, P2221, DOI 10.1007/s00146-023-01710-4
   Council of European Union, 2023, ChatGPT in the Public Sector-overhyped or overlooked?
   Davenport TH, 2018, HARVARD BUS REV, V96, P108
   Davies A, 2021, NATURE, V600, P70, DOI 10.1038/s41586-021-04086-x
   Desouza KC, 2020, BUS HORIZONS, V63, P205, DOI 10.1016/j.bushor.2019.11.004
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   European Parliament, 2023, Compromise Amendments on the Draft Report Proposal for a regulation of the European Parliament and of the Council on harmonized rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union Legislative Acts
   Europol, 2023, ChatGPT - the Impact of Large Language Models on Law Enforcement, a Tech Watch Flash Report from the Europol Innovation Lab
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Flusser V, 2019, Multitudes, V2019, P199, DOI [10.3917/mult.074.0199, DOI 10.3917/MULT.074.0199]
   G7 Group, 2023, G7 Hiroshima Leaders' Communique
   Graham Shawn, 2023, Open Res Eur, V3, P100, DOI 10.12688/openreseurope.16003.1
   Hadwick D., 2021, WORLD TAX J, V13, P609
   Hahn R, 2019, SCIENCE, V364, P534, DOI 10.1126/science.aaw9446
   Henrickson L, 2024, AI SOC, V39, P2647, DOI 10.1007/s00146-023-01752-8
   Henseler H, 2023, ChatGPT as a copilot for investigating digital evidence
   Herbold S, 2023, Arxiv, DOI arXiv:2304.14276
   High-Level Expert Group on Artificial Intelligence, 2019, Ethics guidelines for trustworthy AI, DOI DOI 10.2759/346720
   Huang JS, 2023, AM J CANCER RES, V13, P1148
   Hueber AJ, 2023, RMD OPEN, V9, DOI 10.1136/rmdopen-2023-003248
   ISO, 2020, ISO/IEC TR 24028:2020
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jiao WX, 2023, Arxiv, DOI [arXiv:2301.08745, DOI 10.48550/ARXIV.2301.08745, 10.48550/ARXIV.2301.08745]
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Kirchenbauer J, 2024, Arxiv, DOI [arXiv:2301.10226, 10.48550/arXiv.2301.10226, DOI 10.48550/ARXIV.2301.10226]
   Lasmar Almada MA, 2022, World Tax J, V14
   Longoni Chiara, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P97, DOI 10.1145/3531146.3533077
   Longoni C, 2023, J MARKETING RES, V60, P170, DOI 10.1177/00222437221110139
   Mikuriya K, 2020, World Customs J, V14
   Minelle F, 2023, AI to support PM: a ChatGPT quality assessment (ss test)
   Mokander J, 2023, AI and Ethics, P1
   Motoki F, 2024, PUBLIC CHOICE, V198, P3, DOI 10.1007/s11127-023-01097-2
   Opdahl AL, 2023, DATA KNOWL ENG, V146, DOI 10.1016/j.datak.2023.102182
   OpenAI, 2023, GPT 4 TECHN REP
   Peeters R, 2023, Arxiv, DOI arXiv:2305.03423
   Peeters R, 2018, INFORM POLITY, V23, P267, DOI 10.3233/IP-180074
   Pierce Natalie, 2023, WHY LAW FIRMS MUST R
   Piscopo C, 2008, MIND MACH, V18, P273, DOI 10.1007/s11023-008-9097-3
   Pu DQ, 2023, Arxiv, DOI arXiv:2306.07799
   Rozado D, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12030148
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Sudmann A., 2018, Digital Culture Society, V4, P181, DOI DOI 10.14361/DCS-2018-0111
   Supiot A, 2017, Hart Studies in Comparative Public Law
   United Kingdom Government Secretary of State for Science Innovation and Technology, 2023, A pro-innovation approach to AI regulation
   Vrabie C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15129572
   Wagner MW, 2023, Can Assoc Radiol J
   Xames MD, 2023, ChatGPT for research and publication: opportunities and challenges
   Yang XJ, 2023, Arxiv, DOI arXiv:2302.08081
NR 57
TC 3
Z9 3
U1 19
U2 37
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0951-5666
EI 1435-5655
J9 AI SOC
JI AI Soc.
PD 2024 JAN 18
PY 2024
DI 10.1007/s00146-023-01840-9
EA JAN 2024
PG 12
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA FH2P4
UT WOS:001144809200003
DA 2024-12-25
ER

PT J
AU Ruiz-Rojas, LI
   Salvador-Ullauri, L
   Acosta-Vargas, P
AF Ruiz-Rojas, Lena Ivannova
   Salvador-Ullauri, Luis
   Acosta-Vargas, Patricia
TI Collaborative Working and Critical Thinking: Adoption of Generative
   Artificial Intelligence Tools in Higher Education
SO SUSTAINABILITY
LA English
DT Article
DE collaboration; critical thinking; generative artificial intelligence;
   generative AI; higher education; sustainable development goals; SDG
AB This study explores the impact of generative artificial intelligence tools on critical thinking and collaboration among university students, highlighting the importance of investigating these technologies due to their increasing integration into higher education and their potential to transform traditional pedagogical practices. A predominantly female sample was surveyed to assess their familiarity with and experience and perceptions of these tools. A total of 87% of the respondents had prior knowledge of generative AI tools, with 38% using them occasionally. Among the most popular tools are Canva 2024 (33%), Chat PDF (26%), and YOU.COM (24%). Additionally, 64% of the respondents believe that these tools significantly improve their critical thinking ability. Despite their high familiarity with and occasional use of these tools, the need for continuous training and technical support was identified. While generative AI tools show promising potential for enhancing collaboration and critical thinking in higher education, previous research has limitations, such as the lack of longitudinal data and the inadequacy in addressing ethical considerations and potential biases. More comprehensive research is needed to understand their long-term impact better and maximize their potential benefits.
C1 [Ruiz-Rojas, Lena Ivannova] Univ Fuerzas Armadas, Dept Ciencias Humanas & Sociales, Sangolqui 170550, Ecuador.
   [Salvador-Ullauri, Luis] Univ Alicante, Dept Software & Comp Syst, Alicante 03690, Spain.
   [Acosta-Vargas, Patricia] Univ Amer, Intelligent & Interact Syst Lab, Quito 170125, Ecuador.
   [Acosta-Vargas, Patricia] Univ Amer, Fac Ingn & Ciencias Aplicadas, Carrera Ingn Ind, Quito 170125, Ecuador.
C3 Universitat d'Alacant; Universidad de Las Americas - Ecuador;
   Universidad de Las Americas - Ecuador
RP Acosta-Vargas, P (corresponding author), Univ Amer, Intelligent & Interact Syst Lab, Quito 170125, Ecuador.; Acosta-Vargas, P (corresponding author), Univ Amer, Fac Ingn & Ciencias Aplicadas, Carrera Ingn Ind, Quito 170125, Ecuador.
EM liruiz@espe.edu.ec; lasu1@alu.ua.es; patricia.acosta@udla.edu.ec
RI Acosta-Vargas, Patricia/G-9650-2019; Salvador-Ullauri, Luis/C-3538-2017;
   Acosta-Vargas, Patricia/J-9708-2017
OI Ruiz, Lena/0000-0001-9475-9605; Acosta-Vargas,
   Patricia/0000-0003-4210-0117
FU Universidad de Las Americas-Ecuador [489.A.XIV.24]
FX This research was funded by Universidad de Las Americas-Ecuador as part
   of the internal research project 489.A.XIV.24.
CR Abrami PC, 2015, REV EDUC RES, V85, P275, DOI 10.3102/0034654314551063
   [Anonymous], Google Google Docs
   [Anonymous], 2023, AI Chatbot to Search the Web
   Bansal G, 2024, COMMUN ASSOC INF SYS, V54, DOI 10.17705/1CAIS.05413
   Bezanilla-Albisua María José, 2018, Estud. pedagóg., V44, P89
   Branig M, 2022, LECT NOTE NETW SYST, V389, P21, DOI 10.1007/978-3-030-93904-5_3
   Canva, 2024
   Chen X., 2023, P 2023 10 INT C BEH, P1, DOI [10.1109/BESC59560.2023.10386441, DOI 10.1109/BESC59560.2023.10386441]
   Fotaris P., 2023, Proc. Eur. Conf. Games-Based Learn, V2023, P180, DOI [10.34190/ecgbl.17.1.1870, DOI 10.34190/ECGBL.17.1.1870]
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Guerra E., 2024, STEM Educ, V4, P71, DOI [10.3934/steme.2024005, DOI 10.3934/STEME.2024005]
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Hall P, 2023, ONLINE INFORM REV, V47, P1264, DOI 10.1108/OIR-08-2021-0452
   Hemachandran K, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/1410448
   Ibda H., 2023, Int. J. Eval. Res. Educ, V12, P459, DOI [10.11591/ijere.v12i1.23565, DOI 10.11591/IJERE.V12I1.23565]
   Kang M, 2023, IEEE T PATTERN ANAL, V45, P15725, DOI 10.1109/TPAMI.2023.3306436
   Kim J, 2024, EDUC INF TECHNOL, V29, P8693, DOI 10.1007/s10639-023-12109-5
   Koreshnikova JN, 2020, PSIKHOL NAUK OBRAZOV, V25, P88, DOI 10.17759/pse.2020250608
   Li TT, 2024, ASIA PAC J EDUC, V44, P45, DOI 10.1080/02188791.2024.2305163
   Lin ZC, 2023, ROY SOC OPEN SCI, V10, DOI 10.1098/rsos.230658
   Luan H, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.580820
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Miller D.D., 2020, Hum.-Mach. Shar. Contexts, V10, P205, DOI [10.1016/B978-0-12-820543-3.00010-9, DOI 10.1016/B978-0-12-820543-3.00010-9]
   Muca E, 2022, EDUC SCI, V12, DOI 10.3390/educsci12080573
   Okuogume A, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16062272
   OpenAI, 2024, ChatGPT V4
   Orduo-Osuna J.H., 2023, Facilitating Global Collaboration and Knowledge Sharing in Higher Education with Generative AI, P259
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Peramunugamage A, 2023, J COMPUT EDUC, V10, P83, DOI 10.1007/s40692-022-00223-1
   Ruiz Rojas Lena Ivanova, 2023, Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality. Lecture Notes in Educational Technology, P1062, DOI 10.1007/978-981-99-0942-1_112
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Salinas-Navarro DE, 2024, EDUC SCI, V14, DOI 10.3390/educsci14010083
   Santórum M, 2024, APPL SCI-BASEL, V14, DOI 10.3390/app14020840
   Spiros MC, 2022, SCI JUSTICE, V62, P708, DOI 10.1016/j.scijus.2022.03.008
   Ssemugenyi F, 2023, COGENT EDUC, V10, DOI 10.1080/2331186X.2023.2187943
   Sun YL, 2024, Arxiv, DOI [arXiv:2401.13804, 10.48550/ARXIV.2401.13804, DOI 10.48550/ARXIV.2401.13804]
   Thornhill-Miller B, 2023, J INTELL-BASEL, V11, DOI 10.3390/jintelligence11030054
   Tome M., 2024, Tome AI
   United Nations, 2022, SUSTAINABLE DEV GOAL
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Weng J. C., 2023, Putting Intellectual Robots to Work: Implementing Generative AI Tools in Project Management'
   Yu P., 2023, Facilitating Global Collaboration and Knowledge Sharing in Higher Education with Generative AI, P1
   Zhang Jiehuang, 2023, International Journal of Crowd Science, P32, DOI 10.26599/IJCS.2022.9100033
   Zoom Video Communications Video Communications, 2024, About Us
NR 44
TC 5
Z9 5
U1 132
U2 132
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUL
PY 2024
VL 16
IS 13
AR 5367
DI 10.3390/su16135367
PG 23
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA YQ4M1
UT WOS:001269935800001
OA gold
DA 2024-12-25
ER

PT J
AU Thompson, K
   Corrin, L
   Lodge, JM
AF Thompson, Kate
   Corrin, Linda
   Lodge, Jason M.
TI AI in tertiary education: progress on research and practice
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE generative AI; educational technology; educational research
AB Generative artificial intelligence (AI) has had a significant impact in tertiary education for practitioners and researchers during 2023. We review the way in which academics have made sense of generative AI, revisit our proposed research agenda and reflect on our changing roles as academics in relation to learning, teaching, design and policy.
C1 [Thompson, Kate] Queensland Univ Technol, Brisbane City, Qld, Australia.
   Deakin Univ, Burwood, Vic, Australia.
   [Lodge, Jason M.] Univ Queensland, St Lucia, Qld, Australia.
C3 Queensland University of Technology (QUT); Deakin University; University
   of Queensland
RP Thompson, K (corresponding author), Queensland Univ Technol, Brisbane City, Qld, Australia.
EM kate.j.thompson@qut.edu.au
RI Lodge, Jason/F-8079-2018; Corrin, Linda/AAD-8545-2019
OI Lodge, Jason/0000-0001-6330-6160; Corrin, Linda/0000-0002-1593-3271;
   Thompson, Kate/0000-0003-0738-0205
CR Australian Research Council, 2023, Scheme round statistics for approved applications Discovery projects 2024 round 1
   Bozkurt A., 2023, ASIAN J DISTANCE ED, V18, pi, DOI [DOI 10.5281/ZENODO.8174941, 10.4018/979-8-3693-1351-0]
   Cardona MA., 2023, Artificial intelligence and the future of teaching and learning
   Flinders University, 2023, Good practice guide Designing assessment for artificial intelligence and academic integrity
   FutureLearn, 2023, Generative AI in higher education: Understand the uses and limitations of generative AI to address its challenges and harness its potential for higher education
   Giroux HA, 2015, QUALITATIVE INQUIRY-PAST, PRESENT, AND FUTURE: A CRITICAL READER, P194
   Goodyear P, 2023, AUSTRALAS J EDUC TEC, V39, P1, DOI 10.14742/ajet.9082
   Green S., 2023, ComputerWeekly.ComOctober 17
   Griffith University, 2023, Research integrity resource sheets (RIRS): #17 Artificial intelligence and research outputs
   Hodges C., 2023, EDUCAUSE Review
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   International Association of Universities, 2023, Who does the thinking? The role of generative AI in higher education
   Kayali B, 2023, AUSTRALAS J EDUC TEC, V39, P20, DOI 10.14742/ajet.8915
   Knight S, 2023, AUSTRALAS J EDUC TEC, V39, P101, DOI 10.14742/ajet.8922
   Laverdiere R., 2023, Five ways higher education can leverage generative AI
   Li HF, 2023, AUSTRALAS J EDUC TEC, V39, P40, DOI 10.14742/ajet.8923
   Lodge JM, 2023, Assessment reform for the age of Artificial Intelligence
   Marr B, 2023, FORBES
   Matthews J, 2023, AUSTRALAS J EDUC TEC, V39, P82, DOI 10.14742/ajet.8896
   Parliament of Australia, 2023, Inquiry into the use of generative artificial intelligence in the Australian education system
   Pham T, 2023, AUSTRALAS J EDUC TEC, V39, P1, DOI 10.14742/ajet.8825
   Russell Group, 2023, Russell group principles on the use of generative AI tools in education
   Swift B., 2023, Times Higher EducationJanuary 26
   Tertiary Education Quality and Standards Agency, 2023, Higher education good practice hub: Artificial intellgence
   Thanh BN, 2023, AUSTRALAS J EDUC TEC, V39, P59, DOI 10.14742/ajet.8902
   The University of Sydney, 2023, Teaching@Sydney
   Thompson K., 2023, Technology-enhanced learning and the virtual university, P1, DOI [https://doi.org/10.1007/978-981-19-9438-826-1, DOI 10.1007/978-981-19-9438-826-1]
NR 27
TC 5
Z9 5
U1 53
U2 120
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2023
VL 39
IS 5
DI 10.14742/ajet.9251
PG 7
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA DO3Z6
UT WOS:001132966400006
OA gold
DA 2024-12-25
ER

PT J
AU Eaton, SE
AF Eaton, Sarah Elaine
TI Pre-Service Teacher Education in a Postplagiarism World: Incorporating
   GenAI Into Teacher Training
SO BROCK EDUCATION-A JOURNAL OF EDUCATIONAL RESEARCH AND PRACTICE
LA English
DT Article
DE Pre-service teacher education; postplagiarism; generative artificial
   intelligence (GenAI); AI literacy; educational technology integration
AB This essay explores the integration of generative artificial intelligence (GenAI) into pre-service teacher education amidst contemporary debates on technology in education. It highlights the cautious stance taken by educational authorities, such as the Alberta Teachers' Association, which advises against involving students directly with AI tools. The discussion contrasts this cautionary position with global trends, noting advanced AI curricula in countries like China and Japan. Emphasizing the necessity for hands-on GenAI training for pre-service teachers, the essay advocates equipping future educators with the skills and knowledge to effectively incorporate AI into their practice. It calls for engaging students as partners in learning and rethinking traditional notions of plagiarism in a postplagiarism world where AI co-creation becomes common.
C1 [Eaton, Sarah Elaine] Univ Calgary, Werklund Sch Educ, Calgary, AB, Canada.
C3 University of Calgary
RP Eaton, SE (corresponding author), Univ Calgary, Werklund Sch Educ, Calgary, AB, Canada.
EM seaton@ucalgary.ca
RI Eaton, Sarah/AAB-2731-2019
OI Eaton, Sarah Elaine/0000-0003-0607-6287
CR [Anonymous], 2023, Kyodo NewsJune 22
   [Anonymous], 2019, The Wall Street JournalOctober 1
   [Anonymous], 2022, K-12 AI curricula: A mapping of government-endorsed AI curricula
   Bretag T, 2019, HERDSA Review o f Higher Education, V6
   Bretag T, 2016, HANDBOOK OF ACADEMIC INTEGRITY, P463, DOI 10.1007/978-981-287-098-8_24
   Dawson P., 2023, YouTube
   Eaton SE, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00144-1
   Healey M, 2016, TEACH LEARN INQ, V4, DOI 10.20343/teachlearninqu.4.2.3
   Lancaster T., 2022, CONTRACT CHEATING HI, P219
   McRae P, 2024, Alberta teachers have mixed views on AI
   Student Voice Australia, Student engagement continuum
   Theobald D, 2024, ATA News: Some tips for using AI in school
   United Nations Educational Scientific and Cultural Organization (UNESCO) University of MilanBicocca (Italy) State University of New York & Downstate Health Sciences University, 2023, The risks and challenges of neurotechnologies for human rights.
NR 13
TC 1
Z9 1
U1 9
U2 9
PU BROCK UNIV, FAC EDUCATION
PI CATHARINES
PA 500 GLENRIDGE AVE, ST, CATHARINES, ON L2S 3A1, CANADA
SN 1183-1189
J9 BROCK EDUC
JI Brock Educ.
PY 2024
VL 33
IS 3
SI SI
BP 11
EP 16
PG 6
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D6C5J
UT WOS:001297037800003
DA 2024-12-25
ER

PT J
AU Levin, C
   Suliman, M
   Naimi, E
   Saban, M
AF Levin, Chedva
   Suliman, Moriya
   Naimi, Etti
   Saban, Mor
TI Augmenting intensive care unit nursing practice with generative AI: A
   formative study of diagnostic synergies using simulation-based clinical
   cases
SO JOURNAL OF CLINICAL NURSING
LA English
DT Article; Early Access
DE clinical scenarios; decision-making; diagnostic accuracy; generative
   artificial intelligence; intensive care units
ID DECISION-MAKING
AB Background: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results. Methods: This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response. Results: Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges. Conclusions: While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation. Relevance to Clinical Practice: This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments. Patient or Public Contribution: Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.
C1 [Levin, Chedva] Lev Acad Ctr, Jerusalem Coll Technol, Fac Sch Life & Hlth Sci, Nursing Dept, Jerusalem, Israel.
   [Levin, Chedva] Chaim Sheba Med Ctr, Dept Vasc Surg, Tel Aviv, Israel.
   [Suliman, Moriya] Chaim Sheba Med Ctr, Intens Care Unit, Tel Aviv, Israel.
   [Naimi, Etti; Saban, Mor] Tel Aviv Univ, Fac Med & Hlth Sci, Sch Hlth Profess, Dept Nursing, Tel Aviv, Israel.
C3 Chaim Sheba Medical Center; Chaim Sheba Medical Center; Tel Aviv
   University
RP Saban, M (corresponding author), Tel Aviv Univ, Fac Med & Hlth Sci, Sch Hlth Sci, Tel Aviv, Israel.
EM morsaban1@tauex.tau.ac.il
FX None.
CR Abbasian M, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-024-01074-z
   Aharoni E, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-58087-7
   Alkhaqani A. L., 2023, Al-Rafidain J MedSci, V4, P50, DOI [DOI 10.54133/AJMS.V4I.110, 10.54133/ajms.v4i.110]
   Archibald MM, 2023, J ADV NURS, V79, P3648, DOI 10.1111/jan.15643
   Bahrani S., 2013, Realtime simulations to support operational decision making in healthcare
   Bajaj S, 2024, ACAD RADIOL, V31, P1256, DOI 10.1016/j.acra.2023.08.039
   Barash Y, 2023, J AM COLL RADIOL, V20, P998, DOI 10.1016/j.jacr.2023.06.009
   Borji A., 2023, GPT-4, Claude, and Bard, DOI [10.2139/ssrn.4476855, DOI 10.2139/SSRN.4476855]
   Campbell S, 2020, J RES NURS, V25, P652, DOI 10.1177/1744987120927206
   Gaba DM, 2004, QUAL SAF HEALTH CARE, V13, pI2, DOI 10.1136/qshc.2004.009878
   Gaube S, 2021, NPJ DIGIT MED, V4, DOI 10.1038/s41746-021-00385-9
   Gopalan PD, 2019, J CRIT CARE, V50, P99, DOI 10.1016/j.jcrc.2018.11.027
   Huang JAT, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.36100
   Huh S, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.1
   James Fiona R, 2018, J Intensive Care Soc, V19, P247, DOI 10.1177/1751143717746566
   Jee Marcus, 2023, BMJ Open, V13, pe073099, DOI 10.1136/bmjopen-2023-073099
   Jin MJ, 2021, NURS OPEN, V8, P936, DOI 10.1002/nop2.702
   Johnson Douglas, 2023, Res Sq, DOI 10.21203/rs.3.rs-2566942/v1
   Kao HJ, 2023, MEDICINE, V102, DOI 10.1097/MD.0000000000034068
   Kirova V D., 2023, Journal of Systemics, Cybernetics and Informatics, V21, P42, DOI [DOI 10.54808/JSCI.21.04.42, 10.54808/jsci.21.04.42]
   Levin C, 2024, INT J NURS STUD, V155, DOI 10.1016/j.ijnurstu.2024.104771
   Lyu Q, 2023, VIS COMPUT IND BIOME, V6, DOI 10.1186/s42492-023-00136-5
   Merchant RM, 2020, CIRCULATION, V142, pS337, DOI 10.1161/CIR.0000000000000918
   Miao Hongyu, 2023, Asian Pac Isl Nurs J, V7, pe48136, DOI 10.2196/48136
   Morton PG, 2023, CRITICAL CARE NURSIN
   Nibbelink CW, 2018, J CLIN NURS, V27, P917, DOI 10.1111/jocn.14151
   Nursing PBATAJ of 1982, 2023, From novice to expert
   Rosen S, 2023, INT EMERG NURS, V70, DOI 10.1016/j.ienj.2023.101340
   Rosen S., 2023, European Radiology, V1, P1, DOI [10.1007/S00330-023-10230-0/FIGURES/2, DOI 10.1007/S00330-023-10230-0/FIGURES/2]
   Saban M, 2024, J ADV NURS, DOI 10.1111/jan.16101
   Saintsing D, 2011, J NURS MANAGE, V19, P354, DOI 10.1111/j.1365-2834.2011.01248.x
   Sejnowski TJ, 2023, NEURAL COMPUT, V35, P309, DOI 10.1162/neco_a_01563
   Tam W, 2023, NURS EDUC TODAY, V129, DOI 10.1016/j.nedt.2023.105917
   Tredinnick L., 2023, Blackbox creativity and generative artifical intelligence, DOI [10.1177/02663821231195131, DOI 10.1177/02663821231195131]
   Wong SHV, 2020, NURS EDUC TODAY, V95, DOI 10.1016/j.nedt.2020.104600
   Wu S., 2023, arXiv, V9, P2
   Zaretsky J, 2024, JAMA NETW OPEN, V7, DOI 10.1001/jamanetworkopen.2024.0357
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
NR 38
TC 0
Z9 0
U1 10
U2 10
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0962-1067
EI 1365-2702
J9 J CLIN NURS
JI J. Clin. Nurs.
PD 2024 AUG 5
PY 2024
DI 10.1111/jocn.17384
EA AUG 2024
PG 10
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA A7A2L
UT WOS:001284020500001
PM 39101368
OA hybrid
DA 2024-12-25
ER

PT J
AU Dahal, N
AF Dahal, Niroj
TI How Can Generative AI (GenAI) Enhance or Hinder Qualitative Studies? A
   Critical Appraisal from South Asia, Nepal
SO QUALITATIVE REPORT
LA English
DT Article
DE qualitative data analysis; GenAI; research methods; ethical issues;
   critical appraisal
ID ARTIFIICIAL INTELLIGENCE AI
AB Qualitative researchers can benefit from using generative artificial intelligence (GenAI), such as different versions of ChatGPT-GPT-3.5 or GPT-4, Google Bard-now renamed as a Gemini, and Bing Chat-now renamed as a Copilot, in their studies. The scientific community has used artificial intelligence (AI) tools in various ways. However, using GenAI has generated concerns regarding potential research unreliability, bias, and unethical outcomes in GenAI-generated research results. Considering these concerns, the purpose of this commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal. I explore the controversy surrounding the proper acknowledgment of GenAI or AI use in qualitative studies and how GenAI can support or challenge qualitative studies. First, I discuss what qualitative researchers need to know about GenAI in their research. Second, I examine how GenAI can be a valuable tool in qualitative research as a co-author, a conversational platform, and a research assistant for enhancing and hindering qualitative studies. Third, I address the ethical issues of using GenAI in qualitative studies. Fourth, I share my perspectives on the future of GenAI in qualitative research. I would like to recognize and record the utilization of GenAI and/or AI alongside my cognitive and evaluative abilities in constructing this critical appraisal. I offer ethical guidance on when and how to appropriately recognize the use of GenAI in qualitative studies. Finally, I offer some remarks on the implications of using GenAI in qualitative studies
C1 [Dahal, Niroj] Kathmandu Univ, Sch Educ, Lalitpur, Nepal.
RP Dahal, N (corresponding author), Kathmandu Univ, Sch Educ, Lalitpur, Nepal.
EM niroj@kusoed.edu.np
RI Dahal, Niroj/AEK-2733-2022
OI Dahal, Niroj/0000-0001-7646-1186
FU Google Bard
FX Acknowledgments: I express my sincere gratitude to the reviewer, editor,
   and senior editor - Alicia King, Martha Snyder, and Chip Turner - of TQR
   for their valuable feedback, insightful suggestions, and meticulous
   corrections throughout this commentary. Equally, I wish to acknowledge
   the use of ChatGPT - GPT-3.5 or GPT-4, Google Bard - now renamed as a
   Gemini, and Bing Chat - now renamed as a Copilot in this commentary.
   ChatGPT was used to brainstorm and structure the content. Google Bard
   was employed to distill the key themes from academic papers, while Bing
   Chat was used to refine the language and ensure a consistent flow and
   cohesion throughout the sentences and paragraphs. Thus, I wish to
   recognize and record the application of both GenAI and AI and my
   cognitive and evaluative abilities in the formulation of this
   commentary.
CR Aattouri I., 2023, IAES International Journal of Artificial Intelligence (IJ-AI), V12, P943, DOI [10.11591/ijai.v12.i2.pp943-955, DOI 10.11591/IJAI.V12.I2.PP943-955]
   Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Albalawi U, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19105901
   Amann J, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0279088
   Anis S, 2023, BUS SOC, V62, P1139, DOI 10.1177/00076503231163286
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Christou P, 2023, QUAL REP, V28, P1981, DOI 10.46743/2160-3715/2023.6407
   Christou PA, 2023, QUAL REP, V28, P1968, DOI 10.46743/2160-3715/2023.6406
   Chubb J, 2022, AI SOC, V37, P1439, DOI 10.1007/s00146-021-01259-0
   Ciechanowski L, 2020, J BUS RES, V117, P322, DOI 10.1016/j.jbusres.2020.06.012
   Cingillioglu I, 2023, INT J INF LEARN TECH, V40, P259, DOI 10.1108/IJILT-03-2023-0043
   Dahal N., 2023, P 28 ASIAN TECHNOLOG, V28, P429
   Dahal N., 2023, Critical Roles of Digital Citizenship and Digital Ethics, P249, DOI [10.4018/978-1-6684-8934-5.ch014, DOI 10.4018/978-1-6684-8934-5.CH014]
   Dahal N, 2023, QUAL REP, V28, P2298, DOI 10.46743/2160-3715/2023.6097
   Denzin N.K., 1994, Qualitative Studies in Education, V7, P295, DOI DOI 10.1080/0951839940070401
   Elali FR, 2023, PATTERNS, V4, P1, DOI 10.1016/j.patter.2023.100706
   Feuston Jessica L., 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3479856
   Fransman J., 2018, RES ALL, V2, P185, DOI [10.18546/RFA.02.2.02, DOI 10.18546/RFA.02.2.02]
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gao CA, 2022, bioRxiv, DOI [10.1101/2022.12.23.521610, 10.1101/2022.12.23.521610, DOI 10.1101/2022.12.23.521610V1, DOI 10.1101/2022.12.23.521610]
   Gröger C, 2021, COMMUN ACM, V64, P98, DOI 10.1145/3448247
   Hasija A, 2022, J BUS LOGIST, V43, P388, DOI 10.1111/jbl.12301
   Huang H., 2021, An introduction to qualitative research artificial intelligence technologies
   Jiang Jialun Aaron, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3449168
   Longo L, 2020, ADV INTELL SYST COMP, V1068, P1, DOI 10.1007/978-3-030-31787-4_1
   Marshall D. T., 2023, The ethics of using artificial intelligence in qualitative research, DOI [10.31235/osf.io/3rnbh, DOI 10.31235/OSF.IO/3RNBH]
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Parker JL, 2023, QUAL REP, V28, P2772, DOI 10.46743/2160-3715/2023.6695
   Salinas-Navarro DE, 2024, EDUC SCI, V14, DOI 10.3390/educsci14010083
   Shimizu I, 2023, JMIR MED EDUC, V9, DOI 10.2196/53466
   Traberg CS, 2022, NATURE, V606, P653, DOI 10.1038/d41586-022-01697-w
   Vianello A, 2023, INT J HUM-COMPUT INT, V39, P1405, DOI 10.1080/10447318.2022.2095478
   Yu LH, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1061778
NR 34
TC 1
Z9 1
U1 17
U2 24
PU NOVA SOUTHEASTERN UNIV
PI FORT LAUDERDALE-DAVIE
PA 3301 COLLEGE AVE, FORT LAUDERDALE-DAVIE, FL 33314 USA
SN 1052-0147
EI 2160-3715
J9 QUAL REP
JI Qual. Rep.
PD MAR
PY 2024
VL 29
IS 3
DI 10.46743/2160-3715/2024.6637
PG 15
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA KL9O7
UT WOS:001180241700006
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Shailendra, S
   Kadel, R
   Sharma, A
AF Shailendra, Samar
   Kadel, Rajan
   Sharma, Aakanksha
TI Framework for Adoption of Generative Artificial Intelligence (GenAI) in
   Education
SO IEEE TRANSACTIONS ON EDUCATION
LA English
DT Article
DE Education; Artificial intelligence; Ethics; Privacy; Stakeholders;
   Uncertainty; Training; 4E framework; academic evaluation matrix (AVM);
   academic integrity; education; generative artificial intelligence
   (GenAI)
AB Contributions: An adoption framework to include generative artificial intelligence (GenAI) in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption. Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. GenAI has impacted our perspective on traditional methods of academic integrity and the scholarship of teaching, learning, and research. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved. Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students' learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed 1) for effective redesign of the course curriculum; 2) for enabling staff and students; and 3) to define an evaluation matrix to measure the effectiveness and success of the adoption process.
C1 [Shailendra, Samar; Kadel, Rajan; Sharma, Aakanksha] Melbourne Inst Technol, SITE, Melbourne, Vic 3000, Australia.
RP Sharma, A (corresponding author), Melbourne Inst Technol, SITE, Melbourne, Vic 3000, Australia.
EM aasharma@mit.edu.au
RI Kadel, Rajan/ABA-8977-2021
OI Sharma, Aakanksha/0000-0002-9556-0638; Kadel, Rajan/0000-0001-9207-2148;
   Shailendra, Samar/0000-0001-9961-1001
FU Melbourne Institute of Technology (MIT) administration
FX This work was supported by the Melbourne Institute of Technology (MIT)
   administration.
CR Anderson L. W., 2001, A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objective
   Aust.Curric.,Assess.Rep.Auth.(ACARA), 2012, CURRICULUMDEVELOPMEN
   Aust. Curric. Assess. Rep. Auth. (ACARA), 2019, MEASUREMENT FRAMEWOR
   Bahrini Aram, 2023, 2023 Systems and Information Engineering Design Symposium (SIEDS), P274, DOI 10.1109/SIEDS58326.2023.10137850
   Chan C. K. Y., 2023, INT J EDUC TECHNOL H, V20, P1, DOI DOI 10.1186/S41239-022-00368-0
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Cross K. P., 1988, HDB FACULTY
   Currie GM, 2023, SEMIN NUCL MED, V53, P719, DOI 10.1053/j.semnuclmed.2023.04.008
   Dept. Educ. Skills Employ. Canberra ACT Australia, 2020, TEMPLATE BUILD YOUR
   "Developingandimplementingcurriculumframeworks, 2017, IBE2017OPCD02
   Escotet M., 2023, PROSPECTS, DOI [10.1007/s11125-023-09642-z, DOI 10.1007/S11125-023-09642-Z]
   Gupta M., 2023, IEEEACCESS, V11
   Hoel Tore, 2018, Smart Learning Environments, V5, DOI 10.1186/s40561-018-0052-3
   Holmes W., 2023, Guidance for generative AI in education and research
   Holmes W, 2022, INT J ARTIF INTELL E, V32, P504, DOI 10.1007/s40593-021-00239-1
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Johnson K., 2023, SUMMARYOFINSTITUTION
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kezar A., 2011, Understanding and facilitating organizational change in the 21st century: Recent research and conceptualizations, V28
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lv Z., 2023, Cogn. Robot., V3, P208, DOI [10.1016/j.cogr.2023.06.001, DOI 10.1016/J.COGR.2023.06.001]
   Marzano R. J., 2010, DESIGNING TEACHING L
   Miao F., 2021, AIANDEDUCATIONAGUIDA
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mishra P., 2023, JDIGITLEARNTEACHEDUC, V39, P17
   Montenegro, 2021, IEEEACCESS, V9
   Owlia M.S., 1996, QUAL ASSUR EDUC, V4, P12, DOI DOI 10.1108/09684889610116012
   Partelow S, 2023, J ENVIRON STUD SCI, V13, P510, DOI 10.1007/s13412-023-00833-w
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Rodway P., 2023, Computers and Education: Artificial Intelligence, V5, P1, DOI [DOI 10.1016/J.CAEAI.2023.100150, 10.1016/j.caeai.2023.100150 10.1016/j.caeai.2023.100150]
   Shen L, 2021, PROCEEDINGS OF THE 30TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, CIKM 2021, P1598, DOI 10.1145/3459637.3482352
   Sinakou E, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11215994
   Southworth J., 2023, COMPUTERS ED ARTIFIC, V4, pPG, DOI [10.1016/j.caeai.2023.100127 10.1016/j.caeai.2023.100127, DOI 10.1016/J.CAEAI.2023.100127, 10.1016/j.caeai.2023.100127]
   Suskie L., 2018, Assessing Student Learning: A Common Sense Guide
   TEQSA,Melbourne,VIC,Australia, 2021, HIGHEREDUCATIONSTAND
   Valentini A, 2023, P INT I HIGH ED LAT, P15
   Viennet R., 2017, ED POLICY IMPLEMENTA
   Xu WQ, 2022, EDUC INF TECHNOL, V27, P4195, DOI 10.1007/s10639-021-10774-y
   Yang J.SuandW., 2023, ECNUREVEDUC, V6, P366
NR 40
TC 1
Z9 1
U1 55
U2 55
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9359
EI 1557-9638
J9 IEEE T EDUC
JI IEEE Trans. Educ.
PD OCT
PY 2024
VL 67
IS 5
BP 777
EP 785
DI 10.1109/TE.2024.3432101
EA AUG 2024
PG 9
WC Education, Scientific Disciplines; Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA J6Q6Q
UT WOS:001288200300001
DA 2024-12-25
ER

PT J
AU Lu, WX
AF Lu, Weixu
TI Training the stochastic parrot: Using generative AI to create textual
   materials for communication courses
SO COMMUNICATION TEACHER
LA English
DT Article
AB This article explores the application of generative artificial intelligence (AI), such as ChatGPT, in the development of customized textual materials for communication teaching. It underscores the potential of generative AI to address the scarcity of suitable learning materials and enhance teaching effectiveness. A case study of text generation for a communication theory class highlights the process and efficiency of employing generative AI to produce contextually rich and pedagogically relevant teaching resources.
C1 [Lu, Weixu] Univ Wisconsin La Crosse, Dept Commun Studies, La Crosse, WI USA.
C3 University of Wisconsin System; University of Wisconsin La Crosse
RP Lu, WX (corresponding author), Univ Wisconsin La Crosse, Dept Commun Studies, 4206 Centennial Hall,1725 State St, La Crosse, WI 54601 USA.
EM wlu@uwlax.edu
OI Lu, Weixu/0000-0002-3794-3606
CR [Anonymous], 2015, What should a graduate with a communication degree know, understand, and be able to do?
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bowen J. A., 2024, TEACHING AI PRACTICA
   Chen L., 2023, Harvard Data Science Rev, V6, P1, DOI DOI 10.1162/99608F92.5317DA47
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Giray L, 2023, ANN BIOMED ENG, DOI 10.1007/s10439-023-03272-4
   Lim S, 2024, COMMUN TEACH, V38, P21, DOI 10.1080/17404622.2023.2269258
   Mollick Ethan R., 2024, COINTELLIGENCE LIVIN
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Pierce D., 2023, VERGE           0918
   Sundar S S., 2023, Journalism Communication Monographs, V25, P165, DOI DOI 10.1177/15226379231167135
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
NR 12
TC 1
Z9 1
U1 5
U2 5
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1740-4622
EI 1740-4630
J9 COMMUN TEACH
JI Commun. Teach.
PD OCT 1
PY 2024
VL 38
IS 4
BP 315
EP 322
DI 10.1080/17404622.2024.2385343
EA AUG 2024
PG 8
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA G4W0H
UT WOS:001288898600001
DA 2024-12-25
ER

PT J
AU De Silva, D
   Kaynak, O
   El-Ayoubi, M
   Mills, N
   Alahakoon, D
   Manic, M
AF De Silva, Daswin
   Kaynak, Okyay
   El-Ayoubi, Mona
   Mills, Nishan
   Alahakoon, Damminda
   Manic, Milos
TI Opportunities and Challenges of Generative Artificial Intelligence:
   Research, Education, Industry Engagement, and Social Impact
SO IEEE INDUSTRIAL ELECTRONICS MAGAZINE
LA English
DT Article; Early Access
DE Generative AI; Artificial intelligence; Training; Data models;
   Computational modeling; Solid modeling; Regulation
ID AI
AB Generative artificial intelligence (Generative AI) is transforming the way we live and work. Following several decades of artificial narrow intelligence, Generative AI is signaling a paradigm shift in the intelligence of machines, an increased generalization capability with increased accessibility and equity for nontechnical users. Large language models (LLMs) are leading this charge, specifically conversational interfaces, such as ChatGPT, Gemini, Claude, and Llama (large language model meta AI). Besides language and text, robust and effective Generative AI models have emerged for all other modalities of digital data, image, video, audio, code, and combinations thereof. This article presents the opportunities and challenges of Generative AI in advancing industrial systems and technologies. The article begins with an introduction to Generative AI, which includes its rapid progression to state-of-the-art, the deep learning algorithms, large training datasets, and computing infrastructure used to build Generative AI models, as well as the technical limitations. The contribution, value, and utility of Generative AI is presented in terms of its four capabilities of accelerating academic research, augmenting the learning and teaching experience, supporting industry practice, and increasing social impact. The article concludes with an expeditious message to the academic research and industry practitioner communities to invest time and effort in the training, adoption, and application of Generative AI, with consideration for AI literacy for all stakeholders, human-centricity, and the responsible development and use of AI in industrial settings.
C1 [De Silva, Daswin; Mills, Nishan; Alahakoon, Damminda] La Trobe Univ, Res Ctr Data Analyt & Cognit, Melbourne, Vic 3083, Australia.
   [Kaynak, Okyay] Bogazici Univ, Dept Elect & Elect Engn, TR-34342 Istanbul, Turkiye.
   [El-Ayoubi, Mona] La Trobe Univ, Educ Serv Dept, Melbourne, Vic 3083, Australia.
   [Manic, Milos] Virginia Commonwealth Univ, Dept Comp Sci, Richmond, VA 23284 USA.
C3 La Trobe University; Bogazici University; La Trobe University; Virginia
   Commonwealth University
RP De Silva, D (corresponding author), La Trobe Univ, Res Ctr Data Analyt & Cognit, Melbourne, Vic 3083, Australia.
EM d.desilva@latrobe.edu.au; okyay.kaynak@boun.edu.tr;
   m.el-ayoubi@latrobe.edu.au; n.mills@latrobe.edu.au;
   d.alahakoon@latrobe.edu.au; misko@ieee.org
OI Mills, Nishan/0000-0003-2157-3767; Manic, Milos/0000-0003-1484-7678
CR Adikari A, 2019, IEEE INTL CONF IND I, P183, DOI [10.1109/INDIN41052.2019.8972196, 10.1109/indin41052.2019.8972196]
   Agnese J, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1345
   [Anonymous], STATE IMPLEMENTATION
   [Anonymous], ABB MICROSOFT COLLAB
   [Anonymous], 2022, Blueprint for an AI bill of rights
   [Anonymous], AI STRATEGY GOVT JAP
   [Anonymous], SIEMENS ELEVATES PRE
   [Anonymous], MIT Artificial Intelligence Laboratory
   [Anonymous], GARTNER BOARD BRIEF
   [Anonymous], BOSCH USE GENERATIVE
   [Anonymous], PROPOSAL REGULATION
   [Anonymous], 2013, Mitsubishi electric develops world's best error-correction technology for high-capacity optical communication
   [Anonymous], STATEMENT AI RISK
   [Anonymous], VISION FIELDS INTERE
   Ausat A. M. A., 2023, J EDUC-S AFR, V5, P106
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bandaragoda T, 2020, NEURAL COMPUT APPL, V32, P16057, DOI 10.1007/s00521-020-04736-7
   Barke S, 2023, P ACM PROGRAM LANG, V7, DOI 10.1145/3586030
   Benaich N., 2023, STATE AI REPORT
   Bentley C., 2023, FRAMEWORK RESPONSIBL
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bousmalis K., 2023, arXiv
   Brown T. B., 2020, ARXIV200514165
   Brynjolfsson E., 2023, Generative AI at Work.
   Cao Yunkang, 2023, arXiv
   Chamishka S, 2022, MULTIMED TOOLS APPL, V81, P35173, DOI 10.1007/s11042-022-13363-4
   Cheruvu R, 2022, COMPUTER, V55, P46, DOI 10.1109/MC.2022.3170423
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cyert Richard., 2015, Organizational Behavior, V2, P60
   De Silva D, 2022, PATTERNS, V3, DOI 10.1016/j.patter.2022.100489
   Devlin J., 2018, ARXIV
   Eloundou T., 2023, ARXIV
   European Commission, EU ART INT ACT
   European Union High Level Expert Group on AI, 2019, AI ETHICS GUIDELINES
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Gozalo-Brizuela E. C., 2023, ARXIV
   Gupta, 2019, ARXIV
   Haefner N, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120392
   Hagendorff T, 2020, MIND MACH, V30, P99, DOI 10.1007/s11023-020-09517-8
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   HORNIK K, 1989, NEURAL NETWORKS, V2, P359, DOI 10.1016/0893-6080(89)90020-8
   Hu K., 2023, REUTERS         0202
   IEEE, 2017, ETHI CALLY ALIGNED D, P266
   Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   Lewkowycz A, 2022, ADV NEUR IN
   Li Junyi, 2022, ARXIV
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Liu Z., 2023, P ACM WEB C 2023, P417, DOI DOI 10.1145/3543507.3583386
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Nallaperuma D, 2018, IEEE IND ELEC, P3120, DOI 10.1109/IECON.2018.8591357
   Nawaratne R, 2020, IEEE T IND INFORM, V16, P7756, DOI 10.1109/TII.2019.2957454
   Nawaratne R, 2017, IEEE IND ELEC, P4790, DOI 10.1109/IECON.2017.8216826
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Organisation for Economic Cooperation and Development, OECD AI PRINCIPLES
   Ouyang L., 2022, arXiv
   Patel SB, 2023, LANCET DIGIT HEALTH, V5, pE107, DOI 10.1016/S2589-7500(23)00021-3
   Penalvo F. J. G., 2023, ED KNOWL SOC EKS, V24
   Rathnayaka P, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22103653
   Reed Scott, 2022, arXiv
   Samuelson P, 2023, COMMUN ACM, V66, P20, DOI 10.1145/3597151
   Sanmarchi F, 2024, J PUBLIC HEALTH-HEID, V32, P1761, DOI 10.1007/s10389-023-01936-y
   Stiennon N., 2020, ADV NEURAL IN FORM P, V33, P3021
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Strubell A., 2019, ARXIV
   Suzuki M, 2022, ADV ROBOTICS, V36, P261, DOI 10.1080/01691864.2022.2035253
   The Government of Australia, AUSTRA LIAN GOVT INT
   Thompson N. C., 2020, arXiv
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Tou H., 2023, ARXIV
   Vaswani A., 2017, ADV NEURAL INFORM PR, V2017, P5999
   Wei JS, 2022, ADV NEUR IN
   Wei Jason, 2021, arXiv
   Wen L. L., 2023, ARXIV
   Wu F, 2020, NAT MACH INTELL, V2, P312, DOI 10.1038/s42256-020-0183-4
   Ya n g J., 2023, ARXIV
   Yang J, 2024, IEEE T SYST MAN CY-S, DOI 10.1109/TSMC.2024.3349555
   Yao S., 2023, ARXIV
NR 80
TC 0
Z9 0
U1 71
U2 71
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1932-4529
EI 1941-0115
J9 IEEE IND ELECTRON M
JI IEEE Ind. Electron. Mag.
PD 2024 SEP 16
PY 2024
DI 10.1109/MIE.2024.3382962
EA SEP 2024
PG 16
WC Engineering, Electrical & Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA G7P7O
UT WOS:001318518400001
DA 2024-12-25
ER

PT J
AU Cano-Marin, E
AF Cano-Marin, Enrique
TI The transformative potential of Generative Artificial Intelligence
   (GenAI) in business: a text mining analysis on innovation data sources
SO ESIC MARKET
LA English
DT Article
DE Generative Artificial Intelligence (GenAI); Large-Language Models
   (LLMs); business; innovation; Natural Language Processing (NLP)
AB Objective: This study investigates the transformative potential of Generative Artificial Intelligence (GenAI) within the business domain and the entrepreneurial activity. Methodology: A comprehensive research design is adopted, integrating text-mining techniques to ana-- lyse data obtained from publicly available innovation repositories. A systematic literature review (SLR) is developed based on the literature obtained from all databases indexed in Web of Science (WoS), incorporating preprints from arXiv, alongside industry-related innovation data in the form of patents from Google Patents. This method enables the der-- ivation of valuable insights regarding the impact and prospective developments of GenAI across diverse business sectors and industries by leveraging Natural Language Processing (NLP) and network analysis.<br /> Results: The research outcomes highlight the significant potential of GenAI in enabling informed decision-making, enhancing productivity, and revealing new growth opportunities in the business landscape. The continuously evolving business environment is examined, emphasising GenAI's role as a catalyst for data-driven innovation. However, there are still relevant limitations to overcome. Limitations: The selection of data sources and the study period may have excluded relevant or recently published articles and patents within the scope of the present research. The language of the databases analysed is only English. Practical Implications: The practical implications of this study carry significant weight, serving as a valuable resource for decision-makers, researchers, and practitioners navigating the constantly shifting terrain of business innovation through the lens of GenAI. Understanding the potential advantages and challenges associated with GenAI adoption equips stakeholders to make informed decisions and develop future business strategies.
C1 [Cano-Marin, Enrique] Univ Alcala, Comp Sci Dept, Alcala De Henares, Spain.
C3 Universidad de Alcala
RP Cano-Marin, E (corresponding author), Univ Alcala, Comp Sci Dept, Alcala De Henares, Spain.
EM enrique.canom@uah.es
RI Cano-Marin, Enrique/GZM-2612-2022
OI Cano Marin, Enrique/0000-0002-7948-1657
FX The authors have no competing interests to declare that are relevant to
   the con-tent of this article. This research received no external
   funding.
CR Abonamah AA, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.696346
   Ahamat A., 2022, International Journal of Technoentrepreneurship, V4, P198, DOI [DOI 10.1504/IJTE.2022.127155, 10.1504/IJTE.2022.127155]
   Ahmed I, 2022, IEEE T IND INFORM, V18, P5031, DOI 10.1109/TII.2022.3146552
   Akter S, 2023, TECHNOVATION, V125, DOI 10.1016/j.technovation.2023.102768
   Barreto F., 2023, Intelligent Computing and Networking, P545, DOI [DOI 10.1007/978-981-99-3177-441, 10.1007/978-981-99-3177-4_41, DOI 10.1007/978-981-99-3177-4_41]
   Bastian M., 2009, P INT AAAI C WEB SOC, V3, P361, DOI [10.1136/qshc.2004.010033, 10.1609/icwsm.v3i1.13937, 10.13140/2.1.1341.1520]
   Brandes U, 2008, IEEE T KNOWL DATA EN, V20, P172, DOI 10.1109/TKDE.2007.190689
   Breuer T, 2022, SCIENTOMETRICS, V127, P2455, DOI 10.1007/s11192-022-04319-4
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Cano-Marin E, 2023, ANN OPER RES, DOI 10.1007/s10479-023-05495-z
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Chen LJ, 2023, Arxiv, DOI [arXiv:2305.05176, 10.48550/arXiv.2305.05176]
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dano EB, 2019, 2019 5TH IEEE INTERNATIONAL SYMPOSIUM ON SYSTEMS ENGINEERING (IEEE ISSE 2019), DOI 10.1109/isse46696.2019.8984472
   Daun M, 2023, PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1, P110, DOI 10.1145/3587102.3588815
   Dave T, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1169595
   Du HP, 2023, IEEE T INTELL VEHICL, V8, P2020, DOI 10.1109/TIV.2023.3253281
   Dwivedi R, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3561048
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elia G, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103701
   Enholm IM, 2022, INFORM SYST FRONT, V24, P1709, DOI 10.1007/s10796-021-10186-w
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Faccia A., 2022, P 2022 6 INT C CLOUD, P37, DOI [10.1145/3555962.3555969, DOI 10.1145/3555962.3555969]
   Garg M, 2018, PHYSICA A, V512, P698, DOI 10.1016/j.physa.2018.08.002
   Grünebaum A, 2023, AM J OBSTET GYNECOL, V228, P696, DOI 10.1016/j.ajog.2023.03.009
   Guo JY, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100177
   Hacker P, 2024, Arxiv, DOI [arXiv:2306.00292, 10.48550/arXiv.2306.00292, DOI 10.48550/ARXIV.2306.00292]
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Handa P, 2023, BIOMED SIGNAL PROCES, V86, DOI 10.1016/j.bspc.2023.105292
   Hassani H, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7020062
   Heredia J, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100171
   Hleg A, 2019, B 1049 Brussels
   Isikli E, 2018, SPRINGER SER ADV MAN, P105, DOI 10.1007/978-3-319-57870-5_6
   Jalil S, 2023, IEEE ICST WORKSHOP, P430, DOI 10.1109/ICSTW58534.2023.00078
   Janssen M, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101493
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Li I, 2022, COMPUT SCI REV, V46, DOI 10.1016/j.cosrev.2022.100511
   Liu Y., 2023, Meta-Radiology, DOI [DOI 10.1016/J.METRAD.2023.100017, DOI 10.1016/J.METRAD.2023.1000172]
   Lucchi N, 2024, EUR J RISK REGUL, V15, P602, DOI 10.1017/err.2023.59
   Madani A, 2023, NAT BIOTECHNOL, V41, P1099, DOI 10.1038/s41587-022-01618-2
   Májovsky M, 2023, J MED INTERNET RES, V25, DOI 10.2196/46924
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Martín-Martín A, 2021, SCIENTOMETRICS, V126, P871, DOI 10.1007/s11192-020-03690-4
   Martinez J. M. G., 2022, Sustainable Technology and Entrepreneurship, V1, DOI DOI 10.1016/J.STAE.2022.100006
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Mielli F, 2019, IEEE CEM IND TECH CO, DOI 10.1109/citcon.2019.8729105
   Möhring M, 2022, LECT NOTES BUS INF P, V462, P113, DOI 10.1007/978-3-031-16947-2_8
   Noruzi A., 2014, Webology, V11
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Oduoye MO, 2023, INT J SURG, V109, P2987, DOI 10.1097/JS9.0000000000000552
   Oesterreich TD, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103685
   Ogunrinde A., 2022, ESIC Market, V53, P286, DOI [10.7200/esicm.53.286, DOI 10.7200/ESICM.53.286]
   Okey OD, 2023, COMPUT SECUR, V135, DOI 10.1016/j.cose.2023.103476
   OpenAI, 2022, CHATGPT OPT LANG MOD
   Paranyushkin D., 2011, Nodus Labs, V26, P1
   Paul J, 2021, INT J CONSUM STUD, DOI 10.1111/ijcs.12695
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perifanis NA, 2023, INFORMATION, V14, DOI 10.3390/info14020085
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Qiang JP, 2022, IEEE T KNOWL DATA EN, V34, P1427, DOI 10.1109/TKDE.2020.2992485
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Richard S, 2021, BUS PROCESS MANAG J, V27, P505, DOI 10.1108/BPMJ-05-2020-0216
   Salah M., 2023, Computers in Human Behavior: Artificial Humans, DOI [10.1016/j.chbah.2023.100006, DOI 10.1016/J.CHBAH.2023.100006]
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Schneckenberg D, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103463
   Smith AM, 2018, J LEADERSH STUD, V12, P85, DOI 10.1002/jls.21605
   Statista, 2022, Adoption rate for major milestone internet-of things services and technology in 2022, in days
   Storment J. R., 2023, Cloud FinOps
   Tagscherer F., 2023, Sustain. Technol. Entrep, V2, P100039, DOI DOI 10.1016/J.STAE.2023.100039
   Vaishya R, 2023, DIAB MET SYND CLIN R, V17, DOI 10.1016/j.dsx.2023.102744
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang FY, 2023, IEEE-CAA J AUTOMATIC, V10, P831, DOI 10.1109/JAS.2023.123552
   Wang SH, 2023, Arxiv, DOI arXiv:2304.10428
   Weidinger L, 2021, Arxiv, DOI arXiv:2112.04359
   Zhang YC, 2021, J BUS RES, V131, P374, DOI 10.1016/j.jbusres.2020.11.004
   Zhao AL, 2023, FRONT PHARMACOL, V14, DOI 10.3389/fphar.2023.1194216
   Zhao HY, 2023, Arxiv, DOI [arXiv:2309.01029, DOI 10.48550/ARXIV.2309.01029]
   Zhao RC, 2023, Arxiv, DOI [arXiv:2303.10868, 10.48550/arXiv.2303.10868, DOI 10.48550/ARXIV.2303.10868]
   Zhou C, 2023, Arxiv, DOI [arXiv:2302.09419, DOI 10.48550/ARXIV.2302.09419, 10.48550/arXiv.2302.09419]
NR 81
TC 0
Z9 0
U1 22
U2 22
PU ESCUELA SUPERIOR GESTION COMERCIAL & MARKETING
PI MADRID
PA AVE VALDENIGRALES, S-N, POZUELO ALARCON, MADRID, 28223, SPAIN
SN 0212-1867
EI 1989-3574
J9 ESIC MARK
JI ESIC Mark.
PD MAY-AUG
PY 2024
VL 55
IS 2
AR e333
DI 10.7200/esicm.55.333
PG 23
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA E2T8J
UT WOS:001301585100001
OA gold
DA 2024-12-25
ER

PT J
AU Klein-Avraham, I
   Greussing, E
   Taddicken, M
   Dabran-Zivan, S
   Jonas, E
   Baram-Tsabari, A
AF Klein-Avraham, Inbal
   Greussing, Esther
   Taddicken, Monika
   Dabran-Zivan, Shakked
   Jonas, Evelyn
   Baram-Tsabari, Ayelet
TI How to make sense of generative AI as a science communication
   researcher? A conceptual framework in the context of critical engagement
   with scientific information
SO JCOM-JOURNAL OF SCIENCE COMMUNICATION
LA English
DT Article
DE Informal learning; Public engagement with science and technology;
   Science communication: theory and models
ID MEDIA RICHNESS; INTERACTIVITY; PERFORMANCE; DEFINITION
AB A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments. Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a both a reflection aid and a conceptual baseline for scholarly references.
C1 [Klein-Avraham, Inbal] Technion Israel Inst Technol, Fac Educ Sci andTechnol, Haifa, Israel.
   [Greussing, Esther; Taddicken, Monika; Jonas, Evelyn] Tech Univ Carolo Wilhelmina Braunschweig, Inst Commun Sci, Braunschweig, Germany.
   [Dabran-Zivan, Shakked] Technion Israel Inst Technol, Haifa, Israel.
   [Baram-Tsabari, Ayelet] Technion Israel Inst Technol, Fac Educ Sci & Technol, Sci Educ & Commun, Haifa, Israel.
C3 Technion Israel Institute of Technology; Braunschweig University of
   Technology; Technion Israel Institute of Technology; Technion Israel
   Institute of Technology
RP Klein-Avraham, I (corresponding author), Technion Israel Inst Technol, Fac Educ Sci andTechnol, Haifa, Israel.
EM inbal.klein@campus.technion.ac.il; e.greussing@tu-braunschweig.de;
   m.taddicken@tu-braunschweig.de; shakkeda@gmail.com;
   evelyn.jonas@tu-braunschweig.de; ayelet@technion.ac.il
OI Jonas, Evelyn/0009-0006-1942-4622
FU Niedersaechsisches Vorab program - Lower Saxony Ministry for Science and
   Culture, Germany
FX The authors disclosed receipt of the following financial support for the
   research, authorship, and/or publication of this article: this research
   was supported by the Niedersaechsisches Vorab program, funded by the
   Lower Saxony Ministry for Science and Culture, Germany.
CR Alvarez A, 2024, NAT HUM BEHAV, DOI 10.1038/s41562-024-01846-3
   [Anonymous], 1994, USABILITY INSPECTION, DOI DOI 10.1145/259963.260531
   Bang Y, 2023, Arxiv, DOI arXiv:2302.04023
   Bao LY, 2022, COMPUT HUM BEHAV, V130, DOI 10.1016/j.chb.2022.107182
   Barzilai Sarit, 2023, Computers & Education, DOI 10.1016/j.compedu.2023.104832
   Barzilai S, 2020, LEARN INSTR, V69, DOI 10.1016/j.learninstruc.2020.101367
   Biyela S, 2024, NAT REV PHYS, V6, P162, DOI 10.1038/s42254-024-00691-7
   Brewer R, 2022, ACM T INTERACT INTEL, V12, DOI 10.1145/3484507
   Bromme R., 2010, Personal epistemology in the classroom, P163, DOI DOI 10.1017/CBO9780511691904.006
   Bucchi M, 2014, ROUT INT HANDB, P1
   Canfield KN, 2020, FRONT COMMUN, V5, DOI 10.3389/fcomm.2020.00002
   Chandio AA, 2023, ENVIRON DEV SUSTAIN, V25, P1614, DOI 10.1007/s10668-022-02111-1
   Chattaraman V, 2019, COMPUT HUM BEHAV, V90, P315, DOI 10.1016/j.chb.2018.08.048
   Chinn S, 2018, PUBLIC UNDERST SCI, V27, P807, DOI 10.1177/0963662518791094
   Chong T, 2021, J RETAIL CONSUM SERV, V63, DOI 10.1016/j.jretconser.2021.102735
   Coyle D., 2012, P SIGCHI C HUM FACT, P2025, DOI [DOI 10.1145/2207676.2208350, 10.1145/2207676.2208350]
   Dabran-Zivan S., The importance of science education, scientific knowledge, and evaluation strategies for the successful detection of COVID-19 misinformation
   Dabran-Zivan S, 2023, INTERNET RES, V33, P1774, DOI 10.1108/INTR-07-2022-0560
   DAFT RL, 1987, MIS QUART, V11, P355, DOI 10.2307/248682
   Dambanemuya Henry Kudzanai, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3449157
   Doran D, 2017, Arxiv, DOI arXiv:1710.00794
   Dubovi I, 2021, PUBLIC UNDERST SCI, V30, P759, DOI 10.1177/0963662521990848
   Evans C, 2007, COMPUT EDUC, V49, P1147, DOI 10.1016/j.compedu.2006.01.008
   Fähnrich B, 2021, JCOM-J SCI COMMUN, V20, DOI 10.22323/2.20030402
   Feinstein NW, 2024, J RES SCI TEACH, V61, P2049, DOI 10.1002/tea.21941
   Fortunati Leopoldina, 2022, HMC, V5, P75, DOI [10.30658/hmc.5.3, DOI 10.30658/HMC.5.3]
   Forzani E, 2020, J ADOLESC ADULT LIT, V63, P401, DOI 10.1002/jaal.1004
   Gambino A., 2020, HUMAN MACHINE COMMUN, V1, P71, DOI [DOI 10.30658/HMC.1.5, DOI 10.3316/INFORMIT.097034846749023]
   Greussing E., 2024, SCI COMMUNICATION AG, P43
   Greussing E, 2020, SCI COMMUN, V42, P803, DOI 10.1177/1075547020962100
   Guzman AL, 2020, NEW MEDIA SOC, V22, P70, DOI 10.1177/1461444819858691
   Halpern DianeF., 2014, Thought and Knowledge, V5th, DOI DOI 10.4324/9781315885278
   Hancock JT, 2020, J COMPUT-MEDIAT COMM, V25, P89, DOI 10.1093/jcmc/zmz022
   Hendriks F, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.572744
   Hepp A., 2023, Human-Machine Communication, V6, P41, DOI [https://doi.org/10.30658/hmc.6.4, DOI 10.30688/HMC.6.4]
   Hoffmann AL, 2019, INFORM COMMUN SOC, V22, P900, DOI 10.1080/1369118X.2019.1573912
   Houde S., 2020, arXiv, DOI [10.48550/arXiv.2003.07679, DOI 10.48550/ARXIV.2003.07679]
   Information Technology, 2024, Merriam-Webster.com Dictionary
   Ishii K, 2019, HUM BEHAV EMERG TECH, V1, P124, DOI 10.1002/hbe2.138
   Jungherr A, 2023, COMMUN THEOR, V33, P164, DOI 10.1093/ct/qtad006
   Kang H, 2022, J COMPUT-MEDIAT COMM, V27, DOI 10.1093/jcmc/zmac014
   Kang HJ, 2016, MEDIA PSYCHOL, V19, P561, DOI 10.1080/15213269.2015.1121829
   Keeling K, 2010, J BUS RES, V63, P793, DOI 10.1016/j.jbusres.2008.12.015
   Kidd C, 2023, SCIENCE, V380, P1222, DOI 10.1126/science.adi0248
   Lamb G., 2014, The Science Teacher, V81, P25
   Lammers W, 2024, SCI COMMUN, V46, P332, DOI 10.1177/10755470241231290
   Lasswell H. D., 1948, COMMUNICATION IDEAS, P37
   Lin SS, 2014, INT J SCI MATH EDUC, V12, P1023, DOI 10.1007/s10763-013-9451-7
   Lin Z., 2023, PsyArXiv, DOI [10.31234/osf.io/9yhwz, DOI 10.31234/OSF.IO/9YHWZ]
   Liu SH, 2009, COMPUT EDUC, V52, P599, DOI 10.1016/j.compedu.2008.11.002
   Liu YP, 2002, J ADVERTISING, V31, P53, DOI 10.1080/00913367.2002.10673685
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   López-Pérez L, 2018, JCOM-J SCI COMMUN, V17, DOI 10.22323/2.17020208
   McGrew S, 2023, AERA OPEN, V9, DOI 10.1177/23328584231176451
   Miller T, 2022, ARTIF INTELL, V307, DOI 10.1016/j.artint.2022.103705
   Natale S, 2020, CONVERGENCE-US, V26, P3, DOI 10.1177/1354856517715164
   Nielsen J., 2024, 10 Usability Heuristics for User Interface Design
   Nisbet MC, 2009, AM J BOT, V96, P1767, DOI 10.3732/ajb.0900041
   OECD, 2023, Is education losing the race with technology? AI's progress in maths and reading, DOI [10.1787/73105f99-en, DOI 10.1787/73105F99-EN]
   OECD, 2019, Artificial Intelligence in Society, DOI DOI 10.1787/EEDFEE77-EN
   OECD, 2022, OECD Digital Economy Papers, V323, DOI [10.1787/cb6d9-ca-en, DOI 10.1787/CB6D9-CA-EN]
   OECD, 2021, Capabilities and assessments, V1, DOI [10.1787/5-e71f34-en, DOI 10.1787/5-E71F34-EN]
   Olesk A, 2021, JCOM-J SCI COMMUN, V20, DOI 10.22323/2.20030206
   Osborne J, 2022, SCIENCE, V378, P246, DOI 10.1126/science.abq8093
   Perez SL, 2015, J MED INTERNET RES, V17, DOI 10.2196/jmir.3945
   Polman JL, 2014, SCI EDUC, V98, P766, DOI 10.1002/sce.21114
   Pradhan A, 2020, ACM T COMPUT-HUM INT, V27, DOI 10.1145/3373759
   Qiaosi Wang, 2022, Proceedings of the ACM on Human-Computer Interaction, V6, DOI 10.1145/3512977
   Fernandes GWR, 2020, RES SCI EDUC, V50, P673, DOI 10.1007/s11165-018-9707-x
   Sartori L, 2023, AI SOC, V38, P443, DOI 10.1007/s00146-022-01422-1
   Schäfer MS, 2023, JCOM-J SCI COMMUN, V22, DOI 10.22323/2.22020402
   Sharon AJ, 2020, SCI EDUC, V104, P873, DOI 10.1002/sce.21581
   Shin D, 2024, NEW MEDIA SOC, DOI 10.1177/14614448241234040
   Sohn D, 2011, NEW MEDIA SOC, V13, P1320, DOI 10.1177/1461444811405806
   Spitale G, 2023, SCI ADV, V9, DOI 10.1126/sciadv.adh1850
   Stadtler M, 2014, PROCESSING INACCURATE INFORMATION: THEORETICAL AND APPLIED PERSPECTIVES FROM COGNITIVE SCIENCE AND THE EDUCATIONAL SCIENCES, P379
   Suchman L, 1999, AM BEHAV SCI, V43, P392, DOI 10.1177/00027649921955335
   Sundar SS, 2022, HUM COMMUN RES, V48, P379, DOI 10.1093/hcr/hqac014
   Sundar SS, 2020, J COMPUT-MEDIAT COMM, V25, P74, DOI 10.1093/jcmc/zmz026
   Sundar SS, 2015, HBK COMMUN MEDIA, P47
   Taddicken M, 2021, JCOM-J SCI COMMUN, V20, DOI 10.22323/2.20030205
   Tang KS, 2024, SCI EDUC, V108, P1329, DOI 10.1002/sce.21875
   Tatalovic M, 2018, JCOM-J SCI COMMUN, V17, DOI 10.22323/2.17010501
   Tseng AS, 2021, J RES SCI TEACH, V58, P1152, DOI 10.1002/tea.21696
   van Stekelenburg A, 2022, PSYCHOL SCI, V33, P1989, DOI 10.1177/09567976221083219
   Weingart P, 2016, JCOM-J SCI COMMUN, V15
   West JD, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.1912444117
   Zajko M, 2021, AI SOC, V36, P1047, DOI 10.1007/s00146-021-01153-9
   Zuccon G, 2023, Arxiv, DOI arXiv:2302.13793
NR 90
TC 0
Z9 0
U1 5
U2 5
PU SCUOLA INT SUPERIORE STUDI AVANZATI-S I S S A-INT SCH ADVANCED STUDIES
PI TRIESTE
PA VIA BEIRUT 2-4, TRIESTE, 34014, ITALY
SN 1824-2049
J9 JCOM-J SCI COMMUN
JI JCOM-J. Sci. Commun.
PY 2024
VL 23
IS 6
AR A05
DI 10.22323/2.23060205
PG 19
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA J2J3N
UT WOS:001335377600002
OA gold
DA 2024-12-25
ER

PT J
AU Perkins, M
   Furze, L
   Roe, J
   MacVaugh, J
AF Perkins, Mike
   Furze, Leon
   Roe, Jasper
   MacVaugh, Jason
TI The Artificial Intelligence Assessment Scale (AIAS): A Framework for
   Ethical Integration of Generative AI in Educational Assessment
SO JOURNAL OF UNIVERSITY TEACHING AND LEARNING PRACTICE
LA English
DT Article
ID TECHNOLOGY; IMPACT
AB Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct. Editors Section: Editor Guest Publication Received: Revision: Accepted: Published: Copyright (c) publication. open Creative BY
C1 [Perkins, Mike; MacVaugh, Jason] British Univ Vietnam, Hanoi, Vietnam.
   [Furze, Leon] Deakin Univ, Burwood, Australia.
   [Roe, Jasper] James Cook Univ, Townsville, Australia.
C3 Deakin University; James Cook University
RP Perkins, M (corresponding author), British Univ Vietnam, Hanoi, Vietnam.
RI Roe, Jasper/JOK-3723-2023
CR Abd-Elaal ES, 2022, EUR J ENG EDUC, V47, P725, DOI 10.1080/03043797.2022.2046709
   Anderson N, 2023, BMJ OPEN SPORT EXERC, V9, DOI 10.1136/bmjsem-2023-001568
   Ansari AN, 2024, EDUC INF TECHNOL, V29, P11281, DOI 10.1007/s10639-023-12223-4
   Bader M, 2021, NORD J DIGIT LIT, V16, P21, DOI 10.18261/issn.1891-943x-2021-01-03
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Birks D, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00142-3
   Bissessar C., 2023, Equity in Education & Society, DOI [10.1177/27526461231215083, DOI 10.1177/27526461231215083]
   Black SE, 2001, REV ECON STAT, V83, P434, DOI 10.1162/00346530152480081
   Bretag T, 2019, ASSESS EVAL HIGH EDU, V44, P676, DOI 10.1080/02602938.2018.1527892
   Bretag T, 2014, STUD HIGH EDUC, V39, P1150, DOI 10.1080/03075079.2013.777406
   Burke M. M., 2016, The Accounting Educators' Journal, V26
   Chaka C., 2023, J. Appl. Learn. Teach., V6, DOI DOI 10.37074/JALT.2023.6.2.12
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Cole S., 2000, CAMPUS, V5, P5, DOI [10.1177/108648220000500203, DOI 10.1177/108648220000500203]
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Dawson P., 2020, Defending Assessment Security in a Digital World: Preventing E-Cheating and Supporting Academic Integrity in Higher Education
   Eager B, 2023, J UNIV TEACH LEARN P, V20
   Eaton SE, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00144-1
   Elali FR, 2023, PATTERNS, V4, P1, DOI 10.1016/j.patter.2023.100706
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00140-5
   Ercegovac Z., 2005, Proceedings of the American Society for Information Science and Technology, V42, DOI [10.1002/meet.1450420142, DOI 10.1002/MEET.1450420142]
   Fishman T., 2014, The fundamental values of academic integrity, V2nd
   Fowler S., 2023, Learning Letters, V1, P1, DOI [10.59453/JMTN6001, DOI 10.59453/JMTN6001]
   Fröhling L, 2021, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.443
   Futterer T, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-42227-6
   Grynbaum M.M., 2023, N.Y. Times12-27
   Hartmann J., 2023, SSRN Scholarly Paper 4597899, DOI [10.2139/ssrn.4597899, DOI 10.2139/SSRN.4597899]
   Higgins S., 2012, Full Report
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   Kramm N, 2023, TEACH HIGH EDUC, V28, P2173, DOI 10.1080/13562517.2023.2263839
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Liang WX, 2023, Arxiv, DOI arXiv:2304.02819
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lodge JM, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.7.02
   Lodge JM, 2023, Assessment reform for the age of Artificial Intelligence
   Lodge JM., 2023, Learning: Research and Practice, V9, P117, DOI [10.1080/23735082.2023.2261106, DOI 10.1080/23735082.2023.2261106]
   MacVaugh J, 2010, EUR J INNOV MANAG, V13, P197, DOI 10.1108/14601061011040258
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Milmo D., 2023, GUARDIAN         FEB
   Oldham GR, 2015, COMPUT HUM BEHAV, V42, P5, DOI 10.1016/j.chb.2013.10.041
   Oremus W., 2024, Washington PostJanuary 4
   Orenstrakh MS, 2023, Arxiv, DOI arXiv:2307.07411
   Perkins Mike, 2023, Figshare, DOI 10.6084/m9.figshare.24745749.v1
   Perkins Mike, 2023, F1000Res, V12, P1398, DOI 10.12688/f1000research.142411.2
   Perkins M, 2024, HIGH EDUC POLICY, V37, P633, DOI 10.1057/s41307-023-00323-2
   Perkins M, 2023, Arxiv, DOI arXiv:2305.18081
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Qayyum MA, 2015, INFORM RES, V20
   Risko EF, 2016, TRENDS COGN SCI, V20, P676, DOI 10.1016/j.tics.2016.07.002
   Robinson J., 2017, College Student Journal, V51, P209
   Roe J., 2023, Journal of English and Applied Linguistics, V2, P3, DOI 10.59588/2961-3094.1035
   Roe J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02282-w
   Roe J, 2022, INT J EDUC INTEGR, V18, DOI 10.1007/s40979-022-00109-w
   Rusandi MA, 2023, J PUBLIC HEALTH-UK, V45, pE602, DOI 10.1093/pubmed/fdad049
   Sadasivan VS, 2024, Arxiv, DOI [arXiv:2303.11156, DOI 10.48550/ARXIV.2303.11156]
   Schacter J., 1999, The impact of education technology on student achievement: What the most current research has to say
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Sutherland-Smith W., 2022, Contract Cheating in Higher Education, P91
   Traphagan T, 2014, INTERACT LEARN ENVIR, V22, P253, DOI 10.1080/10494820.2011.641685
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
   Walsh LL, 2021, INT J EDUC INTEGR, V17, DOI 10.1007/s40979-021-00089-3
   Weber-Wulff D, 2023, Arxiv, DOI [arXiv:2306.15666, DOI 10.48550/ARXIV.2306.15666, 10.48550/arXiv.2306.15666]
   Xiao P., 2023, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4458269
NR 66
TC 5
Z9 5
U1 22
U2 22
PU Open Access Publishing Assoc
PI Launceston
PA 28a Brisbane St, Launceston, Tasmania, AUSTRALIA
SN 1449-9789
J9 J UNIV TEACH LEARN P
JI J. Univ. Teach. Learn. Pract.
PY 2024
VL 21
IS 6
AR 36
DI 10.53761/q3azde36
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D5J4K
UT WOS:001296541300010
OA gold
DA 2024-12-25
ER

PT J
AU Jackson, MH
AF Jackson, Michele H.
TI Stances toward generative AI in teaching and learning: An introduction
   to the special issue
SO COMMUNICATION TEACHER
LA English
DT Article; Early Access
AB This article introduces the special issue of Communication Teacher on generative artificial intelligence (AI) in communication education. An important and unique contribution of this issue is that it demonstrates a range of stances that communication educators have toward generative AI. Each article is introduced as illustrating one or more of four stances: Observe, Replicate, Enhance, and Transform.
C1 [Jackson, Michele H.] Michigan State Univ, Dept Lyman Briggs Coll, E Lansing, MI 48824 USA.
C3 Michigan State University
RP Jackson, MH (corresponding author), Michigan State Univ, Dept Lyman Briggs Coll, E Lansing, MI 48824 USA.
EM mhj@msu.edu
CR Altman S., 2024, The intelligence age
   Camp JW, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2392765
   Carabantes D, 2023, PROF INFORM, V32, DOI 10.3145/epi.2023.sep.16
   Duckett J, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2395312
   Flynn MA, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2392764
   Heathco GJ, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2397558
   Hu YF, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2397065
   Isotalus P, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2407910
   Lim S, 2024, COMMUN TEACH, V38, P21, DOI 10.1080/17404622.2023.2269258
   Littell WN, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2405188
   Lu WX, 2024, COMMUN TEACH, V38, P315, DOI 10.1080/17404622.2024.2385343
   McGowan-Kirsch AM, 2024, COMMUN TEACH, V38, P41, DOI 10.1080/17404622.2023.2271548
   Pelzer E, 2022, COMMUN TEACH, V36, P314, DOI 10.1080/17404622.2021.2001551
   Pollino MA, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2396514
   Rister A, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2396014
   Travis ES, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2392766
   Wilson T, 2024, COMMUN TEACH, V38, P242, DOI 10.1080/17404622.2024.2360988
   Youn H, 2024, COMMUN TEACH, DOI 10.1080/17404622.2024.2396005
NR 18
TC 0
Z9 0
U1 0
U2 0
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1740-4622
EI 1740-4630
J9 COMMUN TEACH
JI Commun. Teach.
PD 2024 OCT 31
PY 2024
DI 10.1080/17404622.2024.2419012
EA OCT 2024
PG 5
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA K9G0T
UT WOS:001346897800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Soliman, M
   Ali, RA
   Khalid, J
   Mahmud, I
   Ali, WB
AF Soliman, Mohamed
   Ali, Reham Adel
   Khalid, Jamshed
   Mahmud, Imran
   Ali, Wanamina Bostan
TI Modelling continuous intention to use generative artificial intelligence
   as an educational tool among university students: findings from PLS-SEM
   and ANN
SO JOURNAL OF COMPUTERS IN EDUCATION
LA English
DT Article; Early Access
DE Continuous intention; Generative Artificial Intelligence; Technology
   acceptance model; Self-determination theory; Higher education; Neural
   network; PLS-SEM; ANN
ID SELF-DETERMINATION THEORY; TECHNOLOGY ACCEPTANCE MODEL; NEURAL-NETWORK
   APPROACH; INFORMATION-TECHNOLOGY; BEHAVIORAL INTENTION; PERCEIVED
   USEFULNESS; FIT INDEXES; MOTIVATION; CLASSROOM; AUTONOMY
AB The current study explores continuous intention (CI) to use generative artificial intelligence (GenAI) as an educational tool among university students through the prism of a post-pandemic theoretical framework. Despite GenAI technology's latest launch in the academia sector, very little has been done to evaluate its effects. To examine what factors impact the continuous intention to use GenAI, this paper contemplates incorporating the technology acceptance model with self-determination theory. University students were requested to fill out questionnaire forms that were designed to gather data for the proposed model. A hybrid approach combining a linear partial least squares structural equation modeling model with compensation and a non-linear artificial neural network (ANN) model without compensation is used to investigate the effect of CI on the use of GenAI as an educational tool. The empirical results indicated that perceived usefulness and autonomy are significant predictors of the continued intention to use GenAI in the Thai context. However, the CI was unaffected by perceived ease of use. Additionally, the ANN model indicates that relatedness is the most important predictor. Overall, theoretical and practical ramifications are addressed.
C1 [Soliman, Mohamed] Prince Songkla Univ, Pattani Campus, Pattani, Thailand.
   [Ali, Reham Adel] Ahram Canadian Univ Cairo, Fac Comp Sci & IT, Cairo, Egypt.
   [Khalid, Jamshed] Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management CEM, Nanjing, Peoples R China.
   [Mahmud, Imran] Daffodil Int Univ, Dept Software Engn, Dhaka, Bangladesh.
   [Ali, Wanamina Bostan] Prince Songkla Univ, Fac Management Sci, Hatyai Campus, Hat Yai, Thailand.
C3 Prince of Songkla University; Nanjing University of Aeronautics &
   Astronautics; Daffodil International University; Prince of Songkla
   University
RP Soliman, M (corresponding author), Prince Songkla Univ, Pattani Campus, Pattani, Thailand.
EM mohamed.so@psu.ac.th; reham_akwah@yahoo.com; jamshed.jt@gmail.com;
   imranmahmud@daffodilvarsity.edu.bd; wanamina.w@psu.ac.th
RI Mahmud, Imran/AAU-3754-2021; Mohamed Soliman, Mohamed
   Soliman/I-1974-2018
OI Mohamed Soliman, Mohamed Soliman/0000-0003-2212-3813; Khalid,
   Jamshed/0000-0003-1438-3746
FU Prince of Songkla University
FX No Statement Available
CR Abdullah HO, 2023, J FAM BUS MANAG, V13, P1104, DOI 10.1108/JFBM-09-2022-0113
   Adams C, 2018, J EDUC ADMIN, V56, P382, DOI 10.1108/JEA-04-2017-0036
   Akmese ÖF, 2021, INF TECHNOL LEARN TO, V82, P297, DOI 10.33407/itlt.v82i2.4178
   Al-Ansi AM., 2023, Social Sciences Humanities Open., V8
   Al-Emran M, 2020, EDUC INF TECHNOL, V25, P2899, DOI 10.1007/s10639-019-10094-2
   AL-khatib AW, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102403
   Al-Madhagy Taufiq-Hail G., 2021, Journal of Open Innovation: Technology, Market, and Complexity, V7, P224, DOI DOI 10.3390/JOITMC7040224
   Al-Rahmi WM, 2019, IEEE ACCESS, V7, P26797, DOI 10.1109/ACCESS.2019.2899368
   Al-Shihi H, 2018, EDUC INF TECHNOL, V23, P1805, DOI 10.1007/s10639-018-9691-9
   Alam S, 2022, INT J MANAG EDUC-OXF, V20, DOI 10.1016/j.ijme.2022.100706
   Ali F, 2018, INT J CONTEMP HOSP M, V30, P514, DOI 10.1108/IJCHM-10-2016-0568
   Ali RA, 2018, INT REV RES OPEN DIS, V19, P253
   Alsabawy AY, 2016, COMPUT HUM BEHAV, V64, P843, DOI 10.1016/j.chb.2016.07.065
   Alzaidi MS, 2022, EDUC TRAIN, V64, P305, DOI 10.1108/ET-05-2021-0179
   ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
   Aroonsrimarakot S, 2023, EDUC INF TECHNOL, V28, P8153, DOI 10.1007/s10639-022-11530-6
   Baig MI, 2022, INFORM DEV, V38, P570, DOI 10.1177/02666669211008224
   Becker JM, 2023, INT J CONTEMP HOSP M, V35, P321, DOI 10.1108/IJCHM-04-2022-0474
   BENTLER PM, 1980, PSYCHOL BULL, V88, P588, DOI 10.1037/0033-2909.107.2.238
   Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
   Chang CT, 2017, COMPUT EDUC, V111, P128, DOI 10.1016/j.compedu.2017.04.010
   Chen H, 2017, MOB INF SYST, V2017, DOI 10.1155/2017/3906953
   Chen YP, 2023, INT J HUM-COMPUT INT, V39, P101, DOI 10.1080/10447318.2021.2020007
   Chin W, 2020, IND MANAGE DATA SYST, V120, P2161, DOI 10.1108/IMDS-10-2019-0529
   Cohen J., 1988, STAT POWER ANAL BEHA
   Cortez PM, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e25896
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Dalvi-Esfahani M, 2020, J EDUC COMPUT RES, V57, P1956, DOI 10.1177/0735633118805211
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Deci EL, 2008, CAN PSYCHOL, V49, P182, DOI 10.1037/a0012801
   Diop E, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0216007
   El-Sayad G, 2021, J COMPUT EDUC, V8, P527, DOI 10.1007/s40692-021-00191-y
   Eremeev A., 2022, 2022 6 INT C INF TEC
   Esawe AT, 2023, E-LEARNING DIGITAL M, V20, P162, DOI 10.1177/20427530221107788
   Guillén ME, 2022, J CLEAN PROD, V380, DOI 10.1016/j.jclepro.2022.135057
   Fan PJQ, 2024, SYSTEMS-BASEL, V12, DOI 10.3390/systems12030068
   Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Foroughi B, 2024, INT J HUM-COMPUT INT, V40, P4501, DOI 10.1080/10447318.2023.2226495
   Gan CM, 2017, ELECTRON LIBR, V35, P846, DOI 10.1108/EL-04-2016-0093
   Garad A., 2021, J THEOR APPL INF TEC, V99, P1
   Gbongli K, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11133639
   Ghasemy M, 2020, HIGH EDUC, V80, P1121, DOI 10.1007/s10734-020-00534-1
   Gold AH, 2001, J MANAGE INFORM SYST, V18, P185, DOI 10.1080/07421222.2001.11045669
   Hair J., 2022, Research Methods in Applied Linguistics, V1
   Hair JF, 2014, PRIMER PARTIAL LEAST
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Henseler J, 2014, ORGAN RES METHODS, V17, P182, DOI 10.1177/1094428114526928
   Henseler J, 2009, ADV INT MARKETING, V20, P277, DOI 10.1108/S1474-7979(2009)0000020014
   Hew JJ, 2018, TOURISM MANAGE, V66, P121, DOI 10.1016/j.tourman.2017.10.005
   Hew TS, 2016, IND MANAGE DATA SYST, V116, P1557, DOI 10.1108/IMDS-11-2015-0475
   Hu LT, 1998, PSYCHOL METHODS, V3, P424, DOI 10.1037/1082-989X.3.4.424
   Jaboob M, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2300016
   Javaid M, 2022, J IND INTEGR MANAG, V07, P83, DOI 10.1142/S2424862221300040
   Jeno LM, 2017, COMPUT EDUC, V107, P1, DOI 10.1016/j.compedu.2016.12.011
   Joo YJ, 2018, EDUC TECHNOL SOC, V21, P48
   Racero FJ, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10082711
   Jurayev T.N., 2023, ADV MOB LEARN ED RES, V3, P610, DOI DOI 10.25082/AMLER.2023.01.010
   Karkonasasi K, 2018, Arxiv, DOI arXiv:1806.10744
   Khamkaew S, 2021, Journal of World Englishes and Educational Practices, V3, P53
   Kock N, 2015, INT J E-COLLAB, V11, P1, DOI 10.4018/ijec.2015100101
   Lai HJ, 2020, INTERACT LEARN ENVIR, V28, P890, DOI 10.1080/10494820.2018.1546748
   Lee D, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18010271
   Lee VH, 2024, R&D MANAGE, V54, P750, DOI 10.1111/radm.12596
   Lee Y, 2015, COMPUT HUM BEHAV, V51, P418, DOI 10.1016/j.chb.2015.05.021
   Legris P, 2003, INFORM MANAGE-AMSTER, V40, P191, DOI 10.1016/S0378-7206(01)00143-4
   Leong LY, 2020, INT J INFORM MANAGE, V51, DOI 10.1016/j.ijinfomgt.2019.102047
   Leong LY, 2015, EXPERT SYST APPL, V42, P6620, DOI 10.1016/j.eswa.2015.04.043
   Li H., 2019, Journal o fData Information andManagement, V2, P39, DOI [DOI 10.1007/S42488-019-00017-8, 10.1007/s42488-019-00017-8]
   Liaw S.-S., 2015, The International Review of Research in Open and Distributed Learning, P16, DOI DOI 10.19173/IRRODL.V16I4.2355
   Liaw SS, 2010, COMPUT EDUC, V54, P446, DOI 10.1016/j.compedu.2009.08.029
   Liébana-Cabanillas F, 2017, INT J INFORM MANAGE, V37, P14, DOI 10.1016/j.ijinfomgt.2016.10.008
   Lin FY, 2011, GOV INFORM Q, V28, P271, DOI 10.1016/j.giq.2010.09.004
   Liu YD, 2022, INT J SPORT MARK SPO, V23, P278, DOI 10.1108/IJSMS-12-2020-0232
   Mahmud I, 2017, INFORM SYST, V69, P164, DOI 10.1016/j.is.2017.05.005
   Mazurowski MA, 2020, ACAD RADIOL, V27, P127, DOI 10.1016/j.acra.2019.04.024
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Nasim S. F., 2022, International Journal of Technology, Innovation and Management (IJTIM), V2, P52, DOI DOI 10.54489/IJTIM.V2I2.80
   Niemiec CP, 2009, THEORY RES EDUC, V7, P133, DOI 10.1177/1477878509104318
   Nieto M, 2021, SOFTWAREX, V13, DOI 10.1016/j.softx.2020.100653
   Nikou SA, 2017, COMPUT HUM BEHAV, V68, P83, DOI 10.1016/j.chb.2016.11.020
   Ouyang F, 2022, EDUC INF TECHNOL, V27, P7893, DOI 10.1007/s10639-022-10925-9
   Ramayah T, 2014, INT J E-ADOPT, V6, P1, DOI 10.4018/ijea.2014010101
   Ramayah T., 2017, International Journal of Business and Innovation, V3, P1, DOI [10.1109/RFIC.2004.1320574, DOI 10.1109/RFIC.2004.1320574]
   Ramu M, 2022, INT J EARLY CHILD SP, V14, P10375, DOI 10.9756/INT-JECSE/V14I3.1215
   Razia Bahaa, 2023, Development and Learning in Organizations: An International Journal, P21, DOI 10.1108/DLO-04-2022-0074
   Rezvani A, 2017, COMPUT HUM BEHAV, V76, P263, DOI 10.1016/j.chb.2017.07.032
   Ringle C. M., 2022, SmartPLS, V4
   Roca JC, 2008, COMPUT HUM BEHAV, V24, P1585, DOI 10.1016/j.chb.2007.06.001
   Ronkko M., 2011, PLS marker variable approach to diagnosing and controlling for method variance, P1
   Ryan R., 2009, SOC PSYCHOL-GERMANY, V84, P848
   Ryan RM, 2017, SELF-DETERMINATION THEORY: BASIC PSYCHOLOGICAL NEEDS IN MOTIVATION, DEVELOPMENT, AND WELLNESS, P1, DOI 10.1521/978.14625/28806
   Sarstedt M, 2022, J BUS RES, V138, P398, DOI 10.1016/j.jbusres.2021.08.051
   Sergis S, 2018, COMPUT HUM BEHAV, V78, P368, DOI 10.1016/j.chb.2017.08.011
   Sharma PN, 2023, EUR J MARKETING, V57, P1662, DOI 10.1108/EJM-08-2020-0636
   Sitar-Taut DA, 2021, ONLINE INFORM REV, V45, P1000, DOI 10.1108/OIR-01-2021-0017
   Sorebo O, 2009, COMPUT EDUC, V53, P1177, DOI 10.1016/j.compedu.2009.06.001
   Tan GWH, 2014, COMPUT HUM BEHAV, V36, P198, DOI 10.1016/j.chb.2014.03.052
   Tavakol M, 2011, INT J MED EDUC, V2, P53, DOI 10.5116/ijme.4dfb.8dfd
   Tenenhaus M, 2005, COMPUT STAT DATA AN, V48, P159, DOI 10.1016/j.csda.2004.03.005
   Tiwari CK, 2023, INTERACT TECHNOL SMA, DOI 10.1108/ITSE-04-2023-0061
   Ulla MB, 2021, HELIYON, V7, DOI 10.1016/j.heliyon.2021.e08317
   van Gelderen M, 2010, EDUC TRAIN, V52, P710, DOI 10.1108/00400911011089006
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Wang LYK, 2019, HELIYON, V5, DOI 10.1016/j.heliyon.2019.e01788
   Wang YY, 2023, IEEE ACCESS, V11, P143272, DOI 10.1109/ACCESS.2023.3342055
   Wetzels M, 2009, MIS QUART, V33, P177, DOI 10.2307/20650284
   Wong LW, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.08.005
   Yeralan S., 2023, Sustainable Engineering and Innovation, V5, P107, DOI [DOI 10.37868/SEI.V5I2.ID196, 10.37868/sei.v5i2.id196]
   Yuan YP, 2021, TELEMAT INFORM, V64, DOI 10.1016/j.tele.2021.101676
   Zhang K., 2021, COMPUTERS ED ARTIFIC, V2, P100025, DOI [DOI 10.1016/J.CAEAI.2021.100025, https://doi.org/10.1016/j.caeai.2021.100025, 10.1016/j.caeai.2021.100025]
   Zulkarnain N S., 2023, International Journal of Multidisciplinary Research and Analysis, V6, P2101, DOI DOI 10.47191/IJMRA/V6-I5-34
NR 113
TC 0
Z9 0
U1 21
U2 21
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 2197-9987
EI 2197-9995
J9 J COMPUT EDUC
JI J. Comput. Educ.
PD 2024 AUG 27
PY 2024
DI 10.1007/s40692-024-00333-y
EA AUG 2024
PG 32
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D7E8N
UT WOS:001297785700001
DA 2024-12-25
ER

PT J
AU Lodge, JM
   Thompson, K
   Corrin, L
AF Lodge, Jason M.
   Thompson, Kate
   Corrin, Linda
TI Mapping out a research agenda for generative artificial intelligence in
   tertiary education
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE generative artificial intelligence; research; assessment
AB Generative artificial intelligence (AI) has taken the world by storm. In this editorial, we outline some of the key areas of tertiary education impacted by large language models and associated applications that will require re-thinking and research to address in the short to medium term. Given how rapidly generative AI developments are currently occurring, this editorial is speculative. Although there is a long history of research on AI in education, the current situation is both unprecedented and seemingly not something that the AI in education community fully predicted. We also outline the editorial position of AJET in regards to generative AI to assist authors using tools such as ChatGPT as any part of the research or writing process. This is a rapidly evolving space. We have attempted to provide some clarity in this editorial while acknowledging that we may need to revisit some or all of what we offer here in the weeks and months ahead.
C1 [Lodge, Jason M.] Univ Queensland, St Lucia, Australia.
   [Thompson, Kate] Queensland Univ Technol, Brisbane, Australia.
   [Corrin, Linda] Deakin Univ, Burwood, Australia.
C3 University of Queensland; Queensland University of Technology (QUT);
   Deakin University
RP Lodge, JM (corresponding author), Univ Queensland, St Lucia, Australia.
EM jason.lodge@uq.edu.au
RI Lodge, Jason/F-8079-2018; Corrin, Linda/AAD-8545-2019
OI Lodge, Jason/0000-0001-6330-6160; Corrin, Linda/0000-0002-1593-3271;
   Thompson, Kate/0000-0003-0738-0205
CR Nguyen A, 2023, EDUC INF TECHNOL, V28, P4221, DOI 10.1007/s10639-022-11316-w
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chen XL, 2022, EDUC TECHNOL SOC, V25, P28
   Dai W, 2023, IEEE INT CONF ADV LE, P323, DOI 10.1109/ICALT58122.2023.00100
   Dawson P., 2021, Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education
   Future of Life Institute, 2023, PAUS GIANT AI EXP OP
   Gasevic D., 2023, Computers and Education: Artificial Intelligence, V4, P100130, DOI [10.1016/j.caeai.2023.100130 10.1016/j.caeai.2023.100130, DOI 10.1016/J.CAEAI.2023.100130, 10.1016/j.caeai.2023.100130]
   Jarvela S., IN PRESS
   Knight S., 2015, The Routledge International Handbook of Research on Teaching Thinking, P467
   Kuka L., 2022, Learning with technologies and technologies in learning, V456, DOI [10.1007/978-3-031-04286-7_26, DOI 10.1007/978-3-031-04286-7_26]
   Lodge JM, 2023, AUSTRALAS J EDUC TEC, V39, P18, DOI 10.14742/ajet.8695
   Morze N., 2021, Journal of Physics: Conference Series, V1840, DOI 10.1088/1742-6596/1840/1/012062
   Peng HC, 2019, SMART LEARN ENVIRON, V6, DOI 10.1186/s40561-019-0089-y
   Sabzalieva E., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Xie H, 2019, COMPUT EDUC, V140, DOI 10.1016/j.compedu.2019.103599
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 18
TC 48
Z9 49
U1 101
U2 375
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2023
VL 39
IS 1
BP 18
EP 18
DI 10.14742/ajet.8695
PG 1
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA G4FT6
UT WOS:000988740600002
OA gold
DA 2024-12-25
ER

PT J
AU Miller, RE
AF Miller, Robin Elizabeth
TI Pandora's Can of Worms: A Year of Generative AI in Higher Education
SO PORTAL-LIBRARIES AND THE ACADEMY
LA English
DT Article
AB In the year since ChatGPT was released by OpenAI, librarians, instructors, and higher education administrators have grappled with generative artificial intelligence (AI) and its implications for teaching, learning, research, and writing. Drawn from informal conversations, professional observations, discussion groups, and professional development events, this article reports on the experience of learning about generative AI at one university. This article considers ways that educators may use AI tools and reasons to resist adopting generative AI tools, situating uses on a spectrum of acceptability.
C1 [Miller, Robin Elizabeth] Univ Wisconsin Eau Claire, Lib collect & discovery, Eau Claire, WI 54701 USA.
   [Miller, Robin Elizabeth] Univ Wisconsin Eau Claire, McIntyre Lib, Eau Claire, WI 54701 USA.
C3 University of Wisconsin System; University of Wisconsin Eau Claire;
   University of Wisconsin System; University of Wisconsin Eau Claire
RP Miller, RE (corresponding author), Univ Wisconsin Eau Claire, Lib collect & discovery, Eau Claire, WI 54701 USA.; Miller, RE (corresponding author), Univ Wisconsin Eau Claire, McIntyre Lib, Eau Claire, WI 54701 USA.
EM millerob@uwec.edu
CR American Association of University Professors, Sample Intellectual Property Policy & Contract Language
   Andreou Ashley, 2023, Psychology TodayMarch 9,
   [Anonymous], 2023, NPR (National Public Radio, April 5,
   [Anonymous], 2023, Mata v. Avianca, Inc., 1:222cv01461,Doc.54 (S.D.N.Y. 2023
   Bogle Ariel, 2023, GuardianAugust 25,
   Boysen Ryan, 2023, Law 360June 22,
   Collins Eliza, 2023, Wall Street JournalAugust 28,
   Coulter Martin, 2023, Reuters, February 9
   Ding Jaimie, 2023, Los Angeles TimesFebruary 16,
   Gates Bill, 2023, GatesNotes: The Blog of Bill Gates (blog)April 19,
   Greenwald Ted, 2017, Wall Street Journal, Eastern Edition, March 13
   Houston AB, 2023, TECH SERV Q, V40, P76, DOI 10.1080/07317131.2023.2187110
   Huang K., 2023, New York TimesJan. 16
   International Committee of Medical Journal Editors, 2023, Defining the Role of Authors and Contributors, Part 4
   Korn Jennifer, 2023, CNNFebruary 22
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   McGowan A, 2023, PSYCHIAT RES, V326, DOI 10.1016/j.psychres.2023.115334
   Miller Claire Cain, 2022, New York Times
   Miller Robin, LibGuides: Artificial Intelligence (AI) and Student Learning: What Is It?
   Mollick Ethan, 2023, One Useful ThingJuly 15,
   Mullin Benjamin, 2023, New York TimesJuly 20,
   Nadeem Reem, 2023, Pew Research Center: Internet, Science & Tech (blog)April 20,
   Paris Francesca, 2023, New York TimesApril 14,
   Peres R, 2010, INT J RES MARK, V27, P91, DOI 10.1016/j.ijresmar.2009.12.012
   Reisner Alex, 2023, Atlantic (blog)September 25,
   Reisner Alex, 2023, Atlantic (blog)August 19,
   Rogers E.M., 1962, DIFFUSION INNOVATION
   Rogers EM, 2004, J HEALTH COMMUN, V9, P13, DOI 10.1080/10810730490271449
   Rogin Ali, 2023, PBS NewsSeptember 2,
   SAG-AFTRA, 2023, SAGAFTRA StrikeJuly 13,
   Sarkar Rishik, 2023, Critical AI (blog)July 31,
   Singer Natasha, 2023, Ban or Embrace? Colleges Wrestle with A.I.-Generated Admissions Essays
   @SJSindu, 2023, Create MagazineApril 29,
   Somoye Funmi Looi, 2023, PC Guide
   Sult Leslie, Google Docs
   U.S. Department of Education Office of Educational Technology, 2023, Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations, P15
   Woodie Alex, 2023, DatanamiJanuary 18,
   Writers Guild of America, 2023, Writers Guild of America Calls Strike
NR 38
TC 2
Z9 2
U1 31
U2 51
PU JOHNS HOPKINS UNIV PRESS
PI BALTIMORE
PA JOURNALS PUBLISHING DIVISION, 2715 NORTH CHARLES ST, BALTIMORE, MD
   21218-4363 USA
SN 1531-2542
EI 1530-7131
J9 PORTAL-LIBR ACAD
JI Portal-Libr. Acad
PD JAN
PY 2024
VL 24
IS 1
DI 10.1353/pla.2024.a916988
PG 15
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA NM0A3
UT WOS:001200740700004
DA 2024-12-25
ER

PT J
AU Anderson, OS
   Laubepin, FA
   August, ET
AF Anderson, Olivia S.
   Laubepin, Frederique A.
   August, Ella T.
TI Public Health Students and Instructors Weigh in on Generative Artificial
   Intelligence: Are They on The Same Page?
SO PEDAGOGY IN HEALTH PROMOTION
LA English
DT Article
DE artificial intelligence technology; ChatGPT; education; pedagogy; public
   health
AB Generative artificial intelligence (genAI) technology is used among students, yet it remains unclear how public health students and instructors perceive it to be effective in a learning environment. We described how and why public health students and instructors are using genAI technology along with their perceived benefits and limitations of using genAI, noting where perceptions overlap. We surveyed public health students and instructors at a higher education institution in the United States. Student survey questions covered which genAI technologies they used, which activities they used genAI for, and perceived benefits and limitations of using genAI. Questions for instructors covered which genAI technology they used, course activities genAI was integrated, and perceived benefits and limitations of using genAI. Student respondents (n = 300) indicated using genAI technology for writing or clarifying concepts. Students and instructors (n = 62) agreed genAI technology could save time on tedious tasks and will be part of our future workforce. They agreed that appropriate use in the classroom will better prepare future professionals. Alternatively, students and instructors indicated genAI may impede learning, produce inaccurate information, and pose opportunities for unethical behavior. While students and instructors agree on many aspects of genAI technology, instructors should be explicit about their expectations and rationale for use of genAI technology in classrooms.
C1 [Anderson, Olivia S.; Laubepin, Frederique A.; August, Ella T.] Univ Michigan, Sch Publ Hlth, Dept Nutr Sci, 1415 Washington Hts, Ann Arbor, MI 48109 USA.
C3 University of Michigan System; University of Michigan
RP Anderson, OS (corresponding author), Univ Michigan, Sch Publ Hlth, Dept Nutr Sci, 1415 Washington Hts, Ann Arbor, MI 48109 USA.
EM oliviasa@umich.edu
OI Anderson, Olivia/0000-0001-8313-3682
CR Abramson A., 2023, Monitor on Psychology, V54, P67
   Adler-Kassner L., 2016, THRESHOLD CONCEPTS W
   Amani S., 2023, PREPRINT
   August E., 2024, BRAVE NEW WORDS FRAM, DOI [10.1177/23733799241235119, DOI 10.1177/23733799241235119]
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Lovich D., 2023, FORBES
   Marche S, 2022, ATLANTIC
   Marr Bernard, 2023, Forbes. corn
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Rajabi P, 2023, PROCEEDINGS OF THE 25TH WESTERN CANADIAN CONFERENCE ON COMPUTING EDUCATION, DOI 10.1145/3593342.3593360
   Scott I., 2023, INSIDE HIGHER ED
   Terry O. K., 2023, CHRONICLE HIGHER ED
   The Learner Network, 2023, NEW YORK TIMES
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Trust T., 2023, Contemporary Issues in Technology and Teacher Education, V23
   University of Michigan Information and Technology Services, 2023, U M GENERATIVE AI GU
   Zastudil C., 2023, PREPRINT
   Zhang K., 2021, COMPUTERS ED ARTIFIC, V2, P100025, DOI [DOI 10.1016/J.CAEAI.2021.100025, https://doi.org/10.1016/j.caeai.2021.100025, 10.1016/j.caeai.2021.100025]
NR 19
TC 1
Z9 1
U1 13
U2 13
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 2373-3799
EI 2373-3802
J9 PEDAGOGY HEAL PROMOT
JI Pedagogy Health Promotion
PD SEP
PY 2024
VL 10
IS 3
BP 170
EP 177
DI 10.1177/23733799241246954
EA MAY 2024
PG 8
WC Education & Educational Research; Public, Environmental & Occupational
   Health
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research; Public, Environmental & Occupational
   Health
GA C8S9Z
UT WOS:001217487000001
DA 2024-12-25
ER

PT J
AU Wang, K
   Ruan, QQ
   Zhang, XX
   Fu, CH
   Duan, BY
AF Wang, Kai
   Ruan, Qianqian
   Zhang, Xiaoxuan
   Fu, Chunhua
   Duan, Boyuan
TI Pre-Service Teachers' GenAI Anxiety, Technology Self-Efficacy, and
   TPACK: Their Structural Relations with Behavioral Intention to Design
   GenAI-Assisted Teaching
SO BEHAVIORAL SCIENCES
LA English
DT Article
DE generative artificial intelligence; pre-service teachers; the UTAUT
   model; TPACK; anxiety
ID PEDAGOGICAL CONTENT KNOWLEDGE; ARTIFICIAL-INTELLIGENCE;
   INFORMATION-TECHNOLOGY; UNIVERSITY-STUDENTS; VIRTUAL-REALITY; PERCEIVED
   EASE; UNIFIED THEORY; ACCEPTANCE; EDUCATION; MODEL
AB Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers' viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers' behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers' behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers' perspectives and intentions regarding GenAI technology.
C1 [Wang, Kai] Beijing Normal Univ, Ctr Teacher Educ Res, Beijing 100091, Peoples R China.
   [Ruan, Qianqian; Fu, Chunhua; Duan, Boyuan] Minzu Univ China, Sch Educ, Beijing 100081, Peoples R China.
   [Zhang, Xiaoxuan] Cent China Normal Univ, Sch Educ, Wuhan 430070, Peoples R China.
C3 Beijing Normal University; Central China Normal University
RP Ruan, QQ (corresponding author), Minzu Univ China, Sch Educ, Beijing 100081, Peoples R China.
EM wangkai1991@bnu.edu.cn; 22301422@muc.edu.cn; xiaoxuan19990705@163.com;
   fuchunhua161618@163.com; 21301412@muc.edu.cn
RI ruan, qianqian/HJI-0187-2023; Zhang, Xiaoxuan/KOC-3677-2024
OI Ruan, Qianqian/0009-0004-5886-233X; Duan, Boyuan/0009-0001-5901-8446;
   Zhang, Xiaoxuan666/0009-0002-1524-5038
FU Fundamental Research Funds for the Central Universities
FX We are grateful to all participants in this study.
CR Agyei C, 2022, EDUC INF TECHNOL, V27, P1865, DOI 10.1007/s10639-021-10657-2
   Al-Adwan AS, 2022, EDUC INF TECHNOL, V27, P3567, DOI 10.1007/s10639-021-10758-y
   Al-Shehri M, 2017, INT J ADV COMPUT SC, V8, P442
   Alasmari T, 2019, EDUC INF TECHNOL, V24, P2127, DOI 10.1007/s10639-019-09865-8
   Alkhuwaylidee A. R., 2019, J COMPUT THEOR NANOS, V16, P845, DOI [10.1166/jctn.2019.7964, DOI 10.1166/JCTN.2019.7964, https://doi.org/10.1166/jctn.2019.7964]
   Almisad B, 2020, INT J TECHNOL ENHANC, V12, P1
   Alnoor AM., 2020, Global Business and Organizational Excellence, V39, P41, DOI DOI 10.1002/JOE.21984
   Alotumi M, 2022, EDUC INF TECHNOL, V27, P10035, DOI 10.1007/s10639-022-11051-2
   Altalhi M, 2021, EDUC INF TECHNOL, V26, P1589, DOI 10.1007/s10639-020-10317-x
   Althunibat A, 2015, COMPUT HUM BEHAV, V52, P65, DOI 10.1016/j.chb.2015.05.046
   Alyoussef IY, 2022, HELIYON, V8, DOI 10.1016/j.heliyon.2022.e12529
   An X, 2023, EDUC INF TECHNOL, V28, P5187, DOI 10.1007/s10639-022-11286-z
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   BANDURA A, 1986, J SOC CLIN PSYCHOL, V4, P359, DOI 10.1521/jscp.1986.4.3.359
   Bandura A., 2006, Self-Efficacy Beliefs of Adolescents, V5, P307
   Bandura A., 1997, SELF EFFICACY EXERCI
   Bandura A., 1994, Encyclopedia of human behavior, V4, P71, DOI DOI 10.1002/9780470479216.CORPSY0836
   Bardakci S, 2019, EDUC INF TECHNOL, V24, P2887, DOI 10.1007/s10639-019-09904-4
   Bower M, 2024, EDUC INF TECHNOL, V29, P15403, DOI 10.1007/s10639-023-12405-0
   BRISLIN RW, 1970, J CROSS CULT PSYCHOL, V1, P185, DOI 10.1177/135910457000100301
   Buabeng-Andoh C, 2020, INTERACT TECHNOL SMA, V17, P455, DOI 10.1108/ITSE-02-2020-0028
   Sánchez-Prieto JC, 2017, COMPUT HUM BEHAV, V72, P644, DOI 10.1016/j.chb.2016.09.061
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Celik I, 2022, TECHTRENDS, V66, P616, DOI 10.1007/s11528-022-00715-y
   Chang CT, 2017, COMPUT EDUC, V111, P128, DOI 10.1016/j.compedu.2017.04.010
   Chao CM, 2019, FRONT PSYCHOL, V10, DOI 10.3389/fpsyg.2019.01652
   Chassignol M, 2018, PROCEDIA COMPUT SCI, V136, P16, DOI 10.1016/j.procs.2018.08.233
   Chen HR, 2012, EVAL PROGRAM PLANN, V35, P398, DOI 10.1016/j.evalprogplan.2011.11.007
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chiu TKF, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12145568
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Decman M, 2015, COMPUT HUM BEHAV, V49, P272, DOI 10.1016/j.chb.2015.03.022
   Di Peng, 2019, Foundations and Trends in Smart Learning. Proceedings of 2019 International Conference on Smart Learning Environments. Lecture Notes in Educational Technology (LNET), P161, DOI 10.1007/978-981-13-6908-7_23
   Dong Y, 2020, ASIA-PAC EDUC RES, V29, P147, DOI 10.1007/s40299-019-00461-5
   Durak HY, 2019, J COMPUT HIGH EDUC, V31, P173, DOI 10.1007/s12528-018-9200-6
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2011, IFIP ADV INF COMM TE, V366, P155
   Ertmer PA, 2012, COMPUT EDUC, V59, P423, DOI 10.1016/j.compedu.2012.02.001
   Fangjing Ning, 2019, Foundations and Trends in Smart Learning. Proceedings of 2019 International Conference on Smart Learning Environments. Lecture Notes in Educational Technology (LNET), P125, DOI 10.1007/978-981-13-6908-7_18
   Fathi J, 2020, EDUC INF TECHNOL, V25, P3897, DOI 10.1007/s10639-020-10146-y
   Fernández-Batanero JM, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18020548
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   Gomez FC, 2022, TECHTRENDS, V66, P159, DOI 10.1007/s11528-021-00639-z
   Green B. P., 2020, Artificial Intelligence and Ethics: Sixteen Challenges and Opportunities
   Gunasinghe A., 2019, Eur. J. Soc. Sci. Stud
   Gurer MD, 2021, EDUC INF TECHNOL, V26, P4709, DOI 10.1007/s10639-021-10493-4
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Herft A, 2023, A teacher's prompt guide to ChatGPT aligned with 'What Works Best' guide
   Herodotou C, 2019, ETR&D-EDUC TECH RES, V67, P1273, DOI 10.1007/s11423-019-09685-0
   Holzmann P, 2020, J TECHNOL TRANSFER, V45, P259, DOI 10.1007/s10961-018-9693-1
   Hong JC, 2023, J RES TECHNOL EDUC, V55, P426, DOI 10.1080/15391523.2021.1967818
   Hu SL, 2020, EDUC INF TECHNOL, V25, P4615, DOI 10.1007/s10639-020-10171-x
   Huang L, 2017, PROC INT CONF EDU IN, P283, DOI 10.1109/EITT.2017.75
   Ishak N., 2022, International Journal of Learning, Teaching and Educational Research, V21, P281, DOI [10.26803/ijlter.21.5.15, DOI 10.26803/IJLTER.21.5.15]
   Islamoglu H, 2021, ETR&D-EDUC TECH RES, V69, P1025, DOI 10.1007/s11423-021-09973-8
   Jang J, 2021, IEEE ACCESS, V9, P6798, DOI 10.1109/ACCESS.2020.3048708
   Jena RK, 2015, COMPUT HUM BEHAV, V51, P1116, DOI 10.1016/j.chb.2015.03.020
   Jin L, 2021, MOD LANG J, V105, P412, DOI 10.1111/modl.12712
   Joo YJ, 2018, EDUC TECHNOL SOC, V21, P48
   Joseph GV, 2021, DIGIT EDUC REV, P51
   Jung I, 2015, INNOV EDUC TEACH INT, V52, P243, DOI 10.1080/14703297.2013.805986
   Kafyulilo A, 2016, EDUC INF TECHNOL, V21, P1535, DOI 10.1007/s10639-015-9398-0
   Kamalasena B., 2023, South Asian J. Bus. Insight, V3, P93, DOI [10.4038/sajbi.v3i1.53, DOI 10.4038/SAJBI.V3I1.53]
   Kao CP, 2014, EDUC TECHNOL SOC, V17, P302
   Kapici HO, 2023, EDUC STUD-UK, V49, P76, DOI 10.1080/03055698.2020.1835610
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kikalishvili S, 2024, INTERACT LEARN ENVIR, V32, P5587, DOI 10.1080/10494820.2023.2220401
   Kim B, 2018, INFORM TECHNOL DEV, V24, P706, DOI 10.1080/02681102.2017.1312244
   Kim J, 2022, ASIA PAC J EDUC, V42, P699, DOI 10.1080/02188791.2020.1776213
   Kinzie M. B., 1990, Educational Technology, Research and Development, V38, P5, DOI 10.1007/BF02298244
   Koehler M.J., 2013, HDB RES ED COMMUNICA, V4th, P101, DOI [DOI 10.1007/978-1-4614-3185-59, 10.1007/978-1-4614-3185-5_, DOI 10.1007/978-1-4614-3185-5_9]
   Koh JHL, 2013, INSTR SCI, V41, P793, DOI 10.1007/s11251-012-9249-y
   Kul U., 2019, Online Submission, V11, P198, DOI 10.15345/iojes.2019.01.014
   Wah LL, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13147568
   Lameras P, 2022, INFORMATION, V13, DOI 10.3390/info13010014
   Lassoued Z, 2020, EDUC SCI, V10, DOI 10.3390/educsci10090232
   Leguina A, 2015, INT J RES METHOD EDU, V38, P220, DOI 10.1080/1743727X.2015.1005806
   Li Q, 2023, EDUC INF TECHNOL, V28, P3217, DOI 10.1007/s10639-022-11219-w
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Ling Wang, 2004, Journal of Research on Technology in Education, V36, P231
   Liou YH, 2017, TEACH TEACH, V23, P635, DOI 10.1080/13540602.2016.1218329
   Luckin R., 2022, COMPUTERS ED ARTIFIC, V3, DOI [DOI 10.1016/J.CAEAI.2022.100076, 10.1016/J.CAEAI.2022.100076]
   Maduku DK, 2017, CYBERPSYCH BEH SOC N, V20, P30, DOI 10.1089/cyber.2016.0287
   Maican CI, 2019, COMPUT EDUC, V128, P113, DOI 10.1016/j.compedu.2018.09.010
   Mello RF, 2023, Arxiv, DOI arXiv:2309.12332
   Mercader C, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-0182-x
   Meuter ML, 2003, J BUS RES, V56, P899, DOI 10.1016/S0148-2963(01)00276-4
   Michalsky T, 2021, METACOGN LEARN, V16, P595, DOI 10.1007/s11409-020-09251-7
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Mohammad-Salehi B., 2021, TESL-EJ, V25, P1
   Mohebi L., 2021, Int. J. Learn. Teach. Educ. Res, V20, P232, DOI [10.26803/ijlter.20.12.14, DOI 10.26803/IJLTER.20.12.14]
   Ni AH, 2023, EDUC INF TECHNOL, V28, P3191, DOI 10.1007/s10639-022-11305-z
   Ning YY, 2021, INT SYMP EDUC TECH, P228, DOI 10.1109/ISET52350.2021.00054
   Noar SM, 2003, STRUCT EQU MODELING, V10, P622, DOI 10.1207/S15328007SEM1004_8
   Nyaaba M., 2024, Journal of AI, V8, P1, DOI DOI 10.61969/JAI.1385915
   Obienu AC, 2021, EDUC INF TECHNOL, V26, P2091, DOI 10.1007/s10639-020-10341-x
   OLIVIER TA, 1993, J COMPUT-BASE INSTR, V20, P81
   Oye ND, 2014, EDUC INF TECHNOL, V19, P251, DOI 10.1007/s10639-012-9189-9
   Paraskeva F, 2008, COMPUT EDUC, V50, P1084, DOI 10.1016/j.compedu.2006.10.006
   Peng R, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0286112
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Piniel K, 2013, STUD SECOND LANG LE, V3, P523, DOI 10.14746/ssllt.2013.3.4.5
   Pratama M. P., 2023, Klasikal J. Educ. Lang. Teach. Sci, V5, P350, DOI DOI 10.52208/KLASIKAL.V5I2.877
   Putry A.R.A., 2022, Engl. Educ. J, V12, P151, DOI [10.15294/eej.v12i2.55567, DOI 10.15294/EEJ.V12I2.55567]
   Rahmiati F., 2021, J TECHNOLOGY MANAGEM, V9, P1
   Rahrnawati R. N., 2019, AM J HUM SOC SCI RES, V3, P41
   Sahin F, 2022, SOC PSYCHOL EDUC, V25, P567, DOI 10.1007/s11218-022-09702-w
   Semiatin AM, 2012, AGING MENT HEALTH, V16, P683, DOI 10.1080/13607863.2011.651437
   Shen CW, 2019, VIRTUAL REAL-LONDON, V23, P313, DOI 10.1007/s10055-018-0348-1
   Shulman LS, 2019, PROFESORADO, V23, P269, DOI 10.30827/profesorado.v23i3.11230
   Sidik D, 2020, EDUC INF TECHNOL, V25, P4781, DOI 10.1007/s10639-019-10018-0
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Teo T, 2017, INTERACT LEARN ENVIR, V25, P513, DOI 10.1080/10494820.2016.1143844
   Teo T, 2016, COMPUT EDUC, V94, P77, DOI 10.1016/j.compedu.2015.10.022
   Teo T, 2011, COMPUT EDUC, V57, P2432, DOI 10.1016/j.compedu.2011.06.008
   Thayyib PV, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15054026
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tondeur J, 2018, COMPUT EDUC, V122, P32, DOI 10.1016/j.compedu.2018.03.002
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 1996, DECISION SCI, V27, P451, DOI 10.1111/j.1540-5915.1996.tb01822.x
   Venkatesh V, 2000, INFORM SYST RES, V11, P342, DOI 10.1287/isre.11.4.342.11872
   Wang Q, 2021, BRIT J EDUC TECHNOL, V52, P2319, DOI 10.1111/bjet.13141
   Wang WT, 2009, COMPUT EDUC, V53, P761, DOI 10.1016/j.compedu.2009.02.021
   Wang YY, 2022, INTERACT LEARN ENVIR, V30, P619, DOI 10.1080/10494820.2019.1674887
   Wong GKW, 2015, AUSTRALAS J EDUC TEC, V31, P713
   Wong JYL, 2023, TEACH TEACH EDUC, V136, DOI 10.1016/j.tate.2023.104364
   Wong KT, 2020, INT J EMERG TECHNOL, V15, P163, DOI 10.3991/ijet.v15i01.11497
   Wu CC, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.738959
   Xie T, 2022, VIRTUAL REAL-LONDON, V26, P1725, DOI 10.1007/s10055-022-00656-0
   Xu WQ, 2022, EDUC INF TECHNOL, V27, P4195, DOI 10.1007/s10639-021-10774-y
   Yang JZ, 2021, INTERACT LEARN ENVIR, V29, P1062, DOI 10.1080/10494820.2019.1627560
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhong Z, 2024, EDUC INF TECHNOL, V29, P8369, DOI 10.1007/s10639-023-12152-2
NR 137
TC 7
Z9 7
U1 178
U2 194
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-328X
J9 BEHAV SCI-BASEL
JI Behav. Sci.
PD MAY
PY 2024
VL 14
IS 5
AR 373
DI 10.3390/bs14050373
PG 21
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA SB9Q9
UT WOS:001232121100001
PM 38785864
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Andreoni, M
   Lunardi, WT
   Lawton, G
   Thakkar, S
AF Andreoni, Martin
   Lunardi, Willian Tessaro
   Lawton, George
   Thakkar, Shreekant
TI Enhancing Autonomous System Security and Resilience With Generative AI:
   A Comprehensive Survey
SO IEEE ACCESS
LA English
DT Article
DE Security; Robots; Computer security; Surveys; Artificial intelligence;
   Safety; Task analysis; GenerativeAI; artificial intelligence; autonomous
   systems; security; UxV
AB This survey explores the transformative role of Generative Artificial Intelligence (GenAI) in enhancing the trustworthiness, reliability, and security of autonomous systems such as Unmanned Aerial Vehicles (UAVs), self-driving cars, and robotic arms. As edge robots become increasingly integrated into daily life and critical infrastructure, the complexity and connectivity of these systems introduce formidable challenges in ensuring security, resilience, and safety. GenAI advances from mere data interpretation to autonomously generating new data, proving critical in complex, context-aware environments like edge robotics. Our survey delves into the impact of GenAI technologies-including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer-based models, and Large Language Models (LLMs)-on cybersecurity, decision-making, and the development of resilient architectures. We categorize existing research to highlight how these technologies address operational challenges and innovate predictive maintenance, anomaly detection, and adaptive threat response. Our comprehensive analysis distinguishes this work from existing reviews by mapping out the applications, challenges, and technological advancements of GenAI and their impact on creating secure frameworks for autonomous systems. We discuss significant challenges and future directions for integrating these technologies within security frameworks to address the evolving landscape of cyber-physical threats, underscoring the potential of GenAI to make autonomous systems more adaptive, secure, and efficient.
C1 [Andreoni, Martin; Lunardi, Willian Tessaro; Thakkar, Shreekant] Technol Innovat Inst, Abu Dhabi, U Arab Emirates.
C3 Technology Innovation Institute
RP Andreoni, M (corresponding author), Technol Innovat Inst, Abu Dhabi, U Arab Emirates.
EM martin.andreoni@tii.ae
OI Lawton, George/0009-0003-7309-5100
CR Abdin M., 2024, arXiv
   Achiam J., 2023, arXiv
   Adeboye O, 2022, IEEE ACCESS, V10, P124534, DOI 10.1109/ACCESS.2022.3222834
   Ahn Michael, 2022, ARXIV
   Akleman E., 2020, Computer, V53, P1, DOI [10.1109/MC.2020.3004171, DOI 10.1109/MC.2020.3004171]
   Alahi A, 2016, PROC CVPR IEEE, P961, DOI 10.1109/CVPR.2016.110
   Aldhaheri S, 2023, SENSORS-BASEL, V23, DOI 10.3390/s23187796
   Amine Ferrag M., 2024, ARXIV
   Anil R, 2023, ARXIV
   [Anonymous], 2024, ARXIV
   [Anonymous], 2023, MISTRAL AI
   [Anonymous], 2024, GENERATIVE AI DEFENS
   [Anonymous], 2024, Meta Llama-3
   [Anonymous], 2024, TRUSTWORTHY GENERATI
   [Anonymous], 2024, CLAUDE 3 FAMILY
   Arvinte M., 2022, IEEE T WIRELESS COMM, V1, P1
   Barfoot T.D., 2017, State Estimation For Robotics, DOI 10.1017/9781316671528
   Bayer M, 2024, ACM T PRIV SECUR, V27, DOI 10.1145/3652594
   Bose A., 2023, P IEEE INT C INT SEC, P1
   Brown H., 2024, ARXIV
   Brown TB, 2020, ADV NEUR IN, V33
   Cao L., 2024, ARXIV
   Carlini N, 2023, PROCEEDINGS OF THE 32ND USENIX SECURITY SYMPOSIUM, P5253
   Chen G., 2023, ARXIV
   Chen JD, 2021, SOC INDIC RES, V153, P65, DOI 10.1007/s11205-020-02481-x
   Chen YR, 2024, COMPUT SECUR, V145, DOI 10.1016/j.cose.2024.104016
   Nandakumar SC, 2024, SENSORS-BASEL, V24, DOI 10.3390/s24041330
   Chougule A, 2024, IEEE OPEN J VEH TECH, V5, P142, DOI 10.1109/OJVT.2023.3335180
   Chowdhury A, 2020, IEEE ACCESS, V8, P207308, DOI 10.1109/ACCESS.2020.3037705
   Christodorescu M., 2024, 2024855 CRYPT EPRINT
   Chung SJ, 2018, IEEE T ROBOT, V34, P837, DOI 10.1109/TRO.2018.2857475
   Cleland-Huang J, 2022, I W S E ADAP SM SYS, P120, DOI 10.1145/3524844.3528054
   Cui C, 2024, IEEE WINT C APPL COM, P958, DOI 10.1109/WACVW60836.2024.00106
   Cui YD, 2024, IEEE T INTELL VEHICL, V9, P1450, DOI 10.1109/TIV.2023.3327715
   de Jesus Coelho da Silva G., 2024, ARXIV
   Deitke Matt, 2022, ARXIV
   Delecki H., 2023, P INT C INF FUS, P1
   Demirci D, 2022, IEEE ACCESS, V10, P58488, DOI 10.1109/ACCESS.2022.3179384
   Du BX, 2024, IEEE INTERNET THINGS, V11, P7664, DOI 10.1109/JIOT.2023.3317629
   Du H., 2023, IEEE NETWORK, V1, P1
   Du H. Y., 2023, arXiv
   Dube R., 2024, RG222010726404
   Dunmore A, 2023, IEEE ACCESS, V11, P76071, DOI 10.1109/ACCESS.2023.3296707
   Fan H., 2023, INT J INFOR COMPUT, V5, P28
   Fang R., 2024, ARXIV
   Ferrag MA, 2024, IEEE ACCESS, V12, P23733, DOI 10.1109/ACCESS.2024.3363469
   Fontanesi G, 2023, IEEE INTERNET THINGS, V10, P4998, DOI 10.1109/JIOT.2022.3220981
   Freestone T., 2024, BUILDING TRUST GENER
   Fuertes D, 2023, ENG APPL ARTIF INTEL, V122, DOI 10.1016/j.engappai.2023.106085
   G. Team, 2023, ARXIV
   González GG, 2023, Security and Privacy, P558, DOI 10.1109/EuroSPW59978.2023.00068
   Goecks V. G., 2024, P INT C MIL COMM INF, P1
   Golda A, 2024, IEEE ACCESS, V12, P48126, DOI 10.1109/ACCESS.2024.3381611
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Gupta A., 2022, ARXIV
   Hafeez S., 2024, ARXIV
   Han PH, 2021, IEEE SENS J, V21, P21903, DOI 10.1109/JSEN.2021.3105226
   Hannan A, 2021, PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR), P1, DOI 10.1109/CSR51186.2021.9527980
   Harper J., 2024, PENTAGON TESTING GEN
   Hassanin M., 2024, ARXIV
   He AF, 2018, PROC CVPR IEEE, P4834, DOI 10.1109/CVPR.2018.00508
   He Y, 2019, IEEE SYS MAN CYBERN, P2248, DOI [10.1109/smc.2019.8914585, 10.1109/SMC.2019.8914585]
   Herzalla D, 2023, IEEE ACCESS, V11, P118577, DOI 10.1109/ACCESS.2023.3319213
   Hilario E, 2024, INT J INF SECUR, V23, P2075, DOI 10.1007/s10207-024-00835-x
   Hu P., 2024, P NETW DISTR SYST SE, P1
   Huang H., 2023, WHAT CEOS NEED KNOW
   Huang L., 2024, ARXIV
   Hunt W., 2024, ARXIV
   IEEE Standard for Transparency of Autonomous Systems, 2022, 70012021 IEEE
   Javaid S., 2024, ARXIV
   Jin Hongye, 2024, arXiv
   Khlaisamniang P, 2023, Natural Lang Proc, DOI 10.1109/iSAI-NLP60301.2023.10354608
   Kim Yubin, 2024, ArXiv
   Kingma D. P., 2014, AUTOENCODING VARIATI
   Kirichenko P., 2020, P NIPS, V33, P20578
   Kobyzev I, 2021, IEEE T PATTERN ANAL, V43, P3964, DOI 10.1109/TPAMI.2020.2992934
   Kulyadi S. P., 2021, P 13 INT C EL COMP A, P1
   Lee J, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9204221
   Li Longyuan, 2021, ADV NEUR IN, V34
   Li MLQ, 2021, COMPUT SECUR, V108, DOI 10.1016/j.cose.2021.102371
   Li N., 2024, ARXIV
   Liang C, 2023, ARXIV
   Liang J., 2024, ARXIV
   Liu G., 2024, ARXIV
   Liu Z., 2024, P WORKSH ART INT CYB, P1
   Liu Z., 2023, arXiv
   Lu HD, 2022, IEEE SENS J, V22, P17464, DOI 10.1109/JSEN.2021.3069452
   Lunardi WT, 2023, IEEE T NETW SERV MAN, V20, P1305, DOI 10.1109/TNSM.2022.3229706
   Mai J., 2023, ARXIV
   Marcus G., 2024, 2 WORST100B INVESTME
   Meng R., 2024, P NETW DISTR SYST SE, P1
   Methnani L, 2024, ACM COMPUT SURV, V56, DOI 10.1145/3645090
   Mian Z, 2024, ENG APPL ARTIF INTEL, V127, DOI 10.1016/j.engappai.2023.107357
   Mittal S, 2016, PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, P860, DOI 10.1109/ASONAM.2016.7752338
   Mon Divakaran D., 2024, ARXIV
   Montijano E., 2024, ARXIV
   Mukherjee Subhojyoti, 2023, ARXIV
   Nasr Milad, 2023, ARXIV
   Neupane S., 2023, ARXIV
   Neupane S, 2024, IEEE ACCESS, V12, P22072, DOI 10.1109/ACCESS.2024.3363657
   Nourmohammadzadeh Motlagh F., 2024, ARXIV
   O. X-Embodiment Collaboration, 2023, ARXIV
   Obrenovic B, 2024, AI SOC, DOI 10.1007/s00146-024-01889-0
   Pankajakshan R., 2024, ARXIV
   Park C, 2023, IEEE INTERNET THINGS, V10, P2330, DOI 10.1109/JIOT.2022.3211346
   Pearce H., 2022, ARXIV
   Penedo Guilherme, 2023, arXiv
   Perez-Cerrolaza J, 2024, ACM COMPUT SURV, V56, DOI 10.1145/3626314
   Piggott B, 2023, IEEE ACM Symp Edge C, P287, DOI 10.1145/3583740.3626809
   Qi Y., 2024, ARXIV
   Qiang Fu, 2019, 2019 IEEE 5th International Conference on Computer and Communications (ICCC), P225, DOI 10.1109/ICCC47050.2019.9064184
   Rahali A., 2021, ARXIV
   Ranade P, 2021, IEEE INT CONF BIG DA, P3334, DOI 10.1109/BigData52589.2021.9671824
   Russell S, 2015, NATURE, V521, P415, DOI 10.1038/521415a
   Ruzicka M, 2022, INT J DISTRIB SENS N, V18, DOI 10.1177/15501477221075544
   Sadhu V, 2023, IEEE T ROBOT, V39, P3319, DOI 10.1109/TRO.2023.3269380
   Schnell T, 2022, IEEE INT C INT ROBOT, P4332, DOI 10.1109/IROS47612.2022.9982240
   Sedjelmaci H, 2022, T EMERG TELECOMMUN T, V33, DOI 10.1002/ett.4073
   Shafee S., 2024, ARXIV
   Shayegani E., 2023, arXiv
   Shi Jing, 2023, ARXIV
   Shirajum Munir M., 2024, ARXIV
   Skaltsis George Marios, 2021, 2021 International Conference on Unmanned Aircraft Systems (ICUAS), P488, DOI 10.1109/ICUAS51884.2021.9476736
   Stafford V., 2020, ZERO TRUST ARCHITECT, V800, P207, DOI DOI 10.6028/NIST.SP.800-207
   Sufi F., 2024, Natural Language Processing Journal, V7
   Sun G., 2024, ARXIV
   Sun Y., 2024, ARXIV
   Tagliabue A., 2023, ARXIV
   Tan WL, 2020, IEEE GLOB COMM CONF, DOI 10.1109/GLOBECOM42002.2020.9322377
   Tang AR, 2024, J NURS SCHOLARSHIP, V56, P314, DOI 10.1111/jnu.12938
   Tian Y., 2024, P ADV NEUR INF PROC, V36, P1
   Tong Liang, 2022, 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), P375, DOI 10.1109/ICAICA54878.2022.9844489
   Touvron H., 2023, arXiv
   Vaswani A, 2017, ADV NEUR IN, V30
   Vaz D, 2023, Depend Systems Netwo, P127, DOI 10.1109/DSN-S58398.2023.00037
   Vemprala SH, 2024, IEEE ACCESS, V12, P55682, DOI 10.1109/ACCESS.2024.3387941
   Wang J., 2024, ARXIV
   Wang JC, 2024, IEEE WIREL COMMUN, DOI 10.1109/MWC.013.2300485
   Wang L, 2024, FRONT COMPUT SCI-CHI, V18, DOI 10.1007/s11704-024-40231-1
   Wang YJ, 2024, IEEE T NEUR NET LEAR, V35, P10513, DOI 10.1109/TNNLS.2023.3242323
   Wang Y, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13053136
   Wang ZX, 2019, IEEE INT SYMP PARAL, P975, DOI 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00141
   Weidinger L., 2023, arXiv, P1
   Wu X., 2024, ARXIV
   Wu X., 2023, arXiv
   Wu Y., 2024, ARXIV
   Xia Chunqiu Steven, 2024, P IEEE ACM 46 INT C, P1
   Xia X, 2022, NEUROCOMPUTING, V493, P497, DOI 10.1016/j.neucom.2021.12.093
   Xiaohui Hu, 2020, 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), P927, DOI 10.1109/TrustCom50675.2020.00124
   Xiong ZB, 2021, IEEE T IND INFORM, V17, P6200, DOI 10.1109/TII.2020.3032352
   Xu MR, 2024, IEEE COMMUN SURV TUT, V26, P1127, DOI 10.1109/COMST.2024.3353265
   Yan Q, 2019, INT J MACH LEARN CYB, V10, P3387, DOI 10.1007/s13042-019-00925-6
   Yang LY, 2024, ACM COMPUT SURV, V56, DOI [10.1145/3626235, 10.1145/3648469]
   Yanhua Liu, 2021, 2021 11th International Conference on Information Technology in Medicine and Education (ITME), P157, DOI 10.1109/ITME53901.2021.00041
   Yao YF, 2024, HIGH-CONFID COMPUT, V4, DOI 10.1016/j.hcc.2024.100211
   Yin Shengming, 2023, ARXIV
   Yu HY, 2020, PATTERN RECOGN LETT, V131, P219, DOI 10.1016/j.patrec.2019.12.018
   Yu Xiang, 2022, 2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM), P441, DOI 10.1109/MLCCIM55934.2022.00081
   Zeng F., 2023, arXiv
   Zhang BW, 2023, IEEE INT C INT ROBOT, P7961, DOI 10.1109/IROS55552.2023.10341488
   Zhang C., 2023, BIOMIMETIC INTELL RO, V1
   Zhang J., 2024, arXiv
   Zhang R., 2023, ARXIV
   Zhao JY, 2023, APPL ENERG, V352, DOI 10.1016/j.apenergy.2023.121949
   Zhao Xinqiao, 2024, ARXIV
   Zheng S, 2020, LECT NOTES ARTIF INT, V11908, P621, DOI 10.1007/978-3-030-46133-1_37
   Zhou B, 2022, COMPUT ENVIRON URBAN, V95, DOI 10.1016/j.compenvurbsys.2022.101824
   Zhou YK, 2020, IEEE ACCESS, V8, P183856, DOI 10.1109/ACCESS.2020.3028865
   Zhu B., 2024, ARXIV
   Zolfaghari B., 2024, ACM COMPUT SURV, V56, P1
   Zou Hao, 2023, arXiv
NR 171
TC 0
Z9 0
U1 8
U2 8
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 109470
EP 109493
DI 10.1109/ACCESS.2024.3439363
PG 24
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA C8N7W
UT WOS:001291887400001
OA gold
DA 2024-12-25
ER

PT J
AU Lee, AVY
AF Lee, Alwyn Vwen Yen
TI Staying ahead with generative artificial intelligence for learning:
   challenges and opportunities
SO ASIA PACIFIC JOURNAL OF EDUCATION
LA English
DT Article
DE Generative AI; AI in education; Learning; Challenges and opportunities;
   Emergent Technologies
ID EDUCATION; TEACHER
AB Generative Artificial Intelligence (AI)'s emergence is viewed as a disruptive technological advancement that has been beneficial for most educational purposes but also coupled with emerging challenges and potentially destabilizing effects. Given the unprecedented onset and surge in interests, education stakeholders are often pressured to adopt such emergent technologies with little space and time to seek better understanding and to attain literacy. This paper brings together existing contributions to identify a list of five common themes (5Ts) and various uses of generative AI for improving students learning and future education research. The challenges and opportunities from the use of generative AI in education were also explored, and as part of a rethink of how stakeholders can continue to be relevant in a dynamic learning environment with emerging technologies, three "R" guidelines (3Rs) are also proposed to aid educators and students to stay ahead of the curve in addressing challenges and embracing opportunities arising from the use of generative AI for learning.
C1 [Lee, Alwyn Vwen Yen] Nanyang Technol Univ, Natl Inst Educ, Singapore, Singapore.
   [Lee, Alwyn Vwen Yen] Nanyang Technol Univ, Natl Inst Educ, 1 Nanyang Walk, Singapore 637616, Singapore.
C3 Nanyang Technological University; National Institute of Education (NIE)
   Singapore; Nanyang Technological University; National Institute of
   Education (NIE) Singapore
RP Lee, AVY (corresponding author), Nanyang Technol Univ, Natl Inst Educ, 1 Nanyang Walk, Singapore 637616, Singapore.
EM alwyn.lee@nie.edu.sg
RI Lee, Alwyn Vwen Yen/AAL-2245-2021
OI Lee, Alwyn Vwen Yen/0000-0002-3682-017X
CR Atkinson D, 2008, AUSTRALAS J EDUC TEC, V24, P222
   Barnes B., 2023, ACTORS JOIN WRITERS
   Bian YM, 2021, J MOL MODEL, V27, DOI 10.1007/s00894-021-04674-8
   Biever C, 2023, NATURE, V619, P686, DOI 10.1038/d41586-023-02361-7
   Biswas SS, 2023, ANN BIOMED ENG, V51, P1126, DOI 10.1007/s10439-023-03171-8
   Blain L., 2023, CHATGPT CAN NOW ACCE
   Blinder A., 2009, CREATING NEW TEACHIN, P15
   Buchanan BG, 2005, AI MAG, V26, P53
   Cadamuro J, 2023, CLIN CHEM LAB MED, V61, P1158, DOI 10.1515/cclm-2023-0355
   Collins A., 2018, Rethinking education in the age of technology: The digital revolution and schooling in America
   Coombs C, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102311
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
   Crichton S, 2012, ELECTRON J E-LEARN, V10, P23
   De Cremer D., 2021, AI should augment human intelligence, not replace it
   Delors J., 1996, Learning: The treasure within
   Devlin J., 2018, ARXIV
   DIGNUM V, 2019, ARTIF INTELLFOUND
   Dijkstra R., 2022, CEUR WORKSHOP P
   Dwivedi R, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3561048
   Fahimirad M., 2018, International Journal of Learning and Development, V8, P106, DOI DOI 10.5296/IJLD.V8I4.14057
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fauzi F., 2023, Journal on Education, V5, P14886, DOI [DOI 10.31004/JOE.V5I4.2563, 10.31004/joe.v5i4.2563]
   Felix CV, 2021, INNOV HIGH EDUC TEAC, V33, P33, DOI 10.1108/S2055-364120200000033003
   Firat M., 2023, How chat GPT can transform autodidactic experiences and open education? preprint, DOI DOI 10.31219/OSF.IO/9GE8M
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Friend R, 2000, J ADOLESC ADULT LIT, V44, P320
   Gasevic D., 2023, Computers and Education: Artificial Intelligence, V4, P100130, DOI [10.1016/j.caeai.2023.100130 10.1016/j.caeai.2023.100130, DOI 10.1016/J.CAEAI.2023.100130, 10.1016/j.caeai.2023.100130]
   González-Pérez LI, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14031493
   Guleria P, 2023, EDUC INF TECHNOL, V28, P1081, DOI 10.1007/s10639-022-11221-2
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Haristiani Nuria, 2019, Journal of Physics: Conference Series, V1387, DOI 10.1088/1742-6596/1387/1/012020
   Huang JH, 2021, PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR '21), P580, DOI 10.1145/3460426.3463662
   Johnson Douglas, 2023, Res Sq, DOI 10.21203/rs.3.rs-2566942/v1
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kim S, 2022, EXPERT SYST APPL, V207, DOI 10.1016/j.eswa.2022.117983
   Koh KH., 2017, Authentic assessment, DOI [10.1093/acrefore/9780190264093.013.22, DOI 10.1093/ACREFORE/9780190264093.013.22]
   Lee A. V. Y., 2023, Inf. Technol. Educ. Learn, V3, P1, DOI [10.12937/itel.3.1.Inv.p001, DOI 10.12937/ITEL.3.1.INV.P001]
   Lee AVY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00279-1
   Lee AVY, 2023, STUD EDUC EVAL, V77, DOI 10.1016/j.stueduc.2023.101250
   Lee AVY, 2023, EDUC TECHNOL SOC, V26, P147, DOI 10.30191/ETS.202301_26(1).0011
   Liu LQ, 2018, AAAI CONF ARTIF INTE, P8109
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Luan L., 2023, ARXIV
   Lucy L., 2021, P 3 WORKSHOP NARRATI, P48, DOI [10.18653/v1/2021.nuse-1.5, DOI 10.18653/V1/2021.NUSE-1.5]
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   McCarthy J, 2006, AI MAG, V27, P12
   McCoy RT, 2023, T ASSOC COMPUT LING, V11, P652, DOI 10.1162/tacl_a_00567
   Naveed H, 2023, ARXIV
   Ng T K., 2021, Journal of Education and Training Studies, V9, P49, DOI DOI 10.11114/JETS.V9I1.5105
   Oh C, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P2523, DOI 10.1145/3025453.3025539
   Olari V, 2021, PROCEEDINGS OF THE 16TH WORKSHOP IN PRIMARY AND SECONDARY COMPUTING EDUCATION, WIPSCE 2021, DOI 10.1145/3481312.3481351
   OpenAI, 2022, Introducing ChatGPT
   OpenAI, 2023, GPT-4 API general availability and deprecation of older models in the Completions API
   Owston R., 1997, Educational Researcher, V26, P27, DOI DOI 10.3102/0013189X026002027
   Pappas IO, 2021, FRONT EDUC, V6, DOI 10.3389/feduc.2021.652856
   Pérez JQ, 2020, COMPUT APPL ENG EDUC, V28, P1549, DOI 10.1002/cae.22326
   Perkins M., 2023, ARXIV
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Psiropoulos D, 2016, EDUC INF TECHNOL, V21, P209, DOI 10.1007/s10639-014-9316-x
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Radford A., 2018, IMPROVING LANGUAGE U
   Raffel C, 2020, J MACH LEARN RES, V21
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Resnick M, 2000, J LEARN SCI, V9, P7, DOI 10.1207/s15327809jls0901_3
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Scepanovic S., 2019, MEDD C EMBED COMPUT, V19, P53, DOI [DOI 10.1109/meco.2019.8760114, 10.1109/MECO.2019.8760114]
   Schuck S, 2013, TEACH DEV, V17, P1, DOI 10.1080/13664530.2012.752671
   Serholt S, 2019, PERSP RETHINK REFORM, P77, DOI 10.1007/978-981-13-8161-4_5
   Shawar B. A., 2007, Journal for Language Technology and Computational Linguistics, V22, P29, DOI DOI 10.21248/JLCL.22.2007.88
   Shin R., 2023, TURING TEST MEASURIN
   Siemens G., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022.100107, 10.1016/j.caeai.2022.100107]
   Sousa DA, 2016, BRAIN LEARNS
   Stevenson C., 2022, ARXIV
   Su J., 2022, COMPUTERS ED ARTIFIC, V3, DOI [10.1016/j.caeai.2022.100049, DOI 10.1016/J.CAEAI.2022.100049]
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Surameery NMS., 2023, INT J INFORM TECHNOL, V3, P17, DOI DOI 10.55529/IJITC.31.17.22
   Tarantola A., 2023, CHATGPTS NEW PLUGINS
   Terwiesch C., 2023, Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
   Tsz Kit Ng Davy, 2021, Proceedings of the Association for Information Science and Technology, V58, P504, DOI 10.1002/pra2.487
   Turing A., 2009, Parsing the Turing test. Philosophical and methodological issues in the quest for the thinking computer, P23, DOI DOI 10.1093/MIND/LIX.236.433
   Wong GK., 2020, ACM INROADS, V11, P20, DOI [10.1145/3381884, DOI 10.1145/3381884]
   Wong L.H., 2023, CHANNEL NEWS AS 0404
   Xiao P., 2023, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4458269
   Yue T., 2023, SSRN Electronic Journal, P4346152, DOI [DOI 10.2139/SSRN.4346152, 10.2139/SSRN.4346152]
NR 85
TC 3
Z9 3
U1 39
U2 103
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0218-8791
EI 1742-6855
J9 ASIA PAC J EDUC
JI Asia Pac. J. Educ.
PD JAN 2
PY 2024
VL 44
IS 1
SI SI
BP 81
EP 93
DI 10.1080/02188791.2024.2305171
EA JAN 2024
PG 13
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA JG4I1
UT WOS:001144113700001
DA 2024-12-25
ER

PT J
AU Maung, BM
   McBride, K
   Lucas, JS
   Tabar, M
   Lee, D
AF Maung, Barani Maung
   McBride, Keegan
   Lucas, Jason S.
   Tabar, Maryam
   Lee, Dongwon
TI Generative AI Disproportionately Harms Long Tail Users
SO COMPUTER
LA English
DT Article
AB Generative AI's (GenAI's) risks can be amplified in "long tail" populations and regions. We recommend strategies to mitigate these risks and conclude by calling for a more nuanced and global dialog on GenAI safety.
C1 [Maung, Barani Maung; McBride, Keegan] Univ Oxford, Oxford Internet Inst, Oxford OX1 4BH, England.
   [Lucas, Jason S.] Penn State Univ, University Pk, PA 16802 USA.
   [Tabar, Maryam] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA.
   [Lee, Dongwon] Penn State Univ, Informat Sch, University Pk, PA 16802 USA.
C3 University of Oxford; Pennsylvania Commonwealth System of Higher
   Education (PCSHE); Pennsylvania State University; Pennsylvania State
   University - University Park; University of Texas System; University of
   Texas at San Antonio (UTSA); Pennsylvania Commonwealth System of Higher
   Education (PCSHE); Pennsylvania State University; Pennsylvania State
   University - University Park
RP Lee, D (corresponding author), Penn State Univ, Informat Sch, University Pk, PA 16802 USA.
EM barani.maungmaung@gmail.com; keegan.mcbride@oii.ox.ac.uk;
   jsl5710@psu.edu; maryam.tabar@utsa.edu; dongwon@psu.edu
RI mcbride, keegan/AAG-5897-2019
OI McBride, Keegan/0000-0002-9081-7529; Lee, Dongwon/0000-0001-8371-7629
CR Cazals A, 2023, J COMP ECON, V51, P259, DOI 10.1016/j.jce.2022.09.003
   Chenthamarakshan V, 2023, SCI ADV, V9, DOI 10.1126/sciadv.adg7865
   Cui HT, 2024, NAT METHODS, V21, DOI 10.1038/s41592-024-02201-0
   Goldstein JA, 2024, PNAS NEXUS, V3, DOI 10.1093/pnasnexus/pgae034
   Jittrapirom P, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-30818-2
   Macko Dominik, 2023, P 2023 C EMP METH NA, P9960
   Nkemelu D, 2022, PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES AND DEVELOPMENT, ICTD 2022, DOI 10.1145/3572334.3572372
   Okolo CT, 2023, PROCEEDINGS OF THE 4TH AFRICAN CONFERENCE FOR HUMAN COMPUTER INTERACTION, AFRICHI 2023, P266, DOI 10.1145/3628096.3629066
   Pashentsev E., 2020, Report by the International Center for Social and Political Studies and Consulting
   Solaiman I, 2024, Arxiv, DOI arXiv:2306.05949
NR 10
TC 0
Z9 0
U1 0
U2 0
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD NOV
PY 2024
VL 57
IS 11
BP 82
EP 85
DI 10.1109/MC.2024.3408594
PG 4
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA L0Y6Q
UT WOS:001348069800012
DA 2024-12-25
ER

PT J
AU Yusuf, A
   Pervin, N
   Román-González, M
AF Yusuf, Abdullahi
   Pervin, Nasrin
   Roman-Gonzalez, Marcos
TI Generative AI and the future of higher education: a threat to academic
   integrity or reformation? Evidence from multicultural perspectives
SO INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
LA English
DT Article
DE GenAI; Higher education; Potential; Concerns; Ethical regulations;
   Cultural dimensions
ID LENGTH
AB In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence of generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication of GenAI tools has raised concerns about their potential to automate teaching and research processes. Despite widespread research on GenAI in various fields, there is a lack of multicultural perspectives on its impact and concerns in HE. This study addresses this gap by examining the usage, benefits, and concerns of GenAI in higher education from a multicultural standpoint. We employed an online survey that collected responses from 1217 participants across 76 countries, encompassing a broad range of gender categories, academic disciplines, geographical locations, and cultural orientations. Our findings revealed a high level of awareness and familiarity with GenAI tools among respondents. A significant portion had prior experience and expressed the intention to continue using these tools, primarily for information retrieval and text paraphrasing. The study emphasizes the importance of GenAI integration in higher education, highlighting both its potential benefits and concerns. Notably, there is a strong correlation between cultural dimensions and respondents' views on the benefits and concerns related to GenAI, including its potential as academic dishonesty and the need for ethical guidelines. We, therefore, argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations. We discussed the findings and offered recommendations for researchers, educators, and policymakers, aiming to promote the ethical and effective integration of GenAI tools in higher education.
C1 [Yusuf, Abdullahi] Sokoto State Univ, Dept Sci Educ, Sokoto, Sokoto, Nigeria.
   [Pervin, Nasrin] North South Univ, Dept English & Modern Languages, Dhaka, Bangladesh.
   [Roman-Gonzalez, Marcos] Univ Nacl Educ Distancia, Fac Educ, Madrid, Spain.
C3 North South University (NSU); Universidad Nacional de Educacion a
   Distancia (UNED)
RP Yusuf, A (corresponding author), Sokoto State Univ, Dept Sci Educ, Sokoto, Sokoto, Nigeria.
EM abdullahi.yusuf@ssu.edu.ng
RI Roman-Gonzalez, Marcos/C-5705-2013; Yusuf, Abdullahi/AAR-4510-2021
OI Roman-Gonzalez, Marcos/0000-0001-8506-1715; Yusuf,
   Abdullahi/0000-0003-2487-0564; Pervin, Nasrin/0000-0003-2338-6279
CR Ali O, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100333
   Bagchi K., 2004, J GLOBAL INFORM TECH, V7, P29, DOI [https://doi.org/10.1080/1097198X.2004.10856383, DOI 10.1080/1097198X.2004.10856383]
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bhutoria A., 2022, Comput. Educ. Artif. Intell, V3, P100068, DOI DOI 10.1016/J.CAEAI.2022.100068
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chu HC, 2022, AUSTRALAS J EDUC TEC, V38, P22, DOI 10.14742/ajet.7526
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Creswell JW., 2014, Research design: qualitative, quantitative, and mixed methods approaches, V4, P2014
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   De Cremer D, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1093712
   de la Torre-lopez J, 2023, COMPUTING, V105, P2171, DOI 10.1007/s00607-023-01181-x
   Denejkina A, 2023, Young people's perception and use of Generative AI
   Draper MJ, 2017, INT J EDUC INTEGR, V13, P1, DOI 10.1007/s40979-017-0022-5
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Fang T., 2003, INT J CROSS CULT MAN, V3, P347, DOI DOI 10.1177/1470595803003003006
   Glaser N, 2023, TECHNOL KNOWL LEARN, V28, P1945, DOI 10.1007/s10758-023-09684-4
   Hofstede G., 1989, European Management Journal, V7, P390, DOI [DOI 10.1016/0263-2373, 10.1016/0263-2373(89)90075-3, DOI 10.1016/0263-2373(89)90075-3]
   Hofstede G., 2001, CULTURES CONSEQUENCE
   Jan J, 2024, UNIVERSAL ACCESS INF, V23, P717, DOI 10.1007/s10209-022-00930-7
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Kelly S., 2023, CNN Business
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kovacic Zlatko J., 2009, International Journal of Information Communication Technologies and Human Development, V1, P77, DOI 10.4018/jicthd.2009040104
   Krishnamoorthy Sabitha, 2022, J Assoc Physicians India, V70, P11
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lustig MyronW., 2013, Intercultural Competence: Interpersonal Communication across Cultures, V7th
   Malik A. R., 2023, International Journal of Educational Research Open, V5, DOI [10.1016/j.ijedro.2023.100296, DOI 10.1016/J.IJEDRO.2023.100296]
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Naik N, 2022, FRONT SURG, V9, DOI 10.3389/fsurg.2022.862322
   Parycek P, 2024, J KNOWL ECON, V15, P8390, DOI 10.1007/s13132-023-01433-3
   pek Z., 2023, Educ. Process Int. J, V12, P26, DOI [10.22521/edupij.2023.123.2, DOI 10.22521/EDUPIJ.2023.123.2]
   Pimentel J. L., 2019, International Journal of Sciences: Basic and Applied Research, V45, P183
   Purtill J., 2023, 'bigger than the pandemic': This new AI tool promises to disrupt student assessment
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Revilla M, 2017, INT J MARKET RES, V59, P557, DOI 10.2501/IJMR-2017-039
   Rolstad S, 2011, VALUE HEALTH, V14, P1101, DOI 10.1016/j.jval.2011.06.003
   Stojanov A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00404-7
   Strzelecki A, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13425
   Strzelecki A, 2024, INNOV HIGH EDUC, V49, P223, DOI 10.1007/s10755-023-09686-1
   Sun D, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00446-5
   Sun S, 2019, INT J HOSP MANAG, V77, P89, DOI 10.1016/j.ijhm.2018.06.017
   Tarhini A, 2017, INTERACT LEARN ENVIR, V25, P306, DOI 10.1080/10494820.2015.1122635
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Vincent J., 2023, TOP AI C BANS USE CH
   Yilmaz R., 2023, COMPUT HUM BEHAV, V1, DOI DOI 10.1016/J.CHBAH.2023.100005
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Yusuf A., 2018, Arab Journal of Quality in Education, V5, P7
   Zainuddin M., 2018, P ISERD SCI GLOBE IN
   Zhang CM, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00420-7
NR 51
TC 20
Z9 20
U1 68
U2 111
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2365-9440
J9 INT J EDUC TECHNOL H
JI Int. J. Educ. Technol. High. Educ.
PD MAR 25
PY 2024
VL 21
IS 1
AR 21
DI 10.1186/s41239-024-00453-6
PG 29
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA LV8T4
UT WOS:001189674300001
OA gold
DA 2024-12-25
ER

PT J
AU Bjola, C
   Manor, I
AF Bjola, Corneliu
   Manor, Ilan
TI Digital diplomacy in the age of technological acceleration: three impact
   scenarios of generative artificial intelligence
SO PLACE BRANDING AND PUBLIC DIPLOMACY
LA English
DT Article; Early Access
DE Digital diplomacy; Public diplomacy; Artificial intelligence; Generative
   artificial intelligence; Technological acceleration
AB In this article, we employ the prism of technological acceleration to consider how Generative AI may impact the spatial-temporal dimensions of diplomacy, and what ramifications this transformation may hold for its practice. To do so, we distinguish between Horizontal Acceleration, which relates to the number of diplomatic domains that are progressively impacted by the introduction of a new digital technology, and Vertical Acceleration, which relates to how ripple effects reverberate within the diplomatic domain and how deeply. Next, we outline three possible scenarios in which Generative AI leads to minimal or maximal horizontal and vertical acceleration. In the Dedalus scenario, Generative AI's impact is confined to a few domains, predominantly influencing diplomats' daily routines. In the Heracles scenario, Generative AI systems interact with one another rendering human diplomats as mere messengers. We coin this outcome as 'the end of diplomacy.' Finally, we anticipate that many MFAs may position themselves between these two poles in the Pygmalion scenario.
C1 [Bjola, Corneliu] Univ Oxford, Oxford, England.
   [Manor, Ilan] Ben Gurion Univ Negev, Beer Sheva, Israel.
C3 University of Oxford; Ben Gurion University
RP Manor, I (corresponding author), Ben Gurion Univ Negev, Beer Sheva, Israel.
EM manor.ilan@gmail.com
RI ; Bjola, Corneliu/O-8186-2017
OI Manor, Ilan/0000-0003-2039-3721; Bjola, Corneliu/0000-0003-3609-4240
CR Adler-Nissen R, 2022, EUR J INT RELAT, V28, P640, DOI 10.1177/13540661221107837
   Bjola C., 2015, DIGITAL DIPLOMACY TH
   Bjola C, 2023, INT NEGOT, V28, P69, DOI 10.1163/15718069-bja10060
   Bjola C, 2022, INT AFF, V98, P471, DOI 10.1093/ia/iiac005
   Bjola C, 2018, CAMB REV INT AFF, V31, P3, DOI 10.1080/09557571.2018.1476836
   Bjola Corneliu., 2022, Handbook of Diplomatic Reform and Innovation
   Bjola Corneliu, 2023, Routledge Handbook of Disinformation and National Security, P148, DOI [10.4324/9781003190363-14, DOI 10.4324/9781003190363-14]
   Bjola Corneliu, 2022, Routledge International Handbook of Diaspora Diplomacy, P334, DOI [10.4324/9781003031468-31, DOI 10.4324/9781003031468-31]
   Bjola Corneliu, 2017, Adapting Diplomacy to the Digital Age: Managing the Organisational Culture of Ministries of Foreign Affairs
   Bjola Corneliu, 2021, Research Handbook on Political Propaganda, P80, DOI [10.4337/9781789906424.00013, DOI 10.4337/9781789906424.00013]
   Bouchard Caroline., 2020, Digital Diplomacy and International Organisations: Autonomy, Legitimacy and Contestation
   Cornut J, 2022, INT STUD REV, V24, DOI 10.1093/isr/viac047
   Cull NJ, 2023, PLACE BRANDING PUBLI, V19, P195, DOI 10.1057/s41254-022-00281-3
   Cull NJ, 2013, INT STUD REV, V15, P123, DOI 10.1111/misr.12026
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   Heine Jorge, 2013, The Oxford Handbook of Modern Diplomacy, P60
   Hocking Brian., 2015, DIPLOMACY DIGITAL AG
   Klynge C, 2020, HAGUE J DIPL, V15, P185, DOI 10.1163/1871191X-15101094
   Manor I, 2019, PALGR MAC SER GLOB, P1, DOI 10.1007/978-3-030-04405-3
   Manor I., 2021, PUBLIC DIPLOMACY POL, P111, DOI DOI 10.1007/978-3-030-54552-9_5
   Manor I, 2024, HAGUE J DIPL, V19, P145, DOI 10.1163/1871191X-BJA10173
   Manor I, 2023, INT J COMMUN-US, V17, P860
   Manor I, 2018, MEDIA WAR CONFL, V11, P369, DOI 10.1177/1750635218780564
   Manor Ilan., 2021, J PUBLIC DIPLOMACY, V1, P75, DOI [https://doi.org/10.23045/jpd.2021.1.2.075, DOI 10.23045/JPD.2021.1.2.075]
   Mckinsey & Co, 2023, What's the future of generative AI? An early view in 15 charts
   Neupane S, 2023, Arxiv, DOI arXiv:2306.13033
   Rosa Hartmut, 2003, Constellations, V10, P3, DOI [DOI 10.1111/1467-8675.00309, 10.1111/1467-8675.00309]
   Rosamond Annika Bergman., 2023, The Hague Journal of Diplomacy, V1, P1, DOI [DOI 10.1163/1871191X-BJA10168, 10.1163/1871191x-bja10168]
   Ross Alec., 2012, American Diplomacy
NR 29
TC 0
Z9 0
U1 2
U2 7
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1751-8040
EI 1751-8059
J9 PLACE BRANDING PUBLI
JI Place Branding Public Dipl.
PD 2024 FEB 4
PY 2024
DI 10.1057/s41254-023-00323-4
EA FEB 2024
PG 6
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA GW1T8
UT WOS:001155625700001
DA 2024-12-25
ER

PT J
AU Korzynski, P
   Mazurek, G
   Altmann, A
   Ejdys, J
   Kazlauskaite, R
   Paliszkiewicz, J
   Wach, K
   Ziemba, E
AF Korzynski, Pawel
   Mazurek, Grzegorz
   Altmann, Andreas
   Ejdys, Joanna
   Kazlauskaite, Ruta
   Paliszkiewicz, Joanna
   Wach, Krzysztof
   Ziemba, Ewa
TI Generative artificial intelligence as a new context for management
   theories: analysis of ChatGPT
SO CENTRAL EUROPEAN MANAGEMENT JOURNAL
LA English
DT Article
DE Generative AI; ChatGPT; Management theories; Decision-making; Knowledge
   management; Customer service; Human resource management; Business
   administration
ID USER ACCEPTANCE; PERCEIVED EASE; KNOWLEDGE; TECHNOLOGY; ROBOTS
AB Purpose - The primary purpose of this paper is to examine how generative Artificial Intelligence (AI) such as ChatGPT may serve as a new context for management theories and concepts.Design/methodology/approach - The paper presents the analyses of selected management theories on decision-making, knowledge management, customer service, human resource management and administrative tasks and explains what may change after generative AI adoption.Findings - The paper indicates that some management theories and concepts need to be studied in the generative AI environment that may influence managerial work at the strategic, functional and administrative levels.Research limitations/implications - This paper is an opinion piece article and does not refer to empirical data. It formulates some conclusions to further empirical research studies.Originality/value - The paper analyzes selected management theories in a new technological setting. The paper also provides information about the functions of generative AI that are useful in understanding and overcoming how new technology may change organizations and management.
C1 [Korzynski, Pawel] Kozminski Univ, Dept Human Resource Management, Warsaw, Poland.
   [Mazurek, Grzegorz] Kozminski Univ, Dept Mkt, Warsaw, Poland.
   [Altmann, Andreas] MCI, Entrepreneurial Sch, Dept Econ & Soc, Innsbruck, Austria.
   [Ejdys, Joanna] Bialystok Tech Univ, Fac Engn Management, Bialystok, Poland.
   [Kazlauskaite, Ruta] ISM Univ Management & Econ, Dept Management, Kaunas, Lithuania.
   [Paliszkiewicz, Joanna] Warsaw Univ Life Sci, Management Inst, Warsaw, Poland.
   [Wach, Krzysztof] Cracow Univ Econ, Dept Int Trade, Krakow, Poland.
   [Ziemba, Ewa] Univ Econ Katowice, Katowice, Poland.
C3 Kozminski University; Kozminski University; Bialystok University of
   Technology; ISM University of Management & Economics; Warsaw University
   of Life Sciences; Cracow University of Economics; University of
   Economics in Katowice
RP Mazurek, G (corresponding author), Kozminski Univ, Dept Mkt, Warsaw, Poland.
EM gmazurek@kozminski.edu.pl
RI Korzynski, Pawel/GNO-9916-2022; Ejdys, Joanna/D-6654-2013; Mazurek,
   Grzegorz/O-4302-2019; Kazlauskaite, Ruta/H-4694-2013; Ziemba, Ewa
   Wanda/S-2084-2017; Wach, Krzysztof/A-5253-2011
OI Kazlauskaite, Ruta/0000-0003-4004-0106; Ziemba, Ewa
   Wanda/0000-0002-1084-7497; Wach, Krzysztof/0000-0001-7542-2863
CR [Anonymous], ACAD MANAGEMENT EXEC, DOI [DOI 10.2307/4164720, 10.5465/ame.1987.4275905]
   Argote L, 2003, MANAGE SCI, V49, P571, DOI 10.1287/mnsc.49.4.571.14424
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Becker G. S., 1964, HUMAN CAPITAL
   Belanche D, 2012, J RETAIL CONSUM SERV, V19, P124, DOI 10.1016/j.jretconser.2011.11.001
   Berry L.L., 1991, MARKETING SERVICES
   Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
   Black JS, 2020, BUS HORIZONS, V63, P215, DOI 10.1016/j.bushor.2019.12.001
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   BORKO H, 1968, AM DOC, V19, P3, DOI 10.1002/asi.5090190103
   Bove T., 2023, Bill Gates says ChatGPT will 'change our world' but it doesn't mean your job is at risk Article
   Bruce K, 2006, MANAG ORGAN HIST, V1, P177, DOI 10.1177/1744935906064095
   Colbert A, 2016, ACAD MANAGE J, V59, P731, DOI 10.5465/amj.2016.4003
   Cooley S., 2016, Global Encyclopedia of Public Administration, Public Policy, and Governance, DOI DOI 10.1007/978-3-319-31816-5_2998-1
   Cristofaro M, 2017, J MANAG HIST, V23, P170, DOI 10.1108/JMH-11-2016-0060
   Cullinane N., 2019, ELGAR INTRO THEORIES, DOI [10.4337/9781786439017.00010, DOI 10.4337/9781786439017.00010]
   Dachner AM, 2021, HUM RESOUR MANAGE R, V31, DOI 10.1016/j.hrmr.2019.100732
   Davis E, 2015, COMMUN ACM, V58, P92, DOI 10.1145/2701413
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   de Cicco Roberta, 2021, Journal of Foodservice Business Research, V24, P140, DOI 10.1080/15378020.2020.1826268
   DEAN JW, 1993, DECISION SCI, V24, P1069, DOI 10.1111/j.1540-5915.1993.tb00504.x
   El-Kassas WS, 2021, EXPERT SYST APPL, V165, DOI 10.1016/j.eswa.2020.113679
   Eubanks B., 2022, Artificial Intelligence for HR: Use AI to support and develop a successful workforce, V2nd
   Flavian C, 2021, SERV IND J, V41, P853, DOI 10.1080/02642069.2021.1989177
   Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
   Gentile Chiara, 2007, European Management Journal, V25, P395, DOI 10.1016/j.emj.2007.08.005
   Gordijn B, 2023, MED HEALTH CARE PHIL, V26, P1, DOI 10.1007/s11019-023-10136-0
   Guo JF, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102067
   Hsu CL, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103211
   Hunt SD, 2006, J BUS IND MARK, V21, P72, DOI 10.1108/10610420610651296
   Ji M, 2017, COLUMBIA LAW REV, V117, P1543
   Kao WK, 2023, J HOSP TOUR MANAG, V54, P10, DOI 10.1016/j.jhtm.2022.11.006
   Kolb D. A., 1984, EXPERIENTIAL LEARNIN, DOI DOI 10.1016/B978-0-7506-7223-8.50017-4
   Kumar S., 2021, International Journal of Information Management Data Insights, Elsevier Ltd, V1, P100008, DOI [10.1016/j.jjimei.2021.100008, DOI 10.1016/J.JJIMEI.2021.100008]
   Kumar UA, 2023, LECT NOTE NETW SYST, V475, P659, DOI 10.1007/978-981-19-2840-6_50
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Monteiro P, 2022, ACAD MANAG ANN, V16, P427, DOI 10.5465/annals.2019.0059
   Mustak M, 2021, J BUS RES, V124, P389, DOI 10.1016/j.jbusres.2020.10.044
   Newman J, 2022, FUTURES, V136, DOI 10.1016/j.futures.2021.102886
   Nonaka I., 1995, KNOWLEDGE CREATING C, P995
   O'Dell C., 1998, If Only We Knew What We Know: The Transfer of Internal Knowledge and Best Practices
   Olinga L., 2022, E MUSK SOUNDS ALARM
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Prentic C, 2020, J RETAIL CONSUM SERV, V56, DOI 10.1016/j.jretconser.2020.102186
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Rymarczyk J, 2020, ENTREPR BUS ECON REV, V8, P185, DOI 10.15678/EBER.2020.080110
   Sandholm TW, 1997, ARTIF INTELL, V94, P99, DOI 10.1016/S0004-3702(97)00030-1
   Sieja M, 2019, ENTREPR BUS ECON REV, V7, P117, DOI 10.15678/EBER.2019.070407
   Silva SC, 2023, EUROMED J BUS, V18, P145, DOI 10.1108/EMJB-11-2020-0117
   Sun HS, 2006, J ASSOC INF SYST, V7, P618, DOI 10.17705/1jais.00100
   Sung E, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102382
   Swanson R.A., 2022, Foundations of human resource development, V3rd
   Tapscott D., 2006, Wikinomics: How mass collaboration changes everything
   Taylor F. W., 1911, The principles of scientific management
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Toffler A., 1980, 3 WAVE
   Nguyen TM, 2022, INT MARKET REV, V39, P482, DOI 10.1108/IMR-02-2021-0078
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Votto A.M., 2021, International Journal of Information Management Data Insights, V1, DOI [DOI 10.1016/J.JJIMEI.2021.100047, 10.1016/j.jjimei.2021.100047]
   Wasko MM, 2005, MIS QUART, V29, P35, DOI 10.2307/25148667
   Wheeler A. R., 2021, HR without people?, P45
   Ziemba E, 2022, J COMPUT INFORM SYST, V62, P302, DOI 10.1080/08874417.2020.1808865
NR 62
TC 87
Z9 89
U1 74
U2 239
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2658-0845
EI 2658-2430
J9 CENT EUR MANAG J
JI Cent. Eur. Manag. J.
PD MAY 30
PY 2023
VL 31
IS 1
BP 3
EP 13
DI 10.1108/CEMJ-02-2023-0091
PG 11
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA S2YR1
UT WOS:001069883100001
OA gold
DA 2024-12-25
ER

PT J
AU Acar, OA
AF Acar, Oguz A.
TI Commentary: Reimagining marketing education in the age of generative AI
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE Generative AI; GenAI; Large language models; AI in education; Marketing
   education
ID INTELLIGENT TUTORING SYSTEMS
AB Generative AI (GenAI) holds the potential to revolutionise marketing education by enhancing the learning experience and addressing long-standing pedagogical challenges. This paper explores the transformative impact of GenAI, focusing on three primary dimensions: cost efficiency & scalability, , personalisation & accessibility, , and creativity & innovation. . However, despite these substantial benefits, GenAI also presents important risks and challenges. I therefore underscore the need for strategic and responsible implementation, recommending several approaches such as foundational AI literacy, human oversight, alignment with learning objectives and bespoke pedagogical frameworks to harness GenAI's full potential while mitigating associated risks. Finally, I emphasise that the discussion should evolve from whether we should use GenAI to when and how we should use it.
C1 [Acar, Oguz A.] Kings Coll London, Kings Business Sch, 30 Aldwych, London WC2B 4BG, England.
C3 University of London; King's College London
RP Acar, OA (corresponding author), Kings Coll London, Kings Business Sch, 30 Aldwych, London WC2B 4BG, England.
EM oguz.acar@kcl.ac.uk
RI Acar, Oguz/AAJ-1066-2021
OI Acar, Oguz Ali/0000-0003-1993-0921
CR Acar O A., 2023, Are Your Students Ready for AI?: A Four-Step Framework to Prepare Learners for a ChatGPT
   Acar OA, 2023, Harvard Business ReviewJune
   Acar OA, 2019, TEACH HIGH EDUC, V24, P895, DOI 10.1080/13562517.2018.1516636
   Adesope OO, 2017, REV EDUC RES, V87, P659, DOI 10.3102/0034654316689306
   Bentley C., 2023, FRAMEWORK RESPONSIBL
   Botchu B, 2024, DISABIL REHABIL-ASSI, V19, P2131, DOI 10.1080/17483107.2023.2256805
   Celiktutan B, 2024, INT J RES MARK, V41, P496, DOI 10.1016/j.ijresmar.2024.05.006
   Chi MTH, 2001, COGNITIVE SCI, V25, P471, DOI 10.1016/S0364-0213(01)00044-1
   Davenport T., 2023, Harvard Business Review
   Doshi Anil R, 2023, Generative artificial intelligence enhances creativity
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Girotra K., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4526071, DOI 10.2139/SSRN.4526071]
   Gvirtz A., 2023, Nature
   Gvirtz A., 2023, MIT Sloan Management Review, V65, P1
   Jayman M, 2022, FRONT EDUC, V7, DOI 10.3389/feduc.2022.929335
   Jürgensmeier L, 2024, INT J RES MARK, V41, P468, DOI 10.1016/j.ijresmar.2024.05.005
   Kulik JA, 2016, REV EDUC RES, V86, P42, DOI 10.3102/0034654315581420
   Kumar Harsh, 2023, Math education with large language models: Peril or promise?
   Microsoft, 2024, 2024 Annual Work Trend Index
   Mollick E. R., 2023, The Wharton School Research Paper, DOI DOI 10.2139/SSRN.4391243
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Urban M, 2024, COMPUT EDUC, V215, DOI 10.1016/j.compedu.2024.105031
   VanLehn K, 2011, EDUC PSYCHOL-US, V46, P197, DOI 10.1080/00461520.2011.611369
   Vyletel B, 2024, Exploring faculty burnout through the 2022-23 HMS faculty/staff survey
   Yang CL, 2021, PSYCHOL BULL, V147, P399, DOI 10.1037/bul0000309
NR 26
TC 2
Z9 2
U1 35
U2 35
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD SEP
PY 2024
VL 41
IS 3
BP 489
EP 495
DI 10.1016/j.ijresmar.2024.06.004
EA AUG 2024
PG 7
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA G8O8J
UT WOS:001319173600001
OA hybrid
DA 2024-12-25
ER

PT J
AU Walton, RO
   Watkins, DV
AF Walton, R. O.
   Watkins, D. V.
TI The use of generative AI in research: a production management case study
   from the aviation industry
SO JOURNAL OF MARKETING ANALYTICS
LA English
DT Article; Early Access
DE Additive Manufacturing; Generative Artificial Intelligence; ChatGPT;
   Aviation
AB Generative Artificial Intelligence (GAI) marks a groundbreaking shift in research. Unlike traditional AI, GAI can generate novel insights and content using natural language processing. Using case study methodology, this paper explored GAI's application in identifying research gaps in aviation's use of Additive Manufacturing (AM), focusing on Design Optimization. Recent advances, such as ChatGPT-4, enable GAI to process extensive data and recognize complex patterns. The research method includes paper selection, GAI-driven gap analysis, and thematic extraction. Generative AI uncovered research domains but has limitations in content attribution and accuracy. Nevertheless, GAI promises to revolutionize knowledge discovery and problem-solving across various fields.
C1 [Walton, R. O.; Watkins, D. V.] Embry Riddle Aeronaut Univ, Daytona Beach, FL 32114 USA.
C3 Embry-Riddle Aeronautical University
RP Walton, RO (corresponding author), Embry Riddle Aeronaut Univ, Daytona Beach, FL 32114 USA.
EM waltonr@erau.edu
OI Walton, Robert/0000-0002-6630-4141
CR Adetayo Adebowale Jeremy, 2023, Library Hi Tech News, P18, DOI 10.1108/LHTN-01-2023-0007
   Agrawal R, 2019, RAPID PROTOTYPING J, V25, P1198, DOI 10.1108/RPJ-06-2018-0152
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Brahmbhatt A., 2023, GPT-3.5 vs GPT-4: an in-depth analysis of OpenAI's language models
   Debnath B, 2022, LOGISTICS-BASEL, V6, DOI 10.3390/logistics6020028
   Github.com, 2023, Hallucination-Leaderboard
   Jackson K., 2019, QUALITATIVE DATA ANA
   Lawton G., 2023, ENTERPRISE AI
   Pant M., 2020, Journal of Composite and Advanced Materials, V31, P109
   Wagner SM, 2016, PROD PLAN CONTROL, V27, P1124, DOI 10.1080/09537287.2016.1199824
   Walton RO., 2023, International Journal of Logistics Systems and Management, DOI [10.1504/IJLSM.2023.10054151, DOI 10.1504/IJLSM.2023.10054151]
NR 11
TC 0
Z9 0
U1 20
U2 23
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 2050-3318
EI 2050-3326
J9 J MARK ANAL
JI J. Market. Anal.
PD 2024 MAY 10
PY 2024
DI 10.1057/s41270-024-00317-y
EA MAY 2024
PG 6
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA QI6S1
UT WOS:001220291900001
DA 2024-12-25
ER

PT J
AU Furze, L
   Perkins, M
   Roe, J
   Macvaugh, J
AF Furze, Leon
   Perkins, Mike
   Roe, Jasper
   Macvaugh, Jason
TI The AI Assessment Scale (AIAS) inAaction: A pilot implementation of
   GenAI-supported assessment
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE generative artificial intelligence (GenAI); educational assessment;
   academic integrity; higher education; Artificial Intelligence Assessment
   Scale; higher education policy
AB The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from "no AI" to "full AI," enabling educators to design assessments that focus on areas requiring human input and critical thinking. The pilot study results indicate a significant reduction in academic misconduct cases related to GenAI and enhanced student engagement with GenAI technology. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in higher education, promoting academic integrity while leveraging technology's potential to enhance learning experiences.
C1 [Furze, Leon] Deakin Univ, Geelong, Australia.
   [Perkins, Mike; Macvaugh, Jason] British Univ, Hanoi, Vietnam.
   [Roe, Jasper] James Cook Univ, Singapore, Singapore.
C3 Deakin University; James Cook University
RP Furze, L (corresponding author), Deakin Univ, Geelong, Australia.
EM l.furze@deakin.edu.au
RI Roe, Jasper/JOK-3723-2023
OI Roe, Jasper/0000-0001-7489-2847
CR Abd-Elaal ES, 2022, EUR J ENG EDUC, V47, P725, DOI 10.1080/03043797.2022.2046709
   Abdaljaleel M, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-59011-9
   Amano T, 2023, PLOS BIOL, V21, DOI 10.1371/journal.pbio.3002184
   Anderson N, 2023, BMJ OPEN SPORT EXERC, V9, DOI 10.1136/bmjsem-2023-001568
   [Anonymous], 2022, IFIP Advances in Information and Communication Technology, V664, P310, DOI [10.1007/978-3-031-16411-837, DOI 10.1007/978-3-031-16411-837]
   [Anonymous], 2013, Frequently asked questions. Retrieved from
   Anson C. M., 2022, Composition Studies, V50, P37
   Armstrong P., 2010, Vanderbilt university center for teaching
   Australian National University, 2023, Artificial intelligence including generative AI
   Bearman M, 2024, ASSESS EVAL HIGH EDU, V49, P893, DOI 10.1080/02602938.2024.2335321
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Birks D, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00142-3
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Bretag T, 2019, ASSESS EVAL HIGH EDU, V44, P676, DOI 10.1080/02602938.2018.1527892
   Bussell Chris, 2023, HCI International 2023 Posters: 25th International Conference on Human-Computer Interaction, HCII 2023, Proceedings. Communications in Computer and Information Science (1836), P380, DOI 10.1007/978-3-031-36004-6_52
   Caplan N., 2024, Resisting AI and refocusing on the human. perspectives
   Chaka C., 2023, J. Appl. Learn. Teach., V6, DOI DOI 10.37074/JALT.2023.6.2.12
   Chaka C., 2024, J. Appl. Learn. Teach, V7, P1, DOI DOI 10.37074/JALT.2024.7.1.33
   Chakraborty S, 2023, Arxiv, DOI [arXiv:2304.04736, DOI 10.48550/ARXIV.2304.04736]
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Cheung W., 2023, South China Morning Post
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cole S., 2000, CAMPUS, V5, P5, DOI [10.1177/108648220000500203, DOI 10.1177/108648220000500203]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Courts of New Zealand, 2023, Guidelines for Use of Generative Artificial Intelligence in Courts and Tribunals
   Crawford K., 2021, ATLAS POWER POLITICS, DOI DOI 10.12987/9780300252392
   Cummings Robert E., 2024, Computers and Composition, V71, DOI 10.1016/j.compcom.2024.102827
   Duah JE, 2024, INT J INF LEARN TECH, V41, P180, DOI 10.1108/IJILT-11-2023-0213
   Elali FR, 2023, PATTERNS, V4, P1, DOI 10.1016/j.patter.2023.100706
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00140-5
   European Union Agency for Fundamental RIghts, 2022, Bias in algorithms: Artificial intelligence and discrimination (Report), DOI [10.2811/25847, DOI 10.2811/25847]
   Eynon R., 2022, Learning to live with datafication: Educational case studies and initiatives from across the world, P17
   Forero MG, 2023, Arxiv, DOI [arXiv:2312.02422, DOI 10.48550/ARXIV.2312.02422]
   Fowler S., 2023, Learning Letters, V1, P1, DOI [10.59453/JMTN6001, DOI 10.59453/JMTN6001]
   Freeman J., 2024, HEPI number Policy Note 51)
   Furze L., 2024, Updating the AI Assessment Scale
   Furze L., 2023, The AI Assessment Scale: Version 1
   Google, 2024, The AI for Science Forum: A New Era of Discovery
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Harvard University Information Technology, 2024, Generative artificial intelligence (AI)
   Hemsley-Brown J, 2015, INT J EDUC MANAG, V29, P254, DOI 10.1108/IJEM-10-2013-0150
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   International Center for Academic Integrity, 2021, The Fundamental Values of Academic Integrity
   Jasper R., 2020, Asian Journal of University Education, V16, P13, DOI [DOI 10.24191/AJUE.V16I1.8490, 10.24191/aiue.vl6il.8490, DOI 10.24191/AIUE.VL6IL.8490]
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   Knight S, 2023, AUSTRALAS J EDUC TEC, V39, P101, DOI 10.14742/ajet.8922
   Knowles Alan M., 2024, Computers and Composition, V71, DOI 10.1016/j.compcom.2024.102826
   Leung M., 2023, University World News
   Liang WX, 2023, Arxiv, DOI arXiv:2304.02819
   Liu RX, 2024, PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1, P750, DOI [10.1145/3626252.3630938, 10.1007/978-981-97-4125-0_1]
   Lodge JM, 2023, Assessment reform for the age of Artificial Intelligence
   Luo JH, 2024, ASSESS EVAL HIGH EDU, V49, P651, DOI 10.1080/02602938.2024.2309963
   Nagoya University, Using generative AI in education and research
   OpenAI, 2022, Introducing ChatGPT
   OpenAI, 2024, Video generation models as world simulators
   OpenAI, 2023, GPT-4V(ision) System Card
   Orenstrakh MS, 2024, Ann Int Comp Softw A, P121, DOI 10.1109/COMPSAC61105.2024.00027
   Ozcan S., 2023, The Conversation
   Pangrazio L., 2022, Learning to Live with Datafication: Educational Case Studies and Initiatives from Across the World
   Perkins M., 2019, Pan-Pacific Management Science, V2, P3, DOI [10.13140/RG.2.2.20694.11841, DOI 10.13140/RG.2.2.20694.11841]
   Perkins M, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00487-w
   Perkins M, 2024, J UNIV TEACH LEARN P, V21, DOI 10.53761/q3azde36
   Perkins M, 2024, J ACAD ETHICS, V22, P89, DOI 10.1007/s10805-023-09492-6
   Perkins M, 2024, HIGH EDUC POLICY, V37, P633, DOI 10.1057/s41307-023-00323-2
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Perrotta C, 2024, NEW MEDIA SOC, V26, P1585, DOI 10.1177/14614448221075296
   Pichai S., 2024, The Keyword
   Plata S., 2023, ASIAN J UNI EDU, V19, P743, DOI [DOI 10.24191/AJUE.V19I4.24697, 10.24191/ajue.v19i4.24697]
   Roe J, 2024, Arxiv, DOI [arXiv:2404.15601, 10.53761/2y2np178, DOI 10.53761/2Y2NP178]
   Roe J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02282-w
   Roe J, 2024, HIGH EDUC RES DEV, V43, P966, DOI 10.1080/07294360.2023.2258820
   Roe J, 2022, INT J EDUC INTEGR, V18, DOI 10.1007/s40979-022-00109-w
   Rogerson AM, 2017, INT J EDUC INTEGR, V13, DOI 10.1007/s40979-016-0013-y
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Selwyn N., 2024, Nordisk tidsskrift for pedagogikk og kritikk, V10, P3
   Selwyn N, 2022, EUR J EDUC, V57, P620, DOI 10.1111/ejed.12532
   Sun LH, 2023, Arxiv, DOI [arXiv:2305.10566, DOI 10.48550/ARXIV.2305.10566]
   Thanh BN, 2023, AUSTRALAS J EDUC TEC, V39, P59, DOI 10.14742/ajet.8902
   Turnitin, 2023, The launch of Turnitin's AI writing detector and the road ahead
   University of Adelaide, How to work with artificial intelligence at the University of Adelaide
   University of Melbourne, 2023, Artificial intelligence tools and technologies
   UNSW, 2024, Chat GPT & Generative AI at UNSW
   Uzun L., 2023, Language Education and Technology, V3, P45
   Walters W H., 2023, Open Inf. Sci, V7, DOI [10.1515/opis-2022-0158, DOI 10.1515/OPIS-2022-0158]
   Weber-Wulff D, 2023, Arxiv, DOI [arXiv:2306.15666, DOI 10.48550/ARXIV.2306.15666, 10.48550/arXiv.2306.15666]
   Yale University, 2023, Guidelines for the use of Generative AI Tools
   Yan LX, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13370
   Zwanka RJ, 2024, J INT CONSUM MARK, V36, P267, DOI 10.1080/08961530.2023.2266897
NR 91
TC 0
Z9 0
U1 11
U2 11
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2024
VL 40
IS 4
SI SI
BP 38
EP 55
DI 10.14742/ajet.9434
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M7L6P
UT WOS:001359314500004
OA gold, Green Submitted
DA 2024-12-25
ER

PT J
AU Aldossary, AS
   Aljindi, AA
   Alamri, JM
AF Aldossary, Aminah Saad
   Aljindi, Alia Abdullah
   Alamri, Jamilah Mohammed
TI The role of generative AI in education: Perceptions of Saudi students
SO CONTEMPORARY EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE artificial intelligence; ChatGPT; LLM; technology role; education filed;
   perceptions of Saudi students
AB Purpose: This study aims to provide an analysis of students' perceptions of the role of generative artificial intelligence (GenAI) tools in education, through five axes: (1) level of knowledge and awareness, (2) level of acceptance and readiness, (3) the role of GenAI in education, (4) level of awareness of potential concerns and challenges, and (5) The impact of GenAI tools on achieving the sustainable development goals in education. Materials and methods: The study followed a descriptive quantitative methodology based on surveying through a questionnaire. The sample consisted of 1390 students from 15 Saudi universities. Results: The students have positive perceptions towards the role of GenAI tools in education, as students have a high level of awareness and acceptance of adopting these tools. In addition, students are highly aware of the role of GenAI tools in improving their understanding of complex concepts, developing skills, improving their self-efficacy, learning outcomes, providing feedback, and making learning meaningful. The results also confirm their general awareness of the concerns and challenges. A relationship exists between students' perceptions of GenAI and their scientific specializations, as students in computer sciences showed greater awareness regarding concerns and challenges, whereas students in agricultural sciences showed greater awareness of the impact of GenAI tools on achieving sustainable development goals. Conclusions: The study offers valuable insights on GenAI adoption in higher education, also there is an urgent need to consider developing appropriate use policies, spreading awareness, and creating systems capable of detecting unethical cases.
C1 [Aldossary, Aminah Saad] King Faisal Univ, Dept Curriculum & Instruct, Al Hasa, Saudi Arabia.
   [Aljindi, Alia Abdullah; Alamri, Jamilah Mohammed] King Abdulaziz Univ, Dept Educ Technol, Jeddah, Saudi Arabia.
C3 King Faisal University; King Abdulaziz University
RP Aldossary, AS (corresponding author), King Faisal Univ, Dept Curriculum & Instruct, Al Hasa, Saudi Arabia.
EM aldossary@kfu.edu.sa
FU Deanship of Scientific Research, Vice Presidency for Graduate Studies
   and Scientific Research, King Faisal University, Saudi Arabia
   [KFU242097]
FX Funding: This research is financially supported by the Deanship of
   Scientific Research, Vice Presidency for Graduate Studies and Scientific
   Research, King Faisal University, Saudi Arabia (KFU242097) .
CR Al-Abdullatif AM, 2023, EDUC SCI, V13, DOI 10.3390/educsci13111151
   Atlas S., 2023, CHATGPT HIGHER ED PR
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bernard C., 2011, Digital natives: How do they learn? How to teach them?
   Biggs J.D., 2009, Teaching for quality learning at university: What the student does
   Biggs J, 2012, HIGH EDUC RES DEV, V31, P39, DOI 10.1080/07294360.2012.642839
   Bilgram Volker, 2023, IEEE Engineering Management Review, P18, DOI 10.1109/EMR.2023.3272799
   Castillo-Gonzalez W., 2022, Data and Metadata, V1, DOI DOI 10.56294/DM202223
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cladera M, 2021, J APPL RES HIGH EDUC, V13, P112, DOI 10.1108/JARHE-07-2019-0195
   Creswell J. W., 2014, RES DESIGN QUALITATI, V4th
   da Silva CAG, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031245
   Dehouche N, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e16757
   Digital Transformation, 2023, Digital transformation
   Emsley R, 2023, SCHIZOPHRENIA-UK, V9, DOI 10.1038/s41537-023-00379-4
   Firat Mehmet, 2023, Journal of Applied Learning and Teaching, V3, P1, DOI DOI 10.37074/JALT.2023.6.1.22
   General Food Security Authority (GFSA)-Kingdom of Saudi Arabia, 2024, General Food Security Authority (GFSA)
   Gordon EM, 2023, NATURE, V617, P351, DOI 10.1038/s41586-023-05964-2
   Haensch A.-C., 2023, P BIG DAT MEETS SURV, P1, DOI [10.1109/bigsurv59479.2023.10486710, DOI 10.1109/BIGSURV59479.2023.10486710]
   Hair JF, 2019, RAUSP MANAG J, V54, P490, DOI [10.1108/RAUSP-05-2019-0098, 10.1108/rausp-05-2019-0098]
   Harrer S, 2023, EBIOMEDICINE, V90, DOI 10.1016/j.ebiom.2023.104512
   Idroes G. M., 2023, Journal of Educational Management and Learning, V1, P8, DOI 10.60084
   Jauhiainen JS, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151814025
   Johnston H, 2024, INT J EDUC INTEGR, V20, DOI 10.1007/s40979-024-00149-4
   Kalota F, 2024, EDUC SCI, V14, DOI 10.3390/educsci14020172
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   King Faisal University, 2019, King Faisal University
   Kumar AHS., 2023, Biology, Engineering, Medicine and Science Reports, V9, P24, DOI DOI 10.5530/BEMS.9.1.5
   Kuzdeuov A., 2023, TechRxiv, DOI [10.36227/techrxiv.22047080.v1, DOI 10.36227/TECHRXIV.22047080.V1]
   Limna P., 2023, J. Appl. Learn. Teach, V6, P64, DOI [DOI 10.37074/JALT.2023.6.1.32, https://doi.org/10.37074/jalt.2023.6.1.32]
   Lokmic-Tomkins Z, 2022, NURS EDUC TODAY, V111, DOI 10.1016/j.nedt.2022.105308
   Lyerly E., 2023, Disability Compliance for Higher Education, V28, P2, DOI DOI 10.1002/DHE.31479
   Miao F., 2023, GUIDANCE GENERATIVE, DOI [10.54675/EWZM9535, DOI 10.54675/EWZM9535]
   Obenza B. N., 2023, International Journal of Human Computing Studies, V5, P5, DOI [10.5281/zenodo.10360697, DOI 10.5281/ZENODO.10360697]
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Salinas-Navarro DE, 2024, EDUC SCI, V14, DOI 10.3390/educsci14010083
   Sallam Malik, 2023, Narra J, V3, pe103, DOI 10.52225/narra.v3i1.103
   Schmidt H.G., 2000, Journal on Excellence in College Teaching, V11, P57
   Shailendra S, 2024, IEEE T EDUC, V67, P777, DOI 10.1109/TE.2024.3432101
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Singh H, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090924
   Singleton R.A., 2009, Approaches to social research
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Surameery NMS., 2023, INT J INFORM TECHNOL, V3, P17, DOI DOI 10.55529/IJITC.31.17.22
   Tran T. N., 2023, P ASIACALL INT C, V4, P1, DOI [10.54855/paic.2341, DOI 10.54855/PAIC.2341]
   United Nations General Assembly, 2015, Sustainable development gooals
   United Nations (UN), 2024, Sustainable development Goals
   Williams RT, 2024, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1331607
   Yilmaz H., 2023, International Educational Review, V1, P57, DOI [10.58693/ier.114, DOI 10.58693/IER.114]
   Yilmaz R, 2023, Computers and Education: Artificial Intelligence, V4, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Zastudil Cynthia, 2023, 2023 IEEE FRONT ED C, P1, DOI DOI 10.1109/FIE58773.2023.10343467
NR 56
TC 0
Z9 0
U1 2
U2 2
PU BASTAS PUBL LTD - UK
PI London
PA Bastas Headquarters, 71-75 Shelton St, Convent Garden, London, UNITED
   KINGDOM
EI 1309-517X
J9 CONTEMP EDUC TECHNOL
JI Contemp. Educ. Technol.
PD OCT
PY 2024
VL 16
IS 4
AR ep536
DI 10.30935/cedtech/15496
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA N1Q2P
UT WOS:001362154700002
DA 2024-12-25
ER

PT J
AU Kim, K
   Cho, K
   Jang, R
   Kyung, S
   Lee, S
   Ham, S
   Choi, E
   Hong, GS
   Kim, N
AF Kim, Kiduk
   Cho, Kyungjin
   Jang, Ryoungwoo
   Kyung, Sunggu
   Lee, Soyoung
   Ham, Sungwon
   Choi, Edward
   Hong, Gil-Sun
   Kim, Namkug
TI Updated Primer on Generative Artificial Intelligence and Large Language
   Models in Medical Imaging for Medical Professionals
SO KOREAN JOURNAL OF RADIOLOGY
LA English
DT Article
DE Artificial intelligence; Generative artificial intelligence; Large
   language model; Synthetic data; Medical imaging
ID ADVERSARIAL NETWORK; DOSE CT; REDUCTION; CYCLEGAN
AB The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.
C1 [Kim, Kiduk; Kim, Namkug] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Convergence Med, Seoul, South Korea.
   [Cho, Kyungjin; Kyung, Sunggu; Lee, Soyoung] Univ Ulsan, Asan Med Inst Convergence Sci & Technol, Asan Med Ctr, Dept Biomed Engn,Coll Med, Seoul, South Korea.
   [Jang, Ryoungwoo] Coreline Soft Co Ltd, Seoul, South Korea.
   [Ham, Sungwon] Korea Univ, Coll Med, Ansan Hosp, Healthcare Readiness Inst Unified Korea, Ansan, South Korea.
   [Choi, Edward] Korea Adv Inst Sci & Technol, Daejeon, South Korea.
   [Hong, Gil-Sun; Kim, Namkug] Univ Ulsan, Asan Med Ctr, Coll Med, Dept Radiol, Seoul, South Korea.
   [Hong, Gil-Sun; Kim, Namkug] Univ Ulsan, Res Inst Radiol, Asan Med Ctr, Coll Med, Seoul, South Korea.
   [Hong, Gil-Sun] Univ Ulsan, Coll Med, Dept Radiol, Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
   [Hong, Gil-Sun; Kim, Namkug] Univ Ulsan, Res Inst Radiol, Asan Med Ctr, Coll Med, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
   [Kim, Namkug] Univ Ulsan, Coll Med, Dept Radiol, Dept Convergence Med & Radiol,Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
C3 University of Ulsan; Asan Medical Center; University of Ulsan; Asan
   Medical Center; Korea University; Korea University Medicine (KU
   Medicine); Korea Advanced Institute of Science & Technology (KAIST);
   University of Ulsan; Asan Medical Center; University of Ulsan; Asan
   Medical Center; University of Ulsan; University of Ulsan; University of
   Ulsan
RP Hong, GS (corresponding author), Univ Ulsan, Coll Med, Dept Radiol, Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.; Hong, GS; Kim, N (corresponding author), Univ Ulsan, Res Inst Radiol, Asan Med Ctr, Coll Med, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.; Kim, N (corresponding author), Univ Ulsan, Coll Med, Dept Radiol, Dept Convergence Med & Radiol,Asan Med Ctr, 88 Olymp Ro 43 Gil, Seoul 05505, South Korea.
EM hgs2013@gmail.com; namkugkim@gmail.com
RI KIM, KYUN/Q-3070-2018; Kim, Kiduk/JXM-0052-2024; Choi,
   Edward/AAC-8825-2020
OI Kim, Namkug/0000-0002-3438-2217; Lee, Soyoung/0000-0003-1729-1775; Hong,
   Gil-Sun/0000-0002-0068-9413; Choi, Edward/0000-0002-5958-3509; Jang,
   Ryoungwoo/0000-0002-1511-7469; Kim, Kiduk/0000-0002-9659-897X
FU Korea Health Technology R&D Project through the Korea Health Industry
   Development Institute (KHIDI) - Ministry of Health & Welfare, Republic
   of Korea [HI21C1148, HI22C1723]
FX Funding Statement This research was supported by grants from the Korea
   Health Technology R&D Project through the Korea Health Industry
   Development Institute (KHIDI) , funded by the Ministry of Health &
   Welfare, Republic of Korea (HI21C1148 and HI22C1723) .
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Al Khalil Y, 2023, MED IMAGE ANAL, V84, DOI 10.1016/j.media.2022.102688
   Alayrac Jean-Baptiste., Flamingo: a visual language model for few-shot learning
   Alec Radford, 2018, Improving Language Understanding by Generative Pre-Training
   [Anonymous], A style-based generator architecture for generative adversarial networks
   [Anonymous], Unpaired image-to-image translation using cycle-consistent adversarial networks
   Antol S, VQA: visual question answering
   Arora A, 2023, LANCET, V401, P997, DOI 10.1016/S0140-6736(23)00324-0
   Bae K, 2022, KOREAN J RADIOL, V23, P139, DOI 10.3348/kjr.2021.0146
   Banerjee R, 2022, BRIT J HAEMATOL, V196, P1274, DOI 10.1111/bjh.17945
   Bau D, Seeing what a GAN cannot generate
   Behrendt F, 2023, Arxiv, DOI [arXiv:2303.03758, 10.48550/arXiv.2303.03758]
   Bowles C, Modelling the progression of Alzheimer's disease in MRI using generative adversarial networks, DOI [10.1117/12.2293256, DOI 10.1117/12.2293256]
   Brown T., 2020, C NEUR INF PROC SYST, P1901
   Cao M, 2022, Arxiv, DOI [arXiv:2203.14713, DOI 10.48550/ARXIV.2203.14713]
   Chen RT, Isolating sources of disentanglement in VAEs
   Chen XP, 2018, Arxiv, DOI arXiv:1812.03426
   Cho KYJ, 2023, J DIGIT IMAGING, V36, P902, DOI 10.1007/s10278-023-00782-4
   Choi Yunjey, StarGAN: Unified Generative Adversarial Networks for MultiDomain Imag etoImage Translation
   Chowdhery A, 2022, Arxiv, DOI [arXiv:2204.02311, DOI 10.48550/ARXIV.2204.02311]
   Chung HYJ, 2022, MED IMAGE ANAL, V80, DOI 10.1016/j.media.2022.102479
   Chung J, A recurrent latent variable model for sequential data
   Chung M, 2022, J DIGIT IMAGING, V35, P1061, DOI 10.1007/s10278-022-00608-9
   Conte GM, 2021, RADIOLOGY, V299, P313, DOI 10.1148/radiol.2021203786
   de Rosa GH, 2021, PATTERN RECOGN, V119, DOI 10.1016/j.patcog.2021.108098
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Durall R., 2020, arXiv
   Elreedy D, 2019, INFORM SCIENCES, V505, P32, DOI 10.1016/j.ins.2019.07.070
   Fetty L, 2020, Z MED PHYS, V30, P305, DOI 10.1016/j.zemedi.2020.05.001
   Fontanella A, 2024, Arxiv, DOI [arXiv:2308.02062, 10.48550/arXiv.2308.02062, DOI 10.48550/ARXIV.2308.02062]
   Gao Q, 2023, PREPRINT, DOI [10.1109/TMI.2023.3320812, DOI 10.1109/TMI.2023.3320812]
   Gao Q, 2022, PROC SPIE, V12242, DOI 10.1117/12.2634939
   Ghesu F, 2022, Arxiv, DOI arXiv:2201.01283
   Goodfellow IJ., GENERATIVE ADVERSARI
   Gravina M, 2022, LECT NOTES COMPUT SC, V13231, P100, DOI 10.1007/978-3-031-06427-2_9
   Gregor Karol., Draw: A recurrent neural network for image generation
   Gu J, 2021, IEEE T COMPUT IMAG, V7, P73, DOI 10.1109/TCI.2021.3050266
   Han CHE, 2021, BMC BIOINFORMATICS, V22, DOI 10.1186/s12859-020-03936-1
   Harms J, 2019, MED PHYS, V46, P3998, DOI 10.1002/mp.13656
   Ho Jonathan, DENOISING DIFFUSION
   Hoang TT, 2020, IEEE IJCNN, DOI 10.1109/ijcnn48605.2020.9207181
   Hong GS, 2023, KOREAN J RADIOL, V24, P1061, DOI 10.3348/kjr.2023.0393
   Hossain MZ, 2019, ACM COMPUT SURV, V51, DOI 10.1145/3295748
   Hu CF, 2023, Arxiv, DOI arXiv:2304.08506
   Huang JH, 2023, LECT NOTES COMPUT SC, V14229, P3, DOI 10.1007/978-3-031-43999-5_1
   Hwang SI, 2023, KOREAN J RADIOL, V24, P952, DOI 10.3348/kjr.2023.0773
   Ivanov O, 2019, Arxiv, DOI arXiv:1806.02382
   Jeblick K, 2024, EUR RADIOL, V34, P2817, DOI 10.1007/s00330-023-10213-1
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jia C, Scaling up visual and vision-language representation learning with noisy text supervision
   Jiapeng Zhu, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12362), P592, DOI 10.1007/978-3-030-58520-4_35
   Jung KH, 2023, KOREAN J RADIOL, V24, P1038, DOI 10.3348/kjr.2023.0790
   Kahambing JG, 2023, J PUBLIC HEALTH-UK, V45, pE590, DOI 10.1093/pubmed/fdad028
   Kang E, 2019, MED PHYS, V46, P550, DOI 10.1002/mp.13284
   Karras T, Analyzing and improving the image quality of StyleGAN
   Karras T, Training generative adversarial networks with limited data
   Karras T, 2018, Arxiv, DOI arXiv:1710.10196
   Karras Tero., Alias-Free Generative Adversarial Networks
   Khosla M, 2019, LECT NOTES COMPUT SC, V11861, P301, DOI 10.1007/978-3-030-32692-0_35
   Kim B, 2022, LECT NOTES COMPUT SC, V13691, P347, DOI 10.1007/978-3-031-19821-2_20
   Kim KH, 2018, MED PHYS, V45, P3120, DOI 10.1002/mp.12945
   Kirillov A, 2023, Arxiv, DOI arXiv:2304.02643
   Koga S, 2023, KOREAN J RADIOL, V24, P924, DOI 10.3348/kjr.2023.0738
   KRAMER MA, 1992, COMPUT CHEM ENG, V16, P313, DOI 10.1016/0098-1354(92)80051-A
   Kwon T, 2021, IEEE T COMPUT IMAG, V7, P1354, DOI 10.1109/TCI.2021.3129369
   Lee JS, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0285489
   Lee S, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-31808-0
   Lei Y, 2020, MED PHYS, V47, P530, DOI 10.1002/mp.13933
   Lei Y, 2019, MED PHYS, V46, P3565, DOI 10.1002/mp.13617
   Li CY, 2023, Arxiv, DOI [arXiv:2306.00890, DOI 10.48550/ARXIV.2306.00890]
   Li JP, 2023, LECT NOTES COMPUT SC, V14224, P579, DOI 10.1007/978-3-031-43904-9_56
   Li Q, Ultra-low dose CT image denoising based on conditional denoising diffusion probabilistic model, DOI [10.1109/CyberC55534.2022.00041, DOI 10.1109/CYBERC55534.2022.00041]
   Liang X, 2019, PHYS MED BIOL, V64, DOI 10.1088/1361-6560/ab22f9
   Liao ZB, 2019, LECT NOTES COMPUT SC, V11765, P687, DOI 10.1007/978-3-030-32245-8_76
   Liu HT, 2023, Arxiv, DOI [arXiv:2304.08485, 10.48550/arXiv.2304.08485]
   Liu X, 2023, Arxiv, DOI [arXiv:2305.15887, 10.48550/arXiv.2305.15887, DOI 10.48550/ARXIV.2305.15887]
   Lyu Q, 2022, Arxiv, DOI [arXiv:2209.12104, 10.48550/arXiv.2209.12104]
   Ma J, 2024, NAT COMMUN, V15, DOI 10.1038/s41467-024-44824-z
   Marcel S, 2007, IEEE T PATTERN ANAL, V29, P743, DOI 10.1109/TPAMI.2007.1012
   Maspero M, 2018, PHYS MED BIOL, V63, DOI 10.1088/1361-6560/aada6d
   Mazurowski MA, 2023, MED IMAGE ANAL, V89, DOI 10.1016/j.media.2023.102918
   Mirza M, 2014, Arxiv, DOI arXiv:1411.1784
   Mokady R, Null- text inversion for editing real images using guided diffusion models
   Mündler N, 2024, Arxiv, DOI [arXiv:2305.15852, DOI 10.48550/ARXIV.2305.15852]
   Nagaraja VK, 2016, LECT NOTES COMPUT SC, V9908, P792, DOI 10.1007/978-3-319-46493-0_48
   Nakao T, 2021, J DIGIT IMAGING, V34, P418, DOI 10.1007/s10278-020-00413-2
   Nie D, 2018, IEEE T BIO-MED ENG, V65, P2720, DOI 10.1109/TBME.2018.2814538
   Odena A., 2016, DISTILL, V1, pe3, DOI [10.23915/distill.00003.-URL, 10.23915/distill.00003, DOI 10.23915/DISTILL.00003]
   Ouyang L., Training language models to follow instructions with human feedback
   Özbey M, 2023, IEEE T MED IMAGING, V42, P3524, DOI 10.1109/TMI.2023.3290149
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pan SY, 2023, Arxiv, DOI [arXiv:2305.19467, 10.48550/arXiv.2305.19467]
   Park JE, 2022, KOREAN J RADIOL, V23, P500, DOI 10.3348/kjr.2022.0033
   Park SH, 2023, KOREAN J RADIOL, V24, P715, DOI 10.3348/kjr.2023.0643
   Pinaya WHL, 2022, LECT NOTES COMPUT SC, V13438, P705, DOI 10.1007/978-3-031-16452-1_67
   Preetha CJ, 2021, LANCET DIGIT HEALTH, V3, pE784, DOI 10.1016/S2589-7500(21)00205-3
   Radford A., Learning Transferable Visual Models From Natural Language Supervision
   Radford A., 2019, OPENAI BLOG
   Radford A, 2016, Arxiv, DOI [arXiv:1511.06434, 10.48550/arXiv.1511.06434, DOI 10.48550/ARXIV.1511.06434]
   Rahman A, Ambiguous medical image segmentation using diffusion models
   Rajotte JF, 2022, ISCIENCE, V25, DOI 10.1016/j.isci.2022.105331
   Ramesh A., 2022, arXiv
   Ramesh Aditya, ZERO SHOT TEXT TO IM
   Ren Zhihang, 2022, J Percept Imaging, V5, P0005021, DOI 10.2352/j.percept.imaging.2022.5.000502
   Sandfort V, 2019, SCI REP-UK, V9, DOI 10.1038/s41598-019-52737-x
   Selim M, 2023, Arxiv, DOI arXiv:2301.08815
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Sohail M, 2019, LECT NOTES COMPUT SC, V11827, P22, DOI 10.1007/978-3-030-32778-1_3
   Sohn K., Learning structured output representation using deep conditional generative models
   Song JM, 2022, Arxiv, DOI [arXiv:2010.02502, 10.48550/arXiv.2010.02502]
   Song Y, 2021, Arxiv, DOI [arXiv:2011.13456, 10.48550/arXiv.2011.13456]
   Tang C, 2019, COMPUT MATH METHOD M, V2019, DOI 10.1155/2019/8639825
   Tanner C, 2018, Arxiv, DOI arXiv:1807.07349
   Thorlund K, 2020, CLIN EPIDEMIOL, V12, P457, DOI 10.2147/CLEP.S242097
   Topol EJ, 2021, LANCET, V397, P785, DOI 10.1016/S0140-6736(21)00452-9
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Quan TM, 2018, IEEE T MED IMAGING, V37, P1488, DOI 10.1109/TMI.2018.2820120
   Uppal S, 2022, INFORM FUSION, V77, P149, DOI 10.1016/j.inffus.2021.07.009
   Vahdat A., 2021, NVAE: a deep hierarchical variational autoencoder
   van Breugel B, DECAF: generating fair synthetic data using causally-aware generative networks
   van Hespen KM, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-87013-4
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang JN, 2018, LECT NOTES COMPUT SC, V11070, P3, DOI 10.1007/978-3-030-00928-1_1
   Wang T, High-fidelity GAN inversion for image attribute editing
   Wolleb Julia, 2020, Medical Image Computing and Computer Assisted Intervention - MICCAI 2020. 23rd International Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12264), P14, DOI 10.1007/978-3-030-59719-1_2
   Wolleb J, Diffusion models for implicit image segmentation ensembles
   Wolleb J, 2022, LECT NOTES COMPUT SC, V13438, P35, DOI 10.1007/978-3-031-16452-1_4
   Wolterink JM, 2017, IEEE T MED IMAGING, V36, P2536, DOI 10.1109/TMI.2017.2708987
   Wu CY, 2023, Arxiv, DOI arXiv:2308.02463
   Wu JD, 2023, Arxiv, DOI [arXiv:2301.11798, 10.1609/aaai.v38i6.28418]
   Wu JD, 2022, Arxiv, DOI arXiv:2211.00611
   Xia WH, 2023, IEEE T PATTERN ANAL, V45, P3121, DOI 10.1109/TPAMI.2022.3181070
   Xia WJ, 2022, Arxiv, DOI [arXiv:2209.15136, 10.48550/arXiv.2209.15136]
   Yan CG, 2021, KOREAN J RADIOL, V22, P983, DOI 10.3348/kjr.2020.0988
   Yan PK, 2018, LECT NOTES COMPUT SC, V11046, P197, DOI 10.1007/978-3-030-00919-9_23
   Yang G, 2018, IEEE T MED IMAGING, V37, P1310, DOI 10.1109/TMI.2017.2785879
   Yang S, 2021, IEEE T MED IMAGING, V40, P3015, DOI 10.1109/TMI.2021.3077615
   Yang Z., 2023, arXiv
   Yao Z, 2023, NAT COMMUN, V14, DOI 10.1038/s41467-023-36102-1
   Yin Y, 2023, DiffGAR: model-agnostic restoration from generative artifacts using image-toimage diffusion models, DOI [10.1145/3577530.3577539, DOI 10.1145/3577530.3577539]
   Yuan L., 2021, arXiv
   Zellers Rowan., 2019, From Recognition to Cognition: Visual Commonsense Reasoning
   Zhang KD, 2023, Arxiv, DOI arXiv:2304.13785
   Zhang YC, 2023, Arxiv, DOI arXiv:2305.03678
NR 144
TC 6
Z9 6
U1 30
U2 58
PU KOREAN SOCIETY OF RADIOLOGY
PI SEOUL
PA 71, YANGJAECHEON-RO, SEOCHO-GU, SEOUL, SOUTH KOREA
SN 1229-6929
EI 2005-8330
J9 KOREAN J RADIOL
JI Korean J. Radiol.
PD MAR
PY 2024
VL 25
IS 3
BP 224
EP 242
DI 10.3348/kjr.2023.0818
PG 19
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA KI9I8
UT WOS:001179444600005
PM 38413108
OA Green Published
DA 2024-12-25
ER

PT J
AU Lim, WM
   Gunasekara, A
   Pallant, JL
   Pallant, JI
   Pechenkina, E
AF Lim, Weng Marc
   Gunasekara, Asanka
   Pallant, Jessica Leigh
   Pallant, Jason Ian
   Pechenkina, Ekaterina
TI Generative AI and the future of education: Ragnarok or reformation? A
   paradoxical perspective from management educators
SO INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION
LA English
DT Article
DE Academic integrity; Bard; ChatGPT; Critical analysis; DALL-E; Ethics;
   Future of education; Generative AI; Generative artificial intelligence;
   Google; Education; Educator; Management education; Management educator;
   OpenAI; Paradox; Paradox theory; Ragnarok; Reformation; Transformation;
   Transformative education
AB Generative artificial intelligence (AI) has taken the world by storm, with notable tension transpiring in the field of education. Given that Generative AI is rapidly emerging as a transformative innovation, this article endeavors to offer a seminal rejoinder that aims to (i) reconcile the great debate on Generative AI in order to (ii) lay the foundation for Generative AI to co-exist as a transformative resource in the future of education. Using critical analysis as a method and paradox theory as a theoretical lens (i.e., the "how"), this article (i) defines Generative AI and transformative education (i.e., the "ideas"), (ii) establishes the paradoxes of Generative AI (i.e., the "what"), and (iii) provides implications for the future of education from the perspective of management educators (i.e., the "so what"). Noteworthily, the paradoxes of Generative AI are four-fold: (Paradox #1) Generative AI is a 'friend' yet a 'foe', (Paradox #2) Generative AI is 'capable' yet 'dependent', (Paradox #3) Generative AI is 'accessible' yet 'restrictive', and (Paradox #4) Generative AI gets even 'popular' when 'banned' (i.e., the "what"). Through a position that seeks to embrace rather than reject Generative AI, the lessons and implications that emerge from the discussion herein represent a seminal contribution from management educators on this trending topic and should be useful for approaching Generative AI as a game-changer for education reformation in management and the field of education at large, and by extension, mitigating a situation where Generative AI develops into a Ragnarok that dooms the future of education of which management education is a part of (i.e., the "so what").
C1 [Lim, Weng Marc] Sunway Univ, Sunway Business Sch, Sunway City, Selangor, Malaysia.
   [Lim, Weng Marc; Gunasekara, Asanka; Pallant, Jessica Leigh; Pallant, Jason Ian] Swinburne Univ Technol, Sch Business Law & Entrepreneurship, Hawthorn, Vic, Australia.
   [Lim, Weng Marc] Swinburne Univ Technol Sarawak Campus, Fac Business Design & Arts, Kuching, Sarawak, Malaysia.
   [Pechenkina, Ekaterina] Swinburne Univ Technol, Learning Transformat Unit, Hawthorn, Vic, Australia.
C3 Sunway University; Swinburne University of Technology; Swinburne
   University of Technology Sarawak; Swinburne University of Technology
RP Lim, WM (corresponding author), Sunway Univ, Sunway Business Sch, Sunway City, Selangor, Malaysia.
EM lim@wengmarc.com; agunasekara@swin.edu.au; jlpallant@swin.edu.au;
   jipallant@swin.edu.au; epechenkina@swin.edu.au
RI Lim, Weng Marc/I-1723-2019; Pallant, Jason/AHA-4192-2022; Gunasekara,
   Asanka/AAL-7782-2020
OI Pallant, Jason/0000-0002-1000-6719; Gunasekara,
   Asanka/0000-0002-0858-8668; Lim, Weng Marc/0000-0001-7196-1923;
   Pechenkina, Ekaterina/0000-0001-6997-6974
CR ABC News, 2023, ABC News
   Ali F., 2023, J. Glob. Hosp. Tour, V1, P1
   Alt D, 2017, J COMPUT HIGH EDUC, V29, P388, DOI 10.1007/s12528-017-9149-x
   [Anonymous], 2023, NATURE, V613, P612, DOI 10.1038/d41586-023-00191-1
   Athabasca University, 2020, PRINC ETH PERS STUD
   BREHM JW, 1989, ADV CONSUM RES, V16, P72
   Center for Information Technology and Society, 2023, WHY WE FALL FAK NEWS
   Chatterjee J, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2022.100676
   Cheddadi S., 2021, 16 INT C COMP SCI ED, P18
   Dencik L, 2022, INTERNET POLICY REV, V11, DOI 10.14763/2022.1.1615
   Dibble M., 2023, VOA NEWS
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Fatemi G, 2020, J FURTH HIGH EDUC, V44, P1305, DOI 10.1080/0309877X.2019.1683521
   Ferraro C, 2023, BUS HORIZONS, V66, P667, DOI 10.1016/j.bushor.2023.01.007
   Hair J.F., 2016, A First on Partial Least Squares Structural Equation Modeling (PLS-SEM), V2nd
   Hammer A, 2023, DAILY MAIL
   Harwell D., 2023, WASH POST
   Holden OL, 2021, FRONT EDUC, V6, DOI 10.3389/feduc.2021.639814
   Hu K., 2023, Reuters
   Jaeger C., 2023, AGE
   Jansen SC, 2015, INT J COMMUN-US, V9, P656
   Kelly Samantha Murphy., 2023, CNN
   Kraus S, 2022, REV MANAG SCI, V16, P2577, DOI 10.1007/s11846-022-00588-8
   Kruger J, 1999, J PERS SOC PSYCHOL, V77, P1121, DOI 10.1037/0022-3514.77.6.1121
   Lim W.M., 2021, International Journal of Quality and Innovation, V5, P101
   Lim W M., 2022, Global Business and Organizational Excellence, V41, P23, DOI [DOI 10.1002/JOE.22162, 10.1002/joe.22162]
   Lim WM, 2022, PSYCHOL MARKET, V39, P1129, DOI 10.1002/mar.21654
   Lowenthal P., 2020, J TECHNOLOGY TEACHER, V28, P383, DOI DOI 10.24252/ELITE.V7I1A6
   Lukpat A., 2023, Wall Street Journal
   Marks A., 2022, COVID-19 challenges to university information technology governance, P61, DOI [10.1007/978-3-031-13351-0_3, DOI 10.1007/978-3-031-13351-0_3]
   Nikolopoulou K., 2021, HDB ONLINE LEARNING, P67, DOI [10.1007/978-3-030-67349-9_6, DOI 10.1007/978-3-030-67349-9_6]
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   Open Culture, 2023, OPEN CULTURE
   OpenAI, 2023, GPT-4 technical report
   Paul K., 2023, GUARDIAN
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Pechenkina K., 2023, HIGHER ED GOOD TEACH
   Pichai S, 2023, GOOGLE
   Prayaga P., 2017, INDIGENOUS PATHWAYS, P189, DOI [10.1007/978-981-10-4062-7_12, DOI 10.1007/978-981-10-4062-7_12]
   Rutgers University, 2023, ScienceDaily
   Rychen D.S. E., 2003, KEY COMPETENCIES SUC
   Sahoo S, 2023, J ENTERP INF MANAG, V36, P221, DOI 10.1108/JEIM-01-2022-0025
   Scite, 2023, SEE RES HAS BEEN CIT
   Stokel-Walker Chris, 2022, Nature, DOI 10.1038/d41586-022-04397-7
   Terwiesch C., 2023, Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
   Yap SF, 2024, AUSTRALAS MARK J, V32, P65, DOI 10.1177/14413582221139492
   Young J. R., 2018, SHOULD PROFESSORS US
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhai X, 2022, Chatgpt user experience: Implications for education
NR 49
TC 279
Z9 284
U1 532
U2 1944
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 1472-8117
EI 2352-3565
J9 INT J MANAG EDUC-OXF
JI Int. J. Manag. Educ.
PD JUL
PY 2023
VL 21
IS 2
AR 100790
DI 10.1016/j.ijme.2023.100790
EA MAR 2023
PG 13
WC Business; Education & Educational Research; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Education & Educational Research
GA A1DF9
UT WOS:000952598000001
OA hybrid
DA 2024-12-25
ER

PT J
AU Guettala, M
   Bourekkache, S
   Kazar, O
   Harous, S
AF Guettala, Manel
   Bourekkache, Samir
   Kazar, Okba
   Harous, Saad
TI Generative Artificial Intelligence in Education: Advancing Adaptive and
   Personalized Learning
SO ACTA INFORMATICA PRAGENSIA
LA English
DT Article
DE Ubiquitous learning; AI-driven education; Ethical considerations;
   ChatGPT; GPT-4o; Content generation; Educational transformation;
   Technological integration; Education 4.0; Education 5.0
ID NETWORKS; MODELS
AB The integration of generative artificial intelligence (AI) into adaptive and personalized learning represents a transformative shift in the educational landscape. This research paper investigates the impact of incorporating generative AI into adaptive and personalized learning environments, with a focus on tracing the evolution from conventional artificial intelligence methods to generative AI and identifying its diverse applications in education. The study begins with a comprehensive review of the evolution of generative AI models and frameworks. A framework of selection criteria is established to curate case studies showcasing the applications of generative AI in education. These case studies are analysed to elucidate the benefits and challenges associated with integrating generative AI into adaptive learning frameworks. Through an in-depth analysis of selected case studies, the study reveals tangible benefits derived from generative AI integration, including increased student engagement, improved test scores and accelerated skill development. Ethical, technical and pedagogical challenges related to generative AI integration are identified, emphasizing the need for careful consideration and collaborative efforts between educators and technologists. The findings underscore the transformative potential of generative AI in revolutionizing education. By addressing ethical concerns, navigating technical challenges and embracing human-centric approaches, educators and technologists can collaboratively harness the power of generative AI to create innovative and inclusive learning environments. Additionally, the study highlights the transition from Education 4.0 to Education 5.0, emphasizing the importance of social-emotional learning and human connection alongside personalization in shaping the future of education.
C1 [Guettala, Manel; Bourekkache, Samir] Univ Mohamed Khider Biskra, Dept Comp Sci, Lab INFormat Intelligente LINFI, Biskra, Algeria.
   [Kazar, Okba] Univ Kalba, Coll Arts Sci IT & Commun, Dept Comp Sci, Sharjah, U Arab Emirates.
   [Harous, Saad] Univ Sharjah, Coll Comp & Informat, Dept Comp Sci, Sharjah, U Arab Emirates.
C3 Universite Mohamed Khider Biskra; University of Sharjah
RP Guettala, M (corresponding author), Univ Mohamed Khider Biskra, Dept Comp Sci, Lab INFormat Intelligente LINFI, Biskra, Algeria.
EM manel.guettala@univ-biskra.dz
RI Kazar, Okba/W-2313-2019; Harous, Saad/AAU-6859-2020
OI Harous, Saad/0000-0001-6524-7352; KAZAR, Okba/0000-0003-0522-4954
CR Abdi H., 1999, Neural Networks, V124, DOI [10.4135/9781412985277, DOI 10.4135/9781412985277]
   Abdin M., 2024, Phi-3 technical report: A highly capable language model locally on your phone
   Abe H., 2023, PhD Thesis, DOI [10.14264/971f444, DOI 10.14264/971F444]
   Aidan G. I. Z., 2019, Cohere
   Alam A., 2021, 2021 INT C ADV COMP, P1, DOI DOI 10.1109/ICAC353642.2021.9697300
   Alam MS, 2023, IEEE ACCESS, V11, P43985, DOI 10.1109/ACCESS.2023.3272479
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Anthropic, 2024, Claude
   Aravind Srinivas D. Y., 2022, Perplexity
   Asmus J., 2023, Explainpaper
   Assogba Y., 2023, arXiv, DOI DOI 10.48550/ARXIV.2301.04518
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bankins S, 2021, ETHICS INF TECHNOL, V23, P841, DOI 10.1007/s10676-021-09619-6
   Berrar D., 2019, ENCY BIOINFORMATICS, V1, P542, DOI [10.1016/B978-0-12-809633-8.20349-X, DOI 10.1016/B978-0-12-809633-8.20349-X]
   Binz M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2218523120
   Black A. V. G., 2023, Diffit
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Brandl LC, 2024, EDUC SCI, V14, DOI 10.3390/educsci14030281
   Brynjolfsson E., 2023, Generative AI at Work, DOI DOI 10.3386/W31161
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Chang T.-Y., 2022, P 61 ANN M ASS COMP, V1, P8123
   Chheang V., 2024, 2024 IEEE INT C ART
   Costa VG, 2023, ARTIF INTELL REV, V56, P4765, DOI 10.1007/s10462-022-10275-5
   Creswell A, 2018, IEEE SIGNAL PROC MAG, V35, P53, DOI 10.1109/MSP.2017.2765202
   Cunningham P, 2008, COGN TECHNOL, P21, DOI 10.1007/978-3-540-75171-7_2
   Dhoni P., 2023, TechRxiv, DOI [10.36227/techrxiv.24045792.v1, DOI 10.36227/TECHRXIV.24045792.V1]
   Ding N, 2023, NAT MACH INTELL, V5, P220, DOI 10.1038/s42256-023-00626-4
   Doersch C, 2021, Arxiv, DOI arXiv:1606.05908
   Dogan ME, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13053056
   Doo FX, 2023, J AM COLL RADIOL, V20, P877, DOI 10.1016/j.jacr.2023.07.007
   equipo Xmind, 2023, Chatmind
   Fahes Mohammad, 2023, P IEEECVF INT C COMP, P18623
   Foster ME, 2024, J RES EDUC EFF, V17, P94, DOI 10.1080/19345747.2023.2174919
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gamoura Samia Chehbi, 2024, Advances in Intelligent Manufacturing and Service System Informatics: Proceedings of IMSS 2023. Lecture Notes in Mechanical Engineering, P368, DOI 10.1007/978-981-99-6062-0_34
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   Gimpel H., 2023, Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers. University of Hohenheim
   Girin L, 2021, FOUND TRENDS MACH LE, V15, P1, DOI 10.1561/2200000089
   Goldstein J. A., 2023, PREPRINT, DOI DOI 10.48550/ARXIV.2301.04246
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Goodwin S., 2022, Duolingo English Test: Writing construct: Duolingo Research Report DR-22-03
   Google, 2024, Gemini
   Greek philosopher, 2023, Socratic
   Grinding Gear Games, 2023, POE
   Gruber JB, 2024, Arxiv, DOI [arXiv:2404.07654, 10.48550/arXiv.2404.07654, DOI 10.48550/ARXIV.2404.07654]
   Guettala M., 2023, World Journal on Educational Technology Current Issues, V15, P429, DOI [10.18844/wjet.v15i4.9091, DOI 10.18844/WJET.V15I4.9091]
   Guettala M., 2021, INT C INF SYST ADV T, DOI [10.1109/ICISAT54145.2021.9678434, DOI 10.1109/ICISAT54145.2021.9678434]
   Guettala M., 2022, INT AR C INF TECHN A, DOI [10.1109/ACIT57182.2022.9994205, DOI 10.1109/ACIT57182.2022.9994205]
   Han ZY, 2024, Arxiv, DOI arXiv:2403.14608
   Han ZY, 2018, PHYS REV X, V8, DOI 10.1103/PhysRevX.8.031012
   Hastie TJ., 2009, ELEMENTS STAT LEARNI, DOI 10.1007/978-0-387-84858-7
   Heston T. F., 2023, International Medical Education, V2, P198, DOI [DOI 10.3390/IME2030019, https://doi.org/10.3390/ime2030019]
   Hicks C, 2023, SchoolAI
   Hind Michael, 2020, CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, P1, DOI 10.1145/3334480.3383051
   Hitawala S, 2018, Arxiv, DOI [arXiv:1801.04271, DOI 10.48550/ARXIV.1801.04271]
   Hoffmann J, 2022, ADV NEUR IN
   Howard J, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P328
   Huang W, 2024, Arxiv, DOI [arXiv:2404.14047, 10.48550/arXiv.2404.14047, DOI 10.48550/ARXIV.2404.14047]
   Italki, 2024, Italki
   Ivanovic B, 2021, IEEE ROBOT AUTOM LET, V6, P295, DOI 10.1109/LRA.2020.3043163
   Janssen M, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101493
   Jiang AQ, 2023, Arxiv, DOI [arXiv:2310.06825, 10.48550/arXiv.2310.06825]
   Johri A., 2023, Practice papers
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Kem D., 2022, INT J SOCIAL SCI HUM, V5, P385, DOI DOI 10.47191/IJSSHR/V5-I2-02
   Khan A., 2023, MagicSchool
   Khanzode K.C. A., 2020, International Journal of Library  Information Science, V9, P30
   Khusid A., 2023, Miro
   Kleinbaum D.G., 2002, Logistic regression
   Koedinger KR, 2010, J EDUC COMPUT RES, V43, P489, DOI 10.2190/EC.43.4.d
   Krichen M, 2023, COMPUTERS, V12, DOI 10.3390/computers12080151
   Kurdi G, 2020, INT J ARTIF INTELL E, V30, P121, DOI 10.1007/s40593-019-00186-y
   Lam A. L., 2023, Studdy
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Leiker D, 2023, COMM COM INF SC, V1831, P523, DOI 10.1007/978-3-031-36336-8_81
   Lichtenberger M., 2023, ChatPDF
   Liebrenz M, 2023, LANCET DIGIT HEALTH, V5, pE105, DOI 10.1016/S2589-7500(23)00019-5
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Magee J. F., 1964, Harvard Business Review
   Mao R., 2023, P 2024 JOINT INT C C, P7844
   Mayer RE, 2023, INT J HUM-COMPUT INT, V39, P2229, DOI 10.1080/10447318.2022.2108563
   Melzer P., 2019, A Conceptual Framework for Personalised Learning: Influence Factors, Design, and Support Potentials
   Microsoft, 2023, Copilot
   Mills A., 2023, Journal of Applied Learning and Teaching, V6, DOI DOI 10.37074/JALT.2023.6.1.34
   Moor M, 2023, NATURE, V616, P259, DOI 10.1038/s41586-023-05881-4
   Mote Technologies, 2023, Conker
   Naveed H, 2024, Arxiv, DOI [arXiv:2307.06435, 10.48550/arXiv.2307.06435, DOI 10.48550/ARXIV.2307.06435]
   Noble WS, 2006, NAT BIOTECHNOL, V24, P1565, DOI 10.1038/nbt1206-1565
   Nysom L., 2023, AI Generated Feedback for Students' Assignment Submissions
   Opderbeck DW, 2019, FORDHAM LAW REV, V88, P553
   Openai, 2024, GPT-4o
   Ought, 2023, Elicit: The AI research Assistant
   Patton DU, 2023, J SOC SOC WORK RES, V14, P553, DOI 10.1086/726042
   Patwardhan N, 2023, INFORMATION, V14, DOI 10.3390/info14040242
   Peeperkorn M, 2024, Arxiv, DOI [arXiv:2405.00492, 10.48550/arXiv.2405.00492, DOI 10.48550/ARXIV.2405.00492]
   Peis I, 2022, PATTERN RECOGN, V134, DOI 10.1016/j.patcog.2022.109130
   Pesenti J., 2023, Sizzle
   Pham DT, 2005, P I MECH ENG C-J MEC, V219, P103, DOI 10.1243/095440605X8298
   Poggio T., 1987, Readings in Computer Vision: Issues, Problems, Principles, and Paradigms M, P638, DOI [10.1016/B978-0-08-051581-6.50061-1, DOI 10.1016/B978-0-08-051581-6.50061-1]
   Radford A., 2018, Technical Reports
   Rane N.L., 2024, Innov. Bus. Strateg. Manag, V21, P10, DOI [10.61577/ibsm.2024.100002, DOI 10.61577/IBSM.2024.100002]
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Regenwetter L, 2022, J MECH DESIGN, V144, DOI 10.1115/1.4053859
   Ross J, 2016, groq
   Roy A, 2023, RELIAB ENG SYST SAFE, V233, DOI 10.1016/j.ress.2023.109126
   Schick T, 2022, T ASSOC COMPUT LING, V10, P716, DOI 10.1162/tacl_a_00485
   Shanto S. S., 2024, Tuijin Jishu/Journal of Propulsion Technology, V45, P3019, DOI [10.52783/tjjpt.v45.i01.4680, DOI 10.52783/TJJPT.V45.I01.4680]
   Shoeybi M, 2020, Arxiv, DOI arXiv:1909.08053
   Showbie, 2024, Socrative
   Shrestha R, 2022, IEEE WINT CONF APPL, P2512, DOI 10.1109/WACV51458.2022.00257
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Temsah MH, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.39384
   Tirumala K, 2022, ADV NEUR IN
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Varis D, 2021, 2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021), P8246
   Wang CG, 2019, Arxiv, DOI arXiv:1904.09408
   Wang S, 2023, INTERACT LEARN ENVIR, V31, P793, DOI 10.1080/10494820.2020.1808794
   Wang WJ, 2024, Arxiv, DOI [arXiv:2304.03516, 10.48550/arXiv.2304.03516, DOI 10.48550/ARXIV.2304.03516]
   Watters A., 2023, TEACHING MACHINES HI
   Weisberg S., 2005, Applied Linear Regression
   Wetzel SJ, 2017, PHYS REV E, V96, DOI 10.1103/PhysRevE.96.022140
   Whitham R., 2018, Implications of Generative AI on Learning and Assessment in Higher Education and Design Research Practice
   Wu J, 2023, 3 INT C ED INF MAN S, DOI [10.2991/978-94-6463-264-466, DOI 10.2991/978-94-6463-264-466]
   Yang CR, 2024, Arxiv, DOI arXiv:2309.03409
   Yang LJ, 2021, ACS OMEGA, V6, P33864, DOI 10.1021/acsomega.1c05145
   Yin JL, 2023, J THEOR APPL EL COMM, V18, P237, DOI 10.3390/jtaer18010013
   Zamfir F. S., 2022, 14 INT C EL COMP ART, DOI [10.1109/ECAI54874.2022.9847414, DOI 10.1109/ECAI54874.2022.9847414]
   Zeadally S, 2020, IEEE ACCESS, V8, P23817, DOI 10.1109/ACCESS.2020.2968045
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
   Zhang L, 2020, EDUC RES REV-NETH, V31, DOI 10.1016/j.edurev.2020.100339
   Zheng C., 2023, PLMR
   Zhou L, 2013, IEEE T INF FOREN SEC, V8, P1947, DOI 10.1109/TIFS.2013.2286456
   Zhou Y, 2022, MACH LEARN, V111, P345, DOI 10.1007/s10994-021-06056-w
   Zhu B., 2023, Findings of the Association for Computational Linguistics: ACL 2023, P5506, DOI [10.18653/v1/2023.findings-acl.340, DOI 10.18653/V1/2023.FINDINGS-ACL.340]
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 137
TC 0
Z9 0
U1 49
U2 49
PU Prague Univ Economics and Business
PI PRAGUE 3
PA NAM W CHURCHILLA 4, PRAGUE 3, CZECH REPUBLIC
EI 1805-4951
J9 ACTA INFORM PRAG
JI Acta Inform. Prag.
PY 2024
VL 13
IS 3
BP 460
EP 489
DI 10.18267/j.aip.235
PG 30
WC Computer Science, Interdisciplinary Applications; Social Sciences,
   Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Social Sciences - Other Topics
GA E0D0H
UT WOS:001299784400007
OA gold
DA 2024-12-25
ER

PT J
AU Bannister, P
   Peñalver, EA
   Urbieta, AS
AF Bannister, Peter
   Penalver, Elena Alcalde
   Urbieta, Alexandra Santamaria
TI Transnational higher education cultures and generative AI: a nominal
   group study for policy development in English medium instruction
SO JOURNAL FOR MULTICULTURAL EDUCATION
LA English
DT Article
DE Generative artificial intelligence; English as a medium of instruction;
   Higher education; Academic integrity policy development; Nominal group
   technique
AB PurposeThis purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice.Design/methodology/approachA virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively.FindingsThe GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain.Research limitations/implicationsIf repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation.Practical implicationsWhile grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks.Social implicationsAs texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students.Originality/valueThe novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.
C1 [Bannister, Peter; Urbieta, Alexandra Santamaria] Univ Int La Rioja UNIR, La Rioja, Spain.
   [Penalver, Elena Alcalde] Univ Alcala, Fac Philosophy & Humanities, Alcala De Henares, Spain.
C3 Universidad Internacional de La Rioja (UNIR); Universidad de Alcala
RP Bannister, P (corresponding author), Univ Int La Rioja UNIR, La Rioja, Spain.
EM peter.bannister@unir.net
RI Urbieta, Alexandra/AAH-1820-2020; Peñalver, Elena/Q-7787-2018;
   Bannister, Peter/HDO-4393-2022
OI Bannister, Peter/0000-0002-7216-3912
FU Universidad Internacional de La Rioja (UNIR), Spain as part of the
   Project of Analysis and Development for the Optimization of Assessment
   and Regulation of Generative Artificial Intelligence in Humanities
   (PANDORA) [PP-2023-22]
FX Funding Acknowledgement and Ethics Approval: This research has been
   carried out using funds from Universidad Internacional de La Rioja
   (UNIR), Spain as part of the Project of Analysis and Development for the
   Optimization of Assessment and Regulation of Generative Artificial
   Intelligence in Humanities (PANDORA), with project reference number
   PP-2023-22.
CR Airey J, 2017, HIGH EDUC, V73, P561, DOI 10.1007/s10734-015-9950-2
   Asmus CL, 2005, CREATIVITY RES J, V17, P349, DOI 10.1207/s15326934crj1704_6
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bannister P., 2023, J INT STUDENTS
   Bannister P, 2023, AULA ABIERTA, V52, P401, DOI 10.17811/rifie.52.4.2023.401-409
   Bannister P, 2023, IRAN J LANG TEACH RE, V11, P53, DOI 10.30466/ijltr.2023.121406
   Bao D., 2019, TRANSITIONS J TRANSI, V3, P101, DOI [10.1386/tjtm_00001_2, DOI 10.1386/TJTM_00001_2]
   Bhandari S, 2021, J MANAGE ENG, V37, DOI 10.1061/(ASCE)ME.1943-5479.0000909
   Blattes M., 2018, European Journal of Language Policy, V10, P13, DOI DOI 10.3828/EJLP.2018.2
   Bretag T, 2009, J ACAD ETHICS, V7, P193, DOI 10.1007/s10805-009-9092-1
   Carter SM, 2021, PATIENT, V14, P711, DOI 10.1007/s40271-021-00528-w
   Çelik Ö, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00125-4
   Chaka C., 2023, J. Appl. Learn. Teach., V6, DOI DOI 10.37074/JALT.2023.6.2.12
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Dorussen H, 2005, EUR UNION POLIT, V6, P315, DOI 10.1177/1465116505054835
   Escotet M., 2023, PROSPECTS, DOI [10.1007/s11125-023-09642-z, DOI 10.1007/S11125-023-09642-Z]
   Eshet Y, 2024, EDUC INF TECHNOL, V29, P3279, DOI 10.1007/s10639-023-11967-3
   Fink-Hafner D., 2019, Metodoloski zvezki, V16, P1, DOI DOI 10.51936/FCFM6982
   Foltynek T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00133-4
   Grassini S, 2023, EDUC SCI, V13, DOI 10.3390/educsci13070692
   Green RA, 2014, SAGE OPEN, V4, DOI 10.1177/2158244014529773
   Groves M, 2021, J ENGL ACAD PURP, V50, DOI 10.1016/j.jeap.2021.100957
   Humphrey-Murto S, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0280764
   Ibrahim K, 2023, LANG TEST ASIA, V13, DOI 10.1186/s40468-023-00260-2
   Inbar-Lourie O., 2022, J Engl-Medium Instr, V1, P204, DOI [10.1075/jemi.21014.inb, DOI 10.1075/JEMI.21014.INB]
   Ismail I., 2023, EDUMASPUL JURNAL PEN, V7, P28, DOI [10.33487/edumaspul.v7i1.5392, DOI 10.33487/EDUMASPUL.V7I1.5392]
   Kamwangamalu NM, 2013, WORLD ENGLISH, V32, P325, DOI 10.1111/weng.12034
   Khurshid Faraz, 2023, MedEdPublish (2016), V13, P18, DOI 10.12688/mep.19603.1
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kramm N, 2023, TEACH HIGH EDUC, V28, P2173, DOI 10.1080/13562517.2023.2263839
   Liang W., 2023, ARXIV
   Lim MH, 2022, COMPUT ASSIST LANG L, V35, P2675, DOI 10.1080/09588221.2021.1892768
   Mahboob A, 2017, MULTILING EDUC, V21, P71, DOI 10.1007/978-3-319-51976-0_5
   Manan SA, 2023, LANG EDUC-UK, V37, P88, DOI 10.1080/09500782.2021.1955917
   Manera K., 2019, HDB RES METHODS HLTH, P737, DOI [10.1007/978-981-10-5251-4_100, DOI 10.1007/978-981-10-5251-4_100, 10.1007/978-981-10-5251-4, DOI 10.1007/978-981-10-5251-4]
   Moore E., 2023, ACAD INTEGRITY BROAD, V4, P289, DOI [10.1007/978-3-031-16976-2_16, DOI 10.1007/978-3-031-16976-2_16]
   Mundt K., 2016, European Journal of Higher Education, V6, P387
   Murata K, 2019, ROUT RES LANG ED, P1
   Newton P.M., 2018, Frontiers in Education, V3, DOI DOI 10.3389/FEDUC.2018.00067
   Okaiyeto SA, 2023, INT J AGR BIOL ENG, V16, P285, DOI 10.25165/j.ijabe.20231603.8486
   Ou A. W., 2022, Journal of English-Medium Instruction, V1, P7, DOI [https://doi.org/10.1075/jemi.21021.ou, DOI 10.1075/JEMI.21021.OU]
   Parkinson AL, 2022, ASSESS EVAL HIGH EDU, V47, P1416, DOI 10.1080/02602938.2022.2040947
   Perkins, 2023, DECODING ACAD INTEGR
   Poudel PP, 2021, CURR ISS LANG PLAN, V22, P79, DOI 10.1080/14664208.2020.1741235
   Rana K., 2023, POLICIES POLITICS ID, P48, DOI DOI 10.4324/9781003173120-5
   Rose H., 2020, Investigating Policy and Implementation of English Medium Instruction in Higher Education Institutions in China
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sabate-Dalmau M., 2020, The Secret Life of English-Medium Instruction in Higher Education: examining Microphenomena in Context, P70, DOI DOI 10.4324/9781003005667-4
   Sah P. K., 2022, J ENGLISH MEDIUM INS, V1, P124, DOI [10.1075/jemi.21022.sah, DOI 10.1075/JEMI.21022.SAH]
   Sah PK, 2022, INT J BILING EDUC BI, V25, P742, DOI 10.1080/13670050.2020.1718591
   Sevimel-Sahin, 2023, HDB RES PERSPECTIVES, P306
   Sterling S., 2023, Research Methods in Applied Linguistics, V2, P100040, DOI 10.1016/j.rmal.2022.100040
   Tafazoli D., 2018, Cross-Cultural Perspectives on Technology-Enhanced Language Learning
   Taguchi N, 2014, IRAL-INT REV APPL LI, V52, P89, DOI 10.1515/iral-2014-0004
   Tindle R., 2023, PREPRINT, DOI [10.31234/osf.io/hwkgu, DOI 10.31234/OSF.IO/HWKGU]
   Tran L.T., 2018, MULTILINGUAL ED YB 2, P91, DOI DOI 10.1007/978-3-319-77655-2_6
   Tupas R., 2023, Policies, politics, and ideologies of English medium instruction in Asian universities: Unsettling critical edges, P155
   UNESCO, 2021, AI and Education: Guidance for Policy-Makers
   vander LaenenF., 2015, Crime Science, V4, DOI [10.1186/s40163-014-0016-z, DOI 10.1186/S40163-014-0016-Z, DOI 10.1186/s40163-014-0016-z]
   Walkinshaw I, 2017, MULTILING EDUC, V21, P1, DOI 10.1007/978-3-319-51976-0_1
   Weber-Wulff D., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2306.15666
   Xiao P., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2305.18617
   Zhao Y., 2023, Technology-mediated learning environments for young English learners, P167
NR 63
TC 3
Z9 4
U1 15
U2 34
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2053-535X
J9 J MULTICULT EDUC
JI J. Multicult. Educ.
PD APR 25
PY 2024
VL 18
IS 1/2
SI SI
BP 173
EP 191
DI 10.1108/JME-10-2023-0102
EA DEC 2023
PG 19
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA OJ7B2
UT WOS:001131092800001
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Khosravi, H
   Viberg, O
   Kovanovic, V
   Ferguson, R
AF Khosravi, Hassan
   Viberg, Olga
   Kovanovic, Vitomir
   Ferguson, Rebecca
TI Generative AI and Learning Analytics
SO JOURNAL OF LEARNING ANALYTICS
LA English
DT Article
DE Generative AI,; GenAI; learning analytics; research; practice
ID SCIENCE
AB This editorial looks back at the Journal of Learning Analytics (JLA) in 2023 and forward to 2024. Considering the recent proliferation of large language models such as GPT4 and Bard, the first section of this editorial points to the need for robust Generative AI (GenAI) analytics, calling for consideration of how GenAI may impact learning analytics research and practice. The second section looks back over the past year, providing statistics on submissions and considering the cost of publication in an open-access journal.
C1 [Khosravi, Hassan] Univ Queensland, Inst Teaching & Learning Innovat, Res & Educ Ctr Learning Sci, Brisbane, Qld 4072, Australia.
   [Viberg, Olga] KTH Royal Inst Technol, Dept Human Ctr Technol, Lindstedsvagen 3, S-1004 Stockholm, Sweden.
   [Kovanovic, Vitomir] Univ South Australia, UniSA Educ Futures, Campus Cent City West GPO Box 2471, Adelaide, SA 5001, Australia.
   [Ferguson, Rebecca] Open Univ, Inst Educ Technol, Walton Hall, Milton Keynes MK 6AA, England.
C3 University of Queensland; Royal Institute of Technology; University of
   South Australia; Open University - UK
RP Khosravi, H (corresponding author), Univ Queensland, Inst Teaching & Learning Innovat, Res & Educ Ctr Learning Sci, Brisbane, Qld 4072, Australia.
EM h.khosravi@uq.edu.au; oviberg@kth.se; vitomir.kovanovic@unisa.edu.au;
   rf2656@open.ac.uk
RI Ferguson, Rebecca/HJI-3753-2023; Kovanovic, Vitomir/F-5862-2017
OI Ferguson, Rebecca/0000-0002-8566-8231; Khosravi,
   Hassan/0000-0001-8664-6117; Kovanovic, Vitomir/0000-0001-9694-6033
CR Chen J, 2023, Arxiv, DOI arXiv:2307.16376
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Collyer FM, 2018, CURR SOCIOL, V66, P56, DOI 10.1177/0011392116680020
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Djupe PA, 2015, PS-POLIT SCI POLIT, V48, P346, DOI 10.1017/S1049096514002315
   Hao R, 2023, Arxiv, DOI [arXiv:2305.19278, 10.48550/arXiv.2305.19278, DOI 10.48550/ARXIV.2305.19278]
   Javaid M., 2023, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V3, DOI [10.1016/j.tbench.2023.100115, DOI 10.1016/J.TBENCH.2023.100115]
   Khosravi H., 2022, Computers and Education: Artificial Intelligence, V3, P100074, DOI DOI 10.1016/J.CAEAI.2022.100074
   Liu M., 2023, Future Educ. Res., V1, P72, DOI [DOI 10.1002/FER3.10, 10.1002/fer3.10 10.1002/fer3.10]
   Macneil S, 2023, Arxiv, DOI arXiv:2307.01142
   Moreno J., 2023, Forbes
   Morris S, 2005, LEARN PUBL, V18, P115, DOI 10.1087/0953151053584975
   Mulligan A, 2013, J AM SOC INF SCI TEC, V64, P132, DOI 10.1002/asi.22798
   Nguyen N, 2022, IEEE WORK CONF MIN S, P1, DOI 10.1145/3524842.3528470
   Pinfield S, 2016, J ASSOC INF SCI TECH, V67, P1751, DOI 10.1002/asi.23446
   Puehringer S, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0253226
   Salemi A, 2024, Arxiv, DOI arXiv:2304.11406
   Shen JY, 2019, JMIR MED INF, V7, DOI 10.2196/10010
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Susskind R., 2023, Tomorrow's lawyers: An introduction to your future
   Swiecki Z, 2022, Computers and Education: Artificial Intelligence, V3, DOI DOI 10.1016/J.CAEAI.2022.100075
   Van Noorden R, 2013, NATURE, V495, P426, DOI 10.1038/495426a
   Ware Mark, 2008, Information Services & Use, V28, P109, DOI 10.3233/ISU-2008-0568
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
NR 25
TC 3
Z9 3
U1 26
U2 72
PU SOC LEARNING ANALYTICS RESEARCH-SOLAR
PI BEAUMONT
PA 121 POINTE MARSAN, BEAUMONT, ALBERTA, CANADA
EI 1929-7750
J9 J LEARN ANAL
JI J. Learn. Anal.
PY 2023
VL 10
IS 3
BP 1
EP 6
DI 10.18608/jla.2023.8333
PG 6
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA DO8O5
UT WOS:001133089300002
OA gold, Green Accepted
DA 2024-12-25
ER

PT J
AU Islam, G
   Greenwood, M
AF Islam, Gazi
   Greenwood, Michelle
TI Generative Artificial Intelligence as Hypercommons: Ethics of Authorship
   and Ownership
SO JOURNAL OF BUSINESS ETHICS
LA English
DT Article
DE Business Ethics; Artificial Intelligence; Commons; Authorship; Academic
   Publishing
AB In this editorial essay, we argue that Generative Artificial Intelligence programs (GenAI) draw on what we term a "hypercommons", involving collectively produced inputs and labour that are largely invisible or untraceable. We argue that automatizing the exploitation of common inputs, in ways that remix and reconfigure them, can lead to a crisis of academic authorship in which the moral agency involved in scholarly production is increasingly eroded. We discuss the relationship between the hypercommons and authorship in terms of moral agency and the ethics of academic production, speculating on different responses to the crisis of authorship as posed by GenAI.
C1 [Islam, Gazi] Grenoble Ecole Management, Rue Pierre Semard, F-38003 Grenoble, France.
   [Greenwood, Michelle] Monash Univ, Dept Comp Technol, 900 Dandenong Rd, Caulfield, Vic 3145, Australia.
C3 Grenoble Ecole Management; Monash University
RP Greenwood, M (corresponding author), Monash Univ, Dept Comp Technol, 900 Dandenong Rd, Caulfield, Vic 3145, Australia.
EM gazi.islam@grenoble-em.com; michelle.greenwood@monash.edu
RI Islam, Gazi/D-6466-2012
FU CAUL
FX Open Access funding enabled and organized by CAUL and its Member
   Institutions.
CR [Anonymous], 2010, Blog Theory: Feedback and Capture in the Circuits of Drive
   Benkler Y, 2006, J POLIT PHILOS, V14, P394, DOI 10.1111/j.1467-9760.2006.00235.x
   Butler N, 2024, ORGANIZATION, V31, P720, DOI 10.1177/13505084221145589
   Foucault Michel., 1984, FOUCAULT READER, P101
   Fuchs C, 2019, DIGITAL OBJECTS, DIGITAL SUBJECTS, P53, DOI 10.16997/book29.d
   Gatrell C, 2024, J MANAGE STUD, V61, P739, DOI 10.1111/joms.13045
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Greenwood M, 2023, ORGAN STUD, V44, P523, DOI 10.1177/01708406221107455
   Grimes M, 2023, ACAD MANAGE J, V66, P1617, DOI 10.5465/amj.2023.4006
   Kulkarni M, 2024, J MANAGE INQUIRY, V33, P207, DOI 10.1177/10564926231219622
   Lessig L., 1999, OPEN CODE OPEN CONTE
   Lindebaum D, 2024, J MANAGE STUD, V61, P2724, DOI 10.1111/joms.13032
   Milmo D., 2024, IMPOSSIBLE CREATE AI
   Portuese A., 2010, REV PORTUGUESA FILOS, V66, P819
   Scholz T., 2016, PLATFORM COOPERATIVI, P1
NR 15
TC 0
Z9 0
U1 79
U2 79
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0167-4544
EI 1573-0697
J9 J BUS ETHICS
JI J. Bus. Ethics
PD JUL
PY 2024
VL 192
IS 4
BP 659
EP 663
DI 10.1007/s10551-024-05741-9
EA JUL 2024
PG 5
WC Business; Ethics
WE Social Science Citation Index (SSCI)
SC Business & Economics; Social Sciences - Other Topics
GA YP3H3
UT WOS:001260416300003
OA hybrid
DA 2024-12-25
ER

PT J
AU Wang, HB
AF Wang, Haibo
TI Decoding herding dynamics in the generative AI investment amid key
   technological advancements: A timeline perspective
SO FINANCE RESEARCH LETTERS
LA English
DT Article
DE Herding dynamics; Generative AI (GenAI); Glosten-Jagannathan-Runkle
   generalized; autoregressive conditional heteroskedasticity; (GJR-GARCH)
   model; Market trends; Leverage effects; Drawdown; ChatGPT; Transformer;
   Cross-sectional standard deviation (CSSD); Cross-sectional absolute
   deviation (CSAD)
ID BEHAVIOR
AB This study, using two herding dynamics metrics and Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH) model, forecasts market trends, captures asymmetric volatility, and reveals the generative AI (GenAI) ecosystem's impact on individual assets' returns. Results of this study highlight distinctive traits of each GenAI equity, crucial for strategic positioning, especially for investors in tech stocks tied to GenAI. Herding behavior exhibits greater strength in the initial four months post-announcement of ChatGPT, gradually diminishing. GJR-GARCH reports that most of GenAI stocks do not exhibit statistically significant leverage effects. These findings provide valuable insights to navigate the dynamic landscape of GenAI investments.
C1 [Wang, Haibo] Texas A&M Int Univ, Div Int Business & Technol Studies, AR Sanchez Jr Sch Business, Laredo, TX 78041 USA.
C3 Texas A&M University System; Texas A&M International University
RP Wang, HB (corresponding author), Texas A&M Int Univ, Div Int Business & Technol Studies, AR Sanchez Jr Sch Business, Laredo, TX 78041 USA.
EM hwang@tamiu.edu
RI Haibo, Wang/HZJ-2774-2023
OI Wang, Haibo/0000-0002-8580-829X
CR Akbar M.A., 2023, IEEE Trans. Artif. Intell.
   Avery C, 1998, AM ECON REV, V88, P724
   Bikhchandani S, 1998, J ECON PERSPECT, V12, P151, DOI 10.1257/jep.12.3.151
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Chang EC, 2000, J BANK FINANC, V24, P1651, DOI 10.1016/S0378-4266(99)00096-5
   Chiang TC, 2010, J BANK FINANC, V34, P1911, DOI 10.1016/j.jbankfin.2009.12.014
   Christie William G., 1995, Financial Analysts Journal, V51, P31, DOI [https://doi.org/10.2469/faj.v51.n4.1918, DOI 10.2469/FAJ.V51.N4.1918, 10.2469/faj.v51.n4.1918]
   Mendes BVD, 2017, FINANC RES LETT, V22, P95, DOI 10.1016/j.frl.2017.06.001
   Demirer R, 2019, FINANC RES LETT, V31, P476, DOI 10.1016/j.frl.2018.12.018
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Gavriilidis K, 2016, J ECON BEHAV ORGAN, V132, P23, DOI 10.1016/j.jebo.2015.09.018
   GLOSTEN LR, 1993, J FINANC, V48, P1779, DOI 10.2307/2329067
   JARQUE CM, 1980, ECON LETT, V6, P255, DOI 10.1016/0165-1765(80)90024-5
   Mamidala V, 2023, FINANC RES LETT, V58, DOI 10.1016/j.frl.2023.104428
   Mandaci PE, 2022, FINANC RES LETT, V46, DOI 10.1016/j.frl.2021.102382
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   SCHARFSTEIN DS, 1990, AM ECON REV, V80, P465
   Ukpong I, 2021, FINANC RES LETT, V43, DOI 10.1016/j.frl.2021.101953
   Vidal-Tomás D, 2019, FINANC RES LETT, V30, P181, DOI 10.1016/j.frl.2018.09.008
   Wang QS, 2023, FINANC RES LETT, V58, DOI 10.1016/j.frl.2023.104640
   Yang JY, 2019, INT J MANAG FINANC, V15, P593, DOI 10.1108/IJMF-07-2018-0204
NR 21
TC 2
Z9 2
U1 7
U2 12
PU ACADEMIC PRESS INC ELSEVIER SCIENCE
PI SAN DIEGO
PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
SN 1544-6123
EI 1544-6131
J9 FINANC RES LETT
JI Financ. Res. Lett.
PD JUN
PY 2024
VL 64
AR 105432
DI 10.1016/j.frl.2024.105432
EA APR 2024
PG 10
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA PZ2R7
UT WOS:001217840100001
DA 2024-12-25
ER

PT J
AU Jones, RM
   Rudler, EM
   Preston, C
AF Jones, Rebecca M.
   Rudler, Eva-Maria
   Preston, Conner
TI Exploring the Concept of Valence and the Nature of Science via
   Generative Artificial Intelligence and General Chemistry Textbooks
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE Valence; Generative AI; Nature of Science; General Chemistry
ID STUDENTS
AB Like science itself, our understanding of chemical concepts and the way we teach them change over time. This paper explores historical and modern perspectives of the concept of valence in the context of collegiate general chemistry and draws comparisons to responses from generative artificial intelligence (genAI) tools such as ChatGPT. A fundamental concept in chemistry, valence in the early and mid-20th century was primarily defined as the "combining capacity" of atoms. Twenty-first century textbooks do not include this historical definition but rather use valence as an adjective to modify other nouns, e.g., valence electron or valence orbital. To explore these different perspectives in other information sources that could be used by students, we used a systematic series of prompts about valence to analyze the responses from ChatGPT, Bard, Liner, and ChatSonic from September and December 2023. Our findings show the historical definition is very common in responses to prompts which use valence or valency as a noun but less common when prompts include valence as an adjective. Regarding this concept, the state-of-the-art genAI tools are more consistent with textbooks from the 1950s than modern collegiate general chemistry textbooks. These findings present an opportunity for chemistry educators to observe and discuss with students the nature of science and how our understanding of chemistry changes. Including implications for educators, we present an example activity that may be deployed in general chemistry classes.
C1 [Jones, Rebecca M.; Rudler, Eva-Maria; Preston, Conner] George Mason Univ, Dept Chem & Biochem, Fairfax, VA 22030 USA.
C3 George Mason University
RP Jones, RM (corresponding author), George Mason Univ, Dept Chem & Biochem, Fairfax, VA 22030 USA.
EM rjones22@gmu.edu
OI Jones, Rebecca M./0000-0002-8896-0529
FU Department of Chemistry and Biochemistry at George Mason University;
   College of Science STEM Accelerator
FX The authors acknowledge the support of the Department of Chemistry and
   Biochemistry at George Mason University. R.M.J. thanks Dr. Mary Emenike
   (Rutgers University) for helpful conversation and acknowledges the
   support of the College of Science STEM Accelerator and Dr. Mary Crowe.
   E.R. thanks Dr. Ozlem Dilek and C.P. thanks Dr. Lee Solomon for their
   support of this project.
CR American Chemical Society, MIDDLE HIGH SCH CHEM
   [Anonymous], 2013, NATUREOF SCI NEXT GE, P1
   [Anonymous], 2022, NPR
   [Anonymous], 2023, Time
   [Anonymous], GEMINI
   [Anonymous], NEXT GENERATIONSCIEN
   Ansari T., 2024, AIM
   Bamiro AO, 2015, SAGE OPEN, V5, DOI 10.1177/2158244014564754
   Bard, About us
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Brown T. L., 1963, GEN CHEM
   Bruck LB, 2009, J CHEM EDUC, V86, P820, DOI 10.1021/ed086p820
   ChatSonic, US
   Eichler JF, 2022, J CHEM EDUC, V99, P1503, DOI 10.1021/acs.jchemed.1c01115
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Flynn AB, 2015, CHEM EDUC RES PRACT, V16, P198, DOI 10.1039/c4rp00224e
   getliner, LINER
   Gilbert T. R., 2020, CHEM SCIENCEIN CONTE
   Guo Y, 2023, J CHEM EDUC, V100, P4876, DOI 10.1021/acs.jchemed.3c00505
   Harris E. P., 1888, LECT NOTES GENERALCH
   Ibrahim B, 2009, J RES SCI TEACH, V46, P248, DOI 10.1002/tea.20219
   Julia Angwin, 2016, PROPUBLICA
   Keenan C. W., 1980, GEN COLL CHEM
   Lawrie G, 2023, CHEM EDUC RES PRACT, V24, P392, DOI 10.1039/d3rp90003g
   LEDERMAN NG, 1992, J RES SCI TEACH, V29, P331, DOI 10.1002/tea.3660290404
   Leon AJ, 2023, J CHEM EDUC, V100, P3859, DOI 10.1021/acs.jchemed.3c00288
   Lewis G. N., 1966, VALENCE STRUCTUREOF
   Lewis GN, 1916, J AM CHEM SOC, V38, P762, DOI 10.1021/ja02261a002
   Li F.-F., TIME
   Aparicio JL, 2018, J CHEM EDUC, V95, P1763, DOI 10.1021/acs.jchemed.8b00060
   Moscatel, TOMORROWS JOBS TODAY
   National Science Teachers Association, NATURE SCI POSITION
   Nebergall W. H., 1959, GEN CHEM
   Open AI, 2023, BARD
   Open AI, CHATSONIC VERSION 10
   openai, CHATGPT VERSION 35 9
   openai, CHATGPT VERSION 35 1
   PAULING L, 1948, J CHEM SOC, P1461, DOI 10.1039/jr9480001461
   Pauling L., 1947, GEN CHEM ANINTRODUCT
   Pauling L., 1950, COLL CHEM ANINTRODUC
   Pauling L., 1945, NATURE CHEMICALBOND
   Perrigo, TIME
   Rozear H., DUKE U LIB BLOGS
   Scarlett A. J., 1956, COLL CHEM
   Shultz GV, 2015, J CHEM EDUC, V92, P1325, DOI 10.1021/acs.jchemed.5b00064
   Silberberg M., 2021, CHEMISTRYTHE MOL NAT
   Silberberg M. S., 2013, PRINCIPLESOF GEN CHE
   Smith H. C., 1917, LECT NOTES CHEMISTRY
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   University of Michigan, 2023, GETTING STARTED GEN
   Watts FM, 2023, J CHEM EDUC, V100, P3806, DOI 10.1021/acs.jchemed.3c00664
   Weil E., 2023, NEW YOUR MAGAZINE
   West JK, 2023, J CHEM EDUC, V100, P4351, DOI 10.1021/acs.jchemed.3c00581
   Wolfram S, What Is ChatGPT Doing ...  and Why Does It Work?  ...
   Zumdahl S. S., 2016, CHEM ATOMSFIRST APPR
   Zumdahl S. S., 2016, CHEMISTRY
NR 56
TC 1
Z9 1
U1 11
U2 11
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD JUL 29
PY 2024
VL 101
IS 8
BP 3276
EP 3283
DI 10.1021/acs.jchemed.4c00271
EA JUL 2024
PG 8
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA C6G1N
UT WOS:001280418700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Barrett, A
   Pack, A
AF Barrett, Alex
   Pack, Austin
TI Not quite eye to AI: student and teacher perspectives on the use of
   generative artificial intelligence in the writing process
SO INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
LA English
DT Article
DE Artificial intelligence; Large language model; GPT; Writing education;
   Academic integrity
ID PLAGIARISM
AB Generative artificial intelligence (GenAI) can be used to author academic texts at a similar level to what humans are capable of, causing concern about its misuse in education. Addressing the role of GenAI in teaching and learning has become an urgent task. This study reports the results of a survey comparing educators' (n = 68) and university students' (n = 158) perceptions on the appropriate use of GenAI in the writing process. The survey included representations of user prompts and output from ChatGPT, a GenAI chatbot, for each of six tasks of the writing process (brainstorming, outlining, writing, revising, feedback, and evaluating). Survey respondents were asked to differentiate between various uses of GenAI for these tasks, which were divided between student and teacher use. Results indicate minor disagreement between students and teachers on acceptable use of GenAI tools in the writing process, as well as classroom and institutional-level lack of preparedness for GenAI. These results imply the need for explicit guidelines and teacher professional development on the use of GenAI in educational contexts. This study can contribute to evidence-based guidelines on the integration of GenAI in teaching and learning.
C1 [Barrett, Alex] Florida State Univ, Coll Educ, Stone Bldg,114 West Call St, Tallahassee, FL 32306 USA.
   [Pack, Austin] Brigham Young Univ Hawaii, Fac Educ & Social Work, 55-220 Kulanui St, Laie, HI 96762 USA.
C3 State University System of Florida; Florida State University; Brigham
   Young University; Brigham Young University - Hawaii
RP Barrett, A (corresponding author), Florida State Univ, Coll Educ, Stone Bldg,114 West Call St, Tallahassee, FL 32306 USA.
EM abarrett3@fsu.edu
RI Barrett, Alex/AAC-7531-2021; Pack, Austin/AAG-9689-2021
OI Barrett, Alex/0000-0003-1229-9743
FU Not applicable.
FX Not applicable.
CR Baker RS, 2022, INT J ARTIF INTELL E, V32, P1052, DOI 10.1007/s40593-021-00285-9
   Bland JM, 1997, BRIT MED J, V314, P572, DOI 10.1136/bmj.314.7080.572
   Bonner E., 2023, Teaching English with Technology, V23, P23, DOI DOI 10.56297/BKAM1691/WIEO1749
   Bridgeman B, 2012, APPL MEAS EDUC, V25, P27, DOI 10.1080/08957347.2012.635502
   Carlson M, 2024, TESOL J, V15, DOI 10.1002/tesj.759
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.00280
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   CHOMSKY N., 1991, CHOMSKYAN TURN, P26
   CWPA NCTE & NWP, 2011, National Framework for success in postsecondary writing
   Dehouche N., 2021, Ethic in Science and Environmental Politics, V21, P17, DOI [DOI 10.3354/ESEP00195, 10.3354/esep00195]
   Ely JJ., 2013, Academy of Educational Leadership Journal, V17, P95
   Evering LC, 2012, J ADOLESC ADULT LIT, V56, P35, DOI 10.1002/JAAL.00100
   Fan N, 2023, SAGE OPEN, V13, DOI 10.1177/21582440231181296
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fitria TN., 2021, Metathesis: Journal of English Language, Literature, and Teaching, V5, P65, DOI [10.31002/metathesis.v5i1.3519, DOI 10.31002/METATHESIS.V5I1.3519]
   Gardner J, 2021, J COMPUT ASSIST LEAR, V37, P1207, DOI 10.1111/jcal.12577
   Godwin-Jones R, 2022, LANG LEARN TECHNOL, V26, P5, DOI 10.10125/73474
   Graham M, 2015, GEO-GEOGR ENVIRON, V2, P88, DOI 10.1002/geo2.8
   Graham S, 2020, REV EDUC RES, V90, P179, DOI 10.3102/0034654320914744
   Graham S, 2019, REV RES EDUC, V43, P277, DOI 10.3102/0091732X18821125
   Hockly N, 2019, ELT J, V73, P82, DOI 10.1093/elt/ccy044
   Hu K., 2023, REUTERS         0202
   Huawei S, 2023, EDUC INF TECHNOL, V28, P771, DOI 10.1007/s10639-022-11200-7
   Ingley SJ, 2023, TRENDS ECOL EVOL, V38, P785, DOI 10.1016/j.tree.2023.05.007
   Jackson M., 2021, Journal of Business Technology Law, V16, P299
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Konheim-Kalkstein YL., 2008, Journal of College Character, V, V9, DOI [10.1186/s12909-019-1645-4, DOI 10.1186/S12909-019-1645-4]
   Kumar R, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00130-7
   Lampropoulos G., 2023, SSRN, DOI [10.2139/ssrn.4468181, DOI 10.2139/SSRN.4468181]
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   National Council of Teachers of English, 2013, NCTE POS STAT MACH S
   O'Neill R, 2019, AUSTRALAS J EDUC TEC, V35, P42, DOI 10.14742/ajet.3795
   OpenAI, 2023, Terms of use
   Pack A., 2023, Teaching English with Technology, V23, P4, DOI [10.56297/BUKA4060/VRRO1747, DOI 10.56297/BUKA4060/VRRO1747]
   Pack A, 2023, TESOL QUART, V57, P1571, DOI 10.1002/tesq.3253
   Pennycook A, 1996, TESOL QUART, V30, P201, DOI 10.2307/3588141
   Sadeghi R., 2019, RES ETHICS-UK, V15, P1, DOI DOI 10.1177/1747016116654065
   Seow A., 2002, Methodology in Language Teaching, P315, DOI [10.1017/cbo9780511667190.053, DOI 10.1017/CBO9780511667190.053]
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Sutherland-Smith W., 2005, Journal of English for Academic Purposes, V4, P83, DOI DOI 10.1016/J.JEAP.2004.07.007
   Tatum H.E., 2022, Journal of College and Character, V23, P32, DOI [DOI 10.1080/2194587X.2021.2017977, https://doi.org/10.1080/2194587X.2021.2017977]
   Tseng W., 2023, Journal of China Computer-Assisted Language Learning, V3, P258, DOI https://doi.org/10.1515/jccall-2023-0008
   Urlaub P., 2022, L2 Journal, V14, P45, DOI [DOI 10.5070/L214151790, 10.5070/L214151790]
   Wang ZH, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.909802
   Weigle S. C., 2013, HDB AUTOMATED ESSAY, DOI [10.4324/9780203122761.ch3, DOI 10.4324/9780203122761.CH3]
   Yang M., 2023, THE GUARDIAN
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
   Yeo S., 2007, HIGH ED RES DEV, V26, P199, DOI [DOI 10.1080/072943607013, 10.1080/07294360701310813, DOI 10.1080/07294360701310813, 10 .1080/07294360701310813]
   Yeo S, 2007, QUAL HIGH EDUC, V13, P187, DOI 10.1080/13538320701629202
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   Zhang Z, 2020, ASSESS WRIT, V43, P78, DOI 10.1016/j.asw.2019.100439
NR 52
TC 37
Z9 38
U1 130
U2 374
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2365-9440
J9 INT J EDUC TECHNOL H
JI Int. J. Educ. Technol. High. Educ.
PD NOV 10
PY 2023
VL 20
IS 1
AR 59
DI 10.1186/s41239-023-00427-0
PG 24
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA X5WO5
UT WOS:001099156200001
OA gold
DA 2024-12-25
ER

PT J
AU Liu, XH
AF Liu, Xiaohua
TI Navigating Uncharted Waters: Teachers' Perceptions of and Reactions to
   AI-Induced Challenges to Assessment
SO ASIA-PACIFIC EDUCATION RESEARCHER
LA English
DT Article; Early Access
DE Generative artificial intelligence; ChatGPT; English as a Foreign
   Language; Language assessment; Language teaching; Academic integrity
AB The surge of generative AI (GenAI) tools on the internet has caused unprecedented disruptions to education, and there are growing concerns about them being misused by students to commit plagiarism, hence jeopardizing assessment validity. Despite the foreseeable challenges, it remains unclear how frontline language teachers perceive and react to the potential impact of GenAI on their assessment practice. To explore this, the current study interviewed 17 English language teachers teaching at 10 universities across China. Qualitative analyses of the data revealed that more than half of the participants perceived threats from GenAI to their assessment practice either now or in the future. Meanwhile, there was a lack of guidance and support from their institutions on how to deal with those challenges. Consequently, most of them had to come up with their own coping strategies, including employing detection methods, renovating existing assessments, and promoting AI ethics among students. However, doubts and concerns also arose over the weaknesses of some strategies, demonstrating teachers' struggles for optimal solutions. Implications for future assessment design are discussed.
C1 [Liu, Xiaohua] Chinese Univ Hong Kong Shenzhen, Sch Humanities & Social Sci, 2001 Longxiang Blvd, Shenzhen 518172, Peoples R China.
C3 The Chinese University of Hong Kong, Shenzhen
RP Liu, XH (corresponding author), Chinese Univ Hong Kong Shenzhen, Sch Humanities & Social Sci, 2001 Longxiang Blvd, Shenzhen 518172, Peoples R China.
EM liuxiaohua@cuhk.edu.cn
RI Liu, Xiaohua/D-8966-2013
OI Liu, Xiaohua/0000-0002-5781-5775
FU Guangdong Office of Philosophy and Social Science
FX No Statement Available
CR Abd-Elaal ES, 2022, EUR J ENG EDUC, V47, P725, DOI 10.1080/03043797.2022.2046709
   Adams D, 2023, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12427-8
   Bachman L. F., 1996, Language assessment in practice
   Brown GTL, 2015, ASSESS EDUC, V22, P444, DOI 10.1080/0969594X.2014.996523
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Henning JE, 2017, TEACH DEV, V21, P388, DOI 10.1080/13664530.2016.1243570
   HyScaler, 2023, The Power of AI in Research Hypotheses
   Kane MT, 2013, J EDUC MEAS, V50, P1, DOI 10.1111/jedm.12000
   Karatas F, 2024, EDUC INF TECHNOL, V29, P19343, DOI 10.1007/s10639-024-12574-6
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   King N., 2010, Interviews in qualitative research
   Kirwan A, 2024, IRISH EDUC STUD, V43, P1389, DOI 10.1080/03323315.2023.2284901
   Kiryakova G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13101056
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Liao XF, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13424
   Merriam S.B., 2015, QUALITATIVE RES GUID
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Nguyen HM, 2024, EDUC INF TECHNOL, V29, P15999, DOI 10.1007/s10639-024-12495-4
   Nielsen DC, 2008, TEACH TEACH EDUC, V24, P1288, DOI 10.1016/j.tate.2007.01.015
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Paik M. C., 2003, Statistical methods for rates and proportions, DOI DOI 10.1002/0471445428
   Pecorari D., 2008, ACAD WRITING PLAGIAR
   Pitts J, 2001, MED TEACH, V23, P351, DOI 10.1080/01421590120057021
   Raftery D., 2023, Irish Journal of Technology Enhanced Learning, DOI [10.22554/ijtel.v7i1.114, DOI 10.22554/IJTEL.V7I1.114]
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   State Council of China, 2019, China Education Modernization 2035 (sic)(sic)(sic)(sic)(sic)(sic)2035
   Tan S.C., 2023, Learning: Research and Practice, V9, P125, DOI [10.1080/23735082.2023.2258895, DOI 10.1080/23735082.2023.2258895]
   Thanh BN, 2023, AUSTRALAS J EDUC TEC, V39, P59, DOI 10.14742/ajet.8902
   Wang Q., 2007, INT HDB ED, V15, P87, DOI DOI 10.1007/978-0-387-46301-8_8
   Wen Q., 2024, Foreign Language Teaching and Research, V56, P286, DOI [10.19923/j.cnki.fltr.2024.02.006, DOI 10.19923/J.CNKI.FLTR.2024.02.006]
   Xi XM, 2010, LANG TEST, V27, P147, DOI 10.1177/0265532209349465
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yan Z, 2021, ASSESS EDUC, V28, P228, DOI 10.1080/0969594X.2021.1884042
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
NR 39
TC 1
Z9 1
U1 45
U2 45
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 0119-5646
EI 2243-7908
J9 ASIA-PAC EDUC RES
JI Asia-Pac. Educ. Res.
PD 2024 JUL 11
PY 2024
DI 10.1007/s40299-024-00890-x
EA JUL 2024
PG 12
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA YC3W9
UT WOS:001266259500002
DA 2024-12-25
ER

PT J
AU Li, HL
   Wang, Y
   Luo, SQ
   Huang, C
AF Li, Hanlin
   Wang, Yu
   Luo, Siqi
   Huang, Cui
TI The influence of GenAI on the effectiveness of argumentative writing in
   higher education: evidence from a quasi-experimental study in China
SO JOURNAL OF ASIAN PUBLIC POLICY
LA English
DT Article; Early Access
DE Generative AI; argumentative writing; Chatbot; higher education;
   teaching/learning strategies
ID AI; ENGLISH
AB The revolutionary development of generative artificial intelligence, especially large language model (LLM) chatbots like ChatGPT, has brought disruptive changes to society, especially in the field of higher education, particularly in the domain of writing. Prior studies have primarily examined the opportunities and challenges that LLM Chatbots bring to higher education writing from a theoretical perspective. However, empirical studies on how to effectively and responsibly integrate LLM Chatbots into writing learning and evaluate their effectiveness are still limited. To address this gap, the present study introduces a learning approach termed LLM-powered Chatbot-assisted argumentative writing (LCAW) to support the cultivation of university students' writing abilities. A total of 61 Chinese university students from two classes were invited to participate in a quasi-experimental study with different learning methods as intervention measures, and student perceptions were gathered through interviews. The results show that the proposed LCAW method significantly improves students' writing performance in terms of content quality and language expression, and positively impacts in providing personalized feedback and enhancing writing motivation. This study contributes by proposing an effective method for integrating generative AI into higher education writing instruction and by offering insights for curriculum design policies to enhance higher writing education through GenAI.
C1 [Li, Hanlin; Wang, Yu; Luo, Siqi; Huang, Cui] Zhejiang Univ, Sch Publ Affairs, Hangzhou, Peoples R China.
C3 Zhejiang University
RP Huang, C (corresponding author), Zhejiang Univ, Sch Publ Affairs, Hangzhou, Peoples R China.
EM huangcui@zju.edu.cn
FU National Science and Technology Major Project [2022ZD0116203]; National
   Science and Technology Major Project [72134007]; Key Programme of the
   National Nature Science Foundation of China
FX This work was supported by the National Science and Technology Major
   Project[grant number: 2022ZD0116203], Key Programme of the National
   Nature Science Foundation of China [grant number: 72134007].
CR Adamopoulou E, 2020, MACH LEARN APPL, V2, DOI 10.1016/j.mlwa.2020.100006
   Adams P, 2006, EDUC 3-13, V34, P243, DOI 10.1080/03004270600898893
   Atlas S., 2023, CHATGPT HIGHER ED PR
   Awada G, 2020, COMPUT ASSIST LANG L, V33, P275, DOI 10.1080/09588221.2018.1558254
   Barrot JS, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100745
   Barrot JS, 2023, COMPUT ASSIST LANG L, V36, P584, DOI 10.1080/09588221.2021.1936071
   Beauvais C, 2011, J EDUC PSYCHOL, V103, P415, DOI 10.1037/a0022545
   Boyatzis R., 1998, Transferring qualitative information", V1st
   Brown TB, 2020, ADV NEUR IN, V33
   Burkhart C, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0235209
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Castañeda L, 2018, INT J EDUC TECHNOL H, V15, DOI 10.1186/s41239-018-0109-y
   Chang JL, 2024, J COMPUT ASSIST LEAR, V40, P37, DOI 10.1111/jcal.12856
   Chen M, 2022, J SECOND LANG WRIT, V57, DOI 10.1016/j.jslw.2022.100915
   Cohen L., 2007, RES METHODS ED
   Creswell J. W., 2008, RES DESIGN QUALITATI
   Diklil S, 2014, ASSESS WRIT, V22, P1, DOI 10.1016/j.asw.2014.03.006
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Fu QK, 2024, COMPUT ASSIST LANG L, V37, P179, DOI 10.1080/09588221.2022.2033787
   Futterer T, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-42227-6
   github, OpenBuddy
   Godwin-Jones R, 2022, LANG LEARN TECHNOL, V26, P5, DOI 10.10125/73474
   Graham S, 2019, REV RES EDUC, V43, P277, DOI 10.3102/0091732X18821125
   Graham S, 2011, J EDUC RES, V104, P396, DOI 10.1080/00220671.2010.488703
   Guo K, 2023, COMPUT EDUC, V203, DOI 10.1016/j.compedu.2023.104862
   Guo K, 2022, ASSESS WRIT, V54, DOI 10.1016/j.asw.2022.100666
   Halliday M. A. K., 1976, COHESION ENGLISH
   Harrer S, 2023, EBIOMEDICINE, V90, DOI 10.1016/j.ebiom.2023.104512
   Hartwell K, 2022, ASSESS WRIT, V54, DOI 10.1016/j.asw.2022.100675
   Hegelheimer V, 2016, CALICO J, V33, pI, DOI 10.1558/cj.v33i1.29251
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Jia JY, 2009, AI MAG, V30, P59, DOI 10.1609/aimag.v30i2.2232
   Jonassen D.H., 1995, AM J DISTANCE EDUC, V9, P7, DOI [DOI 10.1080/08923649509526885, 10.1080/08923649509526885]
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kim M, 2024, TECHTRENDS, V68, P37, DOI 10.1007/s11528-023-00899-x
   Kuhn D., 1991, SKILLS ARGUMENT
   Lee YF, 2022, ETR&D-EDUC TECH RES, V70, P1843, DOI 10.1007/s11423-022-10142-8
   Li JR, 2015, J SECOND LANG WRIT, V27, P1, DOI 10.1016/j.jslw.2014.10.004
   Li M, 2023, COMPUT EDUC, V202, DOI 10.1016/j.compedu.2023.104844
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lin MPC, 2020, EDUC TECHNOL SOC, V23, P78
   Mahyoob M., 2023, International Journal of Emerging Technologies in Learning, V18, P282, DOI [https://doi.org/10.3991/ijet.v18i14.41725, DOI 10.3991/IJET.V18I14.41725]
   Markauskaite L., 2022, Computers & Education: Artificial Intelligence, V3, P100056, DOI https://doi.org/10.1016/j.caeai.2022.100056
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Mokmin NAM, 2021, EDUC INF TECHNOL, V26, P6033, DOI 10.1007/s10639-021-10542-y
   Pagano N, 2008, COLL COMPOS COMMUN, V60, P285
   Rahman NAA, 2022, ENVIRON-BEHAV PROC J, V7, P547, DOI 10.21834/ebpj.v7iSI9.4304
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Rowland DR, 2023, J ACAD LANG LEARN, V17, pT31
   Sabzalieva M., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Shang HF, 2022, INTERACT LEARN ENVIR, V30, P4, DOI 10.1080/10494820.2019.1629601
   Song CP, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1260843
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Su YF, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100752
   Topsakal O., 2022, J COGNITIVE SYSTEMS, V7, P33, DOI DOI 10.52876/JCS.1227392
   Tseng W., 2023, Journal of China Computer-Assisted Language Learning, V3, P258, DOI [DOI 10.1515/JCCALL-2023-0008, https://doi.org/10.1515/jccall-2023-0008]
   Urlaub P., 2022, L2 Journal, V14, P45, DOI [DOI 10.5070/L214151790, 10.5070/L214151790]
   Walczak K, 2023, ECON BUS REV-POL, V9, P71, DOI 10.18559/ebr.2023.2.743
   Wang YF, 2017, BRIT J EDUC TECHNOL, V48, P431, DOI 10.1111/bjet.12388
   Williams C, 2019, COMPUT EDUC, V128, P227, DOI 10.1016/j.compedu.2018.09.024
   Wilson J, 2016, COMPUT EDUC, V100, P94, DOI 10.1016/j.compedu.2016.05.004
   Xiao P., 2023, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4458269
   Xiao YY, 2023, LANGUAGES-BASEL, V8, DOI 10.3390/languages8030212
   Xu LL, 2023, ASSESS EVAL HIGH EDU, V48, P995, DOI 10.1080/02602938.2022.2161089
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yang M., 2023, The Guardian
   Yin JQ, 2021, J EDUC COMPUT RES, V59, P154, DOI 10.1177/0735633120952067
   Zhai N, 2023, J EDUC COMPUT RES, V61, P875, DOI 10.1177/07356331221127300
   Zhai X., 2022, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4312418
   Zhang SA, 2023, EDUC INF TECHNOL, V28, P15223, DOI 10.1007/s10639-023-11805-6
   Zhou TQ, 2023, SYSTEM, V118, DOI 10.1016/j.system.2023.103141
   Zou D, 2023, J COMPUT HIGH EDUC, V35, P166, DOI 10.1007/s12528-022-09337-y
   Zou M, 2023, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12397-x
NR 74
TC 1
Z9 1
U1 184
U2 189
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1751-6234
EI 1751-6242
J9 J ASIAN PUBLIC POLIC
JI J. Asian Public Policy
PD 2024 JUN 8
PY 2024
DI 10.1080/17516234.2024.2363128
EA JUN 2024
PG 26
WC Area Studies
WE Social Science Citation Index (SSCI)
SC Area Studies
GA TO0L2
UT WOS:001242084700001
DA 2024-12-25
ER

PT J
AU Bannon, T
   Laplante, P
AF Bannon, Tracy (Trac)
   Laplante, Phil
TI Generative AI in the Software Development Lifecycle
SO COMPUTER
LA English
DT Article
AB We conducted a virtual roundtable on generative AI (GenAI), human/machine teaming, and the future of the software development lifecycle (SDLC). Experts from industry, academia, and government explored how GenAI is transforming the SDLC across mission and business domains.
C1 [Bannon, Tracy (Trac)] MITRE Corp, Bedford, MA 01730 USA.
   [Laplante, Phil] NIST, Gaithersburg, MD 20899 USA.
   [Laplante, Phil] Penn State Univ, Software & Syst Engn, State Coll, PA 16801 USA.
C3 MITRE Corporation; National Institute of Standards & Technology (NIST) -
   USA; Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University
RP Bannon, T (corresponding author), MITRE Corp, Bedford, MA 01730 USA.
EM tbannon@mitre.org; plaplante@psu.edu
OI Bannon, Tracy/0009-0002-7140-1664; Laplante, Phillip/0000-0002-0415-271X
NR 0
TC 0
Z9 0
U1 2
U2 2
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD DEC
PY 2024
VL 57
IS 12
BP 27
EP 34
DI 10.1109/MC.2024.3474789
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA M9B6L
UT WOS:001360415900001
DA 2024-12-25
ER

PT J
AU Isgüzar, S
   Fendoglu, E
   Simsek, AI
AF Isguzar, Seda
   Fendoglu, Eda
   Simsek, Ahmed Ihsan
TI INNOVATIVE APPLICATIONS IN BUSINESSES: AN EVALUATION ON GENERATIVE
   ARTIFICIAL INTELLIGENCE
SO AMFITEATRU ECONOMIC
LA English
DT Article
DE ChatGPT; artificial intelligence (AI); generative artificial
   intelligence (GenAI); OpenAI; business; business management; technology
   adoption; bibliometric analysis
AB The utilisation of Chat Generative Pre -Trained Transformer (ChatGPT) and generative artificial intelligence (GenAI) technologies has started to demonstrate its impact across several domains. The swift shift and widespread implementation of efficient artificial intelligence (AI) present distinct prospects such as optimisation, advancement, enhanced efficiency, boosted sales and marketing, expansion, reduced costs, and heightened profitability. GenAI has the potential to create a competition crisis between technologically advanced enterprises and less developed ones. Additionally, it may give rise to legal, moral, and ethical issues such as copyright infringement and the production of fake and false information. Hence, it is crucial for organisations to ensure that the productivity of AI is maximized in order to maximise its benefits and minimise any potential harm. The aim of this study is to provide suggestions regarding the use and potential of GenAI technologies in the corporate sector and to emphasise the potential research areas of future GenAI. This study contributes to research and practice in business and management and also identifies future research avenues. This study examines the benefits and disadvantages of using GenAI tools in businesses and individual departments, and it highlights the potential risks and dangers. A bibliometric analysis of 198 studies in the discipline of Business & Management from the Scopus database was conducted using the R program's bibliometrix package. The study focuses on descriptive data, annual scientific production, most productive journals, most productive authors and authors dominance factor, most cited publications, and most relevant keywords. The findings show that GenAI is likely to continue with a strong and rapidly rising trend in 2024 and beyond.
C1 [Isguzar, Seda; Fendoglu, Eda] Malatya Turgut Ozal Univ, Malatya, Turkiye.
   [Simsek, Ahmed Ihsan] Firat Univ, Elazig, Turkiye.
C3 Malatya Turgut Ozal University; Firat University
RP Fendoglu, E (corresponding author), Malatya Turgut Ozal Univ, Malatya, Turkiye.
EM eda.fendoglu@ozal.edu.tr
RI Simsek, Ahmed/W-3881-2018; FENDOĞLU, EDA/LWK-7307-2024; ISGUZAR,
   seda/KLY-3036-2024
CR Aguinis H, 2024, ORGAN DYN, V53, DOI 10.1016/j.orgdyn.2024.101029
   Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Amin M. S., 2024, Journal of Computer Science and Technology Studies, V6, P58, DOI [10.32996/jcsts.2024.6.1.7, DOI 10.32996/JCSTS.2024.6.1.7]
   Arenas A.D., 2018, INDIAN J SCI TECHNOL, V11, P1, DOI DOI 10.17485/ijst/2018/v11i18/122604
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Beerbaum D.O., 2023, GENERATIVE ARTIFICIA
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chuma E. L., 2023, Manag Sci Bus Decis, V3, P5, DOI DOI 10.52812/MSBD.63
   Crane D., 1972, Invisible Colleges: Diffusion of Knowledge in Scientific Communities
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Gill SS, 2022, INTERNET THINGS-NETH, V19, DOI 10.1016/j.iot.2022.100514
   GROOS OV, 1969, J DOC, V25, P344, DOI 10.1108/eb026482
   Gupta V., 2023, INTERNET REF SERV Q, P1, DOI DOI 10.1080/10875301.2023.2300114
   Gursoy D, 2022, J HOSP MARKET MANAG, V31, P527, DOI 10.1080/19368623.2022.2072504
   Han J, 2020, J BIOMED INFORM, V109, DOI 10.1016/j.jbi.2020.103516
   Hassani H, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7020062
   Herubel J.P.V.M., 1999, LIBR CULT, V34, P380
   Isguzar S., 2021, Dijital Yozlasma ve Etik
   Iskender A, 2023, EUR J TOUR RES, V34, DOI 10.54055/ejtr.v34i.3169
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Khan MS, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24890
   Knight W., 2017, MIT Technology Review
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Lewis S., 2017, Journal of the Medical Library Association, V105, P385, DOI [10.5195/jmla.2017, DOI 10.5195/JMLA.2017]
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Ruiz-Real JL, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15122699
   Lyu Q, 2023, VIS COMPUT IND BIOME, V6, DOI 10.1186/s42492-023-00136-5
   OpenAI, 2023, Introducing chatgpt enterprise
   OpenAI, 2023, Learning from human preferences
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Pereira V, 2023, HUM RESOUR MANAGE R, V33, DOI 10.1016/j.hrmr.2021.100857
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Rane N., 2023, SSRN Electronic Journal
   Rane N., 2023, ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges and opportunities for industry 4.0, industry 5.0 and society 5.0
   Rane N., 2024, Intelligent Manufacturing through Generative Artificial Intelligence, Such as ChatGPT or Bard
   Rane N., 2023, PREPRINT
   Say C., 2018, 50 Soruda Yapay Zeka
   Serdaliyev Y., 2023, e-journal, V24, P129, DOI [10.47526/2023-1/2524, DOI 10.47526/2023-1/2524]
   Soni V., 2023, Rev. Contemp. Bus. Anal, V6, P133
   Soni V., 2023, Sage Science Review of Applied Machine Learning, V6, P1, DOI [https://doi.org/10.3390/app11083475, DOI 10.3390/APP11083475]
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang Q, 2020, COMPUT SCI REV, V37, DOI 10.1016/j.cosrev.2020.100275
NR 44
TC 0
Z9 0
U1 107
U2 107
PU EDITURA ASE
PI BUCURESTI
PA PIATA ROMANA, NR 6, SECTOR 1, BUCURESTI, 701731, ROMANIA
SN 1582-9146
EI 2247-9104
J9 AMFITEATRU ECON
JI Amfiteatru Econ.
PD MAY
PY 2024
VL 26
IS 66
DI 10.24818/EA/2024/66/511
PG 283
WC Business; Economics; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA UL5C7
UT WOS:001248217500007
OA gold
DA 2024-12-25
ER

PT J
AU Lai, JW
AF Lai, Joel Weijia
TI Adapting Self-Regulated Learning in an Age of Generative Artificial
   Intelligence Chatbots
SO FUTURE INTERNET
LA English
DT Article
DE self-regulated learning; process mining; learning process analytics;
   generative artificial intelligence; chatbot
ID MOTIVATION
AB The increasing use of generative artificial intelligence (GenAI) has led to a rise in conversations about how teachers and students should adopt these tools to enhance the learning process. Self-regulated learning (SRL) research is important for addressing this question. A popular form of GenAI is the large language model chatbot, which allows users to seek answers to their queries. This article seeks to adapt current SRL models to understand student learning with these chatbots. This is achieved by classifying the prompts supplied by a learner to an educational chatbot into learning actions and processes using the process-action library. Subsequently, through process mining, we can analyze these data to provide valuable insights for learners, educators, instructional designers, and researchers into the possible applications of chatbots for SRL.
C1 [Lai, Joel Weijia] Nanyang Technol Univ, Inst Pedag Innovat Res & Excellence, 50 Nanyang Ave, Singapore 639798, Singapore.
C3 Nanyang Technological University
RP Lai, JW (corresponding author), Nanyang Technol Univ, Inst Pedag Innovat Res & Excellence, 50 Nanyang Ave, Singapore 639798, Singapore.
EM joellai@ntu.edu.sg
RI Lai, Joel Weijia/HPF-5537-2023
OI Lai, Joel Weijia/0000-0002-5619-2051
CR Agredo-Delgado V, 2020, ADV INTELL SYST COMP, V1161, P203, DOI 10.1007/978-3-030-45697-9_20
   Artstein R., 2017, Handbook of Linguistic Annotation, P297, DOI [10.1007/978-94-024-0881-2_11, DOI 10.1007/978-94-024-0881-2_11, 10.1007/978-94-024-0881-211]
   Artstein R, 2008, COMPUT LINGUIST, V34, P555, DOI 10.1162/coli.07-034-R2
   Bandura A, 2002, HLTH PSYCHOL READER, P94
   Barry J., 2011, Handbook of Self-Regulation of Learning and Performance, DOI [10.4324/9780203839010, DOI 10.4324/9780203839010]
   Brenner CA, 2022, SMART LEARN ENVIRON, V9, DOI 10.1186/s40561-021-00184-5
   Chiu TKF, 2024, ETR&D-EDUC TECH RES, V72, P2401, DOI 10.1007/s11423-024-10366-w
   Chocarro R, 2023, EDUC STUD-UK, V49, P295, DOI 10.1080/03055698.2020.1850426
   Chung M., 2000, SOOKMYUNG WOMENS U, V1, P55, DOI 10.1007/bf03026146
   Clemons ML., 2020, J. Teach. Learn. Technol, V9
   Daniela P, 2015, PROCD SOC BEHV, V191, P2549, DOI 10.1016/j.sbspro.2015.04.410
   Deci E. L., 2013, Intrinsic motivation and self-determination in human behavior (perspectives in social psychology), DOI DOI 10.1007/978-1-4899-2271-7
   Dilmegani C., 2023, Top 6 Use Cases of Generative AI in Education
   Dunlosky J, 2011, PSYCHOL LEARN MOTIV, V54, P103, DOI 10.1016/B978-0-12-385527-5.00004-8
   Alonso-Mencía ME, 2020, EDUC REV, V72, P319, DOI 10.1080/00131911.2019.1566208
   Fan YZ, 2023, J COMPUT ASSIST LEAR, DOI 10.1111/jcal.12801
   FLAVELL JH, 1979, AM PSYCHOL, V34, P906, DOI 10.1037/0003-066X.34.10.906
   Jansen RS, 2022, J COMPUT ASSIST LEAR, V38, P993, DOI 10.1111/jcal.12675
   Khiat H, 2022, J UNIV TEACH LEARN P, V19
   Kong SC, 2024, IEEE T LEARN TECHNOL, V17, P1588, DOI 10.1109/TLT.2024.3392830
   Kuhail MA, 2023, EDUC INF TECHNOL, V28, P973, DOI 10.1007/s10639-022-11177-3
   Meng JB, 2021, J COMPUT-MEDIAT COMM, V26, P207, DOI 10.1093/jcmc/zmab005
   Molenaar I., 2023, Comput. Hum. Behav, V139, P107540, DOI DOI 10.1016/J.CHB.2022.107540
   Okonkwo C. W., 2021, Comput. Educ. Artif. Intell., V2, DOI [10.1016/j.caeai.2021.100033, DOI 10.1016/J.CAEAI.2021.100033]
   Panadero E, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00422
   Panadero E, 2016, SCAND J EDUC RES, V60, P723, DOI 10.1080/00313831.2015.1066436
   Pintrich P.R., 2000, ROLE GOAL ORIENTATIO, DOI DOI 10.1016/B978-012109890-2/50043-3
   Stahl G., 2021, Theoretical Investigations, P451, DOI [10.1007/978-3-030-49157-420, DOI 10.1007/978-3-030-49157-420]
   Stoeger H., 2010, Gifted Education International, V26, P110, DOI [10.1177/026142941002600113, DOI 10.1177/026142941002600113]
   Wei XM, 2023, INTERNET HIGH EDUC, V56, DOI 10.1016/j.iheduc.2022.100880
   Williamson G., 2015, J. Initial. Teach. Inq, V1, P25, DOI [DOI 10.26021/851, https://doi.org/10.26021/851]
   Winne PH, 2001, SELF-REGULATED LEARNING AND ACADEMIC ACHIEVEMENT, SECOND ED., P153
   WINNE PH, 1995, EDUC PSYCHOL, V30, P173, DOI 10.1207/s15326985ep3004_2
   Winne PH, 1998, EDUC PSYCHOL SER, P277
   Winne PH, 2011, EDUC PSYCHOL HANDB, P15
   Wollny S, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.654924
   Wong J, 2019, INT J HUM-COMPUT INT, V35, P356, DOI 10.1080/10447318.2018.1543084
   Zhang YM, 2022, COMPUT EDUC, V179, DOI 10.1016/j.compedu.2021.104427
   Zimmerman B.J., 2003, The nature of problem solving
   Zimmerman B.J., 2000, HDB SELF REGULATION, P13, DOI [10.1016/B978-012109890-2/50031-74,5, DOI 10.1016/B978-012109890-2/50031-7, 10.1016/B978-012109890-2/50031-7]
   Zimmerman B. J., 2015, SELF REGULATED LEARN, P541, DOI [10.1016/B978-0-08-097086-8.26060-1, DOI 10.1016/B978-0-08-097086-8.26060-1]
   Zimmerman BJ, 2008, AM EDUC RES J, V45, P166, DOI 10.3102/0002831207312909
   ZIMMERMAN BJ, 1986, AM EDUC RES J, V23, P614, DOI 10.3102/00028312023004614
   ZIMMERMAN BJ, 1989, J EDUC PSYCHOL, V81, P329, DOI 10.1037/0022-0663.81.3.329
NR 44
TC 1
Z9 1
U1 35
U2 35
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 1999-5903
J9 FUTURE INTERNET
JI Future Internet
PD JUN
PY 2024
VL 16
IS 6
AR 218
DI 10.3390/fi16060218
PG 12
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA WR6F1
UT WOS:001256632100001
OA gold
DA 2024-12-25
ER

PT J
AU Sofronieva, E
   Beleva, C
   Georgieva, G
   Markov, S
AF Sofronieva, Ekaterina
   Beleva, Christina
   Georgieva, Galina
   Markov, Stefan
TI ARTIFICIAL INTELLIGENCE, ALGORITHM LITERACY, LOCUS OF CONTROL, AND
   ENGLISH LANGUAGE SKILLS: A STUDY AMONG BULGARIAN STUDENTS IN EDUCATION
SO PEDAGOGIKA-PEDAGOGY
LA English
DT Article
DE generative artificial intelligence; algorithm literacy; locus of
   control; English language skills; coding skills
AB This research, conducted in June 2023 at Sofia University "St. Kliment Ohridski", aimed at gaining in-depth insights of the extent to which Bulgarian Internet users in tertiary education had developed comprehension of generative artificial intelligence (AI) models and algorithm literacy. For the purposes of this study, a scale measuring the knowledge of generative AI models was devised and implemented, and an existing algorithm literacy scale was tested. Altogether, 125 university students across various majors in the field of education took part in the research. Findings revealed that the newly developed scale on generative AI models displayed good reliability and correlated positively with the measure for algorithm knowledge and students' self-reported language skills. Group differences in relation to students' university major were found to be significant for knowledge and use of generative AI models, language skills, and coding skills. As hypothesized, students in media education displayed high scores on most scales.
C1 [Sofronieva, Ekaterina; Beleva, Christina; Georgieva, Galina; Markov, Stefan] Sofia Univ St Kliment Ohridski, Sofia, Bulgaria.
C3 University of Sofia
RP Sofronieva, E (corresponding author), Sofia Univ St Kliment Ohridski, Sofia, Bulgaria.
EM e.sofronieva@fppse.uni-sofia.bg; hnbeleva@uni-sofia.bg;
   g.georgieva@fppse.uni-sofia.bg; skmarkov@uni-sofia.bg
RI Beleva, Christina/AAE-2276-2021
FU European Union - NextGenerationEU, through the National Recovery and
   Resilience Plan of the Republic of Bulgaria, project SUMMIT
   [BG-RRP-2.004-0008-C01]
FX This study is financed by the European Union - NextGenerationEU, through
   the National Recovery and Resilience Plan of the Republic of Bulgaria,
   project SUMMIT No BG-RRP-2.004-0008-C01
CR Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Banh L, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00680-1
   Binder Jeffrey M., 2022, Language and the Rise of the Algorithm
   Brodsky JE., 2020, Journal of Media Literacy Education, V12, P43, DOI DOI 10.23860/JMLE-2020-12-3-5
   CHOMSKY N, 2006, Biolinguistics and the human capacity
   CRONBACH LJ, 1955, PSYCHOL BULL, V52, P281, DOI 10.1037/h0040957
   Dodigovic M., 2005, Artificial intelligence in second language learning: Raising error awareness
   Dogruel L, 2022, COMMUN METHODS MEAS, V16, P115, DOI 10.1080/19312458.2021.1968361
   Dogruel L, 2022, INFORM COMMUN SOC, V25, P1311, DOI 10.1080/1369118X.2020.1863999
   EVANS J., 2009, Blackwell Handbook of Language Development, P128
   Hargittai E., 2019, Society and The Internet: How Networks of Information and Communication are Changing Our Lives, V109, P109, DOI 10.1093/oso/9780198843498.003.0007
   Head AJ., 2020, INFORM LITERACY AGE
   HERRERA F., 2023, ENG COMPUTER BASED S, DOI [10.1007/978-3-031-49252-5, DOI 10.1007/978-3-031-49252-5]
   Kashive N, 2021, INT J INF LEARN TECH, V38, P1, DOI 10.1108/IJILT-05-2020-0090
   Kozyreva A, 2021, HUM SOC SCI COMMUN, V8, DOI 10.1057/s41599-021-00787-w
   Kumar L.A., 2023, Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision: Techniques and Use Cases
   Lopes GA, 2023, EDUC SCI, V13, DOI 10.3390/educsci13060619
   Lutz C, 2019, HUM BEHAV EMERG TECH, V1, P141, DOI 10.1002/hbe2.140
   MICHELI M., 2021, Handbook of Digital Inequality, P148
   Miller T, 2019, ARTIF INTELL, V267, P1, DOI 10.1016/j.artint.2018.07.007
   MONDAL P., 2017, Natural Language and Possible Minds: How Language Uncovers the Cognitive Landscape
   Niyogi P, 2006, CURR STUD LINGUIST, V43, P1
   Oeldorf-Hirsch A, 2023, J COMPUT-MEDIAT COMM, V28
   Ogunniye G, 2023, J ARTIF INTELL RES, V76, P163
   Peytcheva-Forsyth R, 2018, EDULEARN PROC, P2311
   ROTTER JB, 1966, PSYCHOL MONOGR, V80, P1, DOI 10.1037/h0092976
   Serra J, 2021, BRIT J EDUC TECHNOL, V52, P1898, DOI 10.1111/bjet.13131
   Sharan NN, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e04572
   Shin D, 2022, INTERNET RES, V32, P1214, DOI 10.1108/INTR-02-2021-0087
   SHOPOV T., 2018, Vsichko da stava za pouka. Prouchvane na faktorite, vliyaeshti na uspeshnoto uchene na vtori ezik
   SOFRONIEVA E., 2020, The Empathy Concept: Measures and Application in Education
   Swart J, 2021, SOC MEDIA SOC, V7, DOI 10.1177/20563051211008828
   Zarouali B, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101607
NR 33
TC 0
Z9 0
U1 19
U2 19
PU NATSIONALNO IZDATELSTVO AZ BUKI
PI SOFIA
PA BUL TSARIGRADSKO SHOSE, 125, BL 5, SOFIA, 1113, BULGARIA
SN 0861-3982
EI 1314-8540
J9 PEDAGOGIKA
JI Pedagogika
PY 2024
VL 96
IS 5
BP 579
EP 599
DI 10.53656/ped2024-5.01
PG 21
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA UV0K4
UT WOS:001250717900001
DA 2024-12-25
ER

PT J
AU Lu, JJ
   Zheng, RX
   Gong, ZK
   Xu, HF
AF Lu, Jijian
   Zheng, Ruxin
   Gong, Zikun
   Xu, Huifen
TI Supporting Teachers' Professional Development With Generative AI: The
   Effects on Higher Order Thinking and Self-Efficacy
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Generative artificial intelligence (AI); higher order thinking;
   preservice teachers; teacher self-efficacy; teaching skills training
ID PRESERVICE; INTERVENTION; SKILLS
AB Generative artificial intelligence (AI) has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted preservice teaching skills training on preservice teachers' self-efficacy and higher order thinking. The participants of this study were 215 preservice mathematics, science, and computer teachers from a university in China. First, a pretest-post-test quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Finally, a semistructured interview comprising open-ended questions was administered to 25 preservice teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of preservice teachers in the experimental group, who used generative AI for teachers' professional development, were considerably higher than those of the control group, both in teacher self-efficacy (F = 8.589, p = 0.0084 < 0.05) and higher order thinking (F = 7.217, p = 0.008 < 0.05). It revealed that generative AI can be effective in supporting teachers' professional development. This study produced a practical teachers' professional development method for preservice teachers with generative AI.
C1 [Lu, Jijian; Gong, Zikun] Hangzhou Normal Univ, Chinese Innovat & Entrepreneurship Educ Res Inst, Hangzhou 311121, Peoples R China.
   [Lu, Jijian; Gong, Zikun] Hangzhou Normal Univ, Chinese Educ Modernizat Res Inst, Hangzhou 311121, Peoples R China.
   [Zheng, Ruxin; Xu, Huifen] Hangzhou Normal Univ, Jing Hengyi Sch Educ, Hangzhou 311121, Peoples R China.
C3 Hangzhou Normal University; Hangzhou Normal University; Hangzhou Normal
   University
RP Gong, ZK (corresponding author), Hangzhou Normal Univ, Chinese Innovat & Entrepreneurship Educ Res Inst, Hangzhou 311121, Peoples R China.; Gong, ZK (corresponding author), Hangzhou Normal Univ, Chinese Educ Modernizat Res Inst, Hangzhou 311121, Peoples R China.; Xu, HF (corresponding author), Hangzhou Normal Univ, Jing Hengyi Sch Educ, Hangzhou 311121, Peoples R China.
EM lujijian@foxmail.com; zrx_email@163.com; zkgong@163.com;
   angela422@foxmail.com
RI zheng, ruxin/IWM-6792-2023; Lu, Jijian/GYV-0784-2022
OI Xu, Huifen/0009-0005-1469-4394; Lu, Jijian/0000-0003-0208-9486; Zheng,
   Ruxin/0009-0004-9842-6946
FU Ministry of Education#x0027;s Artificial Intelligence to Promote the
   Construction of Teachers' Team
FX No Statement Available
CR [Anonymous], 2023, GPT-4 Technical Report
   Archambault Leanna, 2009, Contemporary Issues in Technology and Teacher Education, V9, P71
   Archambault L.M., 2006, Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, P1836
   Arsal Z, 2014, EUR J TEACH EDUC, V37, P453, DOI 10.1080/02619768.2014.912627
   Asiri YA, 2021, IEEE T LEARN TECHNOL, V14, P445, DOI 10.1109/TLT.2021.3104817
   Avci ZY, 2021, EDUC STUD-UK, V47, P383, DOI 10.1080/03055698.2019.1702509
   Aydin-Gunbatar B., 2012, Asia-Pacific Educ. Researcher, V21, P203
   Bandura A., 1978, Psychol Rev, V1, P139, DOI [DOI 10.1016/0146-6402(78)90002-4, 10.1037/0033-295X.84.2.191, DOI 10.1037/0033-295X.84.2.191, 10.1016/0146-6402(78)90002-4, 10.1016/0146-6402]
   Benton-Kupper J., 2001, EDUCATION, V121, P830
   Bommasani R, 2023, ANN NY ACAD SCI, V1525, P140, DOI 10.1111/nyas.15007
   Chao SH, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10051602
   Driana E., 2019, ACITYA J TEACHING ED, V1, P110, DOI [10.30650/ajte.v1i2.233, DOI 10.30650/AJTE.V1I2.233]
   Du H, 2023, arXiv
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fernandez M.L., 2006, EDUCATION, V127, P203
   Fidan M, 2022, J EDUC COMPUT RES, V60, P1716, DOI 10.1177/07356331221077901
   Fung D, 2014, INT J EDUC RES, V66, P45, DOI 10.1016/j.ijer.2014.02.002
   Humphry T, 2023, J CHEM EDUC, V100, P1434, DOI 10.1021/acs.jchemed.3c00006
   Jaipal-Jamani K, 2017, J SCI EDUC TECHNOL, V26, P175, DOI 10.1007/s10956-016-9663-z
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Kpanja E, 2001, BRIT J EDUC TECHNOL, V32, P483, DOI 10.1111/1467-8535.00215
   Lazarides R, 2021, LEARN INSTR, V76, DOI 10.1016/j.learninstruc.2021.101489
   Lee J, 2017, COMPUT EDUC, V115, P143, DOI 10.1016/j.compedu.2017.06.015
   Liu DP, 2022, EDUC INF TECHNOL, V27, P7281, DOI 10.1007/s10639-022-10922-y
   Mergler AG, 2010, TEACH EDUC, V21, P199, DOI 10.1080/10476210902998466
   Ming Y, 2022, IEEE-CAA J AUTOMATIC, V9, P1339, DOI 10.1109/JAS.2022.105734
   Miri B, 2007, RES SCI EDUC, V37, P353, DOI 10.1007/s11165-006-9029-2
   MONTGOMERY GK, 1977, PSYCHOPHYSIOLOGY, V14, P251, DOI 10.1111/j.1469-8986.1977.tb01170.x
   Priyaadharshini M, 2018, COMPUT APPL ENG EDUC, V26, P2237, DOI 10.1002/cae.22035
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Qadir J., 2020, PROC ASEE VIRTUAL AN, P21, DOI [10.18260/1-2-34923, DOI 10.18260/1-2-34923]
   Qin CW, 2023, Arxiv, DOI [arXiv:2302.06476, 10.48550/arXiv.2302.06476, DOI 10.48550/ARXIV.2302.06476]
   Raffel C, 2020, J MACH LEARN RES, V21
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Schina D, 2021, INT J EDUC TECHNOL H, V18, DOI 10.1186/s41239-021-00264-z
   Schneider J, 2016, IEEE T LEARN TECHNOL, V9, P318, DOI 10.1109/TLT.2016.2627043
   Shaughnessy MF, 2004, EDUC PSYCHOL REV, V16, P153, DOI 10.1023/B:EDPR.0000026711.15152.1f
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Tschannen-Moran M, 2007, TEACH TEACH EDUC, V23, P944, DOI 10.1016/j.tate.2006.05.003
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Yan CM, 2017, J EDUC TEACHING, V43, P206, DOI 10.1080/02607476.2017.1286783
NR 41
TC 7
Z9 7
U1 257
U2 355
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PY 2024
VL 17
BP 1279
EP 1289
DI 10.1109/TLT.2024.3369690
PG 11
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA MG0O0
UT WOS:001192357400001
DA 2024-12-25
ER

PT J
AU Abumalloh, RA
   Nilashi, M
   Ooi, KB
   Tan, GWH
   Chan, HK
AF Abumalloh, Rabab Ali
   Nilashi, Mehrbakhsh
   Ooi, Keng Boon
   Tan, Garry Wei Han
   Chan, Hing Kai
TI Impact of generative artificial intelligence models on the performance
   of citizen data scientists in retail firms
SO COMPUTERS IN INDUSTRY
LA English
DT Article
DE Generative AI models; ChatGPT; Citizen Data science; Retail firms;
   Industrial growth; Industrial and innovation
ID BIG DATA; EASE; OPPORTUNITIES; CHATBOT; BANKING
AB Generative Artificial Intelligence (AI) models serve as powerful tools for organizations aiming to integrate advanced data analysis and automation into their applications and services. Citizen data scientists-individuals without formal training but skilled in data analysis-combine domain expertise with analytical skills, making them invaluable assets in the retail sector. Generative AI models can further enhance their performance, offering a cost-effective alternative to hiring professional data scientists. However, it is unclear how AI models can effectively contribute to this development and what challenges may arise. This study explores the impact of generative AI models on citizen data scientists in retail firms. We investigate the strengths, weaknesses, opportunities, and threats of these models. Survey data from 268 retail companies is used to develop and validate a new model. Findings highlight that misinformation, lack of explainability, biased content generation, and data security and privacy concerns in generative AI models are major factors affecting citizen data scientists' performance. Practical implications suggest that generative AI can empower retail firms by enabling advanced data science techniques and real-time decision-making. However, firms must address drawbacks and threats in generative AI models through robust policies and collaboration between domain experts and AI developers.
C1 [Abumalloh, Rabab Ali] Qatar Univ, Dept Comp Sci & Engn, Doha 2713, Qatar.
   [Nilashi, Mehrbakhsh; Ooi, Keng Boon; Tan, Garry Wei Han] UCSI Univ, Ctr Business Informat & Ind Management, UCSI Grad Business Sch, Kuala Lumpur, Malaysia.
   [Ooi, Keng Boon] FORE Sch Management, New Delhi, India.
   [Ooi, Keng Boon; Tan, Garry Wei Han] Swinburne Univ Technol Sarawak Campus, Fac Business Design & Arts, Kuching, Malaysia.
   [Ooi, Keng Boon] Korea Univ, Korea Univ Business Sch KUBS, Seoul, South Korea.
   [Tan, Garry Wei Han] Univ Jordan, Dept Mkt, Amman, Jordan.
   [Chan, Hing Kai] Univ Nottingham Ningbo China, Nottingham Univ, Business Sch China, 199 Taikang East Rd, Ningbo 315100, Peoples R China.
C3 Qatar University; UCSI University; FORE School of Management; Swinburne
   University of Technology Sarawak; Korea University; University of
   Jordan; University of Nottingham Ningbo China
RP Chan, HK (corresponding author), Univ Nottingham Ningbo China, Nottingham Univ, Business Sch China, 199 Taikang East Rd, Ningbo 315100, Peoples R China.
EM rabab.abumalloh@qu.edu.qa; mehrbakhsh@ucsiuniversity.edu.my;
   Ooikengboon@gmail.com; garrytanweihan@gmail.com;
   hingkai.chan@nottingham.edu.cn
RI OOI, Keng-Boon/I-4143-2019; Chan, Hing/A-8343-2008; Abumalloh,
   Rabab/KBQ-4135-2024; Tan Wei Han, Garry/C-6565-2011; Abumalloh,
   Rabab/C-8963-2017
OI Abumalloh, Rabab/0000-0003-2805-3764; Ooi, Keng-Boon/0000-0002-3384-1207
CR Abumalloh R.A., 2020, E-Commer., V61
   Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
   Adamopoulou E, 2020, MACH LEARN APPL, V2, DOI 10.1016/j.mlwa.2020.100006
   Ahani A, 2019, INT J HOSP MANAG, V80, P52, DOI 10.1016/j.ijhm.2019.01.003
   Akter S, 2023, TECHNOVATION, V125, DOI 10.1016/j.technovation.2023.102768
   Alalwan AA, 2017, INT J INFORM MANAGE, V37, P99, DOI 10.1016/j.ijinfomgt.2017.01.002
   Almulla MA, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e32220
   Alpar P, 2022, LECT NOTE NETW SYST, V469, P122, DOI 10.1007/978-3-031-04819-7_13
   An HY, 2024, ECO-ENVIRON HEALTH, V3, P131, DOI 10.1016/j.eehl.2024.01.006
   Awidi I. T., 2024, Comput. Educ. Artif. Intell, V6, DOI [10.1016/j.caeai.2024.100226, DOI 10.1016/J.CAEAI.2024.100203]
   Baabdullah AM, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122951
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Bellini P, 2023, MULTIMED TOOLS APPL, V82, P9989, DOI 10.1007/s11042-021-11837-5
   Bonnevie E, 2021, BIG DATA SOC, V8, DOI 10.1177/20539517211013867
   Bouteraa M., 2024, J. Open Innov. Technol. Mark. Complex., V10
   Bull JW, 2016, ECOSYST SERV, V17, P99, DOI 10.1016/j.ecoser.2015.11.012
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Cruz-Jesus F, 2019, COMPUT IND, V109, P1, DOI 10.1016/j.compind.2019.03.007
   Currie G.M., 2024, Semin. Nucl. Med.
   Davenport TH, 2012, HARVARD BUS REV, V90, P70
   Del Ser J, 2024, INFORM SCIENCES, V655, DOI 10.1016/j.ins.2023.119898
   Derner E, 2023, Arxiv, DOI arXiv:2305.08005
   Dorsey D.W., 2019, Career Pathw. Sch. Retire, V239
   Ferrara E., 2023, Science, V6, P3, DOI [DOI 10.3390/SCI6010003, 10.3390/sci6010003]
   Fischer J.E., 2023, P 5 INT C CONV US IN, P1
   Floyd K, 2014, J RETAILING, V90, P217, DOI 10.1016/j.jretai.2014.04.004
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   Furstenau LB, 2023, DIGIT COMMUN NETW, V9, P856, DOI 10.1016/j.dcan.2023.03.005
   Gala Dhir, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20156438
   Gefen D, 1998, DATA BASE ADV INF SY, V29, P35
   Golda A, 2024, Privacy and security concerns in generative ai: a comprehensive survey
   Groger Christoph, 2018, Datenbank-Spektrum, V18, P5, DOI 10.1007/s13222-018-0273-1
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hair JF, 2014, PRIMER PARTIAL LEAST
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Han RY, 2021, IND MANAGE DATA SYST, V121, P2467, DOI 10.1108/IMDS-05-2021-0300
   Han ZY, 2024, MED TEACH, V46, P657, DOI 10.1080/0142159X.2023.2271159
   Hassani H, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7020062
   Hermann E, 2024, J BUS RES, V180, DOI 10.1016/j.jbusres.2024.114720
   Hermann E, 2022, NEW MEDIA SOC, V24, P1258, DOI 10.1177/14614448211022702
   Hort M., 2023, ACM Journal on Responsible Computing
   Huang HY, 2023, INT J ORAL SCI, V15, DOI 10.1038/s41368-023-00239-y
   Kangaspunta J, 2012, EUR J OPER RES, V223, P264, DOI 10.1016/j.ejor.2012.05.042
   Kar A. K., 2023, Global Journal of Flexible Systems Management, V24, P659, DOI [DOI 10.1007/S40171-023-00356-X, https://doi.org/10.1007/s40171-023-00356-x]
   Karahanna E, 1999, INFORM MANAGE-AMSTER, V35, P237, DOI 10.1016/S0378-7206(98)00096-2
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kava H, 2024, ANN OPER RES, V333, P717, DOI 10.1007/s10479-021-04263-1
   Khan S, 2021, INT J INF RETR RES, V11, P65, DOI 10.4018/IJIRR.2021070105
   Kim M, 2016, PROC INT CONF SOFTW, P96, DOI 10.1145/2884781.2884783
   King KK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2021.102390
   Krugmann J. O., 2024, Customer Needs and Solutions, V11, P3
   Kumar A., 2023, J. Bus. Strategy.
   Lai C. Y., 2023, Computers and Education: Artificial Intelligence, V5, DOI DOI 10.1016/J.CAEAI.2023.100178
   Law K. K., 2024, Computers and Education: Artificial Intelligence, V6
   Lawrence D., 2019, Appl. Clin. Trials, V28, P25
   Lencastre P., 2023, Phys. D: Nonlinear Phenomena, V453
   Lima Gabriel, 2022, FACCT 22 P 2022 ACM, P2103, DOI [10.1145/3531146.3534628, DOI 10.1145/3531146.3534628]
   Line ND, 2020, TOURISM MANAGE, V80, DOI 10.1016/j.tourman.2020.104106
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Mahbooba B, 2021, COMPLEXITY, V2021, DOI 10.1155/2021/6634811
   Malloy T, 2024, FRONT PSYCHOL, V15, DOI 10.3389/fpsyg.2024.1387948
   Noguerol TM, 2019, J AM COLL RADIOL, V16, P1239, DOI 10.1016/j.jacr.2019.05.047
   McCarthy J, 2006, AI MAG, V27, P12
   Merkelbach S., 2022, 2022 IEEE 5 INT C IN, P1
   Meske C, 2022, INFORM SYST MANAGE, V39, P53, DOI 10.1080/10580530.2020.1849465
   Miao H, 2024, IEEE T ENG MANAGE, V71, P465, DOI 10.1109/TEM.2021.3123639
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Monteith S, 2024, BRIT J PSYCHIAT, V224, P33, DOI 10.1192/bjp.2023.136
   Mudarova R., 2024, Int. J. Open Inf. Technol., V12, P39
   Mullarkey MT, 2019, IN SY AP IN WE HC, V11491, P191, DOI 10.1007/978-3-030-19504-5_13
   Nemani P., 2023, Nat. Lang. Process. J.
   Nilashi M., 2023, Harvard Data Science Review, V5, DOI DOI 10.1162/99608F92.545DB2CF
   Niu YF, 2021, INFORM PROCESS MANAG, V58, DOI 10.1016/j.ipm.2021.102725
   Ntoutsi E, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1356
   Okuda T, 2018, FUJITSU SCI TECH J, V54, P4
   Olshannikova E., 2015, J BIG DATA-GER, V2, P22, DOI [10.1186/s40537-015-0031-2, DOI 10.1186/S40537-015-0031-2, 10.1186/S40537-015-0031-2/FIGURES/14]
   Orden-Mejía M, 2022, CURR ISSUES TOUR, V25, P2854, DOI 10.1080/13683500.2021.1997942
   Palanica A, 2019, J MED INTERNET RES, V21, DOI 10.2196/12887
   Pianykh OS, 2020, RADIOLOGY, V297, P6, DOI 10.1148/radiol.2020200038
   Pursnani V., 2023, Computers and Education: Artificial Intelligence, V5
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Qin SJ, 2019, COMPUT CHEM ENG, V126, P465, DOI 10.1016/j.compchemeng.2019.04.003
   Rajnoha R, 2024, IEEE T ENG MANAGE, V71, P90, DOI 10.1109/TEM.2021.3113502
   Ranjan J, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102231
   Raschka S, 2020, INFORMATION, V11, DOI 10.3390/info11040193
   Rodriguez RR, 2009, COMPUT IND, V60, P104, DOI 10.1016/j.compind.2008.09.002
   Rusk N, 2016, NAT METHODS, V13, P35, DOI 10.1038/nmeth.3707
   Saeed W, 2023, KNOWL-BASED SYST, V263, DOI 10.1016/j.knosys.2023.110273
   Sáez C, 2021, J AM MED INFORM ASSN, V28, P360, DOI 10.1093/jamia/ocaa258
   Saura JR, 2022, ENTERP INF SYST-UK, V16, P1694, DOI 10.1080/17517575.2021.1913765
   Schneider CM, 2011, P NATL ACAD SCI USA, V108, P3838, DOI 10.1073/pnas.1009440108
   Schwartz R., 2022, NIST Special Publication, V1270
   Shin D, 2024, NEW MEDIA SOC, DOI 10.1177/14614448241234040
   So C, 2020, 2020 4TH INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND INFORMATION RETRIEVAL, NLPIR 2020, P75, DOI 10.1145/3443279.3443284
   Song IY, 2016, EXPERT SYST, V33, P364, DOI 10.1111/exsy.12130
   Srinivasan R, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P41, DOI 10.1145/3442188.3445869
   Stanula P, 2018, PROC CIRP, V78, P261, DOI 10.1016/j.procir.2018.08.177
   Statista, 2023, Generative AI - Worldwide
   Statista, 2023, Number Data Sci. Employ. Co. Worldw. 2020 2021
   Statista, 2022, Global Big Data Analytics Market Size 2021-2029
   Steinhardt J, 2017, ADV NEUR IN, V30
   Stöger K, 2021, COMPUT LAW SECUR REV, V42, DOI 10.1016/j.clsr.2021.105587
   Stylos N, 2021, INT J CONTEMP HOSP M, V33, P1015, DOI 10.1108/IJCHM-07-2020-0644
   Suhail S, 2023, COMPUT IND, V151, DOI 10.1016/j.compind.2023.103961
   Tchuente D, 2024, COMPUT IND, V155, DOI 10.1016/j.compind.2023.104044
   Tortora L, 2024, FRONT PSYCHIATRY, V15, DOI 10.3389/fpsyt.2024.1346059
   Trust T, 2023, Contemporary Issues in Technology and Teacher Education, V23, P1
   TURN R, 1976, IEEE T COMPUT, V25, P1353, DOI 10.1109/TC.1976.1674604
   Vassakis K., 2018, Mobile big data, P3, DOI [10.1007/978-3-319-67925-91, DOI 10.1007/978-3-319-67925-91]
   Venkatesh V, 1996, DECISION SCI, V27, P451, DOI 10.1111/j.1540-5915.1996.tb01822.x
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang X, 2021, J RETAILING, V97, P658, DOI 10.1016/j.jretai.2020.12.001
   Whang SE, 2023, VLDB J, V32, P791, DOI 10.1007/s00778-022-00775-9
   Wise AF, 2020, J LEARN SCI, V29, P165, DOI 10.1080/10508406.2019.1705678
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Xu L, 2021, JMIR CANCER, V7, DOI 10.2196/27850
   Yadegaridehkordi E, 2020, ELECTRON COMMER R A, V40, DOI 10.1016/j.elerap.2019.100921
   Yallop A, 2020, J TOUR FUTURES, V6, P257, DOI 10.1108/JTF-10-2019-0108
   Yoon SJ, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12199651
   Zacarias AGV, 2023, INT J DATA SCI ANAL, V16, P455, DOI 10.1007/s41060-023-00417-5
   Zacarias AGV, 2021, 2021 IEEE 8TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA), DOI 10.1109/DSAA53316.2021.9564168
   Zhang A, 2024, Arxiv, DOI [arXiv:2310.10108, 10.48550/arXiv.2310.10108, DOI 10.48550/ARXIV.2310.10108]
   Zhang HL, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2021.102762
NR 124
TC 1
Z9 1
U1 34
U2 34
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-3615
EI 1872-6194
J9 COMPUT IND
JI Comput. Ind.
PD OCT
PY 2024
VL 161
AR 104128
DI 10.1016/j.compind.2024.104128
EA JUL 2024
PG 14
WC Computer Science, Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA ZU3L1
UT WOS:001277761100001
DA 2024-12-25
ER

PT J
AU Wang, YY
   Zhang, WN
AF Wang, Yiyang
   Zhang, Weining
TI Factors Influencing the Adoption of Generative AI for Art Designing
   Among Chinese Generation Z: A Structural Equation Modeling Approach
SO IEEE ACCESS
LA English
DT Article
DE Art designing; artificial intelligence; generative; technology readiness
   index; generation Z; trait curiosity; UTAUT2
ID TECHNOLOGY READINESS; CONTINUANCE INTENTION; CONSUMER ACCEPTANCE;
   ARTIFICIAL-INTELLIGENCE; INFORMATION-TECHNOLOGY; PERCEIVED VALUE;
   ATTITUDES; SERVICES; UTAUT2
AB The integration of generative artificial intelligence (GenAI) technology in the realm of art and design has demonstrated significant positive effects on designers and related industries. The current study aimed to explore and evaluate the factors and personal traits that drive Generation Z to embrace GenAI-assisted design. The study model incorporated factors derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology Readiness Index, and the concept of trait curiosity. Empirical validation was conducted using data collected from 326 participants in the southeast of Chinese Mainland. The results of structural equation modeling indicated that: 1) Factors such as effort expectancy, price value, and hedonic motivation from UTAUT2 have a positive influence on the intention to use GenAI, while performance expectancy does not show a statistically significant effect. 2) Both optimism and creativity significantly contribute to performance expectancy, effort expectancy, price value, and hedonic motivation. 3) Trait curiosity has a significant positive impact on both optimism and the intention to use GenAI. The research findings suggest the need for further improvements in the construction and operational strategies of GenAI platforms and provide practical insights for enhancing Generation Z's intention to utilize such platforms.
C1 [Wang, Yiyang] Nanchang Univ, Architecture & Design Coll, Nanchang 330003, Jiangxi, Peoples R China.
   [Zhang, Weining] Shijiazhuang Inst Railway Technol, Shijiazhuang 050061, Hebei, Peoples R China.
C3 Nanchang University; Shijiazhuang Tiedao University
RP Zhang, WN (corresponding author), Shijiazhuang Inst Railway Technol, Shijiazhuang 050061, Hebei, Peoples R China.
EM zhangweining@student.usm.my
RI Zhang, Weining/P-2365-2017
CR Acikgoz F., 2023, International Journal of Information Management Data Insights, V3, DOI DOI 10.1016/J.JJIMEI.2022.100152
   Aini Q, 2019, PROCEDIA COMPUT SCI, V161, P242, DOI 10.1016/j.procs.2019.11.120
   Almaiah MA, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.919198
   Almaiah MA, 2019, IEEE ACCESS, V7, P171907, DOI 10.1109/ACCESS.2019.2956349
   Almaiah MA, 2019, IEEE ACCESS, V7, P174673, DOI 10.1109/ACCESS.2019.2957206
   Alnaser FM, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18930
   Andrews JE, 2021, J ACAD LIBR, V47, DOI 10.1016/j.acalib.2021.102437
   Arbuckle J., 2003, Tech. Rep., P159
   Bagozzi R. P., 1998, J ACAD MARKET SCI, V16, P74, DOI [10.1177/009207038801600107, DOI 10.1007/BF02723327]
   Beck M, 2022, RECH APPL MARKET-ENG, V37, P30, DOI 10.1177/20515707211056468
   Beck M, 2018, J RETAIL CONSUM SERV, V40, P279, DOI 10.1016/j.jretconser.2016.08.006
   Berlyne D. E., 1960, Tech. Rep., P257
   Blut M, 2022, J ASSOC INF SYST, V23, P13, DOI 10.17705/1jais.00719
   Byrne B, 2010, INTERNATIONAL HANDBOOK OF PSYCHOLOGY IN EDUCATION, P3
   Cabrera-Sánchez JP, 2021, TELEMAT INFORM, V58, DOI 10.1016/j.tele.2020.101529
   Cetinic E, 2022, ACM T MULTIM COMPUT, V18, DOI 10.1145/3475799
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.02878
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.00290, 10.48550/arXiv.2305.00290, DOI 10.48550/ARXIV.2305.00290]
   Chang CC, 2013, TECHNOL PEDAGOG EDUC, V22, P373, DOI 10.1080/1475939X.2013.802991
   Chen L., 2019, Euromonitor Int., London, U.K., Tech. Rep., V1, P3
   Chen SC, 2021, SYMMETRY-BASEL, V13, DOI 10.3390/sym13030467
   Chicca J, 2018, TEACH LEARN NURS, V13, P180, DOI 10.1016/j.teln.2018.03.008
   Chin WW, 1998, QUANT METH SER, P295
   CHINDA T., 2012, Proceedings of The 3rd International Conference on Engineering, Project and Production Management, P10
   Chow CSK, 2023, J ELECTRON COMMER RE, V24, P84
   Chu TH, 2022, SAGE OPEN, V12, DOI 10.1177/21582440221142209
   Cox DR., 1980, POINT PROCESSES
   Cruz-Cárdenas J, 2021, J BUS RES, V122, P217, DOI 10.1016/j.jbusres.2020.08.054
   Sebastián MGD, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.993935
   De Peuter S, 2023, AI MAG, V44, P85, DOI 10.1002/aaai.12077
   Dombrosky K., 2018, Tech. Rep. 1, P13
   Duarte P, 2019, J BUS RES, V102, P140, DOI 10.1016/j.jbusres.2019.05.022
   Elliott K., 2012, Services Marketing Quarterly, V33, P311, DOI DOI 10.1080/15332969.2012.715049
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Francis T., 2018, TRUE GEN GENERATION
   Gansser OA, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101535
   Garson G.D., 2006, PUBLIC INFORM TECHNO
   Gilly MC, 2012, J CONSUM AFF, V46, P62, DOI 10.1111/j.1745-6606.2011.01218.x
   Godoe P., 2012, J EUROPEAN PSYCHOL S, V3
   Göring S, 2023, IEEE ACCESS, V11, P38999, DOI 10.1109/ACCESS.2023.3267968
   Guo S., 2021, PROC CHI C HUM FACTO, P1
   Gupta A, 2017, TOUR HOSP MANAG-CROA, V23, P145, DOI 10.20867/thm.23.2.6
   Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
   Hadjistavropoulos HD, 1999, BEHAV RES THER, V37, P671, DOI 10.1016/S0005-7967(98)00159-4
   Ho MT, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.102011
   Hong JC, 2017, COMPUT HUM BEHAV, V67, P264, DOI 10.1016/j.chb.2016.11.001
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Huang CY, 2021, INT J PERFORMABILITY, V17, P422, DOI [DOI 10.23940/IJPE.21.05.P2.422432, 10.23940/ijpe.21.05.p2.422432]
   Hwang Young-Ho, 2018, [Korean Comparative Government Review, 한국비교정부학보], V22, P97
   Jeong So Won, 2020, [The Research Journal of the Costume Culture, 복식문화연구], V28, P409, DOI 10.29049/rjcc.2020.28.4.409
   Jeraj M, 2014, ORGANIZACIJA, V47, P199, DOI 10.2478/orga-2014-0018
   Jin Seok, 2020, [Journal of The Korea Society of Computer and Information, 한국컴퓨터정보학회논문지], V25, P129, DOI 10.9708/jksci.2020.25.03.129
   JooYoungJu, 2014, [The Journal of the Korea Contents Association, 한국콘텐츠학회 논문지], V14, P477, DOI 10.5392/JKCA.2014.14.06.477
   Joreskog K. G., 1993, TESTING STRUCTURAL E, P294, DOI DOI 10.1093/SF/73.3.1161
   Kashdan TB, 2009, J RES PERS, V43, P987, DOI 10.1016/j.jrp.2009.04.011
   Kim J, 2008, J INTERACT MARK, V22, P45, DOI 10.1002/dir.20113
   Kim KJ, 2015, INTERNET RES, V25, P527, DOI 10.1108/IntR-05-2014-0126
   Kline R.B., 2023, PRINCIPLES PRACTICE
   Kline R.B., 1999, Canadian Psychology, V40, P381, DOI [10.1037/h0092500, DOI 10.1037/H0092500]
   Kwak Y, 2022, NURS EDUC TODAY, V119, DOI 10.1016/j.nedt.2022.105541
   LANGEVIN R, 1971, CAN J PSYCHOLOGY, V25, P360, DOI 10.1037/h0082397
   Lee Kwang-Soo, 2015, [Korean Journal of Sport Science, 체육과학연구], V26, P292
   Lee KY, 2021, INTERNET RES, V31, P1899, DOI 10.1108/INTR-06-2020-0327
   Lin C.Y., 2013, Business and Economics, V12, P171
   Lin CH, 2007, PSYCHOL MARKET, V24, P641, DOI 10.1002/mar.20177
   Litman JA, 2010, PERS INDIV DIFFER, V48, P397, DOI 10.1016/j.paid.2009.11.005
   Lyu YR, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app122211312
   McCloskey D., 2020, Pennsylvania Economic Review, V27, P44
   Mezei J., 2022, P 55 HAW INT C SYST, P1458
   Min So Ra, 2022, 2022 IEEE/ACIS 7th International Conference on Big Data, Cloud Computing, and Data Science (BCD), P306, DOI 10.1109/BCD54882.2022.9900521
   Thaker HMT, 2022, J ISLAMIC MARK, V13, P1171, DOI 10.1108/JIMA-11-2019-0228
   Mustafa S, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-24532-8
   Nur Triasesiarta, 2021, 2021 International Conference on Information Management and Technology (ICIMTech), P464, DOI 10.1109/ICIMTech53080.2021.9535003
   Papenhausen C, 2010, J BUS RES, V63, P716, DOI 10.1016/j.jbusres.2009.05.007
   Parasuraman A., 2000, Journal of Service Research, V2, P307, DOI [DOI 10.1177/109467050024001, 10.1177/109467050024001]
   Perry A, 2016, J RETAIL CONSUM SERV, V33, P171, DOI 10.1016/j.jretconser.2016.08.018
   Pramatari K, 2009, EUR J INFORM SYST, V18, P541, DOI 10.1057/ejis.2009.40
   Priporas CV, 2017, COMPUT HUM BEHAV, V77, P374, DOI 10.1016/j.chb.2017.01.058
   Qasem Z, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102254
   Rahi S, 2019, TECHNOL SOC, V58, DOI 10.1016/j.techsoc.2019.03.003
   Rahim NIM, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141912726
   Rashid MHU, 2021, INT J INNOV TECHNOL, V18, DOI 10.1142/S021987702150036X
   Rehman AU, 2022, PLOS ONE, V17, DOI 10.1371/journal.pone.0263652
   Reyes-Mercado P, 2023, J INT EDUC BUS, V16, P91, DOI 10.1108/JIEB-10-2021-0097
   Roy P, 2024, GLOB BUS REV, V25, P832, DOI 10.1177/0972150920939753
   Schaupp LC, 2010, INFORM SYST FRONT, V12, P299, DOI 10.1007/s10796-008-9138-8
   Shek D.T. L., 2014, Human Developmental Research: Experience from Research in Hong Kong, V13, P65, DOI [10.1515/ijdhd-2014-0305, DOI 10.1515/IJDHD-2014-0305]
   Singh A.P., 2016, S ASIAN J MULTIDISCI, V3, P1
   Tabachnick B. G., 2007, Experimental Designs Using ANOVA
   Tran AQ, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.755644
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2012, MIS QUART, V36, P157
   Walczuch R, 2007, INFORM MANAGE-AMSTER, V44, P206, DOI 10.1016/j.im.2006.12.005
   Wang Q, 2023, BRIT J EDUC TECHNOL, V54, DOI 10.1111/bjet.13315
   Wang Y, 2017, J TRAVEL RES, V56, P563, DOI 10.1177/0047287516657891
   Wood S, 2019, ECON GEOGR, V95, P467, DOI 10.1080/00130095.2019.1592672
   Xian XL, 2021, J INTERNET TECHNOL, V22, P697, DOI 10.3966/160792642021052203018
   Xu JP, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13116496
   Yang ZH, 2023, Arxiv, DOI arXiv:2301.13082
   Yildirim N, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3517491
   Yuan CL, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102878
   Zhang H, 2022, PSYCHOL MARKET, V39, P2171, DOI 10.1002/mar.21721
   Zhu YT, 2023, J RES INTERACT MARK, V17, P257, DOI 10.1108/JRIM-10-2021-0246
NR 103
TC 5
Z9 5
U1 50
U2 106
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2023
VL 11
BP 143272
EP 143284
DI 10.1109/ACCESS.2023.3342055
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA CV8Q2
UT WOS:001128106200001
OA gold
DA 2024-12-25
ER

PT J
AU Lan, HY
AF Lan, Haoyong
TI Prompt Engineering for Academic Librarian: Implications and Applications
   of Prompt Engineering in Academic Librarianship
SO JOURNAL OF WEB LIBRARIANSHIP
LA English
DT Article
DE Prompt engineering; artificial intelligence; academic librarianship;
   generative artificial intelligence; large language models; higher
   education; chatbot
AB The rapid advancement of generative artificial intelligence (AI) has transformed research and scholarly communication ecosystems. More and more instructors and researchers have started using generative AI chatbots to help with their academic teaching and research. Prompt engineering is the process of creating and refining inputs for generative AI chatbots to generate desired responses. Known for teaching users to refine academic database keyword searching and promoting critical thinking, academic librarians are well-positioned to use prompt engineering techniques to support academic research, teaching, and learning. This paper provides reviews about the implications and applications of prompt engineering for academic librarians and how they can use prompt engineering to support their work.
C1 [Lan, Haoyong] Carnegie Mellon Univ, Univ Lib, Pittsburgh, PA 15213 USA.
C3 Carnegie Mellon University
RP Lan, HY (corresponding author), Carnegie Mellon Univ, Univ Lib, Pittsburgh, PA 15213 USA.
EM haoyonglan@cmu.edu
OI Lan, Haoyong/0000-0002-6097-964X
CR Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   [Anonymous], 2021, SCITE HELP DESK
   Brown TB, 2020, ADV NEUR IN, V33
   Dinkevych D., 2023, MEDIUM
   Houston AB, 2023, TECH SERV Q, V40, P76, DOI 10.1080/07317131.2023.2187110
   Lo L. S., 2023, Internet Ref. Serv. Q, V27, P203, DOI [10.1080/10875301.2023.2227621, DOI 10.1080/10875301.2023.2227621]
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Lund Brady, 2023, Library Hi Tech News, V40, P6, DOI 10.1108/LHTN-10-2023-0189
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Nicholson JM, 2021, QUANT SCI STUD, V2, P882, DOI 10.1162/qss_a_00146
   Prompt Engineering Guide, 2024, FEW SHOT PROMPTING
   Sanderson K, 2023, NATURE, V616, P639, DOI 10.1038/d41586-023-01273-w
   Van Noorden R, 2023, NATURE, V620, P258, DOI 10.1038/d41586-023-02470-3
   Wei J., 2022, P ADV NEUR INF PROC, V35, P24824, DOI DOI 10.48550/ARXIV.2201.11903
   Zhang Borui, 2023, Medical Reference Services Quarterly, V42, P381, DOI 10.1080/02763869.2023.2250680
NR 16
TC 0
Z9 0
U1 28
U2 28
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1932-2909
EI 1932-2917
J9 J WEB LIBRARIANSH
JI J. Web Llibrariansh.
PD JUL 2
PY 2024
VL 18
IS 3
SI SI
BP 169
EP 175
DI 10.1080/19322909.2024.2399055
EA AUG 2024
PG 7
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA O9G9S
UT WOS:001329487400001
DA 2024-12-25
ER

PT J
AU Akpan, IJ
   Kobara, YM
   Owolabi, J
   Akpan, AA
   Offodile, OF
AF Akpan, Ikpe Justice
   Kobara, Yawo M.
   Owolabi, Josiah
   Akpan, Asuama A.
   Offodile, Onyebuchi Felix
TI Conversational and generative artificial intelligence and human-chatbot
   interaction in education and research
SO INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
LA English
DT Article
DE generative artificial intelligence; quantum computing; disruptive
   technology; conversational chatbot; ChatGPT; human-robot interaction;
   big data analytics; technological transformation
ID AI; PERFORMANCE; SCIENCE; TECHNOLOGIES; COOCCURRENCE; CHATGPT; IMPACT;
   GPT-4; MODEL; ERA
AB Artificial intelligence (AI) as a disruptive technology is not new. However, its recent evolution, engineered by technological transformation, big data analytics, and quantum computing, produces conversational and generative AI (CGAI/GenAI) and human-like chatbots that disrupt conventional operations and methods in different fields. This study investigates the scientific landscape of CGAI and human-chatbot interaction/collaboration and evaluates use cases, benefits, challenges, and policy implications for multidisciplinary education and allied industry operations. The publications trend showed that just 4% (n = 75) occurred during 2006-2018, while 2019-2023 experienced astronomical growth (n = 1763 or 96%). The prominent use cases of CGAI (e.g., ChatGPT) for teaching, learning, and research activities occurred in computer science (multidisciplinary and AI; 32%), medical/healthcare (17%), engineering (7%), and business fields (6%). The intellectual structure shows strong collaboration among eminent multidisciplinary sources in business, information systems, and other areas. The thematic structure highlights prominent CGAI use cases, including improved user experience in human-computer interaction, computer programs/code generation, and systems creation. Widespread CGAI usefulness for teachers, researchers, and learners includes syllabi/course content generation, testing aids, and academic writing. The concerns about abuse and misuse (plagiarism, academic integrity, privacy violations) and issues about misinformation, danger of self-diagnoses, and patient privacy in medical/healthcare applications are prominent. Formulating strategies and policies to address potential CGAI challenges in teaching/learning and practice are priorities. Developing discipline-based automatic detection of GenAI contents to check abuse is proposed. In operational/operations research areas, proper CGAI/GenAI integration with modeling and decision support systems requires further studies.
C1 [Akpan, Ikpe Justice; Offodile, Onyebuchi Felix] Kent State Univ, Dept Informat Syst & Business Analyt, Kent, OH 44242 USA.
   [Kobara, Yawo M.] Univ Windsor, Odette Sch Business, Windsor, ON, Canada.
   [Owolabi, Josiah] Natl Open Univ Nigeria, Fac Educ, Abuja, Nigeria.
   [Akpan, Asuama A.] Ibom Int Ctr Res & Scholarship, Res & Dev, Windsor, ON, Canada.
C3 University System of Ohio; Kent State University; Kent State University
   Kent; Kent State University Salem; University of Windsor
RP Akpan, IJ (corresponding author), Kent State Univ, Dept Informat Syst & Business Analyt, Kent, OH 44242 USA.
EM iakpan@kent.edu; yawo.kobara@uwindsor.ca; joowolabi@noun.edu.ng;
   ibomicrsca@gmail.com
RI Akpan, Ikpe/X-3969-2019; Kobara, Yawo/GRY-1634-2022
OI KOBARA, Yawo Mamoua/0000-0002-2562-3015; Akpan, Ikpe
   Justice/0000-0002-3703-5704
FU Kent State University
FX The authors wish to thank the editors and reviewers whose feedback
   helped improve the quality of this article. We also thank Kent State
   University for funding the open access fee for this article, which
   provides free access for potential readers.
CR Abdalla MHI, 2023, INFORMATION, V14, DOI 10.3390/info14100522
   Abu Rasheed H, 2019, ICERI PROC, P8294
   Adeniyi AE, 2022, LECT NOTES COMPUT SC, V13381, P476, DOI 10.1007/978-3-031-10548-7_35
   Adeshola I, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253858
   Adhikari K, 2024, CURR UROL REP, V25, P1, DOI 10.1007/s11934-023-01185-2
   Ahmad N, 2023, COMPUTER, V56, P72, DOI 10.1109/MC.2023.3263576
   Akpan IJ, 2024, SYSTEMS-BASEL, V12, DOI 10.3390/systems12010026
   Akpan IJ, 2024, INT T OPER RES, V31, P2069, DOI 10.1111/itor.13418
   Akpan IJ, 2024, INT J HEALTHCARE MAN, V17, P743, DOI 10.1080/20479700.2023.2235786
   Akpan IJ, 2021, INT T OPER RES, V28, P2275, DOI 10.1111/itor.12952
   Akpan IJ, 2014, DECIS SUPPORT SYST, V64, P14, DOI 10.1016/j.dss.2014.04.002
   Al-Surmi A, 2022, INT J PROD RES, V60, P4464, DOI 10.1080/00207543.2021.1966540
   AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Aguirre-Núñez JA, 2020, IEEE IJCNN, DOI 10.1109/ijcnn48605.2020.9207155
   AmankwahAmoah J., 2024, INT J INFORM MANAGE, V79, DOI [10.1016/j.ijinfomgt.2024.102759, DOI 10.1016/J.IJINFOMGT.2024.102759]
   Amorim-Lopes M, 2021, INT T OPER RES, V28, P687, DOI 10.1111/itor.12852
   Aria M, 2017, J INFORMETR, V11, P959, DOI 10.1016/j.joi.2017.08.007
   Arnaout JPM, 2010, INT T OPER RES, V17, P595, DOI 10.1111/j.1475-3995.2009.00740.x
   Avenali A, 2023, INT T OPER RES, V30, P2761, DOI 10.1111/itor.13309
   Balderas A, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e19517
   Baxter N., 2017, LONG TERM CARE, V5, P2
   BELGUM E, 1988, COMPUT MUSIC J, V12, P7, DOI 10.2307/3680146
   Benaim AR, 2020, JMIR MED INF, V8, DOI 10.2196/16492
   Bietsch D, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151813690
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Booth F, 2023, JMIR MHEALTH UHEALTH, V11, DOI 10.2196/43052
   Bozinovski S, 2020, INFORM-INT J COMPUT, V44, P291, DOI 10.31449/inf.v44i3.2828
   BREUEL TM, 1990, BEHAV BRAIN SCI, V13, P657, DOI 10.1017/S0140525X00080705
   Bullinaria JA, 2012, BEHAV RES METHODS, V44, P890, DOI 10.3758/s13428-011-0183-8
   Chen Yu-Jou, 2023, MMAsia '23: Proceedings of the 5th ACM International Conference on Multimedia in Asia, DOI 10.1145/3595916.3626370
   Chimbga B., 2023, ARTIFICIAL INTELLIGE, V1976, DOI [10.1007/978-3-031-49002-64, DOI 10.1007/978-3-031-49002-64]
   Chitty-Venkata KT, 2022, IEEE ACCESS, V10, P108374, DOI 10.1109/ACCESS.2022.3212767
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525
   Condrey BJ, 2024, INT J CHRIST EDUC, V28, P198, DOI 10.1177/20569971231196809
   Copeland B. J., 2000, Journal of Logic, Language and Information, V9, P491, DOI 10.1023/A:1008371426608
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Delipetrev B., 2020, Historical evolution of Artificial Intelligence, DOI DOI 10.2760/801580
   Devi K.V., 2023, CHATGPT COMPREHENSIV
   Doerr H.M., 2000, EDUC STUD MATH, V41, P143, DOI [10.1023/A:1003905929557, DOI 10.1023/A:1003905929557]
   Donthu N, 2021, J BUS RES, V133, P285, DOI 10.1016/j.jbusres.2021.04.070
   Doumpos M, 2023, EUR J OPER RES, V306, P1, DOI 10.1016/j.ejor.2022.04.027
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Fadhil A, 2017, ADJUNCT PUBLICATION OF THE 25TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'17), P408, DOI 10.1145/3099023.3099112
   Farina M, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1130913
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Feduzi A, 2014, ORGAN BEHAV HUM DEC, V124, P268, DOI 10.1016/j.obhdp.2014.04.001
   Fethi MD, 2010, EUR J OPER RES, V204, P189, DOI 10.1016/j.ejor.2009.08.003
   Fryer LK, 2019, COMPUT HUM BEHAV, V93, P279, DOI 10.1016/j.chb.2018.12.023
   Fryer LK, 2017, COMPUT HUM BEHAV, V75, P461, DOI 10.1016/j.chb.2017.05.045
   Gabrielli S, 2021, JMIR MHEALTH UHEALTH, V9, DOI 10.2196/27965
   Geerling W., 2023, AM ECON, V68
   Ghani A., 2023, MEDIUM
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gui J, 2023, IEEE T KNOWL DATA EN, V35, P3313, DOI 10.1109/TKDE.2021.3130191
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Haman M, 2024, ACCOUNT RES, V31, P1244, DOI 10.1080/08989621.2023.2185514
   Han J, 2019, IEEE COMPUT INTELL M, V14, P68, DOI 10.1109/MCI.2019.2901088
   Harshvardhan GM, 2020, COMPUT SCI REV, V38, DOI 10.1016/j.cosrev.2020.100285
   Hayes D, 2023, J RES TECHNOL EDUC, V55, P1061, DOI 10.1080/15391523.2022.2107589
   Heng JJY, 2023, POSTGRAD MED J, V99, P1125, DOI 10.1093/postmj/qgad058
   Hoermann S, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7023
   Hosseini M, 2024, ACCOUNT RES, V31, P715, DOI 10.1080/08989621.2023.2168535
   Hu X., 2023, P 11 ANN GEN INT FRA, P109
   Huallpa J.J., 2023, PERIODICALS ENG NATU, V11, P105, DOI DOI 10.21533/PEN.V11I4.3770
   Huang RST, 2023, JMIR MED EDUC, V9, DOI 10.2196/50514
   Huang WJ, 2022, J COMPUT ASSIST LEAR, V38, P237, DOI 10.1111/jcal.12610
   Iskender A, 2023, EUR J TOUR RES, V34, DOI 10.54055/ejtr.v34i.3169
   Jacobsen BN, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517221145372
   Jiao W., 2023, IS CHATGPT GOOD TRAN
   Juhi A, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.36272
   Kalla D., 2023, J EMERGING TECHNOLOG, V10
   Kalyan K.S., 2021, ARXIV PREPRINT ARXIV, P1, DOI 10.48550/arXiv
   Kamalov F, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151612451
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kang Y, 2020, J MANAG ANAL, V7, P139, DOI 10.1080/23270012.2020.1756939
   Kerly A, 2007, KNOWL-BASED SYST, V20, P177, DOI 10.1016/j.knosys.2006.11.014
   Kersting K., 2011, P 11 INT C LOG PROGR, P1
   Kobara YM, 2023, SYSTEMS-BASEL, V11, DOI 10.3390/systems11100524
   Kocon J, 2023, INFORM FUSION, V99, DOI 10.1016/j.inffus.2023.101861
   Kooli C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075614
   Kusal S, 2022, IEEE ACCESS, V10, P92337, DOI 10.1109/ACCESS.2022.3201144
   Le NQK, 2021, BRIEF BIOINFORM, V22, DOI 10.1093/bib/bbab005
   Lee P, 2023, NEW ENGL J MED, V388, P1233, DOI 10.1056/NEJMsr2214184
   Lewis B, 2020, PROC SPIE, V11394, DOI 10.1117/12.2558362
   Li JH, 2023, INT J HUM-COMPUT ST, V179, DOI 10.1016/j.ijhcs.2023.103119
   Liu MJ, 2021, NEUROCOMPUTING, V460, P106, DOI 10.1016/j.neucom.2021.07.007
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Lucchi N, 2024, EUR J RISK REGUL, V15, P602, DOI 10.1017/err.2023.59
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Mane HY, 2023, J PUBLIC HEALTH MAN, V29, P663, DOI 10.1097/PHH.0000000000001781
   Martín-Martín A, 2018, J INFORMETR, V12, P1160, DOI 10.1016/j.joi.2018.09.002
   Mateos-Sanchez M, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14031520
   Matsuura S, 2017, LECT NOTES COMPUT SC, V10279, P233, DOI 10.1007/978-3-319-58700-4_20
   Meera S., 2022, ARTIFICIAL INTELLIGE, P139, DOI [10.1002/9781119821809.ch10, DOI 10.1002/9781119821809.CH10]
   Mellor M.A., 2023, IRENAUT, V1, P152
   Merow C, 2023, NAT ECOL EVOL, V7, P960, DOI 10.1038/s41559-023-02063-3
   Miles O, 2021, DIGIT HEALTH, V7, DOI 10.1177/20552076211063012
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Morrison F.M.M., 2023, J MEDICAL ARTIFICIAL, V6, P1, DOI [10.21037/jmai-23-63, DOI 10.21037/JMAI-23-63]
   Motoki F, 2024, PUBLIC CHOICE, V198, P3, DOI 10.1007/s11127-023-01097-2
   Norris M.E., 2023, HDB ACAD INTEGRITY, P249
   Nyholm S, 2024, CAMB Q HEALTHC ETHIC, V33, P76, DOI 10.1017/S0963180123000464
   Oh N, 2023, ANN SURG TREAT RES, V104, P269, DOI 10.4174/astr.2023.104.5.269
   Opara E., 2023, GLOBAL ACAD J HUMANI, V5, P33, DOI DOI 10.36348/GAJHSS.2023.V05I02.001
   Padillah R, 2023, J PUBLIC HEALTH-UK, V46, pe193, DOI 10.1093/pubmed/fdad169
   Pathak A., 2023, EXPLORING CHATGPT EX, P1, DOI [10.2139/ssrn.4499278, DOI 10.2139/SSRN.4499278]
   Pérez JQ, 2020, COMPUT APPL ENG EDUC, V28, P1549, DOI 10.1002/cae.22326
   Perkins M, 2024, HIGH EDUC POLICY, V37, P633, DOI 10.1057/s41307-023-00323-2
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Piccolo SR, 2023, PLOS COMPUT BIOL, V19, DOI 10.1371/journal.pcbi.1011511
   Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
   Pizzi G, 2021, J BUS RES, V129, P878, DOI 10.1016/j.jbusres.2020.11.006
   Pomerol JC, 1997, EUR J OPER RES, V99, P3, DOI 10.1016/S0377-2217(96)00378-5
   Potts C, 2023, J MED INTERNET RES, V25, DOI 10.2196/43051
   Martinez MAQ, 2021, LECT NOTE NETW SYST, V271, P251, DOI 10.1007/978-3-030-80624-8_32
   Raman R, 2023, ACCOUNT RES, DOI 10.1080/08989621.2023.2273377
   Rapp A, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102630
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Rozado D, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12030148
   Saxena D, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3446374
   Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
   Sebastian G., 2023, INT J SECURITY PRIVA, V15, P1, DOI [10.4018/IJSPPC.325475, DOI 10.4018/IJSPPC.320225, DOI 10.4018/IJSPPC.325475, 10.4018/IJSPPC.320225]
   Sheth A, 2019, IEEE INTELL SYST, V34, P24, DOI [10.1109/MIS.2019.2905748, 10.1109/mis.2019.2905748]
   Shum HY, 2018, FRONT INFORM TECH EL, V19, P10, DOI 10.1631/FITEE.1700826
   Shumanov M, 2021, COMPUT HUM BEHAV, V117, DOI 10.1016/j.chb.2020.106627
   Smutny P, 2020, COMPUT EDUC, V151, DOI 10.1016/j.compedu.2020.103862
   Sok S., 2023, SSRN ELECT J, V3, P110, DOI DOI 10.2139/SSRN.4378735
   Song D, 2017, C HUM SYST INTERACT, P78, DOI 10.1109/HSI.2017.8005002
   Stap D., 2023, P WORKSH NAT LANG PR, P163, DOI DOI 10.18653/V1/2023.AMERICASNLP-1.17
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   van Eck NJ, 2010, SCIENTOMETRICS, V84, P523, DOI 10.1007/s11192-009-0146-3
   Vaswani A, 2017, ADV NEUR IN, V30
   Wan JF, 2018, IEEE INTERNET THINGS, V5, P2272, DOI 10.1109/JIOT.2017.2728722
   Wang M., 2023, ADV ED TECHNOLOGY PS, V7, P128, DOI DOI 10.23977/AETP.2023.070219
   Wienrich C, 2021, FRONT VIRTUAL REAL, V2, DOI 10.3389/frvir.2021.686783
   Wu X., 2024, J INF INTELL, V2, P102, DOI DOI 10.1016/J.JIIXD.2023.10.007
   Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957
   Xygi E, 2023, Art Intel Info Commu, P25, DOI 10.1109/ICAIIC57133.2023.10066979
   Yilmaz R., 2023, COMPUT HUM BEHAV, V1, DOI DOI 10.1016/J.CHBAH.2023.100005
   Yu DJ, 2017, INFORM SCIENCES, V418, P619, DOI 10.1016/j.ins.2017.08.031
   Zamfiroiu Alin, 2023, Informatica Economica, P5, DOI 10.24818/issn14531305/27.1.2023.01
   Zeng J, 2007, KNOWL-BASED SYST, V20, P607, DOI 10.1016/j.knosys.2007.09.001
   Zhang B, 2023, SCI CHINA INFORM SCI, V66, DOI 10.1007/s11432-021-3449-x
   Zhang CF, 2022, IEEE GLOB ENG EDUC C, P998, DOI 10.1109/EDUCON52537.2022.9766384
   Zhu S., 2023, INTELL COMPUT, V2, P0006, DOI [10.34133/icomputing.0006, DOI 10.34133/ICOMPUTING.0006]
NR 149
TC 2
Z9 2
U1 93
U2 93
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0969-6016
EI 1475-3995
J9 INT T OPER RES
JI Int. Trans. Oper. Res.
PD MAY
PY 2025
VL 32
IS 3
BP 1251
EP 1281
DI 10.1111/itor.13522
EA JUL 2024
PG 31
WC Management; Operations Research & Management Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Operations Research & Management Science
GA P5W8A
UT WOS:001280567800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Ananny, M
AF Ananny, Mike
TI Making Generative Artificial Intelligence a Public Problem. Seeing
   Publics and Sociotechnical Problem-Making in Three Scenes of AI Failure
SO JAVNOST-THE PUBLIC
LA English
DT Article
DE GenAI; publics; public problem; sociotechnical system; failure; error
ID ALGORITHM; SCIENCE; FACTS
AB Although Generative Artificial Intelligence (GenAI) has rapidly gained popular attention as something to worry about or be excited by, it is less clear if or how it is a public problem, and what ideals of publicness it illustrates, challenges, or invents. By developing the idea that GenAI is simultaneously an object and agent of public life, by describing how its failures are constructed and animated in three sociotechnical scenes, and by examining those scenes of failure for evidence of publicness, I trace how GenAI might be made into a public problem, and a problem for different ideals of publicness. Tracing how GenAI failures are narrated by charismatic figures, indexed by activists and policymakers, and avoided and repaired by journalists, I suggest that GenAI's public significance stems from its dual identity as both an ontological and epistemological concern and show how that duality plays out in failures that illustrate, combine, and extend ideals of the public.
C1 [Ananny, Mike] Univ Southern Calif, Annenberg Sch Commun & Journalism, Los Angeles, CA 90007 USA.
C3 University of Southern California
RP Ananny, M (corresponding author), Univ Southern Calif, Annenberg Sch Commun & Journalism, Los Angeles, CA 90007 USA.
EM ananny@usc.edu
CR Adut A., 2018, REIGN APPEARANCES MI
   AIAAIC, AB AIAAIC REP
   Ames MG, 2019, CHARISMA MACHINE: THE LIFE, DEATH, AND LEGACY OF ONE LAPTOP PER CHILD, P1
   Ananny M, 2018, NETWORKED PRESS FREEDOM: CREATING INFRASTRUCTURES FOR A PUBLIC RIGHT TO HEAR
   Ananny M, 2023, OSIRIS, V38, P223, DOI 10.1086/725146
   Barassi V, 2022, Research report AI errors and the profiling of humans: mapping the debate in European news media
   Barassi Veronica, 2023, EUROPEAN JOURNALISM
   Barry A, 2021, THEOR CULT SOC, V38, P93, DOI 10.1177/0263276420958043
   Barry Andrew., 2013, MAT POLITICS DISPUTE
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bouk Dan, 2015, OUR DAYS BECAME NUMB
   Bouk Dan., 2022, DEMOCRACYS DATA
   Bowker G., 2005, MEMORY PRACTICES SCI
   Bruns A, 2023, COMMUN THEOR, V33, P70, DOI 10.1093/ct/qtad007
   Burawoy M, 1998, SOCIOL THEOR, V16, P4, DOI 10.1111/0735-2751.00040
   Burema D, 2023, PROCEEDINGS OF THE 2023 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2023, P705, DOI 10.1145/3600211.3604680
   Christopher Simon, 2023, POLICIES PARALLEL CO
   Collins HM, 2002, SOC STUD SCI, V32, P235, DOI 10.1177/0306312702032002003
   Coreynen W., 2021, The Palgrave Handbook of Servitization, P247, DOI [10.1007/978-3-030-75771-716, DOI 10.1007/978-3-030-75771-716]
   Crawford K, 2016, SCI TECHNOL HUM VAL, V41, P77, DOI 10.1177/0162243915589635
   Crawford Kate., 2021, The Atlas of AI
   Culp A., 2022, GUERRILLA GUIDE REFU
   Czernuszenko Martha, 2020, MEDIUM
   Dandurand G, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517231219242
   Dewey John., 1927, PUBLIC ITS PROBLEMS
   DiSalvo Carl., 2012, ADVERSARIAL DESIGN
   Elish MC, 2019, ENGAG SCI TECHNOL SO, V5, P40, DOI 10.17351/ests2019.260
   Engstrom DF, 2023, ANNU REV LAW SOC SCI, V19, P277, DOI 10.1146/annurev-lawsocsci-120522-091626
   Feffer M, 2023, PROCEEDINGS OF 2023 ACM CONFERENCE ON EQUITY AND ACCESS IN ALGORITHMS, MECHANISMS, AND OPTIMIZATION, EAAMO 2023, DOI 10.1145/3617694.3623223
   Fischer R, 2024, PHILOS SOC CRIT, V50, P200, DOI 10.1177/01914537231203535
   Fishkin JS., 2011, When the people speak: deliberative democracy and public consultation
   Fraser N., 1992, Social Text, P109, DOI DOI 10.2307/466240
   Fridman M, 2023, JOURNAL PRACT, DOI 10.1080/17512786.2023.2253797
   Friedland LA, 2023, COMMUN THEOR, V33, P153, DOI 10.1093/ct/qtad010
   Gebru Timnit, 2023, Statement from the listed authors of stochastic parrots on the "AI pause" letter
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Gillespie T., 2018, Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media, DOI [10.12987/9780300235029, DOI 10.12987/9780300235029]
   Gillespie T, 2014, INSIDE TECHNOL, P167
   Gitelman L, 2013, INFRASTRUCT SER, P1
   Glasser T., 1995, GUIL COMMUN
   Gusfield JosephR., 1984, CULTURE PUBLIC PROBL
   Habermas J., 1996, FACTS NORMS CONTRIBU
   Habermas J, 2022, THEOR CULT SOC, V39, P145, DOI 10.1177/02632764221112341
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   Igo Sarah E., 2007, The Averaged American: Surveys, Citizens and the Making of a Mass Public
   Jackson SJ, 2023, COMMUN THEOR, V33, P102, DOI 10.1093/ct/qtad004
   Jacobsen BN, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517221145372
   Jasanoff S., 2015, Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power, DOI [10.7208/chicago/9780226276663.001.0001, DOI 10.7208/CHICAGO/9780226276663.001.0001]
   Jasanoff S., 2016, The Ethics of Invention: Technology and the Human Future
   Jia CY, 2024, JOURNALISM STUD, V25, P38, DOI 10.1080/1461670X.2023.2289881
   Jungherr A, 2023, COMMUN THEOR, V33, P164, DOI 10.1093/ct/qtad006
   Kelty C.M., 2008, Two Bits: The Cultural Significance of Free Software
   Kelty Christopher., 2020, PARTICIPANT CENTURY
   Kieslich Kimon, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2310.06361
   Klimes D, 2022, JAVNOST-PUBLIC, V29, P388, DOI 10.1080/13183222.2022.2147773
   Latour B, 2004, CRIT INQUIRY, V30, P225, DOI 10.1086/421123
   Latour B., 2005, REASSEMBLING SOCIAL
   Le Dantec CA, 2013, SOC STUD SCI, V43, P241, DOI 10.1177/0306312712471581
   LeDantec CA, 2016, DES THINK DES THEOR, P1, DOI 10.7551/mitpress/10513.001.0001
   Lewis SC, 2019, J MASS COMMUN Q, V96, P60, DOI 10.1177/1077699018755983
   Lin Cindy Kaiying, 2023, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3579607
   Lopez MG, 2023, DIGIT JOURNAL, V11, P484, DOI 10.1080/21670811.2022.2043759
   Manovich L., 2000, AI & Society, V14, P176, DOI 10.1007/BF01205448
   Marres N., 2012, Material participation
   Marres N, 2023, SCI TECHNOL HUM VAL, V48, P973, DOI 10.1177/01622439231190884
   Marres N, 2018, ENGAG SCI TECHNOL SO, V4, P423, DOI 10.17351/ests2018.188
   Marres Noortje., 2010, Political Matter: Technoscience, Democracy, and Public Life, P177
   Mattern Shannon., 2015, PLACES J, DOI [https://doi.org/10.22269/150309, DOI 10.22269/150309]
   McGregor S, 2021, AAAI CONF ARTIF INTE, V35, P15458
   Merken Sara, 2023, Reuters26 June
   Mouffe C, 2011, THINK ACTION, P1
   Mukherjee R, 2014, NEW MEDIA SOC, V16, P110, DOI 10.1177/1461444813495184
   Napoli PM, 2019, SOCIAL MEDIA AND THE PUBLIC INTEREST, P1, DOI 10.7312/napo18454
   Nelson Alondra., 2024, FOREIGN AFF
   Nishal Sachita, 2023, CHI 23 GEN AI HCI WO
   OECD, 2024, OECD AI INC MON METH
   Ojala M, 2024, EUR J COMMUN, V39, P145, DOI 10.1177/02673231231210207
   Paul Maria Luisa, 2023, WASHINGTON POST
   Peters J., 1995, GUIL COMMUN, P3
   Pierson J, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1683
   PINCH TJ, 1984, SOC STUD SCI, V14, P399, DOI 10.1177/030631284014003004
   Plantin JC, 2018, NEW MEDIA SOC, V20, P293, DOI 10.1177/1461444816661553
   Rafael Pardinas, 2020, ARXIV, DOI [10.48550/arXiv.2004.01030, DOI 10.48550/ARXIV.2004.01030]
   Raley R, 2023, AM LIT, V95, P185, DOI 10.1215/00029831-10575021
   Roberge J, 2020, BIG DATA SOC, V7, DOI 10.1177/2053951720919968
   Rodrigues R., 2023, Public Governance Administration and Finances Law Review, V8, P17
   Savransky M, 2021, THEOR CULT SOC, V38, P3, DOI 10.1177/0263276420966389
   Schneider R, 2021, UNCERTAIN ARCHIVES, P259
   Schneier Bruce, 2023, NEW YORK TIMES
   Schudson M, 2010, DAEDALUS-US, V139, P100, DOI 10.1162/daed.2010.139.2.100
   Seaver N, 2017, BIG DATA SOC, V4, DOI 10.1177/2053951717738104
   Simon FM, 2024, DIGIT JOURNAL, V12, P149, DOI 10.1080/21670811.2023.2287464
   Simon FM, 2022, DIGIT JOURNAL, DOI 10.1080/21670811.2022.2063150
   Smuha NA, 2021, INTERNET POLICY REV, V10, DOI 10.14763/2021.3.1574
   Splichal S., 2022, DATAFICATION PUBLIC, DOI [10.2307/j.ctv2s2pp3n, DOI 10.2307/J.CTV2S2PP3N]
   Splichal S, 2022, BIG DATA SOC, V9, DOI 10.1177/20539517221097319
   Splichal S, 2022, EUR J COMMUN, V37, P198, DOI 10.1177/02673231211061490
   Squires CR, 2002, COMMUN THEOR, V12, P446, DOI 10.1093/ct/12.4.446
   Stanley Jeff C., 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V67, P489, DOI 10.1177/21695067231198084
   Steinhoff J, 2024, NEW MEDIA SOC, V26, P3290, DOI 10.1177/14614448221099217
   Suchman L, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517231206794
   Turri V, 2023, PROCEEDINGS OF THE 2023 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2023, P576, DOI 10.1145/3600211.3604700
   Urueña S, 2022, SOC STUD SCI, V52, P783, DOI 10.1177/03063127221111469
   Verma P., 2023, WASH. POSTMay 7
   Von Schnitzler Antina., 2016, DEMOCRACYS INFRASTRU
   Wahl-Jorgensen K, 2019, Emotions, media and politics
   Warner M, 2002, PUBLIC CULTURE, V14, P49, DOI 10.1215/08992363-14-1-49
   Wei Mengyi, 2023, P 56 HAW INT C SYST, P4923
   Wessler Hartmut., 2018, Habermas and the Media
   Ytre-Arne B, 2021, MEDIA CULT SOC, V43, P807, DOI 10.1177/0163443720972314
NR 111
TC 1
Z9 1
U1 8
U2 11
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1318-3222
EI 1854-8377
J9 JAVNOST-PUBLIC
JI Javnost-Public
PD JAN 2
PY 2024
VL 31
IS 1
SI SI
BP 89
EP 105
DI 10.1080/13183222.2024.2319000
EA JAN 2024
PG 17
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA NG7Q4
UT WOS:001194225600001
DA 2024-12-25
ER

PT J
AU Kimura, T
AF Kimura, Takuma
TI Exploring the Frontier: Generative AI Applications in Online Consumer
   Behavior Analytics
SO CUADERNOS DE GESTION
LA English
DT Article; Early Access
DE Generative artificial intelligence; Generative adversarial network;
   Variational autoencoders; Autore- gressive model; Generative pre-trained
   transformer; Online consumer behavior analytics
ID ADVERSARIAL NETWORK; CLOTHES TRANSLATION; HUMAN-BODY; MODEL
AB This paper presents a systematic review of the application of generative artificial intelligence (AI) in online consumer behavior analytics (OCBA). With the advent of e-commerce and social media, consumer behavior increasingly occurs online, generating vast amounts of data. This shift necessitates advanced analytical tools, and generative AI emerges as a pivotal technology. Generative AI, distinct from traditional AI, can autonomously generate new content based on learned data patterns, offering innovative approaches to OCBA. Based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology and data synthesis method proposed by Webster and Watson (2002), this study analyzes 28 peer-reviewed papers, focusing on how generative AI is applied in OCBA and how it can enhance OCBA performance. The findings show that generative adversarial networks (GANs) are the most used, followed by variational autoencoders (VAEs) and autoregressive models. This review categorizes the application areas of generative AI in OCBA and examines how these technologies enhance OCBA's effectiveness and efficiency. Furthermore, the paper discusses the challenges associated with generative AI, emphasizing the need to consider ethical issues, such as bias and data privacy. This comprehensive review contributes to a deeper understanding of generative AI's role in OCBA, outlining its applications and functionalities from a technical perspective. It guides future research and practice, highlighting areas for further exploration and improvement in leveraging generative AI for consumer behavior analytics.
C1 [Kimura, Takuma] Hosei Univ, 2-17-1 Fujimi,Chiyoda Ku, Tokyo 1028160, Japan.
   [Kimura, Takuma] Showa Womens Univ, 1-7-57 Taishido,Setagaya Ku, Tokyo 1548533, Japan.
C3 Hosei University
RP Kimura, T (corresponding author), Hosei Univ, 2-17-1 Fujimi,Chiyoda Ku, Tokyo 1028160, Japan.; Kimura, T (corresponding author), Showa Womens Univ, 1-7-57 Taishido,Setagaya Ku, Tokyo 1548533, Japan.
EM ktakuma@hosei.ac.jp
RI Kimura, Takuma/H-4830-2019
OI Kimura, Takuma/0000-0001-7126-188X
CR Adebowale OJ, 2024, SMART SUSTAIN BUILT, V13, P479, DOI 10.1108/SASBE-06-2022-0128
   Almahmood RJK, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app122111256
   [Anonymous], 2004, Standard quality assessment criteria for evaluating primary research papers from a variety of fields
   Anshari Muhammad, 2019, Applied Computing and Informatics, V15, P94, DOI 10.1016/j.aci.2018.05.004
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Asperti A., 2021, SN Computer Science, V2, P301, DOI DOI 10.1007/S42979-021-00702-9
   Baek TH, 2023, J CURR ISS RES AD, V44, P249, DOI 10.1080/10641734.2023.2243496
   Bawack RE, 2022, ELECTRON MARK, V32, P297, DOI 10.1007/s12525-022-00537-z
   Bond-Taylor S, 2022, IEEE T PATTERN ANAL, V44, P7327, DOI 10.1109/TPAMI.2021.3116668
   Breen RL, 2006, J GEOGR HIGHER EDUC, V30, P463, DOI 10.1080/03098260600927575
   Casanova A, 2023, Arxiv, DOI arXiv:2304.13722
   Chawla NV, 2002, J ARTIF INTELL RES, V16, P321, DOI 10.1613/jair.953
   Deshai N, 2023, SOFT COMPUT, V27, P11357, DOI 10.1007/s00500-023-08507-z
   Ding YM, 2023, IEEE ACCESS, V11, P83680, DOI 10.1109/ACCESS.2023.3302339
   Du CH, 2023, IEEE T MULTIMEDIA, V25, P777, DOI 10.1109/TMM.2022.3152367
   Fincato M, 2022, ACM T MULTIM COMPUT, V18, DOI 10.1145/3491226
   Fiore U, 2019, INFORM SCIENCES, V479, P448, DOI 10.1016/j.ins.2017.12.030
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Goti A, 2023, MATHEMATICS-BASEL, V11, DOI 10.3390/math11132943
   Grove S. J., 1992, Journal of the Academy of Marketing Science, V20, P217, DOI [DOI 10.1007/BF02723408, 10.1007/BF02723408]
   Guo DZ, 2023, NEURAL NETWORKS, V166, P273, DOI 10.1016/j.neunet.2023.07.026
   Guo JF, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10173042
   Han Y, 2023, J MECH DESIGN, V145, DOI 10.1115/1.4055736
   Hao YH, 2022, J INTERNET TECHNOL, V23, P1367, DOI 10.53106/160792642022112306019
   Hu KR, 2023, SCI PROGRESS-UK, V106, DOI 10.1177/00368504231180090
   Jiang L, 2013, J SERV MANAGE, V24, P191, DOI 10.1108/09564231311323962
   Kashyap D. A. K., 2022, Journal of Theoretical and Applied Information Technology, V100, P7347
   Kimura T., 2023, Data Science Journal, V22, DOI [10.5334/dsj-2023-022, DOI 10.5334/DSJ-2023-022]
   Kimura T., 2022, Journal of Management Information & Decision Sciences, V25
   Kuhn M., 2013, Applied Predictive Modeling, DOI [10.1007/978-1-4614-6849-3, DOI 10.1007/978-1-4614-6849-3]
   Laenen K, 2022, COMPUTERS, V11, DOI 10.3390/computers11120182
   Li JM, 2022, ALGORITHMS, V15, DOI 10.3390/a15110429
   Li KD, 2023, IEEE T PATTERN ANAL, V45, P12222, DOI 10.1109/TPAMI.2023.3283302
   Lu XY, 2022, IEEE T NETW SERV MAN, V19, P89, DOI 10.1109/TNSM.2021.3112702
   Lungu AJ, 2021, EXPERT REV MED DEVIC, V18, P47, DOI 10.1080/17434440.2021.1860750
   Margaris D, 2022, INFORMATION, V13, DOI 10.3390/info13060302
   Nabeel M, 2019, INT J ADV COMPUT SC, V10, P483
   Nallapati R, 2016, Arxiv, DOI arXiv:1602.06023
   Necula SC, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095531
   Ngai EWT, 2022, J BUS RES, V145, P35, DOI 10.1016/j.jbusres.2022.02.049
   Nguyen MD, 2020, IEEE ACCESS, V8, P132736, DOI 10.1109/ACCESS.2020.3010508
   Notin Pascal, 2021, ADV NEUR IN, V34
   Oshrat Y., 2022, arXiv
   Pan ZQ, 2019, IEEE ACCESS, V7, P36322, DOI 10.1109/ACCESS.2019.2905015
   Papamakarios G, 2021, J MACH LEARN RES, V22
   Perez-Castro A, 2023, TECHNOL FORECAST SOC, V189, DOI 10.1016/j.techfore.2023.122380
   Policarpo LM, 2021, COMPUT SCI REV, V41, DOI 10.1016/j.cosrev.2021.100414
   Postolache E, 2023, Arxiv, DOI arXiv:2301.08562
   Rabbi M. F., 2023, Journal of Information Hiding and Multimedia Signal Processing, V14, P72
   Radford A., 2018, Technical Reports
   Radford A, 2016, Arxiv, DOI [arXiv:1511.06434, 10.48550/arXiv.1511.06434, DOI 10.48550/ARXIV.1511.06434]
   Rethlefsen ML, 2021, SYST REV-LONDON, V10, DOI 10.1186/s13643-020-01542-z
   Roy D, 2022, MULTIMED TOOLS APPL, V81, P5051, DOI 10.1007/s11042-021-11647-9
   Sajid S, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.919334
   SAMUEL AL, 1959, IBM J RES DEV, V3, P211, DOI 10.1147/rd.441.0206
   Singh M, 2020, INT J CLOTH SCI TECH, V32, P177, DOI 10.1108/IJCST-12-2018-0148
   Sun RX, 2021, ADV NEUR IN
   Sun YM, 2009, INT J PATTERN RECOGN, V23, P687, DOI 10.1142/S0218001409007326
   Terzioglu S, 2024, ELECTRON COMMER RES, V24, P1491, DOI 10.1007/s10660-022-09564-6
   Vaswani A, 2017, ADV NEUR IN, V30
   Viktoratos I, 2021, INFORMATION, V12, DOI 10.3390/info12110480
   Wang S, 2021, DATA TECHNOL APPL, V55, P749, DOI 10.1108/DTA-11-2020-0286
   Wang XW, 2020, ISCIENCE, V23, DOI 10.1016/j.isci.2020.101626
   Webster J, 2002, MIS QUART, V26, pXIII
   Wei SH, 2023, EXPERT SYST APPL, V228, DOI 10.1016/j.eswa.2023.120258
   Yang YY, 2022, Arxiv, DOI arXiv:2209.07239
   Yu A, 2020, INT J COMPUT VISION, V128, P2704, DOI 10.1007/s11263-020-01344-9
   Zhang HJ, 2020, IEEE MULTIMEDIA, V27, P58, DOI 10.1109/MMUL.2020.3014037
   Zhang HJ, 2020, NEUROCOMPUTING, V382, P148, DOI 10.1016/j.neucom.2019.11.085
   Zhang HB, 2022, ACM T KNOWL DISCOV D, V16, DOI 10.1145/3470659
   Zhang XL, 2021, NEURAL COMPUT APPL, V33, P8445, DOI 10.1007/s00521-020-05598-9
   Zhao J, 2020, INT J COMPUT VISION, V128, P2185, DOI 10.1007/s11263-019-01181-5
   Zhou XP, 2022, INT J MULTIMED INF R, V11, P199, DOI 10.1007/s13735-022-00244-7
NR 74
TC 0
Z9 0
U1 26
U2 26
PU UNIV PAIS VASCO, INST ECONOMIA APLICADA EMPRESA
PI BILBAO
PA AVE LEHENDAKARI AGIRRE, 8, BILBAO, 48015, SPAIN
SN 1131-6837
EI 1988-2157
J9 CUAD GEST
JI Cuad. Gest.
PD 2024 SEP 11
PY 2024
DI 10.5295/cdg.232121tk
EA SEP 2024
PG 14
WC Business; Business, Finance; Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA F8I4K
UT WOS:001312186400001
OA gold
DA 2024-12-25
ER

PT J
AU Mihaescu, C
   Mihaescu, M
AF Mihaescu, Cristian
   Mihaescu, Manuela
TI The Impact of Generative AI in Music Composition
SO INFORMATION AND COMMUNICATION TECHNOLOGY IN MUSICAL FIELD
LA English
DT Article
DE Generative artificial intelligence; music; generative models; Stable
   Audio; AIVA
AB This paper briefly describes the concept of generative artificial intelligence in music composition, highlighting the innovations and challenges brought by this technology. After an overview of the types of models used in training generative algorithms and specialised software for music composition, the case study analyses two of these applications. The article concludes with a reflection on the challenges brought by this technology and how these systems support musicians in the creative, compositional act.
C1 [Mihaescu, Cristian] TThe Natl Acad Mus Gheorghe Dima Cluj Napoca, Cluj Napoca, Romania.
   [Mihaescu, Cristian] Acad Nationala Muz Gheorghe Dima Cluj Napoca, Cluj Napoca, Romania.
   [Mihaescu, Manuela] Babes Bolyai Univ, Cluj Napoca, Romania.
   [Mihaescu, Manuela] Univ Babes Bolyai, Cluj Napoca, Romania.
C3 Babes Bolyai University from Cluj; Babes Bolyai University from Cluj
RP Mihaescu, C (corresponding author), TThe Natl Acad Mus Gheorghe Dima Cluj Napoca, Cluj Napoca, Romania.; Mihaescu, C (corresponding author), Acad Nationala Muz Gheorghe Dima Cluj Napoca, Cluj Napoca, Romania.
EM cristian.mihaescu@gmail.com; manuela.mihaescu@ubbcluj.ro
RI Mihaescu, Manuela/AAL-1056-2021
CR AIVA, About us
   [Anonymous], 2018, INT C LEARN REPR 201, P1
   [Anonymous], Overview of GAN Structure
   [Anonymous], 2019, Arab Journal of Basic and Applied Sciences, DOI DOI 10.1080/25765299.2019.1649972
   [Anonymous], ?About us"
   Ball P, 2023, NATURE, V624, P22, DOI 10.1038/d41586-023-03817-6
   Chelli G., 2023, Nature, DOI [10.1038/d43978-023-00176-8, DOI 10.1038/D43978-023-00176-8]
   Conroy Gemma, 2023, Nature, DOI 10.1038/d41586-023-02477-w
   Copeland B. I., 2023, Artificial Intelligence
   CronJ, About us
   Gartner, IT GartnerGlossary
   Ghassemi M, 2023, NATURE, V624, P39, DOI 10.1038/d41586-023-03798-6
   GravityWrite, About us
   Hadjeres G, 2017, PR MACH LEARN RES, V70
   Huang AL, 2016, Arxiv, DOI [arXiv:1606.04930, 10.48550/arXiv.1606.04930, DOI 10.48550/ARXIV.1606.04930]
   Huang C.-Z.A., 2019, INT C LEARNING REPRE
   Ji SL, 2020, Arxiv, DOI arXiv:2011.06801
   Karras T, 2018, INT C LEARN REPR ICL, Patent No. 171010196
   Lawton G., 2023, Tech Target, 7 November
   MusicGen, About us
   Naddaf M, 2023, NATURE, V623, P895, DOI 10.1038/d41586-023-03635-w
   OpenAI, US
   Van Noorden R, 2023, NATURE, V621, P672, DOI 10.1038/d41586-023-02980-0
   Yamshchikov IP, 2020, SN APPL SCI, V2, DOI 10.1007/s42452-020-03715-w
   Zeng Y., 2023, Medium
NR 25
TC 1
Z9 1
U1 13
U2 27
PU MEDIA MUSICA
PI CLUJ-NAPOCA
PA STR ICBRATIANU NR 25, CLUJ-NAPOCA, 00000, ROMANIA
SN 2067-9408
EI 2069-654X
J9 INF COMMUN TECHNOL M
JI Inf. Commun. Technol. Musical Field
PY 2023
VL 14
IS 2
BP 93
EP 102
PG 10
WC Music
WE Emerging Sources Citation Index (ESCI)
SC Music
GA DO8D9
UT WOS:001133078100009
DA 2024-12-25
ER

PT J
AU Nguyen, H
   Nguyen, A
AF Nguyen, Ha
   Nguyen, Andy
TI Reflective Practices and Self-Regulated Learning in Designing with
   Generative Artificial Intelligence: An Ordered Network Analysis
SO JOURNAL OF SCIENCE EDUCATION AND TECHNOLOGY
LA English
DT Article; Early Access
DE Generative artificial intelligence; Large language models; Design
   thinking; Self-regulated learning; Network analysis
ID THINKING
AB Advances in generative artificial intelligence (AI) have enabled new forms of human-AI interaction. In this work, we explored the utility of using generative AI, specifically OpenAI's ChatGPT (Chat Generative Pre-trained Transformer) 3.5, to support the design thinking process to identify user needs, ideate, and refine solutions. We examined how 17 students and professionals from a design program engaged in reflective design practices and self-regulated learning (SRL), as they used generative AI to brainstorm ideas. We further explored how participants considered, elaborated upon, and integrated the AI-generated ideas into their design artifacts. Analyses involved qualitative coding of the brainstorming sessions and Ordered Network Analysis, which visualized the co-occurrences between reflective design practices and SRL as indicators of multifaceted learning engagement. Findings illuminate the importance of iterative evaluation and planning of AI-generated ideas, in conjunction with reflection on design moves, to improve design quality. We discuss the importance of reflective practices and SRL in AI-integrated learning.
C1 [Nguyen, Ha] Univ North Carolina Chapel Hill, Sch Educ, 100 Cameron Ave, Chapel Hill, NC 27514 USA.
   [Nguyen, Andy] Univ Oulu, Fac Educ & Psychol, Oulu, Finland.
C3 University of North Carolina School of Medicine; University of North
   Carolina; University of North Carolina Chapel Hill; University of Oulu
RP Nguyen, H (corresponding author), Univ North Carolina Chapel Hill, Sch Educ, 100 Cameron Ave, Chapel Hill, NC 27514 USA.
EM ha.nguyen@unc.edu
RI Nguyen, Ha/HLW-9711-2023
OI Nguyen, Ha/0000-0001-7138-1427
CR Adams R., 2003, Design Studies, V24, P275
   Azevedo R, 2010, EDUC PSYCHOL-US, V45, P210, DOI 10.1080/00461520.2010.515934
   Björklund TA, 2013, DESIGN STUD, V34, P135, DOI 10.1016/j.destud.2012.08.005
   Bowman D., 2021, P 2 INT C QUANTITATI, V2, P91, DOI [10.1007/978-3-030-67788-6_7, 10.1007/978]
   Brohinsky J., 2021, ADV QUANTITATIVE ETH, DOI [10.1007/978-3-030-67788-68, DOI 10.1007/978-3-030-67788-68]
   Brown TB, 2020, ADV NEUR IN, V33
   Cai Carrie J., 2019, Proceedings of the ACM on Human-Computer Interaction, V3, DOI 10.1145/3359206
   Chang DH, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712921
   Chung JJY, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501819
   Clark E, 2018, IUI 2018: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P329, DOI 10.1145/3172944.3172983
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cukurova M, 2024, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13514
   Dakuo Wang, 2019, Proceedings of the ACM on Human-Computer Interaction, V3, DOI 10.1145/3359313
   Daly SR, 2016, J MECH DESIGN, V138, DOI 10.1115/1.4034087
   Doshi AR, 2024, SCI ADV, V10, DOI 10.1126/sciadv.adn5290
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elmoazen R, 2022, IEEE ACCESS, V10, P17330, DOI 10.1109/ACCESS.2022.3149812
   English LD, 2019, INT J TECHNOL DES ED, V29, P1011, DOI 10.1007/s10798-018-9482-z
   Fan YZ, 2023, J COMPUT ASSIST LEAR, V39, P154, DOI 10.1111/jcal.12735
   Greene JA, 2007, REV EDUC RES, V77, P334, DOI 10.3102/003465430303953
   Henriksen D, 2018, J FORMATIVE DES LEAR, V2, P69, DOI 10.1007/s41686-018-0024-6
   Hong YC, 2011, ETR&D-EDUC TECH RES, V59, P687, DOI 10.1007/s11423-011-9202-9
   Irgens GA, 2021, INSTR SCI, V49, P561, DOI 10.1007/s11251-021-09551-8
   Järvelä S, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13325
   Jiang Jialun Aaron, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3449168
   Jung H, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3517539
   Karimi P, 2020, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2020, P221, DOI 10.1145/3377325.3377522
   Kavousi S, 2020, INT J TECHNOL DES ED, V30, P709, DOI 10.1007/s10798-019-09521-9
   Kim J, 2023, INT J DES CREAT INNO, V11, P81, DOI 10.1080/21650349.2023.2167124
   Koh JHL, 2015, DESIGN THINKING FOR EDUCATION: CONCEPTIONS AND APPLICATIONS IN TEACHING AND LEARNING, P1, DOI 10.1007/978-981-287-444-3_1
   Lai Y-R., 2023, Artificial Intelligence, Social Computing and Wearable Technologies, V113, P49
   Li J, 2022, TELEMAT INFORM, V73, DOI 10.1016/j.tele.2022.101862
   Li S., 2022, J LEARN ANAL, V9, P1, DOI [10.18608/jla.2022.7571, DOI 10.18608/JLA.2022.7571]
   Li S, 2022, LEARN INDIVID DIFFER, V95, DOI 10.1016/j.lindif.2022.102144
   Li TT, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12168077
   Licklider JCR., 1960, IRE T HUM FACT ELECT, VHFE-1, P4, DOI [DOI 10.1109/THFE2.1960.4503259, 10.1109/THFE2.1960.4503259]
   Lin YY, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376258
   Louie R, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376739
   Lu YW, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519809
   Melzner N., 2019, INT C QUANT ETHN, P154, DOI [10.1007/978-3-030-33232-713, DOI 10.1007/978-3-030-33232-713]
   Molenaar I., 2022, Handbook of learning analytics, P66
   Molenaar I., 2022, Computers and Education: Artificial Intelligence, V3, DOI DOI 10.1016/J.CAEAI.2022.100070
   Mosely G, 2018, THINK SKILLS CREAT, V27, P177, DOI 10.1016/j.tsc.2018.02.004
   Nguyen A, 2024, STUD HIGH EDUC, V49, P847, DOI 10.1080/03075079.2024.2323593
   Nguyen H, 2021, LEARN INSTR, V74, DOI 10.1016/j.learninstruc.2021.101443
   Nichols K, 2022, INT J TECHNOL DES ED, V32, P2527, DOI 10.1007/s10798-021-09711-4
   Paton B, 2011, DESIGN STUD, V32, P573, DOI 10.1016/j.destud.2011.07.002
   Pintrich PR, 2000, J EDUC PSYCHOL, V92, P544, DOI 10.1037/0022-0663.92.3.544
   Poquet O, 2021, BRIT J EDUC TECHNOL, V52, P1695, DOI 10.1111/bjet.13123
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Razzouk R, 2012, REV EDUC RES, V82, P330, DOI 10.3102/0034654312457429
   Relmasira SC, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151813595
   Rezwana J, 2023, ACM T COMPUT-HUM INT, V30, DOI 10.1145/3519026
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Runco MA, 2012, CREATIVITY RES J, V24, P92, DOI 10.1080/10400419.2012.650092
   Sadokierski Z, 2020, DESIGN STUD, V69, DOI 10.1016/j.destud.2020.03.002
   Saint J, 2021, LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, P333, DOI 10.1145/3448139.3448171
   Saint J, 2020, LAK20: THE TENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, P402, DOI 10.1145/3375462.3375487
   Schleith J, 2022, PROCEEDINGS OF THE 14TH CREATIVITY AND COGNITION, C&C 2022, P29, DOI 10.1145/3527927.3532808
   Schmidt A., 2024, Interactions, V31, P24, DOI DOI 10.1145/3637436
   Schon D.A., 1992, DESIGN STUD, V13, P135, DOI [10.1016/0142-694X(92)90268-F, DOI 10.1016/0142-694X(92)90268-F, 10.1111/j.14678691.1992.tb00031.x, DOI 10.1111/J.14678691.1992.TB00031.X]
   Sch┬u├en, 1983, REFLECTIVE PRACTITIO
   Seidel VP, 2013, J PROD INNOVAT MANAG, V30, P19, DOI 10.1111/jpim.12061
   Shaffer DW., 2017, Handbook of learning analytics, P175, DOI DOI 10.18608/HLA17.015
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Stompff G, 2016, DESIGN STUD, V47, P187, DOI 10.1016/j.destud.2016.09.004
   Strimel Greg., 2019, Journal of STEM Education, V19
   Tan Y., 2022, INT C QUANT ETHN, P101, DOI 10.1007/978-3-031-31726-28
   Tang HH, 2012, AI EDAM, V26, P205, DOI 10.1017/S0890060412000078
   TERVEEN LG, 1995, KNOWL-BASED SYST, V8, P67, DOI 10.1016/0950-7051(95)98369-H
   Tholander J, 2023, DESIGNING INTERACTIVE SYSTEMS CONFERENCE, DIS 2023, P1930, DOI 10.1145/3563657.3596014
   Valkenburg R., 1998, Design Studies, P249
   Van Laer S, 2017, EDUC INF TECHNOL, V22, P1395, DOI 10.1007/s10639-016-9505-x
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang XH, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104703
   Winne PH, 1998, EDUC PSYCHOL SER, P277
   Wu ZH, 2021, LECT NOTES COMPUT SC, V12762, P171, DOI 10.1007/978-3-030-78462-1_13
   Yang D., 2022, JOINT P ACM IUI WORK, V10, P1
   Zhang X, 2016, 34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, P5350, DOI 10.1145/2858036.2858523
   Zimmerman BJ, 2013, EDUC PSYCHOL-US, V48, P135, DOI 10.1080/00461520.2013.794676
   Zimmerman BJ, 2002, THEOR PRACT, V41, P64, DOI 10.1207/s15430421tip4102_2
NR 81
TC 0
Z9 0
U1 41
U2 41
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1059-0145
EI 1573-1839
J9 J SCI EDUC TECHNOL
JI J. Sci. Educ. Technol.
PD 2024 NOV 1
PY 2024
DI 10.1007/s10956-024-10175-z
EA NOV 2024
PG 15
WC Education & Educational Research; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Education & Educational Research
GA K7A5V
UT WOS:001345368100001
DA 2024-12-25
ER

PT J
AU Baek, TH
   Kim, M
AF Baek, Tae Hyun
   Kim, Minseong
TI Is ChatGPT scary good? How user motivations affect creepiness and trust
   in generative artificial intelligence
SO TELEMATICS AND INFORMATICS
LA English
DT Article
DE Generative artificial intelligence (AI); Uses and gratifications theory;
   Creepiness; Trust; Continuance intention
ID SOCIAL MEDIA; GRATIFICATIONS; SITES; INFORMATION; TECHNOLOGY; AVOIDANCE;
   FACEBOOK; QUALITY; MODEL; USAGE
AB Few studies have examined user motivations to use generative artificial intelligence (AI). This research aims to address this gap by examining how user motivations for ChatGPT usage affect perceived creepiness, trust, and the intention to continue using AI chatbot technology. The findings of an online survey (N = 421) reveal a negative relationship between personalization and creepiness, while task efficiency and social interaction are positively associated with creepiness. Increased levels of creepiness, in turn, result in decreased continuance intention. Furthermore, task efficiency and personalization have a positive impact on trust, leading to increased continuance intention. The results contribute to the field of human-computer interaction by investigating the motivations for utilizing generative AI chatbots and advancing our comprehension of AI creepiness, trust, and continuance intention. The practical ramifications of this research can inform the design of user interfaces and the development of features for generative AI chatbots.
C1 [Baek, Tae Hyun] Sungkyunkwan Univ, Dept Media & Commun, Seoul 03063, South Korea.
   [Kim, Minseong] Louisiana State Univ Shreveport, Coll Business, Dept Management & Mkt, Shreveport, LA 71115 USA.
C3 Sungkyunkwan University (SKKU); Louisiana State University System;
   Louisiana State University Shreveport
RP Kim, M (corresponding author), Louisiana State Univ Shreveport, Coll Business, Dept Management & Mkt, Shreveport, LA 71115 USA.
EM tbaek@skku.edu; minseong.kim@lsus.edu
RI Baek, Tae Hyun/JGD-1912-2023; Kim, Minseong/ABA-3512-2021
OI Baek, Tae Hyun/0000-0003-2000-698X
CR ANDERSON JC, 1992, SOCIOL METHOD RES, V20, P321, DOI 10.1177/0049124192020003002
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Baek TH, 2023, J BUS RES, V164, DOI 10.1016/j.jbusres.2023.114039
   Baek TH, 2022, INT J ADVERT, V41, P850, DOI 10.1080/02650487.2021.2011654
   Baek TH, 2022, J BUS RES, V142, P499, DOI 10.1016/j.jbusres.2021.12.066
   Baek TH, 2012, J ADVERTISING, V41, P59, DOI 10.2753/JOA0091-3367410105
   Bakpayev M, 2022, AUSTRALAS MARK J, V30, P90, DOI 10.1016/j.ausmj.2020.04.002
   Bang H, 2018, COMPUT HUM BEHAV, V89, P70, DOI 10.1016/j.chb.2018.07.020
   Brandtzaeg PB, 2017, LECT NOTES COMPUT SC, V10673, P377, DOI 10.1007/978-3-319-70284-1_30
   Chaffey D, 2023, CAN USE I CHATGPT MA
   Chang Y, 2022, INFORM TECHNOL PEOPL, V35, P2115, DOI 10.1108/ITP-09-2020-0632
   Chen SC, 2012, COMPUT HUM BEHAV, V28, P933, DOI 10.1016/j.chb.2011.12.014
   Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
   Cheng XS, 2022, INTERNET RES, V32, P496, DOI 10.1108/INTR-08-2020-0460
   Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
   Choi TR, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101628
   Choung H, 2023, INT J HUM-COMPUT INT, V39, P1727, DOI 10.1080/10447318.2022.2050543
   Cugurullo F, 2024, AI SOC, V39, P1569, DOI 10.1007/s00146-022-01598-6
   Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
   Dekkal M, 2024, J FINANC SERV MARK, V29, P699, DOI 10.1057/s41264-023-00230-y
   Eighmey J, 1998, J BUS RES, V41, P187, DOI 10.1016/S0148-2963(97)00061-1
   Fan H, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2022.102937
   Florenthal B, 2019, J RES INTERACT MARK, V13, P351, DOI 10.1108/JRIM-05-2018-0064
   Gal U., 2023, CHATGPT IS DATA PRIV
   Gratas B., 2023, 50 ChatGPT statistics and facts you need to know
   Gray K, 2012, COGNITION, V125, P125, DOI 10.1016/j.cognition.2012.06.007
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Ho CC, 2010, COMPUT HUM BEHAV, V26, P1508, DOI 10.1016/j.chb.2010.05.015
   Hu P, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106727
   Huang JL, 2018, COMPUT HUM BEHAV, V88, P103, DOI 10.1016/j.chb.2018.06.035
   Huang SL, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2020.103323
   Jin SV, 2023, INT J HUM-COMPUT INT, V39, P1874, DOI 10.1080/10447318.2022.2129277
   Joo J, 2013, COMPUT HUM BEHAV, V29, P2512, DOI 10.1016/j.chb.2013.06.002
   Kamboj S, 2020, ASIA PAC J MARKET LO, V32, P205, DOI 10.1108/APJML-11-2017-0289
   KATZ E, 1973, AM SOCIOL REV, V38, P164, DOI 10.2307/2094393
   KATZ E, 1973, PUBLIC OPIN QUART, V37, P508
   Kees J, 2017, J ADVERTISING, V46, P141, DOI 10.1080/00913367.2016.1269304
   Kim M, 2022, TELEMAT INFORM, V74, DOI 10.1016/j.tele.2022.101881
   Kim SE, 2021, J TRAVEL TOUR MARK, V38, P444, DOI 10.1080/10548408.2021.1943600
   Ko HJ, 2005, J ADVERTISING, V34, P57, DOI 10.1080/00913367.2005.10639191
   Köchling A, 2023, INT J SELECT ASSESS, V31, P45, DOI 10.1111/ijsa.12412
   Krause AE, 2014, COMPUT HUM BEHAV, V39, P71, DOI 10.1016/j.chb.2014.07.001
   Kwon ES, 2014, INT J ADVERT, V33, P657, DOI 10.2501/IJA-33-4-657-680
   Laakasuo M, 2021, INT J SOC ROBOT, V13, P1679, DOI 10.1007/s12369-020-00738-6
   Langer M, 2018, FRONT PSYCHOL, V9, DOI 10.3389/fpsyg.2018.02220
   Lankton NK, 2015, J ASSOC INF SYST, V16, P880, DOI 10.17705/1jais.00411
   Lee H, 2020, INT J ADVERT, V39, P1150, DOI 10.1080/02650487.2020.1765657
   Leung L., 1998, Telematics and Informatics, V15, P253, DOI DOI 10.1016/S0736-5853(98)00016-1
   Liu XD, 2020, BEHAV INFORM TECHNOL, V39, P525, DOI 10.1080/0144929X.2019.1603326
   Liu XH, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.101996
   Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
   Lou C, 2023, J ADVERTISING, V52, P540, DOI 10.1080/00913367.2022.2149641
   Moore R.S., 2015, CURR CONTENTS, V25, P42
   MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
   Mori M., 1970, Energy, V7, P33, DOI DOI 10.1109/MRA.2012.2192811
   Mostafa RB, 2022, EUR J MARKETING, V56, P1748, DOI 10.1108/EJM-02-2020-0084
   Papacharissi Z, 2000, J BROADCAST ELECTRON, V44, P175, DOI 10.1207/s15506878jobem4402_2
   Park G, 2023, BEHAV INFORM TECHNOL, V42, P1998, DOI 10.1080/0144929X.2022.2105746
   Park N, 2010, J COMMUN, V60, P40, DOI 10.1111/j.1460-2466.2009.01440.x
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Phua J, 2017, COMPUT HUM BEHAV, V72, P115, DOI 10.1016/j.chb.2017.02.041
   Puzakova M, 2018, J CONSUM RES, V45, P869, DOI 10.1093/jcr/ucy035
   Rajaobelina L, 2021, PSYCHOL MARKET, V38, P2339, DOI 10.1002/mar.21548
   Raza SA, 2020, TECHNOL SOC, V62, DOI 10.1016/j.techsoc.2020.101331
   RUBIN AM, 1983, J BROADCASTING, V27, P37, DOI 10.1080/08838158309386471
   Ruggiero TE., 2000, Mass Communication & Society, V3, P3, DOI DOI 10.1207/S15327825MCS0301_02
   Rust RT, 2002, J MARKETING, V66, P7, DOI 10.1509/jmkg.66.4.7.18515
   Seymour M, 2021, J ASSOC INF SYST, V22, P591, DOI 10.17705/1jais.00674
   Shank DB, 2019, COMPUT HUM BEHAV, V98, P256, DOI 10.1016/j.chb.2019.04.001
   Shankland S., 2023, Why we're obsessed with the mind-blowing ChatGPT AI chatbot
   Shao C, 2021, HUM BEHAV EMERG TECH, V3, P978, DOI 10.1002/hbe2.293
   Shin D, 2021, INT J HUM-COMPUT ST, V146, DOI 10.1016/j.ijhcs.2020.102551
   Sun Y, 2022, ELECTRON MARK, V32, P17, DOI 10.1007/s12525-021-00483-2
   Wang SS, 2015, REV GEN PSYCHOL, V19, P393, DOI 10.1037/gpr0000056
   Weisman WD, 2021, CYBERPSYCH BEH SOC N, V24, P182, DOI 10.1089/cyber.2020.0175
   Whiting A, 2013, QUAL MARK RES, V16, P362, DOI 10.1108/QMR-06-2013-0041
   Xie CX, 2024, INT J HUM-COMPUT INT, V40, P613, DOI 10.1080/10447318.2022.2121458
   Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
   Zimmermann R, 2023, J RES INTERACT MARK, V17, P273, DOI 10.1108/JRIM-09-2021-0237
NR 79
TC 58
Z9 58
U1 281
U2 625
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0736-5853
J9 TELEMAT INFORM
JI Telemat. Inform.
PD SEP
PY 2023
VL 83
AR 102030
DI 10.1016/j.tele.2023.102030
EA AUG 2023
PG 13
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA P8GT0
UT WOS:001053007200001
DA 2024-12-25
ER

PT J
AU Tassoti, S
AF Tassoti, Sebastian
TI Assessment of Students Use of Generative Artificial Intelligence:
   Prompting Strategies and Prompt Engineering in Chemistry Education
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE General Public; Undergraduate/General; Internet/Web-BasedLearning;
   Generative Artificial Intelligence
AB The rapid integration of generative artificial intelligence (AI) into educational settings prompts an urgent examination of its efficacy and the strategies that students employ to harness its potential. This study focuses on preservice chemistry teachers use of generative AI for chemistry-specific problem-solving and task completion. We found that there is a prevalent reliance on copy-pasting tactics in initial prompting approaches, and students need guidance to improve their prompting abilities. By implementing the "Five S" prompting framework, we explore the evolution of student prompts and the resultant satisfaction with AI-generated responses. Our findings indicate that, while students initially struggle with the nuances of effective prompting, the adoption of structured frameworks significantly enhances their perceived quality of AI-generated answers. This research sheds light on the current state of AI use among students but also underscores the importance of targeted educational frameworks to refine AI interaction in academic contexts. In particular, we suggest critical engagement and methodological prompt engineering strategies to maximize the educational benefits of generative AI technologies.
C1 [Tassoti, Sebastian] Karl Franzens Univ Graz, Inst Chem, Ctr Chem Educ, A-8010 Graz, Austria.
C3 University of Graz
RP Tassoti, S (corresponding author), Karl Franzens Univ Graz, Inst Chem, Ctr Chem Educ, A-8010 Graz, Austria.
EM sebastian.tassoti@uni-graz.at
RI Tassoti, Sebastian/KUF-4070-2024
OI Tassoti, Sebastian/0000-0003-1262-7735
FU Joachim Herz Stiftung; Joachim Herz Stiftung; University of Graz
FX The author thanks Martin Sigot for contributions as a second rater. The
   author thanks Joachim Herz Stiftung for funding of the project AICE -
   Artificial Intelligence in Chemistry Education. The author acknowledges
   the financial support for Open Access publication by the University of
   Graz.
CR Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   AI for Education, PROMPTFRAMEWORK EDUC
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Alneyadi S, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13417
   Bekes E. Rakovac, 2023, 2023 46th MIPRO ICT and Electronics Convention (MIPRO), P636, DOI 10.23919/MIPRO57284.2023.10159734
   Bitzenbauer P, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13176
   Bsharat S. M., 2023, arXiv, P1, DOI [10.48550/arXiv.2312.16171, DOI 10.48550/ARXIV.2312.16171]
   Buriak JM, 2023, ACS ENERGY LETT, V9, P191, DOI 10.1021/acsenergylett.3c02586
   Clark TM, 2023, J CHEM EDUC, V100, P3934, DOI 10.1021/acs.jchemed.3c00500
   Clark TM, 2023, J CHEM EDUC, V100, P1905, DOI 10.1021/acs.jchemed.3c00027
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Deb J, 2024, J CHEM INF MODEL, V64, P799, DOI 10.1021/acs.jcim.3c01702
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   Exintaris B, 2023, J CHEM EDUC, V100, P2972, DOI 10.1021/acs.jchemed.3c00481
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Hasrod T, 2024, J CHEM EDUC, V101, P653, DOI 10.1021/acs.jchemed.3c01170
   Hornberger M., 2023, Comput. Educ, V5, P100165, DOI DOI 10.1016/J.CAEAI.2023.100165
   Humphry T, 2023, J CHEM EDUC, V100, P1434, DOI 10.1021/acs.jchemed.3c00006
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Krupp L., 2023, arXiv, P1, DOI [10.48550/arXiv.2309.03087, DOI 10.48550/ARXIV.2309.03087]
   Kuckartz U., 2012, QUALITATIVE INHALTSA
   Küchemann S, 2023, PHYS REV PHYS EDUC R, V19, DOI 10.1103/PhysRevPhysEducRes.19.020128
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lee H, 2024, ANAT SCI EDUC, V17, P926, DOI 10.1002/ase.2270
   Leon AJ, 2023, J CHEM EDUC, V100, P3859, DOI 10.1021/acs.jchemed.3c00288
   Li Z., 2023, ARXIV, p2302.11520, DOI [10.48550/arXiv.2302.11520, DOI 10.48550/ARXIV.2302.11520]
   Liu J., 2022, ARXIV, p2110.08387, DOI [10.48550/arXiv.2110.08387, DOI 10.48550/ARXIV.2110.08387]
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Nascimento CMC, 2023, J CHEM INF MODEL, V63, P1649, DOI 10.1021/acs.jcim.3c00285
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Polverini G, 2024, EUR J PHYS, V45, DOI 10.1088/1361-6404/ad1420
   Salas-Pilco SZ, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00326-w
   SALLAM M, 2023, HEALTHCARE-BASEL, V11, DOI DOI 10.3390/HEALTHCARE11060887
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   von Garrel J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02304-7
   Wang R, 2023, J CHEM INF MODEL, V63, P7189, DOI 10.1021/acs.jcim.3c01429
   Wang X., 2022, arXiv, P1, DOI DOI 10.48550/ARXIV.2203.11171
   Wei JS, 2022, ADV NEUR IN
   West JK, 2023, J CHEM EDUC, V100, P4351, DOI 10.1021/acs.jchemed.3c00581
   Wu ZX, 2023, J CHEM INF MODEL, V63, P7617, DOI 10.1021/acs.jcim.3c01642
   Yao S., 2023, arXiv, P1, DOI [10.48550/arXiv.2305.10601, DOI 10.48550/ARXIV.2305.10601]
   Zhang P, 2024, EUR J EDUC, V59, DOI 10.1111/ejed.12599
   Zheng ZL, 2023, J AM CHEM SOC, V145, P18048, DOI 10.1021/jacs.3c05819
   Zhou D., 2023, P 11 INT C LEARN REP, DOI [DOI 10.48550/ARXIV.2205.10625, 10.48550/arXiv.2205.10625]
NR 48
TC 10
Z9 10
U1 117
U2 134
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD MAY 22
PY 2024
VL 101
IS 6
BP 2475
EP 2482
DI 10.1021/acs.jchemed.4c00212
EA MAY 2024
PG 8
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA TV6B6
UT WOS:001229501800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Victor, BG
   Sokol, RL
   Goldkind, L
   Perron, BE
AF Victor, Bryan G.
   Sokol, Rebeccah L.
   Goldkind, Lauri
   Perron, Brian E.
TI Recommendations for Social Work Researchers and Journal Editors on the
   Use of Generative AI and Large Language Models
SO JOURNAL OF THE SOCIETY FOR SOCIAL WORK AND RESEARCH
LA English
DT Article
DE ChatGPT; large language models; generative artificial intelligence;
   social work research; social work journals
AB Generative artificial intelligence (AI) and large language models (LLMs) are poised to significantly impact social work research. These technologies can produce high-quality written materials and support qualitative and quantitative data analysis with simple, plain-language prompts from users. However, they also introduce challenges, such as potential bias, data privacy concerns, and generation of misinformation. In this paper, we use a disruptive-disrupting framework to discuss the dual nature of generative AI and LLMs and offer recommendations for social work researchers and journal editors that include guidance around data collection, analysis, interpretation, and dissemination. Researchers must use great caution when deploying generative AI technologies, meticulously examining, verifying, and taking accountability for the text and analyses produced by these instruments. Likewise, journal editors will need to implement quality control procedures and ethical standards to guide and evaluate the use of these technologies in social work research. We consider the recommendations offered here as a point of departure for disciplinary conversations about the role of generative AI and LLMs in social work research.
C1 [Victor, Bryan G.] Wayne State Univ, Sch Social Work, Detroit, MI 48202 USA.
   [Sokol, Rebeccah L.] Univ Michigan Ann Arbor, Sch Social Work, Ann Arbor, MI USA.
   [Sokol, Rebeccah L.] Univ Michigan Ann Arbor, Inst Firearm Injury Prevent, Ann Arbor, MI USA.
   [Goldkind, Lauri] Fordham Univ, Grad Sch Social Serv, Bronx, NY 10458 USA.
   [Victor, Bryan G.] 5447 Wood ward Ave, Detroit, MI 48202 USA.
C3 Wayne State University; University of Michigan System; University of
   Michigan; University of Michigan System; University of Michigan; Fordham
   University
RP Victor, BG (corresponding author), 5447 Wood ward Ave, Detroit, MI 48202 USA.
EM bvictor@wayne.edu
RI Victor, Bryan/T-8349-2019; Goldkind, Lauri/JAC-6492-2023; Perron,
   Brian/AFW-1605-2022
CR Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   ATLAS.ti, 2023, ACC INN DAT AN
   ATLAS.ti, 2023, Introducing: AI coding beta powered by OpenAI
   Checco A, 2021, HUM SOC SCI COMMUN, V8, DOI 10.1057/s41599-020-00703-8
   Committee on Publication Ethics (COPE), 2023, AUTH TOOLS
   Danneels E, 2004, J PROD INNOVAT MANAG, V21, P246, DOI 10.1111/j.0737-6782.2004.00076.x
   Duracinsky M, 2017, BMC MED RES METHODOL, V17, DOI 10.1186/s12874-017-0371-z
   Elsevier, 2023, Publishing Ethics
   Felten Ed, 2023, ARXIV
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V329, P637, DOI 10.1001/jama.2023.1344
   Goldkind L., IN PRESS
   Gordon R., 2023, MIT News
   Ioakimidis V, 2023, BRIT J SOC WORK, V53, P693, DOI 10.1093/bjsw/bcad081
   Kung JY, 2023, J CAN HEALTH LIBRARI, V44, P15, DOI 10.29173/jchla29657
   Markovski Y, 2023, How your data is used to improve model performance
   National Information Standards Organization, 2023, CRediT
   Nature, 2023, NATURE 0124
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   OpenAI, 2023, Terms of use
   Perron B. E., 2023, MEDIUM 0411
   Proceedings of the National Academy of Sciences (PNAS), 2023, PNAS J OUTLINE THEIR
   Sardana D, 2023, J AM DENT ASSOC, V154, P361, DOI 10.1016/j.adaj.2023.02.008
   Scheyett A, 2023, SOC WORK, V68, P101, DOI 10.1093/sw/swad010
   Science, 2023, Science Journals: Editorial Policies
   Scite Inc, 2023, US
   Semantic Scholar, About us
   Singer JB, 2023, J SOC WORK EDUC, V59, P294, DOI 10.1080/10437797.2023.2189878
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Victor BG, 2023, RES SOCIAL WORK PRAC, V33, P511, DOI 10.1177/10497315231166125
   Wang S., 2023, arXiv
NR 30
TC 5
Z9 5
U1 32
U2 124
PU UNIV CHICAGO PRESS
PI CHICAGO
PA 1427 E 60TH ST, CHICAGO, IL 60637-2954 USA
SN 2334-2315
EI 1948-822X
J9 J SOC SOC WORK RES
JI J. Soc. Soc. Work Res.
PD SEP 1
PY 2023
VL 14
IS 3
BP 563
EP 577
DI 10.1086/726021
EA SEP 2023
PG 15
WC Social Work
WE Social Science Citation Index (SSCI)
SC Social Work
GA U1CN0
UT WOS:001039931900002
DA 2024-12-25
ER

PT J
AU Wang, SF
   Zhang, H
AF Wang, Shaofeng
   Zhang, Hao
TI Promoting sustainable development goals through generative artificial
   intelligence in the digital supply chain: Insights from Chinese tourism
   SMEs
SO SUSTAINABLE DEVELOPMENT
LA English
DT Article; Early Access
DE Customer involvement; Digital supply chain innovation; ESG performance;
   Generative artificial intelligence; International SMEs; Sustainable
   development goals
ID DYNAMIC CAPABILITIES; PERFORMANCE; TRANSFORMATION; INNOVATION; SUCCESS
AB Interdisciplinary advancements, such as generative artificial intelligence (AI) and digital supply chains, can significantly contribute to achieving sustainable development goals (SDGs), particularly within tourism. This paper illuminates how it works well, focusing on the underexplored area of Environmental, Social, and Governance (ESG) performance within small and medium-sized tourism enterprises (SMEs) in China. Through a survey of 429 international SMEs, we apply the Resource-Based View and Dynamic Capabilities Theory to investigate how generative AI, such as ChatGPT, in digital supply chains can enhance innovation, collaboration, and, ultimately, ESG performance. The empirical findings underscore the pivotal role of generative AI in augmenting ESG performance via bolstering innovation and collaboration within digital supply chains. Additionally, the moderating effect of customer involvement positively influences the relationship between the digital supply chain and ESG performance. By demonstrating these relations, our study contributes to theoretical and practical efforts toward sustainable tourism and the broader achievement of the SDGs.
C1 [Wang, Shaofeng] Fuzhou Univ Int Studies & Trade, Int Business Sch, Fuzhou, Peoples R China.
   [Zhang, Hao] Zhejiang Shuren Univ, Sch Management, Hangzhou 310015, Peoples R China.
C3 Zhejiang Shuren University
RP Zhang, H (corresponding author), Zhejiang Shuren Univ, Sch Management, Hangzhou 310015, Peoples R China.
EM zhanghao08042022@163.com
RI ; Wang, Shaofeng/N-6103-2017
OI Zhang, Hao/0009-0009-7997-9410; Wang, Shaofeng/0000-0002-0300-2453
FU National Statistical Science Research Project of China [2024LY060];
   Zhejiang Provincial Philosophy and Social Sciences Planning Project
   [45]; Major Humanities and Social Sciences Research Projects in Zhejiang
   higher education institutions [2023QN115]; The 2024 Fuzhou Philosophy
   and Social Science Key Research Base Project [2024FZB26]
FX This work was supported by the National Statistical Science Research
   Project of China [2024LY060]; Zhejiang Provincial Philosophy and Social
   Sciences Planning Project [45]; Major Humanities and Social Sciences
   Research Projects in Zhejiang higher education institutions Grant Number
   [2023QN115]; 2024 Fuzhou Philosophy and Social Science Key Research Base
   Project under Grant [2024FZB26].
CR Abdi Y, 2022, ENVIRON DEV SUSTAIN, V24, P5052, DOI 10.1007/s10668-021-01649-w
   Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Khan SA, 2024, IEEE T ENG MANAGE, V71, P13727, DOI 10.1109/TEM.2021.3052239
   Akter S, 2021, IND MARKET MANAG, V97, P258, DOI 10.1016/j.indmarman.2021.07.014
   Al Amosh H, 2023, ENVIRON SCI POLLUT R, V30, P39978, DOI 10.1007/s11356-022-25050-w
   AlNuaimi BK, 2022, J BUS RES, V145, P636, DOI 10.1016/j.jbusres.2022.03.038
   ARMSTRONG JS, 1977, J MARKETING RES, V14, P396, DOI 10.2307/3150783
   Azeem M, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101635
   Bai CG, 2021, SUSTAIN PROD CONSUMP, V27, P1989, DOI 10.1016/j.spc.2021.04.035
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   Becker JM, 2023, INT J CONTEMP HOSP M, V35, P321, DOI 10.1108/IJCHM-04-2022-0474
   Beisenbina M, 2023, SUSTAIN DEV, V31, P649, DOI 10.1002/sd.2422
   Bekaert G., Sustainable Development
   Belhadi A, 2024, ANN OPER RES, V333, P627, DOI 10.1007/s10479-021-03956-x
   Bell E., 2022, BUSINESS RES METHODS
   BRISLIN RW, 1970, J CROSS CULT PSYCHOL, V1, P185, DOI 10.1177/135910457000100301
   Busulwa R, 2022, INT J HOSP MANAG, V102, DOI 10.1016/j.ijhm.2021.103132
   Camilleri MA, 2024, EXPERT SYST, V41, DOI 10.1111/exsy.13406
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chang K, 2023, APPL ECON LETT, V30, P516, DOI 10.1080/13504851.2021.1996527
   Chen CL, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13031028
   Chen PK, 2024, SUSTAIN DEV, V32, P438, DOI 10.1002/sd.2663
   Chiwaridzo OT, 2024, SUSTAIN DEV, V32, P3021, DOI 10.1002/sd.2822
   Chow MYC, 2024, J FINANC SERV MARK, V29, P1330, DOI 10.1057/s41264-024-00271-x
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Corvello V, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113397
   Creswell J. W., 2008, RES DESIGN QUALITATI
   De Carlo M, 2021, J BUS RES, V129, P936, DOI 10.1016/j.jbusres.2020.09.013
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Etikan I., 2016, AM J THEORETICAL APP, V5, P1, DOI [10.11648/j.ajtas.20160501.11., DOI 10.11648/J.AJTAS.20160501.11]
   Fang MY, 2023, ECON MODEL, V118, DOI 10.1016/j.econmod.2022.106101
   Ghobakhloo M, 2020, J CLEAN PROD, V252, DOI 10.1016/j.jclepro.2019.119869
   Govindan K, 2021, INT J PROD ECON, V231, DOI 10.1016/j.ijpe.2020.107835
   Guntuka L, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142013665
   Gutiérrez-Ponce H, 2023, SUSTAIN DEV, V31, P3008, DOI 10.1002/sd.2566
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hautala-Kankaanpää T, 2022, BUS PROCESS MANAG J, V28, P90, DOI 10.1108/BPMJ-03-2022-0122
   Hirn J, 2022, METHODS ECOL EVOL, V13, P1052, DOI 10.1111/2041-210X.13827
   Hong JT, 2021, INT J PROD ECON, V237, DOI 10.1016/j.ijpe.2021.108147
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Ju X., 2023, Journal of Knowledge Management
   Kanitz R., 2023, The Journal of Applied Behavioral Science
   Knani M, 2022, INT J HOSP MANAG, V107, DOI 10.1016/j.ijhm.2022.103317
   Kranz J, 2021, J STRATEGIC INF SYST, V30, DOI 10.1016/j.jsis.2021.101656
   Kristoffersen E, 2021, INT J PROD ECON, V239, DOI 10.1016/j.ijpe.2021.108205
   Kulkov I, 2024, SUSTAIN DEV, V32, P2253, DOI 10.1002/sd.2773
   Kumar S, 2022, TECHNOL FORECAST SOC, V178, DOI 10.1016/j.techfore.2022.121599
   Lee RP, 2022, IND MARKET MANAG, V104, P276, DOI 10.1016/j.indmarman.2022.05.002
   Li L, 2024, SUSTAIN DEV, V32, P1861, DOI 10.1002/sd.2756
   Liu YY, 2023, SUSTAIN DEV, V31, P2997, DOI 10.1002/sd.2564
   Marcon M, 2023, SUSTAIN DEV, V31, P3513, DOI 10.1002/sd.2601
   Martín-Navarro A, 2023, J BUS RES, V157, DOI 10.1016/j.jbusres.2022.113604
   Minculete G, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141710727
   Nannelli M, 2023, EUR PLAN STUD, V31, P1325, DOI 10.1080/09654313.2023.2180321
   Nayal K, 2022, BUS STRATEG ENVIRON, V31, P845, DOI 10.1002/bse.2921
   Nazir S, 2023, TECHNOL SOC, V72, DOI 10.1016/j.techsoc.2022.102190
   Nguyen NM, 2023, SUSTAIN DEV, V31, P3303, DOI 10.1002/sd.2586
   Niu SJ, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142315639
   O'Connor P, 2023, TOUR REV, V78, P339, DOI 10.1108/TR-09-2022-0431
   Ortiz-Martínez E, 2023, SUSTAIN PROD CONSUMP, V35, P349, DOI 10.1016/j.spc.2022.11.015
   Paladino A, 2007, J PROD INNOVAT MANAG, V24, P534, DOI 10.1111/j.1540-5885.2007.00270.x
   Palinkas LA, 2015, ADM POLICY MENT HLTH, V42, P533, DOI 10.1007/s10488-013-0528-y
   Ping-Kuo C, 2024, SUSTAIN DEV, V32, P1243, DOI 10.1002/sd.2714
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Puriwat W, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511578
   Rather RA, 2022, J TRAVEL RES, V61, P549, DOI 10.1177/0047287521997572
   Molina MER, 2022, BRIT FOOD J, V124, P462, DOI 10.1108/BFJ-12-2020-1171
   Sætra HS, 2023, SUSTAIN DEV, V31, P1027, DOI 10.1002/sd.2438
   Sarpong FA, 2023, COGENT BUS MANAG, V10, DOI 10.1080/23311975.2023.2232159
   Saura JR, 2021, IND MARKET MANAG, V98, P161, DOI 10.1016/j.indmarman.2021.08.006
   Shamim S, 2021, COMPUT HUM BEHAV, V121, DOI 10.1016/j.chb.2021.106777
   Shamim S, 2020, TECHNOL FORECAST SOC, V161, DOI 10.1016/j.techfore.2020.120315
   Shashi, 2020, IND MARKET MANAG, V90, P324, DOI 10.1016/j.indmarman.2020.07.011
   Shimizu K., 2020, Risk Governance and Control: Financial Markets Institutions, V10, P75, DOI 10.22495/rgcv10i3p6
   Singh A, 2024, SUSTAIN DEV, V32, P724, DOI 10.1002/sd.2706
   Soh KL, 2021, J CLEAN PROD, V320, DOI 10.1016/j.jclepro.2021.128858
   Sohn K, 2021, INT J RETAIL DISTRIB, V49, P61, DOI 10.1108/IJRDM-03-2020-0091
   Suder M, 2024, EUR J INNOV MANAG, V27, P1057, DOI 10.1108/EJIM-08-2022-0422
   Sultana S, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121260
   Tamayo-Torres I, 2019, INT J PROD RES, V57, P3719, DOI 10.1080/00207543.2018.1562248
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Tong L., 2022, Frontiers in Environmental Science
   Tourism UN., 2023, Glossary of tourism terms
   Tsolakis N, 2023, ANN OPER RES, V327, P157, DOI 10.1007/s10479-022-04785-2
   Victor BG, 2023, RES SOCIAL WORK PRAC, V33, P511, DOI 10.1177/10497315231166125
   Wang S., 2024, Journal of the Knowledge Economy
   Wang SF, 2024, J CLEAN PROD, V472, DOI 10.1016/j.jclepro.2024.143383
   Wang SF, 2023, J CLEAN PROD, V419, DOI 10.1016/j.jclepro.2023.137980
   Warner KSR, 2019, LONG RANGE PLANN, V52, P326, DOI 10.1016/j.lrp.2018.12.001
   Wei F, 2023, J CLEAN PROD, V405, DOI 10.1016/j.jclepro.2023.136847
   Welford R., 2006, Corporate Social Responsibility and Environmental Management, V13, P166, DOI 10.1002/csr.121
   Welford R, 2008, CORP SOC RESP ENV MA, V15, P52, DOI 10.1002/csr.166
   Wiesböck F, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2020.103389
   World Economic Forum, 2022, Wef travel Tourism development 2021
   World Travel Tourism Council, 2023, Travel Tourism Economic Impact
   Wu LF, 2022, TECHNOL FORECAST SOC, V184, DOI 10.1016/j.techfore.2022.122019
   Xu J, 2021, KYBERNETES, V50, P737, DOI 10.1108/K-12-2019-0793
   Xu ZQ, 2021, J CHEM INF MODEL, V61, P5589, DOI 10.1021/acs.jcim.1c00746
   Yadegaridehkordi E, 2018, TOURISM MANAGE, V66, P364, DOI 10.1016/j.tourman.2017.11.012
   Yin S, 2022, J CLEAN PROD, V363, DOI 10.1016/j.jclepro.2022.132608
   Zheng P, 2019, ADV ENG INFORM, V42, DOI 10.1016/j.aei.2019.100973
   Zhong YJ, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15021319
NR 102
TC 2
Z9 2
U1 91
U2 91
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0968-0802
EI 1099-1719
J9 SUSTAIN DEV
JI Sustain. Dev.
PD 2024 AUG 31
PY 2024
DI 10.1002/sd.3152
EA AUG 2024
PG 18
WC Development Studies; Green & Sustainable Science & Technology; Regional
   & Urban Planning
WE Social Science Citation Index (SSCI)
SC Development Studies; Science & Technology - Other Topics; Public
   Administration
GA E6F4Y
UT WOS:001303942100001
OA Bronze
DA 2024-12-25
ER

PT J
AU Moya, BA
   Eaton, SE
AF Moya, Beatriz Antonieta
   Eaton, Sarah Elaine
TI Examining Recommendations for Generative Artificial Intelligence Use
   with Integrity from a Scholarship of Teaching and Learning Lens
SO RELIEVE-REVISTA ELECTRONICA DE INVESTIGACION Y EVALUACION EDUCATIVA
LA English
DT Article
DE Artificial Intelligence; Generative Artificial Intelligence (GenAI);
   Large Language Models; Academic Integrity; Scholarship of Teaching and
   Learning; Systems Approach
ID SOTL
AB New developments in the Artificial Intelligence (AI) field allowed the development of Generative Artificial Intelligence (GenAI), capable of creating text resembling what humans can produce. As a result, educators' concerns in the higher education sector quickly emerged. Many organizations and experts have addressed these concerns through recommendations. In this conceptual paper, we draw from the Integrated Model for Academic Integrity through a Scholarship of Teaching and Learning Lens to examine and stimulate discussion from twelve documents that focus on using GenAI with integrity. We identified recommendations suitable for the individual (micro), the departmental/program (meso), the institutional (macro), and the interinstitutional/ national/ international (mega) levels concerning two core elements of the model: "high-impact professional learning for individuals and groups" and "local-level leadership and microcultures." Suggestions around the core element "scholarship, research and inquiry" were lacking at the micro and meso levels; likewise, recommendations for the core element "learning spaces, pedagogies, and technologies" were also absent at the meso, macro, and mega levels. We acknowledge that these recommendations focus on learning, involve various stakeholders, and go beyond student conduct, which aligns with current approaches to academic integrity. However, some gaps need further exploration. We highlight the need to develop more specific and practical guidance and resources for educational stakeholders around GenAI issues related to academic integrity, explore how to better support networks and leaders in higher education in creating the conditions for ethical GenAI use, and emphasizing the need for an Equity, Diversity, and Inclusion lens on GenAI.
C1 [Moya, Beatriz Antonieta] Univ Calgary, Werklund Sch Educ, Educ Res Program, Calgary, AB, Canada.
   [Eaton, Sarah Elaine] Univ Calgary, Educ, Calgary, AB, Canada.
C3 University of Calgary; University of Calgary
RP Moya, BA (corresponding author), Univ Calgary, Calgary, AB, Canada.
EM beatriz.moya@ucalgary.ca; seaton@ucalgary.ca
RI Moya, Beatriz/AAM-1585-2020; Eaton, Sarah/AAB-2731-2019
FU University of Calgary Teaching and Learning Grant; Social Sciences and
   Humanities Research Council of Canada (SSHRC) [611-2022-0398]
FX We are grateful to the University of Calgary Teaching and Learning Grant
   and to the Social Sciences and Humanities Research Council of Canada
   (SSHRC) (Grant #611-2022-0398) for supporting this research.
CR [Anonymous], 2016, New Directions for Teaching and Learning, DOI DOI 10.1002/TL.20182
   [Anonymous], 2016, New Directions for Teaching and Learning
   Anson C. M., 2022, Composition Studies, V50, P37
   Australian Academic Integrity Network (AAIN), 2023, AAIN generative artificial intelligence guidelines
   Bearman M., 2020, RE IMAGINING U ASSES, P49, DOI DOI 10.1007/978-3-030-41956-1_5
   Bertram Gallant T., 2008, Academic integrity in the twentyfirst century: A teaching and learning imperative
   Boyer EL., 2016, Scholarship reconsidered: Priorities of the professoriate
   Brake J., 2022, The Absent-Minded Professor
   Bretag T., 2013, Global corruption report: Education, P171, DOI [10.4324/9780203109816, DOI 10.4324/9780203109816]
   Bretag T, 2016, HANDBOOK OF ACADEMIC INTEGRITY, P3, DOI 10.1007/978-981-287-098-8_76
   Bronfenbrenner U., 1976, Educational Researcher, V5, P5
   Canadian Center for Cybersecurity, 2023, Generative artificial intelligence (AI)-ITSAP.00.041
   Delisio L A., 2019, What really works with universal design for learning, P157
   Dignum V, 2021, LOND REV EDUC, V19, DOI 10.14324/LRE.19.1.01
   Eaton S.E, 2023, A comprehensive academic integrity (CAI) framework: An overview
   Eaton S. E., 2023, Learning, Teaching and Leadership
   Eaton S. E., 2023, 10 PRIMER CONGRESO I
   Eaton S. E., 2020, INTEGRIDAD ACAD ENFO
   Eaton SE., 2020, Understanding academic integrity from a teaching and learning perspective: Engaging with the 4M framework
   Eaton SE., 2021, PLAGIARISM HIGHER ED, DOI [10.5040/9798400697142, DOI 10.5040/9798400697142]
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   European Commission, 2022, Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for Educators
   Felten P, 2013, TEACH LEARN INQ, V1, P121, DOI 10.2979/teachlearninqu.1.1.121
   Foltynek T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00133-4
   Fyfe P, 2023, AI SOC, V38, P1395, DOI 10.1007/s00146-022-01397-z
   Gallant TB, 2016, HANDBOOK OF ACADEMIC INTEGRITY, P975, DOI 10.1007/978-981-287-098-8_81
   Hannah ST, 2009, LEADERSHIP QUART, V20, P34, DOI 10.1016/j.leaqua.2008.11.003
   Hemsley B., 2023, The Conversation January 18
   Hubball H, 2013, TEACH LEARN INQ, V1, P41, DOI 10.2979/teachlearninqu.1.1.41
   Hubball H, 2010, CAN J SCHOLARSH TEA, V1, DOI 10.5206/cjsotl-rcacea.2010.1.2
   Hutchings P., 2011, The scholarship of teaching and learning reconsidered: Institutional integration and impact, P1
   Illia L, 2023, BUS ETHICS ENV RESP, V32, P201, DOI 10.1111/beer.12479
   International Center for Academic Integrity, 2021, The Fundamental Values of Academic Integrity
   Kenny N., 2016, NEW DIRECTIONS TEACH, V2016, P87, DOI [https://doi.org/10.1002/tl.20191, DOI 10.1002/TL.20191]
   Kenny N., 2022, ACAD INTEGRITY CANAD, DOI [10.1007/978-3-030-83255-1, DOI 10.1007/978-3-030-83255-1]
   Kenny N, 2017, CAN J SCHOLARSH TEA, V8, DOI 10.5206/cjsotl-rcacea.2017.2.10
   Khan Z. R., 2023, Artificial intelligence content generators in education for schools and universities: A good practice guide
   Kreber C, 2002, STUD HIGH EDUC, V27, P151, DOI 10.1080/03075070220119995
   Kreber C, 2013, TEACH LEARN INQ, V1, P5, DOI 10.2979/teachlearninqu.1.1.5
   Kumar R., 2023, HDB ACAD INTEGRITY, DOI [10.1007/978-981-287-079-7_153-1, DOI 10.1007/978-981-287-079-7_153-1]
   Kumar R., 2022, CANADIAN SOC STUDY H
   Lancaster T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00131-6
   Lesage J, 2024, INT J MECH ENG EDUC, V52, P88, DOI 10.1177/03064190231166665
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Mårtensson K, 2016, EDUC MANAG ADM LEAD, V44, P247, DOI 10.1177/1741143214549977
   Miller--Young J, 2015, TEACH LEARN INQ, V3, P37, DOI 10.2979/teachlearninqu.3.2.37
   Miller-Young JE, 2017, CAN J SCHOLARSH TEA, V8, DOI 10.5206/cjsotl-rcacea.2017.2.4
   Mills A., 2023, What to do about AI text generators
   Mindzak M., 2020, University Affairs February 17
   Munoko I, 2020, J BUS ETHICS, V167, P209, DOI 10.1007/s10551-019-04407-1
   National Academic Integrity Network (NAIN), 2023, Generative artificial intelligence: Guidelines for educators
   O'Brien M., 2008, International Journal for the Scholarship of Teaching and Learning, V2, P1, DOI DOI 10.20429/IJSOTL.2008.020215
   Ouyang F, 2022, EDUC INF TECHNOL, V27, P7893, DOI 10.1007/s10639-022-10925-9
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Poole G, 2013, INT STUD HIGH EDUC, P118
   Roe J., 2023, Journal of English and Applied Linguistics, V2, P3, DOI 10.59588/2961-3094.1035
   Roxa T., 2012, Teacher development in higher education: Existing programs, program impact, and future trends, P1, DOI DOI 10.4324/9780203096826
   Roxå T, 2015, INT J ACAD DEV, V20, P193, DOI 10.1080/1360144X.2015.1029929
   Sabzalieva E., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Sharples M, 2022, INT J ARTIF INTELL E, V32, P1119, DOI 10.1007/s40593-022-00300-7
   Simmons N., 2016, New Directions for Teaching and Learning, V146, P95, DOI DOI 10.1002/TL.20192
   Simmons N, 2019, CAN J SCHOLARSH TEA, V10, DOI 10.5206/cjsotl-rcacea.2019.1.7995
   Stanford University, What is AI? / Basic Questions
   Tauginiene L., Glossary for Academic Integrity
   Taylor KL, 2022, INT J ACAD DEV, V27, P279, DOI 10.1080/1360144X.2021.1899931
   Tertiary Education Quality and Standards Agency (TEQSA), 2017, GOOD PRACTICE NOTE A
   Trigwell K., 2021, University teaching in focus: A learning-centred approach, V2nd, P286, DOI [10.4324/9781003008330, DOI 10.4324/9781003008330]
   UNESCO, 2023, Harnessing the era of artificial intelligence in higher education
   UNESCO, 2021, Ethics of artificial intelligence: the recommendation
   Weber-Wulff D., 2023, Computation and Language
   Whitford E., 2022, Forbes December 9
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 75
TC 0
Z9 0
U1 14
U2 26
PU ASOC INTERUNIVERSITARIA INVESTIGACION PEDAGOGICA
PI VALENCIA
PA AVE BLASCO IBANEZ NO 30, VALENCIA, 46010, SPAIN
SN 1134-4032
J9 RELIEVE
JI RELIEVE
PY 2023
VL 29
IS 2
DI 10.30827/relieve.v29i2.29295
PG 21
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA JF8X8
UT WOS:001171855700010
OA gold
DA 2024-12-25
ER

PT J
AU Chung, KC
AF Chung, Kuo-Cheng
TI The evolution of creativity: how generative AI is reshaping the
   hospitality landscape
SO ENTERPRISE INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Generative artificial intelligence; GEN-AI adoption; organizational
   flexibility; Technology-organisation-environment model
ID ORGANIZATION-ENVIRONMENT FRAMEWORK; INFORMATION-TECHNOLOGY; DIGITAL
   TRANSFORMATION; EXPLORATION
AB This study examines the application of generative AI (GEN-AI) in the hotel industry, using the TOE model to analyze how technological, organizational, and environmental factors influence GEN-AI adoption. Findings reveal that GEN-AI enhances room design, service quality, and marketing innovation, while increasing organizational flexibility and adapting employee roles.
C1 [Chung, Kuo-Cheng] Natl Penghu Univ Sci & Technol, Dept Shipping & Transportat Management, Magong 880, Penghu, Taiwan.
C3 National Penghu University of Science & Technology
RP Chung, KC (corresponding author), Natl Penghu Univ Sci & Technol, Dept Shipping & Transportat Management, Magong 880, Penghu, Taiwan.
EM d9732004@gmail.com
CR Abrokwah-Larbi K., 2023, Ind. Artif. Intell, V1, P11, DOI [10.1007/s44244-023-00012-4, DOI 10.1007/S44244-023-00012-4]
   Assaker G, 2020, INT J CONTEMP HOSP M, V13, P3787, DOI 10.1108/IJCHM-05-2020-0461
   Awa HO, 2017, J ENTERP INF MANAG, V30, P893, DOI 10.1108/JEIM-03-2016-0079
   Aydin O, 2023, Academic Platform Journal of Engineering and Smart Systems, V11, P118
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Baker J, 2010, INTEGR SER INFORM SY, V28, P231, DOI 10.1007/978-1-4419-6108-2_12
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Batinic I., 2016, Journal of Process Management - New Technologies, V4, P25, DOI DOI 10.5937/JPMNT1601025B
   Bonnet D., 2020, MIT Sloan Management Review, V62, P118, DOI [10.7551/mitpress/11633.003.0005, DOI 10.7551/MITPRESS/11633.003.0005]
   Bulchand-Gidumal J., 2023, Current Issues in Tourism, V12, P1, DOI DOI 10.1108/17554211211217316
   Cai RY, 2022, INT J CONTEMP HOSP M, V34, P2807, DOI 10.1108/IJCHM-10-2021-1313
   Calvaresi D, 2023, J TOUR FUTURES, V9, P311, DOI 10.1108/JTF-01-2021-0009
   Chan ESW, 2012, INT J HOSP MANAG, V31, P405, DOI 10.1016/j.ijhm.2011.06.016
   Chin WW, 2003, INFORM SYST RES, V14, P189, DOI 10.1287/isre.14.2.189.16018
   Chung KC, 2020, J HOSP MARKET MANAG, V29, P722, DOI 10.1080/19368623.2020.1693471
   Dhirasasna N, 2021, RENEW ENERG, V163, P1994, DOI 10.1016/j.renene.2020.10.088
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   dos Santos PM, 2023, COMMUN STAT-SIMUL C, V52, P1639, DOI 10.1080/03610918.2021.1888122
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Fan Y., 2024, International Journal of Contemporary Hospitality Management, V96, P102954, DOI [10.1016/j.ijhm.2021.102954, DOI 10.1016/J.IJHM.2021.102954]
   Faul F, 2009, BEHAV RES METHODS, V41, P1149, DOI 10.3758/BRM.41.4.1149
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Fowler F., 2009, The SAGE Handbook of Applied Social Research Methods
   Fuentes-Moraleda L, 2020, TOUR MANAG PERSPECT, V36, DOI 10.1016/j.tmp.2020.100751
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gupta R., 2024, Int J Inf Manage Data Insights, V4, P100232, DOI [10.1016/j.jjimei.2024.100232, DOI 10.1016/J.JJIMEI.2024.100232]
   Gupta V, 2020, INT J TOUR CITIES, V6, P583, DOI 10.1108/IJTC-02-2019-0018
   Gursoy D, 2023, J HOSP MARKET MANAG, V32, P579, DOI 10.1080/19368623.2023.2211993
   Hair J.F., 2016, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hou S, 2022, J SENSORS, V2022, DOI 10.1155/2022/3948298
   Hoyer WD, 2020, J INTERACT MARK, V51, P57, DOI 10.1016/j.intmar.2020.04.001
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Hu Y, 2018, INT J HOSP MANAG, V75, P27, DOI 10.1016/j.ijhm.2018.03.004
   Huang A, 2022, J HOSP TOUR INSIGHTS, V5, P1080, DOI 10.1108/JHTI-01-2021-0021
   Hughes M, 2018, J MARKET MANAG-UK, V34, P178, DOI 10.1080/0267257X.2018.1441175
   Illescas-Manzano M., 2021, Journal of Open Innovation, V7, P125, DOI [https://doi.org/10.3390/joitmc7020125, DOI 10.3390/JOITMC7020125, 10.3390/joitmc7020125]
   Iskender A, 2023, EUR J TOUR RES, V34, DOI 10.54055/ejtr.v34i.3169
   Iyengar M. S., 2024, SCT Proceedings in Interdisciplinary Insights and Innovations, V2, P254, DOI [10.1016/S0140-6736(03)15035-0, DOI 10.1016/S0140-6736(03)15035-0]
   Jafari-Sadeghi V, 2021, J BUS RES, V124, P100, DOI 10.1016/j.jbusres.2020.11.020
   Jani D, 2015, INT J HOSP MANAG, V44, P48, DOI 10.1016/j.ijhm.2014.10.006
   Jattamart A., 2023, Journal of Open Innovation: Technology, Market, and Complexity, V9, P100052, DOI [https://doi.org/10.1016/j.joitmc.2023.100052, DOI 10.1016/J.JOITMC.2023.100052]
   Jibril IA, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14127082
   Johannsen F, 2021, INF SYST E-BUS MANAG, V19, P205, DOI 10.1007/s10257-020-00487-z
   Jooss S, 2019, INT J CONTEMP HOSP M, V31, P3879, DOI 10.1108/IJCHM-10-2018-0849
   Khan U., 2024, J. Glob. Hosp. Tour, V3, P269
   Kim JH, 2023, J TRAVEL TOUR MARK, V40, P779, DOI 10.1080/10548408.2023.2293006
   Kim JH, 2025, J TRAVEL RES, V64, P51, DOI 10.1177/00472875231212996
   Kline R.B., 2023, PRINCIPLES PRACTICE
   Kong HY, 2021, INT J CONTEMP HOSP M, V33, P717, DOI 10.1108/IJCHM-07-2020-0789
   Koo B, 2021, INT J HOSP MANAG, V95, DOI 10.1016/j.ijhm.2020.102763
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   [林懿伦 Lin Yilun], 2018, [自动化学报, Acta Automatica Sinica], V44, P775
   Liu C., 2019, International Journal of Science and Business, V3, P179, DOI [https://doi.org/10.4018/jeco.2010100103, DOI 10.4018/JECO.2010100103]
   Lockett A, 2009, INT J MANAG REV, V11, P9, DOI 10.1111/j.1468-2370.2008.00252.x
   Loureiro SMC, 2024, INT J HUM-COMPUT INT, V40, P782, DOI 10.1080/10447318.2022.2124346
   Manigandan R., 2022, International Journal of Intelligent Systems and Applications in Engineering, V10, P664, DOI [https://doi.org/10.1007/978-981-33-4604-853, DOI 10.1007/978-981-33-4604-853]
   Manoharan A., 2024, International Journal of Hospitality Management, V40, P10, DOI [10.4324/9781315445526-14, DOI 10.4324/9781315445526-14]
   March JG, 1991, ORGAN SCI, V2, P71, DOI 10.1287/orsc.2.1.71
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Miri R., 2016, Journal for Geography, V11, P121, DOI [https://doi.org/10.18690/rg.11.1.3956, DOI 10.18690/RG.11.1.3956]
   Mndzebele Nomsa, 2013, International Journal of Computer and Communication Engineering, V2, P473, DOI 10.7763/IJCCE.2013.V2.229
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Nam K, 2021, ELECTRON MARK, V31, P553, DOI 10.1007/s12525-020-00442-3
   Nikopoulou M, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15032736
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Nuseir M., 2022, International Journal of Data and Network Science, V6/, P885, DOI [DOI 10.5267/J.IJDNS.2022.2.008, https://doi.org/10.5267/j.ijdns.2022.2.008]
   Pantano E, 2020, J RETAIL CONSUM SERV, V55, DOI 10.1016/j.jretconser.2020.102096
   Patton M. Q., 2014, QUALITATIVE RES EVAL
   Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
   Prakarsa G., 2020, International Journal of Global Operations Research, V1, P62, DOI [https://doi.org/10.47194/ijgor.v1i2.36, DOI 10.47194/IJGOR.V1I2.36]
   Randhawa K, 2021, J BUS RES, V130, P618, DOI 10.1016/j.jbusres.2020.05.046
   Rotar C., 2021, Journal of Emerging Technologies and Innovative Research, V8, P799, DOI [10.1002/9781119199717.ch6, DOI 10.1002/9781119199717.CH6]
   Sarstedt M, 2020, INT J MARKET RES, V62, P288, DOI 10.1177/1470785320915686
   Schwertner K., 2017, TRAKIA J SCI, V15, P388, DOI [DOI 10.15547/tjs.2017.s.01.065, 10.15547/tjs.2017.s.01.065]
   Sheehan B, 2020, J BUS RES, V115, P14, DOI 10.1016/j.jbusres.2020.04.030
   Singh P., 2013, Journal of Network and Information Security, V5, P30, DOI [10.5815/ijcnis.2013.06.02, DOI 10.5815/IJCNIS.2013.06.02]
   Skmen A., 2024, Journal of Tourism and Gastronomy Studies, V12, P626, DOI [10.21325/jotags.2024.1398, DOI 10.21325/JOTAGS.2024.1398]
   Soliman M., 2023, ROBONOMICS: The Journal of the Automated Economy, V4, DOI DOI 10.7813/2075-4124.2012/4-6/B.2
   Song SM, 2015, TOUR ANAL, V20, P689, DOI 10.3727/108354215X14464845878156
   Soni V., 2023, Sage Science Review of Applied Machine Learning, V6, P1, DOI [https://doi.org/10.3390/app11083475, DOI 10.3390/APP11083475]
   Tarba SY, 2020, LONG RANGE PLANN, V53, DOI 10.1016/j.lrp.2020.102048
   Tehseen S., 2017, J MANAG SCI, V4, P142, DOI [DOI 10.20547/JMS.2014.1704202, 10.20547/jms.2014.1704202]
   Thaler J, 2024, AI MAG, V45, P111, DOI 10.1002/aaai.12150
   Tornatzky L., 1990, The Processes of Technological Innovation
   Tsou HT, 2015, INT J INFORM MANAGE, V35, P1, DOI 10.1016/j.ijinfomgt.2014.09.001
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Voorhees CM, 2016, J ACAD MARKET SCI, V44, P119, DOI 10.1007/s11747-015-0455-4
   Wang PQ, 2024, CURR ISSUES TOUR, DOI 10.1080/13683500.2023.2300030
   Wegener D. T., 2011, Exploratory Factor Analysis
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Wong IA, 2023, J HOSP TOUR MANAG, V55, P416, DOI 10.1016/j.jhtm.2023.05.005
   Wong K. K., 2013, Marketing Bulletin, V24, P1, DOI DOI 10.1108/EBR-10-2013-0128
   Yang HJ, 2021, INT J HOSP MANAG, V97, DOI 10.1016/j.ijhm.2021.103000
   Yeh CH, 2015, INFORM DEV, V31, P435, DOI 10.1177/0266666913516027
   Zand A, 2020, J MED INTERNET RES, V22, DOI 10.2196/15589
   Zaragoza-Sáez P, 2024, TECHNOL FORECAST SOC, V199, DOI 10.1016/j.techfore.2023.123069
   Zhang XB, 2023, TECHNOL FORECAST SOC, V186, DOI 10.1016/j.techfore.2022.122114
   Zhong LA, 2021, IND MANAGE DATA SYST, V121, P1325, DOI 10.1108/IMDS-11-2019-0603
NR 100
TC 0
Z9 0
U1 22
U2 22
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1751-7575
EI 1751-7583
J9 ENTERP INF SYST-UK
JI Enterp. Inf. Syst.
PD 2024 NOV 15
PY 2024
DI 10.1080/17517575.2024.2427024
EA NOV 2024
PG 27
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA M1D4F
UT WOS:001355014800001
DA 2024-12-25
ER

PT J
AU Larosa, F
   Hoyas, S
   García-Martínez, J
   Conejero, JA
   Nerini, FF
   Vinuesa, R
AF Larosa, Francesca
   Hoyas, Sergio
   Garcia-Martinez, Javier
   Conejero, J. Alberto
   Fuso Nerini, Francesco
   Vinuesa, Ricardo
TI Halting generative AI advancements may slow down progress in climate
   research
SO NATURE CLIMATE CHANGE
LA English
DT Article
ID ARTIFICIAL-INTELLIGENCE
AB Large language models offer an opportunity to advance climate and sustainability research. We believe that a focus on regulation and validation of generative artificial intelligence models would provide more benefits to society than a halt in development.
C1 [Larosa, Francesca; Vinuesa, Ricardo] KTH Royal Inst Technol, FLOW, Engn Mech, Stockholm, Sweden.
   [Larosa, Francesca; Fuso Nerini, Francesco; Vinuesa, Ricardo] KTH Climate Act Ctr, Stockholm, Sweden.
   [Hoyas, Sergio; Conejero, J. Alberto] Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, Valencia, Spain.
   [Garcia-Martinez, Javier] Univ Alicante, Dept Quim Inorgan, Alicante, Spain.
   [Fuso Nerini, Francesco] KTH Royal Inst Technol, KTH Div Energy Syst, Sch Ind Engn & Management, Stockholm, Sweden.
C3 Royal Institute of Technology; Universitat Politecnica de Valencia;
   Universitat d'Alacant; Royal Institute of Technology
RP Larosa, F; Vinuesa, R (corresponding author), KTH Royal Inst Technol, FLOW, Engn Mech, Stockholm, Sweden.; Larosa, F; Vinuesa, R (corresponding author), KTH Climate Act Ctr, Stockholm, Sweden.
EM larosa@kth.se; rvinuesa@mech.kth.se
RI Martinez, Javier/AAG-8670-2020; Larosa, Francesca/JDW-7206-2023;
   Conejero, J. Alberto/A-8589-2008; Vinuesa, Ricardo/ABG-6234-2020; Hoyas,
   Sergio/B-6257-2008
OI garcia-martinez, javier/0000-0002-7089-4973; Hoyas,
   Sergio/0000-0002-8458-7288; Larosa, Francesca/0000-0002-4350-8790;
   Vinuesa, Ricardo/0000-0001-6570-5499
FU Digital Futures, Demonstrator Projects program; Ministerio de Ciencia,
   innovacion y Universidades / FEDER [PID2021-128676OB-I00]; ERDF A way of
   making Europe [PID2021-124618NB-C21, MCIN/AEI/10.13039/501100011033];
   European Union
FX F.L., F.F.N. and R.V. acknowledge financial support from the Digital
   Futures, Demonstrator Projects program. S.H. is partially funded by
   project PID2021-128676OB-I00 at Ministerio de Ciencia, innovacion y
   Universidades / FEDER. J.A.C. is partially funded by grant
   PID2021-124618NB-C21, funded by MCIN/AEI/10.13039/501100011033 and ERDF
   A way of making Europe' by the European Union.
CR [Anonymous], PAUS GIANT AI EXP OP
   [Anonymous], 2023, CLIMATE CHANGE NINA
   [Anonymous], 2019, NAT HUM BEHAV, V3, P103
   [Anonymous], 2023, Stanf. Inst. Hum. -centered Artif. Intell
   Berrang-Ford L, 2021, NAT CLIM CHANGE
   Callaghan MW, 2020, NAT CLIM CHANGE, V10, P118, DOI 10.1038/s41558-019-0684-5
   Carleton TA, 2016, SCIENCE, V353, DOI 10.1126/science.aad9837
   Cattaneo C, 2019, NAT CLIM CHANGE, V9, P907, DOI 10.1038/s41558-019-0646-y
   Falkenberg M, 2022, NAT CLIM CHANGE, V12, P1114, DOI 10.1038/s41558-022-01527-x
   Goh HH, 2021, DISCOV SUSTAIN, V2, DOI 10.1007/s43621-021-00064-5
   Goujard C., 2023, Politico
   Ho DT, 2023, NATURE, V616, P9, DOI 10.1038/d41586-023-00953-x
   Kalluri P, 2020, NATURE, V583, P169, DOI 10.1038/d41586-020-02003-2
   Lee H., 2023, IPCC CLIMATE CHANGE
   Nerini FF, 2019, NAT SUSTAIN, V2, P674, DOI 10.1038/s41893-019-0334-y
   Robitschek E., 2022, TRACKING NATL CLIMAT
   Roe D, 2019, LANCET PLANET HEALTH, V3, pE287, DOI 10.1016/S2542-5196(19)30113-5
   Smith TB, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-01801-6
   Vesco P, 2020, ECOL ECON, V172, DOI 10.1016/j.ecolecon.2020.106633
   Vinuesa R, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-019-14108-y
NR 20
TC 12
Z9 13
U1 12
U2 44
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 1758-678X
EI 1758-6798
J9 NAT CLIM CHANGE
JI Nat. Clim. Chang.
PD JUN
PY 2023
VL 13
IS 6
BP 497
EP 499
DI 10.1038/s41558-023-01686-5
EA MAY 2023
PG 3
WC Environmental Sciences; Environmental Studies; Meteorology & Atmospheric
   Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA I7VQ0
UT WOS:000997023000001
DA 2024-12-25
ER

PT J
AU Dermawan, A
AF Dermawan, Artha
TI Text and data mining exceptions in the development of generative AI
   models: What the EU member states could learn from the Japanese
   "nonenjoyment" purposes?
SO JOURNAL OF WORLD INTELLECTUAL PROPERTY
LA English
DT Article
DE copyright and related rights; freier werkgenuss; generative AI models;
   innovation; text and data mining
AB The European Union (EU) text and data mining (TDM) provisions are a progressive move, but the horizon is still uncertain for both generative artificial intelligence (GenAI) models researchers and developers. This article suggests that to drive innovation and further the commitment to the digital single market, during the national implementation, EU Member States could consider taking the Japanese broad, all-encompassing and "nonenjoyment-based" TDM as an example. The Japanese "nonenjoyment" purposes, however, are not foreign to the European continental view of copyright. A similar concept can be found under the German concept of "Freier Werkgenuss" or enjoyment of the work. A flexible TDM exception built upon the German notion of nonenjoyment purposes could become an opening clause to foster innovation and creativity in the age of GenAI. Moreover, the article argues that an opening clause allowing TDM with "nonenjoyment" purposes could be permissible under the so-called three-step test. This article further suggests, if there is no political will to safeguard "the right to read should be the right to mine" and to provide a welcoming environment for GenAI researchers and developers, when shaping the legal interpretation through national case law, the EU Member States could consider the following: (1) advocate for 72 h of response if technological protection measures (TPMs) are preventing TDM, and (2) Robot Exclusion Standard (robot.txt) as a warning when TDM is not allowed on a website. It is now in the hands of the EU Member States, whether to protect the interests of rightholders or to create a balance between safeguarding "the right to read should be the right to mine," protecting rightholders exclusivity, and creating a supportive environment for the GenAI models researcher and developers.
C1 [Dermawan, Artha] Max Planck Inst Innovat & Competit, Munich, Germany.
   [Dermawan, Artha] Univ Lapland, Fac Law, Law Technol & Design Thinking LTDT Res Grp, Rovaniemi, Finland.
C3 University of Lapland
RP Dermawan, A (corresponding author), Max Planck Inst Innovat & Competit, Munich, Germany.
EM artha.dermawan@ip.mpg.de
RI Dermawan, Artha/IVV-0125-2023
OI Dermawan, Artha/0000-0003-4357-4656
FU Projekt DEAL
FX The author's sincere appreciation goes to the anonymous reviewers of the
   ATRIP Annual Essay Competition 2022 and the editorial team of the
   Journal of World Intellectual Property. The author also would like to
   thank the following: Prof. Rosa Ballardini, Prof. Peter Mezei, Prof.
   Tatsuhiro Ueno, Prof. Jean-Marc Deltorn, Prof. Anne Lauber-Roensberg,
   Prof. Ana Ramalho, Dr. David Linke, Dr. Sven Hetmank, Dr. Gabriele Spina
   Ali, Dr. Daria Kim, Ansgar Kaiser, Natasha Mangal, Ryoko Oshikamo,
   Alexandre Drouet, Miriam Steinhart, Ana Andrijevic and all of the
   participants of the Research Atelier AI and IP at CEIPI, the University
   of Strasbourg (France), and IRGET, TU Dresden (Germany) for the
   wonderful discussion and their endless support during the writing
   process. Open Access funding enabled and organized by Projekt DEAL.
CR Agreement on Trade Related Aspects of Intellectual Property Rights (TRIPs),, 1994, WTO ANNEX IC
   [Anonymous], 2001, DIRECTIVE 200129E
   [Anonymous], AI DA WORLDS 1 ULTRA
   [Anonymous], 2020, Getting the future right -Artificial intelligence and fundamental rights
   [Anonymous], 2019, MUS
   [Anonymous], INFERKIT DEMO
   [Anonymous], 1996, DIRECTIVE 969EC EU
   [Anonymous], 1886, BERNE CONVENTION P
   [Anonymous], 2022, TARGET VARIABLE DATA
   [Anonymous], 2018, JAPAN AMENDS ITS COP
   [Anonymous], 2009, DIRECTIVE 200924E
   [Anonymous], 2017, DAS SUBJEKTIVE VERVI
   [Anonymous], 2016, TECHNOLOGY REV
   [Anonymous], SHORT LECT K VONNEGU
   [Anonymous], 2019, DIRECTIVE 2019790
   [Anonymous], 2010, BGH
   Bernault C, 2017, JURIS ART ETC, V47, P22
   Brady D, 2023, WHAT DEVELOPERS NEED
   Calboli Irene, 2023, Developments and Directions in Intellectual Property Law: 20 Years of The IPKat
   Christensen K, 2019, COPYRIGHT LESSONS MA, P402
   Copyright Research and Information Center (CRIC), 2020, COPYRIGHT LAW JAPAN
   Craig Carys., 2017, Am U Intl L Rev, V33, P1
   Drexl J, 2017, TRADING DATA DIGITAL
   Ducato R, 2019, IIC-INT REV INTELL P, V50, P649, DOI 10.1007/s40319-019-00833-w
   Dusollier S, 2020, COMMON MKT LAW REV, V57, P978
   Dussolier S, 2018, COPYRIGHT RECONSTRUC, P166
   Elgammal Ahmed, 2017, ARXIV
   European Commission, 2020, Communication from the commission to the European parliament, the council, the European economic and social committee and the committee of the regions-sustainable and smart mobility strategy-putting European transport on track for the future (COM/2020/789 final), DOI [10.2779/05068, DOI 10.2779/05068]
   European Copyright Society, 2017, GEN OPINION EU COP, P5
   Fayyad UM, 1996, KNOWLEDGE DISCOVE
   Fayyad Usama, 1996, FROM DATA MINING KNO
   Fiil-Flynn SM, 2022, SCIENCE, V378, P951, DOI 10.1126/science.add6124
   Geiger C, 2019, 201908 CTR INT INT P, P6
   Geiger C, 2018, INTELLECTUAL PROPERT, V5, P95
   Gerrish C., 2019, Stockholm Intellectual Property Law Review, V2, P58
   GP Clermont-Ferrand, PENSEES BLAISE PASCA
   Guibault L, 2010, WHY CHERRY PICKING, V1, P2603
   Han Jiawei, 2001, DATA MINING CONCEPTS
   Hearst MA, 1999, P 37 ANN M ASS COM, V3, P3
   Hilty H, 2019, MAX PLANCK I INNOV, P9
   Hugenholtz PB, 2011, FAIR USE EUROPE SEAR, P29
   Hugenholtz PB, 2008, AMSTERDAM LAW SCH
   Hugenholtz PB, 1989, AUTEURSRECHT INFORMA
   Hugenholtz PB, 2019, THE NEW COPYRIGHT DI
   Japanese Copyright Research and Information Center (CRIC), THE COPYRIGHT JAPAN
   Japanese Copyright Research and Information Center (CRIC), THE HIST COPYRIGHT S
   Joachim v., 2020, URHEBERRECHT
   Jondet N, 2018, PROPRIETES INTELLE, V67, P33
   Kollar P, 2021, MIND MINE STUDY JUST, P3
   Koschwitz L, THE EU JUST TOLD DAT
   Kuhl M, 2019, MACHINE LEARNING A
   Lauber-Ronsberg, 2019, AUTONOME SCHOPFUNG U
   Lawton G, 2022, DATA PREPROCESSING
   Leistner Von, 2006, GRUR, V801, P814
   Lemley M. A., 2020, Texas Law Rev, V99, P447, DOI [10.2139/ssrn.3528447, DOI 10.2139/SSRN.3528447]
   Li Y, 2010, DATA MINING CONCEPTS
   Liber Europe, 2020, EUROPES TDM EXCEPTIO
   Liber Europe, LIBER POSITION STATE
   Margoni T, 2021, A DEEPER LOOK EU TEX
   Mariscal G, 2010, KNOWL ENG REV, V25, P137, DOI 10.1017/S0269888910000032
   Maryam, 2019, FOUNDER AUTOPSY IN
   McDowall RD, 2016, FOCUS QUALITY WHAT E
   McKinsey & Company, 2023, WHAT IS GENERATIVE A
   Meniere Y, PLATFORMS ARTIFICI, P103
   Meys R, 2020, GRUR INT, V69, P457
   Muto, 2017, NEW, P11
   Newitz A, 2017, ARS TECHNICA APRI
   OpenAI, About
   OpenAI, GENERATIVE MODELS
   OpenMinted, TDM STORIES
   Patil S, 2022, STABLE DIFUSSION DIF
   Pihlajarine T, 2019, REGULATING IND INT, P116
   Popescu A, 2019, ECONPAPERS VALUE DAT
   Quintais J., 2020, EUR INTELL PROP R, V1, P28
   Ramesh A, 2014, ARXIV
   Raue, 2020, RECHT ZUGANG, V118
   Reagan AJ, 2016, EPJ DATA SCI, V5, DOI 10.1140/epjds/s13688-016-0093-1
   Reda, F REDA TEXT DATA MIN
   Romero, 2022, DALL E 2 EXPLAINED P
   Rosati, 2021, COPYRIGHT DIGITAL, P68
   Rosati E, 2018, REGARDING APPL TDM, P1
   Rosati E, 2019, ASIA PAC LAW REV, V27, P198, DOI 10.1080/10192557.2019.1705525
   Rosati E, 2018, J INTELLET PROP LAW, V13, P429, DOI 10.1093/jiplp/jpy063
   Sag M, 2019, J COPYRIGHT SOC USA, V66, P291
   Samuelson P, 2019, COMMUN ACM, V62, P26
   Sarker RA, 2002, DATA MIN KNOWL DI
   Schack 'Schutzgegenstand, 2021, AUS, V904, P907
   Schuhmann C, 2022, LAION 5B NEW ERA OPE
   Senftleben, 2021, ENSURING VISIBILITY, P6
   Senftleben M, 2021, ENSURING VISIBILITY, P7
   Shapiro T, 2019, EIPR, V41, P404
   Sharma R, 2020, KDD PROCESS DATA MIN
   The Japanese Government Official Website, ARTIFICIAL INTELLIGE
   Triaille JP, 2014, STUDY LEGAL TEXT DAT, P28
   Ueno T, 2021, THE FLEX, P147
   Ueno T, 2021, THE CAMBRIDGE HDB CO, P213
   Ueno T, 2009, J JAPANESE GROUP AIP, P159
   Ueno T, 2019, COPYRIGHT ISSUES A, V91
   Walter MM, 2010, EUROPEAN COPYRIGHT, P1024
   White B, ARTICLE 3 4 TEXT DAT
NR 100
TC 7
Z9 7
U1 3
U2 28
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1422-2213
EI 1747-1796
J9 J WORLD INTELLECT PR
JI J. World Intellect. Prop.
PD MAR
PY 2024
VL 27
IS 1
BP 44
EP 68
DI 10.1111/jwip.12285
EA MAY 2023
PG 25
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA LD9Y2
UT WOS:000995626800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Yan, Y
   Sun, W
   Zhao, XF
AF Yan, Yi
   Sun, Wei
   Zhao, Xiufeng
TI Metaphorical conceptualizations of generative artificial intelligence
   use by Chinese university EFL learners
SO FRONTIERS IN EDUCATION
LA English
DT Article
DE GenAI; students' perceptions; metaphor analysis; Chinese EFL learners;
   students' attitudes; language learning
ID TEACHERS; STUDENTS; BELIEFS
AB The unveiling of ChatGPT 4o by OpenAI, a multimodal large language model powered by Generative Artificial Intelligence (GenAI), has injected interest and incited debate throughout the echelon of education institutions regarding its prospective benefits and drawbacks. Nonetheless, investigations into the learners' perceptions of GenAI use in learning English as a Foreign Language (EFL) remain markedly insufficient. The study adopts an explorative stance and aims to explore the attitudes and perceptions of Chinese EFL learners toward GenAI use in language learning through the application of metaphor analysis. Data were collected from 281 EFL students of varying majors in four key universities across China by completing a sentence using metaphors to elicit their attitudes and perceptions toward GenAI use in language learning. Through qualitative analysis of metaphorical constructs, including HUMANS, TOOL/MACHINE, BRAIN, RESOURCES, FOOD/DRINK, and MEDICINE metaphors, the study unveils a spectrum of attitudes toward GenAI. While some language learners perceived GenAI as supportive, helpful, and intelligent, others expressed concerns about over-reliance and potential loss of critical thinking skills. The findings underscore the importance of considering learners' diverse attitudes and beliefs toward GenAI use and application in language learning pedagogy. The implications of these findings for the future integration of GenAI in language education are discussed, complemented by recommendations for further research and pedagogical practice.
C1 [Yan, Yi; Sun, Wei; Zhao, Xiufeng] China Univ Petr, Sch Foreign Languages, Beijing, Peoples R China.
C3 China University of Petroleum
RP Yan, Y; Sun, W (corresponding author), China Univ Petr, Sch Foreign Languages, Beijing, Peoples R China.
EM y.yan@cup.edu.cn; swcup1998@163.com
RI Sun, Wei/JCD-7239-2023
OI Sun, Wei/0009-0008-2188-7436
FU General Project of Ministry of Education Foundation on Humanities and
   Social Sciences [21YJA740055]
FX The author(s) declare that financial support was received for the
   research, authorship, and/or publication of this article. This study was
   supported by the General Project of Ministry of Education Foundation on
   Humanities and Social Sciences Grant number: 21YJA740055.
CR Amin TG, 2015, INT J SCI EDUC, V37, P966, DOI 10.1080/09500693.2015.1025313
   Anderson Salena Sampson, 2023, Computers and Composition, DOI 10.1016/j.compcom.2023.102778
   Armstrong S.L., 2008, The International Journal of Learning, V15, P211, DOI [10.18848/1447-9494/CGP/v15i09/45948, DOI 10.18848/1447-9494/CGP/V15I09/45948]
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Barrot JS, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100745
   Bishop L., 2023, Research, and Writing, DOI DOI 10.2139/SSRN.4338981
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Cameron L., 1999, LANG TEACHING, V32, P77, DOI DOI 10.1017/S0261444800013781
   Cameron L., 2010, Metaphor analysis
   Carbonell J, 2016, FUTURES, V84, P145, DOI 10.1016/j.futures.2016.03.019
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Chai CS, 2021, EDUC TECHNOL SOC, V24, P89
   Chatterjee J, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2022.100676
   Nguyen CD, 2016, SYSTEM, V60, P66, DOI 10.1016/j.system.2016.06.004
   de Guerrero M., 2002, Language Teaching Research, V6, P95, DOI [DOI 10.1191/1362168802LR101OA, 10.1191/1362168802lr101oa]
   Delamont S., 2020, Handbook of Qualitative Research in Education, P488, DOI DOI 10.4337/9781788977159.00054
   Dincer A., 2017, Universal Journal of Educational Research, V5, P104, DOI [10.13189/ujer.2017.050113, DOI 10.13189/UJER.2017.050113]
   Dornyei Z., 2002, INDIVIDUAL DIFFERENC, P137, DOI DOI 10.1075/LLLT.2.10DOR
   Erdogan T, 2013, ASIA-PAC EDUC RES, V22, P347, DOI 10.1007/s40299-012-0014-4
   Farrell TSC, 2011, SYSTEM, V39, P54, DOI 10.1016/j.system.2011.01.012
   Fathi J, 2024, SYSTEM, V121, DOI 10.1016/j.system.2024.103254
   Fryer LK, 2019, COMPUT HUM BEHAV, V93, P279, DOI 10.1016/j.chb.2018.12.023
   Gao F, 2023, SYSTEM, V118, DOI 10.1016/j.system.2023.103091
   Guo K, 2024, EDUC INF TECHNOL, V29, P8435, DOI 10.1007/s10639-023-12146-0
   Hawking S., 2014, STEPHEN HAWKING AI C
   Hwang W. Y., 2023, Innovative technologies and learning: ICITL 2023. Lecture Notes in Computer Science, V14099
   Janssen M, 2022, SOC SCI COMPUT REV, V40, P478, DOI 10.1177/0894439320980118
   Jeon J, 2024, COMPUT ASSIST LANG L, V37, P1, DOI 10.1080/09588221.2021.2021241
   Jin L, 2011, RESEARCHING CHINESE LEARNERS: SKILLS, PERCEPTIONS AND INTERCULTURAL ADAPTATIONS, P1, DOI 10.1057/9780230299481
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Keyes O, 2021, INTERDISCIPL SCI REV, V46, P158, DOI 10.1080/03080188.2020.1840224
   Kim A, 2024, SYSTEM, V122, DOI 10.1016/j.system.2024.103256
   Lakoff G., 1993, Metaphor and Thought, P202, DOI [10.1017/CBO9781139173865.013, DOI 10.1017/CBO9781139173865.013]
   Lakoff George., 2008, Metaphors we live by
   Li E, 2022, LECT NOTES ARTIF INT, V13336, P69, DOI 10.1007/978-3-031-05643-7_5
   Liang JC, 2023, INTERACT LEARN ENVIR, V31, P4270, DOI 10.1080/10494820.2021.1958348
   Lim EM, 2024, THINK SKILLS CREAT, V51, DOI 10.1016/j.tsc.2023.101455
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Luo WW, 2024, EARLY EDUC DEV, V35, P96, DOI 10.1080/10409289.2023.2214181
   McGrath I., 2006, J EDUC TEACHING, V32, P303, DOI [10.1080/02607470600782443, DOI 10.1080/02607470600782443]
   Mertala P., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022/100095, 10.1016/j.caeai.2022.100095, DOI 10.1016/J.CAEAI.2022.100095]
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Oxford RL, 2014, SYSTEM, V43, P11, DOI 10.1016/j.system.2014.01.001
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Saban A, 2007, LEARN INSTR, V17, P123, DOI 10.1016/j.learninstruc.2007.01.003
   Schmitt R, 2005, QUAL REP, V10, P358
   Shaw D, 2021, QUAL REP, V26, P3091, DOI 10.46743/2160-3715/2021.4743
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Tabata-Sandom M, 2020, SYSTEM, V94, DOI 10.1016/j.system.2020.102335
   Tobin K., 1990, THEOR PRACT, V29, P122, DOI DOI 10.1080/00405849009543442
   Wan W, 2014, J SECOND LANG WRIT, V23, P53, DOI 10.1016/j.jslw.2014.01.002
   Wan W, 2011, SYSTEM, V39, P403, DOI 10.1016/j.system.2011.07.012
   Wang XF, 2023, LANCET REG HEALTH-W, V41, DOI 10.1016/j.lanwpc.2023.100905
   Wegner E, 2020, LEARN INDIVID DIFFER, V80, DOI 10.1016/j.lindif.2020.101884
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
   Yan D, 2024, KYBERNETES, DOI 10.1108/K-09-2023-1933
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yang HZ, 2024, EDUC INF TECHNOL, V29, P3837, DOI 10.1007/s10639-023-11991-3
   Yu SL, 2023, J SECOND LANG WRIT, V59, DOI 10.1016/j.jslw.2022.100961
   Zhao X, 2023, RELC J, V54, P890, DOI 10.1177/00336882221094089
   Zhou TQ, 2023, SYSTEM, V118, DOI 10.1016/j.system.2023.103141
NR 63
TC 0
Z9 0
U1 23
U2 23
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2504-284X
J9 FRONT EDUC
JI Front. Educ.
PD JUL 30
PY 2024
VL 9
AR 1430494
DI 10.3389/feduc.2024.1430494
PG 14
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA C5D9A
UT WOS:001289579200001
OA gold
DA 2024-12-25
ER

PT J
AU Lv, D
   Sun, R
   Zhu, QH
   Cheng, Y
   Wang, RR
   Qin, SK
AF Lv, Dong
   Sun, Rui
   Zhu, Qiuhua
   Cheng, Yue
   Wang, Rongrong
   Qin, Shukun
TI Language Styles, Recovery Strategies and Users' Willingness to Forgive
   in Generative Artificial Intelligence Service Recovery: A Mixed Study
SO SYSTEMS
LA English
DT Article
DE generative artificial intelligence; service recovery; linguistic style;
   recovery strategies; event-related potentials
ID SATISFACTION; APOLOGY; IMPACT; HUMOR; PERSPECTIVE; ENCOUNTER; SINCERITY;
   COMPONENT; FAILURES; EMPATHY
AB As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users' willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users' willingness to forgive compared to a rational style, primarily through the enhancement of users' perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users' willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.
C1 [Lv, Dong; Sun, Rui; Zhu, Qiuhua; Wang, Rongrong; Qin, Shukun] Huaqiao Univ, Sch Business Adm, Quanzhou 362021, Peoples R China.
   [Cheng, Yue] Tongji Univ, Coll Architecture & Urban Planning, Shanghai 200092, Peoples R China.
C3 Huaqiao University; Tongji University
RP Sun, R (corresponding author), Huaqiao Univ, Sch Business Adm, Quanzhou 362021, Peoples R China.
EM lvdong@stu.hqu.edu.cn; sunrui@hqu.edu.cn; 24028@qztc.edu.cn;
   chengyue0798@tongji.edu.cn; 22013120023@stu.hqu.edu.cn;
   23013120015@stu.hqu.edu.cn
RI cheng, yue/JAC-6644-2023
OI Lv, Dong/0009-0004-4151-8372; Cheng, Yue/0000-0001-9666-9136
FU National Social Sciences-funded general projects, PRC; Humanities and
   Social Sciences Planning Project of the Ministry of Education, PRC
   [20YJA630054, 21FGLB041];  [22BGL006]
FX This research project was supported by the National Social
   Sciences-funded general projects, PRC (Grant No. 22BGL006), and
   Humanities and Social Sciences Planning Project of the Ministry of
   Education, PRC (Grant No. 20YJA630054), and National Social
   Sciences-later-funded projects, PRC (Grant No. 21FGLB041).
CR Abubshait A, 2021, COGN AFFECT BEHAV NE, V21, P763, DOI 10.3758/s13415-021-00895-9
   Agnihotri A, 2024, INT J INFORM MANAGE, V76, DOI 10.1016/j.ijinfomgt.2023.102679
   Biocca F, 2003, PRESENCE-VIRTUAL AUG, V12, P456, DOI 10.1162/105474603322761270
   BITNER MJ, 1990, J MARKETING, V54, P71, DOI 10.2307/1252174
   Bougoure US, 2016, J RETAIL CONSUM SERV, V31, P62, DOI 10.1016/j.jretconser.2016.03.006
   Brakel LAW, 2003, BEHAV BRAIN SCI, V26, P527, DOI 10.1017/S0140525X03210116
   Caruana N, 2019, COGN AFFECT BEHAV NE, V19, P1479, DOI 10.3758/s13415-019-00734-y
   Chen Q., 2014, CUSTOMER NEEDS SOLUT, V1, P288, DOI [10.1007/s40547-014-0023-y, DOI 10.1007/S40547-014-0023-Y]
   Cheng LK, 2023, J CONSUM BEHAV, V22, P67, DOI 10.1002/cb.2112
   Choi GY, 2022, PUBLIC RELAT REV, V48, DOI 10.1016/j.pubrev.2022.102226
   Christensen J, 2024, CURR ISSUES TOUR, DOI 10.1080/13683500.2023.2300032
   Delorme A, 2004, J NEUROSCI METH, V134, P9, DOI 10.1016/j.jneumeth.2003.10.009
   Döring M, 2022, PUBLIC MANAG REV, V24, P790, DOI 10.1080/14719037.2020.1864013
   Finucane ML, 2000, J BEHAV DECIS MAKING, V13, P1, DOI 10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S
   Folstein JR, 2008, PSYCHOPHYSIOLOGY, V45, P152, DOI 10.1111/j.1469-8986.2007.00602.x
   Fratczak P, 2021, INT J IND ERGONOM, V82, DOI 10.1016/j.ergon.2020.103078
   Fu HJ, 2019, NEUROSCI LETT, V713, DOI 10.1016/j.neulet.2019.134522
   Geiger AR, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-97527-6
   Gkorezis P, 2016, LEADERSHIP ORG DEV J, V37, P882, DOI 10.1108/LODJ-11-2014-0231
   Gountas S, 2011, TOUR ANAL, V16, P393, DOI 10.3727/108354211X13149079788819
   Hajcak G, 2010, DEV NEUROPSYCHOL, V35, P129, DOI 10.1080/87565640903526504
   Han E., 2021, P 2021 INT C INF SYS
   Haupt M, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00673-0
   Hinz NA, 2021, ACTA PSYCHOL, V212, DOI 10.1016/j.actpsy.2020.103216
   Honora A, 2024, SERV BUS, V18, P363, DOI 10.1007/s11628-024-00563-1
   Hu YO, 2021, J HOSP TOUR RES, V45, P1022, DOI 10.1177/10963480211011533
   Huang MH, 2024, J MARKETING, V88, P1, DOI 10.1177/00222429231224748
   Huang YY, 2020, INT J CONTEMP HOSP M, V32, P2429, DOI 10.1108/IJCHM-01-2020-0028
   Imai T, 2024, PSYCH J, V13, P79, DOI 10.1002/pchj.682
   Janson A, 2023, COMPUT HUM BEHAV, V149, DOI 10.1016/j.chb.2023.107954
   Kim T, 2021, TELEMAT INFORM, V61, DOI 10.1016/j.tele.2021.101595
   Kobel S, 2021, J BUS RES, V132, P260, DOI 10.1016/j.jbusres.2021.04.034
   Lee JG, 2022, COMPUT HUM BEHAV, V127, DOI 10.1016/j.chb.2021.107015
   Lee S, 2023, TELEMAT INFORM, V82, DOI 10.1016/j.tele.2023.102009
   Li CJ, 2020, INT J HOSP MANAG, V85, DOI 10.1016/j.ijhm.2019.102361
   Li CZE, 2020, J PHYS CONF SER, V1575, DOI 10.1088/1742-6596/1575/1/012192
   Li MC, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103209
   Liu DW, 2023, INT J ADV ROBOT SYST, V20, DOI 10.1177/17298806231191606
   Liu J, 2023, ANN TOURISM RES, V103, DOI 10.1016/j.annals.2023.103668
   Lv D, 2024, J ENVIRON PSYCHOL, V96, DOI 10.1016/j.jenvp.2024.102307
   Lv LX, 2022, J TRAVEL TOUR MARK, V39, P570, DOI 10.1080/10548408.2022.2162659
   Magnini VP, 2009, INT J HOSP MANAG, V28, P540, DOI 10.1016/j.ijhm.2009.03.001
   McGraw AP, 2010, PSYCHOL SCI, V21, P1141, DOI 10.1177/0956797610376073
   Meyer N, 2022, ELECTRON MARK, V32, P2491, DOI 10.1007/s12525-022-00613-4
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Okada Y, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0281604
   Pataranutaporn P, 2023, NAT MACH INTELL, V5, P1076, DOI 10.1038/s42256-023-00720-7
   Peng YH, 2024, J THEOR APPL EL COMM, V19, P1580, DOI 10.3390/jtaer19020077
   Perez-Osorio J, 2022, J COGNITIVE NEUROSCI, V34, P108, DOI 10.1162/jocn_a_01786
   Pion-Tonachini L, 2019, NEUROIMAGE, V198, P181, DOI 10.1016/j.neuroimage.2019.05.026
   Potdevin D, 2021, INT J HUM-COMPUT ST, V150, DOI 10.1016/j.ijhcs.2021.102612
   Prati A, 2017, J ECON BEHAV ORGAN, V143, P78, DOI 10.1016/j.jebo.2017.09.002
   Radu AG, 2019, MARK INTELL PLAN, V37, P358, DOI 10.1108/MIP-03-2018-0080
   Sarofim S, 2022, J CONSUM AFF, V56, P465, DOI 10.1111/joca.12433
   Schumann K, 2012, J SOC PERS RELAT, V29, P997, DOI 10.1177/0265407512448277
   Shams G, 2024, INT J HOSP MANAG, V120, DOI 10.1016/j.ijhm.2024.103782
   Shan MH, 2024, J HOSP MARKET MANAG, V33, P145, DOI 10.1080/19368623.2023.2246456
   Shin H, 2023, INT J CONSUM STUD, V47, P545, DOI 10.1111/ijcs.12849
   Söderlund M, 2018, J RETAIL CONSUM SERV, V42, P55, DOI 10.1016/j.jretconser.2018.01.013
   Song MM, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103323
   Song MM, 2022, ELECTRON COMMER R A, V55, DOI 10.1016/j.elerap.2022.101199
   Sun Y, 2022, ELECTRON MARK, V32, P17, DOI 10.1007/s12525-021-00483-2
   Takaku S, 2001, J LANG SOC PSYCHOL, V20, P144, DOI 10.1177/0261927X01020001007
   Tax SS, 1998, J MARKETING, V62, P60, DOI 10.2307/1252161
   Wang CC, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103325
   Wang K, 2024, KYBERNETES, DOI 10.1108/K-04-2024-0894
   Wang XY, 2023, INT J HOSP TOUR ADM, V24, P540, DOI 10.1080/15256480.2021.2017812
   Warren C, 2016, J PERS SOC PSYCHOL, V110, P407, DOI 10.1037/pspi0000041
   Wei C, 2020, J BUS RES, V118, P321, DOI 10.1016/j.jbusres.2020.06.061
   Wei Q, 2023, PSYCHOL RES BEHAV MA, V16, P3787, DOI 10.2147/PRBM.S416821
   Wiese E, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.01663
   Xie YG, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103599
   Xing XY, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.102049
   Xu XA, 2022, ANN TOURISM RES, V95, DOI 10.1016/j.annals.2022.103439
   Xu X, 2019, J TRAVEL RES, V58, P1034, DOI 10.1177/0047287518789285
   Yang HY, 2022, ANN TOURISM RES, V95, DOI 10.1016/j.annals.2022.103425
   You YF, 2020, J MARKETING, V84, P133, DOI 10.1177/0022242919889894
   Zhang JM, 2023, INT J HOSP MANAG, V108, DOI 10.1016/j.ijhm.2022.103387
   Zhang WK, 2019, PSYCHOL RES BEHAV MA, V12, P913, DOI 10.2147/PRBM.S215751
NR 79
TC 0
Z9 0
U1 19
U2 19
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-8954
J9 SYSTEMS-BASEL
JI Systems-Basel
PD OCT
PY 2024
VL 12
IS 10
AR 430
DI 10.3390/systems12100430
PG 23
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA K3A1A
UT WOS:001342627700001
OA gold
DA 2024-12-25
ER

PT J
AU Kurtz, G
   Amzalag, M
   Shaked, N
   Zaguri, Y
   Kohen-Vacs, D
   Gal, E
   Zailer, G
   Barak-Medina, E
AF Kurtz, Gila
   Amzalag, Meital
   Shaked, Nava
   Zaguri, Yanay
   Kohen-Vacs, Dan
   Gal, Eran
   Zailer, Gideon
   Barak-Medina, Eran
TI Strategies for Integrating Generative AI into Higher Education:
   Navigating Challenges and Leveraging Opportunities
SO EDUCATION SCIENCES
LA English
DT Article
DE higher education (HE); generative AI (GenAI); Generative AI; academic
   teaching
ID CHATGPT
AB The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.
C1 [Kurtz, Gila; Amzalag, Meital; Shaked, Nava; Zaguri, Yanay; Kohen-Vacs, Dan; Gal, Eran; Zailer, Gideon; Barak-Medina, Eran] Holon Inst Technol HIT, Fac Instruct Design, IL-5810201 Holon, Israel.
RP Kurtz, G (corresponding author), Holon Inst Technol HIT, Fac Instruct Design, IL-5810201 Holon, Israel.
EM gilaku@hit.ac.il; meitalam@hit.ac.il; shakedn@hit.ac.il;
   yanayz@hit.ac.il; mrkohen@hit.ac.il; erang@hit.ac.il; gidonz@hit.ac.il;
   eranba@hit.ac.il
RI Kurtz, Gila/CAI-9788-2022; Gal, Eran/AAG-4538-2020; Shaked,
   Nava/AAK-3576-2021
OI Kohen-Vacs, Dan/0000-0002-6225-6365; Kurtz, Gila/0000-0001-7775-1409;
   Shaked, Nava/0000-0002-2362-4663; Gal, Eran/0000-0002-0486-4960
CR AI-Principles Overview OECD, AI
   Akgun Selin, 2022, AI Ethics, V2, P431, DOI 10.1007/s43681-021-00096-7
   Al-Obaydi LH., 2023, International Journal of Emerging Technologies in Learning, V18, P39, DOI [10.3991/ijet.v18i21.42593, DOI 10.3991/IJET.V18I21.42593]
   Ali M., 2021, FOSTERING COMMUNICAT, P36, DOI DOI 10.4018/978-1-7998-4846-2.CH003
   Amani S, 2023, Arxiv, DOI [arXiv:2304.14415, 10.48550/arXiv.2304.14415, DOI 10.48550/ARXIV.2304.14415]
   Amzalag M., unpublished
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bali M. M. E. I., 2022, Journal of Innovation in Educational and Cultural Research, V3, P146, DOI [https://doi.org/10.46843/jiecr.v3i2.88, DOI 10.46843/JIECR.V3I2.88]
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Beck SW, 2023, PHI DELTA KAPPAN, V105, P66, DOI 10.1177/00317217231197487
   Beerbaum Dirk Otto, 2023, SSRN Scholarly Paper, DOI [10.2139/ssrn.4385025, DOI 10.2139/SSRN.4385025]
   Bogert E, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-87480-9
   Broz M., 2023, Midjourney statistics (February 2024)
   Cao Shiye, 2022, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3555572
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   CARLOPIO J, 1988, J OCCUP PSYCHOL, V61, P67, DOI 10.1111/j.2044-8325.1988.tb00272.x
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.02878
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.00290, 10.48550/arXiv.2305.00290, DOI 10.48550/ARXIV.2305.00290]
   Chernenko O., 2020, Pedagog. Educ. Manag. Rev, V2, P52, DOI [10.36690/2733-2039-2020-2-52, DOI 10.36690/2733-2039-2020-2-52]
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Dai W, 2023, IEEE INT CONF ADV LE, P323, DOI 10.1109/ICALT58122.2023.00100
   Díaz-Rodríguez N, 2023, INFORM FUSION, V99, DOI 10.1016/j.inffus.2023.101896
   Dickey E, 2023, Arxiv, DOI [arXiv:2308.12258, 10.48550/arxiv.2308.12258, DOI 10.48550/ARXIV.2308.12258]
   Durate F., 2024, Number of chatgpt users
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ferrara E, 2024, Arxiv, DOI [arXiv:2310.00737, DOI 10.48550/ARXIV.2310.00737]
   Filgueiras F, 2024, EDUC CITIZSH SOC JUS, V19, P349, DOI 10.1177/17461979231160674
   George A. S., 2023, Partners Universal International Research Journal, V2, P36, DOI [10.5281/ZENODO.10421475, DOI 10.5281/ZENODO.10421475]
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Jakesch Maurice, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P310, DOI 10.1145/3531146.3533097
   Johri A, 2023, J ENG EDUC, V112, P572, DOI 10.1002/jee.20537
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Kitchen K., 2023, AI Is a National Security Lifeline, House Armed Services Committee Subcommittee on Cyber, Innovative Technologies, and Information Systems
   Lacey MM, 2023, MICROBIOL AUST, V44, P124, DOI 10.1071/MA23036
   LaGrandeur, 2021, AI ETHICS, V1, P93, DOI DOI 10.1007/S43681-020-00010-7
   Larsen B., 2023, P WORLD EC FOR DAV S
   Lizzio A, 2004, STUD HIGH EDUC, V29, P469, DOI 10.1080/0307507042000236371
   López-Chila R, 2024, EDUC SCI, V14, DOI 10.3390/educsci14010047
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Maathuis C., 2022, EUR C CYB WARF SEC, V21, P170
   Malmstrom H., 2023, Chalmers Stud. Commun. Learn. High. Educ., V1, DOI [10.17196/cls.csclhe/2023/01, DOI 10.17196/CLS.CSCLHE/2023/01]
   Markauskaite L., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022.100056, 10.1016/j.caeai.2022.100056]
   Mondal H, 2023, INDIAN J OPHTHALMOL, V71, P3600, DOI 10.4103/IJO.IJO_718_23
   Muscanell Nicole, 2023, EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report?
   Okonkwo C. W., 2021, Comput. Educ. Artif. Intell., V2, DOI [10.1016/j.caeai.2021.100033, DOI 10.1016/J.CAEAI.2021.100033]
   Pellas N, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00276-4
   Puri A., 2023, Pros. Konf. Linguist. Tah. Atma Jaya (KOLITA), V21, P18, DOI [10.25170/kolita.21.4828, DOI 10.25170/KOLITA.21.4828]
   Sharel Pereira C., 2023, SJCC Management Research Review, V13, P68, DOI [10.35737/sjccmrr/V13/i2/2023/195, DOI 10.35737/SJCCMRR/V13/I2/2023/195]
   Sok S., 2023, SSRN ELECT J, V3, P110, DOI DOI 10.2139/SSRN.4378735
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Sweller J, 2015, CURR DIR PSYCHOL SCI, V24, P190, DOI 10.1177/0963721415569570
   Tahiru F, 2021, J CASES INF TECHNOL, V23, P1, DOI 10.4018/JCIT.2021010101
   Tajik E., 2023, TechRxiv, DOI [10.36227/techrxiv.22589497.v1, DOI 10.36227/TECHRXIV.22589497.V1]
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Torricelli M, 2024, Arxiv, DOI arXiv:2312.00233
   Veiga S.A., 2010, Int. J. Learn, V2, p207218.
   Walczak K, 2023, ECON BUS REV-POL, V9, P71, DOI 10.18559/ebr.2023.2.743
   Wang WJ, 2024, Arxiv, DOI [arXiv:2304.03516, 10.48550/arXiv.2304.03516, DOI 10.48550/ARXIV.2304.03516]
   Xiao YY, 2023, LANGUAGES-BASEL, V8, DOI 10.3390/languages8030212
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 62
TC 8
Z9 8
U1 64
U2 75
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD MAY
PY 2024
VL 14
IS 5
AR 503
DI 10.3390/educsci14050503
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA SE4A9
UT WOS:001232757000001
OA gold
DA 2024-12-25
ER

PT J
AU Ghobakhloo, M
   Fathi, M
   Iranmanesh, M
   Vilkas, M
   Grybauskas, A
   Amran, A
AF Ghobakhloo, Morteza
   Fathi, Masood
   Iranmanesh, Mohammad
   Vilkas, Mantas
   Grybauskas, Andrius
   Amran, Azlan
TI Generative artificial intelligence in manufacturing: opportunities for
   actualizing Industry 5.0 sustainability goals
SO JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT
LA English
DT Article
DE Generative AI impact; Industry 5.0; Resilience; Sustainable development
   goals; Digital transformation; Human centric
AB PurposeThis study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.Design/methodology/approachThe study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.FindingsGenerative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.Practical implicationsWhile each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.Originality/valueThis study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.
C1 [Ghobakhloo, Morteza; Fathi, Masood] Uppsala Univ, Div Ind Engn & Management, Uppsala, Sweden.
   [Ghobakhloo, Morteza; Vilkas, Mantas; Grybauskas, Andrius] Kaunas Univ Technol, Sch Econ & Business, Kaunas, Lithuania.
   [Fathi, Masood] Univ Skovde, Sch Engn Sci, Div Intelligent Prod Syst, Skovde, Sweden.
   [Iranmanesh, Mohammad] La Trobe Univ, Melbourne, Australia.
   [Amran, Azlan] Univ Sains Malaysia, Grad Sch Business, Gelugor, Penang, Malaysia.
C3 Uppsala University; Kaunas University of Technology; University of
   Skovde; La Trobe University; Universiti Sains Malaysia
RP Ghobakhloo, M (corresponding author), Uppsala Univ, Div Ind Engn & Management, Uppsala, Sweden.; Ghobakhloo, M (corresponding author), Kaunas Univ Technol, Sch Econ & Business, Kaunas, Lithuania.
EM morteza_ghobakhloo@yahoo.com; masood.fathi@his.se;
   iranmanesh.mohammad@gmail.com; mantas.vilkas@ktu.lt;
   andrius.grybauskas@ktu.lt; azlan_amran@usm.my
RI Ghobakhloo, Morteza/AAF-2508-2020; Vilkas, Mantas/ABF-4519-2020; Fathi,
   Masood/AAW-7425-2020; Iranmanesh, Mohammad/G-2321-2012
OI Grybauskas, Andrius/0000-0002-3246-645X; Vilkas,
   Mantas/0000-0002-6621-2896
FU European Union [810318]
FX This research has been a part of a project that received funding from
   the European Union's Horizon 2020 research and innovation programme
   under grant agreement No. 810318.
CR Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Badini S, 2023, ADV IND ENG POLY RES, V6, P278, DOI 10.1016/j.aiepr.2023.03.003
   Bendoly E, 2024, DECISION SCI, V55, P325, DOI 10.1111/deci.12619
   Bilgram Volker, 2023, IEEE Engineering Management Review, P18, DOI 10.1109/EMR.2023.3272799
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Carayannis EG, 2022, J KNOWL ECON, V13, P3445, DOI 10.1007/s13132-021-00854-2
   Castañé G, 2023, INT J PROD RES, V61, P2288, DOI 10.1080/00207543.2022.2069525
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Ching NT, 2022, J CLEAN PROD, V334, DOI 10.1016/j.jclepro.2021.130133
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Dwivedi A, 2023, COMPUT IND ENG, V176, DOI 10.1016/j.cie.2022.108927
   Farrukh A, 2024, BUS STRATEG ENVIRON, V33, P2868, DOI 10.1002/bse.3637
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Ghobakhloo M, 2024, INFORM SYST FRONT, DOI 10.1007/s10796-024-10476-z
   Ghobakhloo M, 2023, J CLEAN PROD, V417, DOI 10.1016/j.jclepro.2023.138023
   Ghobakhloo M, 2023, J IND PROD ENG, V40, P432, DOI 10.1080/21681015.2023.2216701
   Ghobakhloo M, 2023, CORP SOC RESP ENV MA, V30, P1473, DOI 10.1002/csr.2431
   Hein-Pensel F, 2023, J MANUF SYST, V66, P200, DOI 10.1016/j.jmsy.2022.12.009
   Huang SH, 2022, J MANUF SYST, V64, P424, DOI 10.1016/j.jmsy.2022.07.010
   Ivanov D, 2023, INT J PROD RES, V61, P1683, DOI 10.1080/00207543.2022.2118892
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kar A. K., 2023, Global Journal of Flexible Systems Management, V24, P659, DOI [DOI 10.1007/S40171-023-00356-X, https://doi.org/10.1007/s40171-023-00356-x]
   Kim B, 2020, DECIS SUPPORT SYST, V134, DOI 10.1016/j.dss.2020.113302
   Leng JW, 2022, J MANUF SYST, V65, P279, DOI 10.1016/j.jmsy.2022.09.017
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Longo F, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10124182
   Maddikunta PKR, 2022, J IND INF INTEGR, V26, DOI 10.1016/j.jii.2021.100257
   Mller J., 2020, RES WORKSH EUR TECHN, DOI [10.2777/082634, DOI 10.2777/082634]
   Mukherjee AA, 2023, INT J PROD ECON, V257, DOI 10.1016/j.ijpe.2023.108770
   Narkhede G, 2023, BUS STRATEGY DEV, V6, P704, DOI 10.1002/bsd2.272
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Ozanne LK, 2016, J PUBLIC POLICY MARK, V35, P249, DOI 10.1509/jppm.15.143
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Raji ID, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P33, DOI 10.1145/3351095.3372873
   Renda A., 2022, Directorate-General for Research and Innovation, DOI [10.2777/17322, DOI 10.2777/17322]
   Sale JEM, 2002, QUAL QUANT, V36, P43, DOI 10.1023/A:1014301607592
   Sindhwani R, 2022, TECHNOL SOC, V68, DOI 10.1016/j.techsoc.2022.101887
   Singh N, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122967
   Valette E, 2023, COMPUT IND ENG, V184, DOI 10.1016/j.cie.2023.109426
   Verma A, 2022, IEEE ACCESS, V10, P69160, DOI 10.1109/ACCESS.2022.3186892
   Wamba SF, 2024, INT J PROD RES, V62, P5676, DOI 10.1080/00207543.2023.2294116
   Wang H, 2023, IET COLL INTEL MANUF, V5, DOI 10.1049/cim2.12078
   Xiang W, 2024, IEEE T IND INFORM, V20, P1055, DOI 10.1109/TII.2023.3274224
   Xu X, 2021, J MANUF SYST, V61, P530, DOI 10.1016/j.jmsy.2021.10.006
   Yang J, 2024, IEEE T SYST MAN CY-S, DOI 10.1109/TSMC.2024.3349555
NR 45
TC 5
Z9 5
U1 88
U2 100
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-038X
EI 1758-7786
J9 J MANUF TECHNOL MANA
JI J. Manuf. Technol. Manag.
PD MAY 28
PY 2024
VL 35
IS 9
BP 94
EP 121
DI 10.1108/JMTM-12-2023-0530
PG 28
WC Engineering, Industrial; Engineering, Manufacturing; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics
GA RX2T5
UT WOS:001230899200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Hua, HW
   Yao, CJ
AF Hua, Haowei
   Yao, Co-Jiayu
TI Investigating generative AI models and detection techniques: impacts of
   tokenization and dataset size on identification of AI-generated text
SO FRONTIERS IN ARTIFICIAL INTELLIGENCE
LA English
DT Article
DE generative artificial intelligence (GenAI); machine learning; natural
   language processing; writing assessment; ChatGPT; Claude; text
   classification
AB Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completing assignments, assessments, or research papers. Current detectors, such as GPT-Zero, struggle to identify modified AI-generated texts and show reduced reliability for English as a Second Language learners. This study investigates detection of academic cheating by use of generative AI in high-stakes writing assessments. Classical machine learning models, including logistic regression, XGBoost, and support vector machine, are used to distinguish between AI-generated and student-written essays. Additionally, large language models including BERT, RoBERTa, and Electra are examined and compared to traditional machine learning models. The analysis focuses on prompt 1 from the ASAP Kaggle competition. To evaluate the effectiveness of various detection methods and generative AI models, we include ChatGPT, Claude, and Gemini in their base, pro, and latest versions. Furthermore, we examine the impact of paraphrasing tools such as GPT-Humanizer and QuillBot and introduce a new method of using synonym information to detect humanized AI texts. Additionally, the relationship between dataset size and model performance is explored to inform data collection in future research.
C1 [Hua, Haowei] Culver Acad, Culver, IN 46511 USA.
   [Yao, Co-Jiayu] Anhui Polytech Univ, Wuhu, Peoples R China.
C3 Anhui Polytechnic University
RP Hua, HW (corresponding author), Culver Acad, Culver, IN 46511 USA.
EM haowei.hua@culver.org
CR Akka M., 2024, Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, P182, DOI [10.4018/979-8-3693-1351-0.ch009, DOI 10.4018/979-8-3693-1351-0.CH009]
   Alexander K., 2023, Teaching English with Technology, V23, P25, DOI [10.56297/BUKA4060/XHLD5365, DOI 10.56297/BUKA4060/XHLD5365]
   Bellini V, 2024, CURR MED RES OPIN, V40, P353, DOI [10.1080/03007995.2024.2408468, 10.1080/03007995.2024.2310086]
   Chaka C., 2023, J. Appl. Learn. Teach., V6, DOI DOI 10.37074/JALT.2023.6.2.12
   Chaka C., 2024, J. Appl. Learn. Teach, V7, P1, DOI DOI 10.37074/JALT.2024.7.1.33
   Chakraborty S, 2023, Arxiv, DOI [arXiv:2304.04736, DOI 10.48550/ARXIV.2304.04736]
   Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
   Cingillioglu I, 2023, INT J INF LEARN TECH, V40, P259, DOI 10.1108/IJILT-03-2023-0043
   Clark K, 2020, Arxiv, DOI arXiv:2003.10555
   Corizzo R., 2023, 2023 INT JOINT C NEU, P1
   Das S., 2015, International Journal of Computer Applications, V115, P31, DOI 10.5120/20182-2402
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Doewes A., 2023, 16 INT C ED DAT MIN, P103
   El Kah A., 2023, The International Conference on Artificial Intelligence and Smart Environment, P242
   Hamner B., 2012, The Hewlett
   Herbold Steffen, 2023, Sci Rep, V13, P18617, DOI 10.1038/s41598-023-45644-9
   Kaur P., 2019, Int. J. Electron. Eng, V11, P393
   Khanzode K.C. A., 2020, International Journal of Library Information Science (IJLIS), V9, P3
   Krishna K., 2024, Advances in Neural Information Processing Systems, V35
   Kumar V, 2020, NATL CONF COMMUN, DOI 10.1109/ncc48643.2020.9056085
   LaValley MP, 2008, CIRCULATION, V117, P2395, DOI 10.1161/CIRCULATIONAHA.106.682658
   Liu YH, 2019, Arxiv, DOI [arXiv:1907.11692, DOI 10.48550/ARXIV.1907.11692]
   Mindner Lorenz, 2023, INT C ART INT ED TEC, P152
   Mitchell E., 2023, P INT C MACH LEARN, V202, P24950
   Mo YH, 2024, Arxiv, DOI [arXiv:2405.06652, 10.48550/arXiv.2405.06652, DOI 10.48550/ARXIV.2405.06652]
   Morales-Mrquez L. E., 2023, Artificial Intelligence-Based Text Classification: Separating Human Writing From Computer Generated Writing
   Mukherjee A., 2013, UIC-CS-03-2013 Technical Report
   Perkins M, 2024, J ACAD ETHICS, V22, P89, DOI 10.1007/s10805-023-09492-6
   Sadasivan VS, 2024, Arxiv, DOI [arXiv:2303.11156, DOI 10.48550/ARXIV.2303.11156]
   Suthaharan S, 2016, INTEGR SER INFORM SY, V36, P207
   Wang H, 2024, Arxiv, DOI arXiv:2405.16422
   Weber-Wulff D, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00146-z
   Yan D., 2023, Psychol. Test. Assessm. Model, V65, P125
   Zeng ZJ, 2024, AAAI CONF ARTIF INTE, P22502
NR 34
TC 0
Z9 0
U1 2
U2 2
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2624-8212
J9 FRONT ARTIF INTELL
JI Front. Artif. Intell.
PD NOV 19
PY 2024
VL 7
AR 1469197
DI 10.3389/frai.2024.1469197
PG 10
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA O0R4A
UT WOS:001368299200001
PM 39628838
OA gold
DA 2024-12-25
ER

PT J
AU Alowibdi, JS
AF Alowibdi, Jalal S.
TI Gender Prediction of Generated Tweets Using Generative AI
SO INFORMATION
LA English
DT Article
DE generative AI; artificial intelligence; linguistic patterns; text
   classification; GenAI-generated; human authored; gender-specific
AB With the use of Generative AI (GenAI), Online Social Networks (OSNs) now generate a huge volume of content data. Yet, user-generated content on OSNs, aided by GenAI, presents challenges in analyzing and understanding its characteristics. In particular, tweets generated by GenAI at the request of authentic human users present difficulties in determining the gendered variation of the content. The vast amount of data generated from tweets' content necessitates a thorough investigation into the gender-specific language used in these tweets. This study explores the task of predicting the gender of text content in tweets generated by GenAI. Through our analysis and experimentation, we have achieved a remarkable 90% accuracy in attributing gender-specific language to these tweets. Our research not only highlights the potential of GenAI in gender prediction but also underscores the sophisticated techniques employed to decipher the refined linguistic cues that differentiate male and female language in GenAI-generated content. This advancement in understanding and predicting gender-specific language in GenAI-generated tweets covers the way for more refined and accurate content analysis in the evolving landscape of OSNs.
C1 [Alowibdi, Jalal S.] Univ Jeddah, Coll Comp Sci & Engn, Dept Comp Sci & Artificial Intelligence, Jeddah 23890, Saudi Arabia.
C3 University of Jeddah
RP Alowibdi, JS (corresponding author), Univ Jeddah, Coll Comp Sci & Engn, Dept Comp Sci & Artificial Intelligence, Jeddah 23890, Saudi Arabia.
EM jalowibdi@uj.edu.sa
CR Ali D, 2024, EDUC SCI, V14, DOI 10.3390/educsci14060643
   Alowibdi J.S., 2013, P 2013 12 INT C MACH, VVolume 1, P365
   Alowibdi J.S., 2024, Eng. Technol. Appl. Sci. Res
   Alowibdi JS, 2015, SOC NETW ANAL MIN, V5, DOI 10.1007/s13278-015-0273-1
   Alowibdi JS, 2013, 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1, P365, DOI 10.1109/ICMLA.2013.74
   Bamman D, 2014, J SOCIOLING, V18, P135, DOI 10.1111/josl.12080
   Celik O., 2019, Sakarya University Journal of Science, V23, P1256
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   Gu JD, 2024, Arxiv, DOI arXiv:2404.05783
   Krger S., 2019, P 2019 IEEE ACM 2 IN
   Kumar R., 2024, Canadian Perspectives on Academic Integrity, V7, DOI [10.55016/ojs/cpai.v7i1.77675, DOI 10.55016/OJS/CPAI.V7I1.77675]
   Lai JW, 2024, FUTURE INTERNET, V16, DOI 10.3390/fi16060218
   Merler M, 2015, IEEE INT CON MULTI
   OpenAI, 2024, ChatGPT (March 15 Version) Large Language Model
   Peersman C., 2011, P 3 INT WORKSH SEARC, P37, DOI DOI 10.1145/2065023.2065035
   Reddy TR, 2017, IEEE INT ADV COMPUT, P860, DOI [10.1109/IACC.2017.0176, 10.1109/IACC.2017.167]
   Susnjak T, 2024, EDUC SCI, V14, DOI 10.3390/educsci14060656
   Yan LX, 2024, FOURTEENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, LAK 2024, P101, DOI 10.1145/3636555.3636856
NR 18
TC 1
Z9 1
U1 2
U2 2
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2078-2489
J9 INFORMATION
JI Information
PD AUG
PY 2024
VL 15
IS 8
AR 452
DI 10.3390/info15080452
PG 14
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA E7P0O
UT WOS:001304875800001
OA gold
DA 2024-12-25
ER

PT J
AU Smolin, M
AF Smolin, Mikhail
TI GenCoder: A Generative AI-Based Adaptive Intra-Vehicle Intrusion
   Detection System
SO IEEE ACCESS
LA English
DT Article
DE Computer crime; Automobiles; Principal component analysis; Intrusion
   detection; Adaptive systems; Protocols; NSL-KDD; Vehicle dynamics;
   Support vector machines; Artificial neural networks; Artificial
   intelligence; Generative AI; Encoding; Adaptive intrusion detection;
   automotive cybersecurity; deep neural network; GenCoder layer;
   generative artificial intelligence; variational autoencoder; vehicular
   intrusion detection system
ID IN-VEHICLE; OPTIMIZATION
AB With the rapid expansion of the vehicular cybersecurity (VCS) market and the increasing sophistication of cyberthreats, developing an adaptive intra-vehicular intrusion detection system (IDS) is crucial. This paper introduces GenCoder, a generative artificial intelligence (GenAI)-based IDS that uniquely addresses the dynamic and evolving nature of vehicular cyberthreats. GenCoder combines a five-layer deep neural network (DNN) with a variational autoencoder (VAE), overseen by a novel communication layer known as the GenCoder layer. This system dynamically adapts to new intrusion patterns by generating and utilizing new training data when deviations from known patterns are detected. The generated samples have a Shannon entropy (SE) value of 1.65 bits for four classes, indicating standard variety among the synthetic data. GenCoder demonstrates exceptional adaptability, pushing the accuracy, precision, recall, and F1-score from 84.79%, 83.58%, 83.70%, and 83.64% to 92.19%, 90.12%, 90.44%, and 90.28%, respectively, after introducing 50% feature deformation to testing data. The novel concept of an adaptive intra-vehicular IDS, the innovative GenCoder layer that establishes seamless communication among the DNN, the VAE, and the dataset, as well as the unique assessment strategies of adaptability make this research exceptional with the potential to create a new dimension in automotive IDS research.
C1 [Smolin, Mikhail] Kyrgyz State Tech Univ, Dept Appl Math & Comp Sci, Bishkek 720044, Kyrgyzstan.
C3 Razzakov Kyrgyz State Technical University
RP Smolin, M (corresponding author), Kyrgyz State Tech Univ, Dept Appl Math & Comp Sci, Bishkek 720044, Kyrgyzstan.
EM mikhail_smolin@kstu.kg
CR Achar S, 2023, IEEE ACCESS, V11, P89205, DOI 10.1109/ACCESS.2023.3305506
   Ahmad A, 2023, IEEE ACCESS, V11, P9042, DOI 10.1109/ACCESS.2023.3240100
   Al Razib M, 2022, IEEE ACCESS, V10, P53015, DOI 10.1109/ACCESS.2022.3172304
   Alalwany E, 2024, ELECTRONICS-SWITZ, V13, DOI 10.3390/electronics13050919
   Ali Z., 2024, Int. Trans. Electr. Eng. Comput. Sci., V3, P41
   Andreica T, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-58694-4
   Anthony C, 2024, ELECTRONICS-SWITZ, V13, DOI 10.3390/electronics13050809
   Avatefipour O, 2019, IEEE ACCESS, V7, P127580, DOI 10.1109/ACCESS.2019.2937576
   Awaad TA, 2022, IEEE ACCESS, V10, P88907, DOI 10.1109/ACCESS.2022.3200375
   Aziz S, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10081267
   Chan B., 2023, Study on communication protocols modbus and CAN bus
   Di Natale M, 2012, UNDERSTANDING AND USING THE CONTROLLER AREA NETWORK COMMUNICATION PROTOCOL: THEORY AND PRACTICE, P1, DOI 10.1007/978-1-4614-0314-2_1
   Dong C, 2023, IEEE ACCESS, V11, P35639, DOI 10.1109/ACCESS.2023.3265018
   Eigenschink P, 2023, IEEE ACCESS, V11, P47304, DOI 10.1109/ACCESS.2023.3275134
   Faruqui Nuruzzaman, 2020, Cyber Security and Computer Science. Second EAI International Conference, ICONCS 2020. Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST 325), P680, DOI 10.1007/978-3-030-52856-0_54
   Faruqui N, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e21520
   Gao ZR, 2019, IEEE ACCESS, V7, P90640, DOI 10.1109/ACCESS.2019.2927009
   Hafeez A., 2020, Tech. Rep. 2020- 01-0721, DOI [10.4271/2020-01-0721, DOI 10.4271/2020-01-0721]
   Hoang TN, 2024, EXPERT SYST APPL, V238, DOI 10.1016/j.eswa.2023.122181
   Hoang TN, 2022, VEH COMMUN, V38, DOI 10.1016/j.vehcom.2022.100520
   Hossain ME, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app132212300
   Hossen T, 2017, NORTH AMER POW SYMP
   Humayed Abdulmalik, 2023, Proceedings of the Second International Conference on Innovations in Computing Research (ICR'23). Lecture Notes in Networks and Systems (721), P256, DOI 10.1007/978-3-031-35308-6_22
   Jeong S, 2024, IEEE T IND INFORM, V20, P4651, DOI 10.1109/TII.2023.3324949
   Kabilan N, 2024, J SAF SCI RESIL, V5, P119, DOI 10.1016/j.jnlssr.2023.12.004
   Kang H., 2021, WORKSH AUT AUT VEH S, V2021, P25
   Khalil A, 2024, AIN SHAMS ENG J, V15, DOI 10.1016/j.asej.2023.102616
   Kishore Ch Ravi, 2024, Journal of the Institution of Engineers (India): Series B (Electrical, Electronics & Telecommunication and Computer Engineering), V105, P541, DOI 10.1007/s40031-023-00987-9
   Kont Kate-Riin, 2024, Library Hi Tech News, V41, P11, DOI 10.1108/LHTN-03-2023-0036
   Lan YC, 2022, INT CONF DAT MIN WOR, P586, DOI 10.1109/ICDMW58026.2022.00081
   Lapin M, 2018, IEEE T PATTERN ANAL, V40, P1533, DOI 10.1109/TPAMI.2017.2751607
   Li L.-H., 2021, P 15 INT C UB INF MA, P1
   Li LF, 2013, INT CONF AFFECT, P312, DOI 10.1109/ACII.2013.58
   Longari S, 2021, IEEE T NETW SERV MAN, V18, P1913, DOI 10.1109/TNSM.2020.3038991
   Masum M, 2021, IEEE INT CONF BIG DA, P5413, DOI 10.1109/BigData52589.2021.9671576
   Micale D, 2024, INT J INF SECUR, V23, P2203, DOI 10.1007/s10207-024-00821-3
   MR World, 2024, Global Automotive Cyber Security Market
   Nandy T, 2024, J KING SAUD UNIV-COM, V36, DOI 10.1016/j.jksuci.2024.101945
   Park M, 2014, INFORM PROCESS LETT, V114, P603, DOI 10.1016/j.ipl.2014.06.002
   Paula LPO, 2023, IEEE ACCESS, V11, P19122, DOI 10.1109/ACCESS.2023.3248509
   Peng Chen, 2020, 2020 IEEE 6th International Conference on Computer and Communications (ICCC), P2059, DOI 10.1109/ICCC51575.2020.9345300
   Preethi D, 2021, INT J SWARM INTELL R, V12, P57, DOI 10.4018/IJSIR.2021040104
   Racherla S, 2024, IEEE ACCESS, V12, P63584, DOI 10.1109/ACCESS.2024.3396461
   Seo E, 2018, ANN CONF PRIV SECUR, P286
   Song HM, 2020, VEH COMMUN, V21, DOI 10.1016/j.vehcom.2019.100198
   Specification C., 1991, ROBERT BOSCH GMBH PO, V50, P15
   Sutradhar K, 2024, COMPUT COMMUN, V219, P1, DOI 10.1016/j.comcom.2024.02.024
   Trivedi S., 2023, P INT C SMART COMP A, P1
   U. Security, 2024, Upstream's 2024 Global Automotive Cybersecurity Report
   Wang SS, 2024, ELECTRONICS-SWITZ, V13, DOI 10.3390/electronics13071317
   Yu L, 2022, EMERG MARK FINANC TR, V58, P472, DOI 10.1080/1540496X.2020.1825935
NR 51
TC 0
Z9 0
U1 1
U2 1
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 150651
EP 150663
DI 10.1109/ACCESS.2024.3476177
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA K0C8H
UT WOS:001340659300001
OA gold
DA 2024-12-25
ER

PT J
AU Ebert, C
   Louridas, P
AF Ebert, Christof
   Louridas, Panos
TI Generative AI for Software Practitioners
SO IEEE SOFTWARE
LA English
DT Article
DE Productivity; Industries; Auditory system; Chatbots; Software;
   Artificial intelligence; Software engineering
AB Generative artificial intelligence (AI) tools, such as Bard, ChatGPT, and CoPilot, have rapidly gained widespread usage. They also have the potential to boost software engineering productivity. In this article, we elaborate technologies and usage of generative AI in the software industry. We address questions, such as: How does generative AI improve software productivity? How to connect generative AI to software development, and what are the risks? Which technologies have what sorts of benefits? Practitioner guidance and case studies are shared from our industry context. I look forward to hearing from you about this column and the technologies that matter most for your work.-Christof Ebert
C1 [Ebert, Christof] Vector Consulting Serv, D-70499 Stuttgart, Germany.
   [Louridas, Panos] Athens Univ Econ & Business, Dept Management Sci & Technol, Athens 10434, Greece.
   [Louridas, Panos] GRNET SA, Res & Dev, Athens 11523, Greece.
C3 Athens University of Economics & Business
RP Ebert, C (corresponding author), Vector Consulting Serv, D-70499 Stuttgart, Germany.
EM christof.ebert@vector.com; louridas@aueb.gr
RI Ebert, Christof/JXM-5500-2024
OI Ebert, Christof/0000-0003-2287-1854
CR Alec Radford, 2018, Improving Language Understanding by Generative Pre-Training
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Chow A. R., 2023, Time
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Ebert C, 2023, IEEE SOFTWARE, V40, P20, DOI 10.1109/MS.2023.3242179
   Ebert C, 2022, IEEE SOFTWARE, V39, P8, DOI 10.1109/MS.2022.3166755
   Lim R., CANTORS PARADISE
   Vaswani A, 2017, ADV NEUR IN, V30
   Wolfram S., 2023, WHAT IS CHATGPT DOIN
NR 9
TC 45
Z9 46
U1 32
U2 125
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0740-7459
EI 1937-4194
J9 IEEE SOFTWARE
JI IEEE Softw.
PD JUL-AUG
PY 2023
VL 40
IS 4
BP 30
EP 38
DI 10.1109/MS.2023.3265877
PG 9
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA M8KI3
UT WOS:001032645500006
DA 2024-12-25
ER

PT J
AU Koh, E
   Zhang, LS
   Lee, AVY
   Wang, HY
AF Koh, Elizabeth
   Zhang, Lishan
   Lee, Alwyn Vwen Yen
   Wang, Hongye
TI Revolutionizing Word Clouds for Teaching and Learning With Generative
   Artificial Intelligence: Cases From China and Singapore
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Artificial intelligence (AI); computer-aided instruction; computer-aided
   learning; data visualization; generative AI; natural language processing
   (NLP)
AB Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning applications. This article examines the word cloud, a toolkit often used to scaffold teaching and learning for reflection, critical thinking, and content learning. Addressing the issues in traditional word clouds, semantic word clouds have been developed but they are technically challenging to develop and still problematic. However, generative AI has the potential to develop efficient, accurate, creative, and accessible word clouds. Three different methods representing three major approaches of word cloud generation were developed, implemented, and user evaluated-traditional (baseline), semantic (natural language processing enhanced), and generative AI (generative pretrained transformer based)-in two different language contexts-Chinese (China case) and English (Singapore case). The findings of the study show the technical robustness of the methods, as well as provide key pedagogical insights from the user perspective of instructors of higher education courses in China and Singapore. Implications to the design of word clouds and their application in teaching and learning are discussed.
C1 [Koh, Elizabeth; Lee, Alwyn Vwen Yen] Nanyang Technol Univ, Natl Inst Educ, Singapore 637616, Singapore.
   [Zhang, Lishan; Wang, Hongye] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
C3 Nanyang Technological University; National Institute of Education (NIE)
   Singapore; Central China Normal University
RP Zhang, LS (corresponding author), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
EM elizabeth.koh@nie.edu.sg; lishan.zhang3@gmail.com; alwyn.lee@nie.edu.sg;
   hongye.wang091@gmail.com
RI Zhang, Lishan/AAM-1156-2020; /ABC-7246-2020; Wang, Hongye/T-5765-2019;
   Lee, Alwyn Vwen Yen/AAL-2245-2021
OI Zhang, Lishan/0000-0003-0830-2399; Koh, Elizabeth/0000-0002-2808-8687;
   Lee, Alwyn Vwen Yen/0000-0002-3682-017X
FU National Natural Science Foundation of China
FX No Statement Available
CR Abdelghani R, 2024, INT J ARTIF INTELL E, V34, P483, DOI 10.1007/s40593-023-00340-7
   Baralt M, 2011, LANG LEARN TECHNOL, V15, P12
   Brown TB, 2020, ADV NEUR IN, V33
   Chi JJ, 2019, PATTERN RECOGN LETT, V123, P53, DOI 10.1016/j.patrec.2019.01.018
   Cohn RJ, 2017, TEACH LEARN MED, V29, P304, DOI 10.1080/10401334.2016.1274658
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   DePaolo CA, 2014, TECHTRENDS, V58, P38, DOI 10.1007/s11528-014-0750-9
   Feinberg J., 2010, Beautiful Visualization: Looking at Data through the Eyes of Experts, P37
   Feng S, 2023, COMPUT EDUC, V203, DOI 10.1016/j.compedu.2023.104864
   Ferrara E., 2023, Should chatGPT be biased? Challenges and risks of bias in large language models, V28
   Hearst MA, 2020, IEEE T VIS COMPUT GR, V26, P2748, DOI 10.1109/TVCG.2019.2904683
   Jo J, 2015, IEEE COMPUT GRAPH, V35, P20, DOI 10.1109/MCG.2015.113
   Joksimovic S., 2023, Comput. Educ. Artif. Intell, V4, DOI [10.1016/j.caeai.2023.100138, DOI 10.1016/J.CAEAI.2023.100138]
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kuo B. Y. L., 2007, 16 INT WORLD WID WEB, DOI DOI 10.1145/1242572.1242766
   Lee A. V. Y., 2023, Inf. Technol. Educ. Learn, V3, P1, DOI [10.12937/itel.3.1.Inv.p001, DOI 10.12937/ITEL.3.1.INV.P001]
   Lee AVY, 2024, ASIA PAC J EDUC, V44, P81, DOI 10.1080/02188791.2024.2305171
   Liu SX, 2014, IEEE CONF VIS ANAL, P183, DOI 10.1109/VAST.2014.7042494
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Mayer RE, 1996, EDUC PSYCHOL, V31, P151, DOI 10.1207/s15326985ep3103&4_1
   Mehrotra R, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P889
   Miley F., 2011, Journal of the Scholarship of Teaching and Learning, P91
   Mollick E., 2023, Inspiring Minds
   OpenAI, US
   Paivio A, 1986, Mental representations: a dual-coding approach
   Piaget J., 1977, The essential Piaget
   Qayyum MA, 2019, J INF KNOWL MANAG, V18, DOI 10.1142/S0219649219500412
   Reyes-Foster B.M., 2016, J TEACHING LEARNING, V5, P16, DOI [10.14434/jotlt.v5n1.13805, DOI 10.14434/JOTLT.V5N1.13805]
   Steele J., 2010, Data Throughthe Eyes of Experts, P37
   Susnjak T, 2024, INT J ARTIF INTELL E, V34, P452, DOI 10.1007/s40593-023-00336-3
   Viégas FB, 2009, IEEE T VIS COMPUT GR, V15, P1137, DOI 10.1109/TVCG.2009.171
   Volkert DR, 2018, NURS EDUC PERSPECT, V39, P53, DOI 10.1097/01.NEP.0000000000000159
   Wang AI, 2014, EDUC RES INT, V2014, DOI 10.1155/2014/259128
   Xie Y, 2019, INTERACT LEARN ENVIR, V27, P478, DOI 10.1080/10494820.2018.1484774
   Zhang LS, 2022, INTERACT LEARN ENVIR, V30, P177, DOI 10.1080/10494820.2019.1648300
   Zheng L., 2024, P ADV NEUR INF PROC, V36
NR 36
TC 1
Z9 1
U1 69
U2 104
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PY 2024
VL 17
BP 1416
EP 1427
DI 10.1109/TLT.2024.3385009
PG 12
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA OF2C0
UT WOS:001205775100001
DA 2024-12-25
ER

PT J
AU Strawn, G
AF Strawn, George
TI Where Deep Learning and Generative AI Started: Masterminds of Artificial
   Neural Networks-McCulloch, Pitts, and Rosenblatt
SO IT PROFESSIONAL
LA English
DT Article
DE Deep learning; Digital computers; Technological innovation; Generative
   AI; Computational modeling; Brain modeling; Biological neural networks
AB Deep learning and generative artificial intelligence originated in 1943, which preceded our age of digital computers. These origins focused on understanding the functioning of the brain and creating an artificial model of it. The three masterminds who initiated this now very important strand of IT technology are highlighted and the elements of their inventions are outlined.
C1 [Strawn, George] Natl Acad Sci, Washington, DC 20001 USA.
C3 National Academies of Sciences, Engineering & Medicine
RP Strawn, G (corresponding author), Natl Acad Sci, Washington, DC 20001 USA.
EM gostrawn@gmail.com
OI Strawn, George/0000-0003-4098-0464
CR [Anonymous], 2024, wikipedia
   McCulloch Warren S., 1943, BULL MATH BIOPHYS, V5, P115, DOI 10.1007/BF02459570
   Rosenblatt F, 1962, PRINCIPLES NEURODYNA
   Strawn G, 2024, IT PROF, V26, P13, DOI 10.1109/MITP.2024.3375568
   Strawn G, 2017, IT PROF, V19, P58, DOI 10.1109/MITP.2017.3680955
   Strawn G, 2016, IT PROF, V18, P62, DOI 10.1109/MITP.2016.116
   Strawn G, 2014, IT PROF, V16, P10, DOI 10.1109/MITP.2014.18
   Strawn GO, 2022, IT PROF, V24, P13, DOI 10.1109/MITP.2022.3172838
NR 8
TC 0
Z9 0
U1 9
U2 9
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1520-9202
EI 1941-045X
J9 IT PROF
JI IT Prof.
PD MAY-JUN
PY 2024
VL 26
IS 3
BP 99
EP 101
DI 10.1109/MITP.2024.3404229
PG 3
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA WX6Y8
UT WOS:001258222000004
DA 2024-12-25
ER

PT J
AU Willet, KBS
   Na, HH
AF Willet, K. Bret Staudt
   Na, Hunhui
TI Generative AI Generating Buzz: Volume, Engagement, and Content of
   Initial Reactions to ChatGPT in Discussions Across Education-Related
   Subreddits
SO ONLINE LEARNING
LA English
DT Article
DE ChatGPT; GenAI; Reddit; affinity spaces; technology reaction; learning
   analytics
ID TECHNOLOGY ACCEPTANCE MODEL; IMPACT
AB The emergence of generative artificial intelligence (GenAI) has ignited debates regarding its potential benefits and detriments for education. Despite widespread discussions, insights into GenAI's impact on education have been limited because early studies hav e often been narrow in scope and focused on specific contexts. Therefore, the purpose of this study is to explore and analyze the volume, engagement, and content of initial reactions to one leading GenAI tool, ChatGPT. Specifically, we collected and analyzed public online discussions of ChatGPT in the first four months following the tool's release. We collected 345 posts and 6,463 comments about ChatGPT from 25 education-focused subreddits. We analyzed the volume, engagement, and content of ChatGPT discussions through descriptive statistics and natural language processing techniques. Findings show relatively low volume of ChatGPT discussions, unevenly spread across education-related subreddits - with the majority of the discussions occurring in two subreddits, while six subreddits did not have any discussions. Despite this, the level of engagement within ChatGPT posts was substantial; for instance, a ChatGPT post hosted a median of 15 comments, and these comments were lengthy, indicating rich engagement rather than superficial. The content of ChatGPT discussions across the six largest education-related subreddits differed in the degree of analytical thinking and emotional tone even while sharing a predominant focus on students and AI. Diverse reactions to and perspectives on GenAI - observed from varied levels of volume, engagement, and content of ChatGPT across educationalrelated subreddits - highlights how diverse educational stakeholders reacted to GenAI differently, offering insights into how to explore, analyze, and comprehend the spread and adoption of technological innovation in education.
C1 [Willet, K. Bret Staudt; Na, Hunhui] Florida State Univ, Tallahassee, FL 32306 USA.
C3 State University System of Florida; Florida State University
RP Willet, KBS (corresponding author), Florida State Univ, Tallahassee, FL 32306 USA.
RI Staudt Willet, Bret/GRY-6181-2022
OI Staudt Willet, K. Bret/0000-0002-6984-416X; Na,
   Hunhui/0000-0003-2157-6685
CR Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Bennett L, 2014, RES LEARN TECHNOL, V22, DOI 10.3402/rlt.v22.21453
   Boehm PJ., 2009, COMMUNITY COLL ENTER, V15, P45
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Bozkurt A., 2023, Asian Journal of Distance Education., V18, DOI [10.5281/zenodo.817494, DOI 10.5281/ZENODO.817494]
   Carpenter J., 2018, P SOC INF TECHN TEAC, P2207
   Carpenter J, 2022, PROF DEV EDUC, V48, P784, DOI 10.1080/19415257.2020.1752287
   Carpenter JP, 2021, TEACH TEACH EDUC, V104, DOI 10.1016/j.tate.2021.103371
   Carvalho A. A. A., 2024, INT C LIF ED LEAD AL, P140
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chen X., 2020, Computers and Education: Artificial Intelligence, V1, P100002, DOI [10.1016/j.caeai.2020.100002 10.1016/j.caeai.2020.100002, DOI 10.1016/J.CAEAI.2020.100002]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Clark R., 2023, Georgia Tech Admission Blog.
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Cuban L., 2003, Oversold and underused: Computers in the classroom
   Dai W, 2023, IEEE INT CONF ADV LE, P323, DOI 10.1109/ICALT58122.2023.00100
   Davies R.S., 2014, Handbook of Research on Educational Communications and Technology, P841
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   ElSayary A, 2024, J COMPUT ASSIST LEAR, V40, P931, DOI 10.1111/jcal.12926
   Frei-Landau R, 2022, EDUC INF TECHNOL, V27, P12811, DOI 10.1007/s10639-022-11148-8
   Gee J.P., 2004, Situated language and learning: A critique of traditional schooling
   Greenhalgh SP, 2020, COMPUT EDUC, V148, DOI 10.1016/j.compedu.2020.103809
   Greenhalgh SP, 2017, TECHTRENDS, V61, P273, DOI 10.1007/s11528-016-0142-4
   Habibi A., 2023, Comput. Educ. Artif. Intell, V5, P100190, DOI [10.1016/j.caeai.2023.100190, DOI 10.1016/J.CAEAI.2023.100190]
   Hagendorff T, 2020, MIND MACH, V30, P99, DOI 10.1007/s11023-020-09517-8
   Haythornthwaite C, 2018, LEARN MEDIA TECHNOL, V43, P219, DOI 10.1080/17439884.2018.1498356
   Henninger NM, 2020, CONVERGENCE-US, V26, P1391, DOI 10.1177/1354856519847329
   Huang AYQ, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104684
   Hung JS, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12070380
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Iqbal N., 2023, Glob J Manag Adm Sci, V3, P171, DOI DOI 10.46568/GJMAS.V3I4.163
   Kalolo JF, 2019, EDUC INF TECHNOL, V24, P345, DOI 10.1007/s10639-018-9778-3
   Kelly Samantha Murphy., 2023, CNN
   Lee RM., 2017, The SAGE Handbook of Online Research Methods, V2nd, DOI DOI 10.4135/9781473957992
   Lee YH, 2011, EDUC TECHNOL SOC, V14, P124
   Lincoln Y. S., 1985, NATURALISTIC INQUIRY, DOI 10.1016/0147-1767(85)90062-8
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Mailizar M, 2021, CONTEMP EDUC TECHNOL, V13, DOI 10.30935/cedtech/9709
   Mandal R., 2023, International Journal of Multidisciplinary Educational Research., V12, P5
   Martin D., 2023, CRN.
   Massanari A, 2017, NEW MEDIA SOC, V19, P329, DOI 10.1177/1461444815608807
   Moore S, 2024, TECHTRENDS, V68, P27, DOI 10.1007/s11528-023-00895-1
   Muljana PS, 2022, J COMPUT HIGH EDUC, V34, P679, DOI 10.1007/s12528-022-09317-2
   Na HH, 2024, J RES TECHNOL EDUC, DOI 10.1080/15391523.2024.2338091
   Na HH, 2024, J RES TECHNOL EDUC, V56, P392, DOI 10.1080/15391523.2022.2150727
   OpenAI, 2022, Introducing ChatGPT
   OpenAI, 2023, GPT-4
   Oravec J. O., 2023, Journal of Interactive Learning Research, V34, P213
   Pennebaker J.W., 2015, Linguistic Inquiry and Word Count: LIWC2015
   Perault Matt, 2023, J FREE SPEECH LAW, V3, P363
   Picciano AG, 2019, ONLINE LEARN, V23, P270, DOI 10.24059/olj.v23i3.2023
   Pichai S., 2023, Introducing Gemini: our largest and most capable AI model
   Pokrivcakova S, 2019, J LANG CULT EDUC, V7, P135, DOI 10.2478/jolace-2019-0025
   PROVENZO EF, 1986, HIST EDUC QUART, V26, P647, DOI 10.2307/369036
   Python Software Foundation, 2024, Python Computer software.
   R Core Team, 2024, R: A language and environment for statistical computing (Version 4.4.1) Computer software. R Foundation for Statistical Computing
   RedditInc, 2024, Homepage-Reddit
   Richard B, 2021, INT J QUAL METH, V20, DOI 10.1177/16094069211012217
   Robinson B, 2023, GAMES CULT, DOI 10.1177/15554120231203134
   Rogers E., 1983, DIFFUSION INNOVATION
   Similarweb, 2024, Reddit.com competitive analysis, marketing mix and traffic
   Simmonds R., 2023, Foundation Marketing.
   Willet KBS, 2021, BRIT J EDUC TECHNOL, V52, P714, DOI 10.1111/bjet.13051
   Willet KBS, 2020, J RES TECHNOL EDUC, V52, P216, DOI 10.1080/15391523.2020.1722978
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Tamim RM, 2011, REV EDUC RES, V81, P4, DOI 10.3102/0034654310393361
   Topsakal O., 2022, J COGNITIVE SYSTEMS, V7, P33, DOI DOI 10.52876/JCS.1227392
   Tov W, 2013, PSYCHOL ASSESSMENT, V25, P1069, DOI 10.1037/a0033007
   Weller M, 2020, ISS ONLINE EDUC, P1, DOI 10.15215/aupress/9781771993050.01
   Wingo NP, 2017, ONLINE LEARN, V21, P15
   Wolf RR, 2023, ONLINE LEARN, V27, P41, DOI 10.24059/olj.v27i3.3974
   Woodruff K., 2023, REIMAGINING ED ROLE, DOI [https://doi.org/10.5772/intechopen.1002741, DOI 10.5772/INTECHOPEN.1002741]
   Zhang K., 2021, COMPUTERS ED ARTIFIC, V2, P100025, DOI [DOI 10.1016/J.CAEAI.2021.100025, https://doi.org/10.1016/j.caeai.2021.100025, 10.1016/j.caeai.2021.100025]
NR 73
TC 1
Z9 1
U1 11
U2 11
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD JUN
PY 2024
VL 28
IS 2
BP 1
EP 24
DI 10.24059/olj.v28i2.4434
PG 24
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA ZX2J9
UT WOS:001278516800001
OA gold
DA 2024-12-25
ER

PT J
AU Ye, XB
   Yan, YH
   Li, J
   Jiang, B
AF Ye, Xiongbiao
   Yan, Yuhong
   Li, Jia
   Jiang, Bo
TI Privacy and personal data risk governance for generative artificial
   intelligence: A Chinese perspective
SO TELECOMMUNICATIONS POLICY
LA English
DT Article
DE Generative AI; Privacy and personal data protection; Risk governance;
   Chinese law
ID DATA PROTECTION
AB The rapid development of generative artificial intelligence (AI) has attracted global attention and posed challenges to existing data governance frameworks. The increased technical complexity and expanded scale of data usage not only make it more difficult to regulate AI but also present challenges for the current legal system. This article, which takes ChatGPT's training data and working principles as a starting point, examines specific privacy risks, data leakage risks, and personal data risks posed by generative AI. It also analyzes the latest practices in privacy and personal data protection in China. This article finds that while China's governance on privacy and personal data protection takes a macro-micro integration approach and a private-and-public law integration approach, there are shortcomings in the legal system. Given that the current personal data protection system centered on individual control is unsuitable for the modes of data processing by generative AI, and that private law is insufficient in safeguarding data privacy, urgent institutional innovation is needed to achieve the objective of "trustworthy AI."
C1 [Ye, Xiongbiao] Cent China Normal Univ, Law Sch, Wuhan, Peoples R China.
   [Yan, Yuhong] Univ Int Business & Econ, Sch Govt, Beijing, Peoples R China.
   [Li, Jia] Univ Int Business & Econ, Sch Law, Beijing, Peoples R China.
   [Li, Jia] Hainan Univ, Sch Int Studies, Haikou, Peoples R China.
   [Jiang, Bo] China Univ Polit Sci & Law, Sch Int Law, Beijing, Peoples R China.
C3 Central China Normal University; University of International Business &
   Economics; University of International Business & Economics; Hainan
   University; China University of Political Science & Law
RP Yan, YH (corresponding author), 10 Huixin East St, Beijing 100029, Peoples R China.
EM 03388@uibe.edu.cn
CR Allen A.L., 1999, WILLIAM MARY LAW REV, V40, P723
   Ananny M, 2016, SCI TECHNOL HUM VAL, V41, P93, DOI 10.1177/0162243915606523
   Anderson RJ, 1996, P IEEE S SECUR PRIV, P30, DOI 10.1109/SECPRI.1996.502667
   [Anonymous], 2019, A definition of AI: main capabilities and disciplines
   Bian G., 2024, When will price discrimination based on big data stop being helpless despite public outcry?
   Bufalieri L, 2020, 2020 IEEE 13TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2020), P75, DOI 10.1109/ICWS49710.2020.00017
   Buzzard J., 2023, 2023 identity fraud study: The butterfly effect
   Cao B, 2022, CNS SPECTRUMS, V27, P428, DOI 10.1017/S1092852921000171
   Ding X., 2024, Law Science, P3
   Edwards L., 2024, Private ordering and generative AI: What can we learn from model terms and conditions?
   Edwards L., 2017, Duke Law Technology Review, V16, P18, DOI DOI 10.2139/SSRN.2972855
   Feng J., 2024, risks, ethical governance and legal regulation faced by artificial intelligence
   Fuchs Christian, 2011, Journal of Information, Communication and Ethics in Society, V9, P220, DOI 10.1108/14779961111191039
   Greenleaf G, 2012, INT DATA PRIV LAW, V2, P68, DOI 10.1093/idpl/ips006
   Guo DH, 2023, J SAF SCI RESIL, V4, P329, DOI 10.1016/j.jnlssr.2023.08.001
   Haughey L., 2023, Are YOUR conversations safe? ChatGPT creator confirms a bug allowed some users to snoop on others' chat histories
   Hou W., 2018, how to regulate unfair competition in the online environment?
   iResearch, 2024, 2023 AIGC scenario application outlook research report
   Knetsch Jonas., 2022, Journal of European Tort Law, V13/, P132, DOI DOI 10.1515/JETL-2022-0008
   Li WL, 2024, COMPUT LAW SECUR REV, V54, DOI 10.1016/j.clsr.2024.105994
   Liu Q., 2021, The Jurist, V5, P1
   Liu X., 2022, Global Law Review, P58
   Loos M. E., 2019, George Washington Law Review Arguendo, V87, P42
   Lynskey O, 2014, INT COMP LAW Q, V63, P569, DOI 10.1017/S0020589314000244
   Mantelero A, 2014, COMPUT LAW SECUR REV, V30, P643, DOI 10.1016/j.clsr.2014.09.004
   Mudaliar A., 2024, ChatGPT Leaks Sensitive User Data, OpenAI Suspects Hack
   Peng N, 2021, Supreme people's court: The national telecommunications network fraud property losses amounted to CNY 35.37 billion last year
   Pieters W, 2011, ETHICS INF TECHNOL, V13, P53, DOI 10.1007/s10676-010-9253-3
   Poremba S., 2023, Security Intelligence
   Quach K., 2021, What happens when your massive text-generating neural net starts spitting out People's phone numbers? If you're OpenAI, you create a filter
   Ranchordas S., 2020, Time, law, and change: An interdisciplinary study, P347, DOI [10.2139/ssrn.3466161, DOI 10.2139/SSRN.3466161]
   Smuha Nathalie A., 2021, Law, Innovation and Technology, V13, P57, DOI 10.1080/17579961.2021.1898300
   Supriyadi D., 2023, Journal of Human Rights, Culture and Legal System, V3, P33, DOI [10.53955/jhcls.v3i1.71, DOI 10.53955/JHCLS.V3I1.71]
   Thompson A. D., 2022, What's in my AI? A comprehensive analysis of datasets used to train GPT-1, GPT-2, GPT-3 GPT-NeoX-20B, megatron-11B, MT-NLG, and gopher
   Tzanou M, 2013, INT DATA PRIV LAW, V3, P88, DOI 10.1093/idpl/ipt004
   van Genderen R. H., 2017, European Data Protection Law Review, P338, DOI DOI 10.21552/EDPL/2017/3/8
   van Ooijen I, 2019, J CONSUM POLICY, V42, P91, DOI 10.1007/s10603-018-9399-7
   Villaronga EF, 2018, COMPUT LAW SECUR REV, V34, P304, DOI 10.1016/j.clsr.2017.08.007
   Villegas-Ch W, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12183786
   Wachter S., 2019, Columbia Business Law Review, V7, P494
   Walch K., 2018, The race for AI dominance is more global than you think
   Wang L., 2012, Journal of Soochow University (Engineering Science Edition), V33, P199
   Wang M., 2024, Asia-Pacific, Security and Maritime affairs, V36-54, P133
   Yang Z., 2019, Journal of Comparetive Law, P1
   Zhang X., 2022, Journal of the East China University of Politics and Law, V25, P6
   Zhang X., 2015, China Legal Science, P38
   Zhao B., 2019, US-China Law Review, V16, P97
   Zhu G., 1997, Statute Law Review, V18, P208
NR 48
TC 1
Z9 1
U1 28
U2 28
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0308-5961
EI 1879-3258
J9 TELECOMMUN POLICY
JI Telecommun. Policy
PD NOV
PY 2024
VL 48
IS 10
AR 102851
DI 10.1016/j.telpol.2024.102851
EA NOV 2024
PG 15
WC Communication; Information Science & Library Science; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Communication; Information Science & Library Science; Telecommunications
GA M7K3F
UT WOS:001359279600001
OA hybrid
DA 2024-12-25
ER

PT J
AU Bar-Gil, O
AF Bar-Gil, Oshri
TI THE DANCE OF AGENCIES IN AI ART-BEYOND THE CREATOR-TOOL DICHOTOMY, A
   NETNOGRAPHIC ANALYSIS OF REDDIT FORUMS
SO JOURNAL OF POSTHUMAN STUDIES-PHILOSOPHY TECHNOLOGY MEDIA
LA English
DT Article
DE generative artificial intelligence; art creation; artistic
   intentionality; algorithmic; rationality; netnography
AB This article analyzes the impact of generative artificial intelligence (AI) systems on contemporary AI-based art generation practices. As algorithms display nascent creativity, they catalyze philosophical questions regarding the role of technology in artistic creation. However, the influence of AI on art creation, perception, and consumption remains unexplored by empirical research. This article integrates the theoretical frameworks of post-phenomenology and actor-network theory to investigate how generative AI technologies mediate creators' perception, agency, and imagination. It explores how human-algorithm assemblages may expand possibilities while also obscuring the anthropocentric constructs underpinning art. It uses netnography of artistic communities in the Reddit website to explore the tensions arising as human rationalities and intentions collide with algorithmic logics and constraints. The findings reveal generative AI's potential to delegate intentionality as well as its potential influence on agency, rationality, and memory. Using empirical grounding, this study elucidates the complex entanglements among artists, algorithms, artwork, and the public in the emerging generative AI terrain.
C1 [Bar-Gil, Oshri] Bar Ilan Univ, Ramat Gan, Israel.
C3 Bar Ilan University
RP Bar-Gil, O (corresponding author), Bar Ilan Univ, Ramat Gan, Israel.
RI Bar-Gil, Oshri/AEP-2909-2022
OI Bar-Gil, Oshri/0000-0003-2601-6409
CR Adams NN, 2024, INT J SOC RES METHOD, V27, P47, DOI 10.1080/13645579.2022.2111816
   Addeo Felice., 2019, ATHENS JOURNAL OF SOCIAL SCIENCES, V7, P9, DOI [10.30958/ajss.7-1-1, DOI 10.30958/AJSS.7-1-1]
   Agrawal Abhinav, 2016, Medium6 December
   Anne-Marie T., 2017, The ORBIT Journal, V1, P1, DOI DOI 10.29297/ORBIT.V1I2.50
   [Anonymous], 2023, bodymemory1. (2023, July 10). "Do You Love Art?" [Reddit Post].
   ANSCOMBE GEM, 1957, P ARISTOTELIAN SOC, V57, P321
   Bar-Gil Oshri, 2020, NECSUS European Journal of Media Studies, V9, P215
   Bartl Michael, 2016, International Journal of Technology Marketing, V11, P165
   Baumgartner J., 2020, ICWSM, P830, DOI DOI 10.1609/ICWSM.V14I1.7347
   Belk R, 2021, MARKETING THEOR, V21, P25, DOI 10.1177/1470593120961461
   Benjamin Walter., 2008, WORK ART AGE ITS TEC
   Bilton C, 2010, INT J CULT POLICY, V16, P255, DOI 10.1080/10286630903128518
   bonabbyteit, 2023, "I Am Scared of My Own Drawing" [Reddit Post].
   Braidotti Rosi., 2013, POSTHUMAN, DOI DOI 10.7551/MITPRESS/9780262034401.003.0004
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Busch Kristen E., 2023, R47569
   Chatterjee A, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.1024449
   Coeckelbergh M, 2020, SCI ENG ETHICS, V26, P2051, DOI 10.1007/s11948-019-00146-8
   Coeckelbergh Mark., 2013, HUMAN BEING RISK ENH
   Crabapple Molly, 2022, Los Angeles Times21 December
   Doneson D, 2019, SOC RES, V86, P871
   Earl Peter E., 2016, Minds, Models and Milieux: Commemorating the Centennial of the Birth of Herbert Simon, P253, DOI [10.1057/978113744250515, DOI 10.1057/978113744250515]
   Elgammal A, 2017, Arxiv, DOI [arXiv:1706.07068, DOI 10.48550/ARXIV.1706.07068]
   Ellul Jacques., 2011, The Technological Society
   Epstein Z, 2020, ISCIENCE, V23, DOI 10.1016/j.isci.2020.101515
   Evans SK, 2017, J COMPUT-MEDIAT COMM, V22, P35, DOI 10.1111/jcc4.12180
   Feenberg Andrew., 2005, Heidegger and Marcuse: The Catastrophe and Redemption of History
   Fisher E, 2020, DIGITAL AGE AND ITS DISCONTENTS, P111, DOI 10.33134/HUP-4-6
   Fisher E, 2019, MEDIA CULT SOC, V41, P1176, DOI 10.1177/0163443719831598
   Flavel Harriet, 2022, Vault 42
   Frey CB., 2020, TECHNOLOGY TRAP CAPI
   Galanter P., 2016, A companion to digital art, P146, DOI [DOI 10.1002/9781118475249.CH5, 10.1002/9781118475249.ch5]
   Gibson James., 1986, ECOLOGICAL APPROACH, DOI DOI 10.4324/9781315740218
   Gonzalez Adrian, 2020, P AAAI C ART INT INT, V16, P337
   Gulacti Ismail Erim, 2021, Medeniyet Sanat Dergisi, V7, P243
   Hayles N.Katherine., 1999, WE BECAME POSTHUMAN
   Heidegger M., 1993, Basic writings, P307
   Hoskins A., 2017, Digital Memory Studies: Media Pasts in Transition, P85, DOI DOI 10.4324/9781315637235-4
   Hoskins A, 2011, MEM STUD, V4, P269, DOI 10.1177/1750698011402570
   Hutson J., 2023, Global Journal of Computer Science and Technology, V23, P1, DOI DOI 10.34257/GJCSTDVOL23IS1PG1
   Ihde D., 1979, TECHNICS PRAXIS
   Ihde D., 1990, TECHNOLOGY LIFEWORLD
   Ihde D., 2009, POSTPHENOMENOLOGY TE
   Jacques L, 2023, PERSPECT SEX REPRO H, V55, P86, DOI 10.1363/psrh.12225
   Jaspers Karl., 1957, Man in the Modern Age
   Koerner Katharina, 2023, Generative AI: Privacy and Tech Perspectives
   KOLAK D, 1990, J AESTHET ART CRITIC, V48, P158, DOI 10.2307/430907
   Kozinets R., 2015, Netnography Redefined
   Kozinets R. V., 2014, SAGE HDB QUALITATIVE, P262, DOI [10.4135/9781446282243.n18, DOI 10.4135/9781446282243.N18]
   Kozinets R.V., 2021, NETNOGRAPHY UNLIMITE
   KUPFER J, 1987, AM PHILOS QUART, V24, P81
   Latour B, 1999, ACTOR NETWORK THEORY AND AFTER, P15
   Latour B, 1996, SOZ WELT, V47, P369
   Latour B., 1994, Common Knowl, V3, P29
   Latour B., 2005, REASSEMBLING SOCIAL
   Latour B, 2008, INSIDE TECHNOL, P151
   LukeTheCyberpunk, 2023, Looking for ways to make my art A'i proof'" [Reddit Post]
   Mazzone M, 2019, ARTS, V8, DOI 10.3390/arts8010026
   Millet K, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107707
   Mitcham Carl., 1994, THINKING TECHNOLOGY
   Nimmo Richie., 2011, Methodological Innovations Online, V6, P108, DOI [DOI 10.4256/mio.2011.010, DOI 10.4256/MIO.2011.010]
   OleanderYuri, 2023, How to Know If Art Really Is Your Passion?" [Reddit Post
   Osborne P., 2015, The Stanford encyclopedia of philosophy
   Pickering Andrew, 2010, The Oxford Handbook of Material Culture Studies, P190
   Pleasure ZH, 2022, J HEALTH COMMUN, V27, P746, DOI 10.1080/10810730.2022.2157911
   Pollock Griselda., 1980, Screen, V21, P57, DOI [10.1093/screen/21.3.57, DOI 10.1093/SCREEN/21.3.57]
   Poscic A, 2020, J SCI TECHNOL ARTS, V12, P45, DOI 10.34632/jsta.2020.9488
   Proferes N., 2021, Social Media + Society, V7, DOI [DOI 10.1177/20563051211019004, 10.1177/20563051211019004]
   Reddit.com, 2024, Reddit by the Numbers
   Refik Anadol Studio, 2021, Machine Hallucinations-Nature Dreams
   ScionoicS, 2023, Important ML Article about Models Trained on Copyright Work Being Transformative and Fair Use. The Ruling Was Upheld All the Way to the US Supreme Court. Generative Models Have Precedence on Their Side" [Reddit Post].
   Searle J, 1983, INTENTIONALITY ESSAY
   Searle JR., 2010, Making the Social World: The Structure of Human Civilization
   Smith MN, 2017, PHILOS EXPLOR, V20, P1, DOI 10.1080/13869795.2017.1356360
   Sorgner S.L., 2010, Journal of Evolution and Technology vol, V21, P1
   Sorgner Stefan Lorenz, 2022, Philosophy of Posthuman Art
   Sternsafari, 2023, "I Lost Everything That Made Me Love My Job through Midjourney over Night." [Reddit Post]
   Verbeek P.P., 2005, What Things Do. Philosophical Reflections on Technology, Agency, and Design
   Wicaksana Galih Dwi, 2023, Open Access Indonesia Journal of Social Sciences, V6, P1023
   Wolfe C., 2010, What is Posthumanism?
   Zammit Marvin, 2022, arXiv
NR 81
TC 0
Z9 0
U1 9
U2 9
PU PENN STATE UNIV PRESS
PI UNIVERSITY PK
PA 820 NORTH UNIV DRIVE, U S B 1, STE C, UNIVERSITY PK, PA 16802 USA
SN 2472-4513
EI 2471-4461
J9 J POSTHUMAN STUD
JI J. Posthuman Stud.
PD DEC
PY 2023
VL 7
IS 2
BP 129
EP 149
DI 10.5325/jpoststud.7.2.0129
PG 21
WC Humanities, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Arts & Humanities - Other Topics
GA F5R8O
UT WOS:001310397900003
DA 2024-12-25
ER

PT J
AU France, SL
AF France, Stephen L.
TI Navigating software development in the ChatGPT and GitHub Copilot era
SO BUSINESS HORIZONS
LA English
DT Article
DE Generative AI; Large language models; Software developers; AI prompting;
   Prompt engineering; Capability maturity model
ID CODE
AB Generative artificial intelligence (GenAI) technologies using LLMs (large language models), such as ChatGPT and GitHub Copilot, with the ability to create code, have the potential to change the software-development landscape. Will this process be incremental, with software developers learning GenAI skills to supplement their existing skills, or will the process be more destructive, with the loss of large numbers of development jobs and a radical change in the responsibilities of the remaining developers? Given the rapid growth of AI capabilities, it is impossible to provide a crystal ball, but this article aims to give insight into the adoption of GenAI with LLMs in software development. The article gives an overview of the software-development industry and of the job functions of software developers. A literature review, combined with a content analysis of online comments from developers, gives insight into how GenAI implemented with LLMs is changing software development and how developers are responding to these changes. The article ties the academic and developer insights together into recommendations for software developers, and it describes a CMM (capability maturity model) framework for assessing and improving LLM development usage. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
C1 [France, Stephen L.] Mississippi State Univ, Mailstop 9582, Mississippi State, MS 39762 USA.
C3 Mississippi State University
RP France, SL (corresponding author), Mississippi State Univ, Mailstop 9582, Mississippi State, MS 39762 USA.
EM sfrance@business.msstate.edu
CR [Anonymous], 2022, Occupational outlook handbook, registered nurses
   Ardimento P, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11030389
   Badampudi D, 2023, ACM T SOFTW ENG METH, V32, DOI 10.1145/3585004
   Bird Christian, 2022, ACM Queue, P35, DOI 10.1145/3582083
   Chatravorti B., 2023, Fortune,June 25
   Ciborowska A, 2018, IEEE WORK CONF MIN S, P94, DOI 10.1145/3196398.3196467
   Davenport TH, 2023, HARVARD BUS REV, V101, P98
   Feng YH, 2023, P INT COMP SOFTW APP, P876, DOI 10.1109/COMPSAC57700.2023.00117
   Gershgorn D., 2021, Verge,June 29
   Hess A. J., 2023, Fast Company,February 16
   Horne J, 2020, J CLEAN PROD, V242, DOI 10.1016/j.jclepro.2019.118052
   Hughes A., 2023, BBC,September 25
   Imai S, 2022, PROC IEEE ACM INT C, P319, DOI [10.1109/ICSE-Companion55297.2022.9793778, 10.1145/3510454.3522684]
   Kumar R, 2018, PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE CONFLUENCE 2018 ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING, P20, DOI 10.1109/CONFLUENCE.2018.8442860
   Legenvre H, 2018, BUS HORIZONS, V61, P95, DOI 10.1016/j.bushor.2017.09.009
   Li A, 2022, 2022 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING EDUCATION AND TRAINING (ICSE-SEET 2022), P69, DOI 10.1109/ICSE-SEET55299.2022.9794240
   MacRae D., 2023, Developer,May 16
   Marr B, 2023, FORBES
   Meyer AN, 2021, IEEE T SOFTWARE ENG, V47, P1872, DOI 10.1109/TSE.2019.2938525
   Nguyen N, 2022, IEEE WORK CONF MIN S, P1, DOI 10.1145/3524842.3528470
   Noll B., 2023, State of the Developer Nation 24th Edition - Q1 2023
   Paulk M.C., 2009, ASQ Software Quality Professional, V1, P5
   Pearce H, 2022, P IEEE S SECUR PRIV, P754, DOI 10.1109/SP46214.2022.00057
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Robillard MP, 2004, IEEE T SOFTWARE ENG, V30, P889, DOI 10.1109/TSE.2004.101
   Sadiq RB, 2021, PEERJ COMPUT SCI, V7, DOI 10.7717/peerj-cs.661
   Sadowski C, 2018, 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING - SOFTWARE ENGINEERING IN PRACTICE TRACK (ICSE-SEIP 2018), P181, DOI 10.1145/3183519.3183525
   Sloyan T., 2021, Forbes,June 8
   Sommers J., 2023, Business Insider,June 13
   Sundberg L, 2023, BUS HORIZONS, V66, P777, DOI 10.1016/j.bushor.2023.04.003
   Trueman C., 2023, Computerworld,June 19
   Vincent J., 2023, Verge,June 13
   Wademan MR, 2007, PERFORM IMPROV Q, V20, P97, DOI 10.1111/j.1937-8327.2007.tb00434.x
   Xia X, 2017, EMPIR SOFTW ENG, V22, P3149, DOI 10.1007/s10664-017-9514-4
   Zhang BQ, 2023, Arxiv, DOI [arXiv:2303.08733, 10.48550/arxiv.2303.08733]
NR 35
TC 1
Z9 1
U1 15
U2 15
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 649
EP 661
DI 10.1016/j.bushor.2024.05.009
EA AUG 2024
PG 13
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2K9N
UT WOS:001301354000001
OA hybrid
DA 2024-12-25
ER

PT J
AU Salinas-Navarro, DE
   Vilalta-Perdomo, E
   Michel-Villarreal, R
   Montesinos, L
AF Salinas-Navarro, David Ernesto
   Vilalta-Perdomo, Eliseo
   Michel-Villarreal, Rosario
   Montesinos, Luis
TI Designing experiential learning activities with generative artificial
   intelligence tools for authentic assessment
SO INTERACTIVE TECHNOLOGY AND SMART EDUCATION
LA English
DT Article
DE Authentic assessment; ChatGPT; Experiential learning; GenAI; Higher
   education; Lean healthcare; Operations management
ID EDUCATION; AI
AB PurposeThis article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.Design/methodology/approachThe study uses "thing ethnography" and "incremental prompting" to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI's potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.FindingsThe findings underscore GenAI's potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI's capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.Originality/valueThis research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.
C1 [Salinas-Navarro, David Ernesto; Vilalta-Perdomo, Eliseo] Aston Univ, Community Resilience & Sustainabil Educ Lab CoRSE, Birmingham, W Midlands, England.
   [Michel-Villarreal, Rosario] Univ Leeds, Sustainabil Res Inst, Sch Earth & Environm, Leeds, W Yorkshire, England.
   [Montesinos, Luis] Tecnol Monterrey, Inst Adv Mat Sustainable Mfg, Mexico City, DF, Mexico.
C3 Aston University; University of Leeds; Tecnologico de Monterrey
RP Montesinos, L (corresponding author), Tecnol Monterrey, Inst Adv Mat Sustainable Mfg, Mexico City, DF, Mexico.
EM lmontesinos@tec.mx
RI Vilalta-Perdomo, Eliseo/AAK-4541-2021; SALINAS-NAVARRO, DAVID
   ERNESTO/ABG-4204-2020; Vilalta-Perdomo, Eliseo/N-9549-2014; Montesinos,
   Luis/J-4255-2018
OI SALINAS-NAVARRO, DAVID ERNESTO/0000-0002-7919-4885; Vilalta-Perdomo,
   Eliseo/0000-0002-4551-8327; Montesinos, Luis/0000-0003-3976-4190
CR Ajjawi R, 2024, ASSESS EVAL HIGH EDU, V49, P499, DOI 10.1080/02602938.2023.2271193
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Albert D., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4516801, DOI 10.2139/SSRN.4516801]
   Alemdag E, 2023, J RES TECHNOL EDUC, DOI 10.1080/15391523.2023.2255698
   Amedu C, 2024, RADIOGRAPHY, V30, P209, DOI 10.1016/j.radi.2023.11.009
   Asch DA., 2023, CATALYST NONISSUE CO, DOI [DOI 10.1056/CAT.23.0043, 10.1056/cat.23.0043]
   Avello-Sáez D, 2023, CAD BRAS TER OCUP, V31, DOI 10.1590/2526-8910.ctoEN271035342
   Barros A, 2023, MANAGE LEARN, V54, P597, DOI 10.1177/13505076231201445
   Bearman M, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13337
   Benkert C., 2015, Experiential Learning: Whats Missing in Most Change Programs Operations
   Bergsteiner H, 2014, STUD CONTIN EDUC, V36, P257, DOI 10.1080/0158037X.2014.904782
   Bergsteiner H, 2010, STUD CONTIN EDUC, V32, P29, DOI 10.1080/01580370903534355
   BIGGS J, 1996, HIGH EDUC, V32, P347, DOI [DOI 10.1007/BF00138871, 10.1007/BF00138871]
   Bockting CL, 2023, NATURE, V622, P693, DOI 10.1038/d41586-023-03266-1
   Bradberry LA, 2019, J POLITICAL SCI EDUC, V15, P94, DOI 10.1080/15512169.2018.1485571
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Chang WW, 2017, DIS'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON DESIGNING INTERACTIVE SYSTEMS, P1001, DOI 10.1145/3064663.3064717
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cila N., 2015, THING CTR NARRATIVES
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   de Zeeuw G., 1996, 7 U HUM CTR SYST INF, V7
   Eager B., 2023, ACHIEVING BETTER RES
   EISENHARDT KM, 1989, ACAD MANAGE REV, V14, P532, DOI 10.2307/258557
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   Geerling W, 2023, 68 AM. ECONOMIST, V68
   Giaccardi E, 2016, DESIGN ANTHROPOLOGICAL FUTURES, P235
   Giaccardi E, 2016, DIS 2016: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON DESIGNING INTERACTIVE SYSTEMS, P377, DOI 10.1145/2901790.2901905
   Guest G., 2012, Applied thematic analysis, P11, DOI DOI 10.4135/9781483384436
   Hamid H, 2023, CURR PHARM TEACH LEA, V15, P1017, DOI 10.1016/j.cptl.2023.10.001
   Holman D, 1997, MANAGE LEARN, V28, P135, DOI 10.1177/1350507697282003
   Ifenthaler D, 2023, J RES TECHNOL EDUC, V55, P1, DOI 10.1080/15391523.2022.2154511
   Iskender A, 2023, EUR J TOUR RES, V34, DOI 10.54055/ejtr.v34i.3169
   Karakose T., 2023, Educ. Process Int. J, V12, P7, DOI [10.22521/edupij.2023.123.1, DOI 10.22521/EDUPIJ.2023.123.1]
   Kassens-Noor E, 2023, ACT LEARN HIGH EDUC, V24, P21, DOI 10.1177/1469787420982546
   King N., 2010, Interviews in qualitative research
   Kivunja C., 2018, INT J HIGHER ED, V7, P44, DOI DOI 10.5430/IJHE.V7N6P44
   Koh J., 2023, J HIGHER ED THEORY P, V23, DOI [10.33423/jhetp.v23i17.6543, DOI 10.33423/JHETP.V23I17.6543]
   Koh K.H., 2017, Oxford Research Encyclopedia of Education, DOI DOI 10.1093/ACREFORE/9780190264093.013.22
   Kohn A., 2018, ARXIV, DOI DOI 10.48550/ARXIV.1805.12518
   Kokoç M, 2021, BEHAV INFORM TECHNOL, V40, P161, DOI 10.1080/0144929X.2019.1680731
   Kolb A., 2018, AEL, V40
   Kolb A. Y., 2017, EXPERIENTIAL LEARNIN, V1, P38, DOI [DOI 10.46787/ELTHE.V1I1.3362, https://doi.org/10.46787/elthe.v1i1.3362]
   Kolb D. A., 1984, EXPERIENTIAL LEARNIN, DOI DOI 10.1016/B978-0-7506-7223-8.50017-4
   Kolb D.A., 1975, Theories of Group Process., P33, DOI DOI 10.1177/0741713612439090
   Kong YT, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.771272
   Lalley J.P., 2007, Education, V128, P64
   LECOMPTE MD, 1982, REV EDUC RES, V52, P31, DOI 10.2307/1170272
   Lin X, 2024, ADULT LEARN, V35, P156, DOI 10.1177/10451595231184928
   McArthur J, 2023, HIGH EDUC, V85, P85, DOI 10.1007/s10734-022-00822-y
   Meron Y, 2023, DES SCI, V9, DOI 10.1017/dsj.2023.28
   Merrett C.G., 2023, ASEE ANN C EXP C P
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mollick E., 2023, USE AI DO PRACTICAL
   Montesinos L, 2023, BMC MED EDUC, V23, DOI 10.1186/s12909-023-04171-x
   Morris N, 2012, BRIT J EDUC STUD, V60, P448, DOI 10.1080/00071005.2012.742279
   Morris TH, 2020, INTERACT LEARN ENVIR, V28, P1064, DOI 10.1080/10494820.2019.1570279
   Nikolic S, 2023, EUR J ENG EDUC, V48, P559, DOI 10.1080/03043797.2023.2213169
   Opara E., 2023, GLOBAL ACAD J HUMANI, V5, P33, DOI DOI 10.36348/GAJHSS.2023.V05I02.001
   Picault J, 2021, J ECON EDUC, V52, P114, DOI 10.1080/00220485.2021.1887030
   Reyes A., 1998, Cybern. Hum. Knowing, V5, P19
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ryan G.W., 2003, Field Methods, V15, P85, DOI [10.1177/1525822X02239569, DOI 10.1177/1525822X02239569]
   Salinas-Navarro DE, 2024, EDUC SCI, V14, DOI 10.3390/educsci14010083
   Salinas-Navarro DE, 2023, EDUC SCI, V13, DOI 10.3390/educsci13010063
   Salinas-Navarro DE, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142013133
   Salinas-Navarro DE, 2020, PR IEEE INT CONF TEA, P1, DOI 10.1109/TALE48869.2020.9368347
   Sane A., 2023, P ACM C GLOB COMP ED, P204
   Santos R.P D., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2305.11890
   Saunders M., 2020, Research Methods for Business Students, V8th ed.
   Smith A, 2023, INT J SOC PSYCHIATR, V69, P1882, DOI 10.1177/00207640231178451
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Tulubas T., 2023, ED PROCESS INT J, V12, DOI [10.22521/edupij.2023.122.6, DOI 10.22521/EDUPIJ.2023.122.6]
   Turney C.S. M., 2009, ACT LEARN HIGH EDUC, V10, P71, DOI DOI 10.1177/1469787408100196
   UNESCO, 2023, GUID GEN AI ED RES
   Vahl M, 1997, SYSTEMS FOR SUSTAINABILITY, P147
   Villarroel V, 2020, INNOV EDUC TEACH INT, V57, P38, DOI 10.1080/14703297.2018.1564882
   Villarroel V, 2018, ASSESS EVAL HIGH EDU, V43, P840, DOI 10.1080/02602938.2017.1412396
   Vince R, 2022, J MANAG EDUC, V46, P983, DOI 10.1177/10525629221114040
   Volante L, 2023, PHI DELTA KAPPAN, V105, P40, DOI 10.1177/00317217231197475
   WIGGINS G, 1989, PHI DELTA KAPPAN, V70, P703, DOI 10.1177/003172171109200721
   Wiggins G., 1990, Practical Assessment, Research Evaluation, V2, P2, DOI [10.7275/ffb1-mm19, DOI 10.7275/FFB1-MM19]
   Yi-Ching Huang, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3479866
NR 85
TC 6
Z9 6
U1 47
U2 69
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-5659
EI 1758-8510
J9 INTERACT TECHNOL SMA
JI Interact. Technol. Smart Educ.
PD OCT 30
PY 2024
VL 21
IS 4
SI SI
BP 708
EP 734
DI 10.1108/ITSE-12-2023-0236
EA MAY 2024
PG 27
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA K4N9Y
UT WOS:001211429500001
OA Green Accepted
DA 2024-12-25
ER

PT J
AU Blonder, R
   Feldman-Maggor, Y
AF Blonder, Ron
   Feldman-Maggor, Yael
TI AI for chemistry teaching: responsible AI and ethical considerations
SO CHEMISTRY TEACHER INTERNATIONAL
LA English
DT Article; Early Access
DE ethics in science; artificial intelligence; web based learning; teacher
   education; teacher professional development
AB This paper discusses the ethical considerations surrounding generative artificial intelligence (GenAI) in chemistry education, aiming to guide teachers toward responsible AI integration. GenAI, driven by advanced AI models like Large Language Models, has shown substantial potential in generating educational content. However, this technology's rapid rise has brought forth ethical concerns regarding general and educational use that require careful attention from educators. The UNESCO framework on GenAI in education provides a comprehensive guide to controversies around generative AI and ethical educational considerations, emphasizing human agency, inclusion, equity, and cultural diversity. Ethical issues include digital poverty, lack of national regulatory adaptation, use of content without consent, unexplainable models used to generate outputs, AI-generated content polluting the internet, lack of understanding of the real world, reducing diversity of opinions, and further marginalizing already marginalized voices and generating deep fakes. The paper delves into these eight controversies, presenting relevant examples from chemistry education to stress the need to evaluate AI-generated content critically. The paper emphasizes the importance of relating these considerations to chemistry teachers' content and pedagogical knowledge and argues that responsible AI usage in education must integrate these insights to prevent the propagation of biases and inaccuracies. The conclusion stresses the necessity for comprehensive teacher training to effectively and ethically employ GenAI in educational practices.
C1 [Blonder, Ron] Weizmann Inst Sci, Dept Sci Teaching, Rehovot, Israel.
   [Feldman-Maggor, Yael] KTH Royal Inst Technol, EECS Sch Elect Engn & Comp Sci Media Technol & Int, Stockholm, Sweden.
C3 Weizmann Institute of Science; Royal Institute of Technology
RP Blonder, R (corresponding author), Weizmann Inst Sci, Dept Sci Teaching, Rehovot, Israel.
EM ron.blonder@weizmann.ac.il; yael.maggor@gmail.com
FU Digital Futures post-doctorate fellowship; Israel Science Foundation
   (ISF) award [73/24]
FX This work was supported by the Digital Futures post-doctorate fellowship
   and the Israel Science Foundation (ISF) award (Grant 73/24).
CR Aggarwal K., 2022, IRAQI J COMPUT SCI M, V3, P115, DOI DOI 10.52866/IJCSM.2022.01.01.013
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Aroch I, 2024, CHEM EDUC RES PRACT, V25, P843, DOI 10.1039/d3rp00307h
   Baker N., 2023, Unlocking a new era for scientific discovery with AI: How Microsofts AI screened over 32 million candidates to find a better battery (Microsoft Blog)
   Balasubramaniam N, 2022, LECT NOTES COMPUT SC, V13216, P3, DOI 10.1007/978-3-030-98464-9_1
   Barrett L., 2017, NYU Rev. L. Soc. Chang, V41, P327, DOI [10.3366/ajicl.2011.0005, DOI 10.3366/AJICL.2011.0005]
   Bengio Y, 2024, SCIENCE, V384, P842, DOI 10.1126/science.adn0117
   Clark TM, 2023, J CHEM EDUC, V100, P1905, DOI 10.1021/acs.jchemed.3c00027
   Easa E, 2024, CHEM EDUC RES PRACT, V25, P1175, DOI 10.1039/d4rp00144c
   Easa E, 2023, CHEM TEACH INT, V5, P125, DOI 10.1515/cti-2023-0005
   Easa E, 2022, CHEM TEACH INT, V4, P71, DOI 10.1515/cti-2021-0022
   Erduran S, 2024, INT J SCI EDUC, V46, P1982, DOI 10.1080/09500693.2024.2306604
   Erduran Sibel, 2023, Science, V382, peadm9788, DOI 10.1126/science.adm9788
   Farazouli A, 2024, ASSESS EVAL HIGH EDU, V49, P363, DOI 10.1080/02602938.2023.2241676
   Feldman-Maggor Y., 2024, proceeding in the Nineteenth European Conference on Technology Enhanced Learning-ECTEL, P99
   Feldman-Maggor Y, 2024, J SCI EDUC TECHNOL, DOI 10.1007/s10956-024-10147-3
   Feldman-Maggor Y, 2016, CHEM EDUC RES PRACT, V17, P283, DOI 10.1039/c5rp00184f
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Figueras Claudia, 2022, SCANDINAVIAN J INFOR, V34, P6
   Fu Y., 2024, Comput. Educ. Artif. Intell, V7, P100306, DOI [10.1016/j.caeai.2024.100306, DOI 10.1016/J.CAEAI.2024.100306]
   Gilbert JK, 2008, MODEL MODEL SCI EDUC, V3, P3
   Guo SC, 2024, EDUC INF TECHNOL, V29, P16387, DOI 10.1007/s10639-024-12491-8
   Holmes W, 2022, INT J ARTIF INTELL E, V32, P504, DOI 10.1007/s40593-021-00239-1
   Kassam K, 2022, J CHEM EDUC, V99, P2773, DOI 10.1021/acs.jchemed.2c00611
   Kwak Y, 2024, BRIT J EDUC TECHNOL, V55, P2039, DOI 10.1111/bjet.13465
   Li T., 2024, Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, P1323
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   MABBOTT GA, 1983, J CHEM EDUC, V60, P697, DOI 10.1021/ed060p697
   Mamlok-Naaman R., 2024, Center for Educational Policy Studies Journal, V14, P13, DOI [https://doi.org/10.26529/cepsj.1718, DOI 10.26529/CEPSJ.1718]
   Markic S., 2024, Center for Educational Policy Studies Journal, V14, P7, DOI [10.26529/cepsj.1886, DOI 10.26529/CEPSJ.1886]
   McDowell SAC, 2020, J CHEM EDUC, V97, P3256, DOI 10.1021/acs.jchemed.0c00611
   Memarian B., 2023, Computers and Education: Artificial Intelligence., DOI DOI 10.1016/J.CAEAI.2023.100152
   Miao F., 2023, GUIDANCE GENERATIVE, DOI [10.54675/EWZM9535, DOI 10.54675/EWZM9535]
   Pargman TC, 2023, J LEARN ANAL, V10, DOI 10.18608/jla.2023.7781
   Pargman TC, 2021, J LEARN ANAL, V8, P123, DOI 10.18608/jla.2021.1
   Pedro F., 2019, Artificial intelligence in education: Challenges and opportunities for sustainable development
   Rap S, 2020, J CHEM EDUC, V97, P3278, DOI 10.1021/acs.jchemed.0c00687
   Rap S, 2016, J SCI EDUC TECHNOL, V25, P62, DOI 10.1007/s10956-015-9577-1
   Reigh E., 2023, Science Children, V60, P26, DOI DOI 10.1080/00368148.2023.12291867
   Richter D, 2011, TEACH TEACH EDUC, V27, P116, DOI 10.1016/j.tate.2010.07.008
   Samoili S., 2020, AI Watch. Defining Artificial Intelligence. Towards an operational definition and taxonomy of artificial intelligence
   Seery M. K.F., 2013, The application of technology to enhance chemistry education, V14, P227, DOI [10.1039/C3RP90006A, DOI 10.1039/C3RP90006A]
   SHULMAN LS, 1987, HARVARD EDUC REV, V57, P1, DOI 10.17763/haer.57.1.j463w79r56455411
   Susnjak T, 2024, EDUC SCI, V14, DOI 10.3390/educsci14060656
   Taddeo M, 2018, SCIENCE, V361, P751, DOI 10.1126/science.aat5991
   Tuvi-Arad I, 2022, ISR J CHEM, V62, DOI 10.1002/ijch.202100042
   Tuvi-Arad I, 2019, ISR J CHEM, V59, P572, DOI 10.1002/ijch.201800076
   UNESCO, 2021, RECOMMENDATIONS ETHI
   Usher M, 2024, INT J STEM EDUC, V11, DOI 10.1186/s40594-024-00493-4
   Watts FM, 2023, J CHEM EDUC, V100, P3806, DOI 10.1021/acs.jchemed.3c00664
   Williams RT, 2024, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1331607
   Wyndham J., 2022, UNESCO recommendation on science and scientific researchers and the United States: An analysis of key themes
   Zhai X., 2023, ChatGPT and AI: The game changer for education, P16
   Zhang M, 2021, FUND RES-CHINA, V1, P831, DOI 10.1016/j.fmre.2021.11.011
NR 54
TC 1
Z9 1
U1 15
U2 15
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
EI 2569-3263
J9 CHEM TEACH INT
JI Chem. Teach. Int.
PD 2024 OCT 16
PY 2024
DI 10.1515/cti-2024-0014
EA OCT 2024
PG 11
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA J3R6W
UT WOS:001336272600001
OA gold
DA 2024-12-25
ER

PT J
AU Currie, G
   Currie, J
   Anderson, S
   Hewis, J
AF Currie, Geoffrey
   Currie, Josie
   Anderson, Sam
   Hewis, Johnathan
TI Gender bias in generative artificial intelligence text-to-image
   depiction of medical students
SO HEALTH EDUCATION JOURNAL
LA English
DT Article
DE Artificial intelligence; DALL-E 3; generative AI; medical education;
   medical students
AB Introduction: In Australia, 54.3% of medical students are women yet they remain under-represented in stereotypical perspectives of medicine. While potentially transformative, generative artificial intelligence (genAI) has the potential for errors, misrepresentations and bias. GenAI text-to-image production could reinforce gender biases making it important to evaluate DALL-E 3 (the text-to-image genAI supported through ChatGPT) representations of Australian medical students.Method: In March 2024, DALL-E 3 was utilised via GPT-4 to generate a series of individual and group images of medical students, specifically Australian undergraduate medical students to eliminate potential confounders. Multiple iterations of images were generated using a variety of prompts. Collectively, 47 images were produced for evaluation of which 33 were individual characters and the remaining 14 images were comprised of multiple (5 to 67) characters. All images were independently analysed by three reviewers for apparent gender and skin tone. Consequently, 33 feature individuals were evaluated and a further 417 characters in groups were evaluated (N = 448). Discrepancies in responses were resolved by consensus.Results: Collectively (individual and group images), 58.8% (N = 258) of medical students were depicted as men, 39.9% (N = 175) as women, 92.0% (N = 404) with a light skin tone, 7.7% (N = 34) with mid skin tone and 0% with dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian medical students for individual images, for group images and for collective images. Among the images of individual medical students (N = 25), DALL-E 3 generated 92% (N = 23) as men and 100% were of light skin tone (N = 25).Conclusion: This evaluation reveals the gender associated with genAI text-to-image generation using DALL-E 3 among Australian undergraduate medical students. Generated images included a disproportionately high proportion of white male medical students which is not representative of the diversity of medical students in Australia. The use of DALL-E 3 to produce depictions of medical students for education or promotion purposes should be done with caution.
C1 [Currie, Geoffrey] Charles Sturt Univ, Sch Dent & Med Sci, Locked Bag 588, Wagga Wagga, NSW 2678, Australia.
   [Currie, Geoffrey] Baylor Coll Med, Dept Radiol, Houston, TX USA.
   [Currie, Josie] Univ NSW, Rural Med Sch, Wagga Wagga, NSW, Australia.
   [Anderson, Sam; Hewis, Johnathan] Charles Sturt Univ, Sch Dent & Med Sci, Port Macquarie, NSW, Australia.
C3 Charles Sturt University; Baylor College of Medicine; University of New
   South Wales Sydney; Charles Sturt University
RP Currie, G (corresponding author), Charles Sturt Univ, Sch Dent & Med Sci, Locked Bag 588, Wagga Wagga, NSW 2678, Australia.
EM gcurrie@csu.edu.au
RI Hewis, Johnathan/X-2382-2019
OI Currie, Geoffrey/0000-0002-6180-8586; Hewis,
   Johnathan/0000-0002-7810-5641
CR Ali R, 2024, JAMA SURG, V159, P87, DOI 10.1001/jamasurg.2023.5695
   Begeny CT, 2023, BRIT J SURG, V110, P1518, DOI 10.1093/bjs/znad242
   Bismark M, 2015, BMJ OPEN, V5, DOI 10.1136/bmjopen-2015-009384
   Cevik J, 2024, ANZ J SURG, V94, P287, DOI 10.1111/ans.18792
   Choudhry HS, 2023, CLIN OPHTHALMOL, V17, P2889, DOI 10.2147/OPTH.S427296
   Crews DC, 2021, JAMA-HEALTH FORUM, V2, DOI 10.1001/jamahealthforum.2021.4820
   Critchley J, 2021, ANAESTHESIA, V76, P14, DOI 10.1111/anae.15359
   Currie G, 2020, EUR J NUCL MED MOL I, V47, P748, DOI 10.1007/s00259-020-04678-1
   Currie G, 2022, SEMIN NUCL MED, V52, P498, DOI 10.1053/j.semnuclmed.2021.11.011
   Currie G, 2020, SEMIN NUCL MED, V51, P120, DOI 10.1053/j.semnuclmed.2020.08.001
   Currie GM, 2023, SEMIN NUCL MED, V53, P719, DOI 10.1053/j.semnuclmed.2023.04.008
   Currie GM, 2023, NUCL MED BIOL, V120, DOI 10.1016/j.nucmedbio.2023.108337
   Ito N, 2023, JMIR MED EDUC, V9, DOI 10.2196/47532
   Klontzas ME., 2022, RADIOGRAPHICS, V42
   Kotek H, 2023, PROCEEDINGS OF THE ACM COLLECTIVE INTELLIGENCE CONFERENCE, CI 2023, P12, DOI 10.1145/3582269.3615599
   Lamb E, 2022, EDUC PRIM CARE, V33, P265, DOI 10.1080/14739879.2022.2079097
   Lee Rosa, 2021, AMA J Ethics, V23, pE912, DOI 10.1001/amajethics.2021.912
   Lett E, 2019, JAMA NETW OPEN, V2, DOI 10.1001/jamanetworkopen.2019.10490
   Lim WY, 2023, J MED IMAG RADIAT ON, V67, P170, DOI 10.1111/1754-9485.13467
   Mahendran GN, 2022, AM J SURG, V224, P266, DOI 10.1016/j.amjsurg.2022.02.008
   Massey D.S., 2003, NIS SKIN COLOR SCALE
   Medical Deans Australia and New Zealand, 2021, STUDENT STAT SNAPSHO
   Moriarty HK, 2023, J MED IMAG RADIAT ON, V67, P146, DOI 10.1111/1754-9485.13397
   Morris DB, 2021, NEW ENGL J MED, V384, P1661, DOI 10.1056/NEJMsr2028487
   Nix S, 2019, SOC SCI-BASEL, V8, DOI 10.3390/socsci8020043
   Puddey IB, 2017, BMC MED EDUC, V17, DOI 10.1186/s12909-016-0842-7
   Torres MB, 2019, JAMA SURG, V154, P868, DOI 10.1001/jamasurg.2019.1648
   Zack Travis, 2024, Lancet Digit Health, V6, pe12, DOI 10.1016/S2589-7500(23)00225-X
NR 28
TC 0
Z9 0
U1 10
U2 10
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0017-8969
EI 1748-8176
J9 HEALTH EDUC J
JI Health Educ. J.
PD NOV
PY 2024
VL 83
IS 7
BP 732
EP 746
DI 10.1177/00178969241274621
EA AUG 2024
PG 15
WC Education & Educational Research; Public, Environmental & Occupational
   Health
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Public, Environmental & Occupational
   Health
GA J7E6M
UT WOS:001299403200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Aldasoro, I
   Armantier, O
   Doerr, S
   Gambacorta, L
   Oliviero, T
AF Aldasoro, Inaki
   Armantier, Olivier
   Doerr, Sebastian
   Gambacorta, Leonardo
   Oliviero, Tommaso
TI The gen AI gender gap☆
SO ECONOMICS LETTERS
LA English
DT Article
DE Generative artificial intelligence; Privacy; Gender
AB Data from the Survey of Consumer Expectations shows that 50% of men use generative AI tools, compared to 37% of women. Privacy concerns and perceived opportunities and risks explain a quarter of the gender gap. Respondents' self-assessed knowledge emerges as the most important factor.
C1 [Aldasoro, Inaki; Doerr, Sebastian; Gambacorta, Leonardo] Bank Int Settlements BIS, Basel, Switzerland.
   [Armantier, Olivier] Fed Reserve Bank New York, New York, NY USA.
   [Doerr, Sebastian; Gambacorta, Leonardo] Ctr Econ Policy Res CEPR, London, England.
   [Oliviero, Tommaso] Univ Naples Federico II, Naples, Italy.
   [Oliviero, Tommaso] CSEF, Naples, Italy.
C3 Bank for International Settlements (BIS); Federal Reserve System - USA;
   Federal Reserve Bank - New York; Centre for Economic Policy Research -
   UK; University of Naples Federico II
RP Oliviero, T (corresponding author), Univ Naples Federico II, Naples, Italy.
EM inaki.aldasoro@bis.org; olivier.armantier@ny.frb.org;
   sebastian.doerr@bis.org; leonardo.Gambacorta@bis.org;
   tommaso.oliviero@unina.it
RI Oliviero, Tommaso/JRX-8137-2023; Gambacorta, Leonardo/KDM-8428-2024
CR Aldasoro I., 2024, BIS Bull., V86
   Armantier O., 2021, BIS Bulletins, V42
   Armantier O., 2024, BIS Working Paper 1187
   Babina T, 2024, J FINANC ECON, V151, DOI 10.1016/j.jfineco.2023.103745
   Brynjolfsson E, 2023, 31161 NBER
   Cazzaniga M., 2024, IMF Staff Discussion Note SDN2024/001
   Chen SR, 2023, J FINANC INTERMED, V54, DOI 10.1016/j.jfi.2023.101026
   Doerr S., 2022, SUERF Policy Briefs, V270
   Gelbach JB, 2016, J LABOR ECON, V34, P509, DOI 10.1086/683668
   Lythreatis S, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121359
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Prince JT, 2022, J ECON MANAGE STRAT, V31, P841, DOI 10.1111/jems.12481
   Tang H., 2024, Working paper, P62
NR 13
TC 0
Z9 0
U1 8
U2 8
PU ELSEVIER SCIENCE SA
PI LAUSANNE
PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND
SN 0165-1765
EI 1873-7374
J9 ECON LETT
JI Econ. Lett.
PD AUG
PY 2024
VL 241
AR 111814
DI 10.1016/j.econlet.2024.111814
EA JUN 2024
PG 3
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA I8U6V
UT WOS:001332955300001
DA 2024-12-25
ER

PT J
AU Chen, BY
   Wu, ZX
   Zhao, RR
AF Chen, Boyang
   Wu, Zongxiao
   Zhao, Ruoran
TI From fiction to fact: the growing role of generative AI in business and
   finance
SO JOURNAL OF CHINESE ECONOMIC AND BUSINESS STUDIES
LA English
DT Article
DE Generative AI; ChatGPT; Natural Language Processing; Sentiment Analysis;
   Practical Applications
ID DEFAULT PREDICTION; CHATGPT; PRODUCTIVITY
AB Generative Artificial Intelligence (AI), such as ChatGPT by OpenAI, has revolutionized the business world, with benefits including improved accessibility, efficiency, and cost reduction. This article reviews recent developments of generative AI in business and finance, summarizes its practical applications, provides examples of the latest generative AI tools, and demonstrates that generative AI can revolutionize data analysis in industry and academia. To test the ability of generative AI to support decision-making in financial markets, we use the ChatGPT to capture corporate sentiments towards environmental policy by inputting text extracted from corporate financial statements. Our results demonstrate that the sentiment scores generated by ChatGPT can predict firms' risk-management capabilities and stock return performance. This study also highlights the potential challenges and limitations associated with generative AI. Finally, we propose several questions for future research at the intersection of generative AI with business and finance.
C1 [Chen, Boyang] China Agr Univ, Coll Econ & Management, Beijing, Peoples R China.
   [Chen, Boyang; Wu, Zongxiao; Zhao, Ruoran] Univ Edinburgh, Business Sch, Edinburgh, Scotland.
C3 China Agricultural University; University of Edinburgh
RP Zhao, RR (corresponding author), Univ Edinburgh, Business Sch, Edinburgh, Scotland.
EM ruoran.zhao@ed.ac.uk
RI Wu, Zongxiao/LKJ-7934-2024
OI Wu, Zongxiao/0000-0002-8899-7575; Zhao, Ruoran/0000-0002-9795-2774
FU State Scholarship Fund [202206350067]
FX This study was supported by the State Scholarship Fund (No.
   202206350067).
CR Agarwal S, 2016, MANAGE SCI, V62, P2218, DOI 10.1287/mnsc.2015.2243
   Ali Z., 2023, EC CHATGPT LABOR MAR, DOI [https://doi.org/10.2139/ssrn.4350925, DOI 10.2139/SSRN.4350925]
   Alshater M., 2022, EXPLORING ROLE ARTIF, DOI [10.2139/ssrn.4312358, DOI 10.2139/SSRN.4312358]
   An JF, 2023, NATURE, V615, P586, DOI 10.1038/d41586-023-00843-2
   Basu T., 2020, MIT Technology Review
   Beam EA, 2023, J DEV ECON, V162, DOI 10.1016/j.jdeveco.2023.103069
   Beerbaum D. O., 2023, GENERATIVE ARTIFICIA, DOI [https://doi.org/10.2139/ssrn.4385025, DOI 10.2139/SSRN.4385025]
   Beraja M, 2023, REV ECON STUD, V90, P1701, DOI 10.1093/restud/rdac056
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Brynjolfsson E., 2023, National Bureau of Economic Research Working Paper 31161
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Cao Y, 2023, J CHIN ECON BUS STUD, V21, P177, DOI 10.1080/14765284.2023.2212434
   Clarke L, 2023, SCIENCE, V380, P120, DOI 10.1126/science.adi2240
   Constantinescu M., 2022, Philosophy Technology, V35, P35, DOI [10.1007/s13347-022-00529-z, DOI 10.1007/S13347-022-00529-Z]
   Debnath R, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-23624-9
   Dixit A.K., 1994, Investment Under Uncertainty, DOI [10.1515/9781400830176, DOI 10.1515/9781400830176]
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eckerli F., 2021, arXiv
   Eisfeldt A. L., 2023, National Bureau of Economic Research working paper
   Fischer C, 2008, J ENVIRON ECON MANAG, V55, P142, DOI 10.1016/j.jeem.2007.11.001
   George A. S., 2023, Partners Universal International Innovation Journal, V1, P9, DOI [DOI 10.5281/ZENODO.7644359, 10.5281/zenodo.7644359]
   Gherhes C, 2023, WORLD DEV, V168, DOI 10.1016/j.worlddev.2023.106252
   Gil D., 2020, The Future of Management in an AI World, P3
   Hamamoto M, 2006, RESOUR ENERGY ECON, V28, P299, DOI 10.1016/j.reseneeco.2005.11.001
   Hansen A. L., 2023, Can ChatGPT Decipher Fedspeak?, DOI [https://doi.org/10.2139/ssrn.4399406, DOI 10.2139/SSRN.4399406]
   Hassan TA, 2019, Q J ECON, V134, P2135, DOI 10.1093/qje/qjz021
   Hassnian A., 2023, SSRN, DOI [https://doi.org/10.2139/ssrn.4403372, DOI 10.2139/SSRN.4403372]
   Herzenstein M, 2011, J MARKETING RES, V48, pS138, DOI 10.1509/jmkr.48.SPL.S138
   Johnstone N, 2010, ENVIRON RESOUR ECON, V45, P133, DOI 10.1007/s10640-009-9309-1
   Korangi K, 2023, EUR J OPER RES, V308, P306, DOI 10.1016/j.ejor.2022.10.032
   Korinek A., 2023, 30957 NAT BUR EC RES, DOI [DOI 10.3386/W30957, 10.3386/w30957]
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Liebrenz M, 2023, LANCET DIGIT HEALTH, V5, pE105, DOI 10.1016/S2589-7500(23)00019-5
   López-Hernández AE, 2024, CLIN EXP OPTOM, DOI 10.1080/08164622.2024.2422473
   Lu QH, 2023, Arxiv, DOI arXiv:2301.05517
   Lu YY, 2021, J ECON SURV, V35, P1045, DOI 10.1111/joes.12422
   Nabavi E, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-01579-0
   Nguyen JH, 2020, J CORP FINANC, V64, DOI 10.1016/j.jcorpfin.2020.101713
   Noy S., 2023, EXPT EVIDENCE PRODUC, DOI [10.2139/ssrn.4375283, DOI 10.2139/SSRN.4375283]
   OpenAI, 2023, ChatGPT plugins
   OpenAI, 2023, Announcing OpenAI's bug bounty program
   Orzechowski P, 2022, SCI ADV, V8, DOI 10.1126/sciadv.abl4747
   Mann SP, 2023, NAT MACH INTELL, V5, P472, DOI 10.1038/s42256-023-00653-1
   Savage N., 2023, Nature
   Scepanovic S, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-022-26796-6
   Seghier ML, 2023, NATURE, V615, P216, DOI 10.1038/d41586-023-00680-3
   Senior M, 2023, NAT BIOTECHNOL, V41, P597, DOI 10.1038/d41587-023-00001-z
   Singh H, 2023, J CHIN ECON BUS STUD, V21, P193, DOI 10.1080/14765284.2023.2210482
   Singh U, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-26432-3
   Stevenson M, 2021, EUR J OPER RES, V295, P758, DOI 10.1016/j.ejor.2021.03.008
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Wan XC, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-82338-6
   Wenzlaff K., 2022, SMARTER HUMANS VALID, DOI [DOI 10.2139/SSRN.4302443, 10.2139/ssrn.4302443]
   Yang Hong, 2023, Nature, DOI 10.1038/d41586-023-01026-9
   Yang KC, 2024, Arxiv, DOI [arXiv:2304.00228, 10.48550/arXiv.2304.00228, DOI 10.48550/ARXIV.2304.00228]
   Yin KD, 2023, ENVIRON SCI POLLUT R, V30, P28066, DOI 10.1007/s11356-022-24088-0
   Zaremba A., 2023, Modern Finance, DOI [10.2139/ssrn.4323643, DOI 10.2139/SSRN.4323643]
   Zhou GS, 2020, CHINA ECON J, V13, P24, DOI 10.1080/17538963.2019.1681201
NR 59
TC 24
Z9 25
U1 92
U2 344
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1476-5284
EI 1476-5292
J9 J CHIN ECON BUS STUD
JI J. Chin. Econ. Bus. Stud.
PD OCT 2
PY 2023
VL 21
IS 4
BP 471
EP 496
DI 10.1080/14765284.2023.2245279
EA AUG 2023
PG 26
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA Y0QH1
UT WOS:001044938900001
OA Green Accepted
DA 2024-12-25
ER

PT J
AU Al-Samarraie, H
   Sarsam, SM
   Alzahrani, AI
   Chatterjee, A
   Swinnerton, BJ
AF Al-Samarraie, Hosam
   Sarsam, Samer Muthana
   Alzahrani, Ahmed Ibrahim
   Chatterjee, Arunangsu
   Swinnerton, Bronwen J.
TI Gender perceptions of generative AI in higher education
SO JOURNAL OF APPLIED RESEARCH IN HIGHER EDUCATION
LA English
DT Article; Early Access
DE Gender; Higher education; AI; Generative AI; Social network analysis
AB PurposeThis study explored the themes and sentiments of online learners regarding the use of Generative Artificial Intelligence (AI) or "generative AI" technology in higher education.Design/methodology/approachEnglish-language tweets were subjected to topic modelling and sentiment analysis. Three prevalent themes were identified and discussed: curriculum development opportunities, lifelong learning prospects and challenges associated with generative AI use.FindingsThe results also indicated a range of topics and emotions towards generative AI in education, which were predominantly positive but also varied across male and female users.Originality/valueThe findings provide insights for educators, policymakers and researchers on the opportunities and challenges associated with the integration of generative AI in educational settings. This includes the importance of identifying AI-supported learning and teaching practices that align with gender-specific preferences to offer a more inclusive and tailored approach to learning.
C1 [Al-Samarraie, Hosam] Univ Leeds, Sch Design, Leeds, England.
   [Al-Samarraie, Hosam] Univ Sains Malaysia, Minden, Malaysia.
   [Sarsam, Samer Muthana] Coventry Univ, Sch Management, Coventry, England.
   [Alzahrani, Ahmed Ibrahim] King Saud Univ, Community Coll, Comp Sci Dept, Riyadh, Saudi Arabia.
   [Chatterjee, Arunangsu] Univ Leeds, Sch Med, Leeds, England.
   [Swinnerton, Bronwen J.] Univ Leeds, Sch Educ, Leeds, England.
C3 University of Leeds; Universiti Sains Malaysia; Coventry University;
   King Saud University; University of Leeds; University of Leeds
RP Al-Samarraie, H (corresponding author), Univ Leeds, Sch Design, Leeds, England.; Al-Samarraie, H (corresponding author), Univ Sains Malaysia, Minden, Malaysia.
EM h.alsamarraie@leeds.ac.uk
RI Alzahrani, Ahmed/AGC-4025-2022; Al-Samarraie, Hosam/AAG-8941-2019;
   Sarsam, Samer/ABH-1955-2020
OI AL-SAMARRAIE, HOSAM/0000-0002-9861-8989
FU King Saud University, Riyadh, Saudi Arabia [RSP 2024/157]
FX This work was funded by the Researchers Supporting Project number (RSP
   2024/157), King Saud University, Riyadh, Saudi Arabia.
CR Ahuja K., 2023, arXiv preprint arXiv:2303.12528
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bhattacharjee U, 2019, INT CONF COMMUN SYST, P726, DOI [10.1109/comsnets.2019.8711427, 10.1109/COMSNETS.2019.8711427]
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chauhan A., 2024, 2024 IEEE 3 INT C AI
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Doshi A. R., 2023, Generative artificial intelligence enhances creativity, DOI DOI 10.2139/SSRN.4535536
   Gross N, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12080435
   Hemachandran K, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/1410448
   Ibrahim H, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-38964-3
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Lee MS, 2021, J AM MED INFORM ASSN, V29, P400, DOI 10.1093/jamia/ocab113
   Mouronte-López ML, 2023, EDUC INF TECHNOL, V28, P10965, DOI 10.1007/s10639-022-11493-8
   Mourelatos E., 2024, GLO Discussion Paper
   Nouraldeen Rasha Mohammad, 2023, Development and Learning in Organizations: An International Journal, P7, DOI 10.1108/DLO-07-2022-0133
   Nyaaba M., 2024, Journal of AI, V8, P45, DOI [10.61969/jai.1400867, DOI 10.61969/JAI.1400867]
   Ofosu-Ampong K., 2023, Journal of Digital Art Humanities, V4, P52, DOI [https://doi.org/10.33847/2712-8149.4.26, DOI 10.33847/2712-8149.4.26]
   Olga A., 2023, Generative AI: Implications and Applications for Education
   Park C, 2019, COMPUT HUM BEHAV, V92, P288, DOI 10.1016/j.chb.2018.11.029
   Singh A, 2024, AAAI CONF ARTIF INTE, P23653
   Strzelecki A, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13425
   Sun LH, 2023, J COMPUT-MEDIAT COMM, V29, DOI 10.1093/jcmc/zmad045
   Thelwall M, 2021, J DATA INFO SCI, V6, P1, DOI 10.2478/jdis-2021-0018
   Uc-Cetina V, 2023, ARTIF INTELL REV, V56, P1543, DOI 10.1007/s10462-022-10205-5
   Vázquez-Cano E, 2017, INT J EDUC TECHNOL H, V14, DOI 10.1186/s41239-017-0065-y
   Xia Q, 2023, EDUC INF TECHNOL, V28, P8691, DOI 10.1007/s10639-022-11547-x
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
NR 28
TC 0
Z9 0
U1 43
U2 43
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2050-7003
EI 1758-1184
J9 J APPL RES HIGH EDUC
JI J. Appl. Res. High. Educ.
PD 2024 SEP 23
PY 2024
DI 10.1108/JARHE-02-2024-0109
EA SEP 2024
PG 15
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA G2X9J
UT WOS:001315333600001
DA 2024-12-25
ER

PT J
AU Franganillo, J
AF Franganillo, Jorge
TI <i>Generative artificial intelligence and its impact on media content
   creation</i>
SO METHAODOS-REVISTA DE CIENCIAS SOCIALES
LA English
DT Article
DE artificial intelligence; creative algorithms; generative models; media
   content; technology ethics.
AB Generative artificial intelligence (AI) is a rapidly advancing field that enables the automated production of high-quality textual, graphic, sound , audiovisual content. This technology has significant implications for journalism, advertising and entertainment, as well as ethical, legal and social issues. This paper examines the possibilities, limitations and risks of generative AI for content production in the media. It analyzes large language models oriented to automated writing, generative adversarial networks oriented to image and short video synthesis , deepfake technology for video manipulation and voice cloning. The implications of these technologies for intellectual property, information veracity, personal identity and human creativity are discussed. It is concluded that generative AI is a powerful and innovative tool for media content creation, but that it also requires careful and ethical use by both content producers and consumers.
C1 [Franganillo, Jorge] Univ Barcelona, Informac & Comunicac, Barcelona, Spain.
C3 University of Barcelona
RP Franganillo, J (corresponding author), Univ Barcelona, Informac & Comunicac, Barcelona, Spain.
EM franganillo@ub.edu
RI Franganillo, Jorge/K-8534-2019
CR Ajder H., 2020, Sensity
   Ajder Henry, 2019, THE STATE OF DEEPFAKES: LANDSCAPE, THREATS, AND IMPACTS
   [Anonymous], 2018, Will Robots Really Steal Our Jobs? An International Analysis of the Potential Long Term Impact of Automation
   Ayuso Silvia, 2023, El Pais11 May
   Barandy K., 2022, Designboom10 de agosto
   BHARGAVA C., 2022, Artificial intelligence: fundamentals and applications
   Boden MA, 2018, Artificial Intelligence: a very short introduction
   Botha J, 2020, PR INT CONF INF WAR, P57, DOI 10.34190/ICCWS.20.085
   Boucher P., 2020, Artificial Intelligence: How Does it Work, Why Does it Matter, and What We Can Do about it?, DOI [10.2861/44572, DOI 10.2861/44572]
   Broderick Ryan., 2023, Polygon 31 May
   Campesato O., 2020, Artificial Intelligence, Machine Learning, and Deep Learning
   Castillo C., 2023, para la inteligencia artificial
   Dale R, 2022, NAT LANG ENG, V28, P401, DOI 10.1017/S1351324922000146
   Davenport T. H., 2022, Harvard Business Review
   Dean I., 2022, Creative bloq11 de agosto
   Giannini S., 2023, Generative AI and the future of education
   Giansiracusa N., 2021, ALGORITHMS CREATE PR, DOI [10.1007/978-1-4842-7155-1, DOI 10.1007/978-1-4842-7155-1]
   Greenhouse, 2023, The Guardian8 de febrero
   Hao Karen, 2021, MIT Technology Review
   Hatzius J., 2023, POTENTIALLY LARGE EF
   Higgins E., 2023, Twitter20 de marzo
   Kreps S, 2022, J EXP POLIT SCI, V9, P104, DOI 10.1017/XPS.2020.37
   Longoni Chiara, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P97, DOI 10.1145/3531146.3533077
   Lopez Delacruz Santiago, 2023, Hipertext.net, P31
   Lyons BA, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2019527118
   Metz C., 2022, The New York Times5 de agosto
   Navigli R, 2023, ACM J DATA INF QUAL, V15, DOI 10.1145/3597307
   Newsguard, 2023, Reports about online misinformation and disinformation from NewsGuard's analysts
   Nightingale SJ, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2120481119
   OpenAI, 2020, The Guardian8 sept.
   Osmanovic-Thunstrom A., 2022, We asked GPT-3 to write an academic paper about itself, then we tried to get it published
   Ousidhoum N., 2021, P 59 ANN M ASS COMP, V1, DOI DOI 10.18653/V1/2021.ACL-LONG.329
   Perez Colome J., 2023, El Pais11 de abril
   Sánchez-García P, 2023, PROF INFORM, V32, DOI 10.3145/epi.2023.mar.08
   Schomer Audrey., 2023, Variety 6 July
   Sola P., 2021, La Vanguardia27 de enero
   Steed R, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P701, DOI 10.1145/3442188.3445932
   Sweney M., 2023, The Guardian7 de marzo
   Warzel C., 2022, The Atlantic17 de agosto
   Yu N, 2021, 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), P14428, DOI 10.1109/ICCV48922.2021.01418
NR 40
TC 2
Z9 2
U1 87
U2 276
PU UNIV REY JUAN CARLOS, FAC CIENCIAS JURIDICAS & SOCIALES
PI MADRID
PA CAMPUS DE VICALVARO, P ARTILLEROS, S-N, MADRID, 28032, SPAIN
SN 2340-8413
J9 METHAODOS
JI Methaodos
PD NOV
PY 2023
VL 11
IS 2
AR m231102a10
DI 10.17502/mrcs.v11i2.710
PG 17
WC Sociology
WE Emerging Sources Citation Index (ESCI)
SC Sociology
GA S2SX7
UT WOS:001069731400001
OA Green Submitted, gold
DA 2024-12-25
ER

PT J
AU Gasaymeh, AM
   Beirat, MA
   Abu Qbeita, AA
AF Gasaymeh, Al-Mothana M.
   Beirat, Mohammad A.
   Abu Qbeita, Asma'a A.
TI University Students' Insights of Generative Artificial Intelligence (AI)
   Writing Tools
SO EDUCATION SCIENCES
LA English
DT Article
DE generative artificial intelligence (AI); writing tools; university
   students' educational majors; Jordan
AB The current study examined university students' insights into generative AI writing tools regarding their familiarity with, perceived concerns about, and perceived benefits of these tools in their academic work. The study used a cross-sectional descriptive research design, and data were collected using a questionnaire instrument. The participants were ninety-five undergraduate and graduate students from a College of Education at a university in Jordan. The results show that university students show moderate familiarity with generative AI writing tools (M = 3.14, SD = 0.81), especially in engagement but lacking technical knowledge. They also have moderate concerns (M = 3.35, SD = 0.85), particularly about misinformation and data security. Despite these concerns, students recognize the benefits (M = 3.62, SD = 0.81), especially regarding the capabilities of these tools in simulating creativity and fostering innovation. In addition, the results showed that gender and educational level appear to have little effect on familiarity, concerns, and perceived benefits regarding these tools. Based on the findings, the study recommends enhancing students' familiarity with generative AI tools through providing technical training, hands-on opportunities, and ethical discussions. In addition, the study recommends addressing students' concerns regarding generative AI writing tools by improving data security related to generative AI, providing ethical guidelines regarding the use of these tools, and boosting AI literacy. Finally, it is recommended to enhance students' perceptions of the benefits of generative AI writing tools by highlighting the creative potential of these tools within the educational setting, using these tools to offer personalized learning experiences that adapt to individual learning styles, and promoting collaboration through generative AI writing tools.
C1 [Gasaymeh, Al-Mothana M.; Abu Qbeita, Asma'a A.] Al Hussein Bin Talal Univ, Fac Educ Sci, Dept Curriculum & Instruct, POB 20, Maan, Jordan.
   [Beirat, Mohammad A.] Al Hussein Bin Talal Univ, Fac Educ Sci, Dept Special Educ, POB 20, Maan, Jordan.
C3 Al-Hussein Bin Talal University; Al-Hussein Bin Talal University
RP Gasaymeh, AM (corresponding author), Al Hussein Bin Talal Univ, Fac Educ Sci, Dept Curriculum & Instruct, POB 20, Maan, Jordan.
EM almothana.m.gasaymeh@ahu.edu.jo; beirat@ahu.edu.jo; asmaa@ahu.edu.jo
RI beirat, mohammad/JQI-7254-2023
OI Abu Qbeita, Asma'a/0000-0002-7352-0256
CR Abouammoh N., 2023, medRxiv, V7, DOI [10.1101/2023.07.13.23292624, DOI 10.1101/2023.07.13.23292624]
   Abumusab S, 2024, AI SOC, V39, P3051, DOI 10.1007/s00146-023-01773-3
   Al-Qahtani A.S., 2021, J. Educ. Psychol. Sci, V22, P163
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   Aron A., 2005, STAT BEHAV SOCIAL SC
   Arowosegbe A, 2024, Preprints, DOI 10.20944/preprints202405.1158.v1
   Awidi I. T., 2024, Comput. Educ. Artif. Intell, V6, DOI [10.1016/j.caeai.2024.100226, DOI 10.1016/J.CAEAI.2024.100203]
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bukar UA, 2024, IEEE ACCESS, V12, P95368, DOI 10.1109/ACCESS.2024.3425172
   Castro D., 2016, Cent Data Innov, V115, P32, DOI [10.7551/mitpress/12385.001.0001, DOI 10.7551/MITPRESS/12385.001.0001]
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dai Yun, 2023, Procedia CIRP, P84, DOI 10.1016/j.procir.2023.05.002
   Duhaylungsod A.V., 2023, J. Namib. Stud. Hist. Politics Cult, V33, P4367
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Fitria T. N., 2023, ELT FORUM, V12, P44, DOI DOI 10.15294/ELT.V12I1.64069
   Fontao C.B., 2024, Int. J. Inf. Educ. Technol, V14, P1035, DOI [10.18178/ijiet.2024.14.8.2131, DOI 10.18178/IJIET.2024.14.8.2131]
   Foster D., 2022, Generative deep learning
   Gammoh LA, 2024, J FURTH HIGHER EDUC, V48, P608, DOI 10.1080/0309877X.2024.2378298
   Gasaymeh AMM, 2019, INT J TECHNOL ENHANC, V11, P136
   Gasaymeh A, 2018, EURASIA J MATH SCI T, V14, P1731, DOI 10.29333/ejmste/85118
   Gasaymeh AM, 2017, EURASIA J MATH SCI T, V13, P7527, DOI 10.12973/ejmste/80014
   George A. S., 2023, Partners Universal International Research Journal, V2, P36, DOI [10.5281/ZENODO.10421475, DOI 10.5281/ZENODO.10421475]
   Goldberg Y, 2016, J ARTIF INTELL RES, V57, P345, DOI 10.1613/jair.4992
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Habes M., 2024, Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom, VVolume 144, DOI [10.1007/978-3-031-52280-29, DOI 10.1007/978-3-031-52280-29]
   Hoy Matthew B., 2018, Medical Reference Services Quarterly, V37, P81, DOI 10.1080/02763869.2018.1404391
   Huallpa J.J., 2023, PERIODICALS ENG NATU, V11, P105, DOI DOI 10.21533/PEN.V11I4.3770
   Huang XY, 2023, EDUC TECHNOL SOC, V26, P112, DOI 10.30191/ETS.202301_26(1).0009
   Hui X., 2023, SSRN, V4527336, DOI DOI 10.2139/SSRN.4544582
   Hutson J., Journal of Intelligent Communication, V4, P20, DOI DOI 10.54963/JIC.V4I1.220
   Imran A.A., 2023, Glob. Soc. Sci. Rev, VVIII, P375, DOI [10.31703/gssr.2023(VIII-I).34, DOI 10.31703/GSSR.2023(VIII-I).34]
   Jowarder M. I., 2023, Indonesian Journal of Innovation and Applied Sciences (IJIAS), V3, P194, DOI [10.47540/ijias.v3i2.878, DOI 10.47540/IJIAS.V3I2.878]
   Khalil M, 2023, LECT NOTES COMPUT SC, V14040, P475, DOI 10.1007/978-3-031-34411-4_32
   Lucchi N, 2024, EUR J RISK REGUL, V15, P602, DOI 10.1017/err.2023.59
   Markos A, 2024, ELECTRONICS-SWITZ, V13, DOI 10.3390/electronics13101985
   Murr P., 2023, Eur. Conf. Innov. Entrep, V18, P660, DOI [10.34190/ecie.18.1.1638, DOI 10.34190/ECIE.18.1.1638]
   Ngo T. T. A., 2023, Int. J. Emerg. Technol. Learn., V18, P4, DOI [DOI 10.3991/IJET.V18I17.39019, https://doi.org/10.3991/ijet.v18i17.39019, 10.3991/ijet.v18i14.39903, DOI 10.3991/IJET.V18I14.39903]
   Nikolopoulou K., 2024, International Journal of Changes in Education, V1, P103, DOI [10.47852/bonviewijce42022489, DOI 10.47852/BONVIEWIJCE42022489]
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Pesovski I, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16073034
   Raman R, 2024, HUM BEHAV EMERG TECH, V2024, DOI 10.1155/2024/3085910
   Reilley M., 2024, The Journalists Toolbox: A Guide to Digital Reporting and AI
   Sevnarayan K., 2024, J. Appl. Learn. Teach, V7, DOI [10.37074/jalt.2024.7.1.41, DOI 10.37074/JALT.2024.7.1.41]
   Shanto S.S., 2024, J. Propuls. Technol, V45, P2024
   Snchez O.V.G., 2023, Rev. Investig. Tecnol. Inf, V11, P98
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   Takale D.G., 2024, J. Inf. Technol. Sci, V10, P20
   Tao Y., 2023, P ICIS 2023 P HYD IN, VVolume 1
   Teubner T, 2023, BUS INFORM SYST ENG+, V65, P95, DOI 10.1007/s12599-023-00795-x
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Xu DN, 2023, PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2023, P9291, DOI 10.1145/3581783.3612704
   Yan LX, 2024, FOURTEENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, LAK 2024, P101, DOI 10.1145/3636555.3636856
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
   Yilmaz H., 2023, International Educational Review, V1, P57, DOI [10.58693/ier.114, DOI 10.58693/IER.114]
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
NR 62
TC 0
Z9 0
U1 20
U2 20
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD OCT
PY 2024
VL 14
IS 10
AR 1062
DI 10.3390/educsci14101062
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA K1Y9Z
UT WOS:001341917900001
OA gold
DA 2024-12-25
ER

PT J
AU Zainurrahman
   Purnawarman, P
   Muslim, AB
AF Zainurrahman, Pupung
   Purnawarman, Pupung
   Muslim, Ahmad Bukhori
TI Ethically Utilizing GenAI Tools to Alleviate Challenges in Conventional
   Feedback Provision
SO JOURNAL OF ACADEMIC ETHICS
LA English
DT Article; Early Access
DE GenAI; Feedback provision; Challenges; Ethics
ID AUTOMATED WRITING EVALUATION
AB Generative artificial intelligence (GenAI) is a subset of artificial intelligence (AI) that can generate content such as texts, images, videos, sounds, etc. While GenAI tools have been utilized in various contexts, their utilization in the academic context is still a controversial topic. Scholars observed that many universities have banned GenAI due to the potential for unethical usage. In this opinion article, we promote the utilization of GenAI tools as feedback agents to alleviate challenges in conventional feedback provision. Feedback is a valuable learning source, especially in writing instructions. However, conventional feedback provision is challenging due to the increase in teacher's workload and social irritation issues in peer feedback. Utilized ethically, GenAI tools can alleviate these challenges. A few critical considerations are briefly presented.
C1 [Zainurrahman, Pupung; Purnawarman, Pupung; Muslim, Ahmad Bukhori] Univ Pendidikan Indonesia, Bandung, Indonesia.
C3 Universitas Pendidikan Indonesia
RP Zainurrahman (corresponding author), Univ Pendidikan Indonesia, Bandung, Indonesia.
EM zainurrahman@upi.edu
RI Purnawarman, Pupung/AAP-6792-2021; Muslim, Ahmad/AAB-5141-2021
OI Bukhori Muslim, Ahmad/0000-0003-0681-9386
FU Lembaga Pengelola Dana Pendidikan - LPDP (Indonesia Endowment Fund for
   Education) under the Ministry of Finance of the Republic of Indonesia
FX The study is conducted as part of the corresponding author's doctoral
   study, which is funded by the Lembaga Pengelola Dana Pendidikan - LPDP
   (Indonesia Endowment Fund for Education) under the Ministry of Finance
   of the Republic of Indonesia.
CR Agostini D., 2024, Research Trends in Humanities, V11, P38
   Almasri F, 2024, RES SCI EDUC, V54, P977, DOI 10.1007/s11165-024-10176-3
   Anders BA, 2023, PATTERNS, V4, P1, DOI 10.1016/j.patter.2023.100694
   Bahammam AS, 2023, Journal of Nature and Science of Medicine, V6, P152, DOI DOI 10.4103/JNSM.JNSM8923
   Banister C, 2023, LANG TEACH RES, V27, P746, DOI 10.1177/1362168820952222
   Casinto CD., 2023, Teaching English as a Second or Foreign LanguageTESL-EJ, V26, P1, DOI [10.55593/ej.26104a8, DOI 10.55593/EJ.26104A8]
   Elbow Peter., 1998, WRITING POWER TECHNI, V2nd
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Gao JM, 2021, INT J EMERG TECHNOL, V16, P322, DOI 10.3991/ijet.v16i11.19657
   Hagerty A., 2019, arXiv, DOI 10.48550/ARXIV.1907.07892
   Hattie J, 2007, REV EDUC RES, V77, P81, DOI 10.3102/003465430298487
   Hyland K, 2019, CAM APPL L, P265
   John P, 2020, CALICO J, V37, P169, DOI 10.1558/cj.36523
   Kerman NT, 2024, INTERACT LEARN ENVIR, V32, P614, DOI 10.1080/10494820.2022.2093914
   Kluger AN, 1996, PSYCHOL BULL, V119, P254, DOI 10.1037/0033-2909.119.2.254
   Koltovskaia S, 2023, RECALL, V35, P290, DOI 10.1017/S0958344022000179
   Law L, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100174
   Lee I., 2017, Classroom writing assessment and feedback in L2 school contexts, V73
   Lozic E, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15100336
   Man DL, 2022, RELC J, DOI 10.1177/00336882221078380
   Okaiyeto SA, 2023, INT J AGR BIOL ENG, V16, P285, DOI 10.25165/j.ijabe.20231603.8486
   Palermo C, 2020, J WRIT RES, V12, P63, DOI 10.17239/jowr-2020.12.01.04
   Parra L, 2019, INT J INSTR, V12, P209, DOI 10.29333/iji.2019.12214a
   Schmohl T., 2020, FUT ED 10 PIX INT C
   Sedaghat S, 2024, J ACAD ETHICS, DOI 10.1007/s10805-024-09533-8
   Sedaghat S, 2024, J AM COLL RADIOL, V21, P344, DOI 10.1016/j.jacr.2023.10.019
   Selvaraj A M., 2021, International Journal of Learning, Teaching and Educational Research, V20, P308, DOI DOI 10.26803/IJLTER.20.1.17
   Stevenson M, 2019, CAM APPL L, P125
   Sumakul D. T., 2022, LEARN Journal: Language Education and Acquisition Research Network, V15, P232
   UNESCO, 2023, UNESCO, DOI [10.54675/EWZM9535, DOI 10.54675/EWZM9535]
   Wang CR, 2024, TECHNOL KNOWL LEARN, DOI 10.1007/s10758-024-09744-3
   Wang S, 2021, Frontiers in Educational Research, V4, P94, DOI [10.25236/FER.2021.041117, DOI 10.25236/FER.2021.041117]
   Weidmann AE, 2024, INT J CLIN PHARM-NET, V46, P751, DOI 10.1007/s11096-024-01705-1
   Yu SL, 2021, J SECOND LANG WRIT, V52, DOI 10.1016/j.jslw.2021.100798
   Yu SL, 2021, ASSESS WRIT, V48, DOI 10.1016/j.asw.2021.100528
   Zainurrahman, 2024, Project: Professional Journal of English Education, V7, P459
NR 36
TC 0
Z9 0
U1 5
U2 5
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 1570-1727
EI 1572-8544
J9 J ACAD ETHICS
JI J. Acad. Ethics
PD 2024 OCT 23
PY 2024
DI 10.1007/s10805-024-09578-9
EA OCT 2024
PG 6
WC Ethics
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA J8A6L
UT WOS:001339235600001
DA 2024-12-25
ER

PT J
AU Bower, M
   Torrington, J
   Lai, JWM
   Petocz, P
   Alfano, M
AF Bower, Matt
   Torrington, Jodie
   Lai, Jennifer W. M.
   Petocz, Peter
   Alfano, Mark
TI How should we change teaching and assessment in response to increasingly
   powerful generative Artificial Intelligence? Outcomes of the ChatGPT
   teacher survey
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Artificial Intelligence; Generative AI; ChatGPT; Teacher beliefs;
   Motivation
ID UNIFIED THEORY; TECHNOLOGY; ACCEPTANCE; AGREEMENT
AB There has been widespread media commentary about the potential impact of generative Artificial Intelligence (AI) such as ChatGPT on the Education field, but little examination at scale of how educators believe teaching and assessment should change as a result of generative AI. This mixed methods study examines the views of educators (n = 318) from a diverse range of teaching levels, experience levels, discipline areas, and regions about the impact of AI on teaching and assessment, the ways that they believe teaching and assessment should change, and the key motivations for changing their practices. The majority of teachers felt that generative AI would have a major or profound impact on teaching and assessment, though a sizeable minority felt it would have a little or no impact. Teaching level, experience, discipline area, region, and gender all significantly influenced perceived impact of generative AI on teaching and assessment. Higher levels of awareness of generative AI predicted higher perceived impact, pointing to the possibility of an 'ignorance effect'. Thematic analysis revealed the specific curriculum, pedagogy, and assessment changes that teachers feel are needed as a result of generative AI, which centre around learning with AI, higher-order thinking, ethical values, a focus on learning processes and face-to-face relational learning. Teachers were most motivated to change their teaching and assessment practices to increase the performance expectancy of their students and themselves. We conclude by discussing the implications of these findings in a world with increasingly prevalent AI.
C1 [Bower, Matt; Torrington, Jodie; Lai, Jennifer W. M.] Macquarie Univ, Macquarie Sch Educ, Sydney, NSW 2109, Australia.
   [Petocz, Peter] Macquarie Univ, Grad Res Acad, Sydney, NSW 2109, Australia.
   [Alfano, Mark] Macquarie Univ, Dept Philosophy, Sydney, NSW 2109, Australia.
C3 Macquarie University; Macquarie University; Macquarie University
RP Lai, JWM (corresponding author), Macquarie Univ, Macquarie Sch Educ, Sydney, NSW 2109, Australia.
EM matt.bower@mq.edu.au; jennifer.lai@mq.edu.au
RI Bower, Matt/J-7574-2016
OI Petocz, Peter/0000-0002-1266-0060; Torrington,
   Jodie/0000-0003-2754-0691; Alfano, Mark/0000-0001-5879-8033; Lai,
   Jennfer W.M./0000-0001-9042-7064; Bower, Matt/0000-0002-4161-5816
FU Macquarie University
FX ChatGPT and generative AI were not used in any way to create this paper.
CR Akgun Selin, 2022, AI Ethics, V2, P431, DOI 10.1007/s43681-021-00096-7
   Almalki S., 2016, J ED LEARNING, V5, P288, DOI [DOI 10.5539/JEL.V5N3P288, 10.5539/jel.v5n3p288]
   Baig MI, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-00223-0
   Baker T., 2019, Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges
   Bower M, 2020, BRIT J EDUC TECHNOL, V51, P2214, DOI 10.1111/bjet.13009
   Bower M, 2017, DESIGN OF TECHNOLOGY-ENHANCED LEARNING: INTEGRATING RESEARCH AND PRACTICE, P35
   Brown TB., 2020, ADV NEURAL INFORM PR, V2020, P1877, DOI [10.48550/ARXIV.2005.14165, DOI 10.48550/ARXIV.2005.14165]
   Carvalho L., 2022, Comput. Educ. Artif. Intell, V3, P100053, DOI [10.1016/j.caeai.2022.100053, DOI 10.1016/J.CAEAI.2022.100053]
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Celik I, 2022, TECHTRENDS, V66, P616, DOI 10.1007/s11528-022-00715-y
   Herting DC, 2020, INNOV HIGH EDUC, V45, P65, DOI 10.1007/s10755-019-09488-4
   Chen X., 2020, Computers and Education: Artificial Intelligence, V1, P100002, DOI [10.1016/j.caeai.2020.100002 10.1016/j.caeai.2020.100002, DOI 10.1016/J.CAEAI.2020.100002]
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   Chounta IA, 2022, INT J ARTIF INTELL E, V32, P725, DOI 10.1007/s40593-021-00243-5
   Churchill R., 2016, Teaching: Making a difference, V3rd
   Clarivate Analytics, 2023, WEB SCI RES DOMAINS
   Clarke V., 2013, SUCCESSFUL QUALITATI
   COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104
   Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dai Y, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12166597
   Devlin J., 2018, ARXIV
   Escueta M., 2017, National Bureau of Economic Research Working Paper No. 23744
   Ferguson C., 2022, COMPUTERS ED ARTIFIC, V3, DOI 10.1016/j.caeai.2022.100089
   Hattie J., 2023, Visible learning, the sequel: A synthesis of over 2,100 meta-analyses relating to achievement, DOI DOI 10.4324/9781003380542
   Hisan UK., 2023, Journal of Pedagogy and Education Science, V2, P71, DOI DOI 10.56741/JPES.V2I01.302
   Holmes W., 2019, Artificial intelligence in education promises and implications for teaching and learning, DOI [10.1046/j.1365-2729.1998.1440251.x, DOI 10.1046/J.1365-2729.1998.1440251.X]
   Kangasharju A., 2022, Comp. Educ. Artif. Intell, V3, P100048, DOI DOI 10.1016/J.CAEAI.2022.100048
   Khosravi H., 2022, Computers and Education: Artificial Intelligence, V3, P100074, DOI DOI 10.1016/J.CAEAI.2022.100074
   Kim J, 2022, EDUC INF TECHNOL, V27, P6069, DOI 10.1007/s10639-021-10831-6
   Lai JWM, 2019, COMPUT EDUC, V133, P27, DOI 10.1016/j.compedu.2019.01.010
   LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
   Luckin R., 2022, COMPUTERS ED ARTIFIC, V3, DOI [DOI 10.1016/J.CAEAI.2022.100076, 10.1016/J.CAEAI.2022.100076]
   Markauskaite L., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022.100056, 10.1016/j.caeai.2022.100056]
   Matzen NJ, 2007, J RES TECHNOL EDUC, V39, P417, DOI 10.1080/15391523.2007.10782490
   Merritt R, 2022, WHAT IS TRANSFORMER
   Miller FA., 2018, OD PRACTITIONER, V50, P8
   Mollman S., 2023, Fortune
   Nardi PM, 2018, DOING SURVEY RESEARCH: A GUIDE TO QUANTITATIVE METHODS, 4TH EDITION, P46
   OpenAI, 2023, INTRO CHATGPT
   Ouyang F., 2021, COMPUTERS ED ARTIFIC, V2, P100020, DOI [DOI 10.1016/J.CAEAI.2021.100020, 10.1016/j.caeai.2021.100020 10.1016/j.caeai.2021.100020]
   Ouyang L, 2022, ADV NEUR IN
   Pei Wang, 2019, Journal of Artificial General Intelligence, V10, P1, DOI 10.2478/jagi-2019-0002
   Pérez-Sanagustín M, 2017, COMPUT EDUC, V104, pA1, DOI 10.1016/j.compedu.2016.09.006
   Popenici Stefan A D, 2017, Res Pract Technol Enhanc Learn, V12, P22, DOI 10.1186/s41039-017-0062-8
   Roose K., 2023, The New York Times
   Schiff D, 2021, AI SOC, V36, P331, DOI 10.1007/s00146-020-01033-8
   Swiecki Z, 2022, Computers and Education: Artificial Intelligence, V3, DOI DOI 10.1016/J.CAEAI.2022.100075
   Tang KY, 2023, INTERACT LEARN ENVIR, V31, P2134, DOI 10.1080/10494820.2021.1875001
   Touretzky D, 2019, AAAI CONF ARTIF INTE, P9795
   Tseng TH, 2022, INTERACT LEARN ENVIR, V30, P635, DOI 10.1080/10494820.2019.1674888
   UNESCO, 2019, Beijing consensus on artificial intelligence and education
   Vaswani A, 2017, ADV NEUR IN, V30
   Venkatesh V, 2016, J ASSOC INF SYST, V17, P328, DOI 10.17705/1jais.00428
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang XH, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104703
   Wingard J, 2023, FORBES
   Xu WQ, 2022, EDUC INF TECHNOL, V27, P4195, DOI 10.1007/s10639-021-10774-y
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhai XS, 2021, COMPLEXITY, V2021, DOI 10.1155/2021/8812542
NR 61
TC 15
Z9 15
U1 273
U2 471
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD AUG
PY 2024
VL 29
IS 12
BP 15403
EP 15439
DI 10.1007/s10639-023-12405-0
EA JAN 2024
PG 37
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA G3S8A
UT WOS:001148511700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Kostis, A
   Lidström, J
   Nair, S
   Holmström, J
AF Kostis, Angelos
   Lidstrom, Johan
   Nair, Sujith
   Holmstrom, Jonny
TI Too Much AI Hype, Too Little Emphasis on Learning? Entrepreneurs
   Designing Business Models Through Learning-by-Conversing With Generative
   AI
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Business; Entrepreneurship; Biological system modeling; Technological
   innovation; Generative AI; Buildings; Uncertainty; Planning; Iterative
   methods; Design methodology; Business model design; entrepreneurship;
   generative AI (GenAI); learning; startup methodology
ID KNOWLEDGE; RESOURCES; CONSTRUCT
AB Entrepreneurs traditionally use "learning-by-doing" and "learning-by-thinking" as alternative approaches to iteratively build business models for their new ventures. However, both approaches face criticism in how they address novelty and uncertainty, which are crucial to successful entrepreneurship. While generative AI (GenAI) is increasingly used in entrepreneurial tasks, the practices through which it becomes a learning resource for entrepreneurs remain unexplored. Based on a qualitative study, we present a process model that illustrates how entrepreneurs incorporate GenAI into business model design through five resourcing practices. These practices transform GenAI into a valuable resource for facilitating learning during the design process. This approach, which we term "learning-by-conversing," introduces a generative startup methodology to complement the lean startup model. We distinguish two modes of learning by conversing-reflexive learning and confirmatory learning-based on how novice and experienced entrepreneurs engage with it. By proposing a learning approach that integrates GenAI with entrepreneurial efforts, we bridge the "thinking" versus "doing" debate in business model generation and deepen our understanding of GenAI's role in entrepreneurship.
C1 [Kostis, Angelos; Lidstrom, Johan] Umea Univ, Umea Sch Business Econ & Stat, S-90187 Umea, Sweden.
   [Kostis, Angelos] Stanford Univ, SCANCOR & Management Sci & Engn, Stanford, CA 94305 USA.
   [Nair, Sujith] BI Norwegian Business Sch, Dept Strategy & Entrepreneurship, N-0484 Oslo, Norway.
   [Holmstrom, Jonny] Umea Univ, Swedish Ctr Digital Innovat, Dept Informat, S-90187 Umea, Sweden.
C3 Umea University; Stanford University; BI Norwegian Business School; Umea
   University
RP Kostis, A (corresponding author), Umea Univ, Umea Sch Business Econ & Stat, S-90187 Umea, Sweden.
EM angelos.kostis@umu.se; johan.lidstrom@umu.se; sujith.nair@bi.no;
   jonny.holmstrom@umu.se
RI Holmstrom, Jonny/N-5703-2019; nair, sujith/Y-4504-2018
OI Holmstrom, Jonny/0000-0002-0905-350X; nair, sujith/0000-0001-8340-9853
FU Handelsbanken Jan Wallanders och Tom Hedelius Stiftelse and Tore
   Browaldhs Stiftelse [W21-0008, Fv23-0047, P21-0035]
FX This work was supported by the Handelsbanken Jan Wallanders och Tom
   Hedelius Stiftelse and Tore Browaldhs Stiftelse under Grant W21-0008,
   Grant Fv23-0047, and Grant P21-0035.
CR Abraham D. C. E. M., Harvard Bus. Rev.
   Alvarez SA, 2005, J MANAGE, V31, P776, DOI 10.1177/0149206305279486
   An HJ, 2016, TECHNOL FORECAST SOC, V102, P132, DOI 10.1016/j.techfore.2015.06.015
   [Anonymous], 2005, Bus. Horiz., DOI [DOI 10.1016/J.BUSHOR.2004.10.014, 10.1016/j.bushor.2004.10.014]
   Avison D, 1999, COMMUN ACM, V42, P94, DOI 10.1145/291469.291479
   Azzam M, 2024, IEEE T ENG MANAGE, V71, P2324, DOI 10.1109/TEM.2022.3179107
   Blank S, 2024, J MANAGE, V50, P3012, DOI 10.1177/01492063231168095
   Boussioux L, 2024, ORGAN SCI, V35, P1589, DOI 10.1287/orsc.2023.18430
   Brem A, 2023, IEEE T ENG MANAGE, V70, P770, DOI 10.1109/TEM.2021.3109983
   Brynjolfsson Erik, 2023, Working Paper No. 31161
   Busse C, 2017, ORGAN RES METHODS, V20, P574, DOI 10.1177/1094428116641191
   Choi J. H., 2023, AI ASSISTANCE LEGAL
   Christensen C.M., 1997, INNOVATORS DILEMMA N
   Contigiani A, 2019, IND CORP CHANGE, V28, P551, DOI 10.1093/icc/dtz013
   Cornish F, 2023, NAT REV METHOD PRIME, V3, DOI 10.1038/s43586-023-00214-1
   Crosina E, 2019, ACAD MANAGE J, V62, P66, DOI 10.5465/amj.2017.0140
   Davenport T.H., 2023, STRATEGY LEADERSHIP, V51, P26, DOI DOI 10.1108/SL-11-2022-0107
   De Cremer D., 2023, Harvard Business Review
   Deken F, 2018, ACAD MANAGE J, V61, P1920, DOI 10.5465/amj.2016.0687
   DellAcqua F., 2023, 24013 HARV BUS SCH T
   Feldman M.S., 2011, OXFORD HDB POSITIVE, P629
   Feldman MS, 2004, ORGAN SCI, V15, P295, DOI 10.1287/orsc.1040.0073
   Felin T, 2020, LONG RANGE PLANN, V53, DOI 10.1016/j.lrp.2019.06.002
   Franzò S, 2024, IEEE T ENG MANAGE, V71, P13646, DOI 10.1109/TEM.2023.3276474
   Gibbon M, 2002, QUAL HEALTH RES, V12, P546, DOI 10.1177/104973202129120061
   Harrison RT, 2000, BRIT J MANAGE, V11, P103, DOI 10.1111/1467-8551.00154
   Hutchinson P, 2021, IEEE T ENG MANAGE, V68, P628, DOI 10.1109/TEM.2020.2977222
   Iversen JH, 2004, MIS QUART, V28, P395
   Kanitz R, 2023, J APPL BEHAV SCI, V59, P345, DOI 10.1177/00218863231168974
   Ladd T., 2016, Harvard Business Review Digital Articles, P2
   Ladd T, 2018, J RES MARK ENTREP, V20, P57, DOI 10.1108/JRME-11-2016-0046
   Larson BZ, 2024, ACAD MANAG LEARN EDU, V23, P373, DOI 10.5465/amle.2024.0338
   Leatherbee M, 2020, STRATEG ENTREP J, V14, P570, DOI 10.1002/sej.1373
   Locke K., 2002, GROUNDED THEORY APPR
   MacDonald C., 2012, CANADIAN J ACTION RE, V13, P34, DOI DOI 10.33524/CJAR.V13I2.37
   Magretta J, 2002, HARVARD BUS REV, V80, P86
   Marzi S, 2023, QUAL RES, V23, P509, DOI 10.1177/14687941211038171
   Mayer A. S., 2024, P AOM
   McKelvey J., 2020, INNOVATION STACK BUI
   Mollick E., 2019, Harvard Bus. Rev., V10, P1
   Muhlroth C, 2022, IEEE T ENG MANAGE, V69, P493, DOI 10.1109/TEM.2020.2989214
   Mukherjee A., 2023, California Manage. Rev., V66
   Nair S., 2023, Paper 19506., V2023
   Nair S, 2022, ACAD MANAGE REV, V47, P162, DOI 10.5465/amr.2019.0040
   Nguyen N, 2022, IEEE WORK CONF MIN S, P1, DOI 10.1145/3524842.3528470
   Osadchaya E, 2024, BUS HORIZONS, V67, P571, DOI 10.1016/j.bushor.2024.05.002
   Osterwalder A., 2009, You're Holding a Handbook for Visionaries, Game Changers, and Challengers Striving to Defy Outmoded Business Models and Design Tomorrow's Enterprises. It's a Book for the ... Business Model Generation  ...
   Otis N., 2023, Working Paper 24-042
   Ottosson S, 2003, TECHNOVATION, V23, P87, DOI 10.1016/S0166-4972(01)00097-9
   Piccoli G., 2022, MIS Quarterly, V46, P2289, DOI DOI 10.25300/MISQ/2022/17061
   Pratt MG, 2019, ADMIN SCI QUART, V64, P398, DOI 10.1177/0001839218769252
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Ramaul L, 2024, BUS HORIZONS, V67, P615, DOI 10.1016/j.bushor.2024.05.006
   Retkowsky J, 2024, BUS HORIZONS, V67, P511, DOI 10.1016/j.bushor.2024.04.009
   Ritala P., 2023, J. Bus. Strategy, V45, P214
   Ritala P, 2024, CALIF MANAGE REV, V66, P80, DOI 10.1177/00081256241252700
   Sense A.J., 2006, INT J SOC RES METHOD, V9, P1, DOI DOI 10.1080/13645570500435546
   Shepherd DA, 2021, ENTREP THEORY PRACT, V45, P967, DOI 10.1177/1042258719899415
   Sonenshein S, 2014, ACAD MANAGE J, V57, P814, DOI 10.5465/amj.2012.0048
   Strauss A. L., 1990, BASICS QUALITATIVE R
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Townsend D M., 2019, Journal of Business Venturing Insights, V11, pe00126, DOI [10.1016/j.jbvi.2019.e00126, DOI 10.1016/J.JBVI.2019.E00126]
   Townsend D. M., 2023, ACAD MANAGE REV
   Townsend DM, 2018, ACAD MANAG ANN, V12, P659, DOI 10.5465/annals.2016.0109
   Wimelius H, 2021, INFORM SYST J, V31, P198, DOI 10.1111/isj.12307
   Yin R., 2003, CASE STUDY RES DESIG
   Zott C, 2010, LONG RANGE PLANN, V43, P216, DOI 10.1016/j.lrp.2009.07.004
NR 67
TC 0
Z9 0
U1 10
U2 10
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 15278
EP 15291
DI 10.1109/TEM.2024.3484750
PG 14
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA L6C8L
UT WOS:001351585500007
DA 2024-12-25
ER

PT J
AU Anas, M
   Saiyeda, A
   Sohail, SS
   Cambria, E
   Hussain, A
AF Anas, Mohammad
   Saiyeda, Anam
   Sohail, Shahab Saquib
   Cambria, Erik
   Hussain, Amir
TI Can Generative AI Models Extract Deeper Sentiments as Compared to
   Traditional Deep Learning Algorithms?
SO IEEE INTELLIGENT SYSTEMS
LA English
DT Article
DE Deep learning; Generative AI; Analytical models; Context modeling;
   Chatbots; Sentiment analysis
AB Recent advances in the context of deep learning have led to the development of generative artificial intelligence (AI) models which have shown remarkable performance in complex language understanding tasks. This study proposes an evaluation of traditional deep learning algorithms and generative AI models for sentiment analysis. Experimental results show that RoBERTa outperforms all models, including ChatGPT and Bard, suggesting that generative AI models are not yet able to capture the nuances and subtleties of sentiment in text. We provide valuable insights into the strengths and weaknesses of different models for sentiment analysis and offer guidance for researchers and practitioners in selecting suitable models for their tasks.
C1 [Anas, Mohammad; Saiyeda, Anam] Jamia Hamdard, New Delhi 110062, India.
   [Sohail, Shahab Saquib] VIT Bhopal Univ, Bhopal 466114, Madhya Pradesh, India.
   [Cambria, Erik] Nanyang Technol Univ, Nanyang 639798, Singapore.
   [Hussain, Amir] Edinburgh Napier Univ, Edinburgh EH11 4BN, Scotland.
C3 Jamia Hamdard University; VIT Bhopal University; Nanyang Technological
   University; Edinburgh Napier University
RP Anas, M (corresponding author), Jamia Hamdard, New Delhi 110062, India.
EM mohammadanas@jamiahamdard.ac.in; anamsaiyeda@jamiahamdard.ac.in;
   shahab.sohail@vitbhopal.ac.in; cambria@ntu.edu.sg;
   a.hussain@napier.ac.uk
RI Hussain, Amir/AAG-6299-2020; sohail, shahab/O-3263-2019; Cambria,
   Erik/C-2103-2013
OI Hussain, Amir/0000-0002-8080-082X; Cambria, Erik/0000-0002-3030-1280;
   Sohail, Shahab Saquib/0000-0002-5944-7371
CR Amin MM, 2023, IEEE INTELL SYST, V38, P15, DOI 10.1109/MIS.2023.3254179
   Amin MM, 2023, IEEE INTELL SYST, V38, P5, DOI 10.1109/MIS.2023.3305861
   Bhardwaz Saumyamani, 2023, 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), P673, DOI 10.1109/ICAAIC56838.2023.10140214
   Cambria E, 2024, 26 INT C HUM COMP IN, V8
   Hassani H, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7020062
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Kalaivani A, 2020, FIGURATIVE LANGUAGE PROCESSING, P72
   Oneto L, 2016, IEEE COMPUT INTELL M, V11, P46, DOI 10.1109/MCI.2016.2572540
   Praveen SV, 2023, ANN BIOMED ENG, V51, P1654, DOI 10.1007/s10439-023-03222-0
   Shobana J, 2021, COMPLEX INTELL SYST, V7, P2485, DOI 10.1007/s40747-021-00436-4
   Wu M., 2023, IEEE 8 INT C SOFTW E, P1, DOI [10.1109/ICSECS58457.2023.10256408.19.J, DOI 10.1109/ICSECS58457.2023.10256408.19.J]
   Xing FZ, 2019, INFORM PROCESS MANAG, V56, P554, DOI 10.1016/j.ipm.2018.11.002
NR 12
TC 6
Z9 6
U1 25
U2 32
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1541-1672
EI 1941-1294
J9 IEEE INTELL SYST
JI IEEE Intell. Syst.
PD MAR-APR
PY 2024
VL 39
IS 2
BP 5
EP 10
DI 10.1109/MIS.2024.3374582
PG 6
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA PL2C0
UT WOS:001214157900004
DA 2024-12-25
ER

PT J
AU Topaz, M
   Peltonen, LM
   Michalowski, M
   Stiglic, G
   Ronquillo, C
   Pruinelli, L
   Song, JY
   O'Connor, S
   Miyagawa, S
   Fukahori, H
AF Topaz, Maxim
   Peltonen, Laura -Maria
   Michalowski, Martin
   Stiglic, Gregor
   Ronquillo, Charlene
   Pruinelli, Lisiane
   Song, Jiyoun
   O'Connor, Siobhan
   Miyagawa, Shoko
   Fukahori, Hiroki
TI The ChatGPT Effect: Nursing Education and Generative Artificial
   Intelligence
SO JOURNAL OF NURSING EDUCATION
LA English
DT Article; Early Access
AB This article examines the potential of generative artiPre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation. Clear institutional guidelines, comprehensive student education on generative AI, and tools to detect AI-generated content are recommended to navigate these challenges. The article concludes by urging nurse educators to harness generative AI's potential responsibly, highlighting the rewards of enhanced learning izing the promise of AI in nursing education.
C1 [Topaz, Maxim] Columbia Univ, Sch Nursing, New York, NY USA.
   [Peltonen, Laura -Maria] Univ Turku, Dept Nursing Sci, Turku, Finland.
   [Peltonen, Laura -Maria] Turku Univ Hosp, Turku, Finland.
   [Michalowski, Martin] Univ Minnesota, Sch Nursing, Minneapolis, MN USA.
   [Stiglic, Gregor] Univ Maribor, Fac Hlth Sci, Maribor, Slovenia.
   [Ronquillo, Charlene] Univ British Columbia Okanagan, Sch Nursing, Kelowna, BC, Canada.
   [Pruinelli, Lisiane] Univ Florida, Coll Nursing, Gainesville, FL 32611 USA.
   [Pruinelli, Lisiane] Univ Florida, Coll Med, Gainesville, FL USA.
   [Song, Jiyoun] Univ Penn, Sch Nursing, Philadelphia, PA USA.
   [O'Connor, Siobhan] Kings Coll London, Fac Nursing Midwifery & Palliat Care, London, England.
   [Miyagawa, Shoko; Fukahori, Hiroki] Keio Univ, Fac Nursing & Med Care, Minato, Japan.
C3 Columbia University; University of Turku; University of Turku;
   University of Minnesota System; University of Minnesota Twin Cities;
   University of Maribor; University of British Columbia; University of
   British Columbia Okanagan; State University System of Florida;
   University of Florida; State University System of Florida; University of
   Florida; University of Pennsylvania; University of London; King's
   College London; Keio University
RP Topaz, M (corresponding author), Columbia Univ, 560 W 168th St, New York, NY 10032 USA.
EM mtopaz80@gmail.com
RI O'Connor, Siobhan/AAI-1035-2019; Michalowski, Martin/AAJ-7931-2020;
   Fukahori, Hiroki/AAI-1705-2021; Stiglic, Gregor/E-5286-2011; Pruinelli,
   Lisiane/AAU-3794-2020; Topaz, Maxim/AAQ-7121-2021; Murtola,
   Laura-Maria/V-2665-2019; O'Connor, Siobhan/D-8140-2015
OI Peltonen, Laura-Maria/0000-0001-5740-6480; O'Connor,
   Siobhan/0000-0001-8579-1718
FX Acknowledgment: This article is significantly informed by insightful
   discussions from the 2nd Workshop on Artificial Intelligence in Nursing
   (https://sites.google.com /view/ainurse23/home/) , and the scientific
   planning committee is grateful to all panelists and attendees for their
   contributions.The au-thors also thank the Nursing and Artificial
   Intelligence Leadership ( www.nailcollab.org) collaborative members for
   their active engagement in writing this article.Their knowledge and
   perspectives have greatly enriched this exploration of artificial
   intelligence in nursing education.
CR Achiam J., 2023, GPT-4 Technical Report, DOI [10.48550/arXiv.2303.08774, DOI 10.48550/ARXIV.2303.08774]
   Bekes E. R., 2023, 2023 46 MIPRO ICT EL
   Betts L, 2020, NURS EDUC PERSPECT, V41, P193, DOI 10.1097/01.NEP.0000000000000472
   Borenstein Jason, 2021, AI Ethics, V1, P61, DOI 10.1007/s43681-020-00002-7
   Borg JS, 2022, AI MAG, V43, P294, DOI 10.1002/aaai.12062
   Bozkurt A., 2023, Asian Journal of Distance Education, V18
   De Gagne Jennie C, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20064884
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Kharpal A., 2023, CNBC
   Komasawa N, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.40940
   Liang WX, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100779
   Mok A., 2023, Amazon, Apple, and 12 other major companies that have restricted employees from using ChatGPT
   Neumann M, 2023, 2023 IEEE/ACM 5TH INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING EDUCATION FOR THE NEXT GENERATION, SEENG, P29, DOI 10.1109/SEENG59157.2023.00010
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   O'Connor Siobhan, 2022, Nurs Outlook, V70, P780, DOI 10.1016/j.outlook.2022.09.003
   Office of Educational Technology, 2023, Articial intelligence and the future of teaching and learning
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Seibert K, 2021, J MED INTERNET RES, V23, DOI 10.2196/26522
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Vynck G. D., 2023, WASHINGTON POST
   Xiang C, 2023, Vice
NR 22
TC 8
Z9 8
U1 50
U2 75
PU SLACK INC
PI THOROFARE
PA 6900 GROVE RD, THOROFARE, NJ 08086 USA
SN 0148-4834
EI 1938-2421
J9 J NURS EDUC
JI J. Nurs. Educ.
PD 2024 FEB 5
PY 2024
DI 10.3928/01484834-20240126-01
EA FEB 2024
PG 4
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA H3V9P
UT WOS:001322761900001
PM 38302101
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Masters, K
   Herrmann-Werner, A
   Festl-Wietek, T
   Taylor, D
AF Masters, Ken
   Herrmann-Werner, Anne
   Festl-Wietek, Teresa
   Taylor, David
TI Preparing for Artificial General Intelligence (AGI) in Health
   Professions Education: AMEE Guide No. 172
SO MEDICAL TEACHER
LA English
DT Article
DE Artificial intelligence; Artificial General Intelligence;
   general-purpose AI; Generative AI; ChatGPT
AB Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI.
C1 [Masters, Ken] Sultan Qaboos Univ, Coll Med & Hlth Sci, Med Educ & Informat Dept, Muscat, Oman.
   [Herrmann-Werner, Anne; Festl-Wietek, Teresa] Univ Tubingen, Tubingen Inst Med Educ, Tubingen, Germany.
   [Taylor, David] Gulf Med Univ, Coll Med, Ctr Leadership & Innovat Hlth Profess Educ, Ajman, U Arab Emirates.
C3 Sultan Qaboos University; Eberhard Karls University of Tubingen
RP Masters, K (corresponding author), Sultan Qaboos Univ, Coll Med & Hlth Sci, Med Educ & Informat Dept, Muscat, Oman.
EM itmeded@gmail.com
RI Taylor, David/H-3670-2013; Masters, Ken/C-6163-2013
OI Masters, Ken/0000-0003-3425-5020; Taylor, David/0000-0002-3296-2963
FX Some of the material in this Guide was presented at the AMSE2023
   Conference, Iasi, Romania, October 2023, and the IMC2024 Conference,
   Lahore, Pakistan, March 2024. We would like to thank the reviewers of a
   previous draft of this Guide for their insightful comments and
   suggestions.
CR Abramson J, 2024, NATURE, V630, DOI 10.1038/s41586-024-07487-w
   Afifi-Sabet K., 2024, SOONER WE THINK SAYS
   Altman S., 2023, Governance of superintelligence
   Altman S., 2024, LEX FRIDMAN PODCAST
   [Anonymous], 2024, OBSERVER INTERNET
   [Anonymous], 2024, CONVERSATION SAM ALT
   [Anonymous], 2024, SUPERIOR COURT CALIF
   [Anonymous], 2020, Artificial intelligence: From ethics to policy
   [Anonymous], 2023, Twitter
   [Anonymous], 2022, EACC EFFECTIVE ACCEL
   [Anonymous], 2023, BUILT AI DOCTOR CHAT
   [Anonymous], 2024, INTRO GPT 4O INTERNE
   Anthropic, 2023, Anthropic's Responsible Scaling Policy
   Aschenbrenner L., 2024, SITUATIONAL AWARENES
   Bai Y., 2022, ARXIV
   Bauwens, 2023, REVIEWER REJECTED MY
   Blum L., 2024, ARXIV
   Bubeck S., 2023, ARXIV
   Butlin P, 2023, ARXIV
   Buttazzo G, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1226990
   Callaway E, 2024, NATURE, V629, P272, DOI 10.1038/d41586-024-01243-w
   Caulfield J., 2023, OVERVIEW LIST INTERN
   Champagne A., 2023, LAW SENSES
   CLARKE AC, 1979, OMNI, V1, P76
   Cook CR, 2018, J SCHOOL PSYCHOL, V66, P4, DOI 10.1016/j.jsp.2017.11.004
   Cornwall J, 2024, ANAT SCI EDUC, V17, P937, DOI 10.1002/ase.2335
   Corona E., 2024, VLOGGER MULTIMODAL D
   Dalberg (Lord Acton) JEED, 1887, ACT CREIGHT CORR
   Daley GQ, 2024, NEW ENGL J MED, V390, P1642, DOI 10.1056/NEJMp2314279
   Daniel, 2024, JUST DONT EXPECTPROD
   deBronkart D., 2024, INCLUDE PATIENT USER
   DMello C., 2024, GLOBAL NEWS
   García-Peñalvo FJ, 2024, RIED-REV IBEROAM EDU, V27, DOI 10.5944/ried.27.1.37716
   Goertzel B., 2007, COGNITIVE TECHNOLOGI
   Gordon M, 2024, MED TEACH, V46, P446, DOI 10.1080/0142159X.2024.2314198
   Gottfredson LS, 2002, GENERAL FACTOR OF INTELLIGENCE: HOW GENERAL IS IT?, P331
   Grosse R., 2023, arXiv
   Gubrud MA., 1997, NANOTECHNOLOGY INT S
   Hendrycks Dan, 2023, ARXIV
   Hess BJ, 2024, MED TEACH, V46, P300, DOI 10.1080/0142159X.2023.2289844
   Horvitz E, 2022, PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, ICMI 2022, P653, DOI 10.1145/3536221.3558175
   Huang K., 2024, ARXIV
   hume.ai, 2024, EMPATHIC VOICE INTER
   Jung C., 2024, TRANSFORMED AI GENER
   Kantrowitz A., 2022, INTERNET
   Kaparthy A., 2016, GENERATIVE MODELS IN
   kevinmise, 2023, SINGULARITY PREDICTI
   Kiyak YS, 2024, MED TEACH, V46, P1018, DOI 10.1080/0142159X.2024.2314723
   Kraljevic Z, 2024, LANCET DIGIT HEALTH, V6, pe281, DOI 10.1016/S2589-7500(24)00025-6
   Kurzweil R., 2006, SINGULARITY IS NEAR
   Kurzweil R, 2024, The Singularity Is Nearer: When We Merge with AI
   Landy, 2024, PROGRAM LEVEL AI CON
   Lawrence DR, 2018, MED LAW REV, V26, P309, DOI 10.1093/medlaw/fwy017
   Legg S., 2007, ARXIV
   Leike J., 2024, ALL OPENAI EMPLOYEES
   Leike Jan, 2023, Introducing superalignment
   Lemoine B., 2022, INTERVIEW INTERNET M
   Li J.-P., 2024, arXiv
   Li Xuan, 2023, ARXIV
   Lin CS, 2024, NAT MED, DOI 10.1038/s41591-024-02961-4
   Littman M. L., 2021, Study Panel Report
   Liu Chenbin, 2023, Meta Radiol, V1, DOI 10.1016/j.metrad.2023.100045
   Madiega T., 2023, General-purpose artificial intelligence (PE 745.708) Internet
   Masters K, 2024, MED TEACH, V46, P752, DOI 10.1080/0142159X.2024.2305365
   Masters K, 2024, MED TEACH, V46, P1175, DOI 10.1080/0142159X.2023.2298756
   Masters K, 2023, MED TEACH, V45, P574, DOI 10.1080/0142159X.2023.2186203
   Masters Ken, 2020, MedEdPublish (2016), V9, P239, DOI 10.15694/mep.2020.000239.1
   Matias Y., 2024, GOOGLE
   McCarthy J, 1955, PROPOSAL DARTMOUTH S
   Meng W., 2024, ARXIV
   Metz C., 2016, WIRED
   MIT, 2024, MIT EXPL GEN AI NOV
   Moor M, 2023, NATURE, V616, P259, DOI 10.1038/s41586-023-05881-4
   Morris MR., 2024, ARXIV
   Munkhdalai T., 2024, ARXIV
   Northoff G, 2022, FRONT COMPUT NEUROSC, V16, DOI 10.3389/fncom.2022.892354
   OpenAI, 2023, GPT 4 TECHN REP INT
   OpenAI, 2024, Hello GPT-4O
   PauseAI, 2023, LIST P DOOM VALUES I
   Plato, 1997, The Complete Works, P971
   Prillaman M, 2024, NATURE, V627, P16, DOI 10.1038/d41586-024-00592-w
   Radford A., 2018, IMPROVING LANGUAGE U
   Ranjbari D, 2024, FAM MED COMMUNITY HE, V12, DOI 10.1136/fmch-2023-002625
   Robert J., 2024, EDUCAUSE AI LANDSCAP
   Rose T., 2015, END AVERAGE WE SUCCE, P247
   Rosenberg GS, 2024, ACTA ORTHOP, V95, P152, DOI 10.2340/17453674.2024.40182
   Saab K., 2024, ARXIV
   Shanahan M., 2015, The Technological Singularity
   Sibbald M, 2024, J EVAL CLIN PRACT, V30, P3, DOI 10.1111/jep.13730
   SingularityNET Fetch.ai, 2024, OCEAN PROTOCOL ARTIF
   Stenseke J, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-024-10732-3
   Thompson AD., 2024, DECLARATION AI CONSC
   Tidy J., 2024, YOUNG PEOPLE TURNING
   Tong A., 2023, INTERNET REUTERS
   Turing A., 1951, INTERNET
   Turing A., 2009, Parsing the Turing test. Philosophical and methodological issues in the quest for the thinking computer, P23, DOI DOI 10.1093/MIND/LIX.236.433
   Turing A., 1951, INTELLIGENT MACHINER
   Turner J., 2024, AIS IMPACT WORKPLACE
   u/adamariefox, 2023, INTERNET RCHATGPT
   Ulam S., 1958, Bull. Amer. Math. Soc, V64, P1, DOI [10.1090/s0002-9904-1958-10189-5, DOI 10.1090/S0002-9904-1958-10189-5]
   Ulanoff L., 2023, TECHRADAR
   Vaswani A., 2017, ADV NEURAL INFORM PR, V2017, P5999
   Vinge V., 1993, WHOLE EARTH REV, P88, DOI 10.1002/9781118555927.ch35
   Vinge V., 2003, TECHNOLOGICAL SINGUL
   Voss P., 2023, ARXIV
   Voss P., 2017, INTERNET INTUITION M
   Voss P., 2007, Chapter in Artificial General Intelligence, P131
   Wang Z., 2023, ARXIV
   Wiblin R., 2023, 80000HOURS
   Wojda T., 2024, ARTIFICIAL INTELLIGE
   Xu XJ, 2024, J EDUC EVAL HEALTH P, V21, DOI 10.3352/jeehp.2024.21.6
NR 111
TC 3
Z9 3
U1 21
U2 21
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0142-159X
EI 1466-187X
J9 MED TEACH
JI Med. Teach.
PD OCT 2
PY 2024
VL 46
IS 10
BP 1258
EP 1271
DI 10.1080/0142159X.2024.2387802
EA AUG 2024
PG 14
WC Education, Scientific Disciplines; Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Health Care Sciences & Services
GA H1R5M
UT WOS:001287670600001
PM 39115700
OA hybrid
DA 2024-12-25
ER

PT J
AU Stevens, ER
   Elmaleh-Sachs, A
   Lofton, H
   Mann, DM
AF Stevens, Elizabeth R.
   Elmaleh-Sachs, Arielle
   Lofton, Holly
   Mann, Devin M.
TI Lightening the Load: Generative AI to Mitigate the Burden of the New Era
   of Obesity Medical Therapy
SO JMIR DIABETES
LA English
DT Article
DE obesity; artificial intelligence; AI; clinical management; GLP-1;
   glucagon-like peptide 1; medical therapy; antiobesity; diabetes;
   medication; agonists; glucose-dependent insulinotropic polypeptide;
   treatment; clinician; health care delivery system; incretin mimetic
ID ARTIFICIAL-INTELLIGENCE; TRUST
AB Highly effective antiobesity and diabetes medications such as glucagon-like peptide 1 (GLP-1) agonists and glucose-dependent insulinotropic polypeptide/GLP-1 (dual) receptor agonists (RAs) have ushered in a new era of treatment of these highly prevalent, morbid conditions that have increased across the globe. However, the rapidly escalating use of GLP-1/dual RA medications is poised to overwhelm an already overburdened health care provider workforce and health care delivery system, stifling its potentially dramatic benefits. Relying on existing systems and resources to address the oncoming rise in GLP-1/dual RA use will be insufficient. Generative artificial intelligence (GenAI) has the potential to offset the clinical and administrative demands associated with the management of patients on these medication types. Early adoption of GenAI to facilitate the management of these GLP-1/dual RAs has the potential to improve health outcomes while decreasing its concomitant workload. Research and development efforts are urgently needed to develop GenAI obesity medication management tools, as well as to ensure their accessibility and use by encouraging their integration into health care delivery systems.
C1 [Stevens, Elizabeth R.; Elmaleh-Sachs, Arielle; Lofton, Holly; Mann, Devin M.] NYU, Grossman Sch Med, Dept Populat Hlth, 227 e 30th st, New York, NY 10016 USA.
   [Elmaleh-Sachs, Arielle; Lofton, Holly; Mann, Devin M.] NYU, Grossman Sch Med, Dept Med, New York, NY USA.
   [Lofton, Holly] NYU, Family Hlth Ctr, Langone Hlth, Brooklyn, NY USA.
   [Lofton, Holly] NYU, Dept Surg, Grossman Sch Med, New York, NY USA.
   [Mann, Devin M.] NYU, MCIT Dept Hlth Informat, Langone Hlth, New York, NY USA.
C3 New York University; New York University; New York University; New York
   University; New York University
RP Stevens, ER (corresponding author), NYU, Grossman Sch Med, Dept Populat Hlth, 227 e 30th st, New York, NY 10016 USA.
EM elizabeth.stevens@nyulangone.org
CR Alqahtani H, 2021, ARCH COMPUT METHOD E, V28, P525, DOI 10.1007/s11831-019-09388-y
   [Anonymous], 2023, PYMNTS
   Bays Harold Edward, 2023, Obes Pillars, V6, P100065, DOI 10.1016/j.obpill.2023.100065
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Chew HSJ, 2022, JMIR MED INF, V10, P4, DOI 10.2196/32578
   Desislavov R, 2023, SUSTAIN COMPUT-INFOR, V38, DOI 10.1016/j.suscom.2023.100857
   Fanni SC, 2023, Natural Language Processing, P87, DOI DOI 10.1007/978-3-031-25928-9
   Golda A, 2024, IEEE ACCESS, V12, P48126, DOI 10.1109/ACCESS.2024.3381611
   Hatherley JJ, 2020, J MED ETHICS, V46, P478, DOI 10.1136/medethics-2019-105935
   Jayakumar P, 2020, J ORTHOP RES, V38, P1414, DOI 10.1002/jor.24614
   Kim Jee Young, 2024, PLOS Digit Health, V3, pe0000390, DOI 10.1371/journal.pdig.0000390
   Laranjo L, 2018, J AM MED INFORM ASSN, V25, P1248, DOI 10.1093/jamia/ocy072
   Lee NS, 2022, NEJM CATAL INNOV CAR, V3, DOI 10.1056/CAT.22.0228
   Margetis G., 2021, Handbook of human factors and ergonomics, P1085, DOI [10.1002/9781119636113, DOI 10.1002/9781119636113]
   Morin O, 2021, NAT CANCER, V2, P709, DOI 10.1038/s43018-021-00236-2
   Murthy V, 2022, NEW ENGL J MED, V387, P577, DOI 10.1056/NEJMp2207252
   Nayak A, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.40232
   Oh YJ, 2021, INT J BEHAV NUTR PHY, V18, DOI 10.1186/s12966-021-01224-6
   Paul M, 2023, ICT EXPRESS, V9, P571, DOI 10.1016/j.icte.2023.02.007
   Reddy S, 2024, IMPLEMENT SCI, V19, DOI 10.1186/s13012-024-01357-9
   Rodriguez DV, 2024, IEEE INT CONF HEALT, P350, DOI 10.1109/ICHI61247.2024.00052
   Rojas JC, 2023, CRIT CARE CLIN, V39, P769, DOI 10.1016/j.ccc.2023.02.004
   Roosan D, 2024, J AM PHARM ASSOC, V64, DOI 10.1016/j.japh.2023.11.023
   Salehinejad H, 2018, Arxiv, DOI arXiv:1801.01078
   Schwartz R, 2022, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, DOI DOI 10.6028/NIST.SP.1270
   Small WR, 2024, JAMA NETW OPEN, V7, DOI 10.1001/jamanetworkopen.2024.22399
   Stein Natalie, 2017, JMIR Diabetes, V2, pe28, DOI 10.2196/diabetes.8590
   Stephens TN, 2019, TRANSL BEHAV MED, V9, P440, DOI 10.1093/tbm/ibz043
   Stevens ER, 2023, J BIOMED INFORM, V147, DOI 10.1016/j.jbi.2023.104525
   Stierman Bryan, 2021, Natl Health Stat Report, DOI 10.15620/cdc:106273
   Tierney AA, 2024, NEJM CATAL INNOV CAR, V5, DOI 10.1056/CAT.23.0404
   Wachter RM, 2024, JAMA-J AM MED ASSOC, V331, P65, DOI 10.1001/jama.2023.25054
   Yu KH, 2018, NAT BIOMED ENG, V2, P719, DOI 10.1038/s41551-018-0305-z
   Yu P, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11202776
   Zembylas M, 2023, LEARN MEDIA TECHNOL, V48, P25, DOI 10.1080/17439884.2021.2010094
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
NR 37
TC 0
Z9 0
U1 0
U2 0
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 2371-4379
J9 JMIR DIABETES
JI JMIR Diabetes
PY 2024
VL 9
AR e58680
DI 10.2196/58680
PG 6
WC Health Care Sciences & Services; Endocrinology & Metabolism
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Endocrinology & Metabolism
GA P2F0Q
UT WOS:001376122900001
PM 39622675
OA gold
DA 2024-12-25
ER

PT J
AU Crowe, B
   Shah, S
   Teng, D
   Ma, SP
   DeCamp, M
   Rosenberg, EI
   Rodriguez, JA
   Collins, BX
   Huber, K
   Karches, K
   Zucker, S
   Kim, EJ
   Rotenstein, L
   Rodman, A
   Jones, D
   Richman, IB
   Henry, TL
   Somlo, D
   Pitts, SI
   Chen, JH
   Mishuris, RG
AF Crowe, Byron
   Shah, Shreya
   Teng, Derek
   Ma, Stephen P.
   DeCamp, Matthew
   Rosenberg, Eric I.
   Rodriguez, Jorge A.
   Collins, Benjamin X.
   Huber, Kathryn
   Karches, Kyle
   Zucker, Shana
   Kim, Eun Ji
   Rotenstein, Lisa
   Rodman, Adam
   Jones, Danielle
   Richman, Ilana B.
   Henry, Tracey L.
   Somlo, Diane
   Pitts, Samantha I.
   Chen, Jonathan H.
   Mishuris, Rebecca G.
TI Recommendations for Clinicians, Technologists, and Healthcare
   Organizations on the Use of Generative Artificial Intelligence in
   Medicine: A Position Statement from the Society of General Internal
   Medicine
SO JOURNAL OF GENERAL INTERNAL MEDICINE
LA English
DT Article; Early Access
DE clinical practice; artificial intelligence; healthcare technology
ID BIAS
AB Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship. Additionally, we highlight our most important generative AI ethics and equity considerations for these stakeholders. For clinicians, we recommend approaching generative AI similarly to other important biomedical advancements, critically appraising its evidence and utility and incorporating it thoughtfully into practice. For technologists developing generative AI for healthcare applications, we recommend a major frameshift in thinking away from the expectation that clinicians will "supervise" generative AI. Rather, these organizations and individuals should hold themselves and their technologies to the same set of high standards expected of the clinical workforce and strive to design high-performing, well-studied tools that improve care and foster the therapeutic relationship, not simply those that improve efficiency or market share. We further recommend deep and ongoing partnerships with clinicians and patients as necessary collaborators in this work. And for healthcare organizations, we recommend pursuing a combination of both incremental and transformative change with generative AI, directing resources toward both endeavors, and avoiding the urge to rapidly displace the human clinical workforce with generative AI. We affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.
C1 [Crowe, Byron; Teng, Derek; Rodman, Adam] Beth Israel Deaconess Med Ctr, Div Gen Internal Med, Boston, MA 02215 USA.
   [Crowe, Byron; Teng, Derek; Rodriguez, Jorge A.; Rodman, Adam; Somlo, Diane; Mishuris, Rebecca G.] Harvard Med Sch, Boston, MA USA.
   [Shah, Shreya] Stanford Univ, Dept Med, Palo Alto, CA USA.
   [Shah, Shreya] Stanford Univ, Stanford Healthcare AI Appl Res Team, Div Primary Care & Populat Hlth, Sch Med, Palo Alto, CA USA.
   [DeCamp, Matthew] Univ Colorado, Dept Med, Aurora, CO USA.
   [Rosenberg, Eric I.] Univ Florida, Coll Med, Dept Med, Div Gen Internal Med, Gainesville, FL USA.
   [Rodriguez, Jorge A.; Mishuris, Rebecca G.] Brigham & Womens Hosp, Div Gen Internal Med, Boston, MA USA.
   [Collins, Benjamin X.] Vanderbilt Univ, Med Ctr, Div Gen Internal Med & Publ Hlth, Nashville, TN 37203 USA.
   [Huber, Kathryn] Kaiser Permanente, Dept Internal Med, Aurora, CO USA.
   [Huber, Kathryn] Univ Colorado, Sch Med, Aurora, CO USA.
   [Karches, Kyle] St Louis Univ, Dept Internal Med, St Louis, MO USA.
   [Zucker, Shana] Univ Miami, Miller Sch Med, Jackson Mem Hosp, Dept Internal Med, Miami, FL USA.
   [Kim, Eun Ji] Northwell Hlth, New Hyde Pk, NY USA.
   [Rotenstein, Lisa] Univ Calif San Francisco, Dept Med, Div Gen Internal Med, San Francisco, CA USA.
   [Rotenstein, Lisa] Univ Calif San Francisco, Dept Med, Div Clin Informat, San Francisco, CA USA.
   [Jones, Danielle; Henry, Tracey L.] Emory Univ, Div Gen Internal Med, Sch Med, Atlanta, GA USA.
   [Richman, Ilana B.] Yale Sch Med, Sect Gen Internal Med, New Haven, CT USA.
   [Somlo, Diane] Massachusetts Gen Hosp, Dept Med, Boston, MA USA.
   [Pitts, Samantha I.] Johns Hopkins Univ, Sch Med, Div Gen Internal Med, Baltimore, MD USA.
   [Chen, Jonathan H.] Stanford Ctr Biomed Informat Res, Stanford, CA USA.
   [Ma, Stephen P.; Chen, Jonathan H.] Div Hosp Med, Stanford, CA USA.
   [Chen, Jonathan H.] Clin Excellence Res Ctr, Stanford, CA USA.
   [Mishuris, Rebecca G.] Mass Gen Brigham, Digital, Somerville, MA USA.
   [Collins, Benjamin X.] Vanderbilt Univ, Dept Biomed Informat, Nashville, TN USA.
C3 Harvard University; Beth Israel Deaconess Medical Center; Harvard
   University; Harvard Medical School; Stanford University; Stanford
   University; University of Colorado System; University of Colorado
   Anschutz Medical Campus; State University System of Florida; University
   of Florida; Harvard University; Brigham & Women's Hospital; Vanderbilt
   University; Kaiser Permanente; University of Colorado System; University
   of Colorado Anschutz Medical Campus; Saint Louis University; University
   of Miami; Northwell Health; University of California System; University
   of California San Francisco; University of California System; University
   of California San Francisco; Emory University; Yale University; Harvard
   University; Massachusetts General Hospital; Johns Hopkins University;
   Vanderbilt University
RP Crowe, B (corresponding author), Beth Israel Deaconess Med Ctr, Div Gen Internal Med, Boston, MA 02215 USA.
EM bcrowe@bidmc.harvard.edu
RI DeCamp, Matthew/N-6028-2014; Shah, Shreya/HDN-1499-2022
OI Ma, Stephen P./0000-0003-3738-9569
CR aapa, 2023, The Patient Experience: Perspectives on Today's Healthcare Internet
   Adler-Milstein J, 2017, HEALTH AFFAIR, V36, P1416, DOI 10.1377/hlthaff.2016.1651
   airc.nist, 2022, AI RMF Playbook Internet
   Allyn B., 2024, Google CEO Pichai says Gemini's AI image results "offended our users
   Anderer S, 2024, JAMA-J AM MED ASSOC, V331, P629, DOI 10.1001/jama.2023.22981
   [Anonymous], 2024, Beckers Health IT Internet citedFeb 29
   [Anonymous], 2022, Survey of Physician Appointment Wait Times and Medicare and Medicaid Acceptance Rates 2022
   [Anonymous], 2021, Physician Specialty Data Report
   [Anonymous], National Trends in Hospital and Physician Adoption of Electronic Health Records - Health IT Quick-Stat #61
   Artificial Intelligence and Machine Learning (AI/ML), 2021, Software as a Medical Device Action Plan Internet
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Bak M, 2022, FRONT GENET, V13, DOI 10.3389/fgene.2022.929453
   Bhasker S, 2023, Tackling healthcare's biggest burdens with generative AI Internet
   Bhattacharyya M, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.39238
   Biden J, 2023, Executive Order 14110
   Bitterman DS, 2020, LANCET DIGIT HEALTH, V2, pE447, DOI 10.1016/S2589-7500(20)30187-4
   Bodenheimer T, 2022, ANN FAM MED, V20, P464, DOI 10.1370/afm.2858
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Byrd TF, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.24176
   Cabral S, 2024, JAMA INTERN MED, V184, P581, DOI 10.1001/jamainternmed.2024.0295
   Chen A, 2023, J GEN INTERN MED, V38, P2613, DOI 10.1007/s11606-023-08190-8
   Corrigan JM., 2000, To err is human: Building a safer health system
   Daneshjou R, 2022, SCI ADV, V8, DOI 10.1126/sciadv.abq6147
   DeCamp M, 2023, SCIENCE, V381, P150, DOI 10.1126/science.adh2713
   Garcia P, 2024, JAMA NETW OPEN, V7, DOI 10.1001/jamanetworkopen.2024.3201
   Gunderson CG, 2020, BMJ QUAL SAF, V29, P1008, DOI 10.1136/bmjqs-2019-010822
   [Institute of Medicine Committee on Quality of Health Care in America], 2001, CROSSING QUALITY CHA
   Jain S., 2022, Forbes
   Kane C, 2023, Telehealth in 2022: Availability Remains Strong but Accounts for a Small Share of Patient Visits for Most Physicians
   Kane CK, 2023, Recent Changes in Physician Practice Arrangements: Shifts Away from Private Practice and Towards Larger Practice Size Continue Through 2022. Policy Research Perspectives
   Kanjee Z, 2023, JAMA-J AM MED ASSOC, V330, P78, DOI 10.1001/jama.2023.8288
   Kingson J., New AI-powered doctor's office allows patients to draw blood, take vitals
   Lee P., 2023, The AI revolution in medicine: GPT-4 and beyond
   Lee TC, 2023, GASTROENTEROLOGY, V165, P509, DOI 10.1053/j.gastro.2023.04.033
   Ligibel JA, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.47894
   Lin S, 2022, MAYO CLIN PROC, V97, P653, DOI 10.1016/j.mayocp.2021.11.039
   McMahon LF, 2021, J GEN INTERN MED, V36, P515, DOI 10.1007/s11606-020-06077-6
   Minaee S, 2024, Arxiv, DOI [arXiv:2402.06196, 10.48550/arXiv.2402.06196, DOI 10.48550/ARXIV.2402.06196]
   Montgomery K., 2006, How Doctors Think: Clinical Judgment and the Practice of Medicine
   nature, Large language models encode clinical knowledge
   Nayak A, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.40232
   Obermeyer Z, 2019, SCIENCE, V366, P447, DOI 10.1126/science.aax2342
   openevidence, OpenEvidence Internet
   Patel SY, 2023, BMJ-BRIT MED J, V382, DOI 10.1136/bmj-2022-073933
   Pearl R., 2023, Forbes Dec 20
   Porter J, 2023, J GEN INTERN MED, V38, P147, DOI 10.1007/s11606-022-07707-x
   President's Council of Advisors on Science and Technology, 2023, Report to the President: A Transformational Effort on Patient Safety
   PRNewswire, 2025, UpDoc Debuts the World's First AI Assistant That Manages Medication Prescriptions and Chronic Conditions
   Rajkomar A, 2018, ANN INTERN MED, V169, P866, DOI 10.7326/M18-1990
   Rodman A, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.47075
   Rotenstein LS, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.44713
   Schwartz R., 2022, NIST Special Publication, V1270
   Shachak A, 2009, J EVAL CLIN PRACT, V15, P641, DOI 10.1111/j.1365-2753.2008.01065.x
   Shah NH, 2023, JAMA-J AM MED ASSOC, V330, P866, DOI 10.1001/jama.2023.14217
   Shanafelt Tait D, 2022, Mayo Clin Proc, V97, P2248, DOI 10.1016/j.mayocp.2022.09.002
   Singh H, 2014, BMJ QUAL SAF, V23, P727, DOI 10.1136/bmjqs-2013-002627
   Sinsky C, 2016, ANN INTERN MED, V165, P753, DOI 10.7326/M16-0961
   Strong E, 2023, JAMA INTERN MED, V183, P1028, DOI 10.1001/jamainternmed.2023.2909
   Tai-Seale M, 2019, HEALTH AFFAIR, V38, P1073, DOI 10.1377/hlthaff.2018.05509
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Tierney AA, 2024, NEJM CATAL INNOV CAR, V5, DOI 10.1056/CAT.23.0404
   Wachter RM, 2024, JAMA-J AM MED ASSOC, V331, P65, DOI 10.1001/jama.2023.25054
   Walsh D., The legal issues presented by generative AI
   whitehouse, Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People Internet
   World Health Organization, 2021, ETHICS GOVERNANCE AR
   Zaretsky J, 2024, JAMA NETW OPEN, V7, DOI 10.1001/jamanetworkopen.2024.0357
   Zeltzer D., 2023, Mayo Clin Proc Digit Health, V1, P480
   Zhang XM, 2020, HUM RESOUR HEALTH, V18, DOI 10.1186/s12960-020-0448-3
NR 68
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0884-8734
EI 1525-1497
J9 J GEN INTERN MED
JI J. Gen. Intern. Med.
PD 2024 NOV 12
PY 2024
DI 10.1007/s11606-024-09102-0
EA NOV 2024
PG 9
WC Health Care Sciences & Services; Medicine, General & Internal
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; General & Internal Medicine
GA L7S3H
UT WOS:001352674400001
PM 39531100
DA 2024-12-25
ER

PT J
AU Hurlburt, GF
AF Hurlburt, George F.
TI Artificial Intelligence, the Workforce and Educational Realities
SO COMPUTER
LA English
DT Article
DE Artificial intelligence; Employment; Business; Generative AI; Education;
   Educational programs; Curriculum development
AB Generative artificial intelligence (GAI) stands poised to eliminate whole occupations. In the future, ongoing education will be required for upskilling as new, unimagined jobs evolve. AI-driven trends are accelerating. They are wake-up calls for academic reform.
C1 [Hurlburt, George F.] Univ Syst Maryland Southern Maryland, California, MD 20619 USA.
RP Hurlburt, GF (corresponding author), Univ Syst Maryland Southern Maryland, California, MD 20619 USA.
EM gfhurlburt@gmail.com
RI Hurlburt, George/JUV-6040-2023
OI Hurlburt, George/0000-0002-4829-3805
CR Georgieva K, IMF Blog
   Hintze A, observer.com
   Hortel R, The ultimate AI skill has nothing to do with tech
   Hurlburt G, 2023, IT PROF, V25, P4, DOI 10.1109/MITP.2023.3267140
   Hurme P., 2017, HUMAN TECHNOLOGY, V13, P145, DOI [DOI 10.17011/HT/URN.201711104209, 10.17011/ht/urn.201711104209]
   Kim S., How non-traditional education is preparing tech talent for the future as university enrollment declines, alternative education is stepping up
   Lee M., 2024, P C N AM CHAPT ASS C, V1, P6537
   Li J., Banishing LLM hallucinations requires rethinking generalization
   Lugar G, 2005, Artificial Intelligence: Structures and Strategies for Complex Problem Solving
   McLuhan M., 1994, Understanding Media. The Extension of Man
   Mei QZ, 2024, P NATL ACAD SCI USA, V121, DOI 10.1073/pnas.2313925121
   Nietzel M. T., FORBES
   Rothwell J., 2020, Assessing the economic gains of eradicating illiteracy nationally and regionally in the United States
   Storm JF, 2024, NEURON, V112, P1531, DOI 10.1016/j.neuron.2024.02.004
   The CFO Survey, U.S. companies ramp up automation and AI as inflation persists
   Zhang TH, 2023, Arxiv, DOI arXiv:2309.10814
NR 16
TC 0
Z9 0
U1 18
U2 18
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD OCT
PY 2024
VL 57
IS 10
BP 94
EP 98
DI 10.1109/MC.2024.3428034
PG 5
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA H8M0N
UT WOS:001325913700015
DA 2024-12-25
ER

PT J
AU Bannister, P
   Penalver, EA
   Urbieta, AS
AF Bannister, Peter
   Penalver, Elena Alcalde
   Urbieta, Alexandra Santamaria
TI International Students and Generative Artificial Intelligence: A
   Cross-Cultural Exploratory Analysis of Higher Education Academic
   Integrity Policy
SO JOURNAL OF INTERNATIONAL STUDENTS
LA English
DT Article
DE academic integrity; generative artificial intelligence; higher
   education; international students; policy analysis
AB This study delves into the GenAI academic integrity policies within tertiary education, with a special focus on international students. Through qualitative analysis of 131 policies from 11 countries, it aims to highlight the overlooked needs of these students amidst the rise of GenAI technologies. The methodology involves a document review and SWOT analysis to assess policy inclusivity. Findings indicate a significant underrepresentation of international students in policy considerations, despite their notable economic impact. This novel research pioneers in its specific focus on international students in examining the intersection of GenAI and academic integrity, revealing a critical need for inclusive policy reform. Despite limitations such as potential selection bias, the study's contributions lie in its call for a more equitable approach to policy development, ensuring international student voices are heard. It concludes with an urgent recommendation for HEIs to integrate diverse student perspectives to uphold academic integrity in the digital era.
C1 [Bannister, Peter; Urbieta, Alexandra Santamaria] Univ Int Rioja, La Rioja, Spain.
   [Penalver, Elena Alcalde] Univ Alcala, Alcala De Henares, Spain.
C3 Universidad Internacional de La Rioja (UNIR); Universidad de Alcala
RP Bannister, P (corresponding author), Univ Int Rioja, La Rioja, Spain.
EM peter.bannister@unir.net; e.alcalde@uah.es;
   alexandra.santamaria@unir.net
RI Urbieta, Alexandra/AAH-1820-2020; Bannister, Peter/HDO-4393-2022
OI Bannister, Peter/0000-0002-7216-3912
FU Project of Analysis and Development for the Optimization of Assessment
   and Regulation of Generative Artificial Intelligence in Humanities
   (PANDORA); Universidad Internacional de La Rioja, Spain [PP-2023-02]
FX This research has been conducted as part of the work of the Project of
   Analysis and Development for the Optimization of Assessment and
   Regulation of Generative Artificial Intelligence in Humanities (PANDORA)
   financed by Universidad Internacional de La Rioja, Spain (PP-2023-02) .
CR Adeshola I, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253858
   [Anonymous], 2021, Ai and education: guidance for policy-makers
   Bannister P, 2023, AULA ABIERTA, V52, P401, DOI 10.17811/rifie.52.4.2023.401-409
   Bannister P, 2023, IRAN J LANG TEACH RE, V11, P53, DOI 10.30466/ijltr.2023.121406
   Bennett L., 2023, Athens Journal of Education, V10, P1
   Benuyenah V., 2023, Journal of Research in Innovative Teaching and Learning, V16, P134, DOI DOI 10.1108/JRIT-03-2023-097
   Birkbeck University of London, 2023, Appendix: Artificial Intelligence (AI) and academic integrity
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Bretag T, 2009, J ACAD ETHICS, V7, P193, DOI 10.1007/s10805-009-9092-1
   Bygrave C, 2014, HORIZON-UK, V22, P199, DOI 10.1108/OTH-05-2014-0016
   Canterbury Christ Church University, 2023, Generative Artificial Intelligence (AI): Guidance for staff
   Cantwell B, 2015, J INT STUDENTS, V5, P512
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chen XL, 2021, LANG LEARN TECHNOL, V25, P151
   Currie GM, 2023, SEMIN NUCL MED, V53, P719, DOI 10.1053/j.semnuclmed.2023.04.008
   Dawson P., 2021, Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education
   de Wit H, 2021, GLOB PERSP HIGHER ED, V50, P303, DOI 10.1163/9789004462717_016
   Denisova-Schmidt E., 2016, International Higher Education, V87, P4, DOI [10.6017/ihe.2016.87.9494, DOI 10.6017/IHE.2016.87.9494]
   Eaton S.E., 2021, Plagiarism in Higher Education: Tackling Tough Topics in Academic Integrity
   Eaton S.S., 2017, Interchange, V48, P271, DOI [DOI 10.1007/S10780-0179300-7, 10.1007/s10780-0179300-7]
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Fass-Holmes B, 2017, J INT STUDENTS, V7, P645, DOI 10.5281/zenodo.570026
   Fatemi G, 2020, J FURTH HIGH EDUC, V44, P1305, DOI 10.1080/0309877X.2019.1683521
   Findlay AM, 2011, INT MIGR, V49, P162, DOI 10.1111/j.1468-2435.2010.00643.x
   Frith KH, 2023, NURS EDUC PERSPECT, V44, P198, DOI 10.1097/01.NEP.0000000000001129
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   Gillespie G. M., 2012, The Mentor: Innovative Scholarship on Academic Advising, V14, DOI [10.26209/mj1461301, DOI 10.26209/MJ1461301]
   Glendinning I., 2022, Contract Cheating in Higher Education: Global Perspectives on Theory, Practice, and Policy, P199, DOI [10.1007/978-3-031-12680-214, DOI 10.1007/978-3-031-12680-2]
   Glendinning I, 2014, INT J EDUC INTEGR, V10, P4
   Groves M, 2021, J ENGL ACAD PURP, V50, DOI 10.1016/j.jeap.2021.100957
   Hertie School, 2023, Artificial Intelligence tools at the Hertie School. Teaching guidelines for faculty and students
   Hou CG, 2022, J ETHN MIGR STUD, V48, P248, DOI 10.1080/1369183X.2020.1797476
   International College of Management Sydney, 2023, Academic integrity policy
   Kilgarriff A., 2014, LEXICOGRAPHY, V1, P7, DOI [10.1007/s40607-014-0009-9, DOI 10.1007/S40607-014-0009-9]
   Liang WX, 2023, Arxiv, DOI arXiv:2304.02819
   Lim MH, 2022, COMPUT ASSIST LANG L, V35, P2675, DOI 10.1080/09588221.2021.1892768
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lodge JM., 2023, Learning: Research and Practice, V9, P117, DOI [10.1080/23735082.2023.2261106, DOI 10.1080/23735082.2023.2261106]
   London School of Economics and Political Science, 2023, Academic integrity and assessment in the context of digitalisation and the rise of generative AI
   Lynch J, 2021, INT J EDUC INTEGR, V17, DOI 10.1007/s40979-021-00086-6
   Marron L, 2023, Handbook of research on redesigning teaching, 167 learning and assessment in the digital era, P326, DOI DOI 10.4018/978-1-6684-8292-6.CH017
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Newton P.M., 2018, Frontiers in Education, V3, DOI DOI 10.3389/FEDUC.2018.00067
   Parkinson AL, 2022, ASSESS EVAL HIGH EDU, V47, P1416, DOI 10.1080/02602938.2022.2040947
   Patton C. V., 2017, Basic Methods of Policy Analysis and Planning
   Pavletic P., 2023, Academic integrity: Broadening practices, technologies, and the role of students. ethics and integrity in educational contexts, P327, DOI [10.1007/978-3-031-16976-2_18, DOI 10.1007/978-3-031-16976-2_18]
   Perkins M, 2024, HIGH EDUC POLICY, V37, P633, DOI 10.1057/s41307-023-00323-2
   Putra FW, 2023, J PUBLIC HEALTH-UK, V45, pe840, DOI 10.1093/pubmed/fdad120
   Reedy AK, 2021, INT J EDUC INTEGR, V17, DOI 10.1007/s40979-021-00080-y
   Rettinger D. A., 2022, Cheating academic integrity: Lessons from 30 years of research
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sanni-Anibire H, 2021, INT J EDUC INTEGR, V17, DOI 10.1007/s40979-021-00088-4
   Sefcik L, 2020, ASSESS EVAL HIGH EDU, V45, P30, DOI 10.1080/02602938.2019.1604942
   Shadiev R, 2024, COMPUT ASSIST LANG L, V37, P841, DOI 10.1080/09588221.2022.2056616
   Shen YQ, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230163
   Simpson D., 2016, Higher Education Politics Economics, V2, P111, DOI [10.32674/hepe.v2i1.22, DOI 10.32674/HEPE.V2I1.22]
   Stockwell G, 2012, COMPUTER-ASSISTED LANGUAGE LEARNING: DIVERSITY IN RESEARCH AND PRACTICE, P147
   Sutton A, 2011, ASSESS EVAL HIGH EDU, V36, P831, DOI 10.1080/02602938.2010.488797
   Tafazoli D., 2022, English language teaching in the European union: Theory and practice across the region, P277, DOI [10.1007/978-981-19-2152-016, DOI 10.1007/978-981-19-2152-016]
   Tafazoli D., 2021, Lenguas Modernas, V58, P55
   Thamrin H, 2017, PROCEDIA COMPUT SCI, V116, P144, DOI 10.1016/j.procs.2017.10.056
   The George Washington University, 2023, Guidelines for using generative Artificial Intelligence at the George Washington University
   Tomlinson M, 2022, DISCOURSE-ABINGDON, V43, P173, DOI 10.1080/01596306.2020.1814996
   Touvron H, 2023, arXiv, DOI [DOI 10.48550/ARXIV, 10.48550/arXiv]
   Tredinnick L., 2023, Business Information Review, V40, P46, DOI [10.1177/02663821231183756, DOI 10.1177/02663821231183756]
   University of Cape Town, 2023, Staff Guide-Assessment and academic integrity in the age of AI
   University of Johannesburg, 2023, Staff Guide: Generative Artificial Intelligence in teaching, learning and research
   University of Reading, 2023, Annex 1: Generative Artificial Intelligence (AI) tools, academic integrity and academic misconduct
   Warschauer M, 2023, J SECOND LANG WRIT, V62, DOI 10.1016/j.jslw.2023.101071
   Winrow A. R., 2015, Global Education Journal, V2
   Xiao P, 2023, Arxiv, DOI [arXiv:2305.18617, 10.48550/arXiv.2305.18617, DOI 10.2139/SSRN.4458269]
   Yao C.W., 2019, JCSCORE, V5, P81, DOI DOI 10.15763/ISSN.2642-2387.2019.5.1.81-109
NR 72
TC 1
Z9 1
U1 11
U2 11
PU UNIV LOUISIANA MONROE
PI MONROE
PA 700 UNIVERSITY AVE, MONROE, LA 71209 USA
SN 2162-3104
EI 2166-3750
J9 J INT STUDENTS
JI J. Int. Students
PY 2024
VL 14
IS 3
BP 149
EP 170
PG 22
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA UB9R5
UT WOS:001245724300008
DA 2024-12-25
ER

PT J
AU Zhou, T
   Zhang, CL
AF Zhou, Tao
   Zhang, Chunlei
TI Examining Generative AI User Intermittent Discontinuance from a C-A-C
   Perspective
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article; Early Access
DE Generative AI; intermittent discontinuance; C-A-C; cognitive dissonance
ID ANTECEDENTS; IMPACT; USAGE
AB As a passive behavior, intermittent discontinuance may lead to user defection and undermine the continuous development of generative artificial intelligence. From a cognition-affect-conation perspective, this research examined the enablers and inhibitors of generative AI user intermittent discontinuance. We adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis. The results indicated that privacy concern and information hallucination influence cognitive dissonance, which further leads to intermittent discontinuance. In contrast, perceived intelligence, anthropomorphism, and personalization influence affective commitment, which prevents intermittent discontinuance. The results imply that generative AI companies need to be concerned with both cognitive dissonance and affective commitment in order to prevent user intermittent discontinuance.
C1 [Zhou, Tao; Zhang, Chunlei] Hangzhou Dianzi Univ, Sch Management, Hangzhou 310018, Peoples R China.
C3 Hangzhou Dianzi University
RP Zhou, T (corresponding author), Hangzhou Dianzi Univ, Sch Management, Hangzhou 310018, Peoples R China.
EM zhoutao@hdu.edu.cn
FU National Natural Science Foundation of China [71771069, 71831006]
FX This work was supported by National Natural Science Foundation of China
   (71771069, 71831006).
CR Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   ALLEN NJ, 1990, J OCCUP PSYCHOL, V63, P1, DOI 10.1111/j.2044-8325.1990.tb00506.x
   Amin M, 2021, J HOSP MARKET MANAG, V30, P845, DOI 10.1080/19368623.2021.1899095
   Cao XF, 2020, INTERNET RES, V30, P1305, DOI 10.1108/INTR-08-2019-0347
   Cao YY, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101629
   Choi N, 2013, J AM SOC INF SCI TEC, V64, P2354, DOI 10.1002/asi.22939
   Dai B, 2020, INTERNET RES, V30, P1455, DOI 10.1108/INTR-06-2019-0225
   Faruk L. I. D., 2023, P 13 INT C ADV INF T, P1, DOI [https://doi.org/10.1145/3628454.3629552, DOI 10.1145/3628454]
   Feng YF, 2024, INT J HUM-COMPUT INT, V40, P1505, DOI 10.1080/10447318.2022.2147714
   FESTINGER L, 1962, SCI AM, V207, P93, DOI 10.1038/scientificamerican1062-93
   Fiss PC, 2011, ACAD MANAGE J, V54, P393, DOI 10.5465/AMJ.2011.60263120
   Greckhamer T, 2018, STRATEG ORGAN, V16, P482, DOI 10.1177/1476127018786487
   Guo YY, 2022, J ENTERP INF MANAG, V35, P774, DOI 10.1108/JEIM-12-2020-0481
   Hatem R, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.44720
   Hou L, 2023, INFORM PROCESS MANAG, V60, DOI 10.1016/j.ipm.2023.103461
   Huang YM, 2019, UNIVERSAL ACCESS INF, V18, P927, DOI 10.1007/s10209-018-0621-9
   Kim JS, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2311971
   Kock Florian, 2021, Tourism Management, V86, DOI 10.1016/j.tourman.2021.104330
   Li C, 2016, COMPUT HUM BEHAV, V54, P25, DOI 10.1016/j.chb.2015.07.049
   Li WY, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2310354
   Lin HX, 2020, J HOSP MARKET MANAG, V29, P530, DOI 10.1080/19368623.2020.1685053
   Lin TC, 2015, INT J INFORM MANAGE, V35, P215, DOI 10.1016/j.ijinfomgt.2015.01.001
   Ma JJ, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2314358
   Ma XY, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102362
   Makkonen M., 2023, CEUR WORKSH P RWTH A, P103
   Man Y, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107482
   Margaret NYM, 2020, COMPUT HUM BEHAV, V103, P48, DOI 10.1016/j.chb.2019.09.019
   Martínez-Navarro J, 2019, J BUS RES, V100, P475, DOI 10.1016/j.jbusres.2018.10.054
   Menon D, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e20962
   Moussawi S, 2019, PROCEEDINGS OF THE 52ND ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, P115
   Moussawi S, 2021, ELECTRON MARK, V31, P343, DOI 10.1007/s12525-020-00411-w
   Pappas IO, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102310
   Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
   PewResearchCenter, 2024, Americans'use of ChatGPT is ticking up, but few trust its election information
   Pham HC, 2024, J RETAIL CONSUM SERV, V78, DOI 10.1016/j.jretconser.2024.103758
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Rafiq F, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10132190
   Ragin, 2008, REDESIGNING SOCIAL I, DOI DOI 10.7208/CHICAGO/9780226702797.001.0001
   Rogers E., 1995, Diffusion of innovations
   Rosenthal S, 2020, MEDIA PSYCHOL, V23, P840, DOI 10.1080/15213269.2019.1648218
   Shen XL, 2018, IND MANAGE DATA SYST, V118, P506, DOI 10.1108/IMDS-05-2017-0222
   Sheng H., 2022, P INT C COMP ENG ART, P642, DOI [https://doi.org/10.1109/ICCEAI55464.2022.00136, DOI 10.1109/ICCEAI55464.2022.00136]
   Singh R, 2022, EUR J MARKETING, V56, P1684, DOI 10.1108/EJM-12-2019-0942
   Song X, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103595
   Vaghefi I, 2020, INTERNET RES, V30, P1427, DOI 10.1108/INTR-10-2019-0418
   Wang JK, 2021, INFORM TECHNOL PEOPL, V34, P1, DOI 10.1108/ITP-10-2018-0483
   Wang R, 2020, ASIAN J COMMUN, V30, P317, DOI 10.1080/01292986.2020.1811737
   Wang S., 2020, Soft Science, V34, P133, DOI [https://doi.org/10.13956/j.ss.1001-8409.2020.10.21, DOI 10.13956/J.SS.1001-8409.2020.10.21]
   Wu X., 2024, J INF INTELL, V2, P102, DOI DOI 10.1016/J.JIIXD.2023.10.007
   Yao XS, 2023, TECHNOL FORECAST SOC, V194, DOI 10.1016/j.techfore.2023.122675
   Zha XJ, 2018, COMPUT HUM BEHAV, V79, P227, DOI 10.1016/j.chb.2017.10.038
   Zhang AD, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107415
   Zhang H., 2022, Information Science, V40, P63, DOI [https://doi.org/10.13833/j.issn.1007-7634.2022.12.008, DOI 10.13833/J.ISSN.1007-7634.2022.12.008]
   Zhang M., 2019, Information and Documentation Services, V40, P84, DOI [https://doi.org/10.12154/j.qbzlgz.2019.04.010, DOI 10.12154/J.QBZLGZ.2019.04.010]
   Zhang M, 2024, INFORM MANAGE-AMSTER, V61, DOI 10.1016/j.im.2023.103902
   Zhang SW, 2016, INFORM MANAGE-AMSTER, V53, P904, DOI 10.1016/j.im.2016.03.006
   Zhu WJ, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2323277
NR 57
TC 1
Z9 1
U1 64
U2 64
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PD 2024 JUL 9
PY 2024
DI 10.1080/10447318.2024.2376370
EA JUL 2024
PG 11
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA YL1R6
UT WOS:001268556900001
DA 2024-12-25
ER

PT J
AU Pence, HE
   Hightower, G
   Forlenza, J
   Leonard, K
   McLellan, A
   Suero, A
   Amoah, B
   Mbow, M
   Borner, S
   Castillo, A
   Pence, LE
AF Pence, Harry E.
   Hightower, Greta
   Forlenza, Jackson
   Leonard, Kaden
   McLellan, Alexandra
   Suero, Amelie
   Amoah, Bridget
   Mbow, Mariama
   Borner, Sara
   Castillo, Alexander
   Pence, Laura E.
TI Using Generative AI Systems for Critical Thinking Engagement in an
   Advanced Chemistry Course: A Case Study
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE Upper-Level Chemistry; Critical Thinking; ArtificialIntelligence;
   Technology; Generative AI; ChatGPT
AB A series of critical thinking assignments was created for students in an advanced chemistry course to interact with and evaluate generative AI (GenAI) systems. Students explored GenAI's facility with producing summaries of C&EN articles, analyzing titration data, and closely reading literature articles. For each assignment, the students evaluated the output using a critical thinking exercise and presented their results using written reports. The students found GenAI to be effective at summarizing news articles, although it demonstrated inaccuracies in mathematical calculations and produced mixed results in answering technical questions based on specific literature articles. The assignments provided valuable practice for students' critical thinking skills.
C1 [Pence, Harry E.] SUNY Coll Oneonta, Dept Chem & Biochem, Oneonta, NY 13820 USA.
   [Hightower, Greta; Forlenza, Jackson; Leonard, Kaden; McLellan, Alexandra; Suero, Amelie; Amoah, Bridget; Mbow, Mariama; Borner, Sara; Castillo, Alexander; Pence, Laura E.] Univ Hartford, Dept Chem, West Hartford, CT 06111 USA.
C3 State University of New York (SUNY) System; University of Hartford
RP Pence, LE (corresponding author), Univ Hartford, Dept Chem, West Hartford, CT 06111 USA.
EM LPence@hartford.edu
OI Pence, Harry E./0000-0002-0412-9018
CR Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   [Anonymous], 2009, Valid Assessment of Learning in Undergraduate Education
   [Anonymous], 2023, ARTIFICIAL INTELLIGE
   Bowen RS, 2022, CHEM EDUC RES PRACT, V23, P725, DOI 10.1039/d2rp00097k
   Clark TM, 2023, J CHEM EDUC, V100, P3934, DOI 10.1021/acs.jchemed.3c00500
   Danczak SM, 2017, CHEM EDUC RES PRACT, V18, P420, DOI 10.1039/c6rp00249h
   Ding L, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00434-1
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   Exintaris B, 2023, J CHEM EDUC, V100, P2972, DOI 10.1021/acs.jchemed.3c00481
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Guo Y, 2023, J CHEM EDUC, V100, P4876, DOI 10.1021/acs.jchemed.3c00505
   Koivisto M, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-40858-3
   Leon AJ, 2023, J CHEM EDUC, V100, P3859, DOI 10.1021/acs.jchemed.3c00288
   Nascimento CMC, 2023, J CHEM INF MODEL, V63, P1649, DOI 10.1021/acs.jcim.3c00285
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   West JK, 2023, J CHEM EDUC, V100, P4351, DOI 10.1021/acs.jchemed.3c00581
   Zheng ZL, 2023, J AM CHEM SOC, V145, P18048, DOI 10.1021/jacs.3c05819
NR 17
TC 1
Z9 1
U1 53
U2 53
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD AUG 12
PY 2024
VL 101
IS 9
BP 3789
EP 3794
DI 10.1021/acs.jchemed.4c00242
EA AUG 2024
PG 6
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA F4G8O
UT WOS:001289815300001
DA 2024-12-25
ER

PT J
AU Sauder, M
   Tritsch, T
   Rajput, V
   Schwartz, G
   Shoja, MM
AF Sauder, Matthew
   Tritsch, Tara
   Rajput, Vijay
   Schwartz, Gary
   Shoja, Mohammadali M.
TI Exploring Generative Artificial IntelligenceAssisted Medical Education:
   Assessing Case-Based Learning for Medical Students
SO CUREUS JOURNAL OF MEDICAL SCIENCE
LA English
DT Article
DE problem-based learning; medical education; generative artificial
   intelligence; chat generative pre-trained; transformer; case-based
   learning
ID HEALTH
AB The recent public release of generative artificial intelligence (GenAI) has brought fresh excitement by making access to GenAI for medical education easier than ever before. It is now incumbent upon both students and faculty to determine the optimal role of GenAI within the medical school curriculum. Given the promise and limitations of GenAI, this study aims to assess the current capabilities of a GenAI (Chat Generative Pre-trained Transformer, ChatGPT), specifically within the framework of a pre-clerkship casebased active learning curriculum. The role of GenAI is explored by evaluating its performance in generating educational materials, creating medical assessment questions, answering medical queries, and engaging in clinical reasoning by prompting it to respond to a problem-based learning scenario. Our results demonstrated that GenAI addressed epidemiology, diagnosis, and treatment questions well. However, there were still instances where it failed to provide comprehensive answers. Responses from GenAI might offer essential information, hint at the need for further inquiry, or sometimes omit critical details. GenAI struggled with generating information on complex topics, raising a significant concern when using it as a 'search engine' for medical student queries. This creates uncertainty for students regarding potentially missed critical information. With the increasing integration of GenAI into medical education, it is imperative for faculty to become well-versed in both its advantages and limitations. This awareness will enable them to educate students on using GenAI effectively in medical education.
C1 [Sauder, Matthew; Tritsch, Tara; Rajput, Vijay; Schwartz, Gary; Shoja, Mohammadali M.] Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Med Educ, Ft Lauderdale, FL 33328 USA.
C3 Nova Southeastern University
RP Shoja, MM (corresponding author), Nova Southeastern Univ, Dr Kiran C Patel Coll Allopath Med, Med Educ, Ft Lauderdale, FL 33328 USA.
EM shoja.m@gmail.com
RI Shoja, Mohammadali/ABH-1563-2020
CR Anton N, 2023, DIAGNOSTICS, V13, DOI 10.3390/diagnostics13010100
   Athaluri SA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37432
   Buhr CR, 2023, JMIR MED EDUC, V9, DOI 10.2196/49183
   Crispen P, 2023, Instructor Guidelines for Student Use of Generative Artificial Intelligence for Academic Work
   Edmunds S, 2013, MedEdPORTAL, V9, P9443, DOI [10.15766/mep_2374-8265.9443, DOI 10.15766/MEP_2374-8265.9443]
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Goddard J, 2023, AM J MED, V136, P1059, DOI 10.1016/j.amjmed.2023.06.012
   Grassini S, 2023, EDUC SCI, V13, DOI 10.3390/educsci13070692
   Hastings J, 2024, LANCET DIGIT HEALTH, V6, pe2, DOI 10.1016/S2589-7500(23)00246-7
   Hatem R, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.44720
   Hilty R, 2020, Artificial Intelligence and Intellectual Property, P20
   Jeyaraman Madhan, 2023, Cureus, V15, pe44316, DOI 10.7759/cureus.44316
   Johnson Douglas, 2023, Res Sq, DOI 10.21203/rs.3.rs-2566942/v1
   Khan RA, 2023, PAK J MED SCI, V39, P605, DOI 10.12669/pjms.39.2.7653
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lee H, 2024, ANAT SCI EDUC, V17, P926, DOI 10.1002/ase.2270
   Li SW, 2023, AM J OBSTET GYNECOL, V229, DOI 10.1016/j.ajog.2023.04.020
   Lum ZC, 2023, CLIN ORTHOP RELAT R, V481, P1623, DOI 10.1097/CORR.0000000000002704
   Masters K, 2019, MED TEACH, V41, P976, DOI 10.1080/0142159X.2019.1595557
   McLean SF, 2016, J MED EDUC CURRIC DE, V3, P39, DOI [10.4137/JMECD.S20377, 10.4137/JMECDECDECD.S20377]
   medium, 2023, ChatGPT prompt for creating case based multiple choice questions for medical education
   Obermeyer Z, 2019, SCIENCE, V366, P447, DOI 10.1126/science.aax2342
   Russell RG, 2023, ACAD MED, V98, P348, DOI 10.1097/ACM.0000000000004963
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Shoja MM, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.40883
   Sim JZT, 2023, FRONT MED TECHNOL, V5, DOI 10.3389/fmedt.2023.1281500
   van de Ridder JMM, 2023, ACAD MED, V98, P867, DOI 10.1097/ACM.0000000000005254
   youtube, 2005, Can a chatbot pass a medical school final?
NR 28
TC 3
Z9 3
U1 9
U2 34
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2168-8184
J9 CUREUS J MED SCIENCE
JI Cureus J Med Sci
PD JAN 9
PY 2024
VL 16
IS 1
AR e51961
DI 10.7759/cureus.51961
PG 24
WC Medicine, General & Internal
WE Emerging Sources Citation Index (ESCI)
SC General & Internal Medicine
GA GM6F7
UT WOS:001153117000025
PM 38333501
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Mallette, JC
AF Mallette, Jennifer C.
TI Preparing Future Technical Editors for an Artificial
   Intelligence-enabled Workplace
SO JOURNAL OF BUSINESS AND TECHNICAL COMMUNICATION
LA English
DT Article
DE technical editing; pedagogy; generative AI; course design;
   microcredential
AB How should instructors adapt technical editing courses to account for generative artificial intelligence (AI)? This article addresses what generative AI means for technical editing pedagogy. While AI tools may be able to address rote editing tasks, expert editors are still needed to provide accessible, ethical, and justice-oriented edits. After reviewing impacts of generative AI on editing praxis, the author focuses on the microcredentials that she built into an editing course in order to address these impacts pedagogically. The goal was to enable students to understand AI, argue for their expertise, and edit from ethical and social justice perspectives.
C1 [Mallette, Jennifer C.] Boise State Univ, Dept Writing Studies, 1910 Univ Dr, Boise, ID 83725 USA.
C3 Boise State University
RP Mallette, JC (corresponding author), Boise State Univ, Dept Writing Studies, 1910 Univ Dr, Boise, ID 83725 USA.
EM jennifermallette@boisestate.edu
CR [Anonymous], GAIN NEW SKILLS KNOW
   [Anonymous], ACC YOUR HEALTHC CAR
   [Anonymous], 2023, WRIT ART US AI TECHN
   [Anonymous], GRAMM WORKS
   Bard FAQ, GOOGL BARD
   Benjamin Sedona, 2021, SIGDOC '21: The 39th ACM International Conference on Design of Communication, P26, DOI 10.1145/3472714.3473619
   Carter A., 2023, PR DAILY
   Clem S., 2023, TECH COMMUN SOCIAL J, V1, P49
   Clem S, 2022, IEEE T PROF COMMUN, V65, P135, DOI 10.1109/TPC.2021.3137666
   D'Agostino S., 2023, Inside Higher Ed
   Eaton A., 2023, KEYWORDS TECHNICAL P, P105, DOI [10.37514/TPC-B.2023.1923.2.11, DOI 10.37514/TPC-B.2023.1923.2.11]
   Haas A. M., 2023, C WORKSH ASS TEACH T
   Hsu T, 2023, NEW YORK TIMES
   Johansen C., WHAT IS MICROCREDENT
   Jones N. N., 2023, KEYWORDS TECHNICAL P, P267, DOI [10.37514/TPC-B.2023.1923.2.11, DOI 10.37514/TPC-B.2023.1923.2.11]
   Mallette Jennifer C., 2018, Communication Design Quarterly Review, V6, P74, DOI 10.1145/3309578.3309586
   McCarthy A., 2023, POTENTIAL IMPACT AI
   Meloncon L., 2019, Editing in the modern classroom, P171
   Micro-Credentials, NAT ED ASS
   Moore Ben., 2020, PCMAG
   Schriver K., 2012, Past, present, and future contributions of cognitive writing research to cognitive psychology, P275
   Tang Y., 2021, 39 ACM INT C DES COM, P380, DOI [10.1145/3472714.3475817, DOI 10.1145/3472714.3475817]
   Wallen J., 2023, TECH REPUBLIC
NR 23
TC 1
Z9 1
U1 23
U2 29
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1050-6519
EI 1552-4574
J9 J BUS TECH COMMUN
JI J. Bus. Tech. Commun.
PD JUL
PY 2024
VL 38
IS 3
SI SI
BP 289
EP 302
DI 10.1177/10506519241239950
EA MAR 2024
PG 14
WC Business; Communication
WE Social Science Citation Index (SSCI)
SC Business & Economics; Communication
GA UE0C6
UT WOS:001190149100001
DA 2024-12-25
ER

PT J
AU Fruehauf, E
   Beman-Cavallaro, A
   Schmidt, L
AF Fruehauf, Evan
   Beman-Cavallaro, Andrew
   Schmidt, Leetta
TI Developing a foundation for the informational needs of generative AI
   users through the means of established interdisciplinary relationships
SO JOURNAL OF ACADEMIC LIBRARIANSHIP
LA English
DT Article
DE Generative artificial intelligence; AI; Libraries; Reference services
AB University faculty immediately had many questions and concerns in response to the public proliferation of generative artificial intelligence programs leveraging large language models to generate complex text responses to simple prompts. Librarians at the University of South Florida (USF) pooled their skills, existing relationships with faculty and professional staff across campus to provide information that answered common questions raised by those faculty on generative artificial intelligence usage within research related topics. Faculty concern regarding the worry of plagiarism, how to instruct students to use the new tools and how to discern the reliability of information generated by artificial intelligence tools were placed at the forefront. By augmenting existing tutorials and instruction sessions, and creating a new information resource, the library was able to build a timely foundation to support future efforts to address the changing information needs of faculty and students using generative artificial intelligence programs and tools.
C1 Univ S Florida, Lib Res & Instruct Dept, Tampa, FL USA.
   Univ S Florida, Lib Special Collect Dept, Tampa, FL USA.
C3 State University System of Florida; University of South Florida; State
   University System of Florida; University of South Florida
RP Fruehauf, E (corresponding author), 4101 USF Apple Dr,LIB 122, Tampa, FL 33620 USA.
EM efruehauf@usf.edu
RI Fruehauf, Evan/KMX-2985-2024
OI Fruehauf, Evan/0009-0005-3178-1641; Schmidt, LeEtta/0000-0002-9567-9065
CR Adetayo A. J., 2023, Internet Reference Services Quarterly, V27, P131, DOI [10.1080/10875301.2023.2203681, DOI 10.1080/10875301.2023.2203681]
   Ajani Y. A., 2022, INTERNET REFERENCE S, V26, P213, DOI [https://doi.org/10.1080/10875301.2022.2086196, DOI 10.1080/10875301.2022.2086196]
   Andrews JE, 2021, J ACAD LIBR, V47, DOI 10.1016/j.acalib.2021.102437
   Asemi A, 2021, LIBR HI TECH, V39, P412, DOI 10.1108/LHT-02-2020-0038
   Bin-Hady WRA, 2023, LIBR HI TECH, DOI 10.1108/LHT-05-2023-0200
   Bishop BW, 2021, B AM METEOROL SOC, V102, pE1384, DOI 10.1175/BAMS-D-20-0163.1
   Borgohain DJ, 2024, LIBR HI TECH, V42, P149, DOI 10.1108/LHT-07-2022-0331
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00284-4
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.01186
   Chen X., 2023, INTERNET REFERENCE S, V27, P121, DOI 10.1080/10875301.2023.2181262
   Chhetri P., 2023, Library Philosophy and Practice, P1
   Chiu T. K. F., 2024, Artificial Intelligence, V6, DOI [10.1016/j.caeai.2023.100197, DOI 10.1016/J.CAEAI.2023.100197]
   Cox AM, 2024, J LIBR INF SCI, V56, P330, DOI 10.1177/09610006221142029
   Emiri O., 2023, Library Philosophy and Practice (e-Journal), P1
   Erb R. A., 2022, The Serials Librarian, V82, P83, DOI [10.1080/0361526X.2022.2018240, DOI 10.1080/0361526X.2022.2018240]
   Gasparini Andrea, 2022, LIBER Quarterly, V32, P1, DOI 10.53377/lq.10934
   Gupta V., 2023, Internet Reference Services Quarterly, V27, P211, DOI [10.1080/10875301.2023.2240773, DOI 10.1080/10875301.2023.2240773]
   Harisanty D, 2024, LIBR HI TECH, V42, P809, DOI 10.1108/LHT-10-2021-0356
   Hervieux S, 2021, J ACAD LIBR, V47, DOI 10.1016/j.acalib.2020.102270
   Houston AB, 2023, TECH SERV Q, V40, P76, DOI 10.1080/07317131.2023.2187110
   Institute for AI+X, Home
   Israel Maria Joseph, 2021, Intelligent Technologies for Interactive Entertainment. 12th EAI International Conference, INTETAIN 2020. Proceedings. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST 377), P25, DOI 10.1007/978-3-030-76426-5_3
   Kiszl P., 2021, REF LIB, V62, P165, DOI [10.1080/02763877.2021.1979164, DOI 10.1080/02763877.2021.1979164]
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102761
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Lund BD, 2020, COLL RES LIBR, V81, P865
   Lysiak L., 2020, Pennsylvania Libraries, V8, P130, DOI [10.5195/palrap.2020.232, DOI 10.5195/PALRAP.2020.232]
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Miller RE, 2024, PORTAL-LIBR ACAD, V24, DOI 10.1353/pla.2024.a916988
   Okunlaya RO, 2022, LIBR HI TECH, V40, P1869, DOI 10.1108/LHT-07-2021-0242
   Passmore J, 2024, J WORK-APPL MANAGE, V16, P4, DOI 10.1108/JWAM-06-2023-0057
   Pence H. E., 2022, The Reference Librarian, V63, P1, DOI [https://doi.org/10.1080/02763877.2022.2140741, DOI 10.1080/02763877.2022.2140741]
   Pierre-Robertson P, 2023, DIGIT LIBR PERSPECT, V39, P620, DOI 10.1108/DLP-04-2023-0026
   So HJ, 2024, ASIA PAC J EDUC, V44, P61, DOI 10.1080/02188791.2023.2294699
   Strzelecki A, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13425
   Subaveerapandiyan A., 2023, Library Philosophy and Practice
   Tait E, 2022, J AUST LIB INF ASSOC, V71, P256, DOI 10.1080/24750158.2022.2081111
   Tella A., 2023, Vjesnik Bibliotekara Hrvatske, V66, DOI [10.30754/vbh.66.1.1031, DOI 10.30754/VBH.66.1.1031]
   Wang T, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13116716
   Weinberger D, 2020, AIB STUD, V60, P213, DOI 10.2426/aibstudi-12478
   Yeralan S., 2023, Sustainable Engineering and Innovation, V5, P107, DOI [DOI 10.37868/SEI.V5I2.ID196, 10.37868/sei.v5i2.id196]
   Yoon J, 2022, LIBR HI TECH, V40, P1893, DOI 10.1108/LHT-07-2021-0229
   Zhao R., 2023, Nongye Tushu Qingbao Xuekan, V35, P29, DOI [10.13998/j.cnki.issn1002-1248.23-0116, DOI 10.13998/J.CNKI.ISSN1002-1248.23-0116]
NR 44
TC 0
Z9 0
U1 42
U2 66
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0099-1333
EI 1879-1999
J9 J ACAD LIBR
JI J. Acad. Librariansh.
PD MAY
PY 2024
VL 50
IS 3
AR 102876
DI 10.1016/j.acalib.2024.102876
EA MAR 2024
PG 6
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA QY3N1
UT WOS:001224391200001
DA 2024-12-25
ER

PT J
AU Lucas, JS
   Maung, BM
   Tabar, M
   Mcbride, K
   Lee, DW
   Murugesan, S
AF Lucas, Jason S.
   Maung, Barani Maung
   Tabar, Maryam
   Mcbride, Keegan
   Lee, Dongwon
   Murugesan, San
TI The Longtail Impact of Generative AI on Disinformation: Harmonizing
   Dichotomous Perspectives
SO IEEE INTELLIGENT SYSTEMS
LA English
DT Article
DE Technological innovation; Generative AI; Navigation; Ecosystems;
   Malware; Risk management; Fake news; Socioeconomics; Intelligent
   systems; Lenses
AB Generative AI (GenAI) poses significant risks in creating convincing yet factually ungrounded content, particularly in "longtail" contexts of high-impact events and resource-limited settings. While some argue that current disinformation ecosystems naturally limit GenAI's impact, we contend that this perspective neglects longtail contexts where disinformation consequences are most profound. This article analyzes the potential impact of GenAI's disinformation in longtail events and settings, focusing on 1) quantity: its ability to flood information ecosystems during critical events; 2) quality: the challenge of distinguishing authentic content from high-quality GenAI content; 3) personalization: its capacity for precise microtargeting exploiting individual vulnerabilities; and 4) hallucination: the danger of unintentional false information generation, especially in high-stakes situations. We then propose strategies to combat disinformation in these contexts. Our analysis underscores the need for proactive measures to mitigate risks, safeguard social unity, and combat the erosion of trust in the GenAI era, particularly in vulnerable communities and during critical events.
C1 [Lucas, Jason S.] Penn State Univ, University Pk, PA 16802 USA.
   [Maung, Barani Maung] Univ Oxford, Oxford Internet Inst, Oxford OX1 4BG, England.
   [Tabar, Maryam] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA.
   [Mcbride, Keegan] Univ Oxford, Oxford Internet Inst, AI Govt & Policy, Oxford OX1 4BH, England.
   [Lee, Dongwon] Penn State Univ, Informat Sch, University Pk, PA 16802 USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park; University of Oxford; University of Texas System;
   University of Texas at San Antonio (UTSA); University of Oxford;
   Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park
RP Lucas, JS (corresponding author), Penn State Univ, University Pk, PA 16802 USA.
EM jsl5710@psu.edu; barani.maungmaung@gmail.com; maryam.tabar@utsa.edu;
   keegan.mcbride@oii.ox.ac.uk; dongwon@psu.edu; san@computer.org
RI mcbride, keegan/AAG-5897-2019
OI McBride, Keegan/0000-0002-9081-7529; Lee, Dongwon/0000-0001-8371-7629
CR Adjin-Tettey TD, 2022, COGENT ARTS HUMANITE, V9, DOI 10.1080/23311983.2022.2037229
   [Anonymous], 2023, GUARDIAN
   Bethany M, 2024, Arxiv, DOI arXiv:2401.09727
   Chen CY, 2024, Arxiv, DOI arXiv:2309.13788
   Chowdhury N, 2023, J PUBLIC HEALTH-HEID, V31, P553, DOI 10.1007/s10389-021-01565-3
   Duboust O., Euronews.com
   Feng SB, 2024, Arxiv, DOI [arXiv:2402.00371, 10.48550/arXiv.2402.00371, DOI 10.48550/ARXIV.2402.00371]
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   Garimella K, 2024, NATURE, V630, P32, DOI 10.1038/d41586-024-01588-2
   Greco A, Encyclopedia Geopolitica
   Heikkila Melissa., 2023, MIT Technology Review
   Karalis M, Georgetown Journal of International Affairs
   Laato S, 2020, EUR J INFORM SYST, V29, P288, DOI 10.1080/0960085X.2020.1770632
   Lucas J., 2023, P C EMP METH NAT LAN, DOI [10.18653/v1/2023.emnlp-main.883, DOI 10.18653/V1/2023.EMNLP-MAIN.883]
   Mazurczyk W, 2024, COMMUN ACM, V67, P36, DOI 10.1145/3624721
   Mirza R., 2024, The Journalist's Resource16 Feb
   Monteith S, 2024, BRIT J PSYCHIAT, V224, P33, DOI 10.1192/bjp.2023.136
   Nahar M, 2024, Arxiv, DOI arXiv:2404.03745
   Okolo C. T, 2024, P BUILD JUST AI EC A
   OpenAI, 2024, AI and Covert Influence operations
   Park PS, 2024, PATTERNS, V5, DOI 10.1016/j.patter.2024.100988
   Pashentsev E., 2020, ICSPSC
   Rocha YM, 2023, J PUBLIC HEALTH-HEID, V31, P1007, DOI 10.1007/s10389-021-01658-z
   Salvi C, 2021, FRONT COMMUN, V5, DOI 10.3389/fcomm.2020.562588
   Schaewitz L, 2020, MASS COMMUN SOC, V23, P484, DOI 10.1080/15205436.2020.1716983
   Sharma PR, 2023, MEMORY, V31, P1, DOI 10.1080/09658211.2022.2120623
   Shoaib MR., 2023, 2023 INT C COMP APPL, P17394, DOI [10.1109/ICCA59364.2023.10401723, DOI 10.1109/ICCA59364.2023.10401723]
   Simchon A, 2024, PNAS NEXUS, V3, DOI 10.1093/pnasnexus/pgae035
   Simon FM., 2023, HARVARD KENNEDY SCH, DOI [10.37016/mr-2020-127, DOI 10.37016/MR-2020-127]
   Siontis KC, 2024, EUR HEART J, V45, P321, DOI 10.1093/eurheartj/ehad766
   Solaiman I, 2024, Arxiv, DOI arXiv:2306.05949
   Spitale G, 2023, SCI ADV, V9, DOI 10.1126/sciadv.adh1850
   Tran T., 2020, DEP INFORM SYSTEMS C, V1186, P1, DOI DOI 10.1007/978-981-15-3817-9_10
   Uchendu A, 2021, FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2021, P2001
   Vysakh C., 2021, Academia Lett., V799, P1
   Zimmerman T, 2023, VACCINE, V41, P136, DOI 10.1016/j.vaccine.2022.11.014
NR 36
TC 0
Z9 0
U1 9
U2 9
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1541-1672
EI 1941-1294
J9 IEEE INTELL SYST
JI IEEE Intell. Syst.
PD SEP
PY 2024
VL 39
IS 5
BP 12
EP 19
DI 10.1109/MIS.2024.3439109
PG 8
WC Computer Science, Artificial Intelligence; Engineering, Electrical &
   Electronic
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering
GA I2Q6Z
UT WOS:001328758600009
DA 2024-12-25
ER

PT J
AU Malhotra, K
   Wiesenfeld, B
   Major, VJ
   Grover, H
   Aphinyanaphongs, Y
   Testa, P
   Austrian, JS
AF Malhotra, Kiran
   Wiesenfeld, Batia
   Major, Vincent J.
   Grover, Himanshu
   Aphinyanaphongs, Yindalon
   Testa, Paul
   Austrian, Jonathan S.
TI Health system-wide access to generative artificial intelligence: the New
   York University Langone Health experience
SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
LA English
DT Article; Early Access
DE clinical informatics; artificial intelligence; medical informatics
   application
AB Objectives: The study aimed to assess the usage and impact of a private and secure instance of a generative artificial intelligence (GenAI) application in a large academic health center. The goal was to understand how employees interact with this technology and the influence on their perception of skill and work performance. Materials and Methods: New York University Langone Health (NYULH) established a secure, private, and managed Azure OpenAI service (GenAI Studio) and granted widespread access to employees. Usage was monitored and users were surveyed about their experiences. Results: Over 6 months, over 1007 individuals applied for access, with high usage among research and clinical departments. Users felt prepared to use the GenAI studio, found it easy to use, and would recommend it to a colleague. Users employed the GenAI studio for diverse tasks such as writing, editing, summarizing, data analysis, and idea generation. Challenges included difficulties in educating the workforce in constructing effective prompts and token and API limitations. Discussion: The study demonstrated high interest in and extensive use of GenAI in a healthcare setting, with users employing the technology for diverse tasks. While users identified several challenges, they also recognized the potential of GenAI and indicated a need for more instruction and guidance on effective usage. Conclusion: The private GenAI studio provided a useful tool for employees to augment their skills and apply GenAI to their daily tasks. The study underscored the importance of workforce education when implementing system-wide GenAI and provided insights into its strengths and weaknesses.
C1 [Malhotra, Kiran; Major, Vincent J.; Grover, Himanshu; Aphinyanaphongs, Yindalon; Testa, Paul; Austrian, Jonathan S.] New York Univ NYU Langone Hlth, Dept Pathol, New York, NY 10016 USA.
   [Malhotra, Kiran; Major, Vincent J.; Grover, Himanshu; Aphinyanaphongs, Yindalon; Testa, Paul; Austrian, Jonathan S.] NYU Langone Hlth, Med Ctr IT, Dept Hlth Informat, New York, NY 10016 USA.
   [Malhotra, Kiran] NYU, Grossman Sch Med, Dept Ophthalmol, New York, NY 10017 USA.
   [Wiesenfeld, Batia] NYU, Stern Sch Business, Dept Management & Org, New York, NY 10012 USA.
   [Major, Vincent J.] NYU, Grossman Sch Med, Dept Populat Hlth, New York, NY 10016 USA.
   [Aphinyanaphongs, Yindalon; Austrian, Jonathan S.] NYU, Grossman Sch Med, Dept Med, New York, NY 10016 USA.
   [Testa, Paul] NYU, Grossman Sch Med, Dept Emergency Med, New York, NY 10016 USA.
C3 NYU Langone Medical Center; New York University; New York University;
   New York University; New York University; New York University
RP Malhotra, K (corresponding author), NYU Langone Hlth, Dept Hlth Informat, 1 Pk Ave, 12th Floor, New York, NY 10016 USA.
EM kiran.malhotra@nyulangone.org
RI Grover, Himanshu/HNR-4326-2023
FU National Science Foundation [2129076]
FX Batia Wiesenfeld and Yindalon Aphinyanaphongs have received funding from
   the National Science Foundation Grant #2129076.
CR Abdelkader OA, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18770
   Austrian J., 2023, Medium, V27
   Brynjolfsson E., 2023, Generative AI at Work.
   Davenport TH., 2023, Harvard Business Rev, V15
   DellAcqua F., 2023, Harvard Business School Technology & Operations Mgt. Unit Working Paper, no. 24-013
   Deloitte, 2023, The Consumer Generative AI Dossier-Deloitte US
   George B, 2023, ADM SCI, V13, DOI 10.3390/admsci13090196
   Hosseini M, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0292216
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Lecler A, 2023, DIAGN INTERV IMAG, V104, P269, DOI 10.1016/j.diii.2023.02.003
   Li SQ, 2024, ANN BIOMED ENG, V52, P441, DOI 10.1007/s10439-023-03300-3
   Liu SR, 2023, J AM MED INFORM ASSN, V30, P1237, DOI 10.1093/jamia/ocad072
   Nazir Anam, 2023, Meta Radiol, V1, DOI 10.1016/j.metrad.2023.100022
   openai, GPT-4
   Pieszko K, 2021, CARDIOL J, V28, P460, DOI 10.5603/CJ.a2020.0093
   Small William R, 2024, PLOS Digit Health, V3, pe0000394, DOI 10.1371/journal.pdig.0000394
NR 16
TC 0
Z9 0
U1 2
U2 2
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1067-5027
EI 1527-974X
J9 J AM MED INFORM ASSN
JI J. Am. Med. Inf. Assoc.
PD 2024 NOV 25
PY 2024
DI 10.1093/jamia/ocae285
EA NOV 2024
PG 7
WC Computer Science, Information Systems; Computer Science,
   Interdisciplinary Applications; Health Care Sciences & Services;
   Information Science & Library Science; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Health Care Sciences & Services; Information Science &
   Library Science; Medical Informatics
GA N3C2Y
UT WOS:001363152600001
PM 39584477
DA 2024-12-25
ER

PT J
AU Yang, SL
   Trainin, G
   Appleget, C
AF Yang, Shuling
   Trainin, Guy
   Appleget, Carin
TI Teacher Use of Generative AI for Read-Aloud Question Prompts
SO READING TEACHER
LA English
DT Article
DE agency; and in-service; ChatGPT; Generative AI; preservice; questions;
   read-aloud; teachers
ID COMPREHENSION
AB The advent of Generative AI technologies, such as ChatGPT, in November 2022, necessitated immediate and critical attention from the educational research community. The impact of GenAI in education, though not yet clear, has the potential to be transformative. More specifically, the focus of this paper is on how to integrate GenAI into elementary literacy education. We, as teacher educators, aim to showcase how to prompt ChatGPT to generate high-quality questions during a read-aloud. We discuss the easy access teachers have to GenAI tools and stress the pivotal role they have in decision-making. We encourage teachers to explore, learn, and understand how to work with GenAI tools to get the most out of it and thus facilitate their agency, teaching, and learning.
C1 [Yang, Shuling] Univ Maryland Baltimore Cty, Literacy, Baltimore, MD 21250 USA.
   [Trainin, Guy] Univ Nebraska Lincoln, Educ, Lincoln, NE USA.
   [Appleget, Carin] Creighton Univ, Educ, Omaha, NE USA.
C3 University System of Maryland; University of Maryland Baltimore County;
   University of Nebraska System; University of Nebraska Lincoln; Creighton
   University
RP Yang, SL (corresponding author), Univ Maryland Baltimore Cty, Literacy, Baltimore, MD 21250 USA.
EM syang9@umbc.edu; gtraining2@unl.edu; carinappleget@creighton.edu
RI Trainin, Guy/B-6597-2008; Yang, Shuling/LMQ-3052-2024
OI Yang, Shuling/0000-0002-4813-3406
CR Anderson L. W., 2001, A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objective
   Babbling Brook Storytime, 2021, FALL HUMPTY DUMPTY G
   Barrentine SJ, 1996, READ TEACH, V50, P36
   Bloom B. S., 1956, TAXONOMY ED OBJECTIV
   Gee J. P., 2011, DO DISCOURSE ANAL TO
   Lane HB, 2007, READ TEACH, V60, P668, DOI 10.1598/RT.60.7.7
   Layne S., 2015, DEFENSE READALOUD SU
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Opitz MF, 2004, READ TEACH, V57, P772
   Raphael TE, 2005, READ TEACH, V59, P206, DOI 10.1598/RT.59.3.1
   RAPHAEL TE, 1986, READ TEACH, V39, P516
   Santat D., 2017, After the fall: How Humpty Dumpty got back up again
   Troyer M, 2023, LIT RES INSTR, V62, P101, DOI 10.1080/19388071.2022.2074328
   van den Berg G, 2023, EDUC SCI, V13, DOI 10.3390/educsci13100998
   WHITEHURST GJ, 1988, DEV PSYCHOL, V24, P552, DOI 10.1037/0012-1649.24.4.552
   Yang S., 2022, J LITERACY INNOVATIO, V7, P58
NR 17
TC 0
Z9 0
U1 15
U2 15
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0034-0561
EI 1936-2714
J9 READ TEACH
JI Read. Teach.
PD JAN
PY 2025
VL 78
IS 4
BP 230
EP 235
DI 10.1002/trtr.2366
EA OCT 2024
PG 6
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA O2V2D
UT WOS:001336829200001
DA 2024-12-25
ER

PT J
AU Lievens, F
   Dunlop, PD
AF Lievens, Filip
   Dunlop, Patrick D.
TI Effects of Applicants' Use of Generative AI in Personnel Selection:
   Towards a More Nuanced View?
SO INTERNATIONAL JOURNAL OF SELECTION AND ASSESSMENT
LA English
DT Article
DE assessment; generative AI; large language models; selection; validity
ID METAANALYSIS; TESTS
AB Generative AI (GenAI) has made rapid inroads in assessment, as a growing number of applicants rely on it as a coach in unproctored assessments of various selection procedures. This had led to assertions that applicants' GenAI use undermines key assumptions of the predictive model underlying selection and is thus disruptive for organizations' current unproctored assessments, thereby invoking various strategies of organizations to deter and detect its use. In this provocation article, we present a more nuanced view. To this end, we start by reviewing the recent research related to the effects of applicants' use of GenAI in assessment and discuss the evidence of the potential of applicant GenAI use to disrupt assessment validity. Next, we draw on test coaching frameworks to discuss three scenarios of how applicants' use of GenAI might affect an assessment's mean scores and criterion-related validity. These perspectives highlight that the use of GenAI might not only exert negative consequences but potentially have also positive consequences for both applicants and organizations. It is pivotal to distinguish among these scenarios because they lead to different strategies for organizations to deal with applicant use of GenAI.
C1 [Lievens, Filip] Singapore Management Univ, Lee Kong Chian Sch Business, Singapore, Singapore.
   [Dunlop, Patrick D.] Curtin Univ, Perth, Australia.
C3 Singapore Management University; Curtin University
RP Lievens, F (corresponding author), Singapore Management Univ, Lee Kong Chian Sch Business, Singapore, Singapore.
EM filiplievens@smu.edu.sg
FU The authors received no specific funding for this work.
FX We would like to thank Richard N. Landers and Chet Robie for their
   insightful comments on a previous version of this paper.
CR Alliger GM, 2000, EDUC PSYCHOL MEAS, V60, P59, DOI 10.1177/00131640021970367
   Arctic Shores, 2024, Why TA Teams Need to Guide Candidates' Use of GenAI and How to Do It
   Arctic Shores, 2023, ChatGPT Has Broken Traditional Aptitude Assessments: Your Solution to Futureproofing Recruitment
   Bangerter A, 2012, J APPL PSYCHOL, V97, P719, DOI 10.1037/a0026078
   Barrick MR, 2009, J APPL PSYCHOL, V94, P1394, DOI 10.1037/a0016532
   BBC, 2023, ChatGPT: How Generative AI Could Change Hiring as We Know It
   Borchert RJ, 2023, JMIR MED EDUC, V9, DOI 10.2196/48978
   Bourdage JS, 2018, PERS PSYCHOL, V71, P597, DOI 10.1111/peps.12285
   Brandt PM, 2023, J PSYCHOLINGUIST RES, V52, P589, DOI 10.1007/s10936-022-09909-0
   Brynjolfsson E., 2023, Generative AI at Work.
   Canagasuriam D, 2025, INT J SELECT ASSESS, V33, DOI 10.1111/ijsa.12491
   Criddle C., 2024, Jobhunters Flood Recruiters With AIGenerated CVs. Financial Times
   DellAcqua F. E., 2023, Working Paper No. 24-013., DOI [10.2139/ssrn.4573321, DOI 10.2139/SSRN.4573321]
   Diekmann J., 2015, EMPLOYEE RECRUITMENT, P117
   Elyoseph Z, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1199058
   Fletcher R., What Does the Public in Six Countries Think of Generative AI in News?, DOI [10.60625/risj-4zb8-cg87, DOI 10.60625/RISJ-4ZB8-CG87]
   Harwood H, 2024, J VOCAT BEHAV, V154, DOI 10.1016/j.jvb.2024.104013
   Hausknecht JP, 2007, J APPL PSYCHOL, V92, P373, DOI 10.1037/0021-9010.92.2.373
   Hickman L, 2024, INT J SELECT ASSESS, V32, P499, DOI 10.1111/ijsa.12479
   Hofman J. M., 2023, Harvard Business Review
   Holtrop D., 2024, The Effect of Applicants' Use of Generative AI on Recruiter Ratings of Cover Letters
   Humlum A. E., 2024, Working paper
   Jaffe S., 2024, Generative AI in Real-World Workplaces: The Second Microsoft Report on AI and Productivity Research
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lievens F, 2005, PERS PSYCHOL, V58, P981, DOI 10.1111/j.1744-6570.2005.00713.x
   Lievens F, 2017, J APPL PSYCHOL, V102, P43, DOI 10.1037/apl0000160
   Lukacik ER, 2022, HUM RESOUR MANAGE R, V32, DOI 10.1016/j.hrmr.2020.100789
   Mitchell M, 2023, Arxiv, DOI [arXiv:2311.09247, 10.48550/arXiv.2311.09247, DOI 10.48550/ARXIV.2311.09247]
   Murphy S.C., 2009, COLL UNIV, V84, P83
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   ONES DS, 1993, J APPL PSYCHOL, V78, P679, DOI 10.1037/0021-9010.78.4.679
   OpenAI, 2023, GPT4 Technical Report
   Phillips J, 2024, PERS INDIV DIFFER, V231, DOI 10.1016/j.paid.2024.112840
   Phillips J, 2024, PERS INDIV DIFFER, V217, DOI 10.1016/j.paid.2023.112434
   Pletzer JL, 2019, J VOCAT BEHAV, V112, P369, DOI 10.1016/j.jvb.2019.04.004
   Reeve CL, 2009, INTELLIGENCE, V37, P34, DOI 10.1016/j.intell.2008.05.003
   Risavy S., 2019, Personnel Assessment and Decisions, V5, DOI DOI 10.25035/PAD.2019.01.004
   Roulin N, 2016, ORGAN PSYCHOL REV, V6, P145, DOI 10.1177/2041386615580875
   SHL, SHL's Exploration of ChatGPT
   Suri S, 2024, Arxiv, DOI [arXiv:2404.04268, 10.48550/arXiv.2404.04268, DOI 10.48550/ARXIV.2404.04268]
   Technology Council of Australia, 2024, Meeting the AI Skills Boom
   Tippins NT, 2006, PERS PSYCHOL, V59, P189, DOI 10.1111/j.1744-6570.2006.00909.x
   Van Iddekinge CH, 2017, ANNU REV ORGAN PSYCH, V4, P445, DOI 10.1146/annurev-orgpsych-032516-113349
   Weber-Wulff D, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00146-z
   Westfall B., 2024, Nearly Half of Job Seekers are Using AI to CheatHere's How Recruiters Can Fight Back
NR 45
TC 0
Z9 0
U1 0
U2 0
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0965-075X
EI 1468-2389
J9 INT J SELECT ASSESS
JI Int. J. Sel. Assess.
PD FEB
PY 2025
VL 33
IS 1
AR e12516
DI 10.1111/ijsa.12516
PG 9
WC Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Psychology; Business & Economics
GA P5T3U
UT WOS:001378527800001
DA 2024-12-25
ER

PT J
AU Henadirage, A
   Gunarathne, N
AF Henadirage, Amali
   Gunarathne, Nuwan
TI Barriers to and Opportunities for the Adoption of Generative Artificial
   Intelligence in Higher Education in the Global South: Insights from Sri
   Lanka
SO INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION
LA English
DT Article; Early Access
DE Barriers; Generative Artificial Intelligence; Global South; Higher
   Education; Opportunities; Sri Lanka
ID TECHNOLOGIES; INNOVATION
AB Numerous studies have explored the intricacies of artificial intelligence (AI) in higher education, predominantly focusing on developed countries. However, there is a notable gap in examining the hindrances and untapped potential of AI implementation in the higher education sector within the South Asian Global South countries. This study aims to identify the barriers to integrating generative artificial intelligence (GenAI) in higher education and explore potential opportunities, with a specific focus on Sri Lanka as the country setting. Using a case study approach, data were gathered from various sources, including interviews and document analysis, concentrating on the largest management faculty in the country. The impediments were analyzed using an extended innovation barriers framework, covering technological, commercial, organizational, societal, and personal challenges. The findings highlight a landscape replete with obstacles. Key among them are the absence of comprehensive policies and guidelines at the university level, uncertainty about the reliability of information provided by GenAI tools, overreliance on these tools by learners, a lack of understanding and expertise among academics, and resistance to embracing technological advancements. Nonetheless, the study identifies several remedial actions that could be adopted to harness the potential of GenAI tools, particularly in South Asian Global South countries such as Sri Lanka.
C1 [Henadirage, Amali; Gunarathne, Nuwan] Univ Sri Jayewardenepura, Fac Management Studies & Commerce, Nugegoda, Sri Lanka.
C3 University Sri Jayewardenepura
RP Gunarathne, N (corresponding author), Univ Sri Jayewardenepura, Fac Management Studies & Commerce, Nugegoda, Sri Lanka.
EM amalihenadirage@sjp.ac.lk; nuwan@sjp.ac.lk
RI Gunarathne, Nuwan/I-1577-2016
OI Gunarathne, Nuwan/0000-0003-3024-9416
CR Akgun Selin, 2022, AI Ethics, V2, P431, DOI 10.1007/s43681-021-00096-7
   AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Alotaibi NS, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151310723
   Anand T. S., 2023, How will India's First AI University-Universal AI University-work?
   Andrew M., 2023, Sri Lanka to introduce artificial intelligence to school curriculum in 2024
   [Anonymous], 1998, World Declaration on Higher Education for the Twenty-first Century: Vision and Action and Framework for Priority Action for Change and Development in Higher education adopted by the World Conference on Higher Education in the Twenty-First Century: Vision and Action
   [Anonymous], 2023, Oxford English Dictionary
   ANSOFF HI, 1957, HARVARD BUS REV, V35, P113
   Attwood A. I., 2020, Srate Journal, V29, P2
   Baker RS, 2016, INT J ARTIF INTELL E, V26, P600, DOI 10.1007/s40593-016-0105-0
   Baker T., 2019, Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges
   Bucea-Manea-Tonis R, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14105842
   Butler-Henderson K, 2020, COMPUT EDUC, V159, DOI 10.1016/j.compedu.2020.104024
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.01185, 10.48550/arXiv.2305.01185, DOI 10.48550/ARXIV.2305.01185]
   Chandra R., 2022, Int J Res Anal Rev, V9, P277
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Ciotti M, 2020, CRIT REV CL LAB SCI, V57, P365, DOI 10.1080/10408363.2020.1783198
   Clarke V., 2013, SUCCESSFUL QUALITATI
   Corrigan-Gibbs Henry, 2015, ACM Transactions on Computer-Human Interaction, V22, DOI 10.1145/2810239
   Dados Nour., 2012, Contexts, V11, P12, DOI [10.1177/1536504212436479, DOI 10.1177/1536504212436479]
   David M., 2023, ChatGPT advantages seem to far outweigh disadvantages: Dr Tara de Mel
   Dehouche N., 2021, Ethic in Science and Environmental Politics, V21, P17, DOI [DOI 10.3354/ESEP00195, 10.3354/esep00195]
   Department of Census and Statistics, 2023, Bulletin: Computer Literacy Statistics-2022 (First Six Months)
   Dobson S., 2023, Why universities should return to oral exams in the AI and ChatGPT era
   Donnelly R., 2004, Critical evaluation of the impact of global educational reform: An Irish export.gov (2019). Sri Lanka-Information and Communication Technologies
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Fjeld J., 2017, Journal of Law and Technology
   FMSC, 2023, About us
   FMSC Prospectus, 2022, Prospectus 2022
   Giovetti O., 2022, How does education affect poverty? It can help end it
   Girasa R., 2020, Artificial Intelligence as a Disruptive Technology, DOI [10.1007/978-3-030-35975-1_1, DOI 10.1007/978-3-030-35975-1_1, 10.1007/978-3-030-35975-11, DOI 10.1007/978-3-030-35975-11]
   González-Calatayud V, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125467
   Hall J, 2011, TECHNOL FORECAST SOC, V78, P1147, DOI 10.1016/j.techfore.2011.02.005
   Hall JK, 2005, R&D MANAGE, V35, P273, DOI 10.1111/j.1467-9310.2005.00389.x
   Henadirage A, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100810
   Hillman AryeL., 2004, Educating Children in Poor Countries
   Holmes W., 2022, Artificial intelligence and education
   Holmes W, 2024, INT J ARTIF INTELL E, V34, P144, DOI 10.1007/s40593-023-00364-z
   Holmes W, 2022, INT J ARTIF INTELL E, V32, P504, DOI 10.1007/s40593-021-00239-1
   Hrastinski S., 2019, Postdigital Science and Education, V1, P427, DOI DOI 10.1007/S42438-019-00046-X
   Hudah F., 2023, National AI policy: Is Sri Lanka falling behind?
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Ikoya PO, 2007, J EDUC ADMIN, V45, P190, DOI 10.1108/09578230710732961
   Kakuchi S., 2023, New government guidelines on the use of AI in education
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Khan S, 2022, EDUC INF TECHNOL, V27, P4141, DOI 10.1007/s10639-021-10784-w
   Kyvik S, 2013, HIGH EDUC, V65, P525, DOI 10.1007/s10734-012-9561-0
   Lv Z., 2023, Cogn. Robot., V3, P208, DOI [10.1016/j.cogr.2023.06.001, DOI 10.1016/J.COGR.2023.06.001]
   Malik MA, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app112412055
   Mason J., 1996, QUALITATIVE RES, P9
   Mhlanga D, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13115788
   Miao F., 2022, K-12 AI curricula: a mapping of government-endorsed AI curricula
   Miao F., 2021, Ai and education: A guidance for policymakers
   Mogaji E., 2022, Re-imagining educational futures in developing countries
   Mogaji E., 2020, Impact of the Pandemic on Higher Education in Emerging Countries: Emerging Opportunities, Challenges and Research Agenda, V2020, P79, DOI DOI 10.2139/SSRN.3622592
   Mphahlele A, 2019, ASSESS EVAL HIGH EDU, V44, P1079, DOI 10.1080/02602938.2019.1573971
   Ndofirepi E, 2020, ROUTLEDGE STUD MARK, V12, P241
   Noor K.B., 2008, American Journal of Applied Sciences, V5, P1602, DOI [10.3844/ajassp.2008.1602.1604, DOI 10.3844/AJASSP.2008.1602.1604]
   Osmani SR., 1994, The Significance of the Sri Lanka Debate', Development and Change, V25, P387, DOI [10.1111/j.1467-7660.1994.tb00520.x, DOI 10.1111/J.1467-7660.1994.TB00520.X]
   Oyelere SS, 2022, IEEE GLOB ENG EDUC C, P1577, DOI 10.1109/EDUCON52537.2022.9766550
   Paiva R, 2020, LECT NOTES ARTIF INT, V12163, P448, DOI 10.1007/978-3-030-52237-7_36
   Patton M. Q., 2014, QUALITATIVE RES EVAL
   Pavaloaia VD, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12051102
   Pedro F., 2019, Artificial intelligence in education: Challenges and opportunities for sustainable development
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Pinkwart N, 2016, INT J ARTIF INTELL E, V26, P771, DOI 10.1007/s40593-016-0099-7
   Popenici Stefan A D, 2017, Res Pract Technol Enhanc Learn, V12, P22, DOI 10.1186/s41039-017-0062-8
   PwC, 2023, Higher Education Leaders Survey 2023
   Qin F, 2020, BRIT J EDUC TECHNOL, V51, P1693, DOI 10.1111/bjet.12994
   Rameez A., 2020, J Educ Soc Res, V10, P341, DOI [10.36941/jesr-2020-0132, DOI 10.36941/JESR-2020-0132]
   Rogers E. M., 2003, DIFFUSION INNOVATION
   Salas-Pilco SZ, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00326-w
   Schiff D, 2022, INT J ARTIF INTELL E, V32, P527, DOI 10.1007/s40593-021-00270-2
   Seidman I., 2012, Interviewing as qualitative research
   Senaratne S, 2022, ACCOUNT EDUC, V31, P536, DOI 10.1080/09639284.2022.2032775
   Sharma H., 2022, Re-imagining Educational Futures in Developing Countries: Lessons from Global Health Crises, P159, DOI [10.1007/978-3-030-88234-19, DOI 10.1007/978-3-030-88234-19]
   Shen XY, 2023, Arxiv, DOI [arXiv:2304.08979, 10.48550/arXiv.2304.08979, 10.48550/arxiv.2304.08979, DOI 10.48550/ARXIV.2304.08979]
   Shen Y, 2024, HUM SOC SCI COMMUN, V11, DOI 10.1057/s41599-024-02647-9
   Skrabut S., 2023, 80 ways to use ChatGPT in the classroom: Using AI to enhance teaching and learning
   Sri Lanka Export Development Board, 2023, Sri Lanka-ICT / BPM Destination of Choice
   Stake R., 1995, ART CASE STUDY RES
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Susnjak T., 2022, PREPRINT, DOI [DOI 10.48550/ARXIV.2212.09292, 10.48550/arXiv.2212.09292]
   Swamy V K., 2023, AI's role in reshaping Indian education
   Tao B., 2019, Arctic Journal, V72, P30
   The Island Online, 2024, The digital divide: AI and its implications for higher education in Sri Lanka
   The World Bank, 2022, The World Bank In Sri Lanka
   The World Bank, 2024, South Asia
   The World Bank, 2023, World Bank Country and Lending Groups
   UNESCO, 2022, Development
   United Nations General Assembly, 1948, UNIVERSAL DECLARATIO
   University Grants Commission of Sri Lanka, 2023, Universities and Higher Educational Institutions established under the purview of the University Grants Commission
   Vinuesa R, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-019-14108-y
   Wall P. J., 2021, PREPRINT
   Walsh Toby., 2017, The ai revolution
   Whisenhunt BL., 2022, Scholarship of Teaching and Learning in Psychology, V8, P140, DOI [10.1037/stl0000242, DOI 10.1037/STL0000242]
   Wickramasinghe V, 2018, INT J EDUC MANAG, V32, P463, DOI 10.1108/IJEM-01-2017-0028
   World Population Review, 2024, About us
   Wu X., 2024, J INF INTELL, V2, P102, DOI DOI 10.1016/J.JIIXD.2023.10.007
   Yasmin K., 2023, Government aims for 'mass use' of AI amid spike in research
   Yin R., 2018, CASE STUDY RES APPL
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhou K. Q., 2023, Mesopotamian Journal of Computer Science, V2023, P16, DOI [10.58496/MJCSC/2023/003, DOI 10.58496/MJCSC/2023/003]
NR 106
TC 0
Z9 0
U1 2
U2 2
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1560-4292
EI 1560-4306
J9 INT J ARTIF INTELL E
JI Int. J. Artif. Intell. Educ.
PD 2024 NOV 27
PY 2024
DI 10.1007/s40593-024-00439-5
EA NOV 2024
PG 37
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA N6A4Y
UT WOS:001365143200001
DA 2024-12-25
ER

PT J
AU Zenni, RD
   Andrew, NR
AF Zenni, Rafael D.
   Andrew, Nigel R.
TI Artificial Intelligence text generators for overcoming language barriers
   in ecological research communication
SO AUSTRAL ECOLOGY
LA English
DT Article
DE chatGPT; generative artificial intelligence text generators; language
   barriers; research communication; science communication
AB Language barriers can impede the dissemination of research findings, restrict collaboration and exclude non-English-speaking researchers from the global scientific community. To overcome this challenge, we explore the potential of Generative Artificial Intelligence (GenAI) text generators to assist non-anglophone researchers in producing high-quality academic texts for publication in scientific journals, with a focus on the field of ecological research. These tools can produce grammatically correct, coherent and contextually appropriate text, improving scientific communication quality. Improving scientific communication is vital in Ecology, where research findings can have important implications for the environment and public policy. GenAI text generators can generate summaries of research findings, abstracts and social media posts promoting research findings. Nonetheless, researchers must exercise caution and use these tools together with human review and editing to ensure accuracy and clarity. As natural language processing and machine learning continue to evolve, the use of GenAI text generators in scientific communication is poised to become increasingly important.
C1 [Zenni, Rafael D.] Univ Fed Lavras, Dept Ecol & Conservacao, Inst Ciencias Nat, Lavras, Brazil.
   [Andrew, Nigel R.] Southern Cross Universty, Fac Sci & Engn, Lismore, NSW, Australia.
   [Zenni, Rafael D.] Univ Fed Lavras, Dept Ecol & Conservacao, Inst Ciencias Nat, BR-37203202 Lavras, Brazil.
C3 Universidade Federal de Lavras; Universidade Federal de Lavras
RP Zenni, RD (corresponding author), Univ Fed Lavras, Dept Ecol & Conservacao, Inst Ciencias Nat, BR-37203202 Lavras, Brazil.
EM rafael.zenni@ufla.br
RI Zenni, Rafael/H-1106-2011
OI Zenni, Rafael/0000-0002-4315-7986
CR Amano T, 2023, PLOS BIOL, V21, DOI 10.1371/journal.pbio.3002184
   Amano T, 2023, NAT SUSTAIN, V6, P845, DOI 10.1038/s41893-023-01087-8
   Amano T, 2021, PLOS BIOL, V19, DOI 10.1371/journal.pbio.3001296
   Andrew NR, 2020, AUSTRAL ECOL, V45, P505, DOI 10.1111/aec.12908
   Fox CW, 2023, FUNCT ECOL, V37, P1144, DOI 10.1111/1365-2435.14259
   Goldstein A, 2022, NAT NEUROSCI, V25, P369, DOI 10.1038/s41593-022-01026-4
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Márquez MC, 2020, FRONT COMMUN, V5, DOI 10.3389/fcomm.2020.00031
   Merow C, 2023, NAT ECOL EVOL, V7, P960, DOI 10.1038/s41559-023-02063-3
   Nuñez MA, 2021, TRENDS ECOL EVOL, V36, P766, DOI 10.1016/j.tree.2021.06.004
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   Trisos CH, 2021, NAT ECOL EVOL, V5, P1205, DOI 10.1038/s41559-021-01460-w
   Wang L, 2024, ANN BIOMED ENG, V52, P754, DOI 10.1007/s10439-023-03324-9
   Woolston Chris, 2019, Nature, V570, P265, DOI 10.1038/d41586-019-01797-0
   Zenni RD, 2023, J APPL ECOL, V60, P380, DOI 10.1111/1365-2664.14370
NR 15
TC 1
Z9 1
U1 7
U2 35
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1442-9985
EI 1442-9993
J9 AUSTRAL ECOL
JI Austral Ecol.
PD NOV
PY 2023
VL 48
IS 7
BP 1225
EP 1229
DI 10.1111/aec.13417
EA AUG 2023
PG 5
WC Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology
GA U3DT1
UT WOS:001052357500001
OA Bronze
DA 2024-12-25
ER

PT J
AU Marimon, F
   Mas-Machuca, M
   Akhmedova, A
AF Marimon, Frederic
   Mas-Machuca, Marta
   Akhmedova, Anna
TI Trusting in Generative AI: Catalyst for Employee Performance and
   Engagement in the Workplace
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article; Early Access
DE Generative AI; work engagement; trust; employee performance
ID DEMANDS-RESOURCES MODEL; WORK ENGAGEMENT; CUSTOMER LOYALTY;
   SELF-EFFICACY; DARK SIDE; MEDIATION; VALIDITY; BURNOUT; INDEX; BARON
AB This paper investigates the impact of the usage of generative AI (GenAI) and services with integrated GenAI on employee performance, alongside with the role of trusting in these tools and services. Employing a mixed methodology, the research first analyzes data from 251 professionals in Spain using a structural equation modeling (SEM) approach, followed by a qualitative survey of 69 top academics in management sciences. Findings indicate that the adoption and effective use of GenAI services does not directly improve workplace performance. Instead, an optimal level of trust in these services plays a critical mediating role, enhancing work engagement and thereby performance. The study draws on the reviewed job demand-resources theory (JD-R) to construct a new theoretical framework of applied in GenAI services, offering insights into how user experience and trust influence engagement and productivity. For managers, these results highlight the importance of building an optimal level of trust in GenAI among employees and users of services with integrated GenAI to boost work engagement and performance.
C1 [Marimon, Frederic; Mas-Machuca, Marta; Akhmedova, Anna] Univ Int Catalunya, Dept Econ & Business Org, Barcelona, Spain.
C3 Universitat Internacional de Catalunya (UIC)
RP Marimon, F (corresponding author), Univ Int Catalunya, Dept Econ & Business Org, Barcelona, Spain.
EM fmarimon@uic.es
RI ; Marimon, Frederic/D-5531-2011; Mas-Machuca, Marta/F-7076-2016
OI Akhmedova, Anna/0000-0002-3734-4689; Marimon,
   Frederic/0000-0002-5572-7341; Mas-Machuca, Marta/0000-0003-1052-7319
CR Acemoglu D., 2023, MIT Sloan Manag. Rev, V64, P1
   Babina T, 2024, J FINANC ECON, V151, DOI 10.1016/j.jfineco.2023.103745
   Bachmann R, 2006, HANDBOOK OF TRUST RESEARCH, P1
   Bakker AB, 2023, ANNU REV ORGAN PSYCH, V10, P25, DOI 10.1146/annurev-orgpsych-120920-053933
   Bankins S, 2024, J ORGAN BEHAV, V45, P159, DOI 10.1002/job.2735
   BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173
   Barrett JAM, 2024, J SERV RES-US, DOI 10.1177/10946705241242901
   Bedué P, 2022, J ENTERP INF MANAG, V35, P530, DOI 10.1108/JEIM-06-2020-0233
   Brodie RJ, 2011, J SERV RES-US, V14, P252, DOI 10.1177/1094670511411703
   Brynjolfsson E., 2023, Generative AI at Work.
   Candrian C, 2022, COMPUT HUM BEHAV, V134, DOI 10.1016/j.chb.2022.107308
   Chan XW, 2017, INT J MANPOWER, V38, P819, DOI 10.1108/IJM-11-2015-0189
   Chatterjee S, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120880
   Chatterjee S, 2021, TECHNOL FORECAST SOC, V168, DOI 10.1016/j.techfore.2021.120783
   Chen Q, 2023, INTERNET RES, V33, P2205, DOI 10.1108/INTR-09-2021-0686
   Christian M.S., 2007, Academy of Management Proceedings, P1, DOI DOI 10.5465/AMBPP.2007.26536346
   Chughtai A.A., 2008, J BEHAV APPL MANAGEM, V10, P47, DOI [https://doi.org/10.21818/001c.17170, DOI 10.21818/001C.17170]
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Consiglio C, 2016, CAREER DEV INT, V21, P125, DOI 10.1108/CDI-03-2015-0045
   Creswell J. W., 2016, QUAL INQ
   Czarnitzki D, 2023, J ECON BEHAV ORGAN, V211, P188, DOI 10.1016/j.jebo.2023.05.008
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Debusscher J, 2016, J OCCUP ORGAN PSYCH, V89, P330, DOI 10.1111/joop.12126
   DECI EL, 1985, J RES PERS, V19, P109, DOI 10.1016/0092-6566(85)90023-6
   Demerouti E, 2001, J APPL PSYCHOL, V86, P499, DOI 10.1037//0021-9010.86.3.499
   DeWitt T, 2008, J SERV RES-US, V10, P269, DOI 10.1177/1094670507310767
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Frank DA, 2023, INFORM TECHNOL PEOPL, V36, P155, DOI 10.1108/ITP-09-2022-0721
   Gargiulo M, 2006, HANDBOOK OF TRUST RESEARCH, P165
   Gkinko L, 2023, J BUS RES, V158, DOI 10.1016/j.jbusres.2023.113707
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hakanen JJ, 2008, WORK STRESS, V22, P224, DOI 10.1080/02678370802379432
   Hakanen JJ, 2006, J SCHOOL PSYCHOL, V43, P495, DOI 10.1016/j.jsp.2005.11.001
   Hayes AF, 2009, COMMUN MONOGR, V76, P408, DOI 10.1080/03637750903310360
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Kashive N, 2021, INT J INF LEARN TECH, V38, P1, DOI 10.1108/IJILT-05-2020-0090
   Kellogg KC, 2020, ACAD MANAG ANN, V14, P366, DOI 10.5465/annals.2018.0174
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Laato S, 2022, INTERNET RES, V32, P1, DOI 10.1108/INTR-08-2021-0600
   Ladhari R, 2012, INT J CONTEMP HOSP M, V24, P628, DOI 10.1108/09596111211217914
   Lai Y L., 2020, Asian Educ. Stud, V5, P10, DOI DOI 10.20849/AES.V5I2.816
   Levine T.R., 1991, COMMUN Q, V39, P325, DOI DOI 10.1080/01463379109369809
   Llorens S, 2007, COMPUT HUM BEHAV, V23, P825, DOI 10.1016/j.chb.2004.11.012
   Marikyan D, 2022, J BUS RES, V142, P572, DOI 10.1016/j.jbusres.2022.01.015
   Maslach C., 1996, MBI Manual, V3rd, DOI DOI 10.1146/ANNUREV.PSYCH.52.1.397
   MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
   Mazzetti G, 2023, PSYCHOL REP, V126, P1069, DOI 10.1177/00332941211051988
   McKnight DH, 2002, J STRATEGIC INF SYST, V11, P297, DOI 10.1016/S0963-8687(02)00020-3
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Nunnally JC., 1994, PSYCHOMETRIC THEORY
   Parasuraman A, 2015, J SERV RES-US, V18, P59, DOI 10.1177/1094670514539730
   Parasuraman A., 2000, Journal of Service Research, V2, P307, DOI [DOI 10.1177/109467050024001, 10.1177/109467050024001]
   Rane N., 2024, Stud. Econ. Bus. Relat., V5, P11
   Rodríguez BP, 2024, EUR J MANAG BUS ECON, V33, P137, DOI 10.1108/EJMBE-06-2022-0177
   Rogers E. M., 2003, DIFFUSION INNOVATION
   Salanova M, 2005, J APPL PSYCHOL, V90, P1217, DOI 10.1037/0021-9010.90.6.1217
   Salanova M., 2006, Journal of Happiness Studies, V7, P1, DOI DOI 10.1007/S10902-005-8854-8
   Samuel J, 2022, INT J INFORM MANAGE, V65, DOI 10.1016/j.ijinfomgt.2022.102505
   Schaufeli WB, 2004, J ORGAN BEHAV, V25, P293, DOI 10.1002/job.248
   Schepman A, 2023, INT J HUM-COMPUT INT, V39, P2724, DOI 10.1080/10447318.2022.2085400
   Skinner D, 2014, ORGANIZATION, V21, P206, DOI 10.1177/1350508412473866
   Umair A, 2023, INTERNET RES, V33, P206, DOI 10.1108/INTR-03-2022-0214
   Wang CX, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18349
   Wijayati DT, 2022, INT J MANPOWER, V43, P486, DOI 10.1108/IJM-07-2021-0423
   Wolfinbarger M, 2003, J RETAILING, V79, P183, DOI 10.1016/S0022-4359(03)00034-4
   Molina-Morales FX, 2011, LONG RANGE PLANN, V44, P118, DOI 10.1016/j.lrp.2011.01.001
   Yang RB, 2022, ELECTRON MARK, V32, P2053, DOI 10.1007/s12525-022-00592-6
   Zhang SY, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.728495
   Zhao XS, 2010, J CONSUM RES, V37, P197, DOI 10.1086/651257
NR 73
TC 1
Z9 1
U1 93
U2 93
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PD 2024 AUG 9
PY 2024
DI 10.1080/10447318.2024.2388482
EA AUG 2024
PG 16
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA C2L4T
UT WOS:001287721900001
DA 2024-12-25
ER

PT J
AU Borge, M
   Smith, BK
   Aldemir, T
AF Borge, M.
   Smith, B. K.
   Aldemir, T.
TI Using generative ai as a simulation to support higher-order thinking
SO INTERNATIONAL JOURNAL OF COMPUTER-SUPPORTED COLLABORATIVE LEARNING
LA English
DT Article
DE Generative AI; GenAI; Higher-order thinking in collaboration; Cultural
   simulation; Collective sensemaking
ID ARGUMENTATIVE KNOWLEDGE CONSTRUCTION; SOCIALLY SHARED REGULATION;
   QUALITY
AB In this paper, and as a tribute to our friend and collaborator Barbara White, we explore how Generative AI (GenAI) technology can create stimulating new learning environments that support complex sense-making activities. We present a case study of expert use of a chat-based generative AI tool to examine the feasibility of using human-computer collaborative interactions to support metacognition and sociometacognition, i.e., knowledge about, awareness of, and ability to regulate individual (meta) and collective (sociometa) cognition. Our questions are: (RQ1) Is it possible for human-GenAI collaborative interactions to support metacognition, and (RQ2) Is it possible for them to support sociometacognition, i.e., knowledge about, awareness of, and ability to regulate individual (meta) and collective (sociometa) cognition. Our initial findings, though limited by the exploratory, case-based methods used, indicate the promise of GenAI as a valuable social interaction and cultural simulation tool for learners to practice collective sensemaking skills. Although the limitations of chat-based GenAI technologies, including their tendency to provide definitive answers unsupported by evidence, are worth mentioning, our findings contribute to the ongoing conversations around how to develop technologies to support learners' argumentation practices. Accordingly, this study has important implications for future research and practice on using chat-based GenAI as a partner for students to practice the knowledge and skills connected to argumentation and scientific claims, especially in larger courses or broader audiences.
C1 [Borge, M.] Penn State Univ, Learning & Performance Syst, University Pk, PA 16802 USA.
   [Smith, B. K.] Boston Coll, Lynch Sch Educ & Human Dev, Dept Comp Sci, Chestnut Hill, Boston, MA USA.
   [Aldemir, T.] Texas A&M Univ, Teaching Learning & Culture, College Stn, TX USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park; Boston College; Texas A&M University System; Texas A&M
   University College Station
RP Borge, M (corresponding author), Penn State Univ, Learning & Performance Syst, University Pk, PA 16802 USA.
EM mbs15@psu.edu; b.smith@bc.edu; taldemir@tamu.edu
RI Smith, Brian/ABC-4549-2020; Aldemir, Tugce/AAX-9073-2020; Smith,
   Brian/A-6499-2008
OI Aldemir, Tugce/0000-0001-5532-7770; Borge, Marcela/0000-0002-8892-8780;
   Smith, Brian/0000-0002-6523-2019
CR Aguilar SJ, 2020, INFORM LEARN SCI, V121, P285, DOI 10.1108/ILS-04-2020-0084
   Aldemir T., 2020, INTERDISCIPLINARITY, P1709
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Atman CJ, 2007, J ENG EDUC, V96, P359, DOI 10.1002/j.2168-9830.2007.tb00945.x
   Baker L., 1984, HDB READING RES, P353
   Baker M, 2007, INT J COMP-SUPP COLL, V2, P315, DOI 10.1007/s11412-007-9022-4
   Ball L. J., 1997, Thinking & Reasoning, V3, P247, DOI 10.1080/135467897394284
   Borge M., 2007, THESIS
   Borge M., 2014, P 18 INT C SUPP GROU, P215, DOI DOI 10.1145/2660398.2660418
   Borge M., 2015, P COMP SUPP COLL LEA, P427
   Borge M., 2019, TECHNOLOGY INSTRUCTI, V11, P193
   Borge M., 2017, INT J INNOV ONLINE E, V22
   Borge M, 2023, J LEARN SCI, V32, P325, DOI 10.1080/10508406.2022.2157725
   Borge M, 2022, ETR&D-EDUC TECH RES, V70, P661, DOI 10.1007/s11423-022-10103-1
   Borge M, 2022, TEACH PSYCHOL, V49, P85, DOI 10.1177/0098628320977421
   Borge M, 2018, INT J COMP-SUPP COLL, V13, P61, DOI 10.1007/s11412-018-9270-5
   Borge M, 2016, COGNITION INSTRUCT, V34, P323, DOI 10.1080/07370008.2016.1215722
   Boston College, 2024, GUIDANCE APPROP RIAT
   Bouckaert M, 2019, RELC J, V50, P439, DOI 10.1177/0033688218810549
   BUTLER DL, 1995, REV EDUC RES, V65, P245, DOI 10.3102/00346543065003245
   CALLAWAY MR, 1984, SOC BEHAV PERSONAL, V12, P157, DOI 10.2224/sbp.1984.12.2.157
   Cannon-Bowers J., 2014, WORKFORCE READINESS, P151
   Carey Stephen., 2011, BEGINNERS GUIDE SCI, V3rd
   COLLINS A, 1993, EDUC PSYCHOL, V28, P25, DOI 10.1207/s15326985ep2801_3
   Collins A., 1991, Teaching advanced skills to at-risk students: Views from research and practice, P216
   Collins A., 1991, AM EDUC, V15, P6, DOI DOI 10.1007/S10833-009-9107-0
   Collins Allan., 1988, THINKING J PHILOS CH, V8, P2
   Darling-Hammond L, 2006, EDUC LEADERSHIP, V64, P14
   Duschl RA., 2002, STUDIES SCI ED, V38, P39, DOI DOI 10.1080/03057260208560187
   Edmondson A, 1999, ADMIN SCI QUART, V44, P350, DOI 10.2307/2666999
   Fiore SM, 2018, NAT HUM BEHAV, V2, P367, DOI 10.1038/s41562-018-0363-y
   Goldhaber D, 2015, EDUC RESEARCHER, V44, P293, DOI 10.3102/0013189X15592622
   Jarvela S, 2019, INT J COMP-SUPP COLL, V14, P425, DOI 10.1007/s11412-019-09313-2
   Järvelä S, 2016, INT J COMP-SUPP COLL, V11, P263, DOI 10.1007/s11412-016-9238-2
   Järvelä S, 2015, ETR&D-EDUC TECH RES, V63, P125, DOI 10.1007/s11423-014-9358-1
   Järvelä S, 2013, EDUC PSYCHOL-US, V48, P25, DOI 10.1080/00461520.2012.748006
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Kokotsaki D, 2016, IMPROV SCH, V19, P267, DOI 10.1177/1365480216659733
   Kozlowski SWJ, 2009, SIOP ORGAN FRONT SER, P113
   Kuhn D, 2013, COGNITION INSTRUCT, V31, P456, DOI 10.1080/07370008.2013.830618
   Lee VR., 2024, COMPUTERS ED ARTIFIC, V7, P100253, DOI 10.1016/j.caeai.2024.100253
   McGrath RG, 1999, ACAD MANAGE REV, V24, P13, DOI 10.2307/259034
   Mehan Hugh., 1979, Theory Into Practice, V18, P285, DOI [DOI 10.1080/00405847909542846, 10.1080/00405847909542846]
   Meier A, 2007, INT J COMP-SUPP COLL, V2, P63, DOI 10.1007/s11412-006-9005-x
   Mok Aaron., 2023, Business Insider
   Nathan MJ, 2007, J LEARN SCI, V16, P523, DOI 10.1080/10508400701525238
   National Center for Education Statistics, 2018, NAEP TECHNOLOGY ENG
   Noguera P., 2015, EQUAL OPPORTUNITY DE
   Noroozi O, 2013, COMPUT EDUC, V61, P59, DOI 10.1016/j.compedu.2012.08.013
   Panadero E, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00422
   Panadero E, 2016, ENABLING POWER ASSES, V4, P311, DOI 10.1007/978-3-319-39211-0_18
   Panadero E, 2012, LEARN INDIVID DIFFER, V22, P806, DOI 10.1016/j.lindif.2012.04.007
   Pennsylvania State University, 2024, ARTIFICIAL INTELLIGE
   Rawte V., 2023, ARXIV
   Reimer T., 2022, Journal of Leadership, Equity, andResearch, V8, P71
   Rogers Y., 2002, DIS 02, P373, DOI DOI 10.1145/778712.778766
   Rummel N, 2011, COMPUT-SUPP COLLAB L, P367, DOI 10.1007/978-1-4419-7710-6_17
   Sawyer R K., 2014, Group Creativity, DOI DOI 10.4324/9781410609090
   Sinclair J., 1975, ANAL DISCOURSE
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Stahl G., 2013, INT HDB COLLABORATIV, P74
   Stahl G., 2006, GROUP COGNITION COMP, DOI [10.7551/mitpress/3372.001.0001, DOI 10.7551/MITPRESS/3372.001.0001]
   Stahl G, 2006, CAMB HANDB PSYCHOL, P409
   Texas A&M University, 2024, BEST PRACTICES GENER
   Vogel F, 2022, INT J COMP-SUPP COLL, V17, P39, DOI 10.1007/s11412-022-09363-z
   Wecker C., 2010, P 9 INT C LEARN SCI, P794
   Wecker C, 2011, LEARN INSTR, V21, P746, DOI 10.1016/j.learninstruc.2011.05.001
   Weinberger A, 2007, COMPUT-SUPP COLLAB L, V6, P191
   White B.Y., 1984, COGNITION INSTRUCT, V1, P69, DOI DOI 10.1207/S1532690XCI0101_4
   White B. Y., 1981, AITR619 MIT ART INT
   WHITE BY, 1985, NEW IDEAS PSYCHOL, V3, P287, DOI 10.1016/0732-118X(85)90025-X
   Xia Y., 2020, INTERDISCIPLINARITY, P1293, DOI [10.22318/icls2020.1293, DOI 10.22318/ICLS2020.1293]
   Xu Z., 2024, ARXIV
   Yu H, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1181712
   ZIMMERMAN BJ, 1989, J EDUC PSYCHOL, V81, P329, DOI 10.1037/0022-0663.81.3.329
NR 75
TC 1
Z9 1
U1 47
U2 47
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1556-1607
EI 1556-1615
J9 INT J COMP-SUPP COLL
JI Int. J. Comp.-Support. Collab. Learn.
PD DEC
PY 2024
VL 19
IS 4
BP 479
EP 532
DI 10.1007/s11412-024-09437-0
EA OCT 2024
PG 54
WC Education & Educational Research; Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Information Science & Library Science
GA O2F8H
UT WOS:001338044700001
DA 2024-12-25
ER

PT J
AU Johnson, CW
   Paulus, T
AF Johnson, Corey W.
   Paulus, Trena
TI Generating a Rflxveleie AI-Assisted Wrflooklw for Academic Writing
SO QUALITATIVE REPORT
LA English
DT Article
DE Generative artificial intelligence (AI); digital workflow; academic
   writing; Chat GPT; technological reflexivity
AB Digital research workflows are study designs that intentionally consider the use of technology in meaningful and reflexive ways. While most scholars use digital tools and spaces in their research process, doing so has consequences that are infrequently considered in an intentional way. Especially in this age of generative AI, technology integration into research studies will have an even greater impact and consequences on study outcomes. This paper documents one digital research workflow, the academic writing process, to demonstrate how inviting generative AI to be a writing partner can be done in a reflexive manner. Drawing on Paulus and Lester's (2023) technological reflexivity framework, we emphasize the need to assess the use and impact of platforms such as ChatGPT on four dimensions of the academic writing workflow: writing methods, writers and their audience, writing outcomes, and the generative AI platform itself. We structure this use case according to Woolf and Silver's (2018) notions of "strategies" and "tactics," applied to the academic writing process, combined with Paulus and Lester's four consequence categories. We include recommendations for navigating and using generative artificial intelligence in future academic writing endeavors.
C1 [Johnson, Corey W.] North Carolina State Univ, Raleigh, NC 27695 USA.
   [Paulus, Trena] East Tennessee State Univ, Johnson City, TN USA.
C3 North Carolina State University; East Tennessee State University
RP Johnson, CW (corresponding author), North Carolina State Univ, Raleigh, NC 27695 USA.
EM cjohns28@ncsu.edu; paulust@etsu.edu
CR American Psychological Association, 2020, Publication manual of the American psychological association, V7th
   Aoun JE, 2017, ROBOT-PROOF: HIGHER EDUCATION IN THE AGE OF ARTIFICIAL INTELLIGENCE, P1
   Buolamwini J., 2023, Unmasking AI: My Mission to Protect What is Human in a World of Machines
   ClickUp, 2024, Chat GPT prompts for academic research
   Correia A.-P., 2024, Ana-Paula Correia's BlogFebruary 20
   Cummings Robert E., 2024, Computers and Composition, V71, DOI 10.1016/j.compcom.2024.102827
   Emerson R. M., 2011, WRITING ETHNOGRAPHIC, DOI DOI 10.7208/CHICAGO/9780226206851.001.0001
   GARFIELD E, 1978, CURR CONTENTS, P5
   Johnson M, 2017, BMC MED RES METHODOL, V17, DOI 10.1186/s12874-017-0290-z
   Lubke J, 2017, REF USER SERV Q, V56, P285
   Mollick E., 2023, One Useful Thing
   Mollick Ethan R., 2024, COINTELLIGENCE LIVIN
   Palmer K., 2024, Inside Higher Education
   Paulus T., 2022, Doing qualitative research in a Digital World
   Paulus TM, 2024, QUAL INQ, DOI 10.1177/10778004241231927
   Paulus TM, 2024, INT J SOC RES METHOD, V27, P621, DOI 10.1080/13645579.2023.2237359
   Richardson L., 2005, SAGE HDB QUALITATIVE, V3rd, P959
   UCT, 2023, The ethics of using AI writing tools
   Williams A., 2022, Noema: Technology & the Human
   Woolf N., 2017, Qualitative analysis using NVivo (first), DOI DOI 10.4324/9781315268569
NR 20
TC 0
Z9 0
U1 1
U2 1
PU NOVA SOUTHEASTERN UNIV
PI FORT LAUDERDALE-DAVIE
PA 3301 COLLEGE AVE, FORT LAUDERDALE-DAVIE, FL 33314 USA
SN 1052-0147
EI 2160-3715
J9 QUAL REP
JI Qual. Rep.
PD OCT
PY 2024
VL 29
IS 10
DI 10.46743/2160-3715/2024.7634
PG 23
WC Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA O0O7E
UT WOS:001368228800006
DA 2024-12-25
ER

PT J
AU Cugurullo, F
   Xu, Y
AF Cugurullo, Federico
   Xu, Ying
TI When AIs become oracles: generative artificial intelligence,
   anticipatory urban governance, and the future of cities
SO POLICY AND SOCIETY
LA English
DT Article; Early Access
DE anticipatory governance; generative artificial intelligence; AI
   urbanism; urban governance; city brains
ID SMART; CITY; RISK
AB Generative Artificial Intelligence (AI) is boosting anticipatory forms of governance, through which state actors seek to predict the future and strategically intervene in the present. In this context, city brains represent an emerging type of generative AI currently employed in urban governance and public policy in a growing number of cities. City brains are large-scale AIs residing in vast digital urban platforms, which manage multiple urban domains including transport, safety, health, and environmental monitoring. They use Large Language Models (LLMs) to generate visions of urban futures: visions that are in turn used by policymakers to generate new urban policies. In this paper, we advance a twofold contribution. Theoretically, we develop a critical theory of anticipatory governance in the age of generative AI. More specifically, we focus on technocratic approaches to anticipatory governance, to explain how the act of governing extends into the future by means of predictive AI technology. Our approach is critical in order to expose the dangers that the use of AI (generative AI, in particular) in urban governance poses, and to identify their causes. These dangers include the formation of a policy process that, under the influence of unintelligible LLMs, risks losing transparency and thus accountability, and the marginalization of human stakeholders (citizens, in particular) as the role of AI in the management of cities keeps growing and governance begins to turn posthuman. Empirically, we critically examine an existing city brain project under development in China and ground our critical theory in a real-life example.
C1 [Cugurullo, Federico; Xu, Ying] Trinity Coll Dublin, Dept Geog, Museum Bldg, Dublin, Ireland.
C3 Trinity College Dublin
RP Cugurullo, F (corresponding author), Trinity Coll Dublin, Dept Geog, Museum Bldg, Dublin, Ireland.
EM cugurulf@tcd.ie
RI Cugurullo, Federico/HGV-1243-2022; XU, YING/HTS-5265-2023
OI XU, YING/0000-0001-6344-9612
FU Irish Research Council [IRCLA/2022/3832]
FX This work is part of the ORACLE project funded by the Irish Research
   Council (project ID: IRCLA/2022/3832).
CR Amoore L, 2011, THEOR CULT SOC, V28, P24, DOI 10.1177/0263276411417430
   Amoore Louise., 2013, The Politics of Possibility: Risk and Security Beyond Probability
   Anderson B, 2010, PROG HUM GEOG, V34, P777, DOI 10.1177/0309132510362600
   Bali AS, 2019, POLICY SOC, V38, P1, DOI 10.1080/14494035.2019.1579502
   Beck U., 1992, Risk society: Towards a new modernity, V17
   Bowden Hugh., 2005, CLASSICAL ATHENS DEL
   Brayne S, 2017, AM SOCIOL REV, V82, P977, DOI 10.1177/0003122417725865
   Bryman Alan., 2016, SOCIAL RES METHODS
   Capano G, 2019, POLICY SOC, V38, P96, DOI 10.1080/14494035.2018.1511194
   Caprotti F, 2022, GEOJOURNAL, V87, P1559, DOI 10.1007/s10708-020-10320-2
   Chun J., 2023, Int. J. Digit. Humanit, V5, P507, DOI [10.1007/s42803-023-00069-8, DOI 10.1007/S42803-023-00069-8]
   Cugurullo F., 2023, Artificial Intelligence and the City: Urbanistic Perspectives on AI
   Cugurullo F., 2021, Frankenstein urbanism: Eco, smart and autonomous cities, artificial intelligence and the end of the city
   Cugurullo F., 2024, Artificial intelligence and the city. Urbanistic perspectives on AI, P361
   Cugurullo F., 2024, AI Ethics, DOI [https://doi.org/10.1007/s43681-024-00476-9, DOI 10.1007/S43681-024-00476-9]
   Cugurullo F, 2024, URBAN STUD, V61, P1168, DOI 10.1177/00420980231203386
   Cugurullo F, 2020, FRONT SUSTAIN CITIES, V2, DOI 10.3389/frsc.2020.00038
   Cugurullo F, 2016, ROUT RES SUSTAIN URB, P195
   Curran D, 2021, URBAN STUD, V58, P487, DOI 10.1177/0042098020927855
   De Barbieri Edward W., 2018, Fla. St. UL Rev, V46, P75, DOI [10.2139/ssrn.3350571, DOI 10.2139/SSRN.3350571]
   Flad RK, 2008, CURR ANTHROPOL, V49, P403, DOI 10.1086/588495
   Foucault M., 1979, DISCIPLINE PUNISH
   Gabrys J, 2014, ENVIRON PLANN D, V32, P30, DOI 10.1068/d16812
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Guston DH, 2014, SOC STUD SCI, V44, P218, DOI 10.1177/0306312713508669
   Guston David H, 2010, Applied Science and Convergence Technology, V19, P432
   Han Byung-Chul., 2017, Psycho-Politics: Neoliberalism and New Technologies of Power
   Hilb M, 2020, J MANAG GOV, V24, P851, DOI 10.1007/s10997-020-09519-9
   Johnson S., 2023, Power and progress: our thousand-year struggle over technology and prosperity
   Karvonen A., 2018, Inside smart cities. Place, politics and urban innovation
   Khanal S, 2024, J CONTEMP CHINA, DOI 10.1080/10670564.2024.2333492
   Kimbell L, 2020, POLICY DES PRACT, V3, P95, DOI 10.1080/25741292.2020.1763545
   Kitchin R, 2024, DATA POWER IN ACTION, P21
   Kitchin R, 2015, REG STUD REG SCI, V2, P6, DOI 10.1080/21681376.2014.983149
   Kitchin R, 2014, GEOJOURNAL, V79, P1, DOI 10.1007/s10708-013-9516-8
   Leszczynski A, 2016, ENVIRON PLANN A, V48, P1691, DOI 10.1177/0308518X16651445
   Maffei S, 2020, POLICY DES PRACT, V3, P123, DOI 10.1080/25741292.2020.1763896
   Magee L, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517231210273
   Marvin S, 2020, INFRASTRUCT SER, P1
   Marvin S, 2022, FRONT SUSTAIN CITIES, V4, DOI 10.3389/frsc.2022.1030318
   McCarroll C, 2022, AI SOC, V37, P791, DOI 10.1007/s00146-021-01334-6
   Miles I, 2010, TECHNOL FORECAST SOC, V77, P1448, DOI 10.1016/j.techfore.2010.07.016
   Nelson JP, 2022, AM J BIOETHICS, V22, P48, DOI 10.1080/15265161.2021.2001109
   Pereboom Derk., 2014, FREE WILL AGENCY MEA
   Quay R, 2010, J AM PLANN ASSOC, V76, P496, DOI 10.1080/01944363.2010.508428
   Radu R, 2021, POLICY SOC, V40, P178, DOI 10.1080/14494035.2021.1929728
   Reith G, 2004, TIME SOC, V13, P383, DOI 10.1177/0961463X04045672
   Richardson R., 2019, NYUL Rev. Online, V94, P15
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Shapiro A., 2023, Artificial intelligence and the city, P240
   Son TH, 2023, SUSTAIN CITIES SOC, V94, DOI 10.1016/j.scs.2023.104562
   State Council, 2017, Notice on the development plan of the new generation of artificial intelligence
   Swyngedouw E, 2017, URBAN STUD, V54, P55, DOI 10.1177/0042098016671475
   Swyngedouw E, 2009, INT J URBAN REGIONAL, V33, P601, DOI 10.1111/j.1468-2427.2009.00859.x
   Taeihagh A, 2021, POLICY SOC, V40, P137, DOI 10.1080/14494035.2021.1928377
   Toffler A., 1970, FUTURE SHOCK
   Welsh M, 2023, COMMUN ACM, V66, P34, DOI 10.1145/3570220
   Wiek A, 2013, J URBAN TECHNOL, V20, P45, DOI 10.1080/10630732.2012.735415
   Wilkinson A, 2016, EUR J FUTURES RES, V4, DOI 10.1007/s40309-016-0094-0
   Winner L., 1977, AUTONOMOUS TECHNOLOG
   Xu Y, 2024, J URBAN TECHNOL, DOI 10.1080/10630732.2023.2292823
NR 61
TC 2
Z9 2
U1 65
U2 65
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1449-4035
EI 1839-3373
J9 POLICY SOC
JI Policy Soc.
PD 2024 AUG 1
PY 2024
DI 10.1093/polsoc/puae025
EA AUG 2024
PG 18
WC Political Science; Public Administration
WE Social Science Citation Index (SSCI)
SC Government & Law; Public Administration
GA A3S1Q
UT WOS:001281754400001
OA gold
DA 2024-12-25
ER

PT J
AU Housley, W
   Dahl, P
AF Housley, William
   Dahl, Patrik
TI Membership categorisation, sociological description and role prompt
   engineering with ChatGPT
SO DISCOURSE & COMMUNICATION
LA English
DT Article
DE Ethnomethodology; ethno-programming; generative artificial intelligence;
   Membership Categorisation Analysis; prompt engineering
AB Large Language Models (LLMs) and generative Artificial Intelligence (A.I.) have become the latest disruptive digital technologies to breach the dividing lines between scientific endeavour and public consciousness. LLMs such as ChatGPT are platformed through commercial providers such as OpenAI, which provide a conduit through which interaction is realised, via a series of exchanges in the form of written natural language text called 'prompt engineering'. In this paper, we use Membership Categorisation Analysis to interrogate a collection of prompt engineering examples gathered from the endogenous ranking of prompting guides hosted on emerging generative AI community and practitioner-relevant social media. We show how both formal and vernacular ideas surrounding 'natural' sociological concepts are mobilised in order to configure LLMs for useful generative output. In addition, we identify some of the interactional limitations and affordances of using role prompt engineering for generating interactional stances with generative AI chatbots and (potentially) other formats. We conclude by reflecting the consequences of these everyday social-technical routines and the rise of 'ethno-programming' for generative AI that is realised through natural language and everyday sociological competencies.
C1 [Housley, William; Dahl, Patrik] Cardiff Univ, Cardiff, Wales.
C3 Cardiff University
RP Housley, W (corresponding author), Cardiff Univ, Sch Social Sci, King Edward VII Ave, Cardiff CF10 3WT, Wales.
EM housleyw@cardiff.ac.uk
CR ALBERT S, 2023, P 5 INT C CONVERSATI, P1
   Albert S, 2019, PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON CONVERSATIONAL USER INTERFACES (CUI 2019), DOI 10.1145/3342775.3342800
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Collins H, 2018, ARTIFICTIONAL INTELL
   Fitzgerald R., 2015, ADV MEMBERSHIP CATEG, DOI DOI 10.1002/9781118611463/WBIELSI018
   Fitzgerald Richard., 2015, Advances in Membership Categorisation Analysis, DOI DOI 10.4135/9781473917873
   Giddens A., 1984, CONSTITUTION SOC OUT
   HALKOWSKI T, 1990, SOC PROBL, V37, P564, DOI 10.1525/sp.1990.37.4.03a00100
   Hester S., 1997, Culture in action: Studies in membership categorization analysis
   Housley W, 1999, SOCIOL RES ONLINE, V4
   Housley W., 2023, SAGE HDB DIGITAL SOC
   Housley W., 2021, SOC DIGITAL AGE INTE
   Housley W., 2000, Text - Interdisciplinary Journal for the Study of Discourse, V20, P83, DOI DOI 10.1515/TEXT.1.2000.20.1.83
   Housley W., 2002, Qualitative Research, V2, P59, DOI DOI 10.1177/146879410200200104
   HOUSLEY W, 2019, HTTF 2019 P HALFWAY, P1
   Reeves S., 2023, SAGE HDB DIGITAL SOC, P573, DOI DOI 10.4135/9781529783193.N32
   ROSE E, 1960, AM SOCIOL REV, V25, P193, DOI 10.2307/2092625
   SACKS H, 1974, LANGUAGE, V50, P696, DOI 10.2307/412243
   Sormani P., 2020, ETHNOGRAPHIC STUDIES, V17, P60, DOI DOI 10.5281/ZENODO.4050539
   Stokoe E., 2018, TALK SCI CONVERSATIO
   Stokoe E, 2020, DISCOURSE STUD, V22, P87, DOI 10.1177/1461445619887537
   Stokoe E, 2012, DISCOURSE STUD, V14, P277, DOI 10.1177/1461445612441534
   Suchman L, 2007, LEARN DOING, P1, DOI 10.2277/052167588X
   Watson R., 2009, ANALYSING PRACTICAL
NR 24
TC 2
Z9 2
U1 28
U2 28
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1750-4813
EI 1750-4821
J9 DISCOURSE COMMUN
JI Discourse Commun.
PD DEC
PY 2024
VL 18
IS 6
SI SI
BP 848
EP 858
DI 10.1177/17504813241267068
EA AUG 2024
PG 11
WC Communication
WE Social Science Citation Index (SSCI)
SC Communication
GA L6P4T
UT WOS:001293162700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Dai, Y
   Lai, SC
   Lim, CP
   Liu, A
AF Dai, Yun
   Lai, Sichen
   Lim, Cher Ping
   Liu, Ang
TI University policies on generative AI in Asia: promising practices, gaps,
   and future directions
SO JOURNAL OF ASIAN PUBLIC POLICY
LA English
DT Article; Early Access
DE Generative AI; artificial intelligence; policy; higher education; Asia;
   scoping review
ID SYSTEMS; CHATGPT
AB Considering the opportunities and challenges raised by generative AI (GenAI) technologies, many universities have been developing policies to guide the integration of GenAI in their academic community. Focusing on Asian universities, this paper presents a scoping review of policy development regarding the GenAI integration. Guided by the theoretical framework of technology integration, this study examined the GenAI policies of 30 universities from the QS top 60 Asian universities with inductive content analysis. The review reveals that these policies prioritized text generation applications, student management, and academic integrity, suggesting an effort to uphold traditional academic values and encourage informed adoption. While the universities were at different stages of understanding and managing GenAI, they tended towards a comprehensive approach in formatting their guidance for internal stakeholders and assessment policies. The review also uncovered gaps in policymaking, such as the exclusion of non-academic staff, limited use of evidence-based practices, international misalignments, and a strong adherence to traditional academic paradigms. This scoping review provides a comprehensive and multifaceted overview of policy development in Asian universities, laying the groundwork for global discussions about the role of GenAI in higher education.
C1 [Dai, Yun; Lai, Sichen] Chinese Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
   [Lim, Cher Ping] Educ Univ Hong Kong, Fac Educ & Human Dev, Hong Kong, Peoples R China.
   [Liu, Ang] Univ New South Wales, Sch Mech & Mfg Engn, Sydney, Australia.
C3 Chinese University of Hong Kong; Education University of Hong Kong
   (EdUHK); University of New South Wales Sydney
RP Dai, Y (corresponding author), Chinese Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
EM yundai@cuhk.edu.hk
RI Dai, Yun/KMA-6593-2024; Lim, Cher Ping/O-6068-2016
OI Dai, Yun/0000-0002-1199-9855; Lim, Cher Ping/0000-0002-4797-1870; Lai,
   Sichen/0009-0002-9436-3816
CR Altbach P., 2004, ASIAN U HIST PERSPEC, P13, DOI [10.56021/9780801880360, DOI 10.56021/9780801880360]
   Arbo P., 2007, OECD ED WORKING PAPE, V9, DOI [10.1787/161208155312, DOI 10.1787/161208155312]
   Arksey H., 2005, INT J SOC RES METHOD, V8, P19, DOI [DOI 10.1080/1364557032000119616, 10.1080/1364557032000119616]
   Armat MR, 2018, QUAL REP, V23, P219
   Aziz KA, 2008, REV POLICY RES, V25, P348, DOI 10.1111/j.1541-1338.2008.00336.x
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Ben-Peretz M., 2009, POLICY MAKING ED HOL
   Castillo E., 2023, These schools and colleges have banned chat GPT and similar AI tools
   Cha Y., 2024, Procedia CIRP
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Conroy G, 2023, NATURE, V622, P234, DOI 10.1038/d41586-023-03144-w
   Craig CJ, 2023, J CURRICULUM STUD, V55, P734, DOI 10.1080/00220272.2023.2257259
   Dai Yun, 2023, Procedia CIRP, P84, DOI 10.1016/j.procir.2023.05.002
   Dai Y, 2023, AUSTRALAS J EDUC TEC, V39, P74, DOI 10.14742/ajet.8843
   Denzin N. K., 1994, Handbook of Qualitative Research, V17, P273, DOI DOI 10.1007/BF00988593
   ETH Zurich, 2024, Chatgpt
   Fauzi F., 2023, Journal on Education, V5, P14886, DOI [DOI 10.31004/JOE.V5I4.2563, 10.31004/joe.v5i4.2563]
   Glaser BG., 2017, The discovery of grounded theory: Strategies for qualitative research, DOI DOI 10.4324/9780203793206
   Hamilton ER, 2016, TECHTRENDS, V60, P433, DOI 10.1007/s11528-016-0091-y
   Heidari A, 2024, INTERNET TECHNOL LET, DOI 10.1002/itl2.530
   Imran M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13605
   Kaur P, 2018, INT J ACAD MED, V4, P60, DOI 10.4103/IJAM.IJAM_7_18
   Kopcha TJ, 2010, ETR&D-EDUC TECH RES, V58, P175, DOI 10.1007/s11423-008-9095-4
   Li HL, 2024, J ASIAN PUBLIC POLIC, DOI 10.1080/17516234.2024.2363128
   Lim CC, 2019, INFECT DIS-NOR, V51, P745, DOI 10.1080/23744235.2019.1648855
   Luo JH, 2024, ASSESS EVAL HIGH EDU, V49, P651, DOI 10.1080/02602938.2024.2309963
   Malik T., 2023, INT WORK C TRANSF DI, P3
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Moore S, 2024, BUS PROF COMMUN Q, V87, P610, DOI 10.1177/23294906241254786
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Papyshev G, 2024, J ASIAN PUBLIC POLIC, DOI 10.1080/17516234.2024.2370716
   Perera P., 2023, Journal of Advances in Education and Philosophy, V7, P246, DOI [10.36348/jaep.2023.v07i08.001, DOI 10.36348/JAEP.2023.V07I08.001, https://doi.org/10.36348/jaep.2023.v07i08.001]
   Pham MT, 2014, RES SYNTH METHODS, V5, P371, DOI 10.1002/jrsm.1123
   Quacquarelli Symonds Limited (QS), 2023, QS World University Rankings 2024: Top global universities
   Sanderson I, 2002, PUBLIC ADMIN, V80, P1, DOI 10.1111/1467-9299.00292
   Shibley I., 2011, Journal of College Science Teaching, V40
   Sohn DW, 2007, WORLD DEV, V35, P991, DOI 10.1016/j.worlddev.2006.05.008
   The World Bank, 2023, Research and development expenditure (% of GDP)
   Tricco AC, 2018, ANN INTERN MED, V169, P467, DOI 10.7326/M18-0850
   UCL, Engaging with AI in your education and assessment
   UNESCO, 2023, UNESCO SURVEY LESS 1
   Vears DF, 2022, FOCUS HEALTH PROF ED, V23, P111
   Vrontis D, 2021, J BUS RES, V128, P812, DOI 10.1016/j.jbusres.2019.03.031
   Yang J., 2023, Advances in Educational Technology and Psychology, V7, P92, DOI [https://doi.org/10.23977/aetp.2023.070914, DOI 10.23977/AETP.2023.070914]
NR 46
TC 0
Z9 0
U1 25
U2 25
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1751-6234
EI 1751-6242
J9 J ASIAN PUBLIC POLIC
JI J. Asian Public Policy
PD 2024 AUG 10
PY 2024
DI 10.1080/17516234.2024.2379070
EA AUG 2024
PG 22
WC Area Studies
WE Social Science Citation Index (SSCI)
SC Area Studies
GA C1C5Q
UT WOS:001286812700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Tuan, NT
   Moore, P
   Thanh, DHV
   Pham, HV
AF Tuan, Nguyen Trung
   Moore, Philip
   Thanh, Dat Ha Vu
   Pham, Hai Van
TI A Generative Artificial Intelligence Using Multilingual Large Language
   Models for ChatGPT Applications
SO APPLIED SCIENCES-BASEL
LA English
DT Article
DE generative AI; language comprehension; multilingual language models;
   large language models; support systems; technological determinism;
   chatbot; ChatGPT
ID DISRUPTIVE INNOVATION; TECHNOLOGY
AB ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question-response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the 'Phoenix' multilingual chatbot model by achieving a 92% performance compared to 'ChatGPT' for the English benchmark.
C1 [Tuan, Nguyen Trung] Natl Econ Univ, Sch Informat Technol & Digital Econ, 207 Giai Phong St, Hanoi 10000, Vietnam.
   [Moore, Philip] Lanzhou Univ, Sch Informat Sci & Engn, Feiyun Bldg,222 Tianshui South Rd, Lanzhou 730030, Peoples R China.
   [Thanh, Dat Ha Vu; Pham, Hai Van] Hanoi Univ Sci & Technol, Sch Informat & Commun Technol, 1 Dai Co Viet, Hanoi 10000, Vietnam.
C3 National Economics University - Vietnam; Lanzhou University; Hanoi
   University of Science & Technology (HUST)
RP Pham, HV (corresponding author), Hanoi Univ Sci & Technol, Sch Informat & Commun Technol, 1 Dai Co Viet, Hanoi 10000, Vietnam.
EM tuannt@neu.edu.vn; ptmbcu@gmail.com; dat.hvthanh@gmail.com;
   haipv@soict.hust.edu.vn
RI Pham, Hai/Q-8380-2019; V. Pham, A/ Prof. Hai/F-4106-2019; Moore,
   Philip/F-3981-2019
OI V. Pham, A/ Prof. Hai/0000-0001-8325-1662; Van Pham,
   Hai/0000-0003-1547-9782; Tuan, Nguyen Trung/0000-0003-3249-2444; Moore,
   Philip/0000-0003-3874-8981
FU National Economics University, Vietnam
FX The authors thank the language experts who participated in the
   experiments using ChatGPT in both English and Vietnamese languages.
CR Aghajanyan A, 2020, Arxiv, DOI arXiv:2012.13255
   Alabool Hamzeh Mohammad, 2023, 2023 International Conference on Information Technology (ICIT), P184, DOI 10.1109/ICIT58056.2023.10225801
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Beeching Edward, 2023, Open llm leaderboard
   Checkland P., 1997, INFORM SYSTEMS INFOR
   Chen W.K., 2021, P 2021 IEEE INT C SO, P1, DOI [10.1109/SSIM49526.2021.9555195, DOI 10.1109/SSIM49526.2021.9555195]
   Chen ZH, 2023, Arxiv, DOI [arXiv:2304.10453, DOI 10.48550/ARXIV]
   Christensen C., 2013, Disruptive innovation
   Christensen CM, 2018, J MANAGE STUD, V55, P1043, DOI 10.1111/joms.12349
   Clarysse B, 2022, TECHNOVATION, V118, DOI 10.1016/j.technovation.2022.102644
   Crosthwaite P., 2023, Applied Corpus Linguistics, V3, DOI [DOI 10.1016/J.ACORP.2023.100066, https://doi.org/10.1016/j.acorp.2023.100066]
   Cuong L.A., 2023, Challenge on Vietnamese Large Language Models 2023 2023
   Dai Yun, 2023, Procedia CIRP, P84, DOI 10.1016/j.procir.2023.05.002
   Deqiang Xin, 2010, 2010 International Conference on E-Business and E-Government (ICEE 2010), P149, DOI 10.1109/ICEE.2010.45
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Espejel Jessica Lopez, 2023, Natural Language Processing Journal, V5, DOI [DOI 10.1016/J.NLP.2023.100032, 10.1016/j.nlp.2023.100032]
   Evans O., 2023, BizEcons Quarterly, V16, P1
   Fleck J, 2001, TECHNOL ANAL STRATEG, V13, P523
   Hagendorff T, 2023, NAT COMPUT SCI, V3, P833, DOI 10.1038/s43588-023-00527-x
   Pham HV, 2023, J INTELL FUZZY SYST, V44, P6775, DOI 10.3233/JIFS-221556
   Hallström J, 2022, INT J TECHNOL DES ED, V32, P17, DOI 10.1007/s10798-020-09600-2
   Han SJ, 2024, COGN SYST RES, V83, DOI 10.1016/j.cogsys.2023.101155
   Hanief S, 2019, 2019 2ND INTERNATIONAL CONFERENCE OF COMPUTER AND INFORMATICS ENGINEERING (IC2IE 2019), P12
   Hu EJ, 2021, Arxiv, DOI arXiv:2106.09685
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kunst JR, 2023, INT J INTERCULT REL, V97, DOI 10.1016/j.ijintrel.2023.101888
   Laurencon Hugo, 2022, ADV NEUR IN
   Lester B, 2021, Arxiv, DOI arXiv:2104.08691
   Li MC, 2023, J RETAIL CONSUM SERV, V71, DOI 10.1016/j.jretconser.2022.103209
   Li RY, 2018, PROCEEDINGS OF THE 2018 1ST IEEE INTERNATIONAL CONFERENCE ON KNOWLEDGE INNOVATION AND INVENTION (ICKII 2018), P362, DOI 10.1109/ICKII.2018.8569132
   Liu X, 2022, PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022): (SHORT PAPERS), VOL 2, P61
   Lybaert C, 2022, LANG COMMUN, V84, P1, DOI 10.1016/j.langcom.2022.01.004
   Mackenzie D, 2023, ENGINEERING-PRC, V25, P9, DOI 10.1016/j.eng.2023.04.004
   Marjanovic Olivera, 2007, 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2007), P448, DOI 10.1109/COLCOM.2007.4553874
   Markides C, 2006, J PROD INNOVAT MANAG, V23, P19, DOI 10.1111/j.1540-5885.2005.00177.x
   Martínez-Plumed F, 2021, TELEMAT INFORM, V58, DOI 10.1016/j.tele.2020.101525
   Moslein K.M., 2015, Wiley Encyclopedia of Management, P1, DOI [10.1002/9781118785317.weom120036, DOI 10.1002/9781118785317.WEOM120036]
   Nazir Anam, 2023, Meta Radiol, V1, DOI 10.1016/j.metrad.2023.100022
   Nguyen DQ, 2024, Arxiv, DOI arXiv:2311.02945
   Nguyen XP, 2024, Arxiv, DOI arXiv:2312.00738
   Pham HV, 2023, INT J FUZZY SYST, V25, P3260, DOI 10.1007/s40815-023-01548-4
   Pham HV, 2023, J ARTIF INTELL SOFT, V13, P165, DOI 10.2478/jaiscr-2023-0013
   Pham V.H., 2022, P INT C INNOVATIVE C, VVolume 471, DOI [10.1007/978-981-19-2535-1_22, DOI 10.1007/978-981-19-2535-1_22]
   Puranam P, 2014, ACAD MANAGE REV, V39, P162, DOI 10.5465/amr.2011.0436
   Ren J, 2021, Arxiv, DOI [arXiv:2101.06840, 10.48550/arXiv.2101.06840]
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Sandrini L, 2023, ECON LETT, V232, DOI 10.1016/j.econlet.2023.111317
   Scao Teven Le, 2023, arXiv, DOI DOI 10.48550/ARXIV.2211.05100
   Smith Noah A, 2021, arXiv
   Sun XH, 2023, Arxiv, DOI arXiv:2304.08109
   Taori R., 2023, Stanford Alpaca: A Strong, Replicable Instruction-Following Model
   Thomas AM, 2014, INT J AD HOC UBIQ CO, V16, P268, DOI 10.1504/IJAHUC.2014.064862
   Thomas RL, 2022, PATTERNS, V3, DOI 10.1016/j.patter.2022.100476
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Utterback JM, 2005, INT J INNOV MANAG, V9, P1, DOI 10.1142/S1363919605001162
   Varghese J, 2024, J HEPATOL, V80, P977, DOI 10.1016/j.jhep.2023.07.028
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wyatt S, 2007, HANDBOOK OF SCIENCE AND TECHNOLOGY STUDIES, THIRD EDITION, P165
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Zheng LM, 2023, Arxiv, DOI [arXiv:2306.05685, 10.48550/arXiv.2306.05685]
NR 61
TC 5
Z9 5
U1 32
U2 45
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2076-3417
J9 APPL SCI-BASEL
JI Appl. Sci.-Basel
PD APR
PY 2024
VL 14
IS 7
AR 3036
DI 10.3390/app14073036
PG 24
WC Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Engineering; Materials Science; Physics
GA NN2D7
UT WOS:001201056200001
OA gold
DA 2024-12-25
ER

PT J
AU Schmitt, B
AF Schmitt, Bernd
TI Transforming qualitative research in phygital settings: the role of
   generative AI
SO QUALITATIVE MARKET RESEARCH
LA English
DT Article
DE Qualitative research; Generative AI; Interpretative methods; New
   methodology
AB Purpose- This commentary discusses the value of generative artificial intelligence (AI) for qualitative research in phygital settings to understand the customer experience.Design/methodology/approach- The critical and logical analysis is based on current knowledge of generative AI.Findings- Generative AI seems very useful for qualitative research in phygital settings to understand the customer experience and should be used in qualitative research projects. Generative AI can provide much-needed validation of the subjective nature of qualitative research and can also generate insights beyond human intuition.Research limitations/implications- The study is based on current technology, which changes fast. In the future, the skills of qualitative researchers may become outdated, relegating them to the role of prompt engineers.Practical implications- Technology, and especially generative AI, will be a key tool for practitioners as they conduct practical research.Social implications- Qualitative researchers should overcome potential anti-technology speciesism and embrace the potential of generative AI.Originality/value- This commentary provides insights into the role of generative AI for qualitative research in phygital settings.
C1 [Schmitt, Bernd] Columbia Business Sch, New York, NY 10027 USA.
C3 Columbia University
RP Schmitt, B (corresponding author), Columbia Business Sch, New York, NY 10027 USA.
EM bhs1@columbia.edu
CR Batat W., 2023, QUALITATIVE RES INT
   Batat W, 2024, J STRATEG MARK, V32, P1220, DOI 10.1080/0965254X.2022.2059775
   Rigby D, 2011, HARVARD BUS REV, V89, P64
   Schmitt B, 2020, MARKET LETT, V31, P3, DOI 10.1007/s11002-019-09499-3
   Schmitt BerndH., 2003, Customer Experience Management
NR 5
TC 3
Z9 3
U1 17
U2 44
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1352-2752
EI 1758-7646
J9 QUAL MARK RES
JI Qual. Mark. Res.
PD JUN 7
PY 2024
VL 27
IS 3
SI SI
BP 523
EP 526
DI 10.1108/QMR-08-2023-0107
EA DEC 2023
PG 4
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA TC5M7
UT WOS:001128787000001
DA 2024-12-25
ER

PT J
AU Brewer, J
   Patel, D
   Kim, D
   Murray, A
AF Brewer, Jordan
   Patel, Dhru
   Kim, Dennie
   Murray, Alex
TI Navigating the challenges of generative technologies: Proposing the
   integration of artificial intelligence and blockchain
SO BUSINESS HORIZONS
LA English
DT Article
DE Blockcain; Generative artificial intelligence (GenAI); ChatGPT; Large
   language models (LLMs); Chatbots
AB The transformative impact of generative AI (GenAI), extending beyond traditional AI, raises numerous concerns including the replacement of human roles and AI misuse in an array of industries. This article introduces blockchain technology as a complementary technological safeguard to address some of these challenges. We emphasize blockchain's role in promoting transparency, verifiability, and decentralization in AI development and usage, thereby offering potential solutions for four distinct challenges: (1) AI toxicity, biases, hallucinations, (2) AI interest misalignment, (3) AI as a black box, and (4) AI misuse. This article proposes ways to ensure responsible and transparent AI usage through the integration of block- chain. We position the convergence of AI and blockchain as a means to manage AI's societal impact and unlock its benefitsdcontingent upon collaborative efforts among various stakeholders such as businesses, developers, and regulatory bodies. We contribute to the discourse on ethical AI usage and the potential of blockchain to enhance AI's reliability and accountability for organizations. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
C1 [Brewer, Jordan; Patel, Dhru; Murray, Alex] Univ Oregon, Lundquist Coll Business, Eugene, OR 97403 USA.
   [Kim, Dennie] Univ Virginia, Darden Sch Business, Charlottesville, VA USA.
C3 University of Oregon; University of Virginia
RP Murray, A (corresponding author), Univ Oregon, Lundquist Coll Business, Eugene, OR 97403 USA.
EM jbrewer3@uoregon.edu; dhrup@uoregon.edu; kimd@darden.virginia.edu;
   amm16@uoregon.edu
OI Murray, Alex/0000-0001-6161-8363
CR Abdelhalim E., 2024, Business Horizons, V67, P487
   Alammar J., 2018, ILLUSTRATED TRANSFOR
   Andreessen Mark, 2023, Andreessen Horowitz
   Anthony C, 2023, ORGAN SCI, V34, P1672, DOI 10.1287/orsc.2022.1651
   Balasubramanian N, 2022, ACAD MANAGE REV, V47, P448, DOI 10.5465/amr.2019.0470
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Berthon P., 2024, Business Horizons, V67, P461
   C3 Generative AI, 2023, C3 AI
   Cai CJ, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300234
   Chainlink, 2023, The blockchain oracle problem
   Choudhary V, 2023, J MANAGE, DOI 10.1177/01492063231194968
   Coulton M., 2023, Yahoo FinanceJuly 4
   Cui YY, 2024, BUS HORIZONS, V67, P583, DOI 10.1016/j.bushor.2024.05.003
   Dukes K., 2023, Working paper, DOI [10.48550/arXiv.2307.11516, DOI 10.48550/ARXIV.2307.11516]
   Feng C, 2024, BUS HORIZONS, V67, P537, DOI 10.1016/j.bushor.2024.04.012
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Future of Life Institute, 2023, Asilomar AI principles
   Gehman S, 2020, Arxiv, DOI arXiv:2009.11462
   Giacomazzo B., 2023, HipHopDXMay 4
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Guzik E., 2023, Journal of Creativity, V33, P100065, DOI [DOI 10.1016/J.YJOC.2023.100065, https://doi.org/10.1016/j.yjoc.2023.100065]
   Haber S., 1991, Journal of Cryptology, V3, P99, DOI 10.1007/BF00196791
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Hashmi N, 2024, BUS HORIZONS, V67, P607, DOI 10.1016/j.bushor.2024.05.005
   Hattenstone S., 2023, The GuardianMarch 23
   Hsu J., 2023, New ScientistNovember 10
   Huckle S, 2017, BIG DATA-US, V5, P356, DOI 10.1089/big.2017.0071
   IBM, 2023, What are smart contracts on blockchain?
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Kaplan S, 2009, J APPL PSYCHOL, V94, P162, DOI 10.1037/a0013115
   Kietzmann J., 2024, Business Horizons, V67, P453
   Krakowski S, 2023, STRATEGIC MANAGE J, V44, P1425, DOI 10.1002/smj.3387
   Lai V, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501999
   Leyer M, 2021, BUS HORIZONS, V64, P711, DOI 10.1016/j.bushor.2021.02.026
   Lowe R., 2022, OpenAI BlogJanuary 27
   Makarius EE, 2020, J BUS RES, V120, P262, DOI 10.1016/j.jbusres.2020.07.045
   March J. G., 1958, ORGANIZATIONS
   Modulus, 2022, Coinmonks
   Murray A, 2023, BUS HORIZONS, V66, P191, DOI 10.1016/j.bushor.2022.06.002
   Murray A, 2021, ACAD MANAGE REV, V46, P552, DOI 10.5465/amr.2019.0186
   Murray A, 2021, ACAD MANAGE PERSPECT, V35, P622, DOI 10.5465/amp.2018.0066
   Nakamoto S., 2008, BITCOIN PEER TO PEER
   ONeill C, 2016, Weapons of Math Destruction. How big Data increases Inequality and threatens Democracy
   Osadchaya E., 2024, Business Horizons, V67, P571
   Ouyang L, 2022, ADV NEUR IN
   Park A, 2023, BUS HORIZONS, V66, P529, DOI 10.1016/j.bushor.2022.10.005
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Ramaul L, 2024, BUS HORIZONS, V67, P615, DOI 10.1016/j.bushor.2024.05.006
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Schaeffer R, 2023, Arxiv, DOI arXiv:2304.15004
   Schwartz Oscar, 2018, GUARDIAN        1112
   Shaffi Sarah, 2023, The Guardian23 January
   Sundberg L, 2024, BUS HORIZONS, V67, P561, DOI 10.1016/j.bushor.2024.04.014
   Szabo N., 1998, SECURE PROPERTY TITL
   The White House, 2023, FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
   The White House, 2023, G7 leaders' statement on economic resilience and economic security
   Wan Q, 2024, Arxiv, DOI arXiv:2307.10811
   Wang Dennis, 2019, Proceedings of the ACM on Human-Computer Interaction, V3, DOI 10.1145/3359194
   World Leader in AI Computing, 2023, NVIDIA
   XIANG Chloe, 2022, Vice 1 Nov.
   Zhang R., 2021, P ACM HUM COMP INT N
NR 61
TC 6
Z9 6
U1 31
U2 31
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 525
EP 535
DI 10.1016/j.bushor.2024.04.011
EA AUG 2024
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2K8V
UT WOS:001301352200001
DA 2024-12-25
ER

PT J
AU Al-Zahrani, AM
AF Al-Zahrani, Abdulrahman M.
TI The impact of generative AI tools on researchers and research:
   Implications for academia in higher education
SO INNOVATIONS IN EDUCATION AND TEACHING INTERNATIONAL
LA English
DT Article
DE Generative AI; research; higher education; ethical considerations;
   awareness; perceived impact
ID ARTIFICIAL-INTELLIGENCE
AB This study explores the impact of Generative AI tools on researchers and research in the context of higher education in Saudi Arabia. An online survey questionnaire was used to collect data on higher education students' perspectives (N = 505). The findings indicate that participants hold positive attitudes and possess a high level of awareness regarding GenAI in research. They recognise the potential of these tools to revolutionise academic research. Participants report highly beneficial experiences using GenAI tools to expand project scope and improve efficiency. Additionally, participants expressed optimism about the future role of GenAI tools, expecting them to become more prevalent and transform the research landscape. However, participants emphasised the importance of adequate training, support, and guidance in using GenAI tools. Ethical considerations emerged as a significant concern, highlighting the participants' commitment to responsible research practices and the need for transparency and addressing potential biases associated with these tools.
C1 [Al-Zahrani, Abdulrahman M.] Univ Jeddah, Educ Technol Dept, Jeddah, Saudi Arabia.
   [Al-Zahrani, Abdulrahman M.] Univ Jeddah, Educ Technol Dept, 8183 Kalthoum Al Awsi, Jeddah 238263259, Saudi Arabia.
C3 University of Jeddah; University of Jeddah
RP Al-Zahrani, AM (corresponding author), Univ Jeddah, Educ Technol Dept, 8183 Kalthoum Al Awsi, Jeddah 238263259, Saudi Arabia.
EM ammzahrani@uj.edu.sa
RI Al-Zahrani, Abdulrahman/JBS-4687-2023
OI Al-Zahrani, Abdulrahman/0009-0007-9885-0730
CR Al-Yateem Nabeel, 2012, Nurse Res, V19, P31
   Alser Muath, 2023, Am J Med Open, V9, P100036, DOI 10.1016/j.ajmo.2023.100036
   Checco A, 2021, HUM SOC SCI COMMUN, V8, DOI 10.1057/s41599-020-00703-8
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Davenport T. H., 2022, GENAI IS CHANGING CR
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Evans JR, 2005, INTERNET RES, V15, P195, DOI 10.1108/10662240510590360
   Exner-Stohr M., 2017, DIGITAL ENTERPRISE C, P61
   Gao CA., 2022, NPJ Digit Med, V1, DOI [10.1038/s41746-023-00819-6, DOI 10.1101/2022.12.23.521610, 10.1101/2022.12.23.521610, DOI 10.1038/S41746-023-00819-6]
   Haleem A., 2022, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V2, DOI [DOI 10.1016/J.TBENCH.2023.100089, 10.1016/j.tbench.2023.100089]
   Keyes O, 2021, INTERDISCIPL SCI REV, V46, P158, DOI 10.1080/03080188.2020.1840224
   Leao CP, 2018, 2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS), P891, DOI 10.1109/IS.2018.8710477
   Maphosa V., 2021, 2021 INT C ARTIFICIA, P1, DOI [10.1109/icABCD51485.2021.9519368, DOI 10.1109/ICABCD51485.2021.9519368]
   Mertens D.M., 2010, Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative and mixed methods, V3rd
   Newcastle University, 2023, CIT CHATGPT OTH GENA
   Razack HIA, 2021, SCI EDIT, V8, P134, DOI 10.6087/kcse.244
   Suh M, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445219
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   The University of Queensland, 2023, CHATGPT OTH GENAI TO
   Thomas R, 2023, SCI EDIT, V10, P27, DOI 10.6087/kcse.294
   Tzirides A. O., 2023, ARXIV
NR 23
TC 11
Z9 12
U1 49
U2 194
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1470-3297
EI 1470-3300
J9 INNOV EDUC TEACH INT
JI Innov. Educ. Teach. Int.
PD SEP 2
PY 2024
VL 61
IS 5
BP 1029
EP 1043
DI 10.1080/14703297.2023.2271445
EA OCT 2023
PG 15
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA F4A8B
UT WOS:001086597100001
DA 2024-12-25
ER

PT J
AU O'Dea, X
AF O'Dea, Xianghan
TI Generative AI: is it a paradigm shift for higher education?
SO STUDIES IN HIGHER EDUCATION
LA English
DT Article
DE Generative AI; higher education; AIEd; ethics; learning and teaching
ID ARTIFICIAL-INTELLIGENCE
AB In this special issue, we explore the opportunities and challenges of using Generative AI (GenAI), in particular, text generators in higher education learning and teaching. As GenAI has become increasingly popular with many staff and students, this special issue provides an overview of the current state of the field and offers insights into future research. This introduction paper consists of four parts. It begins by providing an overview of AI and Generative AI, identifying the gap and framing the special issue relating to the gaps. The second part explores the opportunities and challenges of GenAI in higher education, as identified in the literature. The third part provides an overview of the papers included in the special issue. The final part is the self-reflection of the lead author. The special issue aims to serve as a valuable resource for higher education stakeholders, such as students, practitioners, researchers and managers. We hope this collection will help advance knowledge and future research, encourage innovation and inform evidence-based policy and practices in the field of Generative AI in higher education.
C1 [O'Dea, Xianghan] Kings Coll London, Kings Business Sch, Dept Publ Serv Management & Org PSMO, London, England.
   [O'Dea, Xianghan] Kings Coll London, Kings Business Sch, Dept Publ Serv Management & Org PSMO, Strand, London WC2R 2LS, England.
C3 University of London; King's College London; University of London;
   King's College London
RP O'Dea, X (corresponding author), Kings Coll London, Kings Business Sch, Dept Publ Serv Management & Org PSMO, Strand, London WC2R 2LS, England.
EM xianghan.odea@kcl.ac.uk
CR Atlas S., 2023, CHATGPT HIGHER ED PR
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Bhat S., 2022, P 15 INT C ED DAT MI, V701, P701, DOI DOI 10.5281/ZENODO.6853085
   Bozkurt A., 2023, ASIAN J DISTANCE ED, V18, P50
   Budhathoki T, 2024, STUD HIGH EDUC, V49, P831, DOI 10.1080/03075079.2024.2333937
   Crawford J, 2024, STUD HIGH EDUC, V49, P883, DOI 10.1080/03075079.2024.2326956
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Essien A, 2024, STUD HIGH EDUC, V49, P865, DOI 10.1080/03075079.2024.2316881
   Farhi F., 2023, COMPUTERS ED ARTIFIC, V100180, DOI [10.1016/j.caeai.2023.100180, DOI 10.1016/J.CAEAI.2023.100180, https://doi.org/10.1016/j.caeai.2023.100180]
   Guo Biyang, 2023, ARXIV
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Macneil Stephen, 2022, ICER 2022 V2: Proceedings of the 2022 ACM Conference on International Computing Education Research, P37, DOI 10.1145/3501709.3544280
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Nakatumba-Nabende J., 2023, AI ETHICS HIGHER ED, P39
   Nguyen A, 2024, STUD HIGH EDUC, V49, P847, DOI 10.1080/03075079.2024.2323593
   O'Dea XH, 2023, J UNIV TEACH LEARN P, V20
   ODea X., 2023, Can AI Support Academic Research
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Soni N., 2019, ARXIV
   Study. Com, 2023, PRODUCTIVE TEACHING
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Terwiesch C., 2023, Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
   Tindle R., 2023, PsyArxiv Preprints, V13
   Wild B., 2023, CHATGPT CARDIFF STUD
   Yan L., 2023, arXiv
   Yang M., 2024, STUD HIGH EDUC, P1
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 30
TC 6
Z9 6
U1 130
U2 230
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0307-5079
EI 1470-174X
J9 STUD HIGH EDUC
JI Stud. High. Educ.
PD MAY 3
PY 2024
VL 49
IS 5
SI SI
BP 811
EP 816
DI 10.1080/03075079.2024.2332944
EA MAR 2024
PG 6
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA SX4A9
UT WOS:001189438200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Tang, KS
   Cooper, G
AF Tang, Kok-Sing
   Cooper, Grant
TI The Role of Materiality in an Era of Generative Artificial Intelligence
SO SCIENCE & EDUCATION
LA English
DT Article; Early Access
DE Artificial intelligence; Epistemic insight; Materiality; Scientific
   practice; Science studies
ID PRACTICAL WORK; ARGUMENTATION; SCIENCE
AB The introduction of generative artificial intelligence (GenAI) tools like ChatGPT has raised many challenging questions about the nature of teaching, learning, and assessment in every subject area, including science. Unlike other disciplines, natural science is unique because the ontological and epistemological understanding of nature is fundamentally rooted in our interaction with material objects in the physical world. GenAI, powered by statistical probability arising from a massive corpus of text, is devoid of any connection to the physical world. The use of GenAI thus raises concerns about our connection to reality and its effect on science education. This paper emphasizes the importance of materiality (or material reality) in shaping scientific knowledge and argues for its recognition in the era of GenAI. Drawing on the perspectives of new materialism and science studies, the paper highlights how materiality forms an indispensable aspect of human knowledge and meaning-making, particularly in the discipline of science. It further explains how materiality is central to the epistemic authority of science and cautions the outputs generated by GenAI that lack contextualization to a material reality. The paper concludes by providing recommendations for research and teaching that recognize the role of materiality in the context of GenAI, specifically in practical work, scientific argumentation, and learning with GenAI. As we navigate a future dominated by GenAI, understanding how the epistemic authority of science arises from our connection to the physical world will become a crucial consideration in science education.
C1 [Tang, Kok-Sing; Cooper, Grant] Curtin Univ, Sch Educ, GPO Box U1987, Perth, WA 6845, Australia.
C3 Curtin University
RP Tang, KS (corresponding author), Curtin Univ, Sch Educ, GPO Box U1987, Perth, WA 6845, Australia.
EM kok-sing.tang@curtin.edu.au
RI Tang, Kok-Sing/I-3245-2019
FU Curtin University
FX No Statement Available
CR Abrahams I, 2013, STUD SCI EDUC, V49, P209, DOI 10.1080/03057267.2013.858496
   Abrahams I, 2012, J RES SCI TEACH, V49, P1035, DOI 10.1002/tea.21036
   [Anonymous], 2023, NATURE, V613, P612, DOI 10.1038/d41586-023-00191-1
   Barad K, 2003, SIGNS, V28, P801, DOI 10.1086/345321
   Barad K., 2007, M UNIVERSE HALFWAY
   Bianchini S, 2022, RES POLICY, V51, DOI 10.1016/j.respol.2022.104604
   Billingsley B, 2023, CURRIC J, V34, P335, DOI 10.1002/curj.190
   Buchanan J., 2023, ChatGPT cites economics papers that do not exist
   Chin C, 2010, J RES SCI TEACH, V47, P883, DOI 10.1002/tea.20385
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
   Dilmegani C., 2023, Top 6 use cases of GenAI in education
   Erduran S, 2015, INT J STEM EDUC, V2, DOI 10.1186/s40594-015-0020-1
   Erduran S, 2015, SCI EDUC, V99, P1023, DOI 10.1002/sce.21192
   Ferreira S, 2014, RES SCI EDUC, V44, P53, DOI 10.1007/s11165-013-9377-7
   Ford MJ, 2006, REV RES EDUC, V30, P1, DOI 10.3102/0091732X030001001
   Ford MJ, 2015, SCI EDUC, V99, P1041, DOI 10.1002/sce.21188
   Gamble CN, 2019, ANGELAKI, V24, P111, DOI 10.1080/0969725X.2019.1684704
   Halliday M. A. K., 1978, LANGUAGE SOCIAL SEMI
   Halliday M. A. K., 1994, INTRO FUNCTIONAL GRA, DOI DOI 10.4324/9780203783771
   Hayakawa S. I., 1990, Language in thought and action
   Hetherington L, 2018, STUD SCI EDUC, V54, P141, DOI 10.1080/03057267.2019.1598036
   Holstermann N, 2010, RES SCI EDUC, V40, P743, DOI 10.1007/s11165-009-9142-0
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Jimenez-Aleixandre M. P., 2017, Science Education: An International Course Companion
   Kim H, 2006, RES SCI EDUC, V36, P211, DOI 10.1007/s11165-005-9005-2
   Kim M, 2014, PEDAGOGIES, V9, P300, DOI 10.1080/1554480X.2014.955498
   Kohl HA, 2021, FRONT CLIM, V3, DOI 10.3389/fclim.2021.620497
   Kress G., 2006, READING IMAGES GRAMM
   Latour B., 1979, Laboratory Life: The Construction of Scientific Facts
   Latour Bruno., 1988, SCI ACTION FOLLOW SC
   Lemke J., 1990, Talking Science: Language, Learning and Values
   Lemke J., 1998, Reading Science: Critical and Functional Perspectives on the Discourses of Science, P87, DOI DOI 10.4324/9780203982327
   Manzanarez S, 2022, SENSORS-BASEL, V22, DOI 10.3390/s22186895
   Milne C., 2019, Material Practice and Materiality: Too Long Ignored in Science Education, P9, DOI [DOI 10.1007/978-3-030-01974-7_2, DOI 10.1007/978-3-030-197471]
   Mody CCM, 2015, SCI EDUC, V99, P1026, DOI 10.1002/sce.21190
   O'Halloran K.L., 2006, MULTIMODAL DISCOURSE
   OpenAI, 2023, Terms of use
   OpenAI, 2023, Chatgpt (mar 14 version) large language model
   Osborne J, 2023, SCI EDUC, V107, P553, DOI 10.1002/sce.21790
   Peirce C.S., 1986, WRITINGS CS PEIRCE C
   Pickering A., 2008, MANGLE PRACTICE SCI, DOI [DOI 10.7208/CHICAGO/9780226668253.001.0001, 10.7208/chicago/9780226668253.001.0001]
   Scantlebury K., 2019, Material Practice and Materiality: Too Long Ignored in Science Education, P1, DOI [https://doi.org/10.1007/978-3-030-01974-7_1, DOI 10.1007/978-3-030-01974-7_1]
   Sencindiver S.Y., 2017, Oxford Bibliographies: literary and critical theory, DOI DOI 10.1093/OBO/9780190221911-0016
   Stroupe D, 2015, SCI EDUC, V99, P1033, DOI 10.1002/sce.21191
   Tang K. S., 2021, SCAN, V40, P16, DOI [10.3316/informit.961386803198878, DOI 10.3316/INFORMIT.961386803198878]
   Tang KS, 2022, J RES SCI TEACH, V59, P969, DOI 10.1002/tea.21749
   Vincent J, 2023, VERGE
   Vygotsky L, 2012, THOUGHT AND LANGUAGE, P1
   Walker JP, 2013, J RES SCI TEACH, V50, P561, DOI 10.1002/tea.21082
NR 50
TC 7
Z9 7
U1 65
U2 91
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0926-7220
EI 1573-1901
J9 SCI EDUC-NETHERLANDS
JI Sci. Educ.
PD 2024 FEB 21
PY 2024
DI 10.1007/s11191-024-00508-0
EA FEB 2024
PG 16
WC Education & Educational Research; History & Philosophy Of Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; History & Philosophy of Science
GA IL3H8
UT WOS:001166436900002
OA hybrid
DA 2024-12-25
ER

PT J
AU Prescott, MR
   Yeager, S
   Ham, L
   Saldana, CDR
   Serrano, V
   Narez, J
   Paltin, D
   Delgado, J
   Moore, DJ
   Montoya, J
AF Prescott, Maximo R.
   Yeager, Samantha
   Ham, Lillian
   Saldana, Carlos D. Rivera
   Serrano, Vanessa
   Narez, Joey
   Paltin, Dafna
   Delgado, Jorge
   Moore, David J.
   Montoya, Jessica
TI Comparing the Efficacy and Efficiency of Human and Generative AI:
   Qualitative Thematic Analyses
SO JMIR AI
LA English
DT Article
DE GenAI; generative artificial intelligence; ChatGPT; Bard; qualitative
   research; thematic analysis; digital health
ID ADHERENCE; CHATGPT; HIV
AB Background: Qualitative methods are incredibly beneficial to the dissemination and implementation of new digital health interventions; however, these methods can be time intensive and slow down dissemination when timely knowledge from the data sources is needed in ever-changing health systems. Recent advancements in generative artificial intelligence (GenAI) and their underlying large language models (LLMs) may provide a promising opportunity to expedite the qualitative analysis of textual data, but their efficacy and reliability remain unknown. Objective: The primary objectives of our study were to evaluate the consistency in themes, reliability of coding, and time needed for inductive and deductivethematic analyses between GenAI (ie, ChatGPT and Bard) and human coders. Methods: The qualitative data for this study consisted of 40 brief SMS text message reminder prompts used in a digital health intervention for promoting antiretroviral medication adherence among people with HIV who use methamphetamine. Inductive and deductive thematic analyses of these SMS text messages were conducted by 2 independent teams of human coders. An independent human analyst conducted analyses following both approaches using ChatGPT and Bard. The consistency in themes (or the extent to which the themes were the same) and reliability (or agreement in coding of themes) between methods were compared. Results: The themesgenerated by GenAI (both ChatGPT and Bard) were consistent with 71% (5/7) of the themesidentified by human analysts following inductive thematic analysis. The consistency in themes was lower between humans and GenAI following a deductive thematic analysis procedure (ChatGPT: 6/12, 50%; Bard: 7/12, 58%). The percentage agreement (or intercoder reliability) for these congruent themes between human coders and GenAI ranged from fair to moderate (ChatGPT, inductive: 31/66, 47%; ChatGPT, deductive: 22/59, 37%; Bard, inductive: 20/54, 37%; Bard, deductive: 21/58, 36%). In general, ChatGPT and Bard performed similarly to each other across both types of qualitative analyses in terms of consistency of themes (inductive: 6/6, 100%; deductive: 5/6, 83%) and reliability of coding (inductive: 23/62, 37%; deductive: 22/47, 47%). On average, GenAI required significantly less overall timethan human coders when conducting qualitative analysis (20, SD 3.5 min vs 567, SD 106.5 min). Conclusions:The promising consistency in the themes generated by human coders and GenAI suggests that these technologies hold promise in reducing the resource intensiveness of qualitative thematic analysis; however, the relatively lower reliability in coding between them suggests that hybrid approaches are necessary. Human coders appeared to be better than GenAI at identifying nuanced and interpretativethemes. Future studies should consider how these powerful technologies can be best used in collaboration with human coders to improve the efficiency of qualitative research in hybrid approaches while also mitigating potential ethical risks that they may pose.
C1 [Prescott, Maximo R.; Yeager, Samantha; Ham, Lillian; Saldana, Carlos D. Rivera; Serrano, Vanessa; Narez, Joey; Paltin, Dafna; Delgado, Jorge; Moore, David J.; Montoya, Jessica] Univ Calif San Diego, HIV Neurobehav Res Program, 220 Dickinson St, San Diego, CA 92103 USA.
   [Prescott, Maximo R.; Ham, Lillian; Serrano, Vanessa; Paltin, Dafna] Univ Calif San Diego, San Diego State Univ, Joint Doctoral Program Clin Psychol, San Diego, CA USA.
   [Saldana, Carlos D. Rivera] Univ Calif San Diego, Dept Med, San Diego, CA USA.
   [Moore, David J.; Montoya, Jessica] Univ Calif San Diego, Dept Psychiat, La Jolla, CA USA.
C3 University of California System; University of California San Diego;
   California State University System; San Diego State University;
   University of California System; University of California San Diego;
   University of California System; University of California San Diego;
   University of California System; University of California San Diego
RP Prescott, MR (corresponding author), Univ Calif San Diego, HIV Neurobehav Res Program, 220 Dickinson St, San Diego, CA 92103 USA.
EM mrprescott@health.ucsd.edu
OI Moore, David/0000-0001-9699-318X; Ham, Lillian/0000-0002-6163-9700;
   Delgado, Jorge/0009-0009-5967-7829; Yeager,
   Samantha/0000-0002-0533-1875; Paltin, Dafna/0000-0001-7632-4464
CR AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
   [Anonymous], Researcher Access Program application
   Bandura A., 1986, SOCIAL FDN THOUGHT A
   Biever C, 2023, NATURE, V619, P686, DOI 10.1038/d41586-023-02361-7
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   de Paoli S, 2023, PREPRINT, DOI [10.1177/08944393231220483, DOI 10.1177/08944393231220483]
   de Vries H., 1988, HEALTH EDUC RES, P273, DOI DOI 10.1093/HER/3.3.273
   Firmin RL., 2017, QUAL PSYCHOL, V4, P201, DOI DOI 10.1037/QUP0000050
   Fu ZR, 2024, J MED INTERNET RES, V26, DOI 10.2196/51069
   Gale RC, 2019, IMPLEMENT SCI, V14, DOI 10.1186/s13012-019-0853-y
   Glasgow RE, 2012, CTS-CLIN TRANSL SCI, V5, P48, DOI 10.1111/j.1752-8062.2011.00383.x
   Guetterman TC, 2018, J MED INTERNET RES, V20, DOI 10.2196/jmir.9702
   Hamilton L, 2023, INT J QUAL METH, V22, DOI 10.1177/16094069231201504
   Hruschka DJ., 2004, FIELD METHOD, V16, P307, DOI [DOI 10.1177/1525822X04266540, https://doi.org/10.1177/1525822X04266540]
   Knox WB, 2011, P ICML WORKSH NEW DE
   LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
   Langevin M, 2023, ACS OMEGA, V8, P23148, DOI 10.1021/acsomega.3c00883
   Leech NL, 2007, SCHOOL PSYCHOL QUART, V22, P557, DOI 10.1037/1045-3830.22.4.557
   Leeson W, 2019, INT J QUAL METH, V18, DOI 10.1177/1609406919887021
   Lester JN, 2020, HUM RESOUR DEV REV, V19, P94, DOI 10.1177/1534484320903890
   Lugosi G., 2023, PREPRINT
   MacPhail C, 2016, QUAL RES, V16, P198, DOI 10.1177/1468794115577012
   McNall M, 2007, AM J EVAL, V28, P151, DOI 10.1177/1098214007300895
   Meskó B, 2023, J MED INTERNET RES, V25, DOI 10.2196/50638
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Miles M. B., 1994, QUALITATIVE DATA ANA
   Montoya JL, 2014, AIDS CARE, V26, P1477, DOI 10.1080/09540121.2014.924213
   Moore DJ, 2018, DRUG ALCOHOL DEPEN, V189, P154, DOI 10.1016/j.drugalcdep.2018.05.013
   Morley J, 2023, BMJ-BRIT MED J, V382, DOI 10.1136/bmj.p1551
   Nevedal AL, 2021, IMPLEMENT SCI, V16, DOI 10.1186/s13012-021-01111-5
   Noble Helen, 2015, Evid Based Nurs, V18, P34, DOI 10.1136/eb-2015-102054
   Nowell LS, 2017, INT J QUAL METH, V16, DOI 10.1177/1609406917733847
   O'Connor C, 2020, INT J QUAL METH, V19, DOI 10.1177/1609406919899220
   Park JE, 2022, KOREAN J RADIOL, V23, P500, DOI 10.3348/kjr.2022.0033
   Riley WT, 2013, CLIN TRANSL MED, V2, DOI 10.1186/2001-1326-2-10
   Roberts K, 2019, BMC MED RES METHODOL, V19, DOI 10.1186/s12874-019-0707-y
   ROSENSTOCK IM, 1974, HEALTH EDUC QUART, V2, P354, DOI 10.1177/109019817400200405
   Ruksakulpiwat S, 2023, J MULTIDISCIP HEALTH, V16, P1513, DOI 10.2147/JMDH.S413470
   Skeen SJ, 2022, JMIR HUM FACTORS, V9, DOI 10.2196/37350
   Smith JA, 2003, Qualitative Psychology: A Practical Guide to Research Methods
   Taylor B, 2018, BMJ OPEN, V8, DOI 10.1136/bmjopen-2017-019993
   Thapa S, 2023, ANN BIOMED ENG, DOI 10.1007/s10439-023-03284-0
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Timimi F, 2019, J MED INTERNET RES, V21, DOI 10.2196/14809
   Vindrola-Padros C, 2020, QUAL HEALTH RES, V30, P1596, DOI 10.1177/1049732320921835
   Xiao Z, 2023, COMPANION PROCEEDINGS OF 2023 28TH ANNUAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2023 COMPANION, P75, DOI 10.1145/3581754.3584136
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 50
TC 2
Z9 2
U1 2
U2 2
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
EI 2817-1705
J9 JMIR AI
JI JMIR AI
PY 2024
VL 3
AR e54482
DI 10.2196/54482
PG 13
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA P2V6P
UT WOS:001376554400002
PM 39094113
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Gilreath, H
AF Gilreath, Hanna
TI Generative artificial intelligence integrations and applications
SO JOURNAL OF PRINT AND MEDIA TECHNOLOGY RESEARCH
LA English
DT Article
DE generative artificial intelligence; graphic communication; education
AB Generative artificial intelligence (AI) systems are changing the landscape of communication in every capacity. This is seen in written, oral, and visual methods of communication. For educational degree programs such as graphic communication programs, like those found at Clemson and Cal Poly, this is a difficult technology advancement to navigate. Previously, these programs have been a home for creative students' hopeful to pursue a career in a science and creative communication field within the printing or digital media industries. New technology integrating into the classroom daily such as Chat GPT, Adobe Firefly, and Midjourney are quickly changing the education landscape. This leaves students and educators left to answer the questions of how to adapt these new technologies into the classroom and if it should be part of a formal education program. The first step in making these informed decisions is to better understand the attitudes, apprehensions, and level of comfort of students in Clemson and Cal Poly degree programs toward generative AI systems. To collect metrics on these attitudes, a five-point Likert Scale survey that was distributed to students enrolled in Clemson and Cal Poly graphic communication programs has been formulated. The data collected provided clarity that students have a high level of ethical apprehension toward generative AI systems despite adopting the technology in their everyday lives. In addition, the data results provided clarity that students, regardless of class standing, have a high level of fear surrounding job security and the impact that generative Artificial Intelligence will have on the communication job market post-graduation.
C1 [Gilreath, Hanna] Clemson Univ, 200 Godfrey Hall, Clemson, SC 29634 USA.
C3 Clemson University
RP Gilreath, H (corresponding author), Clemson Univ, 200 Godfrey Hall, Clemson, SC 29634 USA.
EM hgibson@clemson.edu
CR Clemson University, 2023, Department of graphic communication
   Martineau K., 2021, IBM Research Blog
   Routley N., 2023, An AI explains
   WILSON S, 1983, LEONARDO, V16, P15, DOI 10.2307/1575036
NR 4
TC 1
Z9 1
U1 31
U2 31
PU INT ASSOC RESEARCH ORGANIZATIONS INFORM, MEDIA & GRAPHIC ARTS IND
PI DARMSTADT
PA INT ASSOC RESEARCH ORGANIZATIONS INFORM, MEDIA & GRAPHIC ARTS IND,
   DARMSTADT, 00000, GERMANY
SN 2223-8905
EI 2414-6250
J9 J PRINT MEDIA TECHNO
JI J. Print Media Technol. Res.
PD MAR
PY 2024
VL 13
IS 1
BP 35
EP 42
DI 10.14622/JPMTR-2401
PG 8
WC Imaging Science & Photographic Technology
WE Emerging Sources Citation Index (ESCI)
SC Imaging Science & Photographic Technology
GA US6M6
UT WOS:001250086400003
DA 2024-12-25
ER

PT J
AU Doshi, AR
   Hauser, OP
AF Doshi, Anil R.
   Hauser, Oliver P.
TI Generative AI enhances individual creativity but reduces the collective
   diversity of novel content
SO SCIENCE ADVANCES
LA English
DT Article
ID STORY
AB Creativity is core to being human. Generative artificial intelligence (AI)-including powerful large language models (LLMs)-holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.
C1 [Doshi, Anil R.] UCL Sch Management, Dept Strategy & Entrepreneurship, London, England.
   [Hauser, Oliver P.] Univ Exeter, Dept Econ, Exeter, England.
   [Hauser, Oliver P.] Univ Exeter, Inst Data Sci & Artificial Intelligence, Exeter, England.
C3 University of London; University College London; University of Exeter;
   University of Exeter
RP Doshi, AR (corresponding author), UCL Sch Management, Dept Strategy & Entrepreneurship, London, England.; Hauser, OP (corresponding author), Univ Exeter, Dept Econ, Exeter, England.; Hauser, OP (corresponding author), Univ Exeter, Inst Data Sci & Artificial Intelligence, Exeter, England.
EM anil.doshi@ucl.ac.uk; o.hauser@exeter.ac.uk
RI Doshi, Anil/R-9052-2018
OI Hauser, Oliver/0000-0002-9282-0801; Doshi, Anil/0000-0002-8489-3373
FU University of Exeter Business School; UCL School of Management
FX Funding was provided by the University of Exeter Business School and UCL
   School of Management.
CR Agarwal N, 2023, Combining human expertise with artificial intelligence: experimental evidence from radiology
   Agrawal A, 2023, SCIENCE, V381, P155, DOI 10.1126/science.adh9429
   AMABILE TM, 1982, J PERS SOC PSYCHOL, V43, P997, DOI 10.1037/0022-3514.43.5.997
   Ash E, 2023, ANNU REV ECON, V15, P659, DOI 10.1146/annurev-economics-082222-074352
   Brynjolfsson E., 2023, Generative AI at Work.
   Charness G., 2023, NBER Working Paper Series
   Charness G, 2019, J EUR ECON ASSOC, V17, P454, DOI 10.1093/jeea/jvx055
   Doshi A. R., 2024, Generative artificial intelligence and evaluating strategic decisions, DOI [10.2139/ssrn.4714776, DOI 10.2139/SSRN.4714776]
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Epstein Z., 2023, PsyArXiv, DOI [10.31234/osf.io/v4mfz, DOI 10.31234/OSF.IO/V4MFZ]
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Epstein Z, 2020, ISCIENCE, V23, DOI 10.1016/j.isci.2020.101515
   Eshraghian JK, 2020, NAT MACH INTELL, V2, P157, DOI 10.1038/s42256-020-0161-x
   Felten E, 2023, Arxiv, DOI arXiv:2303.01157
   Fishelov D, 2019, NARRATIVE, V27, P30, DOI 10.1353/nar.2019.0001
   Frank MR, 2019, P NATL ACAD SCI USA, V116, P6531, DOI 10.1073/pnas.1900949116
   Girotra K., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4526071, DOI 10.2139/SSRN.4526071]
   HARDIN G, 1968, SCIENCE, V162, P1243, DOI 10.1126/science.162.3859.1243
   HARKINS SG, 1982, J PERS SOC PSYCHOL, V43, P1214, DOI 10.1037/0022-3514.43.6.1214
   Harvey S, 2023, ACAD MANAGE REV, V48, P504, DOI 10.5465/amr.2020.0110
   Jauss R., 1974, Toward an Aesthetic of Reception, P3
   Jia N, 2024, ACAD MANAGE J, V67, P5, DOI 10.5465/amj.2022.0426
   Kenower W., 2020, Writer's Digest
   Korinek Anton, 2023, Language models and cognitive automation for economic research (No. w30957)
   Koster R, 2024, Arxiv, DOI arXiv:2404.15059
   Koster R, 2022, NAT HUM BEHAV, V6, P1398, DOI 10.1038/s41562-022-01383-x
   Lee AL, 2022, LEADERSHIP QUART, V33, DOI 10.1016/j.leaqua.2020.101426
   Lysyakov M, 2023, INFORM SYST RES, V34, P1191, DOI 10.1287/isre.2022.1184
   Matz SC, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-53755-0
   Nelles W, 2012, NARRATIVE, V20, P87
   Nickerson Raymond S., 1998, The Handbook of Creativity, P392
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Olson JA, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2022340118
   Palan S, 2018, J BEHAV EXP FINANC, V17, P22, DOI 10.1016/j.jbef.2017.12.004
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Raj M, 2023, Arxiv, DOI [arXiv:2303.06217, 10.48550/arXiv.2303.06217, DOI 10.48550/ARXIV.2303.06217]
   Redi M, 2014, PROC CVPR IEEE, P4272, DOI 10.1109/CVPR.2014.544
   Sternberg RJ., 1999, HDB CREATIVITY, DOI DOI 10.1017/CB09780511807916.003
   van Inwegen E., 2023, Algorithmic Writing Assistance on Jobseekers' Resumes Increases Hires (No. w30886)
   Wang HC, 2023, NATURE, V620, P47, DOI 10.1038/s41586-023-06221-2
   Wolfradt U, 2001, EUR J PERSONALITY, V15, P297, DOI 10.1002/per.409.abs
   Yanardag P, 2021, C&C'21: PROCEEDINGS OF THE 13TH CONFERENCE ON CREATIVITY AND COGNITION, DOI 10.1145/3450741.3465251
NR 42
TC 5
Z9 5
U1 201
U2 201
PU AMER ASSOC ADVANCEMENT SCIENCE
PI WASHINGTON
PA 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA
SN 2375-2548
J9 SCI ADV
JI Sci. Adv.
PD JUL 12
PY 2024
VL 10
IS 28
AR eadn5290
DI 10.1126/sciadv.adn5290
PG 9
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA A1J0W
UT WOS:001280155200005
PM 38996021
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Spatscheck, N
   Schaschek, M
   Winkelmann, A
AF Spatscheck, Niko
   Schaschek, Myriam
   Winkelmann, Axel
TI The effects of generative AI's human-like competencies on clinical
   decision-making
SO JOURNAL OF DECISION SYSTEMS
LA English
DT Article; Early Access
DE Clinical decision augmentation; generative AI; appropriate reliance
ID ANTHROPOMORPHISM INCREASES TRUST; ARTIFICIAL-INTELLIGENCE;
   INFORMATION-TECHNOLOGY; ALGORITHM AVERSION; AUTOMATION BIAS; COGNITIVE
   LOAD; ARGUMENT QUALITY; INITIAL TRUST; E-COMMERCE; NEED
AB Generative AI (genAI) has revolutionized clinical AI systems by leveraging human language. Yet, challenges remain in its integration into clinical settings, particularly regarding the risk of physicians relying on hallucinated advice. We conducted an experimental study with 368 novice physicians who diagnosed patient cases while being augmented with clinical genAI systems. A theoretical model was empirically tested to examine how anthropomorphism and advice elaboration affect trust and cognitive load as mediators for appropriate reliance. Findings show that augmenting clinical decisions with genAI systems can improve physicians' diagnostic accuracy but also frequently results in inappropriate reliance on hallucinated advice due to miscalibrated trust. Moreover, we emphasize the uncanny familiarity evoked by anthropomorphizing genAI systems, which diminishes trust while reducing cognitive load. Our findings highlight the benefits and ethical challenges of genAI in clinical decision support, underscoring the need to balance its advantages with safeguarding the integrity of physicians' decision agency.
C1 [Spatscheck, Niko; Schaschek, Myriam; Winkelmann, Axel] Julius Maximilians Univ Wuerzburg, Chair Management & Informat Syst, Sanderring 2, D-97070 Wurzburg, Germany.
C3 University of Wurzburg
RP Spatscheck, N (corresponding author), Julius Maximilians Univ Wuerzburg, Chair Management & Informat Syst, Sanderring 2, D-97070 Wurzburg, Germany.
EM niko.spatscheck@uni-wuerzburg.de
CR Almagharbeh WT, 2024, INT NURS REV, DOI 10.1111/inr.13011
   Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Banning M, 2008, J CLIN NURS, V17, P187, DOI 10.1111/j.1365-2702.2006.01791.x
   Benbasat I., 2005, Journal of the Association for Information Systems, V6, P72, DOI [10.17705/1jais.00065, https://doi.org/10.17705/1jais.00065, DOI 10.17705/1JAIS.00065]
   Benlian A, 2020, INFORM SYST J, V30, P1010, DOI 10.1111/isj.12243
   Berger B, 2021, BUS INFORM SYST ENG+, V63, P55, DOI 10.1007/s12599-020-00678-5
   Bhattacherjee A, 2007, EUR J INFORM SYST, V16, P725, DOI 10.1057/palgrave.ejis.3000717
   Bigman YE, 2018, COGNITION, V181, P21, DOI 10.1016/j.cognition.2018.08.003
   Biros DP, 2004, GROUP DECIS NEGOT, V13, P173, DOI 10.1023/B:GRUP.0000021840.85686.57
   Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
   Bonaccio S, 2006, ORGAN BEHAV HUM DEC, V101, P127, DOI 10.1016/j.obhdp.2006.07.001
   Boubin Jayson G., 2017, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V61, P750, DOI 10.1177/1541931213601672
   Breck E., 2019, P 2 SYSML C STANF
   Bucinca Zana, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3449287
   Burleigh TJ, 2013, COMPUT HUM BEHAV, V29, P759, DOI 10.1016/j.chb.2012.11.021
   Burton JW, 2020, J BEHAV DECIS MAKING, V33, P220, DOI 10.1002/bdm.2155
   Bussone A, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2015), P160, DOI 10.1109/ICHI.2015.26
   CACIOPPO JT, 1982, J PERS SOC PSYCHOL, V42, P116, DOI 10.1037/0022-3514.42.1.116
   Cacioppo JT, 1996, PSYCHOL BULL, V119, P197, DOI 10.1037/0033-2909.119.2.197
   CACIOPPO JT, 1983, J PERS SOC PSYCHOL, V45, P805, DOI 10.1037/0022-3514.45.4.805
   Cao Shiye, 2022, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3555572
   Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
   Chandra S, 2022, J MANAGE INFORM SYST, V39, P969, DOI 10.1080/07421222.2022.2127441
   Chang YP, 2024, ACM T INTEL SYST TEC, V15, DOI 10.1145/3641289
   Chattaraman V, 2019, COMPUT HUM BEHAV, V90, P315, DOI 10.1016/j.chb.2018.08.048
   Coelho GLD, 2020, ASSESSMENT, V27, P1870, DOI 10.1177/1073191118793208
   CROSKERRY P., 2005, CAN J ANESTH, V52, pR1, DOI [DOI 10.1007/BF03023077, 10.1007/BF03023077]
   Daschner S, 2022, J DECIS SYST, V31, P77, DOI 10.1080/12460125.2022.2070951
   de Visser EJ, 2016, J EXP PSYCHOL-APPL, V22, P331, DOI 10.1037/xap0000092
   Dellermann D, 2019, BUS INFORM SYST ENG+, V61, P637, DOI 10.1007/s12599-019-00595-2
   Diederich S., 2019, INT C INF SYST 2018
   Dietvorst BJ, 2018, MANAGE SCI, V64, P1155, DOI 10.1287/mnsc.2016.2643
   Dietvorst BJ, 2015, J EXP PSYCHOL GEN, V144, P114, DOI 10.1037/xge0000033
   Dowding D, 2009, J CLIN NURS, V18, P1159, DOI 10.1111/j.1365-2702.2008.02607.x
   Egala SB, 2024, EUR J INFORM SYST, V33, P1016, DOI 10.1080/0960085X.2023.2251927
   Ej D. V., 2012, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V56, P263, DOI [DOI 10.1177/1071181312561062, https://doi.org/10.1177/1071181312561, DOI 10.1177/1071181312561]
   Epley N, 2008, SOC COGNITION, V26, P143, DOI 10.1521/soco.2008.26.2.143
   Epley N, 2007, PSYCHOL REV, V114, P864, DOI 10.1037/0033-295X.114.4.864
   Esteva A, 2019, NAT MED, V25, P24, DOI 10.1038/s41591-018-0316-z
   Evans P, 2024, EDUC PSYCHOL REV, V36, DOI 10.1007/s10648-023-09841-2
   Faul F, 2007, BEHAV RES METHODS, V39, P175, DOI 10.3758/BF03193146
   Fishbein M., 1980, UNDERSTANDING ATTITU
   Fox JR, 2007, COMMUN RES, V34, P277, DOI 10.1177/0093650207300429
   Frith CD, 2008, PHILOS T R SOC B, V363, P2033, DOI 10.1098/rstb.2008.0005
   Gino F, 2007, J BEHAV DECIS MAKING, V20, P21, DOI 10.1002/bdm.539
   Giorgi I, 2023, INT J SOC ROBOT, DOI 10.1007/s12369-023-01019-8
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Gnewuch U., 2018, EUR C INF SYST, P1
   Goddard K, 2014, INT J MED INFORM, V83, P368, DOI 10.1016/j.ijmedinf.2014.01.001
   Goddard K, 2012, J AM MED INFORM ASSN, V19, P121, DOI 10.1136/amiajnl-2011-000089
   Goddard K, 2011, STUD HEALTH TECHNOL, V164, P17, DOI 10.3233/978-1-60750-709-3-17
   Gogoll J, 2018, J BEHAV EXP ECON, V74, P97, DOI 10.1016/j.socec.2018.04.003
   Gong L, 2008, COMPUT HUM BEHAV, V24, P1494, DOI 10.1016/j.chb.2007.05.007
   Gregor S, 1999, MIS QUART, V23, P497, DOI 10.2307/249487
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Haddock G, 2008, PERS SOC PSYCHOL B, V34, P769, DOI 10.1177/0146167208314871
   Hagendorff T, 2023, NAT COMPUT SCI, V3, P833, DOI 10.1038/s43588-023-00527-x
   Hamet P, 2017, METABOLISM, V69, pS36, DOI 10.1016/j.metabol.2017.01.011
   Hansen H, 2013, EUR J MARKETING, V47, P1157, DOI 10.1108/03090561311324264
   Harrer S, 2023, EBIOMEDICINE, V90, DOI 10.1016/j.ebiom.2023.104512
   Hayes A. F., 2017, A regression-based approach, V2nd ed.
   He G, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3581025
   Herm L. V., 2023, 31 EUR C INF SYST, P1
   Hevner AR, 2004, MIS QUART, V28, P75, DOI 10.2307/25148625
   Higgins E.T., 1996, SOCIAL PSYCHOL HDB B
   Hoff KA, 2015, HUM FACTORS, V57, P407, DOI 10.1177/0018720814547570
   HOGARTH RM, 1992, COGNITIVE PSYCHOL, V24, P1, DOI 10.1016/0010-0285(92)90002-J
   Hong WY, 2014, INFORM SYST RES, V25, P111, DOI 10.1287/isre.2013.0501
   Jha S, 2016, JAMA-J AM MED ASSOC, V316, P2353, DOI 10.1001/jama.2016.17438
   Jin D, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11146421
   JOHNSONLAIRD PN, 1993, COGNITION, V49, P1, DOI 10.1016/0010-0277(93)90033-R
   Juravle G, 2020, PROG BRAIN RES, V253, P263, DOI 10.1016/bs.pbr.2020.06.006
   Jussupow E., 2020, EUR C INF SYST, P1
   Jussupow E., 2018, EUR C INF SYST, P1
   Jussupow E, 2022, JMIR FORM RES, V6, DOI 10.2196/28750
   Jussupow E, 2021, INFORM SYST RES, V32, P713, DOI 10.1287/isre.2020.0980
   Kalyuga S, 2011, EDUC PSYCHOL REV, V23, P1, DOI 10.1007/s10648-010-9150-7
   Kim D, 2009, J MANAGE INFORM SYST, V26, P175, DOI 10.2753/MIS0742-1222260306
   Kim J, 2023, COMPUT HUM BEHAV, V139, DOI 10.1016/j.chb.2022.107512
   Komiak SYX, 2006, MIS QUART, V30, P941
   Laban G., 2020, P 10 INT C HUM AG IN, P1
   Lakkaraju H., 2022, PREPRINT
   Lankton NK, 2015, J ASSOC INF SYST, V16, P880, DOI 10.17705/1jais.00411
   Lapointe L, 2005, MIS QUART, V29, P461
   Lebovitz S, 2022, ORGAN SCI, V33, P126, DOI 10.1287/orsc.2021.1549
   Lee JD, 2004, HUM FACTORS, V46, P50, DOI 10.1518/hfes.46.1.50.30392
   Lee MK, 2018, BIG DATA SOC, V5, DOI 10.1177/2053951718756684
   Li JN, 2024, COMPUT METH PROG BIO, V245, DOI 10.1016/j.cmpb.2024.108013
   Li MJ, 2022, ELECTRON MARK, V32, P2245, DOI 10.1007/s12525-022-00591-7
   Li Y., 2022, INT C INF SYST, P1
   Liang HG, 2022, INFORM SYST RES, V33, P737, DOI 10.1287/isre.2021.1082
   Liberati EG, 2017, IMPLEMENT SCI, V12, DOI 10.1186/s13012-017-0644-2
   Lin CL, 2011, SOC BEHAV PERSONAL, V39, P71, DOI 10.2224/sbp.2011.39.1.71
   Liu C, 2019, ASIA PAC J MARKET LO, V31, P378, DOI 10.1108/APJML-05-2018-0170
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Liu SR, 2023, J AM MED INFORM ASSN, V30, P1237, DOI 10.1093/jamia/ocad072
   Lockspeiser TM, 2008, ADV HEALTH SCI EDUC, V13, P361, DOI 10.1007/s10459-006-9049-8
   Loda T, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0222224
   Longoni C, 2019, J CONSUM RES, V46, P629, DOI 10.1093/jcr/ucz013
   Lyell D, 2018, HUM FACTORS, V60, P1008, DOI 10.1177/0018720818781224
   Lyell D, 2017, J AM MED INFORM ASSN, V24, P423, DOI 10.1093/jamia/ocw105
   Matias Y., 2023, Medlm: Generative ai fine-tuned for the healthcare industry
   Matz SC, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-53755-0
   MAYER RC, 1995, ACAD MANAGE REV, V20, P709, DOI 10.2307/258792
   Mcknight D.H., 2005, Multiple Access, V7, P329
   McKnight DH, 2020, J MANAGE INFORM SYST, V37, P1015, DOI 10.1080/07421222.2020.1831772
   McKnight DH, 2017, J STRATEGIC INF SYST, V26, P118, DOI 10.1016/j.jsis.2017.01.001
   Mcknight DH, 1998, ACAD MANAGE REV, V23, P473, DOI 10.5465/AMR.1998.926622
   McKnight DH, 2002, INFORM SYST RES, V13, P334, DOI 10.1287/isre.13.3.334.81
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Meyer J., 2013, The oxford handbook of cognitive engineering, P109, DOI DOI 10.1093/OXFORDHB/9780199757183.001.0001
   Morana S., 2020, EUR C INF SYST, P1
   Mori M., 1970, Energy, V7, P33, DOI DOI 10.1109/MRA.2012.2192811
   Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
   Morrison B. B., 2014, Proceedings of the Tenth Annual Conference on International Computing Education Research, P131, DOI [DOI 10.1145/2632320.2632348, 10.1145/2632320.2632348]
   MOUSAVI SY, 1995, J EDUC PSYCHOL, V87, P319, DOI 10.1037/0022-0663.87.2.319
   Moussawi S, 2021, ELECTRON MARK, V31, P343, DOI 10.1007/s12525-020-00411-w
   Natarajan M, 2020, ACMIEEE INT CONF HUM, P33, DOI 10.1145/3319502.3374839
   Nicolaou AI, 2006, INFORM SYST RES, V17, P332, DOI 10.1287/isre.1060.0103
   Niu DF, 2018, HUM FACTOR ERGON MAN, V28, P352, DOI 10.1002/hfm.20745
   Nori H, 2023, Arxiv, DOI [arXiv:2303.13375, 10.48550/arXiv.2303.13375, DOI 10.48550/ARXIV.2303.13375]
   Nourani M, 2021, IUI '21 - 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P340, DOI 10.1145/3397481.3450639
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   OpenAI, 2023, Fine-tuning
   Orman LV, 2002, INFORM SYST FRONT, V4, P213, DOI 10.1023/A:1016007020846
   PAAS FGWC, 1994, EDUC PSYCHOL REV, V6, P351, DOI 10.1007/BF02213420
   Pak R, 2012, ERGONOMICS, V55, P1059, DOI 10.1080/00140139.2012.691554
   Parkes A, 2017, BEHAV INFORM TECHNOL, V36, P165, DOI 10.1080/0144929X.2016.1209242
   Petty R.E., 1986, COMMUN PERSUATION, P1
   PETTY RE, 1984, J PERS SOC PSYCHOL, V46, P69, DOI 10.1037/0022-3514.46.1.69
   Pfeuffer N, 2019, BUS INFORM SYST ENG+, V61, P523, DOI 10.1007/s12599-019-00599-y
   Phillips-Wren G, 2009, EUR J OPER RES, V195, P642, DOI 10.1016/j.ejor.2007.11.001
   Phillips-Wren G, 2020, J DECIS SYST, V29, P213, DOI 10.1080/12460125.2020.1768680
   Prahl A, 2017, J FORECASTING, V36, P691, DOI 10.1002/for.2464
   Qiu LY, 2009, J MANAGE INFORM SYST, V25, P145, DOI 10.2753/MIS0742-1222250405
   Racherla P, 2012, J CONSUM BEHAV, V11, P94, DOI 10.1002/cb.385
   Risko EF, 2016, TRENDS COGN SCI, V20, P676, DOI 10.1016/j.tics.2016.07.002
   Rubin E, 2023, ACM TRANS MANAG INF, V14, DOI 10.1145/3580479
   Saffarizadeh K, 2024, J ASSOC INF SYST, V25, DOI 10.17705/1jais.00839
   Sameh A. N., 2010, EUR C INF SYST CAP T, P1
   Savage T, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-024-01010-1
   Schaffer J, 2019, PROCEEDINGS OF IUI 2019, P240, DOI 10.1145/3301275.3302308
   Schaschek M., 2024, WIRTSCH 2024 P WUERZ, P1
   Schemmer M., 2023, INT C INF SYST HYD, P1
   Schemmer M, 2023, PROCEEDINGS OF 2023 28TH ANNUAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2023, P410, DOI 10.1145/3581641.3584066
   Schmitt A., 2021, INT C INF SYST AUST, P1
   Schuetz S, 2020, J ASSOC INF SYST, V21, P460, DOI 10.17705/1jais.00608
   Seeger A. M., 2018, INT C INF SYST SAN F, P1
   Seeger AM, 2021, J ASSOC INF SYST, V22, P931, DOI 10.17705/1jais.00685
   Shiffrin R, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2300963120
   Sicilia M, 2005, J ADVERTISING, V34, P31, DOI 10.1080/00913367.2005.10639202
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Singhal K, 2023, Arxiv, DOI [arXiv:2305.09617, DOI 10.48550/ARXIV.2305.09617]
   Siontis KC, 2024, EUR HEART J, V45, P321, DOI 10.1093/eurheartj/ehad766
   SLOVIC P, 1995, AM PSYCHOL, V50, P364, DOI 10.1037/0003-066X.50.5.364
   Smith M., 2008, CLIN REASONING HLTH, V3, P89
   Sniezek JA, 2001, ORGAN BEHAV HUM DEC, V84, P288, DOI 10.1006/obhd.2000.2926
   Sweller J, 1998, EDUC PSYCHOL REV, V10, P251, DOI 10.1023/A:1022193728205
   SWELLER J, 1994, COGNITION INSTRUCT, V12, P185, DOI 10.1207/s1532690xci1203_1
   Sweller J., 1994, Learning and Instruction, V4, P295, DOI [DOI 10.1016/0959-4752, DOI 10.1016/0959-4752(94)90003-5, 10.1016/0959-4752(94)90003-5]
   Sweller J, 2019, EDUC PSYCHOL REV, V31, P261, DOI 10.1007/s10648-019-09465-5
   Tan TF, 2023, OPHTHALMOL SCI, V3, DOI 10.1016/j.xops.2023.100394
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Toulmin S., 1969, The use of arguments
   Tsfati Y, 2005, MEDIA PSYCHOL, V7, P251, DOI 10.1207/S1532785XMEP0703_2
   Uysal E, 2022, J ACAD MARKET SCI, V50, P1153, DOI 10.1007/s11747-022-00856-9
   van Merriënboer JJG, 2005, EDUC PSYCHOL REV, V17, P147, DOI 10.1007/s10648-005-3951-0
   Vasconcelos H., 2023, Proceedings of the ACM on Human-Computer Interaction, V7, P1, DOI [https://doi.org/10.1145/3579605, DOI 10.1145/3579605, 10.1145/3579605]
   Wahn B, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0286102
   Wang WQ, 2016, J MANAGE INFORM SYST, V33, P744, DOI 10.1080/07421222.2016.1243949
   Waytz A, 2014, J EXP SOC PSYCHOL, V52, P113, DOI 10.1016/j.jesp.2014.01.005
   Waytz A, 2010, PERSPECT PSYCHOL SCI, V5, P219, DOI 10.1177/1745691610369336
   Weber R, 2003, MIS QUART, V27, pIII
   Westphal M, 2023, COMPUT HUM BEHAV, V144, DOI 10.1016/j.chb.2023.107714
   Wu M., 2023, Data preparation and analysis for chat model fine-tuning
   Wysocki O, 2023, ARTIF INTELL-AMST, V316, DOI 10.1016/j.artint.2022.103839
   Yang RB, 2022, ELECTRON MARK, V32, P2053, DOI 10.1007/s12525-022-00592-6
   Yang YK, 2024, INFORM MANAGE-AMSTER, V61, DOI 10.1016/j.im.2024.103961
   Yenduri G., 2024, Gpt (generative pre-trained transformer)-a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions
   Yi MY, 2013, DECIS SUPPORT SYST, V55, P284, DOI 10.1016/j.dss.2013.01.029
   You S, 2022, J MANAGE INFORM SYST, V39, P336, DOI 10.1080/07421222.2022.2063553
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
   Zhang YF, 2020, FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, P295, DOI 10.1145/3351095.3372852
   Zhou JL, 2017, LECT NOTES COMPUT SC, V10516, P23, DOI 10.1007/978-3-319-68059-0_2
   Zhou T, 2016, INFORM SYST FRONT, V18, P265, DOI 10.1007/s10796-014-9530-5
NR 186
TC 0
Z9 0
U1 0
U2 0
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1246-0125
EI 2116-7052
J9 J DECIS SYST
JI J. Decis. Syst.
PD 2024 DEC 15
PY 2024
DI 10.1080/12460125.2024.2430731
EA DEC 2024
PG 39
WC Operations Research & Management Science
WE Emerging Sources Citation Index (ESCI)
SC Operations Research & Management Science
GA P4N4T
UT WOS:001377694400001
OA hybrid
DA 2024-12-25
ER

PT J
AU Ghimire, P
   Kim, K
   Acharya, M
AF Ghimire, Prashnna
   Kim, Kyungki
   Acharya, Manoj
TI Opportunities and Challenges of Generative AI in Construction Industry:
   Focusing on Adoption of Text-Based Models
SO BUILDINGS
LA English
DT Article
DE generative AI; implementation framework; construction; AEC; GPT; LLM;
   PaLM; Llama; fine-tuning
ID MANAGEMENT; LOGISTICS; NETWORKS; DISPUTES
AB In the last decade, despite rapid advancements in artificial intelligence (AI) transforming many industry practices, construction largely lags in adoption. Recently, the emergence and rapid adoption of advanced large language models (LLMs) like OpenAI's GPT, Google's PaLM, and Meta's Llama have shown great potential and sparked considerable global interest. However, the current surge lacks a study investigating the opportunities and challenges of implementing Generative AI (GenAI) in the construction sector, creating a critical knowledge gap for researchers and practitioners. This underlines the necessity to explore the prospects and complexities of GenAI integration. Bridging this gap is fundamental to optimizing GenAI's early stage adoption within the construction sector. Given GenAI's unprecedented capabilities to generate human-like content based on learning from existing content, we reflect on two guiding questions: What will the future bring for GenAI in the construction industry? What are the potential opportunities and challenges in implementing GenAI in the construction industry? This study delves into reflected perception in literature, analyzes the industry perception using programming-based word cloud and frequency analysis, and integrates authors' opinions to answer these questions. This paper recommends a conceptual GenAI implementation framework, provides practical recommendations, summarizes future research questions, and builds foundational literature to foster subsequent research expansion in GenAI within the construction and its allied architecture and engineering domains.
C1 [Ghimire, Prashnna; Kim, Kyungki] Univ Nebraska Lincoln, Durham Sch Architectural Engn & Construct, Dept Architectural Engn, Lincoln, NE 68588 USA.
   [Acharya, Manoj] SRI Int, Menlo Pk, CA 94025 USA.
C3 University of Nebraska System; University of Nebraska Lincoln; SRI
   International
RP Ghimire, P (corresponding author), Univ Nebraska Lincoln, Durham Sch Architectural Engn & Construct, Dept Architectural Engn, Lincoln, NE 68588 USA.
EM pghimire3@huskers.unl.edu; kkim13@unl.edu; manoj.acharya@sri.com
OI Kim, Kyungki/0000-0002-7978-4025; Ghimire, Prashnna/0009-0000-2689-9905
CR Abioye SO, 2021, J BUILD ENG, V44, DOI 10.1016/j.jobe.2021.103299
   Afzal F, 2021, INT J MANAG PROJ BUS, V14, P300, DOI 10.1108/IJMPB-02-2019-0047
   AI Caucus Leaders, Introduce Bipartisan Bill to Expand Access to AI Research
   Akepanidtaworn K., 2023, Medium
   Al Qady M, 2010, J CONSTR ENG M, V136, P294, DOI 10.1061/(ASCE)CO.1943-7862.0000131
   Amershi S, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300233
   Andenæs E, 2020, BUILDINGS-BASEL, V10, DOI 10.3390/buildings10100189
   [Anonymous], Activity. Dr. Bradley Hyatt
   [Anonymous], 2023, GPT-3
   [Anonymous], Survey of Generative AI in Architecture and Design
   [Anonymous], Finetuning Large Language Models-DeepLearning
   Baduge SK, 2022, AUTOMAT CONSTR, V141, DOI 10.1016/j.autcon.2022.104440
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Balmer VM, 2022, Arxiv, DOI [arXiv:2211.16406, DOI 10.48550/ARXIV.2211.16406]
   Bassir David, 2023, International Journal for Simulation and Multidisciplinary Design Optimization, DOI 10.1051/smdo/2023005
   Becerik-Gerber B, 2012, J CONSTR ENG M, V138, P431, DOI 10.1061/(ASCE)CO.1943-7862.0000433
   Bengio Y, 2001, ADV NEUR IN, V13, P932
   Birjali M, 2021, KNOWL-BASED SYST, V226, DOI 10.1016/j.knosys.2021.107134
   Bloch T, 2018, AUTOMAT CONSTR, V91, P256, DOI 10.1016/j.autcon.2018.03.018
   Bock T, 2007, AUTON ROBOT, V22, P201, DOI 10.1007/s10514-006-9008-5
   Bommasani R., 2021, arXiv
   Bond-Taylor S, 2022, IEEE T PATTERN ANAL, V44, P7327, DOI 10.1109/TPAMI.2021.3116668
   Brown TB, 2020, ADV NEUR IN, V33
   Cao Y, 2020, J MANAGE ENG, V36, DOI 10.1061/(ASCE)ME.1943-5479.0000784
   Chen JH, 2008, AUTOMAT CONSTR, V17, P773, DOI 10.1016/j.autcon.2008.02.005
   Chen JD, 2017, J COMPUT CIVIL ENG, V31, DOI 10.1061/(ASCE)CP.1943-5487.0000628
   Chen JM, 2023, BUILDINGS-BASEL, V13, DOI 10.3390/buildings13071861
   Cheng MY, 2009, AUTOMAT CONSTR, V18, P164, DOI 10.1016/j.autcon.2008.07.001
   Chokwitthaya C, 2019, Arxiv, DOI arXiv:1906.05767
   Chou JS, 2013, J COMPUT CIVIL ENG, V27, P51, DOI 10.1061/(ASCE)CP.1943-5487.0000197
   Choudhari S, 2017, CONSTR INNOV-ENGL, V17, P158, DOI 10.1108/CI-03-2016-0014
   Chowdhery A, 2022, Arxiv, DOI [arXiv:2204.02311, DOI 10.48550/ARXIV.2204.02311]
   Chua DKH, 2005, J CONSTR ENG M, V131, P715, DOI 10.1061/(ASCE)0733-9364(2005)131:6(715)
   Chung SH, 2023, AUTOMAT CONSTR, V154, DOI 10.1016/j.autcon.2023.105020
   Cohn C., 2023, Towards a formative feedback generation agent: Leveraging a human-in-the-loop, chain-of-thought prompting approach with LLMs to evaluate formative assessment responses in K-12 science
   Coskuner G, 2021, WASTE MANAGE RES, V39, P499, DOI 10.1177/0734242X20935181
   Croitoru FA, 2023, IEEE T PATTERN ANAL, V45, P10850, DOI 10.1109/TPAMI.2023.3261988
   Dai SC, 2023, Arxiv, DOI arXiv:2310.15100
   Darko A, 2020, AUTOMAT CONSTR, V112, DOI 10.1016/j.autcon.2020.103081
   Debrah C, 2022, AUTOMAT CONSTR, V137, DOI 10.1016/j.autcon.2022.104192
   Delgado JMD, 2021, APPL SOFT COMPUT, V112, DOI 10.1016/j.asoc.2021.107836
   Dinh L, 2015, Arxiv, DOI [arXiv:1410.8516, 10.48550/arXiv.1410.8516]
   Dinh L, 2017, Arxiv, DOI arXiv:1605.08803
   Doersch C, 2021, Arxiv, DOI arXiv:1606.05908
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Duan YQ, 2019, INT J INFORM MANAGE, V48, P63, DOI 10.1016/j.ijinfomgt.2019.01.021
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   El Zini J, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3529755
   Elfahham Y, 2019, ALEX ENG J, V58, P499, DOI 10.1016/j.aej.2019.05.002
   Fang WL, 2020, AUTOMAT CONSTR, V110, DOI 10.1016/j.autcon.2019.103013
   Fang Y, 2019, ENG CONSTR ARCHIT MA, V26, P2289, DOI 10.1108/ECAM-09-2018-0386
   Fathi S, 2020, RENEW SUST ENERG REV, V133, DOI 10.1016/j.rser.2020.110287
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   georgeho, Autoregressive Models in Deep Learning-A Brief Survey
   Ghimire P., 2023, P 2 INT C CONSTRUCTI
   Goel R, 2021, INT CONF COMP COMMUN, DOI 10.1109/ICCCI50826.2021.9402337
   Goh YM, 2013, CONSTR MANAG ECON, V31, P460, DOI 10.1080/01446193.2013.797095
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Google AI, PaLM 2-Google AI
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Guan L, 2023, Arxiv, DOI arXiv:2305.14909
   Hassan HAM, 2022, LECT NOTES COMPUT SC, V13286, P215, DOI 10.1007/978-3-031-08473-7_20
   Hatami M, 2022, COMPUTING IN CIVIL ENGINEERING 2021, P1171
   Hatami M, 2022, CONSTRUCTION RESEARCH CONGRESS 2022: COMPUTER APPLICATIONS, AUTOMATION, AND DATA ANALYTICS, P1298
   Heimerl F, 2014, P ANN HICSS, P1833, DOI 10.1109/HICSS.2014.231
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hoskere Vedhus., 2018, arXiv
   Hu WF, 2008, 2008 INTERNATIONAL SEMINAR ON FUTURE INFORMATION TECHNOLOGY AND MANAGEMENT ENGINEERING, PROCEEDINGS, P372, DOI 10.1109/FITME.2008.142
   Huang D, 2019, 2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019), P425, DOI 10.1109/MIPR.2019.00086
   Jang S, 2023, Arxiv, DOI [arXiv:2306.14165, 10.48550/arXiv.2306.14165 2306.14165, DOI 10.48550/ARXIV.2306.141652306.14165]
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Kabir A.I., 2020, Informatica Economica, V24, P55, DOI DOI 10.24818/ISSN14531305/24.4.2020.05
   Kammoun A, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3527850
   Kar AK, 2022, J CLEAN PROD, V376, DOI 10.1016/j.jclepro.2022.134120
   Kazerouni A, 2023, Arxiv, DOI [arXiv:2211.07804, DOI 10.48550/ARXIV.2211.07804]
   Kim J., 2019, P 2019 INT COUNCIL R
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   Kuang WR, 2023, Arxiv, DOI arXiv:2309.00363
   Kumar M, 2020, Arxiv, DOI [arXiv:1903.01434, 10.48550/arXiv.1903.01434]
   Kuo CH, 2021, IEEE ACCESS, V9, P50738, DOI 10.1109/ACCESS.2021.3068269
   Lee Jieun, 2022, MM '22: Proceedings of the 30th ACM International Conference on Multimedia, P1241, DOI 10.1145/3503161.3548094
   Lei T, 2016, Arxiv, DOI [arXiv:1606.04155, DOI 10.48550/ARXIV.1606.04155]
   Li CL, 2018, IEEE IJCNN
   Li Y, 2018, INFORM SCIENCES, V450, P301, DOI 10.1016/j.ins.2018.03.050
   Lin SS, 2021, AUTOMAT CONSTR, V122, DOI 10.1016/j.autcon.2020.103490
   Liu C, 2022, CONSTR INNOV-ENGL, V22, P141, DOI 10.1108/CI-02-2020-0017
   Liu JJ, 2022, AUTOMAT CONSTR, V140, DOI 10.1016/j.autcon.2022.104302
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Liu V, 2023, Arxiv, DOI arXiv:2304.08551
   Liu Y, 2023, J MATERIOMICS, V9, P798, DOI 10.1016/j.jmat.2023.05.001
   Lu WS, 2021, WASTE MANAGE, V134, P78, DOI 10.1016/j.wasman.2021.08.012
   Mahmoodzadeh A, 2022, AUTOMAT CONSTR, V139, DOI 10.1016/j.autcon.2022.104305
   Medhat W, 2014, AIN SHAMS ENG J, V5, P1093, DOI 10.1016/j.asej.2014.04.011
   Mehmood MU, 2019, ENERG BUILDINGS, V202, DOI 10.1016/j.enbuild.2019.109383
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Mishra S, 2023, Arxiv, DOI [arXiv:2302.07440, 10.48550/arXiv.2302.07440 2302.07440, DOI 10.48550/ARXIV.2302.074402302.07440]
   Mo ZB, 2023, LECT NOTES ARTIF INT, V13715, P323, DOI 10.1007/978-3-031-26409-2_20
   Moon S, 2022, AUTOMAT CONSTR, V142, DOI 10.1016/j.autcon.2022.104465
   Mulero-Palencia S, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13126576
   Narazaki Y, 2021, MECH SYST SIGNAL PR, V160, DOI 10.1016/j.ymssp.2021.107850
   NLTK, Natural language toolkit
   openai, GPT-4
   Oyediran H., 2021, P INT S AUTOMATION R
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pan M, 2022, J CONSTR ENG M, V148, DOI 10.1061/(ASCE)CO.1943-7862.0002324
   Paneru S, 2023, BUILDINGS-BASEL, V13, DOI 10.3390/buildings13020552
   Paneru S, 2021, AUTOMAT CONSTR, V132, DOI 10.1016/j.autcon.2021.103940
   Pantoja-Rosero BG, 2022, CONSTR BUILD MATER, V344, DOI 10.1016/j.conbuildmat.2022.128264
   Patton DU, 2023, J SOC SOC WORK RES, V14, P553, DOI 10.1086/726042
   Piñeiro-Martín A, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12143170
   Ploennigs J, 2024, Arxiv, DOI [arXiv:2307.02511, DOI 10.48550/ARXIV.2307.02511]
   Poh CQX, 2018, AUTOMAT CONSTR, V93, P375, DOI 10.1016/j.autcon.2018.03.022
   Pokharel S, 2023, ENVIRON MODELL SOFTW, V166, DOI 10.1016/j.envsoft.2023.105730
   Pournader M, 2021, INT J PROD ECON, V241, DOI 10.1016/j.ijpe.2021.108250
   Prieto SA, 2023, BUILDINGS-BASEL, V13, DOI 10.3390/buildings13040857
   Qwiklabs, Introduction to Generative AI
   Rahimian FP, 2020, AUTOMAT CONSTR, V110, DOI 10.1016/j.autcon.2019.103012
   Sacks R, 2020, DEV BUILT ENVIRON, V4, DOI 10.1016/j.dibe.2020.100011
   Saka A, 2023, Arxiv, DOI [arXiv:2305.18997, 10.1016/j.dibe.2023.100300]
   Saka AB, 2023, ADV ENG INFORM, V55, DOI 10.1016/j.aei.2022.101869
   Sakhakarmi S, 2019, J CONSTR ENG M, V145, DOI 10.1061/(ASCE)CO.1943-7862.0001601
   Sanni-Anibire MO, 2022, INT J CONSTR MANAG, V22, P2134, DOI 10.1080/15623599.2020.1768326
   Saravanan Vignesh, 2018, 2018 5th International Conference on Computational Science and Computational Intelligence (CSCI), P1218, DOI 10.1109/CSCI46756.2018.00234
   Satrio P., 2019, SUSTAIN ENERGY TECHN, V35, P48, DOI DOI 10.1016/j.seta.2019.06.002
   Schneider F., 2023, Archisound: audio generation with diffusion, DOI DOI 10.48550/ARXIV.2301.13267
   Semaan N, 2017, ENG CONSTR ARCHIT MA, V24, P61, DOI 10.1108/ECAM-06-2015-0094
   Seo J, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10207347
   Sha A., 2023, 12 Best Large Language Models (LLMs)
   Spencer BF, 2019, ENGINEERING-PRC, V5, P199, DOI 10.1016/j.eng.2018.11.030
   Succar B, 2009, AUTOMAT CONSTR, V18, P357, DOI 10.1016/j.autcon.2008.10.003
   Tan K, 2018, MATEC WEB CONF, V206, DOI 10.1051/matecconf/201820601008
   Teizer J, 2015, ADV ENG INFORM, V29, P225, DOI 10.1016/j.aei.2015.03.006
   textblob.readthedocs, TextBlob Simplified Text Processing
   Thanaki J., 2017, PYTHON NATURAL LANGU
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Van T.N., 2021, J Appl Sci Technol Trends, V2, P96
   Vaswani A, 2017, ADV NEUR IN, V30
   Vencer Lanz Vincent T., 2023, 2023 8th International Conference on Business and Industrial Research (ICBIR), P392, DOI 10.1109/ICBIR57571.2023.10147684
   Wang KF, 2017, IEEE-CAA J AUTOMATIC, V4, P588, DOI 10.1109/JAS.2017.7510583
   Weng L., What are diffusion models?
   Weng L., Flow-Based Deep Generative Models
   Williams TP, 2014, AUTOMAT CONSTR, V43, P23, DOI 10.1016/j.autcon.2014.02.014
   Wu AN, 2022, BUILD ENVIRON, V223, DOI 10.1016/j.buildenv.2022.109477
   Wu T, 2023, Arxiv, DOI arXiv:2305.09515
   Xie YQ, 2023, Arxiv, DOI arXiv:2302.05128
   Xu GX, 2019, IEEE ACCESS, V7, P51522, DOI 10.1109/ACCESS.2019.2909919
   Xu X, 2014, INT J ADV ROBOT SYST, V11, DOI 10.5772/58445
   Xu YY, 2021, DEV BUILT ENVIRON, V6, DOI 10.1016/j.dibe.2021.100045
   Yang YQ, 2019, SENSORS-BASEL, V19, DOI 10.3390/s19112528
   Yaseen ZM, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12041514
   You A, 2022, EYE VISION, V9, DOI 10.1186/s40662-022-00277-3
   You HX, 2023, Arxiv, DOI [arXiv:2304.11018, DOI 10.48550/ARXIV.2304.11018]
   You K, 2023, AUTOMAT CONSTR, V150, DOI 10.1016/j.autcon.2023.104852
   Yu KH, 2021, ENVIRON IMPACT ASSES, V86, DOI 10.1016/j.ejar.2020.106492
   Yuan Y, 2023, IEEE I CONF COMP VIS, P15964, DOI 10.1109/ICCV51070.2023.01467
   Zabin A, 2022, ADV ENG INFORM, V51, DOI 10.1016/j.aei.2021.101474
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
   Zhang C, 2019, BUILDSYS'19: PROCEEDINGS OF THE 6TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, P287, DOI 10.1145/3360322.3360861
   Zhang SS, 2022, Arxiv, DOI arXiv:2205.01068
   Zheng JW, 2023, AUTOMAT CONSTR, V155, DOI 10.1016/j.autcon.2023.105067
   Zheng JW, 2023, Arxiv, DOI arXiv:2304.09333
   Zhu ZH, 2010, AUTOMAT CONSTR, V19, P944, DOI 10.1016/j.autcon.2010.06.008
NR 163
TC 12
Z9 12
U1 56
U2 116
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2075-5309
J9 BUILDINGS-BASEL
JI BUILDINGS-BASEL
PD JAN
PY 2024
VL 14
IS 1
AR 220
DI 10.3390/buildings14010220
PG 29
WC Construction & Building Technology; Engineering, Civil
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering
GA FX9Z7
UT WOS:001149282600001
OA gold
DA 2024-12-25
ER

PT J
AU Rana, NP
   Pillai, R
   Sivathanu, B
   Malik, N
AF Rana, Nripendra P.
   Pillai, Rajasshrie
   Sivathanu, Brijesh
   Malik, Nishtha
TI Assessing the nexus of Generative AI adoption, ethical considerations
   and organizational performance
SO TECHNOVATION
LA English
DT Article
DE Institutional theory; AI ethics; Generative AI; Organizational
   innovativeness; Organizational performance
ID BIG DATA; PREDICTIVE ANALYTICS; LEARNING ORIENTATION; INSTITUTIONAL
   THEORY; FIRM PERFORMANCE; MEDIATING ROLE; PLS-SEM; INTENTION;
   INNOVATIVENESS; CAPABILITY
AB Numerous enterprises employ Generative AI (GenAI) for a plethora of business operations, which can enhance organizational effectiveness. The adoption might be driven by multiple factors influencing the business landscape. Additionally, numerous ethical considerations could impact the deployment of GenAI. This unique study investigated how organizations adopt GenAI and its effects on their performance. Further, this research utilized institutional theory and ethical guidelines for AI design to develop a research framework examining how organizations adopt GenAI and its impact on their performance. A survey of 384 managers from information technology (IT) and information technology-enabled services (ITeS) companies was conducted. Data analysis was done using PLS-SEM to examine and validate the proposed model. The study outcome reveals that institutional pressures, i.e., coercive, normative and mimetic forces, influence the use of GenAI in organizations. It was also found that fairness, accountability, transparency, accuracy and autonomy influence the use of GenAI. Also, the results divulge that the use of GenAI influences organizational performance and is moderated by organizational innovativeness. This study provides insights to developers of GenAI, senior management of companies, the government and IT policymakers by highlighting the institutional pressures and ethical principles influencing the use of GenAI.
C1 [Rana, Nripendra P.] Queens Univ Belfast, Queens Business Sch, Riddel Hall,185 Stranmillis Rd, Belfast BT9 5EE, North Ireland.
   [Pillai, Rajasshrie] Pune Inst Business Management, Dept Management, Pune, Maharashtra, India.
   [Sivathanu, Brijesh] COEP Technol Univ, Coll Engn Pune, Dept Management, Pune, Maharashtra, India.
   [Malik, Nishtha] Jaipuria Inst Management, Lucknow, India.
C3 Queens University Belfast; College of Engineering Pune
RP Rana, NP (corresponding author), Queens Univ Belfast, Queens Business Sch, Riddel Hall,185 Stranmillis Rd, Belfast BT9 5EE, North Ireland.
EM n.p.rana@qub.ac.uk; rajasshrie1@gmail.com; brij.jesh2002@gmail.com;
   Nishthamalik3@gmail.com
RI Rana, Nripendra/ABA-4719-2020
OI Rana, Nripendra P./0000-0003-1105-8729; MALIK, DR.
   NISHTHA/0000-0002-1582-6855
CR Acar AZ, 2018, INT J INNOV MANAG, V22, DOI 10.1142/S1363919618500093
   Acikgoz Y, 2020, INT J SELECT ASSESS, V28, P399, DOI 10.1111/ijsa.12306
   Agrawal KP, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2286540
   Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Akter S, 2023, TECHNOVATION, V125, DOI 10.1016/j.technovation.2023.102768
   Ali F, 2018, INT J CONTEMP HOSP M, V30, P514, DOI 10.1108/IJCHM-10-2016-0568
   Ameye N, 2023, TECHNOVATION, V127, DOI 10.1016/j.technovation.2023.102846
   [Anonymous], 2023, Generative AI Could Raise Global GDP by 7%
   Ashok M, 2022, INT J INFORM MANAGE, V62, DOI 10.1016/j.ijinfomgt.2021.102433
   Aydiner AS, 2019, J BUS RES, V96, P228, DOI 10.1016/j.jbusres.2018.11.028
   Bag S, 2023, IND MARKET MANAG, V115, P470, DOI 10.1016/j.indmarman.2023.11.003
   Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
   Balasubramaniam N, 2023, INFORM SOFTWARE TECH, V159, DOI 10.1016/j.infsof.2023.107197
   Baruah A., 2023, Business Standards
   Beer JM, 2014, J HUM-ROBOT INTERACT, V3, P74, DOI 10.5898/JHRI.3.2.Beer
   Brüns JD, 2024, J RETAIL CONSUM SERV, V79, DOI 10.1016/j.jretconser.2024.103790
   Budhathoki T, 2024, STUD HIGH EDUC, V49, P831, DOI 10.1080/03075079.2024.2333937
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Calantone RJ, 2002, IND MARKET MANAG, V31, P515, DOI 10.1016/S0019-8501(01)00203-6
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Chandio AA, 2023, ENVIRON DEV SUSTAIN, V25, P1614, DOI 10.1007/s10668-022-02111-1
   Chen YA, 2023, INT J CONTEMP HOSP M, V35, P2868, DOI 10.1108/IJCHM-04-2022-0433
   Chin W, 2020, IND MANAGE DATA SYST, V120, P2161, DOI 10.1108/IMDS-10-2019-0529
   Chin WW, 2012, MIS QUART, V36, P1003
   Chu MN, 2023, IEEE ACCESS, V11, P76427, DOI 10.1109/ACCESS.2023.3297447
   Ciftci I, 2019, INT BUS REV, V28, P90, DOI 10.1016/j.ibusrev.2018.08.004
   Cohen G., 2023, Harv. Bus. Rev., P1
   Cuomo J, IBM Blog
   De Smet A., 2023, The Human Side of Generative AI: Creating a Path to Productivity
   Deloitte, 2019, Conversational AI - the next wave of customer and employee experience, DOI [10.48175/ijarsct-1141, DOI 10.48175/IJARSCT-1141]
   Deloitte, 2023, Global Powers of Retailing 2023 (Revenue growth and continued focus on sustainability), P1
   Díaz-Rodríguez N, 2023, INFORM FUSION, V99, DOI 10.1016/j.inffus.2023.101896
   DiMaggio PJ, 2000, ADV STRATEG MANAGE, V 17, P143, DOI 10.2307/2095101
   Dubey R, 2019, BRIT J MANAGE, V30, P341, DOI 10.1111/1467-8551.12355
   Dubey R, 2019, MANAGE DECIS, V57, P767, DOI 10.1108/MD-04-2018-0396
   Dubey R, 2018, INT J LOGIST MANAG, V29, P485, DOI 10.1108/IJLM-02-2017-0039
   Dubey R, 2016, RESOUR CONSERV RECY, V106, P78, DOI 10.1016/j.resconrec.2015.11.008
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Elliott K, 2021, SOCIETY, V58, P179, DOI 10.1007/s12115-021-00594-8
   Elmore M, 2023, AM J BIOETHICS, V23, P47, DOI 10.1080/15265161.2023.2250335
   Felzmann H, 2019, BIG DATA SOC, V6, DOI 10.1177/2053951719860542
   Floridi L, 2020, SCI ENG ETHICS, V26, P1771, DOI 10.1007/s11948-020-00213-5
   Gartner, 2023, Gartner Says More than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026
   Guizzardi Renata, 2020, Advances in Artificial Intelligence. 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020. Proceedings. Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science (LNAI 12109), P251, DOI 10.1007/978-3-030-47358-7_24
   Gupta R., 2023, Generative AI's Role in Revolutionizing IT Services
   Gupta S, 2020, SUPPLY CHAIN FORUM, V21, P139, DOI 10.1080/16258312.2020.1757369
   Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Hartley JL, 2022, INT J PHYS DISTR LOG, V52, P190, DOI 10.1108/IJPDLM-05-2020-0163
   Heyder T, 2023, J STRATEGIC INF SYST, V32, DOI 10.1016/j.jsis.2023.101772
   Hong WY, 2006, INFORM MANAGE-AMSTER, V43, P204, DOI 10.1016/j.im.2005.06.003
   Hu P, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106727
   Hu Q, 2021, INT J INFORM MANAGE, V56, DOI 10.1016/j.ijinfomgt.2020.102250
   Huang DH, 2021, J INNOV KNOWL, V6, P135, DOI 10.1016/j.jik.2020.09.002
   Kieslich K, 2022, BIG DATA SOC, V9, DOI 10.1177/20539517221092956
   Kock N., 2017, Partial Least Square Path Modeling, P245, DOI [DOI 10.1007/978-3-319-64069-311, 10.1007/978-3-319-64069-3_11, DOI 10.1007/978-3-319-64069-3_11]
   Lai YY, 2018, INT J LOGIST MANAG, V29, P676, DOI 10.1108/IJLM-06-2017-0153
   Liang HG, 2007, MIS QUART, V31, P59
   Lin DP, 2018, IND MANAGE DATA SYST, V118, P589, DOI 10.1108/IMDS-09-2017-0403
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Marchiori DM, 2022, TECHNOL FORECAST SOC, V177, DOI 10.1016/j.techfore.2022.121526
   Mariani MM, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102623
   Marr B., 2023, 10 Amazing Real-World Examples of How Companies Are Using ChatGPT in 2023 WWW Document
   Martin K, 2019, MIS Q EXEC, V18, P129, DOI 10.17705/2msqe.00012
   McKinsey and Company Featured Insights, 2023, What Is Generative AI
   Mckinsey & Company, 2023, What's the Future of Generative AI? An early View in 15 Charts
   Messerschmidt CM, 2013, J STRATEGIC INF SYST, V22, P137, DOI 10.1016/j.jsis.2012.10.005
   Nagtegaal R, 2021, GOV INFORM Q, V38, DOI 10.1016/j.giq.2020.101536
   NASSCOM, 2023, Harnessing the Power of Generative AI - Opportunities for Technology Services
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   OpenAI Blog, 2022, Introducing ChatGPT
   OSullivan I., 2023, 10 Ways Businesses Are Using ChatGPT Right Now WWW Document
   Parteka A, 2023, TECHNOVATION, V125, DOI 10.1016/j.technovation.2023.102764
   Pesamaa O, 2013, J ENG TECHNOL MANAGE, V30, P169, DOI 10.1016/j.jengtecman.2013.01.004
   Pillai R, 2024, BENCHMARKING, V31, P3884, DOI 10.1108/BIJ-05-2023-0288
   Pillai R, 2022, PROD PLAN CONTROL, V33, P1517, DOI 10.1080/09537287.2021.1882689
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Rahimzadeh V, 2023, AM J BIOETHICS, V23, P17, DOI 10.1080/15265161.2023.2233358
   Raj R., 2023, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, DOI [10.1016/j.benchc.2023.100140, DOI 10.1016/J.BENCHC.2023.100140, 10.1016/j.tbench.2023.100140, DOI 10.1016/J.TBENCH.2023.100140]
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Ringle C.M., 2014, REV BRASIL MARK, V13, P56, DOI [10.5585/remark.v13i2.2717, DOI 10.5585/REMARK.V13I2.2717]
   Ronkko M., 2011, PLS marker variable approach to diagnosing and controlling for method variance, P1
   Roush T., 2023, FORBES
   Rubera G, 2012, J MARKETING, V76, P130, DOI 10.1509/jm.10.0494
   Sackey SM, 2016, S AFR J IND ENG, V27, P101
   Sarkis J, 2011, INT J PROD ECON, V130, P1, DOI 10.1016/j.ijpe.2010.11.010
   Scott W.Richard., 2004, Encyclopedia of Social Theory, P408, DOI DOI 10.4135/9781412952552.N155
   Shahzad K, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122989
   Sharma D., 2023, India Today
   Shiau WL, 2016, INFORM MANAGE-AMSTER, V53, P355, DOI 10.1016/j.im.2015.10.004
   Shin D, 2022, COMPUT HUM BEHAV, V133, DOI 10.1016/j.chb.2022.107292
   Shin D, 2021, INT J HUM-COMPUT ST, V146, DOI 10.1016/j.ijhcs.2020.102551
   Shin D, 2020, INT J INFORM MANAGE, V52, DOI 10.1016/j.ijinfomgt.2019.102061
   Shin D, 2019, COMPUT HUM BEHAV, V98, P277, DOI 10.1016/j.chb.2019.04.019
   Sidaoui K, 2024, ORGAN DYN, V53, DOI 10.1016/j.orgdyn.2024.101045
   Simmering MJ, 2015, ORGAN RES METHODS, V18, P473, DOI 10.1177/1094428114560023
   Sivathanu B, 2019, INF RESOUR MANAG J, V32, P52, DOI 10.4018/IRMJ.2019040103
   Sivathanu B, 2018, J ELECTRON COMMER OR, V16, P19, DOI 10.4018/JECO.2018100102
   Sohn K, 2021, INT J RETAIL DISTRIB, V49, P61, DOI 10.1108/IJRDM-03-2020-0091
   Song M, 2000, J INT MARKETING, V8, P61, DOI 10.1509/jimk.8.4.61.19796
   Song MM, 2022, J RETAIL CONSUM SERV, V66, DOI 10.1016/j.jretconser.2021.102900
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Talaei-Khoei A, 2024, TECHNOVATION, V132, DOI 10.1016/j.technovation.2024.102975
   Tsai MC, 2013, INFORM MANAGE-AMSTER, V50, P59, DOI 10.1016/j.im.2012.05.006
   Tsigaris P, 2024, ACCOUNT RES, V31, P973, DOI 10.1080/08989621.2023.2179919
   Tsou HT, 2015, INT J INFORM MANAGE, V35, P1, DOI 10.1016/j.ijinfomgt.2014.09.001
   Tsou J.Y., 2023, Ethical Theory and Technology, VI
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wamba SF, 2016, PROD PLAN CONTROL, V27, P979, DOI 10.1080/09537287.2016.1167981
   Wang YY, 2018, INT J INFORM MANAGE, V38, P7, DOI 10.1016/j.ijinfomgt.2017.07.003
   Willis G.B., 2016, The SAGE Handbook Surv. Methodol., P359, DOI DOI 10.4135/9781473957893
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Zheng DQ, 2013, EUR J INFORM SYST, V22, P221, DOI 10.1057/ejis.2012.28
NR 115
TC 1
Z9 1
U1 137
U2 137
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD JUL
PY 2024
VL 135
AR 103064
DI 10.1016/j.technovation.2024.103064
EA JUL 2024
PG 12
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA XX4S4
UT WOS:001264969000001
OA hybrid
DA 2024-12-25
ER

PT J
AU Hill, PA
   Narine, LK
   Miller, AL
AF Hill, Paul A.
   Narine, Lendel K.
   Miller, Aubree L.
TI Prompt Engineering Principles for Generative AI Use in Extension
SO JOURNAL OF EXTENSION
LA English
DT Article
AB . The prevalence of Generative AI (GenAI) and Large Language Models (LLMs) is increasing rapidly. For Extension professionals, the utilization of prompt engineering is key to leveraging GenAI and LLMs effectively. Prompt engineering involves crafting prompts that elicit desired LLM responses. This article discusses prompt engineering principles, providing examples and guidance. The application of prompt engineering in Extension is explored, showcasing its potential to enhance programs, deliver personalized advice, engage audiences, and disseminate research-based information. By learning prompt engineering skills, Extension professionals can harness the power of GenAI and LLMs, enhancing their ability to address complex challenges in the twenty-first century.
C1 [Hill, Paul A.; Narine, Lendel K.; Miller, Aubree L.] Utah State Univ, Logan, UT 84322 USA.
C3 Utah System of Higher Education; Utah State University
RP Hill, PA (corresponding author), Utah State Univ, Logan, UT 84322 USA.
EM paul.hill@usu.edu; lendel.narine@usu.edu; aubree.miller@usu.edu
CR Andreessen Mark, 2023, Andreessen Horowitz
   Birss D., 2023, LinkedIn Learning
   Brynjolfsson E., 2023, Generative AI at Work (No. W31161)
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Gawdat M., 2021, Scary Smart: The Future of Artificial Intelligence and How You Can save Our World
   Google, 2023, Generative AI on Google Cloud
   Gordon R., 2023, MIT News
   Hill P. A., 2023, Journal of Extension, V61
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   McKinsey & Company, 2023, WHAT IS GENERATIVE A
   Mollick E., 2023, Let ChatGPT be your teaching assistant: Strategies for thoughtfully using AI to lighten your workload
   Toner H., 2023, What are generative AI, large language models, and foundation models
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
   Zewe A., 2023, MIT News
   Zhou YC, 2023, Arxiv, DOI arXiv:2211.01910
NR 15
TC 0
Z9 0
U1 11
U2 11
PU UNIV OF WISCONSIN EXTENSION JOURNAL INC
PI MADISON
PA 605 EXTENSION BLDG 432 NORTH LAKE ST, MADISON, WI 53706 USA
SN 0022-0140
EI 1077-5315
J9 J EXT
JI J. Ext.
PY 2024
VL 62
IS 3
AR 20
PG 7
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA G3C4M
UT WOS:001315451700003
DA 2024-12-25
ER

PT J
AU Xia, Y
   Chen, Y
AF Xia, Yan
   Chen, Yue
TI Driving Factors of Generative AI Adoption in New Product Development
   Teams from a UTAUT Perspective
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article; Early Access
DE New product design; generative AI; technology acceptance; UTAUT
ID COMPUTER SELF-EFFICACY; PERCEIVED RISK; INFORMATION-TECHNOLOGY; MODEL;
   PERSONALITY; ACCEPTANCE; CREATIVITY; TRUST; IDEA; INTELLIGENCE
AB Recent new product development (NPD) teams apply various generative AI (GenAI) tools in the development process, yet it is not fully understood about the factors affecting teams' adoption of these tools. This research identifies factors driving the use and attitudes toward GenAI in NPD tasks based on the Unified Theory of Acceptance and Use of Technology (UTAUT). We interviewed nine GenAI users in NPD teams and conducted a survey study with 309 participants. By exploratory factor analysis and hierarchical regressions, we identified a composite factor of performance expectancy and anthropomorphism as the strongest positive predictor of attitudes, and task-tool fitness as the strongest positive predictor of behavioral intention. Besides, we also identified significant predictors including several other factors in UTAUT and individual differences in AI self-efficacy. The findings can be used for developing UTAUT models and designing GenAI tools specific to NPD purposes.
C1 [Xia, Yan; Chen, Yue] East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China.
C3 East China University of Science & Technology
RP Chen, Y (corresponding author), East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China.
EM chenyue@ecust.edu.cn
OI Chen, Yue/0000-0001-9592-0368
FU National Natural Science Foundation of China
FX No Statement Available
CR Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
   Ali I, 2019, J INNOV KNOWL, V4, P38, DOI 10.1016/j.jik.2017.11.002
   An X, 2023, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2023.2246519
   Apotheker J., 2024, BCG AI radar: From potential to profit with GenAI
   AQSIQ SAC, 2017, Industrial classification for national economic activities (GB/T 47542017)
   Araujo T, 2018, COMPUT HUM BEHAV, V85, P183, DOI 10.1016/j.chb.2018.03.051
   Ariff MSM, 2012, PROCD SOC BEHV, V57, P448, DOI 10.1016/j.sbspro.2012.09.1210
   Bahdanau D, 2016, Arxiv, DOI arXiv:1409.0473
   Barth S, 2017, TELEMAT INFORM, V34, P1038, DOI 10.1016/j.tele.2017.04.013
   Bartneck C, 2009, INT J SOC ROBOT, V1, P71, DOI 10.1007/s12369-008-0001-3
   Batey M, 2006, GENET SOC GEN PSYCH, V132, P355, DOI 10.3200/MONO.132.4.355-430
   Bell JJ, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.1434
   BETTMAN JR, 1973, J MARKETING RES, V10, P184, DOI 10.2307/3149824
   Blut M, 2021, J ACAD MARKET SCI, V49, P632, DOI 10.1007/s11747-020-00762-y
   Booz, 1982, NEW PRODUCTS MANAGEM
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   Cabrera-Sánchez JP, 2021, TELEMAT INFORM, V58, DOI 10.1016/j.tele.2020.101529
   Cai A, 2023, PROCEEDINGS OF THE ACM COLLECTIVE INTELLIGENCE CONFERENCE, CI 2023, P1, DOI 10.1145/3582269.3615596
   Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
   Cao WW, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02325-2
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Celik V, 2013, COMPUT EDUC, V60, P148, DOI 10.1016/j.compedu.2012.06.008
   Chatterjee S, 2020, EDUC INF TECHNOL, V25, P3443, DOI 10.1007/s10639-020-10159-7
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chen Mark, 2021, arXiv
   Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
   Chuang SC, 2015, COMPUT HUM BEHAV, V48, P147, DOI 10.1016/j.chb.2015.01.044
   Chuma E. L., 2023, Manag Sci Bus Decis, V3, P5, DOI DOI 10.52812/MSBD.63
   Chung JJY, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501819
   Coenen A, 2021, Arxiv, DOI arXiv:2107.07430
   Comrey AL, 1992, 1 COURSE FACTOR ANAL
   COOPER RG, 1990, BUS HORIZONS, V33, P44, DOI 10.1016/0007-6813(90)90040-I
   COOPER RG, 1986, J PROD INNOVAT MANAG, V3, P71, DOI 10.1111/1540-5885.320071
   Cooper RG, 2008, J PROD INNOVAT MANAG, V25, P213, DOI 10.1111/j.1540-5885.2008.00296.x
   Cooper Robert G, 2010, Wiley International Encyclopedia of Marketing, DOI [10.1002/9781444316568.wiem05014, DOI 10.1002/9781444316568.WIEM05014]
   Creswell John, 2011, DESIGNING CONDUCTING
   Sebastián MGD, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.993935
   Dean DL, 2006, J ASSOC INF SYST, V7, P646, DOI 10.17705/1jais.00106
   Diederich Stephan, 2019, P 40 INT C INF SYST
   Dollinger StephenJ., 2012, ENCY SCI LEARNING, P2522, DOI [DOI 10.1007/978-1-4419-1428-687, 10.1007/978-1-4419-1428-6_87, DOI 10.1007/978-1-4419-1428-6_87]
   Eapen TT., 2023, HARVARD BUS REV
   Fakhimi A, 2023, AUSTRALAS MARK J, V31, P314, DOI 10.1177/14413582231181140
   Fang Y.-M., 2023, The role of generative AI in industrial design: Enhancing the design process and learning, DOI [https://doi.org/10.1049/icp.2024.0303, DOI 10.1049/ICP.2024.0303]
   Feng ZY, 2020, Arxiv, DOI [arXiv:2002.08155, 10.48550/arXiv.2002.08155, DOI 10.48550/ARXIV.2002.08155]
   Ferioli Marcelo, 2010, International Journal of Product Development, V12, P67, DOI 10.1504/IJPD.2010.034313
   Florén H, 2012, CALIF MANAGE REV, V54, P20, DOI 10.1525/cmr.2012.54.4.20
   Foroughi B, 2024, INT J HUM-COMPUT INT, V40, P4501, DOI 10.1080/10447318.2023.2226495
   Gansser OA, 2021, TECHNOL SOC, V65, DOI 10.1016/j.techsoc.2021.101535
   Girotra K, 2010, MANAGE SCI, V56, P591, DOI 10.1287/mnsc.1090.1144
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
   GOODHUE DL, 1995, MIS QUART, V19, P213, DOI 10.2307/249689
   Gosling SD, 2003, J RES PERS, V37, P504, DOI 10.1016/S0092-6566(03)00046-1
   Guo DY, 2021, Arxiv, DOI arXiv:2009.08366
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hong JW, 2022, INT J COMMUN-US, V16, P172
   Hsiung CP, 2023, HUM FACTOR ERGON MAN, V33, P379, DOI 10.1002/hfm.20998
   Huang L, 2022, CIRP ANN-MANUF TECHN, V71, P121, DOI 10.1016/j.cirp.2022.04.053
   Hui Z, 2024, INT J HUM-COMPUT INT, V40, P7546, DOI 10.1080/10447318.2023.2266254
   Hussein BA, 2014, PROCD SOC BEHV, V119, P702, DOI 10.1016/j.sbspro.2014.03.078
   Jain R, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.893691
   Janson A, 2023, COMPUT HUM BEHAV, V149, DOI 10.1016/j.chb.2023.107954
   Joibi J., 2023, KOR SOC DES STUD AUT
   Joinson AN, 2010, HUM-COMPUT INTERACT, V25, P1, DOI 10.1080/07370020903586662
   Jokisch MR, 2020, COMPUT HUM BEHAV, V111, DOI 10.1016/j.chb.2020.106408
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kaplan AD, 2023, HUM FACTORS, V65, P337, DOI 10.1177/00187208211013988
   Kehr F., 2014, INT C INT SCI
   Kim JS, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2311971
   Kim J, 2021, PSYCHOL MARKET, V38, P1140, DOI 10.1002/mar.21498
   Kim S, 2021, Arxiv, DOI [arXiv:2003.13848, DOI 10.48550/ARXIV.2003.13848]
   Klein M., 2014, SSRN Electronic Journal, DOI [https://doi.org/10.2139/ssrn.2387180, DOI 10.2139/SSRN.2387180]
   Kraus J, 2024, INT J SOC ROBOT, V16, P1223, DOI 10.1007/s12369-022-00952-4
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Kuo YF, 2013, INT J INFORM MANAGE, V33, P948, DOI 10.1016/j.ijinfomgt.2013.08.005
   Lachaux MA, 2020, Arxiv, DOI arXiv:2006.03511
   Lan H, 2024, HUM SOC SCI COMMUN, V11, DOI 10.1057/s41599-024-02600-w
   Larson L, 2020, LEADERSHIP QUART, V31, DOI 10.1016/j.leaqua.2019.101377
   Lee K.M., 2003, P SIGCHI C HUM FACT, P289, DOI [DOI 10.1145/642611.642662, 10.1145/642611.642662]
   Leenders RTAJ, 2003, J ENG TECHNOL MANAGE, V20, P69, DOI 10.1016/S0923-4748(03)00005-5
   Li MJ, 2022, ELECTRON MARK, V32, P2245, DOI 10.1007/s12525-022-00591-7
   Li WY, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2310354
   Li XG, 2021, COMPUT HUM BEHAV, V118, DOI 10.1016/j.chb.2021.106680
   Liebrecht Christine, 2021, Chatbot Research and Design. 4th International Workshop, CONVERSATIONS 2020. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 12604), P16, DOI 10.1007/978-3-030-68288-0_2
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu M, 2022, LECT NOTES COMPUT SC, V13324, P234, DOI 10.1007/978-3-031-05434-1_15
   Maaravi Y, 2021, REV MANAG SCI, V15, P1431, DOI 10.1007/s11846-020-00400-5
   Maican CI, 2023, J ORGAN END USER COM, V35, DOI 10.4018/JOEUC.330019
   Meijer B., 2023, A comparative analysis of human and A.I. feedback on business idea evaluation
   Mesbah S., 2023, P ACM WEB C 2023, P3837, DOI 10.1145/3543507.3583496
   Moussawi S, 2021, BEHAV INFORM TECHNOL, V40, P1603, DOI 10.1080/0144929X.2020.1772368
   Moussawi S, 2021, ELECTRON MARK, V31, P343, DOI 10.1007/s12525-020-00411-w
   Murad M. A. A., 2007, International Journal of Computer Science and Security, P1
   Nass C, 1996, INT J HUM-COMPUT ST, V45, P669, DOI 10.1006/ijhc.1996.0073
   Nov O., 2008, P 41 ANN HAW INT C S, DOI DOI 10.1109/HICSS.2008.348
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Organisciak P, 2023, THINK SKILLS CREAT, V49, DOI 10.1016/j.tsc.2023.101356
   Osta A, 2022, LECT NOTES BUS INF P, V437, P488, DOI 10.1007/978-3-030-95947-0_35
   PAGE AL, 1993, J PROD INNOVAT MANAG, V10, P273, DOI 10.1016/0737-6782(93)90071-W
   Pahl G., 2007, Engineering design: a systematic approach, DOI DOI 10.1007/978-1-84628-319-2
   Park J, 2022, J PSYCHOL, V156, P68, DOI 10.1080/00223980.2021.2012109
   Patterson F., 2009, Characteristics behaviours of innovative people in organisations : Literature review
   Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
   PETER JP, 1976, J MARKETING RES, V13, P184, DOI 10.2307/3150856
   Ribera M., 2019, JOINT P ACM IUI 2019
   Riedl R, 2022, ELECTRON MARK, V32, P2021, DOI 10.1007/s12525-022-00594-4
   Rietz T., 2019, 14 INT C WIRTSCH
   Roh T, 2023, J ELECTRON COMMER RE, V24, P29
   Romero-Rodríguez JM, 2023, J NEW APPROACHES EDU, V12, P323, DOI 10.7821/naer.2023.7.1458
   Rose J., 2023, TheBlue
   Rui Zhang, 2020, Proceedings of the ACM on Human-Computer Interaction, V4, DOI 10.1145/3432945
   Russo D, 2024, ACM T SOFTW ENG METH, V33, DOI 10.1145/3652154
   Said N, 2023, COMPUT HUM BEHAV, V149, DOI 10.1016/j.chb.2023.107855
   Saville JD, 2021, COMPUT HUM BEHAV REP, V4, DOI 10.1016/j.chbr.2021.100124
   Sawyer K, 2011, CREATIVITY RES J, V23, P137, DOI 10.1080/10400419.2011.571191
   Schuetzler RM, 2020, J MANAGE INFORM SYST, V37, P875, DOI 10.1080/07421222.2020.1790204
   Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
   Seeger AM, 2021, J ASSOC INF SYST, V22, P931, DOI 10.17705/1jais.00685
   Selker T, 2023, Arxiv, DOI arXiv:2307.08876
   Selya AS, 2012, FRONT PSYCHOL, V3, DOI 10.3389/fpsyg.2012.00111
   Shamszare H, 2023, Arxiv, DOI [arXiv:2311.05632, 10.48550/arXiv.2311.05632, DOI 10.48550/ARXIV.2311.05632]
   Sharma S, 2024, IEEE T ENG MANAGE, V71, P1846, DOI 10.1109/TEM.2022.3157976
   Shin D, 2021, INT J HUM-COMPUT ST, V146, DOI 10.1016/j.ijhcs.2020.102551
   Shorten Allison, 2017, Evid Based Nurs, V20, P74, DOI 10.1136/eb-2017-102699
   Siemon D, 2022, GROUP DECIS NEGOT, V31, P871, DOI 10.1007/s10726-022-09792-z
   Simon HA, 1955, Q J ECON, V69, P99, DOI 10.2307/1884852
   Sindermann C, 2022, DISCOV PSYCHOL, V2, DOI 10.1007/s44202-022-00020-y
   Spreafico C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15118678
   Stone RN, 1993, Eur. J. Marketing, V27, P39, DOI [10.1108/03090569310026637, DOI 10.1108/03090569310026637]
   Strzelecki A, 2024, INNOV HIGH EDUC, V49, P223, DOI 10.1007/s10755-023-09686-1
   Syverson B., 2020, The rules of brainstorming change when artificial intelligence gets involved. Here's how
   Tabachnick BG., 2007, USING MULTIVARIATE S, V5th, pxxvii
   Thomke S, 2000, J PROD INNOVAT MANAG, V17, P128, DOI 10.1016/S0737-6782(99)00031-4
   Ulfert AS, 2024, EUR J WORK ORGAN PSY, V33, P158, DOI 10.1080/1359432X.2023.2200172
   Ulfert-Blank AS, 2022, COMPUT EDUC, V191, DOI 10.1016/j.compedu.2022.104626
   Van Pinxteren MME, 2020, J SERV MANAGE, V31, P203, DOI 10.1108/JOSM-06-2019-0175
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2012, MIS QUART, V36, P157
   VONHIPPEL E, 1995, RES POLICY, V24, P1, DOI 10.1016/0048-7333(93)00747-H
   Wang YY, 2024, EDUC INF TECHNOL, V29, P4785, DOI 10.1007/s10639-023-12015-w
   Weitzman C., 2023, Unlock the power of AI video summarization now
   Wilson N, 2021, GADJAH MADA INT J BU, V23, P262
   Wu WT, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.870777
   Xi ZH, 2023, Arxiv, DOI arXiv:2309.07864
   Xie YG, 2023, COMPUT HUM BEHAV, V148, DOI 10.1016/j.chb.2023.107878
   Xiong YW, 2024, HUM FACTOR ERGON MAN, V34, P190, DOI 10.1002/hfm.21020
   Yang Q, 2015, COMPUT HUM BEHAV, V50, P9, DOI 10.1016/j.chb.2015.03.058
   Yesil S, 2013, PROCD SOC BEHV, V81, P540, DOI 10.1016/j.sbspro.2013.06.474
   Yesilyurt E, 2016, COMPUT HUM BEHAV, V64, P591, DOI 10.1016/j.chb.2016.07.038
   Yi M, 2023, Arxiv, DOI arXiv:2306.13670
   Yin M, 2023, LECT NOTES COMPUT SC, V14059, P288, DOI 10.1007/978-3-031-48057-7_18
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
   Zhang AD, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107415
   Zhang CZ, 2023, 2023 PROCEEDINGS OF THE 15TH CONFERENCE ON CREATIVITY AND COGNITION, C&C 2023, P254, DOI 10.1145/3591196.3596820
   Zhu WJ, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2323277
NR 155
TC 1
Z9 1
U1 126
U2 126
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PD 2024 JUL 11
PY 2024
DI 10.1080/10447318.2024.2375686
EA JUL 2024
PG 22
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA YR7Z8
UT WOS:001270288300001
DA 2024-12-25
ER

PT J
AU Cacho, RM
AF Cacho, Reynald M.
TI Integrating Generative AI in University Teaching and Learning: A Model
   for Balanced Guidelines
SO ONLINE LEARNING
LA English
DT Article
DE generative artificial intelligence; AI guidelines; ChatGPT; teaching;
   learning; university
AB The study proposes a balanced approach and flexible guidelines for incorporating generative artificial intelligence (AI) into university-level teaching and learning processes at both the university-departmental level and within individual academic autonomy. Building on the AI Ecological Education Policy Framework, the guidelines offer a suggestive frame of reference for faculty and students to integrate generative AI into their coursework. Furthermore, feedback from 118 students and 14 academics at a teacher education institution in the Philippines underscores the guidelines' potential benefits, concerns, usefulness, and necessity in their academic undertakings. While the policy may not cover every detail exhaustively, it seeks to provide practical and context-sensitive recommendations for ethical, honest, responsible, and fair use of AI in course development, implementation, and student engagement. Consequently, other higher education institutions in general, and academics in particular, may adopt and/or modify the guidelines to suit their positions, goals, needs, and directions.
C1 [Cacho, Reynald M.] Philippine Normal Univ South Luzon, Quezon City, Philippines.
RP Cacho, RM (corresponding author), Philippine Normal Univ South Luzon, Quezon City, Philippines.
RI Cacho, Reynald/HJA-8631-2022
OI Cacho, Reynald/0000-0002-0106-5135
FX AI Utilization Declaration. ? I/We declare that Generative AI tools have
   not been used to produce the submitted work. State your reason (s) for
   not using Generative AI tools. The author has no competing interests to
   declare. The study is conducted solely by the author with the
   Institutional Review/Clearance to Proceed REC Code: 2024-111. The
   datasets used and/or analyzed in this project are available from the
   corresponding author upon reasonable request. The research project was
   completed without the benefit of internal or external financial support.
CR Abbas M, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00444-7
   Adeshola I, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253858
   [Anonymous], 2021, Ai and education: guidance for policy-makers
   Bajar JNB, 2024, OPEN PRAX, V16, P208, DOI 10.55982/openpraxis.16.2.586
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Besley T, 2023, EDUC PHILOS THEORY, V55, P272, DOI 10.1080/00131857.2021.2015322
   Bozkurt A., 2023, Asian Journal of Distance Education, V18, P198, DOI DOI 10.5281/ZENODO.7716416
   Bozkurt A., 2022, Asian Journal of Distance Education, V17, DOI DOI 10.5281/ZENODO.6362290
   Bozkurt A, 2024, OPEN PRAX, V16, P283, DOI [10.55982/openpraxis.16.3.739, 10.55982/openpraxis.16.1.654]
   Bozkurt A, 2023, OPEN PRAX, V15, P178, DOI 10.55982/openpraxis.15.3.579
   Cacho R. M., 2014, Asian Journal of Education and E-Learning, V2
   Cacho R, 2023, KNOWL PROCESS MANAG, V30, P398, DOI 10.1002/kpm.1744
   Cacho RM, 2018, ADV SOC SCI EDUC HUM, V212, P582
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00269-3
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Delcker J, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00452-7
   Hong Kong Polytechnic University, 2023, Guidelines for students on the use of generative artificial intelligence (GenAI)
   HyScaler, 2023, The Power of AI in Research Hypotheses
   Johnston H, 2024, INT J EDUC INTEGR, V20, DOI 10.1007/s40979-024-00149-4
   Khallel B, 2024, ONLINE LEARN, V28, P1, DOI 10.24059/olj.v28i2.4397
   Kostka I., 2023, TESL-EJ, V27, P1, DOI [10.55593/ej.27107int, DOI 10.55593/EJ.27107INT]
   Lampou R., 2023, Review of Artificial Intelligence in Education, V4, pe015, DOI [10.37497/rev.artif.intell.educ.v4i00.15, DOI 10.37497/REV.ARTIF.INTELL.EDUC.V4I00.15]
   Lin CC, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00260-y
   London School of Economics and Political Science, 2022, Statement on editorial help for students' written work
   Mayring P., 2004, QUALITATIVE CONTENT, V1, P159, DOI DOI 10.1177/2158244014522633
   McAdoo T., 2024, CITE CHATGPT
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Miyazaki K, 2024, EPJ DATA SCI, V13, DOI 10.1140/epjds/s13688-023-00445-y
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Mollick E. R., 2023, The Wharton School Research Paper, DOI DOI 10.2139/SSRN.4391243
   Nam BH, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00452-5
   Özçelik NP, 2024, SMART LEARN ENVIRON, V11, DOI 10.1186/s40561-024-00296-8
   Popenici Stefan A D, 2017, Res Pract Technol Enhanc Learn, V12, P22, DOI 10.1186/s41039-017-0062-8
   Salhab R, 2024, ONLINE LEARN, V28, DOI 10.24059/olj.v28i2.4426
   Schroeder KT, 2022, ONLINE LEARN, V26, P73
   Sohail SS, 2023, J KING SAUD UNIV-COM, V35, DOI 10.1016/j.jksuci.2023.101675
   Swindell A, 2024, ONLINE LEARN, V28, P7, DOI 10.24059/olj.v28i2.4438
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Trunk A., 2020, Bus. Res., V13, P875, DOI DOI 10.1007/S40685-020-00133-X
   U.S. Copyright Office Library of Congress, 2023, Copyright registration guidance: Works containing material generated by artificial intelligence
   UNESCO, 2023, ChatGPT and Artificial Intelligence in Higher Education: Quick Start Guide.
   Walter Y, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00448-3
   Wang FM, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00417-2
   Wolf RR, 2023, ONLINE LEARN, V27, P41, DOI 10.24059/olj.v27i3.3974
NR 46
TC 0
Z9 0
U1 41
U2 41
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD SEP
PY 2024
VL 28
IS 3
DI 10.24059/olj.v28i3.4508
PG 28
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA F3Z1S
UT WOS:001309228000003
OA gold
DA 2024-12-25
ER

PT J
AU Agrawal, K
AF Agrawal, Kalyan Prasad
TI Towards Adoption of Generative AI in Organizational Settings
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article
DE Generative AI; innovation diffusion; TOE framework
ID INFORMATION-TECHNOLOGY; COMPETITIVE ADVANTAGE; FIRM PERFORMANCE;
   E-BUSINESS; E-COMMERCE; ASSIMILATION; DIFFUSION; INNOVATION;
   PERSPECTIVE; DETERMINANTS
AB As an emerging technology, Generative Artificial Intelligence (AI) holds immense potential for application across various levels of business and management. However, current studies have not yet investigated the elements that impact the acceptance and implementation of generative AI tools, such as ChatGPT, within organizational settings. To fully leverage its benefits, organizations must embrace and integrate Generative AI at a comprehensive and profound level, making it a valuable area of study. This study aims to put forth and examine the influencing factors impacting the adoption of generative AI technology by utilizing the Technology-Organization-Environment framework in conjunction with the institutional theory and the diffusion of innovation theory. Data from 108 organizations in India is collected and analyzed, leading to valuable insights and implications that contribute to a deeper understanding of the key determinants of generative AI adoption. The study digs out valuable knowledge for organizations looking to embrace this technology.
C1 [Agrawal, Kalyan Prasad] Chandragupt Inst Management, Patna, India.
   [Agrawal, Kalyan Prasad] Chandragupt Inst Management Patna CIMP, Mithapur Inst Area, Patna 800001, Bihar, India.
RP Agrawal, K (corresponding author), Chandragupt Inst Management Patna CIMP, Mithapur Inst Area, Patna 800001, Bihar, India.
EM kalyan@cimp.ac.in
CR Agrawal KP, 2015, AMCIS 2015 PROCEEDINGS
   Aydin O., 2022, EMERGING COMPUTER TE, DOI [DOI 10.2139/SSRN.4308687, 10.2139/ssrn.4308687]
   Bharadwaj AS, 2000, MIS QUART, V24, P169, DOI 10.2307/3250983
   Bolloju N, 2007, J ORG COMP ELECT COM, V17, P29
   Chatterjee D, 2002, MIS QUART, V26, P65, DOI 10.2307/4132321
   Chau PYK, 2008, COMMUN ACM, V51, P132, DOI 10.1145/1378727.1404256
   Chau PYK, 1997, MIS QUART, V21, P1, DOI 10.2307/249740
   Chui M., 2022, Generative AI is Here: How Tools Like ChatGPT Could Change Your Business
   Cohen W.M., 1990, ABSORPTIVE CAPACITY, P128, DOI [DOI 10.2307/2393553, 10.2307/2393553]
   COOPER RB, 1990, MANAGE SCI, V36, P123, DOI 10.1287/mnsc.36.2.123
   DAMANPOUR F, 1992, ORGAN STUD, V13, P375, DOI 10.1177/017084069201300304
   DiMaggio PJ, 2000, ADV STRATEG MANAGE, V 17, P143, DOI 10.2307/2095101
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elliot B., 2022, Why is chatgpt making waves in the ai market?
   Fichman RG, 2004, INFORM SYST RES, V15, P132, DOI 10.1287/isre.1040.0021
   Fichman RG, 2001, MIS QUART, V25, P427, DOI 10.2307/3250990
   Fichman RG, 1999, INFORM SYST RES, V10, P255, DOI 10.1287/isre.10.3.255
   Furneaux B, 2011, MIS QUART, V35, P573
   GRANT RM, 1991, CALIF MANAGE REV, V33, P114, DOI 10.2307/41166664
   GROVER V, 1993, DECISION SCI, V24, P603, DOI 10.1111/j.1540-5915.1993.tb01295.x
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hong WY, 2006, INFORM MANAGE-AMSTER, V43, P204, DOI 10.1016/j.im.2005.06.003
   Iacovou CL, 1995, MIS QUART, V19, P465, DOI 10.2307/249629
   Kuan KKY, 2001, INFORM MANAGE-AMSTER, V38, P507, DOI 10.1016/S0378-7206(01)00073-8
   Lane PJ, 2001, STRATEGIC MANAGE J, V22, P1139, DOI 10.1002/smj.206
   Lewin K., 1952, GROUP DECISION SOCIA, P39
   Lin HF, 2006, INFORM MANAGE-AMSTER, V43, P423, DOI 10.1016/j.im.2005.10.004
   Mariani MM, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113364
   Mata F. J., 1995, Management Information Systems Quarterly, V19, P487, DOI 10.2307/249630
   MEYER AD, 1988, ACAD MANAGE J, V31, P897, DOI 10.5465/256344
   Mishra AN, 2007, INFORM SYST RES, V18, P103, DOI 10.1287/isre.1070.0115
   Porter M, 1980, COMPETITIVE STRATEGY
   Premkumar G., 1994, Journal of Management Information Systems, V11, P157
   Ramdani B, 2009, J ENTERP INF MANAG, V22, P10, DOI 10.1108/17410390910922796
   Rogers E., 1995, Diffusion of innovations
   Rogers E. M., 1961, Internet Things, V13
   Sharma S, 2003, INFORM MANAGE-AMSTER, V40, P391, DOI 10.1016/S0378-7206(02)00049-6
   Sharma S, 2000, ACAD MANAGE J, V43, P681, DOI 10.5465/1556361
   Soliman KS, 2004, INFORM MANAGE-AMSTER, V41, P697, DOI 10.1016/j.im.2003.06.001
   Tornatzky L., 1990, The Processes of Technological Innovation
   TORNATZKY LG, 1982, IEEE T ENG MANAGE, V29, P28, DOI 10.1109/TEM.1982.6447463
   Tsai MC, 2010, INFORM MANAGE-AMSTER, V47, P255, DOI 10.1016/j.im.2010.05.001
   Williamson OE., 1983, ORG INNOVATION T COS
   Wu IL, 2010, DECIS SUPPORT SYST, V50, P103, DOI 10.1016/j.dss.2010.07.006
   Zhu K, 2005, INFORM SYST RES, V16, P61, DOI 10.1287/isre.1050.0045
   Zhu K, 2004, J MANAGE INFORM SYST, V21, P17, DOI 10.1080/07421222.2004.11045797
   Zhu K, 2006, EUR J INFORM SYST, V15, P601, DOI 10.1057/palgrave.ejis.3000650
   Zhu K, 2006, MANAGE SCI, V52, P1557, DOI 10.1287/mnsc.1050.0487
NR 48
TC 29
Z9 29
U1 128
U2 470
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD SEP 2
PY 2024
VL 64
IS 5
BP 636
EP 651
DI 10.1080/08874417.2023.2240744
EA JUL 2023
PG 16
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA E6Z3L
UT WOS:001039548700001
DA 2024-12-25
ER

PT J
AU Walczak, K
   Cellary, W
AF Walczak, Krzysztof
   Cellary, Wojciech
TI Challenges for higher education in the era of widespread access to
   Generative AI
SO ECONOMICS AND BUSINESS REVIEW
LA English
DT Article
DE artificial intelligence; Generative Artificial Intelligence; GPT; higher
   education; university transformation
AB The aim of this paper is to discuss the role and impact of Generative Artificial Intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using Generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper's findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI's potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.
C1 [Walczak, Krzysztof] Poznan Univ Econ & Business, Dept Informat Technol, Al Niepodleglosci 10, PL-61875 Poznan, Poland.
   [Cellary, Wojciech] WSB Merito Univ, Inst Appl Res, Ul Powstancow Wielkopolskich 5, PL-61895 Poznan, Poland.
C3 Poznan University of Economics & Business
RP Walczak, K (corresponding author), Poznan Univ Econ & Business, Dept Informat Technol, Al Niepodleglosci 10, PL-61875 Poznan, Poland.
EM krzysztof.walczak@ue.poznan.pl; wojciech.cellary@wsb.poznan.pl
RI Cellary, Wojciech/HJP-7932-2023; Walczak, Krzysztof/AAF-9685-2021
OI Walczak, Krzysztof/0000-0001-8170-7910; Cellary,
   Wojciech/0000-0001-8578-4307
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Aila T., 2017, CoRR
   [Anonymous], 2014, NEURIPS, DOI DOI 10.1145/3422622
   [Anonymous], The effectiveness of data augmentation in image classification using deep learning
   Arjovsky M, 2017, PR MACH LEARN RES, V70
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bengio Y, 2013, IEEE T PATTERN ANAL, V35, P1798, DOI 10.1109/TPAMI.2013.50
   Brown TB, 2020, ADV NEUR IN, V33
   Brunner G., 2018, ARXIV180907600, P747
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Chesney B, 2019, CALIF LAW REV, V107, P1753, DOI 10.15779/Z38RV0D15J
   Christiano PF, 2017, ADV NEUR IN, V30
   Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
   Donahue C, 2019, Arxiv, DOI arXiv:1802.04208
   Engel J, 2017, PR MACH LEARN RES, V70
   Feng Y., 2022, 4 INT C EC MAN CULT, P1622
   Garg N, 2018, P NATL ACAD SCI USA, V115, pE3635, DOI 10.1073/pnas.1720347115
   Gatys LA, 2016, PROC CVPR IEEE, P2414, DOI 10.1109/CVPR.2016.265
   IFR, 2023, INT FED ROB
   Karras T, 2019, PROC CVPR IEEE, P4396, DOI 10.1109/CVPR.2019.00453
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Liang WX, 2023, Arxiv, DOI arXiv:2304.02819
   Liu YH, 2023, Arxiv, DOI [arXiv:2304.01852, DOI 10.1016/J.METRAD.2023.100017]
   Maynez J, 2020, 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), P1906
   Nie Dong, 2017, Med Image Comput Comput Assist Interv, V10435, P417, DOI 10.1007/978-3-319-66179-7_48
   Oord AVD, 2016, arXiv, DOI DOI 10.48550/ARXIV.1609.03499
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Radford A., 2015, INT C LEARN REPR 201
   Raffel C, 2020, J MACH LEARN RES, V21
   Strubell E, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3645
   Vaswani A, 2017, ADV NEUR IN, V30
   Zhao J., 2017, 5 INT C LEARN REPR I, P1
   Zhu JY, 2017, IEEE I CONF COMP VIS, P2242, DOI 10.1109/ICCV.2017.244
NR 33
TC 19
Z9 20
U1 56
U2 245
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2392-1641
EI 2450-0097
J9 ECON BUS REV-POL
JI Econ. Bus. Rev.
PD APR 1
PY 2023
VL 9
IS 2
BP 71
EP 100
DI 10.18559/ebr.2023.2.743
PG 30
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA N5CC9
UT WOS:001037180500005
OA gold
DA 2024-12-25
ER

PT J
AU Duah, JE
   McGivern, P
AF Duah, James Ewert
   McGivern, Paul
TI How generative artificial intelligence has blurred notions of authorial
   identity and academic norms in higher education, necessitating clear
   university usage policies
SO INTERNATIONAL JOURNAL OF INFORMATION AND LEARNING TECHNOLOGY
LA English
DT Article
DE Students; Universities; Artificial intelligence; Education; Educational
   policy; Psychology
AB PurposeThis study examines the impact of generative artificial intelligence (GenAI), particularly ChatGPT, on higher education (HE). The ease with which content can be generated using GenAI has raised concerns across academia regarding its role in academic contexts, particularly regarding summative assessments. This research makes a unique contribution to the literature by examining university student and staff perceptions of current and future issues pertaining to the role of GenAI in universities.Design/methodology/approachA qualitative method involving five one-to-one semi-structured interviews with four students and a lecturer explored the ethical and practical issues of GenAI text generation in academia. An inductive thematic analysis was chosen as it provided nuanced insights aligned with the study's goals.FindingsUse of GenAI was discussed within the context of a range of topics, including perceptions of academic misconduct, authorial integrity and issues pertaining to university policies. Participants universally defined traditional classifications of academic misconduct but were unable to provide clear definitions where the use of GenAI was included for writing summative assessments. Students showed a more open engagement with GenAI, considering it a tool for overcoming obstacles rather than a means to plagiarise. Educators were generally more cautious and less optimistic about the academic role of GenAI. Lack of clear institutional policies surrounding such tools also contributed to ethical ambiguities.Originality/valueThe study highlights diverging perspectives between students and academics, which necessitate a forum for dialogue, ensuring the need to develop clear policies to steer the integration of GenAI in a manner that is beneficial for students and academics.
C1 [Duah, James Ewert; McGivern, Paul] Leeds Trinity Univ, Dept Psychol, Leeds, England.
C3 Leeds Trinity University
RP Duah, JE (corresponding author), Leeds Trinity Univ, Dept Psychol, Leeds, England.
EM jamesewertd@gmail.com; p.mcgivern@leedstrinity.ac.uk
RI Duah, James/JKI-2531-2023
OI Duah, James Ewert/0009-0005-7522-9795
CR Agomuoh F., 2023, 6 BIGGEST PROBLEMS C
   Alam A., 2022, ADV COMPUTING INTELL, V914, DOI [DOI 10.1007/978-981-19-2980-9_32, 10.1007/978-981-19-2980-9_32, 10.1007/978-981-19-2980-932]
   American Psychological Association, 2023, CIT CHATGPT
   [Anonymous], 2020, Facts and statistics
   Anyoha R, 2017, The history of artificial intelligence
   BANDURA A, 1991, ORGAN BEHAV HUM DEC, V50, P248, DOI 10.1016/0749-5978(91)90022-L
   Barnett S., 2023, ChatGPT is making universities rethink plagiarism
   Bass D., 2023, MICROSOFT INVEST 10
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Braun V., 2014, APA HDB RES METHODS, P95, DOI [DOI 10.1007/978-981-10-5251-4103, DOI 10.1037/13620-004, 10.1007/978-981-10-2779-6_103-1]
   Braun V, 2022, QUAL PSYCHOL, V9, P3, DOI 10.1037/qup0000196
   Braun V, 2021, COUNS PSYCHOTHER RES, V21, P37, DOI 10.1002/capr.12360
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Cambridge University, 2023, US GEN AI COURS NOV
   Cambridge University, 2020, PLAG AC MISC
   Caulfield J., 2023, University Policies on AI Writing Tools | Overview List
   Clarke V., 2013, SUCCESSFUL QUALITATI
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   DAgostino S., 2023, GPT-4 Is Here. But Most Faculty Lack AI Policies: Faculty members and administrators are struggling to stay ahead of disruptive AI progress, a new report suggests
   Davidson D., 2023, CHATGPT IS FUN NOT A, DOI [10.1126/science.adg7879, DOI 10.1126/SCIENCE.ADG7879]
   Dhoni P., 2023, PREPRINT, DOI [10.36227/techrxiv.23696709, DOI 10.36227/TECHRXIV.23696709]
   Dolan P., 2023, ZERO CAMBRIDGE STUDE
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00140-5
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00137-0
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Fowler G.A., 2023, WE TESTED NEW CHATGP
   Gao CA., 2022, NPJ Digit Med, V1, DOI [10.1038/s41746-023-00819-6, DOI 10.1101/2022.12.23.521610, 10.1101/2022.12.23.521610, DOI 10.1038/S41746-023-00819-6]
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Haman M, 2024, ACCOUNT RES, V31, P1244, DOI 10.1080/08989621.2023.2185514
   Jalil S, 2023, IEEE ICST WORKSHOP, P430, DOI 10.1109/ICSTW58534.2023.00078
   Javaid M., 2023, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V3, DOI [10.1016/j.tbench.2023.100115, DOI 10.1016/J.TBENCH.2023.100115]
   Katz D. M., 2023, SSRN, DOI [10.2139/SSRN.4389233, 10.2139/ssrn.4389233, DOI 10.2139/SSRN.4389233]
   Kelly S.M., 2023, MICROSOFT IS BRINGIN
   Klimova B, 2023, FRONT PUBLIC HEALTH, V10, DOI 10.3389/fpubh.2022.1118116
   Koebel C., 2023, WHAT ARE REAL EFFECT
   Krasnian V., 2022, CONTRACT CHEATING HI, P29, DOI [https://doi.org/10.1007/978-3-031-12680-2_3, DOI 10.1007/978-3-031-12680-2_3]
   Kurian T., 2023, NEXT GENERATION AI D
   Lewis J., 2023, IMPACT INCREASES COS
   Malinka K., 2022, P 2023 C INN TECHN C, V1, P47, DOI [10.1007/s42438-023-00395-8, DOI 10.1007/S42438-023-00395-8]
   Masle J N., AI INDEX STEERING CO
   Mazar N, 2008, J MARKETING RES, V45, P633, DOI 10.1509/jmkr.45.6.633
   McCallum S., 2023, ChatGPT banned in Italy over privacy concerns WWW Document
   Meuschke N, 2013, INT J EDUC INTEGR, V9, P50
   Mulder LB, 2015, ORGAN BEHAV HUM DEC, V126, P115, DOI 10.1016/j.obhdp.2014.11.002
   Murdock TB, 2006, EDUC PSYCHOL-US, V41, P129, DOI 10.1207/s15326985ep4103_1
   Murray T., 2023, YOUNG PEOPLE COST LI
   Naughton J., 2023, CHATGPT BOT IS CAUSI
   Nazari N, 2021, HELIYON, V7, DOI 10.1016/j.heliyon.2021.e07014
   Newton A., 2023, CREAT AI CROSS MOD W, V37, P2, DOI [10.48550/arXiv.2301.05133, DOI 10.48550/ARXIV.2301.05133]
   Newton PM, 2024, J ACAD ETHICS, V22, P323, DOI 10.1007/s10805-023-09485-5
   Noorbehbahani F, 2022, EDUC INF TECHNOL, V27, P8413, DOI 10.1007/s10639-022-10927-7
   Norris M., 2019, RES HIGHER ED J, V37
   Nurmayanti N., 2023, Jurnal Teknologi Pandidikan, V8, P32, DOI [10.33394/jtp.v8i1.6392, DOI 10.33394/JTP.V8I1.6392]
   O'Connell D.C., 1995, RETHINKING METHODS P, V28, P103, DOI [10.1023/A:1023265024072, DOI 10.1023/A:1023265024072]
   Oates J., 2021, British Psychological Society, DOI [10.53841/bpsrep.2021.inf180, DOI 10.53841/BPSREP.2021.INF180]
   OpenAI, 2023, INTR CHATGPT ENT
   OpenAI, 2023, New AI classifier for indicating AI-written text
   OpenAI, 2023, Technical report
   Parkinson AL, 2022, ASSESS EVAL HIGH EDU, V47, P1416, DOI 10.1080/02602938.2022.2040947
   Prihar A., 2023, CHEATING YALE STUDEN
   Roig M., 2015, AVOIDING PLAGIARISM
   Rozencwajg S, 2023, ANAESTH CRIT CARE PA, V42, DOI 10.1016/j.accpm.2023.101209
   Sadasivan V. S., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2303.11156
   Sakamoto D, 2019, PROCEDIA COMPUT SCI, V159, P1329, DOI 10.1016/j.procs.2019.09.303
   SALLAM M, 2023, HEALTHCARE-BASEL, V11, DOI DOI 10.3390/HEALTHCARE11060887
   Schaefer U, 2021, J BUS ETHICS, V172, P525, DOI 10.1007/s10551-020-04520-6
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   T, 2023, Postdigital Science and Education, V5, P894, DOI DOI 10.1007/S42438-023-00395-8
   turnitin, 2019, TURNITIN U
   Turnitin for Universities, 2023, US
   Uney M., 2022, NEW DIRECTIONS TECHN, P259, DOI [10.1007/978-3-031-13540-8_13, DOI 10.1007/978-3-031-13540-8_13]
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Zhang YX, 2024, HIGH EDUC, V87, P567, DOI 10.1007/s10734-023-01024-w
NR 74
TC 8
Z9 8
U1 23
U2 40
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2056-4880
J9 INT J INF LEARN TECH
JI Int. J. Inf. Learn. Technol.
PD APR 24
PY 2024
VL 41
IS 2
BP 180
EP 193
DI 10.1108/IJILT-11-2023-0213
EA FEB 2024
PG 14
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA OJ4S4
UT WOS:001168101700001
DA 2024-12-25
ER

PT J
AU Lao, YC
   You, YK
AF Lao, Yucong
   You, Yukun
TI Unraveling generative AI in BBC News: application, impact, literacy and
   governance
SO TRANSFORMING GOVERNMENT- PEOPLE PROCESS AND POLICY
LA English
DT Article; Early Access
DE Generative AI; AI literacy; AI governance; BBC News
AB PurposeThis study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.Design/methodology/approachThis study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.FindingsBy analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people's everyday lives. People's generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.Originality/valueBased on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.
C1 [Lao, Yucong] Univ Oulu, Res Unit Hist Culture & Commun, Oulu, Finland.
   Univ Oslo, Dept Media & Commun, Oslo, Norway.
C3 University of Oulu; University of Oslo
RP Lao, YC (corresponding author), Univ Oulu, Res Unit Hist Culture & Commun, Oulu, Finland.
EM Yucong.Lao@oulu.fi
RI You, Yukun/KIG-2855-2024
OI You, Yukun/0000-0003-0188-0751
CR Al Zadjali Halah, 2020, ICEGOV 2020: Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance, P116, DOI 10.1145/3428502.3428516
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   [Anonymous], 2004, BUILDING PUBLIC VALU
   [Anonymous], 2022, K-12 AI curricula: A mapping of government-endorsed AI curricula
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Banerjee I., 2006, PUBLIC SERVICE BROAD
   Baxter P, 2008, QUAL REP, V13, P544
   Bazeley P., 2013, Qualitative Data Analysis with NVivo, DOI DOI 10.1007/978-3-319-23374-1
   Birkstedt T, 2023, INTERNET RES, V33, P133, DOI 10.1108/INTR-01-2022-0042
   Borner I., 2023, Implementing effective AI governance
   Brandtzaeg PB, 2023, LECT NOTES COMPUT SC, V14059, P3, DOI 10.1007/978-3-031-48057-7_1
   Brynjolfsson E., 2023, Generative AI at Work (No. W31161)
   Cao L., 2023, The Next Level Lab at Harvard Graduate School of Education, V5
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Catsaros O., 2023, Bloomberg
   Creswell J. W., 2008, RES DESIGN QUALITATI
   Dafoe A., 2018, Governance of AI Program, Future of Humanity Institute, P1442
   Druga S, 2019, PROCEEDINGS OF 8TH ANNUAL CONFERENCE ON MAKER EDUCATION (FABLEARN 2019), P104, DOI 10.1145/3311890.3311904
   Ebert C, 2023, IEEE SOFTWARE, V40, P30, DOI 10.1109/MS.2023.3265877
   European Commission, 2021, Impact assessment report accompanying the document Directive of the European Parliament and of the Council: amending Directive 2003/ 87/EC establishing a system for greenhouse gas emission allowance trading within the Union, Decision (EU) 2015/1814 concerning the establishment and operation of a market stability reserve for the Union greenhouse gas emission trading scheme and Regulation (EU) 2015/757 - Part 1/4
   Figaredo DD, 2023, BIG DATA SOC, V10, DOI 10.1177/20539517231219958
   Fischer J.E., 2023, Proceedings of the 5th International Conference on Conversational User Interfaces, V5, P1
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gieseke AP, 2020, VANDERBILT LAW REV, V73, P1479
   Gorham Maurice., 1967, Forty Years of Irish Broadcasting
   Grummell B, 2009, EUR J COMMUN, V24, P267, DOI 10.1177/0267323109336756
   Hainsdorf C., 2023, Dawn of the EU's AI act: political agreement reached on world's first comprehensive horizontal AI regulation
   Hirvonen N, 2024, J ASSOC INF SCI TECH, V75, P1152, DOI 10.1002/asi.24860
   Katarya Rahul, 2020, 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), P485, DOI 10.1109/I-SMAC49090.2020.9243588
   Kong SC, 2023, EDUC TECHNOL SOC, V26, P16, DOI 10.30191/ETS.202301_26(1).0002
   Kuziemski M, 2020, TELECOMMUN POLICY, V44, DOI 10.1016/j.telpol.2020.101976
   Lambert J, 2024, COMPUT SCH, V41, P559, DOI 10.1080/07380569.2023.2256710
   Larsson S., 2023, Handbook of critical studies of artificial intelligence, P445
   Lee Irene, 2021, SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, P191, DOI 10.1145/3408877.3432513
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Lütge C, 2021, INT J TECHNOETHICS, V12, P101, DOI 10.4018/IJT.20210101.oa2
   Majid A., 2024, PressGazette
   Mania K, 2024, TRAUMA VIOLENCE ABUS, V25, P117, DOI 10.1177/15248380221143772
   Marshall MN, 1996, FAM PRACT, V13, P522, DOI 10.1093/fampra/13.6.522
   McKinsey and Company, 2023, What is generative AI?
   Meskys E, 2020, J INTELLET PROP LAW, V15, P24, DOI 10.1093/jiplp/jpz167
   Metz R., 2023, The Japan Times
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mntymki M., 2022, AI Ethics, V2, P603, DOI [10.1007/s43681-022-00143-x, DOI 10.1007/S43681-022-00143-X, 10.1007/S43681-022-00143-X]
   National Artificial Intelligence Advisory Committee, 2023, Recommendations: enhancing AI literacy for the United States of America
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Ng D. T. K., 2022, AI literacy in K-16 classrooms, P21, DOI [10.1007/978-3-031-18880-03, DOI 10.1007/978-3-031-18880-03]
   Perchik JD, 2023, ACAD RADIOL, V30, P1472, DOI 10.1016/j.acra.2022.10.002
   Pohontsch NJ, 2019, REHABILITATION, V58, P413, DOI 10.1055/a-0801-5465
   Priya A., 2020, SOCIOL BULL, V70, P94, DOI [10.1177/0038022920970318, DOI 10.1177/0038022920970318, https://doi.org/10.1177/0038022920970318]
   Relmasira SC, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151813595
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Schuller Katharina, 2022, Statistical Journal of the IAOS, V38, P477, DOI 10.3233/SJI-220941
   Semeler A, 2024, EAI ENDORSED TRANS S, V11, DOI 10.4108/eetsis.4067
   Terrasi V., 2023, Searching Engine Journal
   Walters WP, 2020, NAT BIOTECHNOL, V38, P143, DOI 10.1038/s41587-020-0418-2
   Westerlund M, 2019, TECHNOL INNOV MANAG, V9, P39, DOI 10.22215/timreview/1282
   Xian L, 2024, Arxiv, DOI arXiv:2401.08899
   Yi Y., 2021, JAHR, V12, P353, DOI DOI 10.21860/J.12.2.8
NR 59
TC 1
Z9 1
U1 90
U2 111
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1750-6166
EI 1750-6174
J9 TRANSFORM GOV-PEOPLE
JI Transform. Gov.-People Process Policy
PD 2024 MAY 17
PY 2024
DI 10.1108/TG-01-2024-0022
EA MAY 2024
PG 26
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA QR9H4
UT WOS:001222710200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Florido-Benítez, L
AF Florido-Benitez, Lazaro
TI Generative artificial intelligence: a proactive and creative tool to
   achieve hyper-segmentation and hyper-personalization in the tourism
   industry
SO INTERNATIONAL JOURNAL OF TOURISM CITIES
LA English
DT Article; Early Access
DE Generative artificial intelligence; Tourism industry;
   Hyper-segmentation; Hyper-personalization
ID TRAVEL; AI
AB Purpose - The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.<br /> Design/methodology/approach - This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper- segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies. Findings - Findings reveal that GenAI technology exponentially improves consumers'segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.<br /> Originality/value - This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.
C1 [Florido-Benitez, Lazaro] Univ Malaga, Dept Econ & Business Adm, Malaga, Spain.
C3 Universidad de Malaga
RP Florido-Benítez, L (corresponding author), Univ Malaga, Dept Econ & Business Adm, Malaga, Spain.
EM lfb@uma.es
CR Abrokwah-Larbi K, 2024, J ENTREP EMERG ECON, V16, P1090, DOI 10.1108/JEEE-07-2022-0207
   AIM Research, 2024, The emergence of 'segmentation of one' with generative AI
   Akka M., 2024, Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, P182, DOI [10.4018/979-8-3693-1351-0.ch009, DOI 10.4018/979-8-3693-1351-0.CH009]
   Ali F, 2023, INT J HOSP MANAG, V114, DOI 10.1016/j.ijhm.2023.103588
   Almeida S, 2024, TOURISM, V72, P422, DOI 10.37741/t.72.3.10
   Amazon, 2024, How generative AI is transforming customer experience
   [Anonymous], 2024, Forbes
   [Anonymous], 2023, Identity Review
   Atkins C, 2024, COMMUN EARTH ENVIRON, V5, DOI 10.1038/s43247-024-01392-w
   Balkini S., 2023, 28 Powerful email marketing statistics YSK
   Banh L, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00680-1
   Battour M, 2024, J ISLAMIC MARK, V15, P3464, DOI 10.1108/JIMA-11-2023-0379
   Black K., 2018, Business analytics and statistics
   Brüns JD, 2024, J RETAIL CONSUM SERV, V79, DOI 10.1016/j.jretconser.2024.103790
   Buhalis D, 2023, TOURISM MANAGE, V97, DOI 10.1016/j.tourman.2023.104724
   Buhalis D, 2022, J HOSP TOUR TECHNOL, V13, P386, DOI 10.1108/JHTT-03-2021-0104
   Buhalis D, 2015, J DESTIN MARK MANAGE, V4, P151, DOI 10.1016/j.jdmm.2015.04.001
   Cap Gemini, 2017, Hyper-personalization vs. Segmentation: Has big data made customer segmentation redundant?
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chintalapati S, 2022, INT J MARKET RES, V64, P38, DOI 10.1177/14707853211018428
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Christensen J, 2024, CURR ISSUES TOUR, DOI 10.1080/13683500.2023.2300032
   Cities' IQ, 2024, Smart Sity
   Coca-Stefaniak JA, 2019, INT J TOUR CITIES, V5, P513, DOI 10.1108/IJTC-12-2019-163
   Cugurullo F, 2024, POLICY SOC, DOI 10.1093/polsoc/puae025
   Cugurullo F, 2024, URBAN STUD, V61, P1168, DOI 10.1177/00420980231203386
   Dashkevych O, 2024, TECHNOL SOC, V77, DOI 10.1016/j.techsoc.2024.102555
   Dembski F, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12062307
   Dencheva V., 2023, Share of marketers using generative artificial intelligence (AI) in their companies in the United States as of March 2023
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Dolnicar S., 2022, HDB E TOURISM, P849, DOI [https://doi.org/10.1007/978-3-030-48652-5_53, DOI 10.1007/978-3-030-48652-5_53]
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   EU Artificial Intelligence Act, 2024, High-Level Summary of the AI Act
   Expedia Group, 2023, ChatGPT wrote this press release-no, it didn't, but it can now assist with travel planning in the Expedia app
   Feng C, 2024, BUS HORIZONS, V67, P537, DOI 10.1016/j.bushor.2024.04.012
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   Ferrara E, 2023, Arxiv, DOI [arXiv:2304.03738, DOI 10.48550/ARXIV.2304.03738]
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Florido-Benítez L, 2024, INT J TOUR CITIES, V10, P974, DOI 10.1108/IJTC-01-2024-0001
   Florido-Benitez L, 2024, SMART CITIES-BASEL, V7, P475, DOI 10.3390/smartcities7010019
   Florido-Benitez L, 2023, INT J TOUR CITIES, V9, P771, DOI 10.1108/IJTC-06-2023-0119
   Florido-Benítez L, 2024, INT J TOUR CITIES, V10, P261, DOI 10.1108/IJTC-09-2023-0193
   Florido-Benitez L, 2024, TOUR REV, V79, P719, DOI 10.1108/TR-05-2023-0302
   Florido-Benítez L, 2022, INT J TOUR CITIES, V8, P844, DOI 10.1108/IJTC-09-2021-0191
   Ford J, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114124
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   GoogleCloud, 2024, Introducing Google threat intelligence: Actionable threat intelligence at Google scale
   Government of the United Kingdom, 2023, Cybersecurity breaches survey 2023
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Gupta R., 2024, Int J Inf Manage Data Insights, V4, P100232, DOI [10.1016/j.jjimei.2024.100232, DOI 10.1016/J.JJIMEI.2024.100232]
   Gursoy D, 2024, INT J CONTEMP HOSP M, DOI 10.1108/IJCHM-03-2024-0322
   Hajibaba H, 2019, INT J CONTEMP HOSP M, V32, P1393, DOI 10.1108/IJCHM-02-2019-0137
   Hajkowicz S, 2023, TECHNOL SOC, V74, DOI 10.1016/j.techsoc.2023.102260
   Hexaware, 2024, Unlocking Generative AI for Hyper-personalized Customer Experiences
   Hsu CHC, 2024, ANN TOURISM RES, V104, DOI 10.1016/j.annals.2023.103723
   Huang K., 2024, Generative AI Security. Future of Business and Finance, P99, DOI DOI 10.1007/978-3-031-54252-74
   Ivanova-Kadiri I., 2023, Izvestiya Journal of the University of Economics Varna, V67, P101
   Jang K. M., 2023, 12 INT C GEOGR INF S, V41, P1, DOI [10.4230/LIPIcs.GIScience.2023.41, DOI 10.4230/LIPICS.GISCIENCE.2023.41]
   Jauhiainen JS, 2024, INT J INNOV STUD, V8, P262, DOI 10.1016/j.ijis.2024.04.004
   Javaid M., 2022, International Journal of Intelligent Networks, V3, P119, DOI [DOI 10.1016/J.IJIN.2022.08.005, 10.1016/j.ijin.2022.08.005]
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Katsikari C, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12114770
   Kaur G., 2014, Vikalpa: The Journal for Decision Makers, V39, P75, DOI DOI 10.1177/0256090920140406
   Kim JH, 2023, J TRAVEL TOUR MARK, V40, P779, DOI 10.1080/10548408.2023.2293006
   Kim JH, 2025, J TRAVEL RES, V64, P51, DOI 10.1177/00472875231212996
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Ku ECS, 2024, INT J INFORM MANAGE, V76, DOI 10.1016/j.ijinfomgt.2024.102757
   Kumar M., 2023, SSRN, DOI [10.2139/ssrn.4614118, DOI 10.2139/SSRN.4614118]
   Lehto X. Y., 2020, Routledge Handbook of Tourism Cities, P207
   Li DR, 2021, COMPUT URBAN SCI, V1, DOI 10.1007/s43762-021-00005-y
   Li Y, 2024, INT J CONTEMP HOSP M, DOI 10.1108/IJCHM-11-2023-1767
   Londhe Komal, 2024, Procedia Computer Science, V235, P1920, DOI 10.1016/j.procs.2024.04.182
   Long Y, 2021, KYBERNETES, V50, P3246, DOI 10.1108/K-07-2020-0479
   Lukanova G, 2019, ROBOTS, ARTIFICIAL INTELLIGENCE, AND SERVICE AUTOMATION IN TRAVEL, TOURISM AND HOSPITALITY, P157, DOI 10.1108/978-1-78756-687-320191009
   Lv Z., 2023, Cogn. Robot., V3, P208, DOI [10.1016/j.cogr.2023.06.001, DOI 10.1016/J.COGR.2023.06.001]
   Majid GM, 2024, J TRAVEL RES, DOI 10.1177/00472875241247316
   Mariani M, 2022, INT J CONTEMP HOSP M, V34, P231, DOI 10.1108/IJCHM-03-2021-0301
   MarketResearch, 2023, Generative AI in travel market
   Matter N. M., 2024, Eng. Res. J., V181, P1, DOI [10.21608/erj.2024.344313, DOI 10.21608/ERJ.2024.344313]
   Maxim C., 2023, Handbook on sustainable urban tourism
   McKinsey & Company, 2023, AI-Powered Marketing and Sales Reach New Heights with Generative AI
   Mich L, 2023, INF TECHNOL TOUR, V25, P1, DOI 10.1007/s40558-023-00248-x
   Mihalic T, 2024, TOUR REV, DOI 10.1108/TR-09-2023-0609
   Milmo D., 2024, AI chatbots' safeguards can be easily bypassed, say UK researchers
   Mogaji E, 2024, INT J CONTEMP HOSP M, V36, P3324, DOI 10.1108/IJCHM-08-2023-1271
   Moreno-Ibarra M., 2024, Smart cities, P118
   Morrison A. M., 2022, Tourism marketing: in the age of the consumer
   Morrison AM., 2020, ROUTLEDGE HDB TOURIS
   Najafabadi FN, 2023, ATMOSPHERE-BASEL, V14, DOI 10.3390/atmos14101565
   Nusair K, 2023, TOUR MANAG PERSPECT, V48, DOI 10.1016/j.tmp.2023.101148
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Papandreou T., 2024, Generative urban AI is here. Are cities ready?
   Rahmadian E, 2023, ETHICS INF TECHNOL, V25, DOI 10.1007/s10676-023-09730-w
   Raiter O., 2021, International Journal of Contemporary Financial Issues, V1, P39
   Retkowsky J, 2024, BUS HORIZONS, V67, P511, DOI 10.1016/j.bushor.2024.04.009
   Rohden SF, 2023, ELECTRON COMMER RES, V23, P2035, DOI 10.1007/s10660-022-09626-9
   Sai S, 2024, IEEE ACCESS, V12, P53497, DOI 10.1109/ACCESS.2024.3385107
   Singh J.P., 2022, Res. Rev. Sci. Technol, V2, P171
   Singh K, 2024, TECHNOVATION, V133, DOI 10.1016/j.technovation.2024.103021
   Soliman M., 2023, Robonomics: The Journal of the Automated Economy, V4, P1
   Soni V., 2023, Sage Science Review of Applied Machine Learning, V6, P1, DOI [https://doi.org/10.3390/app11083475, DOI 10.3390/APP11083475]
   Sop SA, 2024, J HOSP TOUR TECHNOL, V15, P329, DOI 10.1108/JHTT-08-2023-0237
   Spennemann DHR, 2024, HERITAGE-BASEL, V7, P1453, DOI 10.3390/heritage7030070
   Thavi R, 2024, J ENG DES TECHNOL, V22, P182, DOI 10.1108/JEDT-08-2021-0417
   Tong SL, 2020, J ACAD MARKET SCI, V48, P64, DOI 10.1007/s11747-019-00693-3
   Torkzadeh L, 2024, J POLICY RES TOUR LE, V16, P69, DOI 10.1080/19407963.2021.1988622
   Tuo Y., 2021, INFORM COMMUNICATION, P83
   Tuomi A, 2023, SPR PROC BUS ECON, P323, DOI 10.1007/978-3-031-25752-0_35
   Vapiwala F., 2022, P 2022 INT C DAT AN, P423, DOI [10.1109/ICDABI56818.2022.10041655, DOI 10.1109/ICDABI56818.2022.10041655]
   Vesterinen M, 2024, J MARKET THEORY PRAC, DOI 10.1080/10696679.2024.2322600
   Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
   Vogel D, 2024, J CULIN SCI TECHNOL, V22, P533, DOI 10.1080/15428052.2022.2060890
   Wamba SF, 2017, J BUS RES, V70, P356, DOI 10.1016/j.jbusres.2016.08.009
   Wang JW, 2025, TOURISM MANAGE, V106, DOI 10.1016/j.tourman.2024.105003
   Wang YC, 2023, IEEE OPEN J COMM SOC, V4, P2952, DOI 10.1109/OJCOMS.2023.3320646
   Wang YY, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2316376
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Xiang Z, 2021, ANN TOURISM RES, V86, DOI 10.1016/j.annals.2021.103154
   Yildiz E, 2023, J THEOR APPL EL COMM, V18, P571, DOI 10.3390/jtaer18010029
   Zhang YZ, 2024, ANN TOUR RES EMPIR I, V5, DOI 10.1016/j.annale.2024.100124
   Zhang ZW, 2024, TOUR MANAG PERSPECT, V51, DOI 10.1016/j.tmp.2024.101247
   Zhong LN, 2022, TOUR REV, V77, P1062, DOI 10.1108/TR-06-2021-0276
   Zhu Z., 2024, HCI, P331
   Ziakis C, 2023, INFORMATION, V14, DOI 10.3390/info14120664
NR 127
TC 0
Z9 0
U1 6
U2 6
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2056-5607
EI 2056-5615
J9 INT J TOUR CITIES
JI Int. J. Tour. Cities
PD 2024 OCT 30
PY 2024
DI 10.1108/IJTC-05-2024-0111
EA OCT 2024
PG 21
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA K7O7M
UT WOS:001345736800001
DA 2024-12-25
ER

PT J
AU Lin, ZH
   Ng, YL
AF Lin, Zhihuai
   Ng, Yu-Leung
TI Unraveling Gratifications, Concerns, and Acceptance of Generative
   Artificial Intelligence
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article; Early Access
DE ChatGPT; correlated topic model; generative artificial intelligence;
   technology acceptance; uses and gratifications
ID TECHNOLOGY ACCEPTANCE; MODELS; COMMUNICATION; COMPUTERS; ETHICS; RISE;
   TEXT
AB Despite the burgeoning interest in generative artificial intelligence (AI), specifically ChatGPT, little is known about the underlying user motivations and concerns that may shape technology acceptance indicators, such as post popularity and engagement on online platforms. Guided by the uses and gratifications and technology acceptance approaches, this study utilizes ecologically valid social media data to unearth user-driven topics and themes on Reddit. We extracted six key themes: utilitarian, hedonic, and social gratifications, creativity enhancement, and technical and societal concerns. Our results showed that specific topics of gratifications and concerns predicted post scores and comments in different patterns. Our findings could offer guidance for developers and policymakers, underlining the necessity for user-centric and ethically informed generative AI systems that address technological prowess and societal implications.
C1 [Lin, Zhihuai] Hong Kong Baptist Univ, Sch Commun, Hong Kong, Peoples R China.
   [Ng, Yu-Leung] Hong Kong Baptist Univ, Dept Interact Media, Hong Kong, Peoples R China.
C3 Hong Kong Baptist University; Hong Kong Baptist University
RP Ng, YL (corresponding author), Hong Kong Baptist Univ, Sch Commun, Dept Interact Media, Hong Kong, Peoples R China.
EM yuleungng@hkbu.edu.hk
RI Lin, Zhihuai/GQB-3040-2022; Ng, Yu-Leung/KOD-4235-2024
OI Ng, Yu-Leung/0000-0002-7140-1616
CR Acquisti A, 2015, SCIENCE, V347, P509, DOI 10.1126/science.aaa1465
   Anderson S., 2024, Reddit demographics: What you need to know in2024
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Berger J, 2014, J CONSUM PSYCHOL, V24, P586, DOI 10.1016/j.jcps.2014.05.002
   Bernstein M., 2011, Proceedings of the International AAAI Conference on Web and Social Media, V5, P50, DOI [DOI 10.1609/ICWSM.V5I1.14134, 10.1609/icwsm.v5i1.14134]
   Bischof JonathanM., 2012, Proceedings of the 29th International Conference on Machine Learning, V29, P201, DOI DOI 10.5555/3042573.3042578
   Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114
   Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Boden MA, 2009, AI MAG, V30, P23, DOI 10.1609/aimag.v30i3.2254
   Bogost I., 2007, Persuasive games: The expressive power of videogames
   Bridy Annemarie., 2016, Colum JL Arts, V39, P395, DOI DOI 10.7916/JLA.V39I3.2078
   Brynjolfsson E., 2014, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
   Cameron A. C., 2013, Regression analysis of count data, V2, DOI [DOI 10.1017/CBO9781139013567, 10.1017/CBO9781139013567]
   Chang J., 2009, P 22 INT C NEUR INF, P288
   Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
   Choi TR, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101628
   Crawford K, 2016, NATURE, V538, P311, DOI 10.1038/538311a
   Cugurullo F, 2024, AI SOC, V39, P1569, DOI 10.1007/s00146-022-01598-6
   Dartnall T., 2013, Artificial Intelligence and Creativity, V17, DOI [DOI 10.1007/978-94-017-0793-0, https://doi.org/10.1007/978-94-017-0793-0, 10.1007/978-94-017-0793-0]
   DAVIS FD, 1992, J APPL SOC PSYCHOL, V22, P1111, DOI 10.1111/j.1559-1816.1992.tb00945.x
   Despi J. B. P., 2024, Generative AI reshapes financial landscape
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Floridi L, 2018, MIND MACH, V28, P689, DOI 10.1007/s11023-018-9482-5
   Frank MR, 2019, P NATL ACAD SCI USA, V116, P6531, DOI 10.1073/pnas.1900949116
   GARDNER W, 1995, PSYCHOL BULL, V118, P392, DOI 10.1037/0033-2909.118.3.392
   Gaudette T, 2021, NEW MEDIA SOC, V23, P3491, DOI 10.1177/1461444820958123
   Gillespie T, 2024, BIG DATA SOC, V11, DOI 10.1177/20539517241252131
   Grassini S, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2345430
   Green BP, 2018, SCI FIDES, V6, P9, DOI 10.12775/SetF.2018.015
   Grimmer J, 2013, POLIT ANAL, V21, P267, DOI 10.1093/pan/mps028
   Grün B, 2011, J STAT SOFTW, V40, P1
   Hadi Mogavi Reza, 2024, Computers in Human Behavior: Artificial Humans, V2, DOI 10.1016/j.chbah.2023.100027
   Honnibal M., 2017, SPACY 2 NATURAL LANG
   Hsu CL, 2008, INFORM MANAGE-AMSTER, V45, P65, DOI 10.1016/j.im.2007.11.001
   Hu K., 2023, REUTERS         0202
   Huang MH, 2024, J MARKETING, V88, P1, DOI 10.1177/00222429231224748
   Ibrahim H, 2023, IEEE INTELL SYST, V38, P24, DOI 10.1109/MIS.2023.3255599
   Jo H, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2278283
   Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
   Jurafsky Dan, 2009, Speech and Language Processing, V2nd
   Kantosalo A., 2016, P 7 INT C COMP CREAT, P77
   Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003
   KATZ E, 1973, PUBLIC OPIN QUART, V37, P508
   Khobzi H, 2014, INT J ACCOUNT INF MA, V22, P254, DOI 10.1108/IJAIM-02-2014-0010
   Lee H, 2020, INT J ADVERT, V39, P1150, DOI 10.1080/02650487.2020.1765657
   Li O, 2021, ETHICS INF TECHNOL, V23, P543, DOI 10.1007/s10676-021-09595-x
   Liu IV, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2300015
   Lubart T, 2005, INT J HUM-COMPUT ST, V63, P365, DOI 10.1016/j.ijhcs.2005.04.002
   Lucas C, 2015, POLIT ANAL, V23, P254, DOI 10.1093/pan/mpu019
   Maier D, 2018, COMMUN METHODS MEAS, V12, P93, DOI 10.1080/19312458.2018.1430754
   Makridakis S, 2017, FUTURES, V90, P46, DOI 10.1016/j.futures.2017.03.006
   Manning CD., 2008, Introduction to information retrieval, P99, DOI DOI 10.1017/CBO9780511809071
   Martin K. E., 2020, Strategic Information Management, P450, DOI DOI 10.4324/9780429286797
   Massanari A, 2017, NEW MEDIA SOC, V19, P329, DOI 10.1177/1461444815608807
   Mathieson K, 1991, INFORM SYST RES, V2, P173, DOI 10.1287/isre.2.3.173
   Mazzone M, 2019, ARTS, V8, DOI 10.3390/arts8010026
   McCallum S., 2023, BBC NewsApril 28
   McClure PK, 2018, SOC SCI COMPUT REV, V36, P139, DOI 10.1177/0894439317698637
   McLean G, 2019, COMPUT HUM BEHAV, V99, P28, DOI 10.1016/j.chb.2019.05.009
   Medvedev A.N., 2019, Dynamics on and of complex networks III. DOOCN 2017. Springer Proceedings in Complexity, P183, DOI DOI 10.1007/978-3-030-14683-29
   Miller AI, 2019, ARTIST IN THE MACHINE: THE WORLD OF AI-POWERED CREATIVITY, DOI 10.1080/09540121.2019.1668533
   Mimno D., 2011, P 2011 C EMPIRICAL M, P262, DOI [DOI 10.3115/2145432.2145462, DOI 10.5555/2145432.2145462]
   Mimno David., 2011, Proceedings of the Conference on Empirical Methods in Natural Language Processing, P227
   Mittelstadt BD, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716679679
   Miyazaki K, 2024, EPJ DATA SCI, V13, DOI 10.1140/epjds/s13688-023-00445-y
   Mori M, 2012, IEEE ROBOT AUTOM MAG, V19, P98, DOI 10.1109/MRA.2012.2192811
   Munnik JB, 2024, S AFR J PSYCHOL, V54, P130, DOI 10.1177/00812463231223427
   Murugan M, 2024, J AM MED INFORM ASSN, V31, P1356, DOI 10.1093/jamia/ocae039
   Ng YL, 2024, INFORM TECHNOL PEOPL, DOI 10.1108/ITP-06-2023-0577
   Ng YL, 2024, INT J HUM-COMPUT INT, V40, P2185, DOI 10.1080/10447318.2022.2163347
   Ng YL, 2022, COMPUT HUM BEHAV, V134, DOI 10.1016/j.chb.2022.107326
   Ng YL, 2024, NEW MEDIA SOC, V26, P1429, DOI 10.1177/14614448221074047
   ONeill C, 2016, Weapons of Math Destruction. How big Data increases Inequality and threatens Democracy
   Park JS, 2023, P 36 ANN ACM S US IN, DOI DOI 10.1145/3586183.3606763
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Pitardi V, 2021, PSYCHOL MARKET, V38, P626, DOI 10.1002/mar.21457
   Roberts ME, 2014, AM J POLIT SCI, V58, P1064, DOI 10.1111/ajps.12103
   Rubin AM, 2009, COMMUN SER, P165
   SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0
   Shank DB, 2019, COMPUT HUM BEHAV, V98, P256, DOI 10.1016/j.chb.2019.04.001
   SHANNON CE, 1948, BELL SYST TECH J, V27, P379, DOI 10.1002/j.1538-7305.1948.tb01338.x
   Shin D, 2020, J BROADCAST ELECTRON, V64, P541, DOI 10.1080/08838151.2020.1843357
   Sundar SS, 2020, J COMPUT-MEDIAT COMM, V25, P74, DOI 10.1093/jcmc/zmz026
   Thamik H, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063568
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Topol EJ, 2019, NAT MED, V25, P44, DOI 10.1038/s41591-018-0300-7
   Trunfio M., 2021, Italian Journal of Marketing, V2021, P267, DOI [DOI 10.1007/S43039-021-00035-8, 10.1007/s43039-021-00035-8]
   Turkle Sherry., 2011, Continuing Higher Education Review, V75, P28
   Turney PD, 2010, J ARTIF INTELL RES, V37, P141, DOI 10.1613/jair.2934
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2012, MIS QUART, V36, P157
   Xie ZH, 2024, J COMPUT ASSIST LEAR, DOI 10.1111/jcal.13032
   Yen C, 2021, BEHAV INFORM TECHNOL, V40, P1177, DOI 10.1080/0144929X.2020.1743362
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 95
TC 0
Z9 0
U1 1
U2 1
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PD 2024 DEC 5
PY 2024
DI 10.1080/10447318.2024.2436749
EA DEC 2024
PG 18
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA O4Z7O
UT WOS:001371233000001
OA hybrid
DA 2024-12-25
ER

PT J
AU Rudolph, J
   Ismail, MFB
   Popenici, S
AF Rudolph, Jurgen
   Ismail, Mohamed Fadhil Bin Mohamed
   Popenici, Stefan
TI Higher Education's Generative Artificial Intelligence Paradox: The
   Meaning of Chatbot Mania
SO JOURNAL OF UNIVERSITY TEACHING AND LEARNING PRACTICE
LA English
DT Article
ID AI
AB Higher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, the hype surrounding GenAI and the increasing influence of educational technology business groups over tertiary education. This commentary, prepared for the Special Issue of the Journal of University Teaching & Learning Practice (JUTLP) on "Enhancing student engagement using Artificial Intelligence (AI) and chatbots," delves into the complex landscape of opportunities and threats that AI chatbots, including ChatGPT, introduce to the realm of higher education. We argue that while GenAI offers promise in enhancing pedagogy, research, administration, and student support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, and regulatory gaps necessitate a critical approach. Our commentary advocates for the development of critical AI literacy among educators and students, emphasising the necessity to foster an environment of responsible innovation and informed use of AI. We posit that the successful integration of AI in higher education must be grounded in the principles of ethics, equity, and the prioritisation of educational aims and human values. By offering a critical and nuanced exploration of these issues, our commentary aims to contribute to the ongoing discourse on how higher education institutions can navigate the rise of GenAI, ensuring that technological advancements benefit all stakeholders while upholding core academic values.
C1 [Rudolph, Jurgen; Ismail, Mohamed Fadhil Bin Mohamed] Kaplan Higher Educ Acad, Singapore, Singapore.
   [Popenici, Stefan] Charles Darwin Univ, Casuarina, Australia.
C3 Charles Darwin University
RP Rudolph, J (corresponding author), Kaplan Higher Educ Acad, Singapore, Singapore.
RI Popenici, Stefan/G-4937-2017
FX Thanks to Shannon Tan for creating Figure 1 specifically for this
   commentary.
CR Abdalla M, 2021, AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, P287, DOI 10.1145/3461702.3462563
   Acemoglu D., 2023, Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity
   Adarkwah M. A., 2023, Journal of Applied Learning Teaching, V6, P2, DOI [10.37074/jalt.2023.6.2.26, DOI 10.37074/JALT.2023.6.2.26]
   Adeshola I, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253858
   Adom D., 2020, International Journal of Evaluation and Research in Education, V9, P109
   Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Aithal PS., 2023, INT J CASE STUDIES B, V7, P411, DOI DOI 10.2139/SSRN.4673846
   Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Alam A, 2023, COGENT ENG, V10, DOI 10.1080/23311916.2023.2283282
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Alon-Barkat S, 2023, J PUBL ADM RES THEOR, V33, P153, DOI 10.1093/jopart/muac007
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   Anderson J., 2023, As AI Spreads, Experts predict the best and worst changes in digital life by 2035
   Anft M., 2023, The Chronicle of Higher Education
   [Anonymous], 2023, Kal's cartoon
   [Anonymous], 2024, The Economist
   [Anonymous], 2023, The Economist
   Attenborough D., 2020, LIFE OUR PLANET MY W
   Aydin YO, 2021, IEEE PHOTON CONF, DOI 10.1109/IPC48725.2021.9592954
   Baset A., 2023, Australian Journal of Human Rights, V29, P418, DOI [10.1080/1323238X.2023.2267161, DOI 10.1080/1323238X.2023.2267161]
   Bastani Aaron., 2020, FULLY AUTOMATED LUXU
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Berendt B, 2020, LEARN MEDIA TECHNOL, V45, P312, DOI 10.1080/17439884.2020.1786399
   Berman B. J., 1992, AI & Society, V6, P103, DOI 10.1007/BF02472776
   Bondarenko O, 2020, Arxiv, DOI arXiv:2002.07473
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Brevini B., 2023, Handbook of critical studies of artificial intelligence, P869, DOI [10.4337/9781803928562.00086, DOI 10.4337/9781803928562.00086]
   Brevini B., 2017, Carbon Capitalism and Communication Confronting Climate Crisis, V1st, DOI DOI 10.1007/978-3-319-57876-7
   Broussard M, 2018, ARTIFICIAL UNINTELLIGENCE: HOW COMPUTERS MISUNDERSTAND THE WORLD
   Broussard M., 2023, More than a glitch: Confronting race, gender, and ability bias in tech, DOI [10.1080/1369118X.2023.2267628, DOI 10.1080/1369118X.2023.2267628]
   Brown TB, 2020, ADV NEUR IN, V33
   Bryant D. P., 2019, Teaching students with special needs in inclusive classrooms, DOI [10.1093/elt/ccx042, DOI 10.1093/ELT/CCX042]
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Caldarini G, 2022, INFORMATION, V13, DOI 10.3390/info13010041
   Celik I, 2022, TECHTRENDS, V66, P616, DOI 10.1007/s11528-022-00715-y
   Chaka C., 2023, J. Appl. Learn. Teach., V6, DOI DOI 10.37074/JALT.2023.6.2.12
   Chaka C., 2024, Journal of Applied Learning and Teaching, V7, DOI DOI 10.37074/JALT.2024.7.1.14
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.01185, 10.48550/arXiv.2305.01185, DOI 10.48550/ARXIV.2305.01185]
   Chang CH, 2023, INT RES GEOGR ENVIRO, V32, P85, DOI 10.1080/10382046.2023.2194036
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Cherian J. M., 2024, Reuters
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Claude 3, Anthropic. Introducing the next generation of Claude
   Coffey M., 2023, Journal of Applied Learning Teaching, V6
   COLLINI Stefan., 2012, WHAT ARE U
   Cook L., 1938, Community backgrounds of education: A textbook and educational sociology
   Crawford J., 2023, J UNIV TEACH LEARN P, V20, P1, DOI [10.33327/AJEE-18-6.3-n000319, DOI 10.33327/AJEE-18-6.3-N000319]
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Crawford K., 2021, Atlas ofAI: Power, Politics, and the Planetary Costs of Artificial Intelligence
   Darvishi A, 2022, BRIT J EDUC TECHNOL, V53, P844, DOI 10.1111/bjet.13233
   Devlin H., 2023, The Guardian
   Dieterle E., 2009, ENCY MULTIMEDIA TECH, VII, P1033
   Donelan H, 2024, J COMPUT HIGH EDUC, V36, P435, DOI 10.1007/s12528-023-09360-7
   Doroudi S, 2023, INT J ARTIF INTELL E, V33, P885, DOI 10.1007/s40593-022-00313-2
   Drigas AS, 2009, COMM COM INF SC, V49, P552
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Eager B, 2023, J UNIV TEACH LEARN P, V20
   Eubanks V., 2018, Automating inequality: How high-tech tools profile, police, and punish the poor, DOI [10.5204/lthj.v1i0.1386, DOI 10.5204/LTHJ.V1I0.1386]
   European Parliament, 2023, EU AI ACT 1 REGULATI
   Felluga DinoFranco., 2015, CRITICAL THEORY KEY
   Fenu G, 2022, LECT NOTES COMPUT SC, V13355, P243, DOI 10.1007/978-3-031-11644-5_20
   Fitzpatrick KK, 2017, JMIR MENT HEALTH, V4, DOI 10.2196/mental.7785
   Fleming P., 2021, DARK ACAD U DIE
   Future of Life Institute, 2023, Pause giant AI experiments: An open letter
   Gimpel H., 2023, Unlocking the Power of Generative AI models and Systems such as GPT-4 and ChatGPT for Higher Education: A guide for students and lecturers, DOI [10.13140/RG.2.2.20710.09287/2, DOI 10.13140/RG.2.2.20710.09287/2]
   Ginsberg B., 2011, The fall of the faculty: The rise of the all administrative university and why it matters, DOI DOI 10.1093/OSO/9780199782444.001.0001
   Goita Y., 2023, Multidisciplinary Journal of Akseprin Indonesia, V1
   Gunser Vivian Emily, 2021, INT C HUM COMP INT, V1419, P520, DOI DOI 10.1007/978-3-030-78635-967
   Hanover Research, 2023, Benefits, challenges, and sample use cases of AI in higher education
   Hart R., 2024, Forbes
   Hashem R, 2024, RES PRACT TECH ENHAN, V19, DOI 10.58459/rptel.2024.19023
   Haw M., 2019, The Guardian
   Hawking S., 2014, Stephen Hawking warns artificial intelligence could end mankind
   Hendrikse R, 2022, SCI CULT-UK, V31, P59, DOI 10.1080/09505431.2021.1984423
   Hew KF, 2023, J COMPUT HIGH EDUC, V35, P40, DOI 10.1007/s12528-022-09338-x
   Houser K., 2017, Futurism
   Humble N., 2022, DISCOVER ARTIFICIAL, V2, DOI DOI 10.1007/S44163-022-00039-Z
   Ifelebuegu A., 2023, J APPL LEARNING TEAC, V6, DOI [10.37074/jalt.2023.6.2.2, DOI 10.37074/JALT.2023.6.2.2]
   Jacobides MG, 2021, STRATEG SCI, V6, P412, DOI 10.1287/stsc.2021.0148
   Javaid M., 2023, Journal of Economy and Technology, V1, P127, DOI [DOI 10.1016/J.JECT.2023.08.001, https://doi.org/10.1016/j.ject.2023.08.001]
   Jesse T., 2024, Autism, neurodiversity, and equity in professional preparation programs, P79, DOI [10.4018/979-8-3693-0163-0.ch004, DOI 10.4018/979-8-3693-0163-0.CH004]
   Jiao PC, 2022, ARTIF INTELL REV, V55, P6321, DOI 10.1007/s10462-022-10155-y
   Kabudi T., 2021, Computers and Education: Artificial Intelligence, V2, DOI [10.1016/j.caeai.2021.100017, DOI 10.1016/J.CAEAI.2021.100017, 10.1007/s10639-023-11816-3, DOI 10.1007/S10639-023-11816-3]
   Kadaruddin K., 2023, International Journal of Business, Law, and Education, V4, P618, DOI DOI 10.56442/IJBLE.V4I2.215
   Kassova L., 2024, The Guardian
   Kefalaki M., 2022, Journal of Applied Learning & Teaching, V5, P6, DOI [10.37074/jalt.2022.5.s1.1, DOI 10.37074/JALT.2022.5.S1.1]
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   Khosravi H., 2022, Computers and Education: Artificial Intelligence, V3, P100074, DOI DOI 10.1016/J.CAEAI.2022.100074
   Kim J, 2022, EDUC INF TECHNOL, V27, P6069, DOI 10.1007/s10639-021-10831-6
   Klein E., 2023, The New York Times
   Klein N, 2007, SHOCK DOCTRINE
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kooli C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075614
   Kouam A. W. F., 2024, Journal of Applied Learning and Teaching, V7, DOI [10.37074/jalt.2024.7.1.12, DOI 10.37074/JALT.2024.7.1.12]
   Kurni M., 2023, A beginner's guide to introduce artificial intelligence in teaching and learning, P173, DOI [10.1007/978-3-031-32653-010, DOI 10.1007/978-3-031-32653-010]
   Kurni M., 2023, A beginner's guide to introduce artificial intelligence in teaching and learning, P55, DOI [10.1007/978-3-031-32653-04, DOI 10.1007/978-3-031-32653-04]
   Labadze L, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00426-1
   Lanier J., 2020, Wired
   Lanier J., 2018, The Bodley Head
   Lee KF, 2018, AI SUPERPOWERS CHINA
   Levine E. J., 2020, Inside Higher Ed
   Liang WX, 2022, NAT MACH INTELL, V4, P669, DOI 10.1038/s42256-022-00516-1
   Limna P., 2023, J. Appl. Learn. Teach, V6, P64, DOI [DOI 10.37074/JALT.2023.6.1.32, https://doi.org/10.37074/jalt.2023.6.1.32]
   Lindgren S., 2023, Handbook of Critical Studies of Artificial Intelligence, P1, DOI 10.4337/9781803928562.00005
   Lindgren S., 2023, Critical theory of AI
   Liu M., 2023, Future Educ. Res., V1, P72, DOI [DOI 10.1002/FER3.10, 10.1002/fer3.10 10.1002/fer3.10]
   Lodge JM, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.7.02
   Luan H, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.580820
   Luckin R., 2016, International Journal of Artificial Intelligence in Education, V26, P582
   Luckin R, 2017, NAT HUM BEHAV, V1, DOI 10.1038/s41562-016-0028
   Luckin R, 2019, BRIT J EDUC TECHNOL, V50, P2824, DOI 10.1111/bjet.12861
   Lund B, 2023, SCIENTOMETRICS, V128, P4895, DOI 10.1007/s11192-023-04781-8
   Mac R., 2021, The New York Times
   Madaio Michael, 2022, Proceedings of the ACM on Human-Computer Interaction, V6, DOI 10.1145/3512899
   Madianou M, 2019, SOC MEDIA SOC, V5, DOI 10.1177/2056305119863146
   Mahoney BB, 2023, THINK SKILLS CREAT, V50, DOI 10.1016/j.tsc.2023.101422
   Marcus G., 2023, Gary Marcus substack
   McGuire M. R., 2023, The Howard Journal of Crime and Justice, V62, P441, DOI 10.1111/hojo.12533
   McMurtrie B., 2023, The Chronicle of Higher Education
   Metz C., 2023, The New York Times
   Metz C., 2022, Genius makers. The mavericks who brought AI to Google, Facebook and the world
   Meyer JG, 2023, BIODATA MIN, V16, DOI 10.1186/s13040-023-00339-9
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mills A., 2023, Chronicle of Higher Education
   Mina P. N. R., 2023, International Journal of Educational Innovation and Research, V2, P10, DOI 10.31949/ijeir.v2i1.3001
   Mohammad Karimi E., 2023, Academic Journal of Management and Social Sciences, V6, P105, DOI [10.37074/jalt.2023.6.2.10, DOI 10.54097/AJMSS.V2I2.7669]
   Mollick E., 2022, HARVARD BUSINESS REV
   Mollick E, 2023, RES TECHNOL MANAGE, V66, P11, DOI 10.1080/08956308.2023.2213102
   Mollick E, 2023, Arxiv, DOI arXiv:2306.10052
   Monserrate S.G., 2022, MIT Case Stud. Soc. Ethical Responsib. Comput., DOI DOI 10.21428/2C646DE5.031D4553
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Moradi P., 2020, The Oxford handbook of ethics of AI, P269, DOI DOI 10.1093/OXFORDHB/9780190067397.013.17
   Morozov E., 2013, To save everything, click here: The folly of technological solutionism
   Naseer F., 2024, Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation, P433, DOI 10.4018/979-8-3693
   Noble SU, 2018, ALGORITHMS OF OPPRESSION, P1
   OECD AI, 2023, Sickening new 'deepfake porn' trend sweeping Aussie schools
   ONeill C, 2016, Weapons of Math Destruction. How big Data increases Inequality and threatens Democracy
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   OpenAI, 2023, OpenAI-Educator FAQ
   Orwell George., 1933, OUT PARIS LONDON
   Ouyang F, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-022-00372-4
   Packin N. G., 2018, RES HDB LAW ARTIFICI, DOI [10.4337/9781786439055.00014, DOI 10.4337/9781786439055.00014]
   Pataranutaporn P, 2021, NAT MACH INTELL, V3, P1013, DOI 10.1038/s42256-021-00417-9
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Pimm SL, 2014, SCIENCE, V344, P987, DOI 10.1126/science.1246752
   PIMM SL, 1995, SCIENCE, V269, P347, DOI 10.1126/science.269.5222.347
   Popenici S., 2023, J. Appl. Learn. Teach, V6, P378, DOI [10.37074/jalt.2023.6.2.4, DOI 10.37074/JALT.2023.6.2.4]
   Popenici S., 2023, Artificial Intelligence and learning futures: Critical narratives of technology and imagination in higher education, DOI DOI 10.4324/9781003266563
   Popenici S., 2023, Journal of Applied Learning and Teaching, V6, P1, DOI [10.37074/jalt.2023.6.2.5, DOI 10.37074/JALT.2023.6.2.5]
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Raimundo R, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21217029
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Rikap C., 2023, CITYPERC Working Paper No. 2023-03, DOI [10.2139/ssrn.4472222, DOI 10.2139/SSRN.4472222]
   Ripple WJ, 2017, BIOSCIENCE, V67, P1026, DOI 10.1093/biosci/bix125
   Rodman E, 2024, POLIT THEORY, V52, P548, DOI 10.1177/00905917231200826
   Roscoe R. D., 2022, Artificial Intelligence in STEM Education, P359
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Russell B., 2004, Praise of Idleness And Other Essays
   Rutner S. M., 2022, Information Systems Education Journal (ISEDJ), V20, P4
   Sagre S., 2023, Artificial intelligence: A game-changer in writing, DOI [10.22271/yogic.2023.v8.i2d.1471, DOI 10.22271/YOGIC.2023.V8.I2D.1471]
   Sandoval A, 2018, INTERNET HIGH EDUC, V37, P76, DOI 10.1016/j.iheduc.2018.02.002
   Schneider-Mayerson M., 2020, Eating Chilli Crab in the Anthropocene: Environmental Perspectives on Life in Singapore
   Selwyn Neil., 2019, WHAT IS DIGITAL SOCI
   Sharma S., 2023, Global Journal of Enterprise Information System, V14, P46
   Shelley Mary., 2017, FRANKENSTEIN MODERN
   Shimizu I, 2023, JMIR MED EDUC, V9, DOI 10.2196/53466
   Singer N., 2018, The New York Times
   Singha S., 2024, Transforming education with Gen AI: Prompt engineering and synthetic content creation, P261, DOI [10.4018/979-8-3693-1351-0.ch013, DOI 10.4018/979-8-3693-1351-0.CH013]
   Smart Sebastian., 2017, Critical Planning, V23, P59, DOI [10.5070/CP8231038128, DOI 10.5070/CP8231038128]
   Smith B., 2023, M AI MOMENT ADV FUTU
   Solomon S, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.960649
   Spaeth E., 2023, Journal of Perspectives in Applied Academic Practice, V11, P109, DOI DOI 10.56433/JPAAP.V11I2.517
   Suleyman M., 2023, COMING WAVE AI POWER
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Suryadevara Chaitanya Krishna, 2020, International Journal of Innovations in Engineering Research and Technology, V7, P49
   Susskind D., 2021, A world without work: Technology, automation and how we should respond, DOI [10.1111/padr.12372, DOI 10.1111/PADR.12372]
   Sytsma T., 2024, Technological and economic threats to the US financial system
   Tambo P., 2024, Knowledge@Wharton
   Tan E., 2023, Strategic Management and International Business Policies for Maintaining Competitive Advantage, P256
   Tan S., 2023, Learning intelligence: Innovative and digital transformative learning strategies, P335
   Terzian S., 2019, The Oxford handbook of the history of education, P554, DOI [10.1093/oxfordhb/9780199340033.013.33, DOI 10.1093/OXFORDHB/9780199340033.013.33]
   Thiel P., 2019, New York Times
   Tomic BB, 2023, IEEE T LEARN TECHNOL, V16, P292, DOI 10.1109/TLT.2022.3225432
   Treve M, 2021, HIGH EDUC PEDAGOG, V6, P212, DOI 10.1080/23752696.2021.1951616
   Tsamados A, 2022, AI SOC, V37, P215, DOI 10.1007/s00146-021-01154-8
   Turkle S., 2011, ALONE TOGETHER WHY W
   Van der Kleij FM, 2021, EDUC ASSESS EVAL ACC, V33, P345, DOI [10.1007/s11092-020-09331-x, 10.18046/EUI/expl.13.2020]
   van Dijck J., 2018, The Platform Society, DOI DOI 10.1093/OSO/9780190889760.001.0001
   Van Wyk M., 2024, Journal of Applied Learning and Teaching, V7, DOI [10.37074/jalt.2024.7.1.15, DOI 10.37074/JALT.2024.7.1.15]
   Vashishth TK, 2024, Using Traditional Design Methods to Enhance AI-Driven Decision Making, P206
   Vincent J., 2018, GOOGLE FIXED ITS RAC
   Wagler R, 2011, AM BIOL TEACH, V73, P78, DOI 10.1525/abt.2011.73.2.5
   Wang T, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13116716
   Watters Audrey., 2021, TEACHING MACHINES HI
   Wooldridge M, 2020, ROAD CONSCIOUS MACHI
   Xiaoyi Tian, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3449171
   Yu P. K., 2020, Florida Law Review, V72, P331
   Zajko M, 2021, AI SOC, V36, P1047, DOI 10.1007/s00146-021-01153-9
   Zhai C., 2023, Computers and Education: Artificial Intelligence, DOI DOI 10.1016/J.CAEAI.2023.100134
   Zhang J., 2023, Lecture Notes in Education Psychology and Public Media, V2, P822, DOI DOI 10.54254/2753-7048/2/2022483
NR 202
TC 3
Z9 3
U1 22
U2 22
PU Open Access Publishing Assoc
PI Launceston
PA 28a Brisbane St, Launceston, Tasmania, AUSTRALIA
SN 1449-9789
J9 J UNIV TEACH LEARN P
JI J. Univ. Teach. Learn. Pract.
PY 2024
VL 21
IS 6
AR 77
PG 35
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D5J4K
UT WOS:001296541300009
DA 2024-12-25
ER

PT J
AU Rasul, T
   Nair, S
   Kalendra, D
   Balaji, MS
   Santini, FD
   Ladeira, W Jr
   Rather, RA
   Yasin, N
   Rodriguez, RV
   Kokkalis, P
   Murad, MW
   Hossain, MU
AF Rasul, Tareq
   Nair, Sumesh
   Kalendra, Diane
   Balaji, M. S.
   Santini, Fernando de Oliveira
   Ladeira, Wagner Junior
   Rather, Raouf Ahmad
   Yasin, Naveed
   Rodriguez, Raul V.
   Kokkalis, Panagiotis
   Murad, Md Wahid
   Hossain, Md Uzir
TI Enhancing academic integrity among students in GenAI Era:A holistic
   framework
SO INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION
LA English
DT Article
DE Generative AI; Academic integrity; Higher education; Students;
   Stakeholders
ID HIGHER-EDUCATION; CRITICAL THINKING; INNOVATION; SCIENCE
AB The introduction of Artificial Intelligence (AI), specifically Generative AI (GenAI), has significantly transformed the higher education landscape. Despite the opportunities GenAI offers to students, they pose significant challenges for academic integrity. Thus, it is crucial for higher education institutions (HEI) to balance the use of GenAI for enhancing the learning experience of students with its ethical and responsible use in their educational journey. The present study proposes a comprehensive academic integrity framework focusing on three key stakeholders: students, educators, and institutions. We propose eight strategies ranging from collaborative learning for students to developing a comprehensive GenAI policy for institutions in maintaining academic integrity among students in HEI. Furthermore, we identified four challenges, namely financial, strategic, operational, and cultural, in the implementing a comprehensive academic integrity framework in the GenAI era. This study offers significant insights for HEI to maintain academic integrity among students in the GenAI era.
C1 [Rasul, Tareq; Nair, Sumesh; Kalendra, Diane] Australian Inst Business AIB, Adelaide, Australia.
   [Balaji, M. S.] Rennes Sch Business, Rennes, France.
   [Santini, Fernando de Oliveira; Ladeira, Wagner Junior] Univ Vale Rio dos Sinos UNISINOS, Porto Alegre, Brazil.
   [Santini, Fernando de Oliveira; Ladeira, Wagner Junior; Rodriguez, Raul V.] Woxsen Univ, Sch Business, Hyderabad, India.
   [Rather, Raouf Ahmad; Yasin, Naveed] Abu Dhabi Sch Management, Abu Dhabi, U Arab Emirates.
   [Kokkalis, Panagiotis] Rochester Inst Technol Dubai, Dubai, U Arab Emirates.
   [Murad, Md Wahid] Univ South Australia, Adelaide, Australia.
   [Hossain, Md Uzir] Putra Business Sch, Serdang, Malaysia.
   [Yasin, Naveed] Univ Roehampton London, United Arab Emirates Campus, London, England.
C3 Australian Institute of Business; Universite de Rennes; Woxsen
   University; Rochester Institute of Technology; University of South
   Australia
RP Balaji, MS (corresponding author), Rennes Sch Business, Rennes, France.
EM tfrasul@gmail.com; sumesh.nair@aib.edu.au; diane.kalendra@aib.edu.au;
   balaji.makam@rennes-sb.com; fosantini@unisinos.br; wjladeira@gmail.com;
   r.raouf18@gmail.com; Yasinresearch@gmail.com;
   vice.president@woxsen.edu.in; pxkcad@rit.edu; Wahid.Murad@unisa.edu.au;
   hossainuzir@gmail.com
RI Rasul, T/ABG-4251-2021; Rodriguez, Raul/AAC-5379-2020; Makam,
   Balaji/AAD-6738-2021; Murad, Md Wahid/D-3081-2015
OI Rasul, Tareq/0000-0002-1274-7000; Kalendra, Diane/0000-0002-9168-1440;
   Murad, Md Wahid/0000-0002-2486-2081; RATHER, RAOUF/0000-0002-9242-1165;
   Yasin, Naveed/0000-0003-3927-3762; M S, BALAJI/0000-0002-6003-7644
CR Aalto University, 2023, Guidance for the use of artificial intelligence in teaching and learning at Aalto University
   Abdaljaleel M, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-52549-8
   Adeshola I, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253858
   Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   [Anonymous], 2024, Global Times
   Awosoga O, 2021, INT J EDUC INTEGR, V17, DOI 10.1007/s40979-021-00090-w
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Blind K, 1999, RES POLICY, V28, P451, DOI 10.1016/S0048-7333(99)00007-4
   Brynjolfsson E., 2023, NBER Working Paper No. 31161, V31161, DOI DOI 10.3386/W31161
   Burtsev M., 2023, MIT Sloan Management Review, V65, P8
   Caldwell C, 2010, J BUS ETHICS, V92, P1, DOI 10.1007/s10551-009-0144-7
   Camblin LD, 2000, HIGH EDUC, V39, P1, DOI 10.1023/A:1003827925543
   Carolus A., 2023, Computers in Human Behavior: Artificial Humans, V1, P100014
   Çelik Ö, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00125-4
   Cen L, 2016, INT J COMP-SUPP COLL, V11, P187, DOI 10.1007/s11412-016-9234-6
   CENTRA JA, 1978, J HIGH EDUC, V49, P151, DOI 10.2307/1979280
   Chaisatitkul Atthawut, 2024, International Journal of Information Technology, V16, P137, DOI 10.1007/s41870-023-01661-5
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chauhan PK, 2018, J ACAD ETHICS, V16, P133, DOI 10.1007/s10805-017-9296-8
   Cheng FF, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.713497
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Contrino MF, 2024, SMART LEARN ENVIRON, V11, DOI 10.1186/s40561-024-00292-y
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Dewiyanti S, 2007, COMPUT HUM BEHAV, V23, P496, DOI 10.1016/j.chb.2004.10.021
   Duah JE, 2024, INT J INF LEARN TECH, V41, P180, DOI 10.1108/IJILT-11-2023-0213
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Engel O, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00382-w
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Freeman Josh, 2024, New HEPI policy note finds more than half of students have used generative AI for help on assessments-but only 5% likely to be using AI to cheat
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Harvard University, Initial guidelines for the use of generative AI tools at Harvard
   Herbold Steffen, 2023, Sci Rep, V13, P18617, DOI 10.1038/s41598-023-45644-9
   Hernández R, 2012, HIGH EDUC, V64, P489, DOI 10.1007/s10734-012-9506-7
   Heywood J., 2000, Assessment in higher education: Student learning, teaching, programmes and institutions, V56
   Hmelo-Silver CE, 2008, INSTR SCI, V36, P409, DOI 10.1007/s11251-008-9063-8
   Hobbins J, 2022, ASSESS EVAL HIGH EDU, V47, P1259, DOI 10.1080/02602938.2021.2009439
   Hossain Z, 2022, INT ASS SCH LIBR C I
   HyScaler, 2023, The Power of AI in Research Hypotheses
   intelligent, 2023, Nearly 1 in 3 college students have used ChatGPT on written assignments
   International Center for Academic Integrity, 2021, The Fundamental Values of Academic Integrity
   Jones DLR, 2011, BUS PROF COMMUN Q, V74, P141, DOI 10.1177/1080569911404059
   Kelly A, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.6.12
   Kerr P, 2016, ELT J, V70, P88, DOI 10.1093/elt/ccv055
   Khan ZR, 2024, J ACAD ETHICS, V22, P9, DOI 10.1007/s10805-024-09517-8
   Kier CA, 2022, INT J EDUC INTEGR, V18, DOI 10.1007/s40979-022-00116-x
   Kong SC., 2021, COMPUTERS ED ARTIFIC, V2, P100026, DOI [DOI 10.1016/J.CAEAI.2021.100026, 10.1016/j.caeai.2021.100026]
   Kong SC, 2023, EDUC TECHNOL SOC, V26, P16, DOI 10.30191/ETS.202301_26(1).0002
   Kotsonis A, 2022, ETHICS EDUC, V17, P311, DOI 10.1080/17449642.2022.2111485
   Laupichler MC, 2023, COMPUT HUM BEHAV REP, V12, DOI 10.1016/j.chbr.2023.100338
   Lee VR., 2024, COMPUTERS ED ARTIFIC, V7, P100253, DOI 10.1016/j.caeai.2024.100253
   Li TT, 2024, ASIA PAC J EDUC, V44, P45, DOI 10.1080/02188791.2024.2305163
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lin W, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14116489
   Liu D, 2023, Supporting students to use AI resources responsibly and productively
   Liu HH, 2023, HEALTH INFO LIBR J, V40, P440, DOI 10.1111/hir.12509
   Luo JH, 2024, ASSESS EVAL HIGH EDU, V49, P651, DOI 10.1080/02602938.2024.2309963
   Maastricht University, Project-based learning
   Martin F, 2020, ETR&D-EDUC TECH RES, V68, P1903, DOI 10.1007/s11423-020-09793-2
   Maynooth University, Authentic assessment
   McCabe DL, 2012, CHEATING IN COLLEGE: WHY STUDENTS DO IT AND WHAT EDUCATORS CAN DO ABOUT IT, P1
   Monash University, Learning and teaching: Teach HQ: AI and assessment
   Monteith S, 2024, BRIT J PSYCHIAT, V224, P33, DOI 10.1192/bjp.2023.136
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Newton P, 2024, ASSESS EVAL HIGH EDU, V49, P781, DOI 10.1080/02602938.2023.2299059
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perkins M, 2024, J ACAD ETHICS, V22, P89, DOI 10.1007/s10805-023-09492-6
   Plata S., 2023, ASIAN J UNI EDU, V19, P743, DOI [DOI 10.24191/AJUE.V19I4.24697, 10.24191/ajue.v19i4.24697]
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Rasul T., 2023, Journal of Applied Learning & Teaching, V6
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   RMIT University, 2024, Generative AI for students at RMIT Module
   Rodríguez-Abitia G, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12219069
   Roe J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02282-w
   Rohm AJ, 2021, J MARKET EDUC, V43, P204, DOI 10.1177/02734753211001409
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Schindler LA, 2017, INT J EDUC TECHNOL H, V14, DOI 10.1186/s41239-017-0063-0
   Schneckenberg D, 2009, EDUC RES-UK, V51, P411, DOI 10.1080/00131880903354741
   Sefcik L, 2020, ASSESS EVAL HIGH EDU, V45, P30, DOI 10.1080/02602938.2019.1604942
   Seifert T, 2023, EDUC INF TECHNOL, V28, P7797, DOI 10.1007/s10639-022-11498-3
   Smyrnova-Trybulska E, 2022, EDUC INF TECHNOL, V27, P6787, DOI 10.1007/s10639-021-10830-7
   Southworth J., 2023, COMPUTERS ED ARTIFIC, V4, pPG, DOI [10.1016/j.caeai.2023.100127 10.1016/j.caeai.2023.100127, DOI 10.1016/J.CAEAI.2023.100127, 10.1016/j.caeai.2023.100127]
   Stanford Graduate School of Business, The experience: Collaborative environment
   Stephens JM, 2016, INT J EDUC INTEGR, V12, DOI 10.1007/s40979-016-0010-1
   Stupnisky RH, 2008, RES HIGH EDUC, V49, P513, DOI 10.1007/s11162-008-9093-8
   Swaffield S, 2011, ASSESS EDUC, V18, P433, DOI 10.1080/0969594X.2011.582838
   Tal RT., 2000, Studies in Educational Evaluation, V26, P171, DOI DOI 10.1016/S0191-491X(00)00014-6
   Taras M., 2008, Active Learning in Higher Education, V9, P172, DOI [10.1177/1469787408091655, DOI 10.1177/1469787408091655]
   TEQSA, 2023, Assessment reform for the age of artificial intelligence
   Thanh BN, 2023, AUSTRALAS J EDUC TEC, V39, P59, DOI 10.14742/ajet.8902
   The University of Melbourne, Peer assisted study sessions
   The University of Sydney, 2023, What you need to know about the new academic integrity policy
   Tiruneh D.T., 2014, Higher Education Studies, V4, P1, DOI DOI 10.5539/HES.V4N1P1
   University of Bath, 2023, Support for students to use GenAI tools effectively and responsibly
   University of Oxford, AI in teaching and assessment
   UTS, GenAI learning resources
   Van den Bergh V., 2006, Studies in Educational Evaluation, V32, P345, DOI [10.1016/j.stueduc.2006.10.005, DOI 10.1016/J.STUEDUC.2006.10.005]
   Vuopala E, 2016, ACT LEARN HIGH EDUC, V17, P25, DOI 10.1177/1469787415616730
   WALTERS KS, 1990, J HIGH EDUC, V61, P448, DOI 10.2307/1982080
   Wang YM, 2021, EDUC TECHNOL SOC, V24, P116
   Wangaard D B., 2011, Creating a Culture of Academic Integrity: A Tool Kit for Secondary Schools
   Wood P, 2023, The i
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 110
TC 1
Z9 1
U1 40
U2 40
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 1472-8117
EI 2352-3565
J9 INT J MANAG EDUC-OXF
JI Int. J. Manag. Educ.
PD NOV
PY 2024
VL 22
IS 3
AR 101041
DI 10.1016/j.ijme.2024.101041
EA AUG 2024
PG 13
WC Business; Education & Educational Research; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Education & Educational Research
GA D5W7F
UT WOS:001296886600001
DA 2024-12-25
ER

PT J
AU Herrie, MB
   Maleve, NR
   Philipsen, L
   Staunaes, AB
AF Herrie, Maja Bak
   Maleve, Nicolas Rene
   Philipsen, Lotte
   Staunaes, Asker Bryld
TI Democratization and generative AI image creation: aesthetics,
   citizenship, and practices
SO AI & SOCIETY
LA English
DT Article; Early Access
DE Generative artificial intelligence; Image practices; Democratization;
   Visual citizenship; Explorative workshops
AB The article critically analyzes how contemporary image practices involving generative artificial intelligence are entangled with processes of democratization. We demonstrate and discuss how generative artificial intelligence images raise questions of democratization and citizenship in terms of access, skills, validation, truths, and diversity. First, the article establishes a theoretical framework, which includes theory on democratization and aesthetics and lays the foundations for the analytical concepts of 'formative' and 'generative' visual citizenship. Next, we argue for the use of explorative and collaborative methods to investigate contemporary image practice, before analyzing the central part of our investigation, which takes the form of four collaborative workshops conducted in 2023 with external partners in different domains (the art scene, art therapy, education, and the news media). After analyzing insights from these workshops, the article significantly nuances how visual citizenship is at work in different manners depending on the different concrete image practices using generative artificial intelligence. Finally, we conclude that an aesthetic perspective offers valuable insights into foundational aspects of belonging to contemporary visual communities.
C1 [Herrie, Maja Bak; Maleve, Nicolas Rene; Philipsen, Lotte; Staunaes, Asker Bryld] Aarhus Univ, Ctr Aesthet AI Images, AIIM, Aarhus, Denmark.
C3 Aarhus University
RP Philipsen, L (corresponding author), Aarhus Univ, Ctr Aesthet AI Images, AIIM, Aarhus, Denmark.
EM mbh@cc.au.dk; maleven@cc.au.dk; lottephilipsen@cc.au.dk; abs@cc.au.dk
RI Malevé, Nicolas/HOF-6837-2023; StaunÃ¦s, Asker Bryld/LMP-0894-2024
FU Aarhus Universitet; SHAPE-Shaping Digital Citizenship at Aarhus
   University, Denmark
FX Open access funding provided by Aarhus Universitet. Open access funding
   provided by Aarhus Universitet. The article analyses results from the
   project 'Digital Citizenship and AI Image Practices' which was supported
   by seed funding from SHAPE-Shaping Digital Citizenship at Aarhus
   University, Denmark (100,000 DKK). From Aarhus University Research
   Foundation we received accommodation for our day seminar.
CR Adam M., 2023, ARTIFICIAL INTELLIGE
   Azoulay Ariella., 2008, CIVIL CONTRACT PHOTO
   Bajohr H, 2024, WORD IMAGE, V40, P77, DOI 10.1080/02666286.2024.2330335
   Benjamin Walter., 2007, Illuminations
   Bouko C., 2024, Visual citizenship: communicating political opinions and emotions on social media
   Calvo P., 2024, Algorithmic democracy: a critical perspective based on deliberative democracy
   Claburn T., 2022, The Register
   Coeckelbergh M., 2024, Why AI undermines democracy and what to do about it
   Coeckelbergh Mark, 2022, AI Ethics, P1, DOI 10.1007/s43681-022-00239-4
   Crawford K, 2024, An AI society. Issues
   Crawford K, 2021, AI SOC, DOI 10.1007/s00146-021-01162-8
   Derrida Jacques., 2002, Echographies of Television: Filmed Interviews
   Diwakar A, 2022, TRT world
   Freud S., 1916, Vorlesungen zur Einfhrung in die Psychoanalyse. Erster Teil
   Harari YN  ..., 2018, 21 lessons for the 21st century
   Haraway D., 2016, Staying With the Trouble: Making Kin in the Chthulucene, DOI DOI 10.2307/J.CTV11CW25Q
   Hariman R., 2016, The public image: photography and civic spectatorship, DOI [10.7208/chicago/9780226343099.001.0001, DOI 10.7208/CHICAGO/9780226343099.001.0001]
   Hariman Robert., 2007, NO CAPTION NEEDED IC
   Hoel A., 2020, Mediestetik: en introduktion, P147
   Hoel Aud Sissel, 2018, Image-Action-Space: Situating the Screen in Visual Practice, P11
   Janicka I, 2020, HUMANITIES-BASEL, V9, DOI 10.3390/h9040123
   Krauss R., 1994, The optical unconscious, DOI [10.2307/432046, DOI 10.2307/432046]
   Laclau Ernesto., 2001, Hegemony and Socialist Strategy: Towards a Radical Democratic Politics, VSecond
   MacKenzie A, 2019, THEOR CULT SOC, V36, P3, DOI 10.1177/0263276419847508
   Magnus PD, 2023, AI SOC, DOI 10.1007/s00146-023-01817-8
   Malevé N, 2021, AI SOC, V36, P1117, DOI 10.1007/s00146-020-01093-w
   Meyer R., 2023, Zeitschrift Fr Interdisziplinre Bildwissenschaft, V19, P100, DOI [10.25969/mediarep/22314, DOI 10.25969/MEDIAREP/22314]
   Morreale F, 2024, AI SOC, V39, P2389, DOI 10.1007/s00146-023-01692-3
   Offert F, 2021, AI SOC, V36, P1133, DOI 10.1007/s00146-020-01058-z
   Palmer D., 2024, Media Theory, V8, P159
   Park S, 2024, AI SOC, DOI 10.1007/s00146-024-01948-6
   Pasquinelli M, 2021, AI SOC, V36, P1263, DOI 10.1007/s00146-020-01097-6
   Rancire J., 2004, The Politics of Aesthetics: The Distribution of the Sensible
   Rancire J., 2007, On the shores of politics
   Rao A, 2020, Strategy+Business
   SEKULA A, 1975, ARTFORUM, V13, P36
   Somaini A, 2022, Studies in environmental images, P1, DOI [10.54103/ai/15460, DOI 10.54103/AI/15460]
   Stebbins R.A., 2001, Qualitative research methods: Exploratory research in the social sciences, DOI DOI 10.4135/9781412984249
   Steyerl H, 2023, NEW LEFT REV, P82
   Tagg John., 1988, BURDEN REPRESENTATIO
   Wark McKenzie., 2019, CAPITAL IS DEAD IS T
   Wasielewski A, 2023, The opticality unconscious. Generative methods-AI as collaborator and companion in the social sciences and humanities
   Watney S., 1982, Thinking photography
   Wetherall-Grujic G, 2023, The race to democratize AI
NR 44
TC 0
Z9 0
U1 7
U2 7
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0951-5666
EI 1435-5655
J9 AI SOC
JI AI Soc.
PD 2024 OCT 11
PY 2024
DI 10.1007/s00146-024-02102-y
EA OCT 2024
PG 13
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA I4O3S
UT WOS:001330063900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Chan, CKY
   Lee, KKW
AF Chan, Cecilia Ka Yuk
   Lee, Katherine K. W.
TI The AI generation gap: Are Gen Z students more interested in adopting
   generative AI such as ChatGPT in teaching and learning than their Gen X
   and millennial generation teachers?
SO SMART LEARNING ENVIRONMENTS
LA English
DT Article
DE ChatGPT; Generative AI; AI literacy; Risks; Advantages; Holistic
   competencies; Challenges; Benefits
ID INTERCODER RELIABILITY; OPPORTUNITIES
AB This study aimed to explore the experiences, perceptions, knowledge, concerns, and intentions of Generation Z (Gen Z) students with Generation X (Gen X) and Generation Y (Gen Y) teachers regarding the use of generative AI (GenAI) in higher education. A sample of students and teachers were recruited to investigate the above using a survey consisting of both open and closed questions. The findings showed that Gen Z participants were generally optimistic about the potential benefits of GenAI, including enhanced productivity, efficiency, and personalized learning, and expressed intentions to use GenAI for various educational purposes. Gen X and Gen Y teachers acknowledged the potential benefits of GenAI but expressed heightened concerns about overreliance, ethical and pedagogical implications, emphasizing the need for proper guidelines and policies to ensure responsible use of the technology. The study highlighted the importance of combining technology with traditional teaching methods to provide a more effective learning experience. Implications of the findings include the need to develop evidence-based guidelines and policies for GenAI integration, foster critical thinking and digital literacy skills among students, and promote responsible use of GenAI technologies in higher education.
C1 [Chan, Cecilia Ka Yuk; Lee, Katherine K. W.] Univ Hong Kong, Fac Educ, Teaching & Learning Innovat Ctr TALIC, Room CPD-1-81,Centennial Campus, Hong Kong, Peoples R China.
C3 University of Hong Kong
RP Chan, CKY (corresponding author), Univ Hong Kong, Fac Educ, Teaching & Learning Innovat Ctr TALIC, Room CPD-1-81,Centennial Campus, Hong Kong, Peoples R China.
EM ckchan09@hku.hk
FU The author wishes to thank the students and teachers who participated
   the survey.
FX The author wishes to thank the students and teachers who participated
   the survey.
CR Alam A., 2022, Lecture Notes in Electrical Engineering, V914, DOI DOI 10.1007/978-981-17-5601-6_27
   Bashri M., 2020, Policy Report No. 4
   Bencsik A, 2016, J COMPETITIVENESS, V8, P90, DOI 10.7441/joc.2016.03.06
   Bíró GI, 2014, PROCD SOC BEHV, V141, P148, DOI 10.1016/j.sbspro.2014.05.027
   Bisdas S, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.795284
   Borys M., 2013, Active Citizenship by Knowledge Management Innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013, P819
   Celik I, 2022, TECHTRENDS, V66, P616, DOI 10.1007/s11528-022-00715-y
   Chan C.K.Y., 2023, arXiv
   Chan C.K.Y., 2022, ASSESSMENT EXPERIENT, DOI DOI 10.4324/9781003018391
   Chan C.K.Y., 2023, arXiv
   Chan C.K.Y., 2023, An in-depth look at students' perceptions of 'AI-giarism'
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Cheung KKC, 2023, RES SCI TECHNOL EDUC, V41, P1155, DOI 10.1080/02635143.2021.1993179
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Daniel B, 2015, BRIT J EDUC TECHNOL, V46, P904, DOI 10.1111/bjet.12230
   EAB, 2019, Why Gen Z students prefer YouTube over textbooks
   Eckleberry-Hunt Jodie, 2018, J Grad Med Educ, V10, P378, DOI 10.4300/JGME-D-18-00466.1
   Essel HB, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00362-6
   Feng GC, 2015, METHODOLOGY-EUR, V11, P13, DOI 10.1027/1614-2241/a000086
   Gillissen A, 2022, HEALTHCARE-BASEL, V10, DOI 10.3390/healthcare10040723
   Glum J., 2015, International Business News
   Goodfellow I.J., 2014, ARXIV14062661
   Granitz N, 2021, J EDUC BUS, V96, P299, DOI 10.1080/08832323.2020.1828791
   Hernandez-de-Menendez M, 2020, INT J INTERACT DES M, V14, P847, DOI 10.1007/s12008-020-00674-9
   Hu K., 2023, Reuters
   Isaacs AN, 2020, CURR PHARM TEACH LEA, V12, P1387, DOI 10.1016/j.cptl.2020.07.002
   Jha N, 2022, ADV MED EDUC PRACT, V13, P927, DOI 10.2147/AMEP.S368519
   Krosnick J. A., 2015, Handbook of Survey Research
   Kumar V R., 2022, 2022 IEEE Integrated STEM Education Conference (ISEC), P450, DOI [10.1109/ISEC54952.2022.10025165, DOI 10.1109/ISEC54952.2022.10025165]
   Lai KW, 2015, BRIT J EDUC TECHNOL, V46, P725, DOI 10.1111/bjet.12161
   Liang W., 2023, arXiv
   Linnes C., 2017, Int. J. Manage. Inf. Syst., V21, P11, DOI [10.19030/ijmis.v21i2.10073, DOI 10.19030/IJMIS.V21I2.10073]
   Marr B., 2022, Forbes Innovation Enterprise Tech
   Marshall AL, 2020, MAYO CLIN PROC, V95, P1135, DOI 10.1016/j.mayocp.2020.04.015
   Mclaren Bruce M., 2010, J ARTIFICIAL INTELLI, V1, DOI [10.3233/JAI-2010-0001, DOI 10.3233/JAI-2010-0001]
   Mosca JB, 2019, J BUS DIVERS, V19, P66, DOI [10.33423/jbd.v19i3.2214, DOI 10.33423/JBD.V19I3.2214]
   Oblinger D., 2005, IS IT AGE IT 1 STEPS
   Pearson, 2018, Beyond millennials: The next generation of learners
   Schwieger D., 2018, Information Systems Education Journal (ISEDJ), V16
   Seemiller C., 2016, Generation Z goes to college
   Seemiller C., 2017, CAMPUS, V22, P21, DOI [DOI 10.1002/ABC.21293, 10.1002/ABC.21293]
   Seibert SA, 2021, TEACH LEARN NURS, V16, P85, DOI 10.1016/j.teln.2020.09.002
   Shamma T., 2011, NPR
   Sharma A, 2023, NAT MACH INTELL, V5, P46, DOI 10.1038/s42256-022-00593-2
   Shatto B, 2016, J CONTIN EDUC NURS, V47, P253, DOI 10.3928/00220124-20160518-05
   Shorey S, 2021, NURSE EDUC PRACT, V57, DOI 10.1016/j.nepr.2021.103247
   Terzopoulos George, 2019, P 9 BALK C INF BCI 1, P1, DOI DOI 10.1145/3351556.3351588
   Turner A., 2015, The Journal of Individual Psychology, V71, P103, DOI [DOI 10.1353/JIP.2015.0021, 10.1353/jip.2015.0021]
   Wiedmer T., 2015, Delta Kappa Gamma Bulletin, V82, P51
   Zemke R., 2000, GENERATIONS WORK MAN
NR 52
TC 47
Z9 48
U1 64
U2 186
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
EI 2196-7091
J9 SMART LEARN ENVIRON
JI Smart Learn. Env.
PD NOV 15
PY 2023
VL 10
IS 1
AR 60
DI 10.1186/s40561-023-00269-3
PG 23
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA X9SI7
UT WOS:001101761900002
OA gold
DA 2024-12-25
ER

PT J
AU Liu, ML
   Zhang, LJ
   Biebricher, C
AF Liu, Meilu
   Zhang, Lawrence Jun
   Biebricher, Christine
TI Investigating students' cognitive processes in generative AI-assisted
   digital multimodal composing and traditional writing
SO COMPUTERS & EDUCATION
LA English
DT Article
DE Cognitive process; Generative artificial intelligence; Digital
   multimodal composing; Writing
AB Recently, generative artificial intelligence (AI)-powered chatbots such as ChatGPT and Bing Chat have garnered increasing attention on a global scale. Previous studies have focused mostly on the influence of generative AI on writing while few researchers have investigated how generative AI can facilitate students' multimodal writing process. To fill in this gap, we explored the generative AI-assisted composing processes of two groups of English as a foreign language (EFL) writers over two weeks in this qualitative study. One group completed a multimodal PowerPoint (PPT) project, and the other group completed a traditional argumentative essay project. Our data consist of students' screen recordings with think-aloud protocols, final multimodal texts, and post-project interviews. Our analysis showed different patterns in text production across the two groups. Students in the PPT group tended to construct more bridge texts and examples to corroborate their sub-claims in the hierarchical order. They also inclined to borrow the summarized search results from the Bing Chat to expand texts for their PPT slides. With regard to image generation for PPT slides, descriptions of AI images from ChatGPT were used as effective prompts to generate AI images from Bing Image Creator. Moreover, students were interested in producing and refining AI images following the recommended prompts by Bing Chat. They also evaluated these AI images from different perspectives. We conclude the study with a discussion on the pedagogical implications and suggestions for further study.
C1 [Liu, Meilu; Zhang, Lawrence Jun; Biebricher, Christine] Univ Auckland, Fac Educ & Social Work, Auckland, New Zealand.
   [Zhang, Lawrence Jun] Univ Auckland, Fac Educ & Social Work, Private Bag 92601, Symonds St, Auckland 1150, New Zealand.
C3 University of Auckland; University of Auckland
RP Zhang, LJ (corresponding author), Univ Auckland, Fac Educ & Social Work, Private Bag 92601, Symonds St, Auckland 1150, New Zealand.
EM lj.zhang@auckland.ac.nz
RI Liu, Meilu/KVZ-1908-2024; Zhang, Lawrence Jun/H-1756-2018
OI Zhang, Lawrence Jun/0000-0003-1025-1746; Liu, Meilu/0009-0004-8685-9971
FU University of Auckland; China Scholarship Council of the Ministry of
   Education of China (CSC) [202206090014]
FX This research is funded by a joint doctoral scholarship awarded to Meilu
   Liu by The University of Auckland and the China Scholarship Council of
   the Ministry of Education of China (CSC NO. 202206090014) .
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Barrot JS, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100745
   Bhat Advait, 2023, IUI '23: Proceedings of the 28th International Conference on Intelligent User Interfaces, P436, DOI 10.1145/3581641.3584060
   Chen YJ, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15086692
   Chun D, 2016, MOD LANG J, V100, P64, DOI 10.1111/modl.12302
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Dale R, 2017, NAT LANG ENG, V23, P319, DOI 10.1017/S1351324917000018
   Day T, 2023, PROF GEOGR, V75, P1024, DOI 10.1080/00330124.2023.2190373
   Dehouche N, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e16757
   Deng Y, 2023, ASSESS EVAL HIGH EDU, V48, P1254, DOI 10.1080/02602938.2023.2227358
   Di Zhang E, 2023, INNOV LANG LEARN TEA, V17, P769, DOI 10.1080/17501229.2022.2135712
   Flower L., 1981, College Composition and Communication, V32, P365, DOI [DOI 10.2307/356600, 10.2307/356600]
   Gero KI, 2022, PROCEEDINGS OF THE FIRST WORKSHOP ON INTELLIGENT AND INTERACTIVE WRITING ASSISTANTS (IN2WRITING 2022), P11
   Giannini T., 2023, P EVA LONDON 2023, P211
   Hafner CA, 2020, J SECOND LANG WRIT, V47, DOI 10.1016/j.jslw.2020.100710
   Hafner CA, 2015, TESOL QUART, V49, P486, DOI 10.1002/tesq.238
   Halliday M. A. K., 1994, INTRO FUNCTIONAL GRA, DOI DOI 10.4324/9780203783771
   Hayes JR, 2015, ELEM SCHOOL J, V115, P480, DOI 10.1086/681909
   Hayes JR, 2012, WRIT COMMUN, V29, P369, DOI 10.1177/0741088312451260
   Hunt KMR, 2023, WEATHER, V78, P108, DOI 10.1002/wea.4348
   Jiang LJ, 2022, J SECOND LANG WRIT, V57, DOI 10.1016/j.jslw.2022.100869
   Jiang LJ, 2017, ELT J, V71, P413, DOI 10.1093/elt/ccw098
   Kang S, 2023, INT J APPL LINGUIST, V33, P340, DOI 10.1111/ijal.12473
   Kim Y., 2020, Computers and Composition, V58, P102609, DOI DOI 10.1016/J.COMPCOM.2020.102609
   Kim Y, 2023, J ENGL ACAD PURP, V64, DOI 10.1016/j.jeap.2023.101247
   Kim Y, 2022, SYSTEM, V106, DOI 10.1016/j.system.2022.102722
   Kim Y, 2020, RELC J, V51, P86, DOI 10.1177/0033688220906943
   Kohn K, 2017, COMPUT ASSIST LANG L, V30, P351, DOI 10.1080/09588221.2017.1304966
   Koltovskaia S, 2023, RECALL, V35, P290, DOI 10.1017/S0958344022000179
   Kress G., 2006, READING IMAGES GRAMM
   Leijten M, 2014, J WRIT RES, V5, P285, DOI 10.17239/jowr-2014.05.03.3
   Li AL, 2021, COMPUT VIS IMAGE UND, V211, DOI 10.1016/j.cviu.2021.103259
   Lim J. M., 2020, Doctoral dissertation.
   Lim J, 2024, LANG TEACHING, V57, P183, DOI 10.1017/S0261444823000125
   Liu V, 2022, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, UIST 2022, DOI 10.1145/3526113.3545621
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Lucy L., 2021, P 3 WORKSHOP NARRATI, P48, DOI [10.18653/v1/2021.nuse-1.5, DOI 10.18653/V1/2021.NUSE-1.5]
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Lyle J, 2003, BRIT EDUC RES J, V29, P861, DOI 10.1080/0141192032000137349
   Macken-Horarik Mary., 2004, Visual Communication, V3, P5, DOI DOI 10.1177/1470357204039596
   Mehdi Y, 2023, Official Microsoft Blog Published Febr 7
   Michel M, 2019, J SECOND LANG WRIT, V45, P31, DOI 10.1016/j.jslw.2019.03.002
   Mohsen MA, 2021, LANG TEACH RES, DOI 10.1177/13621688211041292
   Thi NK, 2023, INNOV LANG LEARN TEA, V17, P690, DOI 10.1080/17501229.2022.2122476
   Ng DTK., 2022, COMPUTERS ED ARTIFIC, V3, P100054, DOI [DOI 10.1016/J.CAEAI.2022.100054, 10.1016/j.caeai.2022.100054]
   OpenAI, 2023, DALL-E: Creating images from text
   Otani M, 2023, PROC CVPR IEEE, P14277, DOI 10.1109/CVPR52729.2023.01372
   Qiao H, 2022, PROCEEDINGS OF THE 14TH CREATIVITY AND COGNITION, C&C 2022, P15, DOI 10.1145/3527927.3532792
   Qu WG, 2017, J SECOND LANG WRIT, V38, P92, DOI 10.1016/j.jslw.2017.10.007
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sasaki M., 2000, J SECOND LANG WRIT, V9, P259, DOI DOI 10.1016/S1060-3743(00)00028-X
   Smith B. E., 2021, Multimodal composing in K-16 ESL and EFL education: Multilingual perspectives, P109
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   Su YF, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100752
   Tail AMY, 2023, J ACAD LANG LEARN, V17, pT16
   Tan X, 2023, J SECOND LANG WRIT, V59, DOI 10.1016/j.jslw.2022.100958
   Tecedor M, 2024, APPL LINGUIST, V45, P65, DOI 10.1093/applin/amad002
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Unsworth L., 2006, English Teaching: Practice and Critique, V5, P55
   Walters WH, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-41032-5
   Xu YQ, 2023, COMPUT ASSIST LANG L, V36, P785, DOI 10.1080/09588221.2021.1945635
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
   Yan QZ, 2021, ASIA-PAC EDUC RES, V30, P541, DOI 10.1007/s40299-021-00602-9
   Yang C., 2023, Think-aloud protocols in second language writing: A mixed methods study of their ractivity and veridicality, DOI [10.1007/978-3-031-39574-1, DOI 10.1007/978-3-031-39574-1]
   Yang CS, 2014, J SECOND LANG WRIT, V24, P51, DOI 10.1016/j.jslw.2014.03.002
   Yilmaz R., 2023, COMPUT HUM BEHAV, V1, DOI DOI 10.1016/J.CHBAH.2023.100005
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Yilmaz Ramazan, 2022, Computers and Education: Artificial Intelligence, V3, DOI [10.1016/j.caeai, DOI 10.1016/J.CAEAI.2022.100092]
   Zhan FN, 2023, IEEE T PATTERN ANAL, V45, P15098, DOI 10.1109/TPAMI.2023.3305243
   Zhang ED, 2023, COMPUT ASSIST LANG L, DOI 10.1080/09588221.2023.2177310
   Zhang JH, 2023, LEARN INSTR, V88, DOI 10.1016/j.learninstruc.2023.101808
   Zhang L. J., 2022, Handbook of practical second language teaching and learning, P331, DOI DOI 10.4324/9781003106609-27
   Zhang LJ, 2020, ROUT HANDB APPL, P302
   Zhang MX, 2023, COMPUT ASSIST LANG L, V36, P694, DOI 10.1080/09588221.2021.1942068
   Zhao X, 2023, RELC J, V54, P890, DOI 10.1177/00336882221094089
   Zimmerman A, 2023, ANN SURG ONCOL, V30, P3170, DOI 10.1245/s10434-023-13436-0
NR 76
TC 23
Z9 23
U1 206
U2 384
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 0360-1315
EI 1873-782X
J9 COMPUT EDUC
JI Comput. Educ.
PD APR
PY 2024
VL 211
AR 104977
DI 10.1016/j.compedu.2023.104977
EA DEC 2023
PG 21
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA IH2G9
UT WOS:001165366200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Khanal, S
   Zhang, HZ
   Taeihagh, A
AF Khanal, Shaleen
   Zhang, Hongzhou
   Taeihagh, Araz
TI Why and how is the power of Big Tech increasing in the policy process?
   The case of generative AI
SO POLICY AND SOCIETY
LA English
DT Article; Early Access
DE generative AI; governance; artificial intelligence; big tech; multiple
   streams framework
ID MULTIPLE-STREAMS APPROACH; INSTRUMENT CONSTITUENCIES; PUBLIC-POLICY;
   POLITICS; IMPACT
AB The growing digitalization of our society has led to a meteoric rise of large technology companies (Big Tech), which have amassed tremendous wealth and influence through their ownership of digital infrastructure and platforms. The recent launch of ChatGPT and the rapid popularization of generative artificial intelligence (GenAI) act as a focusing event to further accelerate the concentration of power in the hands of the Big Tech. By using Kingdon's multiple streams framework, this article investigates how Big Tech utilize their technological monopoly and political influence to reshape the policy landscape and establish themselves as key actors in the policy process. It explores the implications of the rise of Big Tech for policy theory in two ways. First, it develops the Big Tech-centric technology stream, highlighting the differing motivations and activities from the traditional innovation-centric technology stream. Second, it underscores the universality of Big Tech exerting ubiquitous influence within and across streams, to primarily serve their self-interests rather than promote innovation. Our findings emphasize the need for a more critical exploration of policy role of Big Tech to ensure balanced and effective policy outcomes in the age of AI.
C1 [Khanal, Shaleen; Taeihagh, Araz] Natl Univ Singapore, Lee Kuan Yew Sch Publ Policy, Singapore, Singapore.
   [Zhang, Hongzhou] Nanyang Technol Univ, S Rajaratnam Sch Int Studies, Singapore, Singapore.
C3 National University of Singapore; Nanyang Technological University
RP Taeihagh, A (corresponding author), Natl Univ Singapore, Lee Kuan Yew Sch Publ Policy, Singapore, Singapore.
EM spparaz@nus.edu.sg
RI Zhang, Hongzhou/JPX-7742-2023; Taeihagh, Araz/D-7856-2014
OI Taeihagh, Araz/0000-0002-4812-4745
FU Ministry of Education - Singapore; Lee Kuan Yew School of Public Policy,
   National University of Singapore
FX A.T. is grateful for the support provided by the Lee Kuan Yew School of
   Public Policy, National University of Singapore.
CR Abdullah Z., 2023, STRAITS TIMES
   Ahmed N, 2023, SCIENCE, V379, P884, DOI 10.1126/science.ade2420
   Alizadeh M., 2022, Journal of Quantitative Description: Digital Media, V2, DOI [https://doi.org/10.51685/jqd.2022.023, DOI 10.51685/JQD.2022.023]
   [Anonymous], 2016, ONLINE PLATFORMS DIG
   [Anonymous], 2012, The Washington Post
   [Anonymous], 2023, GUARDIAN
   Bank M., 2021, LOBBY NETWORK BIG TE
   Béland D, 2016, J COMP POLICY ANAL, V18, P221, DOI 10.1080/13876988.2016.1174410
   Béland D, 2016, J EUR PUBLIC POLICY, V23, P428, DOI 10.1080/13501763.2015.1115533
   Bessen J., 2022, MIT Technology Review
   Birch K, 2022, SCI CULT-UK, V31, P1, DOI 10.1080/09505431.2022.2036118
   Birn AE, 2014, Hypothesis, V12, P8, DOI [DOI 10.5779/HYPOTHESIS.V12I1.229, 10.5779/hypothesis.v12i1.229]
   Bristow T., 2023, POLITICO
   Brownsell A., 2021, Big Tech's share of global ad spend reaches 6%
   Cairney P, 2016, POLICY STUD J, V44, P37, DOI 10.1111/psj.12111
   Chan C., 2023, Asian Management Insight, V10, P10
   Chandrasekaran R., 1999, Washington Post
   Chowdhary M., 2022, State of Big Tech
   Chung J., 2019, Roger Williams University Law Review, V2, P1
   Coleman D., 2018, Michigan Journal of Race and Law, V24, P417, DOI DOI 10.36643/MJRL.24.2.DIGITAL
   Cortellessa E., 2022, Time Mag
   De Vynck G., 2023, WASH POST
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dyer-Witheford N., 2022, The Political Economy of Communication, V9, P2
   Elzen B, 2011, RES POLICY, V40, P263, DOI 10.1016/j.respol.2010.09.018
   Ferrari E, 2020, COMMUN CULT CRIT, V13, P121, DOI 10.1093/ccc/tcz051
   Field H., 2024, CNBC
   Galaz V., 2023, A game changer for misinformation: The rise of generative AI
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Goldenfein J, 2023, CULT STUD, V37, P88, DOI 10.1080/09502386.2022.2042582
   Goldman Sachs, 2023, AI may favor big tech incumbents
   Goyal N, 2021, REGUL GOV, V15, P1020, DOI 10.1111/rego.12387
   Goyal N, 2020, ENVIRON INNOV SOC TR, V34, P311, DOI 10.1016/j.eist.2019.09.002
   Goyal N, 2018, ENERGIES, V11, DOI 10.3390/en11051198
   Haakman R, 2020, J CLEAN PROD, V247, DOI 10.1016/j.jclepro.2019.119175
   Hagendorff T, 2023, AI SOC, V38, P35, DOI 10.1007/s00146-021-01284-z
   Haque MU., 2022, arXiv, DOI [DOI 10.48550/ARXIV.2212.05856, 10.48550/arXiv.2212, 10.48550/arxiv.2212.05856]
   Hart Research Associates, 2022, Tech Oversight 4 State Survey
   Herweg N., 2023, THEORIES POLICY PROC
   Howlett M, 2015, EUR J POLIT RES, V54, P419, DOI 10.1111/1475-6765.12064
   Isaac M., 2021, N. Y. Times
   Jacobides MG, 2021, STRATEG SCI, V6, P412, DOI 10.1287/stsc.2021.0148
   Jullien B, 2021, INF ECON POLICY, V54, DOI 10.1016/j.infoecopol.2020.100880
   Jurowetzki R, 2021, Arxiv, DOI [arXiv:2102.01648, 10.48550/arXiv.2102.01648, DOI 10.48550/ARXIV.2102.01648]
   Karanouh M, 2023, Arxiv, DOI arXiv:2305.18340
   Kingdon J. W., 1984, AGENDAS ALTERNATIVES
   Kitchen K., 2021, GIS Rep
   Kulmer V, 2022, RES POLICY, V51, DOI 10.1016/j.respol.2021.104371
   Kwet M, 2019, RACE CLASS, V60, P3, DOI 10.1177/0306396818823172
   Lascoumes P, 2007, GOVERNANCE, V20, P1, DOI 10.1111/j.1468-0491.2007.00342.x
   Lindman J, 2023, J INF TECHNOL-UK, V38, P144, DOI 10.1177/02683962221113596
   LobbyControl, 2022, The revolving door - from public officials to Big Tech lobbyists
   Luitse D, 2021, BIG DATA SOC, V8, DOI 10.1177/20539517211047734
   Metz C., 2020, N. Y. Times
   Min A. C., 2023, Straits Times
   Monsees L, 2023, INT POLIT SOCIOL, V17, DOI 10.1093/ips/olac020
   Moran M., 2016, The Palgrave Handbook of International Development, P297
   Morozov E., 2014, To Save Everything, Click Here: Technology, solutionism and the urge to fix problems that don't exist, V1st
   Mukherjee I, 2015, POLITICS GOV, V3, P65, DOI 10.17645/pag.v3i2.290
   Nussey S., 2023, Reuters
   Nylen L., 2023, Google says it's No. 1 search tool because users prefer it to rivals
   Olson P., 2023, Amazon, Google scramble to keep pace with OpenAI despite huge AI teams
   OpenSecrets, 2023, OpenSecrets
   Rochford P., 2023, Signal Clevel
   Satariano A., 2019, N. Y. Times
   Schyns C., 2023, Tech. Rep.
   Sevilla J, 2022, IEEE IJCNN, DOI 10.1109/IJCNN55064.2022.9891914
   Sharon T, 2023, INFORM COMMUN SOC, DOI 10.1080/1369118X.2023.2246526
   Sharon T, 2021, ETHICS INF TECHNOL, V23, P45, DOI 10.1007/s10676-020-09547-x
   Shearer E., 2021, PEW RES CTR
   Simons A, 2018, POLICY SOC, V37, P14, DOI 10.1080/14494035.2017.1375248
   Solano Joan Lopez, 2022, Digital Disruption or Crisis Capitalism?: Technology, Power and the Pandemic
   Srivastava S, 2023, PERSPECT POLIT, V21, P989, DOI 10.1017/S1537592721003145
   Stjernfelt F, 2020, YOUR POST HAS BEEN REMOVED: TECH GIANTS AND FREEDOM OF SPEECH, P139, DOI 10.1007/978-3-030-25968-6_12
   Storeng KT, 2021, GLOB PUBLIC HEALTH, V16, P1482, DOI 10.1080/17441692.2021.1882530
   Tong A., 2023, Reuters
   Ulnicane I, 2023, REV POLICY RES, V40, P612, DOI 10.1111/ropr.12574
   Ulnicane I, 2023, REV POLICY RES, V40, P665, DOI 10.1111/ropr.12567
   van Dijck Jose, 2018, The platform society: public values in a connective world, P4, DOI DOI 10.1093/OSO/9780190889760.001.0001
   van Noordt C, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2022.101714
   Voss JP, 2014, ENVIRON POLIT, V23, P735, DOI 10.1080/09644016.2014.923625
   Wagner B.., 2018, Being Profiled: Cogitas Urgo Sum: 10 Years of Profiling the European Citizen, DOI [DOI 10.1515/9789048550180-016, DOI 10.2307/J.CTVHRD092.18]
   Warren E., 2022, Letter to Silvergate Bank re FTX
   Wörsdörfer M, 2022, PHILOS MANAG, V21, P345, DOI 10.1007/s40926-022-00193-5
   Wu X, 2015, POLICY SOC, V34, P165, DOI 10.1016/j.polsoc.2015.09.001
   Yilmaz I., 2023, Digital authoritarianism and its religious legitimization, P1
   YouGov, 2023, The Economist/YouGov
   YouGov & CGO, 2022, CGO/YouGov Tech Poll 2022
   Young JC, 2019, ENVIRON PLANN A, V51, P1424, DOI 10.1177/0308518X19858998
   Zachary G. P., 2010, IEEE Spectrum
   Zahariadis N, 2015, EUR J POLIT RES, V54, P466, DOI 10.1111/1475-6765.12072
   Zhang L., 2023, South China Morning PostJuly 15
NR 92
TC 7
Z9 7
U1 50
U2 79
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1449-4035
EI 1839-3373
J9 POLICY SOC
JI Policy Soc.
PD 2024 MAR 27
PY 2024
DI 10.1093/polsoc/puae012
EA MAR 2024
PG 19
WC Political Science; Public Administration
WE Social Science Citation Index (SSCI)
SC Government & Law; Public Administration
GA MC3B8
UT WOS:001191378400001
OA gold
DA 2024-12-25
ER

PT J
AU Kong, SC
   Lee, JCK
   Tsang, O
AF Kong, Siu-Cheung
   Lee, John Chi -Kin
   Tsang, Olson
TI A pedagogical design for self-regulated learning in academic writing
   using text-based generative artificial intelligence tools: 6-P pedagogy
   of plan, prompt, preview, produce, peer-review, portfolio-tracking
SO RESEARCH AND PRACTICE IN TECHNOLOGY ENHANCED LEARNING
LA English
DT Article
DE 6-P pedagogy; Academic writing; Artificial intelligence literacy;
   ChatGPT; Critical thinking; Pedagogical design; Self -regulated learning
ID CRITICAL THINKING; ACHIEVEMENT; PREDICTORS; LITERACY; QUALITY; SKILLS
AB The emergence and popularity of generative artificial intelligence (AI) tools, particularly text-based ones known as large language models, pose both opportunities and challenges to education. The ability of these tools to generate human-like texts based on minimal instructions causes concerns among educators about students' use of these tools for academic writing, which may constitute a breach of academic integrity. We propose a pedagogical design that models on selfregulated learning and the authoring cycle and develops students' critical thinking and self-regulation when composing academic writing using text-based generative AI tools. It contains six iterative and interactive phases. Students first plan the content and structure of the writing, then generate prompts for text-based generative AI tools. Next, students preview and verify the tools' output, followed by the fourth phase of producing the writing using the corrected output. Fifthly, peer review by fellow students may be required to polish and proofread the writing. Lastly, through portfolio-tracking, students reflect on the writing process, and formulate strategies for future usage of text-based generative AI tools for writing. This pedagogical design helps students and teachers embrace text-based generative AI while addressing the perils these tools present, and guides the development of education interventions and instruments.
C1 [Kong, Siu-Cheung] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [Lee, John Chi -Kin] Educ Univ Hong Kong, Dept Curriculum & Instruct, Hong Kong, Peoples R China.
   [Tsang, Olson] Educ Univ Hong Kong, Artificial Intelligence & Digital Competency Educ, Hong Kong, Peoples R China.
C3 Education University of Hong Kong (EdUHK); Education University of Hong
   Kong (EdUHK); Education University of Hong Kong (EdUHK)
RP Kong, SC (corresponding author), Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
EM sckong@eduhk.hk
OI LEE, Chi Kin John/0000-0002-3235-0967
CR Anwar YAS, 2022, BIOCHEM MOL BIOL EDU, V50, P502, DOI 10.1002/bmb.21655
   Araka E, 2020, RES PRACT TECH ENHAN, V15, DOI 10.1186/s41039-020-00129-5
   Atlas S, 2023, ChatGPT for higher education and professional development: A guide to conversational AI, P41
   Azamfirei R, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04393-x
   Bavli B, 2023, INTERACT LEARN ENVIR, V31, P7040, DOI 10.1080/10494820.2022.2061005
   Biswas G., 2018, Handbook of self-regulation of learning and performance, V2nd, P388, DOI DOI 10.4324/9781315697048-25
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   Buckley S, 2009, MED TEACH, V31, P340, DOI 10.1080/01421590902889897
   Carless D, 2022, ACT LEARN HIGH EDUC, V23, P143, DOI 10.1177/1469787420945845
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chou CY, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-00233-y
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Desaire H, 2023, CELL REP PHYS SCI, V4, DOI 10.1016/j.xcrp.2023.101426
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00140-5
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   FLOWER L., 1989, Planning in writing: The Cognition of a Constructive Process
   Flynn LR., 2013, Case studies for ethics in academic research in the social sciences, P19, DOI DOI 10.4135/9781452269986
   Gao CA, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00819-6
   Glogger I, 2012, J EDUC PSYCHOL, V104, P452, DOI 10.1037/a0026683
   Halpern DF, 1998, AM PSYCHOL, V53, P449, DOI 10.1037/0003-066X.53.4.449
   Hill G, 2021, RES PRACT TECH ENHAN, V16, DOI 10.1186/s41039-021-00166-8
   Hounsell D., 1984, Higher Education Research and Development, V3, P13, DOI [10.1080/0729436840030102, DOI 10.1080/0729436840030102]
   Huisman B, 2019, ASSESS EVAL HIGH EDU, V44, P863, DOI 10.1080/02602938.2018.1545896
   Irvin L., 2010, Writing spaces: Readings on writing, V1, P3
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kaur D, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3491209
   Kim YS, 2013, EARLY CHILD RES Q, V28, P461, DOI 10.1016/j.ecresq.2013.01.001
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kong S.-C., 2023, Working Paper
   Kong SC., 2021, COMPUTERS ED ARTIFIC, V2, P100026, DOI [DOI 10.1016/J.CAEAI.2021.100026, 10.1016/j.caeai.2021.100026]
   Kong SC, 2014, COMPUT EDUC, V78, P160, DOI 10.1016/j.compedu.2014.05.009
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lee H, 2024, ANAT SCI EDUC, V17, P926, DOI 10.1002/ase.2270
   Li Y., 2023, Computers Education: Artificial Intelligence, V4, P100140, DOI DOI 10.1016/J.CAEAI.2023.100140
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Luitse D, 2021, BIG DATA SOC, V8, DOI 10.1177/20539517211047734
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Mejia-Domenzain P, 2022, IEEE T LEARN TECHNOL, V15, P579, DOI 10.1109/TLT.2022.3195881
   Milano S, 2023, NAT MACH INTELL, V5, P333, DOI 10.1038/s42256-023-00644-2
   Mouratidis A, 2013, LEARN INDIVID DIFFER, V23, P179, DOI 10.1016/j.lindif.2012.09.001
   National Writing Project, 2010, Because digital writing matters: Improving student writing in online and multimedia environments, P41
   Noroozi O, 2023, INTERACT LEARN ENVIR, V31, P6302, DOI 10.1080/10494820.2022.2034887
   Nückles M, 2020, EDUC PSYCHOL REV, V32, P1089, DOI 10.1007/s10648-020-09541-1
   Panadero E, 2017, FRONT PSYCHOL, V8, DOI 10.3389/fpsyg.2017.00422
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Pintrich PR, 2004, EDUC PSYCHOL REV, V16, P385, DOI 10.1007/s10648-004-0006-x
   Procter L, 2020, REFLECT PRACT, V21, P444, DOI 10.1080/14623943.2020.1773421
   Rampersad G, 2020, J BUS RES, V116, P68, DOI 10.1016/j.jbusres.2020.05.019
   Reynolds L, 2021, EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21), DOI 10.1145/3411763.3451760
   Saqr M, 2021, RES PRACT TECH ENHAN, V16, DOI 10.1186/s41039-021-00175-7
   Schmid Richard F., 2009, Journal of Computing in Higher Education, V21, P95, DOI 10.1007/s12528-009-9021-8
   Shepherd SJ, 2007, INT J INFORM MANAGE, V27, P3, DOI 10.1016/j.ijinfomgt.2006.06.005
   Short K.G., 2009, Taking the PYP forward, P11
   Short K.G., 1996, CREATING CLASSROOMS
   Sison AJG, 2024, INT J HUM-COMPUT INT, V40, P4853, DOI 10.1080/10447318.2023.2225931
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Su YF, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100752
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   Uhlenbrook S, 2012, HYDROL EARTH SYST SC, V16, P3475, DOI 10.5194/hess-16-3475-2012
   Vardi I., 2015, The Palgrave Handbook of Critical Thinking in Higher Education, P197, DOI 10.1057/9781137378057_13
   Walters WH, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-41032-5
   Wolters CA, 2021, EDUC PSYCHOL REV, V33, P1319, DOI 10.1007/s10648-020-09519-z
   Wong A, 2014, MED EDUC, V48, P489, DOI 10.1111/medu.12382
   Woods H. B., 2023, WELLCOME OPEN RES, V7, P82, DOI DOI 10.12688/WELLCOMEOPENRES.17715.2
   Zhu JX, 2018, EDUC PSYCHOL-UK, V38, P1106, DOI 10.1080/01443410.2018.1497775
   Zimmerman BJ, 2002, THEOR PRACT, V41, P64, DOI 10.1207/s15430421tip4102_2
NR 76
TC 3
Z9 3
U1 89
U2 154
PU ASIA PACIFIC SOC COMPUTERS IN EDUCATION - APSCE
PI  TAOYUAN CITY
PA NO 300, JUNGDA RD, JHONGLI DISTRICT,  TAOYUAN CITY, TAIWAN
EI 1793-7078
J9 RES PRACT TECH ENHAN
JI Res. Pract. Technol. Enhanc. Learn.
PD JAN 1
PY 2024
VL 19
AR p030
DI 10.58459/rptel.2024.19030
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA IN3D3
UT WOS:001166957200001
OA gold
DA 2024-12-25
ER

PT J
AU Kanders, K
   Stupple-Harris, L
   Smith, L
   Gibson, JL
AF Kanders, Karlis
   Stupple-Harris, Louis
   Smith, Laurie
   Gibson, Jenny Louise
TI Perspectives on the impact of generative AI on early-childhood
   development and education
SO INFANT AND CHILD DEVELOPMENT
LA English
DT Article
DE AI technology; childhood; developmental psychology; early years;
   generative artificaI intelligence; infancy
AB Generative artificial intelligence (GAI) is rapidly becoming ubiquitous in many contexts. There is limited scholarship, however, in the fields of Developmental Psychology and Early Childhood Education exploring the implications of generative AI for babies and young children. In this Perspectives piece, we discuss potential use cases, opportunities, and risks for the application of AI in early childhood. Our insights are informed by extensive discussion with stakeholders and by desk research carried out in our roles as academics and analysts in a social innovation foundation. Our aim is to stimulate nuanced and informed discourse on the topic of generative AI in early childhood that can inform innovation in both research and practice.
C1 [Kanders, Karlis; Stupple-Harris, Louis; Smith, Laurie] Nesta, London, England.
   [Gibson, Jenny Louise] Univ Cambridge, Fac Educ, Ctr Res Play Educ Dev & Learning PEDAL, Cambridge, England.
C3 University of Cambridge
RP Gibson, JL (corresponding author), Univ Cambridge, Fac Educ, Ctr Res Play Educ Dev & Learning PEDAL, Cambridge, England.
EM jlg53@cam.ac.uk
RI Gibson, JL/JCO-3108-2023
OI Gibson, Jenny Louise/0000-0002-6172-6265
CR Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Bahrini Aram, 2023, 2023 Systems and Information Engineering Design Symposium (SIEDS), P274, DOI 10.1109/SIEDS58326.2023.10137850
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Baskara F. R., 2023, INT C EARLY CHILDHOO, V2, P351
   Boyd M, 2013, QUAL REP, V18
   Brooks T., 2024, VIDEO GENERATION MOD
   CB Insights, 2023, GEN AI LANDSC TOP ST
   Christofferson A., 2023, WILL GENERATIVE AI C
   DellAcqua F., 2023, Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality, DOI DOI 10.2139/SSRN.4573321
   Department for Education, 2023, GENERATIVE AI ED CAL
   Doroudi S, 2023, INT J ARTIF INTELL E, V33, P885, DOI 10.1007/s40593-022-00313-2
   Edwards S, 2023, AUST J EARLY CHILD, V48, P179, DOI 10.1177/18369391231198910
   Entenberg GA, 2023, FRONT DIGIT HEALTH, V4, DOI 10.3389/fdgth.2022.989022
   Famly, 2023, SIDEKICK WRITING ASS
   Ganguli D., 2023, Challenges in evaluating AI systems
   Ganguli D., 2023, ANTHROPIC
   Grassini S, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-54294-4
   Gwertzman J., 2022, GENERATIVE AI REVOLU
   Hadi Mogavi Reza, 2024, Computers in Human Behavior: Artificial Humans, V2, DOI 10.1016/j.chbah.2023.100027
   Hatzigianni M, 2023, INT J CHILD CARE EDU, V17, DOI 10.1186/s40723-023-00107-6
   Hernandez C., 2023, USE CHATGPT EARLY ED
   Kanders K., 2022, COULD TODDLER TECH H
   Kanders K., 2022, COULD PARENTING APPS
   Kanders K., 2023, FUNDING FUTURE GENER
   Lachman J., 2023, PARENTTEXT OPTIMISAT
   Lee J., 2023, GENERATIVE AI SUPER
   Lee V., 2023, STRAITS TIMES
   Levy-Weiss G., 2023, NEXT ERA GAMING AI W
   Luo WW, 2024, EARLY EDUC DEV, V35, P96, DOI 10.1080/10409289.2023.2214181
   Milo, 2023, MILO
   Navigli R, 2023, ACM J DATA INF QUAL, V15, DOI 10.1145/3597307
   Nesta, 2023, FAIR START
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Nwaogu I., 2023, YAHOO NEWS
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   OpenAI, 2023, Gpt-4v(ision) system card
   PACEY, 2015, BUILD BLOCKS REP STA
   Papert S., 1980, Mindstorms: children, computers, and powerful ideas
   Park E., 2023, PEW RES CTR
   PEDAL, 2022, PEDAL
   Popli N., 2022, TIME
   Rawte V., 2023, The Troubling Emergence of Hallucination in Large Language Models-An Extensive Definition, Quantification, and Prescriptive Remediations
   Selinger E., 2023, The Boston Globe
   Shrivastava R, 2022, Forbes
   Stathoulopoulos K., 2023, DISCOVERY NESTA
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Thorpe K, 2020, EARLY YEARS-ABINGDON, V40, P221, DOI 10.1080/09575146.2018.1443434
   Typecast, 2023, AI VOIC GEN EM TEXT
   Waldman J., 2023, STREAMLINING PRESCHO
   Wang Y., 2023, ALIGNING LARGE LANGU
   Winders D, 2017, MCN-AM J MATERN-CHIL, V42, P248, DOI 10.1097/NMC.0000000000000353
   Zhao W X., A survey of large language models, P2023, DOI DOI 10.48550/ARXIV.2303.18223
NR 52
TC 0
Z9 0
U1 79
U2 98
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 1522-7227
EI 1522-7219
J9 INFANT CHILD DEV
JI Infant Child Dev.
PD JUL
PY 2024
VL 33
IS 4
DI 10.1002/icd.2514
EA MAY 2024
PG 9
WC Psychology, Developmental
WE Social Science Citation Index (SSCI)
SC Psychology
GA D4H9U
UT WOS:001216824200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Summers, A
   El Haddad, M
   Prichard, R
   Clarke, KA
   Lee, JN
   Oprescu, F
AF Summers, Anthony
   El Haddad, May
   Prichard, Roslyn
   Clarke, Karen-Ann
   Lee, Joanne
   Oprescu, Florin
TI Navigating challenges and opportunities: Nursing student's views on
   generative AI in higher education
SO NURSE EDUCATION IN PRACTICE
LA English
DT Article
DE Generative artificial intelligence; ChatGPT; Nursing education; Higher
   education; Student perspectives; Ethical usage; Patient care; Technology
   integration
AB Aim: This qualitative study aims to explore the perspectives of nursing students regarding the application and integration of generative Artificial Intelligence (AI) tools in their studies. Background: With the increasing prevalence of generative AI tools in academic settings, there is a growing interest in their use among students for learning and assessments. Design: Employing a qualitative descriptive design, this study used semi-structured interviews with nursing students to capture the nuanced insights of the participants. Methods: Semi-structured interviews were digitally recorded and then transcribed verbatim. The research team reviewed all the data independently and then convened to discuss and reach a consensus on the identified themes. Results: This study was conducted within the discipline of nursing at a regional Australian university. Thirteen nursing students, from both first and second year of the programme, were interviewed as part of this study. Six distinct themes emerged from the data analysis, including the educational impact of AI tools, equitable learning environment, ethical considerations of AI use, technology integration, safe and practical utility and generational differences. Conclusions: This initial exploration sheds light on the diverse perspectives of nursing students concerning the incorporation of generative AI tools in their education. It underscores the potential for both positive contributions and challenges associated with the integration of generative AI in nursing education and practice.
C1 [Summers, Anthony; El Haddad, May; Prichard, Roslyn; Clarke, Karen-Ann; Lee, Joanne] Univ Sunshine Coast, Sch Hlth, Discipline Nursing, Sippy Downs, Qld 4558, Australia.
   [Oprescu, Florin] Univ Sunshine Coast, Sch Hlth, Discipline Publ Hlth, Sippy Downs, Qld 4558, Australia.
C3 University of the Sunshine Coast; University of the Sunshine Coast
RP Summers, A (corresponding author), Univ Sunshine Coast, Sch Hlth, Discipline Nursing, Sippy Downs, Qld 4558, Australia.
EM asummers@usc.edu.au; melhadda@usc.edu.au; rprichar@usc.edu.au;
   kclarke@usc.edu.au; jlee8@usc.edu.au; foprescu@usc.edu.au
RI Summers, Anthony/U-4099-2019; Haddad, May/ABH-4017-2020
OI Lee, Joanne/0009-0007-3004-9461; El Haddad, May/0000-0003-2328-685X
CR Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.00290, 10.48550/arXiv.2305.00290, DOI 10.48550/ARXIV.2305.00290]
   Christianson KL, 2020, NURS EDUC, V45, pE62, DOI 10.1097/NNE.0000000000000801
   Irwin P, 2023, NURS EDUC TODAY, V127, DOI 10.1016/j.nedt.2023.105835
   Kolb AY, 2005, ACAD MANAG LEARN EDU, V4, P193, DOI 10.5465/AMLE.2005.17268566
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Sandelowski M, 2000, RES NURS HEALTH, V23, P334, DOI 10.1002/1098-240X(200008)23:4<334::AID-NUR9>3.0.CO;2-G
   Sharma M, 2023, NURS EDUC TODAY, V131, DOI 10.1016/j.nedt.2023.105972
   Shirazi F, 2019, J NURS RES, V27, DOI 10.1097/jnr.0000000000000307
   Tam W, 2023, NURS EDUC TODAY, V129, DOI 10.1016/j.nedt.2023.105917
   Tong A, 2007, INT J QUAL HEALTH C, V19, P349, DOI 10.1093/intqhc/mzm042
   Villarroel V, 2018, ASSESS EVAL HIGH EDU, V43, P840, DOI 10.1080/02602938.2017.1412396
NR 13
TC 0
Z9 0
U1 45
U2 45
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 1471-5953
EI 1873-5223
J9 NURSE EDUC PRACT
JI Nurse Educ. Pract.
PD AUG
PY 2024
VL 79
AR 104062
DI 10.1016/j.nepr.2024.104062
EA JUL 2024
PG 5
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA YG5V7
UT WOS:001267357900001
PM 38996582
DA 2024-12-25
ER

PT J
AU So, HJ
   Jang, H
   Kim, M
   Choi, J
AF So, Hyo-Jeong
   Jang, Hyeji
   Kim, Minseon
   Choi, Jieun
TI Exploring public perceptions of generative AI and education: topic
   modelling of YouTube comments in Korea
SO ASIA PACIFIC JOURNAL OF EDUCATION
LA English
DT Article
DE Generative AI; ChatGPT; topic modelling; public perceptions; YouTube
ID CONTENT VALIDITY
AB This study aims to investigate the public's perceptions regarding the integration of Generative AI (GenAI) in education by analysing comments on YouTube news clips. The study collected public comments from YouTube news clips disseminated by three prominent broadcasters in South Korea between December 2022 and June 2023. Two dimensions of public perceptions were examined: sentiments and prevalent topics. Employing machine learning techniques, we conducted sentiment analysis and topic modelling on the crowdsourced dataset of 18,566 comments from 66 YouTube news clips. The first research question focused on public sentiments towards GenAI and education. Findings reveal a predominance of neutral sentiments. Rather than adopting extreme positions of complete acceptance or rejection, the public displayed an inclination to appreciate the intricate nuances of GenAI's implications. The second research question sought to identify the main topics emerging from public comments on GenAI and education. We identified 11 distinct topics where two topics are directly linked to educational implications: demands for changes in learning and assessment methods, and the use of GenAI in higher education. Based on the key findings, we draw implications that can inform a broader understanding of public sentiment and perspective towards GenAI and education.
C1 [So, Hyo-Jeong; Jang, Hyeji; Kim, Minseon; Choi, Jieun] Ewha Womans Univ, Dept Educ Technol, 52 Ewhayeodae Gil, Seoul, South Korea.
C3 Ewha Womans University
RP So, HJ (corresponding author), Ewha Womans Univ, Dept Educ Technol, 52 Ewhayeodae Gil, Seoul, South Korea.
EM hyojeongso@ewha.ac.kr
CR Akkerman SF, 2011, REV EDUC RES, V81, P132, DOI 10.3102/0034654311404435
   ARUN R, 2010, LECT NOTES ARTIF INT, V6118
   Cao J, 2009, NEUROCOMPUTING, V72, P1775, DOI 10.1016/j.neucom.2008.06.011
   Cho A., 2022, KOREA EC DAILY  1221
   박상민, 2018, [Journal of Intelligence and Information Systems, 지능정보연구], V24, P219
   Clark W, 2009, J COMPUT ASSIST LEAR, V25, P56, DOI 10.1111/j.1365-2729.2008.00305.x
   Das D, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.36034
   DEVEAUD R, 2014, DOCUMENT NUMERIQUE, V0017
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eapen TT, 2023, HARVARD BUS REV, V101, P55
   Ekin CC, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104700
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   GMI, 2023, YOUTUBE USERS STAT 2
   Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101
   Grün B, 2011, J STAT SOFTW, V40, P1
   Haensch A.C., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2303.05349
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kemp S., 2023, DIGITAL 2023 S KOREA
   Kim R., 2023, SECONDARY ENGLISH ED, V16, P179, DOI [https://doi.org/10.20487/kasee.16.2.202305.179, DOI 10.20487/KASEE.16.2.202305.179]
   Kim Y., 2021, IT R TEXT MINING
   Kye B.K., 2017, ED INFORM MEDIA RES, V23, P709, DOI [https://doi.org/10.15833/KAFEIAM.23.4.709, DOI 10.15833/KAFEIAM.23.4.709]
   Leiter C., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2302.13795
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu B., 2022, Sentiment Analysis and Opinion Mining
   Maier D, 2018, COMMUN METHODS MEAS, V12, P93, DOI 10.1080/19312458.2018.1430754
   MCCOMBS ME, 1972, PUBLIC OPIN QUART, V36, P176, DOI 10.1086/267990
   Nam S.H., 2023, KOREA U ISSUES GUIDE
   Nguyen H, 2020, AERA OPEN, V6, DOI 10.1177/2332858420979568
   OpenAI, EDUCATOR CONSIDERATI
   Perloff R.M., 2003, DYNAMICS PERSUASION
   Polit DF, 2006, RES NURS HEALTH, V29, P489, DOI 10.1002/nur.20147
   Quinn KM, 2010, AM J POLIT SCI, V54, P209, DOI 10.1111/j.1540-5907.2009.00427.x
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Rampersad G, 2020, J INF TECHNOL POLITI, V17, P1, DOI 10.1080/19331681.2019.1686676
   Rawas S, 2024, EDUC INF TECHNOL, V29, P6895, DOI 10.1007/s10639-023-12114-8
   Rubio DM, 2003, SOC WORK RES, V27, P94, DOI 10.1093/swr/27.2.94
   Selwyn N., 2013, DISTRUSTING ED TECHN
   Selwyn N., 2006, Learning, Media Technology, V31, P5, DOI DOI 10.1080/17439880500515416
   Star S. L., 1989, DISTRIBUTED ARTIFICI, P37, DOI [10.1016/B978-1-55860-092-8.50006-X, DOI 10.1016/B978-1-55860-092-8.50006-X]
   Taecharungroj V, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7010035
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Uddin SMJ, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15097121
   Wan XJ, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2297
   Wang T, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13116716
   Yan D, 2023, EDUC INF TECHNOL, V28, P13943, DOI 10.1007/s10639-023-11742-4
NR 46
TC 2
Z9 2
U1 24
U2 64
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0218-8791
EI 1742-6855
J9 ASIA PAC J EDUC
JI Asia Pac. J. Educ.
PD JAN 2
PY 2024
VL 44
IS 1
SI SI
BP 61
EP 80
DI 10.1080/02188791.2023.2294699
EA DEC 2023
PG 20
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA JG4I1
UT WOS:001125830700001
DA 2024-12-25
ER

PT J
AU O'Dea, X
   Ng, DTK
   O'Dea, M
   Shkuratskyy, V
AF O'Dea, Xianghan
   Ng, Davy Tsz Kit
   O'Dea, Mike
   Shkuratskyy, Viacheslav
TI Factors affecting university students' generative AI literacy: Evidence
   and evaluation in the UK and Hong Kong contexts
SO POLICY FUTURES IN EDUCATION
LA English
DT Article; Early Access
DE Generative AI literacy; AI course; AI in education; higher education; AI
   ethics
ID ARTIFICIAL-INTELLIGENCE; ETHICS
AB Generative AI (GenAI) has become popular with many university students since late 2022 and is incorporated in their daily life. To use GenAI tools effectively and equitably, and also to meet industry expectations, students should be supported to develop GenAI literacy skills during their time at university. Even though some universities have provided AI literacy training courses to students, the courses are not yet widely available to students across the higher education sector. Besides, there has been a shortage of GenAI literacy courses in particular. And more importantly, it is unclear what knowledge and skills that need to be included relating to GenAI literacy, from the student perspective. Building a strong understanding of student perceptions in this area is critical, because it will help enhance appropriateness and content relevance and promote student engagement. Adopting the four-dimensional AI literacy framework as the theoretical foundation, this paper aims to address the gaps. It explores the perceptions of university students on GenAI literacy in the UK and Hong Kong contexts. Survey data from 234 students were collected and analyzed using the descriptive analysis and Kruskal-Wallis test. The results show that factors such as country of studies and prior learning about AI greatly impacted GenAI literacy. However, factors such as age and educational level do not have a significant effect. Built upon the findings and the theoretical foundation, this paper proposes a new GenAI literacy framework. This framework is a revised version of the four-dimensional AI literacy framework and proposes the essential GenAI knowledge and skills for university students. The new framework also acknowledges the impact of the macro, meso, and micro factors on student GenAI literacy development.
C1 [O'Dea, Xianghan] Kings Coll London, Kings Business Sch, London WC2R 2LS, England.
   [Ng, Davy Tsz Kit] Educ Univ Hong Kong, Dept Math & Informat Technol, Hong Kong, Peoples R China.
   [O'Dea, Mike] Univ York, York, England.
   [Shkuratskyy, Viacheslav] York St John Univ, York, England.
C3 University of London; King's College London; Education University of
   Hong Kong (EdUHK); University of York - UK; York Saint John University
RP O'Dea, X (corresponding author), Kings Coll London, Kings Business Sch, London WC2R 2LS, England.
EM xianghan.odea@kcl.ac.uk
RI Ng, Tsz Kit Davy/ADD-3433-2022
CR [Anonymous], 2022, IBM Global AI Adoption Index 2022
   [Anonymous], 2023, Global education monitoring report, 2023: technology in education: a tool on whose terms?
   Baker RS, 2022, INT J ARTIF INTELL E, V32, P1052, DOI 10.1007/s40593-021-00285-9
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bellas F, 2023, INT J ARTIF INTELL E, V33, P399, DOI 10.1007/s40593-022-00315-0
   Berkovich I, 2014, J EDUC ADMIN, V52, P282, DOI 10.1108/JEA-12-2012-0131
   Birhane A, 2023, NAT REV PHYS, V5, P277, DOI 10.1038/s42254-023-00581-4
   Braun V., 2006, QUAL RES PSYCHOL, V3, P77, DOI [DOI 10.1080/14780887.2020.1769238, 10.1080/14780887.2020.1769238]
   Cakiroglu U., 2017, European Journal of Open, Distance and e-Learning, V20, P176
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Celik I, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107468
   Cetindamar D, 2024, IEEE T ENG MANAGE, V71, P810, DOI 10.1109/TEM.2021.3138503
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Chiu TKF, 2021, TECHTRENDS, V65, P796, DOI 10.1007/s11528-021-00637-1
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dijkstra R., 2022, CEUR WORKSHOP P
   El Shazly R, 2021, EXPERT SYST, V38, DOI 10.1111/exsy.12667
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Farina M., 2024, AI and Ethics, P1
   Fisher CR, 2020, HIGH EDUC RES DEV, V39, P1155, DOI 10.1080/07294360.2020.1721441
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Hageman SA., 2023, Journal of Financial Therapy, V14, P3
   Holmes W., 2023, Guidance for generative AI in education and research
   Hornberger M., 2023, Comput. Educ, V5, P100165, DOI DOI 10.1016/J.CAEAI.2023.100165
   Jia QJ, 2021, Arxiv, DOI arXiv:2110.03895
   Kandlhofer M, 2016, PROC FRONT EDUC CONF
   Koh E., 2023, LEARNING RES PRACTIC, V9, P109, DOI [10.1080/23735082.2023.2264086, DOI 10.1080/23735082.2023.2264086]
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kong SC., 2021, COMPUTERS ED ARTIFIC, V2, P100026, DOI [DOI 10.1016/J.CAEAI.2021.100026, 10.1016/j.caeai.2021.100026]
   Kong SC, 2023, EDUC TECHNOL SOC, V26, P16, DOI 10.30191/ETS.202301_26(1).0002
   Lai KW, 2015, BRIT J EDUC TECHNOL, V46, P725, DOI 10.1111/bjet.12161
   Laupichler M.C., 2022, Comput. Educ. Artif. Intell, V3, P100101, DOI DOI 10.1016/J.CAEAI.2022.100101
   Lee Irene, 2021, SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, P191, DOI 10.1145/3408877.3432513
   Lim EM, 2023, EDUC INF TECHNOL, V28, P12969, DOI 10.1007/s10639-023-11724-6
   Lim W., 2022, The International Journal of Management Education, V21(2)
   Lin P, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445377
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Macneil Stephen, 2022, ICER 2022 V2: Proceedings of the 2022 ACM Conference on International Computing Education Research, P37, DOI 10.1145/3501709.3544280
   McKinsey, 2022, MCKINSEY TECHNOLOGY
   Memarian B., 2023, Computers and Education: Artificial Intelligence., DOI DOI 10.1016/J.CAEAI.2023.100152
   Michalos AC., 2014, Encyclopedia of Quality of Life and Well-Being Research, V171
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Moorhouse BL, 2023, COMPUT EDUC OPEN, V5, DOI 10.1016/j.caeo.2023.100151
   Mulders MAM, 2018, J HAND THER, V31, P287, DOI 10.1016/j.jht.2017.10.007
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Ng DTK, 2023, ETR&D-EDUC TECH RES, V71, P137, DOI 10.1007/s11423-023-10203-6
   Ng DTK, 2023, EDUC INF TECHNOL, V28, P8445, DOI 10.1007/s10639-022-11491-w
   Nouraldeen Rasha Mohammad, 2023, Development and Learning in Organizations: An International Journal, P7, DOI 10.1108/DLO-07-2022-0133
   O'Dea X, 2024, STUD HIGH EDUC, V49, P811, DOI 10.1080/03075079.2024.2332944
   O'Dea XH, 2023, J UNIV TEACH LEARN P, V20
   Paray ZA, 2020, J INT EDUC BUS, V13, P55, DOI 10.1108/JIEB-02-2019-0009
   Sanderson Conrad, 2023, IEEE Transactions on Technology and Society, P171, DOI 10.1109/TTS.2023.3257303
   Schlagwein D, 2023, J INF TECHNOL-UK, V38, P232, DOI 10.1177/02683962231200411
   Solyst J, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581378
   Southworth J., 2023, COMPUTERS ED ARTIFIC, V4, pPG, DOI [10.1016/j.caeai.2023.100127 10.1016/j.caeai.2023.100127, DOI 10.1016/J.CAEAI.2023.100127, 10.1016/j.caeai.2023.100127]
   Su JH, 2024, INTERACT LEARN ENVIR, V32, P2848, DOI 10.1080/10494820.2022.2160470
   University of Hong Kong (HKU), 2023, HKU introduces new policy to fully integrate GenAI in Teaching and Learning
   University of Hong Kong (HKU), 2023, About ChatGPT
   Vachovsky M.E., 2016, PROC 47 ACM TECH, P303, DOI DOI 10.1145/2839509.2844620
   Wu TY, 2023, RESOUR CONSERV RECY, V198, DOI 10.1016/j.resconrec.2023.107144
   Wu ZC, 2020, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.517637
   Xia Q, 2022, COMPUT EDUC, V189, DOI 10.1016/j.compedu.2022.104582
   Yu PWD., 2019, Handbook of Research on TPACK in the Digital Age, P47
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhang H, 2023, INT J ARTIF INTELL E, V33, P290, DOI 10.1007/s40593-022-00293-3
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 69
TC 0
Z9 0
U1 50
U2 50
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 1478-2103
J9 POLICY FUTURES EDUC
JI Policy Futures Educ.
PD 2024 SEP 26
PY 2024
DI 10.1177/14782103241287401
EA SEP 2024
PG 22
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA H2E5K
UT WOS:001321623100001
DA 2024-12-25
ER

PT J
AU Lane, SH
   Haley, T
   Brackney, DE
AF Lane, Susan Hayes
   Haley, Tammy
   Brackney, Dana E.
TI Tool or Tyrant: Guiding and Guarding Generative Artificial Intelligence
   Use in Nursing Education
SO CREATIVE NURSING
LA English
DT Article
DE nursing students; nursing education; artificial intelligence (AI);
   generative artificial intelligence (AI); artificial intelligence (AI)
   literacy; ethics
AB As artificial intelligence (AI) continues to evolve rapidly, its integration into nursing education is inevitable. This article presents a narrative exploring the implementation of generative AI in nursing education and offers a guide for its strategic use. The exploration begins with an examination of the broader societal impact and uses of artificial intelligence, recognizing its pervasive presence and the potential it holds. Thematic analysis of strengths, weaknesses, opportunities, and threats collected from nurse educators across the southeastern United States in this case-based descriptive study used four codes: time, innovation, critical thinking, and routine tasks. Findings from the qualitative analysis revealed the overarching themes that AI can serve as both a tool and a tyrant, offering opportunities for efficiency and innovation while posing challenges of transparency, ethical use, and AI literacy. By establishing ethical guidelines, fostering AI literacy, and promoting responsible implementation in nursing education with a clear articulation of expectations, nurse educators can guide and guard the use of generative AI. Despite the concerns, the transformative potential of generative AI to enhance teaching methodologies and prepare students for the interprofessional health-care workforce provides a multitude of innovative opportunities for teaching and learning.
C1 [Lane, Susan Hayes; Haley, Tammy; Brackney, Dana E.] Appalachian State Univ, Beaver Coll Hlth Sci, Dept Nursing, 1179 State Farm Rd, Boone, NC 28607 USA.
C3 University of North Carolina; Appalachian State University
RP Lane, SH (corresponding author), Appalachian State Univ, Beaver Coll Hlth Sci, Dept Nursing, 1179 State Farm Rd, Boone, NC 28607 USA.
EM Lanesh@appstate.edu
CR Abdulai AF, 2023, NURS INQ, V30, DOI 10.1111/nin.12556
   Appalachian State University, CAS CETLESS HOST WOR
   Appalachian State University, ARTIFICIAL INTELLIGE
   Appalachian State University, INITIAL RECOMMENDATI
   Appalachian State University, ACAD AFFAIRS OPTIONA
   Archibald MM, 2023, J ADV NURS, V79, P3648, DOI 10.1111/jan.15643
   Bozkurt A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020800
   CAST, 2018, UNIVERSAL DESIGN LEA
   De Gagne Jennie C, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20064884
   Huss J. A., 2023, INSIGHT J SCHOLARLY, V18, P101
   Kelley PG, 2021, AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, P627, DOI 10.1145/3461702.3462605
   Kiger ME, 2020, MED TEACH, V42, P846, DOI 10.1080/0142159X.2020.1755030
   Knowles MS., 1978, COMMUNITY COLL REV, V5, P9, DOI DOI 10.1177/009155217800500302
   Microsoft, 2022, Microsoft Responsible AI Standard v2 General Requirements
   Okonkwo C. W., 2021, Comput. Educ. Artif. Intell., V2, DOI [10.1016/j.caeai.2021.100033, DOI 10.1016/J.CAEAI.2021.100033]
   OpenAI, 2024, Chatgpt
   Rice JW, 2012, INT J GAMING COMPUT-, V4, P81, DOI 10.4018/jgcms.2012100106
   Robert J., 2024, EDUCAUSE 2024 EDUCAU
   SALLAM M, 2023, HEALTHCARE-BASEL, V11, DOI DOI 10.3390/HEALTHCARE11060887
   Scerri A, 2023, J CLIN NURS, V32, P4211, DOI 10.1111/jocn.16677
   Sun GH, 2023, NURS EDUC, V48, P119, DOI 10.1097/NNE.0000000000001390
   Tanovic E, 2018, COGN AFFECT BEHAV NE, V18, P1207, DOI 10.3758/s13415-018-0632-2
   Teoli D., 2024, SWOT ANAL
NR 23
TC 3
Z9 3
U1 44
U2 54
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1078-4535
EI 1946-1895
J9 CREAT NURS
JI Creat. Nurs.
PD MAY
PY 2024
VL 30
IS 2
SI SI
BP 125
EP 132
DI 10.1177/10784535241247094
EA APR 2024
PG 8
WC Nursing
WE Emerging Sources Citation Index (ESCI)
SC Nursing
GA PZ4X0
UT WOS:001206516700001
PM 38651267
DA 2024-12-25
ER

PT J
AU Omena, JJ
   Autuori, A
   Vasconcelos, EL
   Subet, M
   Botta, M
AF Omena, Janna Joceli
   Autuori, Antonella
   Vasconcelos, Eduardo Leite
   Subet, Matteo
   Botta, Massimo
TI AI Methodology Map. Practical and Theoretical Approach to Engage with
   GenAI for Digital Methods Research
SO SOCIOLOGICA-INTERNATIONAL JOURNAL FOR SOCIOLOGICAL DEBATE
LA English
DT Article
DE Generative Artificial Intelligence; GenAI; Digital Methods; AI in
   Education; Image Networks; Technicity; Algorithmic Race Stereotypes
ID DESIGN
AB This essay accounts for a novel way to explore generative artificial intelligence (GenAI) applications for digital methods research, based on the AI Methodology Map. The map is a pedagogical resource and a theoretical framework designed to structure, visually represent, and explore GenAI web-based applications. As an external object, the map functions as a valuable teaching material and interactive toolkit. As a theoretical framework, it is embodied in a static representation that provides principles for engaging with GenAI. Aligned with digital methods' practical, technical, and theoretical foundations, the map facilitates explorations and critical examinations of GenAI and is supported by visual thinking and data practice documentation. The essay then outlines the map principles, its system of methods, educational entry points, and applications. The organization is as follows: First, we review GenAI methods, discussing how to access them, and their current uses in social research and the classroom context. Second, we define the AI Methodology Map and unpack the theory it embodies by navigating through the three interconnected methods constituting it: making room for, repurposing and designing digital methods-oriented projects with GenAI. Third, we discuss how the map bridges GenAI concepts, technicity, applications and the practice of digital methods, exposing its potential and reproducibility in educational settings. Finally, we demonstrate the AI Methodology Map's application, employing a digital methodology to analyze algorithmic race stereotypes in image collections generated by nine prominent GenAI apps. In conclusion, the essay unveils methodological challenges, presenting provocations and critiques on repurposing GenAI for social research. By encompassing practice, materiality and theoretical perspective, we argued that the AI Methodology Map bridges theoretical and empirical engagement with GenAI, serving them together or separately, thus framing the essay's main contribution. We expect that the AI Methodology Map's reproducibility will likely lead to further discussions, expanding those we present here.
C1 [Omena, Janna Joceli] Kings Coll London, Dept Digital Humanities, London, England.
   [Autuori, Antonella; Subet, Matteo; Botta, Massimo] Univ Appl Sci & Arts Southern Switzerland SUPSI, Manno, Switzerland.
   [Autuori, Antonella] RMIT Univ, Melbourne, Australia.
   [Vasconcelos, Eduardo Leite] Univ Fed Bahia UFBA, Salvador, Brazil.
C3 University of London; King's College London; Royal Melbourne Institute
   of Technology (RMIT); Universidade Federal da Bahia
RP Omena, JJ (corresponding author), Kings Coll London, Dept Digital Humanities, London, England.
EM J.J.Omena@kcl.ac.uk
CR Abid A, 2019, Arxiv, DOI [arXiv:1906.02569, DOI 10.48550/ARXIV.1906.02569, 10.48550/arXiv.1906.02569]
   Agencia Lusa, 2023, Observador8 November
   Amietta R., 2023, DEW
   Anderson C, 2023, IEEE PERVAS COMPUT, V22, P59, DOI 10.1109/MPRV.2022.3229905
   Anderson L. W., 2001, A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objective
   Antolak-Saper N., 2023, AI in Education Learning Circle
   ARNHEIM R, 1980, CRIT INQUIRY, V6, P489, DOI 10.1086/448061
   Arnheim R., 2001, Cabint Magazine26 April
   Báez JM, 2023, FEM MEDIA STUD, V23, P2455, DOI 10.1080/14680777.2022.2056755
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Banh L, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00680-1
   Bastian M., 2009, P INT AAAI C WEB SOC, V3, P361, DOI [10.1136/qshc.2004.010033, 10.1609/icwsm.v3i1.13937, 10.13140/2.1.1341.1520]
   Birhane Abeba, 2022, Nature, V610, P451, DOI 10.1038/d41586-022-03050-7
   Boiret G., 2016, PhantomBuster. Software
   Borra E., 2024, The Medium Is the Methods: Using Large Language Models (LLMs) in Digital Research. Keynote. Digital Methods Winter School
   Borra E., 2023, **DATA OBJECT**, DOI 10.5281/zenodo.10252681
   Botta M., 2024, Designing With: A New Educational Module to Integrate Artificial Intelligence, Machine Learning and Data Visualization in Design Curricula
   Bounegru L, 2018, FIELD GUIDE FAKE NEW
   Bunz M., 2022, YouTube, 24 November
   Buolamwini J., 2018, Proceedings of the 1st Conference on Fairness, P1
   Buolamwini J.A., 2017, Doctoral dissertation
   Burkhardt S, 2024, BIG DATA SOC, V11, DOI 10.1177/20539517241247839
   Castro J.C.M., 2023, Situating Gen-AI Pain & Pleasure: Interpretative Querying Approach Combining Situational Analysis with Digital Methods Presentation slides, DOI [10.13140/RG.2.2.16436.67201, DOI 10.13140/RG.2.2.16436.67201]
   Chao J., 2021, Memespector GUI: Graphical User Interface Client for Computer Vision APIs (Version 0.2.5 beta)
   Chauhan A., 2024, 2024 IEEE 3 INT C AI, P1, DOI [10.1109/ICAIC60265.2024.10433840, DOI 10.1109/ICAIC60265.2024.10433840]
   Ciston S., 2023, A Critical Field Guide for Working with Machine Learning Datasets
   Colombo G., 2023, Digital Methods Summer School 2023
   Cross N, 2001, DES ISSUES, V17, P49, DOI 10.1162/074793601750357196
   Dabkowski P., 2022, ElevenLabs
   Dayma B., 2022, Crayon
   de Seta G., 2023, SocArXiv, DOI DOI 10.31235/OSF.IO/ZVEW4
   Dove G, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P278, DOI 10.1145/3025453.3025739
   Duguay S, 2023, SOC MEDIA SOC, V9, DOI 10.1177/20563051231158822
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Edkie A., 2020, Murf.AI
   Farooq M., 2023, Pakistan Journal of Society, Education and Language (PJSEL), V10, P250
   Ferrara E., 2023, Science, V6, P3, DOI [DOI 10.3390/SCI6010003, 10.3390/sci6010003]
   Franklin U., 1990, REAL WORLD TECHNOLOG
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   Gaspar B., 2023, Cientistas divulgam 10 diretrizes para a Educacao lidar com a Inteligencia Artificial
   Google Creative Lab, 2017, Teachable Machine
   Gorska AM, 2023, FEM MEDIA STUD, V23, P4370, DOI 10.1080/14680777.2023.2263659
   Goulart J., 2024, Brazil Journal16 March
   Gray J., 2022, Digital Culture & Education, V14, P55
   Graziani M, 2023, ARTIF INTELL REV, V56, P3473, DOI 10.1007/s10462-022-10256-8
   Greene C, 2023, RES PUBLICA-NETH, V29, P303, DOI 10.1007/s11158-022-09544-5
   Hartman J., 2023, arXiv, DOI [10.2139/ssrn.4316084, DOI 10.2139/SSRN.4316084]
   Hoel AS, 2012, ERNST CASSIRER ON FORM AND TECHNOLOGY: CONTEMPORARY READINGS, P65
   Honig C., 2023, The Chemical Engineer1 June
   Hooks B, 2015, BLACK LOOKS: RACE AND REPRESENTATION, P21
   Jacomy M, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0098679
   Kendall A., 2016, arXiv, DOI [10.1109/ICCV.2015.336, DOI 10.1109/ICCV.2015.336]
   Koplin JJ, 2023, ETHICS INF TECHNOL, V25, DOI 10.1007/s10676-023-09703-z
   Leshkevich T, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12052712
   Limewire, 2023, BlueWillow. Software
   Maier N., 2004, DownThemAll! (v4.12.1). Web browser plugin
   Manovich L., 2013, The Oxford Handbook of Sound and Image in Digital Media, P252, DOI [10.1093/oxfordhb/9780199757640.013.005, DOI 10.1093/OXFORDHB/9780199757640.013.005]
   Manovich L., 2020, Cultural Analytics, DOI [10.7551/mitpress/11214.001.0001, DOI 10.7551/MITPRESS/11214.001.0001]
   Marres N., 2017, Digital sociology: The reinvention of social research
   Mauri M., 2016, FUT FOC THINK DRS IN, DOI [10.21606/drs.2016.185, DOI 10.21606/DRS.2016.185]
   Mauri M, 2020, INTED PROC, P9034
   Microsoft, 2023, Bing Image Creator
   Midjourney Inc, 2022, Midjourney (Version 5.2)
   Mostaque E., 2019, Stability.ai
   Nicoletti L., 2023, Bloomberg9 June
   Noble S., 2018, ALGORITHMS OPPRESSIO, DOI [10.18574/nyu/9781479833641.001.0001, DOI 10.2307/J.CTT1PWT9W5, DOI 10.18574/NYU/9781479833641.001.0001]
   Noble S.U., 2013, InVisible Culture, V19
   Omena J.J., 2021, Doctoral Dissertation
NR 68
TC 1
Z9 1
U1 1
U2 1
PU UNIV BOLOGNA, DEPT ARTS
PI Bologna
PA Via Zamboni, 33, Bologna, ITALY
EI 1971-8853
J9 SOCIOLOGICA
JI SOCIOLOGICA
PY 2024
VL 18
IS 2
BP 109
EP 144
DI 10.6092/issn.1971-8853/19566
PG 36
WC Sociology
WE Emerging Sources Citation Index (ESCI)
SC Sociology
GA M3P2E
UT WOS:001356688800006
DA 2024-12-25
ER

PT J
AU Chen, X
   Wu, D
AF Chen, Xu
   Wu, Di
TI Automatic Generation of Multimedia Teaching Materials Based on
   Generative AI: Taking Tang Poetry as an Example
SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
LA English
DT Article
DE Education; Generative AI; Videos; Solid modeling; Visualization;
   Painting; Artificial intelligence; Automatic generation; generative
   artificial intelligence (AI); multimedia teaching materials; Tang poetry
   situational videos
AB Generative artificial intelligence (AI) is widely recognized as one of the most influential technologies for the future, having sparked a paradigm shift in scientific research. The field of education has also been greatly impacted by this transformative technology, with researchers exploring the applications of generative AI, particularly ChatGPT, in education. However, existing research primarily focuses on generating text from text, and there remains a relative scarcity of studies on leveraging multimodal generation capabilities to address key challenges in multimodal data supported instruction. In this article, we present a technical framework for generating Tang poetry situational videos, emphasizing the utilization of generative AI to address the need for multimedia teaching resources. Our framework comprises three main modules: textual situational comprehension, image creation, and video generation. Moreover, we have developed a situational video generation system that incorporates various technologies, including text-to-text generation models, text-to-image generation models, image interpolation, text-to-speech synthesis, and video synthesis. To ascertain the efficacy of the modules within the Tang poetry situational video generation system, we undertook a comparative analysis utilizing the prevalent text-to-image and text-to-video generation models. The empirical findings indicate that our approach is capable of generating images that exhibit greater semantic similarity with the poems, thereby enabling a better comprehension of the poem's connotations and its key components. Concurrently, the Tang poetry videos generated can significantly contribute to the reduction of cognitive load and the enhancement of understanding during the learning process. Our research showcases the potential of generative AI in the education field, specifically in the domain of multimodal teaching resources.
C1 [Chen, Xu; Wu, Di] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
C3 Central China Normal University
RP Wu, D (corresponding author), Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
EM chenxu@ccnu.edu.cn; mr.wudi@163.com
OI chen, xu/0000-0002-4200-2253
FU National Natural Science Foundation of China
FX No Statement Available
CR Abdelghani R, 2023, Arxiv, DOI [arXiv:2211.14228, arXiv:2211.14228, arXiv:2211.14228, arXiv:2211.14228]
   Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Alibaba DAMO Academy for Discovery, cv_diffusion_text-toimage-synthesis
   Anmarkrud O, 2019, EDUC PSYCHOL-US, V54, P61, DOI 10.1080/00461520.2018.1554484
   BADDELEY A, 1992, SCIENCE, V255, P556, DOI 10.1126/science.1736359
   Baidu, Wen xin yi ge
   Bihua Wang, 2019, Natural Language Processing and Chinese Computing. 8th CCF International Conference, NLPCC 2019. Proceedings. Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science (LNAI 11839), P426, DOI 10.1007/978-3-030-32236-6_38
   Bommasani R., 2021, arXiv
   Brown TB., 2020, ADV NEURAL INFORM PR, V2020, P1877, DOI [10.48550/ARXIV.2005.14165, DOI 10.48550/ARXIV.2005.14165]
   Bryant J., 2020, How artificial intelligence will impact K-12 teachers
   Chen F, 2016, HUM-COMPUT INT-SPRIN, P1, DOI 10.1007/978-3-319-31700-7
   Chowdhery A, 2022, Arxiv, DOI [arXiv:2204.02311, DOI 10.48550/ARXIV.2204.02311]
   Crow T., 2018, P 20 AUSTR COMP ED C, P53, DOI [DOI 10.1145/3160489.3160492, 10.1145/3160489.3160492]
   Dai W, 2023, IEEE INT CONF ADV LE, P323, DOI 10.1109/ICALT58122.2023.00100
   Deforum, 2023, Deforum-stable-diffusion
   Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
   Dijkstra R., 2022, P 4 INT WORKSH INT T, P4
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Hasan R, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10113894
   Hensel M, 2017, ADV NEUR IN, V30
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Huang ZW, 2022, LECT NOTES COMPUT SC, V13674, P624, DOI 10.1007/978-3-031-19781-9_36
   iFLYTEK, 2023, Summary of the iFLYTEK Xinghuo large model Q&A
   Kalyuga S, 2011, EDUC PSYCHOL REV, V23, P1, DOI 10.1007/s10648-010-9150-7
   Kingma D. P., 2014, AUTOENCODING VARIATI
   Kong Jungil, 2020, ADV NEURAL INFORM PR, V33, P17022
   Leahy W, 2016, INSTR SCI, V44, P107, DOI 10.1007/s11251-015-9362-9
   Li D, 2021, arXiv
   Li NH, 2020, AAAI CONF ARTIF INTE, V34, P8228
   Li ZX, 2023, Arxiv, DOI arXiv:2304.09399
   Liu B, 2018, PROCEEDINGS OF THE 2018 ACM MULTIMEDIA CONFERENCE (MM'18), P783, DOI 10.1145/3240508.3240587
   Low R., 2005, Cambridge Handbook of Multimedia Learning, P147, DOI [DOI 10.1017/CBO9781139547369.010, DOI 10.1017/CBO9781139547369.012]
   Mayer RE, 2009, MULTIMEDIA LEARNING, 2ND EDITION, P1, DOI 10.1017/CBO9780511811678
   Mayer R. E., 2014, Cambridge handbook of multimedia learning, P43, DOI [10.1017/CBO9781139547369.005, DOI 10.1017/CBO9781139547369.005]
   Midjourney, Midjourney
   Mubarak AA, 2021, EDUC INF TECHNOL, V26, P371, DOI 10.1007/s10639-020-10273-6
   Nateraw, 2023, Stable-diffusion-videos
   nytimes, The New York Times
   OpenAI, 2023, GPT-4 deepens the conversation on Duolingo
   OpenAI, DALLE 2
   OpenAI, 2023, Khan academy explores the potential for GPT-4 in a limited pilot program
   Ouyang L, 2022, ADV NEUR IN
   Oz O., 2023, PROC 26 INT ACAD MIN, P144, DOI [10.31224/2861, DOI 10.31224/2861]
   Paas F., 2022, The Cambridge handbook of multimedia learning, V3rd, P73, DOI [DOI 10.1017/CBO9781139547369.004, 10.1017/CBO9781139547369.004]
   Qiao TT, 2019, PROC CVPR IEEE, P1505, DOI 10.1109/CVPR.2019.00160
   Radford A., 2018, Technical Reports
   Radford A, 2021, PR MACH LEARN RES, V139
   Raffel C, 2020, J MACH LEARN RES, V21
   Ramesh A., 2022, arXiv
   Ramesh P, 2022, 38 INT C MACHINE LEA
   Reimers N, 2019, Arxiv, DOI [arXiv:1908.10084, DOI 10.48550/ARXIV.1908.10084]
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Saharia C, 2022, Arxiv, DOI [arXiv:2205.11487, 10.48550/]
   Singer U, 2022, Arxiv, DOI arXiv:2209.14792
   SlideUpLift, How to use ChatGPT to make a PowerPoint presentation?
   Song J., 2023, PROC 16 INT S VIS IN, P1
   Teacher Joe, How to use ChatGPT to improve speaking and writing in English
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Tzirides A.O., 2023, arXiv, DOI DOI 10.48550/ARXIV.2305.07605
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang WH, 2024, Arxiv, DOI [arXiv:2311.03079, DOI 10.48550/ARXIV.2311.03079]
   Xu LL, 2018, AAAI CONF ARTIF INTE, P5618
   Xu T, 2018, PROC CVPR IEEE, P1316, DOI 10.1109/CVPR.2018.00143
   Zhang L, 2023, Arxiv, DOI arXiv:2302.05543
   Zhao GD, 2016, LECT NOTES COMPUT SC, V9757, P73, DOI 10.1007/978-3-319-41165-1_7
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
   Zulkarnain Novan, 2023, ICFET '23: Proceedings of the 2023 9th International Conference on Frontiers of Educational Technologies, P24, DOI 10.1145/3606150.3606155
   Zulko, 2020, GitHub Repository
NR 69
TC 2
Z9 2
U1 109
U2 130
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1939-1382
J9 IEEE T LEARN TECHNOL
JI IEEE Trans. Learn. Technol.
PY 2024
VL 17
BP 1353
EP 1366
DI 10.1109/TLT.2024.3378279
PG 14
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Education & Educational Research
GA NQ1L8
UT WOS:001201826100002
DA 2024-12-25
ER

PT J
AU Chan, CKY
   Hu, WJ
AF Chan, Cecilia Ka Yuk
   Hu, Wenjie
TI Students' voices on generative AI: perceptions, benefits, and challenges
   in higher education
SO INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
LA English
DT Article
DE ChatGPT; Generative AI; Student perception; AI literacy; Risks;
   Advantages; Holistic competencies
AB This study explores university students' perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs' 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students' perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students' perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.
C1 [Chan, Cecilia Ka Yuk; Hu, Wenjie] Univ Hong Kong, Hong Kong, Peoples R China.
C3 University of Hong Kong
RP Chan, CKY (corresponding author), Univ Hong Kong, Hong Kong, Peoples R China.
EM Cecilia.Chan@cetl.hku.hk
CR Abdelwahab HR, 2023, IND HIGHER EDUC, V37, P22, DOI 10.1177/09504222221087614
   Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Atlas S., 2023, CHATGPT HIGHER ED PR
   Bailey D, 2021, INTERACT TECHNOL SMA, V18, P85, DOI 10.1108/ITSE-08-2020-0170
   Berg C., 2023, CASE GENERATIVE AI S
   Bhattacharya K, 2023, INDIAN J SURG, V85, P1346, DOI 10.1007/s12262-023-03727-x
   Biggs J., 2011, Teaching for quality learning at University
   Biggs J, 2012, HIGH EDUC RES DEV, V31, P39, DOI 10.1080/07294360.2012.642839
   Bin Dahmash Abdulmajeed, 2020, BJR Open, V2, P20200037, DOI 10.1259/bjro.20200037
   Bisdas S, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.795284
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Chan CKY, 2023, Arxiv, DOI arXiv:2306.03358
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.02878
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.00280
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.01185, 10.48550/arXiv.2305.01185, DOI 10.48550/ARXIV.2305.01185]
   Chan CKY, 2023, Arxiv, DOI arXiv:2305.01186
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Dehouche N, 2023, Arxiv, DOI [arXiv:2301.01902, 10.48550/arXiv.2301.01902, DOI 10.48550/ARXIV.2301.01902]
   Eggmann F, 2023, J ESTHET RESTOR DENT, V35, P1098, DOI 10.1111/jerd.13046
   Essel HB, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00362-6
   Gayed JM., 2022, COMPUTERS ED ARTIFIC, V3, P100055, DOI DOI 10.1016/J.CAEAI.2022.100055
   Gherhes V, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10093066
   Ghotbi N, 2022, AI SOC, V37, P283, DOI 10.1007/s00146-021-01168-2
   Gillissen A, 2022, HEALTHCARE-BASEL, V10, DOI 10.3390/healthcare10040723
   Gong B, 2019, ACAD RADIOL, V26, P566, DOI 10.1016/j.acra.2018.10.007
   Goodfellow I. J., 2014, arXiv, DOI 10.48550/arXiv.1406.2661
   Harrer S, 2023, EBIOMEDICINE, V90, DOI 10.1016/j.ebiom.2023.104512
   Hew KF, 2023, J COMPUT HIGH EDUC, V35, P40, DOI 10.1007/s12528-022-09338-x
   Hu K., 2023, Reuters
   Jeffrey T., 2020, Journal of Systemics, Cybernetics and Informatics, V18, P8
   Jha N, 2022, ADV MED EDUC PRACT, V13, P927, DOI 10.2147/AMEP.S368519
   Kitamura FC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230171
   Kumar AHS., 2023, Biology, Engineering, Medicine and Science Reports, V9, P24, DOI DOI 10.5530/BEMS.9.1.5
   Landauer TK., 2003, ASSESS EDUC, V10, P295, DOI [10.1080/0969594032000148154, DOI 10.1080/0969594032000148154]
   Lee YF, 2022, ETR&D-EDUC TECH RES, V70, P1843, DOI 10.1007/s11423-022-10142-8
   Lubowitz JH, 2023, ARTHROSCOPY, V39, P1121, DOI 10.1016/j.arthro.2023.01.015
   Maerten AS, 2024, Arxiv, DOI [arXiv:2302.10913, 10.48550/arXiv.2302.10913, DOI 10.48550/ARXIV.2302.10913]
   Mizumoto A., 2023, EXPLORING POTENTIAL, DOI [10.2139/ssrn.4373111, DOI 10.2139/SSRN.4373111]
   Mokmin NAM, 2021, EDUC INF TECHNOL, V26, P6033, DOI 10.1007/s10639-021-10542-y
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Park CJ, 2021, CURR PROBL DIAGN RAD, V50, P614, DOI 10.1067/j.cpradiol.2020.06.011
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Sit C, 2020, INSIGHTS IMAGING, V11, DOI 10.1186/s13244-019-0830-7
   Sumakul D T. Y. G., 2020, Advances in Social Science, Education and Humanities Research, V624, P52
   Terblanche N, 2023, COACHING-INT J THEOR, V16, P100, DOI 10.1080/17521882.2022.2094278
   Warschauer M., 2023, AFFORDANCES CONTRADI, DOI [10.2139/ssrn.4404380, DOI 10.2139/SSRN.4404380]
   Durak HY, 2023, EDUC INF TECHNOL, V28, P471, DOI 10.1007/s10639-022-11149-7
   Yüzbasioglu E, 2021, J DENT EDUC, V85, P60, DOI 10.1002/jdd.12385
   Zhai X., 2022, SSRN ELECT J, DOI [10.2139/ssrn.4312418, DOI 10.2139/SSRN.4312418]
NR 53
TC 179
Z9 181
U1 398
U2 965
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2365-9440
J9 INT J EDUC TECHNOL H
JI Int. J. Educ. Technol. High. Educ.
PD JUL 17
PY 2023
VL 20
IS 1
AR 43
DI 10.1186/s41239-023-00411-8
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M3CH3
UT WOS:001028985800001
OA gold
DA 2024-12-25
ER

PT J
AU Ayemowa, MO
   Ibrahim, R
   Khan, MM
AF Ayemowa, Matthew O.
   Ibrahim, Roliana
   Khan, Muhammad Murad
TI Analysis of Recommender System Using Generative Artificial Intelligence:
   A Systematic Literature Review
SO IEEE ACCESS
LA English
DT Article
DE Recommender systems; Generative AI; Artificial intelligence;
   Systematics; Surveys; Generative adversarial networks; Recommender
   system; generative AI; traditional recommender systems
ID ADVERSARIAL NETWORKS
AB Recommender Systems (RSs), which generate personalized content, have become a technological tool with diverse applications for users. While numerous RSs have been proposed and successfully implemented across various domains, traditional AI-based RSs still encounter certain challenges, such as data sparsity, cold start, and diversity. Generative Artificial Intelligence in recommender systems is a recent advancement used by platforms like Netflix, Spotify, and Amazon to recommend items, news, videos, audios, goods, and services to their customers/users or to personalize experiences for their customers/users. The main purpose of this review is to compare traditional AI-based recommender systems with generative AI-based recommender systems. A total of fifty-two (52) papers, published between 2019 and February 2024, were selected from six major online libraries. To get a more comprehensive understanding of the selected study, we reviewed the selected studies techniques, and the models, datasets, and metrics used. Our systematic review reveals that generative AI models, such as generative adversarial networks (GANs), variational autoencoder (VAEs) and autoencoders have been widely used in recommender systems and they perform better than traditional AI techniques. Among the 30 datasets analyzed, MovieLens was the most frequently used, accounting for 33%, while Amazon datasets accounted for 11%, Recall and RSME are the most commonly used metrics. Our literature review offers understandings into the Generative AI techniques used across different recommender systems and provides suggestions for the future research. Finally, we elaborated on open issues and discussed current and future trends in generative AI-based recommendation systems.
C1 [Ayemowa, Matthew O.; Ibrahim, Roliana] Univ Teknol Malaysia, Fac Comp, Johor Baharu 80000, Johor, Malaysia.
   [Ayemowa, Matthew O.] Gateway ICT Polytech, Dept Comp Sci, Ishara 121116, Ogun, Nigeria.
   [Khan, Muhammad Murad] Govt Coll Univ Faisalabad, Dept Comp Sci, Faisalabad 38000, Pakistan.
C3 Universiti Teknologi Malaysia; Government College University Faisalabad
RP Ayemowa, MO; Ibrahim, R (corresponding author), Univ Teknol Malaysia, Fac Comp, Johor Baharu 80000, Johor, Malaysia.; Ayemowa, MO (corresponding author), Gateway ICT Polytech, Dept Comp Sci, Ishara 121116, Ogun, Nigeria.; Khan, MM (corresponding author), Govt Coll Univ Faisalabad, Dept Comp Sci, Faisalabad 38000, Pakistan.
EM ayemowa@graduate.utm.my; roliana@utm.my; muhammadmurad@gcuf.edu.pk
RI Khan, Muhammad Murad/IVV-6372-2023
CR Agarwal A, 2022, COGENT ENG, V9, DOI 10.1080/23311916.2021.2022568
   Ahmadian S, 2022, EXPERT SYST APPL, V187, DOI 10.1016/j.eswa.2021.115849
   Ahn Y, 2022, ACM T INTERACT INTEL, V12, DOI 10.1145/3484509
   Al-Sbou Mleih, An improved hybrid semi-stacked autoencoder for item-features of recommendation system
   Alatrash R, 2022, COGN SYST RES, V75, P53, DOI 10.1016/j.cogsys.2022.07.002
   Albahri I. A., 2024, Appl. Data Sci. Anal., P1, DOI [10.58496/adsa/2024/001.26C, DOI 10.58496/ADSA/2024/001.26C]
   Aldausari N, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3487891
   Alrashidi M, 2023, IEEE ACCESS, V11, P63874, DOI 10.1109/ACCESS.2023.3276988
   An J., 2015, Special Lect. IE., V1, P1
   Anelli VW, 2020, RECSYS 2020: 14TH ACM CONFERENCE ON RECOMMENDER SYSTEMS, P738, DOI 10.1145/3383313.3411447
   [Anonymous], 2023, Int. J. Inf. Man-age. Data Insights, V3, DOI [10.1016/j.jjimei.2023.100161, DOI 10.1016/J.JJIMEI.2023.100161]
   [Anonymous], 2022, Int. J. Intell. Eng. Syst., V15, P461, DOI [10.22266/ijies2022.1031.40.7, DOI 10.22266/IJIES2022.1031.40.7]
   [Anonymous], 2021, Int. J. Intell. Syst., V36, P778, DOI [10.1002/int.22320.89N, DOI 10.1002/INT.22320.89N]
   [Anonymous], 2024, Int. J. Informat. Appl. Math., V6, P35
   [Anonymous], 2011, Recommender Systems [electronic Resource]: An Introduction
   [Anonymous], 2016, Indian J. Sci. Technol., V9, P1, DOI [10.17485/ijst/2016/v9i47/94892, DOI 10.17485/IJST/2016/V9I47/94892]
   Ay Betul, 2019, 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). Proceedings, P44, DOI 10.1109/Deep-ML.2019.00017
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Ballout M, 2023, Arxiv, DOI [arXiv:2306.12205, 10.48550/arXiv.2306.12205, DOI 10.48550/ARXIV.2306.12205]
   Berahmand K, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-023-10662-6
   Bock A., 2020, AI, V1, P376, DOI [10.3390/ai1030025.56Y, DOI 10.3390/AI1030025.56Y]
   Bondevik JN, 2024, EXPERT SYST APPL, V238, DOI 10.1016/j.eswa.2023.122166
   Brophy E, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3559540
   Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Cao YJ, 2020, PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), P1669, DOI 10.1145/3397271.3401196
   Chae DK, 2019, IEEE ACCESS, V7, P37650, DOI 10.1109/ACCESS.2019.2905876
   Chang WJ, 2023, SENSORS-BASEL, V23, DOI 10.3390/s23020850
   Chen SS, 2023, MATHEMATICS-BASEL, V11, DOI 10.3390/math11081777
   Chen Z., 2024, ACM Trans. Recommender Syst., V1, P1
   Chen Z, 2024, EXPERT SYST APPL, V238, DOI 10.1016/j.eswa.2023.121630
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Chizari N, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11203301
   Cho JH, 2023, J DENT, V138, DOI 10.1016/j.jdent.2023.104739
   Cho YS, 2022, PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), P2482, DOI 10.1145/3485447.3512120
   Chonwiharnphan P, 2020, IEEE ACCESS, V8, P41384, DOI 10.1109/ACCESS.2020.2976491
   Creswell A, 2018, IEEE SIGNAL PROC MAG, V35, P53, DOI 10.1109/MSP.2017.2765202
   Cummings S. M., 2024, Comput. Composition, V71, DOI [10.1016/j.compcom.2024.102827.35B, DOI 10.1016/J.COMPCOM.2024.102827.35B]
   da Silva EQ, 2016, EXPERT SYST APPL, V53, P204, DOI 10.1016/j.eswa.2015.12.050
   Da'u A, 2020, ARTIF INTELL REV, V53, P2709, DOI 10.1007/s10462-019-09744-1
   Deldjoo Y, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3439729
   Dipak Mahajan Arpana, 2023, 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), P91, DOI 10.1109/ICAISS58487.2023.10250700
   Drif A, 2020, IEEE ACCESS, V8, P188335, DOI 10.1109/ACCESS.2020.3030693
   Du L., 2020, P 3 INT C E BUS INF, P1, DOI [10.1145/3453187.3453316.36N, DOI 10.1145/3453187.3453316.36N]
   Etemadi M, 2023, EXPERT SYST APPL, V213, DOI 10.1016/j.eswa.2022.118823
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   GAN, 2023, A Collaborative Filtering Model Based on Improved Generative,School Inf. Sci. Eng.
   Gao M, 2021, INFORM SCIENCES, V546, P1166, DOI 10.1016/j.ins.2020.09.013
   Geng YS, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app122312408
   github, IEEE Transactions on Knowledge and Data Engineering 1 A Survey onAccuracy-Oriented Neural Recommendation: From Collaborative Fil-tering to Information-Rich Recommendation
   Goodfellow I. J., 2014, arXiv, DOI 10.48550/arXiv.1406.2661
   Goodfellow I, 2017, Arxiv, DOI arXiv:1701.00160
   Guo XJ, 2023, AI MAG, V44, P16, DOI 10.1002/aaai.12083
   Hamidi H, 2024, J KING SAUD UNIV-COM, V36, DOI 10.1016/j.jksuci.2024.101964
   Hamza S. U., 2023, P INT C EN POW ENV C, DOI [10.1109/icepecc57281.2023.10209475.109L, DOI 10.1109/ICEPECC57281.2023.10209475.109L]
   Hanafi A. S. H., 2020, Exploit Multi-Layer Deep Learningand Latent Factor to Handle Sparse Data for e-Commerce Recom-mender System, P343, DOI [10.5220/0009910603430351.11C, DOI 10.5220/0009910603430351.11C]
   Haque S, 2022, Arxiv, DOI arXiv:2204.01632
   Haw Su-Cheng, 2022, 2022 2nd International Conference on Big Data Engineering and Education (BDEE), P47, DOI 10.1109/BDEE55929.2022.00015
   Huang ZH, 2022, IEEE T SYST MAN CY-S, V52, P5853, DOI 10.1109/TSMC.2021.3131349
   Jabbar A, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3463475
   Jahanyar B, 2023, COMPUT BIOL MED, V162, DOI 10.1016/j.compbiomed.2023.107024
   Karapantelakis A, 2024, ANN TELECOMMUN, V79, P15, DOI 10.1007/s12243-023-00980-9
   Kersbergen B, 2021, PROC INT CONF DATA, P2447, DOI 10.1109/ICDE51399.2021.00277
   Khan M. I., 2021, Appl. Soft Comput., V109, DOI [10.1016/j.asoc.2021.107552.10N.S.H., DOI 10.1016/J.ASOC.2021.107552.10N.S.H]
   Khan M. M., 2019, Fac. Comput.
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kotkov D, 2016, KNOWL-BASED SYST, V111, P180, DOI 10.1016/j.knosys.2016.08.014
   Kunaver M, 2017, KNOWL-BASED SYST, V123, P154, DOI 10.1016/j.knosys.2017.02.009
   Li CX, 2022, IEEE T PATTERN ANAL, V44, P9629, DOI 10.1109/TPAMI.2021.3127558
   Li I., 2023, IEEE Access, V11, DOI [10.1109/ACCESS.2023.3323353.88H, DOI 10.1109/ACCESS.2023.3323353.88H]
   Li J. Ren, 2011, Knowl.-Based Syst., V220, DOI [10.1016/j.knosys.2021.106948.63RecommenderSystemsHandbook, DOI 10.1016/J.KNOSYS.2021.106948.63RECOMMENDERSYSTEMSHANDBOOK]
   Li J, 2022, PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), P297, DOI 10.1145/3485447.3511958
   Li K., 2023, P INT C POW COMM COM, P1, DOI [10.1145/3630138.3630424.45Y, DOI 10.1145/3630138.3630424.45Y]
   Li P, 2023, ACM T INTEL SYST TEC, V14, DOI 10.1145/3548776
   Liao CL, 2016, ELECTRON COMMER R A, V18, P1, DOI 10.1016/j.elerap.2016.05.001
   Nguyen L, 2019, IEEE INT CONF BIG DA, P1175, DOI 10.1109/BigData47090.2019.9006461
   Liu HF, 2020, WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), P551, DOI 10.1145/3366423.3380138
   Liu TY, 2022, ARTIF INTELL REV, V55, P5953, DOI 10.1007/s10462-022-10135-2
   Liu Y, 2021, WIREL COMMUN MOB COM, V2021, DOI 10.1155/2021/9120864
   Liu YD, 2019, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P2765, DOI 10.1145/3292500.3330707
   Liu YY, 2023, J COMPUT PHYS, V474, DOI 10.1016/j.jcp.2022.111801
   Lyu Z, 2023, IEEE T KNOWL DATA EN, V35, P4954, DOI 10.1109/TKDE.2022.3142260
   Magron C., 2021, arXiv:2102.12369, P1
   Nguyen MD, 2020, IEEE ACCESS, V8, P3761, DOI 10.1109/ACCESS.2019.2962539
   Mirza M, 2014, Arxiv, DOI arXiv:1411.1784
   Movafegh Z, 2023, ENG APPL ARTIF INTEL, V126, DOI 10.1016/j.engappai.2023.107109
   Natarajan S, 2020, EXPERT SYST APPL, V149, DOI 10.1016/j.eswa.2020.113248
   Necula SC, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095531
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Park H, 2023, IEEE ACCESS, V11, P30609, DOI 10.1109/ACCESS.2023.3262026
   Patil A., 2023, P 2023 INT C SUST CO, P730, DOI [10.1109/ICSCDS56580.2023.10104718, DOI 10.1109/ICSCDS56580.2023.10104718]
   Peng LL, 2022, MOB INF SYST, V2022, DOI 10.1155/2022/1265451
   Pennock D M., 2000, Collaborative Filtering By Personality Diagnosis: AHybrid Memory-and Model-Based Approach
   Pereira RR, 2023, PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023, P133, DOI 10.1145/3604237.3626882
   Qiu ZP, 2022, ACM T KNOWL DISCOV D, V16, DOI 10.1145/3511708
   Rajput S, 2023, Arxiv, DOI arXiv:2305.05065
   Roy D, 2022, J BIG DATA-GER, V9, DOI 10.1186/s40537-022-00592-5
   Safavi S, 2022, CONCURR COMP-PRACT E, V34, DOI 10.1002/cpe.6981
   Saifudin I, 2024, IEEE ACCESS, V12, P19827, DOI 10.1109/ACCESS.2024.3359274
   Saxena D, 2022, ACM COMPUT SURV, V54, DOI 10.1145/3446374
   Shafqat W, 2022, IEEE ACCESS, V10, P11036, DOI 10.1109/ACCESS.2022.3141776
   Shalom D., 2019, P 13 ACM C REC SYST, P556, DOI [10.1145/3298689.3347060.128IGAN:ACollaborativeFilteringModelBasedonImprovedGenerative,SchoolInf.Sci.Eng., DOI 10.1145/3298689.3347060.128IGAN:ACOLLABORATIVEFILTERINGMODELBASEDONIMPROVEDGENERATIVE,SCHOOLINF.SCI.ENG]
   Shao Xu, 2020, 2020 International Conference on Computer Information and Big Data Applications (CIBDA). Proceedings, P193, DOI 10.1109/CIBDA50819.2020.00051
   Song J. Qin, 2023, Eng. Appl. Artif. Intell., V124, DOI [10.1016/j.engappai.2023.106569.49, DOI 10.1016/J.ENGAPPAI.2023.106569.49]
   Stohr P., 2024, Inf. Org., V34, DOI [10.1016/j.infoandorg.2024.100503.n34, DOI 10.1016/J.INFOANDORG.2024.100503.N34]
   Su T. M., 2009, Adv. Artif. Intell., P1, DOI [10.1155/2009/421425.n75E.Q.da, DOI 10.1155/2009/421425.N75E.Q.DA]
   Tang CF, 2022, SOFT COMPUT, V26, P10591, DOI 10.1007/s00500-021-06709-x
   Tanuma I, 2022, IEEE ACCESS, V10, P60696, DOI 10.1109/ACCESS.2022.3180051
   Tarus JK, 2017, FUTURE GENER COMP SY, V72, P37, DOI 10.1016/j.future.2017.02.049
   Ul Hassan SZ, 2024, IEEE ACCESS, V12, P16610, DOI 10.1109/ACCESS.2024.3359053
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang SM, 2023, IEEE T SOFTWARE ENG, V49, P1188, DOI 10.1109/TSE.2022.3173346
   Wang ZW, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3439723
   Wu L, 2023, IEEE T KNOWL DATA EN, V35, P4425, DOI 10.1109/TKDE.2022.3145690
   Xia HB, 2023, COMPLEX INTELL SYST, V9, P3819, DOI 10.1007/s40747-022-00849-9
   Yang J, 2024, EXPERT SYST APPL, V241, DOI 10.1016/j.eswa.2023.122771
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Zahid S., 2023, Appl. Data Sci. Anal., P150, DOI [10.58496/adsa/2023/014.29, DOI 10.58496/ADSA/2023/014.29]
   Zhang CN, 2023, Arxiv, DOI arXiv:2303.11717
   Zhang GJ, 2020, FRONT COMPUT SCI-CHI, V14, P430, DOI 10.1007/s11704-018-8052-6
   Zhang Q, 2021, COMPLEX INTELL SYST, V7, P439, DOI 10.1007/s40747-020-00212-w
   Zhang Q, 2017, DECIS SUPPORT SYST, V104, P49, DOI 10.1016/j.dss.2017.10.002
   Zhao JC, 2023, J APPL GEOPHYS, V219, DOI 10.1016/j.jappgeo.2023.105239
   Zhao K., 2022, Knowl.-Based Syst., V257, DOI [10.1016/j.knosys.2022.109900.57Y.-S, DOI 10.1016/J.KNOSYS.2022.109900.57Y.-S]
   Zheng S. Li, 2024, Expert Syst. Appl., V248, DOI [10.1016/j.eswa.2024.123396.48, DOI 10.1016/J.ESWA.2024.123396.48]
   Zhong T., 2020, Session-Based Recommendation via Flow-based Deep Generative Networks andBayesian Inference
   Zhong T, 2020, NEUROCOMPUTING, V391, P129, DOI 10.1016/j.neucom.2020.01.096
   Zhou Y., 2020, TowardsTopic-Guided Conversational Recommender System
   Zhou Y, 2020, Arxiv, DOI arXiv:2012.06901
NR 129
TC 2
Z9 2
U1 44
U2 44
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 87742
EP 87766
DI 10.1109/ACCESS.2024.3416962
PG 25
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA WZ9G1
UT WOS:001258809600001
OA gold
DA 2024-12-25
ER

PT J
AU Henriksen, D
   Woo, L
   Mishra, P
AF Henriksen, Danah
   Woo, Lauren
   Mishra, Punya
TI Unlocking Creativity: Dr. Anna Abraham on Interdisciplinarity, AI, and
   Human Innovation
SO TECHTRENDS
LA English
DT Article
DE Creativity; Technology; Education; Neuroscience; Artificial
   Intelligence; ChatGPT; Generative AI; GenAI; Teachers; Teaching; Teacher
   education
C1 [Henriksen, Danah; Woo, Lauren; Mishra, Punya] Arizona State Univ, Mary Lou Fulton Teachers Coll, Tempe, AZ 85287 USA.
C3 Arizona State University; Arizona State University-Tempe
RP Henriksen, D (corresponding author), Arizona State Univ, Mary Lou Fulton Teachers Coll, Tempe, AZ 85287 USA.
EM danah.henriksen@asu.edu; lauren.woo@asu.edu; punya.mishra@asu.edu
CR Abraham A., 2024, CREATIVE BRAIN MYTHS, DOI [10.7551/mitpress/14313.001.0001, DOI 10.7551/MITPRESS/14313.001.0001]
   Abraham A., 2023, QEIOS, DOI [10.32388/LS88G9, DOI 10.32388/LS88G9]
   Abraham A., 2018, The neuroscience of creativity, DOI [DOI 10.1017/9781316816981, https://doi.org/10.1017/9781316816981]
   Abraham A, 2014, FRONT HUM NEUROSCI, V8, DOI 10.3389/fnhum.2014.00095
   Armstrong L, 2016, BEHAV THER, V47, P287, DOI 10.1016/j.beth.2015.12.005
   Beghetto RA, 2013, KILLING IDEAS SOFTLY?: THE PROMISE AND PERILS OF CREATIVITY IN THE CLASSROOM, P1
   Cropley A, 2016, CREATIVITY RES J, V28, P238, DOI 10.1080/10400419.2016.1195614
   Deresiewicz William., 2014, Excellent Sheep: The Miseducation of the American Elite The Way to a Meaningful Life
   Henriksen D, 2023, TECHTRENDS, V67, P595, DOI 10.1007/s11528-023-00862-w
   Homayoun S., 2018, Journal of Open Innovation: Technology, Market, and Complexity, V4, P1, DOI DOI 10.3390/JOITMC4040055
   Javaras KN, 2019, PERSONAL DISORD, V10, P13, DOI 10.1037/per0000267
   Kapur M, 2008, COGNITION INSTRUCT, V26, P379, DOI 10.1080/07370000802212669
   Kaufman JC, 2018, PERSPECT PSYCHOL SCI, V13, P734, DOI 10.1177/1745691618771981
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P, 2024, TECHTRENDS, V68, P205, DOI 10.1007/s11528-024-00938-1
   Mullen CA, 2021, ANN NY ACAD SCI, V1483, P19, DOI 10.1111/nyas.14176
   Runco MA, 2012, CREATIVITY RES J, V24, P92, DOI 10.1080/10400419.2012.650092
   Runco Mark A., 2023, Journal of Creativity, V33, DOI DOI 10.1016/J.YJOC.2023.100063
   Sacchetti S, 2013, J HAPPINESS STUD, V14, P1789, DOI 10.1007/s10902-012-9410-y
   Steiner E. D., 2022, RESTORING TEACHER PR
   Warr M, 2023, TECHTRENDS, V67, P396, DOI 10.1007/s11528-023-00843-z
   Wingström R, 2024, CREATIVITY RES J, V36, P177, DOI 10.1080/10400419.2022.2107850
   Woo LJ, 2023, TECHTRENDS, V67, P767, DOI 10.1007/s11528-023-00888-0
NR 23
TC 0
Z9 0
U1 9
U2 9
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD SEP
PY 2024
VL 68
IS 5
BP 847
EP 853
DI 10.1007/s11528-024-01002-8
EA SEP 2024
PG 7
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA H9U2J
UT WOS:001320836000001
DA 2024-12-25
ER

PT J
AU Saetra, HS
AF Saetra, Henrik Skaug
TI Generative AI: Here to stay, but for good?
SO TECHNOLOGY IN SOCIETY
LA English
DT Article
DE Generative AI; Large language models; Generative adversarial networks;
   Harms; Power; Inequality
AB Generative AI has taken the world by storm, kicked off for real by ChatGPT and quickly followed by further development and the release of GPT-4 and similar models from OpenAI's competitors. The street has most certainly found its use for generative artificial intelligence (AI), and there is no longer much point in discussing whether generative AI will be influential. It will, and what remains to be discussed it how influential it will be, and what potential harms arise when we use AI to generate text and other forms of content. Technological change entails societal change, and we must always endeavor to ask how new technologies shapes, engenders, or potentially erodes the "good society". In this sense, Generative AI is another instance of politically and culturally disruptive autonomous technology, and in this short commentary I highlight some of the key questions to be asked regarding consequences on the micro, meso, and macro level.
C1 [Saetra, Henrik Skaug] Ostfold Univ Coll, Halden, Norway.
C3 Ostfold University College
RP Saetra, HS (corresponding author), Ostfold Univ Coll, Halden, Norway.
EM Henrik.satra@hiof.no
OI Saetra, Henrik Skaug/0000-0002-7558-6451
CR Bakker MA, 2022, ADV NEUR IN
   Barley S.R., 2020, Work and Technological Change
   BASS D., 2023, Bloomberg
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Brevini B., 2021, POLITY
   Brey P, 2018, TECHNOL SOC, V52, P39, DOI 10.1016/j.techsoc.2017.02.002
   Brown L., 2022, Vulture
   Collingridge David, 1982, The Social Control of Technology
   Danaher J, 2022, ETHICS INF TECHNOL, V24, DOI 10.1007/s10676-022-09661-y
   Edwards B., 2022, Ars Technica
   Ellul J., 1964, The technological society, P229
   Engström E, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101396
   Fish S, 2023, Arxiv, DOI [arXiv:2309.01291, 10.48550/arXiv.2309.01291]
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Griffy-Brown C, 2018, TECHNOL SOC, V52, P1, DOI 10.1016/j.techsoc.2018.01.001
   Koster R, 2022, NAT HUM BEHAV, V6, P1398, DOI 10.1038/s41562-022-01383-x
   Marcus G., 2022, Wired
   Nass A., 1999, Samfunn Okologi, Livsstil, V1971
   Ngo R, 2022, Arxiv, DOI arXiv:2209.00626
   Nordrum E.I., 2023, Technology and Sustainable Development, P97
   Sætra HS, 2022, NAT MACH INTELL, V4, P804, DOI 10.1038/s42256-022-00537-w
   Sætra HS, 2022, TECHNOL SOC, V69, DOI 10.1016/j.techsoc.2022.101973
   Sætra HS, 2019, HUMAN ARENAS, V2, P60, DOI 10.1007/s42087-018-0039-1
   Saetra HS., 2022, J FUTURE ROBOT LIFE, V3, P109, DOI [10.3233/FRL-200023, DOI 10.3233/FRL-200023]
   United Nations, 2015, ARES701
   Welsh M, 2023, COMMUN ACM, V66, P34, DOI 10.1145/3570220
   Widder David Gray, 2023, Concentrated Power, and the Political Economy of Open AI
   Winner L., 1977, AUTONOMOUS TECHNOLOG
NR 28
TC 65
Z9 67
U1 139
U2 346
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0160-791X
EI 1879-3274
J9 TECHNOL SOC
JI Technol. Soc.
PD NOV
PY 2023
VL 75
AR 102372
DI 10.1016/j.techsoc.2023.102372
EA SEP 2023
PG 5
WC Social Issues; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Issues; Social Sciences - Other Topics
GA T5PG5
UT WOS:001078500600001
OA hybrid, Green Published
DA 2024-12-25
ER

PT J
AU Devabe, E
AF Devabe, Esref
TI THE ROLE OF GENERATIVE ARTIFICIAL INTELLIGENCE IN MANAGING SPECULATIVE
   FINANCING RISKS IN ISLAMIC BANKS
SO BILIMNAME
LA English
DT Article
DE Islamic jurisprudence; Generative Artificial Intelligence; Islamic
   Banks; Mudaraba Financing; Risk Management
AB Artificial intelligence (AI) has witnessed unprecedented growth, driven by technological advancements, data proliferation, and the increased precision of machine learning models. The role of AI has expanded beyond merely understanding our environment to actively shaping it, with generative AI, in particular, emerging as a critical tool. Generative AI produces new, innovative content across various fields, including finance, where its applications are transformative. In Islamic banking, early theorists emphasized mudaraba, a profit-sharing mechanism, as a means to effectively mobilize resources for economic and social development. Mudaraba financing promotes fair income distribution and provides a viable way to gather investment capital and allocate financial resources. However, despite its theoretical advantages, mudaraba financing is relatively limited in modern Islamic banks. The primary reason is the associated risk management challenges, which impact the banks, clients, and broader environmental factors, creating an urgent need for effective, practical solutions. This research investigates AI in general and generative AI in particular, focusing on their potential role in managing the risks inherent in mudaraba financing within Islamic banks. It aims to understand how generative AI applications can help mitigate these risks, allowing Islamic banks to protect their funds and broaden their focus beyond traditional debt-based financing. Adopting a descriptive-analytical methodology, the study first describes generative AI's mechanisms, then examines the specific risks of mudaraba financing, and finally analyzes AI's role in managing these risks. By integrating technological advancements, this research seeks to open new avenues for sustainable growth in Islamic finance, enabling Islamic banks to safeguard their investments while supporting broader economic development.
C1 [Devabe, Esref] Istanbul Sabahattin Zaim Univ, Istanbul, Turkiye.
C3 Istanbul Sabahattin Zaim University
RP Devabe, E (corresponding author), Istanbul Sabahattin Zaim Univ, Istanbul, Turkiye.
EM ashraf.dawaba@izu.edu.tr
CR ABDUL KARIM Fadl, 2024, 10 STAT C ISL FIN IS
   ABDUL NOUR Adel, 2005, Artificial Intelligence
   Abu Dhabi Islamic Bank, 2023, Annual Report
   adilet, About us
   AL-DAR QUTNI Ali, Sunan al-Dar Qutni
   AL-KASANI Alaa al-Din, 1328, Baday' al-Sanaye' fi Tartib al-Shara'i
   AL-KHALIFA Hind, Introduction to Generative Artificial Intelligence
   AL-MAWARDI Ali ibn Muhammad, Mudaraba: A Comparative Study Among Jurisprudential Schools
   AL-SHAWKANI Muhammad, Nail al-Awtar
   AL-SUWAFI Ibrahim Nasser, 2021, Isra International Journal of Islamic Finance
   [Anonymous], About Us
   [Anonymous], 2005, Guiding Principles of Risk Management for Institutions (Other than Insurance Institutions) Offering Only Islamic Financial Services
   argaamplus.s3.amazonaws, about us
   Argoverse, US
   Bayhaqi Ahmadibn al-Husayn., al-Sunan al-kubra
   BAZZAZ Halima, 2013, INT C INT FIN SYST I
   DAWABEH Ashraf Muhammad, 2015, Islamic Banking Finance: Theoretical and Practical Foundations
   DAWABEH Ashraf Muhammad, 2020, Islamic Social Finance
   drive.google, US
   Dubai Islamic Bank, 2023, Integrated Annual Report
   ema, About us
   Emirates Islamic Bank, 2023, Annual Report
   Faisal Islamic Bank of Egypt, 2023, Annual Report
   faisalbank, about us
   hussein-hamed, about us
   ibn Anas Malik, 1951, Al-Muwatta
   IBN MUNDHIR Muhammad, Al-Ijma
   Ibn Qudama AbdAllah b. Ahmad b. Muhammad., 1968, AL MUGHNI
   IBN RUSHD Muhammad, 1983, Bidayat al-Mujtahid wa Nihayat al-Muqtasid
   ifsb, about us
   iifa-aifi, about us
   Jordan Islamic Bank, 2023, Annual Report
   jordanheritage, US
   kfh.com, about us
   kuveytturk, about us
   Kuwait Finance House, 2023, Annual Report
   Kuwait Turk Bank, 2023, Consolidated Balance Sheet
   Ministry of Artificial Intelligence Digital Economy and Remote Work Applications, 2023, 100 Applications and Practical Uses for Generative Artificial Intelligence
   MOUSSA Abdullah, 2019, Artificial Intelligence: A Revolution in Modern Technologies
   RUSLAN Muhammad, 2018, Al-Basira Journal
   SABAA Ahmed Saleh, 2018, Al-Mayyadeen Economic Journal, Algeria, Faculty of Economic Sciences, Commercial Sciences, and Management, University of Algeria, V3
   Saudi Data and Artificial Intelligence Authority, 2023, Generative Artificial Intelligence
   unite.ai, about us
NR 43
TC 0
Z9 0
U1 3
U2 3
PU ILAHIYAT BILIMLERI ARASTIRMA VAKFI
PI KAYSERI
PA C/O ERCIYES UNIV ILAHIYAT FAK, MELIKGAZI, KAYSERI, 38039, Turkiye
EI 2148-5860
J9 BILIMNAME
JI Bilimname
PY 2024
VL 52
IS 2
BP 655
EP 685
DI 10.28949/bilimname.1490273
PG 31
WC Religion
WE Emerging Sources Citation Index (ESCI)
SC Religion
GA K9N8B
UT WOS:001347100600001
OA gold
DA 2024-12-25
ER

PT J
AU Daniel, T
   Xuan, J
AF Daniel, Thorin
   Xuan, Jin
TI Responsible use of Generative AI in chemical engineering
SO DIGITAL CHEMICAL ENGINEERING
LA English
DT Article
DE Responsible technology; Ethics; Generative AI
AB Generative Artificial Intelligence is a rapidly developing area being used to create powerful tools which have the potential to change a wide range of professional practices in chemical engineering. As this area develops, new principles on responsible use of Generative AI in chemical engineering are required to ensure that traditional engineering ethics are able to accommodate the new landscape. In this perspective, we assess the current state of engineering ethics, responsible AI principles and suggest how they can combine to ensure that Generative AI can be used responsibly within the chemical engineering sector. Whilst there are many aspect to engineering ethics and responsible AI use, the core principles which include transparency, integrity, and accountability are omnipresent and provide a shared foundation of good practice on which new regulations may be built as the need arises. Future breakthrough will require development on the AI technology itself, the people-centre approach and regulation changes.
C1 [Daniel, Thorin; Xuan, Jin] Univ Surrey, Sch Chem & Chem Engn, Guildford GU2 7XH, England.
C3 University of Surrey
RP Xuan, J (corresponding author), Univ Surrey, Sch Chem & Chem Engn, Guildford GU2 7XH, England.
EM j.xuan@surrey.ac.uk
RI Xuan, Jin/G-5836-2011
OI Daniel, Thorin/0000-0003-4136-9106
FU UK Engineering and Physical Sciences Research Council (EPSRC)
   [EP/W018969/2, EP/V042432/1, EP/V011863/1]; Leverhulme Trust
   [PLP-2022-001]
FX The authors acknowledge the financial support of UK Engineering and
   Physical Sciences Research Council (EPSRC) under the grant numbers
   EP/W018969/2, EP/V042432/1 and EP/V011863/1, and the Leverhulme Trust
   under grant number PLP-2022-001.
CR Ali S, 2023, INFORM FUSION, V99, DOI 10.1016/j.inffus.2023.101805
   Balhorn L.S., What does ChatGPT know about natural science and engineering?
   concentrix, Human-Centered Generative AI Technology-Concentrix
   De Kok T., 2023, GENERATIVE LLMS TEXT
   Deng GL, 2023, Arxiv, DOI arXiv:2307.08715
   digital-strategy.ec.europa, Ethics guidelines for trustworthy AI / Shaping Europe's digital future
   Ekramipooya A, 2023, PROCESS SAF ENVIRON, V176, P65, DOI 10.1016/j.psep.2023.06.004
   Elyan E, 2020, NEURAL NETWORKS, V129, P91, DOI 10.1016/j.neunet.2020.05.025
   engc, STATEMENT OF ETHICAL PRINCIPLES for the engineering profession
   Feng JC, 2020, SCI ADV, V6, DOI 10.1126/sciadv.abc3204
   Gamero N, 2011, J RISK RES, V14, P685, DOI 10.1080/13669877.2010.547260
   Gu JL, 2024, J CONSTR ENG M, V150, DOI 10.1061/JCEMD4.COENG-13944
   He R, 2020, EXPERT SYST APPL, V150, DOI 10.1016/j.eswa.2020.113244
   Hirtreiter E, 2022, Arxiv, DOI arXiv:2211.05583
   IChemE, 2022, Chemical Engineering Matters
   IChemE, Code of Professional Conduct (2020) Annex C to Regulation 3A
   Janssen M, 2020, GOV INFORM Q, V37, DOI 10.1016/j.giq.2020.101493
   Kletz T., 1985, Cheaper, safer plants, or wealth and safety at work: notes on inherently safer and simpler plants
   Leonard KC, 2021, ACS SUSTAIN CHEM ENG, V9, P6126, DOI 10.1021/acssuschemeng.1c02741
   Liang WX, 2022, NAT MACH INTELL, V4, P669, DOI 10.1038/s42256-022-00516-1
   Liao MC, 2022, J IND ECOL, V26, P164, DOI 10.1111/jiec.13214
   Lundberg SM, 2020, NAT MACH INTELL, V2, P56, DOI 10.1038/s42256-019-0138-9
   Marhavilas PK, 2020, SAFETY SCI, V124, DOI 10.1016/j.ssci.2019.104590
   Nikolic S, 2023, EUR J ENG EDUC, V48, P559, DOI 10.1080/03043797.2023.2213169
   Niu ZQ, 2023, ADV ENERGY MATER, V13, DOI 10.1002/aenm.202300244
   Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
   Rane N., 2023, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4598258
   Royal Academy of Engineering, 2011, Engineering ethics in practice: a guide for engineers
   Rozanec JM, 2023, INT J PROD RES, V61, P6847, DOI 10.1080/00207543.2022.2138611
   Savage N, 2023, NAT BIOTECHNOL, V41, P585, DOI 10.1038/s41587-023-01788-7
   Schweidtmann AM, 2021, CHEM-ING-TECH, V93, P2029, DOI 10.1002/cite.202100083
   Thiebes S, 2021, ELECTRON MARK, V31, P447, DOI 10.1007/s12525-020-00441-4
   Tsai ML, 2023, EDUC CHEM ENG, V44, P71, DOI 10.1016/j.ece.2023.05.001
   Vogel G, 2023, COMPUT CHEM ENG, V171, DOI 10.1016/j.compchemeng.2023.108162
   Wing JM, 2021, COMMUN ACM, V64, P64, DOI 10.1145/3448248
NR 35
TC 0
Z9 0
U1 19
U2 19
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 2772-5081
J9 DIGIT CHEM ENG
JI Digit. Chem. Eng.
PD SEP
PY 2024
VL 12
AR 100168
DI 10.1016/j.dche.2024.100168
EA JUN 2024
PG 5
WC Engineering, Chemical
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA XI4M9
UT WOS:001261040500001
OA gold
DA 2024-12-25
ER

PT J
AU van der Ven, H
   Corry, D
   Elnur, R
   Provost, VJ
   Syukron, M
AF van der Ven, Hamish
   Corry, Diego
   Elnur, Rawie
   Provost, Viola Jasmine
   Syukron, Muh
TI Generative AI and Social Media May Exacerbate the Climate Crisis
SO GLOBAL ENVIRONMENTAL POLITICS
LA English
DT Article
DE generative AI; social media; climate; digitalization; LLMs; internet
AB The contributions of generative artificial intelligence (AI) and social media to the climate crisis are often underestimated. To date, much of the focus has been on direct emissions associated with the life cycle of tech products. In this forum article, we argue that this narrow focus misses the adverse and indirect impacts of generative AI and social media on the climate. We outline some of the indirect ways in which generative AI and social media undermine the optimism, focus, creativity, and veracity required to address the climate crisis. Our aim is twofold. First, we seek to balance the tide of optimism about the role of digitalization in addressing the climate crisis by offering a skeptic's perspective. Second, we outline a new research agenda that moves beyond counting directly attributable carbon emissions and proposes a more comprehensive accounting of the indirect ways in which social media and generative AI adversely impact the sociopolitical conditions required to address the climate crisis.
C1 [van der Ven, Hamish] Univ British Columbia, Fac Forestry, Dept Wood Sci, Sustainable Business Management Nat Resources, Vancouver, BC, Canada.
   [Corry, Diego] Univ British Columbia, Business Sustainabil & Technol Lab, Vancouver, BC, Canada.
   [Elnur, Rawie; Provost, Viola Jasmine] Univ British Columbias, Business Sustainabil & Technol Lab, Fac Forestry, Vancouver, BC, Canada.
   [Syukron, Muh] Univ British Columbia UBC, Fac Forestry, Vancouver, BC, Canada.
C3 University of British Columbia; University of British Columbia;
   University of British Columbia; University of British Columbia
RP van der Ven, H (corresponding author), Univ British Columbia, Fac Forestry, Dept Wood Sci, Sustainable Business Management Nat Resources, Vancouver, BC, Canada.
EM hamish.vanderven@ubc.ca
RI Syukron, Muh/LLM-4282-2024
OI Provost, Viola Jasmine/0009-0004-0528-1240; Syukron,
   Muh/0000-0001-9010-8047
FU Social Sciences and Humanities Research Council of Canada; Faculty of
   Forestry at UBC
FX We gratefully acknowledge funding from the Social Sciences and
   Humanities Research Council of Canada and the Faculty of Forestry at
   UBC. We also thank the GEP editors and anonymous reviewers for
   constructive feedback on earlier drafts of this article.
CR Abrams Zara., 2023, Monitor on Psychology, V54
   Adha R, 2023, ENERG ENVIRON-UK, V34, P1619, DOI 10.1177/0958305X221093458
   Ahmad SF, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-01787-8
   Allan JI, 2019, GLOBAL ENVIRON POLIT, V19, P4, DOI 10.1162/glep_a_00488
   Andersen AD, 2021, ENVIRON INNOV SOC TR, V41, P96, DOI 10.1016/j.eist.2021.09.013
   [Anonymous], 2023, Social Media and Youth Mental Health
   Atske Sara, 2018, Artificial Intelligence and the Future of Humans
   Brady WJ, 2023, TRENDS COGN SCI, V27, P947, DOI 10.1016/j.tics.2023.06.008
   Brandt A K., 2023, J. Creat, V33, DOI [10.1016/j.yjoc.2023.100068, DOI 10.1016/J.YJOC.2023.100068]
   Bromley-Trujillo R, 2020, J PUBLIC POLICY, V40, P280, DOI 10.1017/S0143814X18000375
   Carr N., 2010, The shallows: How the internet is changing the way we think, read and remember
   Choudhury M, 2023, NAT HUM BEHAV, DOI 10.1038/s41562-023-01716-4
   Damodar S, 2022, ADOLES PSYCHIAT-NETH, V12, P11, DOI 10.2174/2210676612666220225122720
   Dauvergne P., 2020, AI in the Wild: Sustainability in the Age of Artificial Intelligence (Kindle Edition), DOI DOI 10.7551/MITPRESS/12350.001.0001
   Dauvergne P, 2022, REV INT POLIT ECON, V29, P696, DOI 10.1080/09692290.2020.1814381
   Davidson DJ, 2022, WIRES CLIM CHANGE, V13, DOI 10.1002/wcc.751
   Ding D, 2011, NAT CLIM CHANGE, V1, P462, DOI 10.1038/NCLIMATE1295
   Freitag C, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2021.100340
   Haidt J., 2024, The anxious generation: How the great rewiring of childhood is causing an epidemic of mental illness
   Hari J., 2022, Stolen Focus: Why You Can't Pay Attention and how to Think Deeply Again
   Hayward B, 2020, WIRES CLIM CHANGE, V11, DOI 10.1002/wcc.612
   Hoffman JP, 2023, REMOTE SENS-BASEL, V15, DOI 10.3390/rs15112854
   Hsu AE, 2024, CLIM POLICY, V24, P193, DOI 10.1080/14693062.2023.2237937
   Jones N, 2018, NATURE, V561, P163, DOI 10.1038/d41586-018-06610-y
   Joppa LN, 2017, NATURE, V552, P325, DOI 10.1038/d41586-017-08675-7
   Kaack LH, 2022, NAT CLIM CHANGE, V12, P518, DOI 10.1038/s41558-022-01377-7
   Kakutani Michiko., 2019, THE DEATH OF TRUTH
   Koc-Michalska K, 2017, POLIT COMMUN, V34, P1, DOI 10.1080/10584609.2016.1243178
   Koivisto M, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-40858-3
   Lacombe Romain, 2023, arXiv
   Larosa F, 2023, NAT CLIM CHANGE, V13, P497, DOI 10.1038/s41558-023-01686-5
   Leger-Goodes T, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.872544
   Levin K, 2012, POLICY SCI, V45, P123, DOI 10.1007/s11077-012-9151-0
   Liu M, 2015, INT J CLIN EXP MED, V8, P9943
   Mansharamani Vikram., 2020, Think for Yourself: Restoring Common Sense in an Age of Experts and Artificial Intelligence
   Mytton D, 2022, JOULE, V6, P2032, DOI 10.1016/j.joule.2022.07.011
   Newman N., 2023, Overview and Key Findings of the 2023 Digital News Report
   Patterson D, 2022, COMPUTER, V55, P18, DOI 10.1109/MC.2022.3148714
   Rillig MC, 2023, ENVIRON SCI TECHNOL, DOI 10.1021/acs.est.3c01106
   Roberts JA, 2023, CYBERPSYCH BEH SOC N, V26, P80, DOI 10.1089/cyber.2022.0117
   Rosen LD, 2013, COMPUT HUM BEHAV, V29, P948, DOI 10.1016/j.chb.2012.12.001
   Rostirolla G., 2022, Renewable & Sustainable Energy Reviews, V155, DOI 10.1016/j.rser.2021.111787
   Santarius T, 2023, ENVIRON SCI POLICY, V147, P11, DOI 10.1016/j.envsci.2023.04.020
   Schramowski P, 2022, NAT MACH INTELL, V4, P258, DOI 10.1038/s42256-022-00458-8
   Sha P, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18168820
   Shanmugasundaram M., 2023, Front. Cogn, V2, P1203077, DOI [DOI 10.3389/FCOGN.2023.1203077, 10.3389/fcogn.2023.1203077]
   Siebers T, 2022, MEDIA PSYCHOL, V25, P343, DOI 10.1080/15213269.2021.1959350
   Strubell E, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3645
   Thew H, 2020, GLOBAL ENVIRON CHANG, V61, DOI 10.1016/j.gloenvcha.2020.102036
   Toetzke M, 2023, ENVIRON RES LETT, V18, DOI 10.1088/1748-9326/acf233
   Treen KMD, 2020, WIRES CLIM CHANGE, V11, DOI 10.1002/wcc.665
   van der Ven H, 2017, GLOBAL ENVIRON POLIT, V17, P1, DOI 10.1162/GLEP_a_00387
   Vinuesa R, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-019-14108-y
NR 53
TC 2
Z9 2
U1 22
U2 22
PU MIT PRESS
PI CAMBRIDGE
PA ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA
SN 1526-3800
EI 1536-0091
J9 GLOBAL ENVIRON POLIT
JI Glob. Environ. Polit.
PD MAY 1
PY 2024
VL 24
IS 2
BP 9
EP 18
DI 10.1162/glep_a_00747
PG 10
WC Environmental Studies; International Relations; Political Science
WE Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; International Relations; Government &
   Law
GA G0O5A
UT WOS:001313713000007
OA hybrid
DA 2024-12-25
ER

PT J
AU Nithithanatchinnapat, B
   Maurer, J
   Deng, XN
   Joshi, KD
AF Nithithanatchinnapat, Benyawarath Yaa
   Maurer, Joshua
   Deng, Xuefei (Nancy)
   Joshi, K. D.
TI Future Business Workforce: Crafting a Generative AI-Centric Curriculum
   Today for Tomorrow's Business Education
SO DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS
LA English
DT Article
DE Generative Artificial Intelligence; Generative AI; ChatGPT; Business
   Education; Curriculum; Information Systems Research; Changing Nature of
   Work; Future Workforce
AB In an era where generative AI is reshaping the landscape of business and technology, this editorial addresses the critical imperative for transformative reform in business education. It emphasizes the dual nature of generative AI as both a formidable disruptor and a catalyst for innovation, necessitating a shift in how we educate the future workforce. The editorial calls for a proactive and comprehensive reevaluation of the current educational model, advocating for an integration of AI literacy and ethical considerations into the core of business curricula. We aim to galvanize academia into action, advocating for an educational evolution that not only acknowledges the challenges posed by AI but also harnesses its potential to enrich and advance business education in preparing students for an AI-driven future.
C1 [Nithithanatchinnapat, Benyawarath Yaa] Penn State Behrend, Erie, PA 16563 USA.
   [Maurer, Joshua] Gannon Univ, Erie, PA USA.
   [Deng, Xuefei (Nancy)] Calif State Univ, Carson, CA USA.
   [Joshi, K. D.] Univ Nevada, Reno, NV USA.
C3 Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Gannon University; Nevada System of
   Higher Education (NSHE); University of Nevada Reno
RP Nithithanatchinnapat, B (corresponding author), Penn State Behrend, Erie, PA 16563 USA.
RI Joshi, KD/KWU-9118-2024
OI Joshi, K. D. ; Kshiti, KD/0000-0002-2103-9495; Deng, Xuefei
   (Nancy)/0000-0003-4326-675X; MAURER, JOSHUA/0009-0005-6329-4696
CR Cambria E, 2023, IEEE INTELL SYST, V38, P62, DOI 10.1109/MIS.2023.3329745
   Chomsky Noam, 2023, The New York Times 8 March
   Coffey L., 2023, Inside Higher Ed
   Garrett N, 2020, PROCEEDINGS OF THE 3RD AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY AIES 2020, P272, DOI 10.1145/3375627.3375868
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Hu K., 2023, REUTERS         0202
   Hughes RT, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.604234
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Kadaruddin K., 2023, International Journal of Business, Law, and Education, V4, P618, DOI DOI 10.56442/IJBLE.V4I2.215
   Lakhani K., 2023, Harvard Business Review
   Li YJ, 2019, PROCEEDINGS OF THE 2019 COMPUTERS AND PEOPLE RESEARCH CONFERENCE (SIGMIS-CPR '19), P139, DOI 10.1145/3322385.3322422
   Nguyen B., 2023, ForbesDecember 11
   Ren Yuqing, 2023, ACM SIGMIS Database: the DATABASE for Advances in Information Systems, P6, DOI 10.1145/3614178.3614180
   Shrier D. L., 2023, Harvard Business Review
   Terwiesch C., 2023, Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
   Van Slyke C, 2023, COMMUN ASSOC INF SYS, V53, P1, DOI 10.17705/1CAIS.05301
   Voudoukis N., 2022, EUROPEAN J ED PEDAGO, V3, P288, DOI [10.24018/ejedu.2022.3.3.365, DOI 10.24018/EJEDU.2022.3.3.365]
   World Economic Forum, 2023, Future of Jobs Report 2023
NR 18
TC 5
Z9 5
U1 21
U2 21
PU ASSOC COMPUTING MACHINERY
PI NEW YORK
PA 1601 Broadway, 10th Floor, NEW YORK, NY USA
SN 0095-0033
EI 1532-0936
J9 DATA BASE ADV INF SY
JI Data Base Adv. Inf. Syst.
PD FEB
PY 2024
VL 55
IS 1
BP 6
EP 11
PG 6
WC Computer Science, Information Systems; Information Science & Library
   Science
WE Social Science Citation Index (SSCI)
SC Computer Science; Information Science & Library Science
GA OQ6P7
UT WOS:001208782300001
DA 2024-12-25
ER

PT J
AU Kim, H
   Koo, TKB
AF Kim, Hyoseok
   Koo, Thomas K. B.
TI The Impact of Generative AI on Syllabus Design and Learning
SO JOURNAL OF MARKETING EDUCATION
LA English
DT Article; Early Access
DE generative AI; AI integration; marketing education; course syllabus;
   student perceptions; educational practices
AB This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.
C1 [Kim, Hyoseok] Southern Connecticut State Univ, New Haven, CT USA.
   [Koo, Thomas K. B.] Dalhousie Univ, Halifax, NS, Canada.
C3 Connecticut State University System; Southern Connecticut State
   University; Dalhousie University
RP Kim, H (corresponding author), Southern Connecticut State Univ, Mkt, 10 Wintergreen Ave, New Haven, CT 06515 USA.
EM kimh10@southernct.edu
CR Al-Zahrani AM, 2024, INNOV EDUC TEACH INT, V61, P1029, DOI 10.1080/14703297.2023.2271445
   Allied Market Research, 2023, Generative AI market by component, technology, end user, and region: Global opportunity analysis and industry forecast, 20232032
   Atlas S., 2023, CHATGPT HIGHER ED PR
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Chintalapati S, 2022, INT J MARKET RES, V64, P38, DOI 10.1177/14707853211018428
   Clerwall C, 2014, JOURNAL PRACT, V8, P519, DOI 10.1080/17512786.2014.883116
   Elbanna S., 2023, MANAGEMENT SUSTAINAB, DOI DOI 10.1108/MSAR-03-2023-0016
   Feshbach ND, 2009, SOCIAL NEUROSCIENCE OF EMPATHY, P85
   Graefe A, 2018, JOURNALISM, V19, P595, DOI 10.1177/1464884916641269
   Grewal D, 2024, J MARKET EDUC, DOI 10.1177/02734753241269838
   Guha A, 2024, J MARKET EDUC, V46, P6, DOI 10.1177/02734753231215436
   Hayes AF, 2018, COMMUN MONOGR, V85, P4, DOI 10.1080/03637751.2017.1352100
   Henestrosa AL, 2023, COMPUT HUM BEHAV, V138, DOI 10.1016/j.chb.2022.107445
   Hofeditz L., 2021, P EUR C INF SYST ECI
   Hu K, 2023, ReutersFebruary 1
   Hughes RT, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.604234
   Jarek K, 2019, CENT EUR BUS REV, V8, P46, DOI 10.18267/j.cebr.213
   Kadaruddin K., 2023, International Journal of Business, Law, and Education, V4, P618, DOI DOI 10.56442/IJBLE.V4I2.215
   Kilag O. K., 2023, Excellencia: International Multi-Disciplinary Journal of Education (29949521), V1, P223
   Kumar V, 2024, J MARKET EDUC, DOI 10.1177/02734753241266170
   Ngiam KY, 2019, LANCET ONCOL, V20, pE262, DOI 10.1016/S1470-2045(19)30149-4
   Nurutdinova A. R., 2016, International Journal of Environmental and Science Education, V11, P3807
   O'Donoghue J., 2004, Interactive Educational Multimedia, V9, P63
   OGrady M., 2023, Spend on generative AI will grow 36% annually to 2030
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Ryan A., 2013, Flexible pedagogies: new pedagogical ideas
   Schroeder KT, 2022, ONLINE LEARN, V26, P73
   Schwerdt G, 2011, ECON EDUC REV, V30, P365, DOI 10.1016/j.econedurev.2010.11.005
   Shah R., 2017, Something to believe in: Creating trust and hope in organisations: Stories of transparency, accountability and governance
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Sundar S. S., 2008, The MAIN model: A heuristic approach to understanding technology effects on credibility, P73
   Sundar SS, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300768
   Wheeler L B., 2019, International Journal for the Scholarship of Teaching and Learning, V13, P7
   Wong WH, 2023, HIGH EDUC, V85, P957, DOI 10.1007/s10734-022-00874-0
   Yan H, 2023, IEEE T MULTIMEDIA, V25, P2323, DOI [10.1109/TCSS.2022.3161996, 10.1109/TMM.2022.3146010]
   Zhai X, 2023, COMPLEX INTELL SYST, V9, P2865, DOI 10.1007/s40747-021-00617-1
   Zhou M., 2024, arXiv
   Zulic H., 2019, INSAM J CONT MUSIC A, V1, P100, DOI 10.51191/issn.2637-1898.2019.2.2.100
NR 38
TC 0
Z9 0
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0273-4753
EI 1552-6550
J9 J MARKET EDUC
JI J. Market. Educ.
PD 2024 DEC 12
PY 2024
DI 10.1177/02734753241299024
EA DEC 2024
PG 22
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA P0Z0J
UT WOS:001375287500001
DA 2024-12-25
ER

PT J
AU Bae, H
   Jaesung, H
   Park, J
   Choi, GW
   Moon, J
AF Bae, Haesol
   Jaesung, Hur
   Park, Jaesung
   Choi, Gi Woong
   Moon, Jewoong
TI Pre-Service Teachers' Dual Perspectives on Generative AI: Benefits,
   Challenges, and Integration into their Teaching and Learning
SO ONLINE LEARNING
LA English
DT Article
DE GenAI; ChatGPT; online asynchronous course; instructional design;
   pre-service teachers
ID ARTIFICIAL-INTELLIGENCE; SELF-EFFICACY; EDUCATION; PERCEPTIONS;
   TECHNOLOGY; DIFFUSION; LANGUAGE; ANXIETY; TPACK
AB This study examined pre-service teachers' perspectives on integrating generative AI (GenAI) tools into their own learning and teaching practices. Discussion posts from asynchronous online courses on ChatGPT were analyzed using the Diffusion of Innovations framework to explore awareness, willingness to apply ChatGPT to instruction, and potential benefits, challenges, and concerns about using GenAI in teaching and learning. The course discussions significantly increased pre-service teachers' awareness and foundational knowledge while reducing anxiety towards AI technologies. However, despite exposure to ChatGPT, only a few confirmed uncertainties evidenced by emotional responses, such as worry and concern. Professional development in AI literacy can address these uncertainties and enhance teachers' understanding about using GenAI in class. The study offers insights into responsible GenAI adoption in learning.
C1 [Bae, Haesol; Park, Jaesung] SUNY Albany, Albany, NY 12222 USA.
   [Jaesung, Hur] Florida State Univ, Tallahassee, FL USA.
   [Choi, Gi Woong] Univ Cincinnati, Cincinnati, OH USA.
   [Moon, Jewoong] Univ Alabama, Tuscaloosa, AL USA.
C3 State University of New York (SUNY) System; University at Albany, SUNY;
   State University System of Florida; Florida State University; University
   System of Ohio; University of Cincinnati; University of Alabama System;
   University of Alabama Tuscaloosa
RP Bae, H (corresponding author), SUNY Albany, Albany, NY 12222 USA.
RI Moon, Jewoong/AAB-5647-2019
OI Moon, Jewoong/0000-0001-6311-3019; Choi, Gi Woong/0000-0002-9631-3727
CR Almaiah MA, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11081278
   Ayanwale M A., 2022, Computers and Education: Artificial Intelligence, V3, P100099, DOI DOI 10.1016/J.CAEAI.2022.100099
   Bakir N., 2015, Journal of Digital Learning in Teacher Education, V31, P117, DOI [10.1080/21532974.2015.1040930, DOI 10.1080/21532974.2015.1040930]
   Borg JS, 2022, AI MAG, V43, P294, DOI 10.1002/aaai.12062
   Bozkurt A, 2024, ONLINE LEARN, V28, DOI 10.24059/olj.v28i2.4563
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Chounta IA, 2022, INT J ARTIF INTELL E, V32, P725, DOI 10.1007/s40593-021-00243-5
   Debreli E, 2011, PROCD SOC BEHV, V15, P60, DOI 10.1016/j.sbspro.2011.03.051
   ElSayary A, 2024, J COMPUT ASSIST LEAR, V40, P931, DOI 10.1111/jcal.12926
   Farrell T. S., 2012, Novice-service language teacher development: Bridging the gap between preservice and in-service education and development, DOI [10.24059/olj.v28i2.4563, DOI 10.24059/OLJ.V28I2.4563]
   Farrell TSC, 2012, TESOL QUART, V46, P435, DOI 10.1002/tesq.36
   Frei-Landau R, 2022, TEACH TEACH EDUC, V111, DOI 10.1016/j.tate.2021.103623
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Henderson J, 2021, TECHNOL PEDAGOG EDUC, V30, P573, DOI 10.1080/1475939X.2021.1931426
   Hopcan S, 2024, EDUC INF TECHNOL, V29, P7281, DOI 10.1007/s10639-023-12086-9
   Huang XD, 2021, EDUC INF TECHNOL, V26, P5127, DOI 10.1007/s10639-021-10530-2
   Johnson DG, 2017, J ASSOC INF SCI TECH, V68, P2267, DOI 10.1002/asi.23867
   Jwaifell M., 2013, CONTEMP EDUC TECHNOL, V4, P138, DOI DOI 10.30935/CEDTECH/6098
   Karahan E, 2023, INT J SCI EDUC, V45, P1283, DOI 10.1080/09500693.2023.2200887
   Kaufmann E., 2021, Computers and Education: Artificial Intelligence, V2, P100028, DOI [10.1016/j.caeai.2021.100028, DOI 10.1016/J.CAEAI.2021.100028]
   Kaya F, 2024, INT J HUM-COMPUT INT, V40, P497, DOI 10.1080/10447318.2022.2151730
   Kim K., 2023, Computers and Education: Artificial Intelligence, V4, DOI [10.1016/j.caeai.2023.100137, DOI 10.1016/J.CAEAI.2023.100137]
   Kong SC, 2023, EDUC INF TECHNOL, V28, P4703, DOI 10.1007/s10639-022-11408-7
   Lemon N, 2016, TEACH TEACH, V22, P387, DOI 10.1080/13540602.2015.1058594
   Li J, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101410
   Lin X, 2024, ONLINE LEARN, V28, DOI 10.24059/olj.v28i2.4407
   Lindner A, 2020, PROC FRONT EDUC CONF
   Luik P, 2018, EDUC INF TECHNOL, V23, P741, DOI 10.1007/s10639-017-9633-y
   Lund BD, 2020, COLL RES LIBR, V81, P865
   Markauskaite L., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022.100056, 10.1016/j.caeai.2022.100056]
   MENZIES RG, 1995, CLIN PSYCHOL REV, V15, P23, DOI 10.1016/0272-7358(94)00039-5
   Na HH, 2024, J RES TECHNOL EDUC, DOI 10.1080/15391523.2024.2338091
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Okulu HZ, 2024, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2024.2322462
   Polly D, 2023, COMPUT SCH, V40, P22, DOI 10.1080/07380569.2022.2121107
   Prahani BK, 2022, INT J EMERG TECHNOL, V17, P169, DOI 10.3991/ijet.v17i08.29833
   Qazi Wasim, 2018, International Journal of Business Information Systems, V27, P222
   RACHMAN S, 1977, BEHAV RES THER, V15, P375, DOI 10.1016/0005-7967(77)90041-9
   Raman R, 2021, EDUC INF TECHNOL, V26, P7339, DOI 10.1007/s10639-021-10581-5
   Rogers E. M., 2003, DIFFUSION INNOVATION
   Roscoe R. D., 2022, Artificial Intelligence in STEM Education, P359
   Sahin S, 2012, COMPUT EDUC, V59, P1089, DOI 10.1016/j.compedu.2012.04.007
   Salhab R, 2024, ONLINE LEARN, V28, DOI 10.24059/olj.v28i2.4426
   Seufert S, 2021, COMPUT HUM BEHAV, V115, DOI 10.1016/j.chb.2020.106552
   Song LY, 2011, J INTERACT ONLINE LE, V10, P1
   Sperling K, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100169
   Su J., 2022, COMPUTERS ED ARTIFIC, V3, DOI [DOI 10.1016/J.CAEAI.2022.100065GET, DOI 10.1016/J.CAEAI.2022.100049]
   Swindell A, 2024, ONLINE LEARN, V28, P7, DOI 10.24059/olj.v28i2.4438
   Tondeur J, 2012, COMPUT EDUC, V59, P134, DOI 10.1016/j.compedu.2011.10.009
   Touretzky D, 2019, AAAI CONF ARTIF INTE, P9795
   Uzumcu O, 2024, TECHNOL KNOWL LEARN, V29, P1109, DOI 10.1007/s10758-023-09687-1
   Wang K, 2024, BEHAV SCI-BASEL, V14, DOI 10.3390/bs14050373
   Wang YM, 2024, INTERACT LEARN ENVIR, V32, P2584, DOI 10.1080/10494820.2022.2153147
   Wang YY, 2022, INTERACT LEARN ENVIR, V30, P619, DOI 10.1080/10494820.2019.1674887
   Yang, 2022, COMPUTERS ED ARTIFIC, V3, DOI DOI 10.1016/J.CAEAI.2022.100061
   Yang S.J., 2021, Comput. Educ.: Artif. Intell., V2, DOI [DOI 10.1016/J.CAEAI.2021.100008, 10.1016/j.caeai.2021.100008]
   Yeo MA, 2023, TESOL J, V14, DOI 10.1002/tesj.716
   Yu-Mei Wang, 2002, Journal of Research on Technology in Education, V35, P150
NR 59
TC 0
Z9 0
U1 46
U2 46
PU ONLINE LEARNING CONSORTIUM
PI NEWBURYPORT
PA PO BOX 1238, NEWBURYPORT, MA 01950 USA
SN 2472-5749
EI 2472-5730
J9 ONLINE LEARN
JI Online Learn.
PD SEP
PY 2024
VL 28
IS 3
BP 131
EP 156
DI 10.24059/olj.v28i3.4543
PG 26
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA F3Z1S
UT WOS:001309228000006
OA gold
DA 2024-12-25
ER

PT J
AU Vig, S
AF Vig, Shinu
TI Intersection of generative artificial intelligence and copyright: an
   Indian perspective
SO JOURNAL OF SCIENCE AND TECHNOLOGY POLICY MANAGEMENT
LA English
DT Article; Early Access
DE Artificial intelligence; GenAI; ChatGPT; Copyright; IPR; Legal
   framework; Large language models
AB Purpose - The main objective of this study is to present a compact overview analysis of intellectual property laws, specifically copyright-related provisions applicable to generative artificial intelligence (GenAI) in the Indian context. Design/methodology/approach - The paper adopts a qualitative research methodology that is grounded in secondary sources of information. The data were gathered from the Scopus database for a systematic literature review. Findings - GenAI technology has given rise to numerous questionable issues within the domain of intellectual property that need resolution in the form of policy solutions. Based on the findings of this paper, it can be deduced that Indian copyright laws are not adequate for addressing the rights pertaining to AI and its creations and outputs. Different countries like the United States, European Union and China have approached the regulation and protection of AI-generated content within the realm of copyright law in different ways. The future of law, as it has been established thus far, seems to be on a path of substantial evolution. Practical implications - The study has implications for policymakers globally as there is a need to create feasible policy solutions that can efficiently safeguard against risks stemming from large language models (LLMs) and other GenAI models, while also promoting innovation, technical advancement and adoption. Originality/value - The paper discusses the copyright-related issues in GenAI technology in the context of an emerging economy, India.
C1 [Vig, Shinu] Symbiosis Ctr Management Studies, Noida, India.
   [Vig, Shinu] Symbiosis Int Univ, Pune, India.
C3 Symbiosis International University; Symbiosis Centre for Management
   Studies Noida; Symbiosis International University
RP Vig, S (corresponding author), Symbiosis Ctr Management Studies, Noida, India.; Vig, S (corresponding author), Symbiosis Int Univ, Pune, India.
EM shinu17@gmail.com
RI Vig, Shinu/AAL-4533-2021
CR Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Brittain B, 2023, REUTERS
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eastern Book Company and Ors. V. D. B. Modak and Anr, 2004, Eastern book company and ors. V. D. B. Modak and anr civil appeal no. 6472 of 2004
   Ekhator E.G., 2023, An examination of copyright issues associated with autonomous AI machines
   EUIPO, 2024, Navigating the complexities of generative AI in intellectual property: challenges and opportunities
   Kar A. K., 2023, Global Journal of Flexible Systems Management, V24, P659, DOI [DOI 10.1007/S40171-023-00356-X, https://doi.org/10.1007/s40171-023-00356-x]
   Khanna M., 2022, INDIA TIMES
   Kirmani AR, 2022, ACS ENERGY LETT, V8, P574, DOI 10.1021/acsenergylett.2c02758
   Kretschmer M, 2024, IIC-INT REV INTELL P, V55, P110, DOI 10.1007/s40319-023-01419-3
   Laukyte M., 2012, AISB IACAP WORLD C 2
   Loh E, 2024, BMJ LEAD, V8, P51, DOI 10.1136/leader-2023-000797
   Lucchi N, 2024, EUR J RISK REGUL, V15, P602, DOI 10.1017/err.2023.59
   Luk A., 2024, Law, Innovation and Technology, V16, P1
   Mammen C.E., 2020, FLORIDA INT U LAW RE, V14, P275
   McKinsey, 2023, About Us
   Merkley R., 2023, Lawfareblog
   Padmanabhan A., 2023, IP and ChatGPT: who owns what?
   Parris DL, 2013, J BUS ETHICS, V113, P377, DOI 10.1007/s10551-012-1322-6
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Potter W., 2023, The conversation. Available at if ChatGPT wrote it, who owns the copyright? It depends on where you live, but in Australia it's complicated
   Presuel RC, 2024, INTELIGENCIA ARTIFIC, V27, P14, DOI 10.4114/intartif.vol27iss73pp14-37
   Qadir J., 2023, IEEE GLOB ENG ED C E, P1
   Radford A., 2018, Technical Reports
   Rajya Sabha, 2021, Parliament of India, Rajya Sabha
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Reed L., 2022, ChatGPT for automated testing: from conversation to code
   Rusk N, 2016, NAT METHODS, V13, P35, DOI 10.1038/nmeth.3707
   Samuelson P, 2023, SCIENCE, V381, P158, DOI 10.1126/science.adi0656
   Shi Y., 2023, Science of Law Journal, V2, P17
   Strowel A., 2023, IIC International Review of Intellectual Property and Competition Law, V54, P1
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   USPO, 2023, Federal Register, V88
   Vaswani A, 2017, ADV NEUR IN, V30
   Vig S, 2022, J WORLD INTELLECT PR, V25, P753, DOI 10.1111/jwip.12249
   Vincent J., 2022, Artificial Intelligence
   Wininger A., 2024, Beijing internet court releases translation of Li vs. Liu recognizing copyright in generative AI
   WIPO, 2019, Artificial Intelligence
NR 39
TC 1
Z9 1
U1 19
U2 19
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2053-4620
EI 1758-5538
J9 J SCI TECHNOL POLICY
JI J. Sci. Technol. Policy Manag.
PD 2024 JUL 9
PY 2024
DI 10.1108/JSTPM-08-2023-0145
EA JUL 2024
PG 16
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA XX9J7
UT WOS:001265093000001
DA 2024-12-25
ER

PT J
AU Vesjolijs, A
AF Vesjolijs, Aleksejs
TI The E(G)TL Model: A Novel Approach for Efficient Data Handling and
   Extraction in Multivariate Systems
SO APPLIED SYSTEM INNOVATION
LA English
DT Article
DE generative AI; ETL; data engineering; data mesh; DataOps; hyperloop
AB This paper introduces the EGTL (extract, generate, transfer, load) model, a theoretical framework designed to enhance the traditional ETL processes by integrating a novel 'generate' step utilizing generative artificial intelligence (GenAI). This enhancement optimizes data extraction and processing, presenting a high-level solution architecture that includes innovative data storage concepts: the Fusion and Alliance stores. The Fusion store acts as a virtual space for immediate data cleaning and profiling post-extraction, facilitated by GenAI, while the Alliance store serves as a collaborative data warehouse for both business users and AI processes. EGTL was developed to facilitate advanced data handling and integration within digital ecosystems. This study defines the EGTL solution design, setting the groundwork for future practical implementations and exploring the integration of best practices from data engineering, including DataOps principles and data mesh architecture. This research underscores how EGTL can improve the data engineering pipeline, illustrating the interactions between its components. The EGTL model was tested in the prototype web-based Hyperloop Decision-Making Ecosystem with tasks ranging from data extraction to code generation. Experiments demonstrated an overall success rate of 93% across five difficulty levels. Additionally, the study highlights key risks associated with EGTL implementation and offers comprehensive mitigation strategies.
C1 [Vesjolijs, Aleksejs] Transport & Telecommun Inst, Engn Fac, Lauvas Str 2, LV-1019 Riga, Latvia.
C3 Transport & Telecommunication Institute - Latvia
RP Vesjolijs, A (corresponding author), Transport & Telecommun Inst, Engn Fac, Lauvas Str 2, LV-1019 Riga, Latvia.
EM vesjolijs.a@tsi.lv
CR Adner R, 2017, J MANAGE, V43, P39, DOI 10.1177/0149206316678451
   Almeida A, 2023, J BIG DATA-GER, V10, DOI 10.1186/s40537-023-00760-1
   Amazon, 2024, What Is ETL (Extract Transform Load)?
   Anand N., 2013, P INT C ADV COMP SCI
   [Anonymous], 2024, ISO/IEC 25002:2024
   [Anonymous], 2024, ISO/IEC 25012:2008
   Armbrust M., Diving into Delta Lake: Unpacking the Transaction Log
   Aubakirova A., 2019, Python Tools Evaluation for ETL-Process Development and Maintenance
   Bhattacharjee A, 2019, IEEE INT CONF BIG DA, P1607, DOI 10.1109/BigData47090.2019.9006518
   Bloomberg J., 2019, DataOps: What, Why, and How?
   Chang V, 2015, AD HOC NETW, V35, P65, DOI 10.1016/j.adhoc.2015.07.012
   Daka E, 2014, PROC INT SYMP SOFTW, P201, DOI 10.1109/ISSRE.2014.11
   Das T., 2021, Delta Lake: Up and Running, P75
   Databricks, 2023, WHAT IS MEDALLION AR
   datamesh-architecture, Data Mesh Architecture DataMesh-Architecture.com
   DataOps Principles, 2023, DataOps
   Downey A.B., 2011, Probability and Statistics for Programmers, P19
   Fan W., 2012, FDN DATA QUALITY MAN
   Fernandes D, 2023, PROCEEDINGS OF THE INT'L ACM SYMPOSIUM ON PERFORMANCE EVALUATION OF WIRELESS AD HOC, SENSOR, & UBIQUITOUS NETWORKS, PE-WASUN 2023, P1, DOI 10.1145/3616394.3618270
   Foxley-Marrable M., Medallion Architecture
   Garcia S, 2016, BIG DATA ANAL, V1, P1, DOI 10.1186/s41044-016-0014-0
   General Data Protection Regulation (GDPR), GDPR-info
   Giebler C, 2019, LECT NOTES COMPUT SC, V11708, P179, DOI 10.1007/978-3-030-27520-4_13
   Hunt J, 2019, Advanced Guide to Python 3 Programming, DOI [10.1007/978-3-030-25943-315, DOI 10.1007/978-3-030-25943-315]
   IBM, 2024, What Is ELT?
   Kahneman D., 2000, Choices, Values, and Frames, P321
   Karun AK, 2013, 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT 2013), P132
   Kimpel J.F., 2013, Issues in Information Systems, V14, P376, DOI DOI 10.48009/1IIS2013376-384
   Kirsch L.J., 1996, Accounting, Management and Information Technologies, V6, P221, DOI DOI 10.1016/S0959-8022(96)90015-6
   Knorr E.M., 2002, Ph.D. Thesis, DOI [10.14288/1.0051497, DOI 10.14288/1.0051497]
   Kuznetsov SD, 2023, PROGRAM COMPUT SOFT+, V49, P1, DOI 10.1134/S036176882301005X
   Marr B., 12 New Jobs in The Generative AI Era
   Nandi A., Understanding Gen AI Hallucinations: A Deep Dive into the Phenomenon
   OpenAI, 2024, ChatGPT-4 Artificial Intelligence Language Model
   Oracle, 2024, Data Warehouse Architecture (with a Staging Area)
   Sabtu A, 2017, INT CONF RES INNOV
   Theodorou V, 2016, CONCURR COMP-PRACT E, V28, P3969, DOI 10.1002/cpe.3729
   Ul Hassan CA, 2022, IEEE ACCESS, V10, P13472, DOI 10.1109/ACCESS.2022.3148131
   Weng Y, 2016, SIGNAL PROCESS, V124, P5, DOI 10.1016/j.sigpro.2015.11.003
NR 39
TC 0
Z9 0
U1 4
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2571-5577
J9 APPL SYST INNOV
JI Appl. Syst. Innov.
PD OCT
PY 2024
VL 7
IS 5
AR 92
DI 10.3390/asi7050092
PG 25
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Engineering; Telecommunications
GA K0L1S
UT WOS:001340877100001
OA gold
DA 2024-12-25
ER

PT J
AU Hessari, H
   Bai, A
   Daneshmandi, F
AF Hessari, Hassan
   Bai, Ali
   Daneshmandi, Fatemeh
TI Generative AI: Boosting Adaptability and Reducing Workplace Overload
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE Generative artificial intelligence (AI); employee adaptability;
   perceived work overload; the job demands-resources model (JD-R); the
   broaden-and-build theory; large language models (LLMs)
ID ARTIFICIAL-INTELLIGENCE; DISCRIMINANT VALIDITY; POSITIVE EMOTIONS; BUILD
   THEORY; WORK; MODEL; TURNOVER; ROAD
AB In the evolving digital work environment, the rising prevalence of generative AI tools presents a complex challenge for practitioners: deciding whether to allow or restrict their use in organizational settings. Our research contributes to expanding the broaden-and-build theory and the job demands-resources model (JD-R) within the context of generative AI tools usage. This study investigates the impact of generative AI tools on employees' perceived work overload, focusing on the mediating role of employee adaptability. Utilizing a survey of 307 employees and Structural Equation Modeling (SEM) techniques, findings show that generative AI tools usage by employees not only directly reduces perceived work overload but also significantly boosts employee adaptability, further decreasing perceived work overload. This highlights the dual benefits of generative AI tools in the workplace, offering valuable insights for managers on consciously integrating these technologies to enhance adaptability and reduce workload stress.
C1 [Hessari, Hassan] Virginia Tech, Pamplin Coll Business, Dept Business Informat Technol, Pamplin Hall,880 West Campus Dr, Blacksburg, VA 24061 USA.
   [Bai, Ali] Univ Northern Iowa, Cedar Falls, IA USA.
   [Daneshmandi, Fatemeh] Acad Ctr Educ Culture & Res, Tehran, Iran.
C3 Virginia Polytechnic Institute & State University; University of
   Northern Iowa; Academic Center for Education, Culture & Research (ACECR)
RP Hessari, H (corresponding author), Virginia Tech, Pamplin Coll Business, Dept Business Informat Technol, Pamplin Hall,880 West Campus Dr, Blacksburg, VA 24061 USA.
EM hassanhessari@vt.edu
RI Hessari, Hassan/KHT-8318-2024
CR Ab Hamid MR, 2017, J PHYS CONF SER, V890, DOI 10.1088/1742-6596/890/1/012163
   Abukhait R, 2020, INT J INNOV MANAG, V24, DOI 10.1142/S136391962050070X
   af Wåhlberg AE, 2015, BASIC APPL SOC PSYCH, V37, P336, DOI 10.1080/01973533.2015.1111212
   Agrawal KP, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2286540
   Aguinis H, 2024, ORGAN DYN, V53, DOI 10.1016/j.orgdyn.2024.101029
   Ahmad S., 2016, British Journal of Mathematics Computer Science, V15, P1, DOI [10.9734/BJMCS/2016/25183, DOI 10.9734/BJMCS/2016/25183]
   Ahuja MK, 2007, MIS QUART, V31, P1
   Al Naqbi H, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16031166
   Anam AK., 2020, J Manag Entrep Res, V1, P13, DOI [10.34001/jmer.2020.6.01.1-2, DOI 10.34001/JMER.2020.6.01.1-2]
   [Anonymous], 2023, The economic potential of generative AI the next productivity frontier the economic potential of generative AI: the next productivity Frontier
   [Anonymous], 2023, How generative AI can boost highly skilled workers' productivity
   [Anonymous], 2024, Forbes
   [Anonymous], 2023, Harvard business review you need a generative AI strategy
   [Anonymous], 2023, WALL STREET J
   Azaria A, 2024, DATA INTELLIGENCE, V6, P240, DOI 10.1162/dint_a_00235
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bakker AB, 2017, J OCCUP HEALTH PSYCH, V22, P273, DOI 10.1037/ocp0000056
   Bakker AB, 2010, J PERS PSYCHOL, V9, P3, DOI 10.1027/1866-5888/a000006
   Baldassarre M.T., 2023, P 2023 ACM C INF TEC, P363, DOI [10.1145/3582515.3609555, DOI 10.1145/3582515.3609555]
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bankins S, 2024, J ORGAN BEHAV, V45, P159, DOI 10.1002/job.2735
   Banville D, 2000, J TEACH PHYS EDUC, V19, P374, DOI 10.1123/jtpe.19.3.374
   Beer P, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.00918
   Bilgram Volker, 2023, IEEE Engineering Management Review, P18, DOI 10.1109/EMR.2023.3272799
   Bolarinwa Oladimeji Akeem, 2015, Niger Postgrad Med J, V22, P195, DOI 10.4103/1117-1936.173959
   Bono R, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12010019
   Bowling A, 2005, J PUBLIC HEALTH-UK, V27, P281, DOI 10.1093/pubmed/fdi031
   Bradley C., 2013, Handbook of psychology and diabetes, P438
   Braun V, 2021, INT J SOC RES METHOD, V24, P641, DOI 10.1080/13645579.2020.1805550
   BROWN T. A., 2006, CONFIRMATORY FACTOR
   Brynjolfsson E., 2023, NBER Working Paper No. 31161, V31161, DOI DOI 10.3386/W31161
   Byrne B, 2010, INTERNATIONAL HANDBOOK OF PSYCHOLOGY IN EDUCATION, P3
   Cable DM, 2002, J APPL PSYCHOL, V87, P875, DOI 10.1037//0021-9010.87.875
   Cao JQ, 2025, ASIA-PAC J BUS ADM, V17, P116, DOI 10.1108/APJBA-07-2022-0328
   Centenaro Alexa Pupiara Flores Coelho, 2023, Rev. Latino-Am. Enfermagem, V31, pe4001, DOI 10.1590/1518-8345.6669.4001
   Chuma E. L., 2023, Manag Sci Bus Decis, V3, P5, DOI DOI 10.52812/MSBD.63
   Collie R, 2020, TEACH TEACH, V26, P350, DOI 10.1080/13540602.2020.1832063
   Damman M, 2020, J APPL GERONTOL, V39, P915, DOI 10.1177/0733464818800651
   Ebrahimian M, 2023, BMJ HEALTH CARE INFO, V30, DOI 10.1136/bmjhci-2023-100815
   Elkhatat AM, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00140-5
   Elshaer IA., 2024, J Open Innov Technol Mark Complex, V10, P100208, DOI [10.1016/j.joitmc.2024.100208, DOI 10.1016/J.JOITMC.2024.100208]
   Saghih AMF, 2021, INT J ISLAMIC MIDDLE, V14, P77, DOI 10.1108/IMEFM-11-2019-0489
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Forza C, 1998, INT J PROD ECON, V55, P1, DOI 10.1016/S0925-5273(98)00007-3
   Fredrickson BL, 2004, PHILOS T R SOC B, V359, P1367, DOI 10.1098/rstb.2004.1512
   Fredrickson BL, 2001, AM PSYCHOL, V56, P218, DOI 10.1037/0003-066X.56.3.218
   Freeze R., 2007, ECIS 2007 P, P171
   Fuller CM, 2016, J BUS RES, V69, P3192, DOI 10.1016/j.jbusres.2015.12.008
   Gallagher M.W., 2013, Handbook of Quantitative Methods for Educational Research, P289, DOI [DOI 10.1007/978-94-6209-404-8_14, 10.1007/978-94-6209-404-8_14, DOI 10.1007/978-94-6209-404-814]
   Ganewatta H., 2023, J Bus Econ Finance, V12, P110, DOI [10.17261/Pressacademia.2023.1822, DOI 10.17261/PRESSACADEMIA.2023.1822]
   Gautham KS, 2021, J PERINATOL, V41, P1209, DOI 10.1038/s41372-021-01038-1
   GEORGE D, 2003, SPSS WINDOWS STEP ST
   Gong ZX, 2023, EUR MANAG J, V41, P415, DOI 10.1016/j.emj.2022.03.009
   Griffin MA, 2007, ACAD MANAGE J, V50, P327, DOI 10.5465/AMJ.2007.24634438
   Hair J. F., 2017, International Journal of Multivariate Data Analysis, V1, P107, DOI [10.1504/ijmda.2017.10008574, DOI 10.1504/IJMDA.2017.087624, DOI 10.1504/IJMDA.2017.10008574, https://doi.org/10.1504/IJMDA.2017.087624, 10.1504/IJMDA.2017.087624]
   Hakeem AO, 2023, INT J INNOV MANAG, V27, DOI 10.1142/S1363919623500251
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hessari H, 2024, ASIA-PAC J BUS ADM, DOI 10.1108/APJBA-02-2024-0065
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Huo ML, 2023, HUM RESOUR MANAGE-US, V62, P867, DOI 10.1002/hrm.22167
   Iacobucci D, 2007, J CONSUM PSYCHOL, V17, P139, DOI 10.1016/S1057-7408(07)70020-7
   Jia N, 2024, ACAD MANAGE J, V67, P5, DOI 10.5465/amj.2022.0426
   Jo H, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-57977-0
   Jo H, 2023, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2278283
   Joskowicz Jose, 2024, IEEE Engineering Management Review, V52, P258, DOI 10.1109/EMR.2023.3333794
   Kadaruddin K., 2023, International Journal of Business, Law, and Education, V4, P618, DOI DOI 10.56442/IJBLE.V4I2.215
   Khorshidi Hamid, 2023, Informatics in Medicine Unlocked, DOI 10.1016/j.imu.2023.101314
   Kline R.B., 2023, PRINCIPLES PRACTICE
   Kodden B., 2020, The art of sustainable performance. A model for recruiting, selection, and profes-sional development, DOI [10.1007/978-3-030-46463-9, DOI 10.1007/978-3-030-46463-9]
   Kolbjornsrud V, 2024, CALIF MANAGE REV, V66, P44, DOI 10.1177/00081256231211020
   Lai KK, 2018, STRUCT EQU MODELING, V25, P600, DOI 10.1080/10705511.2017.1392248
   Landers RN, 2023, AM PSYCHOL, V78, P36, DOI 10.1037/amp0000972
   Lee L, 2023, J HOSP TOUR RES, V47, pNP33, DOI 10.1177/10963480221133777
   Lee S., 2017, J APPL BUS RES, V33, P829, DOI [10.19030/jabr.v33i4.10003, DOI 10.19030/JABR.V33I4.10003]
   Lehner OM, 2022, ACCOUNT AUDIT ACCOUN, V35, P109, DOI 10.1108/AAAJ-09-2020-4934
   Lerman SE, 2012, J OCCUP ENVIRON MED, V54, P231, DOI 10.1097/JOM.0b013e318247a3b0
   Loughlin EM, 2021, PROJ LEADERSH SOC, V2, DOI 10.1016/j.plas.2021.100012
   Malik P, 2023, ASIA-PAC J BUS ADM, V15, P325, DOI 10.1108/APJBA-10-2020-0379
   Mannuru NR, 2023, INFORM DEV, DOI 10.1177/02666669231200628
   Marsh HW, 2009, STRUCT EQU MODELING, V16, P439, DOI 10.1080/10705510903008220
   Marsh HW, 2004, PSYCHOL METHODS, V9, P275, DOI 10.1037/1082-989X.9.3.275
   Mazzetti G, 2023, PSYCHOL REP, V126, P1069, DOI 10.1177/00332941211051988
   Meah MR, 2024, J COMPUT INFORM SYST, DOI 10.1080/08874417.2024.2372036
   Méndez-Suárez M, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9151832
   Meyer SC, 2018, INT J ENV RES PUB HE, V15, DOI 10.3390/ijerph15122911
   Mia MM., 2019, J Bus Manag, V21, P56, DOI [DOI 10.9790/487X-2101025661, 10.9790/487X-2101025661, DOI 10.5585/REMARK.V13I2.2718]
   Moore JE, 2000, MIS QUART, V24, P141, DOI 10.2307/3250982
   Mosquera P, 2024, MANAGE DECIS, V62, P2111, DOI 10.1108/MD-06-2023-0970
   Mustafa MZ., 2020, Universal Journal of Educational Research, V8, P127, DOI [DOI 10.13189/UJER.2020.080115, 10.13189/ujer.2020.080115]
   Nakra N, 2023, J CAREER DEV, V50, P1139, DOI 10.1177/08948453231157763
   Nica E., 2023, Contemp Readings In Law Soc Justice, V15, P46
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Oesinghaus A., 2024, ECIS 2024 P
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Parikh NA., 2023, Empowering business transformation: the positive impact and ethical considerations of generative AI in software product management a systematic literature review
   Pavlou PA, 2007, MIS QUART, V31, P105
   Perneger TV, 2015, QUAL LIFE RES, V24, P147, DOI 10.1007/s11136-014-0752-2
   Platt M., 2023, 2023 IEEE 17 INT C A, DOI [10.1109/AICT59525.2023.10313167, DOI 10.1109/AICT59525.2023.10313167]
   Pluta A, 2021, J ORGAN CHANGE MANAG, V34, P590, DOI 10.1108/JOCM-08-2020-0241
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   pub.towardsai, 2022, Generative AI and Future
   Rahman MM., 2021, Int J Technol Transf Commercial, V18, P195, DOI [10.1504/IJTTC.2021.117621, DOI 10.1504/IJTTC.2021.117621]
   Rakap S, 2024, J SPEC EDUC TECHNOL, V39, P339, DOI 10.1177/01626434231211295
   Rane N., 2024, Stud. Econ. Bus. Relat., V5, P11
   Rasool T, 2022, SAGE OPEN, V12, DOI 10.1177/21582440221114320
   Rogala A, 2019, FRONT PSYCHOL, V10, DOI 10.3389/fpsyg.2019.02786
   Rönkkö M, 2022, ORGAN RES METHODS, V25, P6, DOI 10.1177/1094428120968614
   Rosseel Y, 2012, J STAT SOFTW, V48, P1, DOI 10.18637/jss.v048.i02
   Rotenstein LS, 2023, J GEN INTERN MED, V38, P1920, DOI 10.1007/s11606-023-08153-z
   Santilli S, 2024, SOC SCI-BASEL, V13, DOI 10.3390/socsci13020106
   Savinainen M, 2004, ERGONOMICS, V47, P1087, DOI 10.1080/00140130410001686357
   Scholze Alexander, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20166581
   Selenko E, 2022, CURR DIR PSYCHOL SCI, V31, P272, DOI 10.1177/09637214221091823
   Shahriar S, 2023, IEEE ACCESS, V11, P61829, DOI 10.1109/ACCESS.2023.3287195
   Shin YJ, 2019, CAREER DEV Q, V67, P110, DOI 10.1002/cdq.12175
   Siderska J., 2024, Paper presented at: FICC 2024 Lecture Notes in Networks and Systems Advances in information and Communication
   Silva AJ, 2024, PERS REV, V53, P743, DOI 10.1108/PR-09-2022-0629
   Sun Y, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS, CHI 2024, DOI 10.1145/3613904.3642160
   Sundberg L, 2024, BUS HORIZONS, V67, P561, DOI 10.1016/j.bushor.2024.04.014
   Taherdoost H., 2016, INT J ACAD RES MANAG, V5, P28, DOI [10.2139/ssrn.3205040, DOI 10.2139/SSRN.3205040]
   Tavakol M, 2011, INT J MED EDUC, V2, P53, DOI 10.5116/ijme.4dfb.8dfd
   Van Selm M, 2006, QUAL QUANT, V40, P435, DOI 10.1007/s11135-005-8081-8
   Venkatesh V, 2012, MIS QUART, V36, P157
   Walkowiak E, 2023, ECON LETT, V231, DOI 10.1016/j.econlet.2023.111315
   Wang KH, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1106299
   Wong AHK, 2024, ASIAN J BUS ETHICS, V13, P87, DOI 10.1007/s13520-024-00198-5
   Wulf V, 2022, COMPUT SUPP COOP W J, V31, P373, DOI 10.1007/s10606-022-09426-7
   Xanthopoulou D, 2012, EUR J WORK ORGAN PSY, V21, P489, DOI 10.1080/1359432X.2011.584386
   Xu Q, 2024, CURR PSYCHOL, V43, P2104, DOI 10.1007/s12144-023-04373-y
   Yue ZY, 2024, J SOC PERS RELAT, V41, P1279, DOI 10.1177/02654075231188185
   Zhai XX, 2023, APPL PSYCHOL-INT REV, V72, P915, DOI 10.1111/apps.12411
   Ziaei M., 2015, Int J. Occup. Hyg, V7, P53
   Zoghbi-Manrique-de-Lara P, 2021, COGN TECHNOL WORK, V23, P331, DOI 10.1007/s10111-020-00627-y
NR 133
TC 0
Z9 0
U1 30
U2 30
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD 2024 OCT 27
PY 2024
DI 10.1080/08874417.2024.2417672
EA OCT 2024
PG 14
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA K1A2W
UT WOS:001341274500001
DA 2024-12-25
ER

PT J
AU Turchioe, MR
   Kisselev, S
   Van Bulck, L
   Bakken, S
AF Turchioe, Meghan Reading
   Kisselev, Sergey
   Van Bulck, Liesbet
   Bakken, Suzanne
TI Increasing Generative Artificial Intelligence Competency among Students
   Enrolled in Doctoral Nursing Research Coursework
SO APPLIED CLINICAL INFORMATICS
LA English
DT Article
DE artificial intelligence; nursing informatics; informatics; medical
   informatics; nursing students; graduate nursing education; nursing
   education research
AB Background Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way. Objective This study aimed to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in health care. Methods We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat Generative Pretrained Transformer (ChatGPT) 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in health care, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in health care practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students ( n = 10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis. Results Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. All of them reported increasing their self-rated competency in generative AI by one to two points on a five-point rating scale. Conclusion This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.
C1 [Turchioe, Meghan Reading; Kisselev, Sergey; Bakken, Suzanne] Columbia Univ, Sch Nursing, New York, NY USA.
   [Van Bulck, Liesbet] Univ Leuven, KU Leuven, Dept Publ Hlth & Primary Care, Leuven, Belgium.
   [Bakken, Suzanne] Columbia Univ, Dept Biomed Informat, New York, NY USA.
   [Bakken, Suzanne] Columbia Univ, Data Sci Inst, New York, NY USA.
C3 Columbia University; KU Leuven; Columbia University; Columbia University
RP Turchioe, MR (corresponding author), 560 W 168th St, New York, NY 10032 USA.
EM mr3554@cumc.columbia.edu
RI Van Bulck, Liesbet/AAT-1508-2020
FU Columbia Center for Teaching and Learning; National Institute of Nursing
   Research (NINR) of the National Institutes of Health (NIH) [R00NR019124]
FX This project was funded through a grant from the Columbia Center for
   Teaching and Learning. M.R.T. is also funded by National Institute of
   Nursing Research (NINR) of the National Institutes of Health (NIH).
   (grant no.:R00NR019124).
CR aacnnursing, American Association of Colleges of Nursing Nursing Faculty Shortage Fact Sheet
   Acar O A., 2023, Are Your Students Ready for AI?: A Four-Step Framework to Prepare Learners for a ChatGPT
   [Anonymous], 2023, Open AI Governance of superintelligence.
   [Anonymous], 2021, The essentials: Core competencies for professional nursing education
   Armstrong C, 2018, AM FAM PHYSICIAN, V97, P413
   Bakewell-Sachs R., 2022, Addressing the nurse faculty shortage-American Nurse
   Bloom B. S., 1956, TAXONOMY ED OBJECTIV
   Brennan PF, 2015, J NURS SCHOLARSHIP, V47, P477, DOI 10.1111/jnu.12159
   Carroll Whende M, 2023, Nurs Manage, V54, P56, DOI 10.1097/nmg.0000000000000056
   Columbia Center for Teaching and Learning at Columbia University, About us
   De Gagne Jennie C, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20064884
   Douthit BJ, 2022, APPL CLIN INFORM, V13, P161, DOI 10.1055/s-0041-1742218
   Durieux B, 2024, EUR J CARDIOVASC NUR, V23, pe128, DOI 10.1093/eurjcn/zvae016
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eddy N., 2023, Microsoft partner to use generative AI for better EHRs-Healthcare IT News
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Gosak L, 2024, NURSE EDUC PRACT, V75, DOI 10.1016/j.nepr.2024.103888
   Grace JT, 2006, NURS EDUC PERSPECT, V27, P28
   Hsieh HF, 2005, QUAL HEALTH RES, V15, P1277, DOI 10.1177/1049732305276687
   Jo A., 2023, Nature, V614
   Liu JL, 2023, NURS OUTLOOK, V71, DOI 10.1016/j.outlook.2023.102064
   Liu SR, 2023, medRxiv, DOI [10.1101/2023.02.21.23286254, 10.1101/2023.02.21.23286254, DOI 10.1101/2023.02.21.23286254]
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Lugosi G., 2023, PREPRINT
   Lundberg S. M., A Unified Approach to Interpreting Model Predictions
   Moons P, 2023, EUR J CARDIOVASC NUR, V22, pE55, DOI 10.1093/eurjcn/zvad022
   Morris NS, 2021, NURS OUTLOOK, V69, P50, DOI 10.1016/j.outlook.2020.08.002
   Mrayyan MT, 2023, BMJ OPEN, V13, DOI 10.1136/bmjopen-2022-067352
   nurse, Nurse.org Top PhD in nursing programs 2024
   O'Connor S, 2024, NURSE EDUC PRACT, V74, DOI 10.1016/j.nepr.2023.103825
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   OConnor S., 2023, Nursing TimesOctober 9,
   OConnor S., 2023, Nursing Times
   Open AI, 2022, Our approach to alignment research
   openai, Open AI Introducing ChatGPT 2022
   Owens B, 2023, NATURE, V615, P20, DOI 10.1038/d41586-023-00500-8
   Patton MQ, 1999, HEALTH SERV RES, V34, P1189
   Rao A, 2023, J MED INTERNET RES, V25, DOI 10.2196/48659
   Rao A, 2023, J AM COLL RADIOL, V20, P990, DOI 10.1016/j.jacr.2023.05.003
   Raza MM, 2024, NPJ DIGIT MED, V7, DOI 10.1038/s41746-023-00988-4
   Reed JM, 2024, NURS EDUC, V49, P184, DOI 10.1097/NNE.0000000000001590
   Ronquillo CE, 2021, J ADV NURS, V77, P3707, DOI 10.1111/jan.14855
   Russell RG, 2023, ACAD MED, V98, P348, DOI 10.1097/ACM.0000000000004963
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Topaz M, 2024, J NURS EDUC, DOI 10.3928/01484834-20240126-01
   Wing JM, 2006, COMMUN ACM, V49, P33, DOI 10.1145/1118178.1118215
NR 46
TC 0
Z9 0
U1 15
U2 15
PU GEORG THIEME VERLAG KG
PI STUTTGART
PA RUDIGERSTR 14, D-70469 STUTTGART, GERMANY
SN 1869-0327
J9 APPL CLIN INFORM
JI Appl. Clin. Inform.
PD OCT
PY 2024
VL 15
IS 05
BP 842
EP 851
DI 10.1055/a-2373-3151
PG 10
WC Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Medical Informatics
GA J0O6H
UT WOS:001334155700003
PM 39053615
DA 2024-12-25
ER

PT J
AU Hamtini, T
   Assaf, AJ
AF Hamtini, Thair
   Assaf, Abdelbaset J.
TI Exploring the Efficacy of GenAI in Grading SQL Query Tasks: A Case Study
SO CYBERNETICS AND INFORMATION TECHNOLOGIES
LA English
DT Article
DE Grading SQL; ChatGPT; Gemini; Copilot; Automation
AB Numerous techniques, including problem-solving, seeking clarification, and creating questions, have been employed to utilize generative Artificial Intelligence (AI) in education. This study investigates the possibility of using Generate AI (GenAI) to grade Structured Query Language (SQL) queries automatically. Three models were used which are ChatGPT, Gemini, and Copilot. The study uses an experimental approach to assess how well the models perform in evaluating student responses by comparing the models' accuracy with those of human experts. The results showed that despite some inconsistencies, GenAI holds great promise for streamlining. Thus, further research is required in light of inconsistent GenAI performance. If these issues were resolved, GenAI can be utilized in education. However, human oversight and ethical issues must always come first.
C1 [Hamtini, Thair; Assaf, Abdelbaset J.] Univ Jordan, Aqaba, Jordan.
C3 University of Jordan
RP Hamtini, T (corresponding author), Univ Jordan, Aqaba, Jordan.
EM thamtini@ju.edu.jo; ab.assaf@ju.edu.jo
CR Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chen Y., 2020, arXiv
   Jonsson A., 2024, Prompting for Progression: How Well Can GenAI Create a Sense of Progression in a Set of Multiple-Choice Questions?
   Kim SC, 2019, JAMA NETW OPEN, V2, DOI 10.1001/jamanetworkopen.2019.8061
   Ling HC, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.770637
   Luckin R., 2016, Intelligence unleashed: An argument for AI in education
   Luckin R, 2019, BRIT J EDUC TECHNOL, V50, P2824, DOI 10.1111/bjet.12861
   Messer M, 2024, ACM T COMPUT EDUC, V24, DOI 10.1145/3636515
   Moore N. C., 2023, P INT C TEACH ASS LE
   OpenAI, 2024, Bard Large Language Model
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Wang W., 2020, PREPRINT
   Yan LX, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13370
NR 13
TC 0
Z9 0
U1 3
U2 3
PU INST INFORMATION & COMMUNICATION TECHNOLOGIES-BULGARIAN ACAD SCIENCES
PI SOFIA
PA 2, ACAD G BONCHEV, SOFIA, 1113, BULGARIA
SN 1311-9702
EI 1314-4081
J9 CYBERN INF TECHNOL
JI Cybern. Inf. Technol.
PD SEP 1
PY 2024
VL 24
IS 3
BP 102
EP 111
DI 10.2478/cait-2024-0027
PG 10
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA G2X6Y
UT WOS:001315327300001
OA gold
DA 2024-12-25
ER

PT J
AU Mishra, P
   Henriksen, D
AF Mishra, Punya
   Henriksen, Danah
TI Creative Dialogue with Generative AI: Exploring the Possible with Ron
   Beghetto
SO TECHTRENDS
LA English
DT Article
DE Creativity; Technology; Education; Artificial intelligence; Possibility
   thinking; ChatGPT; Generative AI; Futures thinking; Future possibilities
ID MORTIFICATION
AB In this article, we explore the intersection of creativity, education, and technology, with a focus on the impact of Generative AI (GenAI). We delve into the transformative potential of GenAI in redefining educational and creative processes and challenging our existing notions of learning and creativity. Through a conversation with renowned creativity researcher Dr. Ronald Beghetto, we thematically explore how GenAI redefines educational and creative processes and challenges conventional notions of learning and creativity. Dr. Beghetto's work highlights a shift from fearing failure to embracing possibility thinking, advocating for a mindset that views creativity as a dynamic interplay of potential and adaptability. His recent work with GenAI tools illustrates their role as catalysts for possibility thinking, pushing the boundaries towards future-oriented thought and innovation. GenAI can function in multiple ways-including as a reflection of human intellect and values, and as a collaborative partner that enriches human creativity with its unpredictability and generative capabilities. We emphasize the importance of direct, critical, and creative engagement with GenAI in educational settings, cautioning against its passive or uncritical use, and advocating for a balanced approach that leverages its strengths while remaining aware of its limitations. Sharing several possibility thinking tools he has created, Dr. Beghetto offers readers a nuanced perspective on the role of GenAI in education and creativity, advocating for a future where these tools are used responsibly and creatively to unlock new possibilities and enhance human potential.
C1 [Mishra, Punya; Henriksen, Danah] Arizona State Univ, Mary Lou Fulton Teachers Coll, Tempe, AZ 85287 USA.
C3 Arizona State University; Arizona State University-Tempe
RP Mishra, P (corresponding author), Arizona State Univ, Mary Lou Fulton Teachers Coll, Tempe, AZ 85287 USA.
EM punya.mishra@asu.edu; danah.henriksen@asu.edu
CR Barrat J., 2023, OUR FINAL INVENTION
   Beghetto R A., 2022, My Favorite Failure: How Setbacks Can Lead to Learning and Growth
   Beghetto RA., 2023, UNCERTAINTY X DESIGN, DOI [10.1017/9781009071475, DOI 10.1017/9781009071475]
   Beghetto RA, 2016, NEW DIR CHILD ADOLES, V151, P85, DOI 10.1002/cad.20150
   Beghetto RA, 2014, PSYCHOL AESTHET CREA, V8, P266, DOI 10.1037/a0036618
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bhojani AR, 2023, THEOL SCI, V21, P557, DOI 10.1080/14746700.2023.2255944
   Dunnigan J., 2023, SCH LEADERS TAKE CHA, DOI [10.1007/s11528-023-0091, DOI 10.1007/S11528-023-0091]
   Frankfurt HG, 2005, ON BULLSHIT, P1
   Henriksen D, 2023, TECHTRENDS, V67, P595, DOI 10.1007/s11528-023-00862-w
   Mishra P., 2023, Journal of Digital Learning in Teacher Education, V39, P235, DOI DOI 10.1080/21532974.2023.2247480
   Mishra P., 2023, PUNYA MISHRAS WEB
   Mishra P, 2024, TECHTRENDS, V68, P205, DOI 10.1007/s11528-024-00938-1
   Mishra P, 2023, TECHTRENDS, V67, P207, DOI 10.1007/s11528-023-00839-9
   Mishra P, 2009, TECHTRENDS, V53, P48
   Phan T, 2020, EDUC MEDIA INT, V57, P73, DOI 10.1080/09523987.2020.1744859
   Richardson C, 2024, TECHTRENDS, V68, P5, DOI 10.1007/s11528-023-00921-2
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Sawyer R K., 2024, Explaining creativity: The science of human innovation
   Sundar S S., 2023, Journalism Communication Monographs, V25, P165, DOI DOI 10.1177/15226379231167135
   Warr M, 2023, TECHTRENDS, V67, P396, DOI 10.1007/s11528-023-00843-z
   Weizenbaum J., 1976, COMPUTER POWER HUMAN
   Woo LJ, 2023, TECHTRENDS, V67, P767, DOI 10.1007/s11528-023-00888-0
NR 23
TC 2
Z9 2
U1 27
U2 40
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD MAY
PY 2024
VL 68
IS 3
SI SI
BP 395
EP 401
DI 10.1007/s11528-024-00949-y
EA MAR 2024
PG 7
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA SD4A5
UT WOS:001190118300001
DA 2024-12-25
ER

PT J
AU Kim, JS
   Baek, TH
AF Kim, Jeong Soo
   Baek, Tae Hyun
TI Motivational determinants of continuance usage intention for generative
   AI: an investment model approach for ChatGPT users in the United States
SO BEHAVIOUR & INFORMATION TECHNOLOGY
LA English
DT Article; Early Access
DE Generative AI chatbot; ChatGPT; investment model; user-friendliness;
   playfulness
ID TECHNOLOGY ACCEPTANCE MODEL; BRAND RELATIONSHIPS; VOICE ASSISTANTS;
   COMMITMENT; SATISFACTION; UTILITARIAN; CONSUMPTION; ALTERNATIVES;
   ANTECEDENTS; CONSUMERS
AB The proliferation of generative artificial intelligence (AI) chatbots such as ChatGPT has sparked a transformation in the dynamics of human-machine interactions. However, little is known about the psychological mechanisms underlying generative AI chatbot-human relationships. Building on the investment model, this study examined the influence of generative AI user-friendliness and playfulness on ChatGPT commitment and continuance usage intention. The results of an online survey conducted in the United States (N = 446) indicated that user-friendliness and playfulness positively affect satisfaction, investment size, and perceived quality of alternatives. Both satisfaction and investment size positively affect commitment to ChatGPT, thereby influencing the intention to continue using generative AI chatbots. However, the quality of alternatives did not significantly affect user commitment to ChatGPT. The findings of this study contribute to our understanding of building user relationships with generative AI chatbots by identifying user-friendliness (representing utilitarian value) and playfulness (representing hedonic value) as key factors in determining generative AI continuance usage intention.
C1 [Kim, Jeong Soo; Baek, Tae Hyun] Sungkyunkwan Univ, Dept Media & Commun, Seoul 03063, South Korea.
C3 Sungkyunkwan University (SKKU)
RP Baek, TH (corresponding author), Sungkyunkwan Univ, Dept Media & Commun, Seoul 03063, South Korea.
EM tbaek@skku.edu
RI Baek, Tae Hyun/JGD-1912-2023
CR ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   BABIN BJ, 1994, J CONSUM RES, V20, P644, DOI 10.1086/209376
   Baek TH, 2023, TELEMAT INFORM, V83, DOI 10.1016/j.tele.2023.102030
   Baek TH, 2022, INT J ADVERT, V41, P850, DOI 10.1080/02650487.2021.2011654
   Baek TH, 2018, J ADVERTISING, V47, P70, DOI 10.1080/00913367.2017.1405755
   Batra R., 1991, Marketing Letters, V2, P159, DOI DOI 10.1007/BF00436035
   Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
   Bishop C. M., 2006, PATTERN RECOGN
   Bozeman DP, 2001, J APPL PSYCHOL, V86, P161, DOI 10.1037/0021-9010.86.1.161
   Bügel MS, 2011, J RETAIL CONSUM SERV, V11, P247, DOI 10.1016/j.jretconser.2010.11.005
   Byun KA, 2017, J CONSUM MARK, V34, P226, DOI 10.1108/JCM-01-2016-1684
   Campbell WK, 2002, PERS SOC PSYCHOL B, V28, P484, DOI 10.1177/0146167202287006
   Chen LD, 2002, INFORM MANAGE-AMSTER, V39, P705, DOI 10.1016/S0378-7206(01)00127-6
   Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
   Childers TL, 2001, J RETAILING, V77, P511, DOI 10.1016/S0022-4359(01)00056-2
   Chiou JS, 2006, J ACAD MARKET SCI, V34, P613, DOI 10.1177/0092070306286934
   Chiu WS, 2021, INFORM TECHNOL PEOPL, V34, P978, DOI 10.1108/ITP-09-2019-0463
   Chiu W, 2016, INT J SPORT MARK SPO, V17, P243, DOI [10.1108/ijsms-08-2016-013, 10.1108/IJSMS-08-2016-013]
   Choi TR, 2021, TELEMAT INFORM, V62, DOI 10.1016/j.tele.2021.101628
   Choung H, 2023, INT J HUM-COMPUT INT, V39, P1727, DOI 10.1080/10447318.2022.2050543
   Chun H, 2012, CYBERPSYCH BEH SOC N, V15, P473, DOI 10.1089/cyber.2012.0140
   Chung M, 2020, J BUS RES, V117, P587, DOI 10.1016/j.jbusres.2018.10.004
   Coombs S. J., 2000, The International Journal of Creativity Problem Solving, V10, P19
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cyr D, 2006, INFORM MANAGE-AMSTER, V43, P950, DOI 10.1016/j.im.2006.08.009
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
   Dhar R, 2000, J MARKETING RES, V37, P60, DOI 10.1509/jmkr.37.1.60.18718
   Dinh CM, 2023, ELECTRON COMMER RES, DOI 10.1007/s10660-022-09662-5
   Duarte F, 2024, Number of IoT devices
   FARRELL D, 1981, ORGAN BEHAV HUM PERF, V28, P78, DOI 10.1016/0030-5073(81)90016-7
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Ganesh J, 2000, J MARKETING, V64, P65, DOI 10.1509/jmkg.64.3.65.18028
   Geyskens I, 2000, J RETAILING, V76, P11, DOI 10.1016/S0022-4359(99)00021-4
   Gunawardena CN., 1997, American Journal of Distance Education, V11, P8, DOI [10.1080/08923649709526970, DOI 10.1080/08923649709526970]
   Guzman AL, 2020, NEW MEDIA SOC, V22, P70, DOI 10.1177/1461444819858691
   Guzman AL, 2019, COMPUT HUM BEHAV, V90, P343, DOI 10.1016/j.chb.2018.08.009
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hajli N, 2017, IEEE T ENG MANAGE, V64, P594, DOI 10.1109/TEM.2017.2711042
   Harman H. H., 1976, MODERN FACTOR ANAL
   Hartman JB, 2006, PSYCHOL MARKET, V23, P813, DOI 10.1002/mar.20135
   Hassenzahl M, 2006, BEHAV INFORM TECHNOL, V25, P91, DOI 10.1080/01449290500330331
   Hayes A F., 2013, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, V1, P12
   Story J, 2011, J PROD BRAND MANAG, V20, P14, DOI 10.1108/10610421111107987
   HIRSCHMAN EC, 1982, J MARKETING, V46, P92, DOI 10.2307/1251707
   Hsieh SH, 2017, COMPUT HUM BEHAV, V69, P405, DOI 10.1016/j.chb.2016.12.052
   Jan IU, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103440
   Jermias J, 2001, ACCOUNT ORG SOC, V26, P141, DOI 10.1016/S0361-3682(00)00008-8
   Jin SV, 2023, INT J HUM-COMPUT INT, V39, P1874, DOI 10.1080/10447318.2022.2129277
   Jones MA, 2000, J RETAILING, V76, P259, DOI 10.1016/S0022-4359(00)00024-5
   KATZ E, 1973, PUBLIC OPIN QUART, V37, P508
   Kelley H.H., 1978, INTERPERSONAL RELATI
   Kim JS, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2311971
   Kim M, 2022, TELEMAT INFORM, V74, DOI 10.1016/j.tele.2022.101881
   Kim S, 2018, TELEMAT INFORM, V35, P148, DOI 10.1016/j.tele.2017.10.008
   Kock N, 2012, J ASSOC INF SYST, V13, P546, DOI 10.17705/1jais.00302
   Kowalczuk P, 2018, J RES INTERACT MARK, V12, P418, DOI 10.1108/JRIM-01-2018-0022
   Kuberkar Sachin., 2020, International Journal on Emerging Technologies, V11, P948
   Li DH, 2006, DECISION SCI, V37, P427, DOI 10.1111/j.1540-5414.2006.00133.x
   Li L, 2021, ELECTRON MARK, V31, P575, DOI 10.1007/s12525-020-00454-z
   Li MN, 2015, INT J INFORM MANAGE, V35, P229, DOI 10.1016/j.ijinfomgt.2014.12.004
   Li X, 2008, J TRAVEL RES, V47, P25, DOI 10.1177/0047287507312409
   Lin JS, 2016, COMPUT HUM BEHAV, V58, P171, DOI 10.1016/j.chb.2015.12.025
   Litman L, 2017, BEHAV RES METHODS, V49, P433, DOI 10.3758/s13428-016-0727-z
   Liu J, 2020, TECHNOL FORECAST SOC, V158, DOI 10.1016/j.techfore.2020.120142
   Lock Samantha, 2022, The Guardian5 Dec
   Lowe B, 2019, EUR J MARKETING, V53, P1038, DOI 10.1108/EJM-06-2019-966
   Lubbe I, 2021, S AFR J INFORM MANAG, V23, DOI 10.4102/sajim.v23i1.1299
   Luger E, 2016, 34TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2016, P5286, DOI 10.1145/2858036.2858288
   Ma XY, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102362
   Magno F, 2023, TQM J, V35, P1156, DOI 10.1108/TQM-02-2022-0080
   Malhotra Y, 2005, J MANAGE INFORM SYST, V22, P117, DOI 10.1080/07421222.2003.11045840
   Mäntymäki M, 2015, INT J INFORM MANAGE, V35, P124, DOI 10.1016/j.ijinfomgt.2014.10.004
   Massey AP, 2007, DECISION SCI, V38, P277, DOI 10.1111/j.1540-5915.2007.00159.x
   McLean G, 2021, J BUS RES, V124, P312, DOI 10.1016/j.jbusres.2020.11.045
   Moon JW, 2001, INFORM MANAGE-AMSTER, V38, P217, DOI 10.1016/S0378-7206(00)00061-6
   MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
   Morgan-Thomas A, 2013, J BUS RES, V66, P21, DOI 10.1016/j.jbusres.2011.07.019
   Moriuchi E, 2019, PSYCHOL MARKET, V36, P489, DOI 10.1002/mar.21192
   Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
   Nass C., 2005, Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
   Nielsen J., 1994, Usability engineering
   Norman D, 1988, The Psychology of Everyday Things
   Novak TP, 2000, MARKET SCI, V19, P22, DOI 10.1287/mksc.19.1.22.15184
   Padilla-Meléndez A, 2013, COMPUT EDUC, V63, P306, DOI 10.1016/j.compedu.2012.12.014
   Park BW, 2011, COMPUT HUM BEHAV, V27, P2178, DOI 10.1016/j.chb.2011.06.013
   Park G, 2023, BEHAV INFORM TECHNOL, V42, P1998, DOI 10.1080/0144929X.2022.2105746
   Pentina I, 2023, PSYCHOL MARKET, V40, P1593, DOI 10.1002/mar.21853
   Petrovciková K, 2018, EURAS STUD BUS ECON, V9, P53, DOI 10.1007/978-3-319-76288-3_4
   Pillai R, 2020, INT J CONTEMP HOSP M, V32, P3199, DOI 10.1108/IJCHM-04-2020-0259
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Pritchard M. P., 1997, Journal of Travel Research, V35, P2
   Rafiq F, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10132190
   Ramaseshan B, 2006, J RETAILING, V82, P63, DOI 10.1016/j.jretai.2005.11.004
   Rusbult C., 1994, COMMUNICATION RELATI, P115, DOI DOI 10.1207/S15327698JFC0604_1
   RUSBULT CE, 1993, J SOC PERS RELAT, V10, P175, DOI 10.1177/026540759301000202
   Rusbult CE, 1998, PERS RELATIONSHIP, V5, P357, DOI 10.1111/j.1475-6811.1998.tb00177.x
   RUSBULT CE, 1983, J PERS SOC PSYCHOL, V45, P101, DOI 10.1037/0022-3514.45.1.101
   RUSBULT CE, 1980, J EXP SOC PSYCHOL, V16, P172, DOI 10.1016/0022-1031(80)90007-4
   Sharabi LL, 2021, NEW MEDIA SOC, V23, P2926, DOI 10.1177/1461444820937660
   SHETH JN, 1991, J BUS RES, V22, P159, DOI 10.1016/0148-2963(91)90050-8
   Sundar SS, 2020, J COMPUT-MEDIAT COMM, V25, P74, DOI 10.1093/jcmc/zmz026
   Sung YJ, 2010, PSYCHOL MARKET, V27, P1050, DOI 10.1002/mar.20373
   Sung YJ, 2009, J BRAND MANAG, V17, P97, DOI 10.1057/palgrave.bm.2550119
   Ta V, 2020, J MED INTERNET RES, V22, DOI 10.2196/16235
   Talwar S, 2020, INT J HOSP MANAG, V88, DOI 10.1016/j.ijhm.2020.102534
   Tsao WC, 2012, TOTAL QUAL MANAG BUS, V23, P821, DOI 10.1080/14783363.2012.661137
   Uysal A, 2016, COMPUT HUM BEHAV, V61, P357, DOI 10.1016/j.chb.2016.03.028
   van Dam K, 2005, J OCCUP ORGAN PSYCH, V78, P253, DOI 10.1348/096317904X23745
   van der Heijden H, 2004, MIS QUART, V28, P695, DOI 10.2307/25148660
   Veloutsou C, 2007, J MARKET MANAG-UK, V23, P7, DOI 10.1362/026725707X177892
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Wakefield RL, 2006, EUR J INFORM SYST, V15, P292, DOI 10.1057/palgrave.ejis.3000619
   Xie CX, 2024, INT J HUM-COMPUT INT, V40, P613, DOI 10.1080/10447318.2022.2121458
   Yang H, 2019, INF SYST E-BUS MANAG, V17, P65, DOI 10.1007/s10257-018-0375-1
   Yim MC, 2024, INT J HUM-COMPUT INT, V40, P5415, DOI 10.1080/10447318.2023.2234114
   Yuan CL, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102878
   Zhou L, 2020, COMPUT LINGUIST, V46, P53, DOI [10.1162/COLI_a_00368, 10.1162/coli_a_00368]
   Zhou ZY, 2015, EUR J INFORM SYST, V24, P247, DOI 10.1057/ejis.2014.27
NR 120
TC 0
Z9 0
U1 16
U2 16
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0144-929X
EI 1362-3001
J9 BEHAV INFORM TECHNOL
JI Behav. Inf. Technol.
PD 2024 NOV 20
PY 2024
DI 10.1080/0144929X.2024.2429647
EA NOV 2024
PG 17
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA M9A0X
UT WOS:001360375300001
DA 2024-12-25
ER

PT J
AU Wolf, MJ
   Grodzinsky, F
   Miller, KW
AF Wolf, Marty J.
   Grodzinsky, Frances
   Miller, Keith W.
TI Generative AI and Its Implications for Definitions of Trust
SO INFORMATION
LA English
DT Article
DE trust; e-trust; chatbots; generative artificial intelligence
AB In this paper, we undertake a critical analysis of how chatbots built on generative artificial intelligence impact assumptions underlying definitions of trust. We engage a particular definition of trust and the object-oriented model of trust that was built upon it and identify how at least four implicit assumptions may no longer hold. Those assumptions include that people generally provide others with a default level of trust, the ability to identify whether the trusted agent is human or artificial, that risk and trust can be readily quantified or categorized, and that there is no expectation of gain by agents engaged in trust relationships. Based on that analysis, we suggest modifications to the definition and model to accommodate the features of generative AI chatbots. Our changes re-emphasize developers' responsibility for the impacts of their AI artifacts, no matter how sophisticated the artifact may be. The changes also reflect that trust relationships are more fraught when participants in such relationships are not confident in identifying the nature of a potential trust partner.
C1 [Wolf, Marty J.] Bemidji State Univ, Dept Math & Comp Sci, Bemidji, MN 56601 USA.
   [Grodzinsky, Frances] Sacred Heart Univ, Sch Comp Sci & Engn, Fairfield, CT 06825 USA.
   [Miller, Keith W.] Univ Missouri St Louis, Educ Sci & Profess Programs, St Louis, MO 63121 USA.
   [Miller, Keith W.] Univ Missouri St Louis, Dept Comp Sci, St Louis, MO 63121 USA.
C3 Minnesota State Colleges & Universities; Bemidji State University;
   Sacred Heart University; University of Missouri System; University of
   Missouri Saint Louis; Washington University (WUSTL); University of
   Missouri System; University of Missouri Saint Louis
RP Wolf, MJ (corresponding author), Bemidji State Univ, Dept Math & Comp Sci, Bemidji, MN 56601 USA.
EM marty.wolf@bemidjistate.edu; grodzinskyf@sacredheart.edu;
   keith.w.miller@umsl.edu
OI Miller, Keith/0000-0001-5336-8181; Wolf, Marty/0000-0003-0617-942X
CR Bohannon M., Forbes
   Bond Shannon., 2020, NPR
   Cai ZG, 2024, Arxiv, DOI [arXiv:2303.08014, DOI 10.48550/ARXIV.2303.08014]
   Chen H., CNN
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Coeckelbergh M, 2012, ETHICS INF TECHNOL, V14, P53, DOI 10.1007/s10676-011-9279-1
   Essel HB, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00362-6
   Ferrario A., 2020, PHILOS TECHNOLOGY, V33, P523, DOI DOI 10.1007/S13347-019-00378-3
   Floridi L., The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities, P169
   Grodzinsky F., 2020, ROUTLEDGE HDB TRUST, P298, DOI [10.4324/9781315542294-23, DOI 10.4324/9781315542294-23]
   Grodzinsky FS, 2011, ETHICS INF TECHNOL, V13, P17, DOI 10.1007/s10676-010-9255-1
   Grodzinsky Frances S., 2008, Ethics and Information Technology, V10, P27, DOI 10.1007/s10676-008-9163-9
   Hou F, 2023, EMPIR SOFTW ENG, V28, DOI 10.1007/s10664-022-10238-y
   Labadze L, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00426-1
   Lawson G., 5 Examples of Ethical Issues in Software Development
   Mittelstadt BD, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951716679679
   Orseau Laurent, 2011, Artificial General Intelligence. Proceedings 4th International Conference, AGI 2011, P1, DOI 10.1007/978-3-642-22887-2_1
   Parry M., 2009, Chron. High. Educ, V55, pA10
   Shalby C., 2020, LA Times
   Sodji L., Synthesia
   Taddeo M, 2009, INT J TECHNOL HUM IN, V5, P23, DOI 10.4018/jthi.2009040102
   Tenbarge K., 2023, NBC News
   van Rooij I., 2023, PsyArXiv, DOI [10.31234/osf.io/4cbuv, DOI 10.31234/OSF.IO/4CBUV]
   Weise K., The New York Times
   Wolf M. J., 2017, ACM SIGCAS Computers and Society, V47, P54, DOI 10.1145/3144592.3144598
   Wu X., 2024, J INF INTELL, V2, P102, DOI DOI 10.1016/J.JIIXD.2023.10.007
NR 26
TC 1
Z9 1
U1 4
U2 4
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2078-2489
J9 INFORMATION
JI Information
PD SEP
PY 2024
VL 15
IS 9
AR 542
DI 10.3390/info15090542
PG 10
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA H4B1D
UT WOS:001322902200001
OA gold
DA 2024-12-25
ER

PT J
AU Callejo, P
   Alario-Hoyos, C
   Delgado-Kloos, C
AF Callejo, Patricia
   Alario-Hoyos, Carlos
   Delgado-Kloos, Carlos
TI Evaluating the Impact of ChatGPT on Programming Learning Outcomes in a
   Big Data Course
SO INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION
LA English
DT Article
DE Generative Artificial Intelligence; ChatGPT; Programming; Python;
   PySpark; Big Data
AB Recent advances in Generative Artificial Intelligence are leading to major changes in education, both in the way educators teach and in the way students learn. For example, Generative Artificial Intelligence (GenAI) chatbots, such as ChatGPT, can help students by assisting them in problem solving or supporting them in code development tasks. This article aims precisely to explore the effect of ChatGPT in supporting students with different levels of programming experience in a course on Big Data. A Big Data challenge was carried out during one of the sessions with 31 students from different backgrounds. Overall, the students were able to solve the challenge, and the results of the pre- and post-tests indicate that the students improved their grades, i.e. they learned to solve the programming exercise. This quasi-experimental study shows that ChatGPT can be a valuable tool as an assistant in the field of data science and programming for students learning to program (even for the first time), whether they come from engineering programs or other completely different disciplines. It is important not to forget the role of the professor in guiding the students towards the correct use of these GenAI tools.
C1 [Callejo, Patricia; Alario-Hoyos, Carlos; Delgado-Kloos, Carlos] Univ Carlos III Madrid, Telemat Engn Dept, Avda Univ 30, Madrid 29811, Spain.
C3 Universidad Carlos III de Madrid
RP Callejo, P (corresponding author), Univ Carlos III Madrid, Telemat Engn Dept, Avda Univ 30, Madrid 29811, Spain.
EM pcallejo@it.uc3m.es; calario@it.uc3m.es; cdk@it.uc3m.es
RI Alario-Hoyos, Carlos/AAC-2052-2019; Callejo, Patricia/L-5149-2017
FU FEDER/Ministerio de Ciencia, Innovacion y Universidades - Agencia
   Estatal de Investigacion through project H2O Learn
   [PID2020-112584RB-C31]; European Commission through Erasmus+ projects
   MICROCASA [101081924 ERASMUS-EDU-2022-CBHE-STRAND-2]; MICRO-GEAR
   [101127144 ERASMU-SEDU-2023-CBHE-STRAND-3]; POEM-SET
   [2021-FR01-KA220-HED-000032171]; EcoCredGT [101129122
   ERAS-MUS-EDU-2023-CB-VET]
FX The authors acknowledge funding from FEDER/Ministerio de Ciencia,
   Innovacion y Universidades - Agencia Estatal de Investigacion through
   project H2O Learn (PID2020-112584RB-C31) . This research has also
   received partial support from the European Commission through Erasmus+
   projects MICROCASA (101081924 ERASMUS-EDU-2022-CBHE-STRAND-2) ,
   MICRO-GEAR (101127144 ERASMU-SEDU-2023-CBHE-STRAND-3) , POEM-SET
   (2021-FR01-KA220-HED-000032171) and EcoCredGT (101129122
   ERAS-MUS-EDU-2023-CB-VET) . This publication reflects the views only of
   the authors and funders cannot be made responsible for any use which may
   be made of the information contained therein.
CR [Anonymous], 2011, Google+
   [Anonymous], 2013, NBC News
   Apache Spark, Unified engine for large-scale data analytics
   asianews.network, Asia News Networks
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Baron N. S., How ChatGPT robs students of motivation to write and think for themselves
   Bernabei M, 2023, Comput Educ: Artif Intell, V5, DOI [DOI 10.1016/J.CAEAI.2023.100172, 10.1016/j.caeai.2023.100172]
   Bii P., 2013, EDUC RES-UK, V4, P218
   businessinsider, Business Insider
   Castillo A. G. R., 2023, J NAMIB STUDIES, V33, P1, DOI [https://doi.org/10.59670/jns.v33i.411, DOI 10.59670/JNS.V33I.411]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dall-e OpenAI, DALLE 2
   Daun M, 2023, PROCEEDINGS OF THE 2023 CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2023, VOL 1, P110, DOI 10.1145/3587102.3588815
   Ellis AR, 2023, J STAT DATA SCI EDUC, V31, P128, DOI 10.1080/26939169.2023.2223609
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   GitHub Copilot, The world's most widely adopted AI developer tool
   google, Google Code Chat
   Gozalo-Brizuela R, 2023, Arxiv, DOI [arXiv:2306.02781, DOI 10.3844/JCSSP.2024.801.818]
   Ibrahim H, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-38964-3
   Jackman JA, 2021, NAT HUM BEHAV, V5, P542, DOI 10.1038/s41562-021-01074-z
   Judini AI, Code GPT
   Kirova Vassilka D., 2024, SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, P666, DOI 10.1145/3626252.3630927
   Liu RX, 2024, PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1, P750, DOI [10.1145/3626252.3630938, 10.1007/978-981-97-4125-0_1]
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Manco-Chavez JA., 2020, International Journal of Higher Education, V9, P11, DOI DOI 10.5430/IJHE.V9N9P11
   Martin FG, 2012, COMMUN ACM, V55, P26, DOI 10.1145/2240236.2240246
   Meta. Llama3, About us
   Modran H. A., 2023, INT C INT COLL LEARN, P499
   Murphy KP., 2023, Probabilistic Machine Learning: Advanced Topics
   Nikolic S, 2023, EUR J ENG EDUC, V48, P559, DOI 10.1080/03043797.2023.2213169
   OpenAI, CHATGPT
   OpenAI Teaching, Teaching with AI
   PDF.ai, Chat with any PDF document
   Pirzada K., 2013, EUR J BUS MANAGE, V5
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Qureshi B, 2023, Arxiv, DOI [arXiv:2304.11214, DOI 10.48550/ARXIV.2304.11214]
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Rudolph J urgen, 2023, Journal of Applied Learning and Teaching, V6
   Sánchez-Ruiz LM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13106039
   Sarsa Sami, 2022, ICER 2022 V1: Proceedings of the 2022 ACM Conference on International Computing Education Research V.1, P27, DOI 10.1145/3501385.3543957
   Sora, OpenAI
   Synthesia, Turn your text into videos in minutes
   Tabnine, About us
   Yilmaz R., 2023, Computers in Human Behavior: Artificial Humans
   Yu H, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1181712
NR 45
TC 0
Z9 0
U1 25
U2 25
PU TEMPUS PUBLICATIONS
PI DURRUS, BANTRY
PA IJEE , ROSSMORE,, DURRUS, BANTRY, COUNTY CORK 00000, IRELAND
SN 0949-149X
J9 INT J ENG EDUC
JI Int. J. Eng. Educ
PY 2024
VL 40
IS 4
BP 863
EP 872
PG 10
WC Education, Scientific Disciplines; Engineering, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Engineering
GA F4K7J
UT WOS:001309529600013
DA 2024-12-25
ER

PT J
AU Yawson, RM
AF Yawson, Robert M.
TI Perspectives on the promise and perils of generative AI in academia
SO HUMAN RESOURCE DEVELOPMENT INTERNATIONAL
LA English
DT Article; Early Access
DE Generative AI; academia; substantial equivalence; precautionary
   principle
ID HUMAN-RESOURCE DEVELOPMENT; ARTIFICIAL-INTELLIGENCE
AB Recent advances in generative artificial intelligence (AI), spearheaded by models such as GPT-3, DALL-E 2, and ChatGPT, have demonstrated capabilities to produce remarkably human-like text, images, and speech. This has fuelled growing interest in applying these technologies in academic contexts to augment teaching, research, and knowledge creation. However, the integration of emerging technologies into education requires a thoughtful evaluation to ensure responsible and ethical adoption. This essay provides a balanced perspective on both the potential promise and the possible perils of deploying generative AI in academia. It examines key technical factors that require evaluation, discusses risks and limitations, and proposes an informed framework for assessing when and how these technologies could appropriately enhance academic pursuits.
C1 [Yawson, Robert M.] Quinnipiac Univ, Sch Business, Hamden, CT 06518 USA.
C3 Quinnipiac University
RP Yawson, RM (corresponding author), Quinnipiac Univ, Sch Business, Hamden, CT 06518 USA.
EM robert.yawson@qu.edu
RI Yawson, Robert/G-6646-2013
OI Yawson, Robert/0000-0001-6215-4345
CR Alam A., 2022, Towards Excellence, P281, DOI [https://doi.org/10.37867/TE140127, DOI 10.37867/TE140127]
   Ardichvili A, 2022, ADV DEV HUM RESOUR, V24, P78, DOI 10.1177/15234223221077304
   Bakhtin A, 2022, SCIENCE, V378, P1067, DOI 10.1126/science.ade9097
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bhattamisra SK, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7010010
   Brown TB, 2020, ADV NEUR IN, V33
   Bu Q., 2022, Science Insights, V41, P561, DOI DOI 10.15354/SI.22.RE067
   Cannarssa M, 2021, The Cambridge Handbook of Lawyering in the Digital Age, P283, DOI DOI 10.1017/9781108936040.022
   Da gama Mariana Botelho, 2022, Financial Cryptography and Data Security: 26th International Conference, FC 2022, Revised Selected Papers. Lecture Notes in Computer Science (13411), P20, DOI 10.1007/978-3-031-18283-9_2
   Engler A., 2023, Brookings
   European Commission, 2019, Ethics guidelines for trustworthy AI
   FernandezCornejo J., 2014, Genetically Engineered Crops in the United States., DOI [10.2139/ssrn.2503388, DOI 10.2139/SSRN.2503388]
   Fuhr AS, 2022, FRONT MATER, V9, DOI 10.3389/fmats.2022.865270
   Gehrmann S., 2022, P THE 2022 C EMPIRIC, P266, DOI https://doi.org/10.18653/v1/2022.emnlp-demos.27
   Grassmann C, 2021, HUM RESOUR DEV REV, V20, P106, DOI 10.1177/1534484320982891
   Gupta BB, 2023, TECHNOL FORECAST SOC, V186, DOI 10.1016/j.techfore.2022.122152
   Holmes W, 2022, EUR J EDUC, V57, P542, DOI 10.1111/ejed.12533
   Ifenthaler D, 2023, J RES TECHNOL EDUC, V55, P1, DOI 10.1080/15391523.2022.2154511
   Jiang F, 2017, STROKE VASC NEUROL, V2, P230, DOI 10.1136/svn-2017-000101
   Jiang YE, 2022, NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES, P1550
   Kearns P, 1999, NATURE, V401, P640, DOI 10.1038/44260
   Lebovitz S., 2023, MIT Sloan Management Review
   Lilly A, 2022, J STAT MANAG SYST, V25, P1083, DOI 10.1080/09720510.2022.2040859
   Marchant GaryE., 2004, ARBITRARY CAPRICIOUS
   Marcus G., 2019, Rebooting AI: Building Artificial Intelligence We Can Trust
   Marris C, 2001, EMBO REP, V2, P545, DOI 10.1093/embo-reports/kve142
   Millstone E, 1999, NATURE, V401, P525, DOI 10.1038/44006
   Otoo FNK, 2018, EUR J TRAIN DEV, V42, P435, DOI 10.1108/EJTD-12-2017-0113
   Philip J., 2022, Organization Management Journal, V19, P88, DOI [https://doi.org/10.1108/OMJ-10-2020-1063, DOI 10.1108/OMJ-10-2020-1063]
   Raji ID, 2020, PROCEEDINGS OF THE 3RD AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY AIES 2020, P145, DOI 10.1145/3375627.3375820
   Lázaro GRD, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15043507
   Rep. Johnson Eddie Bernice [D-TX-30], 2020, H.R.6216-National Artificial Intelligence Initiative Act of 2020
   Schmitt M., 2023, International Journal of Information Management Data Insights, V3, DOI [DOI 10.1016/J.JJIMEI.2022.100146, 10.1016/j.jjimei.2022.100146]
   Sheng B, 2022, FRONT PUBLIC HEALTH, V10, DOI 10.3389/fpubh.2022.971943
   Shuster K, arXiv
   Simmons SV, 2022, ADV DEV HUM RESOUR, V24, P242, DOI 10.1177/15234223221114359
   Sims CM, 2018, ADV DEV HUM RESOUR, V20, P313, DOI 10.1177/1523422318778009
   Solaiman I, 2021, ADV NEUR IN, V34
   Som C, 2009, J BUS ETHICS, V85, P493, DOI 10.1007/s10551-009-0214-x
   Trabelsi Z, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7010048
   U.S. Food and Drug Administration, 2022, Premarket Notification 510(k)
   Yawson RM, 2012, INT J TECHNOL DES ED, V22, P297, DOI 10.1007/s10798-010-9145-1
NR 42
TC 0
Z9 0
U1 17
U2 29
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1367-8868
EI 1469-8374
J9 HUM RESOUR DEV INT
JI Hum. Resour. Dev. Int.
PD 2024 MAR 31
PY 2024
DI 10.1080/13678868.2024.2334983
EA MAR 2024
PG 12
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA MN4J9
UT WOS:001194284200001
DA 2024-12-25
ER

PT J
AU Claverini, C
AF Claverini, Corrado
TI Tampering with Generative Artificial Intelligence by Jailbreaking
SO TEORIA-RIVISTA DI FILOSOFIA
LA English
DT Article
DE ChatGPT; ethics of artificial intelligence; generative artificial in-
   telligence; jailbreaking; regulation of artificial intelligence
AB In this paper, I will analyse the risks linked to the use of generative artificial intelligence systems and relative risk-reduction strategies, while concentrating in particular on the possibility of tampering with the chatbot ChatGPT by jailbreaking. After examining how a user can tamper with this generative AI, bypassing its ethical and legal restrictions, through a series of prompts, I will turn my focus to the ethical issues raised by the malicious use of this technology: are the transparency requirements requested of generative AI sufficient or should there be tighter restrictions that do not hinder the innovation and development of these technologies? How can the risk of tampering with these AI tools be lowered? And, should a breach take place, who is responsible: the AI developer or the jailbreaker? To what extent could the changes needed to prevent jailbreaking involuntarily generate or strengthen certain biases? In conclusion, I will uphold the necessity of ethical reflection for the sustainable and "human-centric" development of AI.
C1 [Claverini, Corrado] Univ Salento, Lecce, Italy.
C3 University of Salento
RP Claverini, C (corresponding author), Univ Salento, Lecce, Italy.
EM corrado.claverini@unisalento.it
CR [Anonymous], 2023, Aesthetics of Photography 27 giugno
   [Anonymous], 2023, E+T14 febbraio
   [Anonymous], 2023, ARTIFICIAL INTELLIGE
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bordoloi S. K., 2023, sify. com2 luglio
   Chan K.J.D., 2022, SSRN, DOI [10.2139/ssrn.4223016, DOI 10.2139/SSRN.4223016]
   Christian J., 2023, Futurism
   Floridi L, 2023, Philosophy & Technology, V36, P15, DOI DOI 10.1007/S13347-023-00621-Y
   Goodman M., 2023, Flowgpt1 maggio
   Hacker P, 2023, Arxiv, DOI [arXiv:2302.02337, 10.48550/arXiv.2302.02337, 10.48550/ARXIV.2302.02337]
   Harris Gareth, 2023, The Art Newspaper
   Hartmann J, 2023, Arxiv, DOI [arXiv:2301.01768, DOI 10.48550/ARXIV.2301.01768, 10.48550/ARXIV.2301.01768]
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   Kang C., 2023, The New York Times16 maggio
   Kim SC, 2019, JAMA NETW OPEN, V2, DOI 10.1001/jamanetworkopen.2019.8061
   Knight W., 2022, Wired13 luglio
   Li HR, 2023, Arxiv, DOI arXiv:2304.05197
   Liu Y, 2024, Arxiv, DOI arXiv:2305.13860
   Metz C., 2023, The New York Times31 marzo
   OpenAl, ChatGPT Feedback Contest: Official Rules
   Pal K., 2023, Techopedia1 febbraio
   Schroter Jens., 2019, The democratization of artificial intelligence. Net politics in the era of learning algorithms, transcript, P297, DOI DOI 10.25969/MEDIAREP/13546
   Shen XY, 2024, Arxiv, DOI arXiv:2308.03825
   Smith B., 2023, The Official Microsoft Blog2 febbraio
   Varghese J, 2024, J HEPATOL, V80, P977, DOI 10.1016/j.jhep.2023.07.028
   Wolf M. J., 2017, ACM SIGCAS Computers and Society, V47, P54, DOI 10.1145/3144592.3144598
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
   Zhuo TY, 2023, Arxiv, DOI arXiv:2301.12867
NR 30
TC 0
Z9 0
U1 8
U2 8
PU EDIZIONI ETS
PI PISA
PA Lungarno Mediceo 16, 56127 PISA, ITALY
SN 1122-1259
J9 TEORIA
JI Teoria
PY 2024
VL 44
IS 1
DI 10.4454/mg6wax06
PG 172
WC Philosophy
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Philosophy
GA US7H3
UT WOS:001250107100010
DA 2024-12-25
ER

PT J
AU Nguyen, KL
   Uddin, M
   Pham, TM
AF Nguyen, Khuong Le
   Uddin, Minhaz
   Pham, Thong M.
TI Generative artificial intelligence and optimisation framework for
   concrete mixture design with low cost and embodied carbon dioxide
SO CONSTRUCTION AND BUILDING MATERIALS
LA English
DT Article
DE Concrete mixture design; Machine learning approach; Generative AI;
   Compressive strength prediction; Multi-objective optimisation
ID SUPPLEMENTARY CEMENTITIOUS MATERIALS; HIGH-PERFORMANCE CONCRETE; GAS
   EMISSIONS; CO2 EMISSIONS; MODEL
AB This research presents a generative Artificial Intelligence (AI) and design framework that integrates machine learning (ML) and optimisation methodologies to discover new concrete mixture designs. Unlike traditional ML models that predict based on existing data, this framework innovatively generates new concrete mix designs that meet specific requirements such as strength, cost-efficiency, and reduced embodied CO2. To propose a powerful and reliable generative AI model, several advanced ML algorithms were considered, e.g., CatBoost, XGBoost, and LGBM. These models were trained on a unique dataset consisting of 4,936 data points collected from five different batching plants and have not been published yet. Bayesian Optimisation was employed to fine-tune model hyperparameters, resulting in the most effective models attaining R2 values of 0.94 and 0.89 for raw and grouped data, respectively. To verify the trained generative AI model, a case study was conducted, in which the model was requested to provide designs of a mix with pre-determined strength and optimised cost and embodied CO2. The mix designs generated by the framework were successfully validated through experimental tests, corroborating the predictive outcomes. The research culminated in the development of a web application, a tool crafted to streamline the concrete mixture design and optimisation process. This generative AI design framework can be applied to many other aspects of material design and engineering problems.
C1 [Nguyen, Khuong Le] Univ Transport Technol, Dept Civil Engn, Hanoi 100000, Vietnam.
   [Nguyen, Khuong Le] Univ Canberra, Fac Arts & Design, 11 Kirinari St, Bruce, ACT 2617, Australia.
   [Uddin, Minhaz] Stamford Univ Bangladesh, Dept Civil Engn, Dhaka 1217, Bangladesh.
   [Pham, Thong M.] Univ South Australia, UniSA STEM, Mawson Lakes, SA 5095, Australia.
C3 University of Canberra; Stamford University Bangladesh; University of
   South Australia
RP Pham, TM (corresponding author), Univ South Australia, UniSA STEM, Mawson Lakes, SA 5095, Australia.
EM khuongln@utt.edu.vn; thong.pham@unisa.edu.au
RI Pham, Thong/IUQ-8423-2023
OI Pham, Thong/0000-0003-4901-7113; Uddin, Minhaz/0009-0006-7651-144X
CR ACI, 2009, ACI 211.1-91
   Anand S, 2006, J ENVIRON MANAGE, V79, P383, DOI 10.1016/j.jenvman.2005.08.007
   [Anonymous], 2019, ASTM C94/C94M-17
   [Anonymous], 2022, C150C150M ASTM
   [Anonymous], 2021, ASTM C39/C39M-21, DOI DOI 10.1520/C0039_C0039M-21
   [Anonymous], 2017, ASTM C494/C494M-17
   [Anonymous], 2023, ASTM C31/C31M
   [Anonymous], 2023, ASTM D75
   [Anonymous], 2022, ASTM C1602/C1602M
   Asteris PG, 2021, CEMENT CONCRETE RES, V145, DOI 10.1016/j.cemconres.2021.106449
   ASTM, 2018, ASTM C33
   BCMA, 2022, Bangladesh Cement Manufactures Association demands withdrawal of increase to limestone import duty
   Ben Haha M, 2023, CEMENT CONCRETE RES, V174, DOI 10.1016/j.cemconres.2023.107312
   Bengio Y, 2004, J MACH LEARN RES, V5, P1089
   Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324
   Champa-Bujaico E, 2024, COMPOS PART B-ENG, V269, DOI 10.1016/j.compositesb.2023.111099
   Chen G., 2019, ICM Database-Integrated Carbon Metrics Embodied Carbon Life Cycle Inventory Database, DOI [10.26190/5df6aa5d5-ffd, DOI 10.26190/5DF6AA5D5-FFD]
   Chen TQ, 2016, KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P785, DOI 10.1145/2939672.2939785
   Chiew FH, 2017, COMPUT-AIDED CIV INF, V32, P772, DOI 10.1111/mice.12288
   Creswell A, 2018, IEEE SIGNAL PROC MAG, V35, P53, DOI 10.1109/MSP.2017.2765202
   Deb K., 2000, Parallel Problem Solving from Nature PPSN VI. 6th International Conference. Proceedings (Lecture Notes in Computer Science Vol.1917), P849
   Deja J, 2010, INT J GREENH GAS CON, V4, P583, DOI 10.1016/j.ijggc.2010.02.002
   Dorogush AV., 2018, ARXIV
   Duan J, 2021, ENG COMPUT-GERMANY, V37, P3329, DOI 10.1007/s00366-020-01003-0
   Fang Y, 2021, J CLEAN PROD, V328, DOI 10.1016/j.jclepro.2021.129657
   Feng JP, 2022, CONSTR BUILD MATER, V360, DOI 10.1016/j.conbuildmat.2022.129497
   Ferdousi S, 2023, COMPOS PART B-ENG, V265, DOI 10.1016/j.compositesb.2023.110958
   Flower DJM, 2007, INT J LIFE CYCLE ASS, V12, P282, DOI 10.1065/lca2007.05.327
   Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504
   Fung V, 2021, NPJ COMPUT MATER, V7, DOI 10.1038/s41524-021-00670-x
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Hastie TJ., 2009, ELEMENTS STAT LEARNI, DOI 10.1007/978-0-387-84858-7
   Kao CY, 2018, ADV CIV ENG, V2018, DOI 10.1155/2018/4398017
   Ke G., 2017, ADV NEURAL INFORM PR
   Khan MI, 2012, AUTOMAT CONSTR, V22, P516, DOI 10.1016/j.autcon.2011.11.011
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   LGED, 2023, Rate Schedule
   Li KF, 2019, CEMENT CONCRETE RES, V115, P545, DOI 10.1016/j.cemconres.2018.08.006
   Liang MF, 2022, CEMENT CONCRETE RES, V152, DOI 10.1016/j.cemconres.2021.106681
   Likhon N., 2023, bangladeshpost
   Lin RS, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10103369
   Miller SA, 2018, J CLEAN PROD, V178, P587, DOI 10.1016/j.jclepro.2018.01.008
   Nguyen KL, 2024, NEURAL COMPUT APPL, V36, P4207, DOI 10.1007/s00521-023-09296-0
   Nguyen KL, 2024, EXPERT SYST APPL, V239, DOI 10.1016/j.eswa.2023.122458
   Nguyen KL, 2023, ADV CIV ENG, V2023, DOI 10.1155/2023/8853122
   Nguyen KL, 2023, EXPERT SYST APPL, V230, DOI 10.1016/j.eswa.2023.120649
   Parichatprecha R, 2009, COMPUT CONCRETE, V6, P253
   Park S, 2021, MATERIALS, V14, DOI 10.3390/ma14061472
   Rathakrishnan V, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-12890-2
   Robati M, 2016, CONSTR BUILD MATER, V128, P422, DOI 10.1016/j.conbuildmat.2016.10.092
   Sanchez-Lengeling B, 2018, SCIENCE, V361, P360, DOI 10.1126/science.aat2663
   Scrivener KL, 2018, CEMENT CONCRETE RES, V114, P2, DOI 10.1016/j.cemconres.2018.03.015
   Shaban WM, 2021, J CLEAN PROD, V327, DOI 10.1016/j.jclepro.2021.129355
   Shi Y, 2019, J CLEAN PROD, V214, P633, DOI 10.1016/j.jclepro.2018.12.318
   Snoek J, 2012, Arxiv, DOI [arXiv:1206.2944, DOI 10.5555/2999325.2999464, 10.48550/ARXIV.1206.2944]
   Song HW, 2021, CONSTR BUILD MATER, V308, DOI 10.1016/j.conbuildmat.2021.125021
   Suman M., 2023, The Daily Star
   Tao HC, 2021, NAT REV MATER, V6, P701, DOI 10.1038/s41578-021-00337-5
   Tran V, 2022, CONSTR BUILD MATER, V323, DOI 10.1016/j.conbuildmat.2022.126578
   Wang HC, 2023, NATURE, V620, P47, DOI 10.1038/s41586-023-06221-2
   Wu CB, 2018, J CLEAN PROD, V172, P466, DOI 10.1016/j.jclepro.2017.10.216
   Yadollahi A, 2016, PROG NUCL ENERG, V89, P69, DOI 10.1016/j.pnucene.2016.02.010
   Yang KH, 2015, J CLEAN PROD, V103, P774, DOI 10.1016/j.jclepro.2014.03.018
   Yeh IC, 1998, J MATER CIVIL ENG, V10, P263, DOI 10.1061/(ASCE)0899-1561(1998)10:4(263)
   Yu J, 2023, COMPOS PART B-ENG, V266, DOI 10.1016/j.compositesb.2023.110993
   Zhang J, 2024, CONSTR BUILD MATER, V411, DOI 10.1016/j.conbuildmat.2023.134738
NR 66
TC 0
Z9 0
U1 3
U2 3
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0950-0618
EI 1879-0526
J9 CONSTR BUILD MATER
JI Constr. Build. Mater.
PD NOV 15
PY 2024
VL 451
AR 138836
DI 10.1016/j.conbuildmat.2024.138836
EA OCT 2024
PG 18
WC Construction & Building Technology; Engineering, Civil; Materials
   Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Construction & Building Technology; Engineering; Materials Science
GA K2Y1A
UT WOS:001342574100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Bartlett, KA
   Camba, JD
AF Bartlett, Kristin A.
   Camba, Jorge D.
TI Generative Artificial Intelligence in Product Design Education:
   Navigating Concerns of Originality and Ethics
SO INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL
   INTELLIGENCE
LA English
DT Article
DE Education; Generative Artificial Intelligence; Industrial Design;
   Product Design; Text-to- Image
AB Image -generative artificial intelligence (AI) is increasingly being used in the product design process. In this paper, we present examples of how it is being used and discuss the possibilities of how applications may evolve in the future. We discuss the legal and ethical implications of image -generative AI, including concerns about bias, hidden labor, theft from artists, lack of originality in the outputs, and lack of copyright protection. We discuss how these concerns apply to design education and provide recommendations to educators about how AI should be addressed in the design classroom. We recommend that educators introduce AI as one tool among many in the designer's toolkit and encourage it to be used as a process tool rather than for generating final design deliverables. We also provide guidance for how educators might engage students in discussions about AI to enhance their learning.
C1 [Bartlett, Kristin A.] Univ Kentucky, Dept Prod Design, Lexington, KY 40506 USA.
   [Camba, Jorge D.] Purdue Univ, Sch Engn Technol, W Lafayette, IN USA.
C3 University of Kentucky; Purdue University System; Purdue University
RP Bartlett, KA (corresponding author), Univ Kentucky, Dept Prod Design, Lexington, KY 40506 USA.
EM kristibartlett@uky.edu
OI Dorribo Camba, Jorge/0000-0001-5384-3253; Bartlett,
   Kristin/0000-0003-3577-8034
CR Adobe, Adobe firefly
   [Anonymous], 2023, HYPERREAL PROTOTYPIN
   [Anonymous], 2022, AI VS PROCAR DESIGNE
   [Anonymous], 2023, DESIGN AI AI MIDJOUR
   [Anonymous], 2023, AI PRODUCT DESIGN TH
   [Anonymous], 2023, USING AI YOUR DESIGN
   [Anonymous], 2021, AI DESIGNED THIS PRO
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Binyang Song, 2022, Journal of Mechanical Design, V144, DOI 10.1115/1.4051871
   Brittain Blake., 2023, Reuters
   Buonamici F., 2020, COMPUT AIDED DESIGN, V18, P144, DOI [10.14733/cadaps.2021.144-155, DOI 10.14733/CADAPS.2021.144-155]
   Cai A, 2023, PROCEEDINGS OF THE ACM COLLECTIVE INTELLIGENCE CONFERENCE, CI 2023, P1, DOI 10.1145/3582269.3615596
   Camburn B, 2020, J MECH DESIGN, V142, DOI 10.1115/1.4045126
   Carroll J, 2009, ASSESSMENT, LEARNING AND JUDGEMENT IN HIGHER EDUCATION, P115, DOI 10.1007/978-1-4020-8905-3_7
   Chen R, 2023, IEEE I CONF COMP VIS, P22189, DOI 10.1109/ICCV51070.2023.02033
   Cho Jaemin, 2022, ARXIV
   Coorey J., 2018, INT J DESIGN ED, V12, P11
   Dzieza Josh., VERGE
   Economou I, 2011, C P 6 INT DFSA C, P79
   Everitt J., 2016, INT J DESIGN ED, V10
   footwearology_lab, INSTAGRAM
   Fournier-Viger P, 2021, COMM COM INF SC, V1525, P158, DOI 10.1007/978-3-030-93733-1_11
   Gao J., 2022, 36 C NEURAL INFORM P
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   Guo T., 2020, APPL BIG DATA ARTIFI, V5
   Gyory JT, 2022, J MECH DESIGN, V144, DOI 10.1115/1.4052488
   Izadpanah S., 2021, IDIL, V10, DOI [10.7816/idil-10-87-01, DOI 10.7816/IDIL-10-87-01]
   Jain A, 2022, PROC CVPR IEEE, P857, DOI 10.1109/CVPR52688.2022.00094
   Jarrah AM, 2023, ONLINE J COMMUN MEDI, V13, DOI 10.30935/ojcmt/13572
   Koch Janin., 2017, AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems. AAAI Spring Symposium, P415
   Lang Jamie., 2022, Cartoon Brew
   Liao WJ, 2024, AUTOMAT CONSTR, V157, DOI 10.1016/j.autcon.2023.105187
   Lin CH, 2023, PROC CVPR IEEE, P300, DOI 10.1109/CVPR52729.2023.00037
   Midjourney, TERMS SERVICE
   Mostafa M., 2011, INT J CONSTRUCTED EN, V1, P85, DOI [10.18848/2154-8587/CGP/v01i03/37482, DOI 10.18848/2154-8587/CGP/V01I03/37482]
   Nicholls, DIGITAL CAMERA WORLD
   OpenAI, DALLE 2 PREVIEW RISK
   OpenAI, Terms of Use.
   Partnerkin, AI STARTUPSINDIANS F
   Poole B., 2022, ARXIV
   Psoma K., MEDIUM
   Rosamond B., 2002, Politics, V22, P167, DOI [DOI 10.1111/1467-9256.00172, 10.1111/1467-9256.00172]
   Saffo P., 1994, J GRAPHIC DESIGN, V12
   Salkowitz Rob., 2016, FORBES
   Sbai O, 2019, LECT NOTES COMPUT SC, V11131, P37, DOI 10.1007/978-3-030-11015-4_5
   Seidel S, 2019, COMMUN ACM, V62, P50, DOI 10.1145/3210753
   Shekar A, 2014, ASEE ANNU CONF EXPO
   Shen Qiuhong, 2023, ARXIV
   Sheth Sarang., 2017, Yanko Design
   Siebel T., 2023, INNOVATION Q IND DES, V42, P34
   Somepalli G, 2023, PROC CVPR IEEE, P6048, DOI 10.1109/CVPR52729.2023.00586
   Stable Diffusion, FREQ ASK QUEST
   Verganti R, 2020, J PROD INNOVAT MANAG, V37, P212, DOI 10.1111/jpim.12523
   Vincent J., The Verge
   Wang D, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3502121
   Xu JL, 2023, PROC CVPR IEEE, P20908, DOI 10.1109/CVPR52729.2023.02003
   Yang L., 2023, arXiv
   Yuan CX, 2020, IEEE ACCESS, V8, P190710, DOI 10.1109/ACCESS.2020.3032280
   Zhang CZ, 2023, 2023 PROCEEDINGS OF THE 15TH CONFERENCE ON CREATIVITY AND COGNITION, C&C 2023, P254, DOI 10.1145/3591196.3596820
   Zhang GL, 2021, DESIGN STUD, V72, DOI 10.1016/j.destud.2021.100990
NR 60
TC 5
Z9 5
U1 76
U2 122
PU UNIV INT RIOJA-UNIR
PI LOGRONO
PA RECTORADO, AVENIDA DE LA PAZ, 137, LOGRONO, 26006, SPAIN
SN 1989-1660
J9 INT J INTERACT MULTI
JI Int. J. Interact. Multimed. Artif. Intell.
PD MAR
PY 2024
VL 8
IS 5
DI 10.9781/ijimai.2024.02.006
EA FEB 2024
PG 83
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KO8Q8
UT WOS:001179667800001
OA gold, Green Submitted
DA 2024-12-25
ER

PT J
AU Alessandro, G
   Dimitri, O
   Cristina, B
   Anna, M
AF Alessandro, Gabbiadini
   Dimitri, Ognibene
   Cristina, Baldissarri
   Anna, Manfredi
TI The emotional impact of generative AI: negative emotions and perception
   of threat
SO BEHAVIOUR & INFORMATION TECHNOLOGY
LA English
DT Article; Early Access
DE Generative artificial intelligence; ChatGPT; Voicify; negative emotions;
   symbolic threat; realistic threat
ID UNCANNY VALLEY; INTERGROUP; PREJUDICE; EXPECTANCIES; ACCEPTANCE; MODEL
AB Generative Artificial Intelligence (AI) is a rapidly expanding field that aims to develop machines capable of performing tasks that were previously considered unique to humans, such as learning, reasoning, problem-solving, and decision-making. The recent release of several tools based on AI (e.g. ChatGPT) has sparked debates on the potential of this technology and garnered widespread attention in the mainstream media.Using a socio-psychological approach, in three studies (total N = 410), we demonstrate that when faced with Generative AI's ability to reproduce the complexity of human cognitive capabilities, participants reported significantly higher negative emotions than those in the control group. In turn, negative emotions elicited by a specific type of AI (e.g. generative AI) were associated to the perception of threat extended to AI technologies as a whole, understood as a threat to various aspects of human life, including jobs, resources, identity, uniqueness, and value.Our findings emphasise the importance of considering emotional and societal impacts when developing and deploying advanced AI technologies and implementing responsible guidelines to minimise adverse effects. As AI technology advances, addressing public concerns and regulating its usage is crucial for the benefit of society. To achieve this goal, collaboration between experts, policymakers, and the public is necessary.
C1 [Alessandro, Gabbiadini; Dimitri, Ognibene; Cristina, Baldissarri; Anna, Manfredi] Univ Milano Bicocca, Mind & Behav Technol Ctr, Dept Psychol, Milan, Italy.
   [Alessandro, Gabbiadini] Univ Milano Bicocca, Dept Psychol, Piazza Ateneo Nuovo 1, I-20126 Milan, Italy.
C3 University of Milano-Bicocca; University of Milano-Bicocca
RP Alessandro, G (corresponding author), Univ Milano Bicocca, Dept Psychol, Piazza Ateneo Nuovo 1, I-20126 Milan, Italy.
EM alessandro.gabbiadini@unimib.it
RI Gabbiadini, Alessandro/AAQ-4195-2021; Ognibene, Dimitri/AFR-2310-2022
OI Gabbiadini, Alessandro/0000-0002-7593-8007; ognibene,
   dimitri/0000-0002-9454-680X
FU European Association of Social Psychology
FX This work was supported by the European Association of Social
   Psychology.
CR [Anonymous], 2023, GUARDIAN
   Branscombe N. R., 1999, SOCIAL IDENTITY CONT, P35, DOI DOI 10.4135/9781446200919.N23
   Brown TB, 2020, ADV NEUR IN, V33
   Burton JW, 2020, J BEHAV DECIS MAKING, V33, P220, DOI 10.1002/bdm.2155
   Cabitza F, 2023, ARTIF INTELL MED, V138, DOI 10.1016/j.artmed.2023.102506
   Cabitza F, 2017, JAMA-J AM MED ASSOC, V318, P517, DOI 10.1001/jama.2017.7797
   Caprariello PA, 2009, GROUP PROCESS INTERG, V12, P147, DOI 10.1177/1368430208101053
   Cath C, 2018, SCI ENG ETHICS, V24, P505, DOI 10.1007/s11948-017-9901-7
   Cha YJ, 2020, COMPUT HUM BEHAV, V103, P80, DOI 10.1016/j.chb.2019.08.027
   Child R, 2019, Arxiv, DOI arXiv:1904.10509
   Cohen J., 1988, STAT POWER ANAL BEHA
   Connor P, 2021, PERS SOC PSYCHOL B, V47, P89, DOI 10.1177/0146167220916640
   Croom AM, 2015, MUSIC SCI, V19, P44, DOI 10.1177/1029864914561709
   Durante F, 2017, J SOC ISSUES, V73, P138, DOI 10.1111/josi.12208
   Durante F, 2013, BRIT J SOC PSYCHOL, V52, P726, DOI 10.1111/bjso.12005
   Esses V.M., 1993, Affect, cognition, and stereotyping: Interactive processes in group perception, P137, DOI DOI 10.1016/B978-0-08-088579-7.50011-9
   Esses VM, 2001, J SOC ISSUES, V57, P389, DOI 10.1111/0022-4537.00220
   Ford B., 2023, IBM to Pause Hiring for Jobs That AI Could Do
   Gaertig C, 2018, PSYCHOL SCI, V29, P504, DOI 10.1177/0956797617739369
   Gamez-Djokic M, 2020, PSYCHOL SCI, V31, P987, DOI 10.1177/0956797620929977
   Gaube S, 2021, NPJ DIGIT MED, V4, DOI 10.1038/s41746-021-00385-9
   GrandViewResearch, 2022, Artificial Intelligence Market Size, Share & Trends Analysis Report By Solution, By Technology (Deep Learning, Machine Learning), By End-use, By Region, And Segment Forecasts, 2023-2030
   Grant N., 2022, NEW YORK TIMES
   Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
   HAMILTON DL, 1990, J SOC ISSUES, V46, P35, DOI 10.1111/j.1540-4560.1990.tb01922.x
   Haring K. S., 2014, International Journal of Affective Engineering, V13, P149, DOI [DOI 10.5057/IJAE.13.149, 10.5057/ijae.13.149]
   Harmon-Jones C, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0159915
   Hayes A. F., 2018, INTRO MEDIATION MODE
   Hodson G, 2007, PSYCHOL SCI, V18, P691, DOI 10.1111/j.1467-9280.2007.01962.x
   Huang HL, 2021, INT J SOC ROBOT, V13, P1599, DOI 10.1007/s12369-021-00752-2
   Huijts NMA, 2018, ENERGY RES SOC SCI, V44, P138, DOI 10.1016/j.erss.2018.04.042
   Jussupow E., 2020, A Comprehensive Literature Review on Algorithm Aversion
   Jussupow E, 2022, JMIR FORM RES, V6, DOI 10.2196/28750
   Kulviwat S, 2007, PSYCHOL MARKET, V24, P1059, DOI 10.1002/mar.20196
   Larsen JT, 2014, SOC PERSONAL PSYCHOL, V8, P263, DOI 10.1111/spc3.12108
   Legato S., 2023, How Will Chatbots Change Education?
   Lim V, 2021, INT J SOC ROBOT, V13, P1307, DOI 10.1007/s12369-020-00710-4
   Logg JM, 2019, ORGAN BEHAV HUM DEC, V151, P90, DOI 10.1016/j.obhdp.2018.12.005
   Maddux WW, 2008, PERS SOC PSYCHOL B, V34, P74, DOI 10.1177/0146167207309195
   Mahmud H, 2022, TECHNOL FORECAST SOC, V175, DOI 10.1016/j.techfore.2021.121390
   Mandel DR, 2010, REV INT PSYCHOL SOC, V23, P5
   Matthews M, 2012, MOTIV EMOTION, V36, P564, DOI 10.1007/s11031-012-9280-y
   McKee KR, 2023, ISCIENCE, V26, DOI 10.1016/j.isci.2023.107256
   Meyer J., 2023, Computers and Education: Artificial Intelligence, V6, P1, DOI [10.1016/j.caeai.2023.100199, DOI 10.1016/J.CAEAI.2023.100199]
   Miller SL, 2010, J PERS SOC PSYCHOL, V99, P62, DOI 10.1037/a0018086
   Mirbabaie M, 2022, ELECTRON MARK, V32, P73, DOI 10.1007/s12525-021-00496-x
   Morewedge CK, 2022, TRENDS COGN SCI, V26, P824, DOI 10.1016/j.tics.2022.07.007
   Mori M., 1970, Energy, V7, P33, DOI DOI 10.1109/MRA.2012.2192811
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Ngo T. T. A., 2023, Int. J. Emerg. Technol. Learn., V18, P4, DOI [DOI 10.3991/IJET.V18I17.39019, https://doi.org/10.3991/ijet.v18i17.39019, 10.3991/ijet.v18i14.39903, DOI 10.3991/IJET.V18I14.39903]
   Nolan B., 2023, These are the 3 Biggest Fears about AI - and here's how Worried you should be about them
   Olajide O, 2022, INT J ORGAN ANAL, V30, P1771, DOI 10.1108/IJOA-07-2020-2291
   OpenAI, 2023, ChatGPT: Optimizing Language Models for Dialogue
   Ploug T, 2020, ARTIF INTELL MED, V107, DOI 10.1016/j.artmed.2020.101901
   Ransan-Cooper H, 2020, ENERGY RES SOC SCI, V70, DOI 10.1016/j.erss.2020.101656
   Riek BM, 2006, PERS SOC PSYCHOL REV, V10, P336, DOI 10.1207/s15327957pspr1004_4
   Rosenthal-von der Pütten AM, 2014, COMPUT HUM BEHAV, V36, P422, DOI 10.1016/j.chb.2014.03.066
   Rozin P., 2008, DISGUST
   Russell S. J., 1994, Artificial intelligence: A modern approach, V3rd
   Schepman A, 2020, COMPUT HUM BEHAV REP, V1, DOI 10.1016/j.chbr.2020.100014
   Scherer K., 2001, Appraisal processes in emotion: Theory, methods, research, V92, P120, DOI DOI 10.1093/OSO/9780195130072.003.0005
   Schmid K, 2014, PSYCHOL SCI, V25, P665, DOI 10.1177/0956797613508956
   Schoemann AM, 2017, SOC PSYCHOL PERS SCI, V8, P379, DOI 10.1177/1948550617715068
   Sherif M., 1961, Intergroup Conflict and Cooperation: The Robbers Cave Experiment, P155
   Smith E.R., 2008, HDB EMOTIONS, V3rd, P428
   Smith ER, 2015, EMOT REV, V7, P349, DOI 10.1177/1754073915590614
   Spears R, 2002, SELF AND MOTIVATION: EMERGING PSYCHOLOGICAL PERSPECTIVES, P147, DOI 10.1037/10448-006
   Stephan W. G., 2008, IMPROVING INTERGROUP, P55, DOI DOI 10.1002/9781444303117.CH5
   Stephan WG, 2017, CAMBRIDGE HANDBOOK OF THE PSYCHOLOGY OF PREJUDICE, P131
   STEPHAN WG, 1985, J SOC ISSUES, V41, P157, DOI 10.1111/j.1540-4560.1985.tb01134.x
   Stephan WG, 1999, J APPL SOC PSYCHOL, V29, P2221, DOI 10.1111/j.1559-1816.1999.tb00107.x
   TAJFEL H, 1974, SOC SCI INFORM, V13, P65, DOI 10.1177/053901847401300204
   Theophilou E., 2023, INT C ITALIAN ASS AR, V14318, P481, DOI 10.1007/978-3-031-47546-733
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang SS, 2015, REV GEN PSYCHOL, V19, P393, DOI 10.1037/gpr0000056
   Weidinger L, 2021, Arxiv, DOI arXiv:2112.04359
   Wells GL, 1999, PERS SOC PSYCHOL B, V25, P1115, DOI 10.1177/01461672992512005
   Winder D., 2023, Does ChatGPT Pose A Cybersecurity Threat? Here's The AI Bot's Answer
   Ybarra O, 1999, J PERS SOC PSYCHOL, V77, P698, DOI 10.1037/0022-3514.77.4.698
   Yogeeswaran K, 2016, J HUM-ROBOT INTERACT, V5, P29, DOI 10.5898/JHRI.5.2.Yogeeswaran
   Yokoi R, 2021, INT J HUM-COMPUT INT, V37, P981, DOI 10.1080/10447318.2020.1861763
   Yzerbyt V, 2018, J PERS SOC PSYCHOL, V115, P929, DOI 10.1037/pspa0000132
   Zhou YW, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.866124
   Zlotowski JA, 2015, FRONT PSYCHOL, V6, DOI 10.3389/fpsyg.2015.00883
NR 85
TC 3
Z9 3
U1 78
U2 121
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0144-929X
EI 1362-3001
J9 BEHAV INFORM TECHNOL
JI Behav. Inf. Technol.
PD 2024 MAR 26
PY 2024
DI 10.1080/0144929X.2024.2333933
EA MAR 2024
PG 18
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA MA8W2
UT WOS:001191004400001
DA 2024-12-25
ER

PT J
AU Malloy, T
   Gonzalez, C
AF Malloy, Tyler
   Gonzalez, Cleotilde
TI Applying Generative Artificial Intelligence to cognitive models of
   decision making
SO FRONTIERS IN PSYCHOLOGY
LA English
DT Article
DE cognitive modeling; decision making; generative AI; instance based
   learning; natural language; visual learning
AB Introduction Generative Artificial Intelligence has made significant impacts in many fields, including computational cognitive modeling of decision making, although these applications have not yet been theoretically related to each other. This work introduces a categorization of applications of Generative Artificial Intelligence to cognitive models of decision making.Methods This categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate our proposed model in three experimental paradigms. These experiments used for model comparison involve modeling human learning and decision making based on both visual information and natural language, in tasks that vary in realism and complexity. This comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications.Results The best performing model from the ablation we performed used a generative model to both create memory representations as well as predict participant actions. The results of this comparison demonstrates the importance of generative models in both forming memories and predicting actions in decision-modeling research.Discussion In this work, we present a model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods. These results can provide guidelines for cognitive modelers and decision making researchers interested in integrating Generative AI into their methods.
C1 [Malloy, Tyler; Gonzalez, Cleotilde] Carnegie Mellon Univ, Dietrich Coll, Dept Social & Decis Sci, Dynam Decis Making Lab, Pittsburgh, PA 15213 USA.
C3 Carnegie Mellon University
RP Malloy, T (corresponding author), Carnegie Mellon Univ, Dietrich Coll, Dept Social & Decis Sci, Dynam Decis Making Lab, Pittsburgh, PA 15213 USA.
EM tylerjmalloy@cmu.edu
FU Army Research Office10.13039/100000183
FX No Statement Available
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Aggarwal P, 2022, COMPUT SECUR, V117, DOI 10.1016/j.cose.2022.102671
   Aher G, 2023, PR MACH LEARN RES, V202, P337
   Anderson J. R., 2014, ATOMIC COMPONENTS TH, DOI DOI 10.4324/9781315805696
   Anderson JR, 1997, HUM-COMPUT INTERACT, V12, P439, DOI 10.1207/s15327051hci1204_5
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bates C., 2019, CogSci, V43, P1369
   Bates CJ, 2020, PSYCHOL REV, V127, P891, DOI 10.1037/rev0000197
   Begus G, 2020, FRONT ARTIF INTELL, V3, DOI 10.3389/frai.2020.00044
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bhui R, 2021, CURR OPIN BEHAV SCI, V41, P15, DOI 10.1016/j.cobeha.2021.02.015
   Bommasani R., 2021, arXiv
   Brohan A., 2023, C ROBOT LEARNING
   Brown TB, 2020, ADV NEUR IN, V33
   Bugbee E. H., 2022, Proceedings of the Annual Meeting of the Cognitive Science Society, P1
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Chen L., 2021, Adv. Neural Inf. Process. Syst ., V34
   Chevalier-Boisvert Maxime, 2018, Minimalistic gridworld environment for openai gym
   Choi D., 2007, Proceedings of AIIDE, P71, DOI 10.1609/aiide.v3i1.18787
   Chowdhery A, 2023, J MACH LEARN RES, V24
   Cranford E. A., 2021, Proceedings of the 19th Annual Meeting of the International Conference on Cognitive Modeling, P44
   Cranford EA, 2020, TOP COGN SCI, V12, P992, DOI 10.1111/tops.12513
   Cranford Edward A., 2019, Proceedings of the 17th ICCM
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Friston KJ, 2020, NEUROSCI BIOBEHAV R, V118, P42, DOI 10.1016/j.neubiorev.2020.07.005
   Gershman SJ, 2019, FRONT ARTIF INTELL, V2, DOI 10.3389/frai.2019.00018
   Goetschalckx L, 2021, TRENDS COGN SCI, V25, P788, DOI 10.1016/j.tics.2021.06.006
   Gonzalez C, 2003, COGNITIVE SCI, V27, P591, DOI 10.1016/S0364-0213(03)00031-4
   Gonzalez C, 2024, PERSPECT PSYCHOL SCI, V19, P860, DOI 10.1177/17456916231196766
   Gonzalez C, 2013, PROG BRAIN RES, V202, P73, DOI 10.1016/B978-0-444-62604-2.00005-8
   Gonzalez C, 2011, PSYCHOL REV, V118, P523, DOI 10.1037/a0024558
   Griffith S., 2013, Syst, V26, P1, DOI [10.5555/2999792.2999905, DOI 10.5555/2999792.2999905]
   Hedayati S, 2022, NAT HUM BEHAV, V6, P709, DOI 10.1038/s41562-021-01264-9
   Higgins I., 2017, BETA VAE LEARNING BA, P1
   Higgins I, 2017, PR MACH LEARN RES, V70
   Higgins I, 2021, NAT COMMUN, V12, DOI 10.1038/s41467-021-26751-5
   HINTZMAN DL, 1984, BEHAV RES METH INS C, V16, P96, DOI 10.3758/BF03202365
   Hong J, 2012, COMMUN ACM, V55, P74, DOI 10.1145/2063176.2063197
   Huet A, 2021, COMPUT IND, V125, DOI 10.1016/j.compind.2020.103377
   Ivanovic B, 2018, IEEE INT C INT ROBOT, P3088, DOI 10.1109/IROS.2018.8594393
   Kim S, 2019, Arxiv, DOI [arXiv:1811.02155, DOI 10.48550/ARXIV.1811.02155]
   Kirk J. R., 2023, arXiv
   Kirsch L., 2023, NEURIPS 2023 WORKSHO
   Lai L, 2021, PSYCHOL LEARN MOTIV, V74, P195, DOI 10.1016/bs.plm.2021.02.004
   Laird J. E., 2001, Proceedings of the Fifth International Conference on Autonomous Agents, P385, DOI 10.1145/375735.376343
   LAIRD JE, 1987, ARTIF INTELL, V33, P1, DOI 10.1016/0004-3702(87)90050-6
   Laird JE, 2017, AI MAG, V38, P13, DOI 10.1609/aimag.v38i4.2744
   Lejarraga T, 2012, J BEHAV DECIS MAKING, V25, P143, DOI 10.1002/bdm.722
   Li BH, 2020, Arxiv, DOI arXiv:2011.05864
   Li S., 2022, Advances in Neural Information Processing Systems, V35, P31199
   Malloy T., 2022, Reinforcement Learning and Decision Making RLDM
   Malloy T., 2022, NEURIPS 2022 WORKSH
   Malloy T., 2022, J. Vis, V22, P3747, DOI [10.1167/jov.22.14.3747, DOI 10.1167/JOV.22.14.3747]
   Malloy T., 2023, Proceedings of the 2023 AAAI Fall Symposium on Integrating Cognitive Architectures and Generative Models, P1
   McDonald C., 2023, P 2023 AAAI FALL S I, P1
   Mitsopoulos K., 2023, Proceedings of the 2023 AAAI Fall Symposium on Integrating Cognitive Architectures and Generative Models
   Mitsopoulos K, 2023, Arxiv, DOI arXiv:2312.03301
   Morrison D., 2024, PyIBL 5.1.1 Manual
   Navigli R, 2023, ACM J DATA INF QUAL, V15, DOI 10.1145/3597307
   Nguyen TN, 2023, BEHAV RES METHODS, V55, P1734, DOI 10.3758/s13428-022-01848-x
   Nguyen TN, 2022, TOP COGN SCI, V14, P665, DOI 10.1111/tops.12553
   Niv Y, 2015, J NEUROSCI, V35, P8145, DOI 10.1523/JNEUROSCI.2978-14.2015
   Ororbia A., 2023, Proceedings of the 2023 AAAI Fall Symposium on Integrating Cognitive Architectures and Generative Models, P1
   Ororbia A, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-29632-7
   Park JS, 2023, P 36 ANN ACM S US IN
   Radford A., 2018, IMPROVING LANGUAGE U
   Rao RPN, 1999, NAT NEUROSCI, V2, P79, DOI 10.1038/4580
   Reid M., 2022, PREPRINT
   Ren H, 2020, ROBOT AUTON SYST, V124, DOI 10.1016/j.robot.2019.103386
   Shi RZ, 2023, Arxiv, DOI arXiv:2310.20587
   Singh K., 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, P431
   Singh K., 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting
   Singh K, 2023, COMPUT SECUR, V127, DOI 10.1016/j.cose.2023.103105
   Speer R, 2017, AAAI CONF ARTIF INTE, P4444
   Sun R., 2006, Cognition and multi-agent interaction, P79, DOI [DOI 10.1017/CB09780511610721.005, DOI 10.1017/CBO9780511610721.005]
   Swan G, 2014, ATTEN PERCEPT PSYCHO, V76, P2136, DOI 10.3758/s13414-014-0633-3
   Taniguchi T, 2022, NEURAL NETWORKS, V150, P293, DOI 10.1016/j.neunet.2022.02.026
   Taylor ME, 2009, J MACH LEARN RES, V10, P1633
   Wu TH, 2023, Arxiv, DOI arXiv:2310.00212
   Xu Tianhao., 2022, HICSS, P1
   Ziems C, 2024, Arxiv, DOI arXiv:2305.03514
NR 81
TC 2
Z9 2
U1 35
U2 43
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 1664-1078
J9 FRONT PSYCHOL
JI Front. Psychol.
PD MAY 3
PY 2024
VL 15
AR 1387948
DI 10.3389/fpsyg.2024.1387948
PG 16
WC Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA RA1U5
UT WOS:001224869200001
PM 38765837
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Ferrati, F
   Kim, PH
   Muffatto, M
AF Ferrati, Francesco
   Kim, Phillip H.
   Muffatto, Moreno
TI Generative AI in Entrepreneurship Research: Principles and Practical
   Guidance for Intelligence Augmentation
SO FOUNDATIONS AND TRENDS IN ENTREPRENEURSHIP
LA English
DT Article
DE Generative AI; ChatGPT; GPT; artificial intelligence; intelligence
   augmentation; large language models; prompt engineering; research
   process; entrepreneurship.
ID ARTIFICIAL-INTELLIGENCE; TRUST
AB This monograph investigates the integration of generative artificial intelligence (AI) into the academic research process of entrepreneurship. Specifically, we explore using Large Language Models (LLMs) like ChatGPT in several research scenarios to support novice and established researchers. As a practical guide, we introduce researchers to prompt engineering - formulating instructions for the LLMs to generate a desired output. We classify different types of prompts, present various technical strategies, and suggest the design of an effective prompt formula. We illustrate the prompt engineering process with different examples for entrepreneurship research.
C1 [Ferrati, Francesco; Muffatto, Moreno] Univ Padua, Sch Entrepreneurship SCENT, Dept Ind Engn, Padua, Italy.
   [Kim, Phillip H.] Babson Coll, Arthur M Blank Ctr Entrepreneurship, Wellesley, MA USA.
C3 University of Padua; Babson College
RP Ferrati, F (corresponding author), Univ Padua, Sch Entrepreneurship SCENT, Dept Ind Engn, Padua, Italy.
EM francesco.ferrati@unipd.it; pkim1@babson.edu; moreno.muffatto@unipd.it
RI Ferrati, Francesco/KXR-7921-2024
CR Aarts AA, 2015, SCIENCE, V349, DOI 10.1126/science.aac4716
   AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Aldrich HE, 2007, STRATEG ENTREP J, V1, P147, DOI 10.1002/sej.8
   Anderson N, 2023, BMJ OPEN SPORT EXERC, V9, DOI 10.1136/bmjsem-2023-001568
   [Anonymous], 2021, ERA Forum, V22, P147, DOI DOI 10.1007/S12027-020-00648-0
   Ayinde L., 2023, BUS INFORM REV, V40, P137, DOI [https://doi.org/10.1177/02663821231187991, DOI 10.1177/02663821231187991]
   Baker T, 2018, FOUND TRENDS ENTREP, V14, P357, DOI 10.1561/0300000078
   Basta C, 2019, Arxiv, DOI arXiv:1904.08783
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Biswas SS, 2023, ANN BIOMED ENG, V51, P1126, DOI 10.1007/s10439-023-03171-8
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bojanowski Piotr, 2017, Trans. Assoc. Comput. Linguistics, V5, P135, DOI [DOI 10.1162/TACLA00051, 10.1162/tacla00051, DOI 10.1162/TACL_A_00051]
   Bommasani R., 2021, arXiv
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   Bradshaw, 2022, IS BECOMING PROMPT E
   Brants T., 2007, P 2007 JOINT C EMP M, VVolume 1, P858
   Buchanan J., 2024, American Economist, V69, P80, DOI DOI 10.1177/05694345231218454
   Burtsev M., 2023, MIT Sloan Management Review
   Chang YP, 2023, Arxiv, DOI arXiv:2307.03109
   Cho KYHY, 2014, Arxiv, DOI arXiv:1406.1078
   Chowdhary K., 2020, Fundamentals of artificial intelligence, P603, DOI [DOI 10.1007/978-81-322-3972-719, DOI 10.1007/978-81-322-3972-7_19]
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Croidieu G, 2018, ADMIN SCI QUART, V63, P1, DOI 10.1177/0001839216686531
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Dale R, 2021, NAT LANG ENG, V27, P113, DOI 10.1017/S1351324920000601
   Davidsson P., 2023, J BUS VENTURING INSI, V20, P1, DOI 10.1016/j.jbvi.2023.e00413
   Dellermann D, 2019, BUS INFORM SYST ENG+, V61, P637, DOI 10.1007/s12599-019-00595-2
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Du YZ, 2021, J BUS RES, V124, P272, DOI 10.1016/j.jbusres.2020.11.059
   Duong CD, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100883
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ferrati F, 2021, FOUND TRENDS ENTREP, V17, P232, DOI 10.1561/0300000099
   Ganjavi C., 2024, Bmj, P384
   Grant AM, 2011, ACAD MANAGE J, V54, P873, DOI 10.5465/amj.2011.4000
   Grimes M, 2023, ACAD MANAGE J, V66, P1617, DOI 10.5465/amj.2023.4006
   Grudin Jonathan, 2022, From Tool to Partner: The Evolution of HumanComputer Interaction
   Guo C, 2023, IEEE-CAA J AUTOMATIC, V10, P835, DOI 10.1109/JAS.2023.123555
   Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
   Hain D. S., 2020, HDB QUANTITATIVE RES, P176
   Hansson SO, 2007, STUD HIST PHILOS SCI, V38, P523, DOI 10.1016/j.shpsa.2007.06.003
   Harris ZS, 1954, WORD, V10, P146, DOI 10.1080/00437956.1954.11659520
   Henderson P, 2020, J MACH LEARN RES, V21
   Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Hutchinson B., 2020, P 58 ANN M ASS
   Johnson M, 2022, INT J INFORM MANAGE, V64, DOI 10.1016/j.ijinfomgt.2022.102479
   Kim PH, 2016, ACAD MANAGE PERSPECT, V30, P273, DOI 10.5465/amp.2015.0137
   Kim PH, 2014, ORGAN STUD, V35, P359, DOI 10.1177/0170840613499566
   Kim PH, 2005, FOUND TRENDS ENTREP, V1, P55, DOI 10.1561/0300000002
   Kojima T, 2022, ADV NEUR IN
   Korinek A, 2023, J ECON LIT, V61, P1281, DOI 10.1257/jel.20231736
   Korneeva E, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122467
   Kosinski M, 2024, Arxiv, DOI arXiv:2302.02083
   Kotha R, 2018, ACAD MANAGE J, V61, P1307, DOI 10.5465/amj.2015.1233
   Lakatos Imre., 1970, PSA Proceedings, P91
   Leippold M, 2023, FINANC RES LETT, V55, DOI 10.1016/j.frl.2023.103957
   Lévesque M, 2022, ENTREP THEORY PRACT, V46, P803, DOI 10.1177/1042258720927369
   Liga D, 2023, COMPUT LAW SECUR REV, V51, DOI 10.1016/j.clsr.2023.105864
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lindebaum D, 2024, BRIT J MANAGE, V35, P566, DOI 10.1111/1467-8551.12781
   Locke K, 1997, ACAD MANAGE J, V40, P1023, DOI 10.5465/256926
   Lui A, 2018, INF COMMUN TECHNOL L, V27, P267, DOI 10.1080/13600834.2018.1488659
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Luo RQ, 2022, BRIEF BIOINFORM, V23, DOI 10.1093/bib/bbac409
   Mahesh B, 2018, International Journal of Computer Science and Information Technologies, P9, DOI DOI 10.21275/ART20203995
   Mikolov T, ADV NEURAL INFORM PR, P3111, DOI DOI 10.48550/ARXIV.1310.4546
   Mikolov T, 2013, Arxiv, DOI [arXiv:1301.3781, DOI 10.48550/ARXIV.1301.3781]
   Mollick Ethan R., 2024, COINTELLIGENCE LIVIN
   Moore R. C., 2010, P ACL 2010 C SHORT P, P220
   Munafò MR, 2017, NAT HUM BEHAV, V1, DOI 10.1038/s41562-016-0021
   Naveed H, 2024, Arxiv, DOI [arXiv:2307.06435, 10.48550/arXiv.2307.06435, DOI 10.48550/ARXIV.2307.06435]
   Niszczota P, 2023, FINANC RES LETT, V58, DOI 10.1016/j.frl.2023.104333
   Nosek BA, 2022, ANNU REV PSYCHOL, V73, P719, DOI 10.1146/annurev-psych-020821-114157
   Obschonka M, 2020, SMALL BUS ECON, V55, P529, DOI 10.1007/s11187-019-00202-4
   Ostheimer J, 2021, TECHNOL SOC, V66, DOI 10.1016/j.techsoc.2021.101647
   Pennington J., 2014, GLOVE GLOBAL VECTORS, P1532, DOI [DOI 10.3115/V1/D14-1162, 10.3115/V1/D14-1162]
   Puranam P, 2021, J ORGAN DES, V10, P75, DOI 10.1007/s41469-021-00095-2
   Qiu XP, 2020, SCI CHINA TECHNOL SC, V63, P1872, DOI 10.1007/s11431-020-1647-3
   RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626
   Radford A., 2018, Technical Reports
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   Robertson S, 2004, J DOC, V60, P503, DOI 10.1108/00220410410560582
   Robledo S, 2023, J SMALL BUS MANAGE, V61, P1314, DOI 10.1080/00472778.2021.1955125
   Romera-Paredes B, 2024, NATURE, V625, DOI 10.1038/s41586-023-06924-6
   Romero-Brufau S, 2020, BMC MED INFORM DECIS, V20, DOI 10.1186/s12911-020-01158-2
   Rudolph J urgen, 2023, Journal of Applied Learning and Teaching, V6
   Schonberger D., 2018, Deep Copyright: Upand Downstream Questions Related to Artificial Intelligence (AI) and Machine Learning (ML)
   Schwab A, 2019, ENTREP THEORY PRACT, V43, P843, DOI 10.1177/1042258718760841
   Shepherd DA, 2022, J BUS VENTURING, V37, DOI 10.1016/j.jbusvent.2022.106227
   Shinn N., 2023, 37 C NEUR INF PROC S
   Short C. E., 2023, Journal of Business Venturing Insights, V19
   SKAGESTAD P, 1993, J SOC EVOL SYST, V16, P157, DOI 10.1016/1061-7361(93)90026-N
   Strubell E, 2019, Arxiv, DOI arXiv:1906.02243
   Tan YC, 2019, ADV NEUR IN, V32
   Teubner T, 2023, BUS INFORM SYST ENG+, V65, P95, DOI 10.1007/s12599-023-00795-x
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Bansal P, 2011, ACAD MANAGE J, V54, P233, DOI 10.5465/AMJ.2011.60262792
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   van der Aalst WMP, 2021, IJISPM-INT J INF SYS, V9, P5, DOI 10.12821/ijispm090201
   Vaswani A, 2017, ADV NEUR IN, V30
   Vecchiarini M, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100879
   Vedula S, 2019, IND CORP CHANGE, V28, P827, DOI 10.1093/icc/dtz032
   Vincent VU, 2021, BUS HORIZONS, V64, P425, DOI 10.1016/j.bushor.2021.02.008
   Vinsel L, 2023, MIT SLOAN MANAGE REV, V64, P8
   Wei JS, 2022, ADV NEUR IN
   Winkler C., 2023, Entrepreneurship Education and Pedagogy, V6, P579
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
NR 109
TC 0
Z9 0
U1 29
U2 29
PU NOW PUBLISHERS INC
PI HANOVER
PA PO BOX 1024, HANOVER, MA 02339, UNITED STATES
SN 1551-3114
EI 1551-3122
J9 FOUND TRENDS ENTREP
JI Found. Trends Entrep.
PY 2024
VL 20
IS 3
BP 245
EP 383
DI 10.1561/0300000121
PG 139
WC Business
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA XX9A8
UT WOS:001265083800001
DA 2024-12-25
ER

PT S
AU Siroya, H
   Dadhich, M
   Siroya, K
AF Siroya, Heena
   Dadhich, Manish
   Siroya, Kirti
BA Doshi, R
   Dadhich, M
   Poddar, S
   Hiran, KK
BF Doshi, R
   Dadhich, M
   Poddar, S
   Hiran, KK
TI Role of Generative Artificial Intelligence (AI) in Accountability,
   Transparency, and Governance in Higher Education: A Comprehensive Study
SO INTEGRATING GENERATIVE AI IN EDUCATION TO ACHIEVE SUSTAINABLE
   DEVELOPMENT GOALS
SE Advances in Educational Technologies and Instructional Design Book
   Series
LA English
DT Article; Book Chapter
AB This study investigates the impact of Generative Artificial Intelligence (AI) on accountability, transparency, and governance within higher education institutions. Employing a quantitative technique, a structured questionnaire was administered to a convenient sample of 190 academicians, collected via Google Forms from October to December 2023. Utilizing data filtering techniques, hypotheses testing was conducted using Smart-PLS, with both dependent and independent variables identified and analyzed. The findings reveal significant positive relationships between Accountability, Transparency, Governance, and Generative AI, supporting the study's hypotheses. The study underscores the transformative potential of AI integration in higher education, offering policy implications for technologists to implement responsible AI practices and maximize its benefits in governance, transparency, and accountability realms.
C1 [Siroya, Heena] Sir Padampat Singhania Univ, Sch Management, Udaipur, Rajasthan, India.
   [Dadhich, Manish] Sir Padampat Singhania Univ, Udaipur, Rajasthan, India.
   [Siroya, Kirti] All India Inst Med Sci, Johdpur, India.
C3 Sir Padampat Singhania University; Sir Padampat Singhania University
RP Siroya, H (corresponding author), Sir Padampat Singhania Univ, Sch Management, Udaipur, Rajasthan, India.
CR Alfredo R., 2024, Comput. Educ. Artif. Intell, V6, P1, DOI [10.1016/j.caeai.2024.100215, DOI 10.1016/J.CAEAI.2024.100215]
   Bhati S., 2023, 2023 INT C EM TRENDS, P157, DOI [10.1109/ETNCC59188.2023.10284979, DOI 10.1109/ETNCC59188.2023.10284979]
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Danish M.S.S., 2023, Circular Economy, V2, DOI [10.1016/j.cec.2023.100040, DOI 10.1016/J.CEC.2023.100040]
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Heyder T, 2023, J STRATEGIC INF SYST, V32, DOI 10.1016/j.jsis.2023.101772
   Hussain K, 2024, DIGIT BUS, V4, DOI 10.1016/j.digbus.2023.100071
   Krepl V, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e21422
   Memarian B., 2023, Computers and Education: Artificial Intelligence., DOI DOI 10.1016/J.CAEAI.2023.100152
   Perisic A, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e16836
   Sarker M.F., 2023, Soc. Sci. Humanit. Open, V8, P100740, DOI [10.1016/j.ssaho.2023.100740, DOI 10.1016/J.SSAHO.2023.100740]
   Sperling K, 2024, COMPUT EDUC OPEN, V6, DOI 10.1016/j.caeo.2024.100169
   Wu WJ, 2020, ENGINEERING-PRC, V6, P302, DOI 10.1016/j.eng.2019.12.015
   Yang, 2022, COMPUTERS ED ARTIFIC, V3, DOI DOI 10.1016/J.CAEAI.2022.100061
   Yang S.J., 2021, Comput. Educ.: Artif. Intell., V2, DOI [DOI 10.1016/J.CAEAI.2021.100008, 10.1016/j.caeai.2021.100008]
NR 17
TC 0
Z9 0
U1 2
U2 2
PU IGI GLOBAL
PI HERSEY
PA 701 E CHOCOLATE AVE, STE 200, HERSEY, PA 17033-1240 USA
SN 2326-8905
EI 2326-8913
BN 979-8-36934-691-4; 979-8-36932-441-7; 979-8-36932-440-0
J9 ADV EDUC TECHNOL INS
PY 2024
BP 313
EP 321
DI 10.4018/979-8-3693-2440-0.ch017
D2 10.4018/979-8-3693-2440-0
PG 9
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Book Citation Index – Science (BKCI-S)
SC Computer Science; Education & Educational Research
GA BX2XE
UT WOS:001271037500019
DA 2024-12-25
ER

PT J
AU Miao, J
   Thongprayoon, C
   Craici, IM
   Cheungpasitporn, W
AF Miao, Jing
   Thongprayoon, Charat
   Craici, Iasmina M.
   Cheungpasitporn, Wisit
TI How to incorporate generative artificial intelligence in nephrology
   fellowship education
SO JOURNAL OF NEPHROLOGY
LA English
DT Article; Early Access
DE Artificial intelligence; ChatGPT; Nephrology; Fellowship education;
   Personalized learning
AB Traditional nephrology education faces challenges due to expanding medical knowledge, case complexity, and personalized learning needs. Generative artificial intelligence (AI), like ChatGPT, offers potential solutions to enhance nephrology education through dynamic, adaptive, and personalized learning experiences. We discuss integrating generative AI into nephrology education at our institution, highlighting its importance and potential applications. It explores how AI can complement traditional teaching methods by addressing challenges like information overload, diverse learning needs, and continuous learning. Generative AI models should be actively utilized under human supervision to ensure accuracy when summarizing key teaching points, creating discussion topics for journal clubs, and aiding in curriculum development for our Nephrology fellowship. Potential future applications include simulation-based learning, interactive learning modules, personalized learning plans, and enhanced research capabilities. AI can also facilitate mentorship, improve assessment, and support administrative tasks. The integration of AI addresses challenges such as keeping pace with knowledge expansion, providing personalized learning experiences, and improving access to expertise. In summary, the integration of generative AI into nephrology education represents a paradigm shift in preparing future kidney specialists. While AI offers numerous benefits, challenges such as data privacy and maintaining the human element in patient care must be addressed. A balanced approach that preserves human mentorship while employing AI's capabilities is crucial for cultivating well-rounded, competent, and compassionate nephrologists ready to tackle future kidney health challenges.
C1 [Miao, Jing; Thongprayoon, Charat; Craici, Iasmina M.; Cheungpasitporn, Wisit] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Rochester, MN 55905 USA.
C3 Mayo Clinic
RP Cheungpasitporn, W (corresponding author), Mayo Clin, Dept Med, Div Nephrol & Hypertens, Rochester, MN 55905 USA.
EM Cheungpasitporn.Wisit@mayo.edu
CR Aiumtrakul N, 2023, J PERS MED, V13, DOI 10.3390/jpm13101457
   anthropic, Claude 3.5 Sonnet
   Baum MA, 2023, KIDNEY INT, V103, P207, DOI 10.1016/j.kint.2022.07.025
   Cai H, 2019, KIDNEY DIS-BASEL, V5, P204, DOI 10.1159/000502976
   Fatima A, 2024, MEDICINE, V103, DOI 10.1097/MD.0000000000039250
   Gondode P, 2024, INDIAN J ANAESTH, V68, P664, DOI 10.4103/ija.ija_196_24
   Greenberg KI, 2022, ADV CHRONIC KIDNEY D, V29, P510, DOI 10.1053/j.ackd.2022.07.006
   Miao J, 2024, J NEPHROL, DOI 10.1007/s40620-024-01974-z
   Miao J, 2024, J CLIN HYPERTENS, V26, P588, DOI 10.1111/jch.14822
   Miao J, 2024, KIDNEY360, V5, P765, DOI 10.34067/KID.0000000000000430
   Miao J, 2023, J PERS MED, V13, DOI 10.3390/jpm13121681
   Moore CA, 2022, CLIN J AM SOC NEPHRO, V17, P1487, DOI 10.2215/CJN.01850222
   /openai, 2023, Introducing ChatGPT
   Quttainah M, 2024, JMIR AI, V3, DOI 10.2196/51834
   Raff AC, 2021, CURR OPIN NEPHROL HY, V30, P215, DOI 10.1097/MNH.0000000000000676
   Sheikh MS, 2024, BLOOD PURIFICAT, V53, P725, DOI 10.1159/000539065
   Sheridan AM, 2024, CLIN J AM SOC NEPHRO, V19, P554, DOI 10.2215/CJN.0000000000000462
   Touyz RM, 2024, NEW ENGL J MED, V390, P1998, DOI 10.1056/NEJMra1510603
   Valencia OAG, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-59237-7
   Valencia OAG, 2023, J PERS MED, V13, DOI 10.3390/jpm13091363
   Waheed S, 2022, ADV CHRONIC KIDNEY D, V29, P526, DOI 10.1053/j.ackd.2022.06.006
   Wong K, 2024, CUREUS J MED SCIENCE, V16, DOI 10.7759/cureus.61438
   Wu CH, 2024, MED TEACH, DOI 10.1080/0142159X.2024.2339412
NR 23
TC 0
Z9 0
U1 1
U2 1
PU SPRINGER HEIDELBERG
PI HEIDELBERG
PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
SN 1121-8428
EI 1724-6059
J9 J NEPHROL
JI J. Nephrol.
PD 2024 DEC 2
PY 2024
DI 10.1007/s40620-024-02165-6
EA DEC 2024
PG 7
WC Urology & Nephrology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Urology & Nephrology
GA P3E5S
UT WOS:001376786100001
PM 39621255
DA 2024-12-25
ER

PT J
AU Chaccour, C
   Karapantelakis, A
   Murphy, T
   Dohler, M
AF Chaccour, Christina
   Karapantelakis, Athanasios
   Murphy, Timothy
   Dohler, Mischa
TI Telecom's Artificial General Intelligence (AGI) Vision: Beyond the GenAI
   Frontier
SO IEEE NETWORK
LA English
DT Article
DE Artificial intelligence; Transformers; Telecommunications; Decoding;
   Artificial general intelligence; Task analysis; Probability
   distribution; artificial intelligence (AI); machine learning (ML);
   causality; semantic communications; explainability; generative AI
   (GenAI); artificial general intelligence (AGI)
AB This paper unveils the groundbreaking impact of Generative AI (GenAI) as the dawn of a transformative era in 5G/6G networks and beyond. Exploring its disruptive potential across the value chain-from network design to agile and robust automation-we showcase GenAI as a catalyst for innovation and unparalleled efficiency. While tracing its historical journey from conception to practical implementation, the paper positions GenAI not as the sole solution but as the inception of a new era shaping network design, deployment strategies, and synchronous optimization dynamics. We also scrutinize the role of causal AI and transparent frameworks, such as explainable AI and neuro-symbolic AI in fostering trust and seamlessly integrating domain knowledge. Looking ahead beyond GenAI, we envision a future AI landscape composed of semantic communications, collaborative GenAI and discriminative agents. We also examine the challenges related to scalability and complexity that must be overcome to achieve sustainable AI deployments. Finally, we highlight the significance of emerging computing technologies and frameworks, such as quantum and neuromorphic computing, that play a pivotal role in the broader trajectory towards artificial general intelligence.
C1 [Chaccour, Christina] Ericsson Inc, Plano, TX 75024 USA.
   [Karapantelakis, Athanasios] Ericsson Res, S-16480 Kista, Stockholm, Sweden.
   [Murphy, Timothy] Ericsson Inc, BA Cloud Software & Serv, Montreal, PQ H4S 0B6, Canada.
   [Dohler, Mischa] Ericsson Inc, Adv Technol Grp, Santa Clara, CA 95054 USA.
C3 Ericsson; Ericsson; Ericsson
RP Chaccour, C (corresponding author), Ericsson Inc, Plano, TX 75024 USA.
EM christina.chaccour@ericsson.com; athanasios.karapantelakis@ericsson.com;
   timothy.murphy@ericsson.com; mischa.dohler@ericsson.com
RI Dohler, Mischa/G-8670-2012; Chaccour, Christina/ADM-2573-2022
OI Dohler, Mischa/0000-0001-9583-2923
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Chen JY, 2024, IEEE NETWORK, V38, P234, DOI 10.1109/MNET.2024.3366560
   Coman Alexandra, 2011, P AAAI C ART INT, V91, P15
   Du HY, 2024, IEEE NETWORK, V38, P178, DOI 10.1109/MNET.006.2300223
   Guo YW, 2024, Arxiv, DOI arXiv:2307.04725
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hong EK, 2022, J COMMUN NETW-S KOR, V24, P232, DOI 10.23919/JCN.2022.000015
   Leong A., 2023, Heres Why ChatGPT Might be at Capacity for You Still
   Liu YW, 2017, P IEEE, V105, P2347, DOI 10.1109/JPROC.2017.2768666
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Wang JC, 2024, IEEE T MOBILE COMPUT, V23, P10344, DOI 10.1109/TMC.2024.3377226
   Xu MR, 2024, IEEE COMMUN SURV TUT, V26, P1127, DOI 10.1109/COMST.2024.3353265
   Xu MR, 2023, IEEE VEH TECHNOL MAG, V18, P35, DOI 10.1109/MVT.2023.3323757
   Zhang CN, 2023, Arxiv, DOI arXiv:2303.11717
   Zong MY, 2022, Arxiv, DOI arXiv:2212.00857
NR 15
TC 0
Z9 0
U1 7
U2 7
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0890-8044
EI 1558-156X
J9 IEEE NETWORK
JI IEEE Netw.
PD SEP
PY 2024
VL 38
IS 5
BP 21
EP 28
DI 10.1109/MNET.2024.3425594
PG 8
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA H3M7E
UT WOS:001322517900006
DA 2024-12-25
ER

PT S
AU Eybers, OO
AF Eybers, Oscar Oliver
BA Muller, A
   Eybers, OO
BF Muller, A
   Eybers, OO
TI A GenAI Ontology for Academic Literacies Teaching and Learning Practices
SO AI APPROACHES TO LITERACY IN HIGHER EDUCATION
SE Advances in Educational Technologies and Instructional Design Book
   Series
LA English
DT Article; Book Chapter
AB In the ever-evolving higher education landscape, the integration of AI, particularly Generative AI (GenAI), is causing a profound shift. This chapter explores how GenAI is reshaping teaching, learning, and academic literacies. Academic literacies facilitators now navigate a diverse terrain, bridging traditional materials, digital resources, and AI-enhanced texts. They cultivate scholars' proficiency in GenAI tools and pioneer innovative teaching methods. This chapter introduces a GenAI ontology to support this transformative journey. It equips facilitators and students to use GenAI effectively, fostering tailored teaching methods and personalised literacies assessments. In summary, this chapter presents GenAI's potential to innovate, enhance accessibility, and elevate academic prowess in higher education.
C1 [Eybers, Oscar Oliver] Univ Pretoria, Unit Acad Literacy, Pretoria, South Africa.
C3 University of Pretoria
RP Eybers, OO (corresponding author), Univ Pretoria, Unit Acad Literacy, Pretoria, South Africa.
CR Ahuja AS, 2023, INTEGR MED RES, V12, DOI 10.1016/j.imr.2022.100917
   Amen R., 2010, Writing System of Medu Neter: Ancient Egyptian Hieroglyphs
   Ballantyne D, 2021, AFR EDUC RE, V18, P1, DOI 10.1080/18146627.2022.2150245
   Bates T, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-00218-x
   Brannon Lil., 2008, ENGL J, V98, P16
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Cho YH, 2011, INSTR SCI, V39, P629, DOI 10.1007/s11251-010-9146-1
   Cope B, 2015, PEDAGOGY OF MULTILITERACIES: LEARNING BY DESIGN, P1
   De Silva D., 2023, Times Higher EducationJune 20
   De Souza Maira Giovana, 2024, Physics Education, V59, DOI 10.1088/1361-6552/ad1e71
   Escotet M., 2023, PROSPECTS, DOI [10.1007/s11125-023-09642-z, DOI 10.1007/S11125-023-09642-Z]
   Eybers O., 2021, (PhD thesis), DOI [10.13140/RG.2.2.30245.22242, DOI 10.13140/RG.2.2.30245.22242]
   Eybers OO, 2022, S AFR J HIGH EDUC, V36, P115, DOI 10.20853/36-2-4683
   Gao C, 2023, Arxiv, DOI arXiv:2307.14984
   Gao SL, 2022, IEEE-ACM T AUDIO SPE, V30, P2173, DOI 10.1109/TASLP.2022.3153255
   Go E, 2019, COMPUT HUM BEHAV, V97, P304, DOI 10.1016/j.chb.2019.01.020
   Goodier C., 2005, Journal for language teaching, V39, P66, DOI [10.4314/jlt.v39i1.6050, DOI 10.4314/JLT.V39I1.6050]
   Haque Akm Bahalul, 2023, Technological Forecasting and Social Change, DOI 10.1016/j.techfore.2022.122120
   Harb M., 2020, Curriculum Perspectives, V40, P27, DOI [10.1007/s41297-020-00099-0, DOI 10.1007/S41297-020-00099-0]
   Heaven W. D., 2023, MIT Technology ReviewApril 6
   Islam MK, 2022, E-LEARNING DIGITAL M, V19, P36, DOI 10.1177/20427530211027721
   Johnson-Laird P, 2010, WIRES COGN SCI, V1, P8, DOI 10.1002/wcs.20
   Katz S, 2021, INT J ARTIF INTELL E, V31, P397, DOI 10.1007/s40593-020-00226-y
   Le HX, 2022, INTERACT TECHNOL SMA, V19, P510, DOI 10.1108/ITSE-12-2021-0210
   Learning E. L. M, 2022, Adaptive Learning vs. Personalized Learning: A Guide to Both
   Lebuso S., 2023, Accreditation. News24February 2
   Li HF, 2023, AUSTRALAS J EDUC TEC, V39, P40, DOI 10.14742/ajet.8923
   Li Yeping, 2020, J STEM Educ Res, V3, P1, DOI 10.1007/s41979-020-00030-2
   Macagno Fabrizio, 2020, LOGIC ACAD WRITING
   McMurtrie B., 2018, The Chronicle of Higher Education
   Mousavinasab E, 2021, INTERACT LEARN ENVIR, V29, P142, DOI 10.1080/10494820.2018.1558257
   Ojha S. S., 2021, Academia Letters, P1, DOI [10.20935/AL1577, DOI 10.20935/AL1577]
   Qasrawi R., 2020, International Online Journal of Education and Teaching, V7, P744
   Qi Q, 2023, J SECOND LANG WRIT, V62, DOI 10.1016/j.jslw.2023.101052
   Regulski I., 2016, Oxford Handbooks Online, V28, DOI DOI 10.1093/OXFORDHB/9780199935413.013.61
   Sample A, 2020, INFORM TECHNOL LIBR, V39, DOI 10.6017/ital.v39i1.11723
   Seroto J., 2011, Indilinga African Journal of Indigenous Knowledge Systems, V10, P77, DOI DOI 10.10520/EJC61385
   Street B., 2003, CURRENT ISSUES COMP, V5, P77, DOI DOI 10.52214/CICE.V5I2.11369
   Tegegne Habtamu Mengistie., 2015, History in Africa, V42, P433, DOI [10.1017/hia.2014.23, DOI 10.1017/HIA.2014.23]
   Thaheem SK, 2022, ASIAN EDUC DEV STUD, V11, P311, DOI 10.1108/AEDS-08-2020-0189
   Vemula S., 2022, Doctoral dissertation
   Watty K., 2016, J. Accounting Educ., V36, P1, DOI [10.1016/j.jaccedu.2016.03.003, DOI 10.1016/J.JACCEDU.2016.03.003]
   Zhang TB, 2012, INT C MULTIMED INFO, P790, DOI 10.1109/MINES.2012.231
   Zhu YY, 2022, IEEE-ASME T MECH, V27, P846, DOI 10.1109/TMECH.2021.3072675
NR 44
TC 0
Z9 0
U1 0
U2 0
PU IGI GLOBAL
PI HERSEY
PA 701 E CHOCOLATE AVE, STE 200, HERSEY, PA 17033-1240 USA
SN 2326-8905
EI 2326-8913
BN 979-8-36934-471-2; 979-8-36931-055-7; 979-8-36931-054-0
J9 ADV EDUC TECHNOL INS
PY 2024
BP 197
EP 218
DI 10.4018/979-8-3693-1054-0.ch009
D2 10.4018/979-8-3693-1054-0
PG 22
WC Computer Science, Artificial Intelligence; Education & Educational
   Research; Language & Linguistics
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Book Citation Index – Science (BKCI-S)
SC Computer Science; Education & Educational Research; Linguistics
GA BX2XT
UT WOS:001271512600011
DA 2024-12-25
ER

PT J
AU Mishra, P
   Oster, N
   Henriksen, D
AF Mishra, Punya
   Oster, Nicole
   Henriksen, Danah
TI Generative AI, Teacher Knowledge and Educational Research: Bridging
   Short- and Long-Term Perspectives
SO TECHTRENDS
LA English
DT Article
DE Creativity; Technology; Education; Artificial intelligence; ChatGPT;
   Generative AI; Responsible innovation; Futures thinking; Future; Equity
AB This article reflects on the transformative nature of generative AI (GenAI) tools for teaching and teacher education, both reflecting on current innovation and consider future potentials and challenges. In that sense, we aim to position the field of education going forward with the implications of new technologies like GenAI for education and educational research. We argue the need for a dual-lens approach. First and foremost, practice and research should focus on the here-and-now, i.e. how to design powerful learning experiences for pre-service and in-service teachers for them to be productive, creative, critical, and ethical users. But there is also a need for a deeper, longer view-based on sociological and historical trends and patterns that will influence the socio-techno-cultural matrix within which education functions in the long term. We begin with a brief introduction to GenAI technologies. This is followed by an in-depth discussion of the fundamental nature of GenAI tools-their similarities and differences to prior technologies, and the implications for teacher education and research.
C1 [Mishra, Punya; Oster, Nicole; Henriksen, Danah] Arizona State Univ, Teachers Coll, Tempe, AZ 85281 USA.
C3 Arizona State University; Arizona State University-Tempe
RP Mishra, P; Oster, N; Henriksen, D (corresponding author), Arizona State Univ, Teachers Coll, Tempe, AZ 85281 USA.
EM punya.mishra@asu.edu; nicole.oster@asu.edu; danah.henriksen@asu.edu
CR AAP-AACAP-CHA Declaration of a National Emergency in Child and Adolescent Mental Health, 2021, AM ACAD PEDIAT
   Al-Sibai N., 2023, BYTE
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   [Anonymous], 2019, Arts, research, innovation and society, P23, DOI [10.1007/978-3-030-26068-2_3, DOI 10.1007/978-3-030-26068-2_3]
   Arthur R., 2023, RACHEL ARTHUR WRITES
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bartlett R, 2022, J FINANC ECON, V143, P30, DOI 10.1016/j.jfineco.2021.05.047
   Benjamin, 2020, RACE TECHN ABOL
   Bhatia A., 2023, The New York Times .
   Extance A, 2023, NATURE, V623, P474, DOI 10.1038/d41586-023-03507-3
   FAQ, 2023, ELICIT
   Harari Y.N., 2023, The Economist
   Hendrix J., 2023, SUNDAY SHOW
   Henriksen D, 2023, TECHTRENDS, V67, P595, DOI 10.1007/s11528-023-00862-w
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Hutson M., 2022, AI LEARNS WRITE CODE
   Ihde D., 1998, PHILOS TECHNOLOGY
   Kahneman D., 2011, THINKING FAST SLOW
   Kentayya S., 2020, CODED BIAS
   Koehler M.J., 2008, Handbook of technological pedagogical content knowledge (TPCK) for educators, P3, DOI DOI 10.1016/J.COMPEDU.2005.11.012
   Krutka D. G., 2022, J TECHNOLOGY TEACHER, V30, P229
   LI R, 2020, Artificial intelligence revolution: How AI will change our society, economy, and culture
   Marx P., 2023, BIG TECH WONT REVITA
   Mishra P, 2001, J ADOLESC ADULT LIT, V44, P634
   Mishra P., 2019, Journal of Digital Learning in Teacher Education, V35, P76, DOI DOI 10.1080/21532974.2019.1588611
   Mishra P., 2020, Championing technology infusion in teacher preparation: A framework for supporting future educators
   Mishra P., 2023, J DIGITAL LEARNING T, DOI [10.1080/21532974.2023.224748, DOI 10.1080/21532974.2023.224748]
   Mishra P., 2024, IN PRESS
   Mishra P, 2006, TEACH COLL REC, V108, P1017, DOI 10.1111/j.1467-9620.2006.00684.x
   Mishra P, 2021, COMPUT HUM BEHAV, V117, DOI 10.1016/j.chb.2020.106673
   Mitchell A., 2023, New York Post
   ONeill C, 2016, Weapons of Math Destruction. How big Data increases Inequality and threatens Democracy
   Postman Neil., 1998, 5 THINGS WE NEED KNO
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Richardson C, 2024, TECHTRENDS, V68, P5, DOI 10.1007/s11528-023-00921-2
   Roose K., 2022, The New York Times
   Ruiz P., 2023, GLOSSARY ARTIFICIAL
   Snyder K., 2023, FAST CO
   Stubenvoll M., 2021, International Journal of Communication, V15, P22
   Verbeek PP., 2015, INTERACTIONS, V22, P26, DOI DOI 10.1145/2751314
   Warr M., 2023, IMPLICIT BIAS LARGE, DOI [10.2139/ssrn.4625078, DOI 10.2139/SSRN.4625078]
   Warr M, 2023, TECHTRENDS, V67, P396, DOI 10.1007/s11528-023-00843-z
   Warr M, 2020, ETR&D-EDUC TECH RES, V68, P601, DOI 10.1007/s11423-020-09746-9
   Woo LJ, 2023, TECHTRENDS, V67, P767, DOI 10.1007/s11528-023-00888-0
   Zewe A., 2023, MIT News
NR 45
TC 7
Z9 7
U1 26
U2 71
PU SPRINGER INT PUBL AG
PI CHAM
PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
SN 8756-3894
EI 1559-7075
J9 TECHTRENDS
JI TechTrends
PD MAR
PY 2024
VL 68
IS 2
BP 205
EP 210
DI 10.1007/s11528-024-00938-1
EA FEB 2024
PG 6
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA JC0N0
UT WOS:001160410900001
DA 2024-12-25
ER

PT J
AU Jang, KM
   Chen, JD
   Kang, YH
   Kim, J
   Lee, J
   Duarte, F
   Ratti, C
AF Jang, Kee Moon
   Chen, Junda
   Kang, Yuhao
   Kim, Junghwan
   Lee, Jinhyung
   Duarte, Fabio
   Ratti, Carlo
TI Place identity: a generative AI's perspective
SO HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS
LA English
DT Article
ID ATTACHMENT; TOURISM; IMAGE
AB Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL<middle dot>E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.
C1 [Jang, Kee Moon; Kang, Yuhao; Duarte, Fabio; Ratti, Carlo] MIT, Dept Urban Studies & Planning, Senseable City Lab, Cambridge, MA 02139 USA.
   [Chen, Junda] Univ Calif San Diego, Dept Comp Sci & Engn, San Diego, CA USA.
   [Kang, Yuhao] Univ Texas Austin, Dept Geog & Environm, GISense Lab, Austin, TX 78712 USA.
   [Kang, Yuhao] Univ South Carolina, Dept Geog, Columbia, SC 29208 USA.
   [Kim, Junghwan] Virginia Tech, Dept Geog, Blacksburg, VA USA.
   [Lee, Jinhyung] Western Univ, Dept Geog & Environm, London, ON, Canada.
C3 Massachusetts Institute of Technology (MIT); University of California
   System; University of California San Diego; University of Texas System;
   University of Texas Austin; University of South Carolina System;
   University of South Carolina Columbia; Virginia Polytechnic Institute &
   State University; Western University (University of Western Ontario)
RP Kang, YH (corresponding author), MIT, Dept Urban Studies & Planning, Senseable City Lab, Cambridge, MA 02139 USA.; Kang, YH (corresponding author), Univ Texas Austin, Dept Geog & Environm, GISense Lab, Austin, TX 78712 USA.; Kang, YH (corresponding author), Univ South Carolina, Dept Geog, Columbia, SC 29208 USA.
EM yuhao.kang@austin.utexas.edu
RI chen, junda/HHD-0290-2022; Kang, Yuhao/U-2821-2019; Jang, Kee
   Moon/HJA-9762-2022; Kim, Junghwan/KXS-2292-2024
OI Kim, Junghwan/0000-0002-7275-769X; Kang, Yuhao/0000-0003-3810-9450;
   Jang, Kee Moon/0000-0002-4701-283X
FU MIT Libraries
FX The authors would like to thank the members of MIT Senseable City Lab
   who provided feedback on this project. The authors would like to
   acknowledge the financial support of open access from MIT Libraries. Any
   opinions, findings, and conclusions or recommendations expressed in this
   material are those of the author(s) and do not necessarily reflect the
   views of the funders.
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Al-Kodmany K, 2012, ARCHNET-IJAR, V6, P43
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   Atkins C, 2024, COMMUN EARTH ENVIRON, V5, DOI 10.1038/s43247-024-01392-w
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bolojan D., 2022, Proc 27th Int Conf Assoc Computer-Aided Architectural Des Res Asia (CAADRIA), V1, P353
   Canter David., 1977, The Psychology of Place
   Cheon M, 2021, IEEE COMPUT SOC CONF, P433, DOI 10.1109/CVPRW53098.2021.00054
   Choi HS, 2015, URBAN DES INT, V20, P66, DOI 10.1057/udi.2013.38
   Choi S, 2007, TOURISM MANAGE, V28, P118, DOI 10.1016/j.tourman.2006.03.002
   Coghlan A, 2017, TOUR RECREAT RES, V42, P299, DOI 10.1080/02508281.2016.1268744
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dubey A, 2016, LECT NOTES COMPUT SC, V9905, P196, DOI 10.1007/978-3-319-46448-0_12
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ford M., 2015, Rise of the robots: Technology and the threat of a jobless future
   Gao S, 2017, INT J GEOGR INF SCI, V31, P1245, DOI 10.1080/13658816.2016.1273357
   Gao Y, 2022, J GEOGR SYST, V24, P199, DOI 10.1007/s10109-021-00369-z
   Goodchild MF, 2011, SOC DISPAR H H CARE, P21, DOI 10.1007/978-1-4419-7482-2_2
   Gottlieb M, 2023, AM J EMERG MED, V70, P81, DOI 10.1016/j.ajem.2023.05.018
   Hase P, 2021, Arxiv, DOI arXiv:2111.13654
   Hernández B, 2010, J ENVIRON PSYCHOL, V30, P281, DOI 10.1016/j.jenvp.2010.01.009
   Hu YJ, 2019, ANN AM ASSOC GEOGR, V109, P1052, DOI 10.1080/24694452.2018.1535886
   HULL RB, 1994, LANDSCAPE URBAN PLAN, V28, P109, DOI 10.1016/0169-2046(94)90001-9
   Jackson D, 2006, STAT MED, V25, P2688, DOI 10.1002/sim.2481
   Jang KM, 2019, PLOS ONE, V14, DOI 10.1371/journal.pone.0218590
   Jang KM, 2017, 2017 INT C AS PAC PL, P096
   Jenkins A, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0152932
   Kang YH, 2023, Arxiv, DOI arXiv:2304.10743
   Kang YH, 2019, T GIS, V23, P450, DOI 10.1111/tgis.12552
   Kim J, 2023, Findings, DOI DOI 10.32866/001C.72634
   Larsen SC, 2004, ANN ASSOC AM GEOGR, V94, P944
   Latif E, 2024, Arxiv, DOI arXiv:2304.12479
   Lee HK, 2022, MEDIA CULT SOC, V44, P601, DOI 10.1177/01634437221077009
   Lewicka M, 2008, J ENVIRON PSYCHOL, V28, P209, DOI 10.1016/j.jenvp.2008.02.001
   Liu L, 2017, COMPUT ENVIRON URBAN, V65, P113, DOI 10.1016/j.compenvurbsys.2017.06.003
   Mai GC, 2023, Arxiv, DOI arXiv:2304.06798
   Manzo LC, 2006, J PLAN LIT, V20, P335, DOI 10.1177/0885412205286160
   Mishkin P., 2022, Noudettu, V28, P2022
   NASAR JL, 1990, J AM PLANN ASSOC, V56, P41, DOI 10.1080/01944369008975742
   Paananen V, 2024, INT J ARCHIT COMPUT, V22, P458, DOI 10.1177/14780771231222783
   Paasi A, 2003, PROG HUM GEOG, V27, P475, DOI 10.1191/0309132503ph439pr
   Park C, 2023, CITIES, V138, DOI 10.1016/j.cities.2023.104371
   Peng JC, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.00294
   Proshansky H.M., 1983, J ENVIRON PSYCHOL, V3, P57, DOI [10.1016/S0272-4944(83)80021-8, DOI 10.1016/S0272-4944(83)80021-8]
   Relph E., 1976, Place and Placelesness
   Sajjad M, 2023, ANN BIOMED ENG, V51, P1663, DOI 10.1007/s10439-023-03225-x
   Schick T, 2021, Arxiv, DOI [arXiv:2009.07118, DOI 10.48550/ARXIV.2009.07118]
   Seamon David., 2008, KEY TEXTS HUMAN GEOG, DOI DOI 10.4135/9781446213742.N6
   Seneviratne S, 2022, 2022 INT C DIGITAL I, P1, DOI DOI 10.1109/DICTA56598.2022.10034603
   Shen XY, 2023, Arxiv, DOI [arXiv:2304.08979, 10.48550/arXiv.2304.08979, 10.48550/arxiv.2304.08979, DOI 10.48550/ARXIV.2304.08979]
   Shen YQ, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230163
   Shim C, 2014, TOURISM MANAGE, V45, P106, DOI 10.1016/j.tourman.2014.03.001
   Stevens J.P., 2002, Applied Multivariate Statistics for the Social Sciences, V4
   Stewart WP, 2004, LANDSCAPE URBAN PLAN, V69, P315, DOI 10.1016/j.landurbplan.2003.07.005
   Sun Y, 2023, ENVIRON PLAN B-URBAN, V50, P1577, DOI 10.1177/23998083221142191
   Tuan Y.-F., 1979, PHILOS GEOGRAPHY THE, P387
   Tuan Y.-F., 1977, SPACE PLACE PERSPECT
   Turc I, 2019, Arxiv, DOI [arXiv:1908.08962, DOI 10.48550/ARXIV.1908.08962]
   Turchi Tommaso, 2023, End-User Development: 9th International Symposium, IS-EUD 2023, Proceedings. Lecture Notes in Computer Science (13917), P35, DOI 10.1007/978-3-031-34433-6_3
   Wang DJ, 2023, Arxiv, DOI arXiv:2304.03892
   Wang SS, 2015, ANN TOURISM RES, V52, P16, DOI 10.1016/j.annals.2015.02.016
   Wang Wenhui, 2020, MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers, V33
   Wild J, 1965, Existence and the World of Freedom
   Yao Fu., 2023, INT C MACHINE LEARNI, V202 of Proceedings of Machine Learning Research, P10421
   Yokohari M, 2000, LANDSCAPE URBAN PLAN, V47, P159, DOI 10.1016/S0169-2046(99)00084-5
   Zhang F, 2019, ROY SOC OPEN SCI, V6, DOI 10.1098/rsos.181375
   Zhang F, 2018, COMPUT ENVIRON URBAN, V71, P153, DOI 10.1016/j.compenvurbsys.2018.05.005
   Zhang R, 2018, PROC CVPR IEEE, P586, DOI 10.1109/CVPR.2018.00068
NR 68
TC 0
Z9 0
U1 13
U2 13
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2662-9992
J9 HUM SOC SCI COMMUN
JI Hum. Soc. Sci. Commun.
PD SEP 7
PY 2024
VL 11
IS 1
AR 1156
DI 10.1057/s41599-024-03645-7
PG 16
WC Humanities, Multidisciplinary; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Arts & Humanities - Other Topics; Social Sciences - Other Topics
GA I6F5K
UT WOS:001331198000001
OA gold
DA 2024-12-25
ER

PT J
AU Chau, RCW
   Thu, KM
   Yu, OY
   Hsung, RTC
   Lo, ECM
   Lam, WYH
AF Chau, Reinhard Chun Wang
   Thu, Khaing Myat
   Yu, Ollie Yiru
   Hsung, Richard Tai-Chiu
   Lo, Edward Chin Man
   Lam, Walter Yu Hang
TI Performance of Generative Artificial Intelligence in Dental Licensing
   Examinations
SO INTERNATIONAL DENTAL JOURNAL
LA English
DT Article
DE Artificial intelligence; Communication; Dental education; Digital
   technology; Examination questions
AB Objectives: Generative artificial intelligence (GenAI), including large language models (LLMs), has vast potential applications in health care and education. However, it is unclear how proficient LLMs are in interpreting written input and providing accurate answers in dentistry. This study aims to investigate the accuracy of GenAI in answering questions from dental licensing examinations. Methods: A total of 1461 multiple-choice questions from question books for the US and the UK dental licensing examinations were input into 2 versions of ChatGPT 3.5 and 4.0. The passing rates of the US and UK dental examinations were 75.0% and 50.0%, respectively. The performance of the 2 versions of GenAI in individual examinations and dental subjects was analysed and compared. Results: ChatGPT 3.5 correctly answered 68.3% (n = 509) and 43.3% (n = 296) of questions from the US and UK dental licensing examinations, respectively. The scores for ChatGPT 4.0 were 80.7% (n = 601) and 62.7% (n = 429), respectively. ChatGPT 4.0 passed both written dental licensing examinations, whilst ChatGPT 3.5 failed. ChatGPT 4.0 answered 327 more questions correctly and 102 incorrectly compared to ChatGPT 3.5 when comparing the 2 versions. Conclusions: The newer version of GenAI has shown good proficiency in answering multiplechoice questions from dental licensing examinations. Whilst the more recent version of GenAI generally performed better, this observation may not hold true in all scenarios, and further improvements are necessary. The use of GenAI in dentistry will have significant implications for dentist-patient communication and the training of dental professionals. (c) 2023 The Authors. Published by Elsevier Inc. on behalf of FDI World Dental Federation. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
C1 [Chau, Reinhard Chun Wang; Thu, Khaing Myat; Yu, Ollie Yiru; Hsung, Richard Tai-Chiu; Lo, Edward Chin Man; Lam, Walter Yu Hang] Univ Hong Kong, Fac Dent, Hong Kong, Peoples R China.
   [Hsung, Richard Tai-Chiu] Hong Kong Chu Hai Coll, Dept Comp Sci, Hong Kong, Peoples R China.
   [Lam, Walter Yu Hang] Univ Hong Kong, Musketeers Fdn Inst Data Sci, Hong Kong, Peoples R China.
C3 University of Hong Kong; Hong Kong Chu Hai College; University of Hong
   Kong
RP Lam, WYH (corresponding author), Prince Phillip Dent Hosp, 3-F 34 Hosp Rd Sai Ying Pun, Hong Kong, Peoples R China.
EM retlaw@hku.hk
RI Chau, Reinhard/HDL-7203-2022; Lam, Walter/AAS-2168-2020; CHAU, CHUN
   WANG/HGE-8448-2022
OI CHAU, CHUN WANG/0000-0002-5691-6806; Thu, Khaing
   Myat/0000-0001-8710-1709; Lam, Walter/0000-0001-5530-2645
CR AbuSalim S, 2022, HEALTHCARE-BASEL, V10, DOI 10.3390/healthcare10101892
   Achiam J., 2023, arXiv
   Ali Stephen R, 2023, Lancet Digit Health, V5, pe179, DOI 10.1016/S2589-7500(23)00048-1
   Amazon, US NBDE book
   American Dental Association, 2023, Integrated National Board Dental Examination (INBDE) 2023 candidate guide
   [Anonymous], 2023, LANCET REG HEALTH-EU, V30, DOI 10.1016/j.lanepe.2023.100677
   [Anonymous], 2023, QS World University Rankings
   [Anonymous], INBDE History and purpose
   [Anonymous], 2018, Foundation Knowledge for the General Dentist.
   [Anonymous], 2023, Books by Pastest
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Chau RCW., 2023, J California Dent Assoc, V51
   Chau RCW, 2023, DENT J-BASEL, V11, DOI 10.3390/dj11080189
   Chau RCW, 2023, INT DENT J, V73, P724, DOI 10.1016/j.identj.2023.03.007
   Chau RCW, 2024, J PROSTHET DENT, V131, P1111, DOI 10.1016/j.prosdent.2022.12.004
   Dashti M, 2023, J Prosthet Dent, VS0022-3913, P00371
   Dowd FJ, 2007, Mosby's review for the NBDE part two
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Fan K, 2014, MCQs for dentistry, V3rd
   Flores-Cohaila JA, 2023, JMIR MED EDUC, V9, DOI 10.2196/48039
   General Dental Council, 2022, Overseas Registration Examination Part 1 Results for August 2022
   Giannos P, 2023, BMJ NEUROL OPEN, V5, DOI 10.1136/bmjno-2023-000451
   Guo BY, 2023, Arxiv, DOI [arXiv:2301.07597, DOI 10.48550/ARXIV.2301.07597]
   Hammond D, 2014, Best of fives for dentistry, V3rd
   Harris E, 2023, JAMA-J AM MED ASSOC, V330, P792, DOI 10.1001/jama.2023.14311
   Khanagar SB, 2021, J DENT SCI, V16, P482, DOI 10.1016/j.jds.2020.05.022
   Kung T. H, 2023, PLOS Digit Health, V2, DOI DOI 10.1371/JOURNAL.PDIG.0000198.PDIG-D-22-00371
   McClung HJ, 1998, PEDIATRICS, V101, DOI 10.1542/peds.101.6.e2
   de Matos JDM, 2022, DENT J-BASEL, V10, DOI 10.3390/dj10080145
   Reshamwala A., 2013, IRACST Engineering Science and Technology: An International Journal (ESTIJ), V3, P113
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Schwendicke F, 2022, J DENT RES, V101, P21, DOI 10.1177/00220345211020265
   Schwendicke F, 2020, J DENT RES, V99, P769, DOI 10.1177/0022034520915714
   Thurzo A, 2023, EDUC SCI, V13, DOI 10.3390/educsci13020150
   Wen A, 2019, NPJ DIGIT MED, V2, DOI 10.1038/s41746-019-0208-8
   Wenzlaff K., 2022, SMARTER HUMANS VALID, DOI [DOI 10.2139/SSRN.4302443, 10.2139/ssrn.4302443]
NR 36
TC 6
Z9 6
U1 2
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0020-6539
EI 1875-595X
J9 INT DENT J
JI Int. Dent. J.
PD JUN
PY 2024
VL 74
IS 3
BP 616
EP 621
DI 10.1016/j.identj.2023.12.007
EA MAY 2024
PG 6
WC Dentistry, Oral Surgery & Medicine
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Dentistry, Oral Surgery & Medicine
GA C9J1C
UT WOS:001292442000006
PM 38242810
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Ivanov, S
   Soliman, M
   Tuomi, A
   Alkathiri, NA
   Al-Alawi, AN
AF Ivanov, Stanislav
   Soliman, Mohammad
   Tuomi, Aarni
   Alkathiri, Nasser Alhamar
   Al-Alawi, Alamir N.
TI Drivers of generative AI adoption in higher education through the lens
   of the Theory of Planned Behaviour
SO TECHNOLOGY IN SOCIETY
LA English
DT Article
DE Generative AI; Theory of planned behaviour; Higher education
ID TECHNOLOGY; MODEL; ACCEPTANCE; INTERNET
AB Drawing on the Theory of Planned Behaviour (TPB), this study investigates the relationship between the perceived benefits, strengths, weaknesses, and risks of generative AI (GenAI) tools and the fundamental factors of the TPB model (i.e., attitude, subjective norms, and perceived behavioural control). The study also investigates the structural association between the TPB variables and intention to use GenAI tools, and how the latter might affect the actual usage of GenAI tools in higher education. The paper adopts a quantitative approach, relying on an anonymous self-administered online questionnaire to gather primary data from 130 lecturers and 168 students in higher education institutions (HEIs) in several countries, and PLS-SEM for data analysis. The results indicate that although lecturers' and students' perceptions of the risks and weaknesses of GenAI tools differ, the perceived strengths and advantages of GenAI technologies have a significant and positive impact on their attitudes, subjective norms, and perceived behavioural control. The TPB core variables positively and significantly impact lecturers' and students' intentions to use GenAI tools, which in turn significantly and positively impact their adoption of such tools. This paper advances theory by outlining the factors shaping the adoption of GenAI technologies in HEIs. It provides stakeholders with a variety of managerial and policy implications for how to formulate suitable rules and regulations to utilise the advantages of these tools while mitigating the impacts of their disadvantages. Limitations and future research opportunities are also outlined.
C1 [Ivanov, Stanislav] Varna Univ Management, 13A Oborishte Str, Varna 9000, Bulgaria.
   [Ivanov, Stanislav] Zangador Res Inst, Varna 9010, Bulgaria.
   [Soliman, Mohammad] Univ Technol & Appl Sci, Res & Consultat Dept, Salalah, Oman.
   [Soliman, Mohammad] Fayoum Univ, Fac Tourism & Hotels, Al Fayyum, Egypt.
   [Tuomi, Aarni] Haaga Hel Univ Appl Sci, Helsinki, Finland.
   [Alkathiri, Nasser Alhamar] Univ Technol & Appl Sci Salalah, Coll Econ & Business Adm, Business Adm Dept, Salalah, Oman.
   [Al-Alawi, Alamir N.] Univ Technol & Appl Sci, Ibri, Oman.
C3 Varna University of Management; Egyptian Knowledge Bank (EKB); Fayoum
   University
RP Ivanov, S (corresponding author), Varna Univ Management, 13A Oborishte Str, Varna 9000, Bulgaria.
EM stanislav.ivanov@vumk.eu; msoliman.sal@cas.edu.om;
   Aarni.Tuomi@haaga-helia.fi; nasser2014.sal@cas.edu.om;
   alameer.alalawi@utas.edu.om
RI Alkathiri, Nasser/AAW-8450-2020; Tuomi, Aarni/AAK-7168-2021; Soliman,
   Mohammad/AAC-3702-2020; Ivanov, Stanislav/D-7692-2012
OI Soliman, Mohammad/0000-0002-9359-763X; AL-Alawi,
   Alamir/0000-0002-2746-7052; Alhamar Alkathiri, Dr.
   Nasser/0000-0001-9494-7366; Tuomi, Aarni/0000-0002-0515-1313
CR AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
   Ajzen I, 2020, HUM BEHAV EMERG TECH, V2, P314, DOI 10.1002/hbe2.195
   Al-Zahrani AM, 2024, INNOV EDUC TEACH INT, V61, P1029, DOI 10.1080/14703297.2023.2271445
   Ali O, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102402
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Baker-Brunnbauer Josef, 2021, ROBONOMICS: The Journal of the Automated Economy, V2, P17
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Bosnjak M, 2020, EUR J PSYCHOL, V16, P352, DOI 10.5964/ejop.v16i3.3107
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Cheon J, 2012, COMPUT EDUC, V59, P1054, DOI 10.1016/j.compedu.2012.04.015
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Conner M, 1998, J APPL SOC PSYCHOL, V28, P1429, DOI 10.1111/j.1559-1816.1998.tb01685.x
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
   Dubljevic V, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2024.102480
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   FORNELL C, 1981, J MARKETING RES, V18, P382, DOI 10.2307/3150980
   Gilson A, 2022, medRxiv, DOI [10.1101/2022.12.23.22283901, 10.1101/2022.12.23.22283901, DOI 10.1101/2022.12.23.22283901]
   Gundu T, 2023, INT S HUMAN ASPECTS, P418
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
   Han H, 2010, TOURISM MANAGE, V31, P325, DOI 10.1016/j.tourman.2009.03.013
   Henseler J, 2016, IND MANAGE DATA SYST, V116, P2, DOI 10.1108/IMDS-09-2015-0382
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hsu CL, 2007, OMEGA-INT J MANAGE S, V35, P715, DOI 10.1016/j.omega.2006.03.005
   Ivanov S, 2023, SERV IND J, V43, P1055, DOI 10.1080/02642069.2023.2258799
   Ivanov S, 2021, J TOUR FUTURES, V9, P214, DOI 10.1108/JTF-02-2023-0038
   Jaboob M, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2300016
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Knauder H, 2019, TEACH TEACH EDUC, V77, P66, DOI 10.1016/j.tate.2018.09.012
   Kock Florian, 2021, Tourism Management, V86, DOI 10.1016/j.tourman.2021.104330
   Kock N., 2022, WARPPLS USER MANUAL
   Kock N, 2014, INFORM TECHNOL DEV, V20, P23, DOI 10.1080/02681102.2013.832129
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Li ZH, 2023, Arxiv, DOI arXiv:2304.14347
   Megahed FM, 2024, QUAL ENG, V36, P287, DOI 10.1080/08982112.2023.2206479
   Tran MAQ, 2022, MINDFULNESS, V13, P2574, DOI 10.1007/s12671-022-01980-x
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Rice S, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2023.102426
   Rogers E.M., 1962, DIFFUSION INNOVATION
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Shah CS., 2024, TRANSFER DIFFUSION A, P159, DOI [10.1007/978-3-031-50188-3_14, DOI 10.1007/978-3-031-50188-3_14]
   Shanahan M, 2023, NATURE, V623, P493, DOI 10.1038/s41586-023-06647-8
   Soliman M, 2023, PSYCHOL REP, DOI 10.1177/00332941231197165
   Soliman Mohammad, 2021, Revista Turismo & Desenvolvimento, P23
   Stanford, 2023, Responsible AI at Stanford
   Strzelecki A, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13425
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Teo Timothy, 2010, Campus-Wide Information Systems, V27, P60, DOI 10.1108/10650741011033035
   UNESCO, 2023, Generative artificial intelligence in education: what are the opportunities and challenges?
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Vogler R., 2024, ROBONOMICS: J. Autom. Econ., V5, P55
   Walmsley J, 2021, AI SOC, V36, P585, DOI 10.1007/s00146-020-01066-z
   Wang YR, 2020, COMPUT APPL ENG EDUC, V28, P1421, DOI 10.1002/cae.22310
   Wang YY, 2023, IEEE ACCESS, V11, P143272, DOI 10.1109/ACCESS.2023.3342055
   White KM, 2008, J SOC PSYCHOL, V148, P473, DOI 10.3200/SOCP.148.4.473-492
   Wirtz J., 2023, Ital. J. Market., V2023, P289, DOI DOI 10.1007/S43039-023-00076-1
NR 63
TC 20
Z9 20
U1 122
U2 144
PU ELSEVIER SCI LTD
PI London
PA 125 London Wall, London, ENGLAND
SN 0160-791X
EI 1879-3274
J9 TECHNOL SOC
JI Technol. Soc.
PD JUN
PY 2024
VL 77
AR 102521
DI 10.1016/j.techsoc.2024.102521
EA MAR 2024
PG 14
WC Social Issues; Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Issues; Social Sciences - Other Topics
GA RF4M6
UT WOS:001226239900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Garcia, MB
AF Garcia, Manuel B.
TI The Paradox of Artificial Creativity: Challenges and Opportunities of
   Generative AI Artistry
SO CREATIVITY RESEARCH JOURNAL
LA English
DT Article; Early Access
DE Artificial intelligence; generative AI; chatGPT; artificial creativity;
   art
ID ART; INTELLIGENCE; SELF
AB Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundaries, and the development of new collaborative and economic models. The paper posits that the integration of generative AI in art is not just a technological advancement but a significant cultural shift, which necessitates a reevaluation of our understanding of art and the artist. It concludes with a forward-looking perspective, advocating for a collaborative approach to harness the potential of this technology in enriching human creativity and ensuring the vibrant evolution of the art world in the era of AI-driven generation.
C1 [Garcia, Manuel B.] Univ Philippines Diliman, Quezon City, Philippines.
   [Garcia, Manuel B.] FEU Inst Technol, Manila, Philippines.
C3 University of the Philippines System; University of the Philippines
   Diliman; Far Eastern University
RP Garcia, MB (corresponding author), Univ Philippines Diliman, Quezon City, Philippines.; Garcia, MB (corresponding author), FEU Inst Technol, Manila, Philippines.
EM manuelgarciaph@yahoo.com
RI Garcia, Manuel/X-2364-2018
OI Garcia, Manuel/0000-0003-2615-422X
CR Acar S, 2023, CREATIVITY RES J, DOI 10.1080/10400419.2023.2271749
   Akter S, 2023, IND MARKET MANAG, V114, P243, DOI 10.1016/j.indmarman.2023.08.013
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   Atkinson P, 2023, CONVERGENCE-US, V29, P1054, DOI 10.1177/13548565231187730
   Bankins S, 2023, J BUS ETHICS, V185, P725, DOI 10.1007/s10551-023-05339-7
   Bellaiche L, 2023, COGN RES, V8, DOI 10.1186/s41235-023-00499-6
   Birks D, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00142-3
   Blok V., 2022, Philosophy Technology, V35, P1, DOI [10.1007/s13347-022-00559-7, DOI 10.1007/S13347-022-00559-7]
   Browne K, 2022, LEONARDO, V55, P130, DOI 10.1162/leon_a_02092
   BUSHARD B., 2023, Forbes
   Cetinic E, 2022, ACM T MULTIM COMPUT, V18, DOI 10.1145/3475799
   Chatterjee A, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.1024449
   Chesher C, 2023, MEDIA INT AUST, V189, P57, DOI 10.1177/1329878X231193252
   Chiarella SG, 2022, COMPUT HUM BEHAV, V137, DOI 10.1016/j.chb.2022.107406
   Criddle C., 2021, Rembrandts the night watch painting restored by AI
   Cropley D., 2023, Learning Letters, V2, P13, DOI DOI 10.59453/LL.V2.13
   Cropley DH, 2022, PSYCHOL AESTHET CREA, DOI 10.1037/aca0000510
   Das P, 2022, IEEE SIGNAL PROC MAG, V39, P85, DOI 10.1109/MSP.2022.3141365
   Du XJ, 2024, INT J HUM-COMPUT ST, V181, DOI 10.1016/j.ijhcs.2023.103139
   Fenwick M, 2023, COMPUT LAW SECUR REV, V51, DOI 10.1016/j.clsr.2023.105892
   Fields Z., 2023, Multidisciplinary approaches in AI, creativity, innovation, and green collaboration, P1, DOI [10.4018/978-1-6684-6366-6.ch001, DOI 10.4018/978-1-6684-6366-6.CH001]
   Fink A., 2019, Neuroforum, V25, P231, DOI [DOI 10.1515/NF-2019-0006, 10.1515/nf-2019-0006]
   Fox S, 2018, J CONSUM CULT, V18, P169, DOI 10.1177/1469540516659126
   Garcia MB, 2023, APPL SYST INNOV, V6, DOI 10.3390/asi6050096
   Garcia MB, 2024, ANN BIOMED ENG, V52, P139, DOI 10.1007/s10439-023-03299-7
   García-Guimaraes M, 2024, EXPERT REV CARDIOVAS, V22, P167, DOI 10.1080/14779072.2024.2349103
   Grilli L, 2024, CREAT INNOV MANAG, V33, P234, DOI 10.1111/caim.12580
   Gupta V, 2021, EVOL SYST-GER, V12, P439, DOI 10.1007/s12530-019-09303-7
   Guzik E., 2023, Journal of Creativity, V33, P100065, DOI [DOI 10.1016/J.YJOC.2023.100065, https://doi.org/10.1016/j.yjoc.2023.100065]
   Haase J., 2023, Journal of Creativity, V33, DOI DOI 10.1016/J.YJOC.2023.100066
   Hagman G, 2009, ANN NY ACAD SCI, V1159, P164, DOI 10.1111/j.1749-6632.2008.04344.x
   Hong JW, 2019, ACM T MULTIM COMPUT, V15, DOI 10.1145/3326337
   Jiang H, 2023, PROCEEDINGS OF THE 2023 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2023, P363, DOI 10.1145/3600211.3604681
   JOSHI JH, 1976, ANN AM ACAD POLIT SS, V424, P78, DOI 10.1177/000271627642400109
   Kalpokas I, 2023, PHILOS SOC CRIT, DOI 10.1177/01914537231184490
   Karimi P, 2020, PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2020, P221, DOI 10.1145/3377325.3377522
   Ken Gilhooly K., 2024, Journal of Creativity, V34, DOI [10.1016/j.yjoc.2023.100071, DOI 10.1016/J.YJOC.2023.100071]
   Kong FW, 2020, INT J EMERG TECHNOL, V15, P238, DOI 10.3991/ijet.v15i13.15351
   Latikka R, 2023, POETICS, V101, DOI 10.1016/j.poetic.2023.101839
   Li J, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/8803957
   Magni F, 2024, J BUS PSYCHOL, V39, P643, DOI 10.1007/s10869-023-09910-x
   Mannuru NR, 2023, INFORM DEV, DOI 10.1177/02666669231200628
   Mazzone M, 2019, ARTS, V8, DOI 10.3390/arts8010026
   Messer U., 2024, Computers in Human Behavior: Artificial Humans, V2, DOI [10.1016/J.CHBAH.2024.100056, DOI 10.1016/J.CHBAH.2024.100056]
   Mikalonyte ES, 2022, ACM T HUM-ROBOT INTE, V11, DOI 10.1145/3530875
   Morriss-Kay GM, 2010, J ANAT, V216, P158, DOI 10.1111/j.1469-7580.2009.01160.x
   Moruzzi C, 2020, J SCI TECHNOL ARTS, V12, P84, DOI 10.34632/jsta.2020.9481
   Newton Alexis, 2023, arXiv, DOI [10.48550/arXiv.2301.05133, DOI 10.48550/ARXIV.2301.05133]
   Oksanen A., 2023, Computers in Human Behavior: Artificial Humans, P100004, DOI [10.1016/j.chbah.2023.100004, DOI 10.1016/J.CHBAH.2023.100004]
   Oleynick VC, 2014, FRONT HUM NEUROSCI, V8, DOI 10.3389/fnhum.2014.00436
   Piskopani AM, 2022, FIRST INTERNATIONAL SYMPOSIUM ON TRUSTWORTHY AUTONOMOUS SYSTEMS, TAS 2023, DOI 10.1145/3597512.3597528
   Qadri R, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P506, DOI 10.1145/3593013.3594016
   Ramsay B. A., 2012, Fine Art and High Finance, P263, DOI [10.1002/9781119204688.ch11, DOI 10.1002/9781119204688.CH11]
   Rezwana J, 2023, 2023 PROCEEDINGS OF THE 15TH CONFERENCE ON CREATIVITY AND COGNITION, C&C 2023, P62, DOI 10.1145/3591196.3593364
   Richardson L, 2016, ENVIRON PLANN A, V48, P2256, DOI 10.1177/0308518X16653963
   Runco MA, 2023, CREATIVITY RES J, DOI 10.1080/10400419.2023.2257977
   Schei V, 2013, CREATIVITY RES J, V25, P408, DOI 10.1080/10400419.2013.843336
   Shafranskyi V., 2020, Psiholog susplstvo, V2, P89, DOI [10.35774/pis2020.02.089, DOI 10.35774/PIS2020.02.089]
   Shukla A, 2022, CUREUS J MED SCIENCE, V14, DOI 10.7759/cureus.28026
   Sica LS, 2023, IDENTITY, V23, P155, DOI 10.1080/15283488.2022.2050727
   Srinivasan R, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P41, DOI 10.1145/3442188.3445869
   Tavares D., 2023, Instructional technologies in health education and allied disciplines, DOI [10.4018/978-1-6684-7164-7.ch006, DOI 10.4018/978-1-6684-7164-7.CH006]
   Then C., 2023, 2023 IEEE 9 INF TECH, P1, DOI [10.1109/ITIS59651.2023.10420208, DOI 10.1109/ITIS59651.2023.10420208]
   Moura FT, 2023, J CREATIVE BEHAV, V57, P534, DOI 10.1002/jocb.600
   United States Copyright Office, 2023, Re: Zarya of the Dawn (registration # VAu001480196)
   Vinchon F, 2023, J CREATIVE BEHAV, V57, P472, DOI 10.1002/jocb.597
   Wingström R, 2024, CREATIVITY RES J, V36, P177, DOI 10.1080/10400419.2022.2107850
   Yin JT, 2022, BIOL PSYCHOL, V172, DOI 10.1016/j.biopsycho.2022.108359
   Zeilinger M, 2023, LEONARDO, V56, P76, DOI 10.1162/leon_a_02291
   Zhang CZ, 2023, 2023 PROCEEDINGS OF THE 15TH CONFERENCE ON CREATIVITY AND COGNITION, C&C 2023, P254, DOI 10.1145/3591196.3596820
   Zhou E, 2024, PNAS NEXUS, V3, DOI 10.1093/pnasnexus/pgae052
   Zinchenko V., 2020, Humanities Science Current Issues, V3, P190, DOI [10.24919/2308-4863/34-3-30, DOI 10.24919/2308-4863/34-3-30]
   Zubala A, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.600070
NR 73
TC 1
Z9 1
U1 94
U2 109
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1040-0419
EI 1532-6934
J9 CREATIVITY RES J
JI Creativ. Res. J.
PD 2024 MAY 31
PY 2024
DI 10.1080/10400419.2024.2354622
EA MAY 2024
PG 14
WC Psychology, Educational; Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Psychology
GA SO5T8
UT WOS:001235413800001
DA 2024-12-25
ER

PT J
AU Xia, LQ
AF Xia, Lucie Qian
TI Diplomatic relationship-building in the age of generative AI: the
   European Union and China
SO PLACE BRANDING AND PUBLIC DIPLOMACY
LA English
DT Article; Early Access
DE Diplomacy; Generative Artificial Intelligence; China; EU; Geopolitics
ID GOVERNANCE
AB This paper examines the impact of generative AI on international diplomacy through the lens of EU-China diplomatic relationship-building. The first section introduces the broader context of AI's geopolitical impact by distinguishing two different models-the European and Chinese models of regulating and implementing generative AI. The second discuss or explain how the two AI models contrast one another. The third section of the paper focuses on discussing Generative AI and its possible implications for EU-China relations, the extent to which their efforts are likely to strain or facilitate diplomatic relationship-building. The fourth section takes the analysis forward by examining how generative AI could be an unexpected enabling force for EU-China relationship-building. This paper purports that the distinctive EU and Chinese models of generative AI too often belie the opportunities that could potentially enable the EU and China to build their relationship, since generative AI raises shared concerns for the EU and China, and utilising generative AI could make their communications become more efficient, the EU and China may come to reach some kind of shared framework for generative AI development and governance; this could lead to productive talks in other domains such as the trade deficit issue plaguing the EU-China relations or more sensitive cross-strait issues; moreover, in the realm of public diplomacy generative AI could facilitate the EU and China's public diplomatic efforts towards each other.
C1 [Xia, Lucie Qian] Univ Oxford, Dept Polit & Int Relat, Oxford, England.
C3 University of Oxford
RP Xia, LQ (corresponding author), Univ Oxford, Dept Polit & Int Relat, Oxford, England.
EM lucieqian.xia@politics.ox.ac.uk
OI Xia, Lucie Qian/0000-0002-2428-1455
CR [Anonymous], 2020, COM202065
   [Anonymous], 2023, Letter from Ron Wyden, Chairman,
   Arsenault Amelia C., 2022, The Oxford Handbook of AI Governance
   Bjola C, 2022, INT AFF, V98, P471, DOI 10.1093/ia/iiac005
   Coeckelbergh M, 2022, POLITICAL PHILOS INT
   Cohen R., 1997, GLOBAL DIASPORAS INT
   Csernatoni Raluca, 2019, Egmont Security Policy Brief No. 117
   Ding Jeffrey., 2022, The Oxford Handbook of AI Governance
   European Commission, 2019, COM/2019/168 final
   European Commission, 2020, Press Release, EU-China: Commission and China Hold First High-Level Digital Dialogue
   European Commission, 2018, COM2018795 EUR COMM
   European Commission, 2018, COM, P237
   European Commission, 2023, Press release
   Feldstein S, 2023, SURVIVAL, V65, P117, DOI 10.1080/00396338.2023.2261260
   Kissinger Henry., 2015, World Order: Reflections on the Character of Nations and the Course of History
   Roberts H, 2023, INFORM SOC, V39, P79, DOI 10.1080/01972243.2022.2124565
   Zeng JH, 2020, INT AFF, V96, P1441, DOI 10.1093/ia/iiaa172
   Zeng JH, 2017, POLITICS POLICY, V45, P432, DOI 10.1111/polp.12202
NR 18
TC 0
Z9 0
U1 7
U2 12
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1751-8040
EI 1751-8059
J9 PLACE BRANDING PUBLI
JI Place Branding Public Dipl.
PD 2024 FEB 5
PY 2024
DI 10.1057/s41254-023-00321-6
EA FEB 2024
PG 5
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA GY9U1
UT WOS:001156364100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Strawn, G
   Strawn, G
AF Strawn, George
   Strawn, George
TI Masterminds of Generative AI: Vaswani and Altman
SO IT PROFESSIONAL
LA English
DT Article
AB Generative AI (GenAI) is the newest dimension of AI to catch the publics' attention. Some observers think this may be an inflection point on the road to general artificial intelligence. It's too soon to make judgments such as that, but it's not too soon to begin looking at the people who are developing it, not too soon to be seeking a basic understanding of the technology itself. This article will undertake a layman's look at how the technology works. Then it will highlight a technical leader and a business leader in these early days of GenAI.
C1 [Strawn, George; Strawn, George] Natl Acad Sci, NAS Board Res Data & Informat, Washington, DC 20001 USA.
C3 National Academies of Sciences, Engineering & Medicine
RP Strawn, G (corresponding author), Natl Acad Sci, NAS Board Res Data & Informat, Washington, DC 20001 USA.
EM gostrawn@gmail.com; gostrawn@gmail.com
OI Strawn, George/0000-0003-4098-0464
CR Altman Sam, Wikipedia
   [Anonymous], Transformer (deep learning architecture)
   [Anonymous], 2023, National artificial intelligence research resource task force releases final report
   [Anonymous], Attention (machine learning)
   Babcock J, 2021, Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
   ChatGPT, Wikipedia
   Kapur R., 2023, AI Made Simple: A Beginner's Guide to Generative Intelligence
   McLuhan Marshall, 1964, UNDERSTANDING MEDIA
   OpenAI, WIKIPEDIA
   Robertson DouglasS., 1998, The New Renaissance: Computers and the Next Level of Civilization
   Strawn G, 2014, IT PROF, V16, P10, DOI 10.1109/MITP.2014.18
   Strawn GO, 2022, IT PROF, V24, P13, DOI 10.1109/MITP.2022.3172838
   Vaswani A, 2017, ADV NEUR IN, V30
   Vaswani Ashish, Wikipedia
   Y Combinator, Wikipedia
NR 15
TC 1
Z9 1
U1 3
U2 6
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 1520-9202
EI 1941-045X
J9 IT PROF
JI IT Prof.
PD MAR-APR
PY 2024
VL 26
IS 2
BP 13
EP 16
DI 10.1109/MITP.2024.3375568
PG 4
WC Computer Science, Information Systems; Computer Science, Software
   Engineering; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Telecommunications
GA QN4M1
UT WOS:001221538200001
DA 2024-12-25
ER

PT J
AU Khawaldeh, AM
AF Khawaldeh, Ahmed M.
TI Generative AI Hallucinations and Legal Liability in Jordanian Civil
   Courts: Promoting the Responsible Use of Conversational Chat Bots
SO INTERNATIONAL JOURNAL FOR THE SEMIOTICS OF LAW-REVUE INTERNATIONALE DE
   SEMIOTIQUE JURIDIQUE
LA English
DT Article; Early Access
DE Generative AI; Liability; Duty of care; Contracts; Jordan
ID ARTIFICIAL-INTELLIGENCE; ARAB; LAW; AGE
AB Generative Artificial Intelligence (AI) tools produce hallucinations exposing developers and users to a myriad of liabilities in courts. Given the absence of strict laws and regulations structuring how Generative AI content interact with potential allegations of defamation, libel, and slander, judges and attorneys are left with the semiotics of the fragmented articles and rules in each system attempting to settle such cases. The endless interpretations of written and non-verbal signs in the law across the world constitutes a new realm for legal semiotics in the area of Generative AI and defamation. The present analysis examines existing civil liability articles in the Jordanian code to shed light on the litigation and defenses afforded in the defamation realm with respect to Generative AI content. The key finding is that like other countries, civil codes in Jordan are outdated concerning Generative AI content opening a plethora of liability scenarios in libel or slander cases. More importantly, the exercise of semiotic interpretation generates several defenses salvaging users and developers under strict sets of conditions. In sum, new amendments and updated legislative frameworks are needed to protect the healthy development of Generative AI while preserving citizens from damaging defamation, libel, and slander.
C1 [Khawaldeh, Ahmed M.] Amman Arab Univ, Law Sch, Amman, Jordan.
RP Khawaldeh, AM (corresponding author), Amman Arab Univ, Law Sch, Amman, Jordan.
EM a.khawaldeh@aau.edu.jo
OI M. khawaldeh, ahmed/0009-0002-9991-8796
CR Abduljaber M, 2020, J INF KNOWL MANAG, V19, DOI 10.1142/S021964922040002X
   Abduljaber M, 2018, CHANG SOC PERSONAL, V2, P161, DOI 10.15826/csp.2018.2.2.035
   Abduljaber M, 2018, CHIN POLITICAL SCI R, V3, P464, DOI 10.1007/s41111-018-0101-7
   Abduljaber M, 2018, DIG MIDDLE EAST STUD, V27, P97, DOI 10.1111/dome.12132
   Abduljaber MF, 2024, COGENT SOC SCI, V10, DOI 10.1080/23311886.2024.2318859
   Abuanzeh A., 2023, Arab Law Quarterly, V1, P1, DOI [10.1163/15730255-bja10150, DOI 10.1163/15730255-BJA10150]
   Ahmad Al-Qudah Y., 2024, Pakistan Journal of Criminology, V16, P533, DOI [10.62271/pjc.16.1.533.548, DOI 10.62271/PJC.16.1.533.548]
   Al Sarairah I., 2016, Journal of Arts and Social Sciences JASS, V7, P299, DOI [10.53542/jass.v7i2.1122, DOI 10.53542/JASS.V7I2.1122]
   Al-Adwan AS, 2019, J THEOR APPL EL COMM, V14, P51, DOI 10.4067/S0718-18762019000100105
   Al-Amawi M., 2023, Russian Law Journal, V11, P1269
   Al-Billeh T., 2022, Pakistan Journal of Criminology, V14, P1
   Al-Brim A., 2024, Pakistan Journal of Criminology, V16, P1107, DOI [10.62271/pjc.16.2.1107.1118, DOI 10.62271/PJC.16.2.1107.1118]
   Al-Khalaila M., 2023, Journal of Namibian Studies: History Politics Culture, V34, P969, DOI [10.59670/jns.v34i.1136, DOI 10.59670/JNS.V34I.1136]
   Al-Khalailah M., 2012, Journal of Law/Magallat al-Huquq, V36, P11
   Al-Zoubi M., 2023, Intl Rev L, V12, P267, DOI [10.29117/irl.2023.0260, DOI 10.29117/IRL.2023.0260]
   Alabady HS, 2023, INT J CYBER CRIMINOL, V17, P23, DOI 10.5281/zenodo.4766602
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Alkatheiri MS, 2022, COMPUT ELECTR ENG, V101, DOI 10.1016/j.compeleceng.2022.107950
   Alrabei A. M., 2021, J MANAGEMENT INFORM, V24, P1
   Alshurideh M., 2017, Journal of Marketing Communications, V23, P513
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   Arab Legislation Portal, 2023, Law 7 of 2017 on Consumer Protection
   Arcila B.B., 2023, J. Free Speech L., V3, P455
   Aronson J. K., 2023, bmj, V382
   Asran M., 2023, Journal of Positive School Psychology, V7, P973
   Aydin O, 2023, Academic Platform Journal of Engineering and Smart Systems, V11, P118
   Backer L. C., 2023, Research Handbook on Legal Semiotics, P61
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Balas H., 2024, F1000Research, V13, P418, DOI [10.12688/f1000research.147019.1, DOI 10.12688/F1000RESEARCH.147019.1]
   Balevic K., 2022, Business Insider
   BALKIN JM, 1991, TEX LAW REV, V69, P1831
   Balkin JM., 1989, U Miami L Rev, V44, P1119
   Bambauer J., 2023, Journal of Free Speech Law, V3, P343
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Barton H, 2023, Comput Educ Artif Intell, V6, DOI [DOI 10.1016/J.CAEAI.2023.100198, 10.1016/j.caeai.2023.100198]
   Bashayreh M, 2021, INF COMMUN TECHNOL L, V30, P169, DOI 10.1080/13600834.2020.1856025
   Bell J., 2022, Machine Learning and the City, P207, DOI [10.1002/9781119815075.ch18, DOI 10.1002/9781119815075.CH18]
   Bellentani Federico., 2016, PUNCTUM INT J SEMIOT, V2, P28, DOI 10.18680/hss.2016.0004
   Bremmer I, 2021, FOREIGN AFF, V100, P112
   Broekman J. M., 2013, Lawyers Making Meaning: The Semiotics of Law in Legal Education, VII, P127
   Brown N., 2023, Journal of Free Speech Law, V3, P389
   Cabral T. S., 2020, Maastricht Journal of European and Comparative Law, V27, P615
   Cahill R., 2023, Syracuse Law Review
   Cave B., 2022, U Toronto Fac L Rev, V80, P9
   Curran S, 2023, Arxiv, DOI arXiv:2306.11520
   Dagan H., 2022, The American Journal of Jurisprudence, V67, P1, DOI [10.1093/ajj/auac001, DOI 10.1093/AJJ/AUAC001]
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Daradkeh L., 2024, Kurdish Studies, V12, P5993
   De Angelis L, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1166120
   De Silva D, 2022, PATTERNS, V3, DOI 10.1016/j.patter.2022.100489
   Diurni A., 2024, The Italian Law Journal, V9, P473
   Douglas M., 2023, Communications Law Bulletin, V42, P8
   Dreibelbis H., 2021, Duke J Const L Pub Poly, V16, P245
   Duffy MJ, 2017, COMMUN LAW POLICY, V22, P189, DOI 10.1080/10811680.2017.1290984
   Epstein RA., 1995, The University of Toronto Law Journal, V45, P369, DOI DOI 10.2307/825731
   Faqir RSA, 2013, INT J CYBER CRIMINOL, V7, P81
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   Fezari M., 2023, Augmanting reality: The power of generative AI
   Floridi L, 2018, MIND MACH, V28, P689, DOI 10.1007/s11023-018-9482-5
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Garouani M, 2022, INT J ADV MANUF TECH, V120, P1169, DOI 10.1007/s00170-022-08761-9
   Ghandour A., 2024, Ethical Considerations in the Use of ChatGPT: An exploration through the lens of five moral dimensions
   Ghodoosi F, 2022, U ILLINOIS LAW REV, P805
   Ginting O., 2022, International Journal of Economic Technology and Social Sciences (Injects), V3, P190, DOI [10.53695/injects.v3i1.753, DOI 10.53695/INJECTS.V3I1.753]
   Gregory S., 2009, Cardozo Law Review, V31, P2521
   Griffin D., 2002, Jordan Media Strengthening Program, V2
   Grossman M. R., 2023, Duke Law & Technology Review, V23
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   He HH, 2022, CHIN SEMIOT STUD, V18, P147, DOI 10.1515/css-2021-2052
   Heminway JM., 2022, Law Contemp Probs, V85, P131
   Jadalhaq IM, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e16756
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jiang BH, 2024, Data Min, P427
   Kasassbeh F, 2022, INF COMMUN TECHNOL L, V31, P155, DOI 10.1080/13600834.2021.1982192
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kennedy D., 2000, Cardozo L Rev, V22, P1147
   Kevelson R., 1987, LAW SEMIOTICS, V1
   Khasawneh M., 2018, Dirasat: Shari'A and Law Sciences, V45
   Khatsenkova S., 2023, Euro News
   Landers AL., 2020, Ky LJ, V109, P127
   Latifi H, 2024, RELIGIONS, V15, DOI 10.3390/rel15050541
   Li X., 2024, J. Theory Pract. Eng. Sci., V4, P1
   Lloyd HA., 2020, U Rich L Rev, V55, P861
   Lucchi N, 2024, EUR J RISK REGUL, V15, P602, DOI 10.1017/err.2023.59
   Maliha G, 2021, MILBANK Q, V99, P629, DOI 10.1111/1468-0009.12504
   McFadin D., 2024, Arkansas Democrat Gazette
   Metze K, 2024, J PEDIATR SURG, V59, P158, DOI 10.1016/j.jpedsurg.2023.08.018
   Meyer JG, 2023, BIODATA MIN, V16, DOI 10.1186/s13040-023-00339-9
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Morison J., 2024, Research Handbook on Judging and the Judiciary (Elgar-Routledge, Law and Society Series
   Moubaydeen S., 2013, International libel and privacy handbook: A Global Reference for journalists, publishers, webmasters, and lawyers, P505, DOI [10.1002/9781118653784, DOI 10.1002/9781118653784]
   Munir H, 2022, INFORMATION, V13, DOI 10.3390/info13040203
   Naughton J., 2023, Precedent (Sydney NSW), V177, P36
   Nusairat D., 2022, International Journal of Innovation Creativity and Change, V16, P573
   Onder M., 2021, Journal of International Studies, V14, P38, DOI DOI 10.14254/2071-8330.2021/14-2/3
   Onder M., 2019, International Economic Sanctions Outcome: The Influence of Political Agreement
   Onder M., 2023, The Routledge Handbook of the Political Economy of Sanctions, P260
   Onder M, 2022, DIG MIDDLE EAST STUD, V31, P201, DOI 10.1111/dome.12268
   Onder M, 2020, ECONOMIES, V8, DOI 10.3390/economies8010002
   OShea T., Judges issue guidance on use of generative artificial intelligence in court filings in the wake of attorneys citing to nonexistent cases created by ChatGPT
   Osmani N., 2020, Masaryk University Journal of Law and Technology, V14, P53, DOI [https://doi.org/10.5817/mujlt2020-1-3, DOI 10.5817/MUJLT2020-1-3]
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Procaccini FL, 2022, VA LAW REV, V108, P353
   Ricca M., 2023, Research Handbook on Legal Semiotics, P120
   Ricca M, 2022, INT J SEMIOTIC LAW, V35, P179, DOI 10.1007/s11196-020-09771-0
   Roumeliotis KI, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15060192
   Roychowdhury S, 2024, PROCEEDINGS OF THE 17TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, WSDM 2024, P1180, DOI 10.1145/3616855.3635737
   Sanders AK, 2010, COMMUN LAW POLICY, V15, P231, DOI 10.1080/10811680.2010.489845
   Saoudi Y., 2023, Jordanian Journal of Computers and Information Technology (JJCIT), V9
   Sarker Iqbal H, 2022, SN Comput Sci, V3, P158, DOI 10.1007/s42979-022-01043-x
   Scanlon M, 2023, FORENS SCI INT-DIGIT, V46, DOI 10.1016/j.fsidi.2023.301609
   Schmidt Torben, 2022, Anglistik: International Journal of English Studies, V33, P165, DOI 10.33675/angl/2022/1/14
   Schwedler J., 2015, Jordan: The Quiescent Opposition
   Sebastian G., 2023, SSRN, P1, DOI [10.2139/ssrn.4461801, DOI 10.2139/SSRN.4461801]
   Shiyab TM, 2021, COGENT ARTS HUMANITE, V8, DOI 10.1080/23311983.2021.1994112
   Siontis K. C., 2024, ChatGPT hallucinating: can it get any more humanlike?
   Sites B., 2024, University of Miami Law Review, V78, P1025
   Smith H, 2021, AI SOC, V36, P535, DOI 10.1007/s00146-020-01019-6
   Solow-Niederman A., 2023, Columbia Science and Technology Law Review
   Soyer B, 2022, INT J LAW INFORM TEC, V30, P385, DOI 10.1093/ijlit/eaad001
   Spring M, 2022, J OPER MANAG, V68, P592, DOI 10.1002/joom.1215
   Sturgeon TJ, 2021, GLOB STRATEG J, V11, P34, DOI 10.1002/gsj.1364
   Sukereni M., 2023, EPRA International Journal of Research and Development (IJRD), V8, P351, DOI [10.36713/epra13389, DOI 10.36713/EPRA13389]
   Summersfield T., 2003, International Journal for the Semiotics of Law, V16, P155, DOI DOI 10.1023/A:1022809617561
   Swiecki Z, 2022, Computers and Education: Artificial Intelligence, V3, DOI DOI 10.1016/J.CAEAI.2022.100075
   Terzidou K., 2023, MediaLaws
   Tian SB, 2024, BRIEF BIOINFORM, V25, DOI 10.1093/bib/bbad493
   Verenich V., 2012, International Journal of Law Language Discourse, V2, P25
   Villasenor John, 2024, Minnesota Journal of Law, Science & Technology, V25
   Volokh E., 2023, Torts Jotwell
   Ware SJ, 2022, AM BANKRUPT LAW J, V96, P769
   Weisbrod G, 2021, TRANSPORT RES REC, V2675, P417, DOI 10.1177/03611981211002520
   Welch M, 2020, CRIME MEDIA CULT, V16, P7, DOI 10.1177/1741659018822939
   Zavrsnik A., 2020, ERA forum, V20, P4
   Zayed A. B., 2024, Evaluating the fidelity and accuracy of ChatGPT 4 and Google translate in translating legal English documents into Arabic-and vice versa, V1, P87
   Zhan X., 2023, P 5 INT C CONV US IN, P1
   Zhang B, 2023, SCI CHINA INFORM SCI, V66, DOI 10.1007/s11432-021-3449-x
   Zuccon G, 2023, ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL IN THE ASIA PACIFIC REGION, SIGIR-AP 2023, P46, DOI 10.1145/3624918.3625329
NR 138
TC 0
Z9 0
U1 3
U2 3
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0952-8059
EI 1572-8722
J9 INT J SEMIOTIC LAW
JI Int. J. Semiotics Law
PD 2024 SEP 19
PY 2024
DI 10.1007/s11196-024-10199-z
EA SEP 2024
PG 21
WC Law; Language & Linguistics; Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Government & Law; Linguistics; Social Sciences - Other Topics
GA G5A3P
UT WOS:001316759400006
DA 2024-12-25
ER

PT J
AU Migliorini, S
AF Migliorini, Sara
TI China's ' s Interim Measures on generative AI: Origin, content and
   significance
SO COMPUTER LAW & SECURITY REVIEW
LA English
DT Article
DE Generative AI; China; Comparative AI law & policy
AB On 15 August 2023, China's new rules on generative artificial intelligence (AI) entered into force. This article explores the underlying reasons and context for this rapid regulatory development. It argues that China's swift adoption of the Interim Measures on generative AI has been enabled by its traditional approach to digital policy, together with its renewed system of governance and the extensive work that Chinese regulators had conducted on AI ethics and relevant principles. The article also analyses some of the substantial rules laid down by the Interim Measures, offering scholars and policymakers working on generative AI regulation in other jurisdictions the possibility to engage with the solutions chosen by the Chinese regulators. To this end, the article brielfy presents key provisions regarding training data and IP rights; labelling of synthetic content; algorithm registration; accountability for content; and the applicability of existing laws to generative AI. It compares these aspects of the Interim Measures with examples from the European Union and the United States.
C1 [Migliorini, Sara] Univ Macau, Fac Law, Macau, Peoples R China.
   [Migliorini, Sara] LLM Paris 1 Pantheon Sorbonne, Paris, France.
C3 University of Macau
RP Migliorini, S (corresponding author), Univ Macao, Fac Law, E32,Off 2007,Ave Univ, Taipa, Macao, Peoples R China.; Migliorini, S (corresponding author), LLM Paris 1 Pantheon Sorbonne, Paris, France.
EM saramigliorini@um.edu.mo
RI Migliorini, Ph.D., Sara/GQO-8454-2022
OI Migliorini, Sara/0000-0002-1441-3442
CR ajl, About us
   [Anonymous], Provvedimento n. 112 of 30 March 2023.
   [Anonymous], VISION MISSION VALUE
   [Anonymous], 2023, Joint Statement on Enforcement Efforts Against Discrimination and Bias in Automated Systems'
   [Anonymous], 2012, Assistant Professor of Medicine; Director, Oncology Medical Informatics; Associate Director, Cancer Experimental Therapeutics Initiative
   [Anonymous], 2023, FTC Chair Khan and Officials from DOJ, CFPB and EEOC Release Joint Statement on AI'
   [Anonymous], 2021, Law Science
   [Anonymous], 2022, OJL, V227, P1
   [Anonymous], 2019, OJL, V130, P92
   [Anonymous], 2017, Art.8 and Chapter V of the Cybersecurity Law
   [Anonymous], US
   [Anonymous], Groups of plaintiffs sued OpenAI
   [Anonymous], US
   [Anonymous], Justice League: Profiling & Automated Decision -Making (NOYB)
   Bommasani R., 2021, ARXIV
   Bradford A, 2023, FOREIGN AFFAIRS 0623
   Browne Ryan., 2023, CNBC
   Casey Brian, 2020, Texas Law Review, V99, P743
   Deltorn Jean -Marc, 2020, The Oxford Handbook of Music Law and Policy, DOI [10.1093/oxfordhb/9780190872243.013, DOI 10.1093/OXFORDHB/9780190872243.013]
   EPRS, 2023, Artificial intelligence, democracy and elections'
   European Union, 2016, Official Journal of the European Union, L, V119, P1
   Friedmann Danny, 2020, The Oxford Handbook of Online Intermediary Liability, P277
   google, About us
   Heilmann S., 2011, Mao's Invisible Hand: the political foundations of adaptive governance in China
   Hu K., 2023, REUTERS         0202
   Lee Jyh-An, 2018, The SAGE Encyclopedia of the Internet
   Leibowicz Claire, 2023, Technology Review9 August
   Lieberthal KennethG. Oksenberg., 1988, Policy Making in China: Leaders, Structures, and Processes
   Migliorini S., 2023, Elgar Companion to Regulating AI and Big Data in Emerging Economies, P138
   Migliorini S, 2024, EUR J RISK REGUL, V15, P719, DOI 10.1017/err.2024.4
   mofcom, 2019, Commerce Law of the Peoples Republic of China
   Mok Lea, 2023, Hong Kong Free Press4 August
   npc, 2017, The Cybersecurity Law of the People's Republic of China
   Salaymeh L, 2022, RABELS Z AUSL INT PR, V86, P166, DOI 10.1628/rabelsz-2022-0007
   Sheehan M., 2022, WHAT CHINAS ALGORITH
   Sheehan Matt, 2023, China's AI Regulations and How They Get Made
   stablediffusionlitigation, Authors have lodged a class action against Stability AI and Midjourney in the Northern District of California for IP violations
   Stanford, ABOUT US
   stanford, 2022, Behind the Facade of China's Cyber Super -Regulator'
   Tan ZX, 1997, COMMUN ACM, V40, P11, DOI 10.1145/265563.265565
   Tang XY, 2021, FORDHAM LAW REV, V90, P1151
   thomsonreuters, Getty Images sued Stability AI for allegedly infringing its IP rights over millions of images: complaint
   thomsonreuters, plaintiffs seeking to represent those who have posted code on GitHub sued OpenAI and Microsoft, which jointly created a generative AI tool for code, GitHub Copilot, on the grounds that they were improperly monetising open -source code to train such a product.
   Ulbricht L, 2022, REGUL GOV, V16, P3, DOI 10.1111/rego.12437
   Wilman Folkert, 2020, The Responsibility of Online Intermediaries for Illegal User Content in the EU and US
   Yang F, 2014, CHIN J COMMUN, V7, P446, DOI 10.1080/17544750.2014.936954
   Zhang AH., 2022, HARV INT LJ, V63, P457
   US
NR 48
TC 0
Z9 0
U1 37
U2 37
PU ELSEVIER ADVANCED TECHNOLOGY
PI OXFORD
PA OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON,
   OXFORD OX5 1GB, OXON, ENGLAND
SN 0267-3649
J9 COMPUT LAW SECUR REV
JI Comput. Law Secur. Rev.
PD JUL
PY 2024
VL 53
AR 105985
DI 10.1016/j.clsr.2024.105985
EA MAY 2024
PG 7
WC Law
WE Social Science Citation Index (SSCI)
SC Government & Law
GA C9Z6S
UT WOS:001292877200001
DA 2024-12-25
ER

PT J
AU Richards, D
   Worden, D
   Song, XP
   Lavorel, S
AF Richards, Daniel
   Worden, David
   Song, Xiao Ping
   Lavorel, Sandra
TI Harnessing generative artificial intelligence to support nature-based
   solutions
SO PEOPLE AND NATURE
LA English
DT Article
DE chatbot; climate change adaptation; environmental communication;
   extension and outreach; science communication
ID CLIMATE-CHANGE; ADAPTATION; PERSPECTIVE; INFORMATION; INSIGHTS; SCIENCE;
   POLICY
AB The ongoing biodiversity and climate change crises require society to adopt nature-based solutions that integrate and enhance ecosystems. To achieve successful implementation of nature-based solutions, it is vital to communicate scientific information about their benefits and suitability. This article explores the potential of generative artificial intelligence (GenAI) as a tool for automating and scaling up science communication, outreach, and extension for nature-based solutions. To illustrate the potential of GenAI, we present three case study examples; (1) reporting scientific information on ecosystem services, future land use options, and nature-based solutions for farms (2) interactively providing guidance in response to homeowner questions about biodiversity-friendly garden design and (3) visualising potential future scenarios of landscape change that incorporate diverse nature based and technological solutions. These examples demonstrate potential applications which may be relevant to other systems and types of nature-based solutions. While GenAI for nature-based solutions offers significant opportunities, this new technology brings risks of bias, false information, data privacy, mistrust, and high energy usage. Alongside technological development, we require integrated social research into ethics, public acceptability, and user experience, to maximise the benefits of GenAI while limiting these risks. GenAI offers an opportunity to accelerate the dissemination of nature-based design strategies and reach a broader audience, by synthesising information and producing tailored content for specific users and locations. By harnessing the power of GenAI alongside human expertise, we can support nature-based solutions to tackle the complex challenges of future sustainability.Read the free Plain Language Summary for this article on the Journal blog.
   Read the free Plain Language Summary for this article on the Journal blog.
C1 [Richards, Daniel; Lavorel, Sandra] Manaaki Whenua Landcare Res, Lincoln, New Zealand.
   [Worden, David] Manaaki Whenua Landcare Res, Auckland, New Zealand.
   [Song, Xiao Ping] Natl Univ Singapore, Dept Biol Sci, Singapore, Singapore.
   [Song, Xiao Ping] Natl Univ Singapore, Dept Architecture, Singapore, Singapore.
   [Lavorel, Sandra] Univ Grenoble Alpes, Univ Savoie Mont Blanc, Lab Ecol Alpine, CNRS, Grenoble, France.
C3 Landcare Research - New Zealand; Landcare Research - New Zealand;
   National University of Singapore; National University of Singapore;
   Universite Gustave-Eiffel; Communaute Universite Grenoble Alpes;
   Universite Grenoble Alpes (UGA); Centre National de la Recherche
   Scientifique (CNRS); Universite Savoie Mont Blanc
RP Richards, D (corresponding author), Manaaki Whenua Landcare Res, Lincoln, New Zealand.
EM richardsd@landcareresearch.co.nz
RI Lavorel, Sandra/AGM-2903-2022
OI Worden, David/0000-0002-4147-1453
FU New Zealand Ministry of Business, Innovation and Employment
FX No Statement Available
CR Abbott M, 2019, J LANDSC ARCHIT, V14, P6, DOI 10.1080/18626033.2019.1673562
   Albert C, 2021, AMBIO, V50, P1446, DOI 10.1007/s13280-020-01365-1
   Almenar JB, 2021, LAND USE POLICY, V100, DOI 10.1016/j.landusepol.2020.104898
   Amano T, 2021, PLOS BIOL, V19, DOI 10.1371/journal.pbio.3001296
   Yeung JA, 2023, FRONT DIGIT HEALTH, V5, DOI 10.3389/fdgth.2023.1161098
   Ausseil AGE, 2019, ENVIRON MODELL SOFTW, V119, P1, DOI 10.1016/j.envsoft.2019.05.009
   Berdejo-Espinola V, 2023, SCIENCE, V379, P991, DOI 10.1126/science.adg9714
   Birner R, 2021, APPL ECON PERSPECT P, V43, P1260, DOI 10.1002/aepp.13145
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Borgesius FJZ, 2020, INT J HUM RIGHTS, V24, P1572, DOI 10.1080/13642987.2020.1743976
   Brendel AB, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13041974
   Brower A. L., 2018, Journal of New Zealand Grasslands, V80, P47
   Brugger J, 2015, WEATHER CLIM SOC, V7, P18, DOI 10.1175/WCAS-D-13-00036.1
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Calliari E, 2019, SCI TOTAL ENVIRON, V656, P691, DOI 10.1016/j.scitotenv.2018.11.341
   Chausson A, 2020, GLOBAL CHANGE BIOL, V26, P6134, DOI 10.1111/gcb.15310
   Chien AA, 2023, PROCEEDINGS OF THE 2ND ACM WORKSHOP ON SUSTAINABLE COMPUTER SYSTEMS, HOTCARBON 2023, DOI 10.1145/3604930.3605705
   Cohen-Shacham E., 2016, Nature-based solutions to address global societal challenges, DOI [DOI 10.2305/IUCN.CH.2016.13.EN, 10.2305/IUCN.CH.2016.13.en]
   Cohen-Shacham E, 2019, ENVIRON SCI POLICY, V98, P20, DOI 10.1016/j.envsci.2019.04.014
   Cottrell C, 2022, ENVIRON SCI POLICY, V135, P162, DOI 10.1016/j.envsci.2022.05.003
   Daum T, 2022, AGR SYST, V196, DOI 10.1016/j.agsy.2021.103353
   de los Ríos C, 2018, GLOB ECOL CONSERV, V15, DOI 10.1016/j.gecco.2018.e00412
   Ding N, 2023, NAT MACH INTELL, V5, P220, DOI 10.1038/s42256-023-00626-4
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Frantzeskaki N, 2022, BIOSCIENCE, V72, P113, DOI 10.1093/biosci/biab105
   Frantzeskaki N, 2019, ENVIRON SCI POLICY, V93, P101, DOI 10.1016/j.envsci.2018.12.033
   Galecka-Drozda A, 2021, URBAN FOR URBAN GREE, V65, DOI 10.1016/j.ufug.2021.127345
   Goodwin S, 2023, NAT SUSTAIN, V6, DOI 10.1038/s41893-022-01036-x
   Hossen MA, 2022, ENVIRON SCI POLICY, V137, P174, DOI 10.1016/j.envsci.2022.08.028
   Jaung W, 2024, URBAN FOR URBAN GREE, V91, DOI 10.1016/j.ufug.2023.128186
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kattge J, 2020, GLOBAL CHANGE BIOL, V26, P119, DOI 10.1111/gcb.14904
   Kitamura FC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230171
   Klein TM, 2015, ECOSYST SERV, V13, P173, DOI 10.1016/j.ecoser.2015.02.006
   Klein TM, 2016, ECOSYST SERV, V19, P65, DOI 10.1016/j.ecoser.2016.04.002
   Kraus M., 2023, ENHANCING LARGE LANG
   Langemeyer J., 2021, NAT BASED SOLUT, V1, DOI [10.1016/j.nbsj.2021.100006, DOI 10.1016/J.NBSJ.2021.100006]
   Larosa F, 2023, NAT CLIM CHANGE, V13, P497, DOI 10.1038/s41558-023-01686-5
   Lee D, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18010271
   Liang WX, 2022, NAT MACH INTELL, V4, P669, DOI 10.1038/s42256-022-00516-1
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Longoni Chiara, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P97, DOI 10.1145/3531146.3533077
   Lupp G, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13010188
   Ma Z, 2012, SMALL-SCALE FOR, V11, P87, DOI 10.1007/s11842-011-9170-2
   Macey-Dare R., 2023, SSRN ELECT J, DOI DOI 10.2139/SSRN.4366749
   Manning M, 2015, REG ENVIRON CHANGE, V15, P581, DOI 10.1007/s10113-014-0673-1
   Meyer JG, 2023, BIODATA MIN, V16, DOI 10.1186/s13040-023-00339-9
   Morales-Barquero L, 2019, REMOTE SENS-BASEL, V11, DOI 10.3390/rs11192305
   Moser SC, 2014, WIRES CLIM CHANGE, V5, P337, DOI 10.1002/wcc.276
   Mostaco G., 2018, Agronomobot: a smart answering chatbot applied to agricultural sensor networks
   Munn L, 2024, AI SOC, V39, P1673, DOI 10.1007/s00146-023-01636-x
   Murphy C, 2013, ANIM PROD SCI, V53, P917, DOI 10.1071/AN12347
   Nelson DR, 2020, CURR OPIN ENV SUST, V45, P49, DOI 10.1016/j.cosust.2020.09.001
   Nesshöver C, 2017, SCI TOTAL ENVIRON, V579, P1215, DOI 10.1016/j.scitotenv.2016.11.106
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Odubote I. K., 2020, HDB CLIMATE CHANGE R, P1115, DOI [10.1007/978-3-319-71025-9_109-1, DOI 10.1007/978-3-319-71025-9_109-1]
   Ong R. J., 2021, Journal of Physics: Conference Series, V1755, DOI 10.1088/1742-6596/1755/1/012051
   Pasgaard M, 2015, GLOBAL ENVIRON CHANG, V35, P279, DOI 10.1016/j.gloenvcha.2015.09.018
   Peng S, 2023, PREPRINT
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Peters TM, 2023, COMM COM INF SC, V1903, P301, DOI 10.1007/978-3-031-44070-0_15
   Ray C.D., 2007, Journal of Extension, V45, P1
   Richards D, 2023, ECOSYST PEOPLE, V19, DOI 10.1080/26395916.2023.2225647
   Sanchez-Arcilla A., 2022, NATURE BASED SOLUTIO, V2, DOI DOI 10.1016/J.NBSJ.2022.100032
   Seddon N, 2020, PHILOS T R SOC B, V375, DOI 10.1098/rstb.2019.0120
   Shaw A, 2009, GLOBAL ENVIRON CHANG, V19, P447, DOI 10.1016/j.gloenvcha.2009.04.002
   Shr YH, 2019, ECOL ECON, V156, P375, DOI 10.1016/j.ecolecon.2018.10.015
   Subramanian A, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0253377
   Taiuru K., 2020, TREATY WAITANGI TE T
   Telenius A, 2011, NORD J BOT, V29, P378, DOI 10.1111/j.1756-1051.2011.01167.x
   Tomlinson B., 2023, SSRN ELECT J, DOI [DOI 10.2139/SSRN.4399923, 10.2139/ssrn.4399923]
   Tozer L, 2020, CITIES, V107, DOI 10.1016/j.cities.2020.102892
   Vaghefi SA, 2023, COMMUN EARTH ENVIRON, V4, DOI 10.1038/s43247-023-01084-x
   Vartiainen H, 2023, DIGIT CREAT, V34, P1, DOI 10.1080/14626268.2023.2174557
   Watt MS, 2019, FORESTRY, V92, P1, DOI 10.1093/forestry/cpy024
   Wu AN, 2022, BUILD ENVIRON, V223, DOI 10.1016/j.buildenv.2022.109477
   Yang X, 2022, NPJ DIGIT MED, V5, DOI 10.1038/s41746-022-00742-2
   Ye XY, 2022, ENVIRON PLAN B-URBAN, V49, P794, DOI 10.1177/23998083211023516
   Yogarajan V, 2022, FRONT COMP SCI-SWITZ, V4, DOI 10.3389/fcomp.2022.1070493
   Zedler JB, 2000, TRENDS ECOL EVOL, V15, P402, DOI 10.1016/S0169-5347(00)01959-5
   Zhu JJ, 2023, ENVIRON SCI TECHNOL, DOI 10.1021/acs.est.3c01818
   Zmihorski M, 2023, BIOL CONSERV, V288, DOI 10.1016/j.biocon.2023.110371
NR 82
TC 2
Z9 2
U1 24
U2 30
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
EI 2575-8314
J9 PEOPLE NAT
JI People Nat.
PD APR
PY 2024
VL 6
IS 2
BP 882
EP 893
DI 10.1002/pan3.10622
EA FEB 2024
PG 12
WC Biodiversity Conservation; Ecology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biodiversity & Conservation; Environmental Sciences & Ecology
GA MR8T0
UT WOS:001173879800001
OA gold
DA 2024-12-25
ER

PT J
AU Shen, XQ
   Mo, XH
   Xia, TS
AF Shen, Xiaoqi
   Mo, Xiaohong
   Xia, Tiansheng
TI Exploring the attitude and use of GenAI-image among art and design
   college students based on TAM and SDT
SO INTERACTIVE LEARNING ENVIRONMENTS
LA English
DT Article; Early Access
DE Generative artificial intelligence image; Midjourney; technology
   acceptance model; self-determination theory; art and design; college
   students
ID SELF-DETERMINATION THEORY; TECHNOLOGY ACCEPTANCE MODEL; INFORMATION;
   MOTIVATION; EXTENSION; EFFICACY
AB The rapid development of generative artificial intelligence image technology has a tremendous impact on the art and design field and will cause great changes in art and design education. Considering the interrelationships between students' intentions and motivations, this study combines the technology acceptance model with self-determination theory and also considers the possible risks associated with the new technology to construct an integrated theoretical model, aiming to explore the acceptance of GenAI-image among art and design college students. In this study, 308 questionnaires were collected using the questionnaire method and structural equation modeling. The results showed that art and design college students have distinct professional characteristics, and they are more concerned about the originality and privacy of their work than about the replacement of the future jobs. Additionally, they are more likely to use GenAI-image as an effective tool to enhance their creations, and their acceptance of the technology increases when it is capable of connecting other learners with the same objectives. But the power of the technology may also increase their concerns about job substitution and the privacy of originality.
C1 [Shen, Xiaoqi; Mo, Xiaohong; Xia, Tiansheng] Guangdong Univ Technol, Sch Art & Design, Guangzhou, Peoples R China.
C3 Guangdong University of Technology
RP Xia, TS (corresponding author), Guangdong Univ Technol, Sch Art & Design, Guangzhou, Peoples R China.
EM xiatiansheng@gdut.edu.cn
OI XIA, TIANSHENG/0000-0002-6943-2958
FU National Social Science Foundation of China
FX No Statement Available
CR ANDERSON JC, 1988, PSYCHOL BULL, V103, P411, DOI 10.1037/0033-2909.103.3.411
   Asparouhov T, 2009, STRUCT EQU MODELING, V16, P397, DOI 10.1080/10705510903008204
   BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
   Bauer K., 2024, SSRN ELECT J, DOI [https://doi.org/10.2139/ssrn.4782554, DOI 10.2139/SSRN.4782554]
   Benlian A, 2020, INFORM SYST J, V30, P1010, DOI 10.1111/isj.12243
   Bisdas S, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.795284
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Casteleiro-Pitrez J., 2024, Digital, V4, P316, DOI [https://doi.org/10.3390/digital4020016, DOI 10.3390/DIGITAL4020016]
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Cheng YM, 2011, INFORM SYST J, V21, P269, DOI 10.1111/j.1365-2575.2010.00356.x
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dai XL, 2023, Arxiv, DOI arXiv:2309.15807
   Dai Y., 2023, Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education
   Davenport T. H., 2022, Harvard Business Review
   DAVIS FD, 1993, INT J MAN MACH STUD, V38, P475, DOI 10.1006/imms.1993.1022
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   De Vreede T., 2021, HAWAII INT C SYSTEM, DOI DOI 10.24251/HICSS.2021.019
   Deci E. L., 2013, Intrinsic motivation and self-determination in human behavior (perspectives in social psychology), DOI DOI 10.1007/978-1-4899-2271-7
   Deci E.L., 2002, HDB SELF DETERMINATI, P431, DOI DOI 10.1111/BJHP.12054
   Eshraghian JK, 2020, NAT MACH INTELL, V2, P157, DOI 10.1038/s42256-020-0161-x
   Fathali S, 2018, AUSTRALAS J EDUC TEC, V34, P138, DOI 10.14742/ajet.3629
   Foster D., 2022, Generative deep learning
   Franceschelli G, 2022, DATA POLICY, V4, DOI 10.1017/dap.2022.10
   Gagné M, 2022, NAT REV PSYCHOL, V1, P378, DOI 10.1038/s44159-022-00056-w
   Garnier-Villarreal M, 2020, PSYCHOL METHODS, V25, P46, DOI 10.1037/met0000224
   Hong MK, 2023, Arxiv, DOI arXiv:2306.01217
   Hsieh HL, 2022, HEALTHCARE-BASEL, V10, DOI 10.3390/healthcare10102038
   Hutson J., 2024, Forum for Art Studies, V1, P393, DOI [https://doi.org/10.59400/fas.v1i1.393, DOI 10.59400/FAS.V1I1.393]
   JACKSON DN, 1969, PSYCHOL BULL, V72, P30, DOI 10.1037/h0027421
   Jaiswal DP, 2020, PROCEDIA COMPUT SCI, V168, P57, DOI 10.1016/j.procs.2020.02.257
   Johnson DG, 2017, J ASSOC INF SCI TECH, V68, P2267, DOI 10.1002/asi.23867
   Racero FJ, 2020, APPL SCI-BASEL, V10, DOI 10.3390/app10082711
   Kaiser Z, 2019, DES CULT, V11, P173, DOI 10.1080/17547075.2019.1609279
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Khan IU, 2018, TELEMAT INFORM, V35, P964, DOI 10.1016/j.tele.2017.09.009
   Latikka R, 2023, POETICS, V101, DOI 10.1016/j.poetic.2023.101839
   Lee DY, 2013, COMPUT EDUC, V61, P193, DOI 10.1016/j.compedu.2012.10.001
   Lee Y, 2015, COMPUT HUM BEHAV, V51, P418, DOI 10.1016/j.chb.2015.05.021
   Li J, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101410
   Liao SL, 2023, INT J IND ERGONOM, V95, DOI 10.1016/j.ergon.2023.103455
   Liu GX, 2024, INNOV LANG LEARN TEA, V18, P125, DOI 10.1080/17501229.2023.2240316
   Luo YM, 2021, Z ERZIEHWISS, V24, P1379, DOI 10.1007/s11618-021-01042-3
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Moon JW, 2001, INFORM MANAGE-AMSTER, V38, P217, DOI 10.1016/S0378-7206(00)00061-6
   Moradbakhti L, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.855091
   Nikou SA, 2017, COMPUT HUM BEHAV, V68, P83, DOI 10.1016/j.chb.2016.11.020
   Oppenlaender J, 2023, Arxiv, DOI [arXiv:2303.13530, DOI 10.48550/ARXIV.2303.13530, 10.48550/arXiv.2303.13530]
   Oppl S, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14020900
   Pavlov G, 2021, EDUC PSYCHOL MEAS, V81, P110, DOI 10.1177/0013164420926231
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Rahman MS, 2016, COMPUT HUM BEHAV, V58, P12, DOI 10.1016/j.chb.2015.12.016
   Rezvani A, 2017, COMPUT HUM BEHAV, V76, P263, DOI 10.1016/j.chb.2017.07.032
   Ryan RM, 2006, J PERS, V74, P1557, DOI 10.1111/j.1467-6494.2006.00420.x
   Ryan RM, 2017, SELF-DETERMINATION THEORY: BASIC PSYCHOLOGICAL NEEDS IN MOTIVATION, DEVELOPMENT, AND WELLNESS, P1, DOI 10.1521/978.14625/28806
   Sagnier C, 2020, INT J HUM-COMPUT INT, V36, P993, DOI 10.1080/10447318.2019.1708612
   Sahin F, 2022, SOC PSYCHOL EDUC, V25, P567, DOI 10.1007/s11218-022-09702-w
   Sallam M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48254
   Scherer Matthew U., 2016, Harv. JL & Tech., V29, P353
   Schetinger V., 2023, Doom or deliciousness: Challenges and opportunities for visualization in the age of generative models
   Sergis S, 2018, COMPUT HUM BEHAV, V78, P368, DOI 10.1016/j.chb.2017.08.011
   Suryadevara Chaitanya Krishna, 2020, International Journal of Innovations in Engineering Research and Technology, V7, P49
   Tang AR, 2024, J NURS SCHOLARSHIP, V56, P314, DOI 10.1111/jnu.12938
   Tedre M., 2023, How text-to-image generative AI is transforming mediated action?, DOI [10.36227/techrxiv.24449185.v1, DOI 10.36227/TECHRXIV.24449185.V1]
   Teeroovengadum V., 2017, INT J ED DEV USING I, V13
   Teo T, 2009, ASIA PAC EDUC REV, V10, P535, DOI 10.1007/s12564-009-9051-y
   Vallerand RJ, 1997, ADV EXP SOC PSYCHOL, V29, P271, DOI 10.1016/S0065-2601(08)60019-2
   Vartiainen H, 2023, DIGIT CREAT, V34, P1, DOI 10.1080/14626268.2023.2174557
   Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2008, DECISION SCI, V39, P273, DOI 10.1111/j.1540-5915.2008.00192.x
   Wang WT, 2009, COMPUT EDUC, V53, P761, DOI 10.1016/j.compedu.2009.02.021
   Wang YM, 2024, INTERACT LEARN ENVIR, V32, P2584, DOI 10.1080/10494820.2022.2153147
   Wang YY, 2022, INTERACT LEARN ENVIR, V30, P619, DOI 10.1080/10494820.2019.1674887
   Zardari BA, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13116201
   Zhang CM, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00420-7
   Zheng J, 2020, INT J EDUC RES, V102, DOI 10.1016/j.ijer.2020.101612
NR 78
TC 1
Z9 1
U1 76
U2 76
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1049-4820
EI 1744-5191
J9 INTERACT LEARN ENVIR
JI Interact. Learn. Environ.
PD 2024 JUN 14
PY 2024
DI 10.1080/10494820.2024.2365959
EA JUN 2024
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA UU2G3
UT WOS:001250500400001
DA 2024-12-25
ER

PT J
AU Bannister, P
   Santamaría-Urbieta, A
   Alcalde-Peñalver, E
AF Bannister, Peter
   Santamaria-Urbieta, Alexandra
   Alcalde-Penalver, Elena
TI A Delphi Study on Generative Artificial Intelligence and English Medium
   Instruction Assessment: Implications for Social Justice
SO IRANIAN JOURNAL OF LANGUAGE TEACHING RESEARCH
LA English
DT Article
DE academic integrity; assessment; English as a medium of instruction;
   generative artificial intelligence; higher education
ID CONSENSUS
AB The emergence of generative artificial intelligence (GenAI) text generator tools and the potential challenges for higher education (HE) have characterised informal academic discussion on multiple fora. Specifically examining the case of English medium instruction (EMI) assessment academic integrity, this study sought to explore this conundrum by conceptualising threats and possible recommendations to counter these by creating a problem-solution matrix for key stakeholders considering the scarce academic literature available. An exploratory Delphi technique was employed as a way of generating ideas, gauging expert perspectives, and establishing consensus based on the premise of wisdom-of-(expert)-crowds. In the data collection stage, this new use of the mixed-methods methodology in the field included iterative Delphi questionnaire rounds and concurrent focus group sessions with a panel of 26 international experts. Quantitative and qualitative data were analysed using descriptive statistics and thematic analysis, respectively. The resulting GenAI and EMI Assessment Problem-Solution Matrix is an empirically informed instrument for key stakeholders in EMI HE that exemplifies a range of GenAI-induced issues and recommendations as to how to proceed going forward in EMI HE pedagogical settings. This contributes to the field in line with broader theoretical assessment principles, particularly with those seeking to mitigate inequitable practices. Further contextual matters pertaining to social justice were highlighted, such as the effects of the massification and commodification of HE on the role of assessment in both EMI didactic contexts and others. The findings here take a step towards addressing the gaps identified but also represent a means of sparking much-needed further discussion in both extant literature and praxis.
C1 [Bannister, Peter; Santamaria-Urbieta, Alexandra] Univ Int La Rioja UNIR, La Rioja, Spain.
   [Alcalde-Penalver, Elena] Univ Alcala, Alcala De Henares, Spain.
C3 Universidad Internacional de La Rioja (UNIR); Universidad de Alcala
RP Bannister, P (corresponding author), Univ Int La Rioja UNIR, La Rioja, Spain.
EM peter.bannister@unir.net
RI Bannister, Peter/HDO-4393-2022
OI Bannister, Peter/0000-0002-7216-3912
FU Universidad Internacional de La Rioja (UNIR) , Spain [PP-2023-22]
FX This research has been carried out as part of the Project of Analysis
   and Development for the Optimization of Assessment and Regulation of
   Generative Artificial Intelligence in Humanities (PANDORA) , with
   project reference number PP-2023-22, financed by Universidad
   Internacional de La Rioja (UNIR) , Spain. The study was conducted having
   been granted ethics approval from Universidad Internacional de La Rioja
   (UNIR) with ethics approval code PI006/2023.
CR Abd-Elaal ES, 2022, EUR J ENG EDUC, V47, P725, DOI 10.1080/03043797.2022.2046709
   Airey J, 2012, AILA REV, V25, P64, DOI 10.1075/aila.25.05air
   Akins Ralitsa B, 2005, BMC Med Res Methodol, V5, P37, DOI 10.1186/1471-2288-5-37
   Atlas S., 2023, ChatGPT for Education and Professional Development: A guide to conversational AI
   BALEAP, 2022, BALEAP TEAP Individual Accreditation Scheme
   Bishop L., A Computer Wrote this Paper: What ChatGPT Means for Education, Research, and Writing
   Braun V., 2019, HDB RES METHODS HLTH, P843, DOI [DOI 10.1007/978-981-10-5251-4103, DOI 10.1007/978-981-10-5251-4_103]
   Cabaleiro-Cerviño G, 2020, GIST-EDUC LEARN RES, P155, DOI 10.26817/16925777.711
   Carless D., 2015, Excellence in University Assessment: Learning from Award-Winning Practice
   Carless D., 2017, SCALING ASSESSMENT L, V5, DOI [10.1007/978-981-10-3045-1, DOI 10.1007/978-981-10-3045-1]
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chan PTR, 2022, DATA, V7, DOI 10.3390/data7020018
   Chen N. N., 2019, The Politics ofEnglish Second Language Writing Assessment in Global Contexts, P170
   Chew E, 2015, INNOV EDUC TEACH INT, V52, P454, DOI 10.1080/14703297.2013.832633
   Coyle D., 2010, Content and Language Integrated Learning
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Cuhls K, 2001, TECHNOL ANAL STRATEG, V13, P555, DOI 10.1080/09537320120095446
   Dai Yun, 2023, Procedia CIRP, P84, DOI 10.1016/j.procir.2023.05.002
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Dearden J., 2014, ENGLISH MEDIUM INSTR
   Denisova-Schmidt E., 2017, The Challenges of Academic Integrity in Higher Education: Current Trends and Prospects
   Dettori JR, 2020, GLOB SPINE J, V10, P499, DOI 10.1177/2192568220911648
   Diamond IR, 2014, J CLIN EPIDEMIOL, V67, P401, DOI 10.1016/j.jclinepi.2013.12.002
   Doiz A, 2018, TESOL QUART, V52, P657, DOI 10.1002/tesq.452
   Dornyei Z., 2009, QUESTIONNAIRES 2 LAN, DOI DOI 10.4324/9780203864739
   Entwistle N, 2018, STUDENT LEARNING AND ACADEMIC UNDERSTANDING: A RESEARCH PERSPECTIVE WITH IMPLICATIONS FOR TEACHING, P1
   Escotet M., 2023, PROSPECTS, DOI [10.1007/s11125-023-09642-z, DOI 10.1007/S11125-023-09642-Z]
   Farrell TSC, 2020, INT J BILING EDUC BI, V23, P277, DOI 10.1080/13670050.2019.1612840
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Fink-Hafner D., 2019, Metodoloski zvezki, V16, P1, DOI DOI 10.51936/FCFM6982
   Green RA, 2014, SAGE OPEN, V4, DOI 10.1177/2158244014529773
   Greenbaum T. L, 1998, The handbook for focus group research, V2nd, DOI [10.4135/9781412986151.n12, DOI 10.4135/9781412986151.N12]
   Gutierrez-Martin A., 2022, Revista Comunicar, Revista Cientifica de Educomunicacion, VXXX, P21, DOI [10.3916/C70-2022-02, DOI 10.3916/C70-2022-02]
   Hsu C., 2007, PRACT ASSESS RES EVA, V12, DOI [10.7275/pdz9-th90, DOI 10.7275/PDZ9-TH90]
   Hultgren A. K., 2022, Journal of English-Medium Instruction, V1, P105, DOI [10.1075/jemi.21019.hul, DOI 10.1075/JEMI.21019.HUL]
   Inbar-Lourie O., 2022, J Engl-Medium Instr, V1, P204, DOI [10.1075/jemi.21014.inb, DOI 10.1075/JEMI.21014.INB]
   Kaktins L, 2019, ETHICS EDUC, V14, P430, DOI 10.1080/17449642.2019.1660946
   Kao YT., 2017, English as a medium of instruction in higher education: Implementations and classroom practices in Taiwan, P183, DOI [10.1007/978-981-10-4645-211, DOI 10.1007/978-981-10-4645-211]
   Khalil M, 2023, Arxiv, DOI [arXiv:2302.04335, 10.48550/arxiv.2302.04335]
   Lasagabaster D., 2022, Journal of English-Medium Instruction, V1, P48, DOI [DOI 10.1075/JEMI.21011.LAS, 10.1075/jemi.21011.las]
   Lasagabaster D, 2018, LANG TEACHING, V51, P400, DOI 10.1017/S0261444818000113
   Li JR, 2018, LANG LEARN TECHNOL, V22, P27
   Li N., 2018, Language Education and Assessment, V1, P28, DOI [10.29140/lea.v1n1.46, DOI 10.29140/LEA.V1N1.46]
   Liang WX, 2023, Arxiv, DOI arXiv:2304.02819
   Lodge JM, 2023, AUSTRALAS J EDUC TEC, V39, P18, DOI 10.14742/ajet.8695
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lynch B., 2001, LANG TEST, V18, P351, DOI [10.1177/026553220101800403, DOI 10.1177/026553220101800403]
   Macaro E, 2022, LANG TEACHING, V55, P533, DOI 10.1017/S0261444822000052
   Macaro E, 2018, LANG TEACHING, V51, P36, DOI 10.1017/S0261444817000350
   Macfarlane B, 2014, STUD HIGH EDUC, V39, P339, DOI 10.1080/03075079.2012.709495
   MAHAJAN V, 1976, J MARKETING RES, V13, P317, DOI 10.2307/3150755
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Milligan L. O., 2018, English as a medium of instruction in postcolonial contexts: Issues of quality, equity and social justice
   Milligan LO, 2016, COMP EDUC, V52, P277, DOI 10.1080/03050068.2016.1185251
   Morgan, 1997, FOCUS GROUPS QUALITA, DOI [10.4135/9781412984287, DOI 10.4135/9781412984287]
   Morreel S, 2023, MED TEACH, V45, P665, DOI 10.1080/0142159X.2023.2187684
   Mortenson L., 2021, BC TEAL Journal, V6, P106, DOI [10.14288/bctj.v6i1.422, DOI 10.14288/BCTJ.V6I1.422]
   Mortenson L, 2022, ENGL SPECIF PURP, V65, P1, DOI 10.1016/j.esp.2021.08.002
   Otto A, 2021, REV TEMPOS ESPACOS E, V14, DOI 10.20952/revtee.v14i33.15475
   Passey D, 2019, BRIT J EDUC TECHNOL, V50, P972, DOI 10.1111/bjet.12783
   Readings B., 1997, The university in ruins, DOI [10.2307/j.ctv1cbn3kn.9, DOI 10.2307/J.CTV1CBN3KN.9]
   Robinson JP, 2010, EDUC RESEARCHER, V39, P582, DOI 10.3102/0013189X10389811
   Rolfe G., 2013, The university in dissent, DOI DOI 10.4324/9780203084281
   Rothbauer PauletteM., 2008, SAGE ENCY QUALITATIV, P1, DOI DOI 10.4135/9781412963909.N468
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sadasivan VS, 2024, Arxiv, DOI [arXiv:2303.11156, DOI 10.48550/ARXIV.2303.11156]
   Sadler DR, 2010, ASSESS EVAL HIGH EDU, V35, P535, DOI 10.1080/02602930903541015
   Sah P. K., 2022, Journal of English-Medium Instruction, V1, P124, DOI [10.1075/jemi.21022.sah, DOI 10.1075/JEMI.21022.SAH]
   Scheibe M., 2002, The Delphi method: Techniques and applications, P257
   Shamim F., 2023, English as a Medium of Instruction in South Asia, P92, DOI [10.4324/9781003342373-7, DOI 10.4324/9781003342373-7]
   Shen YQ, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230163
   Sterling S., 2023, Research Methods in Applied Linguistics, V2, P100040, DOI 10.1016/j.rmal.2022.100040
   Surowiecki J., 2005, WISDOM CROWDS, DOI DOI 10.5555/1095645
   Tai H-Y., 2015, International Journal of Language and Linguistics, V2
   Thangaratinam S, 2005, OBSTET GYNAECOL, V7, P120, DOI 10.1576/toag.7.2.120.27071
   UCL, 2023, AI, education and assessment
   Uztosun MS, 2018, EUR J TEACH EDUC, V41, P549, DOI 10.1080/02619768.2018.1472569
   Van der Walt Christa., 2013, English-medium instruction at universities: Global challenges, P27
   von der Gracht HA, 2012, TECHNOL FORECAST SOC, V79, P1525, DOI 10.1016/j.techfore.2012.04.013
NR 79
TC 6
Z9 6
U1 9
U2 17
PU URMIA UNIV
PI URMIA
PA FAC HUMANITIES & PERSIAN LITERATURE, URMIA, 57153-1177, IRAN
SN 2322-1291
J9 IRAN J LANG TEACH RE
JI Iran. J. Lang. Teach. Res.
PY 2023
VL 11
IS 3
BP 53
EP 80
DI 10.30466/ijltr.2023.121406
PG 28
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA IF1Q6
UT WOS:001164825200002
DA 2024-12-25
ER

PT J
AU Rister, A
   Velez, M
AF Rister, Alex
   Velez, Meghan
TI Partnering with genAI for communication course design: Innovations,
   challenges, and ethical considerations
SO COMMUNICATION TEACHER
LA English
DT Article; Early Access
AB This article explores the value of generative AI (genAI) tools for much-needed support for instructors in higher education in the realm of course design. Two authors detail their experiences partnering with two distinct tools, ChatGPT and Bard (now known as Gemini), for communication course development, emphasizing iterative collaboration with genAI. Benefits of genAI for creativity and innovation are balanced with challenges and ethical considerations, along with recommendations for faculty and institutions on leveraging genAI for innovative course design.
C1 [Rister, Alex] Embry Riddle Aeronaut Univ, Dept Humanities & Commun, Daytona Beach, FL 32114 USA.
   [Velez, Meghan] Univ Cent Florida, Dept Writing & Rhetor, Orlando, FL USA.
C3 Embry-Riddle Aeronautical University; State University System of
   Florida; University of Central Florida
RP Rister, A (corresponding author), Embry Riddle Aeronaut Univ, Dept Humanities & Commun, Daytona Beach, FL 32114 USA.
EM ristera@erau.edu
CR Brammer SE, 2022, COMMUN EDUC, V71, P155, DOI 10.1080/03634523.2021.2022732
   Cardon P, 2024, BUS PROF COMMUN Q, V87, P223, DOI 10.1177/23294906231208166
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Dejeu EB, 2024, J BUS TECH COMMUN, V38, P225, DOI 10.1177/10506519241239923
   Delaney B., 2022, Journalism and Mass Communication Educator, V77, P5, DOI [DOI 10.1177/10776958211001214, 10.1177/10776958211001214]
   Dickey E, 2024, Arxiv, DOI arXiv:2308.12276
   Getchell KM, 2022, BUS PROF COMMUN Q, V85, P7, DOI 10.1177/23294906221074311
   International Society for Educational Technology, 2023, ADDIE model for instructional design
   Jaschik S., 2019, Faculty attitudes on technology: A study by Inside Higher Ed and Gallup
   Johnson-Eilola J, 2024, J BUS TECH COMMUN, V38, P199, DOI 10.1177/10506519241239641
   Kleinman S, 2005, COMMUN TEACH, V19, P13, DOI 10.1080/1740462042000339212
   Lewis E, 2021, J CONTIN HIGH EDUC, V69, P61, DOI 10.1080/07377363.2020.1776558
   Mallette JC, 2024, J BUS TECH COMMUN, V38, P289, DOI 10.1177/10506519241239950
   Meganck S., 2018, Teaching Journalism Mass Communication, V8, P25
   National Communication Association (NCA), 2023, Online course assignments
   Omizo RM, 2024, J BUS TECH COMMUN, V38, P242, DOI 10.1177/10506519241239927
   Saroyan A., 2004, RETHINKING TEACHING, P3
   Sharma D, 2024, BUS PROF COMMUN Q, V87, P630, DOI 10.1177/23294906241254780
NR 18
TC 1
Z9 1
U1 7
U2 7
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 1740-4622
EI 1740-4630
J9 COMMUN TEACH
JI Commun. Teach.
PD 2024 SEP 4
PY 2024
DI 10.1080/17404622.2024.2396014
EA SEP 2024
PG 10
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA F2J5O
UT WOS:001308136400001
DA 2024-12-25
ER

PT J
AU van Berlo, ZMC
   Campbell, C
   Voorveld, HAM
AF van Berlo, Zeph M. C.
   Campbell, Colin
   Voorveld, Hilde A. M.
TI The MADE Framework: Best Practices for Creating Effective Experimental
   Stimuli Using Generative AI
SO JOURNAL OF ADVERTISING
LA English
DT Article
ID CHATGPT
AB This paper introduces the MADE (Mapping, Assembling, Demonstrating, Executing) framework, a comprehensive set of best practices for the ethical and effective use of generative artificial intelligence (AI) in creating experimental stimuli for advertising research. The framework was developed through an extensive exploration of various emergent generative AI tools used in common experimental manipulations. We apply the MADE framework to demonstrate the creation of high-quality, realistic experimental ads using leading generative AI tools. Our empirical testing shows that AI-generated stimuli are valid, with consumers rating them equally high in quality, appropriateness, and realism compared with professionally created ads. This finding underscores the viability of AI-generated ads in advertising research. Additionally, we discuss the importance of adhering to ethical standards and ensuring transparency in AI use. By combining technological innovation with methodological rigor, this paper aims to guide researchers in leveraging the potential of generative AI while addressing its ethical implications, thereby enhancing the realism and validity of experimental advertising research.
C1 [van Berlo, Zeph M. C.; Voorveld, Hilde A. M.] Univ Amsterdam, Amsterdam Sch Commun Res ASCoR, POB 15791, NL-1001 NG Amsterdam, Netherlands.
   [Campbell, Colin] Univ San Diego, San Diego, CA 92110 USA.
C3 University of Amsterdam; University of San Diego
RP van Berlo, ZMC (corresponding author), Univ Amsterdam, Amsterdam Sch Commun Res ASCoR, POB 15791, NL-1001 NG Amsterdam, Netherlands.
EM Z.M.C.vanBerlo@uva.nl
RI van Berlo, Zeph/O-1903-2019
OI van Berlo, Zeph/0000-0002-1008-8654; Voorveld, Hilde/0000-0002-6667-3529
CR Ahn SJ, 2022, J ADVERTISING, V51, P592, DOI 10.1080/00913367.2022.2111729
   APA (American Psychological Association), 2023, APA J POLICY GENERAT
   Arango L, 2023, J ADVERTISING, V52, P486, DOI 10.1080/00913367.2023.2183285
   Audi, 2020, AUDI E TRON SPORTBAC
   Azrout R, 2024, TIJDSCHR COMMUNWET, V52, P267, DOI 10.5117/TCW2024.3.002.AZRo
   BANAJI MR, 1989, AM PSYCHOL, V44, P1185, DOI 10.1037/0003-066X.44.9.1185
   Bockting CL, 2023, NATURE, V622, P693, DOI 10.1038/d41586-023-03266-1
   brihallofficial, 2018, AD BRINGING END THIS
   Campbell C, 2022, J ADVERTISING RES, V62, P241, DOI 10.2501/JAR-2022-017
   Campbell C, 2022, J ADVERTISING, V51, P22, DOI 10.1080/00913367.2021.1909515
   Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Cohen, 2023, HARVARD BUSINESS REV
   Cook T. D., 1979, Quasi-experimentation design analysis issues for field settings
   Creswell J. W., 2016, QUAL INQ
   De Pelsmacker P, 2021, INT J ADVERT, V40, P835, DOI 10.1080/02650487.2020.1827895
   Deveau Richelle, 2023, AI POWERED MARKETING
   Dom Qwek, DOM QWEK
   Eisinga R, 2013, INT J PUBLIC HEALTH, V58, P637, DOI 10.1007/s00038-012-0416-3
   Endert Julius, 2024, DW AKADEMIE
   European IP Helpdesk, 2023, INTELLECTUAL PROPERT
   Franke C, 2023, J ADVERTISING, V52, P523, DOI 10.1080/00913367.2022.2154721
   Geuens M, 2017, J ADVERTISING, V46, P83, DOI 10.1080/00913367.2016.1225233
   Google, 2024, The AI for Science Forum: A New Era of Discovery
   Grigsby JL, 2023, J ADVERTISING, V52, P594, DOI 10.1080/00913367.2022.2077267
   Hamby A, 2023, J ADVERTISING, V52, P633, DOI 10.1080/00913367.2022.2107121
   Hayes JL, 2021, J ADVERTISING, V50, P81, DOI 10.1080/00913367.2020.1809576
   Hofer Matthias., 2018, INT ENCY COMMUNICATI, DOI [https://doi.org/10.1002/9781118901731.iecrm0039, DOI 10.1002/9781118901731.IECRM0039]
   Huh J, 2023, J ADVERTISING, V52, P477, DOI 10.1080/00913367.2023.2227013
   Lehnert K, 2023, J ADVERTISING, V52, P558, DOI 10.1080/00913367.2022.2053900
   Louis Vuitton, 2018, PERFUMES ADVERTISEME
   MCGRATH JE, 1981, AM BEHAV SCI, V25, P179, DOI 10.1177/000276428102500205
   Miao F., 2023, GUIDANCE GENERATIVE, DOI [10.54675/EWZM9535, DOI 10.54675/EWZM9535]
   Moorman C, 2019, J MARKETING, V83, P1, DOI 10.1177/0022242919867086
   Morales AC, 2017, J CONSUM RES, V44, P465, DOI 10.1093/jcr/ucx048
   PERDUE BC, 1986, J MARKETING RES, V23, P317, DOI 10.2307/3151807
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pot Kim, 2023, FRANKWATCHING
   Reichardt CS, 2002, SOC SERV REV, V76, P510, DOI 10.1086/345281
   Rivas P, 2023, AI-BASEL, V4, P375, DOI 10.3390/ai4020019
   Rodgers S, 2021, J ADVERTISING, V50, P1, DOI 10.1080/00913367.2020.1868233
   Sands S, 2022, EUR J MARKETING, V56, P1721, DOI 10.1108/EJM-12-2019-0949
   Schmidt F L., 2015, Methods of meta-analysis: Correcting error and bias in research findings, DOI DOI 10.4135/9781483398105
   Stam Aleda, 2023, AI WILL IMPACT LEAST
   Terlutter R, 2013, J ADVERTISING, V42, P95, DOI 10.1080/00913367.2013.774610
   Thomas VL, 2021, J ADVERTISING, V50, P11, DOI 10.1080/00913367.2020.1810595
   U.S. Congress. House, 2023, HR 3831 118 C AD MAY
   Vakratsas D, 2020, J ADVERTISING, V50, P39, DOI 10.1080/00913367.2020.1843090
   van Berlo ZMC, 2023, INT J ADVERT, V42, P171, DOI 10.1080/02650487.2022.2143098
   van Berlo ZMC, 2021, J BUS RES, V122, P458, DOI 10.1016/j.jbusres.2020.09.006
   van Esch P, 2021, J ADVERTISING, V50, P63, DOI 10.1080/00913367.2020.1832939
   van Heerde HJ, 2021, J MARKETING, V85, P1, DOI 10.1177/0022242921992383
   Voorveld HAM, 2024, INT J ADVERT, V43, P960, DOI 10.1080/02650487.2023.2264045
   Wang CY, 2024, NEW MEDIA SOC, DOI 10.1177/14614448241232345
   Wu LW, 2024, J BIOPHARM STAT, DOI 10.1080/10543406.2024.2341683
   YIN RK, 1994, EVAL PRACT, V15, P283, DOI 10.1016/0886-1633(94)90023-X
   Yoon Julia, 2024, INFORMING INNOVATION
NR 57
TC 1
Z9 1
U1 64
U2 64
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0091-3367
EI 1557-7805
J9 J ADVERTISING
JI J. Advert.
PD OCT 19
PY 2024
VL 53
IS 5
SI SI
BP 732
EP 753
DI 10.1080/00913367.2024.2397777
EA SEP 2024
PG 22
WC Business; Communication
WE Social Science Citation Index (SSCI)
SC Business & Economics; Communication
GA O5B1R
UT WOS:001314412200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Islam, T
   Miron, A
   Nandy, M
   Choudrie, J
   Liu, XH
   Li, YM
AF Islam, Tasin
   Miron, Alina
   Nandy, Monomita
   Choudrie, Jyoti
   Liu, Xiaohui
   Li, Yongmin
TI Transforming Digital Marketing with Generative AI
SO COMPUTERS
LA English
DT Article
DE generative AI; deep learning; e-commerce; digital marketing
ID SOCIAL MEDIA; PERSONALIZATION; OPTIMIZATION; TECHNOLOGY; CHALLENGES;
   STRATEGIES; COLLECTION
AB The current marketing landscape faces challenges in content creation and innovation, relying heavily on manually created content and traditional channels like social media and search engines. While effective, these methods often lack the creativity and uniqueness needed to stand out in a competitive market. To address this, we introduce MARK-GEN, a conceptual framework that utilises generative artificial intelligence (AI) models to transform marketing content creation. MARK-GEN provides a comprehensive, structured approach for businesses to employ generative AI in producing marketing materials, representing a new method in digital marketing strategies. We present two case studies within the fashion industry, demonstrating how MARK-GEN can generate compelling marketing content using generative AI technologies. This proposition paper builds on our previous technical developments in virtual try-on models, including image-based, multi-pose, and image-to-video techniques, and is intended for a broad audience, particularly those in business management.
C1 [Islam, Tasin; Miron, Alina; Liu, Xiaohui; Li, Yongmin] Brunel Univ London, Dept Comp Sci, London UB8 3PH, England.
   [Nandy, Monomita] Brunel Univ London, Brunel Business Sch, London UB8 3PH, England.
   [Choudrie, Jyoti] Univ Hertfordshire, Hertfordshire Business Sch, Hatfield AL10 9AB, England.
C3 Brunel University; Brunel University; University of Hertfordshire
RP Li, YM (corresponding author), Brunel Univ London, Dept Comp Sci, London UB8 3PH, England.
EM tasin.islam2@brunel.ac.uk; alina.miron@brunel.ac.uk;
   monomita.nandy@brunel.ac.uk; j.choudrie@herts.ac.uk;
   xiaohui.liu@brunel.ac.uk; yongmin.li@brunel.ac.uk
RI Li, Yongmin/KZU-7840-2024; Liu, Xiaohui/B-5046-2013; Nandy,
   Monomita/ITU-0057-2023
OI Nandy, Monomita/0000-0001-8191-2412; Islam, Tasin/0000-0001-7568-9322;
   Miron, Alina/0000-0002-0068-4495; Liu, Xiaohui/0000-0003-1589-1267; Li,
   Yongmin/0000-0003-1668-2440; Choudrie, Jyoti/0000-0001-9349-7690
CR Aguirre E, 2015, J RETAILING, V91, P34, DOI 10.1016/j.jretai.2014.09.005
   Alalwan AA, 2017, TELEMAT INFORM, V34, P1177, DOI 10.1016/j.tele.2017.05.008
   Anandhan A, 2018, IEEE ACCESS, V6, P15608, DOI 10.1109/ACCESS.2018.2810062
   [Anonymous], 2011, IJCAI
   Ansari A, 2003, J MARKETING RES, V40, P131, DOI 10.1509/jmkr.40.2.131.19224
   Aszemi NM, 2019, INT J ADV COMPUT SC, V10, P269
   Bala M., 2018, International Journal of Management, IT Engineering, V8, P321
   Barreto AM, 2014, INT J MARKET RES, V56, P631, DOI 10.2501/IJMR-2014-043
   Boughton S.B., 2005, PERSPECTIVES BUSINES, V2, P29
   Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564
   caffe.berkeleyvision, Caffe|Deep Learning Framework
   Campana Mattia G., 2017, Online Social Networks and Media, V3, P75, DOI 10.1016/j.osnem.2017.10.005
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Chaffey D., 2019, Digital marketing
   Chen HZ, 2012, LECT NOTES COMPUT SC, V7574, P609, DOI 10.1007/978-3-642-33712-3_44
   Chen HJ, 2019, PROC CVPR IEEE, P10034, DOI 10.1109/CVPR.2019.01028
   Chilimbi Trishul M, 2014, 11th USENIX Symposium on Operating Systems Design and Implementation, OSDI '14, Broomfield, CO, USA, October 6-8, 2014, V14, P571, DOI 10.1108/01439911111122716
   Cho KYHY, 2014, Arxiv, DOI arXiv:1406.1078
   Choi S, 2021, PROC CVPR IEEE, P14126, DOI 10.1109/CVPR46437.2021.01391
   De Keyzer F., 2015, Journal of Interactive Advertising, V15, P124, DOI DOI 10.1080/15252019.2015.1082450
   Desai V., 2019, INT J TREND SCI RES, V5, P196, DOI [10.31142/ijtsrd23100, DOI 10.31142/IJTSRD23100]
   Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776
   Dong H, 2019, IEEE I CONF COMP VIS, P9025, DOI 10.1109/ICCV.2019.00912
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Edelman D., 2015, McKinsey Digit, V20036
   Evelina T.Y., 2020, BUSINESS THEORY PRAC, V21, P613, DOI [10.3846/btp.2020.12143, DOI 10.3846/BTP.2020.12143]
   Farris P.W., 2010, Marketing metrics: The definitive guide to measuring marketing performance, V2nd ed.
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Filo K, 2015, SPORT MANAG REV, V18, P166, DOI 10.1016/j.smr.2014.11.001
   García-Sánchez F, 2020, INFORM PROCESS MANAG, V57, DOI 10.1016/j.ipm.2019.102153
   github, GITHUB LETS BUILD HE
   Goodfellow I., 2014, NeurIPS, V27
   Graham S., 2020, How to Balance Consumer Privacy and Personalization in Marketing
   Han K, 2023, IEEE T PATTERN ANAL, V45, P87, DOI 10.1109/TPAMI.2022.3152247
   Han XT, 2018, PROC CVPR IEEE, P7543, DOI 10.1109/CVPR.2018.00787
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   huggingface.co, Hugging Face-The AI Community Building the Future
   Islam T, 2024, NEUROCOMPUTING, V594, DOI 10.1016/j.neucom.2024.127887
   Islam T, 2024, Arxiv, DOI arXiv:2310.00106
   Islam T, 2024, IEEE ACCESS, V12, P29475, DOI 10.1109/ACCESS.2024.3368612
   Islam T, 2022, 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, P369, DOI 10.1109/ICMLA55696.2022.00059
   Kang WC, 2019, PROC CVPR IEEE, P10524, DOI 10.1109/CVPR.2019.01078
   Kapoor KK., 2016, MARKETING REV, V16, P183, DOI DOI 10.1362/146934716X14636478977557
   Khraim H S., 2015, American Journal of Business and Management, V4, P76, DOI 10.11634/216796061504676
   Kiapour MH, 2015, IEEE I CONF COMP VIS, P3343, DOI 10.1109/ICCV.2015.382
   Kim KH, 2021, J BUS RES, V131, P627, DOI 10.1016/j.jbusres.2021.02.052
   Kingma DP., 2014, PREPRINT
   Krishen AS, 2021, J BUS RES, V131, P183, DOI 10.1016/j.jbusres.2021.03.061
   Kritzinger WT, 2013, J ORG COMP ELECT COM, V23, P273, DOI 10.1080/10919392.2013.808124
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Kshetri N, 2019, COMPUTER, V52, P58, DOI 10.1109/MC.2018.2887322
   Kumar V, 2012, MIT SLOAN MANAGE REV, V54, P55
   Kushwaha B. P., 2020, PalArch's Journal of Archaeology of Egypt/Egyptology, V17, P2053
   Lambrecht A, 2013, J MARKETING RES, V50, P561, DOI 10.1509/jmr.11.0503
   Lee J, 2016, INT J INFORM MANAGE, V36, P360, DOI 10.1016/j.ijinfomgt.2016.01.001
   Leung FF, 2022, J ACAD MARKET SCI, V50, P226, DOI 10.1007/s11747-021-00829-4
   Lin CA, 2016, COMPUT HUM BEHAV, V64, P710, DOI 10.1016/j.chb.2016.07.027
   Liu Y, 2019, DECIS SUPPORT SYST, V123, DOI 10.1016/j.dss.2019.113079
   Lu J, 2015, DECIS SUPPORT SYST, V74, P12, DOI 10.1016/j.dss.2015.03.008
   Ma LQ, 2017, ADV NEUR IN, V30
   Martynenko M., 2023, Futurity Economics&Law, V3, P63, DOI [10.57125/FEL.2023.03.25.07, DOI 10.57125/FEL.2023.03.25.07]
   Moens M.-F., 2014, MINING USER GENERATE
   Munro J., 2012, Destination Brands, P141
   Nelson-Field K, 2013, J ADVERTISING RES, V53, P186, DOI 10.2501/JAR-53-2-186-191
   Nuseir MT, 2023, STUD COMPUT INTELL, V1056, P21, DOI 10.1007/978-3-031-12382-5_2
   Okazaki S, 2013, INT MARKET REV, V30, P56, DOI 10.1108/02651331311298573
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pachoulakis I., 2012, INT J MULTIMEDIA ITS, V4, DOI DOI 10.5121/IJMA.2012.4404
   Palmer DE, 2005, J BUS ETHICS, V58, P271, DOI 10.1007/s10551-005-1421-8
   Parise S, 2016, BUS HORIZONS, V59, P411, DOI 10.1016/j.bushor.2016.03.004
   Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization, P325
   play.google, Lensa: AI Photo Editor, Camera-Apps on Google Play
   PyTorch, About us
   Rawson A, 2013, HARVARD BUS REV, V91, P90
   Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121
   Ronneberger O, 2015, LECT NOTES COMPUT SC, V9351, P234, DOI 10.1007/978-3-319-24574-4_28
   Ruder S, 2017, Arxiv, DOI [arXiv:1609.04747, DOI 10.48550/ARXIV.1609.04747]
   Sahni NS, 2018, MARKET SCI, V37, P236, DOI 10.1287/mksc.2017.1066
   Saxena A., 2013, VISION, V17, P17, DOI DOI 10.1177/0972262912469560
   Sen R, 2005, INT J ELECTRON COMM, V10, P9, DOI 10.1080/10864415.2005.11043964
   Sharma D, 2019, 2019 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2019), P687, DOI [10.1109/confluence.2019.8776976, 10.1109/CONFLUENCE.2019.8776976]
   Shrestha YR, 2021, J BUS RES, V123, P588, DOI 10.1016/j.jbusres.2020.09.068
   Singer U, 2022, Arxiv, DOI arXiv:2209.14792
   Smith KT, 2012, J CONSUM MARK, V29, P86, DOI 10.1108/07363761211206339
   Soni V., 2023, Sage Science Review of Applied Machine Learning, V6, P1, DOI [https://doi.org/10.3390/app11083475, DOI 10.3390/APP11083475]
   Swani K, 2017, IND MARKET MANAG, V62, P77, DOI 10.1016/j.indmarman.2016.07.006
   Taylor DG, 2011, J ADVERTISING RES, V51, P258, DOI 10.2501/JAR-51-1-258-275
   Tene Omer, 2008, Utah L. Rev, p1433 1447, DOI DOI 10.2139/SSRN.1021490
   TensorFlow, About us
   Thelwall M, 2001, INTERNET RES, V11, P114, DOI 10.1108/10662240110388224
   Vaswani A, 2017, ADV NEUR IN, V30
   Voulodimos A, 2018, COMPUT INTEL NEUROSC, V2018, DOI 10.1155/2018/7068349
   Wang BC, 2018, LECT NOTES COMPUT SC, V11217, P607, DOI 10.1007/978-3-030-01261-8_36
   Whang SE, 2020, PROC VLDB ENDOW, V13, P3429, DOI 10.14778/3415478.3415562
   Wu CW, 2016, J BUS RES, V69, P5310, DOI 10.1016/j.jbusres.2016.04.130
   Yang L, 2020, NEUROCOMPUTING, V415, P295, DOI 10.1016/j.neucom.2020.07.061
   Yang X, 2023, IND MANAGE DATA SYST, V123, P1717, DOI 10.1108/IMDS-08-2022-0500
   Yasmin A., 2015, INT J MANAG SCI BUS, V1, P69, DOI 10.18775/ijmsba.1849-5664-5419.2014.15.1006
   Ying X, 2018, CHINA PERSPECT-SER, P1, DOI 10.1088/1742-6596/1168/2/022022
   Zablotskaia P, 2019, Arxiv, DOI arXiv:1910.09139
NR 101
TC 2
Z9 2
U1 105
U2 105
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2073-431X
J9 COMPUTERS
JI Computers
PD JUL
PY 2024
VL 13
IS 7
AR 168
DI 10.3390/computers13070168
PG 24
WC Computer Science, Interdisciplinary Applications
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA ZP8J8
UT WOS:001276588700001
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU da Trindade, ASCE
   de Oliveira, HPC
AF da Trindade, Alessandra Stefane Candido Elias
   de Oliveira, Henry Poncio Cruz
TI GENERATIVE ARTIFICIAL INTELLIGENCE (IA) AND INFORMATION LITERACY:
   INFORMATIONAL CAPABILITIES REQUIRED FOR THE USE OF GENERATIVE AI TOOLS
   FOR INFORMATION REQUIREMENTS OF AN ACADEMIC-SCIENTIFIC NATURE
SO PERSPECTIVAS EM CIENCIA DA INFORMACAO
LA English
DT Article
DE Scientific communication; Information competence; Artificial
   Intelligence; Perplexity AI; AI; ChatGPT
ID SKILLS
AB Artificial intelligence (AI) poses new challenges for the acquisition of knowledge, also in an academic-scientific context. Objective: Present the information capabilities required for the efficient use of Generative AI technologies for information requirements of an academic-scientific nature. Methodological approach: The research is exploratory, using a qualitative approach and two data collection techniques, namely bibliographic research and observation. Result: Generative AI tools bring new opportunities and challenges to academia and raise concerns about copyright (plagiarism and intellectual property of the content generated by the technology), integrity of science, reliability of research, fairness and ethics (dissemination of bias) and others. Conclusions: To use generative AI tools effectively and strategically, people need to develop five steps (analyzing information needs; analyzing the tool; planning search strategies (devising commands); analyzing synthesized content; using synthesized content) and apply the 18 information literacies associated with these steps.
C1 [da Trindade, Alessandra Stefane Candido Elias; de Oliveira, Henry Poncio Cruz] Univ Fed Paraiba UFPB, Joao Pessoa, PB, Brazil.
C3 Universidade Federal da Paraiba
RP da Trindade, ASCE (corresponding author), Univ Fed Paraiba UFPB, Joao Pessoa, PB, Brazil.
EM alessandra150196@hotmail.com; henry.poncio@gmail.com
RI ; Trindade, Alessandra Stefane Candido Elias da/IUO-9500-2023
OI Oliveira, Henry Poncio Cruz de/0000-0003-2330-2442; Trindade, Alessandra
   Stefane Candido Elias da/0000-0003-3956-7381
CR [Anonymous], 2023, NATURE, V613, P612, DOI 10.1038/d41586-023-00191-1
   Barry CA, 1997, J INFORM SCI, V23, P225, DOI 10.1177/016555159702300306
   Behimehr Sara, 2020, Journal of Information Management, V8, P18
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Biswas SS, 2023, ANN BIOMED ENG, V51, P1126, DOI 10.1007/s10439-023-03171-8
   Bourdieu P., 1983, BOURDIEU SOCIOLOGIA, P122
   BOURDIEU Pierre., 2015, A economia das trocas simbolicas
   BUFREM L. S., 2020, A dinamica da pesquisa em Ciencia da Informacao
   CAMPOS R. S., 2020, Aoristo: International Journal of Phenomenology, Hermeneutics and Metaphysics, V3, P106
   CASTILHO M. A., 2020, Algoritmos e estruturas de dados, P13
   CHAGAS E. T. O., 2019, Revista Cientifica Multidisciplinar Nucleo do Conhecimento, Sao Paulo, V4
   D'Ancona Matthew., 2018, Pos-Verdade - A nova guerra contra os fatos em tempos de fake news
   De Lucca DM, 2020, PERSPECT CIENC INF, V25, P22, DOI 10.1590/1981-5344/3317
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eisenberg M.B., 1990, Information problem solving, the big six approach to library and information skills instruction
   Eisenberg MB, 2008, DESIDOC J LIB INF TE, V28, P39, DOI 10.14429/djlit.28.2.166
   FERNANDEZ MARCIAL V. F., 2022, Revista Bibliotecas. Anales de Investigacion, Cuba, V18
   FREIRE G. H. A., 2010, Etica da Informacao: conceitos, abordagens, aplicacoes
   Garcia A.C., 2020, Computacao Brasil, P14
   Hill-Yardin EL, 2023, BRAIN BEHAV IMMUN, V110, P152, DOI 10.1016/j.bbi.2023.02.022
   Huang JS, 2023, AM J CANCER RES, V13, P1148
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   KAUFMAN D., 2020, Revista Famecos, Porto Alegre, V27, P1
   Khan NA, 2023, NEPAL J EPIDEMIOL, V13, P1258, DOI 10.3126/nje.v13i1.53721
   Lakatos E.e., 2001, Fundamentos de metodologia cientifica, V4a
   LECARDELLI J., 2007, Revista Brasileira de Biblioteconomia e Documentacao, Sao Paulo, V2
   Lee JY, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.6
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   MAGALHAES A., 2022, Revista de Ciencia Elementar, V10, P1
   Manohar N, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.34616
   MARTINS A. L., 2010, Informacao & Informacao, Londrina, V15, P1
   OPENAI, 2023, What is Chat GPT? s.l.:
   Ortiz AFH, 2023, BRAIN BEHAV IMMUN, V111, P124, DOI 10.1016/j.bbi.2023.04.004
   Outman Alex, 2023, IEEE DataPort, DOI 10.21227/C4MJ-2W23
   Pellegrini E, 2018, PERSPECT CIENC INF, V23, P117, DOI 10.1590/1981-5344/2953
   PERPLEXITY, 2023, About Perplexity.
   Pestana O., 2001, Paginas a, Vb, P41
   Picciano AG, 2019, ONLINE LEARN, V23, P270, DOI 10.24059/olj.v23i3.2023
   Russell Stuart., 2013, Inteligencia Artificial: uma abordagem moderna, ed, V3a
   SALES R., 2007, Revista Digital de Biblioteconomia & Ciencia da Informacao, Campinas, V5, P67
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   TRINDADE A. S. C. E., 2021, Dissertacao (Mestrado Profissional em Gestao da Informacao e do Conhecimento)
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 43
TC 0
Z9 0
U1 50
U2 50
PU UNIV FEDERAL MINAS GERAIS, ESCOLA BIBLIOTECONOMIA
PI BELO HORIZONTE
PA CENTRO, CAIXA POSTAL 1606, BELO HORIZONTE, MG 30161-970, BRAZIL
SN 1413-9936
EI 1981-5344
J9 PERSPECT CIENC INF
JI Pespect. Cienc. Inf.
PY 2024
VL 29
AR e47485
DI 10.1590/1981-5344/47485
PG 27
WC Information Science & Library Science
WE Emerging Sources Citation Index (ESCI)
SC Information Science & Library Science
GA WE2L5
UT WOS:001253126400008
OA gold
DA 2024-12-25
ER

PT J
AU Zhang, RC
   Xiong, K
   Du, HY
   Niyato, D
   Kang, JW
   Shen, XM
   Poor, HV
AF Zhang, Ruichen
   Xiong, Ke
   Du, Hongyang
   Niyato, Dusit
   Kang, Jiawen
   Shen, Xuemin
   Poor, H. Vincent
TI Generative AI-Enabled Vehicular Networks: Fundamentals, Framework, and
   Case Study
SO IEEE NETWORK
LA English
DT Article
DE Generative AI; Data models; Predictive models; Navigation; Accidents;
   Training; Reliability; Vehicular networks; generative AI; multi-modal;
   DRL
AB Recognizing the tremendous improvements that the integration of generative artificial intelligence (AI) can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks.
C1 [Zhang, Ruichen] Beijing Jiaotong Univ, Sch Comp Sci & Technol, Beijing 100044, Peoples R China.
   [Zhang, Ruichen; Du, Hongyang; Niyato, Dusit] Nanyang Technol Univ, Coll Comp & Data Sci, Singapore 639798, Singapore.
   [Xiong, Ke] Beijing Jiaotong Univ, Innovat Ctr Railway Traff Safety, Sch Comp Sci & Technol, Engn Res Ctr Network Management Technol High Speed, Beijing 100044, Peoples R China.
   [Xiong, Ke] Beijing Jiaotong Univ, Natl Engn Res Ctr Adv Network Technol, Beijing 100044, Peoples R China.
   [Kang, Jiawen] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China.
   [Shen, Xuemin] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada.
   [Poor, H. Vincent] Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544 USA.
C3 Beijing Jiaotong University; Nanyang Technological University; Beijing
   Jiaotong University; Beijing Jiaotong University; Guangdong University
   of Technology; University of Waterloo; Princeton University
RP Xiong, K (corresponding author), Beijing Jiaotong Univ, Natl Engn Res Ctr Adv Network Technol, Beijing 100044, Peoples R China.
EM ruichen.zhang@ntu.edu.sg; kxiong@bjtu.edu.cn; hongyang001@e.ntu.edu.sg;
   dniyato@ntu.edu.sg; kavinkang@gdut.edu.cn; sshen@uwaterloo.ca;
   poor@princeton.edu
RI Shen, Xuemin/AAH-2564-2020; Kang, Jiawen/I-9044-2019; Poor,
   H./S-5027-2016; Du, Hongyang/KBQ-0157-2024; Niyato, Dusit/Y-2769-2019
OI Shen, Xuemin (Sherman)/0000-0002-4140-287X; Du,
   Hongyang/0000-0002-8220-6525; Kang, Jiawen/0000-0002-8218-3490; Zhang,
   Ruichen/0000-0002-6859-3645; Poor, H. Vincent/0000-0002-2062-131X
FU Changping Innovation Joint Fund of Beijing Natural Science Foundation
   [L234084]; National Natural Science Foundation of China [62071033,
   92167204, 62072030, 62102099, U22A2054]; Pearl River Talent Recruitment
   Program [2021QN02S643]; National Research Foundation, Singapore, and
   Infocomm Media Development Authority under its Future Communications
   Research and Development Programme, Defence Science Organisation (DSO)
   National Laboratories under the AI Singapore Programme (AISG)
   [AISG2RP-2020-019, FCP-ASTAR-TG-2022-003]; Singapore Ministry of
   Education (MOE) Tier 1 [RG87/22]; U.S. National Science Foundation
   [ECCS-2335876]
FX This work was supported in part by the Changping Innovation Joint Fund
   of Beijing Natural Science Foundation under Grant L234084; in part by
   the National Natural Science Foundation of China under Grant 62071033,
   Grant 92167204, Grant 62072030; in part by the National Natural Science
   Foundation of China under Grant 62102099 and Grant U22A2054; in part by
   the Pearl River Talent Recruitment Program under Grant 2021QN02S643; in
   part by the National Research Foundation, Singapore, and Infocomm Media
   Development Authority under its Future Communications Research and
   Development Programme, Defence Science Organisation (DSO) National
   Laboratories under the AI Singapore Programme (AISG) under Award
   AISG2RP-2020-019 and Grant FCP-ASTAR-TG-2022-003; in part by the
   Singapore Ministry of Education (MOE) Tier 1 under Grant RG87/22; and in
   part by the U.S. National Science Foundation under Grant ECCS-2335876.
CR Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Du HY, 2024, Arxiv, DOI arXiv:2308.05384
   Du HY, 2023, IEEE J SEL AREA COMM, V41, P2158, DOI 10.1109/JSAC.2023.3280978
   Fisher M, 2012, ACM T GRAPHIC, V31, DOI 10.1145/2366145.2366154
   Garcia MHC, 2021, IEEE COMMUN SURV TUT, V23, P1972, DOI 10.1109/COMST.2021.3057017
   Hua YM, 2015, PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTERNET OF THINGS, P1, DOI 10.1109/ICAIOT.2015.7111524
   Jin WG, 2018, PR MACH LEARN RES, V80
   Jing YC, 2020, IEEE T VIS COMPUT GR, V26, P3365, DOI 10.1109/TVCG.2019.2921336
   Kim SW, 2021, PROC CVPR IEEE, P5816, DOI 10.1109/CVPR46437.2021.00576
   Lu RX, 2020, P IEEE, V108, P373, DOI 10.1109/JPROC.2019.2948302
   Mou C, 2023, Arxiv, DOI arXiv:2302.08453
   Xie HQ, 2021, IEEE T SIGNAL PROCES, V69, P2663, DOI 10.1109/TSP.2021.3071210
   Yang ZP, 2020, PROC CVPR IEEE, P11115, DOI 10.1109/CVPR42600.2020.01113
   Zhang RC, 2023, IEEE J SEL AREA COMM, V41, P1413, DOI 10.1109/JSAC.2023.3240707
   Zhang RC, 2022, IEEE J SEL AREA COMM, V40, P677, DOI 10.1109/JSAC.2021.3118397
NR 15
TC 1
Z9 1
U1 13
U2 13
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0890-8044
EI 1558-156X
J9 IEEE NETWORK
JI IEEE Netw.
PD JUL
PY 2024
VL 38
IS 4
BP 259
EP 267
DI 10.1109/MNET.2024.3391767
PG 9
WC Computer Science, Hardware & Architecture; Computer Science, Information
   Systems; Engineering, Electrical & Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA ZD2M0
UT WOS:001273289700011
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Head, A
   Willis, S
AF Head, Amanda
   Willis, Sonya
TI Assessing law students in a GenAI world to create knowledgeable future
   lawyers
SO INTERNATIONAL JOURNAL OF THE LEGAL PROFESSION
LA English
DT Article
AB Assessing law students has always been a challenging task, but the introduction of Generative Artificial Intelligence (GenAI), such as ChatGPT, compounds the problems already caused by increased student numbers, contract cheating and budget cuts in universities. As GenAI rapidly develops, legal educators must find ways to accommodate, and even incorporate, GenAI into their curricula and assessments so that law graduates can understand its capabilities and limitations within legal practice. Simultaneously, many jurisdictions, including Australia, have legislative obligations to deliver law graduates who satisfy legal knowledge-based "eligibility" requirements for admission into practice. This article introduces a knowledge framework for managing GenAI in legal education consisting of three pillars: Substantive Legal Knowledge, GenAI Ethics Knowledge, and GenAI System Knowledge. The authors argue this framework can assist legal educators in designing optimal assessments in an AI-disrupted world. The article employs the knowledge framework to examine the experiences and views of Australian law students' engagement with GenAI outputs in completing a compulsory legal ethics assessment in 2023. This empirical case study demonstrates that effective assessment design incorporating GenAI can enhance law student and graduate outcomes despite the ongoing challenges for legal educators and the profession associated with GenAI.
C1 [Head, Amanda; Willis, Sonya] Macquarie Univ, Law Sch, Sydney, Australia.
C3 Macquarie University
RP Head, A (corresponding author), Macquarie Univ, Law Sch, Sydney, Australia.
EM amanda.head@mq.edu.au
OI Willis, Sonya/0000-0002-3879-0699; Head, Amanda/0000-0002-8964-0252
CR Adamopoulou E, 2020, MACH LEARN APPL, V2, DOI 10.1016/j.mlwa.2020.100006
   Ajevski M, 2023, LAW TEACH, V57, P352, DOI 10.1080/03069400.2023.2207426
   Alarie B, 2018, U TORONTO LAW J, V68, P106, DOI 10.3138/utlj.2017-0052
   Allen & Overy LLP, 2023, ARTIFICIAL INTELLIGE
   American Bar Association (ABA), 2023, RULE 11 COMPETENCE C
   An Q., 2023, ADV ED HUMANITIES SO, V6, P279
   Australian Bureau of Statistics (ABS), 2023, NATL STATE TERRITORY
   Brescia R. H., 2023, WASHBURN LAW J, V62, P507
   Casey T., 2014, CLIN LAW REV, V20, P317
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Choi J. H., 2023, AI ASSISTANCE LEGAL
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Croft L., 2023, LAWYERS WEEKLY
   Dal Pont G. E., 2021, LAWYERS PROFESSIONAL
   De Poloni G., 2023, ABC NEWS
   Farazouli A, 2024, ASSESS EVAL HIGH EDU, V49, P363, DOI 10.1080/02602938.2023.2241676
   Farhi F., 2023, COMPUTERS ED ARTIFIC, V100180, DOI [10.1016/j.caeai.2023.100180, DOI 10.1016/J.CAEAI.2023.100180, https://doi.org/10.1016/j.caeai.2023.100180]
   Firat Mehmet, 2023, Journal of Applied Learning and Teaching, V3, P1, DOI DOI 10.37074/JALT.2023.6.1.22
   Galloway K., 2020, 212 GRIFF U LAW SCH
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Hargreaves S, 2023, LEG EDUC REV, V33, P69
   Herbert-Lowe S., 2021, LAW SOC NSW J ONLINE
   Huallpa J.J., 2023, PERIODICALS ENG NATU, V11, P105, DOI DOI 10.21533/PEN.V11I4.3770
   Hugging Face, 2024, HUGGING FACE AI COMM
   Iu K. Y., 2023, CHATGPT OPENAI END L
   Katz D. M., 2023, SSRN, DOI [10.2139/SSRN.4389233, 10.2139/ssrn.4389233, DOI 10.2139/SSRN.4389233]
   Law Society of New South Wales Professional Support Unit, 2023, LAW SOC NSW J ONLINE
   Leiser F, 2023, PROCEEDINGS OF 2023 MENSCH UND COMPUTER, MUC 2023, P81, DOI 10.1145/3603555.3603565
   Lowe MS, 2020, J ACAD LIBR, V46, DOI 10.1016/j.acalib.2020.102234
   Munoz S., 2023, Social Space, V23, P1
   Murray Michael D, 2023, ARTIFICIAL INTELLIGE
   Perlman A., 2022, 2214 SUFF U LAW SCH
   Plata S., 2023, ASIAN J UNI EDU, V19, P743, DOI [DOI 10.24191/AJUE.V19I4.24697, 10.24191/ajue.v19i4.24697]
   Rothman D., 2022, Transformers for Natural Language Processing: Build, Train, and Fine-Tune Deep Neural Network Architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ryznar M., 2023, WASH LEE LAW REV, V80, P305, DOI DOI 10.2139/SSRN.3684958
   Savelka J., 2023, ARXIV
   Sharma A., 2023, JUS CORPUS LAW J, V3, P106
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Tarves T. K., 2023, LEGAL REFERENCE SERV, V42, P56
   Tertiary Education Quality and Standards Agency (TEQSA), 2023, ASS REF AG ART INT
   Universities Australia, 2017, UA ACAD INTEGRITY BE
   Yim N., 2023, AUSTRALIAN
   Yu Y., 2023, J ED HUMANITIES SOCI, V14, P220, DOI DOI 10.54097/EHSS.V14I.8840
NR 47
TC 1
Z9 1
U1 4
U2 4
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0969-5958
EI 1469-9257
J9 INT J LEG PROF
JI Int. J. Leg. Prof.
PD SEP 1
PY 2024
VL 31
IS 3
BP 293
EP 310
DI 10.1080/09695958.2024.2379785
EA JUL 2024
PG 18
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA P3G3F
UT WOS:001272888800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Modgil, S
   Gupta, S
   Kar, AK
   Tuunanen, T
AF Modgil, Sachin
   Gupta, Shivam
   Kar, Arpan Kumar
   Tuunanen, Tuure
TI How could Generative AI support and add value to non-technology
   companies - A qualitative study
SO TECHNOVATION
LA English
DT Article
DE Qualitative study; Generative artificial intelligence; Business value;
   Business model; Technology appropriation
AB With the spread of generative AI, non-technology companies are also adopting it at a faster rate. Therefore, this study aims to study the appropriation of Generative AI to create value to non-technology businesses through a knowledge based view of the firm. To achieve this objective, we followed a semi-structured interview schedule, where 98 qualitative data points were collected and analysed. We follow open, axial and selective coding along with Gioia methodology for analysis. Findings indicate that companies employ Generative AI for risk management, where potential threats, impact of possible hazards and degree of uncertainty in the business environment are considered in decision-making. Generative AI also helps in knowledge integration, where assimilation, adaptation, application and implementation are achieved. Findings also suggest that an improved business outlook can be achieved regarding accurate demand forecasting, real-time insights, contextual understanding and alignment to the vision through Generative AI. It is also observed that companies are investing in Generative AI to achieve competitive advantage and greater significance. The contribution of this study lies in the development of four propositions and a framework for generative AI-driven value for non-technology companies. The framework also uncovers the internal flow among key elements from risk identification to integration to developing the outlook and driving utility.
C1 [Modgil, Sachin] Int Management Inst IMI Kolkata, Dept Operat Management & Quantiat Tech, 2-4 C,Judges Ct Rd, Kolkata 700027, West Bengal, India.
   [Gupta, Shivam] NEOMA Business Sch, Dept Informat Syst, Supply Chain Management & Decis Support, 59 Rue Pierre Taittinger, F-51100 Reims, France.
   [Kar, Arpan Kumar] Indian Inst Technol Delhi, Dept Management Studies, New Delhi 110016, India.
   [Kar, Arpan Kumar] Delft Univ Technol, Fac Technol Policy & Management, Informat & Commun Technol Grp, Mekelweg 5, NL-2628 CD Delft, Netherlands.
   [Tuunanen, Tuure] Univ Jyvaskyla, Fac Informat Technol, Mattilanniemi 2, Jyvaskyla 40014, Finland.
C3 International Management Institute (IMI) Kolkata; Indian Institute of
   Technology System (IIT System); Indian Institute of Technology (IIT) -
   Delhi; Delft University of Technology; University of Jyvaskyla
RP Kar, AK (corresponding author), Indian Inst Technol Delhi, Dept Management Studies, New Delhi 110016, India.
EM sach.modgil@gmail.com; shivam.gupta@neoma-bs.fr; arpan_kar@yahoo.co.in;
   tuure@tuunanen.fi
RI Gupta, Shivam/R-2996-2016; Kar, Arpan/B-9999-2009
OI Tuunanen, Tuure/0000-0001-7119-1412; Modgil, Sachin/0000-0001-7816-6594
CR Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Akter S, 2023, TECHNOVATION, V125, DOI 10.1016/j.technovation.2023.102768
   Anantrasirichai N, 2022, ARTIF INTELL REV, V55, P589, DOI 10.1007/s10462-021-10039-7
   Bain & Company, 2023, How generative AI will supercharge productivity
   Baltar F, 2012, INTERNET RES, V22, P57, DOI 10.1108/10662241211199960
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   Bendoly E, 2024, DECISION SCI, V55, P325, DOI 10.1111/deci.12619
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Castañé G, 2023, INT J PROD RES, V61, P2288, DOI 10.1080/00207543.2022.2069525
   Chen B., 2023, Computers and Education: Artificial Intelligence, V5, P100184, DOI DOI 10.1016/J.CAEAI.2023.100184
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chinchanikar S, 2022, J MATER ENG PERFORM, V31, P6112, DOI 10.1007/s11665-022-07125-4
   Croitoru FA, 2023, IEEE T PATTERN ANAL, V45, P10850, DOI 10.1109/TPAMI.2023.3261988
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Elad M, 2017, IEEE T IMAGE PROCESS, V26, P2338, DOI 10.1109/TIP.2017.2678168
   Fabra IT, 2023, TECHNOVATION, V127, DOI 10.1016/j.technovation.2023.102851
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Forbes, 2023, Why companies buy generative AI consulting: the 3-month payback factor
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gamoura S.C., 2023, INT S INT MAN SERV S, P368
   Gill SS, 2022, INTERNET THINGS-NETH, V19, DOI 10.1016/j.iot.2022.100514
   Gioia DA, 2013, ORGAN RES METHODS, V16, P15, DOI 10.1177/1094428112452151
   Gopaldas A, 2016, QUAL MARK RES, V19, P115, DOI 10.1108/QMR-08-2015-0074
   Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110
   Harreis H.., 2023, Generative AI: Unlocking the Future of Fashion, P1
   Hussain M, 2023, IEEE ACCESS, V11, P127202, DOI 10.1109/ACCESS.2023.3332468
   Iqbal T, 2022, J KING SAUD UNIV-COM, V34, P2515, DOI 10.1016/j.jksuci.2020.04.001
   Just J, 2024, TECHNOVATION, V129, DOI 10.1016/j.technovation.2023.102883
   Kar A. K., 2023, Global Journal of Flexible Systems Management, V24, P659, DOI [DOI 10.1007/S40171-023-00356-X, https://doi.org/10.1007/s40171-023-00356-x]
   Kar AK, 2022, J CLEAN PROD, V376, DOI 10.1016/j.jclepro.2022.134120
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   KPMG, 2023, Generative AI: from buzz to business value
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Linde L, 2021, TECHNOL FORECAST SOC, V166, DOI 10.1016/j.techfore.2021.120614
   Mariani MM, 2023, TECHNOVATION, V122, DOI 10.1016/j.technovation.2022.102623
   McKinsey & Company, 2023, The state of AI in 2023- Generative AI's breakout year
   Medium.com, 2023, Supply Chain Management
   Meriton R, 2021, INT J PROD RES, V59, P7283, DOI 10.1080/00207543.2020.1832273
   Mor B, 2021, ARCH COMPUT METHOD E, V28, P1429, DOI 10.1007/s11831-020-09422-4
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Pearlson K.E., 2024, Managing and using information systems: A strategic approach, V8th
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Pyrkov A, 2023, DRUG DISCOV TODAY, V28, DOI 10.1016/j.drudis.2023.103675
   Rashid MBMA, 2021, SLAS TECHNOL, V26, P3, DOI 10.1177/2472630320956931
   Robinson G, 2021, AREA, V53, P671, DOI 10.1111/area.12726
   Rogers EM, 2009, COMMUN SER, P418
   Settembre-Blundo D., 2021, Global Journal of Flexible Systems Management, V22, P107, DOI DOI 10.1007/S40171-021-00277-7
   Shahriar S, 2022, DISPLAYS, V73, DOI 10.1016/j.displa.2022.102237
   Singha S, 2023, TECHNOL FORECAST SOC, V197, DOI 10.1016/j.techfore.2023.122908
   Strauss A. L., 1990, BASICS QUALITATIVE R
   Suhaili SM, 2021, EXPERT SYST APPL, V184, DOI 10.1016/j.eswa.2021.115461
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Tsironi E, 2017, NEUROCOMPUTING, V268, P76, DOI 10.1016/j.neucom.2016.12.088
   Wahid R, 2023, ASIA PAC J MARKET LO, V35, P1813, DOI 10.1108/APJML-10-2023-994
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang M, 2024, IEEE T LEARN TECHNOL, V17, P629, DOI 10.1109/TLT.2023.3324714
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Yasuda H, 2005, TECHNOVATION, V25, P763, DOI 10.1016/j.technovation.2004.01.008
   Yuan C, 2021, TECHNOVATION, V103, DOI 10.1016/j.technovation.2021.102225
NR 60
TC 0
Z9 0
U1 43
U2 43
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0166-4972
EI 1879-2383
J9 TECHNOVATION
JI Technovation
PD JAN
PY 2025
VL 139
AR 103124
DI 10.1016/j.technovation.2024.103124
EA OCT 2024
PG 12
WC Engineering, Industrial; Management; Operations Research & Management
   Science
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Engineering; Business & Economics; Operations Research & Management
   Science
GA J9F6R
UT WOS:001340052400001
DA 2024-12-25
ER

PT J
AU Cameron, MJ
   Shahin, J
   Lockerman, N
AF Cameron, Michael J.
   Shahin, Jenifer
   Lockerman, Nicole
TI Commentary on "Collective effort to enhance the quality of research
   evidence in intellectual and developmental disabilities: a case study of
   an academic-practice network"
SO TIZARD LEARNING DISABILITY REVIEW
LA English
DT Article
DE Virtual reality; Generative artificial intelligence; Theories of
   development
AB Purpose-This paper aims to endorse and elaborate on the recommendations put forward by the Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network (SF-DDARIN), emphasising their significance in the field of developmental disabilities. Design/methodology/approach-This paper outlines a specific point of view. The first section focuses on integrating developmental theory and advanced technology in interventions for developmental disabilities. Subsequently, the commentary explores virtual reality (VR) and generative artificial intelligence (AI) for enhancing social skills and personalising support. Finally, the piece highlights innovations like SocialWise VR and Custom Generative Pre-Trained Transformers in aligning interventions with developmental stages. Findings- Technologies like VR and generative AI hold vast potential to revolutionise how clinicians provide timely and relevant knowledge to individuals with developmental disabilities. Research limitations/implications-This is strictly a commentary. Practical implications-Availability of technology. Social implications-Both VR and generative AI will impact service delivery in a meaningful way. Originality/value-The paper advocates for incorporating these technologies into SF-DDARIN's approach, emphasising their potential to revolutionise evidence-based interventions in developmental disabilities.
C1 [Cameron, Michael J.] Univ Southern Calif, Dept Psychol, Los Angeles, CA 90007 USA.
   [Shahin, Jenifer; Lockerman, Nicole] SocialWise VR, London, England.
C3 University of Southern California
RP Cameron, MJ (corresponding author), Univ Southern Calif, Dept Psychol, Los Angeles, CA 90007 USA.
EM came746@usc.edu
RI Cameron, Michael/JFT-1588-2023
CR Grindle C.F., 2024, TIZARD LEARN DISABIL
   Hernandez CD, 2023, BEHAV ANAL PRACT, V16, P1280, DOI 10.1007/s40617-023-00791-3
   Sáez-Suanes GP, 2022, REV J AUTISM DEV DIS, V9, P307, DOI 10.1007/s40489-021-00254-x
   STOKES TF, 1977, J APPL BEHAV ANAL, V10, P349, DOI 10.1901/jaba.1977.10-349
   Taylor J, 2023, INT J HERIT STUD, V29, P199, DOI 10.1080/13527258.2023.2179100
   Vygotsky L.S., 1978, MIND SOC DEV HIGHER, DOI 10.2307/j.ctvjf9vz4
   Waite-Stupiansky S., 2022, Theories of early childhood education, P3, DOI [10.4324/9781003288077-2, DOI 10.4324/9781003288077-2]
NR 7
TC 1
Z9 1
U1 2
U2 2
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1359-5474
EI 2042-8782
J9 TIZARD LEARN DISABIL
JI Tizard Learn. Disabil. Rev.
PD MAY 28
PY 2024
VL 29
IS 1
SI SI
BP 14
EP 19
DI 10.1108/TLDR-11-2023-0031
EA MAR 2024
PG 6
WC Education, Special
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA SC0F6
UT WOS:001180422600001
DA 2024-12-25
ER

PT J
AU Tan, TF
   Quek, C
   Wong, J
   Ting, DSW
AF Tan, Ting Fang
   Quek, Chrystie
   Wong, Joy
   Ting, Daniel S. W.
TI A look at the emerging trends of large language models in ophthalmology
SO CURRENT OPINION IN OPHTHALMOLOGY
LA English
DT Article
DE generative artificial intelligence; generative pretrained transformers;
   large language models; ophthalmology
AB Purpose of reviewAs the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to expand, this review seeks to update physicians of the current progress, to catalyze further work to harness its capabilities to enhance healthcare delivery in ophthalmology.Recent findingsGenerative AI applications have shown promising performance in Ophthalmology. Beyond native LLMs and question-answering based tasks, there has been increasing work in employing novel LLM techniques and exploring wider use case applications.SummaryIn this review, we first look at existing LLM use case applications specific to Ophthalmology, followed by an overview of commonly used LLM techniques. We finally focus on the emerging trends of the generative AI space with an angle from ophthalmology.
C1 [Tan, Ting Fang; Quek, Chrystie; Wong, Joy; Ting, Daniel S. W.] Singapore Eye Res Inst, Singapore Natl Eye Ctr, Singapore, Singapore.
   [Ting, Daniel S. W.] Singapore Hlth Serv, Artificial Intelligence Off, Singapore, Singapore.
   [Ting, Daniel S. W.] Duke NUS Med Sch, Singapore, Singapore.
C3 National University of Singapore; Singapore National Eye Center;
   National University of Singapore
RP Ting, DSW (corresponding author), Singapore Eye Res Inst, Duke NUS Med Sch, Singapore Hlth Serv, AI Off,Head AI & Digital Hlth, 20 Coll Rd,Level 6 Discovery Tower, Singapore 169856, Singapore.
EM daniel.ting45@gmail.com
FU National Medical Research Council Singapore [NMRC/HSRG/0087/2018,
   MOH-000655-00, MOH-001014-00]; Duke-NUS Medical School Singapore
   [Duke-NUS/RSF/2021/0018, 05/FY2020/EX/15-A58]; Agency for Science,
   Technology and Research Singapore [A20H4g2141, H20C6a0032]
FX D.S.W.T. holds a patent on a deep-learning system for the detection of
   retinal diseases. D.W.S.T. is supported by grants from the National
   Medical Research Council Singapore (NMRC/HSRG/0087/2018; MOH-000655-00;
   MOH-001014-00), Duke-NUS Medical School Singapore
   (Duke-NUS/RSF/2021/0018; 05/FY2020/EX/15-A58) and Agency for Science,
   Technology and Research Singapore (A20H4g2141; H20C6a0032), for research
   inartificial intelligence. The remaining authors have no conflicts of
   interest.
CR Akbari H, 2021, ADV NEUR IN
   Antaki F, 2024, JAMA OPHTHALMOL, V142, P573, DOI 10.1001/jamaophthalmol.2024.1165
   Bedi S, 2024, JAMA-J AM MED ASSOC, DOI 10.1001/jama.2024.21700
   Betzler BK, 2023, LANCET DIGIT HEALTH, V5, pE917, DOI 10.1016/S2589-7500(23)00201-7
   Biswas S, 2024, OPHTHAL PHYSL OPT, V44, P641, DOI 10.1111/opo.13284
   Carlà MM, 2024, GRAEF ARCH CLIN EXP, V262, P2945, DOI 10.1007/s00417-024-06470-5
   Chen XL, 2024, ISCIENCE, V27, DOI 10.1016/j.isci.2024.110021
   Chen Z, 2024, Arxiv, DOI [arXiv:2405.08272, 10.48550/arXiv.2405.08272, DOI 10.48550/ARXIV.2405.08272]
   Chia MA, 2024, BRIT J OPHTHALMOL, DOI 10.1136/bjo-2024-325459
   Feng DH, 2023, MATH BIOSCI ENG, V20, P2439, DOI 10.3934/mbe.2023114
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V330, P702, DOI 10.1001/jama.2023.12500
   Hu EJ, 2021, Arxiv, DOI arXiv:2106.09685
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Liu Y, 2023, Arxiv, DOI [arXiv:2308.05374, DOI 10.48550/ARXIV.2308.053742308.05374]
   Luo MJ, 2024, JAMA OPHTHALMOL, V142, P798, DOI 10.1001/jamaophthalmol.2024.2513
   Ma H, 2024, MATHEMATICS-BASEL, V12, DOI 10.3390/math12020286
   Nori H, 2023, Arxiv, DOI [arXiv:2303.13375, 10.48550/arXiv.2303.13375, DOI 10.48550/ARXIV.2303.13375]
   Qiu JN, 2023, Arxiv, DOI arXiv:2310.04992
   Raja H, 2024, JMIR FORM RES, V8, DOI [10.2024/1/e52462, 10.2196/52462]
   Rojas-Carabali W, 2024, ASIA-PAC J OPHTHALMO, V13, DOI 10.1016/j.apjo.2024.100084
   Shi DL, 2024, Arxiv, DOI [arXiv:2405.11338, 10.48550/arXiv.2405.11338, DOI 10.48550/ARXIV.2405.11338]
   Shu FX, 2023, Arxiv, DOI [arXiv:2312.06720, 10.48550/arXiv.2312.06720, DOI 10.48550/ARXIV.2312.06720]
   Singh S, 2023, SEMIN OPHTHALMOL, V38, P503, DOI 10.1080/08820538.2023.2209166
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Sorin V, 2023, arXiv, DOI [10.1101/2023.11.24.23298953, DOI 10.1101/2023.11.24.23298953]
   Tai-Seale M, 2024, JAMA NETW OPEN, V7, DOI 10.1001/jamanetworkopen.2024.6565
   Tan TF, 2024, Arxiv, DOI [arXiv:2407.07666, 10.48550/arXiv.2407.07666, DOI 10.48550/ARXIV.2407.07666]
   Thirunavukarasu Arun James, 2024, PLOS Digit Health, V3, pe0000341, DOI 10.1371/journal.pdig.0000341
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Tu T, 2024, Arxiv, DOI [arXiv:2401.05654, 10.48550/arXiv.2401.05654, DOI 10.48550/ARXIV.2401.05654]
   Wang XZ, 2022, Arxiv, DOI [arXiv:2203.11171, DOI 10.48550/ARXIV.2203.11171]
   Wong M, 2023, BRIT J OPHTHALMOL, DOI 10.1136/bjo-2023-324734
   Zhang Juzhao, 2024, NPJ Digit Med, V7, P108, DOI 10.1038/s41746-024-01109-5
   Zhang Y, 2024, STRUCT CHEM, DOI 10.1007/s11224-024-02405-2
   Zhao H, 2023, Arxiv, DOI [arXiv:2312.04906, 10.48550/arXiv.2312.04906, DOI 10.48550/ARXIV.2312.04906]
   Zhou YK, 2023, NATURE, V622, P156, DOI 10.1038/s41586-023-06555-x
NR 36
TC 0
Z9 0
U1 1
U2 1
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 1040-8738
EI 1531-7021
J9 CURR OPIN OPHTHALMOL
JI Curr. Opin. Ophthalmol.
PD JAN
PY 2025
VL 36
IS 1
BP 83
EP 89
DI 10.1097/ICU.0000000000001097
PG 7
WC Ophthalmology
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Ophthalmology
GA O6Z8O
UT WOS:001372592900013
PM 39446695
DA 2024-12-25
ER

PT J
AU Doo, FX
   Cook, TS
   Siegel, EL
   Joshi, A
   Parekh, V
   Elahi, A
   Yi, PH
AF Doo, Florence X.
   Cook, Tessa S.
   Siegel, Eliot L.
   Joshi, Anupam
   Parekh, Vishwa
   Elahi, Ameena
   Yi, Paul H.
TI Exploring the Clinical Translation of Generative Models Like ChatGPT:
   Promise and Pitfalls in Radiology, From Patients to Population Health
SO JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY
LA English
DT Article
DE generative artificial intelligence; radiology; limitations; large
   language models; ChatGPT
ID ARTIFICIAL-INTELLIGENCE; CLIMATE
AB Generative artificial intelligence (AI) tools such as GPT-4, and the chatbot interface ChatGPT, show promise for a variety of applications in radiology and health care. However, like other AI tools, ChatGPT has limitations and potential pitfalls that must be considered before adopting it for teaching, clinical practice, and beyond. We summarize five major emerging use cases for ChatGPT and generative AI in radiology across the levels of increasing data complexity, along with pitfalls associated with each. As the use of AI in health care continues to grow, it is crucial for radiologists (and all physicians) to stay informed and ensure the safe translation of these new technologies.
C1 [Doo, Florence X.] Univ Maryland, Med Intelligent Imaging Ctr UM2ii, Innovat, Baltimore, MD USA.
   [Doo, Florence X.] ACR Commiss Econ, Comm Econ Acad Radiol, Baltimore, MD USA.
   [Cook, Tessa S.] Emory Univ, Perelman Sch Med, Dept Radiol, Practice Informat, Atlanta, GA USA.
   [Cook, Tessa S.] Penn Med, Dept Radiol, Imaging Informat, Philadelphia, PA USA.
   [Cook, Tessa S.] Penn Med, Dept Radiol, 3D & Advanced Imaging, Philadelphia, PA USA.
   [Cook, Tessa S.] ACR Commiss Patient and Family Ctr Care, RAHSR Affin Grp, Philadelphia, PA USA.
   [Siegel, Eliot L.] Univ Maryland, Res Informat Syst, Baltimore, MD USA.
   [Siegel, Eliot L.] US Dept Vet Affairs Maryland Healthcare Syst, USDepartment Vet Affairs Vet Integrated Serv Netwo, Radiol & Nucl Med Diagnost, Baltimore, MD USA.
   [Joshi, Anupam] Univ Maryland, Comp Sci & Elect Engn, Baltimore, MD USA.
   [Joshi, Anupam] Univ Maryland Baltimore Cty, Ctr Cybersecur, Baltimore, MD USA.
   [Parekh, Vishwa] Univ Maryland, Med Intelligent Imaging UM2ii Ctr, Baltimore, MD USA.
   [Elahi, Ameena] Univ Penn, Philadelphia, PA USA.
   [Elahi, Ameena] Penn Med, Informat Serv, Philadelphia, PA USA.
   [Yi, Paul H.] RAD AID Int, Philadelphia, PA USA.
   [Doo, Florence X.] Stanford Univ, 500 Pasteur Dr, Palo Alto, CA 94304 USA.
C3 University System of Maryland; University of Maryland Baltimore; Emory
   University; University of Pennsylvania; Pennsylvania Medicine;
   University of Pennsylvania; Pennsylvania Medicine; University System of
   Maryland; University of Maryland Baltimore; University System of
   Maryland; University of Maryland Baltimore; University System of
   Maryland; University of Maryland Baltimore County; University System of
   Maryland; University of Maryland Baltimore; University of Pennsylvania;
   University of Pennsylvania; Pennsylvania Medicine; Stanford University
RP Doo, FX (corresponding author), Stanford Univ, 500 Pasteur Dr, Palo Alto, CA 94304 USA.
EM fdoo@som.umaryland.edu
RI Doo, Florence/ABC-8531-2020; Doo, Florence Xini/Q-6640-2018
OI Joshi, Anupam/0000-0002-8641-3193; Doo, Florence
   Xini/0000-0001-6519-5222; Elahi, Ameena/0000-0001-6938-1735
FU AUR GERRAF; NIH,; IBC; RSNA; NIH; ACR
FX Dr Doo declares support from AUR GERRAF; honoraria from AIRE; and a
   leadership role in the University of Maryland Medical Intelligent
   Imaging (UM2ii) Center, and membership on the ABR Initial Certifiying
   Advisory Committee. Dr Cook declares support from NIH, IBC, RSNA;
   honoraria from ISMIE, Icahn, MGH, BJR, Sectra; SIIM Board meeting travel
   reimbursement, PRS Board meeting travel reimbursement; and leadership
   roles as SIIM Board Chair, PRRS and PRS Board Member. Dr Joshi declares
   institutional support from MIPS, DoD, NSF. Dr Elahi declares leadership
   roles as SIIM Board of Directors-Chair of the Membership Committee,
   ABII-Item writer, 10 year review committee. RAD-AID International. Dr Yi
   declares support from NIH, ACR, RSNA; honoraria from SIIM, SNMI; and
   leadership roles as Vice Chair of Annual Program Planning Committee,
   SIIM-Board of Directors, Director of UM2ii, Associate Editor of
   Radiology:Artificial Intelligence, and University of Maryland Baltimore
   Digital Health Faculty Advisory Board. All other authors state that they
   have no conflict of interest related to the material discussed in this
   article. The authors are non-partner/non-partnership track/employees.
CR Abid A, 2021, AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, P298, DOI 10.1145/3461702.3462624
   Abou Elkassem A, 2023, AM J ROENTGENOL, V221, P373, DOI 10.2214/AJR.23.29198
   Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Adams LC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230725
   Ali Stephen R, 2023, Lancet Digit Health, V5, pe179, DOI 10.1016/S2589-7500(23)00048-1
   An JF, 2023, NATURE, V615, P586, DOI 10.1038/d41586-023-00843-2
   [Anonymous], 2023, NAT MED, V29, P505, DOI 10.1038/s41591-023-02289-5
   Asch DA., 2023, CATALYST NONISSUE CO, DOI [DOI 10.1056/CAT.23.0043, 10.1056/cat.23.0043]
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Ayoub NF, 2023, JAMA OTOLARYNGOL, V149, P556, DOI 10.1001/jamaoto.2023.0704
   Bang Y, 2023, Arxiv, DOI arXiv:2302.04023
   Beheshtian E, 2023, RADIOLOGY, V306, DOI 10.1148/radiol.220505
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Borji A, 2023, Arxiv, DOI [arXiv:2302.03494, 10.48550/arxiv.2302.03494, DOI 10.48550/ARXIV.2302.03494]
   Buckley BW, 2021, RADIOLOGY, V300, pE339, DOI 10.1148/radiol.2021210851
   Cheng KM, 2023, ANN BIOMED ENG, V51, P870, DOI 10.1007/s10439-023-03196-z
   Doo FX, 2023, J AM COLL RADIOL, V20, P852, DOI 10.1016/j.jacr.2023.06.014
   Dratsch T, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.222176
   Gao CA, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00819-6
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Glaese Amelia, 2022, arXiv
   Haemmerli J, medRxiv, DOI [10.1101/2023.03.1, DOI 10.1101/2023.03.1]
   Haupt CE, 2023, JAMA-J AM MED ASSOC, V329, P1349, DOI 10.1001/jama.2023.5321
   Haver HL, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230424
   Heitkamp DE, 2016, J AM COLL RADIOL, V13, P1359, DOI 10.1016/j.jacr.2016.05.019
   Hirosawa Takanobu, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20043378
   Hopkins AM, 2023, JNCI CANCER SPECT, V7, DOI 10.1093/jncics/pkad010
   Kanjee Z, 2023, JAMA-J AM MED ASSOC, V330, P78, DOI 10.1001/jama.2023.8288
   Kaplan J, 2020, Arxiv, DOI [arXiv:2001.08361, 10.48550/arXiv.2001.08361]
   Khan RA, 2023, PAK J MED SCI, V39, P605, DOI 10.12669/pjms.39.2.7653
   Kitamura FC, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230171
   Kocon J, 2023, INFORM FUSION, V99, DOI 10.1016/j.inffus.2023.101861
   Krügel S, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-31341-0
   Kung T. H, 2023, PLOS Digit Health, V2, DOI DOI 10.1371/JOURNAL.PDIG.0000198.PDIG-D-22-00371
   Larson DB, 2023, AM J ROENTGENOL, V221, P687, DOI 10.2214/AJR.23.29585
   Lee P, Allen School Distinguished Lecture (Microsoft Research & Incubations)
   Lee P, 2023, NEW ENGL J MED, V388, P1233, DOI 10.1056/NEJMsr2214184
   Lourenco AP, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.231053
   Lyu Q, 2023, VIS COMPUT IND BIOME, V6, DOI 10.1186/s42492-023-00136-5
   Martin-Carreras T, 2019, CLIN IMAG, V54, P116, DOI 10.1016/j.clinimag.2018.12.006
   Medicare and Medicaid programs, Patient Protection and Affordable Care Act; interoperability and patient access for Medicare Advantage organization and Medicaid managed care plans, state Medicaid agencies, CHIP agencies and CHIP managed care entities, issuers of qualified health plans on the federally-facilitated exchanges, and health care providers. Federal Register
   Mello MM, 2023, JAMA-HEALTH FORUM, V4, DOI 10.1001/jamahealthforum.2023.1938
   Metz Rachel, Bloomberg
   Miao QH, 2023, IEEE-CAA J AUTOMATIC, V10, P877, DOI 10.1109/JAS.2023.123561
   Milano S, 2023, NAT MACH INTELL, V5, P333, DOI 10.1038/s42256-023-00644-2
   Moor M, 2023, NATURE, V616, P259, DOI 10.1038/s41586-023-05881-4
   OpenAI, Introducing ChatGPT 2022
   Patel S, 2023, LANCET DIGIT HEALTH, V5, pE102, DOI 10.1016/S2589-7500(23)00023-7
   Rao ARY, 2023, medRxiv, DOI [10.1101/2023.02.21.23285886, 10.1101/2023.02.21.23285886v1, DOI 10.1101/2023.02.21.23285886V1, 10.1101/2023.02.21.23285886, DOI 10.1101/2023.02.21.23285886]
   Rao ARY, 2023, medRxiv, DOI [10.1101/2023.02.02.23285399, 10.1101/2023.02.02.23285399, DOI 10.1101/2023.02.02.23285399]
   Schoen J, 2021, J AM COLL RADIOL, V18, P1041, DOI 10.1016/j.jacr.2021.02.009
   Schramowski P, 2022, NAT MACH INTELL, V4, P258, DOI 10.1038/s42256-022-00458-8
   Schwalbe N, 2020, LANCET, V395, P1579, DOI 10.1016/S0140-6736(20)30226-9
   Schwartz R, 2020, COMMUN ACM, V63, P54, DOI 10.1145/3381831
   Shelmerdine SC, 2022, BMJ-BRIT MED J, V379, DOI 10.1136/bmj-2022-072826
   Shen YQ, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230163
   Siriwardhana S, 2023, T ASSOC COMPUT LING, V11, P1, DOI 10.1162/tacl_a_00530
   Sparrow B, 2011, SCIENCE, V333, P776, DOI 10.1126/science.1207745
   US Department of Health and Human Services, CMS 9115-F FAQs
   Weidinger L, 2021, Arxiv, DOI arXiv:2112.04359
   Yaraghi N., ChatGPT and health care: implications for interoperability and fairness
NR 63
TC 9
Z9 9
U1 21
U2 56
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 1546-1440
EI 1558-349X
J9 J AM COLL RADIOL
JI J. Am. Coll. Radiol.
PD SEP
PY 2023
VL 20
IS 9
BP 877
EP 885
DI 10.1016/j.jacr.2023.07.007
EA OCT 2023
PG 9
WC Radiology, Nuclear Medicine & Medical Imaging
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Radiology, Nuclear Medicine & Medical Imaging
GA W1FP2
UT WOS:001089157200001
PM 37467871
DA 2024-12-25
ER

PT J
AU Knight, S
   -Deane, CD
   Heggart, K
   Kitto, K
   Kozanoglu, DC
   Maher, D
   Narayan, B
   Zarrabi, F
AF Knight, Simon
   -Deane, Camille Dickson
   Heggart, Keith
   Kitto, Kirsty
   Kozanoglu, Dilek Cetindamar
   Maher, Damian
   Narayan, Bhuva
   Zarrabi, Forooq
TI Generative AI in the Australian education system: An open data set of
   stakeholder recommendations and emerging analysis from a public inquiry
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE policy analysis; content analysis; edtech; participatory; AI ethics
AB The launch of new tools in late 2022 heralded significant growth in attention to the impacts of generative AI (GenAI) in education. Claims of the potential impact on education are contested, but there are clear risks of inappropriate use particularly where GenAI aligns poorly with learning aims. In response, in mid-2023, the Australian Federal Government held an inquiry, calling for public submissions. This inquiry offers a lens onto the policy framing of GenAI in education and provides the object of investigation for this paper. We use the inquiry submissions, extracting structured claims from each. This extraction is provided as an open data set for further research, while this paper focuses on our analysis of the policy recommendations made.
C1 [Knight, Simon; -Deane, Camille Dickson; Heggart, Keith; Kitto, Kirsty; Kozanoglu, Dilek Cetindamar; Maher, Damian; Narayan, Bhuva] Univ Technol Sydney, Ctr Res Educ Digital Soc, Ultimo, NSW, Australia.
   [Zarrabi, Forooq] Univ Technol Sydney, TD Sch, Ultimo, NSW, Australia.
   [-Deane, Camille Dickson; Kozanoglu, Dilek Cetindamar] Univ Technol Sydney, Fac Engn & IT, Ultimo, NSW, Australia.
   [Heggart, Keith; Maher, Damian; Narayan, Bhuva] Univ Technol Sydney, Fac Arts & Social Sci, Ultimo, NSW, Australia.
   [Kitto, Kirsty] Univ Technol Sydney, Connected Intelligence Ctr, Ultimo, NSW, Australia.
C3 University of Technology Sydney; University of Technology Sydney;
   University of Technology Sydney; University of Technology Sydney;
   University of Technology Sydney
RP Knight, S (corresponding author), Univ Technol Sydney, Ctr Res Educ Digital Soc, Ultimo, NSW, Australia.
EM simon.knight@uts.edu.au
RI Heggart, Keith/AAR-6183-2020; CETINDAMAR, DILEK/R-9278-2019; Maher,
   Damien/E-3443-2012; Knight, Simon/AAG-7525-2019; Dickson-Deane,
   Camille/N-7364-2016; Narayan, Bhuva/M-4044-2015; Knight,
   Simon/O-1513-2013
OI Dickson-Deane, Camille/0000-0002-5504-7856; Cetindamar,
   Dilek/0000-0002-0457-3258; Narayan, Bhuva/0000-0001-8852-5589; Maher,
   Damian/0000-0002-3566-0805; Kitto, Kirsty/0000-0001-7642-7121; Heggart,
   Keith/0000-0003-2331-1234; Knight, Simon/0000-0002-8709-5780
CR [Anonymous], COMMUNITIES CHILDREN
   Bacchi C., 2012, Engaging with Carol Bacchi, P21
   Bletsas A., 2012, Engaging with Carol Bacchi - strategic interventions and exchanges
   Bromham L, 2016, NATURE, V534, P684, DOI 10.1038/nature18315
   Buchan J., 2010, UNSW Australian School of Business Research Paper 2010 BLAT 01)
   Bussell Chris, 2023, HCI International 2023 Posters: 25th International Conference on Human-Computer Interaction, HCII 2023, Proceedings. Communications in Computer and Information Science (1836), P380, DOI 10.1007/978-3-031-36004-6_52
   Cairney P., 2016, POLITICS EVIDENCE BA, DOI DOI 10.1057/978-1-137-51781-4
   Cardona MA., 2023, Artificial intelligence and the future of teaching and learning
   Cook K., 2017, P 3 INT C PUBLIC POL, P1
   Cox MJ, 2007, EDUC INF TECHNOL, V12, P59, DOI [10.1007/s10639-007-9032-x, 10.1007/sl0639-007-9032-x]
   Creative Commons, 2023, Frequently asked questions
   Department of Business, 2023, Assess if your R&D activities are eligible for the R&D Tax Incentive
   du Boulay B, 2024, INT J ARTIF INTELL E, V34, P116, DOI 10.1007/s40593-023-00355-0
   EduGrowth, 2020, Australian EdTech Ecosystem Snapshot
   Fowler S., 2023, Learning Letters, V1, P1, DOI [10.59453/JMTN6001, DOI 10.59453/JMTN6001]
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Grandbastien M, 2016, INT J ARTIF INTELL E, V26, P1, DOI 10.1007/s40593-015-0092-6
   Hallsworth M., 2011, Policy Making in the Real World: Evidence and Analysis
   Hicks M., 2016, Impact evaluation of key themes funded by the Office for Learning and Teaching 2012-2016
   HolonIQ, 2022, 2022 GLOBAL ED OUTLO
   Homes W, 2024, INT J ARTIF INTELL E, V34, P1, DOI 10.1007/s40593-023-00352-3
   Hsieh HF, 2005, QUAL HEALTH RES, V15, P1277, DOI 10.1177/1049732305276687
   Janssen M, 2012, INFORM SYST MANAGE, V29, P258, DOI 10.1080/10580530.2012.716740
   Johnson L., 2023, A big idea for Australian higher education: The National Centre for Student Success (Submission to the Universities Accord Panel)
   Knight S., 2023, Generative AI in the Australian education system: An open dataset of stakeholder recommendations and emerging analysis from a public inquiry [Data set], DOI [10.26195/jjaa-7p04, DOI 10.26195/JJAA-7P04]
   Knight S., 2023, UTS:CREDS submission in response to the House Standing Committee on Employment, Education and Training's inquiry into the use of generative artificial intelligence in the Australian education system
   Krueger N., 2019, ISTE BlogDecember 25
   LearnPlatform, 2023, 2023 EdTech evidence mid-year report
   Loble L., 2022, Shaping AI to tackle Australia's learning divide, DOI [10.57956/kxye-qd93, DOI 10.57956/KXYE-QD93]
   Meagher G., 2018, CRITICAL APPROACHES, V10, P1
   Oliver K, 2022, EVID POLICY, V18, P691, DOI 10.1332/174426421X16420918447616
   Parliament of Australia, 2023, Inquiry into the use of generative artificial intelligence in the Australian education system
   Parliament of Australia, 2021, Making a submission to a Committee inquiry
   Puttick R., 2018, MAPPING STANDARDS EV
   Regan S., 2019, THESIS AUSTR NATL U
   Rickinson M., 2017, EUROPEAN C ED RES 20
   Rickinson M, 2022, EDUC RES-UK, V64, P133, DOI 10.1080/00131881.2022.2054452
   Rickinson M, 2019, EVID POLICY, V15, P235, DOI 10.1332/174426418X15172393826277
   Ross-Hellauer T, 2022, ROY SOC OPEN SCI, V9, DOI 10.1098/rsos.211032
   Sabzalieva E., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Schiff D, 2022, INT J ARTIF INTELL E, V32, P527, DOI 10.1007/s40593-021-00270-2
   Self J, 2016, INT J ARTIF INTELL E, V26, P4, DOI 10.1007/s40593-015-0040-5
   Sharples J., 2018, Putting evidence to work-A school's guide to implementation
   Sharples M, 2023, Arxiv, DOI arXiv:2306.10063
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Thompson G, 2023, LEARN MEDIA TECHNOL, V48, P240, DOI 10.1080/17439884.2022.2126495
   UK Government, 2023, Generative artificial intelligence (AI) in education
   Verhagen E, 2014, BRIT J SPORT MED, V48, P698, DOI 10.1136/bjsports-2013-092241
   Vezina B., 2020, Why sharing academic publications under "No Derivatives"licenses is misguided
   Weaver-Hightower MB, 2014, J MIX METHOD RES, V8, P115, DOI 10.1177/1558689813490996
   Woelert P, 2013, HIGH EDUC, V66, P755, DOI 10.1007/s10734-013-9634-8
   Zavestoski S, 2006, SCI TECHNOL HUM VAL, V31, P383, DOI 10.1177/0162243906287543
NR 52
TC 2
Z9 2
U1 14
U2 28
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2023
VL 39
IS 5
BP 101
EP 124
DI 10.14742/ajet.8922
PG 24
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA DO3Z6
UT WOS:001132966400004
OA gold
DA 2024-12-25
ER

PT J
AU Seo, WJ
   Kim, M
AF Seo, Won Jin
   Kim, Mihui
TI Utilization of Generative Artificial Intelligence in Nursing Education:
   A Topic Modeling Analysis
SO EDUCATION SCIENCES
LA English
DT Article
DE generative artificial intelligence; nursing; nursing education; nursing
   education research; students; patients; topic modeling
ID CHATGPT; FUTURE
AB The advent of artificial intelligence (AI) has prompted the introduction of novel digital technologies, including mobile learning and metaverse learning, into nursing students' learning environments. This study used text network and topic modeling analyses to identify the research trends in generative AI in nursing education for students and patients in schools, hospitals, and community settings. Additionally, an ego network analysis using strengths, weaknesses, opportunities, and threats (SWOT) words was performed to develop a comprehensive understanding of factors that impact the integration of generative AI in nursing education. The literature was searched from five databases published until July 2024. After excluding studies whose abstracts were not available and removing duplicates, 139 articles were identified. The seven derived topics were labeled as usability in future scientific applications, application and integration of technology, simulation education, utility in image and text analysis, performance in exams, utility in assignments, and patient education. The ego network analysis focusing on the SWOT keywords revealed "healthcare", "use", and "risk" were common keywords. The limited emphasis on "threats", "strengths", and "weaknesses" compared to "opportunities" in the SWOT analysis indicated that these areas are relatively underexplored in nursing education. To integrate generative AI technology into education such as simulation training, teaching activities, and the development of personalized learning, it is necessary to identify relevant internal strengths and weaknesses of schools, hospitals, and communities that apply it, and plan practical application strategies aligned with clear institutional guidelines.
C1 [Seo, Won Jin] Yonsei Univ, Coll Nursing, 50-1 Yonsei Ro, Seoul 03722, South Korea.
   [Kim, Mihui] Jeonju Univ, Dept Nursing Sci, 303 Cheonjam Ro, Jeonju 55069, South Korea.
C3 Yonsei University; Yonsei University Health System; Jeonju University
RP Kim, M (corresponding author), Jeonju Univ, Dept Nursing Sci, 303 Cheonjam Ro, Jeonju 55069, South Korea.
EM wjin.seo@gmail.com; ystelra50@gmail.com
FU Korea government (MSIT) [RS-2024-00357844]; National Research Foundation
   of Korea (NRF) - Korea government (MSIT)
FX This work was supported by the National Research Foundation of Korea
   (NRF) grant funded by the Korea government (MSIT) (RS-2024-00357844).
CR Abdulai AF, 2023, NURS INQ, V30, DOI 10.1111/nin.12556
   Abujaber AA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.48643
   Alshaikh R, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e25361
   Athilingam P, 2024, TEACH LEARN NURS, V19, P97, DOI 10.1016/j.teln.2023.11.004
   Baker C, 2021, J PROF NURS, V37, P86, DOI 10.1016/j.profnurs.2020.10.001
   Benfatah M, 2024, TEACH LEARN NURS, V19, pe486, DOI 10.1016/j.teln.2024.02.005
   Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826
   Bonacaro Antonio, 2024, Stud Health Technol Inform, V315, P200, DOI 10.3233/SHTI240134
   Buchanan Christine, 2021, JMIR Nurs, V4, pe23933, DOI 10.2196/23933
   Cao Q, 2023, LIBR HI TECH, V41, P543, DOI 10.1108/LHT-03-2022-0144
   Cyram, 2022, Netminer
   De Gagne JC, 2023, NURS EDUC, V48, pE73, DOI 10.1097/NNE.0000000000001327
   Demir KA, 2021, SMART LEARN ENVIRON, V8, DOI 10.1186/s40561-021-00170-x
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Egger R, 2022, FRONT SOCIOL, V7, DOI 10.3389/fsoc.2022.886498
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Gunawan J, 2024, NURS EDUC TODAY, V141, DOI 10.1016/j.nedt.2024.106323
   Güzer B, 2014, PROCD SOC BEHV, V116, P4596, DOI 10.1016/j.sbspro.2014.01.992
   Huang HM, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11212855
   Hwang GJ, 2024, INTERACT LEARN ENVIR, V32, P373, DOI 10.1080/10494820.2022.2086579
   Imran M, 2024, SMART LEARN ENVIRON, V11, DOI 10.1186/s40561-024-00310-z
   Jalaparti V, 2012, ANN WORK NETW
   Johnson SB, 2023, JNCI CANCER SPECT, V7, DOI 10.1093/jncics/pkad015
   Kim J, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-024-12878-7
   Lage MJ, 2000, J ECON EDUC, V31, P30, DOI 10.2307/1183338
   Lee GG, 2023, Arxiv, DOI [arXiv:2312.06037, 10.48550/arXiv.2312.06037]
   Lee Sungjick, 2009, [The Journal of Society for e-Business Studies, 한국전자거래학회지], V14, P59
   Liu JL, 2023, NURS OUTLOOK, V71, DOI 10.1016/j.outlook.2023.102064
   Miao YQ, 2024, NURS EDUC, V49, pE338, DOI 10.1097/NNE.0000000000001679
   National Academy of Medicine, 2021, The Future of Nursing 20202030: Charting a Path to Achieve Health Equity
   Neumann M, 2023, 2023 IEEE/ACM 5TH INTERNATIONAL WORKSHOP ON SOFTWARE ENGINEERING EDUCATION FOR THE NEXT GENERATION, SEENG, P29, DOI 10.1109/SEENG59157.2023.00010
   Oberer B., 2016, Int. J. u-and e-Serv. Sci. Technol, V9, P379, DOI [10.14257/ijunesst.2016.9.3.36, DOI 10.14257/IJUNESST.2016.9.3.36]
   Oniani D, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00965-x
   openai, GPT-4
   openai, ChatGPT-Release Notes
   Ozyurt O, 2023, EDUC INF TECHNOL, V28, P4335, DOI 10.1007/s10639-022-11396-8
   Park Jiu, 2022, [The Korean Journal of Educational Methodology Studies, 교육방법연구], V34, P711
   Parker JL, 2023, J NURS EDUC, V62, P721, DOI 10.3928/01484834-20231006-02
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Puyt RW, 2023, LONG RANGE PLANN, V56, DOI 10.1016/j.lrp.2023.102304
   Reed JM, 2024, NURS EDUC, V49, P184, DOI 10.1097/NNE.0000000000001590
   Risling T, 2017, NURSE EDUC PRACT, V22, P89, DOI 10.1016/j.nepr.2016.12.007
   Rodgers DL, 2023, SIMUL HEALTHC, V18, P395, DOI 10.1097/SIH.0000000000000747
   Singh H, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090924
   Topaz M, 2024, J NURS EDUC, DOI 10.3928/01484834-20240126-01
   Van Kraaij J, 2023, NURS EDUC TODAY, V120, DOI 10.1016/j.nedt.2022.105646
   Vaughn J, 2024, CLIN SIMUL NURS, V87, DOI 10.1016/j.ecns.2023.101487
   Yalcinkaya T, 2023, NURSE EDUC PRACT, V71, DOI 10.1016/j.nepr.2023.103714
   Yan MC, 2023, NAT BIOTECHNOL, V41, P1657, DOI 10.1038/s41587-023-02011-3
   Yu P, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11202776
   Zoya S., 2021, IEEE ACCESS, V9, DOI [10.1109/ACCESS.2021.3112620, DOI 10.1109/ACCESS.2021.3112620]
NR 52
TC 0
Z9 0
U1 3
U2 3
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD NOV
PY 2024
VL 14
IS 11
AR 1234
DI 10.3390/educsci14111234
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA N4W5O
UT WOS:001364362300001
DA 2024-12-25
ER

PT J
AU Magee, MD
AF Magee, Michael D.
TI GENERATIVE ARTIFICIAL INTELLIGENCEASATOOLFOR JEWELRY DESIGN
SO GEMS & GEMOLOGY
LA English
DT Article
AB While the jewelry industry is deeply rooted in traditional tools and techniques, generative artificial intelligence(AI) must be recognized today as a revolutionary tool to assist jewelers. The intersection between AI and the cre-ative arts is especially controversial, and the use of generative AI in jewelry design is no different. This paper ex-amines how generative AI creates designs based on text and image prompts, comparing five of the more commonAI programs (Midjourney, DALL.E, Stable Diffusion, Leonardo, and Firefly) with a focus on generating realisticjewelry images. It addresses the ethical, legal, and regulatory questions that have arisen around AI-generated art.Lastly, it explores how this new technology can best be used by jewelry designers to enhance creative expression
C1 [Magee, Michael D.] GIA, Carlsbad, CA 92008 USA.
RP Magee, MD (corresponding author), GIA, Carlsbad, CA 92008 USA.
CR Appel G., 2023, Harvard Business Review
   Autodesk, 2024, ADSK News
   Diffusion Art, 2024, How does Stable Diffusion work!
   Epstein Z, 2023, Arxiv, DOI arXiv:2306.04141
   Gilgamesh, 2023, Medium
   JEMSU, What potential drawbacks could interior designers face with Al-generated content in 2024?
   Metz R., 2024, Yahoo FinanceApril 12
   Sanderson G., 2017, Neural networks
   Siddiqui Y, 2024, Arxiv, DOI [arXiv:2407.02445, 10.48550/arXiv240702445, DOI 10.48550/ARXIV240702445]
   Touvron H, 2023, arXiv, DOI [DOI 10.48550/ARXIV, 10.48550/arXiv]
   United States Copyright Office, 2023, 59942 Federal Register, V88
   Wiggers K., 2023, TechCrunch
   Yup K., 2023, Yale Daily News
NR 13
TC 0
Z9 0
U1 1
U2 1
PU GEMOLOGICAL INST AMER
PI CARLSBAD
PA 5345 ARMADA DR, CARLSBAD, CA 92008 USA
SN 0016-626X
EI 2376-4473
J9 GEMS GEMOL
JI Gems Gemol.
PD FAL
PY 2024
VL 60
IS 3
DI 10.5741/GEMS.60.3.330
PG 167
WC Mineralogy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Mineralogy
GA M0D5S
UT WOS:001354337100004
DA 2024-12-25
ER

PT J
AU Xu, R
   Wang, Z
AF Xu, Rui
   Wang, Zhong
TI Generative artificial intelligence in healthcare from the perspective of
   digital media: Applications, opportunities and challenges
SO HELIYON
LA English
DT Article
DE ChatGPT; Healthcare; Digital media; Applications; Opportunities;
   Challenges; Digital health; Generative artificial intelligence; Large
   language models; Artificial intelligence generated content
ID CHATGPT; MEDICINE
AB Introduction: The emergence and application of generative artificial intelligence/large language models (hereafter GenAI LLMs) have the potential for significant impact on the healthcare industry. However, there is currently a lack of systematic research on GenAI LLMs in healthcare based on reliable data. This article aims to conduct an exploratory study of the application of GenAI LLMs (i.e., ChatGPT) in healthcare from the perspective of digital media (i.e., online news), including the application scenarios, potential opportunities, and challenges. Methods: This research used thematic qualitative text analysis in five steps: firstly, developing main topical categories based on relevant articles; secondly, encoding the search keywords using these categories; thirdly, conducting searches for news articles via Google ; fourthly, encoding the subcategories using the elaborate category system; and finally, conducting category-based analysis and presenting the results. Natural language processing techniques, including the TermRaider and AntConc tool, were applied in the aforementioned steps to assist in text qualitative analysis. Additionally, this study built a framework, using for analyzing the above three topics, from the perspective of five different stakeholders, including healthcare demanders and providers. Results: This study summarizes 26 applications (e.g., provide medical advice, provide diagnosis and triage recommendations, provide mental health support, etc.), 21 opportunities (e.g., make healthcare more accessible, reduce healthcare costs, improve patients care, etc.), and 17 challenges (e.g., generate inaccurate/misleading/wrong answers, raise privacy concerns, lack of transparency, etc.), and analyzes the reasons for the formation of these key items and the links between the three research topics. Conclusions: The application of GenAI LLMs in healthcare is primarily focused on transforming the way healthcare demanders access medical services (i.e., making it more intelligent, refined, and humane) and optimizing the processes through which healthcare providers offer medical services (i.e., simplifying, ensuring timeliness, and reducing errors). As the application becomes more widespread and deepens, GenAI LLMs is expected to have a revolutionary impact on traditional healthcare service models, but it also inevitably raises ethical and security concerns. Furthermore, GenAI LLMs applied in healthcare is still in the initial stage, which can be accelerated from a specific healthcare field (e.g., mental health) or a specific mechanism (e.g., GenAI LLMs' economic benefits allocation mechanism applied to healthcare) with empirical or clinical research.
C1 [Xu, Rui; Wang, Zhong] Guangdong Univ Technol, Sch Econ, Guangzhou, Peoples R China.
   [Wang, Zhong] Guangdong Univ Technol, Key Lab Digital Econ & Data Governance, Guangzhou, Peoples R China.
C3 Guangdong University of Technology; Guangdong University of Technology
RP Wang, Z (corresponding author), Guangdong Univ Technol, Key Lab Digital Econ & Data Governance, Guangzhou, Peoples R China.
EM xur2022@163.com; wz@gdut.edu.cn
OI Xu, Rui/0000-0002-8706-639X
FU National Social Science Fund of China [21BJL038]
FX This research was funded by the National Social Science Fund of China
   (Grant No. 21BJL038) .
CR Ahmed S.K., 2023, ChatGPT's influence on journal impact factors: a paradigm shift, DOI [10.2139/ssrn.4467193, DOI 10.2139/SSRN.4467193]
   Ahmed SK, 2023, ANN BIOMED ENG, V51, P2351, DOI 10.1007/s10439-023-03262-6
   Alhasan K, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.36263
   Ali M.J., 2023, Readership Awareness Series-Paper 4: Chatbots and ChatGPT-Ethical Considerations in Scientific Publications, Seminars in ophthalmology, V4
   Ali Stephen R, 2023, Lancet Digit Health, V5, pe179, DOI 10.1016/S2589-7500(23)00048-1
   Nguyen A, 2020, MEDIA COMMUN-LISBON, V8, P323, DOI 10.17645/mac.v8i2.3352
   Anderson LE, 2020, SEX REPROD HEALTHC, V25, DOI 10.1016/j.srhc.2020.100534
   Arditi C, 2016, BMC HEALTH SERV RES, V16, DOI 10.1186/s12913-016-1816-5
   Awal SS, 2023, J PUBLIC HEALTH-HEID, DOI 10.1007/s10389-023-02170-2
   Aydin O, 2023, Academic Platform Journal of Engineering and Smart Systems, V11, P118
   Berg M., 2012, Interactions: Studies in Communication & Culture, V3, P71, DOI 10.1386/iscc.3.1.71
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Biswas SS, 2023, ANN BIOMED ENG, V51, P868, DOI 10.1007/s10439-023-03172-7
   Biswas SS, 2023, ANN BIOMED ENG, V51, P1126, DOI 10.1007/s10439-023-03171-8
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Brynjolfsson E., 2023, Generative AI at Work.
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Choudhury A, 2023, J MED INTERNET RES, V25, DOI 10.2196/47184
   Couldry N., 2012, MEDIA SOC WORLD SOCI
   Dahmen J, 2023, KNEE SURG SPORT TR A, V31, P1187, DOI 10.1007/s00167-023-07355-6
   Dave T, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1169595
   Eysenbach G., 2008, Credibility of Health Information and Digital Media: New Perspectives and Implications for Youth
   Ferrag MA, 2023, Arxiv, DOI arXiv:2303.11751
   Gunawan J, 2023, BELITUNG NURS J, V9, P1, DOI 10.33546/bnj.2551
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hassani H, 2023, BIG DATA COGN COMPUT, V7, DOI 10.3390/bdcc7020062
   Hu X, 2023, Arxiv, DOI [arXiv:2304.02796, 10.1016/j.procir.2023.05.001, DOI 10.1016/J.PROCIR.2023.05.001]
   Huang HY, 2023, INT J ORAL SCI, V15, DOI 10.1038/s41368-023-00239-y
   Huang P.G. Sonya, 2022, Generative AI: a creative new world
   Iftikhar L., 2023, EC Paediatrics, V12, P45
   Javaid M., 2023, BenchCouncil Trans Benchmarks Standards Eval, V3, P100105, DOI [10.1016/j.tbench.2023.100105, DOI 10.1016/J.TBENCH.2023.100105]
   Johnson Douglas, 2023, Res Sq, DOI 10.21203/rs.3.rs-2566942/v1
   Klang E, 2023, THER ADV GASTROENTER, V16, DOI 10.1177/17562848231218618
   Kleesiek J, 2023, J NUCL MED, V64, P701, DOI 10.2967/jnumed.123.265687
   Kuckartz U., 2013, Qualitative Text Analysis: A Guide to Methods, Practice and Using Software, DOI DOI 10.4135/9781446288719
   Lee H, 2024, ANAT SCI EDUC, V17, P926, DOI 10.1002/ase.2270
   Li JN, 2023, medRxiv, DOI [10.1101/2023.03.30.23287899, 10.1101/2023.03.30.23287899, DOI 10.1101/2023.03.30.23287899, DOI 10.1101/2023.03.30.23287899V1]
   Liebrenz M, 2023, LANCET DIGIT HEALTH, V5, pE105, DOI 10.1016/S2589-7500(23)00019-5
   Liu JL, 2023, J MED INTERNET RES, V25, DOI 10.2196/48568
   Liu YH, 2023, Arxiv, DOI [arXiv:2304.01852, DOI 10.1016/J.METRAD.2023.100017]
   Loh E, 2024, BMJ LEAD, V8, P51, DOI 10.1136/leader-2023-000797
   Lupton D., 2017, Digital Health. Critical and Cross-Disciplinary Perspectives
   Mack CA, 2011, IEEE T SEMICONDUCT M, V24, P202, DOI 10.1109/TSM.2010.2096437
   Mann DL, 2023, JACC-BASIC TRANSL SC, V8, P221, DOI 10.1016/j.jacbts.2023.01.001
   Martínez G, 2023, Arxiv, DOI [arXiv:2303.01255, 10.48550/arXiv.2303.01255, DOI 10.48550/ARXIV.2303.01255]
   Mbakwe Amarachi B, 2023, PLOS Digit Health, V2, pe0000205, DOI 10.1371/journal.pdig.0000205
   McGee R.W., 2023, Technical report, Working Paper
   Muller M, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3503719
   Nguyen J, 2023, CLIN TRANSL MED, V13, DOI 10.1002/ctm2.1324
   Nov O, 2023, medRxiv, DOI [10.1101/2023.01.23.23284735, 10.1101/2023.01.23.23284735, DOI 10.1101/2023.01.23.23284735]
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   Olczak J, 2021, ACTA ORTHOP, V92, P513, DOI 10.1080/17453674.2021.1918389
   Panda A., 2021, VIKALPA, V46, P71, DOI DOI 10.1177/02560909211025361
   Parray A.A., 2023, ChatGPT and global public health: applications, challenges, ethical considerations and mitigation strategies, DOI [10.1016/j.glt.2023.05.001, DOI 10.1016/J.GLT.2023.05.001]
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Roberts Carl W., 2020, TEXT ANAL SOCIAL SCI
   Sallam Malik, 2023, Narra J, V3, pe103, DOI 10.52225/narra.v3i1.103
   Sallam M, 2023, medRxiv, DOI [10.1101/2023.02.19.23286155, 10.1101/2023.02.19.23286155v1, DOI 10.1101/2023.02.19.23286155V1, 10.1101/2023.02.19.23286155, DOI 10.1101/2023.02.19.23286155]
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Schachtner B., 2022, arXiv, DOI DOI 10.48550/ARXIV.2212.14882
   Sedaghat S, 2023, CLIN MED, V23, P278, DOI 10.7861/clinmed.2023-0078
   Singh OP, 2023, INDIAN J PSYCHIAT, V65, P297, DOI 10.4103/indianjpsychiatry.indianjpsychiatry_112_23
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Sinha RK, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35237
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Teebagy S, 2023, medRxiv, DOI [10.1101/2023.04.03.23287957, 10.1101/2023.04.03.23287957, DOI 10.1101/2023.04.03.23287957]
   Thirunavukarasu AJ, 2023, J ROY SOC MED, V116, P181, DOI 10.1177/01410768231173123
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Ufuk F, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230276
   Vaishya R, 2023, DIAB MET SYND CLIN R, V17, DOI 10.1016/j.dsx.2023.102744
   Wang CY, 2023, J MED INTERNET RES, V25, DOI 10.2196/48009
   Wang DJ, 2023, Arxiv, DOI arXiv:2304.03892
   Wang JD, 2023, Arxiv, DOI arXiv:2302.12095
   Wang Z, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1108621
   Wang Z, 2019, ISR J HEALTH POLICY, V8, DOI 10.1186/s13584-019-0293-9
   Wenxiu P., 2015, J ED SOCIAL RES, V5, P245, DOI [DOI 10.5901/JESR.2015.V5N3P245, 10.5901/jesr.2015.v5n3p245]
   West CG, 2023, Arxiv, DOI [arXiv:2303.17012, 10.48550/arXiv.2303.17012, DOI 10.48550/ARXIV.2303.17012]
   Xiao DV, 2023, J PEDIATR SURG, V58, P2410, DOI 10.1016/j.jpedsurg.2023.07.008
   Xie YQ, 2023, NAT MACH INTELL, DOI 10.1038/s42256-023-00765-8
   Xue VW, 2023, CLIN TRANSL MED, V13, DOI 10.1002/ctm2.1216
   Zohny H, 2023, J MED ETHICS, V49, P79, DOI 10.1136/jme-2023-108909
NR 82
TC 2
Z9 2
U1 13
U2 13
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2405-8440
J9 HELIYON
JI Heliyon
PD JUN 30
PY 2024
VL 10
IS 12
AR e32364
DI 10.1016/j.heliyon.2024.e32364
PG 16
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA WE1C0
UT WOS:001253090800001
PM 38975200
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Salah, M
   Abdelfattah, F
   Al Halbusi, H
AF Salah, Mohammed
   Abdelfattah, Fadi
   Al Halbusi, Hussan
TI Generative Artificial Intelligence (ChatGPT & Bard) in Public
   Administration Research: A Double-Edged Sword for Street-Level
   Bureaucracy Studies
SO INTERNATIONAL JOURNAL OF PUBLIC ADMINISTRATION
LA English
DT Article; Early Access
DE Generative AI; ChatGPT; Bard; street-level bureaucracy; methodological
   advancements
AB This manuscript critically examines the adoption of generative AI tools, notably ChatGPT and Bard, within public administration research, especially in street-level bureaucracy. While these tools offer revolutionary insights into bureaucratic behaviors and decision-making, they pose significant ethical dilemmas, including potential biases and data privacy concerns. The paper offers comprehensive recommendations designed to help researchers navigate these challenges, emphasizing the need for robust data validation, enhanced transparency, and continuous adherence to evolving ethical standards. The overarching aim is to facilitate responsible AI integration, ensuring research methodologies' efficacy and preserving ethical integrity in public administration inquiries.
C1 [Salah, Mohammed; Abdelfattah, Fadi] Modern Coll Business & Sci MCBS, Dept Business & Econ, Muscat, Oman.
   [Al Halbusi, Hussan] Ahmed Bin Mohammed Mil Coll ABMMC, Management Dept, Doha, Qatar.
C3 Ahmed Bin Mohammed Military College
RP Salah, M (corresponding author), Modern Coll Business & Sci MCBS, Dept Business & Econ, Muscat, Oman.
EM sala142@yahoo.com
RI SALAH, MOHAMMED/GPK-4620-2022; Abdelfattah, Fadi/L-7441-2014
OI Salah, Mohammed/0000-0002-1742-2790; Abdelfattah,
   Fadi/0000-0002-4665-4777
CR Dignum V, 2018, ETHICS INF TECHNOL, V20, P1, DOI 10.1007/s10676-018-9450-z
   Dunleavy P, 2006, J PUBL ADM RES THEOR, V16, P467, DOI 10.1093/jopart/mui057
   Hassan MS, 2023, INT J PUBLIC ADMIN, V46, P430, DOI 10.1080/01900692.2021.2001008
   Lipsky M, 2010, STREET-LEVEL BUREAUCRACY: DILEMMAS OF THE INDIVIDUAL IN PUBLIC SERVICES, 30TH EDITION, P1
NR 4
TC 11
Z9 11
U1 22
U2 49
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0190-0692
EI 1532-4265
J9 INT J PUBLIC ADMIN
JI Int. J. Public Adm.
PD 2023 NOV 4
PY 2023
DI 10.1080/01900692.2023.2274801
EA NOV 2023
PG 7
WC Public Administration
WE Emerging Sources Citation Index (ESCI)
SC Public Administration
GA X5LA7
UT WOS:001098851800001
OA Bronze
DA 2024-12-25
ER

PT J
AU Chaudhary, G
AF Chaudhary, Gyandeep
TI Charting the Uncharted: Exploring Intellectual Property in the Era of
   Generative AI
SO NTUT JOURNAL OF INTELLECTUAL PROPERTY LAW AND MANAGEMENT
LA English
DT Article
DE Generative Artificial Intelligence; Intellectual Property; Patent;
   Copyright; Infringement
AB Generative artificial intelligence (AI) has brought about a paradigm shift in creative expression, unleashing transformative potential. Recent advancements have enabled machines to produce striking images across artistic styles. Text generators demonstrate remarkable proficiency, albeit with occasional factual embellishments. AI-generated works have received recognition in esteemed exhibitions. In instances where original pieces being are loaned, AI replicas serve as substitutes. This phenomenon has multifaceted legal ramifications, particularly regarding intellectual property rights. Potential copyright infringement, complex ownership, and the need for clear guidelines necessitate thorough examination and evaluation. Generative AI is derived from large datasets carefully selected from extensive archives. Fundamental model training relies on data lakes and question snippets- billions of processed parameters. During training, the models identify patterns, correlate, and develop predictive, responsive rules for prompts. Despite seemingly miraculous novelty, AI-generated content combines pre-existing knowledge and expressions, channelled through human ingenuity in innovative ways. Legal ownership complexities transcend AI developers and instructors. Resolving intricacies necessitates unambiguous terms, agreements, and licensing to ensure fair rights/obligation allocation. This paper thoroughly investigates the intellectual property terrain regarding generative AI. Comprehensive analysis of frameworks, cases, and discourse elucidates copyright, patent, trademark complexities pertaining to AI-generated content. The Objective is to provide significant insights, facilitating ethical AI adoption while mitigating risks. Responsible adoption and, meticulous IP rights consideration enable human creativity-AI collaboration, harnessing transformative capabilities consistently with ethical and legal standards.
C1 [Chaudhary, Gyandeep] Bennett Univ, Sch Law, Law, Greater Noida, India.
RP Chaudhary, G (corresponding author), Bennett Univ, Sch Law, Law, Greater Noida, India.
EM gyan.2889@gmail.com
CR Arai Y, 2011, INF ECON POLICY, V23, P270, DOI 10.1016/j.infoecopol.2011.08.001
   Bridy Annemarie, 2014, Stanford Technology Law Review., V5, P1
   Chaudhary Gyandeep, 2022, Indian J.L. & Just, V13, P212
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Felzmann H, 2020, SCI ENG ETHICS, V26, P3333, DOI 10.1007/s11948-020-00276-4
   Howe ML, 2015, MEMORY, V23, P633, DOI 10.1080/09658211.2015.1010709
   Korteling JE, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.622364
   Lee E, 2010, SOUTH CALIF LAW REV, V83, P797
   Levendowski A, 2018, WASH LAW REV, V93, P579
   Reiling A D., 2020, IJCA, V11, P1, DOI DOI 10.36745/IJCA.343
   Rodrigues R, 2020, J RESPONSIBLE TECHNO, V4, DOI [10.1016/j.jrt.2020.100005, DOI 10.1016/J.JRT.2020.100005]
   Ryan Brendan., 2013, Optimizing Academic Library Services in the Digital Milieu: Digital Devices and Their Emerging Trends, P51
   Samuelson P, 2015, WASH LAW REV, V90, P815
   dos Santos MLB, 2022, ONLINE INFORM REV, V46, P95, DOI 10.1108/OIR-06-2020-0258
   Smith Clifton L., 2013, Security Risk Management
   Vyhmeister E., 2023, AI Ethics, V3, P175, DOI [10.1007/s43681-022-00154-8, DOI 10.1007/S43681-022-00154-8]
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 17
TC 0
Z9 0
U1 6
U2 6
PU NATL TAIPEI UNIV TECHNOLOGY
PI TAIPEI
PA 1, SEC 3, CHUNG-HSIAO E RD, TAIPEI, 10608, TAIWAN
SN 2226-6771
J9 NTUT J INTELLECT PRO
JI NTUT J. Intellect. Prop. Law Manag.
PD JUN
PY 2024
VL 13
IS 1
PG 97
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA J6Y6C
UT WOS:001338504400006
DA 2024-12-25
ER

PT J
AU Dwivedi, YK
   Pandey, N
   Currie, W
   Micu, A
AF Dwivedi, Yogesh K.
   Pandey, Neeraj
   Currie, Wendy
   Micu, Adrian
TI Leveraging ChatGPT and other generative artificial intelligence
   (AI)-based applications in the hospitality and tourism industry:
   practices, challenges and research agenda
SO INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Bard; ChatGPT; Generative AI; Hospitality;
   Tourism
ID GUESTS; HOTEL
AB Purpose - The hospitality and tourism sector has witnessed phenomenal growth in customer numbers during the postpandemic times. This growth has been accompanied by the use of technologies in customer interface and backend activities, including the adoption of self-serving technologies. This study aims to analyze the existing practices and challenges and establish a research agenda for the implementation of generative artificial intelligence (AI) (such as ChatGPT) and similar tools in the hospitality and tourism industry. Design/methodology/approach - This study analyzes the existing literature and practices. This study draws upon these practices to outline a novel research agenda for scholars and practitioners working in this domain.Findings - The integration of generative AI technologies, such as ChatGPT, will have a transformational impact on the hospitality and tourism industry. This study highlights the potential challenges of implementing such technologies from the perspectives of companies, customers and regulators.Research limitations/implications - This study serves as a reference material for those who are planning to use generative AI tools like ChatGPT in their hospitality and tourism businesses. This study also highlights potential pitfalls that ChatGPT-enabled systems may encounter during service delivery processes.Originality/value - This study is a pioneering work that assesses the applications of ChatGPT in the hospitality and tourism industry. This study highlights the potential and challenges in implementing ChatGPT within the hospitality and tourism industry.
C1 [Dwivedi, Yogesh K.] Swansea Univ, Sch Management, Digital Futures Sustainable Business & Soc Res Gr, Swansea, Wales.
   [Dwivedi, Yogesh K.] Symbiosis Int Univ, Inst Business Management, Dept Management, Pune, India.
   [Pandey, Neeraj] Natl Inst Ind Engn NITIE, Dept Mkt, Mumbai, India.
   [Currie, Wendy] Audencia Business Sch, Dept Informat Syst & Supply Chain Management, Nantes, France.
   [Micu, Adrian] Dunarea de Jos Univ Galati, Fac Econ & Business Adm, Galati, Romania.
C3 Swansea University; Symbiosis International University; Indian Institute
   of Management (IIM System); Indian Institute of Management Mumbai;
   Audencia; Dunarea De Jos University Galati
RP Dwivedi, YK (corresponding author), Swansea Univ, Sch Management, Digital Futures Sustainable Business & Soc Res Gr, Swansea, Wales.; Dwivedi, YK (corresponding author), Symbiosis Int Univ, Inst Business Management, Dept Management, Pune, India.
EM y.k.dwivedi@swansea.ac.uk; npandey@nitie.ac.in; wcurrie@audencia.com;
   adrian.micu@ugal.ro
RI Dwivedi, Yogesh/A-5362-2008; Micu, Adrian/AAJ-9641-2020; Pandey,
   Neeraj/D-1968-2013
OI Dwivedi, Yogesh/0000-0002-5547-9990; Micu, Adrian/0000-0003-3161-5748;
   Pandey, Neeraj/0000-0002-6238-6397
CR Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Badr M., 2023, UNLEASHING POWER MIC
   Buhalis DT, 2023, INT J CONTEMP HOSP M, V35, P369, DOI 10.1108/IJCHM-04-2022-0497
   Calvillo M., 2023, WILL AI CHATGPT CHAN
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chang H, 2016, CORNELL HOSP Q, V57, P172, DOI 10.1177/1938965515588132
   Chen SP, 2021, J HOSP MARKET MANAG, V30, P871, DOI 10.1080/19368623.2021.1903644
   Du HP, 2023, IEEE T INTELL VEHICL, V8, P2020, DOI 10.1109/TIV.2023.3253281
   Dwivedi Y.K., 2023, DISCUSSION GENERATIV
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2023, PSYCHOL MARKET, V40, P750, DOI 10.1002/mar.21767
   Dwivedi YK, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102542
   Erdem F., 2003, TEAM PERFORM MANAG, V9, P131, DOI [10.1108/13527590310493846, DOI 10.1108/13527590310493846]
   Gaur L, 2021, INT J CONTEMP HOSP M, V33, P4079, DOI 10.1108/IJCHM-11-2020-1246
   Gautam V., 2023, INDIAS LARGEST IT FI
   Guchait P, 2020, INT J CONTEMP HOSP M, V32, P2029, DOI 10.1108/IJCHM-06-2020-027
   Gursoy D, 2023, J HOSP MARKET MANAG, V32, P579, DOI 10.1080/19368623.2023.2211993
   Haleem A., 2022, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V2, DOI [DOI 10.1016/J.TBENCH.2023.100089, 10.1016/j.tbench.2023.100089]
   Hu X., 2023, ARXIV
   Ivanov S, 2021, J TOUR FUTURES, V9, P214, DOI 10.1108/JTF-02-2023-0038
   Koohang A, 2023, J COMPUT INFORM SYST, V63, P735, DOI 10.1080/08874417.2023.2165197
   Leung R, 2013, CORNELL HOSP Q, V54, P25, DOI 10.1177/1938965512454594
   Liu Siru, 2023, medRxiv, P2023
   Malik Y.K., 2023, GENERATIVE AI CHATGP
   Mich L, 2023, INF TECHNOL TOUR, V25, P1, DOI 10.1007/s40558-023-00248-x
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Okumus F., 2019, Strategic management for hospitality and tourism, V2nd
   OpenAI, 2023, INTR CHATGPT PLUS
   Ozdemir O, 2023, INT J CONTEMP HOSP M, V35, P3305, DOI 10.1108/IJCHM-04-2022-0535
   Pandey N., 2018, EMERALD EMERGING MAR, V8, P1
   Pandey N., 2022, Multidiscip. Bus. Rev., V15, P2
   Pandey N, 2013, INT J INDIAN CULT BU, V6, P314, DOI 10.1504/IJICBM.2013.053105
   Pellinen J., 2003, International Journal of Hospitality Management, V22, P217, DOI 10.1016/S0278-4319(03)00019-7
   Poria Y, 2014, INT J HOSP MANAG, V39, P84, DOI 10.1016/j.ijhm.2014.02.006
   Ramasundaram A, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102599
   Ranson P, 2019, INT J CULT TOUR HOSP, V13, P524, DOI 10.1108/IJCTHR-06-2019-0101
   Sifat RI, 2023, ANN BIOMED ENG, V51, P1357, DOI 10.1007/s10439-023-03204-2
   Tourism and Hospitality Market Forecast, 2023, Tourism and Hospitality Market Size (2022-2027)
NR 38
TC 108
Z9 109
U1 255
U2 755
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0959-6119
EI 1757-1049
J9 INT J CONTEMP HOSP M
JI Int. J. Contemp. Hosp. Manag.
PD JAN 2
PY 2024
VL 36
IS 1
BP 1
EP 12
DI 10.1108/IJCHM-05-2023-0686
EA JUN 2023
PG 12
WC Hospitality, Leisure, Sport & Tourism; Management
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Business & Economics
GA CT8X5
UT WOS:001000819400001
OA Green Accepted
DA 2024-12-25
ER

PT J
AU Currie, G
   John, G
   Hewis, J
AF Currie, Geoffrey
   John, George
   Hewis, Johnathan
TI Gender and ethnicity bias in generative artificial intelligence
   text-to-image depiction of pharmacists
SO INTERNATIONAL JOURNAL OF PHARMACY PRACTICE
LA English
DT Article
DE generative artificial intelligence; text-to-image; diversity; pharmacist
AB Introduction In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases. Methods In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus. Results Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone. Conclusions This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.
C1 [Currie, Geoffrey; John, George] Charles Sturt Univ, Sch Dent & Med Sci, Locked Bag 588, Wagga Wagga, NSW 2678, Australia.
   [Currie, Geoffrey] Baylor Coll Med, Dept Radiol, Houston, TX USA.
   [Hewis, Johnathan] Charles Sturt Univ, Sch Dent & Med Sci, Port Macquarie, Australia.
C3 Charles Sturt University; Baylor College of Medicine; Charles Sturt
   University
RP Currie, G (corresponding author), Charles Sturt Univ, Sch Dent & Med Sci, Locked Bag 588, Wagga Wagga, NSW 2678, Australia.
EM gcurrie@csu.edu.au
RI Hewis, Johnathan/X-2382-2019
OI Hewis, Johnathan/0000-0002-7810-5641; John, George/0000-0002-5131-4312;
   Currie, Geoffrey/0000-0002-6180-8586
CR AHPRA, 2024, PHARM WORKFORCE ANAL
   AHPRA, 2024, AHPRA NATL BOARDS AN
   Ali R, 2024, JAMA SURG, V159, P87, DOI 10.1001/jamasurg.2023.5695
   Bourke CJ, 2019, AUST HEALTH REV, V43, P611, DOI 10.1071/AH18062
   Cevik J, 2024, ANZ J SURG, V94, P287, DOI 10.1111/ans.18792
   Choudhry HS, 2023, CLIN OPHTHALMOL, V17, P2889, DOI 10.2147/OPTH.S427296
   Currie G, 2020, EUR J NUCL MED MOL I, V47, P748, DOI 10.1007/s00259-020-04678-1
   Currie G, 2022, SEMIN NUCL MED, V52, P498, DOI 10.1053/j.semnuclmed.2021.11.011
   Currie G, 2020, SEMIN NUCL MED, V51, P120, DOI 10.1053/j.semnuclmed.2020.08.001
   Currie GM, 2023, SEMIN NUCL MED, V53, P719, DOI 10.1053/j.semnuclmed.2023.04.008
   Currie GM, 2023, NUCL MED BIOL, V120, DOI 10.1016/j.nucmedbio.2023.108337
   Department of Health & Aged Care, 2024, NATL HLTH WORKFORCE
   Ito N, 2023, JMIR MED EDUC, V9, DOI 10.2196/47532
   Kotek H, 2023, PROCEEDINGS OF THE ACM COLLECTIVE INTELLIGENCE CONFERENCE, CI 2023, P12, DOI 10.1145/3582269.3615599
   Lombardo C, 2022, EAT WEIGHT DISORD-ST, V27, P1089, DOI 10.1007/s40519-021-01258-6
   Massey DS., 2023, NIS SKIN COLOR SCALE
   Pharmacy Guild of Australia, 2024, WORKFORCE CAPABILITY
   Raza Muhammad Ahmer, 2022, Innov Pharm, V13, DOI 10.24926/iip.v13i2.4839
   Yanicak A, 2015, J AM PHARM ASSOC, V55, P578, DOI 10.1331/JAPhA.2015.15028
   Yong FR, 2023, EXPLOR RES CLIN SOC, V9, DOI 10.1016/j.rcsop.2023.100247
   Zack Travis, 2024, Lancet Digit Health, V6, pe12, DOI 10.1016/S2589-7500(23)00225-X
NR 21
TC 0
Z9 0
U1 5
U2 5
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0961-7671
EI 2042-7174
J9 INT J PHARM PRACT
JI Int. J. Pharm. Pract.
PD SEP 4
PY 2024
VL 32
IS 6
BP 524
EP 531
DI 10.1093/ijpp/riae049
EA SEP 2024
PG 8
WC Pharmacology & Pharmacy
WE Emerging Sources Citation Index (ESCI)
SC Pharmacology & Pharmacy
GA L9T8Q
UT WOS:001304535700001
PM 39228085
OA hybrid
DA 2024-12-25
ER

PT J
AU Bannister, P
   Carver, M
AF Bannister, Peter
   Carver, Mark
TI 'I don't need professional development; I want institutional
   development': legitimising marginalised epistemic capital that disrupts
   generative AI discourse
SO PROFESSIONAL DEVELOPMENT IN EDUCATION
LA English
DT Article; Early Access
DE Delphi; english for academic purposes; generative artificial
   intelligence; professional learning and development; higher education;
   legitimation code theory
ID ARTIFICIAL-INTELLIGENCE; COMMUNITIES; LEADERSHIP
AB Responding to GenAI technologies, academics press for PLD that informs pedagogical practice and policy development. However, insufficient critical evaluation of whose knowledge informs this and its underlying complexity has resulted in excessively reductive offerings that either champion specific tools or advocate for their prohibition. Engaging with critically complex perspectives provides an opportunity to deepen discourse through untapped funds of knowledge, while considering PLD's broader role in policy and institutional development. English for Academic Purposes (EAP) lecturers are typically peripheral within universities, positioned as a remedial or technical service who 'fix' academic integrity and student language development issues. GenAI disruption has created a heightened demand for language expertise, offering an opportunity for EAP lecturers to advocate for the critical complexity underpinning their funds of knowledge. Using Legitimation Code Theory (LCT) within a Delphi study with senior EAP academics, we argue that engaging with critical complexity can empower institutionally marginalised expertise. The panel established design principles and actionable measures to (i) move beyond individualistic PLD and (ii) formalise the voicing and valuing of diverse expertise through collaboration across institutional hierarchies. Taken together, these offer ways to leverage epistemic relations to disrupt entrenched power structures through transformative PLD and thus catalyse institutional change.
C1 [Bannister, Peter] Univ Int La Rioja, Doctoral Sch, Logrono, Spain.
   [Carver, Mark] Univ St Andrews, Int Educ Inst, St Andrews, Scotland.
C3 Universidad Internacional de La Rioja (UNIR); University of St Andrews
RP Bannister, P (corresponding author), Univ Int La Rioja, Doctoral Sch, Ave de La Paz,137, Logrono 26006, La Rioja, Spain.
EM peter.bannister@unir.net
RI Carver, Mark/GZM-1137-2022; Bannister, Peter/HDO-4393-2022
OI Bannister, Peter/0000-0002-7216-3912; Carver, Mark/0000-0003-4393-8915
CR Amin A, 2008, RES POLICY, V37, P353, DOI 10.1016/j.respol.2007.11.003
   [Anonymous], 2003, Qualitative data: An introduction to coding and analysis (pp. ix
   Bannister P, 2024, J RES APPL LINGUIST, V15, P55, DOI 10.22055/rals.2024.45862.3214
   Bannister P, 2024, J INT STUDENTS, V14, P149
   Bannister P, 2023, IRAN J LANG TEACH RE, V11, P53, DOI 10.30466/ijltr.2023.121406
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bernstein B., 2000, Pedagogy, symbolic control and identity: theory, research, critique
   Bourdieu P., 1977, OUTLINE THEORY PRACT, DOI DOI 10.1017/CBO9780511812507
   Cabezas-Clavijo A, 2024, PUBLISH RES Q, V40, P147, DOI 10.1007/s12109-024-09998-w
   Carpenter JP, 2023, J RES TECHNOL EDUC, V55, P749, DOI 10.1080/15391523.2022.2030267
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Demir EK, 2021, EDUC RES REV-NETH, V33, DOI 10.1016/j.edurev.2021.100391
   Derek Bok Center for Teaching and Learning, 2024, Bringing generative AI to the harvard classroom
   Dikilita K., 2024, Nordic journal of systematic reviews in education, V2, P103, DOI [10.23865/njsre.v2.6227, DOI 10.23865/NJSRE.V2.6227]
   Ding A., 2019, Specialised English, P63
   Eacott S, 2017, SCH LEADERSH MANAG, V37, P413, DOI 10.1080/13632434.2017.1327428
   Forde C, 2017, PROF DEV EDUC, V43, P106, DOI 10.1080/19415257.2015.1131733
   Groves M, 2021, J ENGL ACAD PURP, V50, DOI 10.1016/j.jeap.2021.100957
   Hyland K, 2021, J ENGL ACAD PURP, V49, DOI 10.1016/j.jeap.2020.100929
   Kennedy A, 2015, PROF DEV EDUC, V41, P1, DOI 10.1080/19415257.2014.983787
   Kennedy A, 2014, PROF DEV EDUC, V40, P688, DOI 10.1080/19415257.2014.955122
   King's College London, 2023, Generative AI in higher education
   Kirk S., 2022, Social theory for English for academic purposes, P87
   Lancaster T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00131-6
   Liang WX, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100779
   Lomer S., 2022, Contextualizing English for academic purposes in higher education. Politics, policies, P45
   MacPherson SA, 2022, PROF DEV EDUC, DOI 10.1080/19415257.2022.2143863
   MacQueen K. M., 1998, CULTURAL ANTHR METHO, V10, P31, DOI DOI 10.1177/1525822X980100020301
   Maton K, 2016, KNOWLEDGE-BUILDING: EDUCATIONAL STUDIES IN LEGITIMATION CODE THEORY, P1
   Mercieca BM, 2024, PROF DEV EDUC, DOI 10.1080/19415257.2024.2306995
   Narayanan A., 2024, AI SNAKE OIL WHAT AR
   Olga A., 2023, Generative AI: Implications and Applications for Education
   Peachey N., 2023, ChatGPT in the language classroom
   Pearson, 2024, Generative AI: useful tool or teaching distraction?
   Quality Assurance Agency QAA, 2023, QAA briefs members on artificial intelligence threat to academic integrity
   Rice M., 2025, Applying critically complex theories to professional learning with and about advanced technologies special issue
   Roe J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02282-w
   Saldaa J., 2021, The coding manual for qualitative researchers, V4th ed.
   Swan J, 2002, MANAGE LEARN, V33, P477, DOI 10.1177/1350507602334005
NR 40
TC 0
Z9 0
U1 2
U2 2
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1941-5257
EI 1941-5265
J9 PROF DEV EDUC
JI Prof. Dev. Educ.
PD 2024 NOV 16
PY 2024
DI 10.1080/19415257.2024.2427873
EA NOV 2024
PG 19
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M1E3D
UT WOS:001355038100001
DA 2024-12-25
ER

PT J
AU Berthon, P
   Yalcin, T
   Pehlivan, E
   Rabinovich, T
AF Berthon, Pierre
   Yalcin, Taylan
   Pehlivan, Ekin
   Rabinovich, Tamara
TI Trajectories of AI technologies: Insights for managers
SO BUSINESS HORIZONS
LA English
DT Article
DE Trajectories of technology; Generative AI; Chatbots; ChatGPT; Large
   language models; Social media
AB Generative artificial intelligence (GenAI) has long been considered a technology for the future. With the release of the chatbot ChatGPT 4, many now feel the future has arrived. Long in gestation, this new technology promises many benefits to humankind, but worries persist that as AI technology scales and comes to rival or exceed human intelligence, the servant may become the master. Amid such hyperbole, the more nuanced trajectories of this technology have been neglected. In this article, we use the Trajectories of Technology (ToT) framework developed by Berthon and colleagues to explore the disparate paths that AI has taken and will take in the coming years, especially in the form of chatbots. This framework provides managers with a conceptual tool to strategically plan for the enormous promises and perils of AI in general and of chatbots specifically. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
C1 [Berthon, Pierre] Bentley Univ, 175 Forest St, Waltham, MA 02452 USA.
   [Yalcin, Taylan; Pehlivan, Ekin] Calif State Univ Channel Isl, 1 Univ Dr, Camarillo, CA 93012 USA.
   [Rabinovich, Tamara] Quincy Coll, 1250 Hancock St, Quincy, MA 02169 USA.
C3 Bentley University; California State University System; California State
   University Channel Islands
RP Pehlivan, E (corresponding author), Calif State Univ Channel Isl, 1 Univ Dr, Camarillo, CA 93012 USA.
EM pberthon@bentley.edu; taylan.yalcin@csuci.edu; ekin.pehlivan@csuci.edu;
   Tamara.rabinovich@quincycollege.edu
CR Agatston A, 2012, CIRCULATION, V126, pE3, DOI 10.1161/CIRCULATIONAHA.112.098566
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Appleby C., 2024, The best AI detection tools to catch cheating and plagiarism
   Berthon P, 2005, CALIF MANAGE REV, V48, P110, DOI 10.2307/41166330
   Botpress, 2023, Next-generation chatbots that speak for themselves
   Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Cetinic E, 2022, ACM T MULTIM COMPUT, V18, DOI 10.1145/3475799
   Clayton P, 2009, INT J ENV RES PUB HE, V6, P1235, DOI 10.3390/ijerph6031235
   Davis L. M., 2023, CNETJune 9
   Duolingo, 2023, Introducing duolingo max, a learning experience powered by GPT-4
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Freud Sigmund., 1914, Zur Einfuhrung des Narzissmus
   Fung B., 2023, CNN Business
   Future of Life Institute, 2023, Pause giant AI experiments: An open letter
   Gabriel I, 2020, MIND MACH, V30, P411, DOI 10.1007/s11023-020-09539-2
   Gozalo-Brizuela R., 2023, arXiv, DOI [10.48550/arxiv.2301.04655, DOI 10.48550/ARXIV.2301.04655, 10.48550/arXiv.2301.04655]
   Gupta P., 2023, WritesonicJuly 27
   Haidt J., 2023, The AtlanticMay 5
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Harari Y., 2023, The New York Times
   Hashmi N, 2024, BUS HORIZONS, V67, P607, DOI 10.1016/j.bushor.2024.05.005
   Heidegger M., 1983, The question concerning technology and other essays
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Kabir S., 2023, arXiv, DOI [10.1145/3613904.3642596, DOI 10.1145/3613904.3642596]
   Kan N., 2023, PC MagazineJuly 28
   Kaplan A, 2020, BUS HORIZONS, V63, P37, DOI 10.1016/j.bushor.2019.09.003
   Khan Academy, 2023, World-class AI for education
   Khan S., 2023, How AI Could Save (Not Destroy) Education
   Kietzmann J., 2024, Business Horizons, V67, P453
   Kietzmann J, 2020, BUS HORIZONS, V63, P131, DOI 10.1016/j.bushor.2019.11.005
   Knight W., 2023, WIRED
   Mayor A., 2019, Gods and robots: Myths, machines, and ancient dreams of technology
   Mollick E., 2023, WHY ALL OUR CLASSES
   Murphy K. P., 2022, Probabilistic Machine Learning: An Introduction
   Nadel J., 2023, ItnewsJuly 4
   Ntoutsi E, 2020, WIRES DATA MIN KNOWL, V10, DOI 10.1002/widm.1356
   Popper Karl., 1979, OBJECTIVE KNOWLEDGE
   Pulver A., 2023, The GuardianNovember 9
   Ryan J., 2023, CNETJuly 27
   Scheffler I., 2023, FreethinkJune 17
   Schneier B., 2023, The coming AI hackers
   Schulman A., 2023, New Atlantis, V73, P4
   Schumpeter J., 1942, Capitalism, socialism and democracy
   Shakir U., 2023, VergeJuly 27
   Shneiderman B, 2021, ISSUES SCI TECHNOL, V37, P56
   Shumailov I, 2024, Arxiv, DOI arXiv:2305.17493
   Sottile L., 2013, The AtlanticDecember 19
   Stokel-Walker C., 2023, WiredAugust 2
   Sutherland B., 2023, BloombergJuly 28
   Thorbecke C., 2023, CNN BusinessJuly 22
   Timmer J., 2023, ArsTechnicaMarch 16
   Turing A. M., 1996, Philos Math, V4, P256, DOI DOI 10.1093/PHILMAT/4.3.256
   Veltman C., 2023, NPRJuly 17
   Victor A., 2023, DaffodilFebruary 27
   Wang WY, 2019, J DATABASE MANAGE, V30, P61, DOI 10.4018/JDM.2019010104
   Yudkowsky E., 2023, Time
NR 57
TC 1
Z9 1
U1 28
U2 28
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 461
EP 470
DI 10.1016/j.bushor.2024.03.002
EA AUG 2024
PG 10
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2M0Z
UT WOS:001301383800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Victor, BG
   Kubiak, S
   Angell, B
   Perron, BE
AF Victor, Bryan G.
   Kubiak, Sheryl
   Angell, Beth
   Perron, Brian E.
TI Time to Move Beyond the ASWB Licensing Exams: Can Generative Artificial
   Intelligence Offer a Way Forward for Social Work?
SO RESEARCH ON SOCIAL WORK PRACTICE
LA English
DT Article
DE licensing; social work professionals; generative artificial
   intelligence; validity; dynamic testing
AB Social work scholars have long questioned the validity and utility of the Association of Social Work Boards (ASWB) licensing exams. Data released in 2022 revealed severe disparities in pass rates based on race, age, and language, exacerbating these concerns. In this paper, we explore the potential of generative artificial intelligence (AI) such as ChatGPT to address core problems of the ASWB exams, including the use of a multiple-choice format that does not reflect real-world social work practice. To assess its social work reasoning, we used ChatGPT to answer ASWB-developed practice questions for the Bachelors, Masters, and Clinical exams. ChatGPT scored 76%, 80%, and 64%, respectively, and identified additional validity challenges. Based on this performance, we provide a proof-of-concept for how generative AI might move us toward a more valid and equitable exam. While we strongly support licensure requirements, state regulators and legislators should temporarily suspend the use of the ASWB exams for this purpose.
C1 [Victor, Bryan G.; Kubiak, Sheryl] Wayne State Univ, Sch Social Work, Detroit, MI 48202 USA.
   [Angell, Beth; Perron, Brian E.] Univ Michigan, Sch Social Work, Ann Arbor, MI USA.
   [Victor, Bryan G.] Wayne State Univ, 5447 Woodward Ave, Detroit, MI 48202 USA.
C3 Wayne State University; University of Michigan System; University of
   Michigan; Wayne State University
RP Victor, BG (corresponding author), Wayne State Univ, 5447 Woodward Ave, Detroit, MI 48202 USA.
EM bvictor@wayne.edu
RI Perron, Brian/AFW-1605-2022; Angell, Beth/JED-6032-2023; Victor,
   Bryan/T-8349-2019
OI Victor, Bryan/0000-0002-2092-912X
CR Albright DL, 2010, SOC WORK RES, V34, P229, DOI 10.1093/swr/34.4.229
   Apgar D, 2023, RES SOCIAL WORK PRAC, V33, P5, DOI 10.1177/10497315221124137
   Asakura K, 2020, J TEACH SOC WORK, V40, P501, DOI 10.1080/08841233.2020.1813234
   Association of Social Work Boards, 2023, ASWB SOC WORK EX UPD
   Association of Social Work Boards, 2022, CONT HIST SOCIAL WOR
   Association of Social Work Boards, 2022, ASS SOC WORK BOARDS
   Association of Social Work Boards, 2023, ED PORT GROUP REV PR
   Bonanno GA, 2002, J PERS SOC PSYCHOL, V83, P1150, DOI 10.1037//0022-3514.83.5.1150
   DeCarlo MP, 2022, RES SOCIAL WORK PRAC, V32, P255, DOI 10.1177/10497315211055986
   Frey WR, 2020, SOC SCI COMPUT REV, V38, P42, DOI 10.1177/0894439318788314
   Hammer A, 2023, DAILY MAIL
   Hu K., 2023, Reuters
   Kelly Samantha Murphy, CHATGPT PASSES EXAMS
   Kubler Ross E., 1969, DEATH DYING
   Kubler-Ross Elisabeth., 2005, GRIEF GRIEVING FINDI
   McVean A, ITS TIME LET 5 STAGE
   National Association of Social Workers (NASW)., NASW OPP ASS SOC WOR
   Nienow M, 2023, RES SOCIAL WORK PRAC, V33, P76, DOI 10.1177/10497315221125885
   OpenAI, 2022, CHATGPT OPT LANG MOD
   Perron BE, 2019, CHILD ABUSE NEGLECT, V98, DOI 10.1016/j.chiabu.2019.104180
   Smith MJ, 2023, CRIM JUSTICE BEHAV, V50, P272, DOI 10.1177/00938548221081447
   Stroebe M, 2017, OMEGA-J DEATH DYING, V74, P455, DOI 10.1177/0030222817691870
   Thyer BA, 2011, CLIN SOC WORK J, V39, P296, DOI 10.1007/s10615-009-0253-x
   Victor BG, 2021, J SOC SOC WORK RES, V12, P631, DOI 10.1086/712734
NR 24
TC 17
Z9 17
U1 14
U2 74
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 1049-7315
EI 1552-7581
J9 RES SOCIAL WORK PRAC
JI Res. Soc. Work. Pract.
PD JUL
PY 2023
VL 33
IS 5
BP 511
EP 517
DI 10.1177/10497315231166125
EA MAR 2023
PG 7
WC Social Work
WE Social Science Citation Index (SSCI)
SC Social Work
GA K4MZ8
UT WOS:000955152500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Shahzad, MF
   Xu, S
   Liu, HZ
   Zahid, H
AF Shahzad, Muhammad Farrukh
   Xu, Shuo
   Liu, Huizheng
   Zahid, Hira
TI Generative Artificial Intelligence (ChatGPT-4) and Social Media Impact
   on Academic Performance and Psychological Well-Being in China's Higher
   Education
SO EUROPEAN JOURNAL OF EDUCATION
LA English
DT Article; Early Access
DE academic performance; ChatGPT-4; collaborative learning; higher
   education; psychological well-being; social media
ID STUDENTS; MODELS; USAGE
AB The rapid advancement of generative artificial intelligence (GAI) and the extensive use of social media have transformed how students engage with educational materials and interact with their peers. Collaborative learning (CL) platforms, empowered by artificial intelligence (AI) algorithms, have gained popularity due to their potential to enhance learning outcomes and provide personalised educational experiences. This research examines the effects of generative AI (ChatGPT-4) and social media use on young students' academic performance and psychological well-being, focusing on CL. The study conceptual framework was examined based on a sample of 441 Chinese university students. The statistical technique PLS-SEM is put into practice to measure the structural framework of academic performance and psychological well-being. The findings of this study show that generative AI (ChatGPT-4) and social media positively influence young students' academic performance and psychological well-being. Additionally, the results of this research study show that CL positively mediates between social media, academic performance and psychological well-being. Conversely, it negatively mediates the association between generative AI (ChatGPT-4), academic performance (AP), and psychological well-being. The findings can facilitate a better understanding of the implications of technologies in the educational context and subsequently aid in formulating evidence-based strategies to optimise their impact on students's academic success and well-being.
C1 [Shahzad, Muhammad Farrukh; Xu, Shuo; Liu, Huizheng] Beijing Univ Technol, Coll Econ & Management, Beijing, Peoples R China.
   [Zahid, Hira] Univ Punjab, Hailey Coll Commerce, Lahore, Pakistan.
C3 Beijing University of Technology; University of Punjab
RP Xu, S (corresponding author), Beijing Univ Technol, Coll Econ & Management, Beijing, Peoples R China.
EM xushuo@bjut.edu.cn
RI Xu, Shuo/KVY-0402-2024; Farrukh Shahzad, Muhammad/JJE-9020-2023
OI Xu, Shuo/0000-0002-8602-1819
FU National Natural Science Foundation of China
FX The authors have nothing to report.
CR Al-Marghilani A, 2022, INT J COMPUT INT SYS, V15, DOI 10.1007/s44196-022-00063-y
   Alam MMD, 2021, J BIOMED INFORM, V116, DOI 10.1016/j.jbi.2021.103722
   Alamri MM, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12166471
   Ali Z, 2018, J COMPUT EDUC, V5, P297, DOI 10.1007/s40692-018-0114-0
   Allal-Cherif O, 2021, TECHNOL FORECAST SOC, V169, DOI 10.1016/j.techfore.2021.120822
   Alqahtani T, 2023, RES SOC ADMIN PHARM, V19, P1236, DOI 10.1016/j.sapharm.2023.05.016
   Alshuaibi MSI, 2018, INT J EDUC MANAG, V32, P625, DOI 10.1108/IJEM-08-2016-0182
   Alturki S, 2022, SMART LEARN ENVIRON, V9, DOI 10.1186/s40561-022-00220-y
   Anderson RK, 2018, EDUC SCI, V8, DOI 10.3390/educsci8030098
   Nguyen A, 2023, EDUC INF TECHNOL, V28, P4221, DOI 10.1007/s10639-022-11316-w
   Anser MK, 2024, ENVIRON DEV SUSTAIN, DOI 10.1007/s10668-024-05580-8
   Babin B. J., 2015, Heresies and Sacred Cows in Scholarly Marketing Publications . 16., DOI [10.1016/j.jbusres.2015.12.001, DOI 10.1016/J.JBUSRES.2015.12.001]
   Bagozzi RP, 1998, ORGAN RES METHODS, V1, P45, DOI 10.1177/109442819800100104
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Bailey E, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19031077
   Bandura A., 1969, Handbook of socialization theory and research, P213, DOI DOI 10.1080/19371918.2011.591629
   Bano S, 2019, CHILD YOUTH SERV REV, V103, P200, DOI 10.1016/j.childyouth.2019.06.002
   Bouteraa M, 2024, COMPUT HUM BEHAV REP, V14, DOI 10.1016/j.chbr.2024.100402
   Brougham D, 2018, J MANAGE ORGAN, V24, P239, DOI 10.1017/jmo.2016.55
   Bruggeman H, 2019, COMPUT HUM BEHAV, V101, P104, DOI 10.1016/j.chb.2019.07.015
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00284-4
   Chyung S., 2017, PERFORM IMPROV Q, V10, P15, DOI [https://doi.org/10.1002/pfi.21727, DOI 10.1002/PFI.21727, 10.1002/pfi.21727]
   Correia AB, 2024, COGENT BUS MANAG, V11, DOI 10.1080/23311975.2024.2374625
   Coyne SM, 2020, COMPUT HUM BEHAV, V104, DOI 10.1016/j.chb.2019.106160
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   Delanerolle Gayathri, 2021, Womens Health (Lond), V17, p17455065211018111, DOI 10.1177/17455065211018111
   Donthu N, 2021, J BUS RES, V135, P758, DOI 10.1016/j.jbusres.2021.07.015
   Fan XJ, 2019, TELEMAT INFORM, V41, P86, DOI 10.1016/j.tele.2019.04.001
   Farrukh M., 2024, Industry and Innovation: Textile Industry, P95, DOI [10.1007/978-3-031-57804-5, DOI 10.1007/978-3-031-57804-5]
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Gao ZY, 2024, INT J MANAG EDUC-OXF, V22, DOI 10.1016/j.ijme.2024.100958
   Giunchiglia F, 2018, COMPUT HUM BEHAV, V82, P177, DOI 10.1016/j.chb.2017.12.041
   Guilmette M, 2019, LEARN INDIVID DIFFER, V73, P8, DOI 10.1016/j.lindif.2019.04.006
   Gupta D., 2023, Journal of Reattach Therapy and Developmental Diversities, V6, P184
   Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
   Hardy BW, 2018, COMPUT HUM BEHAV, V85, P282, DOI 10.1016/j.chb.2018.04.005
   Ho MT, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e36251
   Ho MT, 2024, AI SOC, DOI 10.1007/s00146-024-01913-3
   Ho MT, 2023, METHODSX, V10, DOI 10.1016/j.mex.2023.102149
   Ho MT, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.102011
   Horesh D, 2022, HEALTHCARE-BASEL, V10, DOI 10.3390/healthcare10112261
   Hu B, 2023, COMPUT HUM BEHAV, V145, DOI 10.1016/j.chb.2023.107760
   Hussain K, 2024, DIGIT BUS, V4, DOI 10.1016/j.digbus.2023.100071
   Ivanov S, 2024, TECHNOL SOC, V77, DOI 10.1016/j.techsoc.2024.102521
   Javaid M., 2023, BenchCouncil Trans Benchmarks Standards Eval, V3, P100105, DOI [10.1016/j.tbench.2023.100105, DOI 10.1016/J.TBENCH.2023.100105]
   Johnston H, 2024, INT J EDUC INTEGR, V20, DOI 10.1007/s40979-024-00149-4
   Kross E, 2021, TRENDS COGN SCI, V25, P55, DOI 10.1016/j.tics.2020.10.005
   Lau WWF, 2017, COMPUT HUM BEHAV, V68, P286, DOI 10.1016/j.chb.2016.11.043
   Lee ELE, 2021, BIOL PSYCHIAT-COGN N, V6, P856, DOI 10.1016/j.bpsc.2021.02.001
   Li JL, 2022, SUSTAIN CITIES SOC, V83, DOI 10.1016/j.scs.2022.103958
   Li K, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15065221
   Lu HM, 2018, MOBILE NETW APPL, V23, P368, DOI 10.1007/s11036-017-0932-8
   Malik A, 2021, INFORM TECHNOL PEOPL, V34, P557, DOI 10.1108/ITP-06-2019-0289
   Mantello P, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-01837-1
   Maqableh M M., 2015, Commun. Netw, V7, P159, DOI DOI 10.4236/CN.2015.74015
   Nguyen A, 2024, STUD HIGH EDUC, V49, P847, DOI 10.1080/03075079.2024.2323593
   Obrenovic B, 2024, AI SOC, DOI 10.1007/s00146-024-01889-0
   Oueder M., 2019, International Journal of Engineering Research and Technology, V12, P2212
   Park N, 2014, COMPUT HUM BEHAV, V36, P138, DOI 10.1016/j.chb.2014.03.037
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Rehman AU, 2024, TECHNOL SOC, V78, DOI 10.1016/j.techsoc.2024.102655
   Saeidnia H. R., 2022, How May ChatGPT Impact Digital Reference Services?, V6, P1
   Saif N, 2024, COMPUT HUM BEHAV, V154, DOI 10.1016/j.chb.2023.108097
   Shahzad MF, 2024, BRIT EDUC RES J, DOI 10.1002/berj.4084
   Shahzad MF, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-024-12949-9
   Shahzad MF, 2024, J MANUF TECHNOL MANA, DOI 10.1108/JMTM-05-2024-0236
   Shahzad MF, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00478-x
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Tan L, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0274299
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Tsai ML, 2023, EDUC CHEM ENG, V44, P71, DOI 10.1016/j.ece.2023.05.001
   Twenge JM, 2020, J ADOLESCENCE, V79, P91, DOI 10.1016/j.adolescence.2019.12.018
   Urban M, 2024, COMPUT EDUC, V215, DOI 10.1016/j.compedu.2024.105031
   Vuong QH, 2024, AI SOC, DOI 10.1007/s00146-024-01914-2
   Wang QY, 2009, COMPUT EDUC, V53, P1138, DOI 10.1016/j.compedu.2009.05.023
   Whelan E, 2022, INTERNET RES, V32, P280, DOI 10.1108/INTR-06-2021-0394
   Whelan E, 2020, COMPUT EDUC, V143, DOI 10.1016/j.compedu.2019.103692
   Xu S, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-66689-4
   Yosep I, 2023, J MULTIDISCIP HEALTH, V16, P261, DOI 10.2147/JMDH.S400779
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   Yu H, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1181712
   Zang JJ, 2022, FRONT PUBLIC HEALTH, V10, DOI 10.3389/fpubh.2022.964320
   Zhang XY, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12407-y
NR 83
TC 0
Z9 0
U1 22
U2 22
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0141-8211
EI 1465-3435
J9 EUR J EDUC
JI Eur. J. Educ.
PD 2024 NOV 13
PY 2024
DI 10.1111/ejed.12835
EA NOV 2024
PG 15
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA M4F9B
UT WOS:001357127700001
DA 2024-12-25
ER

PT J
AU Di Martino, L
   Ford, H
AF Di Martino, Luigi
   Ford, Heather
TI Navigating uncertainty: public diplomacy vs. AI
SO PLACE BRANDING AND PUBLIC DIPLOMACY
LA English
DT Article; Early Access
DE Uncertainty; Public diplomacy; Artificial intelligence; Generative
   artificial intelligence
AB Some have heralded generative AI models as an opportunity to inform diplomacy and support diplomats' communication campaigns. Others have argued that generative AI is inherently untrustworthy because it simply manages probabilities and doesn't consider the truth value of statements. In this article, we examine how AI applications are built to smooth over uncertainty by providing a single answer among multiple possible answers and by presenting information in a tone and form that demands authority. We contrast this with the practices of public diplomacy professionals who must grapple with both epistemic and aleatory uncertainty head on to effectively manage complexities through negotiation. We argue that the rise of generative AI and its "operationalization of truth" invites us to reflect on the possible shortcoming of AI's application to public diplomacy practices and to recognize how prominent uncertainty is in public diplomacy practices.
C1 [Di Martino, Luigi] Western Sydney Univ, Young & Resilient Res Ctr, Bldg EM,Parramatta Campus,Locked Bag 1797, Penrith, NSW 2751, Australia.
   [Ford, Heather] Univ Technol, Sch Commun, Bldg 10,Level 5,15 Broadway Ave, Ultimo, NSW 2007, Australia.
C3 Western Sydney University; University of Technology Sydney
RP Di Martino, L (corresponding author), Western Sydney Univ, Young & Resilient Res Ctr, Bldg EM,Parramatta Campus,Locked Bag 1797, Penrith, NSW 2751, Australia.
EM L.DiMartino@westernsydney.edu.au; heather.ford@uts.edu.au
RI Di Martino, Luigi/AAR-2822-2021
OI Di Martino, Luigi/0000-0001-8490-0804
FU Western Sydney University
FX No Statement Available
CR Amoore L, 2019, THEOR CULT SOC, V36, P147, DOI 10.1177/0263276419851846
   Aroyo L, 2015, AI MAG, V36, P15, DOI 10.1609/aimag.v36i1.2564
   Auer C., 2016, The Handbook of International Crisis Communication Research, P119
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bickford Susan., 1996, The dissonance of democracy: Listening, conflict, and citizenship, DOI [10.7591/9781501722202, DOI 10.7591/9781501722202]
   Di Martino L, 2020, PLACE BRANDING PUBLI, V16, P131, DOI 10.1057/s41254-019-00135-5
   Frankfurt HG, 2005, ON BULLSHIT, P1
   Gass R.H., 2009, Routledge handbook of public diplomacy, P154
   Goodall Bud, 2006, Strategic ambiguity, communication, and public diplomacy in an uncertain world: Principles and practices, P1
   Graham SE, 2014, INT STUD REV, V16, P522, DOI 10.1111/misr.12156
   Hone Katharina E., 2022, Science diplomacy capacity development: Reflections on DiPLO's 2021 course and the road ahead
   Kouw M, 2018, SCI TECHNOL STUD, V31, P52
   Latour Bruno., 1988, SCI ACTION FOLLOW SC
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Malik K, 2022, OBSERVER
   Manfredi-Sánchez JL, 2023, J COMMUN MANAG, V27, P241, DOI 10.1108/JCOM-04-2022-0037
   Manor Ilan, 2023, International Affairs Blog
   Melissen J, 2005, NEW PUBLIC DIPLOMACY: SOFT POWER IN INTERNATIONAL RELATIONS, P3
   Mills Laura., 2021, Public diplomacy and the politics of uncertainty, P277, DOI [10.1007/978-3-030-54552-9_11, DOI 10.1007/978-3-030-54552-9_11]
   Munn L., 2023, arXiv, DOI [10.48550/arXiv.2301.12066, DOI 10.48550/ARXIV.2301.12066]
   Rolfe M, 2014, HAGUE J DIPL, V9, P76, DOI 10.1163/1871191X-12341266
   Sevin E, 2017, POLITICS POLICY, V45, P879, DOI 10.1111/polp.12227
   Surowiec Pawel., 2021, Public diplomacy and the politics of uncertainty, DOI [10.1007/978-3-030-54552-9, DOI 10.1007/978-3-030-54552-9]
   Taylor M, 2014, J PUBLIC RELAT RES, V26, P384, DOI 10.1080/1062726X.2014.956106
   U.S. Advisory Commission on Public Diplomacy, 2023, The use of artificial intelligence in public diplomacy
   van der Bles AM, 2019, ROY SOC OPEN SCI, V6, DOI 10.1098/rsos.181870
   van Ham P, 2003, SECUR DIALOGUE, V34, P427, DOI 10.1177/0967010603344004
   Vincent J, 2022, AI-generated answers temporarily banned on coding Q&A site Stack Overflow
   WINHAM GR, 1977, WORLD POLIT, V30, P87, DOI 10.2307/2010076
   Zaharna Rhonda S., 2003, Foreign Policy in Focus
   Zaharna RS., 2023, A Research Agenda for Public Diplomacy
NR 31
TC 1
Z9 1
U1 7
U2 10
PU PALGRAVE MACMILLAN LTD
PI BASINGSTOKE
PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND
SN 1751-8040
EI 1751-8059
J9 PLACE BRANDING PUBLI
JI Place Branding Public Dipl.
PD 2024 FEB 28
PY 2024
DI 10.1057/s41254-024-00330-z
EA FEB 2024
PG 5
WC Hospitality, Leisure, Sport & Tourism
WE Emerging Sources Citation Index (ESCI)
SC Social Sciences - Other Topics
GA JE1C0
UT WOS:001171385300001
OA hybrid
DA 2024-12-25
ER

PT J
AU Kim, T
   Kim, MJ
   Promsivapallop, P
AF Kim, Taekyung
   Kim, Myung Ja
   Promsivapallop, Pornpisanu
TI Investigating the influence of generative AI's credibility and utility
   on travel consumer behaviour and recommendations through the lens of
   personal innovativeness
SO CURRENT ISSUES IN TOURISM
LA English
DT Article; Early Access
DE Generative AI; personal innovativeness; behaviour intention; willingness
   to recommend
AB This study examines the application of generative artificial intelligence (AI) in the tourism industry, focusing on its potential to enhance personalised travel services. Specifically, it explores the influence of the perceived credibility and utility of generative AI on tourists' intention to use and recommend these technologies, while also assessing the moderating impact of personal innovativeness (PI) on these perceptions. Utilising a comprehensively integrated methodological approach that includes both quantitative and qualitative analyses, the findings reveal that, while trust and usage in AI significantly affect intentions to use and recommend AI services, PI does not act as a significant moderator in these relationships. These results highlight the broad appeal of AI technologies and suggest the need for further research towards developing customised strategies for increasing AI acceptance among tourists.
C1 [Kim, Taekyung] Kyung Hee Univ, Sch Management, Big Data Analyt, Seoul, South Korea.
   [Kim, Myung Ja; Promsivapallop, Pornpisanu] Prince Songkla Univ, Fac Hospitality & Tourism, Phuket, Thailand.
   [Kim, Myung Ja] Kyung Hee Univ, Coll Hotel & Tourism Management, Seoul, South Korea.
C3 Kyung Hee University; Prince of Songkla University; Kyung Hee University
RP Kim, MJ (corresponding author), Prince Songkla Univ, Fac Hospitality & Tourism, Phuket, Thailand.; Kim, MJ (corresponding author), Kyung Hee Univ, Coll Hotel & Tourism Management, Seoul, South Korea.
EM silver@khu.ac.kr
RI Kim, Taekyung/JDX-0821-2023; Kim, Myung Ja/AAI-8460-2020;
   Promsivapallop, Pornpisanu/GMW-8834-2022
OI Kim, Myung Ja/0000-0002-8077-8503; Kim, Taekyung/0000-0001-5089-2914
CR Christensen J, 2024, CURR ISSUES TOUR, DOI 10.1080/13683500.2023.2300032
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Gretzel U, 2015, ELECTRON MARK, V25, P179, DOI 10.1007/s12525-015-0196-8
   Hateftabar F, 2023, CURR ISSUES TOUR, V26, P1861, DOI 10.1080/13683500.2022.2071682
   Kim MJ, 2024, CURR ISSUES TOUR, V27, P1666, DOI 10.1080/13683500.2023.2214721
   Kim MJ, 2023, CURR ISSUES TOUR, V26, P1389, DOI 10.1080/13683500.2023.2176744
   Lewis W, 2003, MIS QUART, V27, P657
   Seyitoglu F, 2021, CURR ISSUES TOUR, V24, P1631, DOI 10.1080/13683500.2020.1774518
   Shin DH, 2017, COMPUT HUM BEHAV, V67, P292, DOI 10.1016/j.chb.2016.11.007
   Vorobeva D, 2024, CURR ISSUES TOUR, V27, P1551, DOI 10.1080/13683500.2023.2214353
NR 10
TC 0
Z9 0
U1 108
U2 108
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1368-3500
EI 1747-7603
J9 CURR ISSUES TOUR
JI Curr. Issues Tour.
PD 2024 JUN 14
PY 2024
DI 10.1080/13683500.2024.2364764
EA JUN 2024
PG 5
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA UN1W0
UT WOS:001248657100001
DA 2024-12-25
ER

PT J
AU Guha, A
   Grewal, D
   Atlas, S
AF Guha, Abhijit
   Grewal, Dhruv
   Atlas, Stephen
TI Generative AI and Marketing Education: What the Future Holds
SO JOURNAL OF MARKETING EDUCATION
LA English
DT Article
DE ChatGPT; generative AI; marketing education
AB To understand why and how marketing educators can best use generative artificial intelligence (AI), such as ChatGPT, this article integrates a literature survey, interviews with both marketing educators and managers, and surveys of both marketing educators and students. In leveraging these inputs, the authors argue that generative AI can significantly shape and improve the future of marketing education. Specifically, by including ChatGPT in their lessons, marketing educators can both materially enhance learning experiences and better prepare students for future jobs with marketing firms that rely on ChatGPT in practice. Noting that ChatGPT has downsides, this research identifies several steps educators should take to minimize the risks. Finally, the authors propose an agenda for continued research into how marketing educators can and should use ChatGPT, with the explicit recognition that ChatGPT is evolving rapidly, so that, the research agenda will need to adapt as well.
C1 [Guha, Abhijit] Univ South Carolina, Columbia, SC USA.
   [Grewal, Dhruv] Babson Coll, Babson Pk, MA USA.
   [Grewal, Dhruv] Univ Bath, Bath, England.
   [Grewal, Dhruv] Tecnol Monterrey, Monterrey, Mexico.
   [Atlas, Stephen] Univ Rhode Isl, Kingston, RI USA.
   [Grewal, Dhruv] Babson Coll, Dept Mkt, Babson Pk, MA 02457 USA.
C3 University of South Carolina System; University of South Carolina
   Columbia; Babson College; University of Bath; Tecnologico de Monterrey;
   University of Rhode Island; Babson College
RP Grewal, D (corresponding author), Babson Coll, Dept Mkt, Babson Pk, MA 02457 USA.
EM dgrewal@babson.edu
RI Guha, Abhijit/GNP-5652-2022; Grewal, Dhruv/B-7264-2013
OI Grewal, Dhruv/0000-0002-7046-6063
CR Abid A., 2023, IS CHATGPT BECOMING
   Ahmad A., 2023, WRITE BETTER OUTLINE
   ANDREASEN AR, 1985, HARVARD BUS REV, V63, P176
   [Anonymous], 2023, Reuters
   Athaluri SA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37432
   Atlas S., 2023, CHATGPT HIGHER ED PR
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bal AS, 2015, J MARKET EDUC, V37, P190, DOI 10.1177/0273475315593380
   Barrett T., 2023, UPLEVEL YOUR PROMPT
   Biddle S., 2022, The Intercept
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bowman E., 2022, National Public Radio
   Brand James, 2023, Using GPT for market research
   Clark B., 2023, MEDIUM          0614
   Cost B., 2023, New York Post
   Crittenden VL, 2019, J MARKET EDUC, V41, P75, DOI 10.1177/0273475319849652
   Dobson S., 2023, The Conversation19 April
   Dorsey D., 2023, ABC7NEWS        0617
   Eapen TT., 2023, HARVARD BUS REV
   Elbanna S., 2023, Management & Sustainability: An Arab Review
   Esposito F., 2023, 9TO5MAC         0518
   Frackiewicz M., 2023, STEM HUMANITIES CHAT
   Gray I., 2023, POSSIBLE SIDE EFFECT
   Grewal D, 2018, J MARKET EDUC, V40, P85, DOI 10.1177/0273475318755838
   Harris L., 2023, ARE CHATGPT OTHER AI
   Hirsh-Pasek K., 2023, Brookings
   Knight W., 2023, WIRED
   Lucariello K., 2023, GRAMMARLY ANNOUNCES
   McAdoo T., 2023, APA Style
   McCormack M., 2023, EDUCAUSE QuickPoll Results: Adopting and Adapting to Generative AI in Higher Ed Tech
   Nelson J., 2023, PERPLEXITY AI CHATBO
   Nolan B., 2023, SHAKY START SOME COL
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Puranam P., 2023, CHATGPT FUTURE BUSIN
   Reavey B, 2021, J MARKET EDUC, V43, P333, DOI 10.1177/02734753211043925
   Ruby D., 2023, DEMANDSAGE      1108
   Rutinowski J., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2304.07333
   Schlegelmilch BB, 2020, J MARKET EDUC, V42, P93, DOI 10.1177/0273475320922285
   Scott I., 2023, INSIDE HIGHER ED
   Shimek C., 2023, UM RES AI TESTS TOP
   Sinha P., 2023, Harvard Business Review
   Smith M, 2023, ChatGPT is the hottest new job skill that can help you get hired, according to HR experts
   Terwiesch C., 2023, Would chatgpt get a wharton MBA? a prediction based on its performance in the operations management course. mack institute for innovation management at the wharton school
   Thorbecke C., 2023, DONT TELL ANYTHING C
   Uta J., 2023, 9 BEST USES CHATGPT
   Wallbank A., 2023, PROMPT ENG ACAD SKIL
   Wiggers K., 2023, CHATGPT PROMPTS OPTI
   Wood P., 2023, OXFORD CAMBRIDGE BAN
   Zhai X., 2022, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4312418
NR 49
TC 21
Z9 21
U1 31
U2 120
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0273-4753
EI 1552-6550
J9 J MARKET EDUC
JI J. Market. Educ.
PD APR
PY 2024
VL 46
IS 1
BP 6
EP 17
DI 10.1177/02734753231215436
EA DEC 2023
PG 12
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA KJ1R0
UT WOS:001112276000001
DA 2024-12-25
ER

PT J
AU Reed, JM
   Dodson, TM
AF Reed, Janet M.
   Dodson, Tracy M.
TI Generative AI Backstories for Simulation Preparation
SO NURSE EDUCATOR
LA English
DT Article
DE AI images; artificial intelligence; nursing education; patient
   simulation; presimulation materials
ID ARTIFICIAL-INTELLIGENCE; EDUCATION; IMAGES; EMPATHY
AB Background:Developing engaging presimulation learning materials that provide contextualized patient information is needed to best prepare students for nursing simulation. One emerging strategy that can be used by educators to create visual images for storytelling is generative artificial intelligence (AI).Purpose:The purpose of this pilot study was to determine how the use of generative AI-created patient backstories as a presimulation strategy might affect student engagement and learning in nursing simulation.Methods:A qualitative cross-sectional survey with content analysis was completed with undergraduate nursing students following an acute care simulation.Results:Student surveys point to positive pedagogical outcomes of using AI image generation as a strategy to prepare for simulation such as decreased anxiety in simulation, increased preparatory knowledge, and increased emotional connection with the patient's story.Conclusions:Images created with generative AI hold promise for future research and transforming nursing education.
C1 [Reed, Janet M.; Dodson, Tracy M.] Kent State Univ, 800 E Summit St, Kent, OH 44242 USA.
C3 University System of Ohio; Kent State University; Kent State University
   Salem; Kent State University Kent
RP Reed, JM (corresponding author), Kent State Univ, 800 E Summit St, Kent, OH 44242 USA.
EM Jreed56@kent.edu; tdodson4@kent.edu
RI Reed, Janet/HZH-7451-2023
OI Reed, Janet/0000-0003-3905-4156
CR Ahmad S, 2022, CIN-COMPUT INFORM NU, V40, P139, DOI 10.1097/CIN.0000000000000871
   Andersen P, 2022, CLIN SIMUL NURS, V67, P49, DOI 10.1016/j.ecns.2022.02.014
   Byrne M, 2023, CIN-COMPUT INFORM NU, V41, P479, DOI 10.1097/CIN.0000000000001044
   de la Croix A, 2011, MED EDUC, V45, P1090, DOI 10.1111/j.1365-2923.2011.04060.x
   Dodson TM, 2021, CLIN SIMUL NURS, V59, P52, DOI 10.1016/j.ecns.2021.05.007
   Dreifuerst KT, 2015, CLIN SIMUL NURS, V11, P268, DOI 10.1016/j.ecns.2015.01.005
   Embree JL, 2021, J CONTIN EDUC NURS, V52, P454, DOI 10.3928/00220124-20210913-04
   Ferdig RE., 2023, J Interact Learn Res, V34, P185
   Fitzpatrick JJ, 2018, NURS EDUC PERSPECT, V39, P60, DOI 10.1097/01.NEP.0000000000000298
   Frith KH, 2019, NURS EDUC PERSPECT, V40, P261, DOI 10.1097/01.NEP.0000000000000543
   Hwang GJ, 2024, INTERACT LEARN ENVIR, V32, P373, DOI 10.1080/10494820.2022.2086579
   Kaplan-Rakowski R., 2023, J INTERACTIVE LEARNI, V34, P313, DOI DOI 10.11113/ITLJ.V7.137
   Kim J, 2022, EDUC INF TECHNOL, V27, P6069, DOI 10.1007/s10639-021-10831-6
   Lapum JL, 2016, NURS EDUC, V41, P112, DOI 10.1097/NNE.0000000000000214
   Lee D, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18010271
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   MacDonnell JA, 2011, J TRANSFORM EDUC, V9, P203, DOI 10.1177/1541344612441083
   McDermott DS, 2021, CLIN SIMUL NURS, V58, P9, DOI 10.1016/j.ecns.2021.08.008
   McMillian-Bohler J, 2022, J NURS EDUC, V61, P489, DOI 10.3928/01484834-20220602-11
   Mills B, 2016, NURS EDUC TODAY, V45, P9, DOI 10.1016/j.nedt.2016.06.006
   Plunkett L., 2022, KOTAKU NEWSLETT 0825
   Reed J., 2023, J INTERACT LEARN RES, V34, P369
   Reed JM, 2023, NURS EDUC, V48, P246, DOI 10.1097/NNE.0000000000001453
   Reed JM, 2022, CLIN SIMUL NURS, V73, P21, DOI 10.1016/j.ecns.2022.08.005
   Rieger Kendra L, 2016, JBI Database System Rev Implement Rep, V14, P139
   Roberts ML, 2023, NURS EDUC, V48, P260, DOI 10.1097/NNE.0000000000001419
   Rogers BA, 2021, NURS EDUC TODAY, V99, DOI 10.1016/j.nedt.2021.104815
   Slota M, 2018, J PROF NURS, V34, P357, DOI 10.1016/j.profnurs.2017.12.007
   Tanner CA, 2006, J NURS EDUC, V45, P204, DOI 10.3928/01484834-20060601-04
   Tyerman J., 2021, Journal of Nursing Education and Practice, V11, P10, DOI [https://doi.org/10.5430/jnep.v11n7p10, DOI 10.5430/JNEP.V11N7P10]
   Tyerman J, 2019, CLIN SIMUL NURS, V27, P12, DOI 10.1016/j.ecns.2018.11.002
   von Gerich H, 2022, INT J NURS STUD, V127, DOI 10.1016/j.ijnurstu.2021.104153
   Yockey J, 2019, CLIN SIMUL NURS, V29, P29, DOI 10.1016/j.ecns.2018.12.004
   Yu JP, 2021, NURS OPEN, V8, P2813, DOI 10.1002/nop2.860
NR 34
TC 5
Z9 5
U1 23
U2 26
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 0363-3624
EI 1538-9855
J9 NURS EDUC
JI Nurs. Educ.
PD JUL-AUG
PY 2024
VL 49
IS 4
BP 184
EP 188
DI 10.1097/NNE.0000000000001590
EA DEC 2023
PG 5
WC Nursing
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Nursing
GA WR2U8
UT WOS:001225478800003
PM 38151702
DA 2024-12-25
ER

PT J
AU Arslan, M
   Munawar, S
   Cruz, C
AF Arslan, Muhammad
   Munawar, Saba
   Cruz, Christophe
TI Political-RAG: using generative AI to extract political information from
   media content
SO JOURNAL OF INFORMATION TECHNOLOGY & POLITICS
LA English
DT Article; Early Access
DE Natural Language Processing; Information Extraction (IE); Large Language
   Models (LLMs); Retrieval-Augmented Generation (RAG); Political events
   extraction
AB In the digital era, media content is crucial for political analysis, providing valuable insights through news articles, social media posts, speeches, and reports. Natural Language Processing (NLP) has transformed Political Information Extraction (IE), automating tasks such as event extraction and sentiment analysis. Traditional NLP methods, while effective, are often task-specific and require specialized expertise. In contrast, Large Language Models (LLMs) powered by Generative Artificial Intelligence (GenAI) offer a more integrated solution. However, domain-specific challenges persist, which led to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by incorporating external data retrieval, addressing issues related to data availability. To demonstrate RAG's capabilities, we introduce the Political-RAG system, designed to extract political event information from media content, including Twitter data and news articles. Initially developed for event extraction, the Political-RAG system lays the foundation for developing various complex Political IE tasks. These include detecting hate speech, analyzing conflicts, assessing political bias, and evaluating social trends, sentiment, and opinions.
C1 [Arslan, Muhammad] Univ West England, Sch Architecture & Environm, Bristol, England.
   [Arslan, Muhammad; Cruz, Christophe] Univ Bourgogne, Lab Interdisciplinaire Carnot Bourgogne ICB, Dijon, France.
   [Cruz, Christophe] Univ Bourgogne, Comp Sci Dept, IUT Dijon Auxerre, Dijon, France.
C3 University of West England; Universite de Technologie de
   Belfort-Montbeliard (UTBM); Universite de Bourgogne; Universite de
   Bourgogne
RP Arslan, M (corresponding author), Univ West England, Sch Architecture & Environm, Bristol, England.; Arslan, M (corresponding author), Univ Bourgogne, Lab Interdisciplinaire Carnot Bourgogne ICB, Dijon, France.
EM muhammad.arslan@u-bourgogne.fr
RI CRUZ, Christophe/I-2419-2012; Munawar, Saba/AAK-3494-2021; Arslan,
   Muhammad/T-6589-2018
OI Munawar, Saba/0009-0007-1255-9425; Arslan, Muhammad/0000-0003-3682-7002
FU Agence Nationale de la Recherche, France; University of the West of
   England, Bristol, UK
FX The work was supported by the Agence Nationale de la Recherche, France
   and the University of the West of England, Bristol, UK.
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Al Ghadban Y., 2023, medRxiv
   Ali O., 2024, 2024 5 INT C ADV COM, P1
   Almatrafi O, 2015, ACM IMCOM 2015, PROCEEDINGS, DOI 10.1145/2701126.2701129
   Alsarra S., 2023, P 14 INT C REC ADV N, P98
   Armenta-Segura J., 2023, P 6 WORKSH CHALL APP, P53
   Avanthika K., 2023, P 6 WORKSH CHALL APP, P66
   Aziz A., 2023, P 6 WORKSH CHALL APP, P101, DOI [https://doi.org/10.26615/978-954-452-089-2014, DOI 10.26615/978-954-452-089-2014]
   Barbera Pablo., 2020, The SAGE Handbook of Research Methods in Political Science and International Relations, P404, DOI [DOI 10.4135/9781526486387, DOI 10.4135/9781526486387.N26]
   Bestvater SE, 2023, POLIT ANAL, V31, P235, DOI 10.1017/pan.2022.10
   Bor D, 2023, Arxiv, DOI [arXiv:2302.07775, 10.48550/arXiv.2302.07775, DOI 10.48550/ARXIV.2302.07775]
   Bose R, 2019, SMART INNOV SYST TEC, V107, P427, DOI 10.1007/978-981-13-1747-7_41
   Burley T, 2020, PRACTICE AND EXPERIENCE IN ADVANCED RESEARCH COMPUTING 2020, PEARC 2020, P152, DOI 10.1145/3311790.3397343
   Buyukoz Berfu, 2020, P WORKSHOP AUTOMATED, P9
   Chen W., 2022, arXiv preprint arXiv, V2209, P14491
   Colverd G, 2023, Arxiv, DOI arXiv:2311.02597
   Costa Carlos, 2021, SIGDOC '21: The 39th ACM International Conference on Design of Communication, P63, DOI 10.1145/3472714.3473624
   Davenport C, 2002, J CONFLICT RESOLUT, V46, P427, DOI 10.1177/0022002702046003005
   Delucia A., 2023, P 6 WORKSH CHALL APP, P18
   Demidov D., 2023, Political bias of news content: Classification based on individual articles and media
   Demiros I, 2008, J INF TECHNOL POLITI, V5, P133, DOI 10.1080/19331680802149632
   Doumit S., 2011, INT C COMPL SYST
   Doumit S, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P3068, DOI 10.1109/IJCNN.2011.6033626
   Efat A. A., 2023, EMPOLITICON: NLP and ML based approach for context and emotion classification of political speeches from transcripts
   Esackimuthu S., 2023, P 6 WORKSH CHALL APP, P79
   Falcon LLM Team, 2023, Arxiv, DOI [arXiv:2311.16867, 10.48550/arXiv.2311.16867]
   Ge J., 2023, medRxiv, DOI [10.1101/2023.11.10.23298364, DOI 10.1101/2023.11.10.23298364]
   Gode S, 2023, AI MAG, V44, P248, DOI 10.1002/aaai.12104
   Halterman A., 2021, Three essays on natural language processing and information extraction with applications to political violence and international security
   Halterman A., 2020, Technical report MIT
   Halterman A., 2018, MIT political science department research paper No. 2018-21, DOI [https://doi.org/10.2139/ssrn.3267476, DOI 10.2139/SSRN.3267476]
   Han ZF, 2024, Arxiv, DOI [arXiv:2402.14594, DOI 10.48550/ARXIV.2402.14594]
   Hettiarachchi H, 2021, CASE 2021: THE 4TH WORKSHOP ON CHALLENGES AND APPLICATIONS OF AUTOMATED EXTRACTION OF SOCIO-POLITICAL EVENTS FROM TEXT (CASE), P120
   Hey A. M., 2023, Using NLP analysis to categorise statements to values on the political compass
   Hitesh MSR, 2019, 2019 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT COMMUNICATION AND COMPUTATIONAL TECHNIQUES (ICCT), P146, DOI [10.1109/ICCT46177.2019.8969049, 10.1109/icct46177.2019.8969049]
   Hobbs Jerry R., 2010, Handbook of natural language processing, V15, P16
   Hodorog A, 2022, SUSTAIN CITIES SOC, V85, DOI 10.1016/j.scs.2022.104026
   Hoffmann J, 2022, Arxiv, DOI [arXiv:2203.15556, 10.48550/arXiv.2203.15556]
   Hu Y., 2022, Conflibert: A pre-trained language model for political conflict and violence
   Hürriyetoglu A, 2021, CASE 2021: THE 4TH WORKSHOP ON CHALLENGES AND APPLICATIONS OF AUTOMATED EXTRACTION OF SOCIO-POLITICAL EVENTS FROM TEXT (CASE), P79
   Javed I., 2023, 2023 5 INT C HUM COM, P1, DOI [https://doi.org/10.1109/HORA58378.2023.10156766, DOI 10.1109/HORA58378.2023.10156766]
   Jeffrey Pennington R. S. C. D., 2014, P 2014 C EMP METH NA, P1532
   Jeong M, 2024, BIOINFORMATICS, V40, pi119, DOI 10.1093/bioinformatics/btae238
   Jimeno Yepes A., 2024, arXiv
   Jin Z., 2022, Handbook of computational social science for policy, P141
   Jo E, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581503
   Johnson K., 2016, P 1 WORKSHOP NLP COM, P66
   Katre PD, 2019, INT CONF ADV COMPU, P108, DOI [10.1109/IACC48062.2019.8971605, 10.1109/iacc48062.2019.8971605]
   Kawintiranon K, 2022, LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P7360
   Kim J., 2020, P 28 INT C COMP LING, P2284
   Kowsik VVS, 2024, J INTELL INF SYST, V62, P765, DOI 10.1007/s10844-024-00842-3
   Kuamri S, 2017, INT CONF COMPUT
   Lei YY, 2024, Arxiv, DOI arXiv:2404.01715
   Lewis P, 2020, ADV NEUR IN, V33
   Liang Y, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), P88, DOI 10.1109/IRI.2018.00020
   Linegar M, 2023, FRONT POLIT SCI, V5, DOI 10.3389/fpos.2023.1257092
   Loerakker M., 2024, P 7 WORKSH CHALL APP, P6
   Lorenzini J, 2022, AM BEHAV SCI, V66, P555, DOI 10.1177/00027642211021650
   Lukito J., 2019, P 3 WORKSH NAT PROC, P54, DOI [https://doi.org/10.18653/v1/W19-2107, DOI 10.18653/V1/W19-2107]
   Makarov P., 2018, P 2 JOINT SIGHUM WOR, P103
   Mallavarapu C., 2018, SMU Data Science Review, V1, P10
   Manathunga SS, 2023, VIROL J, V20, DOI 10.1186/s12985-023-02018-x
   Maynard D, 2012, LECT NOTES COMPUT SC, V7117, P88, DOI 10.1007/978-3-642-25953-1_8
   Miao JJ, 2022, EVOL INTELL, V15, P1545, DOI 10.1007/s12065-021-00565-2
   Mikolov T, ADV NEURAL INFORM PR, P3111, DOI DOI 10.48550/ARXIV.1310.4546
   Misra R, 2022, Arxiv, DOI arXiv:2209.11429
   Orellana S, 2023, INFORMATION, V14, DOI 10.3390/info14030152
   Osorio J, 2020, IEEE IJCNN, DOI 10.1109/ijcnn48605.2020.9207039
   Oyewola DO, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e14836
   Pair E, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.712646
   Pan F., 2022, End-to-end table question answering via retrieval-augmented generation
   Piskorski J., 2013, MULTISOURCE MULTILIN, P23, DOI DOI 10.1007/978-3-642-28569-1_2
   Rackauckas Z., 2024, International Journal on Natural Language Computing, V13, P37, DOI DOI 10.5121/IJNLC.2024.13103
   Rahmati A., 2023, AUT Journal of Mathematics and Computing, V4, P145
   Raiaan M. A. K., 2024, A review on large language models: Architectures, applications, taxonomies, open issues and challenges
   Sawhney Ramit, 2020, P 28 INT C COMP LING, P4847, DOI DOI 10.18653/V1/2020.COLING-MAIN.426
   Scao Teven Le, 2023, arXiv, DOI DOI 10.48550/ARXIV.2211.05100
   Sha YC, 2023, MATHEMATICS-BASEL, V11, DOI 10.3390/math11153269
   Sharber B., 2020, Analyzing political polarization in news media with natural language processing
   Shi X., 2024, Advances in Neural Information Processing Systems, V36, DOI [https://doi.org/10.48550/arXiv.2305.16646, DOI 10.48550/ARXIV.2305.16646]
   Singh J, 2023, MULTIMED TOOLS APPL, DOI 10.1007/s11042-023-16263-3
   Singh K., 2023, P 6 WORKSH CHALL APP, P136
   Small SG, 2014, NEURAL COMPUT APPL, V25, P533, DOI 10.1007/s00521-013-1516-6
   Sufi F, 2023, INFORMATION, V14, DOI 10.3390/info14090485
   Suri M., 2022, P 5 WORKSH CHALL APP, P161, DOI [10.18653/v1/2022.case-1.23, DOI 10.18653/V1/2022.CASE-1.23]
   Tanev H., 2023, P 6 WORKSH CHALL APP, P160
   Tanev H., 2024, P 7 WORKSH CHALL APP, P32
   Thapa S, 2024, P 7 WORKSH CHALL APP, P234
   Touvron H, 2023, Arxiv, DOI [arXiv:2307.09288, 10.48550/arXiv.2307.09288]
   Ul Haq E, 2020, IEEE ACCESS, V8, P197379, DOI 10.1109/ACCESS.2020.3034983
   Varma Sagi Harshad, 2022, Innovations in Computational Intelligence and Computer Vision: Proceedings of ICICV 2021. Advances in Intelligent Systems and Computing (1424), P19, DOI 10.1007/978-981-19-0475-2_3
   Wang Michael D., 2023, Machine Learning and Data Mining Annual, V2023, DOI [10.5772/intechopen.110794, DOI 10.5772/INTECHOPEN.110794]
   Wang WS, 2023, PROCEEDINGS OF THE 31ST ACM JOINT MEETING EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, ESEC/FSE 2023, P146, DOI 10.1145/3611643.3616256
   Won D, 2017, PROCEEDINGS OF THE 2017 ACM MULTIMEDIA CONFERENCE (MM'17), P786, DOI 10.1145/3123266.3123282
   Xia ML, 2024, Arxiv, DOI arXiv:2308.04215
   Xiong GZ, 2024, Arxiv, DOI arXiv:2402.13178
   Yamagishi Y., 2024, P 7 WORKSH CHALL APP, P60
   Yang JF, 2024, ACM T KNOWL DISCOV D, V18, DOI 10.1145/3649506
   Yu H, 2023, PR MACH LEARN RES, V225, P650
   Zakka Cyril, 2024, NEJM AI, V1, DOI 10.1056/aioa2300068
   Zhou M, 2020, ENGINEERING-PRC, V6, P275, DOI 10.1016/j.eng.2019.12.014
NR 101
TC 0
Z9 0
U1 20
U2 20
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1933-1681
EI 1933-169X
J9 J INF TECHNOL POLITI
JI J. Inf. Technol. Politics
PD 2024 OCT 24
PY 2024
DI 10.1080/19331681.2024.2417263
EA OCT 2024
PG 16
WC Communication; Political Science
WE Social Science Citation Index (SSCI)
SC Communication; Government & Law
GA J8K1C
UT WOS:001339485200001
DA 2024-12-25
ER

PT J
AU Moore, S
   Lookadoo, K
AF Moore, Sarah
   Lookadoo, Kathryn
TI Communicating Clear Guidance: Advice for Generative AI Policy
   Development in Higher Education
SO BUSINESS AND PROFESSIONAL COMMUNICATION QUARTERLY
LA English
DT Article
DE classroom policy; business communication instruction; AI; pedagogy;
   plagiarism; syllabus policy; generative AI
AB This article presents the ongoing conversation about generative AI guidance and policy in higher education. The article examines syllabus policies, including analyzing sentiment, emotion, and common themes in GenAI policies. Findings show that policies should be audience-focused, clearly written, and grounded in strategies to promote ethical AI use in academia and the workforce. Practical tips for policy writing and sample policies are provided.
C1 [Moore, Sarah; Lookadoo, Kathryn] Univ Texas Dallas, 800 W Campbell, Richardson, TX 75083 USA.
C3 University of Texas System; University of Texas Dallas
RP Moore, S (corresponding author), Univ Texas Dallas, 800 W Campbell, Richardson, TX 75083 USA.
EM semoore@utdallas.edu
RI Moore, Sarah/KFQ-7530-2024
OI Moore, Sarah/0000-0001-5285-7741
CR American Psychological Association, 2023, APA PUBLISHING POLIC
   [Anonymous], Welcome to copilot in windows-Microsoft support.
   Brown J., 2023, STUDENT DESIGNED POL
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chronicle of Higher Education, 2023, PERSPECTIVES GENERAT
   Durand Cheryl, 2023, Am J Pharm Educ, V87, pajpe9025, DOI 10.5688/ajpe9025
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eaton L., SYLLABI POLICIES AI
   Geiger R. S., 2023, SAN DIEGO UNION TRIB
   George G., 2014, Journal of Academic and Business Ethics, V8, P1
   Getchell KM, 2022, BUS PROF COMMUN Q, V85, P7, DOI 10.1177/23294906221074311
   Gurman M., 2023, SAMSUNG BANS CHATGPT
   Hanson M., 2024, COLL ENROLLMENT STAT
   Jaschik S., 2007, Inside Higher Ed
   Kelly M. L., 2023, NPR
   Konieczny P, 2016, J ASSOC INF SCI TECH, V67, P1523, DOI 10.1002/asi.23616
   Larson C., 1989, TEAMWORK WHAT MUST G
   Luo JH, 2024, ASSESS EVAL HIGH EDU, V49, P651, DOI 10.1080/02602938.2024.2309963
   Maehre J., 2009, COLL TEACH, V57, P229
   McAdoo T., 2024, CITE CHATGPT
   MLA-CCCC Joint Task Force on Writing and AI, 2023, MLA CCCC JOINT TASK
   Moore S., 2023, BACK TO SCH WEBINAR
   Mouton A., 2023, FEDERATION BUSINESS, V14, P3
   Neuburger J., 2023, INSIGHTS CORPORATE S, V37, P3
   OpenAI, IS CHATGPT SAFE ALL
   Pappu A., 2023, GOOGLE WORKSPACE BLO
   Qualtrics, SENTIMENT ANAL LEVER
   Shanahan M., 2022, PREPRINT
   Society of Human Resource Management (SHRM), 2023, GENERATIVE ARTIFICIA
   Sunds J, 2023, INT J STUD EDUC, V5, DOI 10.46328/ijonse.174
   The Hamilton Project, 2023, AGE DISTRIBUTION UND
   Tiruye T, 2023, EUR J CANCER CARE, V2023, DOI 10.1155/2023/6660371
   Williams T., 2024, TIMES HIGHER ED THE
   Winkelmann ZK, 2021, J ATHL TRAINING, V56, P20, DOI 10.4085/1062-6050-0275.20
   Xiao P., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2305.18617
   Young J.R., 2006, The Chronicle of Higher Education
   Zanin AC, 2016, MANAGE COMMUN Q, V30, P147, DOI 10.1177/0893318915619755
   Zimmerman J., 2023, WASHINGTON POST
NR 39
TC 3
Z9 3
U1 14
U2 17
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 2329-4906
EI 2329-4922
J9 BUS PROF COMMUN Q
JI Bus. Prof. Commun. Q.
PD DEC
PY 2024
VL 87
IS 4
BP 610
EP 629
DI 10.1177/23294906241254786
EA MAY 2024
PG 20
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA L2O0Y
UT WOS:001235910000001
OA hybrid
DA 2024-12-25
ER

PT J
AU Reddy, MR
   Walter, NG
   Sevryugina, YV
AF Reddy, Manik R.
   Walter, Nils G.
   Sevryugina, Yulia V.
TI Implementation and Evaluation of a ChatGPT-Assisted Special Topics
   Writing Assignment in Biochemistry
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE General Public; Upper-Division Undergraduate; Graduate
   Education/Research; Communication/Writing; Computer-Based Learning;
   Biochemistry; NucleicAcids/DNA/RNA
AB The effective and responsible educational application of ChatGPT and other generative artificial intelligence (GenAI) tools constitutes an active area of exploration. This study describes and assesses the implementation of a structured, GenAI-assisted scientific essay writing assignment in nucleic acid biochemistry. Briefly, students created, evaluated, and iteratively refined ChatGPT essays in response to feedback and independent literature research, identifying several strengths and shortcomings of large language model writing and citation practices. The scaffolded assignment structure aimed to prepare students for GenAI-assisted writing, and the majority of the class cohort ultimately indicated an improved understanding of GenAI functionality and prompt engineering, as well as interest in additional GenAI usage and applications. Moreover, students valued the instructional guidance on engagement with GenAI tools and the prompt engineering opportunities afforded by this exercise. However, discontentment with AI-produced citations was common, and 26% of supporting references were found to be nonexistent. The content evaluation and prompt generation strategies uncovered here may facilitate successful ChatGPT-guided writing assignments in other scientific contexts.
C1 [Reddy, Manik R.; Walter, Nils G.] Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA.
   [Sevryugina, Yulia V.] Univ Michigan Lib, Ann Arbor, MI 48109 USA.
C3 University of Michigan System; University of Michigan; University of
   Michigan System; University of Michigan
RP Walter, NG (corresponding author), Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA.; Sevryugina, YV (corresponding author), Univ Michigan Lib, Ann Arbor, MI 48109 USA.
EM nwalter@umich.edu; yulias@umich.edu
RI Sevryugina, Yulia/GXF-5259-2022; Walter, Nils/G-7504-2016
OI Walter, Nils/0000-0002-7301-1275; Sevryugina, Yulia/0000-0003-1844-952X;
   Reddy, Manik/0009-0000-0741-0916
FU NIH [GM131922]
FX The authors would like to acknowledge the Fall 2023 cohort of Biol/Chem
   455/505 students at the University of Michigan for their survey
   responses and engagement with the GenAI writing exercise documented in
   this report. The authors also wish to thank the NIH for funding grant
   GM131922 to NGW.
CR Achiam J., 2023, arXiv
   [Anonymous], U M GPT DEPTHU M INF
   [Anonymous], CHATGPT CAN NOW SEE
   [Anonymous], YOUR DATAIS USED IMP
   [Anonymous], Introducing ChatGPT
   [Anonymous], NEW YORK TIMES
   [Anonymous], Chatgpt
   [Anonymous], ITS AI SERVICESFAQU
   [Anonymous], LLAMA 2
   [Anonymous], NEW AI CLASSIFIERFOR
   Beeler C., 2023, ARXIV
   Bhattacharyya M, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.39238
   Boiko DA, 2023, NATURE, V624, P570, DOI 10.1038/s41586-023-06792-0
   Charmaz K., 2003, QUALITATIVE PSYCHOL, P81
   Clark TM, 2023, J CHEM EDUC, V100, P3934, DOI 10.1021/acs.jchemed.3c00500
   Clark TM, 2023, J CHEM EDUC, V100, P1905, DOI 10.1021/acs.jchemed.3c00027
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Desaire H, 2023, CELL REP PHYS SCI, V4, DOI 10.1016/j.xcrp.2023.101672
   Desaire H, 2023, CELL REP PHYS SCI, V4, DOI 10.1016/j.xcrp.2023.101426
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   Exintaris B, 2023, J CHEM EDUC, V100, P2972, DOI 10.1021/acs.jchemed.3c00481
   Extance A, 2023, NATURE, V623, P474, DOI 10.1038/d41586-023-03507-3
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Futterer T, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-42227-6
   Guo Y, 2023, J CHEM EDUC, V100, P4876, DOI 10.1021/acs.jchemed.3c00505
   Hasrod T, 2024, J CHEM EDUC, V101, P653, DOI 10.1021/acs.jchemed.3c01170
   Hie BL, 2024, NAT BIOTECHNOL, V42, DOI 10.1038/s41587-023-01763-2
   Hill-Yardin EL, 2023, BRAIN BEHAV IMMUN, V110, P152, DOI 10.1016/j.bbi.2023.02.022
   Huang S, 2023, DIGIT DISCOV, V2, P1710, DOI 10.1039/d3dd00159h
   Humphry T, 2023, J CHEM EDUC, V100, P1434, DOI 10.1021/acs.jchemed.3c00006
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Leon AJ, 2023, J CHEM EDUC, V100, P3859, DOI 10.1021/acs.jchemed.3c00288
   Lewis P, 2020, ADV NEUR IN, V33
   Liang WX, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100779
   Roose K., 2022, NY TIMES        1205
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sevryugina Y., 2024, HARNESSING POWER AI, DOI [10.7302/22173, DOI 10.7302/22173]
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Sullivan M., 2023, J APPL LEARNING TEAC, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.17
   Talanquer V, 2023, J CHEM EDUC, V100, P2821, DOI 10.1021/acs.jchemed.3c00472
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Trager R., FIGHTING FIRE FIREAI
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   Van Noorden R, 2023, NATURE, V621, P672, DOI 10.1038/d41586-023-02980-0
   Walters WH, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-41032-5
   Watts FM, 2023, J CHEM EDUC, V100, P3806, DOI 10.1021/acs.jchemed.3c00664
   West JK, 2023, J CHEM EDUC, V100, P4351, DOI 10.1021/acs.jchemed.3c00581
   Xie ZK, 2024, CHEM SCI, V15, P500, DOI 10.1039/d3sc04610a
   Zheng ZL, 2023, J AM CHEM SOC, V145, P28284, DOI 10.1021/jacs.3c12086
   Zheng ZL, 2023, ACS CENTRAL SCI, V9, P2161, DOI 10.1021/acscentsci.3c01087
   Zheng ZL, 2023, ANGEW CHEM INT EDIT, V62, DOI 10.1002/anie.202311983
   Zheng ZL, 2023, J AM CHEM SOC, V145, P18048, DOI 10.1021/jacs.3c05819
NR 54
TC 4
Z9 4
U1 45
U2 45
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD JUN 17
PY 2024
VL 101
IS 7
BP 2740
EP 2748
DI 10.1021/acs.jchemed.4c00226
EA JUN 2024
PG 9
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA XY6C3
UT WOS:001250625500001
DA 2024-12-25
ER

PT J
AU Bahi, A
   Gharib, J
   Gahi, Y
AF Bahi, Anas
   Gharib, Jihane
   Gahi, Youssef
TI Integrating Generative AI for Advancing Agile Software Development and
   Mitigating Project Management Challenges
SO INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
LA English
DT Article
DE Artificial intelligence; software engineering; Agile software
   development
AB Agile software development emphasizes iterative progress, adaptability, and stakeholder collaboration. It champions flexible planning, continuous improvement, and rapid delivery, aiming to respond swiftly to change and deliver value efficiently. Integrating Generative Artificial Intelligence (AI) into Agile software development processes presents a promising avenue for overcoming project management challenges and enhancing the efficiency and effectiveness of software development endeavors. This paper explores the potential benefits of leveraging Generative AI in Agile methodologies, aiming to streamline development workflows, foster innovation, and mitigate common project management challenges. By harnessing the capabilities of Generative AI for tasks such as code generation, automated testing, and predictive analytics, Agile teams can augment their productivity, accelerate delivery cycles, and improve the quality of software products. Additionally, Generative AI offers opportunities for enhancing collaboration, facilitating decision-making, and addressing uncertainties inherent in Agile project management. Through an in-depth analysis of the integration of Generative AI within Agile frameworks, this paper provides insights into how organizations can harness the transformative potential of AI to advance Agile software development practices and navigate the complexities of modern software projects more effectively.
C1 [Bahi, Anas] Mohammed V Univ Rabat, Mohammadia Sch Engineers, Lab Appl Geophys Geotech Engn Geol & Environm, Rabat, Morocco.
   [Gharib, Jihane; Gahi, Youssef] Ibn Tofail Univ, Natl Sch Appl Sci, Lab Engn Sci, Kenitra, Morocco.
   [Gahi, Youssef] Univ Ottawa, Sch Elect Engn & Comp Sci, 800 King Edward Ave, Ottawa, ON, Canada.
C3 Mohammed V University in Rabat; Ibn Tofail University of Kenitra;
   University of Ottawa
RP Bahi, A (corresponding author), Mohammed V Univ Rabat, Mohammadia Sch Engineers, Lab Appl Geophys Geotech Engn Geol & Environm, Rabat, Morocco.
CR [Anonymous], 2017, Agile Practice Guide, P57
   Astridita A, 2024, INT J ADV COMPUT SC, V15, P232
   Moniruzzaman ABM, 2013, Arxiv, DOI [arXiv:1307.3356, 10.48550/arXiv.1307.3356, DOI 10.48550/ARXIV.1307.3356]
   Barke H, 2019, PEERJ COMPUT SCI, DOI 10.7717/peerj-cs.241
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Colonese E, 2016, ADV INTELL SYST, V422, P59, DOI 10.1007/978-3-319-27896-4_6
   Conboy K, 2019, IEEE SOFTWARE, V36, P44, DOI 10.1109/MS.2018.2884865
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dikert K, 2016, J SYST SOFTWARE, V119, P87, DOI 10.1016/j.jss.2016.06.013
   Ebert C, 2023, IEEE SOFTWARE, V40, P30, DOI 10.1109/MS.2023.3265877
   gptbot, Mastering ChatGPT: How to Craft Effective Prompts (Full Guide + Examples)
   Gregory P, 2016, INFORM SOFTWARE TECH, V77, P92, DOI 10.1016/j.infsof.2016.04.006
   hbr, How AI Will Transform Project Management
   Hoda R, 2016, J SYST SOFTWARE, V117, P245, DOI 10.1016/j.jss.2016.02.049
   Koutsikouri D, 2020, LECT NOTES BUS INF P, V396, P155, DOI 10.1007/978-3-030-58858-8_16
   Kreutzer R.T., 2018, Change Management: Shaping Change Successfully, P197, DOI DOI 10.1007/978-3-662-56548-3_3
   Kula E, 2022, IEEE T SOFTWARE ENG, V48, P3573, DOI 10.1109/TSE.2021.3101192
   learning.oreilly, Managing Software Requirements the Agile Way | Managing Software Requirements the AgileWay
   Moe NB, 2008, ASWEC 2008: 19TH AUSTRALIAN SOFTWARE ENGINEERING CONFERENCE, PROCEEDINGS, P76, DOI 10.1109/ASWEC.2008.28
   Nuottila J, 2016, IJISPM-INT J INF SYS, V4, P65, DOI 10.12821/ijispm040304
   OReilly Mr, In-Depth Report Engaging people and building processes to accelerate results The Drivers of Agility
   Paasivaara M, 2018, EMPIR SOFTW ENG, V23, P2550, DOI 10.1007/s10664-017-9555-8
   Patel J., Making Sense of Resistance to Agile Adoption Mak-ing Sense of Resistance to Agile Adoption in Waterfall Organizations: Social In-telligence and Leadership
   Radford Alec, 2018, OpenAI BlogJune 11
   Raharjo Teguh, 2020, ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management, P123, DOI 10.1145/3378936.3378949
   Reunamäki R, 2023, BUS HORIZONS, V66, P505, DOI 10.1016/j.bushor.2022.10.003
   Sainio Kari, 2023, Master's Thesis
   Sallam Sara Hassan Ahmed, 2024, Journal of Advanced Research in Applied Sciences and Engineering Technology, V33, P154, DOI [10.37934/araset.33.3.154168, DOI 10.37934/ARASET.33.3.154168]
   Sarker Iqbal., 2015, International Journal of Software Engineering and its Applications, V9, P55, DOI DOI 10.14257/IJSEIA.2015.9.11.05
   Shameem M, 2018, J SOFTW-EVOL PROC, V30, DOI 10.1002/smr.1979
   Sithambaram J, 2021, INT J PROJ MANAG, V39, P474, DOI 10.1016/j.ijproman.2021.03.002
   Vlaanderen K, 2011, INFORM SOFTWARE TECH, V53, P58, DOI 10.1016/j.infsof.2010.08.004
   web.s.ebscohost, AI Smart Kit: Agile Decision -Making on AI (Abridged Version)
   White J., 2023, A prompt pattern catalog to enhance prompt engineering with chatgpt
   Wolfram S., 2023, WHAT IS CHATGPT DOIN
NR 35
TC 0
Z9 0
U1 20
U2 20
PU SCIENCE & INFORMATION SAI ORGANIZATION LTD
PI WEST YORKSHIRE
PA 19 BOLLING RD, BRADFORD, WEST YORKSHIRE, 00000, ENGLAND
SN 2158-107X
EI 2156-5570
J9 INT J ADV COMPUT SC
JI Int. J. Adv. Comput. Sci. Appl.
PD MAR
PY 2024
VL 15
IS 3
BP 54
EP 61
PG 8
WC Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA G6B2T
UT WOS:001317462700006
DA 2024-12-25
ER

PT J
AU Wood, M
AF Wood, Maury
TI High Current Density Power Modules Mitigate the Environmental Impact of
   Power-Intensive GenAI
SO IEEE POWER ELECTRONICS MAGAZINE
LA English
DT Article
DE Training; Program processors; Power demand; Net zero; Multichip modules;
   Pressing; Optical fiber networks
AB Datacenters consume a stunning amount of energy to power and cool generative artificial intelligence (genAI), compute and infrastructure hardware. The training of genAI artificial neural network models typically consumes months of time, with thousands of multi-billion transistor processors, high-bandwidth semiconductor and magnetic memories, and optical network processors operating perpetually [1], [2]. The New York Times has reported that "In a middle-ground scenario, by 2027 AI servers could use between 85 to 134 terawatt hours (TWh) annually [3]." GenAI model training presents a daunting and pressing power consumption challenge which is misaligned with societal net zero and greenhouse gas reduction objectives.<br /> This article discusses genAI processor power delivery options, and how advanced high current density power modules and vertical power delivery methods can realize a significant improvement in processing performance, while reducing power losses, and saving terawatts of energy annually at the global scale.
C1 [Wood, Maury] Vicor Corp, Strateg Mkt, Andover, MA 01810 USA.
RP Wood, M (corresponding author), Vicor Corp, Strateg Mkt, Andover, MA 01810 USA.
EM mwood@vicr.com
CR [Anonymous], 2024, Real-Time Trillion Parameter Model NVIDIA GB200 NVL72
NR 1
TC 0
Z9 0
U1 2
U2 2
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2329-9207
EI 2329-9215
J9 IEEE POWER ELECTRON
JI IEEE Power Electron. Mag.
PD JUN
PY 2024
VL 11
IS 2
BP 20
EP 25
DI 10.1109/MPEL.2024.3398448
PG 6
WC Engineering, Electrical & Electronic
WE Emerging Sources Citation Index (ESCI)
SC Engineering
GA XD0F9
UT WOS:001259622600018
DA 2024-12-25
ER

PT J
AU Kim, TS
   Ignacio, MJ
   Yu, S
   Jin, HL
   Kim, YG
AF Kim, Tae-Seok
   Ignacio, Marvin John
   Yu, Seunghee
   Jin, Hulin
   Kim, Yong-Guk
TI UI/UX for Generative AI: Taxonomy, Trend, and Challenge
SO IEEE ACCESS
LA English
DT Article
DE Artificial intelligence; Chatbots; Taxonomy; User experience; Internet;
   Encoding; Bidirectional control; Generators; Decoding; Audio;
   explainable AI; generative AI; image; multimodal-GPT; text; UI/UX; UI/UX
AB Current technological advancements in Information Technology are closely linked to Generative Artificial Intelligence, enabling the automation of complex tasks such as generating documents, images, videos, audio, and actions. As such tasks can save much human labor and resources, diverse industries are trying to adopt this technology. However, developing a product utilizing Generative AI is a challenging task, partly because it is a new technology and many users are not familiar with it yet. This paper's primary goal is to find a better way to design Generative AI systems, especially from the Human-Computer Interaction perspective. To begin, we propose a taxonomy for Generative AI systems based on their modality, such as text-based, image-based, audio-based, and multi-modal-based systems, and then evaluate them in terms of their usability, because their functionalities should be aligned with the User Interface (UI), leading to a better User Experience (UX). We survey important trends in this area and introduce future applications by touching upon the issue of explainable AI. Although Generative AI has a bright future, it faces formidable challenges in our industries and society. It is hoped that the taxonomy and research findings presented here will be a useful framework for future research in Generative AI systems and their UI/UX.
C1 [Kim, Tae-Seok; Ignacio, Marvin John; Kim, Yong-Guk] Sejong Univ, Dept Comp Engn, Seoul 05006, South Korea.
   [Yu, Seunghee] Sejong Univ, Dept Business Adm, Seoul 05006, South Korea.
   [Jin, Hulin] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China.
C3 Sejong University; Sejong University; Anhui University
RP Kim, YG (corresponding author), Sejong Univ, Dept Comp Engn, Seoul 05006, South Korea.
EM ykim@sejong.ac.kr
FU Information Technology Research Center (ITRC) Support Program
   [IITP-2022-RS-2022-00156354]; Korean Government [Ministry of Science and
   Information Technology (MSIT)] from the Institute of Information and
   Communications Technology Planning and Evaluation (IITP)
   [RS-2019-II190231]; National Research Foundation of Korea (NRF) -
   Ministry of Education [2020R1A6A1A03038540]
FX This work was supported in part by the Information Technology Research
   Center (ITRC) Support Program under Grant IITP-2022-RS-2022-00156354, in
   part by Korean Government [Ministry of Science and Information
   Technology (MSIT)] from the Institute of Information and Communications
   Technology Planning and Evaluation (IITP) under Grant RS-2019-II190231,
   and in part by the Basic Science Research Program through the National
   Research Foundation of Korea (NRF) funded by the Ministry of Education
   under Grant 2020R1A6A1A03038540.
CR adobe, Adobe Firefly-Free Generative AI for Creatives
   Agostinelli A., 2015, arXiv
   Alvarez-Cortés V, 2007, ELECT ROBOT AUTO MEC, P312, DOI 10.1109/CERMA.2007.4367705
   Anderson P, 2018, PROC CVPR IEEE, P3674, DOI 10.1109/CVPR.2018.00387
   Anil GTGR, 2023, Arxiv, DOI [arXiv:2312.11805, 10.48550/arXiv.2312.11805]
   [Anonymous], 2024, Guardian
   [Anonymous], [26] (accessed: April 2024). (), [Online]. Available: https://www.energy.ca.gov/programs-andtopics/programs/building-energy-efficiency-standards.
   Avdeyev P., 2023, P INT C MACH LEARN, P1276
   Barros A, 2023, MANAGE LEARN, V54, P597, DOI 10.1177/13505076231201445
   boomy, Unleash Your Creativity Make Music With Boomy AI
   Brown T. B., 2020, ARXIV200514165
   Chamola Vinay, 2024, IEEE Internet of Things Magazine, V7, P126, DOI 10.1109/IOTM.001.2300174
   Choi S, 2021, AAAI CONF ARTIF INTE, V35, P1166
   Chowdhery A, 2023, J MACH LEARN RES, V24
   Chung YA, 2021, 2021 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU), P244, DOI 10.1109/ASRU51503.2021.9688253
   Clark K, 2020, Arxiv, DOI arXiv:2003.10555
   Clark L, 2019, INTERACT COMPUT, V31, P349, DOI 10.1093/iwc/iwz016
   cohere, Cohere | the Leading AI Platform for Enterprise.
   craiyon, Craiyon-Your Free AI Image Generator Tool:Create AI Art!
   creator.nightcafe.studio, Free AI Art Generator: Create, Share and Explore
   CULLIS CA, 1986, TRENDS GENET, V2, P307, DOI 10.1016/0168-9525(86)90285-4
   Dakhel AM, 2023, J SYST SOFTWARE, V203, DOI 10.1016/j.jss.2023.111734
   Descript, Edit Videos & Podcasts Like a Doc | AI Video Editor
   Designs AI, About  us
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dhariwal P, 2021, ADV NEUR IN, V34
   Dhariwal P, 2020, Arxiv, DOI arXiv:2005.00341
   Doshi AR, 2024, Arxiv, DOI arXiv:2312.00506
   Du HY, 2023, Arxiv, DOI [arXiv:2311.00947, DOI 10.48550/ARXIV.2311.00947]
   Du ZX, 2022, Arxiv, DOI [arXiv:2103.10360, 10.48550/arXiv.2103.10360]
   elevenlabs, ElevenLabs Generative Voice AI
   Engage AI, About us
   enhanced.ai, AI+ Automation = 10x Output for Your Bus. or Client
   Esser P, 2021, PROC CVPR IEEE, P12868, DOI 10.1109/CVPR46437.2021.01268
   Extracellular, About us
   Frey C. B., 2024, Brown J. World Affairs, V30, P161
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gao J, 2022, ADV NEUR IN
   Garrett J.J., 2002, New Riders
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Harreis H., 2023, Tech. Rep.
   Huang QQ, 2022, Arxiv, DOI arXiv:2208.12415
   Jakesch Maurice, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P310, DOI 10.1145/3531146.3533097
   kakaocorp, Kakaotalk
   Killoran N., 2017, arXiv, DOI [DOI 10.48550/ARXIV:1712.06148, DOI 10.48550/ARXIV.1712.06148]
   Laion, About us
   Lan ZZ, 2020, Arxiv, DOI arXiv:1909.11942
   Le VT, 2023, APPL INTELL, V53, P3240, DOI 10.1007/s10489-022-03613-1
   Lewis C., 1997, Handbook of human-computer interaction, P717, DOI [10.1016/B978-044481862-1.50096-0, DOI 10.1016/B978-044481862-1.50096-0]
   Lewis M, 2019, Arxiv, DOI [arXiv:1910.13461, 10.48550/arXiv.1910.13461]
   Li JY, 2023, AAAI CONF ARTIF INTE, P11578, DOI 10.1109/CVPR52729.2023.01114
   Li JN, 2022, PR MACH LEARN RES
   Li Z., 2022, arXiv, DOI 10.48550/arXiv.2203.11239
   Liang P, 2023, Arxiv, DOI arXiv:2211.09110
   Liu YH, 2019, Arxiv, DOI [arXiv:1907.11692, DOI 10.48550/ARXIV.1907.11692]
   Liu YX, 2024, Arxiv, DOI [arXiv:2402.17177, 10.48550/arXiv.2402.17177, DOI 10.48550/ARXIV.2402.17177]
   Liu ZC, 2001, J VISUAL COMP ANIMAT, V12, P227, DOI 10.1002/vis.260
   Lutova LA, 2001, RUSS J PLANT PHYSL+, V48, P662, DOI 10.1023/A:1016772422463
   Lv H, 2024, Arxiv, DOI arXiv:2401.05702
   Lv Z., 2023, Cogn. Robot., V3, P208, DOI [10.1016/j.cogr.2023.06.001, DOI 10.1016/J.COGR.2023.06.001]
   McKiernan EC, 2023, PLOS BIOL, V21, DOI 10.1371/journal.pbio.3002204
   Memecam, About us
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Microsoft Bing, About us
   Murf AI, About us
   Nguyen T. T., 2023, Knowl.-Based Syst., V277
   Nielsen J., 1990, SIGCHI Bulletin, P249
   Ning YL, 2024, Arxiv, DOI [arXiv:2311.02107, 10.48550/arXiv.2311.02107, DOI 10.48550/ARXIV.2311.02107]
   Nyberg EP, 2022, RISK ANAL, V42, P1155, DOI 10.1111/risa.13759
   O'Meara J, 2023, CONVERGENCE-US, V29, P1070, DOI 10.1177/13548565231185865
   Omar Bahiyah, 2020, International Journal of Interactive Mobile Technologies, V14, P121, DOI 10.3991/ijim.v14i04.12429
   OpenAI, 2024, ChatGPT: GPT-4 Language Model
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   openai, Hello GPT-4o
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Petko Georgiev, 2024, arXiv, DOI [10.48550/arXiv.2403.05530, DOI 10.48550/ARXIV.2403.05530]
   Podell D, 2023, Arxiv, DOI arXiv:2307.01952
   Popov V, 2021, PR MACH LEARN RES, V139
   Quicktools by Picsart, About us
   Quizify, About us
   Radford A., 2018, Technical Reports
   Radford A., 2019, OPENAI BLOG
   Radford A, 2021, PR MACH LEARN RES, V139
   Raffel C, 2020, J MACH LEARN RES, V21
   Ramesh A., 2022, arXiv
   Ramesh A, 2021, PR MACH LEARN RES, V139
   Rane N., 2023, Challenges Opportunities Ind., V4, P1
   replicate, Lambdal/Text-to-pokemon-Run With an API on Replicate
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   RoomGPT, About us
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Saharia Chitwan, 2022, ACM SIGGRAPH C P
   Sanh Victor, 2021, arXiv, DOI 10.48550/arXiv.2110.08207
   Shah N., 2020, Tech. Rep.
   Sherstinsky A, 2021, Arxiv, DOI [arXiv:1808.03314, DOI 10.1016/J.PHYSD.2019.132306]
   Shirodkar S. S., 2023, Int. J. Res. Appl. Sci. Eng. Technol., V11, P1010, DOI [DOI 10.22214/IJRASET.2023.55792, 10.22214/IJRASET.2023.55792]
   Shutsko A, 2020, IN SY AP IN WE HC, V12195, P108, DOI 10.1007/978-3-030-49576-3_8
   Solaiman I, 2024, Arxiv, DOI arXiv:2306.05949
   speechify, Lyrebird AI
   Stappen L, 2023, IEEE INT C INTELL TR, P5790, DOI 10.1109/ITSC57777.2023.10422003
   streamlabs, StreamLabs Podcast Editor
   Synthesia, About  us
   Tigard D. W., 2021, AI Ethics, V1, P113, DOI [10.1007/s43681-020-00009-0, DOI 10.1007/S43681-020-00009-0]
   Udo H, 2023, Arxiv, DOI arXiv:2305.02932
   van den Oord A, 2017, ADV NEUR IN, V30
   voicemod, Voicemod-Free Real-Time Voice Changer
   Wang L, 2024, Arxiv, DOI arXiv:2212.03533
   Wang TF, 2023, PROC CVPR IEEE, P4563, DOI 10.1109/CVPR52729.2023.00443
   Wang YF, 2012, LECT NOTE INFORMTECH, V13, P1
   Wei Jason, 2022, arXiv
   Wen Jinbo, 2024, IEEE Internet of Things Magazine, V7, P30, DOI 10.1109/IOTM.001.2300255
   Wu C.-Y., 2021, arXiv
   Wu Yue, 2022, ADV NEURAL INFORM PR, V35, P36188, DOI DOI 10.48550/ARXIV.2210.06465
   Xue L., 2020, arXiv
   Ye JJ, 2023, Arxiv, DOI [arXiv:2303.10420, 10.48550/ARXIV.2303.10420]
   Yu W, 2019, LECT NOTES COMPUT SC, V11554, P3, DOI 10.1007/978-3-030-22796-8_1
   Zeghidour N, 2022, IEEE-ACM T AUDIO SPE, V30, P495, DOI 10.1109/TASLP.2021.3129994
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
   Zhang ZY, 2019, Arxiv, DOI [arXiv:1905.07129, DOI 10.18653/V1/P19-1139]
   Zhu Liming., 2022, Humanity driven AI: Productivity, well-being, sustainability and partnership, P15, DOI [DOI 10.1007/978-3-030-72188-6_2, 10.1007/978-3-030-72188-6_2, 10.1007/978-3-030-72188-62]
NR 120
TC 0
Z9 0
U1 0
U2 0
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 179891
EP 179911
DI 10.1109/ACCESS.2024.3502628
PG 21
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA O8U0Y
UT WOS:001373800700042
DA 2024-12-25
ER

PT J
AU De Maio, C
AF De Maio, Carmela
TI Institutional responses to ChatGPT: Analysing the academic integrity
   policies of four public and private institutions of higher education in
   Australia
SO JOURNAL OF ACADEMIC LANGUAGE AND LEARNING
LA English
DT Article
DE academic integrity; policies; institutional responses; generative
   artificial intelligence (GenAI); ChatGPT
AB ChatGPT and other generative artificial intelligence (GenAI) tools have disrupted teaching and learning in higher education and pose a potential threat to academic integrity. Although most tertiary institutions have in place policies on how to respond to breaches of academic integrity by students, these policies may not always be clear on how to best approach the potential impacts of GenAI to ensure academic integrity can be maintained. Consequently, this paper presents an analysis of the academic integrity policies and procedures of four Australian public and private institutions of higher education where I teach. Applying the elements of access, approach, responsibility, detail, and support from the framework developed by Bretag and her colleagues (2011), and including the additional elements of currency and flexibility, findings from document analysis of these policies suggests that not all of them contain all these elements so may not be effective enough to respond to the unauthorised use of ChatGPT by students. I argue that not only do policies need to be more regularly updated, but that more clarity and guidance is required for all stakeholders. Timely communication of relevant policy would be one way to maintain a positive culture of academic integrity in institutions of higher learning.
C1 [De Maio, Carmela] Edith Cowan Univ, Joondalup, Australia.
C3 Edith Cowan University
RP De Maio, C (corresponding author), Edith Cowan Univ, Joondalup, Australia.
EM c.demaio@ecu.edu.au
RI De Maio, Carmela/ABD-9141-2021
CR Abd-Elaal ES, 2022, EUR J ENG EDUC, V47, P725, DOI 10.1080/03043797.2022.2046709
   Bergerot CD, 2023, CANCER INVEST, V41, P224, DOI 10.1080/07357907.2023.2167210
   Bretag T, 2014, Australian Government report
   Bretag T, 2011, INT J EDUC INTEGR, V7, P3
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crompton H, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00392-8
   De Maio C., 2015, Doctoral dis- sertation
   de Maio C, 2020, J HIGH EDUC POLICY M, V42, P102, DOI 10.1080/1360080X.2019.1662927
   Foltynek T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00133-4
   Miles M.B., 1994, Qualitative data analysis: an expanded sourcebook
   Miron J, 2021, J ACAD ETHICS, V19, P441, DOI 10.1007/s10805-021-09412-6
   Möller A, 2023, J FURTH HIGHER EDUC, V47, P338, DOI 10.1080/0309877X.2022.2130195
   Munoz A., 2023, Australian Academic Integ- rity Network.
   National Academic Integrity Network, 2023, Generative Artificial Intelligence: Guidelines for Educators
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   scu, Academic Quality, Standards and Integrity Policy
   Stoesz BM, 2019, INT J EDUC INTEGR, V15, DOI 10.1007/s40979-019-0042-4
   Tertiary Education Quality and Standards Agency [TEQSA], 2023, Sector update: Maintaining up to date academic integrity policies and procedures
   Tertiary Education Quality and Standards Agency [TEQSA], 2023, Artificial intelligenceMay 9
   Tertiary Education Quality and Standards Agency [TEQSA], 2023, Protecting academic integrity
NR 20
TC 3
Z9 3
U1 6
U2 7
PU ASSOC ACAD LANGUAGE LEARNING
PI BRISBANE
PA C/O DAVID ROWLAND, STUDENT SERVICES, BRISBANE, QLD 4072, AUSTRALIA
SN 1835-5196
J9 J ACAD LANG LEARN
JI J. Acad. Lang. Learn.
PY 2024
VL 18
IS 1
BP T1
EP T8
PG 8
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA NK1M0
UT WOS:001200255600001
DA 2024-12-25
ER

PT J
AU Wu, D
   Zhang, SL
   Ma, ZY
   Yue, XG
   Dong, RK
AF Wu, Di
   Zhang, Shuling
   Ma, Zhiyuan
   Yue, Xiao-Guang
   Dong, Rebecca Kechen
TI Unlocking Potential: Key Factors Shaping Undergraduate Self-Directed
   Learning in AI-Enhanced Educational Environments
SO SYSTEMS
LA English
DT Article
DE generative AI; self-directed learning; teacher support; learning
   strategies; technology acceptance; self-efficacy
ID GENERATIVE AI
AB This study investigates the factors influencing undergraduate students' self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students' acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students' self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students' self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.
C1 [Wu, Di; Zhang, Shuling] Hubei Univ, Educ Coll, Wuhan 430062, Peoples R China.
   [Ma, Zhiyuan] Zhongnan Univ Econ & Law, China Korea Inst New Media, Wuhan 430073, Peoples R China.
   [Yue, Xiao-Guang] European Univ Cyprus, Dept Comp Sci & Engn, CY-2404 Nicosia, Cyprus.
   [Dong, Rebecca Kechen] Univ Technol Sydney, UTS Business Sch, Management Discipline, Sydney, NSW 2007, Australia.
C3 Hubei University; Zhongnan University of Economics & Law; European
   University Cyprus; University of Technology Sydney
RP Ma, ZY (corresponding author), Zhongnan Univ Econ & Law, China Korea Inst New Media, Wuhan 430073, Peoples R China.
EM wudi@hubu.edu.cn; 202221124010817@stu.hubu.edu.cn; z0004877@zuel.edu.cn;
   x.yue@external.euc.ac.cy; rebecca.dong@uts.edu.au
RI Yue, XiaoGuang/AAC-7781-2019
OI Zhang, Shuling/0009-0002-7268-2584; Dong, Rebecca
   Kechen/0000-0002-2486-4511
FU Humanities and Social Sciences Youth Foundation of Ministry of Education
   of China; Fundamental Research Funds for the Central Universities;
   Zhongnan University of Economics and Law [2722024BQ063];  [19YJC880093]
FX This work was financially supported by the Humanities and Social
   Sciences Youth Foundation of Ministry of Education of China "Research on
   Online Learning Ability Assessment and Personalized Recommendation
   Mechanism Based on Cognitive Diagnosis" (grant number: 19YJC880093) and
   the Fundamental Research Funds for the Central Universities, Zhongnan
   University of Economics and Law (grant number: 2722024BQ063).
CR Abdel-Karim BM, 2023, MIS QUART, V47, P1395, DOI 10.25300/MISQ/2022/16773
   An FH, 2024, EDUC INF TECHNOL, V29, P2605, DOI 10.1007/s10639-023-11959-3
   An FH, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su141610390
   Bagozzi RP, 2012, J ACAD MARKET SCI, V40, P8, DOI 10.1007/s11747-011-0278-x
   Bai SR, 2024, IEEE T LEARN TECHNOL, V17, P1313, DOI 10.1109/TLT.2024.3378306
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Brod G, 2021, EDUC PSYCHOL REV, V33, P1295, DOI 10.1007/s10648-020-09571-9
   Cain W, 2024, TECHTRENDS, V68, P47, DOI 10.1007/s11528-023-00896-0
   Chen J, 2022, LANG TEACH RES, DOI 10.1177/13621688221134967
   Chen SY, 2024, LIBR HI TECH, V42, P392, DOI 10.1108/LHT-12-2021-0480
   Chen XJ, 2024, EDUC INF TECHNOL, V29, P17485, DOI 10.1007/s10639-024-12549-7
   Chiu MC, 2024, INTERACT LEARN ENVIR, V32, P824, DOI 10.1080/10494820.2022.2100426
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Chiu TKF, 2022, IEEE T EDUC, V65, P30, DOI 10.1109/TE.2021.3085878
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Dhananjaya GM, 2024, IEEE ACCESS, V12, P34019, DOI 10.1109/ACCESS.2024.3369901
   Diwan C., 2023, Computers and Education: Artificial Intelligence, V4, P100, DOI DOI 10.1016/J.CAEAI.2022.100110
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Esiyok E, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2303557
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Fiorella L, 2023, EDUC PSYCHOL REV, V35, DOI 10.1007/s10648-023-09769-7
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Geng S, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0147-0
   Goetschalckx L, 2021, TRENDS COGN SCI, V25, P788, DOI 10.1016/j.tics.2021.06.006
   Gunjal A., 2024, P AAAI C ART INT
   Han FF, 2021, INT J EDUC TECHNOL H, V18, DOI 10.1186/s41239-021-00303-9
   Hoferichter F, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.992497
   Huang AYQ, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104684
   Huang KL, 2024, THINK SKILLS CREAT, V52, DOI 10.1016/j.tsc.2024.101508
   Huang L, 2023, BEHAV SCI-BASEL, V13, DOI 10.3390/bs13090704
   Jaboob M, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2023.2300016
   Klingenberg S, 2023, J COMPUT ASSIST LEAR, V39, P218, DOI 10.1111/jcal.12741
   Li B, 2024, IEEE T LEARN TECHNOL, V17, P1515, DOI 10.1109/TLT.2024.3386098
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu ML, 2024, COMPUT EDUC, V211, DOI 10.1016/j.compedu.2023.104977
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Lo KWK, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.825902
   Lodge JM, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.7.02
   Loeng S, 2020, EDUC RES INT, V2020, DOI 10.1155/2020/3816132
   Lu JJ, 2024, IEEE T LEARN TECHNOL, V17, P1279, DOI 10.1109/TLT.2024.3369690
   Ma SY, 2024, ASIA PAC J EDUC, V44, P94, DOI 10.1080/02188791.2024.2305155
   Marougkas A, 2024, MULTIMED TOOLS APPL, V83, P18185, DOI 10.1007/s11042-023-15986-7
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Moser S, 2024, EUR J PSYCHOL EDUC, DOI 10.1007/s10212-024-00813-w
   Naidoo K, 2023, RES SCI TECHNOL EDUC, V41, P211, DOI 10.1080/02635143.2021.1895098
   Ng DTK, 2024, BRIT J EDUC TECHNOL, V55, P1328, DOI 10.1111/bjet.13454
   Ng DTK, 2023, ETR&D-EDUC TECH RES, V71, P137, DOI 10.1007/s11423-023-10203-6
   Nunnally JC., 1994, PSYCHOMETRIC THEORY
   O'Dea X, 2024, STUD HIGH EDUC, V49, P811, DOI 10.1080/03075079.2024.2332944
   Pan XQ, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.564294
   Pellas N, 2023, EDUC SCI, V13, DOI 10.3390/educsci13111155
   Saif N, 2024, COMPUT HUM BEHAV, V154, DOI 10.1016/j.chb.2023.108097
   Schiavo G, 2024, TECHNOL SOC, V77, DOI 10.1016/j.techsoc.2024.102537
   Shao C, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2331858
   Shen S, 2024, SAGE OPEN, V14, DOI 10.1177/21582440241245369
   Stanton JD, 2021, CBE-LIFE SCI EDUC, V20, DOI 10.1187/cbe.20-12-0289
   Su X, 2020, IEEE T NEUR NET LEAR, V31, P1884, DOI 10.1109/TNNLS.2019.2927369
   Tao Y, 2024, BMC PSYCHOL, V12, DOI 10.1186/s40359-024-01572-5
   Taylor TAH, 2023, MED EDUC ONLINE, V28, DOI 10.1080/10872981.2023.2189553
   UnitedNations, 2024, SDG 4 Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning Opportunities for All
   Wakhata R., 2023, Eurasia J. Math. Sci. Technol. Educ, V19, P2231, DOI [10.29333/ejmste/12963, DOI 10.29333/EJMSTE/12963]
   Wang YP, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.817968
   Wei L, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1261955
   Yilmaz Ramazan, 2022, Computers and Education: Artificial Intelligence, V3, DOI [10.1016/j.caeai, DOI 10.1016/J.CAEAI.2022.100092]
   Zhang ZH, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e25370
NR 66
TC 0
Z9 0
U1 62
U2 62
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-8954
J9 SYSTEMS-BASEL
JI Systems-Basel
PD SEP
PY 2024
VL 12
IS 9
AR 332
DI 10.3390/systems12090332
PG 19
WC Social Sciences, Interdisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA H5M4K
UT WOS:001323879500001
OA gold
DA 2024-12-25
ER

PT J
AU Ding, L
   Li, T
   Jiang, SY
   Gapud, A
AF Ding, Lu
   Li, Tong
   Jiang, Shiyan
   Gapud, Albert
TI Students' perceptions of using ChatGPT in a physics class as a virtual
   tutor
SO INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION
LA English
DT Article
DE GenAI; ChatGPT; Perception; Misconception; Physics problems
ID HELP
AB The latest development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has drawn the attention of educational researchers and practitioners. We have witnessed many innovative uses of ChatGPT in STEM classrooms. However, studies regarding students' perceptions of ChatGPT as a virtual tutoring tool in STEM education are rare. The current study investigated undergraduate students' perceptions of using ChatGPT in a physics class as an assistant tool for addressing physics questions. Specifically, the study examined the accuracy of ChatGPT in answering physics questions, the relationship between students' ChatGPT trust levels and answer accuracy, and the influence of trust on students' perceptions of ChatGPT. Our finding indicates that despite the inaccuracy of GenAI in question answering, most students trust its ability to provide correct answers. Trust in GenAI is also associated with students' perceptions of GenAI. In addition, this study sheds light on students' misconceptions toward GenAI and provides suggestions for future considerations in AI literacy teaching and research.
C1 [Ding, Lu] Univ S Alabama, Dept Counselling & Instruct Sci, UCOM 3858, Mobile, AL 36688 USA.
   [Li, Tong] Ball State Univ, Ctr Emerging Media Design & Dev, Muncie, IN USA.
   [Jiang, Shiyan] North Carolina State Univ, Teacher Educ & Learning Sci, Raleigh, NC USA.
   [Gapud, Albert] Univ S Alabama, Dept Phys, Mobile, AL USA.
C3 University of South Alabama; Ball State University; North Carolina State
   University; University of South Alabama
RP Ding, L (corresponding author), Univ S Alabama, Dept Counselling & Instruct Sci, UCOM 3858, Mobile, AL 36688 USA.
EM luding@southalabama.edu
RI Ding, Lu/JNE-3772-2023; Li, Tong/JXO-0453-2024
OI Li, Tong/0000-0002-1166-625X; Ding, Lu/0000-0002-1454-1532
CR Aaker JL, 2012, J CONSUM PSYCHOL, V22, P191, DOI 10.1016/j.jcps.2011.11.012
   Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Airenti G, 2015, INT J SOC ROBOT, V7, P117, DOI 10.1007/s12369-014-0263-x
   Alshater M., 2022, EXPLORING ROLE ARTIF, DOI [10.2139/ssrn.4312358, DOI 10.2139/SSRN.4312358]
   Belanche D, 2021, PSYCHOL MARKET, V38, P2357, DOI 10.1002/mar.21532
   Bewersdorff A, 2023, Computers and Education: Artificial Intelligence, V4, DOI [10.1016/j.caeai.2023.100143, DOI 10.1016/J.CAEAI.2023.100143]
   Bingham A. J., 2022, ANAL INTERPRETING QU, DOI DOI 10.3102/1682697
   Bisdas S, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.795284
   Bitzenbauer P, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13176
   Brown T. B., Advances in Neural Information Processing Systems, V33, P1877
   Buabbas AJ, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11091298
   Buolamwini J., 2018, Proceedings of the 1st Conference on Fairness, P1
   Chan CKY, 2023, Arxiv, DOI [arXiv:2305.00290, 10.48550/arXiv.2305.00290, DOI 10.48550/ARXIV.2305.00290]
   Chatterjee J, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2022.100676
   Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Ding L., 2023, Mis)conceptions and perceptions of artificial intelligence: A scoping review
   Epley N, 2007, PSYCHOL REV, V114, P864, DOI 10.1037/0033-295X.114.4.864
   Falode O.C., 2018, Malaysian Online Journal of Educational Technology, V6, P63, DOI [DOI 10.17220/MOJET.2018.03.005, 10.17220/mojet.2018.03.005]
   Ferrara E, 2023, Arxiv, DOI [arXiv:2304.03738, DOI 10.48550/ARXIV.2304.03738]
   Field A., 2009, Discovering statistics with SPSS, V3rd
   Finson Kevin., 2002, SCH SCI MATH, V102, P335, DOI [10.1111/j.1949-8594.2002.tb18217.x, DOI 10.1111/J.1949-8594.2002.TB18217.X]
   Gillissen A, 2022, HEALTHCARE-BASEL, V10, DOI 10.3390/healthcare10040723
   GILPEREZ D, 1990, SCI EDUC, V74, P531, DOI 10.1002/sce.3730740504
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Gong B, 2019, ACAD RADIOL, V26, P566, DOI 10.1016/j.acra.2018.10.007
   Gonzalez-Jimenez H, 2018, FUTURES, V98, P49, DOI 10.1016/j.futures.2018.01.004
   Graesser AC, 2016, INT J ARTIF INTELL E, V26, P124, DOI 10.1007/s40593-015-0086-4
   Hair J. F., 2010, MULTIVARIATE DATA AN, V7
   Hancer E, 2017, SWARM EVOL COMPUT, V32, P49, DOI 10.1016/j.swevo.2016.06.004
   Hu K., 2023, REUTERS         0202
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Karabenick SA, 2003, CONTEMP EDUC PSYCHOL, V28, P37, DOI 10.1016/S0361-476X(02)00012-7
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kim K, 2023, EDUC INF TECHNOL, V28, P9827, DOI 10.1007/s10639-023-11600-3
   LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310
   Liao Q. Vera, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P1257, DOI 10.1145/3531146.3533182
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Lockey S, 2021, Hawaii Int Con Sys S, P5463
   Matsuda N, 2013, J EDUC PSYCHOL, V105, P1152, DOI 10.1037/a0031955
   McNamara DS, 2013, BEHAV RES METHODS, V45, P499, DOI 10.3758/s13428-012-0258-1
   Mertala P., 2022, Computers and Education: Artificial Intelligence, V3, DOI [DOI 10.1016/J.CAEAI.2022/100095, 10.1016/j.caeai.2022.100095, DOI 10.1016/J.CAEAI.2022.100095]
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Michaeli T., 2023, IFIP WCCE 2022 WORLD, DOI [10.48550/arXiv.2305.06450, DOI 10.48550/ARXIV.2305.06450]
   Nazaretsky T., 2021, CONFIRMATION BIAS TR
   O'Connor S, 2023, NURSE EDUC PRACT, V66, DOI 10.1016/j.nepr.2022.103537
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Pizzi G, 2023, PSYCHOL MARKET, V40, P1372, DOI 10.1002/mar.21813
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Qin F, 2020, BRIT J EDUC TECHNOL, V51, P1693, DOI 10.1111/bjet.12994
   Rücker MT, 2016, J SCI EDUC TECHNOL, V25, P274, DOI 10.1007/s10956-015-9592-2
   Sahoo S, 2023, J ENTERP INF MANAG, V36, P221, DOI 10.1108/JEIM-01-2022-0025
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Schmidt-Fajlik R., 2023, ASIACALL ONLINE J, V14, P105, DOI [DOI 10.54855/ACOJ.231417, 10.54855/acoj.231417]
   Smith J. P., 1994, The Journal of the Learning Sciences, V3, P115, DOI [10.1207/S15327809JLS0302_1, DOI 10.1207/S15327809JLS0302_1, 10.1207/s15327809jls0302_1]
   Su YF, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100752
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Sundar SS, 2019, CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3290605.3300768
NR 60
TC 20
Z9 20
U1 118
U2 325
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 2365-9440
J9 INT J EDUC TECHNOL H
JI Int. J. Educ. Technol. High. Educ.
PD DEC 22
PY 2023
VL 20
IS 1
AR 63
DI 10.1186/s41239-023-00434-1
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA DA5V6
UT WOS:001129334900001
OA gold
DA 2024-12-25
ER

PT J
AU Ouanes, K
AF Ouanes, Khaled
TI Generative artificial intelligence in healthcare: current status and
   future directions
SO ITALIAN JOURNAL OF MEDICINE
LA English
DT Article
DE artificial intelligence; generative AI; healthcare; health informatics
AB Generative artificial intelligence (GAI) is rapidly transforming the healthcare landscape, offering innovative solutions in areas such as medical imaging, drug discovery, and clinical decision support. This comprehensive review examines the current role of GAI in healthcare, its potential benefits, drawbacks, challenges, and future research directions. By synthesizing recent literature and expert perspectives, this review provides a critical analysis of GAI's impact on healthcare delivery, patient outcomes, and ethical considerations. While GAI shows promise in enhancing diagnostic accuracy, accelerating drug development, and improving healthcare efficiency, it also faces significant challenges related to data privacy, regulatory compliance, and ethical implementation. This review aims to inform healthcare professionals, researchers, and policymakers about the current state and future potential of GAI in healthcare, emphasizing the need for responsible development and deployment of these technologies.
C1 [Ouanes, Khaled] Saudi Elect Univ, Coll Hlth Sci, Dept Hlth Informat, Dammam, Saudi Arabia.
C3 Imam Abdulrahman Bin Faisal University; Saudi Electronic University
RP Ouanes, K (corresponding author), Saudi Elect Univ, Coll Hlth Sci, Dept Hlth Informat, Dammam, Saudi Arabia.
EM k.ouanes@seu.edu.sa
CR Arora Anmol, 2022, Future Healthc J, V9, P190, DOI 10.7861/fhj.2022-0013
   Burlina P, 2021, TRANSL VIS SCI TECHN, V10, DOI 10.1167/tvst.10.2.13
   Chen H, 2019, Trends Pharmacol Sci, V40, P592
   Ding Y, 2019, IEEE ACCESS, V7, P149736, DOI 10.1109/ACCESS.2019.2947194
   Hirosawa Takanobu, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20043378
   Jadon A, 2023, Arxiv, DOI [arXiv:2305.05247, 10.48550/ARXIV.2305.05247]
   Kadurin A, 2017, MOL PHARMACEUT, V14, P3098, DOI 10.1021/acs.molpharmaceut.7b00346
   Kuzlu M, 2023, 12 MED C EMB COMP ME, P1
   Lin E, 2020, MOLECULES, V25, DOI 10.3390/molecules25143250
   Mansouri H, 2023, All Sciences Abstracts, V1, P8
   Musalamadugu T, 2023, Future Med AI, V1
   Oniani D, 2023, Arxiv, DOI [arXiv:2308.02448, 10.1038/s41746-023-00965-x]
   Schubert MC, 2023, medRxiv, DOI [10.1101/2023.11.01.23297938, 10.1101/2023.11.01.2 3297938v1, DOI 10.1101/2023.11.01.23297938V1]
   Shah C, 2023, PROCEEDINGS OF THE 32ND ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2023, P3, DOI 10.1145/3583780.3615317
   Sharma Brihat, 2023, Proc Conf Assoc Comput Linguist Meet, V2023, P78
   Shokrollahi Y, 2023, Arxiv, DOI arXiv:2310.00795
   Tong X, 2021, J Med Chem, V64, P6305
   Walters WP, 2020, NAT BIOTECHNOL, V38, P143, DOI 10.1038/s41587-020-0418-2
   Yu P, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11202776
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
   Zhavoronkov A, 2020, NAT BIOTECHNOL, V38, P146, DOI 10.1038/s41587-020-0417-3
   Zhavoronkov A, 2019, NAT BIOTECHNOL, V37, P1038, DOI 10.1038/s41587-019-0224-x
NR 22
TC 0
Z9 0
U1 8
U2 8
PU PAGEPRESS PUBL
PI PAVIA
PA MEDITGROUP, VIA G BELLI, 4, PAVIA, 27100, ITALY
SN 1877-9344
EI 1877-9352
J9 ITAL J MED
JI Ital. J. Med.
PY 2024
VL 18
IS 3
AR 1782
DI 10.4081/itjm.2024.1782
PG 4
WC Medicine, General & Internal
WE Emerging Sources Citation Index (ESCI)
SC General & Internal Medicine
GA I4Z7Z
UT WOS:001330361300002
OA gold
DA 2024-12-25
ER

PT J
AU Shan, RC
AF Shan, Richard
TI Certifying Generative AI: Retrieval-Augmented Generation Chatbots in
   High-Stakes Environments
SO COMPUTER
LA English
DT Article
DE Technological innovation; Ethics; Generative AI; Data integrity;
   Computational modeling; Mission critical systems; Chatbots
AB This article emphasizes the urgent need for certifying RAG GenAI chatbots in mission-critical systems, focused on data integrity and model reliability, with a comprehensive framework of technical, ethical, and operational assessments, which demonstrate its necessity through real-world implementations, advocating for continuous innovation and adaptable regulations.
EM m@richardshan.com
CR Ala-Pietila P., 2020, AI HLEG
   [Anonymous], 2024, The responsible AI certifica-tion
   [Anonymous], 2024, AI risk management framework
   [Anonymous], 2024, Al checklist: Self-assessment tool: Document
   Augenstein I, 2023, Arxiv, DOI arXiv:2310.05189
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bewersdorff A, 2024, Arxiv, DOI arXiv:2401.00832
   Cinà G, 2022, Arxiv, DOI [arXiv:2206.15363, DOI 10.48550/ARXIV.2206.15363]
   Ferrara E, 2024, Arxiv, DOI [arXiv:2310.00737, DOI 10.48550/ARXIV.2310.00737]
   Flemming Moos, 2024, CR, P442
   Gariel M, 2023, Arxiv, DOI arXiv:2302.11049
   GenAl Forum, About us
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hou XY, 2024, Arxiv, DOI [arXiv:2308.10620, 10.1145/3695988, DOI 10.1145/3695988]
   Kang MT, 2024, Arxiv, DOI arXiv:2402.03181
   Lewis P, 2020, ADV NEUR IN, V33
   Li SY, 2022, Arxiv, DOI [arXiv:2210.06726, DOI 10.48550/ARXIV.2210.06726]
   Liu BB, 2023, Arxiv, DOI arXiv:2312.09241
   Liu Y., 2023, Meta-Radiology, DOI [DOI 10.1016/J.METRAD.2023.100017, DOI 10.1016/J.METRAD.2023.1000172]
   The Global Partnership on Artificial Intelligence (GAPI), About us
   Trager RF, 2023, Arxiv, DOI arXiv:2308.15514
   Wei JS, 2022, Arxiv, DOI [arXiv:2206.07682, DOI 10.48550/ARXIV.2206.07682]
NR 22
TC 0
Z9 0
U1 6
U2 6
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0018-9162
EI 1558-0814
J9 COMPUTER
JI Computer
PD SEP
PY 2024
VL 57
IS 9
BP 35
EP 44
DI 10.1109/MC.2024.3401085
PG 10
WC Computer Science, Hardware & Architecture; Computer Science, Software
   Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA E8Z2P
UT WOS:001305820000003
DA 2024-12-25
ER

PT J
AU Rice, M
AF Rice, Mary
TI The micropolitical landscape of publicly discoverable policies for
   generative AI in large US school districts
SO TECHNOLOGY PEDAGOGY AND EDUCATION
LA English
DT Article; Early Access
DE Assemblage in educational policy; generative AI educational policies;
   K12 school technology policies; large school districts' policies; US
   school districts
AB While the use of generative AI (genAI) in K12 schools is increasing, it is poorly understood from a policy perspective. There are significant tensions in technology integration in education between public interests for social good and pressure from educational technology companies to view schools as markets. This study examined genAI policy efforts in the 50 largest US school districts by student enrolment in fall 2023 and spring 2024. A content analysis of documents from districts, states and media sources revealed micropolitical, rather than top-down, activities where districts were attempting policy making about genAI in varied ways and at varied paces. Educational technology vendors seemed to be opening space for substantial influence in policy decisions simultaneously. Understandings about these micropolitical activities of policy making have the potential to support shared decision-making and co-responsibility as important opportunities for disrupting current dynamics of influence on educational policy making in US schools.
C1 [Rice, Mary] Univ New Mexico, Dept Language Literacy & Sociocultural Studies, Albuquerque, NM 87106 USA.
C3 University of New Mexico
RP Rice, M (corresponding author), Univ New Mexico, Dept Language Literacy & Sociocultural Studies, Albuquerque, NM 87106 USA.
EM maryrice@unm.edu
OI Rice, Mary F./0000-0002-8138-512X
CR Arnesen K.T., 2019, J ONLINE LEARNING RE, V5, P251
   Boninger F., 2019, Personalized learning and the digital privatization of curriculum and teaching
   Bowman A., 2020, WestEd
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cramer M, 2010, IEEE PERVAS COMPUT, V9, P37, DOI 10.1109/MPRV.2010.42
   Dallas ISD, 2023, Student handbook
   DiMaggio PJ, 2000, ADV STRATEG MANAGE, V 17, P143, DOI 10.2307/2095101
   Elmore R., 1993, DESIGNING COHERENT E, P96
   Emmel J. A., 2022, Doctoral dissertation
   Fox N.J., 2017, Sociology and the New Materialism
   Hadi M., 2023, TechRxiv, DOI DOI 10.36227/TECHRXIV.23589741.V4
   Hill B., 2023, ChatGPT and school instruction: Navigating between evolution and caution
   Hine E, 2024, AI SOC, V39, P257, DOI 10.1007/s00146-022-01499-8
   Honig M.I., 2006, NEW DIRECTIONS ED PO
   Infante E., 2023, Honolulu Star-Advertiser8 August
   Innovate Ohio, 2023, AI toolkit: Guidance and resources for policymakers, teachers, and parents to advance AI readiness in Ohio schools
   Jeon J, 2023, EDUC INF TECHNOL, V28, P11963, DOI 10.1007/s10639-023-11656-1
   Jessop B., 2003, Governance as social and political communication
   Kaka SJ, 2024, J EDUC-US, V204, P663, DOI 10.1177/00220574241231695
   Ketcham J., 2023, Shelter from the storm: Better options for New York Citys asylum-seeker crisis
   Klein Alyson., 2024, Education Week
   Klosky JV, 2022, J SCHOOL HEALTH, V92, P656, DOI 10.1111/josh.13185
   Knox J, 2020, LEARN MEDIA TECHNOL, V45, P298, DOI 10.1080/17439884.2020.1754236
   LaRoque L., 1986, Canadian Journal of Education, V11, P486, DOI DOI 10.2307/1494585
   Li K., 2024, Bernard Shaw, automata, robots, and artificial intelligence, P49, DOI [10.1007/978-3-031-49226-64, DOI 10.1007/978-3-031-49226-64]
   Lieberman M., 2023, Education Week10 August
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Luo WW, 2024, EARLY EDUC DEV, V35, P96, DOI 10.1080/10409289.2023.2214181
   Maryland General Assembly, 2024, Education Artificial intelligence Study and regulations
   Mintz J, 2023, COMPUT SCH, V40, P325, DOI 10.1080/07380569.2023.2279870
   Monteleone C, 2023, MATH EDUC RES J, V35, P339, DOI 10.1007/s13394-023-00445-1
   Murgia E, 2023, 2023 ADJUNCT PROCEEDINGS OF THE 31ST ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2023, P22, DOI 10.1145/3563359.3597399
   Murgia E, 2023, 2023 ADJUNCT PROCEEDINGS OF THE 31ST ACM CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION, UMAP 2023, P311, DOI 10.1145/3563359.3596996
   National Center for Educational Statistics, 2019, Enrollment, poverty, and federal funds for the 120 largest school districts, by enrollment size in 2017: 201617 and fiscal year 2019
   North Carolina Department of Public Instruction, 2023, North Carolina generative AI implementation recommendations and considerations for PK-13 public schools
   Oregon Department of Education, 2023, Generative artificial intelligence (AI) in K-12 classrooms
   Parker G., 2023, Town and Country Planning, V92, P381
   Pennsylvania Training and Technical Assistance Network, 2018, Curriculum and instruction
   Perry SL, 2021, PUBLIC UNDERST SCI, V30, P930, DOI 10.1177/09636625211006271
   Pier J., 2023, Pedagog.ai
   Plata S., 2023, ASIAN J UNI EDU, V19, P743, DOI [DOI 10.24191/AJUE.V19I4.24697, 10.24191/ajue.v19i4.24697]
   Pleyers G., 2020, Journal of Civil Society, V16, P295, DOI [DOI 10.1080/17448689.2020.1794398, 10.1080/17448689.2020.1794398]
   Rice M., 2023, Handbook of research on digital ethics and digital citizenship, DOI [10.4018/978-1-6684-8934-5.ch005, DOI 10.4018/978-1-6684-8934-5.CH005]
   Rice M, 2024, PROF DEV EDUC, DOI 10.1080/19415257.2024.2407413
   Rice MF, 2023, EDUC INF TECHNOL, V28, P6927, DOI 10.1007/s10639-022-11482-x
   Rice MF., 2022, Management in Education, DOI [DOI 10.1177/08920206221107102, 10.1177/08920206221107102]
   Sandelowski M, 2010, RES NURS HEALTH, V33, P77, DOI 10.1002/nur.20362
   Schwartz S., 2023, Education Week
   Selwyn N, 2022, EUR J EDUC, V57, P620, DOI 10.1111/ejed.12532
   Shipan C.R., 2021, WHY BAD POLICIES SPR
   South Carolina Department of Education, 2023, South Carolina artificial intelligence standards framework
   Stinnette E., 2023, Loudoun Times-Mirror23 December
   The White House, 2023, FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence
   U.S. Department of Education, 2021, The federal role in education
   United Nations System Chief Executives Board for Coordination, 2022, Summary of deliberations addendum. Principles of the ethical use of artificial intelligence in the United Nations System
   White MD, 2006, LIBR TRENDS, V55, P22, DOI 10.1353/lib.2006.0053
   Whitfield S., 2023, KHOU11
   Williamson B, 2024, INT J ARTIF INTELL E, V34, P97, DOI 10.1007/s40593-023-00342-5
   Wu R, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13334
   Zhang Y., 2009, Applications of social research methods to questions in information and library science, P309
NR 60
TC 0
Z9 0
U1 2
U2 2
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1475-939X
EI 1747-5139
J9 TECHNOL PEDAGOG EDUC
JI Technol. Pedagag. Educ.
PD 2024 NOV 3
PY 2024
DI 10.1080/1475939X.2024.2421494
EA NOV 2024
PG 17
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA L1J8Q
UT WOS:001348364100001
DA 2024-12-25
ER

PT J
AU Wheatley, A
   Hervieux, S
AF Wheatley, Amanda
   Hervieux, Sandy
TI Comparing generative artificial intelligence tools to voice assistants
   using reference interactions
SO JOURNAL OF ACADEMIC LIBRARIANSHIP
LA English
DT Article
DE Artificial intelligence; Voice assistants; Generative AI; Reference
ID CHAT
AB This study investigates the ability of voice assistants and generative AI tools to respond to reference questions traditionally received by academic librarians. The authors created a sample of 25 questions based on queries received on the virtual reference service at their institution. They then created a rubric to evaluate the quality of the answers that the AI powered tools provided. The authors determined that the tools understand reference questions well and provide relevant answers but that the quality of the references provided, and the accuracy of the answers can be lacking. They suggest that more research needs to be done to understand the place of AI powered tools in reference services.
C1 [Wheatley, Amanda; Hervieux, Sandy] McGill Univ, McGill Univ Lib, Montreal, PQ, Canada.
C3 McGill University
RP Wheatley, A (corresponding author), 3459 McTavish St, Montreal, PQ H3A 0C9, Canada.
EM amanda.wheatley@mcgill.ca
CR Abdullah Malak, 2022, 2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS), P1, DOI 10.1109/SNAMS58071.2022.10062688
   Aithal S., 2023, International Journal of Management, Technology, and Social Sciences (IJMTS), V8, P95, DOI [https://doi.org/10.47992/IJMTS.2581.6012.0272, DOI 10.47992/IJMTS.2581.6012.0272]
   Allison D, 2012, LIBR HI TECH, V30, P95, DOI 10.1108/07378831211213238
   Ammari T, 2019, ACM T COMPUT-HUM INT, V26, DOI 10.1145/3311956
   Armann-Keown V, 2015, REF SERV REV, V43, P656, DOI 10.1108/RSR-04-2015-0024
   Austin John Langshaw, 1975, DO THINGS WORDS
   Bergen H., 2016, Word and Text: A Journal of Literary Studies and Linguistics, VVI, P95
   Cambre J., 2019, P ACM HUMAN COMPUTE, V3, DOI [10.1145/3359325(CSCW),223:1-223:19, DOI 10.1145/3359325(CSCW),223:1-223:19]
   Chen X., 2023, INTERNET REFERENCE S, V27, P121, DOI 10.1080/10875301.2023.2181262
   Dawson K, 2023, COMMUN PUBLIC, V8, P266, DOI 10.1177/20570473231187188
   Dempsey PR, 2017, COLL RES LIBR, V78, P898, DOI 10.5860/crl.78.7.898
   Forrestal V., 2023, ChoiceMay 15
   Frederick Donna Ellen, 2023, Library Hi Tech News, P4, DOI 10.1108/LHTN-04-2023-0063
   Hervieux S, 2018, REF SERV REV, V46, P529, DOI 10.1108/RSR-07-2018-0060
   Hoy Matthew B., 2018, Medical Reference Services Quarterly, V37, P81, DOI 10.1080/02763869.2018.1404391
   Humphry J, 2021, NEW MEDIA SOC, V23, P1971, DOI 10.1177/1461444820923679
   Insider Intelligence, 2023, Voice assistant users: US 2023-2027
   Insider Intelligence, 2023, ChatGPT users: US 2022-2025
   Kumar A, 2021, J EXP PSYCHOL GEN, V150, P595, DOI 10.1037/xge0000962
   Lai KT, 2023, COLL RES LIBR, V84, P974
   Lappalainen Y, 2023, J WEB LIBRARIANSH, V17, P37, DOI 10.1080/19322909.2023.2221477
   Li XC, 2019, TECHNOL CULT, V60, pS129, DOI 10.1353/tech.2019.0066
   Matteson ML, 2011, REF USER SERV Q, V51, P172, DOI 10.5860/rusq.51n2.172
   Nass C., 2005, Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
   OpenAI, ChatGPT can now see, hear, and speak
   Panda Subhajit, 2023, Library Hi Tech News, P22, DOI 10.1108/LHTN-02-2023-0032
   Phan T., 2017, Transformations, V29, P24
   Reinsfelder TL, 2023, J WEB LIBRARIANSH, V17, P95, DOI 10.1080/19322909.2023.2268832
   Rodriguez S, 2022, REF SERV REV, V50, P392, DOI 10.1108/RSR-05-2022-0020
   Smith K. F., 1986, Robot at the reference desk?, DOI [10.5860/crl4705486, DOI 10.5860/CRL4705486]
   Soofastaei A., 2021, Virtual assistants, DOI [10.5772/intechopen.91579, DOI 10.5772/INTECHOPEN.91579]
   Springshare, 2023, Introducing your library-powered answering engine
   WhatsApp, 2022, We're making voice messages even better
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
NR 34
TC 1
Z9 1
U1 17
U2 17
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0099-1333
EI 1879-1999
J9 J ACAD LIBR
JI J. Acad. Librariansh.
PD SEP
PY 2024
VL 50
IS 5
AR 102942
DI 10.1016/j.acalib.2024.102942
EA AUG 2024
PG 7
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA E3S5W
UT WOS:001302239700001
OA hybrid
DA 2024-12-25
ER

PT J
AU Christiansen, MB
   Rafsanjani, A
   Jorgensen, J
AF Christiansen, Mads Bering
   Rafsanjani, Ahmad
   Jorgensen, Jonas
TI Nature redux: interrogating biomorphism and soft robot aesthetics
   through generative AI
SO FRONTIERS IN ROBOTICS AND AI
LA English
DT Article
DE generative artificial intelligence; soft robotics; robot design; design
   aesthetics; biomorphism
ID DESIGN; BIOPHILIA
AB Artificial Intelligence (AI) has rapidly become a widespread design aid through the recent proliferation of generative AI tools. In this work we use generative AI to explore soft robotics designs, specifically Soft Biomorphism, an aesthetic design paradigm emphasizing the inherent biomorphic qualities of soft robots to leverage them as affordances for interactions with humans. The work comprises two experiments aimed at uncovering how generative AI can articulate and expand the design space of soft biomorphic robotics using text-to-image (TTI) and image-to-image (ITI) generation techniques. Through TTI generation, Experiment 1 uncovered alternative interpretations of soft biomorphism, emphasizing the novel incorporation of, e.g., fur, which adds a new dimension to the material aesthetics of soft robotics. In Experiment 2, TTI and ITI generation were combined and a category of hybrid techno-organic robot designs discovered, which combined rigid and pliable materials. The work thus demonstrates in practice the specific ways in which AI image generation can contribute towards expanding the design space of soft robotics.
C1 [Christiansen, Mads Bering; Rafsanjani, Ahmad; Jorgensen, Jonas] Univ Southern Denmark, Maersk Mc Kinney Moller Inst, SDU Soft Robot, SDU Biorobot, DK-5230 Odense, Denmark.
C3 University of Southern Denmark
RP Jorgensen, J (corresponding author), Univ Southern Denmark, Maersk Mc Kinney Moller Inst, SDU Soft Robot, SDU Biorobot, DK-5230 Odense, Denmark.
EM jonj@sdu.dk
RI Christiansen, Mads/AAY-9346-2021; Jørgensen, Jonas/AEK-5054-2022;
   Rafsanjani Abbasi, Ahmad/IYT-1443-2023
OI Rafsanjani Abbasi, Ahmad/0000-0003-4950-2303; Jorgensen,
   Jonas/0000-0001-9598-3414; Christiansen, Mads Bering/0000-0002-9313-9604
FU University of Southern Denmark; Digital Autonomous Production (SDU I4.0
   DAP) program
FX The author(s) declare that financial support was received for the
   research, authorship, and/or publication of this article. Funding for
   carrying out the research was provided by the University of Southern
   Denmark, Digital Autonomous Production (SDU I4.0 DAP) program.
CR Adnan Myasar Mundher, 2020, 2020 3rd International Conference on Engineering Technology and its Applications (IICETA), P203, DOI 10.1109/IICETA50496.2020.9318911
   Agkathidis Asterios., 2017, BIOMORPHIC STRUCTURE, V2nd
   [Anonymous], 2023, BBC News
   Arnold T, 2017, SOFT ROBOT, V4, P81, DOI 10.1089/soro.2017.0032
   Barison M, 2021, Aesthetica Prepr, V117
   Barr A. H., 1936, Mus. Mod. Art, V1936
   Bell P., 2001, Handbook of Visual Analysis, P10
   Belling AS, 2021, PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON TANGIBLE, EMBEDDED, AND EMBODIED INTERACTION, TEI 2021, DOI 10.1145/3430524.3442470
   Bewley H, 2018, DIS 2018: PROCEEDINGS OF THE 2018 DESIGNING INTERACTIVE SYSTEMS CONFERENCE, P1069, DOI 10.1145/3196709.3196817
   Bongard JC, 2013, COMMUN ACM, V56, P74, DOI [10.1145/2493883, 10.1145/2492007.2493883]
   Botar Oliver I., 2016, The Routledge Encyclopedia of Modernism, DOI DOI 10.4324/9781135000356-REM770-1
   Breazeal C. L., 2004, DESIGNING SOCIABLE R
   Brewster T, 2024, Forbes
   Britannica Dictionary, 2024, Soft. Definition and meaning
   Broo DG, 2023, IEEE SPECTRUM, V60, P44, DOI 10.1109/MSPEC.2023.10309274
   Burnham J, 1969, Real time systems
   Cambridge Dictionary, 2024, Soft-definition
   Cameron J., 1984, Orion Pictures
   Chaillou S., 2020, ARCHITECTURAL INTELL, P117, DOI [DOI 10.1007/978-981-15-6568-7_8, DOI 10.1007/978-981-15-6568-78, 10.1007/978-981-15-6568-7_8]
   Chan HC, 2024, LECT NOTES COMPUT SC, V14707, P16, DOI 10.1007/978-3-031-61044-8_2
   Choi SK, 2018, DIGIT CREAT, V29, P96, DOI 10.1080/14626268.2018.1423995
   Christiansen M. B., 2024, able
   Christiansen MB, 2023, IEEE ROMAN, P370, DOI 10.1109/RO-MAN57019.2023.10309420
   Christiansen MB, 2024, INT J SOC ROBOT, V16, P835, DOI 10.1007/s12369-023-01037-6
   Christiansen MB, 2020, ACMIEEE INT CONF HUM, P133, DOI 10.1145/3371382.3378328
   Christiansen MB., 2020, J ARTISTIC RES, DOI [10.22501/jar.549014, DOI 10.22501/JAR.549014]
   Croitoru FA, 2023, IEEE T PATTERN ANAL, V45, P10850, DOI 10.1109/TPAMI.2023.3261988
   Crowther Paul., 2012, MEANINGS ABSTRACT AR
   Das R, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-28873-w
   Della Sciucca L, 2022, LECT NOTES COMPUT SC, V13374, P191, DOI 10.1007/978-3-031-13324-4_17
   DIXON Steve., 2007, DIGITAL PERFORMANCE
   Doncieux S, 2015, FRONT ROBOT AI, DOI 10.3389/frobt.2015.00004
   Dotson K, 2020, Blue River harnesses AI to help farmers combat invasive weeds
   Dunstan B. J., 2023, Cultural robotics: social robots and their emergent cultural ecologies, P13, DOI [DOI 10.1007/978-3-031-28138, 10.1007/978-3-031-28138-92, DOI 10.1007/978-3-031-28138-92]
   Figoli F, 2022, DRS2022 Bilbao, DOI [10.21606/drs.2022.414, DOI 10.21606/DRS.2022.414]
   Flagg A., 2012, 2012 IEEE Haptics Symposium (HAPTICS), P99, DOI 10.1109/HAPTIC.2012.6183776
   Fong T, 2003, ROBOT AUTON SYST, V42, P143, DOI 10.1016/S0921-8890(02)00372-X
   Fowler G., 2023, Wash. Post
   Gaekwad JS, 2023, J ENVIRON PSYCHOL, V90, DOI 10.1016/j.jenvp.2023.102085
   Gan Y, 2021, INT J IND ERGONOM, V83, DOI 10.1016/j.ergon.2021.103128
   Gore H, 2018, J MOD CRAFT, V11, P17, DOI 10.1080/17496772.2018.1440808
   Goris K, 2011, INT J HUM ROBOT, V8, P481, DOI 10.1142/S0219843611002563
   Gregory A., 2023, Guard
   Grinde B, 2009, INT J ENV RES PUB HE, V6, P2332, DOI 10.3390/ijerph6092332
   Haddon AlfredC., 1895, EVOLUTION ART ILLUST
   HALLPIKE CR, 1969, MAN, V4, P256, DOI 10.2307/2799572
   Hartson R., 2012, The UX Book: Process and guidelines for ensuring a quality user experience
   Hoggenmueller M, 2023, COMPANION OF THE ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION, HRI 2023, P40, DOI 10.1145/3568294.3580035
   Holtzblatt K., 2015, Contextual design: evolved, DOI [10.1007/978-3-031-02207-4, DOI 10.1007/978-3-031-02207-4]
   Hugging Face, 2022, What is Image-to-Image?
   Husbands P., 2010, Encyclopedia of machine learning, P373, DOI [10.1007/978-0-387-30164-8285, DOI 10.1007/978-0-387-30164-8285]
   Imani M., 2017, Int. J. Archit. Environ. Eng, V11, P1156, DOI [10.5281/zenodo.1132046, DOI 10.5281/ZENODO.1132046]
   Jorgensen J, 2017, ISEA 2017 MANIZALES, P153
   Sandoval JMJ, 2021, EGA-REV EXPRES GRAF, V26, P142, DOI 10.4995/ega.2021.13919
   Joye Y, 2011, ENVIRON VALUE, V20, P189, DOI 10.3197/096327111X12997574391724
   Jrgensen J., 2019, PhD thesis
   Juler Edward., 2015, Grown but Not Made: British Modernist Sculpture and the New Biology
   Jung D, 2023, J ENVIRON PSYCHOL, V89, DOI 10.1016/j.jenvp.2023.102033
   Kac Eduardo., 2001, The Journal of research into New Media Technologies, V7, P76, DOI DOI 10.1177/135485650100700108
   Kakoudaki D., 2014, Anatomy of a robot: literature, cinema, and the cultural work of artificial people, DOI [10.36019/9780813562179, DOI 10.36019/9780813562179]
   Kellert SR., 1993, The Biophilia Hypothesis
   Kim J, 2023, INT J DES CREAT INNO, V11, P81, DOI 10.1080/21650349.2023.2167124
   Kriegmana S, 2020, P NATL ACAD SCI USA, V117, P1853, DOI 10.1073/pnas.1910837117
   Larsen B, 2022, 2022 IEEE 5TH INTERNATIONAL CONFERENCE ON SOFT ROBOTICS (ROBOSOFT), P201, DOI 10.1109/ROBOSOFT54090.2022.9762174
   Laschi C, 2016, SCI ROBOT, V1, DOI 10.1126/scirobotics.aah3690
   Laschi C, 2017, BIOSYST BIOROBOT, V17, P1, DOI 10.1007/978-3-319-46460-2_1
   Lee YK, 2022, THINK SKILLS CREAT, V46, DOI 10.1016/j.tsc.2022.101137
   Lee YH, 2023, LECT NOTES COMPUT SC, V14015, P502, DOI 10.1007/978-3-031-35132-7_38
   Lesnik-Oberstein K., 2011, The last taboo: women and body hair, DOI [10.7228/manchester/9780719075001.001.0001, DOI 10.7228/MANCHESTER/9780719075001.001.0001]
   Löffler D, 2020, ACMIEEE INT CONF HUM, P261, DOI 10.1145/3319502.3374788
   Ma Y., 2023, Stable diffusion prompt guide for beginners
   Majidi C, 2014, SOFT ROBOT, V1, P5, DOI 10.1089/soro.2013.0001
   Manovich L, 2024, Artificial aesthetics: generative AI
   Merriam-Webster, 2024, Soft
   Midjourney Inc, 2024, Midjourney
   Mikubill, 2023, ControlNet for Stable Diffusion WebUI
   Mitsui T, 2001, IROS 2001: PROCEEDINGS OF THE 2001 IEEE/RJS INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-4, P1189, DOI 10.1109/IROS.2001.976330
   Monge J. C., 2023, How to write the best stable diffusion prompts in 2023
   Müller S, 2015, LECT NOTES ARTIF INT, V9245, P49, DOI 10.1007/978-3-319-22876-1_5
   Nayeri F., 2023, N. Y. Times
   OpenAI, 2024, DALLE 3
   Orive G, 2020, ADV HEALTHC MATER, V9, DOI 10.1002/adhm.201901023
   Oxman N, 2014, 3D PRINT ADDIT MANUF, V1, P108, DOI 10.1089/3dp.2014.1505
   Park SJ, 2016, SCIENCE, V353, P158, DOI 10.1126/science.aaf4292
   Pinterest Inc, 2024, Pinterest
   Quan HF, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13169433
   Rafsanjani A, 2018, SCI ROBOT, V3, DOI 10.1126/scirobotics.aar7555
   Reddy A, 2022, DIGIT CREAT, V33, P295, DOI 10.1080/14626268.2022.2138452
   Ricotti L, 2017, SCI ROBOT, V2, DOI 10.1126/scirobotics.aaq0495
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Rus D, 2015, NATURE, V521, P467, DOI 10.1038/nature14543
   Sabinson EB, 2021, IEEE ROMAN, P1153, DOI 10.1109/RO-MAN50785.2021.9515499
   Sandry E, 2015, INT J SOC ROBOT, V7, P335, DOI 10.1007/s12369-014-0278-3
   Savage M., 2023, BBC News
   Schellin Heidi, 2020, 2020 Systems and Information Engineering Design Symposium (SIEDS), DOI 10.1109/SIEDS49339.2020.9106587
   Schiebel T, 2022, J ENVIRON PSYCHOL, V79, DOI 10.1016/j.jenvp.2021.101725
   Scott R. (Director)., 1979, 20th Century Fox
   Silverberg D., 2023, BBC News
   Singh A, 2013, LECT NOTES COMPUT SC, V8118, P403
   Sparrow R., 2002, Ethics and Information Technology, V4, P305, DOI 10.1023/A:1021386708994
   Stability AI, 2023, Generative models by stability AI
   Stable Diffusion Art, 2023, Stable Diffusion prompt: a definitive guide
   Stella F, 2023, NAT MACH INTELL, V5, P561, DOI 10.1038/s42256-023-00669-7
   Stiehl WD, 2006, CONSUM COMM NETWORK, P1290
   Stokes AA, 2014, SOFT ROBOT, V1, P70, DOI 10.1089/soro.2013.0002
   The Art Story, 2024, Art Story
   Tirado J, 2024, ADV SCI, V11, DOI 10.1002/advs.202400012
   Vartiainen H, 2023, DIGIT CREAT, V34, P1, DOI 10.1080/14626268.2023.2174557
   Walker J, 2020, ACTUATORS, V9, DOI 10.3390/act9010003
   Warwick K, 2012, Encyclopedia of applied ethics, VSecond Edition, P699, DOI [10.1016/B978-0-12-373932-2.00028-4, DOI 10.1016/B978-0-12-373932-2.00028-4]
   Westlund JK, 2016, ACMIEEE INT CONF HUM, P561, DOI 10.1109/HRI.2016.7451856
   Wilson E.O., 1984, P1
   Wnsche I, 2012, Meanings of abstract art
   Yan R., 2007, WORKSHOP MULTIMEDIA, P13, DOI [10.1145/1290067.1290071, DOI 10.1145/1290067.1290071]
   Yang LY, 2024, ACM COMPUT SURV, V56, DOI [10.1145/3626235, 10.1145/3648469]
   Zhang CS, 2024, Arxiv, DOI arXiv:2303.07909
NR 116
TC 0
Z9 0
U1 2
U2 2
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
SN 2296-9144
J9 FRONT ROBOT AI
JI Front. Robot. AI
PD OCT 25
PY 2024
VL 11
AR 1472051
DI 10.3389/frobt.2024.1472051
PG 17
WC Robotics
WE Emerging Sources Citation Index (ESCI)
SC Robotics
GA L3S1Q
UT WOS:001349945000001
PM 39524149
OA gold
DA 2024-12-25
ER

PT J
AU Ben Tayeb, M
   Tikhonchuk, V
   Feugeas, JL
AF Ben Tayeb, M.
   Tikhonchuk, V.
   Feugeas, J. -L
TI ICF target optimization using generative AI
SO PHYSICS OF PLASMAS
LA English
DT Article
AB This paper explores the capabilities of generative artificial intelligence (AI) for target optimization in inertial confinement fusion for energy production. For demonstration purposes, the focus is on optimizing the laser illumination temporal profile assuming a spherical implosion and a given target structure. An optimization protocol is based on the generative AI tool and a dataset for a shock ignition scheme produced with a reference hydrodynamic code. In a first optimization process, the generative AI proposed a family of laser power profiles by introducing a plateau before the shock that doubles the energy gain value of the reference configuration. In a second optimization process, the number of parameters defining the laser power profile is increased according to the results of the first step. The generative AI then suggested more general solutions including multiple plateaus and classical profiles without shock that further double the gain for half the laser energy required. The suggested optimization method can be extended to other configurations of laser-target interaction. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license
C1 [Ben Tayeb, M.; Tikhonchuk, V.; Feugeas, J. -L] Univ Bordeaux CNRS CEA, Ctr Lasers Intenses & Applicat, F-33405 Talence, France.
   [Tikhonchuk, V.] ELI Beamlines Facil, Extreme Light Infrastruct ERIC, Dolni Brezany 25241, Czech Republic.
C3 CEA; Centre National de la Recherche Scientifique (CNRS)
RP Ben Tayeb, M (corresponding author), Univ Bordeaux CNRS CEA, Ctr Lasers Intenses & Applicat, F-33405 Talence, France.
EM morad.ben-tayeb@u-bordeaux.fr
RI Tikhonchuk, Vladimir/S-1160-2018
OI Tikhonchuk, Vladimir/0000-0001-7532-5879
FU Commissariat a l'Energie Atomique et aux Energies Alternatives
FX We acknowledge the Commissariat a l'Energie Atomique et aux Energies
   Alternatives for funding this work. We thank Didier Raffestin for his
   attention and fruitful discussions in this groundbreaking field. We
   thank Aurelia Maiolo for her invaluable assistance in providing us with
   results from the CHIC simulation code. We thank the CELIA IT team for
   their support.
CR Abu-Shawareb H, 2022, PHYS REV LETT, V129, DOI 10.1103/PhysRevLett.129.075001
   Anderson KS, 2013, PHYS PLASMAS, V20, DOI 10.1063/1.4804635
   Atzeni S., 2004, The Physics of Inertial Fusion: Beam Plasma Interaction, Hydrodynamics, Hot Dense Matter
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Batani D, 2014, NUCL FUSION, V54, DOI 10.1088/0029-5515/54/5/054009
   Betti R, 2007, PHYS REV LETT, V98, DOI 10.1103/PhysRevLett.98.155001
   Breil J, 2011, COMPUT FLUIDS, V46, P161, DOI 10.1016/j.compfluid.2010.06.017
   Döpp A, 2023, HIGH POWER LASER SCI, V11, DOI 10.1017/hpl.2023.47
   Gaffney JA, 2019, PHYS PLASMAS, V26, DOI 10.1063/1.5108667
   Goodfellow I. J., 2014, arXiv, DOI 10.48550/arXiv.1406.2661
   Hatfield PW, 2019, PHYS PLASMAS, V26, DOI 10.1063/1.5091985
   HiPER Project Team, 2003, Final report, Grant Agreement Number 211737
   Karras T, 2018, Arxiv, DOI arXiv:1710.10196
   KIDDER RE, 1974, NUCL FUSION, V14, P797, DOI 10.1088/0029-5515/14/6/005
   KRAMER MA, 1991, AICHE J, V37, P233, DOI 10.1002/aic.690370209
   KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694
   Lafon M, 2010, PHYS PLASMAS, V17, DOI 10.1063/1.3407623
   Li Z, 2024, PLASMA PHYS CONTR F, V66, DOI 10.1088/1361-6587/ad0e21
   Nora R, 2011, PHYS PLASMAS, V18, DOI 10.1063/1.3619827
   Kingma DP, 2014, Arxiv, DOI arXiv:1312.6114
   Pearson K, 1901, PHILOS MAG, V2, P559, DOI 10.1080/14786440109462720
   Radford A, 2016, Arxiv, DOI [arXiv:1511.06434, 10.48550/arXiv.1511.06434, DOI 10.48550/ARXIV.1511.06434]
   Scott RHH, 2022, PHYS REV LETT, V129, DOI 10.1103/PhysRevLett.129.195001
   U.S. Department of Energy, 2024, Basic Research Needs for HED Density Laboratory Physics
   Vallet A, 2013, PHYS PLASMAS, V20, DOI 10.1063/1.4817292
   Wu FY, 2022, HIGH POWER LASER SCI, V10, DOI 10.1017/hpl.2022.4
NR 26
TC 0
Z9 0
U1 6
U2 6
PU AIP Publishing
PI MELVILLE
PA 1305 WALT WHITMAN RD, STE 300, MELVILLE, NY 11747-4501 USA
SN 1070-664X
EI 1089-7674
J9 PHYS PLASMAS
JI Phys. Plasmas
PD OCT
PY 2024
VL 31
IS 10
AR 103903
DI 10.1063/5.0228824
PG 8
WC Physics, Fluids & Plasmas
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Physics
GA J4A8F
UT WOS:001336513500002
OA hybrid
DA 2024-12-25
ER

PT J
AU Wojtkiewicz, K
AF Wojtkiewicz, Kathryn
TI How Do You Solve a Problem like DALL-E 2?
SO JOURNAL OF AESTHETICS AND ART CRITICISM
LA English
DT Article
AB The arrival of image-making generative artificial intelligence (AI) programs has been met with a broad rebuke: to many, it feels inherently wrong to regard images made using generative AI programs as artworks. I am skeptical of this sentiment, and in what follows I aim to demonstrate why. I suspect AI generated images can be considered artworks; more specifically, that generative AI programs are, in many cases, just another tool artists can use to realize their creative intent. I begin with an overview of how generative AI programs, like OpenAI's DALL-E 2, work. Then, leveraging work by Claire Anscomb, I argue that generative AI programs are a new technique of automatic image-making that affords creative agency to its users, thereby qualifying the images they create as artworks. Finally, I show many of the objections brought against AI artworks-including accusations of plagiarism and artistic devaluation-are due to the social backdrop in which we currently find them, rather than the technology itself. In the end, I aim to open the door to further aesthetic debate concerning AI generated images and art.
C1 [Wojtkiewicz, Kathryn] Georgetown Univ, Philosophy Dept, Washington, DC 20007 USA.
C3 Georgetown University
RP Wojtkiewicz, K (corresponding author), Georgetown Univ, Philosophy Dept, Washington, DC 20007 USA.
EM kate.wojtkiewicz@georgetown.edu
CR Arcas BAY, 2017, ARTS, V6, DOI 10.3390/arts6040018
   Allyn B., 2022, NPR
   Anscomb C, 2021, J AESTHET ART CRITIC, V79, P415, DOI 10.1093/jaac/kpab054
   Bacharach S., 2020, World authorship, P61, DOI [10.1093/oxfordhb/9780198819653.013.5, DOI 10.1093/OXFORDHB/9780198819653.013.5]
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   CARROLL N, 1995, J AESTHET ART CRITIC, V53, P251, DOI 10.2307/431350
   Chalmers David, 2022, NEURIPS C
   Davies Stephen., 2013, ROUTLEDGE COMPANION, P213
   Dickie George., 1983, Proceedings of the 8th International Wittgenstein Symposium, V10, P57
   Escalante-De Mattei S., 2022, ART NEWS
   Escalante-de Mattei Shanti, 2023, ART NEWS
   Flash Art, UNCANNY VALLEY
   Greenberg Alex, 2023, ART NEWS
   Irvin S, 2005, BRIT J AESTHET, V45, P123, DOI 10.1093/aesthj/ayi015
   Irvin S, 2006, PHILOS COMPASS, V1, P114, DOI 10.1111/j.1747-9991.2006.00016.x
   Kim JW, 2021, Clip: connecting text and images
   Kuta S., 2022, SMITHSONIAN MAG 0906
   Levinson Jerrold., 2011, Music, Art,Metaphysics, P3
   Mag Uidhir Christy., 2013, ART ART ATTEMPTS, P57
   Metz C, 2023, NEW YORK TIMES
   Montemayor C, 2021, MIND MACH, V31, P471, DOI 10.1007/s11023-021-09568-5
   Newport C., 2023, The New Yorker
   OpenAI, 2022, DALL E INTRO OUTPAIN
   Parsons Guy, 2022, DALL E 2 PROMPT BOOK
   Posture Julien, 2022, EYE DESIGN      0727
   Ramesh Aditya, 2021, DALL-E: Creating Images from Text
   Robertson Adi, 2022, VERGE           0902
   Roose K., 2023, The New York Times
   Roose K., 2022, The New York Times
   Shane J., 2019, You look like a thing and I love you
   Stecker Robert., 2010, AESTHETICS PHILOS AR, P77
   Steinert Steffen., 2016, PHILOS TECHNOLOGY, V30, P267
   Weinberg Justin, 2020, DAILYNOUS       0730
NR 33
TC 1
Z9 1
U1 11
U2 28
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0021-8529
EI 1540-6245
J9 J AESTHET ART CRITIC
JI J. Aesthet. Art Crit.
PD DEC 31
PY 2023
VL 81
IS 4
BP 454
EP 467
DI 10.1093/jaac/kpad046
EA JAN 2024
PG 14
WC Art; Humanities, Multidisciplinary; Philosophy
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Art; Arts & Humanities - Other Topics; Philosophy
GA IF0J7
UT WOS:001145392700001
OA Bronze
DA 2024-12-25
ER

PT J
AU Akhavan, A
   Jalali, MS
AF Akhavan, Ali
   Jalali, Mohammad
TI Generative AI and simulation modeling: how should you (not) use large
   language models like ChatGPT
SO SYSTEM DYNAMICS REVIEW
LA English
DT Article
AB Generative Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) and chatbots like ChatGPT, hold promise for advancing simulation modeling. Despite their growing prominence and associated debates, there remains a gap in comprehending the potential of generative AI in this field and a lack of guidelines for its effective deployment. This article endeavors to bridge these gaps. We discuss the applications of ChatGPT through an example of modeling COVID-19's impact on economic growth in the United States. However, our guidelines are generic and can be applied to a broader range of generative AI tools. Our work presents a systematic approach for integrating generative AI across the simulation research continuum, from problem articulation to insight derivation and documentation, independent of the specific simulation modeling method. We emphasize while these tools offer enhancements in refining ideas and expediting processes, they should complement rather than replace critical thinking inherent to research. Copyright (c) 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.
C1 [Akhavan, Ali; Jalali, Mohammad] Harvard Med Sch, MGH Inst Technol Assessment, Boston, MA USA.
   [Jalali, Mohammad] MIT, Sloan Sch Management, Cambridge, MA USA.
   [Jalali, Mohammad] MGH Inst Technol Assessment, 125 Nashua St, Boston, MA 02114 USA.
C3 Harvard University; Harvard Medical School; Massachusetts Institute of
   Technology (MIT)
RP Jalali, MS (corresponding author), MGH Inst Technol Assessment, 125 Nashua St, Boston, MA 02114 USA.
EM msjalali@mgh.harvard.edu
RI Akhavan, Ali/GXG-0891-2022; Jalali, Mohammad/Q-2188-2019
OI Jalali, Mohammad S./0000-0001-6769-2732; Akhavan,
   Ali/0000-0002-7077-3442
FU Centers for Disease Control and Prevention
FX The authors thank Satoshi Koiso, Hannah Lee, Hesam Mahmoudi, Elizabeth
   Mason, Amir Salarkia, and Melike Yildirim, who provided constructive
   feedback on earlier versions of this article.
CR [Anonymous], 2023, NATURE, V613, P612, DOI 10.1038/d41586-023-00191-1
   Ariyaratne S, 2023, BRIT J SURG, V110, P1213, DOI 10.1093/bjs/znad198
   Banks J, 2005, Discrete-event system simulation, V4th
   Biswas Som S, 2023, J Pediatr Pharmacol Ther, V28, P576, DOI 10.5863/1551-6776-28.6.576
   Castellanos-Gomez A., 2023, Nanomanufacturing, V3, P135, DOI [DOI 10.3390/NANOMANUFACTURING3020009, 10.3390/nanomanufacturing3020009]
   Davis JP, 2007, ACAD MANAGE REV, V32, P480, DOI 10.5465/AMR.2007.24351453
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V329, P637, DOI 10.1001/jama.2023.1344
   Ghaffarzadegan N, 2024, SYST DYNAM REV, V40, DOI 10.1002/sdr.1761
   Graf A, 2023, NEUROSCIENCE, V515, P71, DOI 10.1016/j.neuroscience.2023.02.008
   Guleria A, 2023, J INFECT DEV COUNTR, V17, P1292, DOI 10.3855/jidc.18738
   Harrison JR, 2007, ACAD MANAGE REV, V32, P1229, DOI 10.5465/AMR.2007.26586485
   Homer JB, 1996, SYST DYNAM REV, V12, P1, DOI 10.1002/(SICI)1099-1727(199621)12:1<1::AID-SDR93>3.0.CO;2-P
   Jalali MS, 2024, SYST DYNAM REV, V40, DOI 10.1002/sdr.1753
   Jalali MS, 2021, EPIDEMIOL REV, V43, P166, DOI 10.1093/epirev/mxab006
   Jalali MS., 2024, INTEGRATING AI LANGU, DOI [10.1002/sdr.1772, DOI 10.1002/SDR.1772]
   Law A.M., 1982, Simulation Modeling and Analysis
   Low G., 1980, ELEMENTS SYSTEM DYNA, P76
   Meyer JG, 2023, BIODATA MIN, V16, DOI 10.1186/s13040-023-00339-9
   Monks T, 2019, J SIMUL, V13, P55, DOI 10.1080/17477778.2018.1442155
   Rahmandad H, 2012, SYST DYNAM REV, V28, P396, DOI 10.1002/sdr.1481
   Samuelson PA, 1939, REV ECON STATISTICS, V21, P75, DOI 10.2307/1927758
   Shen YQ, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230163
   Sterman J. D, 2000, BUSINESS DYNAMICS SY
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Thapa S, 2023, ANN BIOMED ENG, DOI 10.1007/s10439-023-03284-0
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Van Noorden R, 2023, NATURE, V624, P509, DOI 10.1038/d41586-023-03930-6
   Velasquez-Henao Juan David, 2023, DYNA, V90, P9, DOI DOI 10.15446/DYNA.V90N230.111700
   Wang XF, 2023, LANCET REG HEALTH-W, V41, DOI 10.1016/j.lanwpc.2023.100905
   White J., 2023, PROMPT PATTERN CATAL, DOI DOI 10.48550/ARXIV.2302.11382
   White J., 2023, CHATGPT PROMPT PATTE, DOI DOI 10.48550/ARXIV.2303.07839
   Xiang LJ, 2021, FRONT PUBLIC HEALTH, V9, DOI 10.3389/fpubh.2021.741525
NR 33
TC 2
Z9 2
U1 25
U2 29
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0883-7066
EI 1099-1727
J9 SYST DYNAM REV
JI Syst. Dyn. Rev.
PD JUL
PY 2024
VL 40
IS 3
DI 10.1002/sdr.1773
EA MAY 2024
PG 27
WC Management; Social Sciences, Mathematical Methods
WE Social Science Citation Index (SSCI)
SC Business & Economics; Mathematical Methods In Social Sciences
GA A8Z1Q
UT WOS:001220741900001
OA hybrid
DA 2024-12-25
ER

PT J
AU Karlovs-Karlovskis, U
AF Karlovs-Karlovskis, Uldis
TI Generative Artificial Intelligence Use in Optimising Software
   Engineering Process: A Systematic Literature Review
SO APPLIED COMPUTER SYSTEMS
LA English
DT Article
DE Agile; DevOps; generative AI; software engineering; systematic
   literature review
AB Generative AI is only a few years old but already being applied in Software Engineering (SE). This literature review examines the most popular SE sub-fields of such cases and research methods that are typically used. 117 studies starting from 2020 have been assessed, and literature review has shown that the most active research is ongoing in the code generation area. It is not clearly defined by researchers, but the majority of the methods can be assumed as experiments. It is concluded that researchers often do not define the used research method with exclusions such as literature review or opinion survey. However, different validation methods are highly valued and applied thoroughly.
C1 [Karlovs-Karlovskis, Uldis] Riga Tech Univ, Riga, Latvia.
C3 Riga Technical University
RP Karlovs-Karlovskis, U (corresponding author), Riga Tech Univ, Riga, Latvia.
EM Uldis.Karlovs-Karlovskis@rtu.lv
OI Karlovs-Karlovskis, Uldis/0009-0008-7816-3770
CR [Anonymous], Nicole Forsgren biography
   [Anonymous], OpenAI official web site
   Bird C, 2023, COMMUN ACM, V66, P56, DOI 10.1145/3589996
   Brie Paul, 2023, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3593230
   Dinella E, 2023, IEEE T SOFTWARE ENG, V49, P1599, DOI 10.1109/TSE.2022.3183955
   Ebert C, 2023, IEEE SOFTWARE, V40, P30, DOI 10.1109/MS.2023.3265877
   Fan AEL, 2023, Arxiv, DOI arXiv:2310.03533
   Forsgren N, 2018, COMMUN ACM, V61, P44, DOI 10.1145/3159169
   Fu M, 2023, IEEE T SOFTWARE ENG, V49, P611, DOI 10.1109/TSE.2022.3158252
   Giray G, 2021, J SYST SOFTWARE, V180, DOI 10.1016/j.jss.2021.111031
   Jiang N, 2024, Arxiv, DOI arXiv:2311.13721
   Kitchenham B, 2013, INFORM SOFTWARE TECH, V55, P2049, DOI 10.1016/j.infsof.2013.07.010
   Mehra A., GPT history
   microsoft, Microsoft Research web site - Bird
   Mudgal P, 2023, Arxiv, DOI [arXiv:2309.07938, DOI 10.48550/ARXIV.2309.07938]
   nicolefv, Nicole Forsgren official web site
   Noda A, 2023, COMMUN ACM, V66, P44, DOI 10.1145/3610285
   Ozkaya I, 2023, IEEE SOFTWARE, V40, P4, DOI 10.1109/MS.2023.3306641
   Ozkaya I, 2023, IEEE SOFTWARE, V40, P4, DOI 10.1109/MS.2023.3278056
   Parikh NA, 2023, Arxiv, DOI arXiv:2306.04605
   Park Y, 2023, IEEE ACCESS, V11, P39037, DOI 10.1109/ACCESS.2023.3268638
   Shetty M, 2021, 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE (ICSE-SEIP 2021), P218, DOI 10.1109/ICSE-SEIP52600.2021.00031
   Tihanyi N, 2024, Arxiv, DOI arXiv:2305.14752
   Wang JJ, 2024, Arxiv, DOI arXiv:2307.07221
   wikipedia, Mik Kersten biography
   Zheng ZB, 2024, Arxiv, DOI [arXiv:2308.11396, 10.48550/arXiv.2308.11396, DOI 10.48550/ARXIV.2308.11396]
NR 26
TC 0
Z9 0
U1 2
U2 2
PU SCIENDO
PI WARSAW
PA BOGUMILA ZUGA 32A, WARSAW, MAZOVIA, POLAND
SN 2255-8683
EI 2255-8691
J9 APPL COMPUT SYST
JI Appl. Comput. Syst.
PD JUN 1
PY 2024
VL 29
IS 1
BP 68
EP 77
DI 10.2478/acss-2024-0009
PG 10
WC Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA C8D8A
UT WOS:001291627800011
OA gold
DA 2024-12-25
ER

PT J
AU Chen, Y
   Esmaeilzadeh, P
AF Chen, Yan
   Esmaeilzadeh, Pouyan
TI Generative AI in Medical Practice: In-Depth Exploration of Privacy and
   Security Challenges
SO JOURNAL OF MEDICAL INTERNET RESEARCH
LA English
DT Article
DE artificial intelligence; AI; generative artificial intelligence;
   generative AI; medical practices; potential benefits; security and
   privacy threats
ID HEALTH RECORDS; SYNTHETIC DATA; ATTACKS; MODELS
AB As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data -intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.
C1 [Chen, Yan; Esmaeilzadeh, Pouyan] Florida Int Univ, Coll Business, Dept Informat Syst & Business Analyt, Modesto A Maidique Campus,1200 SW 8th St,RB 261 B, Miami, FL 33199 USA.
C3 State University System of Florida; Florida International University
RP Esmaeilzadeh, P (corresponding author), Florida Int Univ, Coll Business, Dept Informat Syst & Business Analyt, Modesto A Maidique Campus,1200 SW 8th St,RB 261 B, Miami, FL 33199 USA.
EM pesmaeil@fiu.edu
OI Chen, Yan/0000-0001-9212-3566; Esmaeilzadeh, Pouyan/0000-0002-3885-8112
CR Abadir PM, 2023, NATURE AGING, V3, P629, DOI 10.1038/s43587-023-00430-0
   Ahmad K, 2022, COMPUT SCI REV, V43, DOI 10.1016/j.cosrev.2021.100452
   Albahri AS, 2023, INFORM FUSION, V96, P156, DOI 10.1016/j.inffus.2023.03.008
   Ali M, 2023, IEEE J BIOMED HEALTH, V27, P778, DOI 10.1109/JBHI.2022.3181823
   Alqahtani H, 2021, ARCH COMPUT METHOD E, V28, P525, DOI 10.1007/s11831-019-09388-y
   [Anonymous], The legal issues presented by generative AI
   [Anonymous], Synthetic data is enabling better healthcare tools - here's how
   [Anonymous], 2023, EU AI Act: first regulation on artificial intelligence
   [Anonymous], 2023, Artificial Intelligence Risk Management Framework (AIRMF1.0), DOI [10.6028/NIST.AI.100-1, DOI 10.6028/NIST.AI.100-1]
   [Anonymous], BLUEPR AI BILL RIGHT
   Arora A, 2023, LANCET, V401, P641, DOI 10.1016/S0140-6736(23)00216-7
   Athaluri SA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37432
   Bale A.S., 2023, INT J INTELL SYST, V12, P697
   Bohr A., 2020, Artificial intelligence in healthcare
   Brown Hannah, 2022, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, P2280, DOI 10.1145/3531146.3534642
   Byrne DW, 2022, Artificial Intelligence for Improved Patient Outcomes: Principles for Moving Forward with Rigorous Science
   Cai ZP, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3459992
   Chen CS, 2012, SOC BEHAV PERSONAL, V40, P639, DOI 10.2224/sbp.2012.40.4.639
   Chen DF, 2020, CCS '20: PROCEEDINGS OF THE 2020 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P343, DOI 10.1145/3372297.3417238
   Chen RJ, 2021, NAT BIOMED ENG, V5, P493, DOI 10.1038/s41551-021-00751-8
   Chen Y, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2020.103394
   Choi E, 2017, P 2017 MACH LEARN HL, P1
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Fernandes M, 2023, ADV COMPUT, V129, P39, DOI 10.1016/bs.adcom.2022.08.009
   Fredrikson M, 2015, CCS'15: PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1322, DOI 10.1145/2810103.2813677
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gesk TS, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2022.101704
   Ghosheh GO, 2024, ACM COMPUT SURV, V56, DOI 10.1145/3636424
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Han C, 2019, IEEE ACCESS, V7, P156966, DOI 10.1109/ACCESS.2019.2947606
   Harrer S, 2023, EBIOMEDICINE, V90, DOI 10.1016/j.ebiom.2023.104512
   Hernandez M, 2022, NEUROCOMPUTING, V493, P28, DOI 10.1016/j.neucom.2022.04.053
   Hu YP, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3487890
   Huang C, 2017, ENTROPY-SWITZ, V19, DOI 10.3390/e19120656
   Jain S, 2022, J INTELL MANUF, V33, P1007, DOI 10.1007/s10845-020-01710-x
   Javaid M., 2023, BenchCouncil Trans Benchmarks Standards Eval, V3, P100105, DOI [10.1016/j.tbench.2023.100105, DOI 10.1016/J.TBENCH.2023.100105]
   Jiang S, 2022, J MECH DESIGN, V144, DOI 10.1115/1.4051681
   Kasirzadeh A, 2022, PHILOS TECHNOL, DOI DOI 10.1007/S13347-023-00606-X
   Khan S, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/9985933
   Kim BN, 2021, IEEE T MED IMAGING, V40, P1737, DOI 10.1109/TMI.2021.3065727
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lankton NK, 2015, J ASSOC INF SYST, V16, P880, DOI 10.17705/1jais.00411
   Learned K, 2019, SCI DATA, V6, DOI 10.1038/s41597-019-0096-4
   Lee D, 2020, J AM MED INFORM ASSN, V27, P1411, DOI 10.1093/jamia/ocaa119
   Lee P, 2023, NEW ENGL J MED, V388, P1233, DOI 10.1056/NEJMsr2214184
   Li X., 2021, Discov Artif Intell, V1, P1, DOI DOI 10.1007/S44163-021-00006-0
   Liu Q, 2018, IEEE ACCESS, V6, P12103, DOI 10.1109/ACCESS.2018.2805680
   Lu TK, 2009, NAT BIOTECHNOL, V27, P1139, DOI 10.1038/nbt.1591
   Lugosi G., 2023, PREPRINT
   Mahmood F, 2018, IEEE T MED IMAGING, V37, P2572, DOI 10.1109/TMI.2018.2842767
   Martín-Noguerol T, 2023, EUR J RADIOL, V161, DOI 10.1016/j.ejrad.2023.110726
   Martinelli DD, 2022, COMPUT BIOL MED, V145, DOI 10.1016/j.compbiomed.2022.105403
   Mattioli J, 2023, P SAFE AI 2023 AAAIS
   Matwin S, 2015, Advanced Research in Data Privacy
   McCoy LG, 2022, J CLIN EPIDEMIOL, V142, P252, DOI 10.1016/j.jclinepi.2021.11.001
   Mcknight D.H., 2011, ACM Transactions on Management Information Systems, V2, P1, DOI [10.1145/1985347.1985353, DOI 10.1145/1985347.1985353]
   McMahan HB, 2017, PR MACH LEARN RES, V54, P1273
   Mopuri KR, 2018, LECT NOTES COMPUT SC, V11213, P20, DOI 10.1007/978-3-030-01240-3_2
   Mosqueira-Rey E, 2023, ARTIF INTELL REV, V56, P3005, DOI 10.1007/s10462-022-10246-w
   Nasr M, 2018, PROCEEDINGS OF THE 2018 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'18), P634, DOI 10.1145/3243734.3243855
   Noorbakhsh-Sabet N, 2019, AM J MED, V132, P795, DOI 10.1016/j.amjmed.2019.01.017
   Nova K., 2023, J. Adv. Anal. Healthc. Manag, V7, P115
   Park Y., 2016, P 10 USENIX WORKSH O
   Paul D, 2020, DRUG DISCOV TODAY, V26, P80, DOI 10.1016/j.drudis.2020.10.010
   Quiring E, 2020, PROCEEDINGS OF THE 29TH USENIX SECURITY SYMPOSIUM, P1363
   Randall JE., 2022, INTELL BASED MED, V6, P100068, DOI [DOI 10.1016/J.IBMED.2022.100068, 10.1016/j.ibmed.2022.100068]
   Rane N., 2023, Challenges Opportunities Ind., V4, P1
   Reichman Benjamin, 2021, Pattern Recognition. ICPR 2020 International Workshops and Challenges. Proceedings. Lecture Notes in Computer Science (LNCS 12661), P266, DOI 10.1007/978-3-030-68763-2_20
   Shafahi A, 2018, ADV NEUR IN, V31
   Shokri R, 2017, P IEEE S SECUR PRIV, P3, DOI 10.1109/SP.2017.41
   Shokri R, 2015, CCS'15: PROCEEDINGS OF THE 22ND ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P1310, DOI 10.1145/2810103.2813687
   Shoukry Y, 2013, LECT NOTES COMPUT SC, V8086, P55, DOI 10.1007/978-3-642-40349-1_4
   Song Y, 2018, Arxiv, DOI arXiv:1710.10766
   Summerfield C., 2022, NATURAL GEN INTELLIG
   Sun H, 2023, IEEE T KNOWL DATA EN, V35, P3367, DOI 10.1109/TKDE.2021.3130903
   Tang X, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12112437
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Topol Eric J, 2023, Science, V381, padk6139, DOI 10.1126/science.adk6139
   Tripathy A, 2019, ANN ALLERTON CONF, P495, DOI [10.1109/ALLERTON.2019.8919758, 10.1109/allerton.2019.8919758]
   Tseng BW, 2020, IEEE T INF FOREN SEC, V15, P2499, DOI 10.1109/TIFS.2020.2968188
   van Bussel MJP, 2022, BMC HEALTH SERV RES, V22, DOI 10.1186/s12913-022-08189-7
   Walker HL, 2023, J MED INTERNET RES, V25, DOI 10.2196/47479
   Wang ZY, 2020, PROC CVPR IEEE, P8916, DOI 10.1109/CVPR42600.2020.00894
   Wang ZB, 2019, IEEE INFOCOM SER, P2512, DOI [10.1109/infocom.2019.8737416, 10.1109/INFOCOM.2019.8737416]
   Xiao QX, 2019, PROCEEDINGS OF THE 28TH USENIX SECURITY SYMPOSIUM, P443
   Xie Qianqian, 2023, Res Sq, DOI 10.21203/rs.3.rs-3661764/v1
   Xu L, 2021, JMIR CANCER, V7, DOI 10.2196/27850
   Yale A, 2020, NEUROCOMPUTING, V416, P244, DOI 10.1016/j.neucom.2019.12.136
   Zeng XX, 2022, CELL REP MED, V3, DOI 10.1016/j.xcrm.2022.100794
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
NR 91
TC 27
Z9 27
U1 95
U2 133
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 1438-8871
J9 J MED INTERNET RES
JI J. Med. Internet Res.
PD MAR 8
PY 2024
VL 26
AR e53008
DI 10.2024/1/e53008
PG 19
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA LI4B4
UT WOS:001186136400004
PM 38457208
DA 2024-12-25
ER

PT J
AU Bartelt, C
   Röser, AM
AF Bartelt, Cedric
   Roeser, Alexander Maximilian
TI Transforming the Operational Components of Marketing Processes with
   GenAI: A Paradigm Shift
SO ADVANCES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
LA English
DT Article
DE Artificial intelligence; ChatGPT; Digital transformation; Generative
   artificial intel-; ligence; Large language models; AI-Marketing;
   Marketing process.
ID CHALLENGES
AB Generative Artificial Intelligence (GenAI) is transforming marketing through advanced content creation, personalized customer interactions, and strategic optimization. This study explores GenAI's impact on marketing processes, current applications, and related ethical and strategic challenges. A meta-analysis of the existing literature was conducted, focusing on GenAI integration in marketing. Relevant research was collected from various databases and rigorously evaluated, with case studies illustrating practical applications and benefits. The findings show that GenAI enhances marketing efficiency by automating content creation, improving customer service, and optimizing strategies using specialized AI tools. However, GenAI's use is currently fragmented, focusing more on operational than strategic tasks. This study underscores the necessity for a comprehensive and integrated approach to fully leverage the potential of GenAI in marketing processes, with the intention of effecting a paradigm shift, particularly in operational marketing tasks. Future research should focus on developing integrated GenAI solutions that comprehensively address all facets of the marketing process, including strategic decision- making, ethical and regulatory considerations, and long-term impact assessment, while exploring innovative applications and optimizing existing technologies to fully harness GenAI's potential in driving a holistic digital transformation of marketing.
C1 [Bartelt, Cedric] FOM Univ Appl Sci Econ & Management, Essen, Germany.
   [Bartelt, Cedric; Roeser, Alexander Maximilian] Univ Sopron, Istvan Szechenyi Econ & Management Doctoral Sch, Sopron, Hungary.
   [Roeser, Alexander Maximilian] FOM Univ Appl Sci Econ & Management, ISF Inst Strateg Finance, Essen, Germany.
C3 University of West Hungary
RP Röser, AM (corresponding author), Univ Sopron, Istvan Szechenyi Econ & Management Doctoral Sch, Sopron, Hungary.; Röser, AM (corresponding author), FOM Univ Appl Sci Econ & Management, ISF Inst Strateg Finance, Essen, Germany.
EM CEDRIC.BARTELT@FOM-NET.DE; ALEXANDER_MAXIMILIAN.ROESER@FOM-NET.DE
FX [39] Coyle J, Jeske S. The Rise of AI Copilots: How Llms Turn Data Into
   Actions, Advance the Business Intelligence Industry and Make Data
   Accessible Company-Wide. Appl Mark Anal. 2023;9:207-214. [7] Enshassi M,
   Nathan RJ, Soekmawati S, Al-Mulali U, Ismail H. Potentials of Artificial
   Intelligence in Digital Marketing and Financial Technology for Small and
   Medium Enterprises. IAES Int. 2024;13:2089.
CR Abrokwah-Larbi K, 2023, J Entrep Emerg Econ, V4604, P2053
   Bagale GS, 2023, ANN OPER RES, V326, P3, DOI 10.1007/s10479-021-04235-5
   Bartelt C, 2024, Mark Rev St. Gallen, V2024, P64
   Bishop C. M., 2006, Pattern Recognition and Machine Learning, DOI [DOI 10.1007/978-0-387-45528-0, 10.1007/978-0-387-45528-0]
   Bodemann M, 2022, Digitalisierung UND Nachhaltigkeit - Transformation von Geschaftsmodellen UND Unternehmenspraxis. Organisationskompetenz Zukunftsfahigkeit
   Bruhn Manfred, 2019, Marketing: Grundlagen Fur Studium UND Praxis. Lehrbuch
   Chen J, 2023, J Comput Inf Syst, P1
   Chen Y, 2024, AUSTRALAS MARK J, DOI 10.1177/14413582241252904
   Cioppi M., 2023, Italian Journal of Marketing, DOI DOI 10.1007/S43039-023-00067-2
   Coyle J, 2023, Appl Mark Anal., V9, P207
   Dahm MH, 2020, Die Digitale Transformation von Unternehmen - Unternehmenskultur Im Fokus: Ein Konzept Zur Strukturier ten UND Zielgerichteten Kulturtransformation Fur Deutsche Traditionsunternehmen, P127
   Doring N., 2023, FORSCHUNGSMETHODEN E
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2022, IND MARKET MANAG, V105, P109, DOI 10.1016/j.indmarman.2022.06.001
   Enshassi M, 2024, IAES Int, V13, P2089
   Flick U., 2020, Gutekriterien Qualitativer Forschung, P1
   Glauner P., 2024, Zeitschrift Fur Die Digitale Anwendung - Ltz, V2024, P24
   Golab-Andrzejak E., 2023, Procedia Comput Sci
   Gupta R, 2024, Int J Inf Manag Data Insights, V4
   Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
   Hauer G, 2021, INNOV MANAG REV, V18, P164, DOI 10.1108/INMR-05-2019-0066
   He AZ, 2023, J RES INTERACT MARK, V17, P620, DOI 10.1108/JRIM-03-2022-0082
   Hicham N., 2023, J. Intell Manag. Decis, V2, P139, DOI [10.56578/jimd020304, DOI 10.56578/JIMD020304]
   Homburg C, 2020, Marketingmanagement: Strategie - Instrumente - Umsetzung - Unternehmensfuhrung
   Hwang DH, 2021, MICROMACHINES-BASEL, V12, DOI 10.3390/mi12070838
   Kakkar T, 2021, Generative Adversarial Networks: A Survey
   Kshetri N, 2024, IT PROF, V26, P90, DOI 10.1109/MITP.2024.3375569
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Kumar V, 2024, INT J INFORM MANAGE, V77, DOI 10.1016/j.ijinfomgt.2024.102783
   Medjedovic I., 2014, Qualitative Sekundaranalyse: Zum Potenzial einer neuen Forschungsstrategie in der empirischen Sozialforschung
   Meffert H, 2019, Marketing: Grundlagen marktorientierter Unternehmensfuhrung. Konzepte - Instrumente - Praxisbeispiele, V13th
   Mikalef P, 2023, J BUS RES, V164, DOI 10.1016/j.jbusres.2023.113998
   MITCHELL T, 1989, ANNU REV COMPUT SCI, V4, P417
   Omelyanenko V, 2022, Econ Educ., V7, P14
   Runia P, 2019, Marketing: ProzessUND Praxisorientierte Grundlagen
   RUSSELL SJ, 2016, ARTIFICIAL INTELLIGE
   Sow M., 2018, Business and Economic Research, V8, P139
   TEICHERT R, 2019, ACTA UNIV SIL MEND B, V67, P1673, DOI [DOI 10.11118/actaun201967061673, 10.11118/actaun201967061673]
   Wittig F, 2002, GI WORKSH AD BEN INT, V10
NR 39
TC 0
Z9 0
U1 3
U2 3
PU Shimur Publications
PI New Moti Nagar
PA Tc-1/7, TC-1, New Moti Nagar, Delhi, INDIA
EI 2582-9793
J9 ADV ARTIF INTELL MAC
JI Adv. Artif. Intell. Mach. Learn.
PD JUL
PY 2024
VL 4
IS 3
BP 2535
EP 2544
PG 10
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA I1F4X
UT WOS:001327784400009
DA 2024-12-25
ER

PT J
AU Ma, H
   Li, NN
AF Ma, Hui
   Li, Nana
TI Exploring User Behavioral Intentions and Their Relationship With AI
   Design Tools: A Future Outlook on Intelligent Design
SO IEEE ACCESS
LA English
DT Article
DE Generative artificial intelligence; Generative artificial intelligence;
   design tools; design tools; social influence; social influence; user
   behavioral intentions; user behavioral intentions; structural equation
   modeling; structural equation modeling
ID INFORMATION-SYSTEMS; CONTINUANCE; TECHNOLOGY; SATISFACTION; ACCEPTANCE;
   ALGORITHMS; SITES; UTAUT; MODEL
AB In the context of the swift advancement and integration of generative artificial intelligence (AI) technologies into the design realm, comprehending user acceptance of these advanced tools has emerged as a pivotal issue. The primary purpose of this research is to examine and understand the factors influencing user acceptance and behavioral intentions towards generative artificial intelligence (AI) technologies in the design field. To achieve this, this research delineates a multifaceted theoretical model, underpinned by an analysis of user behavioral intentions and their driving factors. The model encapsulates eight principal constructs: intention to continue use, self-efficacy, perceived usefulness, satisfaction, facilitating conditions, expectation confirmation, trust in technology, and social influence. An empirical examination of 13 related hypotheses was conducted. Utilizing data from 339 valid questionnaires, the outcomes of the structural equation modeling lent support to all posited hypotheses. The research delineates that users' sustained intention to utilize generative AI technology is directly contingent upon factors such as perceived usefulness, satisfaction, self-efficacy, and trust in technology. Notably, perceived usefulness and self-efficacy are identified as pivotal determinants of satisfaction. Furthermore, social influence and expectation confirmation are found to augment perceived usefulness, while facilitating conditions to enhance both self-efficacy and expectation confirmation. These findings yield novel insights into the theory of user behavior, charting a course for the refinement of generative AI design tools. Such enhancements are aimed at fostering a more extensive application and acceptance of these tools in the relevant fields.
C1 [Ma, Hui] Anhui Wenda Univ Informat Engn, Sch Intelligent Mfg, Hefei 230000, Peoples R China.
   [Li, Nana] Hefei Univ Econ, Coll Art & Design, Hefei 230000, Peoples R China.
RP Ma, H (corresponding author), Anhui Wenda Univ Informat Engn, Sch Intelligent Mfg, Hefei 230000, Peoples R China.
EM mahuidesign@gmail.com
RI Li, Nana/ACN-6625-2022
FU Anhui Provincial Key Scientific Research Projects in Colleges and
   Universities Program [2022AH052617]
FX This work was supported by Anhui Provincial Key Scientific Research
   Projects in Colleges and Universities Program under Grant 2022AH052617.
CR Abbad MMM, 2021, EDUC INF TECHNOL, V26, P7205, DOI 10.1007/s10639-021-10573-5
   Adam M, 2021, ELECTRON MARK, V31, P427, DOI 10.1007/s12525-020-00414-7
   Afroogh S, 2024, Arxiv, DOI arXiv:2403.14680
   AJZEN I, 1991, ORGAN BEHAV HUM DEC, V50, P179, DOI 10.1016/0749-5978(91)90020-T
   Al-Adwan AS, 2023, EDUC INF TECHNOL, V28, P15381, DOI 10.1007/s10639-023-11816-3
   Al-Debei MM, 2022, J INNOV KNOWL, V7, DOI 10.1016/j.jik.2022.100242
   Alam S, 2022, INT J MANAG EDUC-OXF, V20, DOI 10.1016/j.ijme.2022.100706
   Alqudah O., 2023, Int. J. Data Netw. Sci., V7, P657
   Alshurideh M, 2023, INTERACT LEARN ENVIR, V31, P1214, DOI 10.1080/10494820.2020.1826982
   Alshurideh M, 2020, ADV INTELL SYST COMP, V1058, P406, DOI 10.1007/978-3-030-31129-2_37
   Balakrishnan J, 2022, TECHNOL FORECAST SOC, V180, DOI 10.1016/j.techfore.2022.121692
   BANDURA A, 1977, PSYCHOL REV, V84, P191, DOI 10.1037/0033-295X.84.2.191
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Bawack RE, 2018, INT J MED INFORM, V109, P15, DOI 10.1016/j.ijmedinf.2017.10.016
   Becker BA, 2023, PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, P500, DOI 10.1145/3545945.3569759
   Bhattacherjee A, 2001, MIS QUART, V25, P351, DOI 10.2307/3250921
   Bhattacherjee A, 2008, J COMPUT INFORM SYST, V49, P17, DOI 10.1080/08874417.2008.11645302
   Bi N. C., 2023, J. Social Media Soc., V12, P209
   Brynjolfsson E., 2023, Generative AI at Work.
   Cai A, 2023, PROCEEDINGS OF THE ACM COLLECTIVE INTELLIGENCE CONFERENCE, CI 2023, P1, DOI 10.1145/3582269.3615596
   Chan W M., 2021, Asian Journal of Business Research, V11, DOI [DOI 10.14707/AJBR.210098, https://doi.org/10.14707/ajbr.210098]
   Chaudhry BM, 2024, EXTENDED ABSTRACTS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2024, DOI 10.1145/3613905.3650878
   Cheng Y, 2020, J BROADCAST ELECTRON, V64, P592, DOI 10.1080/08838151.2020.1834296
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Choudhury A, 2023, J MED INTERNET RES, V25, DOI 10.2196/47184
   Choung H, 2023, INT J HUM-COMPUT INT, V39, P1727, DOI 10.1080/10447318.2022.2050543
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Dai HM, 2020, COMPUT EDUC, V150, DOI 10.1016/j.compedu.2020.103850
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   DeLone WH., 2016, FDN TRENDS INFORM SY, V2, P1, DOI DOI 10.1561/2900000005
   Dermeval D, 2018, INT J ARTIF INTELL E, V28, P336, DOI 10.1007/s40593-017-0157-9
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Figueroa-Armijos M, 2023, J BUS ETHICS, V186, P179, DOI 10.1007/s10551-022-05166-2
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Frey C. B., 2024, Brown J. World Affairs, V30, P161
   Fui-Hoon Nah F., 2023, Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration, V25, P277, DOI DOI 10.1080/15228053.2023.22338
   Gao B., 2023, P INT C AI GEN CONT, P205
   Gatys LA, 2016, PROC CVPR IEEE, P2414, DOI 10.1109/CVPR.2016.265
   Gaw F, 2022, MEDIA CULT SOC, V44, P706, DOI 10.1177/01634437211053767
   Gui J, 2023, IEEE T KNOWL DATA EN, V35, P3313, DOI 10.1109/TKDE.2021.3130191
   Gulati S, 2019, BEHAV INFORM TECHNOL, V38, P1004, DOI 10.1080/0144929X.2019.1656779
   Gupta A, 2021, BEHAV INFORM TECHNOL, V40, P1341, DOI 10.1080/0144929X.2020.1748715
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hair J.F., 2017, Int. J. Res. Method Educ, V38, P220
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hill D, 2017, INT J TECHNOL ASSESS, V33, P160, DOI 10.1017/S0266462317000460
   Huang KL, 2024, LEARN INSTR, V92, DOI 10.1016/j.learninstruc.2024.101929
   Jiang MT, 2022, EDUC GERONTOL, V48, P114, DOI 10.1080/03601277.2021.2021730
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Kim B, 2011, CYBERPSYCH BEH SOC N, V14, P199, DOI 10.1089/cyber.2010.0009
   Kwon E, 2023, DESIGN STUD, V88, DOI 10.1016/j.destud.2023.101202
   Lam M. W., 2024, P ADV NEUR INF PROC, V36, P1
   Lee D, 2015, INFORM MANAGE-AMSTER, V52, P295, DOI 10.1016/j.im.2014.12.001
   Lee SY, 2014, COMPUT HUM BEHAV, V32, P253, DOI 10.1016/j.chb.2013.12.009
   Lee S, 2024, Arxiv, DOI arXiv:2305.03509
   Li CL, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2023.101939
   Li J, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS, CHI 2024, DOI 10.1145/3613904.3642114
   Li X., 2024, J. Theory Pract. Eng. Sci., V4, P1
   Li X, 2008, J STRATEGIC INF SYST, V17, P39, DOI 10.1016/j.jsis.2008.01.001
   Liao WJ, 2024, AUTOMAT CONSTR, V157, DOI 10.1016/j.autcon.2023.105187
   Lin H, 2014, INFORM MANAGE-AMSTER, V51, P595, DOI 10.1016/j.im.2014.03.010
   Liu CH, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.830842
   Liu GX, 2024, INNOV LANG LEARN TEA, V18, P125, DOI 10.1080/17501229.2023.2240316
   Liu ILB, 2016, J ASSOC INF SCI TECH, V67, P56, DOI 10.1002/asi.23371
   Lu YF, 2019, J ELECTRON COMMER RE, V20, P105
   Marandu EE, 2023, J APPL RES HIGH EDUC, V15, P681, DOI 10.1108/JARHE-02-2022-0061
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Min F, 2021, 2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS AND COMPUTER ENGINEERING (ICCECE), P55, DOI 10.1109/ICCECE51280.2021.9342098
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Nguyen M, 2020, CURR ALLERGY ASTHM R, V20, DOI 10.1007/s11882-020-00969-7
   Nunnally J. C., 1978, PSYCHOMETRIC THEORY
   OLIVER RL, 1980, J MARKETING RES, V17, P460, DOI 10.2307/3150499
   Park E, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101318
   Pink S, 2020, DESIGN STUD, V69, DOI 10.1016/j.destud.2020.04.002
   Raykov T., 2011, INTRO PSYCHOMETRIC T
   Shao Z, 2019, ELECTRON COMMER R A, V33, DOI 10.1016/j.elerap.2018.100823
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Stokel-Walker Chris, 2022, Nature, DOI 10.1038/d41586-022-04397-7
   Tabri N, 2012, CAN GRAD J SOCIOL CR, V1, P59, DOI 10.15353/cgjsc-rcessc.v1i1.25
   Talukder MS, 2020, TECHNOL FORECAST SOC, V150, DOI 10.1016/j.techfore.2019.119793
   Tam C, 2020, INFORM SYST FRONT, V22, P243, DOI 10.1007/s10796-018-9864-5
   Tao D, 2020, COMPUT HUM BEHAV, V104, DOI 10.1016/j.chb.2019.09.023
   Tenenhaus M, 2005, COMPUT STAT DATA AN, V48, P159, DOI 10.1016/j.csda.2004.03.005
   THOMPSON RL, 1991, MIS QUART, V15, P125, DOI 10.2307/249443
   Tran Van-Dat, 2019, Global Business and Finance Review, V24, P29
   Urquhart L, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12073682
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang CX, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18349
   Wang KF, 2017, IEEE-CAA J AUTOMATIC, V4, P588, DOI 10.1109/JAS.2017.7510583
   Wang T, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13084586
   Wei Q., 2024, J. Educ., Humanities Social Sci., V32, P67
   Winter E, 2021, IRISH EDUC STUD, V40, P235, DOI 10.1080/03323315.2021.1916559
   Wong LW, 2024, INTERNET RES, V34, P343, DOI 10.1108/INTR-07-2021-0446
   Wu JF, 2024, J ENG DESIGN, DOI 10.1080/09544828.2024.2362587
   Yang XW, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e29584
   Zhang H, 2022, PSYCHOL MARKET, V39, P2171, DOI 10.1002/mar.21721
   Zhang KZK, 2016, ELECTRON COMMER R A, V15, P14, DOI 10.1016/j.elerap.2015.12.001
   Zhou E, 2024, PNAS NEXUS, V3, DOI 10.1093/pnasnexus/pgae052
   Zhou T, 2010, COMPUT HUM BEHAV, V26, P760, DOI 10.1016/j.chb.2010.01.013
NR 101
TC 0
Z9 0
U1 23
U2 23
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 149192
EP 149205
DI 10.1109/ACCESS.2024.3441088
PG 14
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA J7U4V
UT WOS:001339074800001
OA gold
DA 2024-12-25
ER

PT J
AU Chan, CKY
   Tsi, LHY
AF Chan, Cecilia Ka Yuk
   Tsi, Louisa H. Y.
TI Will generative AI replace teachers in higher education? A study of
   teacher and student perceptions
SO STUDIES IN EDUCATIONAL EVALUATION
LA English
DT Article
DE ChatGPT; Generative AI; AI Literacy; Social-emotional competencies;
   Holistic competencies
ID ARTIFICIAL-INTELLIGENCE; AL
AB This paper evaluates the potential of generative artificial intelligence (AI) in higher education, specifically its capacity to replace or assist human teachers. By reviewing relevant literature and analysing survey data from students and teachers, this mixed-methods study provides a comprehensive perspective on the future role of educators in the face of advancing generative AI technologies. An online survey was conducted to explore the perceptions of 399 university students and 184 teachers across different disciplines in eight higher education institutions in Hong Kong concerning the use of generative AI technologies. Findings suggest that although some believed generative AI may eventually replace teachers, the majority of participants argued that human teachers possess unique qualities, including critical thinking and emotions, which make them irreplaceable. Similarly, findings also emphasized the importance of social-emotional competencies developed through human interactions, something which generative AI technologies cannot currently replicate. Crucially, this study further found that students value and respect their human teachers, even as generative AI becomes more prevalent. As such, the authors propose that teachers can seek to effectively integrate generative AI to enhance teaching and learning without viewing it as their replacement. To do so, they must understand how generative AI can work well with teachers and students, avoid potential pitfalls, develop AI literacy, and address practical issues including ethics and privacy. Recommendations are offered on how universities, teachers, and students can adopt generative AI technologies in an approach that balances the strengths of human educators with generative AI technologies. As the future of education lies in the synergy between human teachers and generative AI, teachers, students, and universities should all understand and refine their unique qualities in order to effectively navigate the integration of generative AI, ensuring well-rounded and impactful learning experiences.
C1 [Chan, Cecilia Ka Yuk; Tsi, Louisa H. Y.] Univ Hong Kong, Hong Kong, Peoples R China.
C3 University of Hong Kong
RP Chan, CKY (corresponding author), Univ Hong Kong, Fac Educ, Teaching & Learning Innovat Ctr TALIC, Room CPD-1-81,Centennial Campus, Hong Kong, Peoples R China.
EM ckchan09@hku.hk; louisa94@connect.hku.hk
FX The author wishes to thank the teachers and students who partici-pated
   the survey.
CR Alhashmi SFS, 2020, ADV INTELL SYST, V1058, P393, DOI 10.1007/978-3-030-31129-2_36
   [Anonymous], 2021, Ai and education: guidance for policy-makers
   Baarslag Tim, 2017, Autonomous Agents and Multiagent Systems. AAMAS 2017 Workshops, Visionary Papers. Revised Selected Papers. LNAI 10643, P143, DOI 10.1007/978-3-319-71679-4_10
   Banihashem SK, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00455-4
   Bashir T., 2023, Pakistan Journal of Educational Research and Evaluation (PJERE), V11
   Blaschke LM, 2012, INT REV RES OPEN DIS, V13, P56, DOI 10.19173/irrodl.v13i1.1076
   Braga A, 2017, INFORMATION, V8, DOI 10.3390/info8040156
   Briggs J., 2023, The Potentially Large Effects of Artificial Intelligence on Economic Growth online
   Brooks R, 2008, CAN J SCH PSYCHOL, V23, P114, DOI 10.1177/0829573508316597
   Bryant J., 2020, How artificial intelligence will impact K-12 teachers
   Bühler MM, 2022, EDUC SCI, V12, DOI 10.3390/educsci12110782
   Celik I, 2022, TECHTRENDS, V66, P616, DOI 10.1007/s11528-022-00715-y
   Cerullo M., 2023, MoneyWatchApril 5
   Chan C. K. Y., 2024, Generative AI in higher education: The ChatGPT effect, DOI [10.4324/9781003459026, DOI 10.4324/9781003459026]
   Chan C.K.Y., 2022, ASSESSMENT EXPERIENT, DOI DOI 10.4324/9781003018391
   Chan C.K.Y., 2023, Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of 'AI-giarism'
   Chan CKY, 2024, Arxiv, DOI arXiv:2407.10777
   Chan CKY, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00284-4
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chatterjee S, 2020, EDUC INF TECHNOL, V25, P3443, DOI 10.1007/s10639-020-10159-7
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Chiu T.K.F., 2024, COMPUTERS ED ARTIFIC, V6, DOI [DOI 10.1016/J.CAEAI.2023.100197, 10.1016/j.caeai.2023.100197]
   Cloutier C, 2021, STRATEG ORGAN, V19, P113, DOI 10.1177/1476127020979329
   Cope B, 2021, EDUC PHILOS THEORY, V53, P1229, DOI 10.1080/00131857.2020.1728732
   Education Bureau, 2023, Education Bureau circular memorandum No. 109/2023
   Edwards C, 2018, COMMUN EDUC, V67, P473, DOI 10.1080/03634523.2018.1502459
   Etiubon R. U., 2023, Asian Journal of Educational Technology (AJET), V2, P12, DOI [10.53402/ajet.v2i1.185, DOI 10.53402/AJET.V2I1.185]
   Farnell A., 2023, Times Higher EducationJanuary 19
   Felix CV, 2021, INNOV HIGH EDUC TEAC, V33, P33, DOI 10.1108/S2055-364120200000033003
   Goksel N., 2019, Handbook of research on learning in the age of transhumanism, P224, DOI [10.4018/978-1-5225-8431-5.ch014, DOI 10.4018/978-1-5225-8431-5.CH014]
   Guilherme A, 2019, AI SOC, V34, P47, DOI 10.1007/s00146-017-0693-8
   Hase S., 2000, ANDRAGOGY HEUTAGOGY
   Hashmi N, 2024, BUS HORIZONS, V67, P607, DOI 10.1016/j.bushor.2024.05.005
   Holmes W, 2024, INT J ARTIF INTELL E, V34, P144, DOI 10.1007/s40593-023-00364-z
   Huang AYQ, 2023, COMPUT EDUC, V194, DOI 10.1016/j.compedu.2022.104684
   Ibrahim Ishaq, 2024, 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), P213, DOI 10.1109/ICETSIS61505.2024.10459491
   Jarrahi MH, 2018, BUS HORIZONS, V61, P577, DOI 10.1016/j.bushor.2018.03.007
   Ji CZ, 2022, NEURAL COMPUT APPL, V34, P16367, DOI 10.1007/s00521-022-07588-5
   Johnson RB., 2004, ED RES, V33, P14, DOI DOI 10.3102/0013189X033007014
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kennedy MM, 2016, REV EDUC RES, V86, P945, DOI 10.3102/0034654315626800
   Kim J, 2022, EDUC INF TECHNOL, V27, P6069, DOI 10.1007/s10639-021-10831-6
   Köbis L, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.624050
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kolchenko V., 2018, HAPS Educator, V22, P249, DOI DOI 10.21692/HAPS.2018.032
   Koltsakis E., 2023, Introduction to artificial intelligence, P1
   Kong FW, 2020, INT J EMERG TECHNOL, V15, P238, DOI 10.3991/ijet.v15i13.15351
   Lampropoulos G., 2023, Augmented reality and artificial intelligence: The fusion of advanced technologies, P137, DOI [10.1007/ 978-3-031-27166-3_8, DOI 10.1007/978]
   Lee D., 2024, Comput Educ Artif Intell, V6, DOI [10.1016/J.CAEAI.2024.100221, DOI 10.1016/J.CAEAI.2024.100221]
   Lee HS, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13010351
   Lunenberg M, 2007, TEACH TEACH EDUC, V23, P586, DOI 10.1016/j.tate.2006.11.001
   Malik G, 2019, ADV INTELL SYST, V707, P407, DOI 10.1007/978-981-10-8639-7_42
   Mann G, 2024, TEACH TEACH, V30, P102, DOI 10.1080/13540602.2023.2241020
   Marinucci L, 2023, AI SOC, V38, P747, DOI 10.1007/s00146-022-01474-3
   Martin AJ, 2009, REV EDUC RES, V79, P327, DOI 10.3102/0034654308325583
   McGuire A, 2024, INT J TECHNOL EDUC, V7, P326, DOI 10.46328/ijte.639
   Mohamad W. N., 2024, Journal of Intelligent Systems and Internet of Things, V11, P30, DOI [10.54216/JISIoT.110203, DOI 10.54216/JISIOT.110203]
   Montemayor C, 2022, AI SOC, V37, P1353, DOI 10.1007/s00146-021-01230-z
   Moorhouse BL, 2024, SYSTEM, V122, DOI 10.1016/j.system.2024.103290
   Morris MR, 2023, Arxiv, DOI [arXiv:2304.01420, DOI 10.48550/ARXIV.2304.01420]
   Noroozi O, 2024, INT J TECHNOL EDUC, V7, P373, DOI 10.46328/ijte.845
   Noroozi O, 2024, ASSESS EVAL HIGH EDU, DOI 10.1080/02602938.2024.2345669
   NWANA HS, 1990, ARTIF INTELL REV, V4, P251, DOI 10.1007/BF00168958
   Obenza B. N., 2023, International Journal of Human Computing Studies, V5, P5, DOI [10.5281/zenodo.10360697, DOI 10.5281/ZENODO.10360697]
   OpenAI, CHATGPT
   Parsons SA, 2018, REV EDUC RES, V88, P205, DOI 10.3102/0034654317743198
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Quigley C., 2019, An educator's guide to STEAM: Engaging students using real-world problems
   Ramirez IAL, 2024, INT J TECHNOL EDUC, V7, P417, DOI 10.46328/ijte.593
   Renz A., 2020, International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), V2, P14, DOI DOI 10.3991/IJAI.V2I1.12675
   Ryan M, 2020, SCI ENG ETHICS, V26, P2749, DOI 10.1007/s11948-020-00228-y
   Saarna C, 2024, INT J TECHNOL EDUC, V7, P611, DOI 10.46328/ijte.773
   Sallam Malik, 2023, Narra J, V3, pe103, DOI 10.52225/narra.v3i1.103
   Sapkota B, 2024, INT J TECHNOL EDUC, V7, P218, DOI 10.46328/ijte.677
   Schiff D, 2021, AI SOC, V36, P331, DOI 10.1007/s00146-020-01033-8
   Tala ML, 2024, AMFITEATRU ECON, V26, P71, DOI 10.24818/EA/2024/65/71
   Terzopoulos George, 2019, P 9 BALK C INF BCI 1, P1, DOI DOI 10.1145/3351556.3351588
   ThinkML Team, 2022, Potential use of Robotics in education system
   Timms MJ, 2016, INT J ARTIF INTELL E, V26, P701, DOI 10.1007/s40593-016-0095-y
   University Grants Committee, General statistics on UGC-funded institutions/ programmes
   Vu HT, 2022, BEHAV INFORM TECHNOL, V41, P1515, DOI 10.1080/0144929X.2021.1884288
   Watermeyer R., 2023, Postdigital Science and Education, DOI [DOI 10.1007/S42438-023-00440-6, 10.1007/s42438-023-00440-6]
   Wilson J, 2018, HARVARD BUS REV, V96, P115
   Yang JY, 2019, APPL SCI-BASEL, V9, DOI 10.3390/app9102078
   Yun WS, 2024, INT J TECHNOL EDUC, V7, P650, DOI 10.46328/ijte.823
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 89
TC 2
Z9 2
U1 136
U2 136
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0191-491X
J9 STUD EDUC EVAL
JI Stud. Educ. Eval.
PD DEC
PY 2024
VL 83
AR 101395
DI 10.1016/j.stueduc.2024.101395
EA AUG 2024
PG 13
WC Education & Educational Research; Psychology, Educational
WE Social Science Citation Index (SSCI)
SC Education & Educational Research; Psychology
GA G0M4S
UT WOS:001313658900001
DA 2024-12-25
ER

PT J
AU Cimino, A
   Felicetti, AM
   Corvello, V
   Ndou, V
   Longo, F
AF Cimino, Antonio
   Felicetti, Alberto Michele
   Corvello, Vincenzo
   Ndou, Valentina
   Longo, Francesco
TI Generative artificial intelligence (AI) tools in innovation management:
   a study on the appropriation of ChatGPT by innovation managers
SO MANAGEMENT DECISION
LA English
DT Article; Early Access
DE Innovation; Innovation managers; Generative artificial intelligence;
   ChatGPT; Appropriation theory
ID STRUCTURATION THEORY; RIGDONS RETHINKING; CREATIVITY; PLS; TECHNOLOGY;
   ACCEPTANCE; COMPLEXITY; VARIABLES; CONTEXT; MODELS
AB PurposeUsing AI to strengthen creativity and problem-solving capabilities of professionals involved in innovation management holds huge potential for improving organizational decision-making. However, there is a lack of research on the use of AI technologies by innovation managers. The study uses the theory of appropriation to explore how specific factors - agile leadership (AL), innovation orientation (IO) and individual creativity (IC) - impact innovation managers' use of generative AI tools, such as ChatGPT (CGA).Design/methodology/approachThe research model is tested through a large-scale survey of 222 Italian innovation managers. Data have been analyzed using structural equation modeling following a two-step approach. First, the measurement model was assessed to ensure the constructs reliability. Subsequently, the structural model was analyzed to draw the conclusions on theorized model relationships and their statistical significance.FindingsThe research findings reveal positive associations between IO and IC with CGA, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT. However, the study did not confirm a significant association between AL and CGA.Originality/valueOur findings have important implications for organizations seeking to maximize the potential of generative AI in innovation management. Understanding the factors that drive the adoption and customization of generative AI tools can inform strategies for better integration into the innovation process, thereby leading to enhanced innovation outcomes and improved decision-making processes.
C1 [Cimino, Antonio; Ndou, Valentina] Univ Salento, Dept Engn Innovat, Lecce, Italy.
   [Felicetti, Alberto Michele] Univ Calabria, Dept Mech Energy & Management Engn, Arcavacata Di Rende, Italy.
   [Corvello, Vincenzo] Univ Calabria, Dept Engn Management, Arcavacata Di Rende, Italy.
   [Longo, Francesco] Univ Calabria, Arcavacata Di Rende, Italy.
C3 University of Salento; University of Calabria; University of Calabria;
   University of Calabria
RP Ndou, V (corresponding author), Univ Salento, Dept Engn Innovat, Lecce, Italy.
EM valentina.ndou@unisalento.it
RI Corvello, Vincenzo/AAI-2177-2021; FELICETTI, ALBERTO
   MICHELE/ACE-0225-2022; Cimino, Antonio/IQU-9860-2023; Longo,
   Francesco/I-4336-2016; Longo, Francesco/C-1182-2013
OI Felicetti, Alberto Michele/0000-0002-3692-2643; Longo,
   Francesco/0000-0002-8538-9857; ndou, valentina/0000-0003-2133-7887
CR Açikgöz A, 2016, CREAT INNOV MANAG, V25, P445, DOI 10.1111/caim.12173
   Akkaya B, 2022, INT J ENV RES PUB HE, V19, DOI 10.3390/ijerph19084834
   Al-Sadi A., 2023, Int. J. Technol. Innov. Manag, V3, P1, DOI DOI 10.54489/IJTIM.V3I1.195
   Aldahdouh T Z., 2019, International Journal of Innovation Studies, P23, DOI [10.1016/j.ijis.2019.06.001, DOI 10.1016/J.IJIS.2019.06.001]
   Amabile T.M., 1988, INDIVIDUAL CREATIVIT
   Arfi S, 2023, CURR PHARM BIOTECHNO, V24, P1784, DOI 10.2174/1389201024666230411091057
   Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
   Bagnoli Carlo, 2019, International Journal of E-Services and Mobile Applications, V11, P34, DOI 10.4018/IJESMA.2019070103
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bass BM, 2003, J APPL PSYCHOL, V88, P207, DOI 10.1037/0021-9010.88.2.207
   Beer P, 2020, FRONT PSYCHOL, V11, DOI 10.3389/fpsyg.2020.00918
   Bentler PM, 2014, LONG RANGE PLANN, V47, P138, DOI 10.1016/j.lrp.2014.02.005
   Booyse D, 2024, MANAG RES REV, V47, P64, DOI 10.1108/MRR-09-2021-0701
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   Brand M, 2021, REV MANAG SCI, V15, P157, DOI 10.1007/s11846-019-00373-0
   Brynjolfsson E., 2017, HARVARD BUS REV, P1
   Bubou G M., 2020, Journal of Research in Innovative Teaching Learning, V15, P2, DOI [10.1108/JRIT-12-2019-0079, DOI 10.1108/JRIT-12-2019-0079]
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
   Chae HC, 2023, J COMPUT INFORM SYST, V63, P1460, DOI 10.1080/08874417.2023.2169847
   Chen CJ, 2011, INT J HUM RESOUR MAN, V22, P3447, DOI 10.1080/09585192.2011.599940
   Chubb J, 2022, AI SOC, V37, P1439, DOI 10.1007/s00146-021-01259-0
   Chui M., 2023, McKinsey Digital Report, V14, P2023
   Ciftci O., 2021, P  ENTER ETOURISM C, P162
   Cooper R.G., 1986, Winning at New Products, V26
   Cui GD, 2023, CURR PSYCHOL, V42, P25233, DOI 10.1007/s12144-022-03633-7
   de Jong JPJ, 2007, EUR J INNOV MANAG, V10, P41, DOI 10.1108/14601060710720546
   DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
   Dennis AR, 2003, MIS QUART, V27, P289
   Dennis AR, 2001, J MANAGE INFORM SYST, V18, P235
   DESANCTIS G, 1994, ORGAN SCI, V5, P121, DOI 10.1287/orsc.5.2.121
   Dijkstra TK, 2015, MIS QUART, V39, P297, DOI 10.25300/MISQ/2015/39.2.02
   Dijkstra TK, 2014, LONG RANGE PLANN, V47, P146, DOI 10.1016/j.lrp.2014.02.004
   Dolata M, 2023, J MANAGE INFORM SYST, V40, P56, DOI 10.1080/07421222.2023.2172775
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   European Union, 2023, European Innovation Scoreboard 2023 executive summary, DOI [10.2777/572275, DOI 10.2777/572275]
   Fachrunnisa O., 2020, J Small Bus Strateg, V30, P65
   Faullant R, 2012, CREAT INNOV MANAG, V21, P76, DOI 10.1111/j.1467-8691.2012.00626.x
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Füller J, 2022, TECHNOL FORECAST SOC, V178, DOI 10.1016/j.techfore.2022.121598
   Giddens A., 1979, CENTRAL PROBLEMS SOC
   Gkinko L, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102568
   Guzmán VE, 2020, PROCEDIA MANUF, V43, P543, DOI 10.1016/j.promfg.2020.02.167
   Haefner N, 2023, TECHNOL FORECAST SOC, V197, DOI 10.1016/j.techfore.2023.122878
   Haefner N, 2021, TECHNOL FORECAST SOC, V162, DOI 10.1016/j.techfore.2020.120392
   Hair J.F., 2018, ADV ISSUES PARTIAL L
   Hair J.F., 2021, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Cham, DOI [10.1007/978-3-030-80519-7, DOI 10.1007/978-3-030-80519-7, 10.3926/oss.407, DOI 10.1007/978-3-030-80519-71]
   Hair JF, 2014, PRIMER PARTIAL LEAST
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Hofman M, 2023, PROJ MANAG J, V54, P285, DOI 10.1177/87569728221150436
   Janson A, 2017, AIS Trans HumanComput Interact, V9, P173
   Jones MR, 2008, MIS QUART, V32, P127
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kang S, 2012, INFORM SYST RES, V23, P214, DOI 10.1287/isre.1100.0342
   Ko EJ, 2021, TECHNOL FORECAST SOC, V171, DOI 10.1016/j.techfore.2021.120994
   Kolade O, 2022, TECHNOL SOC, V71, DOI 10.1016/j.techsoc.2022.102086
   Kumar V, 2019, CALIF MANAGE REV, V61, P135, DOI 10.1177/0008125619859317
   Kummitha RKR, 2020, TECHNOL FORECAST SOC, V157, DOI 10.1016/j.techfore.2020.120087
   Lee K, 2013, J BUS RES, V66, P2634, DOI 10.1016/j.jbusres.2012.05.024
   Lohmller J.-B., 2013, Latent Variable Path Modeling with Partial Least Squares
   Maier MA, 2018, REV MANAG SCI, V12, P1055, DOI 10.1007/s11846-017-0238-z
   Mamoshina P, 2016, MOL PHARMACEUT, V13, P1445, DOI 10.1021/acs.molpharmaceut.5b00982
   Manohar SS, 2014, J BUS ETHICS, V125, P667, DOI 10.1007/s10551-013-1926-5
   Mariani MM, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113364
   Miranda SM, 2015, MIS QUART, V39, P591, DOI 10.25300/MISQ/2015/39.3.04
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Muhammad U, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0249311
   ORLIKOWSKI WJ, 1992, ORGAN SCI, V3, P398, DOI 10.1287/orsc.3.3.398
   Orlikowski WJ, 2001, INFORM SYST RES, V12, P121, DOI 10.1287/isre.12.2.121.9700
   Oztemel E, 2020, J INTELL MANUF, V31, P127, DOI 10.1007/s10845-018-1433-8
   Paganin G, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph18179366
   Peifer Y, 2022, PROCEDIA COMPUT SCI, V200, P1024, DOI 10.1016/j.procs.2022.01.301
   Proctor T., 2010, Creative Problem Solving for Managers: Developing Skills for Decision Making and Innovation, DOI [10.4324/9780203859827, DOI 10.4324/9780203859827]
   Rogers E. M., 2003, DIFFUSION INNOVATION
   Romanow D, 2018, MIS QUART, V42, P189, DOI 10.25300/MISQ/2018/13275
   Rothwell R., 1994, INT MARKET REV, V11, P7, DOI [DOI 10.1108/02651339410057491, 10.1108/02651339410057491, DOI 10.1080/00343400500128457]
   Saghafian M, 2021, FRONT PSYCHOL, V12, DOI 10.3389/fpsyg.2021.630145
   Sahoo S, 2022, J MANUF SYST, V64, P236, DOI 10.1016/j.jmsy.2022.06.008
   SCHEWE G, 1994, J ENG TECHNOL MANAGE, V11, P25, DOI 10.1016/0923-4748(94)90023-X
   Schmitz KW, 2016, MIS QUART, V40, P663, DOI 10.25300/MISQ/2016/40.3.07
   Scott C.R., 1998, COMMUN Q, V46, P353, DOI DOI 10.1080/01463379809370107
   Setiawan B., 2021, Technology, Market, and Complexity, V7, P188, DOI [10.3390/joitmc7030188, DOI 10.3390/JOITMC7030188]
   Shao Z, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103597
   Shao Z, 2017, INFORM MANAGE-AMSTER, V54, P902, DOI 10.1016/j.im.2017.01.005
   Shao Z, 2022, EXPERT SYST APPL, V209, DOI 10.1016/j.eswa.2022.118221
   Siguaw JA, 2006, J PROD INNOVAT MANAG, V23, P556, DOI 10.1111/j.1540-5885.2006.00224.x
   Slåtten T, 2020, BMC HEALTH SERV RES, V20, DOI 10.1186/s12913-020-05954-4
   Standing C, 2016, INT J INNOV LEARN, V19, P44, DOI 10.1504/IJIL.2016.073288
   Sternberg RJ, 2005, EDUC PSYCHOL REV, V17, P191, DOI 10.1007/s10648-005-5617-2
   Sternberg RJ, 1996, AM PSYCHOL, V51, P677, DOI 10.1037/0003-066X.51.7.677
   Suchman L., 1987, PLANS SITUATED ACTIO
   Tidd J., 2021, Managing innovation: integrating technological, market and organizational change, V7th
   Tran K, 2018, NAT CATAL, V1, P696, DOI 10.1038/s41929-018-0142-1
   Nguyen T, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103534
   Tredinnick L., 2023, Business Information Review, V40, P98
   Tshitoyan V, 2019, NATURE, V571, P95, DOI 10.1038/s41586-019-1335-8
   Vaidya S., 2018, Proc. Manuf, V20, P233, DOI [10.1016/j.promfg.2018.02.034, DOI 10.1016/J.PROMFG.2018.02, DOI 10.1016/J.PROMFG.2018.02.034]
   van der Zant T., 2013, Philosophy and Theory of Artificial Intelligence, V5, P107, DOI 10.1007/978-3-642-31674-6
   van Wijk J, 2013, ACAD MANAGE J, V56, P358, DOI 10.5465/amj.2008.0355
   Veit F., 2017, P BPM DEMO TRACK BUS, P1
   Verdegem P, 2011, TECHNOVATION, V31, P411, DOI 10.1016/j.technovation.2011.02.004
   Verganti R, 2020, J PROD INNOVAT MANAG, V37, P212, DOI 10.1111/jpim.12523
   Wang T, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122581
   Willaby HW, 2015, PERS INDIV DIFFER, V84, P73, DOI 10.1016/j.paid.2014.09.008
   WOLD H., 1975, Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling, P307, DOI [10.1016/B978-0-12-103950-9.50017-4, 10.1016/b978-0-12-103950-9.50017-4]
   Wold H., 1982, SYSTEMS INDIRECT OBS
   Yi MY, 2006, DECISION SCI, V37, P393, DOI 10.1111/j.1540-5414.2006.00132.x
   Zhong RY, 2017, ENGINEERING-PRC, V3, P616, DOI 10.1016/J.ENG.2017.05.015
   Zhou J, 2003, RES PERS H, V22, P165
NR 111
TC 1
Z9 1
U1 149
U2 157
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0025-1747
EI 1758-6070
J9 MANAGE DECIS
JI Manag. Decis.
PD 2024 MAY 30
PY 2024
DI 10.1108/MD-10-2023-1968
EA MAY 2024
PG 23
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA SH5X0
UT WOS:001233588000001
DA 2024-12-25
ER

PT J
AU Osadcha, K
   Osadcha, M
AF Osadcha, Kateryna
   Osadcha, Maryna
TI GENERATIVE ARTIFICIAL INTELLIGENCE VS HUMANS IN THE PROCESS OF CREATING
   CORPORATE IDENTITY ELEMENTS
SO INFORMATION TECHNOLOGIES AND LEARNING TOOLS
LA English
DT Article
DE generative artificial intelligence; advertising graphic; logotype;
   digital design; professional training; higher education
AB The emergence of new tools, the appearance of new technologies and improvements to existing ones have resulted in expansion of generative artificial intelligence. The technologies of generative artificial intelligence have already been used by people to perform not only intellectual tasks, but also creative ones, in particular in the field of design. Therefore, their capabilities in graphic design need to be studied. One of the routine tasks of a designer is the development of corporate identity elements (a logo, font, and colour). Designers can spend a lot of time on this, choosing different style options. Therefore, delegating this routine work to generative artificial intelligence may be appropriate. With this practical need in mind, the capabilities of modern AI tools for image and logo generation were studied in the research, and the results of AI logo generation compared to the work of novice designers were analysed. As a result, conclusions were drawn about the expediency of using generative AI technology in the work of designers, in particular, for the development of corporate identity elements, and the appropriateness of studying generative artificial intelligence technology in the training of future designers. These conclusions were made on the basis of a survey of 41 experts in the field of design, information technology and artificial intelligence. Based on the findings of the survey, we can note that it was difficult for experts to distinguish between logos generated by artificial intelligence and logos created by novice designers. Logos developed by novice designers (5) were recognized as the most attractive among the 45 logos presented in the survey. Images generated in some AI tools (Tailor Brands, Hatchful) are considered attractive by design, information technology and artificial intelligence professionals. Therefore, they can be used to create corporate identity elements. Thus, the vast majority of experts agreed that artificial intelligence tools for generating images and logos should be used in the process of creating corporate identity elements. In addition, the vast majority of experts found it advisable to use generative artificial intelligence technologies in the process of professional training of future designers.
C1 [Osadcha, Kateryna] Norwegian Univ Sci & Technol, Dept Comp Sci, Trondheim, Norway.
   [Osadcha, Maryna] Univ Tsukuba, Tsukuba, Japan.
   [Osadcha, Maryna] Bogdan Khmelnitsky Melitopol State Pedag Univ, Zaporizhzhia, Ukraine.
C3 Norwegian University of Science & Technology (NTNU); University of
   Tsukuba
RP Osadcha, K (corresponding author), Norwegian Univ Sci & Technol, Dept Comp Sci, Trondheim, Norway.
EM katheryna.osadcha@ntnu.no; s2258003@u.tsukuba.ac.jp
RI Osadcha, Kateryna/L-5268-2016
OI Osadcha, Kateryna/0000-0003-0653-6423
CR Alto V., 2023, Modern Generative AI with ChatGPT and OpenAI Models: Leverage the Capabilities of OpenAI's LLM for Productivity and Innovation with GPT3 and GPT4
   [Anonymous], 2023, Principles for the Development, Deployment, and Use of Generative AI Technologies
   As I, 2018, INT J ARCHIT COMPUT, V16, P306, DOI 10.1177/1478077118800982
   Barale A, 2021, PHILOS INQ, V9, P199, DOI 10.4454/philinq.v9i2.367
   Daniel I., 2021, Evaluating the Effectiveness of Artificial Intelligence Systems in Intelligence Analysis, DOI [10.7249/RR-A464-1, DOI 10.7249/RR-A464-1]
   Fields Z., 2023, Multidisciplinary approaches in AI, creativity, innovation, and green collaboration, P1, DOI [10.4018/978-1-6684-6366-6.ch001, DOI 10.4018/978-1-6684-6366-6.CH001]
   Garces K., 2023, Penji
   Gong YJ, 2021, ECOL INFORM, V63, DOI 10.1016/j.ecoinf.2021.101304
   Gwira C., 2023, Elegant Themes
   Karaata E., 2018, Online J art Des, V6, P183
   Lawton G., 2023, ENTERPRISE AI
   Liu CY, 2021, J SENSORS, V2021, DOI 10.1155/2021/8153783
   Martinkenaite I, 2021, NorwAI Annual Report 2021, P5
   Marzano G., 2022, Sustaining Creativity and the Arts in the Digital Age, DOI [10.4018/978-1-7998-7840-7, DOI 10.4018/978-1-7998-7840-7]
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Mures O. A., 2016, Handbook of Research on Visual Computing and Emerging Geometrical Design Tools, P78, DOI [10.4018/978-1-5225-0029-2.ch004, DOI 10.4018/978-1-5225-0029-2.CH004]
   Nake Frieder., 2012, Computers and Creativity, P61, DOI DOI 10.1007/978-3-642-31727-9_3
   Ogata T., 2019, Post-narratology Through Computational and Cognitive Approaches, P164, DOI [10.4018/978-1-5225-7979-3.ch004, DOI 10.4018/978-1-5225-7979-3.CH004]
   Ortiz S., 2023, What is ChatGPT and why does it matter? Here's what you need to know
   Shaikh E, 2023, Demand Sage
   Steenson M.W., 2022, Architectural Intelligence: How Designers and Architects Created the Digital Landscape
   Tao F, 2022, CRIT ARTS, V36, P110, DOI 10.1080/02560046.2022.2112725
   Wang B, 2021, MOB INF SYST, V2021, DOI 10.1155/2021/4894131
NR 23
TC 0
Z9 0
U1 39
U2 62
PU NATL ACAD EDUCATIONAL SCIENCES UKRAINE, INST DIGITALISATION EDUCATION
PI KYIV
PA VUL M BERLYNSKOHO 9, KYIV, 04060, UKRAINE
SN 2076-8184
J9 INF TECHNOL LEARN TO
JI Inf. Technol. Learn. Tools
PY 2023
VL 98
IS 6
BP 212
EP 230
DI 10.33407/itlt.v98i6.5494
PG 19
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA ED1V2
UT WOS:001136895900011
OA gold
DA 2024-12-25
ER

PT J
AU Hsiao, SK
   Treat, RM
   Javan, R
AF Hsiao, Sabrina K.
   Treat, Rachel M.
   Javan, Ramin
TI Establishing a Multi-Society Generative AI Task Force Within Radiology
SO CUREUS JOURNAL OF MEDICAL SCIENCE
LA English
DT Article
DE medical education technology; general radiology; radiology ai; chatgpt4;
   chatbot; task force; education; artificial intelligence; technology;
   health policy
AB Traditional artificial intelligence (AI) tools have already been implemented in clinical radiology for lesion detection and decision-making. Generative AI (GenAI), comparingly, is a new subset of machine learning that functions based on data probabilities to create content, offering numerous capabilities yet also uncertainties. Multidisciplinary collaboration is essential in safely harnessing the power of GenAI as it transforms medicine. This paper proposes creating a GenAI task force among radiological societies, including the American College of Radiology (ACR), Society of Imaging Informatics in Medicine (SIIM), care, health policy, and education. In this paper, we explore how a task force with guidelines will help radiologists and trainees develop essential strategies for integrating evolving AI-related technologies into clinical practice.
C1 [Hsiao, Sabrina K.; Treat, Rachel M.; Javan, Ramin] George Washington Univ, Sch Med & Hlth Sci, Dept Radiol, Washington, DC 20052 USA.
C3 George Washington University
RP Javan, R (corresponding author), George Washington Univ, Sch Med & Hlth Sci, Dept Radiol, Washington, DC 20052 USA.
EM rjavan@mfa.gwu.edu
RI Javan, Ramin/AAN-8503-2020
CR Abou Elkassem A, 2023, AM J ROENTGENOL, V221, P373, DOI 10.2214/AJR.23.29198
   [Anonymous], 2023, The ethics of AI in healthcare
   [Anonymous], Generative AI: radiology meets chatGPT
   [Anonymous], 2024, Executive order on the safe, secure, and trustworthy development and use of artificial intelligence
   [Anonymous], 2023, Global initiative on AI for health
   Bader Raneem, 2024, Radiol Case Rep, V19, P2106, DOI 10.1016/j.radcr.2024.02.037
   Bajaj S, 2024, ACAD RADIOL, V31, P1256, DOI 10.1016/j.acra.2023.08.039
   Brady AP, 2024, INSIGHTS IMAGING, V15, DOI 10.1186/s13244-023-01541-3
   Han RY, 2024, LANCET DIGIT HEALTH, V6, pe367, DOI 10.1016/S2589-7500(24)00047-5
   Park J, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-63824-z
NR 10
TC 0
Z9 0
U1 4
U2 4
PU SPRINGERNATURE
PI LONDON
PA CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND
EI 2168-8184
J9 CUREUS J MED SCIENCE
JI Cureus J Med Sci
PD JUL 13
PY 2024
VL 16
IS 7
AR e64475
DI 10.7759/cureus.64475
PG 7
WC Medicine, General & Internal
WE Emerging Sources Citation Index (ESCI)
SC General & Internal Medicine
GA YL4K2
UT WOS:001268628100010
PM 39139317
OA gold, Green Accepted
DA 2024-12-25
ER

PT J
AU Chadha, N
   Popil, E
   Gregory, J
   Armstrong-Davies, L
   Justin, G
AF Chadha, Nisha
   Popil, Erik
   Gregory, Jill
   Armstrong-Davies, Lily
   Justin, Gale
TI How do we teach generative artificial intelligence to medical educators?
   Pilot of a faculty development workshop using ChatGPT
SO MEDICAL TEACHER
LA English
DT Article; Early Access
DE Generative artificial intelligence; faculty development; clinical skills
AB PurposeArtificial intelligence (AI) is already impacting the practice of medicine and it is therefore important for future healthcare professionals and medical educators to gain experience with the benefits, limitations, and applications of this technology. The purpose of this project was to develop, implement, and evaluate a faculty development workshop on generative AI using ChatGPT, to familiarise participants with AI.Materials and methodsA brief workshop introducing faculty to generative AI and its applications in medical education was developed for preclinical clinical skills preceptors at our institution. During the workshop, faculty were given prompts to enter into ChatGPT that were relevant to their teaching activities, including generating differential diagnoses and providing feedback on student notes. Participant feedback was collected using an anonymous survey.Results27/36 participants completed the survey. Prior to the workshop, 15% of participants indicated having used ChatGPT, and approximately half were familiar with AI applications in medical education. Interest in using the tool increased from 43% to 65% following the workshop, yet participants expressed concerns regarding accuracy and privacy with use of ChatGPT.ConclusionThis brief workshop serves as a model for faculty development in AI applications in medical education. The workshop increased interest in using ChatGPT for educational purposes, and was well received.
C1 [Chadha, Nisha; Justin, Gale] Icahn Sch Med Mt Sinai, New York Eye & Ear Infirm, Eye & Vis Res Inst, Dept Ophthalmol & Med Educ, 17 E 102nd St,8th Floor West, New York, NY 10029 USA.
   [Popil, Erik; Gregory, Jill; Armstrong-Davies, Lily] Icahn Sch Med Mt Sinai, Instruct Technol Grp, Digital & Technol Partners, New York, NY USA.
C3 New York Eye & Ear Infirmary of Mount Sinai; Icahn School of Medicine at
   Mount Sinai; Icahn School of Medicine at Mount Sinai
RP Chadha, N (corresponding author), Icahn Sch Med Mt Sinai, New York Eye & Ear Infirm, Eye & Vis Res Inst, Dept Ophthalmol & Med Educ, 17 E 102nd St,8th Floor West, New York, NY 10029 USA.
EM nisha.chadha@mssm.edu
CR Breeding T, 2024, AM SURGEON, V90, P560, DOI 10.1177/00031348231180950
   Heng JJY, 2023, POSTGRAD MED J, V99, P1125, DOI 10.1093/postmj/qgad058
   Hwang JY, 2024, MED TEACH, V46, P291, DOI 10.1080/0142159X.2023.2259068
   Lee J, 2021, ACAD MED, V96, pS62, DOI 10.1097/ACM.0000000000004291
   Masters K, 2023, MED TEACH, V45, P673, DOI 10.1080/0142159X.2023.2208731
   Russell RG, 2023, ACAD MED, V98, P348, DOI 10.1097/ACM.0000000000004963
   Smith D., 2023, The Scholarly Kitchen
   Tsang R, 2023, J MED EDUC CURRIC DE, V10, DOI 10.1177/23821205231178449
   Wood EA, 2021, J MED EDUC CURRIC DE, V8, DOI 10.1177/23821205211024078
NR 9
TC 2
Z9 2
U1 12
U2 19
PU TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
SN 0142-159X
EI 1466-187X
J9 MED TEACH
JI Med. Teach.
PD 2024 APR 19
PY 2024
DI 10.1080/0142159X.2024.2341806
EA APR 2024
PG 3
WC Education, Scientific Disciplines; Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Health Care Sciences & Services
GA OK3W5
UT WOS:001207135400001
PM 38648540
DA 2024-12-25
ER

PT J
AU Hanai, A
   Ishikawa, T
   Kawauchi, S
   Iida, Y
   Kawakami, E
AF Hanai, Akiko
   Ishikawa, Tetsuo
   Kawauchi, Shoichiro
   Iida, Yuta
   Kawakami, Eiryo
TI Generative artificial intelligence and non-pharmacological bias: an
   experimental study on cancer patient sexual health communications
SO BMJ HEALTH & CARE INFORMATICS
LA English
DT Article
DE Artificial intelligence; Health Communication; Clinical Governance;
   Evidence-Based Medicine
AB Objectives The objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss. Methods We employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate 100 questions from the AI, based on epidemiological survey data regarding sexual difficulties among cancer survivors. These questions were submitted to Bot1 (standard GPT) and Bot2 (sourced from two clinical guidelines). Results No censorship of sexual expressions or medical terms occurred. Despite the lack of reflection on guideline recommendations, 'consultation' was significantly more prevalent in both bots' responses compared with pharmacological interventions, with ORs of 47.3 (p<0.001) in Bot1 and 97.2 (p<0.001) in Bot2. Discussion Generative AI can serve to provide health information on sensitive topics such as sexual health, despite the potential for policy-restricted content. Responses were biased towards non-pharmacological interventions, which is probably due to a GPT model designed with the 's prohibition policy on replying to medical topics. This shift warrants attention as it could potentially trigger patients' expectations for non-pharmacological interventions.
C1 [Hanai, Akiko; Ishikawa, Tetsuo; Kawauchi, Shoichiro; Iida, Yuta; Kawakami, Eiryo] Adv Data Sci Project, Med Data Math Reasoning Team, Adv Data Sci Project, RIKEN, Yokohama, Japan.
   [Hanai, Akiko; Ishikawa, Tetsuo; Kawakami, Eiryo] Chiba Univ, Grad Sch Med, Dept Artificial Intelligence Med, Chiba, Japan.
   [Ishikawa, Tetsuo] Keio Univ, Dept Extended Intelligence Med, Ishii Ishibashi Lab, Sch Med, Tokyo, Japan.
   [Ishikawa, Tetsuo] Univ Tokyo, Grad Sch Arts & Sci, Collect Intelligence Res Lab, Tokyo, Japan.
C3 Chiba University; Keio University; University of Tokyo
RP Hanai, A (corresponding author), Adv Data Sci Project, Med Data Math Reasoning Team, Adv Data Sci Project, RIKEN, Yokohama, Japan.; Hanai, A (corresponding author), Chiba Univ, Grad Sch Med, Dept Artificial Intelligence Med, Chiba, Japan.
EM hanaaki0803@gmail.com
RI Hanai, Akiko/AFL-4949-2022
OI Iida, Yuta/0009-0002-4273-4080; Hanai, Akiko/0000-0003-4468-1488
FU RIKEN; RIKEN grant
FX We thank Manami Kato and Ayaka Mori for their assistance with data
   management. This work was supported by a RIKEN grant.
CR Ayoub NF, 2024, OTOLARYNG HEAD NECK, V170, P1484, DOI 10.1002/ohn.465
   Carter J, 2018, J CLIN ONCOL, V36, P492, DOI 10.1200/JCO.2017.75.8995
   Melisko ME, 2016, J NATL COMPR CANC NE, V14, P685, DOI 10.6004/jnccn.2016.0193
   Meskó B, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00873-0
   Open AI, Usage policies
   Raggio GA, 2014, PSYCHOL HEALTH, V29, P632, DOI 10.1080/08870446.2013.879136
   Reese JB, 2017, J CANCER SURVIV, V11, P175, DOI 10.1007/s11764-016-0577-9
   Walker HL, 2023, J MED INTERNET RES, V25, DOI 10.2196/47479
NR 8
TC 0
Z9 0
U1 6
U2 7
PU BMJ PUBLISHING GROUP
PI LONDON
PA BRITISH MED ASSOC HOUSE, TAVISTOCK SQUARE, LONDON WC1H 9JR, ENGLAND
EI 2632-1009
J9 BMJ HEALTH CARE INFO
JI BMJ Health Care Inform.
PD APR
PY 2024
VL 31
IS 1
AR e100924
DI 10.1136/bmjhci-2023-100924
PG 3
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA NQ3G8
UT WOS:001201874400002
PM 38575326
OA gold
DA 2024-12-25
ER

PT J
AU Bojic, S
   Radovanovic, N
   Radovic, M
   Stamenkovic, D
AF Bojic, Suzana
   Radovanovic, Nemanja
   Radovic, Milica
   Stamenkovic, Dusica
TI Could generative artificial intelligence replace fieldwork in pain
   research?
SO SCANDINAVIAN JOURNAL OF PAIN
LA English
DT Article
DE acute pain; artificial intelligence; climbing; generative pretrained
   transformer
AB Background- Generative artificial intelligence (AI) models offer potential assistance in pain research data acquisition, yet concerns persist regarding data accuracy and reliability. In a comparative study, we evaluated open generative AI models' capacity to acquire data on acute pain in rock climbers comparable to field research. Methods- Fifty-two rock climbers (33 m/19 f; age 29.0 [24.0-35.75] years) were asked to report pain location and intensity during a single climbing session. Five generative pretrained transformer models were tasked with responses to the same questions. Results- Climbers identified the back of the forearm (19.2%) and toes (17.3%) as primary pain sites, with reported median pain intensity at 4 [3-5] and median maximum pain intensity at 7 [5-8]. Conversely, AI models yielded divergent findings, indicating fingers, hands, shoulders, legs, and feet as primary pain localizations with average and maximum pain intensity ranging from 3 to 4.4 and 5 to 10, respectively. Only two AI models provided references that were untraceable in PubMed and Google searches. Conclusion- Our findings reveal that, currently, open generative AI models cannot match the quality of field-collected data on acute pain in rock climbers. Moreover, the models generated nonexistent references, raising concerns about their reliability.
C1 [Bojic, Suzana] Univ Belgrade, Fac Med, Dept Anesthesiol & Intens Care, Dr Subot 8, Belgrade 11000, Serbia.
   [Bojic, Suzana] Univ Clin Hosp Ctr Dr Dragisa Misov Dedinje, Dept Anesthesiol & Intens Care, Heroja Milana Tepica 1, Belgrade 11000, Serbia.
   [Radovanovic, Nemanja] Univ Clin Ctr Serbia, Dept Anesthesiol & Intens Care, Belgrade 11000, Serbia.
   [Radovic, Milica] Univ Clin Hosp Ctr Zemun, Intens Care Unit, Belgrade 11000, Serbia.
   [Stamenkovic, Dusica] Univ Def, Med Fac, Dept Anesthesiol & Intens Care, Veljka Luk Kurjaka 1, Belgrade 11000, Serbia.
   [Stamenkovic, Dusica] Mil Med Acad, Dept Anesthesiol & Intens Care, Crnotravska 17, Belgrade 11000, Serbia.
C3 University of Belgrade; Clinical Centre of Serbia
RP Bojic, S (corresponding author), Univ Belgrade, Fac Med, Dept Anesthesiol & Intens Care, Dr Subot 8, Belgrade 11000, Serbia.
EM subojic@yahoo.com; radovanovicz@gmail.com; milicaradovic16@gmail.com;
   dusicastamenkovic@yahoo.com
RI Stamenkovic, Dusica/A-3319-2008
OI Radovanovic, Nemanja/0009-0008-8300-6622
CR Borji A, 2023, Arxiv, DOI [arXiv:2302.03494, 10.48550/arxiv.2302.03494, DOI 10.48550/ARXIV.2302.03494]
   Schedlbauer J, 2021, INT J MED INFORM, V150, DOI 10.1016/j.ijmedinf.2021.104453
   Spitale G, 2023, SCI ADV, V9, DOI 10.1126/sciadv.adh1850
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Zhang N, 2023, Front Neurol, V14, DOI [10.3389/fneur.2023.1225223, DOI 10.3389/FNEUR.2023.1225223]
NR 5
TC 0
Z9 0
U1 3
U2 3
PU WALTER DE GRUYTER GMBH
PI BERLIN
PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY
SN 1877-8860
EI 1877-8879
J9 SCAND J PAIN
JI Scand. J. Pain
PD MAR 7
PY 2024
VL 24
IS 1
AR 20230136
DI 10.1515/sjpain-2023-0136
PG 3
WC Clinical Neurology
WE Emerging Sources Citation Index (ESCI)
SC Neurosciences & Neurology
GA MV1G7
UT WOS:001196313500001
PM 38452184
OA gold
DA 2024-12-25
ER

PT J
AU Hinds, P
   von Krogh, G
AF Hinds, Pamela
   von Krogh, Georg
TI Generative AI, Emerging Technology, and Organizing: Towards a theory of
   progressive encapsulation
SO ORGANIZATION THEORY
LA English
DT Article
DE AI in organizations; AI in teams; information systems; relational
   perspective; team work; teams; technology
ID ARTIFICIAL-INTELLIGENCE; AUTOMATION; ORGANIZATIONS; DISCOVERY
AB Generative AI is upon us and is changing organizations and organizing. In this essay, we extend the relational perspective on technology, which argues for moving away from an entity-based view of technology to one that focuses instead on the evolving relations and functions between people, technologies, and organizations. We do so by introducing the concept of "progressive encapsulation" which captures GenAI's potential ability to increasingly expand the "black box" and reduce visibility into and control over the relations and functions performed. We argue that progressive encapsulation is critical in our theorizing about GenAI. As an illustration and thought experiment we consider how GenAI and progressive encapsulation may necessitate changes in our theorizing about groups and teams in organizations.
C1 [Hinds, Pamela] Stanford Univ, Stanford, CA USA.
   [von Krogh, Georg] Swiss Fed Inst Technol, Strateg Management & Innovat, Zurich, Switzerland.
C3 Stanford University; Swiss Federal Institutes of Technology Domain; ETH
   Zurich
RP Hinds, P (corresponding author), Stanford Univ, Huang Engn Ctr, Stanford, CA 94305 USA.
EM phinds@stanford.edu
OI Hinds, Pamela/0000-0003-1478-0602
FU National Science Foundation [2211943]; Swiss National Science Foundation
   [100013_197763]; Swiss National Science Foundation (SNF) [100013_197763]
   Funding Source: Swiss National Science Foundation (SNF)
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This work
   is based on research supported by the National Science Foundation under
   Grant No. 2211943 to the first author and a Research Grant from the
   Swiss National Science Foundation (Grant no: 100013_197763) to the
   second author.
CR Acemoglu D, 2019, J ECON PERSPECT, V33, P3, DOI 10.1257/jep.33.2.3
   Bailey DE, 2022, ORGAN SCI, V33, P1, DOI 10.1287/orsc.2021.1562
   BARLEY SR, 1990, ADMIN SCI QUART, V35, P61, DOI 10.2307/2393551
   Ben-Menahem SM, 2016, ACAD MANAGE J, V59, P1308, DOI 10.5465/amj.2013.1214
   Berg JM, 2023, ACAD MANAG DISCOV, V9, P424, DOI 10.5465/amd.2023.0106
   Cameron LD, 2022, ORGAN SCI, V33, P231, DOI 10.1287/orsc.2021.1547
   Cloutier C, 2020, ORGAN THEOR, V1, DOI 10.1177/2631787720902473
   Cornelissen J, 2024, ORGAN THEOR, V5, DOI 10.1177/26317877241239056
   Ekins S, 2013, DRUG DISCOV TODAY, V18, P265, DOI 10.1016/j.drudis.2012.10.007
   Epstein Z, 2023, SCIENCE, V380, P1110, DOI 10.1126/science.adh4451
   Eshraghian JK, 2020, NAT MACH INTELL, V2, P157, DOI 10.1038/s42256-020-0161-x
   Faraj S, 2018, INFORM ORGAN-UK, V28, P62, DOI 10.1016/j.infoandorg.2018.02.005
   FAUNCE WA, 1965, SOC PROBL, V13, P149, DOI 10.1525/sp.1965.13.2.03a00050
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Gmsay A. A., 2024, Organization Theory, V1, P1
   Grimes M, 2023, ACAD MANAGE J, V66, P1617, DOI 10.5465/amj.2023.4006
   Haase J., 2023, Journal of Creativity, V33
   Jarrahi MH, 2023, J ASSOC INF SCI TECH, V74, P303, DOI 10.1002/asi.24730
   Larson L, 2020, LEADERSHIP QUART, V31, DOI 10.1016/j.leaqua.2019.101377
   Massey AP, 2003, J MANAGE INFORM SYST, V19, P129
   Mayo AT, 2022, ADMIN SCI QUART, V67, P821, DOI 10.1177/00018392221096451
   Mortensen M, 2018, ORGAN SCI, V29, P341, DOI 10.1287/orsc.2017.1198
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Ooi Keng-Boon., 2023, Journal of Computer Information Systems
   Orlikowski WJ, 2000, ORGAN SCI, V11, P404, DOI 10.1287/orsc.11.4.404.14600
   Puranam P, 2012, ACAD MANAGE REV, V37, P419, DOI 10.5465/amr.2010.0535
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Reed MI, 2012, ORGAN STUD, V33, P203, DOI 10.1177/0170840611430590
   Sneed HM, 2000, ANN SOFTW ENG, V9, P293, DOI 10.1023/A:1018989111417
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Tschang FT, 2021, ACAD MANAGE PERSPECT, V35, P642, DOI 10.5465/amp.2019.0062
   von Krogh G, 2018, ACAD MANAG DISCOV, V4, P404, DOI 10.5465/amd.2018.0084
   Waardenburg L, 2022, ORGAN SCI, V33, P59, DOI 10.1287/orsc.2021.1544
   Wachter RM, 2024, JAMA-J AM MED ASSOC, V331, P65, DOI 10.1001/jama.2023.25054
   Wang HC, 2023, NATURE, V620, P47, DOI 10.1038/s41586-023-06221-2
   WEICK KE, 1993, ADMIN SCI QUART, V38, P357, DOI 10.2307/2393372
   Zaccaro SJ, 2020, ANNU REV ORGAN PSYCH, V7, P479, DOI 10.1146/annurev-orgpsych-012119-045418
NR 37
TC 0
Z9 0
U1 7
U2 7
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
EI 2631-7877
J9 ORGAN THEOR
JI Organ. Theor.
PD OCT
PY 2024
VL 5
IS 4
AR 26317877241293478
DI 10.1177/26317877241293478
PG 14
WC Management
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA K7K6I
UT WOS:001345629500001
OA gold
DA 2024-12-25
ER

PT J
AU Agbon, G
AF Agbon, Gildas
TI Who speaks through the machine? Generative AI as discourse and
   implications for management
SO CRITICAL PERSPECTIVES ON ACCOUNTING
LA English
DT Article
DE Generative AI; ChatGPT; Discourse; Technological solutionism;
   Management; Organization
ID ARTIFICIAL-INTELLIGENCE; GRAND CHALLENGES; COMMUNICATION
AB This article draws on the Foucauldian concept of "discursive formation" to conceptualize generative artificial intelligence (GAI)'s potential influence on management. It shows how ChatGPT, a typical GAI, can affect practices, decision-making responsibility and the management disciplines through a dual discourse. The first one emanates from technological solutionism, a belief that any problem can be solved with the assistance of technology (the technosolutionist discourse), and the second one concerns the utterances generated by ChatGPT itself, which are shaped by various algorithmic, epistemic and linguistic influences (the generative discourse). In simpler terms, what ChatGPT can do to management appears to depend on "what is said about it" and "what it says". In contrast to the existing literature on ChatGPT's potential influence on organizations, this article, through its discursive approach, takes a non-normative position, to reveal the subtler influences of generative artificial intelligence, and highlight the individual and organizational responsibilities of actors interacting with these two discourses. The conclusions may be of interest to management readers in general, and of more particular interest to the accounting profession, as the conceptualization is based more broadly on examples taken from accounting, given the close link between the accounting profession and information technologies.
C1 [Agbon, Gildas] Univ Laval, Fac Sci Adm, Pavillon Palasis Prince,2325 Rue Terrasse, Quebec City, PQ G1V 0A6, Canada.
C3 Laval University
RP Agbon, G (corresponding author), Univ Laval, Fac Sci Adm, Pavillon Palasis Prince,2325 Rue Terrasse, Quebec City, PQ G1V 0A6, Canada.
EM gildas.agbon.1@ulaval.ca
OI Agbon, Gildas/0000-0001-8871-0186
FU Laval University's Faculty of Business Administration
FX I gratefully acknowledge financial support from the Laval University's
   Faculty of Business Administration in translating the manuscript. I am
   particularly grateful to Professor Yves Gendron, co-editor, for inviting
   me to this special issue, and Professor Luc Bres for his supportive
   feedback on early drafts of this paper. I also thank Professor Jeremy
   Morales and the two anonymous reviewers for their insightful comments
   and guidance, as well as Ann Gallon for her meticulous translation work.
   Finally, I extend my gratitude to the participants of the Organization
   Studies Division at the 2024 annual conference of the Administrative
   Sciences Association of Canada (ASAC) for their constructive feedback.
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Ahrne G, 2016, EUR MANAG J, V34, P93, DOI 10.1016/j.emj.2016.02.003
   Alasuutari P., 1995, Qualitative Method and Cultural Studies, P47
   Anderson C, 2008, WIRED, V16
   Andrew J, 2022, CRIT PERSPECT ACCOUN, V87, DOI 10.1016/j.cpa.2022.102478
   [Anonymous], ACAD MANAGEMENT EXEC, DOI [DOI 10.2307/4164720, 10.5465/ame.1987.4275905]
   Ayinde L., 2023, BUS INFORM REV, V40, P137, DOI [https://doi.org/10.1177/02663821231187991, DOI 10.1177/02663821231187991]
   Bai HR, 2022, FILOS-SOCIOL, V33, P40
   Ballantine J, 2024, CRIT PERSPECT ACCOUN, V99, DOI 10.1016/j.cpa.2024.102711
   Barad K, 2003, SIGNS, V28, P801, DOI 10.1086/345321
   Bass D., 2019, Move Over Elon Musk: Microsoft to Invest $1 Billion in Partnership With OpenAI Article
   Bass D., 2023, OpenAI makes ChatGPT available for companies to integrate in apps
   Bass D., 2023, These are OpenAI's strongest competitors right now Article
   Bass D., 2023, It's raining money for ChatGPT company OpenAI as Microsoft officially throws down a $10 billion investment Article
   Bass D., 2023, Microsoft Hopes OpenAI's Chatbot Will Make Bing Smarter Article
   Beerbaum D.O., 2023, GENERATIVE ARTIFICIA
   Benveniste E., 1970, LANGAGES, P12
   Berghel H, 2023, COMPUTER, V56, P130, DOI 10.1109/MC.2023.3252379
   Berlinski E., 2022, Revue francaise de gestion, V48, P65
   Berlinski E, 2024, CRIT PERSPECT ACCOUN, V98, DOI 10.1016/j.cpa.2023.102697
   Beukeboom CJ, 2019, REV COMMUN RES, V7, P1, DOI 10.12840/issn.2255-4165.017
   Blodgett SL, 2020, Arxiv, DOI arXiv:2005.14050
   Blomberg A, 2014, J ORGAN CHANGE MANAG, V27, P935, DOI 10.1108/JOCM-12-2013-0252
   Boltanski Luc., 1999, NOUVEL ESPRIT CAPITA
   Bove T., 2023, Bill Gates says ChatGPT will 'change our world' but it doesn't mean your job is at risk Article
   Brivot M, 2011, ACCOUNT ORG SOC, V36, P135, DOI 10.1016/j.aos.2011.03.003
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Burrell J, 2021, ANNU REV SOCIOL, V47, P213, DOI 10.1146/annurev-soc-090820-020800
   Burrell J, 2016, BIG DATA SOC, V3, P1, DOI 10.1177/2053951715622512
   Cai K., 2023, Six Things You Didn't Know About ChatGPT, Stable Diffusion and The Future of Generative AI
   Cameron LD, 2022, ORGAN SCI, V33, P38, DOI 10.1287/orsc.2021.1557
   Cao Y, 2023, J CHIN ECON BUS STUD, V21, P177, DOI 10.1080/14765284.2023.2212434
   Carabantes M, 2020, AI SOC, V35, P309, DOI 10.1007/s00146-019-00888-w
   Cardon D., 2015, vent les algorithmes: Nos vies a l'heure des big data
   Casilli AntonioA., 2019, En attendant les robots: enquete sur le travail du clic
   Chalmers Alan., 2013, What is This Thing Called Science?, V4th ed
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chomsky N., 1957, SYNTACTIC STRUCTURES, DOI [10.1515/9783112316009, DOI 10.1515/9783112316009]
   Christiano PF, 2017, ADV NEUR IN, V30
   Chuma E. L., 2023, Manag Sci Bus Decis, V3, P5, DOI DOI 10.52812/MSBD.63
   Collins P. H., 2022, Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cornuejols A., 2018, Apprentissage artificiel: Deep learning, concepts et algorithmes, V3e
   Counts A., 2023, Musk wants to build own ChatGPT AI to rival Microsoft and Google Article
   Crozier M., 1977, L'acteur et le systeme: Les contraintes de l'action collective
   Das D, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.36034
   de Saussure Ferdinand., 1916, Cours de linguistique gnrale
   Deloitte, 2023, Generative AI: Navigating risks and ethics
   Dreyfus H.L., 1983, M FOUCAULT STRUCTURA, V2nd
   Durocher S, 2014, ACCOUNT BUS RES, V44, P630, DOI 10.1080/00014788.2014.938012
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Fairclough N, 2005, ORGAN STUD, V26, P915, DOI 10.1177/0170840605054610
   Farnadi G., 2023, Angles morts de la gouvernance de l'IA, P31
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Ferraro F, 2015, ORGAN STUD, V36, P363, DOI 10.1177/0170840614563742
   Foucault Michel, 2002, ARCHAEOLOGY KNOWLEDG
   Foucault Michel, 1990, MOTS CHOSES
   Foucault Michel., 1969, L'archeologie du savoir
   Fricker M, 2008, THEORIA-SPAIN, V23, P69
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Funnell W., 2022, Critical Perspectives on Accounting, V89, DOI [10.1016/j.cpa.2021.102329, DOI 10.1016/J.CPA.2021.102329]
   Gates B., 2023, Gates Notes
   Gaudet Stephanieet., 2018, L'aventure de la recherche qualitative: Du questionnement a la redaction scientifique
   Gendron Y, 2022, CRIT PERSPECT ACCOUN, V87, DOI 10.1016/j.cpa.2021.102411
   George G, 2016, ACAD MANAGE J, V59, P1880, DOI 10.5465/amj.2016.4007
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Hacking Ian., 1986, Reconstructing Individualism: Autonomy, Individuality, and the Self in Western Thought, P222
   Hasan A.R., 2021, Open Journal of Business and Management, V10, P440, DOI [10.4236/OJBM.2022.101026, DOI 10.4236/OJBM.2022.101026]
   Hatchuel A., 1995, Experts in organizations: A knowledge-based perspective on organizational change
   Hayek F.A., 1976, ROAD SERFDOM
   Higginbotham S., 2016, Elon Musk, Amazon Create Artificial Intelligence Research Center Article
   Huh S, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.1
   Jin B., 2022, Wall Street Journal
   Johnson Arianna, 2023, Is ChatGPT Partisan? Poems about Trump and Biden raise questions about the AI bot's bias-Here's what experts think
   Kahn J., 2023, Fortune.com
   Kahn J., 2023, The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft Article
   Korzynski P, 2023, ENTREPR BUS ECON REV, V11, P25, DOI 10.15678/EBER.2023.110302
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Krause R., 2023, Investors Business Daily
   Lassila EM, 2022, CRIT PERSPECT ACCOUN, V87, DOI 10.1016/j.cpa.2022.102434
   Latour B., 2020, Petites lecons de sociologie des sciences
   Lindebaum D, 2023, ACAD MANAGE REV, V48, P575, DOI 10.5465/amr.2021.0159
   Lopez-Lira A, 2024, Arxiv, DOI arXiv:2304.07619
   Lyon D., 2007, Surveillance Studies: An Overview
   Maass Anne., 1999, Advances in experimental social psychology, V31, P79, DOI [10.1016/S0065-2601(08)60272-5, DOI 10.1016/S0065-2601(08)60272-5]
   MEHRABIAN A, 1967, J CONSULT PSYCHOL, V31, P248, DOI 10.1037/h0024648
   Mintzberg H., 1982, STRUCTURE DYNAMIQUE
   Mollman S., 2023, Google is investing $300M in an OpenAI challenger that will take on ChatGPT while focusing on A.I. safety Article
   Morozov E., 2014, To Save Everything, Click Here: Technology, solutionism and the urge to fix problems that don't exist, V1st
   Niburski K, 2023, J CONS HLTH INTERNET, V27, P12, DOI 10.1080/15398285.2022.2133832
   NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14
   Nyberg D, 2009, ORGAN STUD, V30, P1181, DOI 10.1177/0170840609337955
   Oliphant T, 2021, J ASSOC INF SCI TECH, V72, P951, DOI 10.1002/asi.24461
   ONeill C, 2016, Weapons of Math Destruction. How big Data increases Inequality and threatens Democracy
   Orlikowski WJ, 2010, CAMB J ECON, V34, P125, DOI 10.1093/cje/bep058
   Ouyang L, 2022, ADV NEUR IN
   Pasquale D, 2016, The Black Box Society: The Secret Algorithms That Control Money and Information
   Pauchant T. C., 2023, Adam Smith, l'antidote ultime au capitalisme: Sa theorie du capabilisme
   Payzan-LeNestour E, 2022, J FINANC ECON, V143, P1316, DOI 10.1016/j.jfineco.2021.06.019
   Picard CF, 2018, AUDITING-J PRACT TH, V37, P191, DOI 10.2308/ajpt-51752
   Porter ME, 2011, HARVARD BUS REV, V89, P62
   Rabinow P., 1991, FOUCAULT READER INTR
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Rau A., 2018, The SAGE handbook of qualitative data collection, P300, DOI [DOI 10.4135/9781526416070.N19, 10.4135/9781526416070.N19]
   Roff HM, 2019, ETHICS INT AFF, V33, P127, DOI 10.1017/S0892679419000121
   Rogers E.M., 1962, DIFFUSION INNOVATION
   Roy N, 2023, DECISION-INDIA, V50, P11, DOI 10.1007/s40622-023-00342-3
   Russo F, 2024, AI SOC, V39, P1585, DOI 10.1007/s00146-022-01617-6
   Sandvig C., 2014, ANN M INT COMM ASS S, P1
   Schmidt CTA, 2007, AI SOC, V21, P537, DOI 10.1007/s00146-007-0083-8
   Scott James C., 1999, SEEING STATE CERTAIN
   Shirky C., 2008, Here Comes Everybody: The Power of Organizing Without Organizations
   Simon HA., 1996, SCI ARTIFICIAL
   Simon HA, 1955, Q J ECON, V69, P99, DOI 10.2307/1884852
   Singer Peter., 2019, The Life You Can Save: How to Do Your Part to End World Poverty
   Singh H, 2023, J CHIN ECON BUS STUD, V21, P193, DOI 10.1080/14765284.2023.2210482
   Smith A., 1937, WEALTH NATIONS
   Sundström A, 2024, CRIT PERSPECT ACCOUN, V99, DOI 10.1016/j.cpa.2023.102701
   Tool Advisor, 2024, ChatGPT a plus de 180 millions d'utilisateurs actifs
   Trittin-Ulbrich H, 2021, ORGANIZATION, V28, P8, DOI 10.1177/1350508420968184
   Vaidhyanathan S, 2011, GOOGLIZATION OF EVERYTHING: AND WHY WE SHOULD WORRY, P1
   van Dijk J.A., 2020, THE DIGITAL DIVIDE
   Varzaru AA, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11142256
   Vasarhelyi MA, 2015, ACCOUNT HORIZ, V29, P381, DOI 10.2308/acch-51071
   Vaswani A, 2017, ADV NEUR IN, V30
   Viale T, 2017, ACCOUNT AUDIT ACCOUN, V30, P270, DOI 10.1108/AAAJ-12-2014-1887
   Wei T, 2023, INT REV ECON FINANC, V88, P1389, DOI 10.1016/j.iref.2023.07.108
   Weise K., 2023, The New York Times
   Widdowson H. G., 1973, An applied linguistic approach to discourse analysis
   Wolf Z. B., 2023, AI CAN BE RACIST SEX
   Zamfiroiu Alin, 2023, Informatica Economica, P5, DOI 10.24818/issn14531305/27.1.2023.01
   Zhao JN, 2024, J CORP ACCOUNT FINAN, V35, P269, DOI 10.1002/jcaf.22663
NR 132
TC 0
Z9 0
U1 34
U2 34
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1045-2354
EI 1095-9955
J9 CRIT PERSPECT ACCOUN
JI Crit. Perspect. Account.
PD DEC
PY 2024
VL 100
AR 102761
DI 10.1016/j.cpa.2024.102761
EA AUG 2024
PG 16
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA C3Z7F
UT WOS:001288782400001
OA hybrid
DA 2024-12-25
ER

PT J
AU Fu, BY
   Hadid, A
   Damer, N
AF Fu, Biying
   Hadid, Abdenour
   Damer, Naser
TI Generative AI in the context of assistive technologies: Trends,
   limitations and future directions
SO IMAGE AND VISION COMPUTING
LA English
DT Article
DE Assistive AI; Generative AI; Generative models; Assistive systems;
   Assistive technologies and services
ID LONELINESS; RECOGNITION; ASSISTANCE; HEALTH; OLDER; MODEL; RISK
AB With the tremendous successes of Large Language Models (LLMs) like ChatGPT for text generation and Dall-E for high-quality image generation, generative Artificial Intelligence (AI) models have shown a hype in our society. Generative AI seamlessly delved into different aspects of society ranging from economy, education, legislation, computer science, finance, and even healthcare. This article provides a comprehensive survey on the increased and promising use of generative AI in assistive technologies benefiting different parties, ranging from the assistive system developers, medical practitioners, care workforce, to the people who need the care and the comfort. Ethical concerns, biases, lack of transparency, insufficient explainability, and limited trustworthiness are major challenges when using generative AI in assistive technologies, particularly in systems that impact people directly. Key future research directions to address these issues include creating standardized rules, establishing commonly accepted evaluation metrics and benchmarks for explainability and reasoning processes, and making further advancements in understanding and reducing bias and its potential harms. Beyond showing the current trends of applying generative AI in the scope of assistive technologies in four identified key domains, which include care sectors, medical sectors, helping people in need, and co-working, the survey also discusses the current limitations and provides promising future research directions to foster better integration of generative AI in assistive technologies.
C1 [Fu, Biying] RheinMain Univ Appl Sci, D-65195 Wiesbaden, Hessen, Germany.
   [Fu, Biying; Damer, Naser] Fraunhofer Inst Comp Graph Res, D-64283 Darmstadt, Germany.
   [Hadid, Abdenour] Sorbonne Univ Abu Dhabi, Sorbonne Ctr Artificial Intelligence, Abu Dhabi, U Arab Emirates.
   [Damer, Naser] Tech Univ Darmstadt, Dept Comp Sci, D-64283 Darmstadt, Hessen, Germany.
C3 Fraunhofer Gesellschaft; Technical University of Darmstadt
RP Fu, BY (corresponding author), RheinMain Univ Appl Sci, D-65195 Wiesbaden, Hessen, Germany.
EM biying.fu@hs-rm.de
FU German Federal Ministry of Education and Research; Hessian Ministry of
   Higher Education, Research, Science and the Arts
FX This research work has been funded by the German Federal Ministry of
   Education and Research and the Hessian Ministry of Higher Education,
   Research, Science and the Arts within their joint support of the
   National Research Center for Applied Cybersecurity ATHENE.
CR Abu Elzein EME, 1999, REV SCI TECH OIE, V18, P672
   Accountability Act, 1996, Health insurance portability and accountability act of 1996 (HIPAA) privacy rule, V104, P191
   Adedeji A, 2024, Arxiv, DOI arXiv:2402.07658
   [Anonymous], 44. World Health Organization. 2024. https://www.who.int/news-room/fact-sheets/detail/ecoli
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Bala A., 2024, Multimodal LLM using Federated Visual Instruction Tuning for Visually Impaired
   Bian YM, 2021, J MOL MODEL, V27, DOI 10.1007/s00894-021-04674-8
   Bissoto A, 2021, IEEE COMPUT SOC CONF, P1847, DOI 10.1109/CVPRW53098.2021.00204
   Biswas A, 2024, Arxiv, DOI arXiv:2405.18346
   Bommasani R, 2023, ANN NY ACAD SCI, V1525, P140, DOI 10.1111/nyas.15007
   Boujarwah FA, 2011, ASSETS 11: PROCEEDINGS OF THE 13TH INTERNATIONAL ACM SIGACCESS CONFERENCE ON COMPUTERS AND ACCESSIBILITY, P19
   Boutros F., 2023, IEEE INT JOINT C BIO, P1, DOI DOI 10.1109/IJCB57857.2023.10449036
   Boutros F, 2024, IEEE T BIOM BEHAV ID, V6, P290, DOI 10.1109/TBIOM.2024.3371502
   Boutros F, 2023, IEEE I CONF COMP VIS, P19593, DOI 10.1109/ICCV51070.2023.01800
   Boutros F, 2023, IMAGE VISION COMPUT, V135, DOI 10.1016/j.imavis.2023.104688
   Boutros F, 2022, 2022 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS (IJCB), DOI 10.1109/IJCB54206.2022.10007961
   Bryant BR, 1998, J LEARN DISABIL, V31, P4, DOI 10.1177/002221949803100102
   Burkart K, 2022, LANCET PLANET HEALTH, V6, pE586, DOI 10.1016/S2542-5196(22)00122-X
   Cambria E, 2024, Arxiv, DOI arXiv:2407.15248
   Cetin I, 2023, COMPUT MED IMAG GRAP, V104, DOI 10.1016/j.compmedimag.2022.102158
   Chen BY, 2023, J CHIN ECON BUS STUD, V21, P471, DOI 10.1080/14765284.2023.2245279
   Chen Mark, 2021, arXiv
   Chen YZ, 2022, COMPUT BIOL MED, V144, DOI 10.1016/j.compbiomed.2022.105382
   Chi WQ, 2020, IEEE INT CONF ROBOT, P2414, DOI [10.1109/icra40945.2020.9196912, 10.1109/ICRA40945.2020.9196912]
   Chiang WL, 2024, Arxiv, DOI arXiv:2403.04132
   Chowdhery A, 2023, J MACH LEARN RES, V24
   Cobianchi L, 2023, WORLD J EMERG SURG, V18, DOI 10.1186/s13017-022-00467-3
   Cohn C, 2024, AAAI CONF ARTIF INTE, P23182
   Courtin E, 2017, HEALTH SOC CARE COMM, V25, P799, DOI 10.1111/hsc.12311
   Dakhel AM, 2023, J SYST SOFTWARE, V203, DOI 10.1016/j.jss.2023.111734
   DALL-E OpenAI, 2022, DALL-E now available without waitlist
   Damer N., 2023, 2023 11 INT WORKSH B, P1
   Das BC, 2024, Arxiv, DOI arXiv:2402.00888
   Deniz S., 2019, International Journal of Health Services Research and Policy, V4, P214, DOI DOI 10.23884/IJHSRP.2019.4.3.06
   Devi KP, 2024, DENT TRAUMATOL, V40, P91, DOI 10.1111/edt.12882
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dhingra B., 2019, arXiv, DOI 10.48550/arXiv.1909.06146
   Du YL, 2023, Arxiv, DOI arXiv:2302.00111
   Ebers M, 2021, J, V4, P589, DOI [10.3390/j4040043, DOI 10.3390/J4040043]
   Eddy SR, 1996, CURR OPIN STRUC BIOL, V6, P361, DOI 10.1016/S0959-440X(96)80056-X
   Edgar DL, 2007, LANG SPEECH HEAR SER, V38, P31, DOI 10.1044/0161-1461(2007/004)
   Elbro C, 2011, INT J LANG COMM DIS, V46, P437, DOI 10.1111/j.1460-6984.2011.00004.x
   Eldawlatly Seif, 2024, BMC Biomed Eng, V6, P4, DOI 10.1186/s42490-024-00080-2
   Fazli S, 2015, P IEEE, V103, P891, DOI 10.1109/JPROC.2015.2413993
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Fiora A., 2024, INTED2024 P, P4479, DOI [10.31234/osf.io/epgfy, DOI 10.31234/OSF.IO/EPGFY]
   Gao SC, 2023, PROC CVPR IEEE, P10021, DOI 10.1109/CVPR52729.2023.00966
   Genevay A, 2017, Arxiv, DOI [arXiv:1706.01807, DOI 10.48550/ARXIV.1706.01807]
   Gibney E, 2024, NATURE, V626, P938, DOI 10.1038/d41586-024-00497-8
   Golda A., 2024, IEEE Access
   Goodey CF, 2011, HISTORY OF INTELLIGENCE AND "INTELLECTUAL DISABILITY": THE SHAPING OF PSYCHOLOGY IN EARLY MODERN EUROPE, P1
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Gozzi N, 2022, KNOWL-BASED SYST, V240, DOI 10.1016/j.knosys.2021.108053
   Gribbin J., 2018, Richard Feynman: A Life in Science
   Griffith H, 2023, PROC CONF MOBILE SEC, DOI 10.1109/MOBISECSERV58080.2023.10329224
   Guo XY, 2022, LECT NOTES COMPUT SC, V13559, P187, DOI 10.1007/978-3-031-16760-7_18
   Gursoy D, 2019, INT J INFORM MANAGE, V49, P157, DOI 10.1016/j.ijinfomgt.2019.03.008
   Hajij Mustafa, 2022, 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), P46, DOI 10.1109/HI-POCT54491.2022.9744072
   Han C, 2018, I S BIOMED IMAGING, P734, DOI 10.1109/ISBI.2018.8363678
   He KM, 2016, LECT NOTES COMPUT SC, V9908, P630, DOI 10.1007/978-3-319-46493-0_38
   Heikkinen RL, 2004, ARCH GERONTOL GERIAT, V38, P239, DOI 10.1016/j.archger.2003.10.004
   Helen D., 2024, Revolut. Healthc. Sec. AI, P79
   Hendrycks D, 2021, Arxiv, DOI [arXiv:2009.03300, 10.48550/arXiv.2009.03300]
   Heylen L, 2010, AGEING SOC, V30, P1177, DOI 10.1017/S0144686X10000292
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Holmes W., 2023, Guidance for Generative AI in Education and Research
   Huang D, 2024, Arxiv, DOI [arXiv:2309.14345, DOI 10.48550/ARXIV.2309.14345]
   Huang Y., 2023, Behavior-driven query similarity prediction based on pre-trained language models for e-commerce search
   Iaia V., 2022, GRUR Int., V71, P793
   Islam S, 2024, IEEE ACCESS, V12, P35728, DOI 10.1109/ACCESS.2024.3370848
   Jelassi M, 2024, DIAGNOSTICS, V14, DOI 10.3390/diagnostics14090895
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jin D, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11146421
   Jin LC, 2020, COMPUT INTEL NEUROSC, V2020, DOI 10.1155/2020/1459107
   Jouppi NP, 2023, CONF PROC INT SYMP C, P1147, DOI 10.1145/3579371.3589350
   Karabacak M, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.39305
   Karabacak M, 2023, JMIR MED EDUC, V9, DOI 10.2196/48163
   Karnewar A, 2023, IEEE I CONF COMP VIS, P22919, DOI 10.1109/ICCV51070.2023.02100
   Kazeminia S, 2020, ARTIF INTELL MED, V109, DOI 10.1016/j.artmed.2020.101938
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   Klys J, 2018, ADV NEUR IN, V31
   Korinek A, 2023, J ECON LIT, V61, P1281, DOI 10.1257/jel.20231736
   Korn O., 2012, P 5 INT C PERV TECHN, P1
   Korn O., 2015, Gamification: Concepts, Methodologies, Tools, and Applications, P1936
   Kuzlu M., 2023, 2023 12 MED C EMB CO, P1
   LANGDELL T, 1978, J CHILD PSYCHOL PSYC, V19, P255, DOI 10.1111/j.1469-7610.1978.tb00468.x
   Lemley M.A., 2023, How generative AI turns copyright law on its head
   Lewis P, 2020, ADV NEUR IN, V33
   Li B, 2021, J NEUROL, V268, P2042, DOI 10.1007/s00415-019-09596-3
   Li J., 2023, PMLR, P19730, DOI DOI 10.48550/ARXIV.2301.12597
   Li YJ, 2024, Arxiv, DOI arXiv:2308.10149
   Li YT, 2018, AAAI CONF ARTIF INTE, P7065
   Lim KL, 2020, IEEE SIGNAL PROC LET, V27, P231, DOI 10.1109/LSP.2020.2965328
   Liu HT, 2023, Arxiv, DOI [arXiv:2304.08485, 10.48550/arXiv.2304.08485]
   Lopez R, 2020, MOL SYST BIOL, V16, DOI 10.15252/msb.20199198
   Lucy L., 2021, P 3 WORKSHOP NARRATI, P48, DOI [10.18653/v1/2021.nuse-1.5, DOI 10.18653/V1/2021.NUSE-1.5]
   Lugmayr A, 2022, PROC CVPR IEEE, P11451, DOI 10.1109/CVPR52688.2022.01117
   Lyu Y, 2024, Arxiv, DOI arXiv:2404.15576
   Ma XY, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102362
   Madanian Samaneh, 2023, PEC Innov, V2, P100171, DOI 10.1016/j.pecinn.2023.100171
   Manakul P, 2023, Arxiv, DOI [arXiv:2303.08896, 10.48550/arXiv.2303.08896, DOI 10.48550/ARXIV.2303.08896]
   Mckenna N, 2023, Arxiv, DOI arXiv:2305.14552
   Mercorio Fabio, 2020, Machine Learning and Knowledge Extraction. 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9. International Cross-Domain Conference, CD-MAKE 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12279), P159, DOI 10.1007/978-3-030-57321-8_9
   Merullo J, 2023, Arxiv, DOI arXiv:2209.15162
   Mesnard T., 2024, arXiv, DOI 10.48550/arXiv.2403.08295
   Midjourney @Midjourney, 2022, Twitter Twitter, We're officially moving to openbeta!
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Montagna S., 2023, P 2023 ACM C INF TEC, P205
   Motoki F, 2024, PUBLIC CHOICE, V198, P3, DOI 10.1007/s11127-023-01097-2
   Mukherkjee D, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-12646-y
   Nova K., 2023, J. Adv. Anal. Healthc. Manag, V7, P115
   Nugent Chris, 2023, Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). Lecture Notes in Networks and Systems (835), P202, DOI 10.1007/978-3-031-48306-6_20
   Omiye JA, 2024, ANN INTERN MED, V177, DOI 10.7326/M23-2772
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Ozcelik H, 2018, ACAD MANAGE J, V61, P2343, DOI 10.5465/amj.2015.1066
   Padmanabha A, 2024, Arxiv, DOI arXiv:2404.04066
   Pal A, 2022, PR MACH LEARN RES, V174, P248
   Pani B., 2024, Appl. Gener. AI, P261
   Peng D, 2024, Arxiv, DOI arXiv:2407.01031
   Pu YC, 2016, ADV NEUR IN, V29
   Puryear B., 2022, J. Comput. Sci. Colleges, V38, P37
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Qiao TT, 2019, PROC CVPR IEEE, P1505, DOI 10.1109/CVPR.2019.00160
   Qiu S, 2023, UNIVERSAL ACCESS INF, V22, P609, DOI 10.1007/s10209-021-00852-w
   Radford A., 2023, INT C MACHINE LEARNI, P28492
   Radford A, 2021, PR MACH LEARN RES, V139
   Ramesh A, 2021, PR MACH LEARN RES, V139
   Rauker T, 2023, 2023 IEEE CONFERENCE ON SECURE AND TRUSTWORTHY MACHINE LEARNING, SATML, P464, DOI 10.1109/SaTML54575.2023.00039
   Revell G., 2024, Applications of Generative AI, P189, DOI [10.1007/978-3-031-46238-29, DOI 10.1007/978-3-031-46238-29]
   Reynolds D.A., 2009, Encyclopedia of Biometrics, V741, P659, DOI [DOI 10.1007/978-0-387-73003-5_196, DOI 10.1007/978-0-387-73003-5196]
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Sabo R, 2024, HUM AFF, V34, P224, DOI 10.1515/humaff-2023-0134
   Sai S, 2024, IEEE ACCESS, V12, P31078, DOI 10.1109/ACCESS.2024.3367715
   Saparov A, 2023, Arxiv, DOI arXiv:2210.01240
   Seymour W, 2024, PROCEEDINGS OF THE 6TH CONFERENCE ON ACM CONVERSATIONAL USER INTERFACES, CUI 2024, DOI 10.1145/3640794.3665888
   Shah NH, 2023, JAMA-J AM MED ASSOC, V330, P866, DOI 10.1001/jama.2023.14217
   Silver T., 2022, NEURIPS 2022 FDN MOD
   Singer U, 2022, Arxiv, DOI arXiv:2209.14792
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Stability.ai, 2022, Stability AI, Stable diffusion public release
   Sundström G, 2009, EUR J AGEING, V6, P267, DOI 10.1007/s10433-009-0134-8
   Tang M, 2022, AM J NEURORADIOL, V43, P1164, DOI 10.3174/ajnr.A7576
   Tang YL, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642899
   Tanwani AK, 2017, IEEE INT C INT ROBOT, P43, DOI 10.1109/IROS.2017.8202136
   Tirumala K, 2023, ADV NEUR IN
   Tong XC, 2021, J MED CHEM, V64, P14011, DOI 10.1021/acs.jmedchem.1c00927
   Tonmoy STI, 2024, Arxiv, DOI [arXiv:2401.01313, DOI 10.48550/ARXIV.2401.01313]
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Tseng PH, 2013, J NEUROL, V260, P275, DOI 10.1007/s00415-012-6631-2
   Nguyen TD, 2017, ADV NEUR IN, V30
   Turpin M, 2023, Arxiv, DOI [arXiv:2305.04388, DOI 10.48550/ARXIV.2305.04388, 10.48550/arXiv.2305.04388]
   Tytarenko A, 2024, Arxiv, DOI arXiv:2405.07603
   UNESCO, 2021, Recommendation on the Ethics of Artificial Intelligence, 23 November 2021, SHS/BIO/PI/2021/1
   United Nations Educational Scientific and Cultural Organization (UNESCO), 2021, REIMAGINING OUR FUTU
   Vaswani A, 2017, ADV NEUR IN, V30
   Victor CR, 2002, AGEING SOC, V22, P585, DOI 10.1017/S0144686X02008784
   Wang T, 2024, Arxiv, DOI arXiv:2309.09435
   Wang T, 2024, IEEE T CONSUM ELECTR, V70, P2949, DOI [10.1109/TCE.2022.3225088, 10.1007/s13762-022-03987-2]
   Wang Z, 2024, IEEE T AUTOM SCI ENG, V21, P2469, DOI [10.1109/TASE.2023.3261891, 10.1109/TCNS.2023.3235425, 10.1109/ICASSP49357.2023.10094992]
   Wang ZR, 2022, Arxiv, DOI arXiv:2108.10904
   Wei JS, 2022, Arxiv, DOI [arXiv:2206.07682, DOI 10.48550/ARXIV.2206.07682]
   WEINER MF, 1975, INT J GROUP PSYCHOTH, V25, P239, DOI 10.1080/00207284.1975.11491894
   Welker Yonah, 2023, Generative AI holds great potential for those with disabilities-but it needs policy to shape it
   Wermelinger M, 2023, PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023, P172, DOI 10.1145/3545945.3569830
   West P., 2023, 12 INT C LEARN REPR
   Wilson RS, 2007, ARCH GEN PSYCHIAT, V64, P234, DOI 10.1001/archpsyc.64.2.234
   Wiratunga N, 2024, LECT NOTES ARTIF INT, V14775, P445, DOI 10.1007/978-3-031-63646-2_29
   Wright S.L., 2005, Loneliness in the Workplace
   Wu JZ, 2023, IEEE I CONF COMP VIS, P7589, DOI 10.1109/ICCV51070.2023.00701
   Wu X., 2024, J INF INTELL, V2, P102, DOI DOI 10.1016/J.JIIXD.2023.10.007
   Wu Y., 2023, J. Adv. Res. Educ., V2, P6
   Xie ZH, 2024, J COMPUT ASSIST LEAR, DOI 10.1111/jcal.13032
   Xue J, 2014, GENOME BIOL, V15, DOI 10.1186/s13059-014-0521-0
   Xun SY, 2022, COMPUT BIOL MED, V140, DOI 10.1016/j.compbiomed.2021.105063
   Yang BF, 2024, Arxiv, DOI arXiv:2404.02508
   Yang DC, 2023, IEEE-ACM T AUDIO SPE, V31, P1720, DOI 10.1109/TASLP.2023.3268730
   Yang JF, 2024, ACM T KNOWL DISCOV D, V18, DOI 10.1145/3649506
   Yao JY, 2024, Arxiv, DOI arXiv:2310.01469
   You A, 2022, EYE VISION, V9, DOI 10.1186/s40662-022-00277-3
   Yu Jiang, 2021, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), P3029, DOI 10.1109/BIBM52615.2021.9669554
   Zaccolo S., 2020, Artificial Intelligence as a Creativity Companion
   Zeng Fanlong, 2023, 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), P840, DOI 10.1109/ICPADS60453.2023.00126
   Zhang DZ, 2024, Arxiv, DOI [arXiv:2401.13601, 10.48550/arXiv.240113601, DOI 10.48550/ARXIV.240113601]
   Zhang LM, 2023, IEEE I CONF COMP VIS, P3813, DOI 10.1109/ICCV51070.2023.00355
   Zhang SQ, 2024, Arxiv, DOI arXiv:2407.04418
   Zhang X, 2024, Arxiv, DOI arXiv:2406.09136
   Zhang Z., 2023, NEURIPS 2023 WORKSH
   Zhao SJ, 2019, AAAI CONF ARTIF INTE, P5885
   Zhou X., 2018, Psychology, V09, P1005, DOI [DOI 10.4236/PSYCH.2018.95064, 10.4236/psych.2018.95064]
   Zhu XY, 2024, Arxiv, DOI arXiv:2308.07633
   Ziheng Jiang, 2024, Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation. NSDI '24, P745
NR 193
TC 0
Z9 0
U1 0
U2 0
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0262-8856
EI 1872-8138
J9 IMAGE VISION COMPUT
JI Image Vis. Comput.
PD FEB
PY 2025
VL 154
AR 105347
DI 10.1016/j.imavis.2024.105347
PG 15
WC Computer Science, Artificial Intelligence; Computer Science, Software
   Engineering; Computer Science, Theory & Methods; Engineering, Electrical
   & Electronic; Optics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Optics
GA O9F9A
UT WOS:001374108400001
OA hybrid
DA 2024-12-25
ER

PT J
AU Thanh, BN
   Vo, DTH
   Nhat, MN
   Pham, TTT
   Trung, HT
   Zuan, SH
AF Thanh, Binh Nguyen
   Vo, Diem Thi Hong
   Nhat, Minh Nguyen
   Pham, Thi Thu Tra
   Trung, Hieu Thai
   Zuan, Son Ha
TI Race with the machines: Assessing the capability of generative AI in
   solving authentic assessments
SO AUSTRALASIAN JOURNAL OF EDUCATIONAL TECHNOLOGY
LA English
DT Article
DE authentic assessment; Bloom's taxonomy; generative AI; AI in tertiary
   education; quantitative; case study
ID BLOOMS TAXONOMY; DESIGN; FRAMEWORK; TOOL
AB In this study, we introduce a framework designed to help educators assess the effectiveness of popular generative artificial intelligence (AI) tools in solving authentic assessments. We employed Bloom's taxonomy as a guiding principle to create authentic assessments that evaluate the capabilities of generative AI tools. We applied this framework to assess the abilities of ChatGPT-4, ChatGPT-3.5, Google Bard and Microsoft Bing in solving authentic assessments in economics. We found that generative AI tools perform very well at the lower levels of Bloom's taxonomy while still maintaining a decent level of performance at the higher levels, with "create" being the weakest level of performance. Interestingly, these tools are better able to address numeric-based questions than text-based ones. Moreover, all the generative AI tools exhibit weaknesses in building arguments based on theoretical frameworks, maintaining the coherence of different arguments and providing appropriate references. Our study provides educators with a framework to assess the capabilities of generative AI tools, enabling them to make more informed decisions regarding assessments and learning activities. Our findings demand a strategic reimagining of educational goals and assessments, emphasising higher cognitive skills and calling for a concerted effort to enhance the capabilities of educators in preparing students for a rapidly transforming professional environment.
C1 [Thanh, Binh Nguyen; Vo, Diem Thi Hong; Nhat, Minh Nguyen; Pham, Thi Thu Tra; Trung, Hieu Thai] RMIT Univ Vietnam, Business Sch, Ho Chi Minh City, Vietnam.
C3 Royal Melbourne Institute of Technology (RMIT)
RP Vo, DTH (corresponding author), RMIT Univ Vietnam, Business Sch, Ho Chi Minh City, Vietnam.
EM diem.vo@rmit.edu.vn
RI Vo, Diem/IRY-9015-2023
OI Vo, Diem Thi Hong/0000-0002-5289-2325; Thai, Hieu/0000-0001-9774-4388;
   Pham, Thi Thu Tra/0000-0001-7052-3323; , Binh/0000-0002-5036-8458
CR Nguyen A, 2023, EDUC INF TECHNOL, V28, P4221, DOI 10.1007/s10639-022-11316-w
   Armstrong P., 2010, BLOOMS TAXONOMY
   Ashford-Rowe K, 2014, ASSESS EVAL HIGH EDU, V39, P205, DOI 10.1080/02602938.2013.819566
   Athanassiou N., 2003, Journal of Management Education, V27, P533, DOI DOI 10.1177/1052562903252515
   Attia AS, 2021, J APPL SCI ENG, V24, P315, DOI 10.6180/jase.202106_24(3).0006
   Bloom B. S., 1956, TAXONOMY ED OBJECTIV
   Bosco AM, 2014, ASIA-PAC J COOP EDUC, V15, P281
   Callaghan-Koru JA, 2022, PEDAGOGY HEAL PROMOT, V8, P75, DOI 10.1177/2373379920979684
   Chiu TKF, 2024, INTERACT LEARN ENVIR, V32, P3240, DOI 10.1080/10494820.2023.2172044
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   Crowe A, 2008, CBE-LIFE SCI EDUC, V7, P368, DOI 10.1187/cbe.08-05-0024
   Dai W, 2023, IEEE INT CONF ADV LE, P323, DOI 10.1109/ICALT58122.2023.00100
   Darling-Hammond L, 2000, TEACH TEACH EDUC, V16, P523, DOI 10.1016/S0742-051X(00)00015-9
   Dawson P., 2020, Defending assessment security in a digital world: Preventing e-cheating and supporting academic integrity in higher education, V1st, DOI [10.4324/9780429324178, DOI 10.4324/9780429324178]
   DeMara RF, 2019, EDUC INF TECHNOL, V24, P1147, DOI 10.1007/s10639-018-9812-5
   Easterly W., 2005, HDB EC GROWTH
   Freeman M., 1997, Australian Journal of Educational Technology, V13, P23
   Greenstein L., 2012, Assessing 21st century skills: A guide to evaluating mastery and authentic learning
   Gulikers JTM, 2004, ETR&D-EDUC TECH RES, V52, P67, DOI 10.1007/BF02504676
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Herrington J, 2000, ETR&D-EDUC TECH RES, V48, P23, DOI 10.1007/BF02319856
   Ho HK, 2021, CURR PHARM TEACH LEA, V13, P536, DOI 10.1016/j.cptl.2021.01.007
   Hussey T., 2002, Active learning in higher education, V3, P220, DOI 10.1177/1469787402003003003
   James LT, 2018, STUD HIGH EDUC, V43, P401, DOI 10.1080/03075079.2016.1165659
   Kar S. P., 2022, Learning in the age of digital and green transition, V634, P747, DOI [10.1007/978-3-031-26190, DOI 10.1007/978-3-031-26190]
   Karanja E, 2021, J INT EDUC BUS, V14, P197, DOI 10.1108/JIEB-05-2020-0038
   Katz A, 2023, Arxiv, DOI arXiv:2305.11882
   Kibble JD, 2011, ADV PHYSIOL EDUC, V35, P396, DOI 10.1152/advan.00062.2011
   Koh K.H., 2017, Oxford Research Encyclopedia of Education, DOI DOI 10.1093/ACREFORE/9780190264093.013.22
   Krathwohl DR, 2002, THEOR PRACT, V41, P212, DOI 10.1207/s15430421tip4104_2
   Lau KH, 2018, BENCHMARKING, V25, P2828, DOI 10.1108/BIJ-10-2017-0286
   Lodge JM, 2023, AUSTRALAS J EDUC TEC, V39, P18, DOI 10.14742/ajet.8695
   Manville G., 2022, GILE Journal of Skills Development, V2, P73, DOI [10.52398/gjsd.2022.v2.i2, DOI 10.52398/GJSD.2022.V2.I2]
   Matore MEEM, 2021, CMC-COMPUT MATER CON, V68, P1235, DOI 10.32604/cmc.2021.016143
   Megahed FM, 2024, QUAL ENG, V36, P287, DOI 10.1080/08982112.2023.2206479
   Na Seung-Joo, 2021, Korean J Med Educ, V33, P191, DOI 10.3946/kjme.2021.199
   Neely P., 2012, American Journal of Business Education, V5, P449, DOI [https://doi.org/10.19030/ajbe.v5i4.7122, DOI 10.19030/AJBE.V5I4.7122]
   Pappas E, 2013, J CLEAN PROD, V48, P54, DOI 10.1016/j.jclepro.2012.09.039
   Parwata I. G. A. L., 2023, Emerging Science Journal, V7, P569, DOI [10.28991/ESJ-2023-07-02-019, DOI 10.28991/ESJ-2023-07-02-019]
   Pepin M, 2021, J SMALL BUS ENTERP D, V28, P570, DOI 10.1108/JSBED-02-2020-0035
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Ramirez TV, 2017, J PERS ASSESS, V99, P146, DOI 10.1080/00223891.2016.1167059
   Samuelson P.A., 2013, Economics, V19th
   SHANE HG, 1981, PHI DELTA KAPPAN, V62, P311
   Stanny CJ, 2016, EDUC SCI, V6, DOI 10.3390/educsci6040037
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   Su JH, 2023, ECNU REV EDUC, V6, P355, DOI 10.1177/20965311231168423
   Su YF, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100752
   Tai J, 2018, HIGH EDUC, V76, P467, DOI 10.1007/s10734-017-0220-3
   Taylor JB, 2000, AM ECON REV, V90, P90, DOI 10.1257/aer.90.2.90
   Thompson AR, 2015, ANAT SCI EDUC, V8, P493, DOI 10.1002/ase.1507
   Thompson AR, 2013, ADV PHYSIOL EDUC, V37, P370, DOI 10.1152/advan.00015.2013
   Tian YC, 2024, Arxiv, DOI [arXiv:2305.18149, 10.48550/ARXIV.2305.18149]
   Villarroel V, 2018, ASSESS EVAL HIGH EDU, V43, P840, DOI 10.1080/02602938.2017.1412396
   West J., 2023, Social Sciences Humanities Open, V8, P100620, DOI DOI 10.1016/J.SSAHO.2023.100620
   Wiewiora A, 2019, ASSESS EVAL HIGH EDU, V44, P415, DOI 10.1080/02602938.2018.1516730
   Wiggins G.P., 1993, Assessing student pe1jormance: Exploring the purpose and limits qj"testing
   Woldab ZE., 2013, ACAD J INTERDISCIPLI, V1, P197
   Zaidi NB, 2017, ANAT SCI EDUC, V10, P456, DOI 10.1002/ase.1685
   Zheng AY, 2008, SCIENCE, V319, P414, DOI 10.1126/science.1147852
NR 62
TC 14
Z9 14
U1 45
U2 97
PU AUSTRALASIAN SOC COMPUTERS LEARNING TERTIARY EDUCATION-ASCILITE
PI TUGUN
PA UNIT 5, 202 COODE ST, PO BOX 350, TUGUN, 4224, AUSTRALIA
SN 1449-3098
EI 1449-5554
J9 AUSTRALAS J EDUC TEC
JI Australas. J. Educ. Technol.
PY 2023
VL 39
IS 5
BP 59
EP 81
DI 10.14742/ajet.8902
PG 23
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA DO3Z6
UT WOS:001132966400001
OA gold
DA 2024-12-25
ER

PT J
AU Niu, ZQ
   Zhao, WH
   Deng, H
   Tian, L
   Pinfield, VJ
   Ming, PW
   Wang, Y
AF Niu, Zhiqiang
   Zhao, Wanhui
   Deng, Hao
   Tian, Lu
   Pinfield, Valerie J.
   Ming, Pingwen
   Wang, Yun
TI Generative Artificial Intelligence for Designing Multi-Scale Hydrogen
   Fuel Cell Catalyst Layer Nanostructures
SO ACS NANO
LA English
DT Article
DE fuel cells; generative artificial intelligence; multiscale design;
   multiphysics; catalyst layer
ID LATTICE BOLTZMANN-EQUATION; MASS-TRANSFER; PERFORMANCE; IONOMER; MODEL
AB Multiscale design of catalyst layers (CLs) is important to advancing hydrogen electrochemical conversion devices toward commercialized deployment, which has nevertheless been greatly hampered by the complex interplay among multiscale CL components, high synthesis cost and vast design space. We lack rational design and optimization techniques that can accurately reflect the nanostructure-performance relationship and cost-effectively search the design space. Here, we fill this gap with a deep generative artificial intelligence (AI) framework, GLIDER, that integrates recent generative AI, data-driven surrogate techniques and collective intelligence to efficiently search the optimal CL nanostructures driven by their electrochemical performance. GLIDER achieves realistic multiscale CL digital generation by leveraging the dimensionality-reduction ability of quantized vector-variational autoencoder. The powerful generative capability of GLIDER allows the efficient search of the optimal design parameters for the Pt-carbon-ionomer nanostructures of CLs. We also demonstrate that GLIDER is transferable to other fuel cell electrode microstructure generation, e.g., fibrous gas diffusion layers and solid oxide fuel cell anode. GLIDER is of potential as a digital tool for the design and optimization of broad electrochemical energy devices.
C1 [Niu, Zhiqiang; Tian, Lu] Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, England.
   [Zhao, Wanhui] Civil Aviat Univ China, Coll Aeronaut Engn, Tianjin 300300, Peoples R China.
   [Deng, Hao] Shanghai Hydrogen Prop Technol Co Ltd, Shanghai, Peoples R China.
   [Pinfield, Valerie J.] Loughborough Univ, Dept Chem Engn, Loughborough LE11 3TU, England.
   [Ming, Pingwen] Tongji Univ, Clean Energy Automot Engn Ctr, Sch Automot Studies, Shanghai 201804, Peoples R China.
   [Wang, Yun] Univ Calif Irvine, Dept Mech & Aerosp Engn, Renewable Energy Resources Lab, Irvine, CA 92697 USA.
C3 Loughborough University; Civil Aviation University of China;
   Loughborough University; Tongji University; University of California
   System; University of California Irvine
RP Niu, ZQ (corresponding author), Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, England.; Wang, Y (corresponding author), Univ Calif Irvine, Dept Mech & Aerosp Engn, Renewable Energy Resources Lab, Irvine, CA 92697 USA.
EM z.niu@lboro.ac.uk; yunw@uci.edu
RI MING, Pingwen/AFW-8740-2022; wang, yun/A-7748-2010; Niu,
   Zhiqiang/JVO-1192-2024
OI Niu, Zhiqiang/0000-0001-9220-282X; wang, yun/0000-0003-2035-3148
FU National Natural Science Foundation of China [52206187]
FX The authors would like to acknowledge the support of National Natural
   Science Foundation of China (grant no. 52206187).
CR Babu KV, 2022, INT J HYDROGEN ENERG, V47, P4018, DOI 10.1016/j.ijhydene.2021.11.006
   Chen L, 2020, CHEM ENG J, V391, DOI 10.1016/j.cej.2019.123590
   Chong L, 2018, SCIENCE, V362, P1276, DOI 10.1126/science.aau0630
   Dahari A, 2023, ADV ENERGY MATER, V13, DOI 10.1002/aenm.202202407
   Denisart L, 2024, BATTERIES SUPERCAPS, V7, DOI 10.1002/batt.202300268
   Ebrahimi S, 2017, RENEW ENERG, V113, P846, DOI 10.1016/j.renene.2017.06.067
   Franco AA, 2020, BATTERIES SUPERCAPS, V3, P1147, DOI 10.1002/batt.202000120
   Girod R, 2023, NAT CATAL, V6, P383, DOI 10.1038/s41929-023-00947-y
   He YS, 2023, J ELECTROCHEM SOC, V170, DOI 10.1149/1945-7111/acc551
   Hsu T, 2018, J POWER SOURCES, V386, P1, DOI 10.1016/j.jpowsour.2018.03.025
   Inoue G, 2016, J POWER SOURCES, V327, P1, DOI 10.1016/j.jpowsour.2016.07.037
   Ishikawa H, 2018, J POWER SOURCES, V374, P196, DOI 10.1016/j.jpowsour.2017.11.026
   Kang YH, 2023, NAT MACH INTELL, V5, P309, DOI 10.1038/s42256-023-00628-2
   Kench S, 2021, NAT MACH INTELL, V3, P299, DOI 10.1038/s42256-021-00322-1
   Lai C.-H., 2016, PARTICLE SWARM OPTIM
   Lang JT, 2023, CHEM REV, DOI 10.1021/acs.chemrev.2c00873
   Li X, 2022, NANOSCALE HORIZ, V7, DOI 10.1039/d1nh00501d
   Lu JH, 2019, CHEM ENG SCI, V199, P319, DOI 10.1016/j.ces.2019.01.021
   Lu JH, 2019, INT J HEAT MASS TRAN, V132, P519, DOI 10.1016/j.ijheatmasstransfer.2018.12.010
   Niu ZQ, 2023, ADV ENERGY MATER, V13, DOI 10.1002/aenm.202300244
   Niu ZQ, 2021, ENERG ENVIRON SCI, V14, P2549, DOI 10.1039/d1ee00398d
   Niu ZQ, 2018, J ELECTROCHEM SOC, V165, pF986, DOI 10.1149/2.1191811jes
   Normile SJ, 2019, SOLID STATE IONICS, V335, P38, DOI 10.1016/j.ssi.2019.02.017
   Peng X, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-17370-7
   Razavi A, 2019, ADV NEUR IN, V32
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Sadeghi MA, 2024, APPL ENERG, V353, DOI 10.1016/j.apenergy.2023.122004
   Sanchez-Lengeling B, 2018, SCIENCE, V361, P360, DOI 10.1126/science.aat2663
   Schilter O, 2023, DIGIT DISCOV, V2, P728, DOI 10.1039/d2dd00125j
   Schneider P, 2023, J ELECTROCHEM SOC, V170, DOI 10.1149/1945-7111/acb8df
   Steinbach AJ, 2018, JOULE, V2, P1297, DOI 10.1016/j.joule.2018.03.022
   Suter TAM, 2021, ADV ENERGY MATER, V11, DOI 10.1002/aenm.202101025
   Tsukamoto T, 2021, J POWER SOURCES, V488, DOI 10.1016/j.jpowsour.2020.229412
   US DRIVE Partnership, FUEL CELL TECHNICAL
   van den Oord A, 2016, ADV NEUR IN, V29
   van den Oord A, 2017, ADV NEUR IN, V30
   Wang Y, 2005, ELECTROCHIM ACTA, V50, P1307, DOI 10.1016/j.electacta.2004.08.022
   Wang Y, 2022, ENERG ENVIRON SCI, V15, P2288, DOI 10.1039/d2ee00790h
   Westermayr J, 2023, NAT COMPUT SCI, V3, P139, DOI 10.1038/s43588-022-00391-1
   Yang TT, 2023, J AM CHEM SOC, V145, P26817, DOI 10.1021/jacs.3c09299
   Yao ZP, 2021, NAT MACH INTELL, V3, P76, DOI 10.1038/s42256-020-00271-1
   Zenyuk IV, 2016, J POWER SOURCES, V328, P364, DOI 10.1016/j.jpowsour.2016.08.020
   Zhao WH, 2023, ENERG CONVERS MANAGE, V280, DOI 10.1016/j.enconman.2023.116791
   Zheng WB, 2018, J ELECTROCHEM SOC, V165, pF468, DOI 10.1149/2.0711807jes
NR 44
TC 4
Z9 4
U1 23
U2 23
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 1936-0851
EI 1936-086X
J9 ACS NANO
JI ACS Nano
PD JUL 10
PY 2024
VL 18
IS 31
BP 20504
EP 20517
DI 10.1021/acsnano.4c04943
EA JUL 2024
PG 14
WC Chemistry, Multidisciplinary; Chemistry, Physical; Nanoscience &
   Nanotechnology; Materials Science, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Science & Technology - Other Topics; Materials Science
GA A9R0U
UT WOS:001279790700001
PM 38984372
OA hybrid, Green Published
DA 2024-12-25
ER

PT J
AU Ulnicane, I
AF Ulnicane, Inga
TI Governance fix? Power and politics in controversies about governing
   generative AI
SO POLICY AND SOCIETY
LA English
DT Article; Early Access
DE generative AI; governance; artificial intelligence; Responsible
   Innovation; risk
ID ARTIFICIAL-INTELLIGENCE; TECHNOLOGY; INNOVATION
AB The launch of ChatGPT in late 2022 led to major controversies about the governance of generative artificial intelligence (AI). This article examines the first international governance and policy initiatives dedicated specifically to generative AI: the G7 Hiroshima process, the Organisation for Economic Cooperation and Development reports, and the UK AI Safety Summit. This analysis is informed by policy framing and governance literature, in particular by the work on technology governance and Responsible Innovation. Emerging governance of generative AI exhibits characteristics of polycentric governance, where multiple and overlapping centers of decision-making are in collaborative relationships. However, it is dominated by a limited number of developed countries. The governance of generative AI is mostly framed in terms of the risk management, largely neglecting issues of purpose and direction of innovation, and assigning rather limited roles to the public. We can see a "paradox of generative AI governance" emerging, namely, that while this technology is being widely used by the public, its governance is rather narrow. This article coins the term "governance fix" to capture this rather narrow and technocratic approach to governing generative AI. As an alternative, it suggests embracing the politics of polycentric governance and Responsible Innovation that highlight democratic and participatory co-shaping of technology for social benefit. In the context of the highly unequal distribution of power in generative AI characterized by a high concentration of power in a small number of large tech companies, the government has a special role in reshaping the power imbalances by enabling wide-ranging public participation in the governance of generative AI.
C1 [Ulnicane, Inga] Univ Birmingham, Birmingham B15 2TT, England.
C3 University of Birmingham
RP Ulnicane, I (corresponding author), Univ Birmingham, Birmingham B15 2TT, England.
EM i.ulnicane@bham.ac.uk
RI Ulnicane, Inga/ABI-5607-2020
OI Ulnicane, Inga/0000-0003-2051-1265
FX Helpful comments and suggestions from two anonymous reviewers are
   gratefully acknowledged. Many thanks to the special issue editor
   Professor Araz Taeihagh for his encouragement and support. This article
   has benefited from discussions of earlier versions at the special issue
   workshop at the Lee Kuan Yew School of Public Policy at the National
   University of Singapore, October 2023 and at the workshop 'AI and
   ChatGPT in public policy and decision making' at the Center for
   Computing and Social Responsibility, De Montfort University, Leicester,
   December 2023.
CR Aguerre C., 2024, Global digital data governance: Polycentric perspectives
   AI Fringe, 2024, AI for everyone. 30 October-3 November 2023. Perspectives from the AI Fringe
   AI Safety Summit, 2023, AI SAF SUMM NOV 1 2
   [Anonymous], 2019, Organisation for Economic Co-Operation and Development Website
   [Anonymous], 2023, Hiroshima Process International Guiding Principles for Organizations Developing Advanced AI System, P5
   Ansell C., 2022, Handbook on theories of governance
   Bacchi Carol., 2000, DISCOURSE, V21, P45
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bertuzzi L., Euractiv
   Browne Jude, 2023, Feminist AI: Critical Perspectives on Algorithms, Data, and Intelligent Machines, P328, DOI [10.1093/oso/9780192889898.003.0019, DOI 10.1093/OSO/9780192889898.003.0019]
   Carlisle K, 2019, POLICY STUD J, V47, P921, DOI 10.1111/psj.12212
   Center for AI Safety, 2023, Statement on AI risk
   Chhotray V, 2008, GOVERNANCE THEORY AND PRACTICE - A CROSS-DISCIPLINARY APPROACH, P1, DOI 10.1057/9780230583344
   Cihon P, 2020, GLOB POLICY, V11, P545, DOI 10.1111/1758-5899.12890
   Clarke L., Politico
   Connected by Data etal, 2023, AI safety summit: Open letter to the UK Prime Minister
   Crawford K, 2024, NATURE, V626, P693, DOI 10.1038/d41586-024-00478-x
   Crawford Kate., 2021, The Atlas of AI
   de Saille S, 2015, J RESPONSIBLE INNOV, V2, P152, DOI 10.1080/23299460.2015.1045280
   DSIT (Department for Science Innovation Technology), 2023, AI Safety Summit, hosted by the UK
   European Commission, 2023, Commission welcomes G7 leaders' agreement on Guiding Principles and a Code of Conduct on Artificial Intelligence
   Filgueiras F., 2021, GOVERNANCE DIGITAL W
   Fisher E, 2024, J RESPONSIBLE INNOV, V11, DOI 10.1080/23299460.2024.2309060
   Future of Life Institute, 2023, Pause giant AI experiments: An open letter
   G7, 2023, G7 Leaders' Statement on Economic Resilience and Economic Security
   G7, 2023, G7 Hiroshima Leaders' Communique
   Galanos V, 2019, TECHNOL ANAL STRATEG, V31, P421, DOI 10.1080/09537325.2018.1518521
   Gebru T, 2023, letter
   Giles P, 2019, J CULT ECON-UK, V12, P612, DOI 10.1080/17530350.2019.1639068
   Johnston SF, 2017, HIST TECHNOL, V33, P196, DOI 10.1080/07341512.2017.1336851
   Khanal S, 2024, POLICY SOC, DOI 10.1093/polsoc/puae012
   Levi-Faur David, 2012, The Oxford Handbook of Governance
   McQuillan D, 2018, SOC MEDIA SOC, V4, DOI 10.1177/2056305118768303
   Morozov Evgeny., 2011, The Net Delusion: How Not to Liberate the World
   Mouriquand D., Euronews
   Mügge D, 2024, J EUR PUBLIC POLICY, V31, P2200, DOI 10.1080/13501763.2024.2318475
   Noble SU, 2018, ALGORITHMS OF OPPRESSION, P1
   OECD, 2023, OECD Digital Economy Papers. April 2023 No. 352
   OECD, 2023, OECD Artificial Intelligence papers September 2023 No. 1
   OECD, 2023, Report prepared for the 2023 G7 presidency and the G7 digital and tech working group
   Ostrom E, 2010, GLOBAL ENVIRON CHANG, V20, P550, DOI 10.1016/j.gloenvcha.2010.07.004
   Owen R, 2012, SCI PUBL POLICY, V39, P751, DOI 10.1093/scipol/scs093
   Peters G.B., 2012, The Oxford Handbook of Governance, P19
   Pierre Jon., 2021, ADV INTRO GOVERNANCE
   Pringle Eleanor., 2023, Fortune
   Radu R, 2021, POLICY SOC, V40, P178, DOI 10.1080/14494035.2021.1929728
   Rein Martin., 1996, Knowledge Policy, V9, P85, DOI [DOI 10.1007/BF02832235, 10.1007/BF02832235]
   Roberts H, 2024, INT AFF, V100, P1275, DOI 10.1093/ia/iiae073
   Rosner Lisa., 2004, The Technological Fix: How People Use Technology to Create and Solve Problems
   Schiff DS, 2023, REV POLICY RES, V40, P729, DOI 10.1111/ropr.12535
   Schmitt L., 2022, AI and Ethics, V2, P303, DOI [DOI 10.1007/S43681-021-00083-Y, 10.1007/s43681-021-00083y, DOI 10.1007/S43681-021-00083Y]
   Schon D.A.M. Rein., 1994, FRAME REFLECTION RES
   Stilgoe J, 2013, RES POLICY, V42, P1568, DOI 10.1016/j.respol.2013.05.008
   Sunak R., 2023, The Royal Society
   Taeihagh A, 2021, POLICY SOC, V40, P137, DOI 10.1080/14494035.2021.1928377
   Taylor Josh, 2023, The Guardian
   The White House, 2023, Remarks by Vice President Kamala Harris on the Future of Artificial Intelligence
   UK Government, 2023, Introduction to the AI Safety Summit
   Ulnicane I, 2023, REV POLICY RES, V40, P612, DOI 10.1111/ropr.12574
   Ulnicane I, 2022, GLOB PUBLIC POLICY G, V2, P326, DOI 10.1007/s43508-022-00049-8
   Ulnicane I, 2021, POLICY SOC, V40, P158, DOI 10.1080/14494035.2020.1855800
   van Hulst M, 2016, AM REV PUBLIC ADM, V46, P92, DOI 10.1177/0275074014533142
   Veale M, 2023, ANNU REV LAW SOC SCI, V19, P255, DOI 10.1146/annurev-lawsocsci-020223-040749
   Vilakazi T, 2019, REV AFR POLIT ECON, V46, P369, DOI 10.1080/03056244.2018.1536974
   Von der Leyen U., 2023, Strasbourg
   WEINBERG AM, 1966, B ATOM SCI, V22, P4, DOI 10.1080/00963402.1966.11454993
   Whittaker M., 2021, Interactions, V28, P50, DOI [DOI 10.1145/3488666, 10.1145/3488666]
   Wong J.C., 2020, GUARDIAN
NR 68
TC 1
Z9 1
U1 91
U2 91
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1449-4035
EI 1839-3373
J9 POLICY SOC
JI Policy Soc.
PD 2024 JUL 2
PY 2024
DI 10.1093/polsoc/puae022
EA JUL 2024
PG 15
WC Political Science; Public Administration
WE Social Science Citation Index (SSCI)
SC Government & Law; Public Administration
GA XD2U3
UT WOS:001259689200001
OA gold
DA 2024-12-25
ER

PT S
AU Sandhu, R
   Cheema, GS
   Channi, HK
   Kaur, M
   Ghai, D
AF Sandhu, Ramandeep
   Cheema, Gagandeep Singh
   Channi, Harpreet Kaur
   Kaur, Mandeep
   Ghai, Deepika
BA Doshi, R
   Dadhich, M
   Poddar, S
   Hiran, KK
BF Doshi, R
   Dadhich, M
   Poddar, S
   Hiran, KK
TI An Introduction to Generative AI Tools for Education 2030
SO INTEGRATING GENERATIVE AI IN EDUCATION TO ACHIEVE SUSTAINABLE
   DEVELOPMENT GOALS
SE Advances in Educational Technologies and Instructional Design Book
   Series
LA English
DT Article; Book Chapter
AB The year 2030 marks a significant juncture in the evolution of education, where Generative Artificial Intelligence (AI) tools are poised to revolutionize the learning experience. In education society, the importance of generative AI is to improve the accessibility of learning at the global level so that personalized learning experiences can be provided to every learner as per their needs. This chapter explores the multifaceted role of generative AI tools in reshaping educational practices, envisioning a future where these tools foster personalized, adaptive, and engaging learning environments. Generative AI tools, characterized by their ability to create and adapt content autonomously, are instrumental in tailoring educational materials to individual learner needs. This chapter surveys the landscape of generative AI applications in education, including content generation, interactive simulations, intelligent tutoring systems, and dynamic learning pathways. These tools aim to provide adaptive, context-aware learning experiences that cater to diverse learning styles and preferences. The adaptability of generative AI tools extends to the creation of personalized learning pathways. By leveraging data analytics and machine learning algorithms, these tools dynamically adjust content delivery, pacing, and complexity, ensuring that each learner's educational journey is optimized for their unique requirements. The discussion encompasses the potential of generative AI tools to support both formal and informal learning settings. Generative AI tools also play a crucial role in promoting inclusivity in education. By generating diverse and culturally relevant content, these tools contribute to breaking down barriers and addressing disparities in access to quality education. This chapter explores how generative AI can be leveraged to create content that resonates with learners from different backgrounds, fostering a more inclusive educational landscape.
C1 [Sandhu, Ramandeep; Cheema, Gagandeep Singh; Ghai, Deepika] Lovely Profess Univ, Sch Comp Sci & Engn, Phagwara, Punjab, India.
   [Channi, Harpreet Kaur] Chandigarh Univ, Mohali, Punjab, India.
   [Kaur, Mandeep] Chandigarh Univ, Dept Comp Sci & Engn, Mohali, Punjab, India.
C3 Lovely Professional University; Chandigarh University; Chandigarh
   University
RP Sandhu, R (corresponding author), Lovely Profess Univ, Sch Comp Sci & Engn, Phagwara, Punjab, India.
RI Lyhne, Ivar/HII-7094-2022; Geissler, Gesa/D-5578-2018
OI Jiricka-Purrer, Alexandra/0000-0002-6842-1835; Sandhu,
   Dr.Ramandeep/0000-0003-2595-4030; Geissler, Gesa/0000-0001-8559-9320;
   Sandfort, Robin/0000-0001-7452-5959; Uhlhorn,
   Birthe/0000-0001-7287-117X; Lyhne, Ivar/0000-0002-0338-0593
CR Abdullah MA, 2022, I COMP CONF WAVELET, DOI 10.1109/ICCWAMTIP56608.2022.10016485
   Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Akgun Selin, 2022, AI Ethics, V2, P431, DOI 10.1007/s43681-021-00096-7
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Castelli M, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12094127
   Dai J, 2020, IEEE-ASME T MECH, V25, P2252, DOI 10.1109/TMECH.2020.3012179
   De Oliveira Silva A., 2020, REV ARTIFICIAL INTEL, V1, P05, DOI [10.37497/rev.artif.intell.education.v1i00.5, DOI 10.37497/REV.ARTIF.INTELL.EDUCATION.V1I00.5]
   Doroudi S, 2023, INT J ARTIF INTELL E, V33, P885, DOI 10.1007/s40593-022-00313-2
   Eglash R, 2020, BRIT J EDUC TECHNOL, V51, P1334, DOI 10.1111/bjet.12963
   Faiz M, 2023, International Journal of Intelligent Systems and Applications in Engineering, V11, P685
   Feng YH, 2023, P INT COMP SOFTW APP, P876, DOI 10.1109/COMPSAC57700.2023.00117
   Ferrara E, 2024, J COMPUT SOC SCI, V7, P549, DOI 10.1007/s42001-024-00250-1
   Furey H., 2019, AI Matters, V4, P13, DOI [DOI 10.1145/3299758.3299764, 10.1145/3299758.3299764]
   Gao P, 2021, MOBILE NETW APPL, V26, P2123, DOI 10.1007/s11036-021-01777-7
   Ghai D., 2022, MACHINE LEARNING ALG, DOI [10.1002/9781119861850, DOI 10.1002/9781119861850]
   Huang XD, 2021, EDUC INF TECHNOL, V26, P5127, DOI 10.1007/s10639-021-10530-2
   Hughes RT, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.604234
   Humble N., 2022, DISCOVER ARTIFICIAL, V2, DOI DOI 10.1007/S44163-022-00039-Z
   Karras T, 2019, PROC CVPR IEEE, P4396, DOI 10.1109/CVPR.2019.00453
   Kingma DP, 2019, FOUND TRENDS MACH LE, V12, P4, DOI 10.1561/2200000056
   Laupichler M.C., 2022, Comput. Educ. Artif. Intell, V3, P100101, DOI DOI 10.1016/J.CAEAI.2022.100101
   Lehmann Florian, 2020, i-com: Journal of Interactive Media, V19, P251, DOI 10.1515/icom-2020-0025
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Macpherson T, 2021, NEURAL NETWORKS, V144, P603, DOI 10.1016/j.neunet.2021.09.018
   Megahed FM, 2024, QUAL ENG, V36, P287, DOI 10.1080/08982112.2023.2206479
   Mogavi R. H., 2023, ARXIV
   Ovalle A., 2023, ARXIV
   Salas-Pilco SZ, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00326-w
   Santos R. P. D., 2023, ARXIV
   Terwiesch C., 2023, Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
   Zastudil C., 2023, 2023 IEEE FRONT ED C, P1
   Zhai XS, 2021, COMPLEXITY, V2021, DOI 10.1155/2021/8812542
   Zhang YD, 2022, COMPUT INTEL NEUROSC, V2022, DOI 10.1155/2022/7655001
NR 34
TC 7
Z9 7
U1 13
U2 17
PU IGI GLOBAL
PI HERSEY
PA 701 E CHOCOLATE AVE, STE 200, HERSEY, PA 17033-1240 USA
SN 2326-8905
EI 2326-8913
BN 979-8-36934-691-4; 979-8-36932-441-7; 979-8-36932-440-0
J9 ADV EDUC TECHNOL INS
PY 2024
BP 1
EP 28
DI 10.4018/979-8-3693-2440-0.ch001
D2 10.4018/979-8-3693-2440-0
PG 28
WC Computer Science, Interdisciplinary Applications; Education &
   Educational Research; Education, Scientific Disciplines
WE Book Citation Index – Social Sciences & Humanities (BKCI-SSH); Book Citation Index – Science (BKCI-S)
SC Computer Science; Education & Educational Research
GA BX2XE
UT WOS:001271037500003
DA 2024-12-25
ER

PT J
AU Mattalo, B
AF Mattalo, Brandon
TI Artificial Intelligence: The Future of Pedagogy
SO JOURNAL OF LEGAL STUDIES EDUCATION
LA English
DT Article
AB While it is important to research the negative impact of generative artificial intelligence on academic integrity, academics should focus most of their efforts on the opportunities these technologies present for improving pedagogical practices. In this note, I attempt to flip the narrative from one of fear to one of opportunity. I suggest that academics should research the use of generative AI to improve teaching effectiveness and efficiency. I offer various practical suggestions on how these tools can be used to advance pedagogical practices, with specific business law examples.
C1 [Mattalo, Brandon] Wilfrid Laurier Univ Waterloo, Lazaridis Sch Business & Econ, Waterloo, ON, Canada.
RP Mattalo, B (corresponding author), Wilfrid Laurier Univ Waterloo, Lazaridis Sch Business & Econ, Waterloo, ON, Canada.
EM bmattalo@wlu.ca
NR 0
TC 0
Z9 0
U1 2
U2 9
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0896-5811
EI 1744-1722
J9 J LEG STUD EDUC
JI J. Leg. Stud. Educ.
PD DEC
PY 2024
VL 41
IS 1
BP 49
EP 71
DI 10.1111/jlse.12146
EA FEB 2024
PG 23
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA KA6I4
UT WOS:001154667400001
OA hybrid
DA 2024-12-25
ER

PT J
AU Xing, YX
AF Xing, Yixun
TI Exploring the use of ChatGPT in learning and instructing statistics and
   data analytics
SO TEACHING STATISTICS
LA English
DT Article
DE data science; generative artificial intelligence; teaching statistics
AB Generative artificial intelligence (AI) has shown the potential to reshape the world and redefine daily workflows. One specific instance of generative AI, ChatGPT, specializes in understanding natural language and generating human-like conversational text. Its free access, user-friendly interface, and instant feedback have propelled its popularity within and beyond education. Given its extensive knowledge of traditional statistics and contemporary data science, it can be considered for integration into modern statistics education. However, there have been ongoing questions and serious concerns regarding the accuracy and accountability of the responses generated by ChatGPT. This study explores ChatGPT's capabilities in addressing conceptual problems, implementing analytical techniques, and facilitating teaching while considering its disadvantages and ongoing development. With continued practice and deeper insights into this novel technology, its benefits can be cautiously leveraged in teaching and learning activities.
C1 [Xing, Yixun] Univ North Texas, Dept Adv Data Analyt, Denton, TX 76203 USA.
C3 University of North Texas System; University of North Texas Denton
RP Xing, YX (corresponding author), Univ North Texas, Dept Adv Data Analyt, Denton, TX 76203 USA.
EM yixun.xing@unt.edu
CR Almahri FAJ, 2020, 2020 THE 6TH IEEE INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT (ICIM 2020), P284, DOI [10.1109/ICIM49319.2020.244712, 10.1109/icim49319.2020.244712]
   [Anonymous], 2023, PREPRINTS
   Beltagy I, 2019, 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019), P3615
   Brown T. B., 2020, ARXIV200514165
   Cain W, 2024, TECHTRENDS, V68, P47, DOI 10.1007/s11528-023-00896-0
   Chocarro R, 2023, EDUC STUD-UK, V49, P295, DOI 10.1080/03055698.2020.1850426
   Clark TM, 2023, J CHEM EDUC, V100, P1905, DOI 10.1021/acs.jchemed.3c00027
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Day T, 2023, PROF GEOGR, V75, P1024, DOI 10.1080/00330124.2023.2190373
   Devlin J., 2018, ARXIV
   Ellis AR, 2023, J STAT DATA SCI EDUC, V31, P128, DOI 10.1080/26939169.2023.2223609
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Gregorcic B., 2023, Physics Education, V58
   Hwang GJ, 2023, EDUC TECHNOL SOC, V26, DOI 10.30191/ETS.202304_26(2).0014
   Jungwirth David, 2023, Int J Environ Res Public Health, V20, DOI 10.3390/ijerph20054541
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Lee J, 2020, BIOINFORMATICS, V36, P1234, DOI 10.1093/bioinformatics/btz682
   Lee U., 2023, ED INFORM TECHNOL, DOI [10.1007/s10639-023-12249-8, DOI 10.1007/S10639]
   Liu Y., 2019, RoBERTa: A Robustly Optimized BERT Pretraining Approach
   Siegerink B, 2023, NURSE EDUC PRACT, V68, DOI 10.1016/j.nepr.2023.103599
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   Sun GH, 2023, NURS EDUC, V48, P119, DOI 10.1097/NNE.0000000000001390
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Wang J, 2023, AM J PHYS, V91, P255, DOI 10.1119/5.0145897
   Wang LKP, 2023, MED TEACH, V45, P925, DOI 10.1080/0142159X.2023.2198663
   Wardat Y., 2023, Eurasia Journal of Mathematics, Science and Technology Education, V19, DOI DOI 10.29333/EJMSTE/13272
   Yang JF, 2024, ACM T KNOWL DISCOV D, V18, DOI 10.1145/3649506
   Yang ZL, 2019, ADV NEUR IN, V32
   Zhu JJ, 2023, ENVIRON SCI TECHNOL, DOI 10.1021/acs.est.3c01818
NR 30
TC 2
Z9 2
U1 10
U2 26
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0141-982X
EI 1467-9639
J9 TEACH STAT
JI Teach. Stat.
PD APR
PY 2024
VL 46
IS 2
BP 95
EP 104
DI 10.1111/test.12367
EA APR 2024
PG 10
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA NS1G7
UT WOS:001199868700001
DA 2024-12-25
ER

PT J
AU Golda, A
   Mekonen, K
   Pandey, A
   Singh, A
   Hassija, V
   Chamola, V
   Sikdar, B
AF Golda, Abenezer
   Mekonen, Kidus
   Pandey, Amit
   Singh, Anushka
   Hassija, Vikas
   Chamola, Vinay
   Sikdar, Biplab
TI Privacy and Security Concerns in Generative AI: A Comprehensive Survey
SO IEEE ACCESS
LA English
DT Article
DE Generative artificial intelligence; privacy concerns; security concerns;
   deep learning; adversarial attacks; synthetic data; Deepfake; ethical
   implications; cybersecurity; machine learning; privacy protection;
   ethical responsibility; misinformation; social engineering; regulatory
   compliance; artificial intelligence; privacy preservation; data
   security; threat analysis
ID ARTIFICIAL-INTELLIGENCE; NEURAL-NETWORK; DEEP; REPRESENTATION;
   AUTOENCODERS; RECOGNITION; INFORMATION; BLOCKCHAIN; ATTACKS; SYSTEM
AB Generative Artificial Intelligence (GAI) has sparked a transformative wave across various domains, including machine learning, healthcare, business, and entertainment, owing to its remarkable ability to generate lifelike data. This comprehensive survey offers a meticulous examination of the privacy and security challenges inherent to GAI. It provides five pivotal perspectives essential for a comprehensive understanding of these intricacies. The paper encompasses discussions on GAI architectures, diverse generative model types, practical applications, and recent advancements within the field. In addition, it highlights current security strategies and proposes sustainable solutions, emphasizing user, developer, institutional, and policymaker involvement.
C1 [Golda, Abenezer; Mekonen, Kidus; Singh, Anushka; Hassija, Vikas] Kalinga Inst Ind Technol, Sch Comp Sci Engn, Bhubaneswar 751024, India.
   [Pandey, Amit] Bennett Univ, Sch Comp Sci Engn & Technol, Greater Noida 201310, India.
   [Chamola, Vinay] Birla Inst Technol & Sci BITS Pilani, Dept Elect & Elect, Pilani 333031, Rajasthan, India.
   [Chamola, Vinay] Birla Inst Technol & Sci BITS Pilani, APPCAIR, Pilani 333031, Rajasthan, India.
   [Sikdar, Biplab] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119077, Singapore.
C3 Kalinga Institute of Industrial Technology (KIIT); Birla Institute of
   Technology & Science Pilani (BITS Pilani); Birla Institute of Technology
   & Science Pilani (BITS Pilani); National University of Singapore
RP Chamola, V (corresponding author), Birla Inst Technol & Sci BITS Pilani, Dept Elect & Elect, Pilani 333031, Rajasthan, India.; Chamola, V (corresponding author), Birla Inst Technol & Sci BITS Pilani, APPCAIR, Pilani 333031, Rajasthan, India.
EM vinay.chamola@pilani.bits-pilani.ac.in
RI Hassija, Vikas/AAY-3020-2021
OI Golda, Abenezer/0009-0005-8896-8992; , Kidus/0009-0004-0222-8479;
   Chamola, Vinay/0000-0002-6730-3060; Singh, Anushka/0009-0003-0274-808X
CR Al-Huthaifi R, 2023, INFORM SCIENCES, V632, P833, DOI 10.1016/j.ins.2023.03.033
   Bai T, 2021, IEEE IMAGE PROC, P2543, DOI 10.1109/ICIP42928.2021.9506278
   Baker S, 2023, IEEE COMMUN SURV TUT, V25, P1261, DOI 10.1109/COMST.2023.3256323
   Benaddi H, 2022, IEEE GLOB COMM CONF, P2788, DOI 10.1109/GLOBECOM48099.2022.10000726
   Benzaid C, 2020, IEEE NETWORK, V34, P140, DOI 10.1109/MNET.011.2000088
   Benzaïd C, 2020, IEEE NETWORK, V34, P124, DOI 10.1109/MNET.001.1900273
   Bernard S, 2021, IEEE T INF FOREN SEC, V16, P812, DOI 10.1109/TIFS.2020.3021913
   Betz J, 2022, IEEE OPEN J INTEL TR, V3, P458, DOI 10.1109/OJITS.2022.3181510
   Bhaskara A, 2020, IEEE T HUM-MACH SYST, V50, P215, DOI 10.1109/THMS.2020.2965529
   Cai HM, 2017, IEEE INTERNET THINGS, V4, P75, DOI 10.1109/JIOT.2016.2619369
   Cai ZN, 2020, IEEE ACCESS, V8, P164144, DOI 10.1109/ACCESS.2020.3021523
   Casillo M, 2022, 2022 IEEE INTERNATIONAL CONFERENCE ON SMART COMPUTING (SMARTCOMP 2022), P252, DOI 10.1109/SMARTCOMP55677.2022.00063
   Chakraborty S., 2023, Intellectual Property in the Era of Generative AI
   Chaudhury S, 2021, IEEE ACCESS, V9, P109241, DOI 10.1109/ACCESS.2021.3101282
   Chauhan PS, 2021, COMPUTER, V54, P125, DOI 10.1109/MC.2021.3083916
   Chen CLP, 2020, IEEE T CYBERNETICS, V50, P2237, DOI 10.1109/TCYB.2018.2869902
   Chen PH, 2020, IEEE T IMAGE PROCESS, V29, P8292, DOI 10.1109/TIP.2020.3009820
   Chen ZL, 2023, INFORM FUSION, V97, DOI 10.1016/j.inffus.2023.101819
   De S, 2022, IEEE T IND INFORM, V18, P5728, DOI 10.1109/TII.2022.3155656
   Dietzel S, 2010, IEEE NETWORK, V24, P26, DOI 10.1109/MNET.2010.5395780
   Ding F, 2022, IEEE T INTELL TRANSP, V23, P9430, DOI 10.1109/TITS.2021.3120075
   Dinh TN, 2018, COMPUTER, V51, P48, DOI 10.1109/MC.2018.3620971
   Diop L, 2021, IEEE INT CONF BIG DA, P5882, DOI 10.1109/BigData52589.2021.9672071
   Dunmore A, 2023, IEEE ACCESS, V11, P76071, DOI 10.1109/ACCESS.2023.3296707
   Dutta IK, 2020, 2020 11TH IEEE ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON), P399, DOI [10.1109/uemcon51285.2020.9298135, 10.1109/UEMCON51285.2020.9298135]
   Eiffert S, 2020, IEEE ROBOT AUTOM LET, V5, P5026, DOI 10.1109/LRA.2020.3004324
   Eigenschink P, 2023, IEEE ACCESS, V11, P47304, DOI 10.1109/ACCESS.2023.3275134
   Emami H, 2021, IEEE T MULTIMEDIA, V23, P391, DOI 10.1109/TMM.2020.2975961
   Esteban C, 2017, Arxiv, DOI arXiv:1706.02633
   Fang YK, 2020, IEEE SENS J, V20, P9359, DOI 10.1109/JSEN.2020.2987841
   Fei JW, 2022, IEEE INT WORKS INFOR, DOI 10.1109/WIFS55849.2022.9975409
   Fereidooni H, 2021, 2021 IEEE SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (SPW 2021), P56, DOI 10.1109/SPW53761.2021.00017
   Fernandes S., 2019, P INT C ADV COMP COM, P1
   Franceschelli G, 2022, DATA POLICY, V4, DOI 10.1017/dap.2022.10
   Gonog L, 2019, C IND ELECT APPL, P505, DOI [10.1109/iciea.2019.8833686, 10.1109/ICIEA.2019.8833686]
   Guaman DS, 2021, IEEE ACCESS, V9, P15961, DOI 10.1109/ACCESS.2021.3053130
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Hafeez T, 2021, IEEE ACCESS, V9, P49355, DOI 10.1109/ACCESS.2021.3069137
   Hetzner C., 2023, Fortune.com
   Hosseini-Asl E, 2016, IEEE T NEUR NET LEAR, V27, P2486, DOI 10.1109/TNNLS.2015.2479223
   Hua MY, 2015, IEEE T ELECTRON DEV, V62, P3215, DOI 10.1109/TED.2015.2469716
   Huang Changwu, 2023, IEEE Transactions on Artificial Intelligence, P799, DOI 10.1109/TAI.2022.3194503
   Hurlburt G, 2023, IT PROF, V25, P4, DOI 10.1109/MITP.2023.3267140
   Java S, 2019, PROCEEDINGS 2019 AMITY INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AICAI), P593, DOI [10.1109/aicai.2019.8701330, 10.1109/AICAI.2019.8701330]
   Jiang B, 2021, IEEE INTERNET THINGS, V8, P10430, DOI 10.1109/JIOT.2021.3057419
   Jones ML, 2018, IEEE SECUR PRIV, V16, P64, DOI 10.1109/MSP.2018.2701155
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Khalid H, 2020, IEEE COMPUT SOC CONF, P2794, DOI 10.1109/CVPRW50498.2020.00336
   Korshunov P, 2022, IEEE T BIOM BEHAV ID, V4, P386, DOI 10.1109/TBIOM.2022.3143404
   Kuzlu M., 2023, P 12 MED C EMB COMP
   Lau MM, 2019, 2019 7TH INTERNATIONAL CONFERENCE ON SMART COMPUTING & COMMUNICATIONS (ICSCC), P99
   Lee H, 2021, IEEE ACCESS, V9, P132652, DOI 10.1109/ACCESS.2021.3113186
   Lee WS, 2022, IEEE ACCESS, V10, P130424, DOI 10.1109/ACCESS.2022.3227969
   Leenders G., 2019, Becoming Human: Artificial Intelligence Magazine, V13
   Li C, 2019, NOTRE DAME LAW REV, V94, P2211
   Li CS, 2021, ACM T KNOWL DISCOV D, V15, DOI 10.1145/3426238
   Li LX, 2020, IEEE ACCESS, V8, P139110, DOI 10.1109/ACCESS.2020.3011028
   Li S, 2022, IEEE T AFFECT COMPUT, V13, P1195, DOI 10.1109/TAFFC.2020.2981446
   Li SC, 2022, IEEE INTERNET THINGS, V9, P14542, DOI 10.1109/JIOT.2021.3066427
   Li XH, 2022, IEEE T KNOWL DATA EN, V34, P29, DOI 10.1109/TKDE.2020.2983930
   Liu Y, 2020, CHINA COMMUN, V17, P105, DOI 10.23919/JCC.2020.09.009
   Maddigan P, 2023, IEEE ACCESS, V11, P45181, DOI 10.1109/ACCESS.2023.3274199
   Majeed A, 2023, IEEE ACCESS, V11, P76177, DOI 10.1109/ACCESS.2023.3297646
   Michael Katina, 2023, IEEE Transactions on Technology and Society, P104, DOI 10.1109/TTS.2023.3280109
   Monteiro NRC, 2021, IEEE ACM T COMPUT BI, V18, P2364, DOI 10.1109/TCBB.2020.2977335
   Nasrin Sayeda Samia, 2020, 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), P196, DOI 10.1109/WIECON-ECE52138.2020.9398005
   Nguyen DC, 2021, IEEE INTERNET THINGS, V8, P12806, DOI 10.1109/JIOT.2021.3072611
   Ong DS, 2021, PROC CVPR IEEE, P3629, DOI 10.1109/CVPR46437.2021.00363
   Ouyang LW, 2022, IEEE INTERNET THINGS, V9, P14273, DOI 10.1109/JIOT.2020.3032706
   Pan ZQ, 2020, IEEE T EM TOP COMP I, V4, P500, DOI 10.1109/TETCI.2020.2991774
   Pant K, 2020, AACL-IJCNLP 2020: THE 1ST CONFERENCE OF THE ASIA-PACIFIC CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 10TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING: PROCEEDINGS OF THE STUDENT RESEARCH WORKSHOP, P37
   Peters Dorian, 2020, IEEE Transactions on Technology and Society, V1, P34, DOI 10.1109/TTS.2020.2974991
   Phong LT, 2018, IEEE T INF FOREN SEC, V13, P1333, DOI 10.1109/TIFS.2017.2787987
   Pitt J, 2019, IEEE TECHNOL SOC MAG, V38, P5, DOI 10.1109/MTS.2019.2913064
   Powell O., 2023, OpenAI Confirms ChatGPT Data BreachCshub.com
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Radanliev P, 2021, IEEE ACCESS, V9, P109986, DOI 10.1109/ACCESS.2021.3101579
   Rajabiyazdi F, 2020, IEEE SYS MAN CYBERN, P302, DOI [10.1109/SMC42975.2020.9282970, 10.1109/smc42975.2020.9282970]
   Ramesh A, 2021, 20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021), P825, DOI 10.1109/ICMLA52953.2021.00136
   Rana MS, 2022, IEEE ACCESS, V10, P25494, DOI 10.1109/ACCESS.2022.3154404
   Rawal Atul, 2022, IEEE Transactions on Artificial Intelligence, V3, P852, DOI 10.1109/TAI.2021.3133846
   Ribeiro AH, 2020, PR MACH LEARN RES, V108, P2370
   Rishi R., 2022, 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), P1085, DOI 10.1109/ICAC3N56670.2022.10074420
   Rodriguez-Almeida AJ, 2023, IEEE J BIOMED HEALTH, V27, P2670, DOI 10.1109/JBHI.2022.3196697
   Ru BS, 2019, IEEE T NANOBIOSCI, V18, P324, DOI 10.1109/TNB.2019.2909094
   Saheed YK, 2021, IEEE ACCESS, V9, P161546, DOI 10.1109/ACCESS.2021.3128837
   Sattler F, 2020, IEEE T NEUR NET LEAR, V31, P3400, DOI 10.1109/TNNLS.2019.2944481
   Shahid W, 2022, IEEE ACCESS, V10, P27069, DOI 10.1109/ACCESS.2022.3157724
   Shoufan A, 2023, IEEE ACCESS, V11, P38805, DOI 10.1109/ACCESS.2023.3268224
   Stallings W, 2020, IEEE SECUR PRIV, V18, P61, DOI 10.1109/MSEC.2019.2953324
   Subakan YC, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P26, DOI 10.1109/ICASSP.2018.8461671
   Sun H, 2023, IEEE T KNOWL DATA EN, V35, P3367, DOI 10.1109/TKDE.2021.3130903
   Taimoor N, 2022, IEEE ACCESS, V10, P535, DOI 10.1109/ACCESS.2021.3137364
   Tanuwidjaja HC, 2020, IEEE ACCESS, V8, P167425, DOI 10.1109/ACCESS.2020.3023084
   Tran NT, 2021, IEEE T IMAGE PROCESS, V30, P1882, DOI 10.1109/TIP.2021.3049346
   Vasan D, 2020, IEEE T COMPUT, V69, P1654, DOI 10.1109/TC.2020.3015584
   Vaswani A., 2023, P ADV NEUR INF PROC, P5999
   Verdoliva L, 2020, IEEE J-STSP, V14, P910, DOI 10.1109/JSTSP.2020.3002101
   Volz V, 2020, IEEE CONF COMPU INTE, P399, DOI [10.1109/CoG47356.2020.9231944, 10.1109/cog47356.2020.9231944]
   Wang KF, 2017, IEEE-CAA J AUTOMATIC, V4, P588, DOI 10.1109/JAS.2017.7510583
   Wang SX, 2022, IEEE T INTELL TRANSP, V23, P6347, DOI 10.1109/TITS.2021.3055838
   Wang YCF, 2023, 2023 INTERNATIONAL VLSI SYMPOSIUM ON TECHNOLOGY, SYSTEMS AND APPLICATIONS, VLSI-TSA/VLSI-DAT, DOI 10.1109/VLSI-TSA/VLSI-DAT57221.2023.10134215
   Waqas N, 2022, IEEE ACCESS, V10, P80847, DOI 10.1109/ACCESS.2022.3193668
   Wei RQ, 2021, IEEE ACCESS, V9, P4939, DOI 10.1109/ACCESS.2020.3048309
   Wigan Marcus, 2022, IEEE Transactions on Technology and Society, V3, P185, DOI 10.1109/TTS.2022.3190766
   Winfield AFT, 2021, FRONT ROBOT AI, V8, DOI 10.3389/frobt.2021.665729
   Wu NB, 2020, IEEE ACCESS, V8, P216741, DOI 10.1109/ACCESS.2020.3041854
   Wu YD, 2023, IEEE T DEPEND SECURE, V20, P1744, DOI 10.1109/TDSC.2022.3162623
   Wu ZM, 2021, J GLOBAL OPTIM, V79, P617, DOI 10.1007/s10898-020-00943-7
   Xia WJ, 2023, SCIENTOMETRICS, V128, P543, DOI 10.1007/s11192-022-04547-8
   Xiao AR, 2023, IEEE T PATTERN ANAL, V45, P11321, DOI 10.1109/TPAMI.2023.3262786
   Xiong JB, 2022, IEEE INTERNET THINGS, V9, P2787, DOI 10.1109/JIOT.2021.3093573
   Xue Tong, 2021, 2021 IEEE Sustainable Power and Energy Conference (iSPEC), P4250, DOI 10.1109/iSPEC53008.2021.9735442
   Yadav SP, 2022, ARCH COMPUT METHOD E, V29, P1753, DOI 10.1007/s11831-021-09647-x
   Yang HL, 2021, IEEE J SEL AREA COMM, V39, P3144, DOI 10.1109/JSAC.2021.3088655
   Yang XY, 2021, IEEE T BIG DATA, V7, P729, DOI 10.1109/TBDATA.2017.2715334
   Yang YM, 2018, IEEE T SYST MAN CY-S, V48, P1065, DOI 10.1109/TSMC.2016.2637279
   Yazdinejad A, 2020, IEEE GLOBE WORK, DOI 10.1109/GCWkshps50303.2020.9367545
   Ye F, 2023, IEEE T NEUR NET LEAR, V34, P461, DOI 10.1109/TNNLS.2021.3096457
   Yeh CY, 2020, IEEE WINT C APPL COM, P53, DOI [10.1109/WACVW50321.2020.9096939, 10.1109/wacvw50321.2020.9096939]
   Yoon J, 2020, IEEE J BIOMED HEALTH, V24, P2378, DOI 10.1109/JBHI.2020.2980262
   Zhang Q, 2021, IEEE INTERNET THINGS, V8, P10412, DOI 10.1109/JIOT.2021.3058638
   Zheng Chuang, 2022, 2022 11th International Conference of Information and Communication Technology (ICTech)), P355, DOI 10.1109/ICTech55460.2022.00077
   Zhong P, 2017, IEEE T GEOSCI REMOTE, V55, P3516, DOI 10.1109/TGRS.2017.2675902
   Zhou QY, 2023, IEEE T COGN DEV SYST, V15, P724, DOI 10.1109/TCDS.2022.3176977
   Zhou XH, 2021, IEEE WIREL COMMUN LE, V10, P1552, DOI 10.1109/LWC.2021.3074135
   Zhu Y, 2022, IEEE T IMAGE PROCESS, V31, P6487, DOI 10.1109/TIP.2022.3211736
   Zihan Chen, 2020, 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), P155, DOI 10.1109/ICIVC50857.2020.9177494
NR 128
TC 6
Z9 6
U1 60
U2 88
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 2169-3536
J9 IEEE ACCESS
JI IEEE Access
PY 2024
VL 12
BP 48126
EP 48144
DI 10.1109/ACCESS.2024.3381611
PG 19
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Telecommunications
GA NA6S8
UT WOS:001197763200001
OA gold
DA 2024-12-25
ER

PT J
AU Requejo, WS
   Martínez, FF
   Vega, CA
   Martínez, RZ
   Cendrero, AM
   Lantada, AD
AF Requejo, William Solorzano
   Martinez, Francisco Franco
   Vega, Carlos Aguilar
   Martinez, Rodrigo Zapata
   Cendrero, Adrian Martinez
   Lantada, Andres Diaz
TI Fostering creativity in engineering design through constructive
   dialogues with generative artificial intelligence
SO CELL REPORTS PHYSICAL SCIENCE
LA English
DT Article
ID MATERIALS DISCOVERY
AB Artificial intelligence (AI) is progressively reshaping the way that researchers design and study highly complex systems. In this perspective, we introduce an engineering design methodology aimed at fostering creativity through "constructive dialogues with a generative AI"and exemplify its potential through a set of methodically developed case studies. This creativity promotion approach starts with computer-aided design (CAD) models of lattices, metamaterials, and architected materials, which are provided as initial inputs to a generative AI through a chat. Then, the conversation starts with researchers asking the generative AI to modify the provided CAD model images by incorporating new elements, placing them in quasi-real-life environments, or adapting the provided designs to the structures of new products. To illustrate the methodology, a varied set of selected case studies of constructive dialogues leading to highly innovative designs are provided, bridging the gap between tissue engineering scaffolds and building architectures, biohybrid materials and product design, and innovative structures and medical devices, to cite a few.
C1 [Requejo, William Solorzano; Martinez, Francisco Franco; Vega, Carlos Aguilar; Martinez, Rodrigo Zapata; Cendrero, Adrian Martinez; Lantada, Andres Diaz] Univ Politecn Madrid, Mech Engn Dept, Prod Dev Lab, ETSI Ind, c Jose Gutierrez Abascal 2, E-28006 Madrid, Spain.
C3 Universidad Politecnica de Madrid
RP Lantada, AD (corresponding author), Univ Politecn Madrid, Mech Engn Dept, Prod Dev Lab, ETSI Ind, c Jose Gutierrez Abascal 2, E-28006 Madrid, Spain.
EM andres.diaz@upm.es
RI Franco-Martinez, Francisco/HSG-1726-2023; Diaz Lantada,
   Andres/ABH-2560-2020
OI Diaz Lantada, Andres/0000-0002-0358-9186; Franco-Martinez,
   Francisco/0000-0002-7894-7478; Aguilar, Carlos/0000-0003-0291-3041;
   Solorzano Requejo, William Gabriel/0000-0002-2989-9166
FU Ministerio de Ciencia, Innovacio<acute accent>n y Universidades, la
   Agencia Estatal de Investigacio<acute accent>n y el Centro para el
   Desarrollo Tecnolo<acute accent>gico y la Innovacio<acute accent>n
   E.P.E. [PLEC2023-010237, MIG-20232050]
FX The research presented has been supported by the following research and
   innovation projects: "iMPLANTS-CM: Impresion de metamateriales empleando
   aleaciones con memoria y gradientes funcionales para una nueva
   generacion de implantes inteligentes," from the "Convocatoria 2020 de
   ayudas para la realizacion de proyectos sinergicos de I + D,"funded by
   Comunidad Autonoma de Madrid (reference: Y2020/BIO-6756) ; "INKplant:
   Ink-based hybrid multi-material fabrication of next generation
   implants"funded by the European Union's Horizon 2020 Research and
   Innovation Programme under grant agreement no. 953134; and "METALIA:
   Tecnolog & imath;as Habilitadoras para la Implementacion de la
   Inteligencia Artificial en la cadena de valor dela Fabricacion Aditiva
   de nuevas aleaciones metalicas"funded by Ministerio de Ciencia,
   Innovacion y Universidades, la Agencia Estatal de Investigacion y el
   Centro para el Desarrollo Tecnologico y la Innovacion E.P.E. within the
   "Transmisiones 2023"call (references: PLEC2023-010237 [AEI] and
   MIG-20232050 [CDTI] ) . The authors acknowledge the reviewers for their
   constructive comments, insightful questions, and suggestions for
   improvement, which have led to a clearer paper with a more objective
   presentation of results.r la Fabricacio<acute accent>n Aditiva de nuevas
   aleaciones meta<acute accent>licas"funded by Ministerio de Ciencia,
   Innovacio<acute accent>n y Universidades, la Agencia Estatal de
   Investigacio<acute accent>n y el Centro para el Desarrollo Tecnolo<acute
   accent>gico y la Innovacio<acute accent>n E.P.E. within the
   "Transmisiones 2023"call (references: PLEC2023-010237 [AEI] and
   MIG-20232050 [CDTI] ) . The au-thors acknowledge the reviewers for their
   constructive comments, insightful ques-tions, and suggestions for
   improvement, which have led to a clearer paper with a more objective
   presentation of results.
CR Abdallah Yomna K., 2023, Designs, DOI 10.3390/designs7020048
   Altshuller H., 1984, Creativity as an Exact Science
   Anderson M, 2014, AAAI CONF ARTIF INTE, P253
   Beauchamp TL., 2001, Principles of biomedical ethics, V5e, P1
   Benyus J.M., 2002, Biomimicry : Innovation inspired by nature
   Barrera MDB, 2021, MATERIALS, V14, DOI 10.3390/ma14185278
   Buchanan R., 1992, DESIGN ISSUES, V8, P5, DOI [DOI 10.2307/1511637, 10.2307/1511637]
   Crawley E.F., 2014, Rethinking engineering education: The CDIO approach, V2nd
   Lantada AD, 2020, NANOMATERIALS-BASEL, V10, DOI 10.3390/nano10112287
   Jose R, 2018, APPL MATER TODAY, V10, P127, DOI 10.1016/j.apmt.2017.12.015
   Kirchner J. H., 2023, New AI classifier for indicating AI-written text
   Kriegmana S, 2020, P NATL ACAD SCI USA, V117, P1853, DOI 10.1073/pnas.1910837117
   Lantada A., 2024, P 17 INT JOINT C BIO, P42, DOI [10.5220/0012363800003657, DOI 10.5220/0012363800003657]
   Lantada AD, 2023, IEEE PULSE, V14, P24, DOI 10.1109/MPULS.2023.3324241
   Li BH, 2017, FRONT INFORM TECH EL, V18, P86, DOI 10.1631/FITEE.1601885
   Liu L, 2024, INT J HUM-COMPUT INT, V40, P915, DOI 10.1080/10447318.2022.2041907
   Liu RM, 2015, METAB ENG, V32, P143, DOI 10.1016/j.ymben.2015.09.013
   Liu ZK, 2014, CHINESE SCI BULL, V59, P1619, DOI 10.1007/s11434-013-0072-x
   Lu WC, 2017, J MATERIOMICS, V3, P191, DOI 10.1016/j.jmat.2017.08.003
   Núñez JLM, 2020, INT J ENG EDUC, V36, P1740
   Microsoft Corp, Responsible AI Standard
   Microsoft Corp, Responsible AI in progress
   Murugesan S, 2023, COMPUTER, V56, P116, DOI 10.1109/MC.2023.3253292
   National Institute of Standards and Technology (NIST), 2021, ABOUT US
   Pelvis D.G., 2016, CAD Model from Thingiverse under Creative Commons - Attribution License
   Pena MLC, 2021, AUTOMAT CONSTR, V124, DOI 10.1016/j.autcon.2021.103550
   Peres RS, 2020, IEEE ACCESS, V8, P220121, DOI 10.1109/ACCESS.2020.3042874
   Qian C, 2015, SMALL, V11, P64, DOI 10.1002/smll.201402197
   Raccuglia P, 2016, NATURE, V533, P73, DOI 10.1038/nature17439
   Radford A., 2018, Technical Reports
   Radford A., 2019, OPENAI BLOG
   Schicktanz S., 2023, Front. Genet., V14
   Smits J., 2022, LAW ARTIFICIAL INTEL, V35, P323, DOI DOI 10.1007/978-94-6265-523-2_17
   Soori M., 2023, Cogn. Robot, V3, P54, DOI DOI 10.1016/J.COGR.2023.04.001
   the AI Whisperer J., 2023, The Generator
   US Federal Government. Materials Genome Initiative, 2021, ABOUT US
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
   Winkler DA, 2017, BEILSTEIN J ORG CHEM, V13, P1288, DOI 10.3762/bjoc.13.125
   Yin H, 2023, SYSTEMS-BASEL, V11, DOI 10.3390/systems11120566
   Yu KH, 2018, NAT BIOMED ENG, V2, P719, DOI 10.1038/s41551-018-0305-z
NR 40
TC 0
Z9 0
U1 13
U2 13
PU CELL PRESS
PI CAMBRIDGE
PA 50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA
EI 2666-3864
J9 CELL REP PHYS SCI
JI Cell Rep. Phys. Sci.
PD SEP 18
PY 2024
VL 5
IS 9
AR 102157
DI 10.1016/j.xcrp.2024.102157
PG 17
WC Chemistry, Multidisciplinary; Energy & Fuels; Materials Science,
   Multidisciplinary; Physics, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Energy & Fuels; Materials Science; Physics
GA G8K4C
UT WOS:001319058500001
OA gold
DA 2024-12-25
ER

PT J
AU Schmitt, M
   Flechais, I
AF Schmitt, Marc
   Flechais, Ivan
TI Digital deception: generative artificial intelligence in social
   engineering and phishing
SO ARTIFICIAL INTELLIGENCE REVIEW
LA English
DT Article
DE Artificial intelligence; Machine learning; Social engineering; Phishing;
   ChatGPT; Large language models
AB The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks-the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.
C1 [Schmitt, Marc; Flechais, Ivan] Univ Oxford, Dept Comp Sci, Oxford, England.
C3 University of Oxford
RP Schmitt, M (corresponding author), Univ Oxford, Dept Comp Sci, Oxford, England.
EM marcschmitt@hotmail.de
OI Schmitt, Marc/0000-0003-4550-2963
CR Ahmad R, 2023, ARTIF INTELL REV, V56, P10733, DOI 10.1007/s10462-023-10437-z
   Aleroud A, 2017, COMPUT SECUR, V68, P160, DOI 10.1016/j.cose.2017.04.006
   Anil R., 2023, PALM 2 TECHNICAL REP
   Archana R, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-023-10631-z
   Basit A, 2021, TELECOMMUN SYST, V76, P139, DOI 10.1007/s11235-020-00733-2
   Bécue A, 2021, ARTIF INTELL REV, V54, P3849, DOI 10.1007/s10462-020-09942-2
   Boyne S., 2023, SSRN Electronic Journal, DOI [DOI 10.2139/SSRN.4168458, 10.2139/ssrn.4518501, DOI 10.2139/SSRN.4443356]
   Bray SD, 2023, J CYBERSECURITY, V9, DOI 10.1093/cybsec/tyad011
   Briggs J., 2023, The Potentially Large Effects of Artificial Intelligence on Economic Growth online
   Cavaliere Fabio, 2020, Network Security, V2020, P9, DOI 10.1016/S1353-4858(20)30105-7
   Chen X, 2024, J INF SECUR APPL, V81, DOI 10.1016/j.jisa.2024.103708
   Chui M., 2023, The economic potential of generative AI: The next productivity frontier
   Cutillo CM, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-0254-2
   Daniel K., 2013, Thinking, fast and slow
   Davenport TH, 2018, J BUS ANAL, V1, P73, DOI 10.1080/2573234X.2018.1543535
   Desolda G, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3469886
   Distler V, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581170
   Dorrenbacher J, 2023, C HUM FACT COMP SYST
   Fahl S, 2021, The Cyber Security Body of Knowledge
   Fleming P, 2023, J CYBERSECURITY, V9, DOI 10.1093/cybsec/tyad005
   Gambín AF, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-023-10679-x
   Ganesh A, 2023, C HUM FACT COMP SYST
   Glas M, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3581046
   Goodfellow IJ, 2014, ADV NEUR IN, V27, P2672
   Greshake K, 2023, Not what you've signed up for: compromising real-world llm-integrated applications with indirect prompt injection
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Jansen Pascal, 2020, CHI PLAY '20: Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play, P59, DOI 10.1145/3383668.3419917
   Kaur A, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-024-10810-6
   Kaur R, 2023, INFORM FUSION, V97, DOI 10.1016/j.inffus.2023.101804
   Ke JP, 2023, NEURAL NETWORKS, V160, P216, DOI 10.1016/j.neunet.2023.01.001
   Kim SSY, 2023, C HUM FACT COMP SYST
   Kosch T, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3582272
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   Malanowska A, 2024, IEEE ACCESS, V12, P36311, DOI 10.1109/ACCESS.2024.3374201
   Malatji M., 2024, AI Ethics, P13
   Marin IA, 2023, C HUM FACT COMP SYST
   Mouton F, 2014, INFO SECUR S AFR
   Naqvi B, 2023, COMPUT SECUR, V132, DOI 10.1016/j.cose.2023.103387
   Nowrozy R, 2024, ACM COMPUT SURV, V56, DOI 10.1145/3653297
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Rapp A, 2023, C HUM FACT COMP SYST
   Renaud K, 2023, M CYB THREAT GEN AI
   Schmitt M, 2023, J IND INF INTEGR, V36, DOI 10.1016/j.jii.2023.100520
   Schmitt M, 2023, INTELL SYST APPL, V18, DOI 10.1016/j.iswa.2023.200188
   Soliman MM, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-023-10641-x
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Taddeo M, 2019, NAT MACH INTELL, V1, P557, DOI 10.1038/s42256-019-0109-1
   Tang X, 2023, NAT ELECTRON, V6, P109, DOI 10.1038/s41928-022-00913-9
   Teichmann F., 2023, Int. Cybersecur. Law Rev., V4, P399, DOI [10.1365/s43439-023-00094-x, DOI 10.1365/S43439-023-00094-X]
   Tikkinen-Piri C, 2018, COMPUT LAW SECUR REV, V34, P134, DOI 10.1016/j.clsr.2017.05.015
   Vaswani A, 2017, ADV NEUR IN, V30
   Vondrácek M, 2023, COMPUT SECUR, V127, DOI 10.1016/j.cose.2022.102923
   Webb T, 2023, NAT HUM BEHAV, V7, P1526, DOI 10.1038/s41562-023-01659-w
   World Economic Forum, 2024, Global Cybersecurity Outlook 2024
   Yamamura N, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581169
   Yamin MM, 2021, J INF SECUR APPL, V57, DOI 10.1016/j.jisa.2020.102722
   Zhang YX, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-024-10744-z
   Zou A., 2023, UNIVERSAL TRANSFERAB
   Zscaler, 2024, Zscaler ThreatLabz 2024 Phishing Report
NR 59
TC 0
Z9 0
U1 8
U2 8
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0269-2821
EI 1573-7462
J9 ARTIF INTELL REV
JI Artif. Intell. Rev.
PD OCT 12
PY 2024
VL 57
IS 12
AR 324
DI 10.1007/s10462-024-10973-2
PG 23
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA J3X2X
UT WOS:001336421600003
OA hybrid
DA 2024-12-25
ER

PT J
AU Simkute, A
   Tankelevitch, L
   Kewenig, V
   Scott, AE
   Sellen, A
   Rintel, S
AF Simkute, Auste
   Tankelevitch, Lev
   Kewenig, Viktor
   Scott, Ava Elizabeth
   Sellen, Abigail
   Rintel, Sean
TI Ironies of Generative AI: Understanding and Mitigating Productivity Loss
   in Human-AI Interaction
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article; Early Access
ID FLIGHT-DECK AUTOMATION; SITUATION AWARENESS; TRAFFIC MANAGEMENT;
   TRADE-OFFS; PERFORMANCE; WORKLOAD; MODEL; INTERRUPTIONS; CONTROLLER;
   ALLOCATION
AB Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these 'ironies of automation' have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users' roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.
C1 [Simkute, Auste] Univ Edinburgh, Edinburgh, Scotland.
   [Tankelevitch, Lev; Sellen, Abigail; Rintel, Sean] Microsoft Res, Cambridge, England.
   [Kewenig, Viktor; Scott, Ava Elizabeth] UCL, London, England.
C3 University of Edinburgh; Microsoft; University of London; University
   College London
RP Rintel, S (corresponding author), Microsoft Res, Cambridge, England.
EM serintel@microsoft.com
OI Tankelevitch, Lev/0000-0003-1286-5194; Scott, Ava
   Elizabeth/0000-0002-4469-4556; Kewenig, Viktor/0009-0009-5912-0676
CR Altmann EM, 2002, COGNITIVE SCI, V26, P39, DOI 10.1207/s15516709cog2601_2
   Altmann EM, 2015, INT J HUM-COMPUT ST, V79, P51, DOI 10.1016/j.ijhcs.2014.12.007
   Altmann EM, 2014, J EXP PSYCHOL GEN, V143, P215, DOI 10.1037/a0030986
   Andrew A. M., 2003, Robotica, V21, P345, DOI [10.1017/S0263574702274858, DOI 10.1017/S0263574702274858]
   Arnold K. C., 2021, JOINT P ACM IUI 2021
   Bailey BP, 2008, ACM T COMPUT-HUM INT, V14, DOI 10.1145/1314683.1314689
   BAINBRIDGE L, 1983, AUTOMATICA, V19, P775, DOI 10.1016/0005-1098(83)90046-8
   Barke S, 2023, P ACM PROGRAM LANG, V7, DOI 10.1145/3586030
   Bauer K, 2023, INFORM SYST RES, V34, P1582, DOI 10.1287/isre.2023.1199
   Baxter G., 2012, Ecce, P65, DOI [https://doi.org/10.1145/2448136.2448149, DOI 10.1145/2448136.2448149]
   Bhat A, 2023, PROCEEDINGS OF 2023 28TH ANNUAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2023, P436, DOI 10.1145/3581641.3584060
   BILLINGS C., 1991, The International Journal of Aviation Psychology, V1, P261, DOI [https://doi.org/10.1207/s15327108ijap01041, DOI 10.1207/S15327108IJAP01041]
   Bird Christian, 2022, ACM Queue, P35, DOI 10.1145/3582083
   Brumby DP, 2013, J EXP PSYCHOL-APPL, V19, P95, DOI 10.1037/a0032696
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Calderwood A., 2020, IUI 2020 WORKSHOPS
   Carayon Pascale, 2019, Yearb Med Inform, V28, P71, DOI 10.1055/s-0039-1677907
   Chen J, 2024, LECT NOTES COMPUT SC, V14621, P341, DOI 10.1007/978-3-031-61175-9_23
   Chen X, 2023, Arxiv, DOI [arXiv:2306.15774, 10.48550/arXiv.2306.15774]
   Chen Z., 2023, Large language model in creative work: The role of collaboration modality and user expertise, DOI [10.2139/ssrn.4575598, DOI 10.2139/SSRN.4575598]
   Chignell M, 2023, ACM T COMPUT-HUM INT, V30, DOI 10.1145/3557891
   Choi J. H., 2023, J. LEGAL EDUC, DOI DOI 10.2139/SSRN.4539836
   CHU YY, 1979, IEEE T SYST MAN CYB, V9, P769, DOI 10.1109/TSMC.1979.4310128
   Clark E, 2018, IUI 2018: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P329, DOI 10.1145/3172944.3172983
   Cook R. I., 1991, NASA, Lyndon B. Johnson Space Center
   Cook RI, 1996, HUM FACTORS, V38, P593, DOI 10.1518/001872096778827224
   Cork RD, 1998, J AM MED INFORM ASSN, V5, P164, DOI 10.1136/jamia.1998.0050164
   Cutrell E., 2000, EXTENDED ABSTRACTS C, P99, DOI [10.1145/633292.633351, DOI 10.1145/633292.633351]
   Cutrell E, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P407
   Czerwinski M., 2004, P SIGCHI C HUM FACT, P175, DOI [DOI 10.1145/985692.985715, 10.1145/985692.985715]
   Dang H, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580969
   DeGrave AJ, 2024, LANCET, V403, P717, DOI 10.1038/s41551-023-01160-9
   DellAcqua F. E., 2023, Working Paper No. 24-013., DOI [10.2139/ssrn.4573321, DOI 10.2139/SSRN.4573321]
   Dixon SR, 2005, HUM FACTORS, V47, P479, DOI 10.1518/001872005774860005
   Drosos I, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20)
   Effken JA, 1997, ERGONOMICS, V40, P1, DOI 10.1080/001401397188341
   Endsley M.R., 1997, Effect of free flight conditions on controller performance, workload, and situation awareness
   Endsley M.R., 2003, P HUMAN FACTORS ERGO, P268, DOI [DOI 10.1177/154193120304700304, 10.1177/154193120304700304]
   Endsley M.R., 1997, Distribution of attention, situation awareness, and workload in a passive air traffic control task: Implications for operational errors and automation, DOI DOI 10.2514/ATCQ.6.1.21
   Endsley MR, 2017, HUM FACTORS, V59, P5, DOI 10.1177/0018720816681350
   Endsley MR, 2023, ERGONOMICS, V66, P1656, DOI 10.1080/00140139.2023.2243404
   ENDSLEY MR, 1995, HUM FACTORS, V37, P65, DOI 10.1518/001872095779049499
   ENSTROM KD, 1977, IEEE T SYST MAN CYB, V7, P153, DOI 10.1109/TSMC.1977.4309679
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Frey C. B., 2023, Brown Journal of World Affairs
   Friedman N., 2021, Introducing GitHub Copilot: your AI pair programmer
   Funk K, 1999, INT J AVIAT PSYCHOL, V9, P109, DOI 10.1207/s15327108ijap0902_2
   Galster S M., 2001, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V45, P321, DOI https://doi.org/10.1177/154193120104500412
   Gmeiner F, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580999
   Goodrich MA, 2003, IEEE SYS MAN CYBERN, P3943
   GRUBB PL, 1995, HUM FAC ERG SOC P, P1360
   Gu K, 2024, Arxiv, DOI [arXiv:2309.10947, 10.48550/arXiv.2309.10947]
   Gu K, 2024, Arxiv, DOI [arXiv:2309.10108, 10.48550/arXiv.2309.10108]
   Haldane AG, 2011, NATURE, V469, DOI 10.1038/nature09659
   Huang JAT, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.36100
   Iqbal S. T., 2005, CHI 05 EXTENDED ABST, P1489, DOI [DOI 10.1145/1056808.1056948, https://doi.org/10.1145/1056808.1056948]
   Iqbal ST, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P93
   Janssen CP, 2015, INT J HUM-COMPUT ST, V79, P1, DOI 10.1016/j.ijhcs.2015.03.002
   Janssen CP, 2011, TOP COGN SCI, V3, P123, DOI 10.1111/j.1756-8765.2010.01125.x
   Janssen CP, 2010, COGNITIVE SCI, V34, P1548, DOI 10.1111/j.1551-6709.2010.01124.x
   Jayagopal D, 2022, PROCEEDINGS OF THE 35TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, UIST 2022, DOI 10.1145/3526113.3545659
   Jiang E, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501870
   Jones DG, 1996, AVIAT SPACE ENVIR MD, V67, P507
   Kazemitabaar M, 2023, Arxiv, DOI [arXiv:2309.14049, 10.48550/arXiv.2309.14049]
   King BJ, 2022, APPL ERGON, V99, DOI 10.1016/j.apergo.2021.103643
   Klein G, 2006, IEEE INTELL SYST, V21, P70, DOI 10.1109/MIS.2006.75
   Kulkarni C, 2023, Arxiv, DOI [arXiv:2303.12647, 10.48550/arXiv.2303.12647]
   Lee JD, 2009, SPRINGER HANDBOOK OF AUTOMATION, P417, DOI 10.1007/978-3-540-78831-7_25
   Liang JT, 2023, Arxiv, DOI [arXiv:2303.17125, 10.48550/arXiv.2303.17125]
   Liao QV, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580652
   Liao QV, 2023, Arxiv, DOI [arXiv:2306.01941, 10.48550/arXiv.2306.01941, DOI 10.48550/ARXIV.2306.01941]
   Lindgren I, 2023, TOGETHER IN THE UNSTABLE WORLD: DIGITAL GOVERNMENT AND SOLIDARITY, P395, DOI 10.1145/3598469.3598514
   Loft S, 2007, HUM FACTORS, V49, P376, DOI 10.1518/001872007X197017
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Manzey D., 2006, Proceedings of the Human Factors and Ergonomics Society 50th Annual Meeting, V50, P220, DOI [10.1177/154193120605000303, DOI 10.1177/154193120605000303]
   Manzey D, 2012, J COGN ENG DECIS MAK, V6, P57, DOI 10.1177/1555343411433844
   Mark G., 2012, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, P555
   Mark G, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P107
   McBride SE, 2011, HUM FACTORS, V53, P672, DOI 10.1177/0018720811421909
   McIlroy RC, 2015, IEEE T HUM-MACH SYST, V45, P145, DOI 10.1109/THMS.2014.2369372
   McNutt A, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023, DOI 10.1145/3544548.3580940
   Metzger U, 2005, HUM FACTORS, V47, P35, DOI 10.1518/0018720053653802
   Metzger U, 2001, HUM FACTORS, V43, P519, DOI 10.1518/001872001775870421
   Monk CA, 2008, J EXP PSYCHOL-APPL, V14, P299, DOI 10.1037/a0014402
   MORAY N, 1986, IEEE T SYST MAN CYB, V16, P497, DOI 10.1109/TSMC.1986.289252
   Moreno L, 2015, 2015 IEEE/ACM 37TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, VOL 1, P880, DOI 10.1109/ICSE.2015.98
   NORMAN DA, 1990, PHILOS T R SOC B, V327, P585, DOI 10.1098/rstb.1990.0101
   Nova K., 2023, J. Adv. Anal. Healthc. Manag, V7, P115
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Oppenlaender Jonas, 2022, Academic Mindtrek 2022: 25th International Academic Mindtrek conference, P192, DOI 10.1145/3569219.3569352
   Parasuraman R, 1996, HUM FACTORS, V38, P665, DOI 10.1518/001872096778827279
   Parasuraman R, 1997, HUM FACTORS, V39, P230, DOI 10.1518/001872097778543886
   Parasuraman R, 2000, IEEE T SYST MAN CY A, V30, P286, DOI 10.1109/3468.844354
   Parasuraman R., 1993, The International Journal of Aviation Psychology, V3, P1, DOI [DOI 10.1207/S15327108IJAP0301_1, 10.1207/s15327108ijap03011, DOI 10.1207/S15327108IJAP03011]
   Paris C. L., 1988, Computational Linguistics, V14, P64
   Parnin C, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P93
   Pasquale F., 2023, Generative AI, explainability, and score-based natural language processing in benefits administration
   Paul CL, 2015, INT J HUM-COMPUT ST, V79, P20, DOI 10.1016/j.ijhcs.2015.02.001
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Pennefather P. P., 2023, Creative prototyping with generative AI: Augmenting creative workflows with generative AI, P339, DOI [10.1007/978-1-4842-9579-312, DOI 10.1007/978-1-4842-9579-312]
   Pennefather P. P., 2023, Creative Prototyping with Generative AI: Augmenting Creative Workflows with Generative AI, P387, DOI https://doi.org/10.1007/978-1-4842-9579-313
   Prather J, 2023, Arxiv, DOI [arXiv:2304.02491, 10.48550/arXiv.2304.02491]
   Preiksaitis C, 2023, NAT MED, V29, P1296, DOI 10.1038/s41591-023-02341-4
   Ragavan SS, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P345, DOI 10.1145/3490099.3511161
   RASMUSSEN J, 1989, INT J MAN MACH STUD, V31, P517, DOI 10.1016/0020-7373(89)90014-X
   REASON J, 1990, PHILOS T R SOC B, V327, P475, DOI 10.1098/rstb.1990.0090
   Ross SI, 2023, PROCEEDINGS OF 2023 28TH ANNUAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2023, P491, DOI 10.1145/3581641.3584037
   Rudisill M., 1995, P 8 INT S AV PSYCH
   Salvucci DD, 2011, TOP COGN SCI, V3, P227, DOI 10.1111/j.1756-8765.2011.01134.x
   Sarkar A, 2023, PROCEEDINGS OF THE 2ND ANNUAL MEETING OF THE SYMPOSIUM ON HUMAN-COMPUTER INTERACTION FOR WORK, CHIWORK 2023, DOI 10.1145/3596671.3597650
   Sarkar A, 2022, Arxiv, DOI [arXiv:2208.06213, 10.48550/arXiv.2208.06213]
   Schellaert W, 2023, J ARTIF INTELL RES, V77, P377
   Sheridan T. B., 2005, Reviews of Human Factors and Ergonomics, V1, P89, DOI [DOI 10.1518/155723405783703082, 10.1518/155723405783703082, https://doi.org/10.1518/155723405783703082]
   Sheridan T.B., 2012, HDB HUMAN FACTORS ER, V4th, P990
   SINAIKO HW, 1972, APPL ERGON, V3, P3
   Smith H. P. R., 1979, Technical Report NASA-TM-78482
   Stoner H. A., 2003, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V47, P444, DOI [https://doi.org/10.1177/154193120304700341, DOI 10.1177/154193120304700341]
   Sukhera Javeed, 2022, J Grad Med Educ, V14, P414, DOI 10.4300/JGME-D-22-00480.1
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Taekman JM, 2010, INT ANESTHESIOL CLIN, V48, P101, DOI 10.1097/AIA.0b013e3181eace73
   Tankelevitch L, 2024, PROCEEDINGS OF THE 2024 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYTEMS (CHI 2024), DOI 10.1145/3613904.3642902
   Ulfsnes R., 2024, Generative AI for effective software development, P219, DOI [https://doi.org/10.1007/978-3-031-55642-510, DOI 10.1007/978-3-031-55642-510]
   Vaithilingam P, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519665
   Vasconcelos H, 2024, Arxiv, DOI [arXiv:2302.07248, arXiv:2302.07248, 10.48550/ARXIV.2302.07248, 10.48550/arXiv.2302.07248]
   Wang RT, 2024, PROCEEDINGS OF THE 2024 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, ACM FACCT 2024, P1475, DOI 10.1145/3630106.3658984
   Warm JS, 2008, HUM FACTORS, V50, P433, DOI 10.1518/001872008X312152
   Weisz JD, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P369, DOI 10.1145/3490099.3511157
   Weisz JD, 2021, IUI '21 - 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P402, DOI 10.1145/3397481.3450656
   Wickens C.D., 2000, TRANSPORTATION HUMAN, V2, P99, DOI DOI 10.1207/STHF0202_01
   WIENER EL, 1980, ERGONOMICS, V23, P995, DOI 10.1080/00140138008924809
   Woodruff A, 2024, Arxiv, DOI [arXiv:2310.06778, DOI 10.48550/ARXIV.2310.06778]
   Wu TS, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3517582
   Xu FF, 2022, ACM T SOFTW ENG METH, V31, DOI 10.1145/3487569
   Yuan A, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P841, DOI 10.1145/3490099.3511105
   Zajac HD, 2023, ACM T COMPUT-HUM INT, V30, DOI 10.1145/3582430
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
NR 136
TC 1
Z9 1
U1 17
U2 17
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PD 2024 OCT 14
PY 2024
DI 10.1080/10447318.2024.2405782
EA OCT 2024
PG 22
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA I7E0A
UT WOS:001331838900001
DA 2024-12-25
ER

PT J
AU Ray, PP
AF Ray, Partha Pratim
TI Generative AI and Its Impact on Sugarcane Industry: An Insight into
   Modern Agricultural Practices
SO SUGAR TECH
LA English
DT Article
DE Generative AI; Sugarcane industry; Sugar technology; Sustainable
   development goal
AB Generative Artificial Intelligence (GAI) offers remarkable opportunities for transforming sugarcane agriculture, promising significant improvements in crop cultivation, processing, and sustainability. This short communication delineates GAI's prospective applications in the sugar industry, emphasizing tangible benefits and outlining the necessary steps toward integrating this innovative technology effectively. Implementing GAI, while challenging, primarily due to the required investment, infrastructure development, data privacy concerns, and workforce training, is crucial for optimizing crop yield, sustainability, and enhancing bioethanol production from sugarcane. Recognizing and addressing these challenges through multi-stakeholder collaboration, policy formulation, and robust data security measures is fundamental, as is the need for ongoing research and development in applying GAI to the sugar industry to realize global sustainability goals.
C1 [Ray, Partha Pratim] Sikkim Univ, Gangtok, India.
C3 Sikkim University
RP Ray, PP (corresponding author), Sikkim Univ, Gangtok, India.
EM ppray@cus.ac.in
RI Ray, Partha Pratim/HRC-0757-2023
OI Ray, Partha Pratim/0000-0003-2306-2792
CR Akkem Y, 2023, ENG APPL ARTIF INTEL, V120, DOI 10.1016/j.engappai.2023.105899
   Hartatik Sri, 2023, IOP Conference Series: Earth and Environmental Science, DOI 10.1088/1755-1315/1177/1/012009
   Hasnain M, 2023, WASTE BIOMASS VALORI, V14, P23, DOI 10.1007/s12649-022-01864-0
   Javaid M., 2023, Advanced Agrochem, V2, P15, DOI DOI 10.1016/J.AAC.2022.10.001
   Kalopesa E, 2023, SENSORS-BASEL, V23, DOI 10.3390/s23031065
   Modi RU, 2023, FIELD CROP RES, V291, DOI 10.1016/j.fcr.2022.108797
   Mukherjee E, 2023, SUGAR TECH, V25, P269, DOI 10.1007/s12355-022-01176-6
   Solaiman I, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P111, DOI 10.1145/3593013.3593981
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
NR 9
TC 1
Z9 1
U1 7
U2 15
PU SPRINGER INDIA
PI NEW DELHI
PA 7TH FLOOR, VIJAYA BUILDING, 17, BARAKHAMBA ROAD, NEW DELHI, 110 001,
   INDIA
SN 0972-1525
EI 0974-0740
J9 SUGAR TECH
JI Sugar Tech.
PD APR
PY 2024
VL 26
IS 2
BP 325
EP 332
DI 10.1007/s12355-023-01358-w
EA JAN 2024
PG 8
WC Agronomy
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Agriculture
GA MR6S9
UT WOS:001150777100001
DA 2024-12-25
ER

PT J
AU Zhu, LX
   Mou, WM
   Hong, CL
   Yang, T
   Lai, YC
   Qi, C
   Lin, AQ
   Zhang, J
   Luo, P
AF Zhu, Lingxuan
   Mou, Weiming
   Hong, Chenglin
   Yang, Tao
   Lai, Yancheng
   Qi, Chang
   Lin, Anqi
   Zhang, Jian
   Luo, Peng
TI The Evaluation of Generative AI Should Include Repetition to Assess
   Stability
SO JMIR MHEALTH AND UHEALTH
LA English
DT Article
DE large language model; generative AI; ChatGPT; artificial intelligence;
   health care
AB The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.
C1 [Zhu, Lingxuan; Hong, Chenglin; Lai, Yancheng; Lin, Anqi; Zhang, Jian; Luo, Peng] Southern Med Univ, Zhujiang Hosp, Dept Oncol, 253 Ind Ave, Guangzhou, Peoples R China.
   [Mou, Weiming] Shanghai Jiao Tong Univ, Shanghai Gen Hosp, Sch Med, Dept Urol, Shanghai, Peoples R China.
   [Yang, Tao] Chinese Acad Med Sci, Canc Hosp, Dept Med Oncol, Natl Clin Res Ctr Canc,Natl Canc Ctr, Beijing, Peoples R China.
   [Yang, Tao] Peking Union Med Coll, Beijing, Peoples R China.
   [Qi, Chang] TU Wien, Inst L & Computat, Vienna, Austria.
C3 Southern Medical University - China; Shanghai Jiao Tong University;
   Chinese Academy of Medical Sciences - Peking Union Medical College;
   Cancer Institute & Hospital - CAMS; Chinese Academy of Medical Sciences
   - Peking Union Medical College; Peking Union Medical College; Technische
   Universitat Wien
RP Luo, P (corresponding author), Southern Med Univ, Zhujiang Hosp, Dept Oncol, 253 Ind Ave, Guangzhou, Peoples R China.
EM luopeng@smu.edu.cn
RI Lin, Anqi/ABV-1026-2022; Mou, Weiming/GRY-0057-2022; Yang,
   Tao/KHV-7528-2024; Lai, Yancheng/HOA-8285-2023; Zhu,
   Lingxuan/GRX-9995-2022; Luo, Peng/C-5323-2017
OI HONG, CHENGLIN/0009-0009-3565-3486; Zhu, Lingxuan/0009-0001-9077-408X;
   Luo, Peng/0000-0002-8215-2045; Lai, Yancheng/0009-0004-8444-7535; Mou,
   Weiming/0009-0007-1089-6516; Lin, Anqi/0000-0002-6324-0410
CR Ali Stephen R, 2023, Lancet Digit Health, V5, pe179, DOI 10.1016/S2589-7500(23)00048-1
   Chen JH, 2023, Arxiv, DOI [arXiv:2312.10074, 10.48550/arXiv.2312.10074]
   Cheng SL, 2023, J MED INTERNET RES, V25, DOI 10.2196/51229
   Fijaoko N, 2023, RESUSCITATION, V185, DOI 10.1016/j.resuscitation.2023.109732
   Giannos Panagiotis, 2023, JMIR Med Educ, V9, pe47737, DOI 10.2196/47737
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Grünebaum A, 2023, AM J OBSTET GYNECOL, V228, P696, DOI 10.1016/j.ajog.2023.03.009
   Howard A, 2023, LANCET INFECT DIS, V23, P405, DOI 10.1016/S1473-3099(23)00113-5
   Hsu HY, 2023, JMIR MED EDUC, V9, DOI 10.2196/48433
   Long C, 2024, JMIR MED EDUC, V10, DOI 10.2196/49970
   Meyer A, 2024, JMIR MED EDUC, V10, DOI 10.2196/50965
   Patel SB, 2023, LANCET DIGIT HEALTH, V5, pE107, DOI 10.1016/S2589-7500(23)00021-3
   Sarraju A, 2023, JAMA-J AM MED ASSOC, V329, P842, DOI 10.1001/jama.2023.1044
   Shao CY, 2023, INTERACT J MED RES, V12, DOI 10.2196/46900
   von Wedel D, 2024, JAMA-J AM MED ASSOC, V331, P252, DOI 10.1001/jama.2023.24641
   Yanagita Y, 2023, JMIR FORM RES, V7, DOI 10.2196/48023
   Zhu LX, 2024, JAMA INTERN MED, V184, DOI 10.1001/jamainternmed.2024.0020
   Zhu LX, 2023, RESUSCITATION, V188, DOI 10.1016/j.resuscitation.2023.109783
   Zhu LX, 2023, J TRANSL MED, V21, DOI 10.1186/s12967-023-04123-5
NR 19
TC 3
Z9 3
U1 15
U2 19
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 2291-5222
J9 JMIR MHEALTH UHEALTH
JI JMIR mHealth uHealth
PY 2024
VL 12
AR e57978
DI 10.2196/57978
PG 4
WC Health Care Sciences & Services; Medical Informatics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Health Care Sciences & Services; Medical Informatics
GA RH1Q3
UT WOS:001226687200002
PM 38688841
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Wang, ZZ
AF Wang, Zhaozhe
TI Post-Rhetoric: A Rhetorical Profile of the Generative Artificial
   Intelligence Chatbot
SO RHETORIC REVIEW
LA English
DT Article
ID CIRCULATION
AB The generative AI chatbot, as an artificial rhetorical agent participating in the invention and circulation of public discourse, shakes the foundations of rhetorical tenets such as agency, ethos, circulation, and justice; and in doing so, it further isolates rhetoric as amoral, ateleological techn & emacr; concerned with mere calculated effects and consequences, and may ultimately contribute to a post-rhetoric condition. This article depicts a rhetorical profile of the generative AI chatbot characterized by stochastic rhetoric, which is distinguished from the conventional understanding of rhetoric as (human) conscious and purposeful use of language to induce change. Making a case for the possibility of a post-rhetoric condition, the article considers what it might mean for our conceptualization of ethos, circulation, and justice, and suggests ways of adapting to it.
C1 [Wang, Zhaozhe] Univ Toronto, Inst Study Univ Pedag, Ontario Inst Studies Educ, Toronto, ON, Canada.
C3 University of Toronto; University Health Network Toronto
RP Wang, ZZ (corresponding author), Univ Toronto, Inst Study Univ Pedag, Ontario Inst Studies Educ, Toronto, ON, Canada.
EM zhaozhe.wang@utoronto.ca
RI Wang, Zhaozhe/HRE-2605-2023
OI Wang, Zhaozhe/0000-0001-6608-7530
CR Abbott DP, 2007, PHILOS RHETORIC, V40, P274, DOI 10.1353/par.2007.0025
   [Anonymous], 1964, Understanding Media: The Extensions of Man
   Beck Estee N., 2015, Computers and Composition, V35, P125, DOI 10.1016/j.compcom.2015.01.005
   Beck Estee N., 2016, enculturation, V23
   Bender EM, 2020, P 58 ANN M ASS COMP, P5185, DOI DOI 10.18653/V1/2020.ACL-MAIN.463
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Beveridge Aaron., 2020, Computers and Composition, V57, P102594
   Bogost I., 2007, Persuasive games: The expressive power of videogames
   Bolter JayDavid Richard Grusin., 1999, REMEDIATION UNDERSTA
   Boyle C, 2018, RHETORIC AS A POSTHUMAN PRACTICE, P1, DOI 10.26818/9780814213803
   Brock K, 2019, SWEETLAND DIG RHET C, P1, DOI 10.3998/mpub.10019291
   Brock Kevin, 2016, Computers and Composition, V42, P17, DOI 10.1016/j.compcom.2016.08.007
   Broussard M, 2018, ARTIFICIAL UNINTELLIGENCE: HOW COMPUTERS MISUNDERSTAND THE WORLD
   Carnegie Teena A. M., 2009, Computers and Composition, V26, P164, DOI 10.1016/j.compcom.2009.05.005
   Chaput C, 2010, PHILOS RHETORIC, V43, P1
   Davidson Helen, 2023, The Guardian
   Dencik L, 2019, INFORM COMMUN SOC, V22, P873, DOI 10.1080/1369118X.2019.1606268
   Esposito E., 2022, Artificial communication: how algorithms produce social intelligence, DOI DOI 10.7551/MITPRESS/14189.001.0001
   Fogg B., 2003, Persuasive technology: Using computers to change what we think and do, DOI [DOI 10.1016/B978-1-55860-643-2.X5000-8, 10.1016/B978-1-55860-643-2.X5000-8, 10.1016/B978-155860643-2/50011-1, DOI 10.1016/B978-155860643-2/50011-1]
   Frankfurt HG, 2005, ON BULLSHIT, P1
   Gallagher John, 2020, Computers and Composition, P29, DOI 10.1016/j.compcom.2020.102583
   Gries L.E., 2018, Circulation, writing rhetoric, P3
   Gries LE, 2015, STILL LIFE WITH RHETORIC: A NEW MATERIALIST APPROACH FOR VISUAL RHETORICS, pXIII
   Gurak LauraJ., 2001, Cyberliteracy: Navigating the Internet with awareness
   Heaven W. D., 2022, TECHNOLOGY REV
   Heidegger Martin., 1998, PATHMARKS, P239
   Heikkila M., 2023, MIT Technology Review
   Hillen Andrew J., 2020, Extending Ethos in Digital Rhetorics
   Ingraham Chris., 2014, Digital Rhetoric and Global Literacies: Communication Modes and Digital Practices in the Networked World, P62
   Johnson JD, 2018, THEORIZING DIGITAL RHETORIC, P196
   Lyons SR, 2000, COLL COMPOS COMMUN, V51, P447, DOI 10.2307/358744
   Maher Jennifer., 2016, Computational Culture, V5
   Marjanovic O, 2022, EUR J INFORM SYST, V31, P269, DOI 10.1080/0960085X.2021.1934130
   Mayer-Schonberger Viktor, 2013, Big data: A revolution that will transform how we live, work, and think
   McComiskey Bruce., 2017, POSTTRUTH RHETORIC C
   Miller CarolynR., 2007, Rhetoric Society Quarterly, V37, P137, DOI [10.1080/02773940601021197, DOI 10.1080/02773940601021197]
   Musiol Martin, 2023, ABOUT US
   OpenAI, 2022, Snapshot of ChatGPT Model Behavior Guidelines
   Pulver CJ, 2020, METABOLIZING CAPITAL, P1, DOI 10.7330/ 9781607329688
   Reid Alex., 2020, enculturation, V31
   Rickert T.J., 2014, Ambient Rhetoric: The Attunements of Rhetorical Being
   Ridolfo Jim., 2009, KAIROS, V13
   SELFE CL, 1994, COLL COMPOS COMMUN, V45, P480, DOI 10.2307/358761
   Smith CraigR., 2017, Rhetoric and Human Consciousness: A History, V5th
   The Editors, 2022, Bloomberg
   Vallor S., 2016, TECHNOLOGY VIRTUES P, DOI [10.1093/acprof:oso/9780190498511.001.0001, DOI 10.1093/ACPROF:OSO/9780190498511.001.0001]
   Vincent James, 2023, The Verge
   Wang ZZ, 2023, RHETOR SOC Q, V53, P670, DOI 10.1080/02773945.2023.2191215
   Wang ZZ, 2022, Q J SPEECH, V108, P382, DOI 10.1080/00335630.2022.2128201
   Wang ZZ, 2021, RHETOR REV, V40, P395, DOI 10.1080/07350198.2021.1963041
   Wittgenstein L., 1969, Notebooks 1914-1916
   Woolley S.C., 2019, COMPUTATIONAL PROPAG, P241
   Zappen JP, 2005, TECH COMMUN Q, V14, P319, DOI 10.1207/s15427625tcq1403_10
NR 53
TC 0
Z9 0
U1 10
U2 10
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0735-0198
EI 1532-7981
J9 RHETOR REV
JI Rhetor. Rev.
PD JUL 2
PY 2024
VL 43
IS 3
BP 155
EP 172
DI 10.1080/07350198.2024.2351723
PG 18
WC Language & Linguistics; Literature
WE Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Linguistics; Literature
GA UW5P6
UT WOS:001251117700003
DA 2024-12-25
ER

PT J
AU Mattusch, M
AF Mattusch, Matthias
TI Generative AI for European asset pricing: alleviating the momentum
   anomaly
SO EUROPEAN JOURNAL OF FINANCE
LA English
DT Article; Early Access
DE Momentum anomaly; factor zoo; Sharpe ratio; generative AI; C45; G11;
   G12; G15
ID CROSS-SECTION; SIZE; LIQUIDITY; RETURNS; EQUITY
AB We challenge the notion of classical factor models that concentrated factors, particularly the anomalous momentum factor, dominate the European stock market. We use a generative artificial intelligence (generative AI) asset pricing model that incorporates the economic rationale of no-arbitrage and treats the European capital market as a complex system. This model outperforms all European benchmarks over 16 years out-of-sample, with an annualized Sharpe ratio of 3.68, a cross-sectional $ R<^>2 $ R2 of over 22%, and an explained variation of over 13%. Using interpretable AI techniques, we find that the model sees a zoo of factors in the European market rather than just a concentrated set. These excellent results stem from time-conditional modeling, which requires momentum, especially for tangency portfolio weights. Conditional betas can substitute momentum more efficiently. Overall, the risk-sharing mechanism for European assets is more complex than previously thought.
C1 [Mattusch, Matthias] Tech Univ Dresden, Chair Finance & Financial Technol, Dresden, Germany.
RP Mattusch, M (corresponding author), Tech Univ Dresden, Chair Finance & Financial Technol, Dresden, Germany.
EM matthias.mattusch@tu-dresden.de
NR 0
TC 0
Z9 0
U1 0
U2 0
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1351-847X
EI 1466-4364
J9 EUR J FINANC
JI Eur. J. Financ.
PD 2024 DEC 14
PY 2024
DI 10.1080/1351847X.2024.2439979
EA DEC 2024
PG 39
WC Business, Finance
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA P7N4W
UT WOS:001379729100001
DA 2024-12-25
ER

PT J
AU Lan, YJ
   Chen, NS
AF Lan, Yu-Ju
   Chen, Nian-Shing
TI Teachers'' agency in the era agency of LLM and generative AI: Designing
   pedagogical AI agents
SO EDUCATIONAL TECHNOLOGY & SOCIETY
LA English
DT Article
DE Generative artificial intelligence (GAI); Pedagogical AI agent;
   Instructional design; Personalized learning
AB The purpose of this study is to explore the existing problems associated with using generative AI in education and to propose a potential solution for addressing those issues through the design of pedagogical AI agents. The existing problems are examined from two different perspectives: those of teachers and students. The proposed solutions for designing pedagogical AI agents are systematically presented, including main concepts, design considerations, functions, procedures, and structure/templates. An example of how to apply the proposed solution in designing a pedagogical AI agent is provided, illustrating its application in teaching order words (or sequencing words). Finally, the paper concludes with a discussion of potential topics for further research.
C1 [Lan, Yu-Ju] Natl Taiwan Normal Univ, Taipei, Taiwan.
   [Chen, Nian-Shing] Natl Taiwan Normal Univ, Inst Res Excellence Learning Sci, Program Learning Sci, Taipei, Taiwan.
C3 National Taiwan Normal University; National Taiwan Normal University
RP Chen, NS (corresponding author), Natl Taiwan Normal Univ, Inst Res Excellence Learning Sci, Program Learning Sci, Taipei, Taiwan.
EM yujulan@gmail.com; nianshing@gmail.com
RI Chen, Nian-Shing/B-7035-2009
NR 0
TC 14
Z9 14
U1 211
U2 228
PU INT FORUM EDUCATIONAL TECHNOLOGY & SOC, NATL TAIWAN NORMAL UNIV
PI Taipei City
PA No.162, Sec. 1, Heping E. Rd., Da-an Dist, Taipei City, TAIWAN
SN 1176-3647
EI 1436-4522
J9 EDUC TECHNOL SOC
JI Educ. Technol. Soc.
PD JAN
PY 2024
VL 27
IS 1
DI 10.30191/ETS.202401_27(1).PP01
PG 18
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA IQ9L1
UT WOS:001167911000001
DA 2024-12-25
ER

PT J
AU Sundberg, L
   Holmström, J
AF Sundberg, Leif
   Holmstrom, Jonny
TI Innovating by prompting: How to facilitate innovation in the age of
   generative AI
SO BUSINESS HORIZONS
LA English
DT Article
DE Prompt engineering; Iterative prompting; Generative AI; Large language
   models; licenses/by/4.0/).
ID DIGITAL INNOVATION; BIG DATA
AB This article focuses on how recent advances in artificial intelligence (AI), particularly chatbots based on large language models (LLMs), such as ChatGPT, can be used for innovation purposes. The article begins with a brief overview of the development and characteristics of generative AI (GenAI). Elaborating on the implications of GenAI, we provide examples to demonstrate four mechanisms of LLMs: translation, summarization, classification, and amplification. These mechanisms inform a framework that highlights how LLMs enable the creation of innovative solutions for organizations through capacities in two dimensions: context awareness and content awareness. The strength of LLMs lies in the combination of capacities in both these dimensions, which enables them to comprehend and amplify content. Four managerial suggestions are presented, ranging from starting out with smallscale projects and data exploration, to scaling through integration efforts and educating prompt engineers. By presenting the framework, recommendations, and examples of use cases in various contexts, the article contributes to the emerging literature on GenAI and innovation. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/
C1 [Sundberg, Leif; Holmstrom, Jonny] Umea Univ, Swedish Ctr Digital Innovat SCDI, Dept Informat, Umea, Sweden.
C3 Umea University
RP Sundberg, L (corresponding author), Umea Univ, Swedish Ctr Digital Innovat SCDI, Dept Informat, Umea, Sweden.
EM leif.sundberg@umu.se; jonny.holmstrom@umu.se
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   [Anonymous], 2023, The GuardianApril 17
   Azamfirei R, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04393-x
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Berente N, 2021, MIS QUART, V45, P1433, DOI DOI 10.25300/MISQ/2021/16274
   Berthon P., 2024, Business Horizons, V67, P461
   Bouschery SG, 2023, J PROD INNOVAT MANAG, V40, P139, DOI 10.1111/jpim.12656
   BRESNAHAN TF, 1995, J ECONOMETRICS, V65, P83, DOI 10.1016/0304-4076(94)01598-T
   Brown TB, 2020, ADV NEUR IN, V33
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   Coursera, 2023, Prompt engineering for ChatGPT
   Cramer J. S., 2002, Tinbergen Inst. Work. Pap, DOI [10.2139/ssrn.360300, DOI 10.2139/SSRN.360300]
   Dataconomy, 2022, AI artwork wins art competition
   DeepLearning.AI, 2023, ChatGPT prompt engineering for developers
   Denton E, 2021, BIG DATA SOC, V8, DOI 10.1177/20539517211035955
   Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, 10.48550/arXiv.1810.04805, DOI 10.48550/ARXIV.1810.04805]
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   FUKUSHIMA K, 1988, NEURAL NETWORKS, V1, P119, DOI 10.1016/0893-6080(88)90014-7
   Gandhi P. A., 2023, Indian Journal of Medical Sciences, V75, P1, DOI DOI 10.25259/IJMS342023
   GILL TG, 1995, MIS QUART, V19, P51, DOI 10.2307/249711
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Günther WA, 2017, J STRATEGIC INF SYST, V26, P191, DOI 10.1016/j.jsis.2017.07.003
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Hautala J, 2023, FUTURES, V153, DOI 10.1016/j.futures.2023.103247
   Holmström J, 2018, INFORM ORGAN-UK, V28, P107, DOI 10.1016/j.infoandorg.2018.04.002
   Hu K., 2023, REUTERS         0202
   Huang F, 2023, Arxiv, DOI [arXiv:2302.07736, DOI 10.1145/3543873.3587368, DOI 10.48550/ARXIV.2302.07736]
   IKEA, 2023, BILLY bookcase
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kasirzadeh A, 2022, PHILOS TECHNOL, DOI DOI 10.1007/S13347-023-00606-X
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Khan RA, 2023, PAK J MED SCI, V39, P605, DOI 10.12669/pjms.39.2.7653
   Kietzmann J., 2024, Business Horizons, V67, P453
   Kietzmann J, 2020, BUS HORIZONS, V63, P131, DOI 10.1016/j.bushor.2019.11.005
   Kietzmann J, 2020, BUS HORIZONS, V63, P135, DOI 10.1016/j.bushor.2019.11.006
   Krizhevsky A, 2017, COMMUN ACM, V60, P84, DOI 10.1145/3065386
   LeCun Y, 2015, NATURE, V521, P436, DOI 10.1038/nature14539
   LeCun Yann, 1989, Advances in Neural Information Processing Systems, V2
   Leippold M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2022.103617
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Lo LS, 2023, J ACAD LIBR, V49, DOI 10.1016/j.acalib.2023.102720
   Lund Brady D., 2023, Library Hi Tech News, P26, DOI 10.1108/LHTN-01-2023-0009
   Lycett M, 2013, EUR J INFORM SYST, V22, P381, DOI 10.1057/ejis.2013.10
   Mackenzie A, 2017, MACHINE LEARNERS: ARCHAEOLOGY OF A DATA PRACTICE, P1, DOI 10.7551/mitpress/10302.001.0001
   Mariani MM, 2023, J BUS RES, V155, DOI 10.1016/j.jbusres.2022.113364
   Öhman J, 2023, Arxiv, DOI [arXiv:2303.17183, 10.48550/arXiv.2303.17183]
   Paschen U, 2020, BUS HORIZONS, V63, P147, DOI 10.1016/j.bushor.2019.10.004
   Recker J, 2023, CALIF MANAGE REV, V65, P27, DOI 10.1177/00081256231170028
   Ritala P., 2023, J. Bus. Strategy, V45, P214
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Russell S. J., 1994, Artificial intelligence: A modern approach, V3rd
   Sabherwal R, 2024, J ASSOC INF SYST, V25, DOI 10.17705/1jais.00860
   Sahlgren M., 2022, MediumSeptember 22
   Shen YQ, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.230163
   Short C. E., 2023, Journal of Business Venturing Insights, V19
   Sundberg L, 2023, BUS HORIZONS, V66, P777, DOI 10.1016/j.bushor.2023.04.003
   Teubner T, 2023, BUS INFORM SYST ENG+, V65, P95, DOI 10.1007/s12599-023-00795-x
   Turing A., 2009, Parsing the Turing test. Philosophical and methodological issues in the quest for the thinking computer, P23, DOI DOI 10.1093/MIND/LIX.236.433
   Yoo YJ, 2010, INFORM SYST RES, V21, P724, DOI 10.1287/isre.1100.0322
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
   Zhou KY, 2022, INT J COMPUT VISION, V130, P2337, DOI 10.1007/s11263-022-01653-1
NR 63
TC 12
Z9 12
U1 100
U2 100
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 561
EP 570
DI 10.1016/j.bushor.2024.04.014
EA AUG 2024
PG 10
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2M8A
UT WOS:001301402100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Rodriguez, DV
   Lawrence, K
   Gonzalez, J
   Brandfield-Harvey, B
   Xu, L
   Tasneem, S
   Levine, DL
   Mann, D
AF Rodriguez, Danissa, V
   Lawrence, Katharine
   Gonzalez, Javier
   Brandfield-Harvey, Beatrix
   Xu, Lynn
   Tasneem, Sumaiya
   Levine, Defne L.
   Mann, Devin
TI Leveraging Generative AI Tools to Support the Development of Digital
   Solutions in Health Care Research: Case Study
SO JMIR HUMAN FACTORS
LA English
DT Article
DE digital health; GenAI; generative; artificial intelligence; ChatGPT;
   software engineering; mHealth; mobile health; app; apps; application;
   applications; diabetes; diabetic; diabetes prevention; digital
   prescription; software; engagement; behaviour change; behavior change;
   developer; developers; LLM; LLMs; language model; language models; NLP;
   natural language processing
AB Background: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. Objective: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. Methods: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human -generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. Results: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high -quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy -to -build computational solutions for medical technologies. Conclusions: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. Trial Registration: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500
C1 [Rodriguez, Danissa, V; Lawrence, Katharine; Brandfield-Harvey, Beatrix; Xu, Lynn; Tasneem, Sumaiya; Levine, Defne L.; Mann, Devin] NYU, Grossman Sch Med, Dept Populat Hlth, New York, NY USA.
   [Lawrence, Katharine; Gonzalez, Javier; Mann, Devin] New York Univ Langone Hlth, Med Ctr Informat Technol, Dept Hlth Informat, New York, NY USA.
   [Rodriguez, Danissa, V] NYU, Grossman Sch Med, Dept Populat Hlth, 227 East 30th St,6th Floor, New York, NY 10016 USA.
C3 New York University; New York University; New York University
RP Rodriguez, DV (corresponding author), NYU, Grossman Sch Med, Dept Populat Hlth, 227 East 30th St,6th Floor, New York, NY 10016 USA.
EM danissa.rodriguez@nyulangone.org
OI Rodriguez, Danissa Victoria/0000-0003-4642-6798; Tasneem,
   Sumaiya/0000-0001-5721-1819; Levine, Defne Leyla/0009-0000-5238-3144;
   Mann, Devin M/0000-0002-2099-0852; Gonzalez, Javier/0000-0002-7562-6070;
   , Lynn Xu/0009-0009-8586-9955
CR Ahmad A, 2023, 27TH INTERNATIONAL CONFERENCE ON EVALUATION AND ASSESSMENT IN SOFTWARE ENGINEERING, EASE 2023, P279, DOI 10.1145/3593434.3593468
   Akbar MA., 2023, IEEE Transactions on Artificial Intelligence, DOI [10.1109/TAI.2023.3318183, DOI 10.1109/TAI.2023.3318183]
   Alshuqayran N, 2016, IEEE INT CONF SERV, P44, DOI 10.1109/SOCA.2016.15
   [Anonymous], 2022, The impact of artificial intelligence on the future of workforces in the European Union and the United States of America
   [Anonymous], 2016, Microservice Architecture: Aligning Principles, Practices, and Culture
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Biswas S, 2023, OPHTHAL PHYSL OPT, V43, P1562, DOI 10.1111/opo.13207
   Brynjolfsson E., 2023, Generative AI at Work.
   Dave T, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1169595
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Lawrence K, 2021, JMIR RES PROTOC, V10, DOI 10.2196/26750
   Liu SR, 2023, J AM MED INFORM ASSN, V30, P1237, DOI 10.1093/jamia/ocad072
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Lugosi G., 2023, PREPRINT
   Mathew S., 2014, Amazon Whitepapers
   Nayak A, 2023, JAMA INTERN MED, V183, P1026, DOI 10.1001/jamainternmed.2023.2561
   Risling TL, 2020, J RES NURS, V25, P226, DOI 10.1177/1744987120913590
   Rodriguez DV, 2021, J AM MED INFORM ASSN, V29, P155, DOI 10.1093/jamia/ocab206
   Strong E, 2023, JAMA INTERN MED, V183, P1028, DOI 10.1001/jamainternmed.2023.2909
   Surameery NMS., 2023, INT J INFORM TECHNOL, V3, P17, DOI DOI 10.55529/IJITC.31.17.22
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Wang J., 2023, Meta-Radiology, P100047, DOI DOI 10.1016/J.METRAD.2023.100047
   White J, 2023, Arxiv, DOI [arXiv:2303.07839, 10.48550/arXiv.2303.07839]
   White J, 2023, Arxiv, DOI [arXiv:2302.11382, 10.48550/ARXIV.2302.11382]
NR 24
TC 5
Z9 5
U1 17
U2 32
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
SN 2292-9495
J9 JMIR HUM FACTORS
JI JMIR Hum. Factors
PY 2024
VL 11
AR e52885
DI 10.2196/52885
PG 14
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA KX9B5
UT WOS:001183371900001
PM 38446539
OA gold, Green Published
DA 2024-12-25
ER

PT J
AU Osadchaya, E
   Marder, B
   Yule, JA
   Yau, A
   Lavertu, L
   Stylos, N
   Oliver, S
   Angell, R
   de Regt, A
   Gao, LY
   Qi, K
   Zhang, WZ
   Zhang, YW
   Li, JY
   Alrabiah, S
AF Osadchaya, Elena
   Marder, Ben
   Yule, Jennifer A.
   Yau, Amy
   Lavertu, Laura
   Stylos, Nikolaos
   Oliver, Sebastian
   Angell, Rob
   de Regt, Anouk
   Gao, Liyu
   Qi, Kang
   Zhang, Will Zhiyuan
   Zhang, Yiwei
   Li, Jiayuan
   Alrabiah, Sara
TI To ChatGPT, or not to ChatGPT: Navigating the paradoxes of generative AI
   in the advertising industry
SO BUSINESS HORIZONS
LA English
DT Article
DE ChatGPT; Generative AI; Paradoxes; Advertising; Chatbots
ID CREATIVITY
AB Generative AI (GenAI) technology is evoking both excitement and fear about its potential impact across a host of industriesdincluding advertising, where it is expected to have a significant disruptive effect. This article utilizes the paradox lens to explore the implications of text-to-text GenAI in the form of ChatGPT for the advertising industry. Drawing on 48 interviews with advertising professionals, we identify three operational paradoxes that are associated with conducting research, creativity, efficiency, and one psychological paradox related to work identity. To gain a competitive advantage, we urge practitioners to adopt a confrontation-based coping strategy to navigate these paradoxes. This can be mobilized via an ambidexterity or contingency paradox management approach. We outline specific tactics in this article. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
C1 [Osadchaya, Elena] Univ Leicester, Sch Business, Leicester LE2 1RQ, England.
   [Marder, Ben; Yule, Jennifer A.; Gao, Liyu; Qi, Kang; Zhang, Will Zhiyuan; Zhang, Yiwei; Li, Jiayuan] Univ Edinburgh, Business Sch, Edinburgh EH8 9JS, Scotland.
   [Yau, Amy] Cardiff Univ, Cardiff Business Sch, Cardiff CF10 3EU, Wales.
   [Lavertu, Laura] Strathclyde Business Sch, Glasgow G4 0QU, Scotland.
   [Stylos, Nikolaos] Univ Bristol, Business Sch, Bristol BS8 1SD, England.
   [Oliver, Sebastian; Angell, Rob] Univ Southampton, Southampton Business Sch, Southampton SO17 1BJ, England.
   [de Regt, Anouk] Univ Utrecht, Sch Econ, NL-3584EC Utrecht, Netherlands.
   [Alrabiah, Sara] Inst Publ Adm, Riyadh 11141, Saudi Arabia.
C3 University of Leicester; University of Edinburgh; Cardiff University;
   University of Strathclyde; University of Bristol; University of
   Southampton; Solent University; Utrecht University; Institute of Public
   Administration - Saudi Arabia
RP Osadchaya, E (corresponding author), Univ Leicester, Sch Business, Leicester LE2 1RQ, England.
EM eo156@leicester.ac.uk
RI regt, A/AGR-4072-2022; Lavertu, Laura/AAN-2117-2021; Oliver,
   Sebastian/JGC-7946-2023
OI Osadchaya, Elena/0000-0002-7226-350X; Oliver,
   Sebastian/0000-0001-8600-7964; Stylos, Nikolaos/0000-0003-1626-0088;
   Angell, Rob/0000-0001-8554-2092; de Regt, Anouk/0000-0001-9961-0025
CR Abdelhalim E., 2024, Business Horizons, V67, P487
   ANA, 2023, The continued rise of the in-house agency
   Bartz D., 2023, White House Says, ReutersJuly 21
   Berthon P., 2024, Business Horizons, V67, P461
   Brower T., 2023, Forbes
   Brown R., 2023, Welcome to Jasper AI-Quick, easy content creation powered by AI! Medium
   Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
   Cannon T., 1996, Welcome to the revolution: Managing paradox in the 21st century
   Cellerin T., 2023, How ChatGPT is already disrupting the advertising industry
   Clegg SR, 2002, HUM RELAT, V55, P483, DOI 10.1177/0018726702055005425
   Clugston R., 2023, Adhesives Age
   Cui YY, 2024, BUS HORIZONS, V67, P583, DOI 10.1016/j.bushor.2024.05.003
   Demsar V, 2021, BUS HORIZONS, V64, P415, DOI 10.1016/j.bushor.2021.02.007
   Dischler J., 2023, Introducing a new era of AI-powered ads with Google
   Dowling L., 2023, Pathmonk
   Farhi P., 2023, Washington Post
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Fischer S., 2023, AXIOS,May 30
   Frey Carl Benedikt, 2019, MIT Sloan Management ReviewNovember 12
   Gebauer H, 2020, BUS HORIZONS, V63, P313, DOI 10.1016/j.bushor.2020.01.005
   Grand View Research, 2023, Generative AI market size to reach $109.37 billion by 2030
   Haggin P., 2023, The Wall Street Journal,April 16
   Haluza D, 2023, SYSTEMS-BASEL, V11, DOI 10.3390/systems11030120
   Hannigan TR, 2024, BUS HORIZONS, V67, P471, DOI 10.1016/j.bushor.2024.03.001
   Hu K., 2023, REUTERS         0202
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Jarvenpaa SL, 2005, INFORM SYST MANAGE, V22, P7, DOI 10.1201/1078.10580530/45520.22.4.20050901/90026.2
   Kietzmann J., 2024, Business Horizons, V67, P453
   Koslow S, 2003, J ADVERTISING RES, V43, P96, DOI 10.2501/JAR-43-1-96-110
   Kulp P., 2023, Ad Week,March 14
   Lawton G., 2023, ENTERPRISE AI
   Lehnert K, 2013, INT J ADVERT, V32, P211, DOI 10.2501/IJA-32-2-211-231
   Lewis MW, 2000, ACAD MANAGE REV, V25, P760, DOI 10.2307/259204
   Loten A., 2023, The Wall Street Journal,March 6
   Mauran C., 2023, WHOOPS SAMSUNG WORKE
   McCarthy IP, 2020, BUS HORIZONS, V63, P253, DOI 10.1016/j.bushor.2020.01.001
   Meta, 2023, Press release
   Mick DG, 1998, J CONSUM RES, V25, P123, DOI 10.1086/209531
   Muliyil A., 2023, ET Brand Equity,February 14
   Omnicom Group, 2023, Press release
   OpenAI, 2023, GPT-4
   Ortiz S., 2023, ZDNET,October 18
   Pedersen CL, 2023, BUS HORIZONS, V66, P765, DOI 10.1016/j.bushor.2023.04.002
   Cunha MPE, 2023, BUS HORIZONS, V66, P453, DOI 10.1016/j.bushor.2022.09.004
   Robertson J, 2024, BUS HORIZONS, V67, P499, DOI 10.1016/j.bushor.2024.04.008
   Ross C., 2023, Medium,May 10
   Smith RE, 2007, MARKET SCI, V26, P819, DOI 10.1287/mksc.1070.0272
   Sundberg L, 2024, BUS HORIZONS, V67, P561, DOI 10.1016/j.bushor.2024.04.014
   Victor A. M., 2023, Medium,May 16
NR 49
TC 5
Z9 5
U1 42
U2 42
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 571
EP 581
DI 10.1016/j.bushor.2024.05.002
EA AUG 2024
PG 11
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2S0X
UT WOS:001301539600001
OA Green Accepted, hybrid
DA 2024-12-25
ER

PT J
AU Lee, H
   Park, S
AF Lee, Hyunju
   Park, Soobin
TI Information amount, accuracy, and relevance of generative artificial
   intelligence platforms' answers regarding learning objectives of medical
   arthropodology evaluated in English and Korean queries in December 2023:
   a descriptive study
SO JOURNAL OF EDUCATIONAL EVALUATION FOR HEALTH PROFESSIONS
LA English
DT Article
DE Artificial intelligence; Deep learning; Language; Self efficacy;
   Republic of Korea
AB Purpose: This study assessed the performance of 6 generative artificial intelligence (AI) platforms on the learning objectives of medical arthropodology in a parasitology class in Korea. We examined the AI platforms' performance by querying in Korean and English to determine their information amount, accuracy, and relevance in prompts in both languages.
   Methods: From December 15 to 17, 2023, 6 generative AI platforms-Bard, Bing, Claude, Clova X, GPT-4, and Wrtn-were tested on 7 medical arthropodology learning objectives in English and Korean. Clova X and Wrtn are platforms from Korean companies. Responses were evaluated using specific criteria for the English and Korean queries.
   Results: Bard had abundant information but was fourth in accuracy and relevance. GPT-4, with high information content, ranked first in accuracy and relevance. Clova X was 4th in amount but 2nd in accuracy and relevance. Bing provided less information, with moderate accuracy and relevance. Wrtn's answers were short, with average accuracy and relevance. Claude AI had reasonable information, but lower accuracy and relevance. The responses in English were superior in all aspects. Clova X was notably optimized for Korean, leading in relevance.
   Conclusion: In a study of 6 generative AI platforms applied to medical arthropodology, GPT-4 excelled overall, while Clova X, a Korea-based AI product, achieved 100% relevance in Korean queries, the highest among its peers. Utilizing these AI platforms in classrooms improved the authors' self-efficacy and interest in the subject, offering a positive experience of interacting with generative AI platforms to question and receive information.
C1 [Lee, Hyunju; Park, Soobin] Hallym Univ, Coll Med, Chunchon, South Korea.
C3 Hallym University
RP Park, S (corresponding author), Hallym Univ, Coll Med, Chunchon, South Korea.
EM 20216132@hallym.ac.kr
OI Lee, Hyunju/0009-0003-2762-3654; Park, Soobin/0009-0000-8359-8834
FU College of Medicine, Hallym University [HLMC 2023-2-1]
FX This study is supported by the student research grant of the College of
   Medicine, Hallym University (HLMC 2023-2-1). The funder had no role in
   study design, data collection and analysis, or manuscript preparation.
CR Blakeslee S., 2004, LOEX Quarterly
   Huh S, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.1
   Hultgren C, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.32
   Hwang JY, 2024, MED TEACH, V46, P291, DOI 10.1080/0142159X.2023.2259068
   Park J, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.29
   Preiksaitis C, 2023, JMIR MED EDUC, V9, DOI 10.2196/48785
   Seth I, 2023, AESTHET SURG J OPEN, V5, DOI 10.1093/asjof/ojad084
   Song HF, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-02021-3
   Torres-Zegarra BC, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.30
NR 9
TC 3
Z9 3
U1 3
U2 10
PU KOREA HEALTH PERSONNEL LICENSING EXAMINATION INST
PI SEOUL
PA JAYANG-RO 45, GWANGJIN-GU, SEOUL, 05103, SOUTH KOREA
SN 1975-5937
J9 J EDUC EVAL HEALTH P
JI J. Educ. Eval. Health Prof.
PD DEC 28
PY 2023
VL 20
AR 39
DI 10.3352/jeehp.2023.20.39
PG 8
WC Education, Scientific Disciplines
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA MR4L0
UT WOS:001195345100001
PM 38151711
OA gold
DA 2024-12-25
ER

PT J
AU Westphal, E
   Seitz, H
AF Westphal, Erik
   Seitz, Hermann
TI Generative Artificial Intelligence: Analyzing Its Future Applications in
   Additive Manufacturing
SO BIG DATA AND COGNITIVE COMPUTING
LA English
DT Article
DE generative artificial intelligence; additive manufacturing; chatbot;
   Text-to-image; Text-to-3D
AB New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.
C1 [Westphal, Erik; Seitz, Hermann] Univ Rostock, Chair Microfluid, D-18059 Rostock, Germany.
   [Seitz, Hermann] Univ Rostock, Dept Life Light & Matter, D-18059 Rostock, Germany.
C3 University of Rostock; University of Rostock
RP Westphal, E (corresponding author), Univ Rostock, Chair Microfluid, D-18059 Rostock, Germany.
EM erik.westphal@uni-rostock.de; hermann.seitz@uni-rostock.de
RI Westphal, Erik/AAT-9992-2021; Seitz, Hermann/G-1657-2015
OI Seitz, Hermann/0000-0003-3401-0090
FU European Regional Development Fund (ERDF); Ministry for Economics,
   Employment and Health of Mecklenburg-Vorpommern, Germany
   [TBI-V-1-345-VBW-118, TBI-1-026-W-009]
FX This research was funded by the European Regional Development Fund
   (ERDF) and the Ministry for Economics, Employment and Health of
   Mecklenburg-Vorpommern, Germany, grant numbers TBI-V-1-345-VBW-118 and
   TBI-1-026-W-009.
CR Ai Build Limited, Talk to AiSync
   Ali S., 2021, arXiv
   Authentise Inc, Authentise brings ChatGPT Capabilities to Additive Manufacturing
   Awasthi P, 2021, ADDIT MANUF, V46, DOI 10.1016/j.addma.2021.102177
   Badini S, 2023, ADV IND ENG POLY RES, V6, P278, DOI 10.1016/j.aiepr.2023.03.003
   Bahrini A, 2023, Arxiv, DOI [arXiv:2304.09103, 10.48550/arXiv.2304.09103, DOI 10.48550/ARXIV.2304.09103, 10.48550/arxiv.2304.09103]
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Ballagas R, 2019, IEEE PERVAS COMPUT, V18, P20, DOI 10.1109/MPRV.2019.2929130
   Banh L, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00680-1
   Brisco R., 2023, Proceedings of the Design Society, V3, P1835, DOI [10.1017/pds.2023.184, DOI 10.1017/PDS.2023.184]
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Carlini N, 2021, PROCEEDINGS OF THE 30TH USENIX SECURITY SYMPOSIUM, P2633
   Chowdhary K., 2020, Fundamentals of artificial intelligence, P603, DOI [DOI 10.1007/978-81-322-3972-719, DOI 10.1007/978-81-322-3972-7_19]
   Chun Hyunjin, 2020, IOP Conference Series: Materials Science and Engineering, V727, DOI 10.1088/1757-899X/727/1/012010
   Corchado JM, 2023, ADCAIJ-ADV DISTRIB C, V12, DOI 10.14201/adcaij.31704
   Gao J, 2022, Arxiv, DOI [arXiv:2209.11163, DOI 10.48550/ARXIV.2209.11163, 10.48550/arXiv.2209.11163]
   Gibson I., 2021, ADDITIVE MANUFACTURI
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Goodfellow I. J., 2014, arXiv, DOI 10.48550/arXiv.1406.2661
   Gozalo-Brizuela R, 2023, Arxiv, DOI [arXiv:2306.02781, DOI 10.3844/JCSSP.2024.801.818]
   Ho JAT, 2020, Arxiv, DOI arXiv:2006.11239
   Jain A, 2022, Arxiv, DOI arXiv:2112.01455
   Jaruga-Rozdolska A., 2022, Architectus, V3, P95, DOI 10.37190/arc220310
   Jasche Florian, 2023, i-com: Journal of Interactive Media, P3, DOI 10.1515/icom-2022-0045
   Joublin F, 2023, Arxiv, DOI arXiv:2305.06087
   Jun H, 2023, Arxiv, DOI [arXiv:2305.02463, DOI 10.48550/ARXIV.2305.02463]
   Khalid NM, 2022, PROCEEDINGS SIGGRAPH ASIA 2022, DOI 10.1145/3550469.3555392
   Khorasani M, 2021, RAPID PROTOTYPING J, DOI 10.1108/RPJ-01-2021-0009
   Kietzmann J, 2015, BUS HORIZONS, V58, P209, DOI 10.1016/j.bushor.2014.11.005
   Kristiawan RB, 2021, OPEN ENG, V11, P639, DOI 10.1515/eng-2021-0063
   Kulkarni P, 2019, 2019 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, CONTROL AND AUTOMATION (ICCUBEA), DOI [10.1109/ICCUBEA47591.2019.9129347, 10.1109/iccubea47591.2019.9129347]
   Li B., 2019, Advances in Neural Information Processing Systems
   Li CH, 2024, Arxiv, DOI arXiv:2305.06131
   Li YP, 2023, MACHINES, V11, DOI 10.3390/machines11020170
   Lin CH, 2023, Arxiv, DOI arXiv:2211.10440
   Liu VV, 2022, Arxiv, DOI arXiv:2210.11603
   Nahavandi S, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11164371
   Oh S, 2019, J MECH DESIGN, V141, DOI 10.1115/1.4044229
   Oppenlaender Jonas, 2022, Academic Mindtrek 2022: 25th International Academic Mindtrek conference, P192, DOI 10.1145/3569219.3569352
   Poole B., 2022, arXiv
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Saka AB, 2023, ADV ENG INFORM, V55, DOI 10.1016/j.aei.2022.101869
   Szeliski R., 2022, Computer vision: algorithms and applications
   Tian HY, 2023, Arxiv, DOI arXiv:2304.11938
   Tofail SAM, 2018, MATER TODAY, V21, P22, DOI 10.1016/j.mattod.2017.07.001
   Ultimaker B.V., 2020, Ultimaker S3 and Ultimaker S5: Installation and User Manual
   Vaswani A, 2023, Arxiv, DOI [arXiv:1706.03762, 10.48550/arXiv.1706.03762, DOI 10.48550/ARXIV.1706.03762]
   Wang WJ, 2024, Arxiv, DOI [arXiv:2304.03516, 10.48550/arXiv.2304.03516, DOI 10.48550/ARXIV.2304.03516]
   Wong Kaufui V., 2012, ISRN Mechanical Engineering, DOI 10.5402/2012/208760
   Yuan CX, 2020, IEEE ACCESS, V8, P190710, DOI 10.1109/ACCESS.2020.3032280
   Zhao WX, 2023, Arxiv, DOI [arXiv:2303.18223, DOI 10.48550/ARXIV.2303.18223]
NR 52
TC 2
Z9 2
U1 14
U2 14
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2504-2289
J9 BIG DATA COGN COMPUT
JI Big Data Cogn. Comput.
PD JUL
PY 2024
VL 8
IS 7
AR 74
DI 10.3390/bdcc8070074
PG 21
WC Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory & Methods
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA ZP9C2
UT WOS:001276607200001
OA gold
DA 2024-12-25
ER

PT J
AU Lazaroiu, G
   Gedeon, T
   Rogalska, E
   Valaskova, K
   Nagy, M
   Musa, H
   Zvarikova, K
   Poliak, M
   Horak, J
   Cretoiu, RI
   Krulicky, T
   Ionescu, L
   Popa, C
   Hurloiu, LR
   Nistor, F
   Avram, LG
   Braga, V
AF Lazaroiu, George
   Gedeon, Tom
   Rogalska, Elzbieta
   Valaskova, Katarina
   Nagy, Marek
   Musa, Hussam
   Zvarikova, Katarina
   Poliak, Milos
   Horak, Jakub
   Cretoiu, Raluca Ionela
   Krulicky, Tomas
   Ionescu, Luminia
   Popa, Catalin
   Hurloiu, Lacramioara Rodica
   Nistor, Filip
   Avram, Laurentia Georgeta
   Braga, Viorica
TI Digital twin-based cyber-physical manufacturing systems, extended
   reality metaverse enterprise and production management algorithms, and
   Internet of Things financial and labor market technologies in generative
   artificial intelligence economics
SO OECONOMIA COPERNICANA
LA English
DT Article
DE generative artificial intelligence economics; fintech; labor market;
   metaverse enterprise; production management; cyber-physical
   manufacturing
ID SERVICES
AB Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and laborintensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization.Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms.
   Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.
C1 [Lazaroiu, George; Gedeon, Tom] Curtin Univ, Perth, Australia.
   [Lazaroiu, George] Toronto Metropolitan Univ, Toronto, ON, Canada.
   [Lazaroiu, George] Cardiff Metropolitan Univ, Cardiff, Wales.
   [Lazaroiu, George; Cretoiu, Raluca Ionela; Ionescu, Luminia; Hurloiu, Lacramioara Rodica; Avram, Laurentia Georgeta; Braga, Viorica] Spiru Haret Univ, Campulung, Romania.
   [Rogalska, Elzbieta] Univ Warmia & Mazury, Olsztyn, Poland.
   [Valaskova, Katarina; Nagy, Marek; Zvarikova, Katarina; Poliak, Milos] Univ Zilina, Zilina, Slovakia.
   [Musa, Hussam] Matej Bel Univ, Banska Bystrica, Slovakia.
   [Horak, Jakub; Krulicky, Tomas] Inst Technol & Business, Ceske Budejovice, Czech Republic.
   [Popa, Catalin; Nistor, Filip] Mircea Cel Batran Naval Acad, Constanta, Romania.
C3 Curtin University; Toronto Metropolitan University; Cardiff Metropolitan
   University; Spiru Haret University; University of Warmia & Mazury;
   University of Zilina; Matej Bel University; Institute of Technology &
   Business, Ceske Budejovice; Mircea cel Batran Naval Academy
RP Lazaroiu, G (corresponding author), Curtin Univ, Perth, Australia.; Lazaroiu, G (corresponding author), Toronto Metropolitan Univ, Toronto, ON, Canada.; Lazaroiu, G (corresponding author), Cardiff Metropolitan Univ, Cardiff, Wales.; Lazaroiu, G (corresponding author), Spiru Haret Univ, Campulung, Romania.
EM george.lazaroiu@spiruharet.ro
RI Rogalska, Elżbieta/AAB-7586-2020; Poliak, Milos/R-5949-2016; Gedeon,
   Tom/JQW-6232-2023; Horák, Jakub/T-7022-2019; Nistor, Filip/H-8336-2017;
   Nagy, Marek/DEM-8067-2022; Lazaroiu, George/A-2262-2015; Musa,
   Hussam/C-9944-2019; Zvarikova, Katarina/P-2328-2015; Popa,
   Catalin/KAL-8706-2024; Valaskova, Katarina/O-9250-2015
OI Musa, Hussam/0000-0002-4492-8770; Zvarikova,
   Katarina/0000-0001-5278-9275; Popa, Catalin/0000-0002-4419-7867; Gedeon,
   Tom/0000-0001-8356-4909; Nagy, Marek/0000-0003-0740-6268; Horak,
   Jakub/0000-0001-6364-9745; Valaskova, Katarina/0000-0003-4223-7519
FU Slovak Research and Development Agency Grant VEGA [1/0494/24]
FX This research was financially supported by the Slovak Research and
   Development Agency Grant VEGA 1/0494/24: Metamorphoses and causalities
   of indebtedness, liquidity and solvency of companies in the context of
   the global environment.
CR Aguinis H, 2024, ORGAN DYN, V53, DOI 10.1016/j.orgdyn.2024.101029
   AmankwahAmoah J., 2024, INT J INFORM MANAGE, V79, DOI [10.1016/j.ijinfomgt.2024.102759, DOI 10.1016/J.IJINFOMGT.2024.102759]
   Andronie M, 2023, OECON COPERNIC, V14, P769, DOI 10.24136/oc.2023.023
   Andronie M, 2023, ISPRS INT J GEO-INF, V12, DOI 10.3390/ijgi12020035
   Andronie M, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12010022
   Andronie M, 2021, ELECTRONICS-SWITZ, V10, DOI 10.3390/electronics10202497
   Aysan AF, 2024, TECHNOL FORECAST SOC, V202, DOI 10.1016/j.techfore.2024.123323
   Bankins S, 2024, J ORGAN BEHAV, V45, P159, DOI 10.1002/job.2735
   Barbu CM, 2021, J THEOR APPL EL COMM, V16, P1415, DOI 10.3390/jtaer16050080
   Cao SS, 2024, PAC-BASIN FINANC J, V84, DOI 10.1016/j.pacfin.2024.102307
   Chen Y, 2024, EUR J FINANC, V30, P2157, DOI 10.1080/1351847X.2024.2358940
   de la Rosa W, 2024, CURR OPIN PSYCHOL, V58, DOI 10.1016/j.copsyc.2024.101843
   Eisikovits N., 2024, ACCOUNT HORIZ, DOI [10.2308/HORIZONS-2023-042, DOI 10.2308/HORIZONS-2023-042]
   Fan SK, 2024, DECIS SUPPORT SYST, V182, DOI 10.1016/j.dss.2024.114251
   Giudici P, 2024, EXPERT SYST APPL, V235, DOI 10.1016/j.eswa.2023.121220
   Holmstrm J., 2024, Business Horizons, DOI [10.1016/j.bushor.2024.02.010, DOI 10.1016/J.BUSHOR.2024.02.010]
   Jia N, 2024, ACAD MANAGE J, V67, P5, DOI 10.5465/amj.2022.0426
   Kang M, 2024, KNOWL-BASED SYST, V300, DOI 10.1016/j.knosys.2024.112017
   Khan HH, 2024, FINANC RES LETT, V67, DOI 10.1016/j.frl.2024.105772
   Khan MS, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24890
   Kshetri N, 2024, COMPUTER, V57, P102, DOI 10.1109/MC.2024.3382452
   Kueschnig M, 2024, FINANC RES LETT, V59, DOI 10.1016/j.frl.2023.104779
   Lazaroiu G, 2023, OECON COPERNIC, V14, P707, DOI 10.24136/oc.2023.021
   Lazaroiu G, 2023, OECON COPERNIC, V14, P703, DOI 10.24136/oc.2023.020
   Lazaroiu G, 2022, ISPRS INT J GEO-INF, V11, DOI 10.3390/ijgi11050277
   Li JC, 2024, IEEE INTERNET THINGS, V11, P21763, DOI 10.1109/JIOT.2024.3376748
   Lim T, 2024, ARTIF INTELL REV, V57, DOI 10.1007/s10462-024-10708-3
   Lin HX, 2024, COMPUT HUM BEHAV, V152, DOI 10.1016/j.chb.2023.108092
   Nagy M, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13031681
   Nagy M, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10193543
   Oehler A, 2024, FINANC RES LETT, V60, DOI 10.1016/j.frl.2023.104898
   Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
   RAMAUL L, 2024, BUS HORIZONS, DOI DOI 10.1016/J.BUSHOR.2024.05.006
   Retkowsky J, 2024, BUS HORIZONS, V67, P511, DOI 10.1016/j.bushor.2024.04.009
   Sachan S, 2024, INT REV FINANC ANAL, V93, DOI 10.1016/j.irfa.2024.103149
   Tigges M, 2024, TECHNOL FORECAST SOC, V205, DOI 10.1016/j.techfore.2024.123491
   Valaskova K, 2023, EQUILIBRIUM, V18, P1133, DOI 10.24136/eq.2023.036
   Valaskova K, 2022, MATHEMATICS-BASEL, V10, DOI 10.3390/math10142452
   Zhao CJ, 2024, FINANC RES LETT, V60, DOI 10.1016/j.frl.2023.104843
   Zhu H, 2024, J BUS RES, V174, DOI 10.1016/j.jbusres.2023.114494
NR 40
TC 0
Z9 0
U1 27
U2 27
PU INST BADAN GOSPODARCZYCH
PI OLSZTYN
PA UL KS ROBERTA BILITEWSKIEGO NR 5, LOK 19, OLSZTYN, 10-693, POLAND
SN 2083-1277
EI 2353-1827
J9 OECON COPERNIC
JI Oecon. Copernic.
PD SEP
PY 2024
VL 15
IS 3
BP 837
EP 870
DI 10.24136/oc.3183
PG 34
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA J1Q3K
UT WOS:001334882100002
OA gold
DA 2024-12-25
ER

PT J
AU Kolding, S
   Lundin, RM
   Hansen, L
   Ostergaard, SD
AF Kolding, Sara
   Lundin, Robert M.
   Hansen, Lasse
   Ostergaard, Soren Dinesen
TI Use of generative artificial intelligence (AI) in psychiatry and mental
   health care: a systematic review
SO ACTA NEUROPSYCHIATRICA
LA English
DT Article; Early Access
DE Artificial intelligence; machine learning; psychiatry; mental health;
   systematic review
ID RATING-SCALE; CHATGPT; CHATBOT; STATES
AB Objectives: Tools based on generative artificial intelligence (AI) such as ChatGPT have the potential to transform modern society, including the field of medicine. Due to the prominent role of language in psychiatry, e.g., for diagnostic assessment and psychotherapy, these tools may be particularly useful within this medical field. Therefore, the aim of this study was to systematically review the literature on generative AI applications in psychiatry and mental health. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted across three databases, and the resulting articles were screened independently by two researchers. The content, themes, and findings of the articles were qualitatively assessed. Results: The search and screening process resulted in the inclusion of 40 studies. The median year of publication was 2023. The themes covered in the articles were mainly mental health and well-being in general - with less emphasis on specific mental disorders (substance use disorder being the most prevalent). The majority of studies were conducted as prompt experiments, with the remaining studies comprising surveys, pilot studies, and case reports. Most studies focused on models that generate language, ChatGPT in particular. Conclusions: Generative AI in psychiatry and mental health is a nascent but quickly expanding field. The literature mainly focuses on applications of ChatGPT, and finds that generative AI performs well, but notes that it is limited by significant safety and ethical concerns. Future research should strive to enhance transparency of methods, use experimental designs, ensure clinical relevance, and involve users/patients in the design phase.
C1 [Kolding, Sara; Hansen, Lasse; Ostergaard, Soren Dinesen] Aarhus Univ, Dept Clin Med, Aarhus, Denmark.
   [Kolding, Sara; Hansen, Lasse; Ostergaard, Soren Dinesen] Aarhus Univ Hosp Psychiat, Dept Affect Disorders, Aarhus, Denmark.
   [Kolding, Sara; Hansen, Lasse] Aarhus Univ, Ctr Humanities Comp, Aarhus, Denmark.
   [Lundin, Robert M.] Deakin Univ, Inst Mental & Phys Hlth & Clin Translat IMPACT, Geelong, Vic, Australia.
   [Lundin, Robert M.] Mildura Base Publ Hosp, Mental Hlth Serv, Alcohol & Other Drugs Integrated Treatment Team, Mildura, Vic, Australia.
   [Lundin, Robert M.] Barwon Hlth, Mental Hlth Drugs & Alcohol Serv, Change Improve Mental Hlth CHIME, Geelong, Vic, Australia.
C3 Aarhus University; Aarhus University; Deakin University; Barwon Health
RP Ostergaard, SD (corresponding author), Aarhus Univ, Dept Clin Med, Aarhus, Denmark.; Ostergaard, SD (corresponding author), Aarhus Univ Hosp Psychiat, Dept Affect Disorders, Aarhus, Denmark.
EM soeoes@rm.dk
RI ; Lundin, Robert/Q-5362-2018
OI Kolding, Sara/0009-0003-1946-6687; Ostergaard, Soren
   Dinesen/0000-0002-8032-6208; Lundin, Robert/0000-0002-5992-2822; Hansen,
   Lasse/0000-0003-1113-4779
FU Novo Nordisk Foundation [NNF20SA0062874]; Lundbeck Foundation
   [R358-2020-2341, R344- 2020-1073]; Danish Cancer Society [R283-A16461];
   Central Denmark Region Fund for Strengthening of Health Science
   [1-36-72-4-20]; Danish Agency for Digitisation Investment Fund for New
   Technologies [2020-6720]; Independent Research Fund Denmark
   [7016-00048B, 2096-00055A]
FX There was no specific funding for this study. Outside this study, SDO is
   supported by the Novo Nordisk Foundation (grant number:NNF20SA0062874),
   the Lundbeck Foundation (grant numbers: R358-2020-2341 and R344-
   2020-1073), the Danish Cancer Society (grant number: R283-A16461), the
   Central Denmark Region Fund for Strengthening of Health Science (grant
   number: 1-36-72-4-20), The Danish Agency for Digitisation Investment
   Fund for New Technologies (grant number 2020-6720), and Independent
   Research Fund Denmark (grant number: 7016-00048B and 2096-00055A).
CR Alanezi F, 2024, J MULTIDISCIP HEALTH, V17, P461, DOI 10.2147/JMDH.S447368
   Almusharraf F, 2020, J MED INTERNET RES, V22, DOI 10.2196/20251
   American Psychiatric Association, 2013, DIAGN STAT MAN MENT, V5th ed., DOI DOI 10.1176/APPI.BOOKS.9780890425596
   Amin S, 2023, TOB CONTROL, DOI 10.1136/tc-2023-058009
   Ayers JW, 2023, JAMA INTERN MED, V183, P589, DOI 10.1001/jamainternmed.2023.1838
   Beilharz F, 2021, J MED INTERNET RES, V23, DOI 10.2196/27807
   Blease C, 2024, PSYCHIAT RES, V333, DOI 10.1016/j.psychres.2024.115724
   Carpenter KA, 2023, BIOMOLECULES, V13, DOI 10.3390/biom13020387
   D'Souza RF, 2023, ASIAN J PSYCHIATR, V89, DOI 10.1016/j.ajp.2023.103770
   De Freitas J, 2024, J CONSUM PSYCHOL, V34, P481, DOI 10.1002/jcpy.1393
   de Leon J, 2023, J CLIN PSYCHOPHARM, V43, P400, DOI 10.1097/JCP.0000000000001734
   Denecke K, 2018, METHOD INFORM MED, V57, P243, DOI 10.1055/s-0038-1675822
   Dergaa I, 2024, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1277756
   Draffan E A, 2023, Stud Health Technol Inform, V306, P215, DOI 10.3233/SHTI230622
   Else H, 2023, NATURE, V613, P423, DOI 10.1038/d41586-023-00056-7
   Elyoseph Z, 2024, JMIR MENT HEALTH, V11, DOI 10.2196/54369
   Elyoseph Z, 2024, FAM MED COMMUNITY HE, V12, DOI 10.1136/fmch-2023-002583
   Elyoseph Z, 2023, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1213141
   Elyoseph Z, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1199058
   Galido PV, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.38166
   Gallifant J., 2024, PREPRINT, DOI [10.1101/2024.07.24.24310930, DOI 10.1101/2024.07.24.24310930]
   Gao YJ, 2022, J AM MED INFORM ASSN, V29, P1797, DOI 10.1093/jamia/ocac127
   Gifu Daniela, 2022, Procedia Comput Sci, V214, P503, DOI 10.1016/j.procs.2022.11.205
   Graham S, 2019, CURR PSYCHIAT REP, V21, DOI 10.1007/s11920-019-1094-0
   Hadar-Shoval D, 2023, FRONT PSYCHIATRY, V14, DOI 10.3389/fpsyt.2023.1234397
   Haman M, 2023, ANN BIOMED ENG, V51, P1128, DOI 10.1007/s10439-023-03201-5
   HAMILTON M, 1960, J NEUROL NEUROSUR PS, V23, P56, DOI 10.1136/jnnp.23.1.56
   HAMILTON M, 1959, BRIT J MED PSYCHOL, V32, P50, DOI 10.1111/j.2044-8341.1959.tb00467.x
   Hansen L, 2021, ACTA NEUROPSYCHIATR, V33, P323, DOI 10.1017/neu.2021.22
   Haug CJ, 2023, NEW ENGL J MED, V388, P1201, DOI 10.1056/NEJMra2302038
   Heinz MV, 2023, DIGIT HEALTH, V9, DOI 10.1177/20552076231170499
   Herrmann-Werner A, 2024, J MED INTERNET RES, V26, DOI 10.2196/52113
   Heston TF., 2023, Cureus, V15
   Hristidis V, 2023, J MED INTERNET RES, V25, DOI 10.2196/48966
   Hu K., 2023, REUTERS         0202
   Hwang G, 2024, PSYCHIAT RES, V331, DOI 10.1016/j.psychres.2023.115655
   KAY SR, 1987, SCHIZOPHRENIA BULL, V13, P261, DOI 10.1093/schbul/13.2.261
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lee P, 2023, NEW ENGL J MED, V388, P1233, DOI 10.1056/NEJMsr2214184
   Li H, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00979-5
   LINGJAERDE O, 1987, ACTA PSYCHIAT SCAND, V76, P7
   Luka Inc, 2024, Replika
   Lundin RM, 2023, J ECT, V39, P130, DOI 10.1097/YCT.0000000000000941
   Luykx JJ, 2023, WORLD PSYCHIATRY, V22, P479, DOI 10.1002/wps.21145
   Ma Zilin, 2023, AMIA Annu Symp Proc, V2023, P1105
   Mcfayden TC, 2024, CYBERPSYCH BEH SOC N, V27, P135, DOI 10.1089/cyber.2023.0202
   McGowan A, 2023, PSYCHIAT RES, V326, DOI 10.1016/j.psychres.2023.115334
   Moher D, 2009, ANN INTERN MED, V151, P264, DOI [10.1136/bmj.b2700, 10.1136/bmj.b2535, 10.1371/journal.pmed.1000097, 10.1186/2046-4053-4-1, 10.1016/j.ijsu.2010.07.299, 10.1136/bmj.i4086, 10.1016/j.ijsu.2010.02.007]
   Nadkarni PM, 2011, J AM MED INFORM ASSN, V18, P544, DOI 10.1136/amiajnl-2011-000464
   OpenAI, 2023, DallE 2
   OpenAI, 2024, Chatgpt
   Ostergaard SD, 2024, ACTA PSYCHIAT SCAND, V149, P441, DOI 10.1111/acps.13680
   Ostergaard SD, 2023, SCHIZOPHRENIA BULL, V49, P1105, DOI 10.1093/schbul/sbad068
   Parker G, 2024, BIPOLAR DISORD, V26, P249, DOI 10.1111/bdi.13379
   Prada Paco, 2023, Rev Med Suisse, V19, P532, DOI 10.53738/REVMED.2023.19.818.532
   Randhawa J, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.46548
   Rudan D, 2023, J GLOB HEALTH, V13, DOI 10.7189/jogh.13.04102
   Sabour S, 2023, FRONT DIGIT HEALTH, V5, DOI 10.3389/fdgth.2023.1133987
   Salah M, 2024, CURR PSYCHOL, V43, P7843, DOI 10.1007/s12144-023-04989-0
   Schumacher E, 2024, Arxiv, DOI [arXiv:2311.08303, DOI 10.48550/ARXIV.2311.08303, 10.48550/arXiv.2311.08303]
   Sezgin E, 2023, J MED INTERNET RES, V25, DOI 10.2196/49240
   Smith A, 2023, INT J SOC PSYCHIATR, V69, P1882, DOI 10.1177/00207640231178451
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Sora, Creating video from text
   Spallek S, 2023, JMIR MED EDUC, V9, DOI 10.2196/51243
   Takefuji Y, 2023, ASIAN J PSYCHIATR, V88, DOI 10.1016/j.ajp.2023.103736
   Vaidyam AN, 2021, CAN J PSYCHIAT, V66, P339, DOI 10.1177/0706743720966429
   Vaswani A, 2017, ADV NEUR IN, V30
   Veritas Health Innovation, 2024, Covidence systematic review software
   Wang R, 2023, J CHEM INF MODEL, V63, P7189, DOI 10.1021/acs.jcim.3c01429
   Woodnutt S, 2024, J PSYCHIATR MENT HLT, V31, P79, DOI 10.1111/jpm.12965
   World Health Organization, 1993, ICD 10 CLASS MENT BE
NR 72
TC 0
Z9 0
U1 7
U2 7
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 0924-2708
EI 1601-5215
J9 ACTA NEUROPSYCHIATR
JI Acta Neuropsychiatr.
PD 2024 NOV 11
PY 2024
DI 10.1017/neu.2024.50
EA NOV 2024
PG 14
WC Neurosciences; Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Neurosciences & Neurology; Psychiatry
GA L7R3V
UT WOS:001352649800001
PM 39523628
OA hybrid
DA 2024-12-25
ER

PT J
AU Dong, WJ
   Pan, DH
   Kim, S
AF Dong, Wanjin
   Pan, Daohua
   Kim, Soonbae
TI Exploring the integration of IoT and Generative AI in English language
   education: Smart tools for personalized learning experiences
SO JOURNAL OF COMPUTATIONAL SCIENCE
LA English
DT Article
DE IoT; Generative AI; Smart tools; Adaptive learning environment
ID SPEECH
AB English language education is undergoing a transformative shift, propelled by advancements in technology. This research explores the integration of the Internet of Things (IoT) and Generative Artificial Intelligence (Generative AI) in the context of English language education, with a focus on developing a personalized oral assessment method. The proposed method leverages real-time data collection from IoT devices and Generative AI's language generation capabilities to create a dynamic and adaptive learning environment. The study addresses historical challenges in traditional teaching methodologies, emphasizing the need for AI approaches. The research objectives encompass a comprehensive exploration of the historical context, challenges, and existing technological interventions in English language education. A novel, technology-driven oral assessment method is designed, implemented, and rigorously evaluated using datasets such as Librispeech and L2Arctic. The ablation study investigates the impact of training dataset proportions and model learning rates on the method's performance. Results from the study highlight the importance of maintaining a balance in dataset proportions, selecting an optimal learning rate, and considering model depth in achieving optimal performance.
C1 [Dong, Wanjin] Nanyang Med Coll, Nanyang, Peoples R China.
   [Dong, Wanjin; Kim, Soonbae] Chungbuk Natl Univ, Chungbuk, South Korea.
   [Pan, Daohua] Heilongjiang Vocat Coll Nationalities, Harbin 150066, Peoples R China.
C3 Nanyang Medical College; Chungbuk National University
RP Kim, S (corresponding author), Chungbuk Natl Univ, Chungbuk, South Korea.
EM donglele@chungbuk.ac.kr; pandaohua@ftcl.hit.edu.cn;
   pearlpoet@chungbuk.ac.kr
FX The authors declare that they have no known competing interests or
   personal relationships that could have appeared the work reported in
   this paper.
CR Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Berndt D. J., 1994, P 3 INT C KNOWL DISC, P359, DOI DOI 10.5555/3000850.3000887
   Carion Nicolas, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12346), P213, DOI 10.1007/978-3-030-58452-8_13
   Chan W, 2016, INT CONF ACOUST SPEE, P4960, DOI 10.1109/ICASSP.2016.7472621
   Chimbga Bridget, 2023, Artificial Intelligence Research: 4th Southern African Conference, SACAIR 2023, Proceedings. Communications in Computer and Information Science (1976), P44, DOI 10.1007/978-3-031-49002-6_4
   Choi YJ, 2021, BIG DATA-US, V9, P279, DOI 10.1089/big.2020.0274
   Dong LH, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P5884, DOI 10.1109/ICASSP.2018.8462506
   Graves A., 2006, P 23 INT C MACH LEAR, P369, DOI DOI 10.1145/1143844.1143891
   GRIFFIN DW, 1984, IEEE T ACOUST SPEECH, V32, P236, DOI 10.1109/TASSP.1984.1164317
   Higuchi Y, 2020, Arxiv, DOI arXiv:2005.08700
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Kumar K, 2019, ADV NEUR IN, V32
   Lee A, 2016, INT CONF ACOUST SPEE, P6145, DOI 10.1109/ICASSP.2016.7472858
   Lee A, 2013, INT CONF ACOUST SPEE, P8227, DOI 10.1109/ICASSP.2013.6639269
   Lee A, 2012, IEEE W SP LANG TECH, P382, DOI 10.1109/SLT.2012.6424254
   Leung WK, 2019, INT CONF ACOUST SPEE, P8132, DOI 10.1109/ICASSP.2019.8682654
   Li NH, 2019, AAAI CONF ARTIF INTE, P6706
   Liu RL, 2020, INT CONF ACOUST SPEE, P7759, DOI [10.1109/ICASSP40776.2020.9054523, 10.1109/icassp40776.2020.9054523]
   Madakam S, 2015, J Comput Commun, V3, P164, DOI [10.4236/jcc.2015.35021, DOI 10.4236/JCC.2015.35021]
   Mang Q, 2020, INT CONF ACOUST SPEE, P7829, DOI [10.1109/icassp40776.2020.9053896, 10.1109/ICASSP40776.2020.9053896]
   McAuliffe M, 2017, INTERSPEECH, P498, DOI 10.21437/Interspeech.2017-1386
   Mohamed AR, 2010, INT CONF ACOUST SPEE, P4354, DOI 10.1109/ICASSP.2010.5495651
   Moritz N, 2020, INT CONF ACOUST SPEE, P6074, DOI [10.1109/icassp40776.2020.9054476, 10.1109/ICASSP40776.2020.9054476]
   Okamoto T, 2020, INT CONF ACOUST SPEE, P6729, DOI [10.1109/icassp40776.2020.9053915, 10.1109/ICASSP40776.2020.9053915]
   Panayotov V, 2015, INT CONF ACOUST SPEE, P5206, DOI 10.1109/ICASSP.2015.7178964
   Povey D., 2011, IEEE 2011 WORKSHOP A
   Povey D, 2010, INT CONF ACOUST SPEE, P4330, DOI 10.1109/ICASSP.2010.5495662
   Ren Y, 2019, ADV NEUR IN, V32
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang YX, 2017, Arxiv, DOI [arXiv:1703.10135, DOI 10.48550/ARXIV.1703.10135]
   Watanabe S, 2018, Arxiv, DOI arXiv:1804.00015
   Witt S.M., 2000, Use of speech recognition in computer-assisted language learning
   Won Jinhee, 2022, Journal of Multimedia Information System, V9, P345, DOI 10.33851/JMIS.2022.9.4.345
   Yan BC, 2020, INTERSPEECH, P3032, DOI 10.21437/Interspeech.2020-1616
   YANG WJ, 1988, IEEE T ACOUST SPEECH, V36, P988, DOI 10.1109/29.1620
   Zhang L, 2020, SENSORS-BASEL, V20, DOI 10.3390/s20071809
   Zhang YL, 2022, COMPUT ELECTR ENG, V102, DOI 10.1016/j.compeleceng.2022.108115
   Zhao GL, 2018, INTERSPEECH, P2783, DOI 10.21437/Interspeech.2018-1110
NR 38
TC 1
Z9 1
U1 51
U2 51
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 1877-7503
EI 1877-7511
J9 J COMPUT SCI-NETH
JI J. Comput. Sci.
PD OCT
PY 2024
VL 82
AR 102397
DI 10.1016/j.jocs.2024.102397
EA AUG 2024
PG 9
WC Computer Science, Interdisciplinary Applications; Computer Science,
   Theory & Methods
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA D6J5N
UT WOS:001297220400001
DA 2024-12-25
ER

PT J
AU Dorta-González, P
   López-Puig, AJ
   Dorta-González, MI
   González-Betancor, SM
AF Dorta-Gonzalez, Pablo
   Lopez-Puig, Alexis Jorge
   Dorta-Gonzalez, Maria Isabel
   Gonzalez-Betancor, Sara M.
TI Generative artificial intelligence usage by researchers at work: Effects
   of gender, career stage, type of workplace, and perceived barriers
SO TELEMATICS AND INFORMATICS
LA English
DT Article
DE Artificial intelligence; Use of AI by researchers in the workplace;
   Challenges in implementing AI; Gender imbalance
ID CHATGPT; AI
AB The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments. As survey data may be influenced by a bias towards scientists interested in AI, potentially skewing the results towards the perspectives of these researchers, this study uses a regression model to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, and scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11 % increase in tool use, while those who cite insufficient training resources experience an 8 % decrease. Female researchers experience a 7 % decrease in AI tool usage compared to men, while advanced career researchers experience a significant 19 % decrease. Researchers associated with government advisory groups are 45 % more likely to use AI tools frequently than those in government roles. Researchers in for-profit companies show an increase of 19 %, while those in medical research institutions and hospitals show an increase of 16 % and 15 %, respectively. This paper contributes to a deeper understanding of the mechanisms driving the use of generative AI tools amongst researchers, with valuable implications for both academia and industry.
C1 [Dorta-Gonzalez, Pablo] Univ Las Palmas Gran Canaria, Inst Tourism & Sustainable Econ Dev TIDES, Campus Tafira, Las Palmas Gran Canaria 35017, Spain.
   [Lopez-Puig, Alexis Jorge] Agencia Canaria Cal Univ & Evaluac Educ ACCUEE, C Alamo 54-2 Planta, Las Palmas Gran Canaria 35014, Spain.
   [Lopez-Puig, Alexis Jorge] Univ Las Palmas Gran Canaria, Dept Math, Campus Tafira, Las Palmas Gran Canaria 35017, Spain.
   [Dorta-Gonzalez, Maria Isabel] Univ La Laguna, Dept Comp & Syst Engn, Ave Astrofisico Francisco Sanchez S-N, San Cristobal la Laguna 38271, Spain.
   [Gonzalez-Betancor, Sara M.] Univ Las Palmas Gran Canaria, Dept Quantitat Methods Econ & Management, Campus Tafira, Las Palmas Gran Canaria 35017, Spain.
C3 Universidad de Las Palmas de Gran Canaria; Universidad de Las Palmas de
   Gran Canaria; Universidad de la Laguna; Universidad de Las Palmas de
   Gran Canaria
RP Dorta-González, P (corresponding author), Univ Las Palmas Gran Canaria, Inst Tourism & Sustainable Econ Dev TIDES, Campus Tafira, Las Palmas Gran Canaria 35017, Spain.
EM pablo.dorta@ulpgc.es
RI Gonzalez-Betancor, Sara M./P-8315-2015; Dorta-Gonzalez,
   Pablo/C-6425-2009
OI Gonzalez-Betancor, Sara M./0000-0002-2209-1922; Dorta-Gonzalez,
   Pablo/0000-0003-0494-2903
CR Agathokleous E, 2024, TRENDS PLANT SCI, V29, P210, DOI 10.1016/j.tplants.2023.06.008
   Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   Altmäe S, 2023, REPROD BIOMED ONLINE, V47, P3, DOI 10.1016/j.rbmo.2023.04.009
   Berman A, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2024.102471
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Briganti G, 2020, FRONT MED-LAUSANNE, V7, DOI 10.3389/fmed.2020.00027
   Chen TJ, 2023, J CHIN MED ASSOC, V86, P351, DOI 10.1097/JCMA.0000000000000900
   Davidson R., 2004, Econometric Theory and Methods
   Davidson R., 1993, ESTIMATION INFERENCE
   De Angelis L, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1166120
   ERC, 2023, Foresight: Use and Impact of Artificial Intelligence in the Scientific Process
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   Gao AZ, 2021, SCI ROBOT, V6, DOI 10.1126/scirobotics.abf1462
   Gao CA, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00819-6
   Glickman M., 2024, Harv. Data Sci. Rev, V6, P1, DOI [10.1162/99608f92.7f9220ff, DOI 10.1162/99608F92.7F9220FF]
   Grace K, 2024, Arxiv, DOI [arXiv:2401.02843, DOI 10.48550/ARXIV.2401.02843, 10.48550/arXiv.2401.02843]
   Hangl J, 2023, TECHNOL SOC, V74, DOI 10.1016/j.techsoc.2023.102299
   Holler J, 2019, TRENDS COGN SCI, V23, P639, DOI 10.1016/j.tics.2019.05.006
   Huang JS, 2023, AM J CANCER RES, V13, P1148
   Hutson M, 2022, NATURE, V611, P192, DOI 10.1038/d41586-022-03479-w
   Kung Tiffany H, 2023, PLOS Digit Health, V2, pe0000198, DOI 10.1371/journal.pdig.0000198
   Lee JY, 2023, J EDUC EVAL HEALTH P, V20, DOI 10.3352/jeehp.2023.20.6
   Li YJ, 2024, TECHNOL SOC, V77, DOI 10.1016/j.techsoc.2024.102518
   Marquez R, 2023, EDUC CHEM ENG, V44, P164, DOI 10.1016/j.ece.2023.05.005
   Nature, 2023, NATURE
   Nature, 2023, NATURE
   Nature, 2023, Science and the new age of AI
   Neumeister L, 2023, Associated Press News
   Norris C, 2023, ANN BIOMED ENG, V51, P1121, DOI 10.1007/s10439-023-03212-2
   Rahimi F, 2023, ARCH MED RES, V54, P272, DOI 10.1016/j.arcmed.2023.03.004
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   Thirunavukarasu AJ, 2023, NAT MED, V29, P1930, DOI 10.1038/s41591-023-02448-8
   Trenfield SJ, 2022, ADV DRUG DELIVER REV, V182, DOI 10.1016/j.addr.2021.114098
   Van Noorden R, 2023, NATURE, V621, P672, DOI 10.1038/d41586-023-02980-0
   Wang HC, 2023, NATURE, V620, P47, DOI 10.1038/s41586-023-06221-2
   Xu YJ, 2021, INNOVATION-AMSTERDAM, V2, DOI 10.1016/j.xinn.2021.100179
NR 37
TC 0
Z9 0
U1 53
U2 53
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0736-5853
J9 TELEMAT INFORM
JI Telemat. Inform.
PD OCT
PY 2024
VL 94
AR 102187
DI 10.1016/j.tele.2024.102187
EA SEP 2024
PG 12
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA F1T3C
UT WOS:001307706500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Wang, HC
   Du, QX
   Wang, Y
   Xu, HW
   Wei, Z
   Wang, XW
AF Wang, Haochen
   Du, Qixiu
   Wang, Ye
   Xu, Hanwen
   Wei, Zheng
   Wang, Xiaowo
TI GPro: generative AI-empowered toolkit for promoter design
SO BIOINFORMATICS
LA English
DT Article
AB Motivation Promoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks.Results Here, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines.Availability and implementation GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.
C1 [Wang, Haochen; Du, Qixiu; Wang, Ye; Xu, Hanwen; Wei, Zheng; Wang, Xiaowo] Tsinghua Univ, Minist Educ, Key Lab Bioinformat, Beijing 100084, Peoples R China.
   [Wang, Haochen; Du, Qixiu; Wang, Ye; Xu, Hanwen; Wei, Zheng; Wang, Xiaowo] Tsinghua Univ, Ctr Synthet & Syst Biol, Beijing 100084, Peoples R China.
   [Wang, Haochen; Du, Qixiu; Wang, Ye; Xu, Hanwen; Wei, Zheng; Wang, Xiaowo] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China.
   [Wang, Haochen; Du, Qixiu; Wang, Ye; Xu, Hanwen; Wei, Zheng; Wang, Xiaowo] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
C3 Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua
   University
RP Wang, XW (corresponding author), Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China.
EM xwwang@tsinghua.edu.cn
RI wang, haochen/KIK-2593-2024; Wang, Xiaowo/K-4886-2012; Wei,
   Zheng/KCK-9968-2024
OI Wei, Zheng/0000-0003-2060-7486; Wang, Haochen/0000-0003-1191-6682; Du,
   Qixiu/0009-0008-8351-618X
FU National Natural Science Foundation of China [62250007, 62225307];
   Beijing Municipal Natural Science Foundation [Z230015]; Guoqiang
   Institute, Tsinghua University
FX This work was supported by the National Natural Science Foundation of
   China (Nos. 62250007 and 62225307), Beijing Municipal Natural Science
   Foundation (Z230015), and the grant from the Guoqiang Institute,
   Tsinghua University.
CR Arjovsky M, 2017, PR MACH LEARN RES, V70
   de Almeida BP, 2024, NATURE, V626, DOI 10.1038/s41586-023-06905-9
   Gupta A, 2019, NAT MACH INTELL, V1, P105, DOI 10.1038/s42256-019-0017-4
   Khalil AS, 2010, NAT REV GENET, V11, P367, DOI 10.1038/nrg2775
   Kotopka BJ, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-15977-4
   Vaishnav ED, 2022, NATURE, V603, P455, DOI 10.1038/s41586-022-04506-6
   Valeri JA, 2023, CELL SYST, V14, P525, DOI 10.1016/j.cels.2023.05.007
   Van Brempt M, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-020-19446-w
   Wang Y, 2020, NUCLEIC ACIDS RES, V48, P6403, DOI 10.1093/nar/gkaa325
   Zhang PC, 2023, NAT COMMUN, V14, DOI 10.1038/s41467-023-41899-y
   Zrimec J, 2022, NAT COMMUN, V13, DOI 10.1038/s41467-022-32818-8
NR 11
TC 4
Z9 4
U1 10
U2 19
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 1367-4803
EI 1367-4811
J9 BIOINFORMATICS
JI Bioinformatics
PD MAR 4
PY 2024
VL 40
IS 3
AR btae123
DI 10.1093/bioinformatics/btae123
EA MAR 2024
PG 4
WC Biochemical Research Methods; Biotechnology & Applied Microbiology;
   Computer Science, Interdisciplinary Applications; Mathematical &
   Computational Biology; Statistics & Probability
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology;
   Computer Science; Mathematical & Computational Biology; Mathematics
GA LC5I8
UT WOS:001184589800001
PM 38429953
OA Green Published, gold
DA 2024-12-25
ER

PT J
AU Watts, FM
   Dood, AJ
   Shultz, GV
   Rodriguez, JMG
AF Watts, Field M.
   Dood, Amber J.
   Shultz, Ginger V.
   Rodriguez, Jon-Marc G.
TI Comparing Student and Generative Artificial Intelligence Chatbot
   Responses to Organic Chemistry Writing-to-Learn Assignments
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE Second-Year Undergraduate; Organic Chemistry; Writing; Problem Solving;
   Assessment; Mechanisms of Reactions; Internet/Web-Based Learning;
   Chemical Education Research
ID MECHANISMS; FRAMEWORK; THINKING
AB Chemistry education research demonstrates the value of open-ended writing tasks, such as writing-to-learn (WTL) assignments, for supporting students' learning with topics including reasoning about reaction mechanisms. The emergence of generative artificial intelligence (AI) technology, such as chatbots ChatGPT and Bard, raises concerns regarding the value of open-ended writing tasks in the classroom; one concern involves academic integrity and whether students will use these chatbots to produce sufficient responses to open-ended writing tasks. The present study investigates the degree to which generative AI chatbots exhibit mechanistic reasoning in response to organic chemistry WTL assignments. We produced responses from three generative AI chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) to two WTL assignments developed to elicit students' mechanistic reasoning. Using previously reported machine learning models for analyzing student writing in response to the WTL assignments, we analyzed the chatbot responses for the inclusion of features pertinent to mechanistic reasoning. Herein, we report quantitative analyses of (1) the differences between chatbot responses on the two assignments and (2) the differences between chatbot and authentic student responses. Findings indicate that chatbots respond differently to different WTL assignments. Additionally, the chatbots rarely incorporated the discussion of electron movement, a key feature of mechanistic reasoning. Furthermore, the chatbots, in general, do not engage in mechanistic reasoning at the same level as students. We contextualize the results by considering academic integrity with the assumption that students' intentions are to engage in academically honest behavior, and we focus on understanding the ethical uses of generative AI for classroom assignments.
C1 [Dood, Amber J.; Shultz, Ginger V.] Univ Michigan, Dept Chem, Ann Arbor, MI 48109 USA.
   [Watts, Field M.; Rodriguez, Jon-Marc G.] Univ Wisconsin, Dept Chem & Biochem, Milwaukee, WI 53211 USA.
C3 University of Michigan System; University of Michigan; University of
   Wisconsin System; University of Wisconsin Milwaukee
RP Watts, FM (corresponding author), Univ Wisconsin, Dept Chem & Biochem, Milwaukee, WI 53211 USA.
EM wattsf@uwm.edu
RI Watts, Field/ABA-1960-2021; Rodriguez, Jon-Marc G./GRR-2933-2022
OI Rodriguez, Jon-Marc G./0000-0001-6949-6823; Watts,
   Field/0000-0002-1800-1816; Dood, Amber/0000-0003-4572-1402
FU University of Michigan Provost's Third Century Initiative
FX The authors would like to thank the University of Michigan Provost's
   Third Century Initiative for funding. We would like to thank the
   participating students, as well as Solaire Finkenstaedt-Quinn, Ina
   Zaimi, and Michael Petterson for their assistance in developing the two
   WTL assignments. The authors would additionally like to thank Solaire
   FinkenstaedtQuinn for discussions related to the preparation of this
   manuscript.
CR Achiam J., 2023, arXiv
   Anderson P, 2015, RES TEACH ENGL, V50, P199
   [Anonymous], 2000, Information literacy competency standards for higher education
   [Anonymous], THAL
   [Anonymous], Chatgpt
   [Anonymous], BARD AI EXPT GOOGLE
   Arnold K. C., 2021, JOINT P ACM IUI 2021
   Ashenhurst J., WITTIG REACTION
   Asmussen G., 2022, ADV CHEM ED SERIES S, P90, DOI DOI 10.1039/9781839167782-00090
   Beeler C., 2023, ARXIV
   Bodé NE, 2019, J CHEM EDUC, V96, P1068, DOI 10.1021/acs.jchemed.8b00719
   Brandfonbrener PB, 2021, J CHEM EDUC, V98, P3431, DOI 10.1021/acs.jchemed.1c00660
   Buriak JM, 2023, ACS NANO, V17, P4091, DOI 10.1021/acsnano.3c01544
   Burke DD, 2018, J LEG STUD EDUC, V35, P5, DOI 10.1111/jlse.12068
   Caspari I, 2018, CHEM EDUC RES PRACT, V19, P1117, DOI 10.1039/c8rp00131f
   Caspari I., 2019, Int. J. Phys. Chem. Educ., V11, P31, DOI [10.12973/ijpce/211359, DOI 10.12973/IJPCE/211359]
   Clark TM, 2023, J CHEM EDUC, V100, P1905, DOI 10.1021/acs.jchemed.3c00027
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   DeKorver BK, 2023, J CHEM EDUC, V100, P91, DOI 10.1021/acs.jchemed.2c00206
   Dood AJ, 2023, J CHEM EDUC, V100, P53, DOI 10.1021/acs.jchemed.2c00572
   Dood AJ, 2022, J CHEM EDUC, DOI 10.1021/acs.jchemed.2c00313
   Dood AJ, 2020, J CHEM EDUC, V97, P3551, DOI 10.1021/acs.jchemed.0c00569
   Dood AJ, 2020, CHEM EDUC RES PRACT, V21, P267, DOI 10.1039/c9rp00148d
   Dood AJ, 2019, CAN J CHEM, V97, P711, DOI 10.1139/cjc-2018-0479
   Dood AJ, 2018, J CHEM EDUC, V95, P1267, DOI 10.1021/acs.jchemed.8b00177
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   Exintaris B, 2023, J CHEM EDUC, V100, P2972, DOI 10.1021/acs.jchemed.3c00481
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Finkenstaedt-Quinn S., 2023, INT J SCHOLARSHIP TE, V17, P1, DOI [10.20429/ijsotl.2023.17118, DOI 10.20429/IJSOTL.2023.17118]
   Finkenstaedt-Quinn SA, 2021, J CHEM EDUC, V98, P1548, DOI 10.1021/acs.jchemed.0c01482
   Frost SJH, 2023, CHEM EDUC RES PRACT, V24, P706, DOI 10.1039/d2rp00327a
   Gere AR, 2019, WRIT COMMUN, V36, P99, DOI 10.1177/0741088318804820
   Graulich N, 2019, CHEM EDUC RES PRACT, V20, P924, DOI 10.1039/c9rp00054b
   Graulich N, 2018, J CHEM EDUC, V95, P376, DOI 10.1021/acs.jchemed.7b00672
   Graves BC, 2023, ASSESS WRIT, V57, DOI 10.1016/j.asw.2023.100754
   Gupte T, 2021, CHEM EDUC RES PRACT, V22, P396, DOI 10.1039/d0rp00266f
   Haudek KC, 2012, CBE-LIFE SCI EDUC, V11, P283, DOI 10.1187/cbe.11-08-0084
   Howitz WJ, 2021, J CHEM EDUC, V98, P385, DOI 10.1021/acs.jchemed.0c00450
   Humphry T, 2023, J CHEM EDUC, V100, P1434, DOI 10.1021/acs.jchemed.3c00006
   Jacques V, 2015, P NATL ACAD SCI USA, V112, pE1471, DOI 10.1073/pnas.1417832112
   Jamieson MV, 2020, J CHEM EDUC, V97, P2768, DOI 10.1021/acs.jchemed.0c00785
   Kranz D, 2023, CHEM EDUC RES PRACT, V24, P453, DOI 10.1039/d2rp00132b
   Krist C, 2019, J LEARN SCI, V28, P160, DOI 10.1080/10508406.2018.1510404
   Lawrie G, 2023, CHEM EDUC RES PRACT, V24, P392, DOI 10.1039/d3rp90003g
   Martin PP, 2023, CHEM EDUC RES PRACT, V24, P407, DOI 10.1039/d2rp00287f
   Moon A, 2019, CHEM EDUC RES PRACT, V20, P484, DOI 10.1039/c9rp00005d
   Nguyen JG, 2020, J CHEM EDUC, V97, P3429, DOI 10.1021/acs.jchemed.0c00790
   Noyes K, 2020, J CHEM EDUC, V97, P3923, DOI 10.1021/acs.jchemed.0c00445
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Petterson MN, 2022, CHEM EDUC RES PRACT, V23, P189, DOI 10.1039/d1rp00181g
   Raje S, 2020, J CHEM EDUC, V97, P3436, DOI 10.1021/acs.jchemed.0c00797
   Raker JR., 2022, Student reasoning in organic chemistry: Research advances and evidence-based instructional practices, P304, DOI [10.1039/9781839167782-00304, DOI 10.1039/9781839167782-00304]
   Russ RS, 2008, SCI EDUC, V92, P499, DOI 10.1002/sce.20264
   Salomon G., 1991, EDUC RESEARCHER, V20, P2, DOI DOI 10.3102/0013189X020003002
   Schmidt-McCormack JA, 2019, CHEM EDUC RES PRACT, V20, P383, DOI 10.1039/c8rp00260f
   Sheskin D. J., 2011, HDB PARAMETRIC NONPA
   Shibani A., 2023, P 16 INT C ED DAT MI, P283
   Stowe RL, 2017, J CHEM EDUC, V94, P1852, DOI 10.1021/acs.jchemed.7b00335
   Talanquer V, 2023, J CHEM EDUC, V100, P2821, DOI 10.1021/acs.jchemed.3c00472
   Toledo S, 2017, J CHEM EDUC, V94, P1043, DOI 10.1021/acs.jchemed.6b00651
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   Waltzer T, 2023, ETHICS BEHAV, V33, P130, DOI 10.1080/10508422.2022.2026775
   Watts F. M., 2022, STUDENT REASONING OR, P285, DOI DOI 10.1039/9781839167782-00285
   Watts F.M., 2023, ACM International Conference Proceeding Series, P531, DOI DOI 10.1145/3576050.3576053
   Watts FM, 2022, CHEM EDUC RES PRACT, V23, P486, DOI 10.1039/d1rp00301a
   Watts FM, 2021, CHEM EDUC RES PRACT, V22, P364, DOI 10.1039/d0rp00298d
   Watts FM, 2020, CHEM EDUC RES PRACT, V21, P1148, DOI 10.1039/c9rp00185a
   West C. G., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2303.17012
   Winograd B, 2021, LAK21 CONFERENCE PROCEEDINGS: THE ELEVENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, P586, DOI 10.1145/3448139.3448202
   Xu WQ, 2022, INT J STEM EDUC, V9, DOI 10.1186/s40594-022-00377-5
   Yik BJ, 2023, CHEM EDUC RES PRACT, V24, P263, DOI 10.1039/d2rp00184e
   Yik BJ, 2021, CHEM EDUC RES PRACT, V22, P866, DOI 10.1039/d1rp00111f
   Zhai XM, 2022, J RES SCI TEACH, V59, P1765, DOI 10.1002/tea.21773
   Zhai XM, 2020, STUD SCI EDUC, V56, P111, DOI 10.1080/03057267.2020.1735757
NR 75
TC 26
Z9 26
U1 44
U2 158
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD SEP 7
PY 2023
VL 100
IS 10
BP 3806
EP 3817
DI 10.1021/acs.jchemed.3c00664
EA SEP 2023
PG 12
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA T5DC4
UT WOS:001070107700001
DA 2024-12-25
ER

PT J
AU Yilmaz, FGK
   Yilmaz, R
   Ceylan, M
AF Yilmaz, Fatma Gizem Karaoglan
   Yilmaz, Ramazan
   Ceylan, Mehmet
TI Generative Artificial Intelligence Acceptance Scale: A Validity and
   Reliability Study
SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
LA English
DT Article
DE Generative artificial intelligent; ChatGPT; students; technology
   acceptance; UTAUT model
ID INFORMATION-TECHNOLOGY; USER ACCEPTANCE; UNIFIED THEORY
AB The purpose of this study is to formulate an acceptance scale grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The scale is designed to scrutinize students' acceptance of generative artificial intelligence (AI) applications. This tool assesses students' acceptance levels toward generative AI applications. The scale development study was conducted in three phases, encompassing 627 university students from various faculties who have utilized generative AI tools such as ChatGPT during the 2022-2023 academic year. To evaluate the face and content validity of the scale, input was sought from professionals with expertise in the field. The initial sample group (n = 338) underwent exploratory factor analysis (EFA) to explore the underlying factors, while the subsequent sample group (n = 250) underwent confirmatory factor analysis (CFA) for the verification of factor structure. Later, it was seen that four factors comprising 20 items accounted for 78.349% of total variance due to EFA. CFA results confirmed that structure of the scale, featuring 20 items and four factors (performance expectancy, effort expectancy, facilitating conditions, and social influence), was compatible with the obtained data. Reliability analysis yielded Cronbach's alpha coefficient of 0.97, and the test-retest method demonstrated a reliability coefficient of 0.95. To evaluate the discriminative power of the items, a comparative analysis was conducted between the lower 27% and upper 27% of participants, with subsequent calculation of corrected item-total correlations. The results demonstrate that the generative AI acceptance scale exhibits robust validity and reliability, thus affirming its effectiveness as a robust measurement instrument.
C1 [Yilmaz, Fatma Gizem Karaoglan; Yilmaz, Ramazan] Bartin Univ, Fac Sci, Dept Comp Technol & Informat Syst, Bartin, Turkiye.
   [Ceylan, Mehmet] Bartin Univ, Dept Commun Coordinat, Bartin, Turkiye.
C3 Bartin University; Bartin University
RP Yilmaz, FGK (corresponding author), Bartin Univ, Fac Sci, Dept Comp Technol & Informat Syst, Bartin, Turkiye.
RI CEYLAN, Mehmet/AAA-3159-2021; Yilmaz, Ramazan/F-9517-2019; KARAOGLAN
   YILMAZ, Fatma Gizem/W-2168-2017
OI Yilmaz, Ramazan/0000-0002-2041-1750; KARAOGLAN YILMAZ, Fatma
   Gizem/0000-0003-4963-8083
CR Agrawal K, 2024, J COMPUT INFORM SYST, V64, P636, DOI 10.1080/08874417.2023.2240744
   Akbulut Y., 2010, Sosyal bilimlerde SPSS uygulamalari: Sik kullanilan istatistiksel analizler ve aciklamali SPSS cozumleri
   Akiba D, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090885
   Almaiah MA, 2019, IEEE ACCESS, V7, P174673, DOI 10.1109/ACCESS.2019.2957206
   Altalhi M, 2021, EDUC INF TECHNOL, V26, P1589, DOI 10.1007/s10639-020-10317-x
   [Anonymous], 2023, What is generative AI? An AI explains
   [Anonymous], 2022, MIT TECHNOL REV
   [Anonymous], 2023, New York Times
   [Anonymous], 2000, Turkish Psychology Articles
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Balci A., 2001, Sosyal Bilimlerde Arastirma
   Baytak A., 2023, CURR PERSPECT ED RES, V6, P7, DOI DOI 10.46303/CUPER.2023.2
   Buyukozturk S., 2010, Sosyal bilimler icin veri analizi el kitabi
   Buyukozturk S., 2018, BILIMSEL ARA T RMA Y
   Byrne B, 2010, INTERNATIONAL HANDBOOK OF PSYCHOLOGY IN EDUCATION, P3
   Chang M., 2022, Technology in Society, V71, DOI [DOI 10.1016/J.TECHSOC.2022.102027, 10.2139/ssrn.4017406]
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Cokluk O., 2012, Sosyal Bilimler Icin Cok Degiskenli Istatistik: SPSS ve LISREL Uygulamalari
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Creswell, 2012, ED RES PLANNING COND
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Fraenkel J.R., 2012, How to design and evaluate research in education, V8th
   Ghahramani Z, 2015, NATURE, V521, P452, DOI 10.1038/nature14541
   Hair JF., 1979, Multivariate data analysis: With readings
   Harshvardhan GM, 2020, COMPUT SCI REV, V38, DOI 10.1016/j.cosrev.2020.100285
   Hooper D., 2008, ELECT J BUSINESS RES, V6, P53, DOI [DOI 10.3109/03005364000000039, 10.21427/D7CF7R]
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Hu X., 2023, P 11 ANN GEN INT FRA, P109
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Hwang GJ, 2021, MATHEMATICS-BASEL, V9, DOI 10.3390/math9060584
   Ilhan M, 2014, EGIT BILIM, V39, P31
   Jang Y, 2022, EDUC INF TECHNOL, V27, P11635, DOI 10.1007/s10639-022-11086-5
   Jeon J, 2024, INTERACT LEARN ENVIR, V32, P4613, DOI 10.1080/10494820.2023.2204343
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Kline P., 2014, An easy guide to factor analysis
   Kline R. B., 2015, PRINCIPLES PRACTICE
   Kuchemann S., 2023, ARXIV
   Li B, 2023, LANGUAGES-BASEL, V8, DOI 10.3390/languages8030197
   Li ZX, 2023, NEURAL COMPUT APPL, V35, P24369, DOI 10.1007/s00521-023-08989-w
   Lieto A, 2018, COGN SYST RES, V48, P1, DOI 10.1016/j.cogsys.2017.08.003
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   McKinsey, 2023, WHAT IS GENERATIVE A
   Miles J, 2007, PERS INDIV DIFFER, V42, P869, DOI 10.1016/j.paid.2006.09.022
   Mitchell M., 2019, Artificial Intelligence
   Ouyang F., 2021, COMPUTERS ED ARTIFIC, V2, P100020, DOI [DOI 10.1016/J.CAEAI.2021.100020, 10.1016/j.caeai.2021.100020 10.1016/j.caeai.2021.100020]
   Pallant J., 2010, SPSS SURVIVAL MANUAL, DOI DOI 10.4324/9781003117407
   Park YJ, 2021, FUTURE OF DIGITAL SURVEILLANCE, P1, DOI 10.3998/mpub.10211441
   Park YW, 2023, ASIA PAC BUS REV, V29, P1105, DOI 10.1080/13602381.2022.2059955
   Raffaghelli JE, 2022, COMPUT EDUC, V182, DOI 10.1016/j.compedu.2022.104468
   Roll I, 2016, INT J ARTIF INTELL E, V26, P582, DOI 10.1007/s40593-016-0110-3
   Ruiz-Rojas LI, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151511524
   Sallam M., 2023, JMIR MED EDUC, V9, pe48254, DOI [https://doi.org/10.2196/preprints.48254, DOI 10.2196/PREPRINTS.48254]
   Sezer B, 2019, AUSTRALAS J EDUC TEC, V35, P15, DOI 10.14742/ajet.3959
   Shidiq M, 2023, P INT C ED SOC HUM, V1, P353
   Sohn K, 2020, TELEMAT INFORM, V47, DOI 10.1016/j.tele.2019.101324
   Stevens J., 1996, APPL MULTIVARIATE ST
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   TABACHNICK BG, 2001, USING MULTIVARIATE S
   Tamilmani K, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2020.102269
   Tegmark M., 2018, Life 3.0: Being Human in the Age of Artificial Intelligence
   Teng ZQ, 2022, MOB INF SYST, V2022, DOI 10.1155/2022/5479215
   Tezbasaran A., 1997, LIKERT TIPI L EK HAZ
   Toh SY, 2023, EDUC INF TECHNOL, V28, P2529, DOI 10.1007/s10639-022-11288-x
   Ustun AB, 2023, VIRTUAL REAL-LONDON, V27, P1063, DOI 10.1007/s10055-022-00717-4
   Varzaru AA, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11142256
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang HY, 2010, SOC BEHAV PERSONAL, V38, P415, DOI 10.2224/sbp.2010.38.3.415
   Yilmaz H., 2023, International Educational Review, V1, P57, DOI [10.58693/ier.114, DOI 10.58693/IER.114]
   Yilmaz R., 2023, COMPUT HUM BEHAV, V1, DOI DOI 10.1016/J.CHBAH.2023.100005
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Yilmaz Ramazan, 2022, Computers and Education: Artificial Intelligence, V3, DOI [10.1016/j.caeai, DOI 10.1016/J.CAEAI.2022.100092]
NR 75
TC 25
Z9 25
U1 120
U2 267
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 1044-7318
EI 1532-7590
J9 INT J HUM-COMPUT INT
JI Int. J. Hum.-Comput. Interact.
PD DEC 16
PY 2024
VL 40
IS 24
BP 8703
EP 8715
DI 10.1080/10447318.2023.2288730
EA DEC 2023
PG 13
WC Computer Science, Cybernetics; Ergonomics
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Computer Science; Engineering
GA P1U5B
UT WOS:001123440600001
DA 2024-12-25
ER

PT J
AU Al-Emran, M
   Abu-Hijleh, B
   Alsewari, AA
AF Al-Emran, Mostafa
   Abu-Hijleh, Bassam
   Alsewari, AbdulRahman A.
TI Examining the impact of Generative AI on social sustainability by
   integrating the information system success model and
   technology-environmental, economic, and social sustainability theory
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Generative AI; IS success model; Privacy concerns; Social
   sustainability; T-EESST
AB Generative Artificial Intelligence (AI) refers to advanced systems capable of creating new content by learning from vast datasets, including text, images, and code. These AI tools are increasingly being integrated into various sectors, including education, where they have the potential to enhance learning experiences. While the existing literature has primarily focused on the immediate educational benefits of these tools, such as enhanced learning and efficiency, less attention has been given to how these tools influence broader social sustainability goals, including equitable access and inclusive learning environments. Therefore, this study aims to fill this gap by developing a theoretical research model that combines the information system (IS) success model, technology-environmental, economic, and social sustainability theory (T-EESST), and privacy concerns. To evaluate the developed model, data were collected from 773 university students who were active users of Generative AI and analyzed using the PLS-SEM technique. The findings showed that service quality, system quality, and information quality have a significant positive effect on user satisfaction. Using Generative AI tools is found to be positively affected by user satisfaction. Interestingly, the findings supported the positive role of Generative AI in promoting social sustainability. However, no significant negative correlation was found between privacy concerns and Generative AI use. The findings provide several theoretical contributions and offer insights for various stakeholders in developing, implementing, and managing Generative AI tools in educational settings.
C1 [Al-Emran, Mostafa; Abu-Hijleh, Bassam] British Univ Dubai, Fac Engn & IT, Dubai, U Arab Emirates.
   [Al-Emran, Mostafa] Al Bayan Univ, Engn Tech Coll, Baghdad, Iraq.
   [Alsewari, AbdulRahman A.] Birmingham City Univ, Coll Comp, Fac Comp Engn & Built Environm, Birmingham, England.
C3 Al-Bayan University; Birmingham City University
RP Al-Emran, M (corresponding author), British Univ Dubai, Fac Engn & IT, Dubai, U Arab Emirates.; Al-Emran, M (corresponding author), Al Bayan Univ, Engn Tech Coll, Baghdad, Iraq.
EM mustafa.n.alemran@gmail.com; bassam.abuhijleh@buid.ac.ae;
   rahman.alsewari@bcu.ac.uk
RI Al-Emran, Mostafa/W-4466-2018
FU British University in Dubai [INFO019]
FX This research is funded by The British University in Dubai under the
   Grant Number: INFO019.
CR AbdelKader AF, 2022, J ACAD LIBR, V48, DOI 10.1016/j.acalib.2022.102506
   Al-Emran M, 2024, IEEE T ENG MANAGE, V71, P14512, DOI 10.1109/TEM.2024.3454169
   Al-Emran M, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102383
   Al-Hattami HM, 2024, INFORM TECHNOL DEV, V30, P472, DOI 10.1080/02681102.2022.2073325
   Al-Qaysi N, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2361210
   Al-Sharafi MA, 2023, INTERACT LEARN ENVIR, V31, P7491, DOI 10.1080/10494820.2022.2075014
   Albanna H, 2022, INT J INFORM MANAGE, V63, DOI 10.1016/j.ijinfomgt.2021.102452
   Alsharhan A, 2024, IEEE T ENG MANAGE, V71, P10232, DOI 10.1109/TEM.2023.3298360
   Alyoussef IY, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e13751
   Ashfaq M, 2020, TELEMAT INFORM, V54, DOI 10.1016/j.tele.2020.101473
   Bahroun Z, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712983
   Boubker O, 2024, EXPERT SYST APPL, V238, DOI 10.1016/j.eswa.2023.121820
   Brunetti F, 2020, TQM J, V32, P697, DOI 10.1108/TQM-12-2019-0309
   Cabral A. R., 2023, The National News
   Çelik K, 2022, EDUC INF TECHNOL, V27, P4709, DOI 10.1007/s10639-021-10798-4
   Creswell J. W., 2014, RES DESIGN QUALITATI, V4th
   DeLone WH, 2003, J MANAGE INFORM SYST, V19, P9, DOI 10.1080/07421222.2003.11045748
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   El-Bassiouny N. M., 2022, Management Sustainability: An Arab Review, V1, P1
   Elbanna S., 2023, MANAGEMENT SUSTAINAB, DOI DOI 10.1108/MSAR-03-2023-0016
   Foroughi B, 2024, INT J HUM-COMPUT INT, V40, P4501, DOI 10.1080/10447318.2023.2226495
   Grandviewresearch, 2023, AI In Education Market Size, Share & Trends Analysis Report By Component (Solutions, Services), By Deployment, By Technology, By Application, By End-use, By Region, And Segment Forecasts, 2022-2030
   Hair J. F., 2021, PRIMER PARTIAL LEAST
   Hair J.F., 2016, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
   Hair J, 2017, IND MANAGE DATA SYST, V117, P442, DOI 10.1108/IMDS-04-2016-0130
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Rehman IHU, 2023, COGENT BUS MANAG, V10, DOI 10.1080/23311975.2023.2186739
   Ischen Carolin, 2020, Chatbot Research and Design. Third International Workshop, CONVERSATIONS 2019. Revised Selected Papers. Lecture Notes in Computer Science (LNCS 11970), P34, DOI 10.1007/978-3-030-39540-7_3
   Ivanov S, 2021, J TOUR FUTURES, V9, P214, DOI 10.1108/JTF-02-2023-0038
   Jaspers EDT, 2022, J BUS RES, V142, P255, DOI 10.1016/j.jbusres.2021.12.043
   Jayashree S, 2022, SUSTAIN PROD CONSUMP, V31, P313, DOI 10.1016/j.spc.2022.02.015
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu YL, 2023, COMPUT HUM BEHAV, V143, DOI 10.1016/j.chb.2023.107716
   Ma YY, 2021, TELEMAT INFORM, V65, DOI 10.1016/j.tele.2021.101707
   Mamakou XJ, 2024, INFORM SYST MANAGE, V41, P357, DOI 10.1080/10580530.2023.2279075
   Mannuru NR, 2023, INFORM DEV, DOI 10.1177/02666669231200628
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Park A, 2024, J COMPUT INFORM SYST, V64, P728, DOI 10.1080/08874417.2023.2251416
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Polese Mario., 2000, SOCIAL SUSTAINABILIT, DOI DOI 10.1016/j.habitatint.2010.03.006
   Saura JR, 2022, GOV INFORM Q, V39, DOI 10.1016/j.giq.2022.101679
   Rane N., 2024, SSRN, 4603244, DOI [10.2139/ssrn.4603244, DOI 10.2139/SSRN.4603244]
   Ratten V, 2023, J FAM BUS MANAG, V13, P821, DOI 10.1108/JFBM-12-2023-199
   Ringle CM, 2023, DATA BRIEF, V48, DOI 10.1016/j.dib.2023.109074
   Ritala P., 2023, Journal of Business Strategy, DOI [10.1108/JBS-05-2023-0094/FULL/PDF, DOI 10.1108/JBS-05-2023-0094/FULL/PDF]
   Sayaf AM, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e13014
   Sekaran U., 2010, RES METHODS BUSINESS
   Stamatonikolos Y., 2023, Illuminem
   Tetteh N, 2020, SUSTAIN CITIES SOC, V63, DOI 10.1016/j.scs.2020.102434
   Thabet Z, 2024, IEEE T ENG MANAGE, V71, P8938, DOI 10.1109/TEM.2023.3296132
   Trkman M, 2023, GOV INFORM Q, V40, DOI 10.1016/j.giq.2022.101787
   Wang YS, 2016, INT J MANAG EDUC-OXF, V14, P379, DOI 10.1016/j.ijme.2016.09.002
   Wang YM, 2024, INT J HUM-COMPUT INT, V40, P5087, DOI 10.1080/10447318.2023.2231278
   Wei CL, 2022, INT J MANAG EDUC-OXF, V20, DOI 10.1016/j.ijme.2022.100634
   Yao Q, 2024, TECHNOL FORECAST SOC, V198, DOI 10.1016/j.techfore.2023.122948
   Zahid M, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101764
   Zhong JY, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103267
NR 57
TC 0
Z9 0
U1 4
U2 4
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 DEC 3
PY 2024
DI 10.1007/s10639-024-13201-0
EA DEC 2024
PG 22
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA O2F5T
UT WOS:001369353600001
DA 2024-12-25
ER

PT J
AU Singh, S
   Singh, S
   Kraus, S
   Sharma, A
   Dhir, S
AF Singh, Shiwangi
   Singh, Surabhi
   Kraus, Sascha
   Sharma, Anuj
   Dhir, Sanjay
TI Characterizing generative artificial fi cial intelligence applications:
   Text- mining-enabled technology roadmapping
SO JOURNAL OF INNOVATION & KNOWLEDGE
LA English
DT Article
DE Generative AI; Technology roadmapping; Patents; Text-mining; Structural
   topic modeling; Patent data mining
ID TRM; FRAMEWORK
AB This study aims to identify generative AI (GenAI) applications and develop a roadmap for the near, mid, and far future. Structural topic modeling (STM) is used to discover latent semantic patterns and identify the key application areas from a text corpus comprising 2,398 patents published between 2017 and 2023. The study identifies six latent topics of GenAI application, including object detection and identification; medical applications; intelligent conversational agents; image generation and processing; financial and information security applications; and cyber-physical systems. Emergent topic terms are listed for each topic, and inter-topic correlations are explored to understand the thematic structures and summarize the semantic relationships among GenAI application areas. Finally, a technology roadmap is developed for each identified application area for the near, mid, and far future. This study provides valuable insights into the evolving GenAI landscape and helps practitioners make strategic business decisions based on the GenAI roadmap. (c) 2024 The Authors. Published by Elsevier Espa & ntilde;a, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
C1 [Singh, Shiwangi] Indian Inst Management, Ranchi, Jharkhand, India.
   [Singh, Surabhi; Sharma, Anuj] OP Jindal Global Univ, Jindal Global Business Sch, Sonipat, Haryana, India.
   [Kraus, Sascha] Free Univ Bozen Bolzano, Fac Econ & Management, Piazza Univ 1, I-39100 Bolzano, Italy.
   [Kraus, Sascha] Univ Johannesburg, Dept Business Management, Johannesburg, South Africa.
   [Dhir, Sanjay] Indian Inst Technol Delhi, Dept Management Studies, New Delhi, India.
C3 Indian Institute of Management (IIM System); Indian Institute of
   Management Ranchi; O.P. Jindal Global University; Free University of
   Bozen-Bolzano; University of Johannesburg; Indian Institute of
   Technology System (IIT System); Indian Institute of Technology (IIT) -
   Delhi
RP Kraus, S (corresponding author), Free Univ Bozen Bolzano, Fac Econ & Management, Piazza Univ 1, I-39100 Bolzano, Italy.; Kraus, S (corresponding author), Univ Johannesburg, Dept Business Management, Johannesburg, South Africa.
EM shiwangi.singh@iimranchi.ac.in; surabhi.iitd1@gmail.com;
   sascha.kraus@zfke.de; f09anujs@iimidr.ac.in; sanjaydhir.iitd@gmail.com
RI Singh, Shiwangi/AAQ-6680-2021; Singh, Surabhi/IQX-1574-2023; Kraus,
   Sascha/HCH-3626-2022; Sharma, Anuj/AAE-5767-2020
OI Singh, Surabhi/0000-0001-9687-1676; Kraus, Sascha/0000-0003-4886-7482;
   Singh, Shiwangi/0000-0001-8693-5947
FU Free University of Bozen-Bolzano
FX This work was supported by the Open Access Publishing Fund provided by
   the Free University of Bozen-Bolzano.
CR Allouch M, 2021, SENSORS-BASEL, V21, DOI 10.3390/s21248448
   AmankwahAmoah J., 2024, INT J INFORM MANAGE, V79, DOI [10.1016/j.ijinfomgt.2024.102759, DOI 10.1016/J.IJINFOMGT.2024.102759]
   Ameen N, 2023, SERV IND J, V43, P125, DOI 10.1080/02642069.2023.2185934
   Åström J, 2022, REV MANAG SCI, V16, P2111, DOI 10.1007/s11846-022-00521-z
   Aydin O., 2022, EMERGING COMPUTER TE, DOI [DOI 10.2139/SSRN.4308687, 10.2139/ssrn.4308687]
   Bengesi S, 2024, IEEE ACCESS, V12, P69812, DOI 10.1109/ACCESS.2024.3397775
   da Silveira LAB Jr, 2018, TECHNOL FORECAST SOC, V126, P194, DOI 10.1016/j.techfore.2017.08.011
   Brophy E, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3559540
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Calof J, 2020, FORESIGHT, V22, P14, DOI 10.1108/FS-02-2019-0011
   Carvalho MM, 2013, TECHNOL FORECAST SOC, V80, P1418, DOI 10.1016/j.techfore.2012.11.008
   Chakraborty S, 2022, TECHNOL FORECAST SOC, V179, DOI 10.1016/j.techfore.2021.121141
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Cook S, 2024, HARVARD BUS REV, V102, P118
   de Alcantara DP, 2019, TECHNOL FORECAST SOC, V138, P127, DOI 10.1016/j.techfore.2018.08.014
   Ding BJ, 2023, J ENG TECHNOL MANAGE, V67, DOI 10.1016/j.jengtecman.2023.101731
   Dwivedi YK, 2024, INT J INFORM MANAGE, V76, DOI 10.1016/j.ijinfomgt.2023.102725
   Dwivedi YK, 2023, TECHNOL FORECAST SOC, V192, DOI 10.1016/j.techfore.2023.122579
   Eapen TT, 2023, HARVARD BUS REV, V101, P55
   Fink A., 2000, Competitive Intelligence Review, V11, P37, DOI [10.1002/(SICI)1520-6386(200031)11:1<37::AID-CIR6>3.0.CO;2-W, DOI 10.1002/(SICI)1520-6386(200031)11:1<37::AID-CIR6>3.0.CO;2-W, DOI 10.1002/(SICI)1520-6386(200031)11:1]
   Gao QY, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100433
   Gershman M, 2016, TECHNOL FORECAST SOC, V110, P187, DOI 10.1016/j.techfore.2015.11.018
   Goodell JW, 2022, INT REV ECON FINANC, V82, P511, DOI 10.1016/j.iref.2022.06.020
   Gordon AV, 2020, TECHNOL FORECAST SOC, V154, DOI 10.1016/j.techfore.2020.119966
   Gu SY, 2022, PROC CVPR IEEE, P10686, DOI 10.1109/CVPR52688.2022.01043
   Hakmaoui A, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121139
   Haupt M, 2024, REV MANAG SCI, DOI 10.1007/s11846-024-00748-y
   Hazra D, 2020, BIOLOGY-BASEL, V9, DOI 10.3390/biology9120441
   Hendriksen C, 2023, J SUPPLY CHAIN MANAG, V59, P65, DOI 10.1111/jscm.12304
   Hoffman G, 2024, ANNU REV CONTR ROBOT, V7, P73, DOI 10.1146/annurev-control-071223-105834
   Idrees H, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100350
   Jeong Y, 2021, SCIENTOMETRICS, V126, P3697, DOI 10.1007/s11192-021-03945-8
   Kalota F, 2024, EDUC SCI, V14, DOI 10.3390/educsci14020172
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kim J, 2021, TECHNOL FORECAST SOC, V171, DOI 10.1016/j.techfore.2021.120972
   Kraus S, 2023, TECHNOL FORECAST SOC, V189, DOI 10.1016/j.techfore.2023.122381
   Kraus S, 2022, REV MANAG SCI, V16, P2577, DOI 10.1007/s11846-022-00588-8
   Lee JH, 2013, TECHNOL FORECAST SOC, V80, P286, DOI 10.1016/j.techfore.2012.09.020
   Lee S, 2005, TECHNOL FORECAST SOC, V72, P567, DOI 10.1016/j.techfore.2004.11.006
   Lee S, 2008, R&D MANAGE, V38, P169, DOI 10.1111/j.1467-9310.2008.00509.x
   Lee S, 2007, TECHNOVATION, V27, P433, DOI 10.1016/j.technovation.2007.02.011
   Letaba PT, 2022, IEEE T ENG MANAGE, V69, P195, DOI 10.1109/TEM.2021.3050812
   Liu MY, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100332
   Liu MY, 2021, P IEEE, V109, P839, DOI 10.1109/JPROC.2021.3049196
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Mekni M., 2021, J. Softw. Eng. Appl, V14, P455, DOI [10.4236/jsea.2021.149027, DOI 10.4236/JSEA.2021.149027]
   Miao H, 2022, IEEE T ENG MANAGE, V69, P262, DOI 10.1109/TEM.2020.2970972
   Nayak J., 2024, Machine Learning for Cyber Physical System: Advances and Challenges, DOI [10.1007/978-3-031-54038-7, DOI 10.1007/978-3-031-54038-7]
   Nazarenko A, 2022, TECHNOVATION, V110, DOI 10.1016/j.technovation.2021.102364
   Nazarko J, 2022, IEEE T ENG MANAGE, V69, P179, DOI 10.1109/TEM.2020.3004549
   Nguyen-Duc A, 2023, Arxiv, DOI arXiv:2310.18648
   Noh H, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120452
   Obreja DM, 2024, J INNOV KNOWL, V9, DOI 10.1016/j.jik.2024.100465
   Ozcan S, 2022, IEEE T ENG MANAGE, V69, P228, DOI 10.1109/TEM.2021.3068310
   Park H, 2020, TECHNOL FORECAST SOC, V154, DOI 10.1016/j.techfore.2020.119965
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Phaal R, 2005, TECHNOLOGY MANAGEMENT: A UNIFYING DISCIPLINE FOR MELTING THE BOUNDARIES, P99
   Phaal R, 2004, TECHNOL FORECAST SOC, V71, P5, DOI 10.1016/S0040-1625(03)00072-6
   Provan G., 2021, Artificial Intelligence Methods For Software Engineering, P211
   Ramos AG, 2022, TECHNOL FORECAST SOC, V174, DOI 10.1016/j.techfore.2021.121213
   Rane N., 2023, ROLE CHALLENGES CHAT, DOI [10.2139/ssrn.4603206, DOI 10.2139/SSRN.4603206]
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Roberts ME, 2016, J AM STAT ASSOC, V111, P988, DOI 10.1080/01621459.2016.1141684
   Roppelt JS, 2024, MANAGE DECIS, V62, P2986, DOI 10.1108/MD-07-2023-1194
   Roppelt JS, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2023.102443
   Rubel O., 2022, SSRN 3493361, DOI [10.2139/ssrn.3493361, DOI 10.2139/SSRN.3493361]
   Sánchez-Franco MJ, 2023, J INNOV KNOWL, V8, DOI 10.1016/j.jik.2023.100380
   Santana M, 2023, REV MANAG SCI, V17, P1971, DOI 10.1007/s11846-022-00613-w
   Sauer PC, 2023, REV MANAG SCI, V17, P1899, DOI 10.1007/s11846-023-00668-3
   Sharma A, 2023, J COMPUT INFORM SYST, V63, P37, DOI 10.1080/08874417.2021.2021114
   Sharma A, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102316
   Silard A, 2023, REV MANAG SCI, V17, P2035, DOI 10.1007/s11846-022-00586-w
   Singh S, 2024, J RETAIL CONSUM SERV, V81, DOI 10.1016/j.jretconser.2024.103977
   Singh S, 2020, J ADV MANAG RES, V17, P262, DOI 10.1108/JAMR-08-2019-0164
   Singh S, 2023, IND MANAGE DATA SYST, V123, P2079, DOI 10.1108/IMDS-02-2023-0126
   Singh S, 2023, INT J ENTREP INNOV, DOI 10.1177/14657503231179597
   Spanjol J, 2023, J PROD INNOVAT MANAG, V40, P383, DOI 10.1111/jpim.12689
   Su YS, 2023, TECHNOL FORECAST SOC, V196, DOI 10.1016/j.techfore.2023.122817
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Vasconcellos Eduardo, 2014, International Journal of Automotive Technology and Management, V14, P25
   Wamba SF, 2024, INT J PROD RES, V62, P5676, DOI 10.1080/00207543.2023.2294116
   Wamba SF, 2024, INT J PROD ECON, V268, DOI 10.1016/j.ijpe.2023.109131
   Watanabe M, 2022, IEEE T ENG MANAGE, V69, P17, DOI 10.1109/TEM.2020.3032603
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Wu AN, 2022, BUILD ENVIRON, V223, DOI 10.1016/j.buildenv.2022.109477
   Yi X, 2019, MED IMAGE ANAL, V58, DOI 10.1016/j.media.2019.101552
   Zhang H, 2021, TECHNOL FORECAST SOC, V167, DOI 10.1016/j.techfore.2021.120729
   Zhang Y, 2016, TECHNOL FORECAST SOC, V110, P175, DOI 10.1016/j.techfore.2015.11.029
   Zheng XL, 2024, IEEE T COMPUT SOC SY, V11, P3457, DOI 10.1109/TCSS.2023.3334306
NR 90
TC 0
Z9 0
U1 34
U2 34
PU ELSEVIER ESPANA
PI MADRID
PA CALLE DE ZURBANO, 76-4TH FLR LEFT, MADRID, 28010, SPAIN
SN 2530-7614
EI 2444-569X
J9 J INNOV KNOWL
JI J. Innov. Knowl.
PD JUL-SEP
PY 2024
VL 9
IS 3
AR 100531
DI 10.1016/j.jik.2024.100531
EA AUG 2024
PG 12
WC Business; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA D0G6U
UT WOS:001293061200001
OA gold
DA 2024-12-25
ER

PT J
AU Ma, L
   Yu, P
   Zhang, X
   Wang, GS
   Hao, FF
AF Ma, Liang
   Yu, Peng
   Zhang, Xin
   Wang, Gaoshan
   Hao, Feifei
TI How AI use in organizations contributes to employee competitive
   advantage: The moderating role of perceived organization support
SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
LA English
DT Article
DE Work-related generative AI use; Employee competitive advantage; Employee
   boundary spanning; Employee agility; Resource base view model
ID ENTERPRISE SOCIAL MEDIA; ARTIFICIAL-INTELLIGENCE; PERFORMANCE; BOUNDARY;
   RESILIENCE; AGILITY; COMPETENCES; MANAGEMENT; RESOURCE; EFFICACY
AB Although the use of generative artificial intelligence (AI) within organizations is becoming increasingly common, research on how to enhance employees' competitive advantage through generative AI use within organizations is very limited. Using a resource-based view model, this study investigates the relationship between generative AI use and employees' competitive advantage, as well as the moderating role of perceived organization support. From an analysis of data from 264 employees from 200 organizations, it is found that work-related generative AI use has a positive effect on employee boundary spanning, which contributes to employee competitive advantage. Secondly, work-related generative AI use also has a positive effect on employee agility, including employee resilience and employee adaptability, which further contributes to employee competitive advantage. However, work-related generative AI use has a positive effect on employee proactivity, while the effect of employee proactivity on employee competitive advantage is not significant. Thirdly, perceived organizational support can enhance the effect between employee boundary spanning and employee competitive advantage. However, it is interesting to observe that perceived organizational support enhances the effect between employee adaptability and employee competitive advantage, while weakening the effect between employee proactivity and employee competitive advantage. It does not exert a moderating effect between employee resilience and employee competitive advantage. These findings can help deepen the current understanding of the relationship between generative AI use in the organization and employee competitive advantage, and provide suggestions for business managers on how to use generative AI to improve employee competitive advantage.
C1 [Ma, Liang; Yu, Peng; Zhang, Xin; Wang, Gaoshan] Shandong Univ Finance & Econ, Jinan 250014, Peoples R China.
   [Hao, Feifei] Shandong Univ Tradit Chinese Med, Jinan 250014, Peoples R China.
C3 Shandong University of Finance & Economics; Shandong University of
   Traditional Chinese Medicine
RP Hao, FF (corresponding author), Shandong Univ Tradit Chinese Med, Jinan 250014, Peoples R China.
EM aifeifei0210@126.com
RI wang, gaoshan/R-7729-2019
FU Shandong Provincial Key R & D Plan (Soft Science) [2023RZB02021];
   General Program of Shandong Natural Science Foundation [ZR2024MG002];
   The 2023 Higher Education Excellent Youth Innovation Team of Shandong
   Province [2023RW064]; The 2023 International Cooperation Research
   Project of Shandong University of Finance and Economics [04]; Jinan
   Municipal School Integration Development Strategy Project "Digital
   Consumption and Manufacturing Digital Transformation Collaborative
   Innovation Center Construction" [JNSX2023052]
FX This work was supported by Shandong Provincial Key R & D Plan (Soft
   Science) under Project No: 2023RZB02021; the General Program of Shandong
   Natural Science Foundation under Project No: ZR2024MG002; 2023 Higher
   Education Excellent Youth Innovation Team of Shandong Province under
   Project No: 2023RW064; 2023 International Cooperation Research Project
   of Shandong University of Finance and Economics under Project No: 04;
   and Jinan Municipal School Integration Development Strategy Project
   "Digital Consumption and Manufacturing Digital Transformation
   Collaborative Innovation Center Construction" under Project No:
   JNSX2023052.
CR Aguinis H, 2021, J MANAGE, V47, P823, DOI 10.1177/0149206320969787
   Akter S, 2021, INT J INFORM MANAGE, V60, DOI 10.1016/j.ijinfomgt.2021.102387
   Alavi S, 2014, INT J PROD RES, V52, P6273, DOI 10.1080/00207543.2014.919420
   ANCONA DG, 1992, ADMIN SCI QUART, V37, P634, DOI 10.2307/2393475
   Arias-Pérez J, 2022, J KNOWL MANAG, V26, P1476, DOI 10.1108/JKM-01-2021-0058
   Awamleh FT, 2022, SAGE OPEN, V12, DOI 10.1177/21582440221119478
   Baham C, 2022, INFORM SYST J, V32, P103, DOI 10.1111/isj.12336
   Baranik LE, 2010, J VOCAT BEHAV, V76, P366, DOI 10.1016/j.jvb.2009.07.004
   Braun TJ, 2017, IND ORGAN PSYCHOL-US, V10, P702, DOI 10.1017/iop.2017.79
   Cai Z, 2018, INT J INFORM MANAGE, V38, P52, DOI 10.1016/j.ijinfomgt.2017.09.001
   Chen Q, 2022, J BUS RES, V145, P552, DOI 10.1016/j.jbusres.2022.02.088
   Cheng B, 2023, J BUS RES, V164, DOI 10.1016/j.jbusres.2023.113987
   Collins CJ, 2021, INT J HUM RESOUR MAN, V32, P331, DOI 10.1080/09585192.2019.1711442
   Cooke FL, 2019, INT J HUM RESOUR MAN, V30, P1239, DOI 10.1080/09585192.2015.1137618
   Díaz-Fernández M, 2013, INT J HUM RESOUR MAN, V24, P643, DOI 10.1080/09585192.2012.677461
   Dutta D, 2023, INT J HUM RESOUR MAN, V34, P2451, DOI 10.1080/09585192.2022.2085525
   Elbaz AM, 2018, TOURISM MANAGE, V67, P3, DOI 10.1016/j.tourman.2018.01.002
   Fang YC, 2021, LEADERSHIP ORG DEV J, V42, P480, DOI 10.1108/LODJ-11-2019-0484
   Faraj S, 2009, J APPL PSYCHOL, V94, P604, DOI 10.1037/a0014367
   Haenlein M, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619864925
   He CQ, 2024, INT J CONTEMP HOSP M, V36, P975, DOI 10.1108/IJCHM-07-2022-0848
   Hossain MA, 2022, IND MARKET MANAG, V106, P240, DOI 10.1016/j.indmarman.2022.08.017
   Hu Q, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103339
   Hu Q, 2023, BEHAV INFORM TECHNOL, V42, P2654, DOI 10.1080/0144929X.2022.2137698
   Huang DL, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103600
   Jaiswal A, 2022, INT J HUM RESOUR MAN, V33, P1179, DOI 10.1080/09585192.2021.1891114
   Jia N, 2024, ACAD MANAGE J, V67, P5, DOI 10.5465/amj.2022.0426
   Jolly PM, 2021, J ORGAN BEHAV, V42, P229, DOI 10.1002/job.2485
   Kamprath M, 2015, TECHNOL FORECAST SOC, V95, P252, DOI 10.1016/j.techfore.2015.01.011
   Kemp A., 2023, Academy of Management Review, V49, P618
   Kim HK, 2022, BALT J MANAG, V17, P192, DOI 10.1108/BJM-06-2021-0225
   Kong HY, 2024, J HOSP MARKET MANAG, V33, P261, DOI 10.1080/19368623.2023.2258116
   Kraaijenbrink J, 2010, J MANAGE, V36, P349, DOI 10.1177/0149206309350775
   Krakowski S, 2023, STRATEGIC MANAGE J, V44, P1425, DOI 10.1002/smj.3387
   Kuntz J, 2017, CAREER DEV INT, V22, P419, DOI 10.1108/CDI-11-2016-0208
   Kurtessis JN, 2017, J MANAGE, V43, P1854, DOI 10.1177/0149206315575554
   Lam LW, 2016, HUM RELAT, V69, P345, DOI 10.1177/0018726715584689
   Lee MCM, 2023, INFORM MANAGE-AMSTER, V60, DOI 10.1016/j.im.2023.103816
   Liang HG, 2007, MIS QUART, V31, P59
   Liang M, 2021, J ENTERP INF MANAG, V34, P922, DOI 10.1108/JEIM-10-2019-0321
   Liu SB, 2018, INT J HUM RESOUR MAN, V29, P1879, DOI 10.1080/09585192.2016.1216872
   Luengo-Oroz M, 2021, IEEE TECHNOL SOC MAG, V40, P71, DOI 10.1109/MTS.2021.3056282
   Luthans F, 2004, ORGAN DYN, V33, P143, DOI 10.1016/j.orgdyn.2004.01.003
   Ma L, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2312452
   Ma L, 2023, INFORM TECHNOL PEOPL, DOI 10.1108/ITP-02-2022-0103
   Ma L, 2021, INTERNET RES, V31, P1823, DOI 10.1108/INTR-01-2020-0022
   Ma L, 2020, J KNOWL MANAG, V24, P2149, DOI 10.1108/JKM-03-2020-0234
   Maden-Eyiusta C, 2022, J MANAGE PSYCHOL, V37, P153, DOI 10.1108/JMP-05-2020-0249
   Malik N, 2022, INT J MANPOWER, V43, P334, DOI 10.1108/IJM-03-2021-0173
   Mell JN, 2022, J APPL PSYCHOL, V107, P1009, DOI 10.1037/apl0000960
   Merhi MI, 2023, INT J INFORM MANAGE, V69, DOI 10.1016/j.ijinfomgt.2022.102545
   Mikalef P, 2023, J BUS RES, V164, DOI 10.1016/j.jbusres.2023.113998
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Monod E, 2024, INFORM ORGAN-UK, V34, DOI 10.1016/j.infoandorg.2023.100498
   Narayanan S, 2022, KNOWL MAN RES PRACT, DOI 10.1080/14778238.2022.2150579
   Odugbesan JA, 2023, J KNOWL MANAG, V27, P696, DOI 10.1108/JKM-08-2021-0601
   Pereira V, 2021, J BUS RES, V132, P557, DOI 10.1016/j.jbusres.2021.04.021
   Pitafi AH, 2024, INFORM TECHNOL PEOPL, DOI 10.1108/ITP-10-2022-0791
   Pitafi AH, 2020, TECHNOL SOC, V63, DOI 10.1016/j.techsoc.2020.101333
   Pitafi AH, 2020, TELEMAT INFORM, V55, DOI 10.1016/j.tele.2020.101451
   Pitafi AH, 2018, TELEMAT INFORM, V35, P2157, DOI 10.1016/j.tele.2018.08.001
   Porter ME, 2002, HARVARD BUS REV, V80, P56
   Prebensen NK, 2017, TOURISM MANAGE, V60, P166, DOI 10.1016/j.tourman.2016.12.001
   Prentice C, 2023, J RETAIL CONSUM SERV, V73, DOI 10.1016/j.jretconser.2023.103376
   Raetze S, 2021, GROUP ORGAN MANAGE, V46, P607, DOI 10.1177/10596011211032129
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Rana NP, 2022, EUR J INFORM SYST, V31, P364, DOI 10.1080/0960085X.2021.1955628
   Rasheed MI, 2023, TECHNOL FORECAST SOC, V194, DOI 10.1016/j.techfore.2023.122717
   Riggle RJ, 2009, J BUS RES, V62, P1027, DOI 10.1016/j.jbusres.2008.05.003
   Sahoo S, 2024, IND MARKET MANAG, V117, P28, DOI 10.1016/j.indmarman.2023.12.008
   Shaikh F, 2023, ASIA PAC J HUM RESOU, V61, P794, DOI 10.1111/1744-7941.12385
   Shet SV, 2024, PERS REV, V53, P674, DOI 10.1108/PR-10-2023-0873
   Singh SK, 2019, TECHNOL FORECAST SOC, V146, P203, DOI 10.1016/j.techfore.2019.05.032
   Song X, 2022, INFORM MANAGE-AMSTER, V59, DOI 10.1016/j.im.2022.103595
   Sullivan Y, 2023, J ASSOC INF SYST, V24, P745, DOI 10.17705/1jais.00807
   Sun JQ, 2021, J APPL PSYCHOL, V106, P250, DOI 10.1037/apl0000494
   Sung SY, 2018, HUM RESOUR MANAGE-US, V57, P1339, DOI 10.1002/hrm.21909
   Talwar S, 2023, TECHNOL FORECAST SOC, V195, DOI 10.1016/j.techfore.2023.122759
   Tang PM, 2023, J APPL PSYCHOL, V108, P1766, DOI 10.1037/apl0001103
   Tang PM, 2022, ACAD MANAGE J, V65, P1019, DOI 10.5465/amj.2020.1516
   Teece DJ, 2014, ACAD MANAGE PERSPECT, V28, P328, DOI 10.5465/amp.2013.0116
   Thibeault A, 2023, RESOUR POLICY, V85, DOI 10.1016/j.resourpol.2023.103795
   Tong SL, 2021, STRATEGIC MANAGE J, V42, P1600, DOI 10.1002/smj.3322
   van Esch E, 2018, INT J HUM RESOUR MAN, V29, P1683, DOI 10.1080/09585192.2016.1206031
   Van Osch W, 2018, J MANAGE INFORM SYST, V35, P647, DOI 10.1080/07421222.2018.1451961
   Van Osch W, 2016, J INF TECHNOL, V31, P207, DOI 10.1057/jit.2016.12
   Verma S, 2022, COMPUT HUM BEHAV, V131, DOI 10.1016/j.chb.2022.107215
   Wamba SF, 2022, INT J INFORM MANAGE, V67, DOI 10.1016/j.ijinfomgt.2022.102544
   Wang XQ, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102535
   Wang ZX, 2017, J VOCAT BEHAV, V101, P90, DOI 10.1016/j.jvb.2017.04.002
   Wijayati DT, 2022, INT J MANPOWER, V43, P486, DOI 10.1108/IJM-07-2021-0423
   Wong LW, 2024, INTERNET RES, V34, P343, DOI 10.1108/INTR-07-2021-0446
   Yang HY, 2022, PSYCHOL RES BEHAV MA, V15, P2421, DOI 10.2147/PRBM.S378141
   Yin M, 2024, COMPUT HUM BEHAV, V150, DOI 10.1016/j.chb.2023.107987
   Zhang JR, 2022, INT J INFORM MANAGE, V64, DOI 10.1016/j.ijinfomgt.2022.102490
   Zhang XB, 2023, TECHNOL FORECAST SOC, V186, DOI 10.1016/j.techfore.2022.122114
   Zhang X, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2379719
   Zhang X, 2019, INFORM MANAGE-AMSTER, V56, DOI 10.1016/j.im.2018.12.004
   Zhu MY, 2021, INTERNET RES, V31, P931, DOI 10.1108/INTR-07-2020-0409
   Zhu YQ, 2023, IND MANAGE DATA SYST, V123, P515, DOI 10.1108/IMDS-02-2022-0114
   Zirar A, 2023, TECHNOVATION, V124, DOI 10.1016/j.technovation.2023.102747
NR 101
TC 0
Z9 0
U1 153
U2 153
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0040-1625
EI 1873-5509
J9 TECHNOL FORECAST SOC
JI Technol. Forecast. Soc. Chang.
PD DEC
PY 2024
VL 209
AR 123801
DI 10.1016/j.techfore.2024.123801
EA OCT 2024
PG 14
WC Business; Regional & Urban Planning
WE Social Science Citation Index (SSCI)
SC Business & Economics; Public Administration
GA I8N0H
UT WOS:001332755500001
DA 2024-12-25
ER

PT J
AU Christensen, J
   Hansen, JM
   Wilson, P
AF Christensen, Jeff
   Hansen, Jared M.
   Wilson, Paul
TI Understanding the role and impact of Generative Artificial Intelligence
   (AI) hallucination within consumers' tourism decision-making processes
SO CURRENT ISSUES IN TOURISM
LA English
DT Article; Early Access
DE Generative Artificial Intelligence (AI); ChatGPT; tourism behaviour;
   hallucination misinformation; challenges in technology usage in tourism;
   Technology Acceptance Model (TAM)
ID USER ACCEPTANCE; PERCEIVED USEFULNESS; TECHNOLOGY; EASE
AB ChatGPT, which launched only a year ago, is the fastest-growing website in the world today. When generative AI software such as ChatGPT generates ideas for people, they often generate false ideas. This occurrence has been called 'AI Hallucination'. It can include generating false text output that is extremely believable to completely gibberish. This source of potential misinformation has significant potential implications for the travel and tourism industry. Using survey responses from 900 consumers, this empirical study contributes to theorizing and examination of how consumers' awareness of AI Hallucination potential combines with existing concepts from the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) when it comes to the decision to use generative AI platforms such as ChatGPT for tourism planning. This research also examines if the consumers are actually able to discern AI Hallucination and why they select to use AI technologies over other tourism information sources, such as aggregated peer review websites like TripAdvisor, government tourism websites, or social media influencers. The results indicate that many consumers chose error-filled AI tourism itineraries over other options because they trust the AI to be more impartial and customized than the other sources.
C1 [Christensen, Jeff] Brigham Young Univ Hawaii, Fac Business & Govt, Ctr Hospitality & Tourism, Laie, HI USA.
   [Hansen, Jared M.] Utah State Univ, Jon M Huntsman Sch Business, Dept Mkt & Strategy, Logan, UT 84322 USA.
   [Wilson, Paul] Brigham Young Univ Hawaii, Fac Business & Govt, Willes Ctr Int Entrepreneurship, Laie, HI USA.
C3 Brigham Young University; Brigham Young University - Hawaii; Utah System
   of Higher Education; Utah State University; Brigham Young University;
   Brigham Young University - Hawaii
RP Hansen, JM (corresponding author), Utah State Univ, Jon M Huntsman Sch Business, Dept Mkt & Strategy, Logan, UT 84322 USA.
EM jared@usu.edu
CR Albayrak T, 2023, J VACAT MARK, V29, P3, DOI 10.1177/13567667211066544
   Baumgartner H, 2021, J ACAD MARKET SCI, V49, P221, DOI 10.1007/s11747-020-00766-8
   Brameier Devon T, 2023, J Bone Joint Surg Am, V105, P1388, DOI 10.2106/JBJS.23.00473
   Brender TD, 2023, JAMA INTERN MED, V183, P1177, DOI 10.1001/jamainternmed.2023.3875
   Bulchand-Gidumal J, 2024, CURR ISSUES TOUR, V27, P2345, DOI 10.1080/13683500.2023.2229480
   Caber M, 2024, CURR ISSUES TOUR, V27, P4276, DOI 10.1080/13683500.2023.2278747
   Caber M, 2020, J OUTDOOR REC TOUR, V31, DOI 10.1016/j.jort.2020.100327
   Chang JL, 2022, CURR ISSUES TOUR, V25, P2338, DOI 10.1080/13683500.2021.2014792
   Chuang CM, 2020, CURR ISSUES TOUR, V23, P2333, DOI 10.1080/13683500.2019.1631266
   DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Dieck MCT, 2018, CURR ISSUES TOUR, V21, P154, DOI 10.1080/13683500.2015.1070801
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Grégoire Y, 2006, MARKET LETT, V17, P31, DOI 10.1007/s11002-006-3796-4
   Guo Q, 2023, INT J CONTEMP HOSP M, V35, P2437, DOI 10.1108/IJCHM-06-2022-0701
   Guo Q, 2024, J HOSP TOUR RES, V48, P450, DOI 10.1177/10963480221108906
   Han H, 2014, ASIA PAC J TOUR RES, V19, P428, DOI 10.1080/10941665.2013.764333
   Han W, 2023, CURR ISSUES TOUR, V26, P1797, DOI 10.1080/13683500.2022.2070457
   Hansen JM, 2018, COMPUT HUM BEHAV, V80, P197, DOI 10.1016/j.chb.2017.11.010
   Hateftabar F, 2023, CURR ISSUES TOUR, V26, P1861, DOI 10.1080/13683500.2022.2071682
   Heaven W.D., 2023, MIT Technology Review
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Horowitz MC, 2024, AI SOC, V39, P1721, DOI 10.1007/s00146-023-01666-5
   Huang YC, 2016, INT J TOUR RES, V18, P116, DOI 10.1002/jtr.2038
   Huang YC, 2013, TOURISM MANAGE, V36, P490, DOI 10.1016/j.tourman.2012.09.009
   Huang YC, 2010, J TEACH TRAVEL TOUR, V10, P312, DOI 10.1080/15313220.2010.525425
   Kirtil IG, 2021, ADV HOSP TOUR RES-AH, V9, P205, DOI 10.30519/ahtr.801690
   Kucukusta D, 2015, INT J CONTEMP HOSP M, V27, P185, DOI 10.1108/IJCHM-09-2013-0413
   Levin M.A., 2008, COLL STUD J, V42, P665
   Medai N, 2023, CURR ISSUES TOUR, V26, P1132, DOI 10.1080/13683500.2022.2048807
   Metz Rachel, 2023, Bloomberg
   Nannelli M, 2023, EUR PLAN STUD, V31, P1325, DOI 10.1080/09654313.2023.2180321
   OBrien M., 2023, AP News
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Pop RA, 2022, CURR ISSUES TOUR, V25, P823, DOI 10.1080/13683500.2021.1895729
   Pophal L., 2023, What is AI hallucination?
   Quan W, 2024, CURR ISSUES TOUR, V27, P2797, DOI 10.1080/13683500.2023.2240474
   Roeschke L., 2023, morning consult
   Rönkkö M, 2022, ORGAN RES METHODS, V25, P6, DOI 10.1177/1094428120968614
   Seçilmis C, 2022, CURR ISSUES TOUR, V25, P2789, DOI 10.1080/13683500.2021.1994528
   Singh N, 2009, J TEACH TRAVEL TOUR, V8, P315, DOI 10.1080/15313220903047896
   Smironva E, 2020, CURR ISSUES TOUR, V23, P1191, DOI 10.1080/13683500.2019.1599828
   Tiwari V, 2024, CURR ISSUES TOUR, V27, P3079, DOI 10.1080/13683500.2023.2247534
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Vorobeva D, 2024, CURR ISSUES TOUR, V27, P1551, DOI 10.1080/13683500.2023.2214353
   Wang FY, 2023, IEEE-CAA J AUTOMATIC, V10, P575, DOI 10.1109/JAS.2023.123486
   Yang H, 2022, CURR ISSUES TOUR, V25, P1046, DOI 10.1080/13683500.2022.2035700
   Yung R, 2019, CURR ISSUES TOUR, V22, P2056, DOI 10.1080/13683500.2017.1417359
   Zhu ZX, 2022, J HOSP TOUR TECHNOL, V13, P715, DOI 10.1108/JHTT-10-2020-0284
NR 49
TC 14
Z9 14
U1 296
U2 492
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1368-3500
EI 1747-7603
J9 CURR ISSUES TOUR
JI Curr. Issues Tour.
PD 2024 JAN 19
PY 2024
DI 10.1080/13683500.2023.2300032
EA JAN 2024
PG 16
WC Hospitality, Leisure, Sport & Tourism
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics
GA FE4Q5
UT WOS:001144077200001
DA 2024-12-25
ER

PT J
AU Chan, E
   Gore, KN
   Jiang, E
AF Chan, Elizabeth
   Gore, Kiran Nasir
   Jiang, Eliza
TI HARNESSING ARTIFICIAL INTELLIGENCE IN INTERNATIONAL ARBITRATION PRACTICE
SO CONTEMPORARY ASIA ARBITRATION JOURNAL
LA English
DT Article
DE generative artificial intelligence in arbitration; artificially
   intelligent legal technology use cases in arbitration; artificial
   intelligence practical applications in arbitration; large language
   models' use in arbitration; artificial intelligence regulation in
   arbitration; future applications of artificial intelligence in
   arbitration practice
AB Since the beginning of 2023, generative artificial intelligence (hereinafter "Generative AI") in the form of large language models (LLMs) like ChatGPT-4 has taken the world by storm. Legal practice is no exception. Among other stories, worldwide headlines have featured the fact that ChatGPT-4 is capable of passing the New York Bar Exam, that courts are adopting Generative AI in their decision-making, and that a New York lawyer has been sanctioned by a judge for relying upon non-existent case law precedent that he obtained from ChatGPT-4 and did not double-check.
   Yet, putting aside these newsworthy developments, tools powered by other forms of artificial intelligence (hereinafter "AI") have already been relied upon in legal practice for many years. This article introduces how AI supports successful international arbitration practice, including uses and methods that are already available and those that are anticipated to become helpful. This article also addresses the challenges and pitfalls that accompany these opportunities. Overall, this article concludes that the brave new world of AI in international arbitration is an exciting one that, through careful and thoughtful deployment of best practices, can add significant value to international arbitration teams in the decades to come.
C1 [Chan, Elizabeth; Gore, Kiran Nasir; Jiang, Eliza] George Washington Univ, Law, Law Sch, Washington, DC 20052 USA.
C3 George Washington University
RP Chan, E (corresponding author), George Washington Univ, Law, Law Sch, Washington, DC 20052 USA.
RI Gore, Kiran/JMR-1003-2023
CR Alloghani M., 2020, A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science, P3, DOI [10.1007/, 10.1007/978-3-030-22475-21, DOI 10.1007/978-3-030-22475-21, 10.1007/978-3-030-22475-2_1, 10.1007/978-3-030-22475-2]
   [Anonymous], WESTLAW
   [Anonymous], Dispute Resolution Office Consultation Document on Uniform International Commercial Mediation Act
   [Anonymous], 2023, MIT COMPUTATIONAL LAW REPORT
   [Anonymous], Arbitrator Research Tool
   [Anonymous], Kluwer Arbitration
   [Anonymous], 2023, CHINA DAILYSeptember 1
   [Anonymous], 2023, Dentons to Launch Client Secure Version of ChatGPT
   [Anonymous], 2023, THE ECONOMISTJune 6
   [Anonymous], 2023, YouTube
   [Anonymous], Practical Tools
   [Anonymous], 2023, Guidelines on the Use of Artificial Intelligence in Arbitration: Draft of 31 August 2023
   [Anonymous], 2023, REUTERSSeptember 27
   [Anonymous], AI and the Rule of Law: Capacity Building for Judicial Systems
   [Anonymous], 2023, Stanf. Inst. Hum. -centered Artif. Intell
   Aponyi Andras, 2021, TAUS: THE LANGUAGE DATA NETWORK
   ARBILEX, ABOUT US
   ARBITRATION INTELLIGENCE, ABOUT US
   Arkoudas K, 2023, Arxiv, DOI arXiv:2308.03762
   Armstrong Kathryn, 2023, BBC27 May
   Attard Simon, 2023, MEDIUMMarch 23
   Cartwright-Finch Ula, 2023, CORTEX CAPITAL, V1
   CASETEXT, ABOUT US
   Chan Elizabeth., The Hong Kong Law Society Young Solicitors' Group Think Tank Competition 2023: Regulating AI in the Legal Profession, Submission for the Hong Kong Lawyer
   Chan KV, 2023, J INT ARBITR, V40, P521
   Charlotin Damien, 2023, LARGE LANGUAGE MODEL
   Cole Jonathan, 2023, LAW360May 26
   Corfield Daniel, AI for Speech Recognition and Transcription in Law and Legal
   Deckard Rick, 2023, Generative AI in the Law: Where Could This All Be Headed?, P1
   DeepL, US
   DISCO, About us
   Efstathiou Stefanie G., 2023, KLUWER ARBITRATION BLOGJuly 22
   EVERLAW, ABOUT US
   Fadel Aline Tanielian, 2021, AMERICAN REVIEW OF INTERNATIONAL ARBITRATION
   FATHOM, ABOUT US
   Flynn Shannon, 2021, LAW TECHNOLOGY TODAYJune 9
   Gareth, 2023, THE TELEGRAPHSeptember 14
   HARVEY, ABOUT US
   Honorable Starr Brantley, Mandatory Certification Regarding Gerenative Artificial Intelligence [Standing Order] (North District Texas)
   JUS CONNECT, ABOUT US
   JUS MUNDI, ABOUT US
   Katz D. M., 2023, GPT-4 Passes the Bar Exam
   LAWDIFY, ABOUT US
   LEX MACHINA, ABOUT US
   LExisNExis, About us
   LUMINANCE, ABOUT US
   Merken Sara, 2023, REUTERSApril 27
   Namjoshi Nikita, Understanding and Applying Text Embeddings
   NEW ERA ADR, About us
   OTTER.AI, ABOUT US
   Pereira Fernanda Romero G., 2023, CIARB NEWSMay 10
   Pichai Sundar, 2023, Google Blog
   Prithiv S, 2022, Legal OCR for Processing Legal Documents
   RELATIVITY, ABOUT US
   Simson Caroline, 2023, LAW360April 5
   Singh Chauhan Aditya, 2020, KLUWER ARBITRATION BLOGSeptember 26
   Socha George, 2017, JUDICATURE, V101, P10
   Susskind R., 2023, Tomorrow's lawyers: An introduction to your future
   The Tea on International Arbitration, 2023, Silicon Valley Weighs in on AI in International Arbitration
   Thomsen Jacqueline, 2023, REUTERSJune 3
   United States District Court North District Texas Dallas Division, Template Certificate Regarding Judge Specific Requirements
   Vonau Manuel, 2023, ANDROID POLICENovember 4
   Wagh Rupali, 2013, INTERNATIONAL JOURNAL COMPUTER APPLICATIONS, V66, P32
   Weiser B., 2023, The New York Times
   Zekos G. I., 2022, Advanced artificial intelligence and robo-justice
   Zhang Aston, 2023, DIVE INTO DEEP LEARNING, P745
NR 66
TC 0
Z9 0
U1 5
U2 10
PU ASIAN CENTER WTO & INT HEALTH LAW & POLICY, COLL LAW
PI TAIPEI
PA NATL TAIWAN UNIV-ACWH, NO 1, SEC 4, ROOSEVELT RD, TAIPEI, 106, TAIWAN
SN 1999-9747
J9 CONTEMP ASIA ARBITAT
JI Contemp. Asia Arbitat. J.
PY 2023
VL 16
IS 2
BP 263
EP 299
PG 37
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA Z3SJ9
UT WOS:001111303600003
DA 2024-12-25
ER

PT J
AU Dornburg, A
   Davin, KJ
AF Dornburg, Alex
   Davin, Kristin J.
TI ChatGPT in foreign language lesson plan creation: Trends, variability,
   and historical biases
SO RECALL
LA English
DT Article; Early Access
DE ChatGPT; generative artificial intelligence; zero-shot prompting; AI
   literacy; lesson planning
ID ASSEMBLAGES
AB The advent of generative artificial intelligence (AI) models holds potential for aiding teachers in the generation of pedagogical materials. However, numerous knowledge gaps concerning the behavior of these models obfuscate the generation of research-informed guidance for their effective usage. Here, we assess trends in prompt specificity, variability, and weaknesses in foreign language teacher lesson plans generated by zero-shot prompting in ChatGPT. Iterating a series of prompts that increased in complexity, we found that output lesson plans were generally high quality, though additional context and specificity to a prompt did not guarantee a concomitant increase in quality. Additionally, we observed extreme cases of variability in outputs generated by the same prompt. In many cases, this variability reflected a conflict between outdated (e.g. reciting scripted dialogues) and more current research-based pedagogical practices (e.g. a focus on communication). These results suggest that the training of generative AI models on classic texts concerning pedagogical practices may bias generated content toward teaching practices that have been long refuted by research. Collectively, our results offer immediate translational implications for practicing and training foreign language teachers on the use of AI tools. More broadly, these findings highlight trends in generative AI output that have implications for the development of pedagogical materials across a diversity of content areas.
C1 [Dornburg, Alex; Davin, Kristin J.] Univ N Carolina, Charlotte, NC 28223 USA.
C3 University of North Carolina; University of North Carolina Charlotte
RP Dornburg, A (corresponding author), Univ N Carolina, Charlotte, NC 28223 USA.
EM adornbur@charlotte.edu; kdavin@charlotte.edu
CR ACTFL, 2012, ACTFL proficiency guidelines
   Ai HY, 2017, RECALL, V29, P313, DOI 10.1017/S095834401700012X
   Anderson MJ, 2001, AUSTRAL ECOL, V26, P32, DOI 10.1046/j.1442-9993.2001.01070.x
   Ateia S, 2023, Arxiv, DOI [arXiv:2306.16108, 10.48550/ARXIV.2306.16108, DOI 10.48550/ARXIV.2306.16108, DOI 10.48550/ARXIV:2306.16108]
   Brown TB, 2020, Arxiv, DOI [arXiv:2005.14165, 10.48550/arXiv.2005.14165]
   Basic Z, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02269-7
   Bekou A., 2024, Focus on ELT Journal, V6, P87, DOI [DOI 10.14744/FELT.6.1.7, 10.14744/felt.6.1.7]
   BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x
   Bozkurt A., 2023, ASIAN J DISTANCE ED, V18, pi, DOI [DOI 10.5281/ZENODO.8174941, 10.4018/979-8-3693-1351-0]
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Chapman MG, 1999, MAR ECOL PROG SER, V180, P257, DOI 10.3354/meps180257
   Chowdhary K., 2020, Fundamentals of artificial intelligence, P603, DOI [DOI 10.1007/978-81-322-3972-719, DOI 10.1007/978-81-322-3972-7_19]
   CLARKE KR, 1993, AUST J ECOL, V18, P117, DOI 10.1111/j.1442-9993.1993.tb00438.x
   Corp A., 2023, The Texas Forum of Teacher Education, V14, P116
   Davin KJ, 2024, MOD LANG J, V108, P513, DOI 10.1111/modl.12925
   Davis J, 2024, JMIR HUM FACTORS, V11, DOI 10.2196/53559
   DellAcqua F. E., 2023, Working Paper No. 24-013., DOI [10.2139/ssrn.4573321, DOI 10.2139/SSRN.4573321]
   Dixon P, 2003, J VEG SCI, V14, P927, DOI 10.1111/j.1654-1103.2003.tb02228.x
   Dornburg A, 2016, B PEABODY MUS NAT HI, V57, P147
   Gao XS, 2024, MOD LANG J, V108, P556, DOI 10.1111/modl.12930
   Giray L, 2023, ANN BIOMED ENG, DOI 10.1007/s10439-023-03272-4
   Gu WS, 2023, Arxiv, DOI [arXiv:2303.15587, 10.48550/ARXIV.2303.15587, DOI 10.48550/ARXIV.2303.15587]
   Guichon N, 2024, MOD LANG J, V108, P563, DOI 10.1111/modl.12931
   Hao MH, 2019, FOR ECOSYST, V6, DOI 10.1186/s40663-019-0188-9
   Hatakeyama-Sato K, 2023, SCI TECH ADV MAT-MET, V3, DOI 10.1080/27660400.2023.2260300
   Heston T. F., 2023, International Medical Education, V2, P198, DOI [DOI 10.3390/IME2030019, https://doi.org/10.3390/ime2030019]
   Hildebrandt SA, 2014, FOREIGN LANG ANN, V47, P576, DOI 10.1111/flan.12117
   Hong, 2023, J ED TECHNOLOGY INNO, V5, pArticl, DOI [10.61414/jeti.v5i1.103, DOI 10.61414/JETI.V5I1.103]
   Hu DQ, 2024, INT J MED INFORM, V183, DOI 10.1016/j.ijmedinf.2023.105321
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Imran M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13605
   Jacobsen L. J., 2023, PREPRINT, DOI [10.31219/osf.io/cr257, DOI 10.31219/OSF.IO/CR257]
   Jalil S, 2023, IEEE ICST WORKSHOP, P430, DOI 10.1109/ICSTW58534.2023.00078
   Jonsson A., 2007, Educational Research Review, V2, P130, DOI [10.1016/j.edurev.2007.05.0, DOI 10.1016/J.EDUREV.2007.05.002]
   Karaman MR, 2024, INT J TECHNOL EDUC, V7, P107, DOI 10.46328/ijte.607
   Kern R, 2024, MOD LANG J, V108, P515, DOI 10.1111/modl.12924
   Khattab O, 2023, Arxiv, DOI [arXiv:2310.03714, 10.48550/ARXIV.2310.03714, DOI 10.48550/ARXIV.2310.03714]
   Koc FS, 2024, RECALL, DOI 10.1017/S0958344024000168
   Kohnke L, 2023, RELC J, V54, P537, DOI 10.1177/00336882231162868
   Kojima T, 2022, Arxiv, DOI arXiv:2205.11916
   Koraishi O., 2023, Language Education Technology, V3, P55
   Kramsch C., 1991, Foreign language research in cross-cultural perspective, P217, DOI DOI 10.1075/SIBIL.2.21KRA
   Krause D. S., 2023, Proper generative AI prompting for financial analysis, DOI DOI 10.2139/SSRN.4453664
   Lee UG, 2024, EDUC INF TECHNOL, V29, P11483, DOI 10.1007/s10639-023-12249-8
   Li B, 2023, LANGUAGES-BASEL, V8, DOI 10.3390/languages8030197
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Lubiana T, 2023, PLOS COMPUT BIOL, V19, DOI 10.1371/journal.pcbi.1011319
   Lusin N., 2023, Enrollments in languages other than English in US institutions of higher education, fall 2021
   Mason S., 2017, NECTFL Review, V80, P47
   Meskó B, 2023, J MED INTERNET RES, V25, DOI 10.2196/50638
   OpenAI, 2024, ChatGPT v4 (Mar 14 version) Large language model
   Ouyang SY, 2024, Arxiv, DOI [arXiv:2308.02828, 10.48550/ARXIV.2308.02828, DOI 10.48550/ARXIV.2308.02828]
   Pettit E., 2023, Scholars see dangerous precedent in West Virginia U.s plan to cut foreign languages
   Ricotta C, 2017, ECOL COMPLEX, V31, P201, DOI 10.1016/j.ecocom.2017.07.003
   Roe J, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02282-w
   Roumeliotis KI, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15060192
   Singh W, 2011, ICES J MAR SCI, V68, P189, DOI 10.1093/icesjms/fsq144
   Team RDC, 2009, R: A Language and Environment for Statistical Computing
   Thorne SL, 2024, MOD LANG J, V108, P567, DOI 10.1111/modl.12932
   Vijaya Sharma S., 2019, 2019 INT C MACH LEAR, P568, DOI [10.1109/COMITCon.2019.8862232, DOI 10.1109/COMITCON.2019.8862232, 10.1109/comitcon.2019.8862232]
   Wang XZ, 2022, Arxiv, DOI [arXiv:2203.11171, DOI 10.48550/ARXIV.2203.11171]
   Wei X, 2024, Arxiv, DOI [arXiv:2302.10205, 10.48550/arXiv.2302.10205]
   Wiggins J., 2005, Understanding by design
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Yao SY, 2023, Arxiv, DOI arXiv:2305.10601
   Yeadon Will, 2024, Physics Education, V59, DOI 10.1088/1361-6552/ad1fa2
NR 66
TC 0
Z9 0
U1 0
U2 0
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 0958-3440
EI 1474-0109
J9 RECALL
JI ReCALL
PD 2024 DEC 11
PY 2024
DI 10.1017/S0958344024000272
EA DEC 2024
PG 16
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA P5F8H
UT WOS:001378171600001
OA hybrid, Green Submitted
DA 2024-12-25
ER

PT J
AU Ironsi, CS
   Ironsi, SS
AF Ironsi, Chinaza Solomon
   Ironsi, Sarah Solomon
TI Experimental evidence for the efficacy of generative AI in improving
   students' writing skills
SO QUALITY ASSURANCE IN EDUCATION
LA English
DT Article; Early Access
DE Artificial intelligence; Generative AI; ChatGPT; Writing
ID ARTIFICIAL-INTELLIGENCE
AB PurposeGiven continued debates on the potentials of newly emerging artificial intelligence (AI) like generative AI (GenAI), this study aims to contribute to corporal studies by investigating the efficacy of GenAI in improving students writing skills.Design/methodology/approachA mixed-methods research design with an experimental approach was used to elicit information from 70 undergraduate students studying at a private university. A writing course was designed and used to elicit information from the participants on the efficacy of using ChatGPT in their writing instruction.FindingsAfter collecting data through experiments and interviews, the result indicates that although ChatGPT may assist students in providing ideas in writing lessons, it may not improve their overall writing skills.Originality/valueThis study provides empirical evidence limited to the scholarly literature on the role of ChatGPT in improving students' writing skills. This study adds to scholarly discussions on the potential of ChatGPT which has recently sparked debates in academia.
C1 [Ironsi, Chinaza Solomon] Akdeniz Karpaz Univ, Foreign Languages & English Preparatory Sch, Lefkosa, Turkiye.
   [Ironsi, Sarah Solomon] Near East Univ, Dept English Language Teaching, Nicosia, Turkiye.
C3 Near East University
RP Ironsi, CS (corresponding author), Akdeniz Karpaz Univ, Foreign Languages & English Preparatory Sch, Lefkosa, Turkiye.
EM solomon.chinaza@akun.edu.tr
RI Ironsi, Chinaza/GRI-9789-2022
OI Ironsi, Chinaza Solomon/0000-0001-8644-710X
FX The authors wish to acknowledge Dr Onuoha Onwukwe Ironsi, Mrs Sylvia
   Egeonu Ironsi, Mr & Mrs Peter Ekpenyong Igajah. Thank you immensely.
CR Adiguzel T, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13152
   Al-Zahrani AM, 2024, INNOV EDUC TEACH INT, V61, P1029, DOI 10.1080/14703297.2023.2271445
   AlAfnan M.A., 2023, J ARTIFICIAL INTELLI, V3, P60, DOI DOI 10.37965/JAIT.2023.0184
   Alshater M., 2022, EXPLORING ROLE ARTIF, DOI [10.2139/ssrn.4312358, DOI 10.2139/SSRN.4312358]
   Bartneck C, 2021, An introduction to ethics in robotics and AI, P61, DOI DOI 10.1007/978-3-030-51110-48
   Carroll J.M., 2009, Interaction Design Encyclopedia
   Clark KR, 2018, RADIOL TECHNOL, V90, P180
   Collie R.J., 2024, Computers and Education: Artificial Intelligence
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elbanna S., 2023, MANAGEMENT SUSTAINAB, DOI DOI 10.1108/MSAR-03-2023-0016
   Fang X., 2023, Education and Information Technologies, V29, P1
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Firat M., 2023, How chat GPT can transform autodidactic experiences and open education?
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gill S. S., 2023, Internet of Things and Cyber-Physical Systems, V3, P262, DOI DOI 10.1016/J.IOTCPS.2023.05.004
   Holmes W., 2023, Guidance for generative AI in education and research
   Ironsi C. S., 2022, Education+ Training, V65, P232
   Ironsi C. S., 2024, Facilitating globalcollaboration and knowledge sharing in higher education with generative AI, P162
   Jiang X, 2024, STUD EDUC EVAL, V83, DOI 10.1016/j.stueduc.2024.101385
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Lee P, 2023, NEW ENGL J MED, V388, P1233, DOI 10.1056/NEJMsr2214184
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   McKendrick J.H., 2020, INT ENCY HUMAN GEOGR, V1, P125, DOI DOI 10.1016/B978-0-08-102295-5.10291-4
   Mhlanga D, 2023, Fintech and Artificial Intelligence for Sustainable Development, DOI 10.2139/ssrn.4354422
   Murdoch B, 2021, BMC MED ETHICS, V22, DOI 10.1186/s12910-021-00687-3
   Murtaza M, 2022, IEEE ACCESS, V10, P81323, DOI 10.1109/ACCESS.2022.3193938
   Packer MJ, 2000, EDUC PSYCHOL-US, V35, P227, DOI 10.1207/S15326985EP3504_02
   Qadir J., 2023, TechRxiv, P1, DOI 10.36227/techrxiv.21789434.v1
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Rasul T., 2023, Journal of Applied Learning and Teaching, V6, P41, DOI [DOI 10.37074/JALT.2023.6.1.29, 10.37074/JALT.2023.6.1.29, 10.37074/jalt]
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Shidiq M, 2023, P INT C ED SOC HUM, V1, P353
   Siriwardhana Y, 2021, EUR CONF NETW COMMUN, P616, DOI [10.1109/EuCNC/6GSummit51104.2021.9482503, 10.1109/EUCNC/6GSUMMIT51104.2021.9482503]
   Soomro KA, 2020, INT J EDUC TECHNOL H, V17, DOI 10.1186/s41239-020-00191-5
   Tapalova O, 2022, ELECTRON J E-LEARN, V20, P639
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Yu H, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1183162
NR 41
TC 0
Z9 0
U1 45
U2 45
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0968-4883
EI 1758-7662
J9 QUAL ASSUR EDUC
JI QUALITY ASSURANCE EDUCATION
PD 2024 AUG 26
PY 2024
DI 10.1108/QAE-04-2024-0065
EA AUG 2024
PG 16
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D4M1D
UT WOS:001295929800001
DA 2024-12-25
ER

PT J
AU Lazaroiu, G
   Gedeon, T
   Valaskova, K
   Vrbka, J
   Suler, P
   Zvarikova, K
   Kramarova, K
   Rowland, Z
   Stehel, V
   Gajanova, L
   Horák, J
   Grupac, M
   Caha, Z
   Blazek, R
   Kovalova, E
   Nagy, M
AF Lazaroiu, George
   Gedeon, Tom
   Valaskova, Katarina
   Vrbka, Jaromir
   Suler, Petr
   Zvarikova, Katarina
   Kramarova, Katarina
   Rowland, Zuzana
   Stehel, Vojtech
   Gajanova, Lubica
   Horak, Jakub
   Grupac, Marian
   Caha, Zdenek
   Blazek, Roman
   Kovalova, Erika
   Nagy, Marek
TI Cognitive digital twin-based Internet of Robotic Things, multi-sensory
   extended reality and simulation modeling technologies, and generative
   artificial intelligence and cyber-physical manufacturing systems in the
   immersive industrial metaverse
SO EQUILIBRIUM-QUARTERLY JOURNAL OF ECONOMICS AND ECONOMIC POLICY
LA English
DT Article
DE cognitive digital twin; Internet of Robotic Things; extended reality;
   simulation modeling; generative artificial intelligence; cyber-physical
   manufacturing system; immersive industrial metaverse; sensor
ID INTEGRATION
AB Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly opera-tions in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes. Purpose of the article: We show that generative AI and cyber-physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing. Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox. Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber-physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber-physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber-physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.
C1 [Lazaroiu, George; Gedeon, Tom] Curtin Univ, Perth, Australia.
   [Lazaroiu, George] Toronto Metropolitan Univ, Toronto, ON, Canada.
   [Lazaroiu, George] Cardiff Metropolitan Univ, Cardiff, Wales.
   [Lazaroiu, George] Spiru Haret Univ, Bucharest, Romania.
   [Valaskova, Katarina; Zvarikova, Katarina; Kramarova, Katarina; Gajanova, Lubica; Grupac, Marian; Blazek, Roman; Kovalova, Erika] Univ Zilina, Zilina, Slovakia.
   [Vrbka, Jaromir; Suler, Petr; Rowland, Zuzana; Stehel, Vojtech; Horak, Jakub; Caha, Zdenek] Inst Technol & Business Ceske Budejovice, Ceske Budejovice, Czech Republic.
   [Nagy, Marek] Univ Econ Bratislava, Bratislava, Slovakia.
C3 Curtin University; Toronto Metropolitan University; Cardiff Metropolitan
   University; Spiru Haret University; University of Zilina; Institute of
   Technology & Business, Ceske Budejovice; University of Economics
   Bratislava
RP Lazaroiu, G (corresponding author), Curtin Univ, Perth, Australia.; Lazaroiu, G (corresponding author), Toronto Metropolitan Univ, Toronto, ON, Canada.; Lazaroiu, G (corresponding author), Cardiff Metropolitan Univ, Cardiff, Wales.; Lazaroiu, G (corresponding author), Spiru Haret Univ, Bucharest, Romania.
EM phd_lazaroiu@yahoo.com
RI Rowland, Zuzana/AAF-9592-2021; Caha, Zdeněk/AAN-2017-2020; Vrbka,
   Jaromir/AAH-4429-2019; Blazek, Roman/ABH-9458-2020; Lazaroiu,
   George/A-2262-2015; Gedeon, Tom/JQW-6232-2023; Stehel,
   Vojtech/AAL-1058-2021; Nagy, Marek/DEM-8067-2022
CR Agarwal A, 2023, TRANSFORM GOV-PEOPLE, V17, P688, DOI 10.1108/TG-03-2023-0036
   Aggogeri F, 2024, ROBOTICS, V13, DOI 10.3390/robotics13030042
   Amaizu GC, 2024, ICT EXPRESS, V10, P233, DOI 10.1016/j.icte.2024.02.010
   Anwar MS, 2024, APPL SOFT COMPUT, V159, DOI 10.1016/j.asoc.2024.111577
   Aromaa S, 2024, INT J HUM FACT ERGON, V11, DOI 10.1504/IJHFE.2024.137128
   Aung N, 2024, TSINGHUA SCI TECHNOL, V29, P795, DOI 10.26599/TST.2023.9010052
   Bellalouna Fahmi, 2023, Procedia CIRP, P638, DOI 10.1016/j.procir.2023.03.116
   Bhattacharya P, 2023, MATHEMATICS-BASEL, V11, DOI 10.3390/math11040941
   Cao J, 2023, IEEE WIREL COMMUN, V30, P40, DOI 10.1109/MWC.2001.2200396
   Carrión C, 2024, J SUPERCOMPUT, V80, P1598, DOI 10.1007/s11227-023-05544-1
   Chang Luyi, 2022, Journal of Communications and Information Networks, V7, P107, DOI 10.23919/JCIN.2022.9815195
   Chen C, 2023, J MANUF SYST, V71, P581, DOI 10.1016/j.jmsy.2023.10.010
   Chen CL, 2023, BIOMIMETICS-BASEL, V8, DOI 10.3390/biomimetics8040343
   Chen Yiqiang, 2023, International Journal of Crowd Science, P180, DOI 10.26599/IJCS.2023.9100028
   Chowdhury M, 2023, INT J AD HOC UBIQ CO, V44, P167, DOI 10.1504/IJAHUC.2023.134763
   Cui ZX, 2023, J MANUF SYST, V70, P264, DOI 10.1016/j.jmsy.2023.07.016
   Dzedzickis A, 2024, J INTELL MANUF, DOI 10.1007/s10845-024-02362-x
   Endres H, 2024, IND MARKET MANAG, V119, P90, DOI 10.1016/j.indmarman.2024.03.006
   Erman B, 2023, IEEE NETWORK, V37, P286, DOI 10.1109/MNET.002.2200515
   Fabra L., 2024, Application of Neural Radiance Fields (NeRFs) for 3D model representation in the industrial metaverse, DOI [10.3390/app14051825, DOI 10.3390/APP14051825]
   Ferrari F, 2023, DISTINKTION, V24, P338, DOI 10.1080/1600910X.2022.2137546
   Gattullo M, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app122412600
   Ghobakhloo M, 2024, ASIA-PAC J BUS ADM, DOI 10.1108/APJBA-08-2023-0384
   Grieves M, 2023, MACHINES, V11, DOI 10.3390/machines11080808
   Hajian A., 2024, Technological Forecasting and Social Change, DOI [10.1016/j.techfore.2024.123224.201,123224, DOI 10.1016/J.TECHFORE.2024.123224.201,123224]
   Hong Y., 2024, Human cognition modeling for the metaverse-oriented design system, DOI [10.1109/MNET.2024.3377909.Network, DOI 10.1109/MNET.2024.3377909.NETWORK]
   Hou XW, 2024, IEEE J SEL AREA COMM, V42, P850, DOI 10.1109/JSAC.2023.3345393
   Jagatheesaperumal SK, 2023, IEEE WIREL COMMUN, V30, P38, DOI 10.1109/MWC.003.2200616
   Jagatheesaperumal SK, 2022, IT PROF, V24, P34, DOI 10.1109/MITP.2022.3225064
   Jaimini U, 2022, IEEE INTERNET COMPUT, V26, P59, DOI 10.1109/MIC.2022.3212085
   Jauhiainen JS, 2024, INT J INNOV STUD, V8, P262, DOI 10.1016/j.ijis.2024.04.004
   Kaarlela T, 2023, ACTUATORS, V12, DOI 10.3390/act12060219
   Kaarlela T, 2023, MACHINES, V11, DOI 10.3390/machines11010013
   Kaigom EG, 2024, IEEE T IND INFORM, V20, P5725, DOI 10.1109/TII.2023.3337380
   Keegan BJ, 2024, BUS HORIZONS, V67, P107, DOI 10.1016/j.bushor.2023.09.002
   Kshetri N, 2023, CENT EUR MANAG J, V31, P511, DOI 10.1108/CEMJ-08-2023-0336
   Kumar A, 2024, J RETAIL CONSUM SERV, V78, DOI 10.1016/j.jretconser.2024.103767
   Kuo HT, 2024, TRANSPORT RES E-LOG, V185, DOI 10.1016/j.tre.2024.103496
   Laviola E, 2022, INT J ADV MANUF TECH, V119, P1769, DOI 10.1007/s00170-021-08449-6
   Lee J, 2022, MANUF LETT, V34, P12, DOI 10.1016/j.mfglet.2022.08.012
   Li X, 2023, IEEE T SYST MAN CY-S, V53, P2148, DOI 10.1109/TSMC.2022.3228594
   Liu SG, 2023, DISPLAYS, V79, DOI 10.1016/j.displa.2023.102463
   Lyu Z, 2024, J ADV RES, V66, P31, DOI 10.1016/j.jare.2023.11.019
   Magalhaes LC, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app12199864
   Mahmoud KH, 2024, AUTOMATION-BASEL, V5, P13, DOI 10.3390/automation5010002
   Mancuso I, 2024, BUS HORIZONS, V67, P331, DOI 10.1016/j.bushor.2024.04.005
   Martínez-Gutiérrez A, 2024, ROBOT CIM-INT MANUF, V89, DOI 10.1016/j.rcim.2024.102764
   Meng Z, 2024, IEEE J SEL AREA COMM, V42, P752, DOI 10.1109/JSAC.2023.3345398
   Mourad N, 2024, IEEE T CONSUM ELECTR, V70, P3212, DOI 10.1109/TCE.2023.3326047
   Nagy M, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13031681
   Negri E, 2023, IFAC PAPERSONLINE, V56, P6351, DOI 10.1016/j.ifacol.2023.10.818
   Ooi KB, 2024, IEEE T ENG MANAGE, V71, P13882, DOI 10.1109/TEM.2023.3307562
   Patterson EA, 2024, J STRAIN ANAL ENG, V59, P303, DOI 10.1177/03093247241233325
   Qu Q, 2024, FUTURE INTERNET, V16, DOI 10.3390/fi16020060
   Ren L, 2024, IEEE T CYBERNETICS, V54, P2683, DOI 10.1109/TCYB.2024.3372591
   Sai S, 2024, IEEE T CONSUM ELECTR, V70, P3194, DOI 10.1109/TCE.2024.3351441
   Sarwatt DS, 2024, IEEE T INTELL TRANSP, V25, P6290, DOI 10.1109/TITS.2023.3347280
   Starly B, 2023, MANUF LETT, V37, P50, DOI 10.1016/j.mfglet.2023.07.021
   Stary C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su152216062
   Stavroulakis GE, 2022, APPL SCI-BASEL, V12, DOI 10.3390/app122311997
   Tantawi K, 2024, INT J INTERACT DES M, DOI 10.1007/s12008-024-01750-0
   Tlili A, 2023, SERV IND J, V43, P260, DOI 10.1080/02642069.2023.2178644
   Tu XY, 2023, FRONT VIRTUAL REAL, V4, DOI 10.3389/frvir.2023.1019080
   Tuli EA, 2024, IEEE ACCESS, V12, P29995, DOI 10.1109/ACCESS.2024.3366527
   Wang X, 2024, INFORM FUSION, V107, DOI 10.1016/j.inffus.2024.102321
   Wang YT, 2022, IEEE-CAA J AUTOMATIC, V9, P2071, DOI 10.1109/JAS.2022.106091
   Yang J, 2022, IEEE-CAA J AUTOMATIC, V9, P2063, DOI 10.1109/JAS.2022.106097
   Yao XF, 2024, J INTELL MANUF, V35, P235, DOI 10.1007/s10845-022-02027-7
   Zaidan AA, 2023, IEEE SYST J, V17, P5303, DOI 10.1109/JSYST.2023.3266842
   Zhang LK, 2023, ELECTRONICS-SWITZ, V12, DOI 10.3390/electronics12173651
   Zheng T, 2024, INT J PROD RES, V62, P8022, DOI 10.1080/00207543.2024.2330631
NR 71
TC 0
Z9 0
U1 6
U2 6
PU INST ECONOMIC RESEARCH-POLAND
PI OLSZTYN
PA Ul Ks. Roberta Bilitewskiego, Nr 5, Lok 19, OLSZTYN, POLAND
SN 1689-765X
EI 2353-3293
J9 EQUILIBRIUM
JI Equilibrium
PD SEP
PY 2024
VL 19
IS 3
BP 719
EP 748
DI 10.24136/eq.3131
PG 30
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA O0L6B
UT WOS:001368147400001
OA gold
DA 2024-12-25
ER

PT J
AU Franceschelli, G
   Musolesi, M
AF Franceschelli, Giorgio
   Musolesi, Mirco
TI Reinforcement Learning for Generative AI: State of the Art,
   Opportunities and Open Research Challenges
SO JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
LA English
DT Article
AB Generative Artificial Intelligence (AI) is one of the most exciting developments in Computer Science of the last decade. At the same time, Reinforcement Learning (RL) has emerged as a very successful paradigm for a variety of machine learning tasks. In this survey, we discuss the state of the art, opportunities and open research questions in applying RL to generative AI. In particular, we will discuss three types of applications, namely, RL as an alternative way for generation without specified objectives; as a way for generating outputs while concurrently maximizing an objective function; and, finally, as a way of embedding desired characteristics, which cannot be easily captured by means of an objective function, into the generative process. We conclude the survey with an in-depth discussion of the opportunities and challenges in this fascinating emerging area.
C1 [Franceschelli, Giorgio; Musolesi, Mirco] Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy.
   [Musolesi, Mirco] UCL, Dept Comp Sci, London, England.
C3 University of Bologna; University of London; University College London
RP Franceschelli, G (corresponding author), Univ Bologna, Dept Comp Sci & Engn, Bologna, Italy.
EM GIORGIO.FRANCESCHELLI@UNIBO.IT; M.MUSOLESI@UCL.AC.UK
RI Franceschelli, Giorgio/LWJ-9430-2024; Musolesi, Mirco/C-9329-2014
OI Franceschelli, Giorgio/0000-0003-3210-3015; Musolesi,
   Mirco/0000-0001-9712-4090
CR Ammanabrolu P, 2020, P 8 INT C LEARN REPR
   Anil GTGR, 2023, Arxiv, DOI [arXiv:2312.11805, 10.48550/arXiv.2312.11805]
   [Anonymous], 1984, Temporal credit assignment in reinforcement learning
   [Anonymous], 2016, J Vision, DOI [DOI 10.1167/16.12.326, 10.1167/16.12.326, DOI 10.1109/CVPR.2016.265]
   [Anonymous], 2018, P 6 INT C LEARN REPR
   Anthropic, 2023, Report
   Atance SR, 2022, J CHEM INF MODEL, V62, P4863, DOI 10.1021/acs.jcim.2c00838
   Aubret A, 2019, Arxiv, DOI arXiv:1908.06976
   Bachman P, 2015, ADV NEUR IN, V28
   Bahdanau Dzmitry, 2017, P ICLR
   Baheti A, 2024, Arxiv, DOI arXiv:2305.14718
   Bai Y., 2022, arXiv
   Bai YT, 2022, Arxiv, DOI arXiv:2212.08073
   Black Kevin, 2023, ICML 23 WORKSH EFF S
   Blaschke T, 2020, J CHEM INF MODEL, V60, P5918, DOI 10.1021/acs.jcim.0c00915
   Bohm F., 2019, P 2019 C EMP METH NA
   Bommasani R., 2021, arXiv
   Brown T. B., 2020, ARXIV200514165
   Casper S, 2023, Arxiv, DOI [arXiv:2307.15217, 10.48550/arXiv.2307.15217]
   Ceron J. S. O., 2021, P 38 INT C MACH LEAR
   Chaganty AT, 2018, PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL), VOL 1, P643
   Cho K., 2014, ARXIV, DOI 10.3115/v1/w14-4012
   Cho W. S., 2019, P NAACL 19 WORKSH NA
   Choshen L., 2020, P 8 INT C LEARN REPR
   Christiano PF, 2017, ADV NEUR IN, V30
   Christies, 2018, Is artificial intelligence set to become art's next medium?
   De Cao N., 2018, P ICML 18 WORKSH THE
   Deshpande A., 2023, FINDINGS ASS COMPUTA
   Dodge J, 2020, Arxiv, DOI arXiv:2002.06305
   Dong YH, 2024, Arxiv, DOI [arXiv:2304.07590, DOI 10.48550/ARXIV.2304.07590]
   Du YL, 2023, Arxiv, DOI arXiv:2305.14325
   Fan Y, 2023, Arxiv, DOI arXiv:2305.16381
   Feffer M., 2023, P 37 AAAI C ART INT
   Fernandes P, 2023, Arxiv, DOI arXiv:2305.00955
   Foster D, 2023, Generative Deep Learning, V2nd
   Franceschelli G, 2024, Arxiv, DOI arXiv:2304.00008
   Franceschelli G, 2024, Arxiv, DOI arXiv:2104.02726
   Franceschelli G, 2022, DATA POLICY, V4, DOI 10.1017/dap.2022.10
   Gao Yifan, 2019, P 28 INT JOINT C ART
   Gaudin T., 2019, 2 NIPS 19 WORKSH MAC
   Glaese Amelia, 2022, arXiv
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Goodhart C., 1975, Pap. Monet. Econ, P1
   Guimaraes GL, 2018, Arxiv, DOI arXiv:1705.10843
   Guo JX, 2018, AAAI CONF ARTIF INTE, P5141
   Ha D., 2018, Advances in Neural Information Processing Systems, DOI [10.5281/zenodo.1207631, DOI 10.5281/ZENODO.1207631]
   Haarnoja T, 2019, Arxiv, DOI arXiv:1812.05905
   Hadfield-Menell D, 2016, ADV NEUR IN, V29
   Hafner D., 2020, INT C LEARNING REPRE
   Han Z., 2020, P 2020 DIGITALFUTURE
   Hao Y, 2023, P 37 C NEUR INF PROC
   Henderson P, 2023, Arxiv, DOI [arXiv:2303.15715, 10.48550/arXiv.2303.15715]
   Ho J., 2020, ADV NEURAL INFORM PR, V33, P6840
   Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
   Holtzman Ari., 2020, 8 INT C LEARN REPR I
   Huang AL, 2016, Arxiv, DOI [arXiv:1606.04930, 10.48550/arXiv.1606.04930, DOI 10.48550/ARXIV.1606.04930]
   Huang ZW, 2019, IEEE I CONF COMP VIS, P8708, DOI 10.1109/ICCV.2019.00880
   Gulrajani I, 2017, ADV NEUR IN, V30
   Jaques N, 2016, NIPS 16 WORKSH DEEP
   Jaques N, 2017, ICLR 17 WORKSH
   Jaques N, 2017, PR MACH LEARN RES, V70
   Jaques N, 2020, PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), P3985
   Jiang N, 2020, P 34 AAAI C ART INT
   Kandasamy K, 2017, P 5 INT C LEARN REPR
   Kiegeland S, 2021, 2021 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL-HLT 2021), P1673
   Kingma DP., 2014, PREPRINT
   Krenn M, 2020, MACH LEARN-SCI TECHN, V1, DOI 10.1088/2632-2153/aba947
   Kreutzer J, 2018, P 2018 C N AM CHAPT, V3
   Lagutin E, 2021, 16TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2021), P1432
   Lamprier S, 2022, PR MACH LEARN RES
   Lavie A., 2007, P 2 WORKSH STAT MACH, P228
   Lazaridis A, 2020, J ARTIF INTELL RES, V69, P1421
   Le Hung, 2022, Advances in Neural Information Processing Systems
   Lee G., 2023, P IEEE CVF INT C COM
   Lee H, 2024, Arxiv, DOI [arXiv:2309.00267, 10.48550/arXiv.2309.00267]
   Lee KM, 2023, Arxiv, DOI arXiv:2302.12192
   Leike J, 2018, Arxiv, DOI arXiv:1811.07871
   Li C., 2022, P 31 INT JOINT C ART
   Li J, 2016, P 2016 C EMP METH NA, P1192, DOI [10.18653/v1/D16-1127, DOI 10.18653/V1/D16-1127]
   Li Jiwei, 2017, P 2017 C EMP METH NA
   Li Yujia, 2018, ARXIV180303324
   Li ZM, 2019, AAAI CONF ARTIF INTE, P6722
   Lillicrap T.P., 2016, ICLR POSTER
   Lin C.-Y., 2004, TEXT SUMMARIZATION B, P74, DOI DOI 10.1253/JCJ.34.1213
   Linke C, 2020, J ARTIF INTELL RES, V69, P1287
   Liu Jiacheng, 2022, P 2022 C EMP METH NA
   Liu VV, 2022, PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22), DOI 10.1145/3491102.3501825
   Lu Ximing, 2022, Advances in Neural Information Processing Systems
   Ziegler DM, 2020, Arxiv, DOI arXiv:1909.08593
   Martin A., 2022, P 2022 C N AM CHAPT
   Mercado R, 2021, MACH LEARN-SCI TECHN, V2, DOI 10.1088/2632-2153/abcf91
   Mnih V., 2013, NIPS 16 WORKSH DEEP
   Mnih V, 2016, PR MACH LEARN RES, V48
   Nardo C, 2023, The Waluigi Effect (mega-post)
   Ng AY., 2000, P 17 INT C MACH LEAR, VVol. 1, P663
   Nguyen Kim Anh, 2017, P 2017 C EMP METH NA
   Nguyen PCH, 2022, SCI REP-UK, V12, DOI 10.1038/s41598-022-12845-7
   Olivecrona M, 2017, J CHEMINFORMATICS, V9, DOI 10.1186/s13321-017-0235-x
   OpenAI, 2022, INTR CHATGPT
   OpenAI, 2023, GPT-4 technical report.
   Ouyang L, 2022, ADV NEUR IN
   Pang R. Y., 2021, P 9 INT C LEARN REPR
   Papineni K, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P311, DOI 10.3115/1073083.1073135
   Pardinas R, 2023, P AAAI 23 WORKSH CRE
   Pasunuru Ramakanth, 2018, P 2018 C N AM CHAPT, V2
   Pateria S, 2021, ACM COMPUT SURV, V54, DOI 10.1145/3453160
   Pathak D, 2017, IEEE COMPUT SOC CONF, P488, DOI 10.1109/CVPRW.2017.70
   Paulus R., 2018, P INT C LEARN REPR I
   Peng B., 2017, P 2017 C EMP METH NA
   Popova M, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aap7885
   Post Matt, 2018, P 3 C MACHINE TRANSL, P186, DOI DOI 10.18653/V1/W18-6319
   Precup D., 2000, Computer Science Department Faculty Publication Series, P759
   Radford A., 2019, OPENAI BLOG
   Rafailov R, 2024, Arxiv, DOI arXiv:2305.18290
   Ramamurthy R, 2023, P 11 INT C LEARN REP
   Ramesh A., 2022, arXiv
   Ramesh A, 2021, PR MACH LEARN RES, V139
   Ranzato M., 2016, ICLR
   Rennie SJ, 2017, PROC CVPR IEEE, P1179, DOI 10.1109/CVPR.2017.131
   Rezende DJ, 2014, PR MACH LEARN RES, V32, P1278
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Schaul T, 2016, P 33 INT C MACH LEAR
   Schuhmann Christoph, 2022, LAION AESTHETICS
   Schull J, 2015, ASSETS'15: PROCEEDINGS OF THE 17TH INTERNATIONAL ACM SIGACCESS CONFERENCE ON COMPUTERS & ACCESSIBILITY, P1, DOI 10.1145/2700648.2809870
   Schulman J, 2017, Arxiv, DOI [arXiv:1707.06347, 10.48550/arXiv.1707.06347, DOI 10.48550/ARXIV.1707.06347]
   Schulman J, 2015, PR MACH LEARN RES, V37, P1889
   Scialom T, 2020, P 34 INT C NEUR INF
   Sellam Thibault, 2020, P 58 ANN M ASS COMP
   Shi Z, 2018, PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P4361
   Shinn N, 2023, Arxiv, DOI arXiv:2303.11366
   Shojaee P, 2023, Transactions on Machine Learning Research
   Singh J, 2021, PROC CVPR IEEE, P16382, DOI 10.1109/CVPR46437.2021.01612
   Singh Jaskirat, 2022, P 17 EUR C COMP VIS
   Singh Satinder, 2004, Advances in Neural Information Processing Systems
   Skalse Joar Max Viktor, 2022, Advances in Neural Information Processing Systems
   Small CT, 2021, RECERCA, V26, DOI 10.6035/recerca.5516
   Snell Charlie V., 2023, P 11 INT C LEARN REP
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Song FF, 2024, Arxiv, DOI arXiv:2306.17492
   Stiennon N, 2020, Advances in Neural Information Processing Systems
   Strobelt Hendrik, 2023, IEEE Trans Vis Comput Graph, V29, P1146, DOI 10.1109/TVCG.2022.3209479
   Su PH, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1, P2431
   Sutskever I, 2014, ADV NEUR IN, V27
   Sutton RS, 2018, ADAPT COMPUT MACH LE, P1
   Tambwekar P, 2019, PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P5982
   Thiede LA, 2022, MACH LEARN-SCI TECHN, V3, DOI 10.1088/2632-2153/ac7ddc
   Thoppilan R., 2022, arXiv
   Tien J., 2023, P 11 INT C LEARN REP
   Touvron H, 2023, Arxiv, DOI [arXiv:2307.09288, 10.48550/arXiv.2307.09288]
   van Hasselt H, 2016, AAAI CONF ARTIF INTE, P2094
   Vanhaelen Q, 2020, ACS MED CHEM LETT, V11, P1496, DOI 10.1021/acsmedchemlett.0c00088
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang ZY, 2016, PR MACH LEARN RES, V48
   WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005
   Whittlestone J, 2021, J ARTIF INTELL RES, V70, P1003
   WILLIAMS RJ, 1992, MACH LEARN, V8, P229, DOI 10.1007/BF00992696
   Williams RJ, 1989, NEURAL COMPUT, V1, P270, DOI 10.1162/neco.1989.1.2.270
   Wolf Y, 2024, Arxiv, DOI arXiv:2304.11082
   Wu J., 2021, arXiv
   Wu YH, 2016, Arxiv, DOI arXiv:1609.08144
   Wu YX, 2018, AAAI CONF ARTIF INTE, P5602
   Xie N., 2012, P 29 INT C MACH LEAR
   Xu JZ, 2023, Arxiv, DOI [arXiv:2304.05977, 10.48550/arXiv.2304.05977]
   Yi XY, 2018, 2018 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2018), P3143
   You JX, 2018, ADV NEUR IN, V31
   Young S, 2013, P IEEE, V101, P1160, DOI 10.1109/JPROC.2012.2225812
   Yu L, 2017, PROCEEDINGS OF THE ASME 36TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, 2017, VOL 3A
   Zhang K., 2021, HDB REINFORCEMENT LE, P321
   Zhang T., 2020, 8 INT C LEARNING REP
   Zhavoronkov A, 2019, NAT BIOTECHNOL, V37, P1038, DOI 10.1038/s41587-019-0224-x
   Zhou L., 2017, P NIPS 17 WORKSH CON
   Zhou T, 2018, Arxiv, DOI arXiv:1810.05977
NR 172
TC 6
Z9 7
U1 8
U2 17
PU AI ACCESS FOUNDATION
PI MARINA DEL REY
PA USC INFORMATION SCIENCES INST, 4676 ADMIRALITY WAY, MARINA DEL REY, CA
   90292-6695 USA
SN 1076-9757
EI 1943-5037
J9 J ARTIF INTELL RES
JI J. Artif. Intell. Res.
PY 2024
VL 79
BP 417
EP 446
PG 30
WC Computer Science, Artificial Intelligence
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA IS4L8
UT WOS:001168307300001
DA 2024-12-25
ER

PT J
AU Hmoud, M
   Swaity, H
   Hamad, N
   Karram, O
   Daher, W
   Troussas, C
   Krouska, A
   Mylonas, P
   Kabassi, K
   Caro, J
   Sgouropoulou, C
AF Hmoud, Mohammad
   Swaity, Hadeel
   Hamad, Nardin
   Karram, Omar
   Daher, Wajeeh
   Troussas, Christos
   Krouska, Akrivi
   Mylonas, Phivos
   Kabassi, Katerina
   Caro, Jaime
   Sgouropoulou, Cleo
TI Higher Education Students' Task Motivation in the Generative Artificial
   Intelligence Context: The Case of ChatGPT
SO INFORMATION
LA English
DT Article
DE artificial intelligence; task motivation; ChatGPT; higher education
ID SELF-DETERMINATION THEORY; ACHIEVEMENT; SYSTEMS
AB Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students' learning, such as the motivational aspect. The present research intended to investigate the characteristics of students' task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students' motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students' motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students' motivation to learn and thus raise their learning achievement.
C1 [Hmoud, Mohammad; Swaity, Hadeel; Hamad, Nardin; Karram, Omar; Daher, Wajeeh] Annajah Natl Univ, Fac Educ Sci, Nablus 9992200, Palestine.
   [Daher, Wajeeh] Al Qasemi Acad Coll Educ, Fac Sci, IL-3010000 Baqa, Israel.
RP Daher, W (corresponding author), Annajah Natl Univ, Fac Educ Sci, Nablus 9992200, Palestine.; Daher, W (corresponding author), Al Qasemi Acad Coll Educ, Fac Sci, IL-3010000 Baqa, Israel.
EM hmoud.tech@gmail.com; hadeelswaity17@gmail.com;
   nardin.research@gmail.com; okarram@gmail.com; wajeehdaher@najah.edu
RI Sgouropoulou, Cleo/AAB-7645-2022; Krouska, Akrivi/ABA-8421-2021; Hmoud,
   Mohammad/JTS-6840-2023; Daher, Wajeeh/V-8742-2018
OI Swaity, Hadeel/0009-0000-6569-964X; Hmoud, Mohammad/0000-0003-0693-8465;
   Daher, Wajeeh/0000-0002-8207-0250
CR Ailincai R., 2018, Eurasia Journal of Mathematics, Science and Technology Education, V14, DOI DOI 10.29333/EJMSTE/93380
   Alfahel E., 2023, Eurasia J. Math. Sci. Technol. Educ, V19, pem2291, DOI [10.29333/ejmste/13299, DOI 10.29333/EJMSTE/13299]
   Boekaerts M., 2002, Advances in motivation and achievement: New directions in measures and methods, P77
   Braun V., 2014, APA HDB RES METHODS, P95, DOI [DOI 10.1007/978-981-10-5251-4103, DOI 10.1037/13620-004, 10.1007/978-981-10-2779-6_103-1]
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Bryman A, 2010, Social Research Methods, P157, DOI [10.4324/9780203381175_chapter_9, DOI 10.4324/9780203381175_CHAPTER_9]
   Chen XY, 2022, THINK SKILLS CREAT, V44, DOI 10.1016/j.tsc.2022.101035
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Daher W, 2014, INT J EMERG TECHNOL, V9, P16, DOI 10.3991/ijet.v9i8.3722
   Daher W., 2021, EURASIA J MATH SCI T, V17, pem1999, DOI [10.29333/ejmste/11127, DOI 10.29333/EJMSTE/11127]
   Daher W., 2021, Eurasia J Math Sci Technol Educ, V17, P1, DOI [10.29333/ejmste/10829, DOI 10.29333/EJMSTE/10829]
   Daher W, 2023, INFORMATION, V14, DOI 10.3390/info14070409
   Daher W, 2023, EDUC SCI, V13, DOI 10.3390/educsci13020098
   Daher W, 2022, COMPUT SCH, V39, P230, DOI 10.1080/07380569.2022.2071227
   Daher W, 2020, SYMMETRY-BASEL, V12, DOI 10.3390/sym12101627
   Deci E. L., 2013, Intrinsic motivation and self-determination in human behavior (perspectives in social psychology), DOI DOI 10.1007/978-1-4899-2271-7
   Degachi C., 2023, P CHI 2023 EXTENDED, DOI [10.1145/3544549.3585780, DOI 10.1145/3544549.3585780]
   Earle F, 2015, MOTIV EMOTION, V39, P467, DOI 10.1007/s11031-015-9481-2
   Elo S, 2014, SAGE OPEN, V4, DOI 10.1177/2158244014522633
   Feng SH, 2021, INT J ARTIF INTELL E, V31, P277, DOI 10.1007/s40593-021-00244-4
   Flick O., 2009, INTRO QUALITATIVE RE
   Graesser AC, 2001, AI MAG, V22, P39
   Guo K, 2023, COMPUT EDUC, V203, DOI 10.1016/j.compedu.2023.104862
   Guo K, 2024, INTERACT LEARN ENVIR, V32, P4917, DOI 10.1080/10494820.2023.2207181
   How ML, 2020, INFORMATION, V11, DOI 10.3390/info11010039
   Huang WJ, 2022, J COMPUT ASSIST LEAR, V38, P237, DOI 10.1111/jcal.12610
   Jeno LM, 2019, COMPUT EDUC, V128, P398, DOI 10.1016/j.compedu.2018.10.008
   Julkunen K., 1989, Ph.D. Thesis
   Kooli C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075614
   Lamb M., 2020, The Palgrave handbook of motivation for language learning
   Lambert C, 2017, LANG TEACH RES, V21, P665, DOI 10.1177/1362168816683559
   Lameras P, 2022, INFORMATION, V13, DOI 10.3390/info13010014
   Lin CC, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15054012
   Lincoln, 1985, NATURALISTIC INQUIRY, P289, DOI DOI 10.1016/0147-1767(85)90062-8
   Liu H, 2022, INTERNET INTERV, V27, DOI 10.1016/j.invent.2022.100495
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Mendoza NB, 2023, EUR J PSYCHOL EDUC, V38, P607, DOI 10.1007/s10212-022-00620-1
   Mogavi R., 2023, arXiv, DOI [10.13140/RG.2.2.15524.86401/1 2305.13114, DOI 10.13140/RG.2.2.15524.86401/12305.13114]
   PARKER EB, 1970, AM SOCIOL REV, V35, P356, DOI 10.2307/2093233
   Polit D.F., 2020, Nursing Research: Generating and assessing evidence for nursing practice, V11th
   Poupore G, 2014, CAN J APPL LINGUIST, V17, P69
   Poupore G, 2013, CAN MOD LANG REV, V69, P91, DOI 10.3138/cmlr.1139
   Rapp A, 2021, INT J HUM-COMPUT ST, V151, DOI 10.1016/j.ijhcs.2021.102630
   Raschka S, 2020, INFORMATION, V11, DOI 10.3390/info11040193
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Ryan RM, 2017, SELF-DETERMINATION THEORY: BASIC PSYCHOLOGICAL NEEDS IN MOTIVATION, DEVELOPMENT, AND WELLNESS, P1, DOI 10.1521/978.14625/28806
   Ryan RM, 2000, AM PSYCHOL, V55, P68, DOI 10.1037/0003-066X.55.1.68
   Sabzalieva E., 2023, ChatGPT and artificial intelligence in higher education: Quick start guide
   Safdari S., 2021, Issues Lang. Teach, V10, P203, DOI [10.22054/ilt.2022.63219.625, DOI 10.22054/ILT.2022.63219.625]
   Saldana J., 2021, CODING MANUAL QUALIT
   Smutny P, 2020, COMPUT EDUC, V151, DOI 10.1016/j.compedu.2020.103862
   Talan T., 2023, International Journal of Management Information Systems and Computer Science, V7, P33, DOI DOI 10.33461/UYBISBBD.1244777
   Tegos S, 2020, LECT NOTES COMPUT SC, V12149, P284, DOI 10.1007/978-3-030-49663-0_34
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   Walters S, 2008, ADULT EDUC QUART, V59, P84, DOI 10.1177/0741713608322826
   Wang J., 2021, Comput. Educ. Artif. Intell, V2, P100023, DOI [DOI 10.1016/J.CAEAI.2021.100023, https://doi.org/10.1016/j.caeai.2021.100023]
   Wardat Y., 2023, Eurasia Journal of Mathematics, Science and Technology Education, V19, DOI DOI 10.29333/EJMSTE/13272
   Wigfield A, 2000, CONTEMP EDUC PSYCHOL, V25, P68, DOI 10.1006/ceps.1999.1015
   Wigfield A, 2019, ADV MOTIV A, V20, P15, DOI 10.1108/S0749-742320190000020002
   Wu R, 2024, BRIT J EDUC TECHNOL, V55, DOI 10.1111/bjet.13334
   Yang, 2022, COMPUTERS ED ARTIFIC, V3, DOI DOI 10.1016/J.CAEAI.2022.100061
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Zare J, 2022, J MULTILING MULTICUL, DOI 10.1080/01434632.2022.2134881
   Zhang RF, 2023, INNOV LANG LEARN TEA, V17, P932, DOI 10.1080/17501229.2023.2197417
   Zhou L., 2023, Educ. Sci. Manag, V1, P19, DOI [10.56578/esm010103, DOI 10.56578/ESM010103]
NR 66
TC 10
Z9 10
U1 47
U2 129
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2078-2489
J9 INFORMATION
JI Information
PD JAN
PY 2024
VL 15
IS 1
AR 33
DI 10.3390/info15010033
PG 18
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA GG1N7
UT WOS:001151421700001
OA gold
DA 2024-12-25
ER

PT J
AU Shahzad, MF
   Xu, S
   Asif, M
AF Shahzad, Muhammad Farrukh
   Xu, Shuo
   Asif, Muhammad
TI Factors affecting generative artificial intelligence, such as ChatGPT,
   use in higher education: An application of technology acceptance model
SO BRITISH EDUCATIONAL RESEARCH JOURNAL
LA English
DT Article; Early Access
DE ChatGPT actual use; ChatGPT ease of use; ChatGPT self-efficacy; ChatGPT
   trust; ChatGPT usefulness; generative artificial intelligence
ID PLS-SEM; VALIDITY
AB The adoption of generative artificial intelligence (GAI) tools, such as ChatGPT, in higher education presents numerous opportunities and challenges. The use of GAI technologies in various fields, including education, has accelerated as technology develops. The widely used language model ChatGPT, developed by OpenAI, has become progressively more important, especially in the field of education. This study employs the technology acceptance model to investigate the factors influencing the employment of ChatGPT within the higher education sector of Pakistan. This study employed the PLS-SEM method for probing data collected from 368 Pakistani university students. The findings indicate that ChatGPT trust positively mediates the affiliation between ChatGPT self-efficacy, ChatGPT actual use, ChatGPT use for information and ChatGPT use for interaction. Further, ChatGPT usefulness and ChatGPT ease of use significantly moderate the association between ChatGPT self-efficacy and ChatGPT trust. Educators must encourage students to use ChatGPT safely to preserve their critical thinking, problem-solving abilities and creativity during assessments. This study contributes to understanding generative AI tools such as ChatGPT that are used in educational settings and provides insights for administrators and policymakers aiming to implement these technologies effectively.
C1 [Shahzad, Muhammad Farrukh; Xu, Shuo] Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China.
   [Asif, Muhammad] Univ Educ, UE Business Sch, Lahore, Pakistan.
C3 Beijing University of Technology
RP Xu, S (corresponding author), Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China.
EM xushuo@bjut.edu.cn
RI Farrukh Shahzad, Muhammad/JJE-9020-2023; Xu, Shuo/KVY-0402-2024; Asif,
   Muhammad/KIK-0105-2024
OI Xu, Shuo/0000-0002-8602-1819; Asif, Muhammad/0000-0003-0408-7628
FU National Natural Science Foundation of China
FX All the authors contributed equally to this work.
CR Abdelkader OA, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18770
   Adarkwah M. A., 2023, Journal of Applied Learning Teaching, V6, P2, DOI [10.37074/jalt.2023.6.2.26, DOI 10.37074/JALT.2023.6.2.26]
   Al-Abdullatif AM, 2023, EDUC SCI, V13, DOI 10.3390/educsci13111151
   Al-Adwan AS, 2023, EDUC INF TECHNOL, V28, P15381, DOI 10.1007/s10639-023-11816-3
   Ali F, 2023, INT J HOSP MANAG, V114, DOI 10.1016/j.ijhm.2023.103588
   Alqahtani T, 2023, RES SOC ADMIN PHARM, V19, P1236, DOI 10.1016/j.sapharm.2023.05.016
   Ayinde L., 2023, BUS INFORM REV, V40, P137, DOI [https://doi.org/10.1177/02663821231187991, DOI 10.1177/02663821231187991]
   Baek TH, 2023, TELEMAT INFORM, V83, DOI 10.1016/j.tele.2023.102030
   Bernabei M, 2023, Comput Educ: Artif Intell, V5, DOI [DOI 10.1016/J.CAEAI.2023.100172, 10.1016/j.caeai.2023.100172]
   Bilquise G, 2024, EDUC INF TECHNOL, V29, P6357, DOI 10.1007/s10639-023-12076-x
   Bin-Nashwan SA, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102370
   Bubou G M., 2020, Journal of Research in Innovative Teaching Learning, V15, P2, DOI [10.1108/JRIT-12-2019-0079, DOI 10.1108/JRIT-12-2019-0079]
   Compeau D. R., 1995, Management Information Systems Quarterly, V19, P189, DOI 10.2307/249688
   DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   de Andrés-Sánchez J, 2023, J THEOR APPL EL COMM, V18, P1217, DOI 10.3390/jtaer18030062
   Faqih KMS, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101787
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Gefen D, 2005, COMMUN ASSOC INF SYS, V16, P91, DOI 10.17705/1CAIS.01605
   Habibi A., 2023, Comput. Educ. Artif. Intell, V5, P100190, DOI [10.1016/j.caeai.2023.100190, DOI 10.1016/J.CAEAI.2023.100190]
   Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
   Haluza D, 2023, SYSTEMS-BASEL, V11, DOI 10.3390/systems11030120
   Henseler J, 2015, J ACAD MARKET SCI, V43, P115, DOI 10.1007/s11747-014-0403-8
   Kim N, 2019, TECHNOL FORECAST SOC, V139, P277, DOI 10.1016/j.techfore.2018.11.014
   Lai C. Y., 2023, Computers and Education: Artificial Intelligence, V5, DOI DOI 10.1016/J.CAEAI.2023.100178
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Liu Z, 2024, COMPUT EDUC, V219, DOI 10.1016/j.compedu.2024.105109
   Ma XY, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102362
   Maheshwari G, 2024, EDUC INF TECHNOL, V29, P12167, DOI 10.1007/s10639-023-12333-z
   Menon D, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e20962
   Mlambo S, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e03730
   Niu B, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103562
   Paul J, 2023, INT J CONSUM STUD, V47, P1213, DOI 10.1111/ijcs.12928
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   ROSCOE AM, 1975, J MARKETING, V39, P20, DOI 10.2307/1250111
   Sahari Y, 2023, J PSYCHOLINGUIST RES, V52, P2937, DOI 10.1007/s10936-023-10031-y
   Saif N, 2024, COMPUT HUM BEHAV, V154, DOI 10.1016/j.chb.2023.108097
   Sarstedt M, 2024, J MARK ANAL, V12, P746, DOI 10.1057/s41270-024-00325-y
   Sarstedt M, 2014, LONG RANGE PLANN, V47, P132, DOI 10.1016/j.lrp.2014.02.008
   Sarstedt M, 2014, J FAM BUS STRATEG, V5, P105, DOI 10.1016/j.jfbs.2014.01.002
   Shahzad MF, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-024-12949-9
   Shahzad MF, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00478-x
   Shahzad MF, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e29523
   Shahzad MF, 2024, HUM SOC SCI COMMUN, V11, DOI 10.1057/s41599-024-02777-0
   Strzelecki A, 2024, INNOV HIGH EDUC, V49, P223, DOI 10.1007/s10755-023-09686-1
   Tarhini A, 2017, J INT EDUC BUS, V10, P164, DOI 10.1108/JIEB-09-2016-0032
   Wang F, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su142113965
   Xu S, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-66689-4
   Xu TG, 2024, EDUC INF TECHNOL, V29, P2067, DOI 10.1007/s10639-023-11861-y
   Zhang B, 2023, SCI CHINA INFORM SCI, V66, DOI 10.1007/s11432-021-3449-x
   Zhang Z, 2023, COMPUT EDUC, V193, DOI 10.1016/j.compedu.2022.104673
NR 52
TC 1
Z9 1
U1 141
U2 141
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0141-1926
EI 1469-3518
J9 BRIT EDUC RES J
JI Br. Educ. Res. J.
PD 2024 OCT 19
PY 2024
DI 10.1002/berj.4084
EA OCT 2024
PG 25
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA J7T4A
UT WOS:001339046700001
DA 2024-12-25
ER

PT J
AU Aristeidou, C
   Dimitropoulos, N
   Michalos, G
AF Aristeidou, Christoforos
   Dimitropoulos, Nikos
   Michalos, George
TI Generative AI and neural networks towards advanced robot cognition
SO CIRP ANNALS-MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Cognitive robotics; Neural networks; Generative artificial intelligence
AB Enhancing autonomy and applicability of robotic systems across diverse scenarios, requires efficient environment perception. Conventional vision systems are highly performing but limited to simple tasks, while AI based ones require extensive data collection, processing and training. This paper presents a framework leveraging generative AI and Neural Networks to implement a dynamically updateable perception system. A multimodal conditional Generative Adversarial Network generates large image datasets which are automatically annotated by a Large Multimodal Model. A Convolutional Neural Network performs further dataset augmentation. A case study on the inspection of aircraft fuel tanks is used to showcase the potential of the approach. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
C1 [Aristeidou, Christoforos; Dimitropoulos, Nikos; Michalos, George] Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras 26504, Greece.
C3 University of Patras
RP Michalos, G (corresponding author), Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat, Patras 26504, Greece.
EM michalos@lms.mech.upatras.gr
RI Michalos, George/AGQ-4841-2022
FU CONVERGING-Social industrial collaborative environments integrating AI,
   Big Data and Robotics for smart manufacturing project - European
   Commission Horizon Europe research and innovation programme [101058521];
   Horizon Europe - Pillar II [101058521] Funding Source: Horizon Europe -
   Pillar II
FX This work has been supported by "CONVERGING-Social industrial
   collaborative environments integrating AI, Big Data and Robotics for
   smart manufacturing" project, funded by the European Commission Horizon
   Europe research and innovation programme under grant agreement No
   101058521.
CR Chryssolouris G., 2006, Manufacturing Systems: Theory and Practice
   Dimitropoulos N, 2021, APPL SCI-BASEL, V11, DOI 10.3390/app11125699
   ElMaraghy H, 2021, CIRP ANN-MANUF TECHN, V70, P635, DOI 10.1016/j.cirp.2021.05.008
   Girshick R, 2016, IEEE T PATTERN ANAL, V38, P142, DOI 10.1109/TPAMI.2015.2437384
   Li LH, 2022, Arxiv, DOI arXiv:2112.03857
   Jiang PY, 2022, PROCEDIA COMPUT SCI, V199, P1066, DOI 10.1016/j.procs.2022.01.135
   Leonardo A.P.I, About us
   Li YG, 2019, CIRP ANN-MANUF TECHN, V68, P487, DOI 10.1016/j.cirp.2019.03.010
   Lichtenwalter D., 2021, P CIRP, V99, P615, DOI DOI 10.1016/J.PROCIR.2021.03.083
   Liu S., 2023, ARXIV
   Liu W, 2016, LECT NOTES COMPUT SC, V9905, P21, DOI 10.1007/978-3-319-46448-0_2
   Mirza M., 2014, ARXIV14111784, P1, DOI DOI 10.48550/ARXIV.1411.1784
   Shorten C, 2019, J BIG DATA-GER, V6, DOI 10.1186/s40537-019-0197-0
   Wang Xingzhi, 2023, Procedia CIRP, P7, DOI 10.1016/j.procir.2023.04.001
   Weimer D, 2016, CIRP ANN-MANUF TECHN, V65, P417, DOI 10.1016/j.cirp.2016.04.072
   Whang SE, 2023, VLDB J, V32, P791, DOI 10.1007/s00778-022-00775-9
   YOLOv8, About us
   Zheng P, 2023, CIRP ANN-MANUF TECHN, V72, P1, DOI 10.1016/j.cirp.2023.04.057
   Zheng P, 2022, CIRP ANN-MANUF TECHN, V71, P377, DOI 10.1016/j.cirp.2022.04.016
   Zhuang FZ, 2021, P IEEE, V109, P43, DOI 10.1109/JPROC.2020.3004555
NR 20
TC 0
Z9 0
U1 5
U2 5
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-8506
EI 1726-0604
J9 CIRP ANN-MANUF TECHN
JI CIRP Ann-Manuf. Technol.
PY 2024
VL 73
IS 1
BP 21
EP 24
DI 10.1016/j.cirp.2024.04.013
EA JUL 2024
PG 4
WC Engineering, Industrial; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA ZQ9D2
UT WOS:001276869100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Sylvia, JJ
   Reeves, C
AF Sylvia, J. J.
   Reeves, Carol
TI Beyond the Robot Tropes: Embracing Nuance and Context in the Adoption of
   Generative AI
SO JOURNAL OF TECHNICAL WRITING AND COMMUNICATION
LA English
DT Article
DE GAI; artificial intelligence; ChatGPT; writing pedagogy
AB This introductory article examines the evolving landscape of generative artificial intelligence (GAI) tools, contextualizing their impact through historical tropes of automation as both helper and threat. The authors argue that GAI tools are neither sentient helpers nor existential threats but complex systems that require careful integration into educational and research settings. The article underscores the importance of nuanced, evidence-based approaches, advocating for a balanced understanding of GAI's potential and limitations. It emphasizes ethical considerations and promotes reflective adoption over reactionary measures.
C1 [Sylvia, J. J.] Fitchburg State Univ, Dept Commun Media, 160 Pearl St, Fitchburg, MA 01420 USA.
   [Reeves, Carol] Butler Univ, Dept English, Indianapolis, IN USA.
C3 Massachusetts System of Public Higher Education; Fitchburg State
   College; Butler University
RP Sylvia, JJ (corresponding author), Fitchburg State Univ, Dept Commun Media, 160 Pearl St, Fitchburg, MA 01420 USA.
EM jsylvia3@fitchburgstate.edu
RI Sylvia, J.J./AAP-4013-2020
CR Asimov Isaac, 2004, robot, V1
   Bagchi S., 2023, Gizmodo
   Brooks M., 1974, Young Frankenstein Comedy
   Broussard M., 2021, Statement by Meredith Broussard Associate Professor
   Capek K., 1923, R. U. R. (Rossum's Universal Robots): A Fantastic Melodrama in Three Acts and an Epilogue
   Eisenstein ElizabethL., 2009, The Printing Press as an Agent of Change: Communications and Cultural Transformations in Early-Modern Europe, V1st
   Emden Christian., 2005, Nietzsche on Language, Consciousness, and the Body
   Guo Y., 2024, ANN C N AM CHAPT ASS
   Kirschenbaum M.G., 2016, Track changes: The literary history of word processing
   Kittler FreidrichA., 1999, Gramophone, Film Typewriter
   Ong WJ, 2012, NEW ACCENT, P1
   Roose K., 2024, Is A.I. Already Taking Jobs? + A Filmmaker Tries Sora + The XZ Backdoor Caper
   Sparrow B, 2011, SCIENCE, V333, P776, DOI 10.1126/science.1207745
   Sylvia J. J., 2012, The effects of TV: How to be happy and live the good life
   Sylvia J. J., 2024, The data renaissance: Analyzing the disciplinary effects of big data, artificial intelligence, and beyond, P71
NR 15
TC 0
Z9 0
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0047-2816
EI 1541-3780
J9 J TECH WRIT COMMUN
JI J. Teach. Writ. Commun.
PD OCT
PY 2024
VL 54
IS 4
SI SI
BP 359
EP 368
DI 10.1177/00472816241260035
PG 10
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA M0J3X
UT WOS:001354491500002
DA 2024-12-25
ER

PT J
AU Van Slyke, C
   Johnson, RD
   Sarabadani, J
AF Van Slyke, Craig
   Johnson, Richard D.
   Sarabadani, Jalal
TI Generative Artificial Intelligence in Information Systems Education:
   Challenges, Consequences, and Responses
SO COMMUNICATIONS OF THE ASSOCIATION FOR INFORMATION SYSTEMS
LA English
DT Article
DE Information Systems Education; Artificial Intelligence; Generative
   Artificial Intelligence; ChatGPT
ID TECHNOLOGY; COMPUTER; EXPERT; IMPACT; AI
AB ChatGPT, an interactive, generative artificial intelligence (AI) system, was introduced in late 2022, quickly becoming one of the most rapidly adopted technologies in history. The rapid emergence of ChatGPT and similar AI tools, such as Google's Bard, and GPT-enabled Bing from Microsoft have led to intense discussions about how they will affect various aspects of society, including higher education. Information systems (IS) education will not escape the impact of AI tools. Our goal for this paper is to develop a better understanding of the range of possible impacts of ChatGPT on IS education and to describe how IS educators might respond to these potential impacts. To that end, we discuss challenges for IS education brought on by generative AI tools, and discuss potential future scenarios based on the emergence of such tools, ranging from AI having little impact on IS education to AI serving as competition for IS educators. We examine the challenges and consequences of each scenario. We also discuss potential responses, ranging from doing nothing to embracing AI tools as legitimate learning aids. We then provide several specific recommendations that will allow IS educators to effectively respond to the rise of AI tools.
C1 [Van Slyke, Craig] Louisiana Tech Univ, Comp Informat Syst, Ruston, LA 71272 USA.
   [Johnson, Richard D.] Washington State Univ, Management Informat Syst & Entrepreneurship, Pullman, WA USA.
   [Sarabadani, Jalal] San Jose State Univ, Lucas Coll, San Jose, CA USA.
   [Sarabadani, Jalal] San Jose State Univ, Grad Sch Business, San Jose, CA USA.
C3 University of Louisiana System; Louisiana Technical University;
   Washington State University; California State University System; San
   Jose State University; California State University System; San Jose
   State University
RP Van Slyke, C (corresponding author), Louisiana Tech Univ, Comp Informat Syst, Ruston, LA 71272 USA.
EM vanslyke@latech.edu
RI Sarabadani, Jalal/AAU-8582-2020; Van Slyke, Craig/F-3712-2014; Johnson,
   Richard/R-4628-2018
OI Johnson, Richard/0000-0001-9367-8889
CR Alkhadher O., 1994, EUR J WORK ORGAN PSY, V4, P169
   [Anonymous], 2014, ACCELERATING DEV EXP
   Anthony C, 2021, ADMIN SCI QUART, V66, P1173, DOI 10.1177/00018392211016755
   Beane M, 2019, ADMIN SCI QUART, V64, P87, DOI 10.1177/0001839217751692
   Behrend TS, 2011, COMPUT HUM BEHAV, V27, P1201, DOI 10.1016/j.chb.2010.12.016
   Bensinger G., 2023, CHATGPT LAUNCHES BOO
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eubanks B., 2018, ARTIF INTELL
   Fowler G., 2023, Wash. Post
   Fronius T., 2016, RESTORATIVE JUSTICE
   Houser K., 2017, TEACHERS COULD HELP
   Hu K., 2023, REUTERS         0202
   Hwang GJ, 2003, COMPUT EDUC, V40, P217, DOI 10.1016/S0360-1315(02)00121-5
   Johnson RD, 2006, INT J HUM-COMPUT ST, V64, P446, DOI 10.1016/j.ijhcs.2005.09.002
   Kara F., 2010, Contemporary Justice Review, V13, P443, DOI 10.1080/10282580.2010.517981
   Klein Ezra., 2023, The New York Times
   Krathwohl DR, 2002, THEOR PRACT, V41, P212, DOI 10.1207/s15430421tip4104_2
   Langer M, 2019, INT J SELECT ASSESS, V27, P217, DOI 10.1111/ijsa.12246
   Langer M, 2016, INT J SELECT ASSESS, V24, P312, DOI 10.1111/ijsa.12150
   Laudon K.C., 2021, E COMMERCE BUSINESS
   Lazarus R. S., 1984, STRESS APPRAISAL COP
   Milmo D., 2023, CHATGPT ATTRACTS 100
   Nass C, 2000, J SOC ISSUES, V56, P81, DOI 10.1111/0022-4537.00153
   Nastjuk I, 2024, EUR J INFORM SYST, V33, P361, DOI 10.1080/0960085X.2022.2154712
   Nolan B., 2023, HERE ARE SCH COLL HA
   Oesch T., 2018, TRAINING IND 0705
   OpenAI, 2022, INTR CHATGPT
   Overton RC, 1997, PERS PSYCHOL, V50, P171, DOI 10.1111/j.1744-6570.1997.tb00907.x
   Peltier JW, 2005, J MARKET EDUC, V27, P250, DOI 10.1177/0273475305279657
   Shankland S., 2023, WHY WERE ALL OBSESSE
   Strich F, 2021, J ASSOC INF SYST, V22, P304, DOI 10.17705/1jais.00663
   Tangermann V., 2023, COLL STUDENT CAUGHT
   Terwiesch C, 2023, Would Chat GPT3 get a Wharton MBA? A prediction based on its performance in the operations management course
   Tippins NT, 2009, IND ORGAN PSYCHOL-US, V2, P2, DOI 10.1111/j.1754-9434.2008.01097.x
   Tippins NT, 2006, PERS PSYCHOL, V59, P189, DOI 10.1111/j.1744-6570.2006.00909.x
   Tonidandel S, 2002, J APPL PSYCHOL, V87, P320, DOI 10.1037//0021-9010.87.2.320
   van den Broek E, 2021, MIS QUART, V45, P1557, DOI 10.25300/MISQ/2021/16559
   Wang D., 2016, ARXIV
   WAY WD, 2001, ETS RES REPORT SERIE, V2001, pR1
   Wiggers K., 2023, Openai releases tool to detect ai-generated text, including from chatgpt-techcrunch
   WILL RP, 1991, COMPUT HUM BEHAV, V7, P171, DOI 10.1016/0747-5632(91)90006-M
   Yellin DM, 2023, COMMUN ACM, V66, P41, DOI 10.1145/3555367
NR 42
TC 8
Z9 8
U1 22
U2 158
PU ASSOC INFORMATION SYSTEMS
PI ATLANTA
PA GEORGIA STATE UNIV, 35 BROAD STREET, STE 916-917, ATLANTA, GA 30303 USA
SN 1529-3181
J9 COMMUN ASSOC INF SYS
JI Commun. Assoc. Inf. Syst.
PY 2023
VL 53
BP 1
EP 21
DI 10.17705/1CAIS.05301
PG 22
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA L3ZP8
UT WOS:001022678600001
DA 2024-12-25
ER

PT J
AU Boscardin, CK
   Gin, B
   Golde, PB
   Hauer, KE
AF Boscardin, Christy K.
   Gin, Brian
   Golde, Polo Black
   Hauer, Karen E.
TI ChatGPT and Generative Artificial Intelligence for Medical Education:
   Potential Impact and Opportunity
SO ACADEMIC MEDICINE
LA English
DT Article
ID PERFORMANCE
AB ChatGPT has ushered in a new era of artificial intelligence (AI) that already has significant consequences for many industries, including health care and education. Generative AI tools, such as ChatGPT, refer to AI that is designed to create or generate new content, such as text, images, or music, from their trained parameters. With free access online and an easy-to-use conversational interface, ChatGPT quickly accumulated more than 100 million users within the first few months of its launch. Recent headlines in the popular press have ignited concerns relevant to medical education over the possible implications of cheating and plagiarism in assessments as well as excitement over new opportunities for learning, assessment, and research. In this Scholarly Perspective, the authors offer insights and recommendations about generative AI for medical educators based on literature review, including the AI literacy framework. The authors provide a definition of generative AI, introduce an AI literacy framework and competencies, and offer considerations for potential impacts and opportunities to optimize integration of generative AI for admissions, learning, assessment, and medical education research to help medical educators navigate and start planning for this new environment. As generative AI tools continue to expand, educators need to increase their AI literacy through education and vigilance around new advances in the technology and serve as stewards of AI literacy to foster social responsibility and ethical awareness around the use of AI.
C1 [Boscardin, Christy K.] UCSF Off Med Educ, Dept Med & Anesthesia, Box 0710,521 Parnassus Ave,Mail Room 0104, San Francisco, CA 94143 USA.
   [Boscardin, Christy K.] Univ Calif San Francisco, Sch Med, Dept Med, San Francisco, CA USA.
   [Boscardin, Christy K.] Univ Calif San Francisco, Sch Med, Dept Anesthesia & Perioperat Care, San Francisco, CA USA.
   [Boscardin, Christy K.] Univ Calif San Francisco, Sch Med, Student Assessment, San Francisco, CA USA.
   [Gin, Brian] Univ Calif San Francisco, Dept Pediat, San Francisco, CA USA.
   [Golde, Polo Black] Univ Calif San Francisco, Sch Med, Data & Analyt, San Francisco, CA USA.
   [Hauer, Karen E.] Univ Calif San Francisco, Competency Assessment & Profess Stand, San Francisco, CA USA.
   [Hauer, Karen E.] Univ Calif San Francisco, Med, San Francisco, CA USA.
C3 University of California System; University of California San Francisco;
   University of California System; University of California San Francisco;
   University of California System; University of California San Francisco;
   University of California System; University of California San Francisco;
   University of California System; University of California San Francisco;
   University of California System; University of California San Francisco;
   University of California System; University of California San Francisco
RP Boscardin, CK (corresponding author), UCSF Off Med Educ, Dept Med & Anesthesia, Box 0710,521 Parnassus Ave,Mail Room 0104, San Francisco, CA 94143 USA.
EM christy.boscardin@ucsf.edu; brian.gin@ucsf.edu;
   polo.blackgolde@ucsf.edu; karen.hauer@ucsf.edu
OI Gin, Brian/0000-0001-7655-3750
CR [Anonymous], 2022, forbesDec 9,
   [Anonymous], 2023, TechRadarFebruary 15
   Biswas S, 2023, RADIOLOGY, V307, DOI 10.1148/radiol.223312
   Cardona MA., 2023, Artificial intelligence and the future of teaching and learning
   Choi JH, 2022, J LEGAL EDUC, V71, P387
   Cowen VS, 2016, MED EDUC, V50, P311, DOI 10.1111/medu.12878
   Cutrer WB, 2021, MED TEACH, V43, pS17, DOI 10.1080/0142159X.2021.1925234
   Dong T, 2013, TEACH LEARN MED, V25, P55, DOI 10.1080/10401334.2012.741536
   Epstein RM, 2007, NEW ENGL J MED, V356, P387, DOI 10.1056/NEJMra054784
   Flanagin A, 2023, JAMA-J AM MED ASSOC, V329, P637, DOI 10.1001/jama.2023.1344
   Gong X., 2020, 2020 IEEE INT C NETW, P1, DOI DOI 10.1109/ICNSC48988.2020.9238087
   Guo BY, 2023, Arxiv, DOI [arXiv:2301.07597, DOI 10.48550/ARXIV.2301.07597]
   Hu K., 2023, Reuters
   Hung AJ, 2018, J ENDOUROL, V32, P438, DOI 10.1089/end.2018.0035
   Khademi A., 2023, Journal of Applied Learning & Teaching, V6, P1, DOI DOI 10.37074/JALT.2023.6.1.1
   Kind T, 2021, MED SCI EDUC, V31, P1957, DOI 10.1007/s40670-021-01409-5
   Kung TH, 2022, medRxiv, DOI [10.1101/2022.12.19.22283643, 10.1101/2022.12.19.22283643, DOI 10.1101/2022.12.19.22283643]
   Laupichler M.C., 2022, Comput. Educ. Artif. Intell, V3, P100101, DOI DOI 10.1016/J.CAEAI.2022.100101
   Liang HY, 2019, NAT MED, V25, P433, DOI 10.1038/s41591-018-0335-9
   Lomis Kimberly, 2021, NAM Perspect, V2021, DOI 10.31478/202109a
   Lugosi G., 2023, PREPRINT
   McCoy LG, 2020, NPJ DIGIT MED, V3, DOI 10.1038/s41746-020-0294-7
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Noorbehbahani F, 2022, EDUC INF TECHNOL, V27, P8413, DOI 10.1007/s10639-022-10927-7
   Russell RG, 2023, ACAD MED, V98, P348, DOI 10.1097/ACM.0000000000004963
   saraharberson, Will ChatGPT Eliminate the College Admissions Essay?
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   Terwiesch T., White Paper
   Tolsgaard MG, 2020, ADV HEALTH SCI EDUC, V25, P1057, DOI 10.1007/s10459-020-10009-8
   Topol EJ, 2019, NAT MED, V25, P44, DOI 10.1038/s41591-018-0300-7
   Turban S., How will ChatGPT change research paper writing?
   van der Vleuten CPM, 2012, MED TEACH, V34, P205, DOI 10.3109/0142159X.2012.652239
   Weissman J., ChatGPT is a plague upon education
NR 33
TC 64
Z9 66
U1 281
U2 420
PU LIPPINCOTT WILLIAMS & WILKINS
PI PHILADELPHIA
PA TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA
SN 1040-2446
EI 1938-808X
J9 ACAD MED
JI Acad. Med.
PD JAN
PY 2024
VL 99
IS 1
BP 22
EP 27
DI 10.1097/ACM.0000000000005439
PG 6
WC Education, Scientific Disciplines; Health Care Sciences & Services
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Education & Educational Research; Health Care Sciences & Services
GA DV3N8
UT WOS:001134819400001
PM 37651677
OA Bronze
DA 2024-12-25
ER

PT J
AU Locke, LG
   Hodgdon, G
AF Locke, Larry G.
   Hodgdon, Grace
TI Gender bias in visual generative artificial intelligence systems and the
   socialization of AI
SO AI & SOCIETY
LA English
DT Article; Early Access
DE Gender bias; AI; Generative AI; Generative artificial intelligence;
   Visual AI
AB Substantial research over the last ten years has indicated that many generative artificial intelligence systems ("GAI") have the potential to produce biased results, particularly with respect to gender. This potential for bias has grown progressively more important in recent years as GAI has become increasingly integrated in multiple critical sectors, such as healthcare, consumer lending, and employment. While much of the study of gender bias in popular GAI systems is focused on text-based GAI such as OpenAI's ChatGPT and Google's Gemini (formerly Bard), this article describes the results of a confirmatory experiment of gender bias in visual GAI systems. The authors argue that the potential for gender bias in visual GAI systems is potentially more troubling than bias in textual GAI because of the superior memorability of images and the capacity for emotional communication that images represent. They go on to offer four potential approaches to gender bias in visual GAI based on the roles visual GAI could play in modern society. The article concludes with a discussion of how dominant societal values could influence a choice between those four potential approaches to gender bias in visual GAI and some suggestions for further research.
C1 [Locke, Larry G.; Hodgdon, Grace] Univ Mary Hardin Baylor, Belton, TX 76513 USA.
   [Locke, Larry G.] LCC Int Univ, Klaipeda, Lithuania.
C3 University Mary Hardin Baylor; LCC International University
RP Locke, LG (corresponding author), Univ Mary Hardin Baylor, Belton, TX 76513 USA.; Locke, LG (corresponding author), LCC Int Univ, Klaipeda, Lithuania.
EM llocke@UMHB.edu
FU SCELC, Statewide California Electronic Library Consortium
FX Open access funding provided by SCELC, Statewide California Electronic
   Library Consortium.
CR Abedin B., 2022, Australas J Inf Syst, DOI [10.1109/TVCG.2019.2934262, DOI 10.1109/TVCG.2019.2934262]
   Brennan Center for Justice, 2024, Artificial intelligence legislation tracker
   Busln N., 2023, Frontiers, DOI [10.1038/s41591-021-01614-0, DOI 10.1038/S41591-021-01614-0]
   Christman J., 2020, Stanford encyclopedia of philosophy, V2020
   Department of Regulatory Agencies, Unfair discrimination. 3 CCR 702-10. Regulation 10-1-1 Governance and Risk Management Framework Requirements for Life Insurers' Use of External Consumer Data and Information Sources, Algorithms, and Predictive Models-Google Drive
   Dewan P, Partnership., V10
   Diaz J, 2024, Fast Company. Innovation by Design 2024
   Dobos AR, 2015, PUBLIC UNDERST SCI, V24, P712, DOI 10.1177/0963662514533623
   Gorska AM, 2023, FEM MEDIA STUD, V23, P4370, DOI 10.1080/14680777.2023.2263659
   Government Accountability Office, 2021, ARTIFICIAL INTELLIGE
   Gross N, 2023, What ChatGPT tells us about gender: a cautionary tale about performativity and gender biases in AI
   Hreha J, Thebehavioralscientist.com
   Hu K., 2023, REUTERS         0202
   Jiang H, 2023, PROCEEDINGS OF THE 2023 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2023, P363, DOI 10.1145/3600211.3604681
   Katatikarn J, 2024, AI art in statistics: the ultimate list in 2024
   Koenig PD, 2024, AI SOC, DOI 10.1007/s00146-024-01987-z
   Konrad A, 2023, Forbes.
   KPMG, 2024, Supercharge your finance workforce with GenAI
   Locke LG, 2024, Christian scholar's review
   Nadeem A., 2020, Gender bias in AI: a review of contributing factors and mitigating strategies, V27
   O'Connor S, 2024, AI SOC, V39, P2045, DOI 10.1007/s00146-023-01675-4
   OpenArt, About
   Park S, 2024, AI SOC, DOI 10.1007/s00146-024-01948-6
   Perez S., 2013, TechCrunch
   Rabinovich M, 2024, AI SOC, DOI 10.1007/s00146-024-01991-3
   Schwartz R., 2022, NIST Special Publication, V1270
   Singla A., 2024, The state of AI in early 2024
   Smith D, 2023, Medium
   Smith G., 2021, Stanford Social Innovation Review, DOI [DOI 10.48558/A179-B138, 10.48558/a179-b138]
   Usborne E, 2014, J THEOR SOC BEHAV, V44, P436, DOI 10.1111/jtsb.12061
   Walsh M, 2024, Canva statistics-all the key facts and figures
   Whitehouse, 2023, Fact sheet: President Biden issues executive order on safe, secure, and trustworthy artificial intelligence
   Whitehouse AJO, 2006, BRIT J DEV PSYCHOL, V24, P767, DOI 10.1348/026151005X74153
   Whitehouse OSTP, 2022, Blueprint for an AI bill of rights: Making automated systems work for the American people
   Women's Bureau, 2022, Employment and earnings by occupation
   Xavier B, 2024, AI SOC, DOI 10.1007/s00146-024-01985-1
NR 36
TC 0
Z9 0
U1 3
U2 3
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 0951-5666
EI 1435-5655
J9 AI SOC
JI AI Soc.
PD 2024 NOV 25
PY 2024
DI 10.1007/s00146-024-02129-1
EA NOV 2024
PG 8
WC Computer Science, Artificial Intelligence
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA N2C7K
UT WOS:001362481200001
OA hybrid
DA 2024-12-25
ER

PT J
AU Staron, M
   Abrahao, S
   Gay, G
   Serebrenik, A
AF Staron, Miroslaw
   Abrahao, Silvia
   Gay, Gregory
   Serebrenik, Alexander
TI Testing, Debugging, and Log Analysis With Modern AI Tools
SO IEEE SOFTWARE
LA English
DT Article
DE Software testing; Generative AI; Debugging; Software engineering
AB This edition of the "Practitioners' Digest" covers recent papers employing generative artificial intelligence in support of testing, debugging, and log analysis that were presented at the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023) and the 16th IEEE International Conference on Software, Testing, Verification and Validation (ICST 2023). Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send us and the authors of the paper(s) a note about your experiences.
C1 [Staron, Miroslaw; Gay, Gregory] Chalmers Univ Technol, Comp Sci & Engn Dept, Interact Design & Software Engn Div, SE-41296 Gothenburg, Sweden.
   [Staron, Miroslaw; Gay, Gregory] Univ Gothenburg, SE-41296 Gothenburg, Sweden.
   [Abrahao, Silvia] Univ Politecn Valencia, Dept Comp Syst & Computat, Valencia 46022, Spain.
   [Serebrenik, Alexander] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands.
C3 Chalmers University of Technology; University of Gothenburg; Universitat
   Politecnica de Valencia; Eindhoven University of Technology
RP Abrahao, S (corresponding author), Univ Politecn Valencia, Dept Comp Syst & Computat, Valencia 46022, Spain.
EM miroslaw.staron@cse.gu.se; sabrahao@dsic.upv.es; greg@greggay.com;
   a.serebrenik@tue.nl
RI Abrahão, Silvia/AAK-6976-2020; Abrahao, Silvia/L-3835-2013
OI Abrahao, Silvia/0000-0003-3580-2014; Staron,
   Miroslaw/0000-0002-9052-0864; Gay, Gregory/0000-0001-6794-9585
NR 0
TC 0
Z9 0
U1 7
U2 12
PU IEEE COMPUTER SOC
PI LOS ALAMITOS
PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
SN 0740-7459
EI 1937-4194
J9 IEEE SOFTWARE
JI IEEE Softw.
PD MAR-APR
PY 2024
VL 41
IS 2
BP 99
EP 102
DI 10.1109/MS.2023.3339408
PG 4
WC Computer Science, Software Engineering
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA KH3E4
UT WOS:001179020800002
DA 2024-12-25
ER

PT J
AU Huang, MH
   Rust, RT
AF Huang, Ming-Hui
   Rust, Roland T.
TI The Caring Machine: Feeling AI for Customer Care
SO JOURNAL OF MARKETING
LA English
DT Article
DE artificial intelligence; feeling AI; generative AI (GenAI); artificial
   empathy; emotion recognition; emotion understanding; emotion management;
   affective computing
ID ARTIFICIAL-INTELLIGENCE; EMOTIONAL CONTAGION; SERVICE; EMPATHY;
   FRAMEWORK; CONSUMERS
AB Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI's emotion understanding, employing "response engineering" to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.
C1 [Huang, Ming-Hui] Natl Taiwan Univ, Coll Management, Dept Informat Management, Taipei, Taiwan.
   [Rust, Roland T.] Univ Maryland, Ctr Excellence Serv, Robert H Smith Sch Business, College Pk, MD USA.
C3 National Taiwan University; University System of Maryland; University of
   Maryland College Park
RP Huang, MH (corresponding author), Natl Taiwan Univ, Coll Management, Dept Informat Management, Taipei, Taiwan.
EM huangmh@ntu.edu.tw; rrust@umd.edu
RI Rust, Roland T./IXX-2982-2023
FU National Science and Technology Council, Taiwan [MOST
   107-2410-H-002-115-MY3, MOST 110-2410-H-002-101-MY3, NSTC
   112-2410-H-002-048-MY3]
FX The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: This
   research was supported by grants (MOST 107-2410-H-002-115-MY3, MOST
   110-2410-H-002-101-MY3 and NSTC 112-2410-H-002-048-MY3) from the
   National Science and Technology Council, Taiwan.
CR Aaker D.A., 1988, Psychology and Marketing, V5, P1, DOI DOI 10.1002/MAR.4220050102
   Allard T, 2020, J MARKETING, V84, P86, DOI 10.1177/0022242920924389
   [Anonymous], 1997, Affective Computing
   [Anonymous], 2001, ECONOMIST       0621
   [Anonymous], 1925, The Psychology of Selling and Advertising
   Asada M, 2015, NEUROSCI RES, V90, P41, DOI 10.1016/j.neures.2014.12.002
   ASHFORTH BE, 1993, ACAD MANAGE REV, V18, P88, DOI 10.2307/258824
   Bach SH, 2022, PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022): PROCEEDINGS OF SYSTEM DEMONSTRATIONS, P93
   Bagozzi RP, 2022, J SERV RES-US, V25, P499, DOI 10.1177/10946705221118579
   BAGOZZI RP, 1994, J MARKETING, V58, P56, DOI 10.2307/1252251
   Bagozzi RP, 1999, J ACAD MARKET SCI, V27, P184, DOI 10.1177/0092070399272005
   Bai Yuntao, 2022, arXiv, DOI [10.48550/arXiv.2204.05862, DOI 10.48550/ARXIV.2204.05862]
   Barrett LF, 2019, PSYCHOL SCI PUBL INT, V20, P1, DOI 10.1177/1529100619832930
   Batra R., 1990, PSYCHOL MARKET, V7, P11, DOI DOI 10.1002/MAR.4220070103
   BATSON CD, 1983, J PERS SOC PSYCHOL, V45, P706, DOI 10.1037//0022-3514.45.3.706
   Belch G.E., 2004, Advertising and Promotion. An Integrated Marketing Communications Perspective, V6
   Berry LL, 2000, J ACAD MARKET SCI, V28, P128, DOI 10.1177/0092070300281012
   Bharadwaj N, 2022, J MARKETING, V86, P27, DOI 10.1177/00222429211013042
   Bommasani R., 2022, ARXIV
   BURKE MC, 1989, J MARKETING RES, V26, P69, DOI 10.2307/3172670
   CARPENDALE Jeremy, 2006, How children develop social understanding
   Caruelle D, 2022, MARKET LETT, V33, P163, DOI 10.1007/s11002-021-09609-0
   Chen M, 2020, PR MACH LEARN RES, V119
   Chernev Alexander., 2014, Strategic Marketing Management
   Chung TS, 2016, J ACAD MARKET SCI, V44, P66, DOI 10.1007/s11747-015-0441-x
   Chung TS, 2009, MARKET SCI, V28, P52, DOI 10.1287/mksc.1080.0371
   Crolic C, 2022, J MARKETING, V86, P132, DOI 10.1177/00222429211045687
   Dallimore KS, 2007, J SERV RES-US, V10, P78, DOI 10.1177/1094670507304694
   Danaher TS, 2023, J SERV RES-US, V26, P493, DOI 10.1177/10946705231190018
   Davenport T., 2022, Working with AI: Real Stories of Human-Machine Collaboration
   David Court, 2017, MCKIN Q
   Davis E, 2015, COMMUN ACM, V58, P92, DOI 10.1145/2701413
   DAVIS MH, 1983, J PERSONALITY SOCIAL, V0044
   Deidre McPhillips, 2023, CNN HLTH
   Devezer B, 2014, J MARKETING, V78, P118, DOI 10.1509/jm.11.0599
   DeWitt T, 2008, J SERV RES-US, V10, P269, DOI 10.1177/1094670507310767
   Dinesh Nirmal, 2023, IBM BLOG        0907
   Dukach Dagny., 2023, HARVARD BUS REV
   Edelman DC, 2015, HARVARD BUS REV, V93, P90
   Ekman Paul., 2003, EMOTIONS REVEALED
   Forrester, 2010, TIME BURY MARKETING
   Franceschelli Giorgio., 2022, ARXIV, DOI DOI 10.48550/ARXIV.2104.02726
   Hajarolasvadi N, 2020, IEEE ACCESS, V8, P218499, DOI 10.1109/ACCESS.2020.3042328
   Hamilton R, 2021, J MARKETING, V85, P68, DOI 10.1177/0022242920908227
   Harris P.L., 2008, HDB EMOTION, V3rd, P320
   Harwood MD, 2006, BRIT J DEV PSYCHOL, V24, P401, DOI 10.1348/026151005X50302
   Hennig-Thurau T, 2006, J MARKETING, V70, P58, DOI 10.1509/jmkg.70.3.58
   Herhausen D, 2023, J MARKETING, V87, P210, DOI 10.1177/00222429221119977
   Hoffman M.L., 1984, EMOTION COGNITION BE, P103
   Holthöwer J, 2023, J ACAD MARKET SCI, V51, P767, DOI 10.1007/s11747-022-00862-x
   Howard J.A., 1969, THEORY BUYER BEHAV, V63, P145
   Huang MH, 2001, J BUS PSYCHOL, V16, P239, DOI 10.1023/A:1011109200392
   Huang MH, 2022, J RETAILING, V98, P209, DOI 10.1016/j.jretai.2021.03.001
   Huang MH, 2021, J ACAD MARKET SCI, V49, P30, DOI 10.1007/s11747-020-00749-9
   Huang MH, 2020, INT J RES MARK, V37, P281, DOI 10.1016/j.ijresmar.2019.10.001
   Huang MH, 2019, CALIF MANAGE REV, V61, P43, DOI 10.1177/0008125619863436
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   Huang MH, 2017, J ACAD MARKET SCI, V45, P906, DOI 10.1007/s11747-017-0545-6
   Huang Ming-Hui, 2023, EVOLVING AI GENERATI
   Jeff Berg, 2022, STATE CUSTOMER CARE
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Kiron D, 2019, MIT SLOAN MANAGE REV, V60, P30
   Kopalle PK, 2022, INT J RES MARK, V39, P522, DOI 10.1016/j.ijresmar.2021.11.002
   Kosinski M., 2023, ARXIV
   Lazarus R.S., 1984, Stress, Appraisal
   LAZARUS RS, 1982, AM PSYCHOL, V37, P1019, DOI 10.1037/0003-066X.37.9.1019
   LeCun Y, 2022, OPEN REV, V62
   Lee Noah., 2023, ARXIV, DOI DOI 10.48550/ARXIV.2305.13788
   Lemon KN, 2016, J MARKETING, V80, P69, DOI 10.1509/jm.15.0420
   Lewis E., 1908, Financial Advertising
   Li Chengyin, 2023, ARXIV
   Li H, 2022, COMMUN ACM, V65, P56, DOI 10.1145/3490443
   Liu M., 2022, ARXIV
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Liu XY, 2019, J SERV RES-US, V22, P285, DOI 10.1177/1094670519835309
   Liu X, 2018, J MARKETING, V82, P86, DOI 10.1509/jm.16.0048
   Liu-Thompkins Y, 2022, J ACAD MARKET SCI, V50, P1198, DOI 10.1007/s11747-022-00892-5
   Lois Melkonian, 2021, BETTERUP        0211
   Luccioni, 2023, ARXIV
   Magids S, 2015, HARVARD BUS REV, V93, P68
   Marcin Frackiewica, 2023, TS2 SPACE       0430
   Mende M, 2019, J MARKETING RES, V56, P535, DOI 10.1177/0022243718822827
   Monika Kisielewska, 2022, TIDIO           0403
   Moorman C, 1999, J MARKETING, V63, P180, DOI 10.2307/1252111
   Moradi Milad., 2022, ARXIV, DOI DOI 10.48550/ARXIV.2109.02555
   Nyer PU, 1997, J ACAD MARKET SCI, V25, P296, DOI 10.1177/0092070397254002
   Oliver Whang, 2023, NEW YORK TIMES  0327
   ONG LML, 1995, SOC SCI MED, V40, P903, DOI 10.1016/0277-9536(94)00155-M
   OpenAI, 2023, GPT 4 TECHN REP, DOI DOI 10.48550/ARXIV.2303.08774
   Osburg VS, 2022, J SERV RES-US, V25, P630, DOI 10.1177/10946705221118233
   Ouyang L, 2022, ADV NEUR IN
   PARASURAMAN A, 1988, J RETAILING, V64, P12
   Parasuraman A, 2021, J SERV MANAGE, V32, P1, DOI 10.1108/JOSM-03-2019-0094
   Puccinelli NM, 2009, J RETAILING, V85, P15, DOI 10.1016/j.jretai.2008.11.003
   Puntoni S, 2021, J MARKETING, V85, P131, DOI 10.1177/0022242920953847
   Rashkin H, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P5370
   Reddy Javaji Shashidhar, 2023, MICROSOFT ED DE 0501
   Richardson A., 2010, Harvard Business Review, V15, P2
   Richins ML, 1997, J CONSUM RES, V24, P127, DOI 10.1086/209499
   RUSSELL JA, 1977, J RES PERS, V11, P273, DOI 10.1016/0092-6566(77)90037-X
   Rust R.T., 2000, DRIVING CUSTOMER EQU
   Rust R.T., 2021, FEELING EC ARTIFICIA
   Rust RT, 2021, J MARKETING, V85, P21, DOI 10.1177/0022242921995173
   Rust RT, 2016, INT J RES MARK, V33, P155, DOI 10.1016/j.ijresmar.2015.07.005
   Rust RT, 2014, MARKET SCI, V33, P206, DOI 10.1287/mksc.2013.0836
   Rust RT, 2004, J MARKETING, V68, P109, DOI 10.1509/jmkg.68.1.109.24030
   Rust RT, 2000, J ACAD MARKET SCI, V28, P86, DOI 10.1177/0092070300281008
   Santhanam S, 2019, ARXIV
   Schuller BW, 2018, COMMUN ACM, V61, P90, DOI 10.1145/3129340
   Sue Poremba, 2023, SECURITY INTELLIGENC
   Sundar SS, 2020, J COMPUT-MEDIAT COMM, V25, P74, DOI 10.1093/jcmc/zmz026
   Tak Ala N., 2023, ARXIV
   Tchiki Davis, 2021, PSYCHOL TODAY   0714
   Thomson M, 2005, J CONSUM PSYCHOL, V15, P77, DOI 10.1207/s15327663jcp1501_10
   Venkatesan R, 2004, J MARKETING, V68, P106, DOI 10.1509/jmkg.68.4.106.42728
   Wailin Wong, 2023, NPR MORNING EDITION
   Waken.ai, 2023, GLOBAL NEWSWIRE 0111
   Wedel M, 2016, J MARKETING, V80, P97, DOI 10.1509/jm.15.0413
   Wellman H.M., 1990, The child's theory of mind
   Weng Yixuan., 2023, ARXIV
   White J., 2023, ARXIV
   Wilson J, 2018, HARVARD BUS REV, V96, P115
   Yejin Choi, 2023, TED
   Zachary Cavanell, 2023, MICROSOFT MECH  0406
NR 124
TC 21
Z9 21
U1 598
U2 818
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0022-2429
EI 1547-7185
J9 J MARKETING
JI J. Mark.
PD SEP
PY 2024
VL 88
IS 5
BP 1
EP 23
DI 10.1177/00222429231224748
EA MAR 2024
PG 23
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA C1X6U
UT WOS:001189079300001
OA hybrid
DA 2024-12-25
ER

PT J
AU Cooper, MM
   Klymkowsky, MW
AF Cooper, Melanie M.
   Klymkowsky, Michael W.
TI Let Us Not Squander the Affordances of LLMs for the Sake of Expedience:
   Using Retrieval Augmented Generative AI Chatbots to Support and Evaluate
   Student Reasoning
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE First-Year Undergraduate; General; Curriculum; Generative AI; Learning
   Theories; Student CenteredLearning
ID ORGANIC-CHEMISTRY; LEWIS STRUCTURES; EXPLANATIONS
AB The use of large language model Generative AI (GenAI) systems by students and instructors is increasing rapidly, and there is little choice but to adapt to this new situation. Many, but not all, students are using GenAI for homework and assignments, which means that we need to provide equitable access for all students to AI systems that can support and enhance their learning. At the same time, we need to think carefully about just what we want teaching and learning to look like as GenAI systems become readily available. Here we propose that "business as usual" is not a responsible option. Although chatbots can readily answer questions, produce summaries of content, and make the process of education more efficient, there is scant evidence that such time saving is effective, and indeed, it is important that we not allow the use of GenAI systems to circumvent or undermine the learning process. The availability of so-called Retrieval Augmented Generative (RAG) AI systems allows us to expand what we expect students to know and do, by 1) supporting instructors in the design of more complex tasks (that can, for example, elicit evidence of three-dimensional learning (3DL)), 2) supporting students as they reason through such scaffolded tasks, and 3) by evaluating student responses, individually and in aggregate. We present examples of each of these affordances with the associated training materials and bot personas, along with caveats about their use.
C1 [Cooper, Melanie M.] Michigan State Univ, Dept Chem, E Lansing, MI 48824 USA.
   [Klymkowsky, Michael W.] Univ Colorado, Mol Cellular & Dev Biol, Boulder, CO 80309 USA.
C3 Michigan State University; University of Colorado System; University of
   Colorado Boulder
RP Cooper, MM (corresponding author), Michigan State Univ, Dept Chem, E Lansing, MI 48824 USA.
EM mmc@msu.edu
RI Cooper, Melanie/ACU-9643-2022
OI Klymkowsky, Mike/0000-0001-5816-9771; Cooper,
   Melanie/0000-0002-7050-8649
FX We would like to acknowledge Nate Kocho from CustomGPT for help and
   productive conversations.
CR Adcroft A, 2011, HIGH EDUC RES DEV, V30, P405, DOI 10.1080/07294360.2010.526096
   [Anonymous], 2024, CHRONICLE HIGHER ED
   [Anonymous], 2024, NOTEBOOKLM NOTE TAKI
   [Anonymous], 2024, TONICAI
   [Anonymous], 2024, MAGICSCHOOLAI AI TEA
   [Anonymous], 2024, BESOCRATIC
   [Anonymous], 2024, EXPLORE AI STUDY TOO
   [Anonymous], 2024, CUSTOMGPTAI CUSTOM G
   [Anonymous], 2024, ChatGPT
   [Anonymous], 2024, MEET KHANMIGOKHAN AC
   [Anonymous], 2024, TOP HAT
   [Anonymous], 2023, CHRONICLE HIGHEREDUC
   Bferro, 2024, TYTON PARTNERS
   Chang DH, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151712921
   Chase CC, 2009, J SCI EDUC TECHNOL, V18, P334, DOI 10.1007/s10956-009-9180-4
   Cooper M. M., 2020, CLUE CHEM LIFETHE UN
   Cooper M. M., 2020, OCLUE ORGANIC CHEMIS
   Cooper M. M., 2017, CLUE CHEM LIFETHE UN
   Cooper MM, 2019, J CHEM EDUC, V96, P1858, DOI 10.1021/acs.jchemed.9b00401
   Cooper MM, 2016, J CHEM EDUC, V93, P1703, DOI 10.1021/acs.jchemed.6b00417
   Cooper MM, 2015, SCIENCE, V350, P281, DOI 10.1126/science.aab0933
   Cooper MM, 2015, J CHEM EDUC, V92, P1273, DOI 10.1021/acs.jchemed.5b00203
   Cooper MM, 2012, CHEM EDUC RES PRACT, V13, P195, DOI 10.1039/c2rp00010e
   Cooper MM, 2010, J CHEM EDUC, V87, P869, DOI 10.1021/ed900004y
   Crandell OM, 2020, J CHEM EDUC, V97, P313, DOI 10.1021/acs.jchemed.9b00815
   Crandell OM, 2019, J CHEM EDUC, V96, P213, DOI 10.1021/acs.jchemed.8b00784
   Delannoy P.-A., 2024, DUBMIXRAG CHATBOT
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   Exintaris B, 2023, J CHEM EDUC, V100, P2972, DOI 10.1021/acs.jchemed.3c00481
   Guo Y, 2023, J CHEM EDUC, V100, P4876, DOI 10.1021/acs.jchemed.3c00505
   Hattie J, 2007, REV EDUC RES, V77, P81, DOI 10.3102/003465430298487
   Hicks MT, 2024, ETHICS INF TECHNOL, V26, DOI 10.1007/s10676-024-09775-5
   Houchlei SK, 2021, J CHEM EDUC, V98, P2751, DOI 10.1021/acs.jchemed.1c00099
   Jonsson A, 2013, ACT LEARN HIGH EDUC, V14, P63, DOI 10.1177/1469787412467125
   Klymkowsky M., 2024, ARXIV
   Klymkowsky MW, 2021, DEV BIOL, V476, P308, DOI 10.1016/j.ydbio.2021.04.004
   Liles J., 2024, SNOPES
   McKinsey & Co, 2024, McKinsey insights
   Messeri L, 2024, NATURE, V627, P49, DOI 10.1038/s41586-024-07146-0
   Mollick ER., 2024, The Wharton School Research Paper, DOI [10.2139/ssrn.4802463, DOI 10.2139/SSRN.4802463]
   Moog R.S., 2008, Process oriented guided inquiry learning POGIL, DOI DOI 10.1021/ACS.CHEMREV.1C00846
   Natl Res Council, 2012, FRAMEWORK FOR K-12 SCIENCE EDUCATION: PRACTICES, CROSSCUTTING CONCEPTS, AND CORE IDEAS, P1
   Noyes K, 2022, J CHEM EDUC, V99, P874, DOI 10.1021/acs.jchemed.1c00959
   Noyes K, 2020, J CHEM EDUC, V97, P3923, DOI 10.1021/acs.jchemed.0c00445
   Noyes K, 2019, J CHEM EDUC, V96, P1821, DOI 10.1021/acs.jchemed.9b00455
   Pashler H., 2007, IESPRACTICE GUIDE NC
   Ralph VR, 2022, JACS AU, DOI 10.1021/jacsau.2c00221
   Tassoti S, 2024, J CHEM EDUC, V101, P2475, DOI 10.1021/acs.jchemed.4c00212
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   Underwood SM, 2016, CHEM EDUC RES PRACT, V17, P365, DOI 10.1039/C5RP00217F
   Williams LC, 2015, J CHEM EDUC, V92, P1979, DOI 10.1021/acs.jchemed.5b00619
   Winstone NE, 2017, STUD HIGH EDUC, V42, P2026, DOI 10.1080/03075079.2015.1130032
   Young JD, 2024, J CHEM EDUC, V101, P2466, DOI 10.1021/acs.jchemed.4c00154
NR 53
TC 1
Z9 1
U1 8
U2 8
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD OCT 15
PY 2024
VL 101
IS 11
BP 4847
EP 4856
DI 10.1021/acs.jchemed.4c00765
EA OCT 2024
PG 10
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA L8K0R
UT WOS:001333420400001
OA hybrid
DA 2024-12-25
ER

PT J
AU Markowitz, DM
AF Markowitz, David M.
TI Can generative AI infer thinking style from language? Evaluating the
   utility of AI as a psychological text analysis tool
SO BEHAVIOR RESEARCH METHODS
LA English
DT Article
DE Analytic thinking; Text analysis; Generative AI; Large language models;
   LIWC
ID CULTURE; THOUGHT; PAIN
AB Generative AI, short for Generative Artificial Intelligence, a class of artificial intelligence systems, is not currently the choice technology for text analysis, but prior work suggests it may have some utility to assess dynamics like emotion. The current work builds upon this empirical foundation to consider how analytic thinking scores from a large language model chatbot, ChatGPT, were linked to analytic thinking scores from dictionary-based tools like Linguistic Inquiry and Word Count (LIWC). Using over 16,000 texts from four samples and tested against three prompts and two large language models (GPT-3.5, GPT-4), the evidence suggests there were small associations between ChatGPT and LIWC analytic thinking scores (meta-analytic effect sizes: .058 < rs < .304; ps < .001). When given the formula to calculate the LIWC analytic thinking index, ChatGPT performed incorrect mathematical operations in 22% of the cases, suggesting basic word and number processing may be unreliable with large language models. Researchers should be cautious when using AI for text analysis.
C1 [Markowitz, David M.] Michigan State Univ, Dept Commun, E Lansing, MI 48824 USA.
C3 Michigan State University
RP Markowitz, DM (corresponding author), Michigan State Univ, Dept Commun, E Lansing, MI 48824 USA.
EM dmm@msu.edu
RI Markowitz, David/L-5563-2019
OI Markowitz, David/0000-0002-7159-7014
CR Alkaissi H, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.35179
   arXiv, 2023, ARXIV
   Blackburn KG, 2018, APPETITE, V123, P390, DOI 10.1016/j.appet.2018.01.022
   Borji A., 2023, CATEGORICAL ARCHIVE
   Boyd R. L., 2022, DEV PSYCHOMETRIC PRO
   Boyd RL, 2021, J LANG SOC PSYCHOL, V40, P21, DOI [10.1177/0261927x20967028, 10.1177/0261927X20967028]
   Boyd RL, 2020, SCI ADV, V6, DOI 10.1126/sciadv.aba2196
   Boyd RL, 2016, CONSUMER PSYCHOLOGY IN A SOCIAL MEDIA WORLD, P222
   Charlesworth TES, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2121798119
   Chung C, 2007, FRONT SOC PSYCHOL, P343
   Cicchetti DV, 1994, Psychol. Assess, V6, P284, DOI DOI 10.1037/1040-3590.6.4.284
   Cintron A, 2006, J PALLIAT MED, V9, P1454, DOI 10.1089/jpm.2006.9.1454
   Clark Elizabeth., 2021, Long Papers, P7282, DOI [DOI 10.18653/V1/2021.ACL-LONG.565, 10.18653/v1/2021.acl-long.565]
   Demszky D, 2023, NAT REV PSYCHOL, V2, P688, DOI 10.1038/s44159-023-00241-5
   DIEDENHOFEN B, 2015, PLOS ONE, V10, DOI [DOI 10.1371/JOURNAL.PONE.0121945, 10.1371/journal.pone.0121945]
   Eichstaedt JC, 2021, PSYCHOL METHODS, V26, P398, DOI 10.1037/met0000349
   Frieder S., 2023, MATH CAPABILITIES CH
   Goranson A, 2017, PSYCHOL SCI, V28, P988, DOI 10.1177/0956797617701186
   Graesser AC, 2014, ELEM SCHOOL J, V115, P210, DOI 10.1086/678293
   Grimmer J, 2013, POLIT ANAL, V21, P267, DOI 10.1093/pan/mps028
   Hoffman KM, 2016, P NATL ACAD SCI USA, V113, P4296, DOI 10.1073/pnas.1516047113
   Ireland M. E., 2014, OXFORD HDB LANGUAGE, P201, DOI DOI 10.1093/OXFORDHB/9780199838639.013.034
   Ireland ME, 2010, J PERS SOC PSYCHOL, V99, P549, DOI 10.1037/a0020386
   Jakesch M, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2208839120
   Jordan KN, 2019, P NATL ACAD SCI USA, V116, P3476, DOI 10.1073/pnas.1811987116
   Kacewicz E, 2014, J LANG SOC PSYCHOL, V33, P125, DOI 10.1177/0261927X13502654
   Kahneman D., 2011, THINKING FAST SLOW
   Kennedy B., 2022, HDB LANGUAGE ANAL PS
   Kern ML, 2014, ASSESSMENT, V21, P158, DOI 10.1177/1073191113514104
   Köbis N, 2021, COMPUT HUM BEHAV, V114, DOI 10.1016/j.chb.2020.106553
   Kreps S, 2022, J EXP POLIT SCI, V9, P104, DOI 10.1017/XPS.2020.37
   Krosnick JA., 2018, The Palgrave Handbook of Survey Research, P439, DOI DOI 10.1007/978-3-319-54395-6_53
   MAASS A, 1989, J PERS SOC PSYCHOL, V57, P981, DOI 10.1037/0022-3514.57.6.981
   Markowitz DM, 2022, PNAS NEXUS, V1, DOI 10.1093/pnasnexus/pgac157
   Markowitz DM, 2023, APPL COGNITIVE PSYCH, V37, P643, DOI 10.1002/acp.4057
   Markowitz DM, 2023, J PERS SOC PSYCHOL, V124, P1133, DOI 10.1037/pspa0000333
   Markowitz DM, 2022, J LANG SOC PSYCHOL, V41, P209, DOI 10.1177/0261927X211026346
   Markowitz DM, 2016, J LANG SOC PSYCHOL, V35, P435, DOI 10.1177/0261927X15614605
   Mehl M.R., 2006, HDB MULTIMETHOD MEAS, P141, DOI [10.1037/11383-011, DOI 10.1037/11383-011]
   Meier T, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0243637
   Nisbett RE, 2001, PSYCHOL REV, V108, P291, DOI 10.1037//0033-295X.108.2.291
   OpenAI, 2023, PRICING
   Pennebaker JW, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0115844
   Pennebaker JW, 2011, NEW SCI, V211, P42, DOI 10.1016/S0262-4079(11)62167-2
   Pennebaker JW., 2022, Linguistic Inquiry and Word Count: LIWC-22. Pennebaker Conglomerates
   Petty R. E., 2009, Handbook of individual differences in social behavior, P318
   Rathje S., 2023, GPT IS EFFECTIVE TOO, DOI [10.31234/osf.io/sekf5, DOI 10.31234/OSF.IO/SEKF5]
   Seraj S, 2021, P NATL ACAD SCI USA, V118, DOI 10.1073/pnas.2017154118
   Tausczik YR, 2010, J LANG SOC PSYCHOL, V29, P24, DOI 10.1177/0261927X09351676
   Voigt R, 2017, P NATL ACAD SCI USA, V114, P6521, DOI 10.1073/pnas.1702413114
   Wang Z., 2023, Is ChatGPT a good sentiment analyzer? A preliminary study
   Wilkerson J, 2017, ANNU REV POLIT SCI, V20, P529, DOI 10.1146/annurev-polisci-052615-025542
   Yelp, 2023, YELP DAT
   Zhou Y. e. a., 2023, Large language models are human-level prompt engineers
NR 54
TC 4
Z9 4
U1 42
U2 96
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1554-351X
EI 1554-3528
J9 BEHAV RES METHODS
JI Behav. Res. Methods
PD JUN 1
PY 2024
VL 56
IS 4
BP 3548
EP 3559
DI 10.3758/s13428-024-02344-0
EA JAN 2024
PG 12
WC Psychology, Mathematical; Psychology, Experimental
WE Social Science Citation Index (SSCI)
SC Psychology
GA G7N6L
UT WOS:001151115700002
PM 38277084
DA 2024-12-25
ER

PT J
AU Moorhouse, BL
   Wan, YW
   Ho, TY
   Lin, AMY
AF Moorhouse, Benjamin Luke
   Wan, Yuwei
   Ho, Tsz Ying
   Lin, Angel M. Y.
TI Generative AI-assisted, evidence-informed use of L1 in L2 classrooms
SO ELT JOURNAL
LA English
DT Article
DE generative AI tools; L1 in L2 classrooms; evidence-informed use of L1;
   ChatGPT
AB Purposeful and strategic use of L1 can help with L2 learning. However, in many contexts, monolingual immersion approaches dominate, leading language teachers to refrain from using L1. It can also mean that teachers are not professionally prepared to implement evidence-informed uses of L1. In this article, we share the findings of an intervention study that aimed to raise preservice English language teachers' awareness of purposeful L1 use while co-exploring ways generative artificial intelligence (AI) tools (e.g. ChatGPT) can aid teachers' knowledge development and strategic utilization of L1 in L2 classrooms. Data were collected from fifty-six preservice language teachers in Hong Kong through a pre- and post-intervention mixed-method survey and follow-up group interviews. The findings show that explicit instruction on the use of L1 in L2 classrooms can increase preservice teachers' intention to use L1 as well as their knowledge about the evidence-informed use of L1 and the ways in which generative AI tools can assist their implementation of L1.
C1 [Moorhouse, Benjamin Luke] Hong Kong Baptist Univ HKBU, Dept Educ Studies, Hong Kong, Peoples R China.
   [Moorhouse, Benjamin Luke] HKBU, GenAI Taskforce, Hong Kong, Peoples R China.
   [Wan, Yuwei; Ho, Tsz Ying] Hong Kong Baptist Univ, Dept Educ Studies, Hong Kong, Peoples R China.
   [Lin, Angel M. Y.] Educ Univ Hong Kong, Dept English Language Educ, Hong Kong, Peoples R China.
   [Lin, Angel M. Y.] Amer Educ Res Assoc, Special Interest Grp SIG, Semiot Educ Signs Meanings & Multimodal, Washington, DC USA.
C3 Hong Kong Baptist University; Education University of Hong Kong (EdUHK)
RP Moorhouse, BL (corresponding author), Hong Kong Baptist Univ HKBU, Dept Educ Studies, Hong Kong, Peoples R China.; Moorhouse, BL (corresponding author), HKBU, GenAI Taskforce, Hong Kong, Peoples R China.
EM blmoorhouse@hkbu.edu.hk; yuweiwan@life.hkbu.edu.hk;
   joey0909@hkbu.edu.hk; angellin@eduhk.hk
RI Wan, Yuwei/LCD-5710-2024; Lin, Angel M Y/JZD-6097-2024; Moorhouse,
   Benjamin/U-2683-2019; /A-3893-2010
OI Moorhouse, Benjamin Luke/0000-0002-3913-5194; /0000-0002-6204-8021
CR Ali O, 2024, TECHNOL FORECAST SOC, V199, DOI 10.1016/j.techfore.2023.123076
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cummins J, 2007, CAN J APPL LINGUIST, V10, P221
   Harbord J., 1992, ELT J, V46, P350, DOI [10.1093/elt/46.4.350, DOI 10.1093/ELT/46.4.350]
   Huang LS, 2010, ELT J, V64, P155, DOI 10.1093/elt/ccp039
   Hwang GJ, 2023, EDUC TECHNOL SOC, V26, DOI 10.30191/ETS.202304_26(2).0014
   Kerr P, 2014, CAMB HBK LANG TEACH, P1
   Lin AMY, 2015, LANG CULT CURRIC, V28, P74, DOI 10.1080/07908318.2014.1000926
   McManus K, 2017, STUD SECOND LANG ACQ, V39, P459, DOI 10.1017/S027226311600022X
   Meniado JC, 2023, RELC J, V54, P461, DOI 10.1177/00336882231160610
   Moorhouse BL, 2024, ELT J, V78, P378, DOI 10.1093/elt/ccae032
   OpenAI, 2024, ChatGPT: A large language model for natural language processing (used for scientific editing). Version GPT-4
   Shin JY, 2020, J MULTILING MULTICUL, V41, P406, DOI 10.1080/01434632.2019.1684928
   Zhao T, 2016, INT J APPL LINGUIST, V26, P75, DOI 10.1111/ijal.12080
NR 15
TC 0
Z9 0
U1 40
U2 40
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
SN 0951-0893
EI 1477-4526
J9 ELT J
JI ELT J.
PD JUL 18
PY 2024
VL 78
IS 4
BP 453
EP 465
DI 10.1093/elt/ccae033
EA JUL 2024
PG 13
WC Education & Educational Research; Linguistics; Language & Linguistics
WE Social Science Citation Index (SSCI); Arts &amp; Humanities Citation Index (A&amp;HCI)
SC Education & Educational Research; Linguistics
GA L1Q3W
UT WOS:001271059700001
DA 2024-12-25
ER

PT J
AU Nixon, N
   Lin, YW
   Snow, L
AF Nixon, Nia
   Lin, Yiwen
   Snow, Lauren
TI Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion
   and Diversity
SO POLICY INSIGHTS FROM THE BEHAVIORAL AND BRAIN SCIENCES
LA English
DT Article
DE generative artificial intelligence; STEM inclusion; diversity in STEM;
   collaborative problem-solving; natural language processing
ID GENDER COMPOSITION; STEREOTYPE THREAT; PERFORMANCE; OUTCOMES;
   COLLABORATION; INTERVENTION; SCIENCE; IMPACT; BIAS
AB Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, and policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.
C1 [Nixon, Nia; Lin, Yiwen; Snow, Lauren] Univ Calif Irvine, Irvine, CA USA.
   [Nixon, Nia] Univ Calif Irvine, Sch Educ, EDUC 3361A, Irvine, CA 92697 USA.
C3 University of California System; University of California Irvine;
   University of California System; University of California Irvine
RP Nixon, N (corresponding author), Univ Calif Irvine, Sch Educ, EDUC 3361A, Irvine, CA 92697 USA.
EM dowelln@uci.edu
OI Lin, Yiwen/0000-0003-3602-8454; Dowell, Nia/0000-0002-9839-8947
FU National Institutes of Health
FX No Statement Available
CR Andrews-Todd J, 2020, COMPUT HUM BEHAV, V104, DOI 10.1016/j.chb.2018.10.025
   [Anonymous], 1994, J. Educ. Bus.
   Apesteguia J, 2012, MANAGE SCI, V58, P78, DOI 10.1287/mnsc.1110.1348
   Ashburn-Nardo L, 2008, SOC JUSTICE RES, V21, P490, DOI 10.1007/s11211-008-0078-8
   Asher MW, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2300463120
   Baker R, 2016, HANDBOOK ON PERSONALIZED LEARNING FOR STATES, DISTRICTS, AND SCHOOLS, P165
   Bear JB, 2011, INTERDISCIPL SCI REV, V36, P146, DOI 10.1179/030801811X13013181961473
   Brooks C., 2020, P 7 ACM C LEARN SCAL, P225, DOI DOI 10.1145/3386527.3405935
   Casad BJ, 2018, GROUP PROCESS INTERG, V21, P767, DOI 10.1177/1368430218767034
   Chatman JA, 2005, ACAD MANAGE J, V48, P321, DOI 10.2307/20159658
   Chen JN, 2019, LEADERSHIP QUART, V30, DOI 10.1016/j.leaqua.2019.101340
   Chine DR, 2022, LECT NOTES COMPUT SC, V13355, P366, DOI 10.1007/978-3-031-11644-5_30
   Chiu Thomas K.F., 2023, Computers and Education: Artificial Intelligence, V4, DOI [DOI 10.1016/J.CAEAI.2022.100118, 10.1016/j.caeai.2022.100118]
   Cohen GL, 2006, SCIENCE, V313, P1307, DOI 10.1126/science.1128317
   Collins PH, 2015, ANNU REV SOCIOL, V41, P1, DOI 10.1146/annurev-soc-073014-112142
   Crenshaw Kimberle., 1989, University of Chicago Legal Forum, V1, P139
   Dasgupta N, 2015, P NATL ACAD SCI USA, V112, P4988, DOI 10.1073/pnas.1422822112
   Dowell N., 2022, HDB LEARNING ANAL, V64, P82, DOI DOI 10.18608/HLA22.011
   Dowell N, 2019, LECT NOTES ARTIF INT, V11625, P207, DOI 10.1007/978-3-030-23204-7_18
   Dowell NMM, 2021, COMPUT HUM BEHAV, V119, DOI 10.1016/j.chb.2021.106709
   Dowell NMM, 2020, J LEARN ANAL, V7, P38, DOI 10.18608/jla.2020.71.4
   Edwards D., 2019, 2019 IEEE INT S TECH, P1, DOI [10.1109/HST47167.2019.9032906, DOI 10.1109/HST47167.2019.9032906]
   Fry R., 2021, STEM jobs see uneven progress in increasing gender, racial and ethnic diversity
   Graesser A. C., Building intelligent tutoring systems for teams: What matters, P173
   Graesser AC, 2018, PSYCHOL SCI PUBL INT, V19, P59, DOI 10.1177/1529100618808244
   Grenander M, 2021, AAAI CONF ARTIF INTE, V35, P15534
   Grunspan DZ, 2016, PLOS ONE, V11, DOI 10.1371/journal.pone.0148405
   Gueldenzoph L.E., 2002, Business Communication Quarterly, V65, P9, DOI DOI 10.1177/108056990206500102
   Hall WM, 2015, SOC PSYCHOL PERS SCI, V6, P528, DOI 10.1177/1948550615572637
   Heilman ME, 2005, J APPL PSYCHOL, V90, P905, DOI 10.1037/0021-9010.90.5.905
   Hirudayaraj M, 2021, EDUC SCI, V11, DOI 10.3390/educsci11100641
   Leibnitz GM, 2022, FRONT SOCIOL, V6, DOI 10.3389/fsoc.2021.784399
   Leslie SJ, 2015, SCIENCE, V347, P262, DOI 10.1126/science.1261375
   Lewis A, 2022, NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES: PROCEEDINGS OF THE STUDENT RESEARCH WORKSHOP, P202
   Lin YW, 2020, LECT NOTES ARTIF INT, V12163, P333, DOI 10.1007/978-3-030-52237-7_27
   Lin YW, 2019, PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE (LAK'19), P431, DOI 10.1145/3303772.3303837
   Logel C, 2009, J PERS SOC PSYCHOL, V96, P1089, DOI 10.1037/a0015703
   Mercier E., 2023, International Handbook of Engineering Education Research, P402
   Moghe S, 2021, PLOS ONE, V16, DOI 10.1371/journal.pone.0260646
   National Science Foundation, 2019, Women, minorities. and persons with disabilities in science and engineering
   Neumann AT, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.668220
   Niler AA, 2020, SEX ROLES, V82, P142, DOI 10.1007/s11199-019-01046-8
   Nkambou R, 2010, STUD COMPUT INTELL, V308, P1, DOI 10.1007/978-3-642-14363-2
   O'Brien LT, 2020, CULT DIVERS ETHN MIN, V26, P163, DOI 10.1037/cdp0000289
   OECD, 2017, Pisa 2015 collaborative problem-solving framework
   Ouyang F., 2021, COMPUTERS ED ARTIFIC, V2, P100020, DOI [DOI 10.1016/J.CAEAI.2021.100020, 10.1016/j.caeai.2021.100020 10.1016/j.caeai.2021.100020]
   Page SE, 2007, DIFFERENCE: HOW THE POWER OF DIVERSITY CREATES BETTER GROUPS, FIRMS, SCHOOLS, AND SOCIETIES, P1
   Paletz SBR, 2004, SMALL GR RES, V35, P128, DOI 10.1177/1046496403258793
   Ployhart RE, 2003, HUM PERFORM, V16, P231, DOI 10.1207/S15327043HUP1603_4
   Pollak KI, 1998, J APPL SOC PSYCHOL, V28, P954, DOI 10.1111/j.1559-1816.1998.tb01662.x
   Rios JA, 2020, EDUC RESEARCHER, V49, P80, DOI 10.3102/0013189X19890600
   Roscoe R. D., 2022, Artificial Intelligence in STEM Education, P359
   Rovira S, 2017, PLOS ONE, V12, DOI 10.1371/journal.pone.0171207
   Salazar M., 2017, The Oxford handbook of multicultural identity, P97
   Schneider B, 2021, J LEARN ANAL, V8, P1, DOI 10.18608/jla.2021.7447
   Seeber I, 2020, INFORM MANAGE-AMSTER, V57, DOI 10.1016/j.im.2019.103174
   Sekaquaptewa D, 2007, CULT DIVERS ETHN MIN, V13, P321, DOI 10.1037/1099-9809.13.4.321
   Settles IH, 2006, PSYCHOL WOMEN QUART, V30, P47, DOI 10.1111/j.1471-6402.2006.00261.x
   Sloane J. D., 2021, Proc NABT Res Symp
   Stangor C, 1998, J PERS SOC PSYCHOL, V75, P1191, DOI 10.1037/0022-3514.75.5.1191
   STEELE CM, 1995, J PERS SOC PSYCHOL, V69, P797, DOI 10.1037/0022-3514.69.5.797
   Stout J. G., 2014, POL INS BEH BRAIN SC, V1, P21, DOI [DOI 10.1177/2372732214549471, 10.1177/2372732214549471]
   Stout JG, 2011, J PERS SOC PSYCHOL, V100, P255, DOI 10.1037/a0021385
   Tucker R, 2014, ASSESS EVAL HIGH EDU, V39, P293, DOI 10.1080/02602938.2013.830282
   Walton GM, 2011, SCIENCE, V331, P1447, DOI 10.1126/science.1198364
   West TV, 2012, J EXP SOC PSYCHOL, V48, P1209, DOI 10.1016/j.jesp.2012.04.012
   Xu WQ, 2022, INT J STEM EDUC, V9, DOI 10.1186/s40594-022-00377-5
   Yang Y, 2022, P NATL ACAD SCI USA, V119, DOI 10.1073/pnas.2200841119
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
NR 69
TC 2
Z9 2
U1 41
U2 52
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 2372-7322
EI 2372-7330
J9 POL INS BEH BRAIN SC
JI Policy Insight Behav. Brain Sci.
PD MAR
PY 2024
VL 11
IS 1
BP 85
EP 92
DI 10.1177/23727322231220356
PG 8
WC Education & Educational Research; Psychology, Multidisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research; Psychology
GA LO9N7
UT WOS:001187863100012
PM 38516055
OA Green Submitted, Green Published, hybrid
DA 2024-12-25
ER

PT J
AU Celiktutan, B
   Klesse, AK
   Tuk, MA
AF Celiktutan, Begum
   Klesse, Anne-Kathrin
   Tuk, Mirjam A.
TI Acceptability lies in the eye of the beholder: Self-other biases in
   GenAI collaborations
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE GenAI; ChatGPT; Inferred contribution; Intellectual ownership;
   Self-other difference; Biased self-evaluation
ID ENHANCEMENT; PERCEPTIONS; ASSESSMENTS; AVERAGE; AI
AB Since the release of ChatGPT, heated discussions have focused on the acceptable uses of generative artificial intelligence (GenAI) in education, science, and business practices. A salient question in these debates pertains to perceptions of the extent to which creators contribute to the co-produced output. As the current research establishes, the answer to this question depends on the evaluation target. Nine studies (seven preregistered, total N = 4498) document that people evaluate their own contributions to co-produced outputs with ChatGPT as higher than those of others. This systematic self-other difference stems from differential inferences regarding types of GenAI usage behavior: People think that they predominantly use GenAI for inspiration, but others use it to outsource work. These self-other differences in turn have direct ramifications for GenAI acceptability perceptions, such that usage is considered more acceptable for the self than for others. The authors discuss the implications of these findings for science, education, and marketing. (c) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.
C1 [Celiktutan, Begum; Klesse, Anne-Kathrin; Tuk, Mirjam A.] Erasmus Univ, Rotterdam Sch Management, NL-3062 PA Rotterdam, Netherlands.
C3 Erasmus University Rotterdam; Erasmus University Rotterdam - Excl
   Erasmus MC
RP Klesse, AK (corresponding author), Erasmus Univ, Rotterdam Sch Management, NL-3062 PA Rotterdam, Netherlands.
EM klesse@rsm.nl
FU Erasmus Research Institute of Management (ERIM)
FX We thank Romain Cadario for his excellent comments on an earlier draft
   of this manuscript as well as Jordi Quoidbach for suggesting the
   follow-up analysis with an AI detector tool. We also thank the Erasmus
   Research Institute of Management (ERIM) for funding the data collection,
   Erik Kemperman from the Research Software Engineering and Consulting
   (RSEC) team at the Rotterdam School of Management, Erasmus University
   for his technical support with the data collection of study 2, and
   Danial Hayati for his help with collecting the data of Study 3.
CR Acar OA, 2023, Harvard Business ReviewJune
   Agarwal S, 2024, J ASSOC CONSUM RES, V9, P269, DOI 10.1086/729900
   ALICKE MD, 1985, J PERS SOC PSYCHOL, V49, P1621, DOI 10.1037/0022-3514.49.6.1621
   Barrick EM, 2022, J EXP SOC PSYCHOL, V101, DOI 10.1016/j.jesp.2022.104344
   Barros A, 2023, MANAGE LEARN, V54, P597, DOI 10.1177/13505076231201445
   Bergner AS, 2023, J CONSUM RES, V50, P742, DOI 10.1093/jcr/ucad014
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bockting CL, 2023, NATURE, V622, P693, DOI 10.1038/d41586-023-03266-1
   Bonezzi A, 2021, J EXP PSYCHOL-APPL, V27, P447, DOI 10.1037/xap0000294
   Botti S, 2004, J PERS SOC PSYCHOL, V87, P312, DOI 10.1037/0022-3514.87.3.312
   BROWN JD, 1986, SOC COGNITION, V4, P353, DOI 10.1521/soco.1986.4.4.353
   Brown JD, 2012, PERS SOC PSYCHOL B, V38, P209, DOI 10.1177/0146167211432763
   Bruk A, 2018, J PERS SOC PSYCHOL, V115, P192, DOI 10.1037/pspa0000120
   Cadario R, 2021, NAT HUM BEHAV, V5, P1636, DOI 10.1038/s41562-021-01146-0
   Castelo N, 2023, J CONSUM RES, V50, P848, DOI 10.1093/jcr/ucad023
   Castelo N, 2019, J MARKETING RES, V56, P809, DOI 10.1177/0022243719851788
   Chambers JR, 2004, PSYCHOL BULL, V130, P813, DOI 10.1037/0033-2909.130.5.813
   Chui M., 2023, The state of ai in 2023: Generative ai's breakout year
   Cordova DI, 1996, J EDUC PSYCHOL, V88, P715, DOI 10.1037/0022-0663.88.4.715
   Davenport T, 2020, J ACAD MARKET SCI, V48, P24, DOI 10.1007/s11747-019-00696-0
   De La Garza A., 2020, TIME
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elyoseph Z, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1199058
   Epley N, 2000, J PERS SOC PSYCHOL, V79, P861, DOI 10.1037//0022-3514.79.6.861
   Futterer T, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-42227-6
   Gai PJ, 2019, J MARKETING, V83, P61, DOI 10.1177/0022242919873901
   Goldman S., 2023, Who owns DALL-E images? Legal AI experts weigh in
   Hayes A. F., 2018, INTRO MEDIATION MODE
   Howe PDL, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1281255
   Hutson M, 2023, NATURE, V620, P260, DOI 10.1038/d41586-023-02491-y
   Iyengar SS, 1999, J PERS SOC PSYCHOL, V76, P349, DOI 10.1037/0022-3514.76.3.349
   Jago AS, 2024, PERS SOC PSYCHOL B, V50, P793, DOI 10.1177/01461672221149815
   Jones EE., 1972, ATTRIBUTION PERCEIV, DOI DOI 10.1037//0022-3514.71.2.375
   Jung MH, 2020, J EXP PSYCHOL GEN, V149, P1193, DOI 10.1037/xge0000700
   Khan Academy, World-class AI for education
   Klesse AK, 2019, J MARKETING RES, V56, P879, DOI 10.1177/0022243719846063
   Kruger J, 2004, PERS SOC PSYCHOL B, V30, P328, DOI 10.1177/0146167203259932
   KUNDA Z, 1990, PSYCHOL BULL, V108, P480, DOI 10.1037/0033-2909.108.3.480
   Longoni C., 2023, PsyArxiv, DOI [10.31234/osf.io/na3wb, DOI 10.31234/OSF.IO/NA3WB]
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Orrù G, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1199350
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Polman E, 2022, J CONSUM RES, V49, P132, DOI 10.1093/jcr/ucab048
   Pronin E, 2002, PERS SOC PSYCHOL B, V28, P369, DOI 10.1177/0146167202286008
   Pronin E, 2008, SCIENCE, V320, P1177, DOI 10.1126/science.1154199
   Roose K., 2023, The New York Times: Teach with it
   Ross L., 1977, Advances in Experimental Social Psychology, V10, P173, DOI DOI 10.1016/S0065-2601(08)60357-3
   Sun D., 2023, Forbes
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Walters WH, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-41032-5
   WATSON D, 1982, PSYCHOL BULL, V92, P682, DOI 10.1037/0033-2909.92.3.682
   Williams EF, 2014, J CONSUM RES, V41, P506, DOI 10.1086/676750
   Williams EF, 2012, PERS SOC PSYCHOL B, V38, P143, DOI 10.1177/0146167211421937
   Yalcin G, 2023, ARTIF INTELL LAW, V31, P269, DOI 10.1007/s10506-022-09312-z
   Zhang Y, 2024, J ASSOC CONSUM RES, V9, P344, DOI 10.1086/730710
   Zhang YH, 2023, JUDGM DECIS MAK, V18, DOI 10.1017/jdm.2023.37
NR 56
TC 2
Z9 2
U1 44
U2 44
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD SEP
PY 2024
VL 41
IS 3
BP 496
EP 512
DI 10.1016/j.ijresmar.2024.05.006
EA AUG 2024
PG 17
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA G8H6B
UT WOS:001318985500001
OA hybrid
DA 2024-12-25
ER

PT J
AU Jürgensmeier, L
   Skiera, B
AF Jurgensmeier, Lukas
   Skiera, Bernd
TI Generative AI for scalable feedback to multimodal exercises
SO INTERNATIONAL JOURNAL OF RESEARCH IN MARKETING
LA English
DT Article
DE Generative AI; Automated Feedback; Marketing Analytics; Learning;
   Education
AB Detailed feedback on exercises helps learners become proficient but is time-consuming for educators and, thus, hardly scalable. This manuscript evaluates how well Generative Artificial Intelligence (AI) provides automated feedback on complex multimodal exercises requiring coding, statistics, and economic reasoning. Besides providing this technology through an easily accessible web application, this article evaluates the technology's performance by comparing the quantitative feedback (i.e., points achieved) from Generative AI models with human expert feedback for 4,349 solutions to marketing analytics exercises. The results show that automated feedback produced by Generative AI (GPT-4) provides almost unbiased evaluations while correlating highly with (r = 0.94) and deviating only 6 % from human evaluations. GPT-4 performs best among seven Generative AI models, albeit at the highest cost. Comparing the models' performance with costs shows that GPT-4, Mistral Large, Claude 3 Opus, and Gemini 1.0 Pro dominate three other Generative AI models (Claude 3 Sonnet, GPT-3.5, and Gemini 1.5 Pro). Expert assessment of the qualitative feedback (i.e., the AI's textual response) indicates that it is mostly correct, sufficient, and appropriate for learners. A survey of marketing analytics learners shows that they highly recommend the app and its Generative AI feedback. An advantage of the app is its subject-agnosticism-it does not require any subject- or exercise-specific training. Thus, it is immediately usable for new exercises in marketing analytics and other subjects. (c) 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C1 [Jurgensmeier, Lukas; Skiera, Bernd] Goethe Univ Frankfurt Main, Fac Business & Econ, Theodor W Adorno Pl 4, D-60323 Frankfurt, Germany.
   [Skiera, Bernd] Deakin Business Sch, 221 Burwood Highway, Burwood, Vic 3125, Australia.
C3 Goethe University Frankfurt; Deakin University
RP Skiera, B (corresponding author), Goethe Univ Frankfurt Main, Fac Business & Econ, Theodor W Adorno Pl 4, D-60323 Frankfurt, Germany.
EM juergensmeier@wiwi.uni-frankfurt.de; skiera@wiwi.uni-frankfurt.de
RI Skiera, Bernd/B-6978-2013
OI Skiera, Bernd/0000-0001-9285-2013; Jurgensmeier,
   Lukas/0000-0002-0808-5147
FU Efl - the Data Science Institute; Digital Teaching and Learning Lab
   (DigiTeLL); Goethe University Frankfurt
FX We thank the IJRM co-editor, David Schweidel, the Area Editor, Michael
   Haenlein, and three anonymous reviewers for excellent feedback.
   Additionally, we thank Karim Zibo, Matti Vennen, and David Miesner from
   Zavi AI for their excellent support in developing the web application,
   which is available through StudyLabs.ai. We also thank Jan Bischoff,
   Jost Kaufmann, and Ryan Grabowski for their excellent support of the
   project. This work benefitted from funding by the "efl - the Data
   Science Institute" and the "Digital Teaching and Learning Lab"
   (DigiTeLL) , a project at Goethe University Frankfurt funded through
   Stiftung Innovation in der Hochschullehre (Foundation for Innovation in
   Higher Education) .
CR Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Albers S, 2012, INT J RES MARK, V29, P111, DOI 10.1016/j.ijresmar.2012.03.001
   [Anonymous], 2023, Digest of education statistics
   Anthropic, 2024, Papers with Code
   Bangor A, 2008, INT J HUM-COMPUT INT, V24, P574, DOI 10.1080/10447310802205776
   BARNETT GO, 1978, MED CARE, V16, P962, DOI 10.1097/00005650-197811000-00007
   Botelho A, 2023, J COMPUT ASSIST LEAR, V39, P823, DOI 10.1111/jcal.12793
   Brand J., 2023, Using gpt for market research
   Brooke J., 1996, USABILITY EVALUATION, P189
   Brown G., 2017, Frontiers in Education, V2, DOI DOI 10.3389/FEDUC.2017.00024
   CHICKERING AW, 1989, BIOCHEM EDUC, V17, P140, DOI 10.1016/0307-4412(89)90094-0
   Czaplewski A., 2009, Marketing Education Review, V19, P29
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Deeva G, 2021, COMPUT EDUC, V162, DOI 10.1016/j.compedu.2020.104094
   Dixson DD, 2016, THEOR PRACT, V55, P153, DOI 10.1080/00405841.2016.1148989
   Eisenbeiss M., 2020, Case 9B20A027
   Germann F, 2013, INT J RES MARK, V30, P114, DOI 10.1016/j.ijresmar.2012.10.001
   Gibbs G., 2005, LEARNING TEACHING HI, P3, DOI DOI 10.1007/978-3-8348-9837-1
   Goli A, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.0306
   Google Gemini Team, 2024, Technical Report
   Guha A, 2024, J MARKET EDUC, V46, P6, DOI 10.1177/02734753231215436
   Heron G, 2011, BRIT J SOC WORK, V41, P276, DOI 10.1093/bjsw/bcq049
   Huang M H., 2023, Journal of Marketing
   Jansen Tijmen, 2023, Working Paper
   Koltovskaia S, 2020, ASSESS WRIT, V44, DOI 10.1016/j.asw.2020.100450
   Kumar Harsh, 2023, Math education with large language models: Peril or promise?
   Li PY, 2024, MARKET SCI, V43, DOI 10.1287/mksc.2023.0454
   Liu X., 2018, Marketing Education Review, V28, P28, DOI DOI 10.1080/10528008.2017.1421049
   McAfee A, 2012, HARVARD BUS REV, V90, P60
   Mistral AI, 2024, Au large: Mistral large
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   OpenAI, 2023, OpenAI Platform
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Radford A., 2018, Technical Reports
   Radford A., 2019, OPENAI BLOG
   Reisenbichler M, 2022, MARKET SCI, V41, P441, DOI 10.1287/mksc.2022.1354
   Ringel Daniel, 2023, Working Paper
   Sauro J, 2012, QUANTIFYING THE USER EXPERIENCE: PRACTICAL STATISTICS FOR USER RESEARCH, P1
   Singh R, 2013, ACM SIGPLAN NOTICES, V48, P15, DOI 10.1145/2499370.2462195
   Skiera B, 2024, J MARK ANAL, V12, P209, DOI 10.1057/s41270-024-00313-2
   Zhang Z, 2018, ASSESS WRIT, V36, P90, DOI 10.1016/j.asw.2018.02.004
NR 41
TC 1
Z9 1
U1 50
U2 50
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0167-8116
EI 1873-8001
J9 INT J RES MARK
JI Int. J. Res. Mark.
PD SEP
PY 2024
VL 41
IS 3
BP 468
EP 488
DI 10.1016/j.ijresmar.2024.05.005
EA AUG 2024
PG 21
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA G8N5J
UT WOS:001319139800001
OA hybrid
DA 2024-12-25
ER

PT J
AU Zhao, FZ
   Sun, Y
   Feng, L
   Zhang, L
   Zhao, DZ
AF Zhao, Fangzhou
   Sun, Yao
   Feng, Lei
   Zhang, Lan
   Zhao, Dezong
TI Enhancing Reasoning Ability in Semantic Communication Through Generative
   AI-Assisted Knowledge Construction
SO IEEE COMMUNICATIONS LETTERS
LA English
DT Article
DE Semantics; Training; Decoding; Mutual information; Knowledge
   engineering; Encoding; Transceivers; Semantic communication; generative
   artificial intelligence; background knowledge construction
AB Semantic communication (SemCom), a pioneering paradigm that places emphasis on conveying the meaning of information, faces challenges in constructing background knowledge to drive precise reasoning of semantic coding models. Fortunately, the recent emergence of Generative Artificial Intelligence (GAI) technology is promising to create high-quality content that can be harnessed to assist knowledge construction in SemCom, enhancing the reasoning ability of semantic coding models. In this letter, we propose a GAI-assisted SemCom framework, named Gen-SC, where sufficient samples for training SemCom transceivers are generated using GAI as per user contextual information. In addition, to guide the GAI model in producing contextually relevant content, a discriminator is incorporated into Gen-SC to measure the disparity between generated samples and actual samples. The simulation results demonstrate that the Gen-SC achieves higher semantic accuracy, especially when the original training samples are insufficient, in contrast to traditional SemCom without knowledge enhancement.
C1 [Zhao, Fangzhou; Sun, Yao; Zhao, Dezong] Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Scotland.
   [Feng, Lei] Beijing Univ Posts & Telecommun BUPT, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.
   [Zhang, Lan] Clemson Univ, Dept Elect & Comp Engn, Clemson, SC 29634 USA.
C3 University of Glasgow; Clemson University
RP Sun, Y (corresponding author), Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Scotland.
EM f.zhao.1@research.gla.ac.uk; Yao.Sun@glasgow.ac.uk; fenglei@bupt.edu.cn;
   lan7@clemson.edu; dezong.zhao@glasgow.ac.uk
RI Cheng, Ching-Yu/Y-2229-2019; zhao, fangzhou/LMO-9032-2024
OI zhao, fangzhou/0009-0003-8301-4087; Feng, Lei/0000-0003-3494-5590
CR Chaccour C, 2022, Arxiv, DOI [arXiv:2211.14343, DOI 10.1109/COMST.2024.3412852]
   Choi J, 2022, IEEE VTS VEH TECHNOL, DOI 10.1109/VTC2022-Fall57202.2022.10012860
   Choi J, 2022, IEEE ACCESS, V10, P129806, DOI 10.1109/ACCESS.2022.3228563
   Du HY, 2023, IEEE J SEL AREA COMM, V41, P2547, DOI 10.1109/JSAC.2023.3288231
   Hagan M.T., 1996, NEURAL NETWORK DESIG
   Han T., 2023, P IEEE INT C AC SPEE, P1
   Liang CS, 2024, Arxiv, DOI arXiv:2401.00124
   Rajpurkar P, 2018, Arxiv, DOI [arXiv:1806.03822, DOI 10.48550/ARXIV.1806.03822]
   Uysal E, 2022, IEEE NETWORK, V36, P233, DOI 10.1109/MNET.106.2100636
   Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686
   Wu Q., 2020, arXiv, V2020
   Xia L, 2024, Arxiv, DOI arXiv:2308.15483
   Xie HQ, 2021, IEEE T SIGNAL PROCES, V69, P2663, DOI 10.1109/TSP.2021.3071210
   Zhao FZ, 2023, IEEE VTS VEH TECHNOL, DOI 10.1109/VTC2023-Fall60731.2023.10333793
   Zhou QY, 2022, IEEE OPEN J COMM SOC, V3, P1076, DOI 10.1109/OJCOMS.2022.3189023
NR 15
TC 0
Z9 0
U1 6
U2 9
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 1089-7798
EI 1558-2558
J9 IEEE COMMUN LETT
JI IEEE Commun. Lett.
PD APR
PY 2024
VL 28
IS 4
BP 832
EP 836
DI 10.1109/LCOMM.2024.3365158
PG 5
WC Telecommunications
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Telecommunications
GA NK0K3
UT WOS:001200227900032
DA 2024-12-25
ER

PT J
AU King, S
   Prasetyo, J
AF King, Stephen
   Prasetyo, Judhi
TI Assessing generative AI through the lens of the 2023 Gartner Hype Cycle
   for Emerging Technologies: a collaborative autoethnography
SO FRONTIERS IN EDUCATION
LA English
DT Article
DE generative AI; problematic use of Internet; autoethnography; Gartner
   Technology Hype Cycle; curriculum design
AB This brief research report examines claims made across contemporary media channels that generative artificial intelligence can be used to develop educational materials, in an experiment to develop a new course for advertising, PR and branding professionals. A collaborative auto-ethnography is employed to examine the journey and unintended consequences experienced by a non-technology lecturer engaging with generative AI for the first-time and is examined under the lens of the 2023 Gartner Hype Cycle for Emerging Technologies. The researchers were able to map lived experiences to stages of the Gartner model, presenting evidence that this tool could have extended utility in the field of human resources for the support of technology integration projects. They also recorded several potential manifestations of symptoms related to the problematic use of Internet (PUI). The implications of the findings contribute to ongoing public discourse regarding the introduction of artificial intelligence within education, with insights for policy development and governance, as well as faculty and student wellbeing.
C1 [King, Stephen] Middlesex Univ Dubai, Media Dept, Dubai, U Arab Emirates.
   [Prasetyo, Judhi] Middlesex Univ Dubai, Comp Engn & Informat, Dubai, U Arab Emirates.
C3 Middlesex University; Middlesex University
RP King, S (corresponding author), Middlesex Univ Dubai, Media Dept, Dubai, U Arab Emirates.
EM s.king@mdx.ac.ae
OI King, Stephen/0000-0002-0659-1353
CR AI Avalanche, 2023, How to make an online course using ChatGPT
   Chandrasekaran A., 2023, Gartner
   Chang H., 2008, Autoethnography as method
   Cooper R, 2022, QUAL REP, V27, P197, DOI 10.46743/2160-3715/2022.5288
   Dawson P., 2023, Understanding Gartner's Hype Cycles
   Deitering A.-M., 2017, SELF SUBJECT AUTOETH
   Gartner, 2023, Gartner Hype Cycle
   Ghapanchi A., 2023, How generative AI like ChatGPT is pushing assessment reform
   Gregory A., 2020, The effects of AI on the Professions
   Gregory A., 2023, Humans needed more than ever
   Its Alan Ayoubi, 2023, Now You can create online courses using Ai
   Moretta T, 2022, COMPR PSYCHIAT, V112, DOI 10.1016/j.comppsych.2021.152286
   Roy R, 2020, QUAL RES J, V20, P383, DOI 10.1108/QRJ-06-2020-0054
   Shelton C., 2020, Contemp. Issues Tech. Teach. Educ, V20
   Taber N, 2010, QUAL RES, V10, P5, DOI 10.1177/1468794109348680
   Terry Owen Kichizo, 2023, CHRON HIGHER EDUC
   UAE Ministry of Artificial Intelligence Digital Economy and Remote Work Applications, 2023, 100 practical applications and use cases of generative AI
   Valin J., 2018, AI: humans still needed
   Van Maanen John., 1988, TALES FIELD
   Weale S., 2023, The Guadian
NR 20
TC 3
Z9 3
U1 7
U2 31
PU FRONTIERS MEDIA SA
PI LAUSANNE
PA AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND
EI 2504-284X
J9 FRONT EDUC
JI Front. Educ.
PD DEC 1
PY 2023
VL 8
AR 1300391
DI 10.3389/feduc.2023.1300391
PG 9
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA CI0W9
UT WOS:001124517000001
OA gold
DA 2024-12-25
ER

PT J
AU Ali, H
   Aysan, AF
AF Ali, Hassnian
   Aysan, Ahmet Faruk
TI Ethical dimensions of generative AI: a cross-domain analysis using
   machine learning structural topic modeling
SO INTERNATIONAL JOURNAL OF ETHICS AND SYSTEMS
LA English
DT Article; Early Access
DE Generative AI; Ethics; Structure topic modeling; Governance; Regulation;
   I23; I31; K11; O33.
ID ARTIFICIAL-INTELLIGENCE; HIGHER-EDUCATION; CHATGPT; TRENDS
AB PurposeThe purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).Design/methodology/approachLeveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.FindingsThe results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.Research limitations/implicationsThis study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI's evolving ethical landscape, offering a model for future research and policymaking in diverse fields.Originality/valueThe study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.
C1 [Ali, Hassnian; Aysan, Ahmet Faruk] Hamad Bin Khalifa Univ, Coll Islamic Studies, Doha, Qatar.
   [Ali, Hassnian] Minhaj Univ Lahore, Int Ctr Res Islamic Econ, ICRIE, Lahore, Pakistan.
C3 Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar; Minhaj
   University
RP Ali, H (corresponding author), Hamad Bin Khalifa Univ, Coll Islamic Studies, Doha, Qatar.
EM haal50943@hbku.edu.qa
RI Aysan, Ahmet/ABD-9167-2021
CR Abu-Farha R, 2023, J AM PHARM ASSOC, V63, DOI 10.1016/j.japh.2023.08.020
   Abubakar J., 2022, EC PERSP P 36 EUR BU
   Adhikari K, 2024, CURR UROL REP, V25, P1, DOI 10.1007/s11934-023-01185-2
   Al Lily AE, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02154-3
   Ali H., 2023, Modern Finance, V1, P116, DOI [10.61351/mf.v1i1.67, DOI 10.61351/MF.V1I1.67]
   Alier M, 2024, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2024.02.011
   Alter IL, 2024, EUR ARCH OTO-RHINO-L, V281, P2723, DOI 10.1007/s00405-024-08512-4
   Andrieux P, 2024, ORGAN DYN, V53, DOI 10.1016/j.orgdyn.2024.101032
   Arslan S, 2024, CLIN NUTR ESPEN, V60, P285, DOI 10.1016/j.clnesp.2024.02.022
   Asia Securities Industry and Financial Markets Association, 2024, Enabling an efficient regulatory environment for AI-practical considerations for generative AI
   Association of Southeast Asian Nations, 2024, ASEAN GUIDE AI GOVER
   Au KH, 2023, INT J SURG, V109, P3940, DOI 10.1097/JS9.0000000000000686
   Awal SS, 2023, J PUBLIC HEALTH-HEID, DOI 10.1007/s10389-023-02170-2
   Aysan A F., 2023, World Scientific Annual Review of Islamic Finance, 01, P41, DOI DOI 10.1142/S2811023423500028
   Aysan AF, 2021, J RISK FINANC MANAG, V14, DOI 10.3390/jrfm14090427
   Bai XW, 2021, TRANSPORT POLICY, V102, P11, DOI 10.1016/j.tranpol.2020.12.013
   Baker HK, 2021, INT REV FINANC ANAL, V78, DOI 10.1016/j.irfa.2021.101946
   Baker HK, 2020, EUR FINANC MANAG, V26, P1224, DOI 10.1111/eufm.12286
   Bartlett KA, 2024, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2024.02.006
   Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993
   Briganti G, 2023, EUR ARCH OTO-RHINO-L, DOI 10.1007/s00405-023-08337-7
   Bringula R, 2024, FRONT EDUC, V9, DOI 10.3389/feduc.2024.1248705
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Carnevale A, 2023, HUMANA MENTE, V16, P33
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Cascella M, 2023, J MED SYST, V47, DOI 10.1007/s10916-023-01925-4
   CEDPO, 2023, Generative AI: the data protection implications
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chavanayarn S., 2023, Bull. Sci. Technol. Soc, V43, P105, DOI [10.1177/02704676231216355, DOI 10.1177/02704676231216355]
   Chen J, 2024, J MED ETHICS, V50, P97, DOI 10.1136/jme-2023-109366
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Cohen IG, 2023, AM J BIOETHICS, V23, P8, DOI 10.1080/15265161.2023.2233357
   Cooper G, 2024, J SCI EDUC TECHNOL, V33, P556, DOI 10.1007/s10956-024-10104-0
   Currie GM, 2023, SEMIN NUCL MED, V53, P719, DOI 10.1053/j.semnuclmed.2023.04.008
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   Das K, 2023, J BUS RES, V154, DOI 10.1016/j.jbusres.2022.113384
   de Winter JCF, 2023, INFORMATICS-BASEL, V10, DOI 10.3390/informatics10040087
   Dergaa I, 2023, BIOL SPORT, V40, P615, DOI 10.5114/biolsport.2023.125623
   Duah JE, 2024, INT J INF LEARN TECH, V41, P180, DOI 10.1108/IJILT-11-2023-0213
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Egbemhenghe A. U., 2023, Environmental Challenges, V13, P100782, DOI [10.1016/j.envc.2023.100782, DOI 10.1016/J.ENVC.2023.100782]
   Farina M, 2023, FRONT ARTIF INTELL, V6, DOI 10.3389/frai.2023.1130913
   Ferrara E., 2023, First Monday, V28, DOI [DOI 10.5210/FM.V28I11.13346, 10.5210/fm.v28i11.13346]
   Francisco GL, 2019, INT BUS REV, V28, P713, DOI 10.1016/j.ibusrev.2019.02.001
   Fuchs K., 2023, Integrating artificial intelligence in higher education: empirical insights from students about using ChatGPT, V13, DOI [10.18178/ijiet.2023.13.9.1939, DOI 10.18178/IJIET.2023.13.9.1939]
   Gallent-Torres C, 2023, RELIEVE, V29, DOI 10.30827/relieve.v29i2.29134
   Gill S. S., 2023, Internet of Things and Cyber-Physical Systems, V3, P262, DOI DOI 10.1016/J.IOTCPS.2023.05.004
   Glaser N, 2023, TECHNOL KNOWL LEARN, V28, P1945, DOI 10.1007/s10758-023-09684-4
   Gromova EA, 2023, Rev Bras Altern Disput Resolut J Altern Disput Resolut, V5, P153
   Gross N, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12080435
   Grünebaum A, 2023, AM J OBSTET GYNECOL, V228, P696, DOI 10.1016/j.ajog.2023.03.009
   Guleria A, 2024, MED SCI LAW, V64, P150, DOI 10.1177/00258024231191829
   Gupta M, 2023, IEEE ACCESS, V11, P80218, DOI 10.1109/ACCESS.2023.3300381
   Hasanein AM, 2023, EUR J INVEST HEALTH, V13, P2599, DOI 10.3390/ejihpe13110181
   Heikkila M., 2023, MIT Technology Review25 March
   Hung JS, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12070380
   Infocomm Media Development Authority, 2024, Proposed model AI governance framework for generative AI: fostering a trusted ecosystem
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Jiang HC, 2016, RENEW SUST ENERG REV, V57, P226, DOI 10.1016/j.rser.2015.12.194
   Jobin A, 2019, NAT MACH INTELL, V1, P389, DOI 10.1038/s42256-019-0088-2
   Karakose T., 2023, Educ. Process Int. J, V12, DOI [DOI 10.22521/EDUPIJ.2023.122.1, 10.22521/edupij.2023.122.1]
   Kazim E, 2021, PATTERNS, V2, DOI 10.1016/j.patter.2021.100314
   Khan MS, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24890
   Kim JK, 2023, J PEDIATR UROL, V19, P598, DOI 10.1016/j.jpurol.2023.05.018
   Klang E, 2023, THER ADV GASTROENTER, V16, DOI 10.1177/17562848231218618
   Klenk M, 2024, ETHICS INF TECHNOL, V26, DOI 10.1007/s10676-024-09745-x
   Knott A, 2023, ETHICS INF TECHNOL, V25, DOI 10.1007/s10676-023-09728-4
   Kuhn KD, 2018, TRANSPORT RES C-EMER, V87, P105, DOI 10.1016/j.trc.2017.12.018
   Laker L.F., 2023, Issues Inf. Syst, V24, P153, DOI [10.48009/2iis2023113, DOI 10.48009/2IIS2023113]
   Langis UP, 2023, RELIG ARTS, V27, P385, DOI 10.1163/15685292-02703004
   Levkovich I, 2023, FAM MED COMMUNITY HE, V11, DOI 10.1136/fmch-2023-002391
   Li LY, 2024, EDUC INF TECHNOL, V29, P10729, DOI 10.1007/s10639-023-12256-9
   Lim B, 2023, J CLIN MED, V12, DOI 10.3390/jcm12206524
   Livberber T, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e19688
   Lund BD, 2024, HEALTH INFO LIBR J, V41, P4, DOI 10.1111/hir.12518
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Lynch CJ, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15120375
   Martin John Levi, 2023, Journal of Social Computing, P1, DOI 10.23919/JSC.2023.0003
   Mehta S, 2024, CURR PROB CARDIOLOGY, V49, DOI 10.1016/j.cpcardiol.2024.102393
   Meron Y, 2023, DES SCI, V9, DOI 10.1017/dsj.2023.28
   Miao J, 2023, J PERS MED, V13, DOI 10.3390/jpm13121681
   Mishra P, 2024, TECHTRENDS, V68, P205, DOI 10.1007/s11528-024-00938-1
   Mittal A., 2023, Economic and Political Weekly, V58, P62
   Mittelstadt B, 2019, NAT MACH INTELL, V1, P501, DOI 10.1038/s42256-019-0114-4
   Nam BH, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00452-5
   Obrenovic B, 2024, AI SOC, DOI 10.1007/s00146-024-01889-0
   Oddone K, 2024, J AUST LIB INF ASSOC, V73, P3, DOI 10.1080/24750158.2023.2289093
   Ong JCL, 2024, CELL REP MED, V5, DOI 10.1016/j.xcrm.2023.101356
   Oniani D, 2023, NPJ DIGIT MED, V6, DOI 10.1038/s41746-023-00965-x
   Pack A, 2023, TESOL QUART, V57, P1571, DOI 10.1002/tesq.3253
   Patton DU, 2023, J SOC SOC WORK RES, V14, P553, DOI 10.1086/726042
   Popowicz-Pazdej A., 2023, Journal of Data Protection and Privacy, V6, P153
   Qadri HMUD, 2024, J ISLAMIC ACCOUNT BU, V15, P291, DOI 10.1108/JIABR-02-2022-0055
   Raman R, 2024, COMPUT SECUR, V140, DOI 10.1016/j.cose.2024.103804
   Raman R, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e27026
   Raman R, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24727
   Ray P. P., 2023, BenchCouncil Transactions on Benchmarks, Standards and Evaluations, V3
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Reis LO, 2023, INT BRAZ J UROL, V49, P652, DOI [10.1590/S1677-5538.IBJU.2023.0112, 10.1590/s1677-5538.ibju.2023.0112]
   Rialp A, 2019, INT BUS REV, V28, DOI 10.1016/j.ibusrev.2019.101587
   Richards D, 2024, PEOPLE NAT, V6, P882, DOI 10.1002/pan3.10622
   Roberts M.E., 2013, NIPS 2013 WORKSHOP T, P2
   Roberts ME, 2014, AM J POLIT SCI, V58, P1064, DOI 10.1111/ajps.12103
   Robledo Dave Arthur R., 2023, International Journal of Information and Education Technology, V13, P1582, DOI 10.18178/ijiet.2023.13.10.1965
   Rozado D, 2023, SOC SCI-BASEL, V12, DOI 10.3390/socsci12030148
   Rusandi MA, 2023, J PUBLIC HEALTH-UK, V45, pE602, DOI 10.1093/pubmed/fdad049
   Sahu PK, 2024, POSTGRAD MED J, V100, P50, DOI 10.1093/postmj/qgad090
   Sallam M, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11060887
   Sauvola J, 2024, AUTOMAT SOFTW ENG, V31, DOI 10.1007/s10515-024-00426-z
   Sharma A, 2021, INT J INFORM MANAGE, V58, DOI 10.1016/j.ijinfomgt.2021.102316
   Song NY, 2024, J CONTING CRISIS MAN, V32, DOI 10.1111/1468-5973.12532
   Sop SA, 2024, J HOSP TOUR TECHNOL, V15, P329, DOI 10.1108/JHTT-08-2023-0237
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Tahir M., 2023, slam Ekonomisi ve Finans Dergisi (EFD), V9, P125
   Ullah E, 2024, DIAGN PATHOL, V19, DOI 10.1186/s13000-024-01464-7
   Umer F, 2024, BDJ OPEN, V10, DOI 10.1038/s41405-024-00198-4
   Vandamme F., 2023, Scientia Paedagogica Experimentalis, V60, P95
   Vargas-Murillo AR., 2023, International Journal of Learning, Teaching and Educational Research, V22, P122, DOI [DOI 10.26803/IJLTER.22.7.7, 10.26803/ijlter.22.7.7]
   Vartiainen H, 2024, IEEE COMPUT GRAPH, V44, P12, DOI 10.1109/MCG.2024.3355808
   Vetter Matthew A., 2024, Computers and Composition, V71, DOI 10.1016/j.compcom.2024.102831
   Vignesh R., 2023, Education in Medicine Journal, V15, P103
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Walczak K, 2023, ECON BUS REV-POL, V9, P71, DOI 10.18559/ebr.2023.2.743
   Wang CY, 2023, J MED INTERNET RES, V25, DOI 10.2196/48009
   Wang N, 2024, ASIA PAC J EDUC, V44, P139, DOI 10.1080/02188791.2024.2305156
   Wang YT, 2023, IEEE OPEN J COMP SOC, V4, P280, DOI 10.1109/OJCS.2023.3300321
   World Economic Forum, 2024, Generative AI governance: shaping a collective global future
   Yu H, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e24289
   Zheng Y, 2024, FRONT ARTIF INTELL, V7, DOI 10.3389/frai.2024.1338433
   Zhu WJ, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2323277
NR 130
TC 0
Z9 0
U1 24
U2 24
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 2514-9369
EI 2514-9377
J9 INT J ETHICS SYST
JI Int. J. Ethics Syst.
PD 2024 SEP 5
PY 2024
DI 10.1108/IJOES-04-2024-0112
EA SEP 2024
PG 32
WC Economics
WE Emerging Sources Citation Index (ESCI)
SC Business & Economics
GA E5J7Q
UT WOS:001303369400001
DA 2024-12-25
ER

PT J
AU Carayannis, EG
   Dumitrescu, R
   Falkowski, T
   Zota, NR
AF Carayannis, Elias G.
   Dumitrescu, Roman
   Falkowski, Tommy
   Zota, Nikos-Rigert
TI Empowering SMEs "Harnessing the Potential of Gen AI for Resilience and
   Competitiveness"
SO IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT
LA English
DT Article
DE Artificial intelligence; Technological innovation; Resilience;
   Generative AI; Navigation; Decision making; Chatbots; AI-driven
   innovation; AI integration; generative artificial intelligence (Gen AI);
   small and medium enterprise (SME) growth; SME resilience; technological
   adaptation
ID ARTIFICIAL-INTELLIGENCE; ENTERPRISES
AB This study investigates how generative artificial intelligence (Gen AI) can enhance the resilience and competitiveness of small and medium enterprises (SMEs). The central question addressed is: How can SMEs leverage Gen AI to navigate challenges and capitalize on opportunities in an evolving digital landscape? We argue that Gen AI offers transformative potential for SMEs by automating processes, enhancing decision-making and fostering innovation, thereby improving their ability to adapt and thrive amidst market uncertainties. Through a comprehensive analysis of SMEs and Gen AI, this article underscores the importance of strategic AI integration, addresses the associated challenges, and provides policy recommendations to support SMEs in harnessing AI for sustainable growth. By exploring real-world examples and theoretical insights, we aim to equip SMEs with the directions, actions, and strategies necessary to succeed in the Gen AI era.
C1 [Carayannis, Elias G.] George Washington Univ, Sch Business, Washington, DC 20052 USA.
   [Dumitrescu, Roman] Univ Paderborn, Heinz Nixdorf Inst, Dept Elect Engn Comp Sci & Math, D-33102 Paderborn, Germany.
   [Falkowski, Tommy] Univ Paderborn, D-33102 Paderborn, Germany.
   [Zota, Nikos-Rigert] Univ Aegean, Dept Informat & Commun Syst Engn, Mitilini 45444, Greece.
C3 George Washington University; University of Paderborn; University of
   Paderborn; University of Aegean
RP Carayannis, EG (corresponding author), George Washington Univ, Sch Business, Washington, DC 20052 USA.
EM caraye@gwu.edu; roman.dumitrescu@iem.fraunhofer.de;
   tommy.falkowski@iem.fraunhofer.de; zotas_nikos@yahoo.gr
RI ; CARAYANNIS, ELIAS/H-3075-2014
OI Zotas, Nikos - Rigert/0009-0007-0536-1357; CARAYANNIS,
   ELIAS/0000-0003-2348-4311
CR Abosede A.J., 2016, INT REV MANAGEMENT B, V5, P315
   Adomako S, 2022, INT BUS REV, V31, DOI 10.1016/j.ibusrev.2022.102032
   Agustina T. S.., 2023, INOBIS JURNAL INOVAS, V6, P149, DOI [10.31842/jurnalinobis.v6i2.265, DOI 10.31842/JURNALINOBIS.V6I2.265]
   Ahmad I., 2024, J INTERCULTURAL COMM, V24, P43, DOI [10.36923/jicc.v24i1.94, DOI 10.36923/JICC.V24I1.94]
   AlAli R., 2024, INT J RELIG, V5, P784, DOI [10.61707/8y29gv34, DOI 10.61707/8Y29GV34]
   [Anonymous], 2021, SME definition
   [Anonymous], 2019, Doing business 2019: Training for reform
   [Anonymous], 2023, Bloomberg
   Bachtiar N. K., 2023, J INNOVATION ENTREPR, V12
   BADGHISH S, 2024, INNOV MANAGE ORG PER, V16
   Bamfo BA, 2019, COGENT BUS MANAG, V6, DOI 10.1080/23311975.2019.1605703
   Bekata AT, 2024, COGENT BUS MANAG, V11, DOI 10.1080/23311975.2024.2320462
   Bilgram Volker, 2023, IEEE Engineering Management Review, P18, DOI 10.1109/EMR.2023.3272799
   Bowman S.R., 2023, ARXIV
   Carayannis E.G., 2009, International Journal of Innovation and Regional Development, V1, P235, DOI [10.1504/IJIRD.2009.021845, DOI 10.1504/IJIRD.2009.021845]
   Carayannis E. G., 2023, HDB RES ARTIFICIAL I, P474
   Carayannis EG, 2022, EUR J OPER RES, V300, P791, DOI 10.1016/j.ejor.2021.10.030
   Carayannis EG, 2023, J KNOWL ECON, V14, P2420, DOI 10.1007/s13132-022-00991-2
   Choi JK, 2019, RESOUR CONSERV RECY, V147, P19, DOI 10.1016/j.resconrec.2019.04.015
   Conz E, 2020, EUR MANAG J, V38, P400, DOI 10.1016/j.emj.2019.12.004
   Cowen-Rivers A. I., 2020, ARXIV
   DellAcqua F. E., 2023, Working Paper No. 24-013., DOI [10.2139/ssrn.4573321, DOI 10.2139/SSRN.4573321]
   Deloitte, 2024, STATE GENERATIVE AI
   Drydakis N, 2022, INFORM SYST FRONT, V24, P1223, DOI 10.1007/s10796-022-10249-6
   Dumitrescu R., 2024, GENAITOOLS REVOLUTIO
   Dvorsky J, 2021, E M EKON MANAG, V24, P102, DOI 10.15240/tul/001/2021-1-007
   Esteva A, 2019, NAT MED, V25, P24, DOI 10.1038/s41591-018-0316-z
   França TJF, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e14694
   Frederickson S., 2023, SEEN MICROSOFT STORI
   Gehrmann, 2020, DATA DRIVEN DECISION
   Goodfellow I. J., 2014, ARXIV, DOI [10.48550/arXiv.1406.266, DOI 10.48550/ARXIV.1406.266]
   Häring K, 2023, LOGISTICS-BASEL, V7, DOI 10.3390/logistics7040099
   Howarth, 2024, 55 NEW GENERATIVE AI
   Hussain R. R. Atif, 2020, ARXIV, DOI DOI 10.48550/ARXIV.2408.11825
   Hussein Esmat H., 2022, J BUSINESS MANAGEMEN, V10, P247
   Ietto B, 2024, IEEE T ENG MANAGE, V71, P1057, DOI 10.1109/TEM.2022.3144881
   Istiqaroh C. R., 2022, J THEOR APPL MANAGE, V15, P449, DOI [10.20473/jmtt.v15i3.37640, DOI 10.20473/JMTT.V15I3.37640]
   Justo-Hanani R, 2022, POLICY SCI, V55, P137, DOI 10.1007/s11077-022-09452-8
   Kallmuenzer A, 2024, REV MANAG SCI, DOI 10.1007/s11846-024-00744-2
   Karras Tero, 2020, ADV NEURAL INFORM PR, V33, P12104
   Kergroach S., 2021, Going Digital Toolkit Note
   Klepic I., 2021, Nase Gospodarstvo/Our Economy, V67, P1, DOI [DOI 10.2478/NGOE-2021-0013, 10.2478/ngoe-2021-0013%0A,2021]
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Kumar M, 2024, PROD PLAN CONTROL, V35, P1639, DOI 10.1080/09537287.2022.2131620
   Kumar S., 2023, SCHOLEDGE INT J BUS, V10
   Kumar S., 2024, INT J BUS POLICY GOV, V10, DOI [10.19085/sijbpg100201, DOI 10.19085/SIJBPG100201]
   Lestari ED, 2024, COGENT BUS MANAG, V11, DOI 10.1080/23311975.2023.2301135
   Madanchian M., 2017, Journal of Economics and Management Systems, V2, P240
   Makany T, 2023, PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON CONVERSATIONAL USER INTERFACES, CUI 2023, DOI 10.1145/3571884.3604315
   Mallett O, 2019, INT J MANAG REV, V21, P294, DOI 10.1111/ijmr.12191
   Novilia F., 2023, W SCI SOCIAL HUMANIT, V1
   openai, 2024, LIFESPAN USES GPT 4
   Papenkordt J., 2022, KUNSTLICHE INTELLIGE
   Poufinas T., 2018, Theoretical Economics Letters, V8, P2788, DOI DOI 10.4236/TEL.2018.813175
   Pramod Dhanya, 2023, 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), P1, DOI 10.1109/OTCON56053.2023.10113959
   Radford A., 2015, INT C LEARN REPR 201
   Rahman J., 2023, INDONESIAN J BUS TEC, V1, P1
   Rajaram K, 2024, BUS HORIZONS, V67, P629, DOI 10.1016/j.bushor.2024.05.008
   Rane N., 2024, ARTIFICIAL INTELLIGE, DOI [10.2139/ssrn.4831911, DOI 10.2139/SSRN.4831911]
   Saunila M, 2020, J INNOV KNOWL, V5, P260, DOI 10.1016/j.jik.2019.11.002
   Sauser B, 2018, NAT HAZARDS, V90, P79, DOI 10.1007/s11069-017-3034-9
   Shahadat MMH, 2023, GLOB BUS REV, DOI 10.1177/09721509221137199
   Shama, 2023, International Journal of Technological Learning, Innovation and Development, V15, P162, DOI 10.1504/IJTLID.2023.135341
   Shepherd DA, 2011, ENTREP THEORY PRACT, V35, P137, DOI 10.1111/j.1540-6520.2010.00426.x
   Shore A., 2024, BUILDING ENTREPRENEU, DOI [10.1016/j.technovation.2024.103063, DOI 10.1016/J.TECHNOVATION.2024.103063]
   Simoes JD, 2017, HEALT SYST TRANSIT, V19, P1
   Soudi M., 2024, DISO, V3
   Sovrano F., 2023, CEUR WORKSHOP P, P54, DOI 10.5167/uzh-257180
   Statista, 2024, GENERATIVE AI WORLDW
   Tolner F, 2021, 2021 IEEE 19TH WORLD SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI 2021), P495, DOI 10.1109/SAMI50585.2021.9378637
   Ullah, 2023, IMPACT ARTIFICIAL IN
   Verma S., 2021, International Journal of Information Management Data Insights, V1, DOI [DOI 10.1016/J.JJIMEI.2020.100002, 10.1016/j.jjimei.2020.100002]
   Villa A, 2017, PROCEDIA MANUF, V13, P1297, DOI 10.1016/j.promfg.2017.09.060
   Vlacic B, 2021, J BUS RES, V128, P187, DOI 10.1016/j.jbusres.2021.01.055
   Yassin M., 2024, OVERCOMING CHALLENGE
   Yazdi M, 2024, SAFETY, V10, DOI 10.3390/safety10020042
   Zamani SZ, 2022, EUR J INNOV MANAG, V25, P735, DOI 10.1108/EJIM-07-2021-0360
   Zhu JH, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-02250-4
NR 78
TC 0
Z9 0
U1 62
U2 62
PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
PI PISCATAWAY
PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
SN 0018-9391
EI 1558-0040
J9 IEEE T ENG MANAGE
JI IEEE Trans. Eng. Manage.
PY 2024
VL 71
BP 14754
EP 14774
DI 10.1109/TEM.2024.3456820
PG 21
WC Business; Engineering, Industrial; Management
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering
GA I0R7H
UT WOS:001327421900005
DA 2024-12-25
ER

PT J
AU Manresa, A
   Sammour, A
   Mas-Machuca, M
   Chen, WF
   Botchie, D
AF Manresa, Alba
   Sammour, Ammar
   Mas-Machuca, Marta
   Chen, Weifeng
   Botchie, David
TI Humanizing GenAI at work: bridging the gap between technological
   innovation and employee engagement
SO JOURNAL OF MANAGERIAL PSYCHOLOGY
LA English
DT Article; Early Access
DE GenAI; Management; Work engagement; Technology acceptance; Employee
   performance
ID TURNOVER INTENTION; TRUST; READINESS; SATISFACTION; RESOURCES;
   ATTITUDES; BUSINESS; ADOPTION; FUTURE; USAGE
AB PurposeThis paper seeks to explore the influence of generative artificial intelligence (GenAI) on employee performance in the workplace, viewed from a managerial perspective. It concentrates on key elements such as employee engagement, trust in GenAI and attitudes toward its implementation. This exploration is motivated by the ongoing evolution of GenAI, which presents managers with the crucial task of understanding and integrating this technology into their strategic frameworks.Design/methodology/approachWe collected 251 responses from managers and senior managers representing companies that have embraced GenAI in Spain. A hierarchical regression analysis was employed to examine the hypotheses. Subsequently, mediating effects and moderated mediation effects were scrutinized using the bias-corrected bootstrapping method.FindingsThe data analysis suggests a significant enhancement in employee engagement and performance from a managerial perspective, attributed to improved attitudes and trust toward the adoption of GenAI. This conclusion is drawn from our research conducted with samples collected in Spain. Notably, our findings indicate that while positive attitudes toward GenAI correlate with enhanced engagement and performance, there exists a weakening effect on the significant positive impact of GenAI adoption in the workplace. This suggests that GenAI is still in its early stages of adoption within these companies, necessitating additional time for managers to develop greater confidence in its efficacy.Originality/valueThis study represents one of the pioneering investigations centered on the implementation of GenAI within the workplace context. It contributes significantly to the existing body of literature concerning the stimulus-organism-response (S-O-R) model in technology innovation adoption within work environments.
C1 [Manresa, Alba; Mas-Machuca, Marta] Univ Int Catalunya, Barcelona, Spain.
   [Sammour, Ammar] Birkbeck Univ London, Sch Business Econ & Informat, London, England.
   [Chen, Weifeng; Botchie, David] Brunel Univ London, Uxbridge, England.
C3 Universitat Internacional de Catalunya (UIC); University of London;
   Birkbeck University London; Brunel University
RP Sammour, A (corresponding author), Birkbeck Univ London, Sch Business Econ & Informat, London, England.
EM amanresa@uic.es
RI Manresa, Alba/F-4205-2017
OI Manresa, Alba/0000-0002-1686-2719; Chen, Weifeng/0000-0002-5850-0759
CR Aguinis H, 2024, ORGAN DYN, V53, DOI 10.1016/j.orgdyn.2024.101029
   Aldhaen Esra, 2023, Development and Learning in Organizations: An International Journal, P11, DOI 10.1108/DLO-06-2022-0108
   [Anonymous], 2018, The macroeconomic impact of artificial intelligence
   Arntz M., 2016, OECD Social, Employment and Migration Working Papers, V189, P1, DOI [10.1787/5jlz9h56dvq7-en, DOI 10.1787/1815199X]
   Bal PM, 2013, EUR J WORK ORGAN PSY, V22, P107, DOI 10.1080/1359432X.2011.626198
   Bates S., 2004, Human Resources, V49, P44
   Bedarkar M, 2014, PROCD SOC BEHV, V133, P106, DOI 10.1016/j.sbspro.2014.04.174
   Braganza A, 2021, J BUS RES, V131, P485, DOI 10.1016/j.jbusres.2020.08.018
   Brislin R.W., 1980, HDB CROSS CULTURAL P, V2, P389
   Brynjolfsson E., 2023, NBER Working Paper No. 31161, V31161, DOI DOI 10.3386/W31161
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Cao GM, 2021, TECHNOVATION, V106, DOI 10.1016/j.technovation.2021.102312
   Chatterjee S, 2021, TECHNOL FORECAST SOC, V170, DOI 10.1016/j.techfore.2021.120880
   Chen Q, 2023, INTERNET RES, V33, P2205, DOI 10.1108/INTR-09-2021-0686
   Loureiro SMC, 2021, J BUS RES, V129, P911, DOI 10.1016/j.jbusres.2020.11.001
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   Ding L, 2021, INT J CONTEMP HOSP M, V33, P2950, DOI 10.1108/IJCHM-09-2020-1038
   Eldor L, 2017, INT J HUM RESOUR MAN, V28, P526, DOI 10.1080/09585192.2016.1180312
   Flavián C, 2022, J SERV MANAGE, V33, P293, DOI 10.1108/JOSM-10-2020-0378
   Frank DA, 2023, INFORM TECHNOL PEOPL, V36, P155, DOI 10.1108/ITP-09-2022-0721
   Ghosh R, 2024, HUM RESOUR DEV INT, V27, P319, DOI 10.1080/13678868.2024.2354627
   Glikson E, 2020, ACAD MANAG ANN, V14, P627, DOI 10.5465/annals.2018.0057
   Hakanen JJ, 2008, WORK STRESS, V22, P224, DOI 10.1080/02678370802379432
   Harter JK, 2002, J APPL PSYCHOL, V87, P268, DOI 10.1037/0021-9010.87.2.268
   Hatzius J., 2023, Goldman Sachs Economic Reports
   Hayes A. F., 2022, INTRO MEDIATION MODE
   Holzinger A, 2018, LECT NOTES COMPUT SC, V11015, P1, DOI 10.1007/978-3-319-99740-7_1
   Hu LT, 1999, STRUCT EQU MODELING, V6, P1, DOI 10.1080/10705519909540118
   Janahi Yusuf Mohamed, 2023, Development and Learning in Organizations: An International Journal, V37, P29, DOI 10.1108/DLO-09-2022-0183
   Kawaguchi K, 2021, MANAGE SCI, V67, DOI 10.1287/mnsc.2020.3599
   Kim J, 2021, PSYCHOL MARKET, V38, P1140, DOI 10.1002/mar.21498
   Kong HY, 2024, J HOSP MARKET MANAG, V33, P261, DOI 10.1080/19368623.2023.2258116
   Koo B, 2021, INT J HOSP MANAG, V95, DOI 10.1016/j.ijhm.2020.102763
   Lee Y, 2018, J BUS RES, V91, P286, DOI 10.1016/j.jbusres.2018.06.022
   Leonard P, 2023, NEW TECH WORK EMPLOY, V38, P291, DOI 10.1111/ntwe.12226
   Lin CP, 2010, J BUS ETHICS, V94, P517, DOI 10.1007/s10551-009-0279-6
   Lu J, 2012, J ELECTRON COMMER RE, V13, P50
   Lu L, 2016, INT J CONTEMP HOSP M, V28, P737, DOI 10.1108/IJCHM-07-2014-0360
   Luqman A, 2017, COMPUT HUM BEHAV, V70, P544, DOI 10.1016/j.chb.2017.01.020
   Malik N, 2022, INT J MANPOWER, V43, P334, DOI 10.1108/IJM-03-2021-0173
   Manyika J., 2016, INDEPENDENT WORK CHO, P1
   McKnight DH, 2002, J STRATEGIC INF SYST, V11, P297, DOI 10.1016/S0963-8687(02)00020-3
   Mellahi K, 2016, BRIT J MANAGE, V27, P426, DOI 10.1111/1467-8551.12154
   Milanzes A., 2023, OECD Social, Employment and Migration Working Papers Nr. 289; OECD Social, Employment and Migration Working Papers,, V289, DOI [10.1787/2247ce58-en, DOI 10.1787/2247CE58-EN]
   Mittal, 2023, 9 INT C ADV COMP COM, P2502, DOI [10.1109/ICACCS57279.2023.10112957, DOI 10.1109/ICACCS57279.2023.10112957]
   Mogaji E, 2024, INT J CONTEMP HOSP M, V36, P3324, DOI 10.1108/IJCHM-08-2023-1271
   Mokyr J, 2015, J ECON PERSPECT, V29, P31, DOI 10.1257/jep.29.3.31
   Murray A., 2015, Fortune
   Muthen L.K., 2016, Regression and Mediation Analysis Using Mplus, DOI DOI 10.1201/9781315117430-28/MPLUS-BENGT-MUTH%C3%A9N-LINDA-MUTH%C3%A9N
   Nam T, 2019, TECHNOL FORECAST SOC, V138, P155, DOI 10.1016/j.techfore.2018.08.017
   Neuber L, 2022, EUR J WORK ORGAN PSY, V31, P292, DOI 10.1080/1359432X.2021.1953989
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Parasuraman A, 2015, J SERV RES-US, V18, P59, DOI 10.1177/1094670514539730
   Parasuraman A., 2000, Journal of Service Research, V2, P307, DOI [DOI 10.1177/109467050024001, 10.1177/109467050024001]
   Peng C, 2014, J INTERNET COMMER, V13, P159, DOI 10.1080/15332861.2014.944437
   Pluta A, 2016, J ORGAN CHANGE MANAG, V29, P293, DOI 10.1108/JOCM-11-2014-0210
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Preacher KJ, 2007, MULTIVAR BEHAV RES, V42, P185, DOI 10.1080/00273170701341316
   Rich BL, 2010, ACAD MANAGE J, V53, P617, DOI 10.5465/AMJ.2010.51468988
   Richman A., 2006, Workspan, V1, P36
   Russell J.A., 1974, APPROACH ENV PSYCHOL
   Saks A.M., 2006, J MANAGERIAL PSYCHOL, V21, P600, DOI [10.1108/02683940610690169, DOI 10.1108/02683940610690169]
   Sari RE., 2020, International Research Journal of Business Studies, V13, P173, DOI [10.21632/irjbs, DOI 10.21632/IRJBS]
   Schaufeli W.B., 2007, RES SOCIAL ISSUES MA, V5, P135, DOI DOI 10.1108/CDI-09-2013-0114
   Secinaro S., 2023, New Technologies in Supporting ESG Criteria and the Implementation in the New Normal: Mapping the Field and Proving Future Research Paths, DOI [10.3280/cgrds1-2023oa15788, DOI 10.3280/CGRDS1-2023OA15788]
   Sowa K, 2021, J BUS RES, V125, P135, DOI 10.1016/j.jbusres.2020.11.038
   Sowa Konrad, 2020, Digital Coworker: human-AI collaboration in work environment, on the example of virtual assistants for management professions, P179
   Spadaro G, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0237934
   Suhartanto D, 2018, J BUS RES, V83, P130, DOI 10.1016/j.jbusres.2017.10.039
   Sultan P, 2021, J CLEAN PROD, V312, DOI 10.1016/j.jclepro.2021.127807
   Tian HN, 2023, TECHNOL FORECAST SOC, V194, DOI 10.1016/j.techfore.2023.122732
   Nguyen TM, 2022, INT MARKET REV, V39, P482, DOI 10.1108/IMR-02-2021-0078
   van den Heuvel S, 2017, LEADERSHIP ORG DEV J, V38, P398, DOI 10.1108/LODJ-03-2015-0052
   Walczuch R, 2007, INFORM MANAGE-AMSTER, V44, P206, DOI 10.1016/j.im.2006.12.005
   Wang CX, 2023, HELIYON, V9, DOI 10.1016/j.heliyon.2023.e18349
   Wijayati DT, 2022, INT J MANPOWER, V43, P486, DOI 10.1108/IJM-07-2021-0423
   Wong W.K.O., 2024, J. Open Innov. Technol. Mark. Complex, V10, P100278, DOI [10.1016/j.joitmc.2024.100278, DOI 10.1016/J.JOITMC.2024.100278]
   Yalabik ZY, 2013, INT J HUM RESOUR MAN, V24, P2799, DOI 10.1080/09585192.2013.763844
   Zhu Y, 2023, J HOSP TOUR TECHNOL, V14, P208, DOI 10.1108/JHTT-02-2022-0041
NR 80
TC 1
Z9 1
U1 66
U2 66
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0268-3946
EI 1758-7778
J9 J MANAGE PSYCHOL
JI J. Manage. Psychol.
PD 2024 AUG 30
PY 2024
DI 10.1108/JMP-05-2024-0356
EA AUG 2024
PG 21
WC Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Psychology; Business & Economics
GA E0T6Q
UT WOS:001300221500001
DA 2024-12-25
ER

PT J
AU Farangi, MR
   Nejadghanbar, H
   Hu, GW
AF Farangi, Mohamad Reza
   Nejadghanbar, Hassan
   Hu, Guangwei
TI Use of generative AI in research: ethical considerations and emotional
   experiences
SO ETHICS & BEHAVIOR
LA English
DT Article; Early Access
DE GenAI; emotional reactions to AI; ethical concerns with AI; ChatGPT;
   research
ID ARTIFICIAL-INTELLIGENCE; EDUCATION
AB This study examines researchers' ethical concerns toward the deployment of GenAI in research and their emotional responses. To acquire an in-depth understanding, we used narrative frames and follow-up interviews to collect data from 22 researchers who reported extensive experience with GenAI. An inductive thematic analysis revealed three themes capturing ethical concerns that invoked three types of emotional reactions. From an ethical perspective, our participants were concerned with "human ethical agency in AI research practices," "cognitive impacts of overreliance on GenAI in research," and "ethical issues of access, accuracy, and privacy." From an emotional perspective, they showed "mixed emotions," "positive emotions," and "negative emotions" when dealing with GenAI tools. There were close connections between the ethical implications of GenAI and the emotional reactions to them. In this light, we conclude that emotional reactions to GenAI, which are determinants of future use, should be taken more seriously in further research.
C1 [Farangi, Mohamad Reza] Kashmar Inst Higher Educ, Dept Translat Studies, Kashmar, Iran.
   [Nejadghanbar, Hassan; Hu, Guangwei] Hong Kong Polytech Univ, Dept English & Commun, Hong Kong, Peoples R China.
C3 Hong Kong Polytechnic University
RP Nejadghanbar, H (corresponding author), Hong Kong Polytech Univ, Dept English & Commun, Hong Kong, Peoples R China.
EM hassan.nejadghanbar@polyu.edu.hk
RI Hu, Guangwei/B-1732-2016; Farangi, Mohamad Reza/AAU-8197-2021;
   Nejadghanbar, Hassan/AAX-7018-2021
OI Farangi, Mohamad Reza/0000-0001-5481-7815; Hu,
   Guangwei/0000-0002-2297-4784; Nejadghanbar, Hassan/0000-0002-7821-8128
CR Abeliansky AL, 2024, RES POLICY, V53, DOI 10.1016/j.respol.2024.104956
   Ahmad SF, 2023, HUM SOC SCI COMMUN, V10, DOI 10.1057/s41599-023-01787-8
   Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Alotaibi NS, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su151310723
   [Anonymous], 2023, NATURE, V613, P612, DOI 10.1038/d41586-023-00191-1
   Ary D, 2018, Introduction to research in education
   Bagozzi RP, 2022, J SERV RES-US, V25, P499, DOI 10.1177/10946705221118579
   Bak M, 2022, FRONT GENET, V13, DOI 10.3389/fgene.2022.929453
   Barkhuizen G, 2008, SYSTEM, V36, P372, DOI 10.1016/j.system.2008.02.002
   Bearman M, 2023, HIGH EDUC, V86, P369, DOI 10.1007/s10734-022-00937-2
   Benchekroun S, 2024, LEARN PUBL, V37, P66, DOI 10.1002/leap.1595
   Bond M, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-023-00436-z
   Braun V, 2021, QUAL RES PSYCHOL, V18, P328, DOI 10.1080/14780887.2020.1769238
   Charow R, 2021, JMIR MED EDUC, V7, DOI 10.2196/31043
   Chu HC, 2022, AUSTRALAS J EDUC TEC, V38, P22, DOI 10.14742/ajet.7526
   Eke DO., 2023, Journal of Responsible Technology, V13, P100060, DOI [DOI 10.1016/J.JRT.2023.100060, 10.1016/J.JRT.2023.100060]
   Farangi M. R., 2024, Investigating questionable research practices among Iranian applied linguists: Prevalence, severity, and the role of AI tools system, P125, DOI [https://doi.org/10.1016/j.system.2024.103427, DOI 10.1016/J.SYSTEM.2024.103427]
   Farangi M. R., 2023, Teaching English as a Second Language Quarterly, V42, P1, DOI [https://doi.org/10.22099/tesl.2023.47604.3194, DOI 10.22099/TESL.2023.47604.3194]
   Farangi MR, 2024, J ACAD ETHICS, V22, P359, DOI 10.1007/s10805-023-09489-1
   Firth J, 2019, WORLD PSYCHIATRY, V18, P119, DOI 10.1002/wps.20617
   Fleckenstein J., 2024, Computers and Education: Artificial Intelligence, V6, DOI [10.1016/j.caeai.2024.100209, DOI 10.1016/J.CAEAI.2024.100209]
   Frederick B., 2023, Search engine journal
   Griffin Tricia A., 2023, AI and Ethics, DOI [https://doi.org/10.1007/s43681-022-00256-3, DOI 10.1007/S43681-022-00256-3]
   Gruenhagen JH., 2024, Comput Educ: Artif Intel, V7, P100273
   Han ELZB, 2023, INFORM SYST RES, V34, P1296, DOI 10.1287/isre.2022.1179
   Heersmink R, 2024, NAT HUM BEHAV, V8, P805, DOI 10.1038/s41562-024-01859-y
   Hetzscholdt P, 2024, LEARN PUBL, V37, P63, DOI 10.1002/leap.1593
   Hu GW, 2024, ACCOUNT RES, V31, P978, DOI 10.1080/08989621.2023.2184262
   Huang FW, 2024, EUR J EDUC, V59, DOI 10.1111/ejed.12770
   Jeanes E, 2017, ORGANIZATION, V24, P174, DOI 10.1177/1350508416656930
   Kartal G, 2024, J EDUC TEACHING, V50, P627, DOI 10.1080/02607476.2024.2326502
   Keegin Joseph M., 2023, CHRON HIGHER EDUC
   Köchling A, 2023, REV MANAG SCI, V17, P2109, DOI 10.1007/s11846-021-00514-4
   La Botie E, 1577, Discours de la servitude volontaire
   Lakhani K., 2023, Harvard Business Review
   Langer M, 2018, COMPUT HUM BEHAV, V81, P19, DOI 10.1016/j.chb.2017.11.036
   Layard R., 2017, The economics of mental health
   Lee J, 2021, ACAD MED, V96, pS62, DOI 10.1097/ACM.0000000000004291
   Mannuru NR, 2023, INFORM DEV, DOI 10.1177/02666669231200628
   McGrath McGrath C. C., 2023, Computers and Education: Artificial Intelligence, V4 4, P100139, DOI [DOI 10.1016/J.CAEAI.2023.100139, 10.1016/j.caeai.2023.100139 10.1016/j.caeai.2023.100139]
   Morikawa M., 2017, GLO Discussion Paper
   Nazari M, 2023, SYSTEM, V117, DOI 10.1016/j.system.2023.103111
   Nejadghanbar H, 2024, TESOL QUART, V58, P1734, DOI 10.1002/tesq.3312
   Nejadghanbar H, 2023, LANG TEACHING, V56, P297, DOI 10.1017/S0261444822000490
   Nolan A., 2024, Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research, DOI [https://doi.org/10.1787/a8d820bd-en, DOI 10.1787/A8D820BD-EN]
   Nugroho A, 2024, INNOV EDUC TEACH INT, DOI 10.1080/14703297.2024.2319184
   Barea MAP, 2023, AI SOC, DOI 10.1007/s00146-023-01804-z
   Perkins M, 2024, J ACAD ETHICS, V22, P89, DOI 10.1007/s10805-023-09492-6
   Polyportis A, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2317364
   Salas-Pilco SZ, 2022, INT J EDUC TECHNOL H, V19, DOI 10.1186/s41239-022-00326-w
   Schmauder C, 2023, TOPOI-INT REV PHILOS, V42, P799, DOI 10.1007/s11245-023-09907-4
   Schwabe H, 2020, PLOS ONE, V15, DOI 10.1371/journal.pone.0242929
   Smith C., 2023, IEEE Spectrum
   Song JY, 2024, TESOL J, V15, DOI 10.1002/tesj.877
   Sorell T, 2022, J MED ETHICS, V48, P278, DOI 10.1136/medethics-2020-107024
   Stahl BC, 2024, INT J INFORM MANAGE, V74, DOI 10.1016/j.ijinfomgt.2023.102700
   Stokel-Walker C., 2024, AI Chatbots Have Thoroughly Infiltrated Scientific Publishing
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   US Department of State, 2022, Declaration for the future of the internet
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Waluyo B., 2024, Social Sciences & Humanities Open, V10
   Weale S., 2023, The Guardian
   Xu QW, 2022, LECT NOTES ARTIF INT, V13336, P513, DOI 10.1007/978-3-031-05643-7_33
   Zhang LX, 2024, BEHAV INFORM TECHNOL, DOI 10.1080/0144929X.2024.2325023
   Zhang P, 2013, MIS QUART, V37, P247
   Zou Z, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-53255-1
NR 66
TC 1
Z9 1
U1 16
U2 16
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 1050-8422
EI 1532-7019
J9 ETHICS BEHAV
JI Ethics Behav.
PD 2024 OCT 26
PY 2024
DI 10.1080/10508422.2024.2420133
EA OCT 2024
PG 17
WC Ethics; Psychology, Multidisciplinary
WE Social Science Citation Index (SSCI)
SC Social Sciences - Other Topics; Psychology
GA K0W7Q
UT WOS:001341182800001
DA 2024-12-25
ER

PT J
AU Ye, J
   Wang, SY
   Tsai, SB
AF Ye, Jing
   Wang, Shuyang
   Tsai, Sang-Bing
TI Impact of Generative AI on Enterprise Performance in China: Mediating
   Role of Managerial Relationships
SO JOURNAL OF GLOBAL INFORMATION MANAGEMENT
LA English
DT Article
DE Artificial Intelligence Technologies; Chinese Enterprises; Corporate
   Performance; Generative Artificial Intelligence; Managerial
   Relationships
ID ARTIFICIAL-INTELLIGENCE; MANAGEMENT; INNOVATION; BUSINESS; TIES
AB This study examines the impact of generative artificial intelligence (GAI) on Chinese enterprises. 320 participants completed electronic surveys, revealing a positive relationship between GAI and corporate performance. Managerial relationships were found to play a crucial role, mediating the influence of GAI on performance. Additionally, the speed of technological change in the industry was identified as a moderator, highlighting the dynamic nature of GAI's impact on managerial relationships and firm performance. These findings underscore the strategic importance of GAI in cultivating managerial relationships and improving enterprise performance, deepening our understanding of the integration of advanced AI technologies and management practices in the Chinese market. Practical implications are offered for decision-makers in technology-intensive industries, providing valuable insights for leveraging GAI to gain a competitive edge and achieve sustainable growth in a rapidly evolving technological landscape.
C1 [Ye, Jing] Zhejiang Ind Polytech Coll, Shaoxing, Peoples R China.
   [Wang, Shuyang] Shaoxing Univ, Yuanpei Coll, Shaoxing, Peoples R China.
   [Tsai, Sang-Bing] Int Engn & Technol Inst, Hongkong, Peoples R China.
C3 Shaoxing University
RP Wang, SY (corresponding author), Shaoxing Univ, Yuanpei Coll, Shaoxing, Peoples R China.
EM wangshuyang777777@gmail.com
CR Alzoubi Haitham M., 2022, International Journal of Business Excellence, V27, P94, DOI 10.1504/IJBEX.2022.123036
   [Anonymous], ACAD MANAGEMENT EXEC, DOI [DOI 10.2307/4164720, 10.5465/ame.1987.4275905]
   Baek TH, 2023, TELEMAT INFORM, V83, DOI 10.1016/j.tele.2023.102030
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Blankenau WF, 2011, APPL ECON, V43, P3129, DOI 10.1080/00036840903476361
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Cui F, 2022, SUSTAINABILITY-BASEL, V14, DOI 10.3390/su14063249
   Dwivedi YK, 2021, INT J INFORM MANAGE, V59, DOI 10.1016/j.ijinfomgt.2020.102168
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Garcia F, 2023, J GLOB INF MANAG, V31, DOI 10.4018/JGIM.313187
   Grisoni F, 2021, SCI ADV, V7, DOI 10.1126/sciadv.abg3338
   Guerola-Navarro V, 2021, ECON RES-EKON ISTRAZ, V34, P2669, DOI 10.1080/1331677X.2020.1836992
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Kayal A, 1999, TECHNOL FORECAST SOC, V60, P237, DOI 10.1016/S0040-1625(98)00030-4
   Keary M, 2023, CONTEMP POLIT THEORY, V22, P70, DOI 10.1057/s41296-021-00541-6
   Korzynski P, 2023, CENT EUR MANAG J, V31, P3, DOI 10.1108/CEMJ-02-2023-0091
   Kshetri N, 2023, IT PROF, V25, P71, DOI 10.1109/MITP.2023.3314325
   Li JJ, 2009, J INT BUS STUD, V40, P339, DOI 10.1057/jibs.2008.76
   Mannuru NR, 2023, INFORM DEV, DOI 10.1177/02666669231200628
   Men L. R., 2023, Handbook on Digital Corporate Communication, P103
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Mohan K, 2021, J AM STAT ASSOC, V116, P1023, DOI 10.1080/01621459.2021.1874961
   Monod E, 2023, J DECIS SYST, V32, P542, DOI 10.1080/12460125.2022.2066051
   Newman MB, 2021, PROF CASE MANAG, V26, P304, DOI 10.1097/NCM.0000000000000533
   Ning X, 2024, INFORM SCIENCES, V660, DOI 10.1016/j.ins.2024.120130
   Ning X, 2024, INFORM FUSION, V102, DOI 10.1016/j.inffus.2023.102033
   Ning X, 2023, PATTERN RECOGN, V136, DOI 10.1016/j.patcog.2022.109216
   NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14
   Papazoglou ME, 2023, J KNOWL ECON, V14, P3724, DOI 10.1007/s13132-022-01023-9
   Pedrini M, 2019, CORP GOV-INT J BUS S, V19, P44, DOI 10.1108/CG-08-2017-0172
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Petruzzelli AM, 2012, TECHNOL ANAL STRATEG, V24, P453, DOI 10.1080/09537325.2012.674668
   Ran H, 2024, INFORM PROCESS MANAG, V61, DOI 10.1016/j.ipm.2024.103664
   Ratten V, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100857
   Seshadri S, 2004, IND MARKET MANAG, V33, P513, DOI 10.1016/j.indmarman.2004.03.004
   Sheng SB, 2011, J MARKETING, V75, P1, DOI 10.1509/jmkg.75.1.1
   Taherparvar N, 2014, J KNOWL MANAG, V18, P591, DOI 10.1108/JKM-11-2013-0446
   Utomo H.J.N., 2023, Journal of Law and Sustainable Development, V11, pe417
   Wang J., 2024, IEEE T CIRC SYST VID
   Wang LX, 2023, J GLOB INF MANAG, V31, DOI 10.4018/JGIM.323205
   Wibowo A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13010189
   Yang MH, 2019, SUSTAINABILITY-BASEL, V11, DOI 10.3390/su11205656
   Yu ZY, 2024, IEEE T CIRC SYST VID, V34, P5589, DOI 10.1109/TCSVT.2024.3358850
NR 44
TC 0
Z9 0
U1 58
U2 58
PU IGI GLOBAL
PI HERSHEY
PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA
SN 1062-7375
EI 1533-7995
J9 J GLOB INF MANAG
JI J. Glob. Inf. Manag.
PY 2024
VL 32
IS 1
AR 347501
DI 10.4018/JGIM.347501
PG 20
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA A4P7D
UT WOS:001282374400003
OA gold
DA 2024-12-25
ER

PT J
AU Andrieux, P
   Johnson, RD
   Sarabadani, J
   Van Slyke, C
AF Andrieux, Pierre
   Johnson, Richard D.
   Sarabadani, Jalal
   Van Slyke, Craig
TI Ethical considerations of generative AI-enabled human resource
   management
SO ORGANIZATIONAL DYNAMICS
LA English
DT Article
DE Generative artificial intelligence (GAI); Human resource management
   (HRM); Affordance; Ethics; Decision-making
AB This paper examines critical ethical considerations linked to making human resources management (HRM) decisions based on the potential capabilities (affordances) offered by generative artificial intelligence (GAI). We first provide a broad overview of the status quo surrounding the use of GAI in the HRM context. Then, we introduce the concept of "affordance" and explain how it provides a useful perspective for human resource (HR) managers to use when evaluating potential benefits and/or harm resulting from the implementation of a potential GAI-based capability to support HRM processes decisions. We discuss concrete examples of how GAI HRM affordances could be implemented in different HRM functions and the ethical questions that arise from their use. Finally, we present an ethics-based framework, the Two-Rule Method, along with ethics-specific recommendations to guide HR managers through the complex issues that arise because of the use of GAI-enabled HR tools.
C1 [Andrieux, Pierre; Van Slyke, Craig] Louisiana Tech Univ, Ruston, LA 71272 USA.
   [Johnson, Richard D.] Washington State Univ, Pullman, WA USA.
   [Sarabadani, Jalal] San Jose State Univ, San Jose, CA USA.
C3 University of Louisiana System; Louisiana Technical University;
   Washington State University; California State University System; San
   Jose State University
RP Van Slyke, C (corresponding author), Louisiana Tech Univ, Ruston, LA 71272 USA.
EM vanslyke@latech.edu
RI Sarabadani, Jalal/AAU-8582-2020; Van Slyke, Craig/F-3712-2014; Johnson,
   Richard/R-4628-2018
OI Andrieux, Pierre/0000-0001-7780-3934; Johnson,
   Richard/0000-0001-9367-8889
CR Dervishaj J., 2020, AI HLEG - Assessment list for trustworthy artificial intelligence (ALTAI)
   Hancock B., 2023, Generative AI and the future of HR
   HR Brew, About us
   Newstead T., Organizational Dynamics
   Shneiderman B, 2021, ISSUES SCI TECHNOL, V37, P56
   Tinguely P. N., 2023, Journal of Organization Design, P1
NR 6
TC 3
Z9 3
U1 58
U2 94
PU ELSEVIER SCIENCE INC
PI NEW YORK
PA STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA
SN 0090-2616
EI 1873-3530
J9 ORGAN DYN
JI Organ. Dyn.
PD JAN-MAR
PY 2024
VL 53
IS 1
AR 101032
DI 10.1016/j.orgdyn.2024.101032
EA FEB 2024
PG 8
WC Business; Psychology, Applied; Management
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA NF4L2
UT WOS:001199023800001
DA 2024-12-25
ER

PT J
AU Simchon, A
   Edwards, M
   Lewandowsky, S
AF Simchon, Almog
   Edwards, Matthew
   Lewandowsky, Stephan
TI The persuasive effects of political microtargeting in the age of
   generative artificial intelligence
SO PNAS NEXUS
LA English
DT Article
DE microtargeting; persuasion; GPT; AI
ID METAANALYSIS
AB The increasing availability of microtargeted advertising and the accessibility of generative artificial intelligence (AI) tools, such as ChatGPT, have raised concerns about the potential misuse of large language models in scaling microtargeting efforts for political purposes. Recent technological advancements, involving generative AI and personality inference from consumed text, can potentially create a highly scalable "manipulation machine" that targets individuals based on their unique vulnerabilities without requiring human input. This paper presents four studies examining the effectiveness of this putative "manipulation machine." The results demonstrate that personalized political ads tailored to individuals' personalities are more effective than nonpersonalized ads (studies 1a and 1b). Additionally, we showcase the feasibility of automatically generating and validating these personalized ads on a large scale (studies 2a and 2b). These findings highlight the potential risks of utilizing AI and microtargeting to craft political messages that resonate with individuals based on their personality traits. This should be an area of concern to ethicists and policy makers.
C1 [Simchon, Almog; Lewandowsky, Stephan] Univ Bristol, Sch Psychol Sci, Bristol BS8 1QU, England.
   [Simchon, Almog] Ben Gurion Univ Negev, Dept Psychol, IL-841050 Beer Sheva, Israel.
   [Edwards, Matthew] Univ Bristol, Dept Comp Sci, Bristol BS8 1QU, England.
   [Lewandowsky, Stephan] Univ Western Australia, Sch Psychol Sci, Perth, WA 6009, Australia.
   [Lewandowsky, Stephan] Univ Potsdam, Dept Psychol, D-14476 Potsdam, Germany.
C3 University of Bristol; Ben Gurion University; University of Bristol;
   University of Western Australia; University of Potsdam
RP Simchon, A (corresponding author), Univ Bristol, Sch Psychol Sci, Bristol BS8 1QU, England.; Simchon, A (corresponding author), Ben Gurion Univ Negev, Dept Psychol, IL-841050 Beer Sheva, Israel.
EM almog.si@post.bgu.ac.il
RI Lewandowsky, Stephan/L-4272-2019; Lewandowsky, Stephan/H-5285-2014
OI Simchon, Almog/0000-0003-2629-2913; Lewandowsky,
   Stephan/0000-0003-1655-2013; Edwards, Matthew/0000-0001-8099-0646
FU Volkswagen Foundation; Humboldt Foundation in Germany; European Research
   Council (ERC) Advanced Grant [101020961]; European Research Council
   (ERC) [101020961] Funding Source: European Research Council (ERC)
FX This research was supported by a large grant from the Volkswagen
   Foundation ("Reclaiming individual autonomy and democratic discourse
   online"). S.L. was also supported by funding from the Humboldt
   Foundation in Germany and by the European Research Council (ERC)
   Advanced Grant (101020961, PRODEMINFO)
CR Baldwin-Philippi J, 2017, POLIT COMMUN, V34, P627, DOI 10.1080/10584609.2017.1372999
   Cadwalladr C., 2020, The Guardian
   Coppock A, 2020, SCI ADV, V6, DOI 10.1126/sciadv.abc4046
   Digital Culture Media and Sport Committee, 2019, DIS FAK NEWS FIN REP
   Dillard JP, 2007, J COMMUN, V57, P613, DOI 10.1111/j.1460-2466.2007.00360.x
   Haenschen K, 2023, POLIT BEHAV, V45, P1661, DOI 10.1007/s11109-022-09781-7
   Hinds J, 2018, PLOS ONE, V13, DOI 10.1371/journal.pone.0207112
   Hirsh JB, 2012, PSYCHOL SCI, V23, P578, DOI 10.1177/0956797611436349
   Joyal-Desmarais K, 2022, PSYCHOL BULL, V148, P465, DOI 10.1037/bul0000377
   Kim T, 2019, J CONSUM RES, V45, P906, DOI 10.1093/jcr/ucy039
   Lavigne M, 2021, PARTY POLIT, V27, P965, DOI 10.1177/1354068820918387
   Lewandowsky Stephan, 2022, Mem Mind Media, V1, DOI 10.1017/mem.2021.7
   Lorenz-Spreen P, 2021, SCI REP-UK, V11, DOI 10.1038/s41598-021-94796-z
   Machkovech S., 2017, Report: facebook helped advertisers target teens who feel "worthless
   Matz S., 2023, The Potential of Generative AI for Personalized Persuasion at Scale, DOI [10.31234/osf.io/rn97c, DOI 10.31234/OSF.IO/RN97C]
   Matz SC, 2017, P NATL ACAD SCI USA, V114, P12714, DOI 10.1073/pnas.1710966114
   Simchon A., 2023, PNAS Nexus, V2
   Soto CJ, 2017, J PERS SOC PSYCHOL, V113, P117, DOI 10.1037/pspp0000096
   Tappin B, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2216261120
   Wu YY, 2015, P NATL ACAD SCI USA, V112, P1036, DOI 10.1073/pnas.1418680112
NR 20
TC 14
Z9 14
U1 20
U2 26
PU OXFORD UNIV PRESS
PI OXFORD
PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND
EI 2752-6542
J9 PNAS NEXUS
JI PNAS Nexus
PD FEB 1
PY 2024
VL 3
IS 2
AR pgae035
DI 10.1093/pnasnexus/pgae035
EA FEB 2024
PG 5
WC Multidisciplinary Sciences; Social Sciences, Interdisciplinary
WE Emerging Sources Citation Index (ESCI)
SC Science & Technology - Other Topics; Social Sciences - Other Topics
GA IA7V7
UT WOS:001163675400006
PM 38328785
OA gold, Green Published, Green Submitted
DA 2024-12-25
ER

PT J
AU Mladenovic, D
   Beheshti, M
   Kolar, T
   Ismagilova, E
   Dwivedi, YK
AF Mladenovic, Dusan
   Beheshti, Moein
   Kolar, Tomaz
   Ismagilova, Elvira
   Dwivedi, Yogesh K.
TI Synthetic WOM? The Emergence of Generative Artificial
   Intelligence-Induced Recommendations
SO JOURNAL OF COMPUTER INFORMATION SYSTEMS
LA English
DT Article; Early Access
DE WOM; generative AI; synthetic WOM; tourism; hospitality; information
   seeking; syWOM
ID WORD-OF-MOUTH; SOCIAL NETWORKING SITES; UNITED-STATES; VISUAL CUES;
   EWOM; CONSUMERS; INTENTION; DETERMINANTS; TRUST
AB This paper examines how Generative Artificial Intelligence (GAI) influences word-of-mouth (WOM) in travel and hospitality, focusing on synthetic WOM (syWOM). It explores how GAI-driven WOM reshapes traveler interactions and decision-making in an experience-centric industry. Using a literature review and conceptual analysis approach1, this study examines the integration of GAI tools, such as ChatGPT, to enhance travel experiences. The analysis presented in this study highlights GAI's potential in inducing syWOM and its effects on traveler perceptions and behaviors. Additionally, it addresses the emerging role of GAI in WOM, emphasizing the need for further research on its impact on travel planning and engagement. This study presents a fresh view of the interaction of syWOM with GAI in travel, aiming to inform future research and practical applications of personalized traveler engagement.
C1 [Mladenovic, Dusan; Beheshti, Moein] Masaryk Univ, Fac Econ & Adm, Dept Business Management, Lipova 41a, Brno 60200, Czech Republic.
   [Kolar, Tomaz] Univ Ljubljana, Fac Econ, Sch Econ & Business, Ljubljana, Slovenia.
   [Ismagilova, Elvira] Swansea Univ, Sch Management, Bay Campus, Swansea, Wales.
   [Dwivedi, Yogesh K.] Swansea Univ, Sch Management, Digital Futures Sustainable Business & Soc Res Grp, Bay Campus, Swansea, Wales.
C3 Masaryk University Brno; University of Ljubljana; Swansea University;
   Swansea University
RP Mladenovic, D (corresponding author), Masaryk Univ, Fac Econ & Adm, Dept Business Management, Lipova 41a, Brno 60200, Czech Republic.
EM dusan.mladenovic@econ.muni.cz
RI Mladenović, Dušan/AAA-2569-2020; Dwivedi, Yogesh/A-5362-2008
CR Abubakar AM, 2017, J HOSP TOUR MANAG, V31, P220, DOI 10.1016/j.jhtm.2016.12.005
   Ali F, 2023, INT J HOSP MANAG, V114, DOI 10.1016/j.ijhm.2023.103588
   Amos C., 2024, Telemat Informatics, P93
   Bandi A, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15080260
   Bastos W, 2021, J BUS RES, V130, P110, DOI 10.1016/j.jbusres.2021.03.022
   Belanche D., 2021, J Retail Consum Serv, P61
   Belk RW, 2023, SERV BUS, V17, P1, DOI 10.1007/s11628-023-00528-w
   Birim SÖ, 2022, J BUS RES, V149, P884, DOI 10.1016/j.jbusres.2022.05.081
   Bockting CL, 2023, NATURE, V614, P224, DOI 10.1038/d41586-023-00288-7
   Bronner F, 2011, J TRAVEL RES, V50, P15, DOI 10.1177/0047287509355324
   Bulchand-Gidumal J., 2023, Curr Issues Tour
   Bulchand-Gidumal J, 2024, CURR ISSUES TOUR, V27, P739, DOI 10.1080/13683500.2023.2173054
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Carvalho I, 2024, TOUR REV, V79, P290, DOI 10.1108/TR-02-2023-0088
   Chakraborty D, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103509
   Chu SC, 2011, J GLOB MARK, V24, P263
   Chu SC, 2011, INT J ADVERT, V30, P47, DOI 10.2501/IJA-30-1-047-075
   Dogru T, 2023, J HOSP TOUR RES, DOI 10.1177/10963480231188663
   Donthu N, 2021, J BUS RES, V135, P758, DOI 10.1016/j.jbusres.2021.07.015
   Dwivedi YK, 2024, INT J CONTEMP HOSP M, V36, P1, DOI 10.1108/IJCHM-05-2023-0686
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Farías P, 2017, INT J ADVERT, V36, P852, DOI 10.1080/02650487.2017.1364033
   Filieri R, 2021, J BUS RES, V135, P663, DOI 10.1016/j.jbusres.2021.06.055
   Filieri R, 2018, INFORM MANAGE-AMSTER, V55, P956, DOI 10.1016/j.im.2018.04.010
   Filieri R, 2018, COMPUT HUM BEHAV, V88, P134, DOI 10.1016/j.chb.2018.05.042
   Fong LHN, 2023, INT J CONTEMP HOSP M, V35, P2916, DOI 10.1108/IJCHM-06-2022-0764
   González-Rodríguez MR, 2022, J HOSP TOUR TECHNOL, V13, P855, DOI 10.1108/JHTT-11-2021-0321
   Grundner L, 2021, J DESTIN MARK MANAGE, V19, DOI 10.1016/j.jdmm.2020.100511
   Gursoy D, 2023, J HOSP MARKET MANAG, V32, P579, DOI 10.1080/19368623.2023.2211993
   Han TY, 2024, TOURISM MANAGE, V102, DOI 10.1016/j.tourman.2024.104884
   Hennig-Thurau T, 2004, J INTERACT MARK, V18, P38, DOI 10.1002/dir.10073
   Hu YU, 2023, INT J HOSP MANAG, V110, DOI 10.1016/j.ijhm.2023.103437
   Ismagilova E, 2021, EUR J MARKETING, V55, P1067, DOI 10.1108/EJM-07-2018-0472
   Ismagilova E, 2020, J RETAIL CONSUM SERV, V53, DOI 10.1016/j.jretconser.2019.02.002
   Ivanov S, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2023.102430
   Ivanov S, 2023, SERV IND J, V43, P1055, DOI 10.1080/02642069.2023.2258799
   Jeong N, 2024, SUSTAINABILITY-BASEL, V16, DOI 10.3390/su16041640
   Kelly S, 2023, TELEMAT INFORM, V77, DOI 10.1016/j.tele.2022.101925
   Kim JH, 2023, J TRAVEL TOUR MARK, V40, P779, DOI 10.1080/10548408.2023.2293006
   Kim JH, 2025, J TRAVEL RES, V64, P51, DOI 10.1177/00472875231212996
   Kim J, 2023, J RETAIL CONSUM SERV, V75, DOI 10.1016/j.jretconser.2023.103494
   Kim J, 2021, PSYCHOL MARKET, V38, P1140, DOI 10.1002/mar.21498
   Kim MJ, 2024, CURR ISSUES TOUR, DOI 10.1080/13683500.2024.2355556
   Kirshner SN, 2024, J RETAIL CONSUM SERV, V76, DOI 10.1016/j.jretconser.2023.103580
   Klaus P, 2022, J RETAIL CONSUM SERV, V65, DOI 10.1016/j.jretconser.2021.102490
   Koc E, 2023, TECHNOL SOC, V74, DOI 10.1016/j.techsoc.2023.102333
   Kshetri N, 2024, INT J INFORM MANAGE, V75, DOI 10.1016/j.ijinfomgt.2023.102716
   Ladhari R, 2015, INT J HOSP MANAG, V46, P36, DOI 10.1016/j.ijhm.2015.01.010
   Leal CC, 2020, INT J HOSP MANAG, V91, DOI 10.1016/j.ijhm.2019.102410
   Lee H, 2021, J VACAT MARK, V27, P237, DOI 10.1177/1356766720987872
   Lee KY, 2019, COMPUT HUM BEHAV, V94, P9, DOI 10.1016/j.chb.2018.12.025
   Lian Y, 2024, TECHNOL SOC, V76, DOI 10.1016/j.techsoc.2023.102442
   Line ND, 2024, INT J HOSP MANAG, V117, DOI 10.1016/j.ijhm.2023.103644
   Ma XY, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102362
   MacInnis DJ, 2011, J MARKETING, V75, P136, DOI 10.1509/jmkg.75.4.136
   Marder B, 2021, J TRAVEL RES, V60, P31, DOI 10.1177/0047287519895125
   Mardumyan A, 2023, J BUS RES, V160, DOI 10.1016/j.jbusres.2023.113756
   Markowitz DM, 2024, J LANG SOC PSYCHOL, V43, P63, DOI 10.1177/0261927X231200201
   Miao L, 2023, ANN TOURISM RES, V102, DOI 10.1016/j.annals.2023.103642
   Mich L, 2023, INF TECHNOL TOUR, V25, P1, DOI 10.1007/s40558-023-00248-x
   Mladenovic D, 2024, INT J CONTEMP HOSP M, V36, P2144, DOI 10.1108/IJCHM-04-2023-0474
   Mladenovic D, 2023, TELEMAT INFORM, V79, DOI 10.1016/j.tele.2023.101966
   Mladenovic D, 2021, J INT CONSUM MARK, V33, P418, DOI 10.1080/08961530.2020.1800547
   Mladenovic D, 2019, INT J CULT TOUR HOSP, V13, P244, DOI [10.1108/IJCTHR-12-2018-0169, 10.1108/ijcthr-12-2018-0169]
   Papadopoulou NM, 2023, J TRAVEL RES, V62, P644, DOI 10.1177/00472875221089049
   Pathania A, 2024, J MARK COMMUN, V30, P438, DOI 10.1080/13527266.2022.2140183
   Praveen SV., 2024, J Retail Consum Serv, P81
   Remountakis M., 2023, Inf, V14
   Rosario AB, 2020, J ACAD MARKET SCI, V48, P422, DOI 10.1007/s11747-019-00706-1
   Rudez HN, 2015, TOUR HOSP MANAG-CROA, V21, P179, DOI 10.20867/thm.21.2.5
   Sadiq MW, 2024, SERV IND J, V44, P173, DOI 10.1080/02642069.2023.2278463
   Sameeni MS., 2024, J Bus Res, P173
   Shen YC, 2021, ONLINE INFORM REV, V45, P1227, DOI 10.1108/OIR-08-2020-0374
   Sigala M, 2024, J HOSP TOUR MANAG, V60, P384, DOI 10.1016/j.jhtm.2024.08.004
   Tassiello V, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2349362
   Verma D, 2023, J BUS RES, V154, DOI 10.1016/j.jbusres.2022.08.056
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
NR 77
TC 0
Z9 0
U1 12
U2 12
PU TAYLOR & FRANCIS INC
PI PHILADELPHIA
PA 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA
SN 0887-4417
EI 2380-2057
J9 J COMPUT INFORM SYST
JI J. Comput. Inf. Syst.
PD 2024 OCT 27
PY 2024
DI 10.1080/08874417.2024.2409252
EA OCT 2024
PG 18
WC Computer Science, Information Systems
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA K0Y5Y
UT WOS:001341230400001
DA 2024-12-25
ER

PT J
AU Reeves, C
   Sylvia, JJ
AF Reeves, Carol
   Sylvia, J. J.
TI Generative AI in Technical Communication: A Review of Research from 2023
   to 2024
SO JOURNAL OF TECHNICAL WRITING AND COMMUNICATION
LA English
DT Article
DE generative artificial intelligence; writing pedagogy; ChatGPT; GAI in
   education; GAI in writing; prompt engineering; critical thinking
ID CHATGPT
AB Since its release in late 2022, ChatGPT and subsequent generative artificial intelligence (GAI) tools have raised a wide variety of questions and concerns for the field of technical communication: How will these tools be incorporated into professional settings? How might we appropriately integrate these tools into our research and teaching? In this review, we examine research published in 2023-2024 addressing these questions (N = 28). Overall, we find preliminary evidence that GAI tools can positively impact student writing and assessment; they also have the potential to assist with some aspects of academic and medical research and writing. However, there are concerns about their reliability and the ethical conundrums raised when they are used inappropriately or when their outputs cannot be distinguished from humans. More research is needed for evidence-based teaching and research strategies as well as policies guiding ethical use. We offer suggestions for new research avenues and methods.
C1 [Reeves, Carol] Butler Univ, Dept English, Indianapolis, IN USA.
   [Sylvia, J. J.] Fitchburg State Univ, Dept Commun Media, 160 Pearl St, Fitchburg, MA 01420 USA.
C3 Butler University; Massachusetts System of Public Higher Education;
   Fitchburg State College
RP Sylvia, JJ (corresponding author), Fitchburg State Univ, Dept Commun Media, 160 Pearl St, Fitchburg, MA 01420 USA.
EM jsylvia3@fitchburgstate.edu
RI Sylvia, J.J./AAP-4013-2020
CR Ariyaratne S, 2023, SKELETAL RADIOL, V52, P1755, DOI 10.1007/s00256-023-04340-5
   Athaluri SA, 2023, CUREUS J MED SCIENCE, V15, DOI 10.7759/cureus.37432
   Babl FE, 2023, EMERG MED AUSTRALAS, V35, P809, DOI 10.1111/1742-6723.14233
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Cardon P, 2024, BUS PROF COMMUN Q, V87, P223, DOI 10.1177/23294906231208166
   Cardon P, 2023, BUS PROF COMMUN Q, V86, P257, DOI 10.1177/23294906231176517
   Casal J.E., 2023, Res Methods Appl Linguist, V2, DOI DOI 10.1016/J.RMAL.2023.100068
   Chamurliyski P., 2023, Acta Scientifica Naturalis, V10, P73, DOI [10.2478/asn-2023-0023, DOI 10.2478/ASN-2023-0023]
   Curry N., 2024, Applied Corpus Linguistics, V4, P100082, DOI [10.1016/j.acorp.2023.100082, DOI 10.1016/J.ACORP.2023.100082]
   Escalante J, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00425-2
   Faiz Rabia, 2023, Linguistic Forum-A Journal of Linguistics, V5, P24, DOI [10.53057/linfo/2023.5.3.3, DOI 10.53057/LINFO/2023.5.3.3]
   Gravel J., 2023, Mayo Clinic Proceedings: Digital Health, V1, P226
   Guo K, 2024, EDUC INF TECHNOL, V29, P8435, DOI 10.1007/s10639-023-12146-0
   Harunasari S. Y., 2023, International Journal of Progressive Sciences and Technologies, V39, P357, DOI [10.52155/ijpsat.v39.2.5516, DOI 10.52155/IJPSAT.V39.2.5516]
   Herbold Steffen, 2023, Sci Rep, V13, P18617, DOI 10.1038/s41598-023-45644-9
   Ho WLJ, 2023, SURG PRACT SCI, V14, DOI 10.1016/j.sipas.2023.100185
   Ibrahim K, 2023, LANG TEST ASIA, V13, DOI 10.1186/s40468-023-00260-2
   Jiang ZL, 2023, LANGUAGES-BASEL, V8, DOI 10.3390/languages8040247
   Leong A. P., 2023, Explorations in English Language and Linguistics, V11, P99, DOI [10.2478/exell-2023-0008, DOI 10.2478/EXELL-2023-0008]
   Lyu Q, 2023, VIS COMPUT IND BIOME, V6, DOI 10.1186/s42492-023-00136-5
   Markowitz DM, 2024, J LANG SOC PSYCHOL, V43, P261, DOI 10.1177/0261927X231220404
   Markowitz DM, 2024, J LANG SOC PSYCHOL, V43, P63, DOI 10.1177/0261927X231200201
   Mizumoto A., 2023, Res. Methods Appl. Linguist, V2, P100050, DOI DOI 10.1016/J.RMAL.2023.100050
   Nguyen Thi Thu H., 2023, INT J LANGUAGE INSTR, V2, P1, DOI DOI 10.54855/IJLI.23231
   Parker JL, 2023, J NURS EDUC, V62, P721, DOI 10.3928/01484834-20231006-02
   Song CP, 2023, FRONT PSYCHOL, V14, DOI 10.3389/fpsyg.2023.1260843
   Stokel-Walker C, 2023, NATURE, V613, P620, DOI 10.1038/d41586-023-00107-z
   Wu YX, 2024, EPILEPSY BEHAV, V151, DOI 10.1016/j.yebeh.2024.109645
   Xiao YY, 2023, LANGUAGES-BASEL, V8, DOI 10.3390/languages8030212
NR 29
TC 1
Z9 1
U1 0
U2 0
PU SAGE PUBLICATIONS INC
PI THOUSAND OAKS
PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA
SN 0047-2816
EI 1541-3780
J9 J TECH WRIT COMMUN
JI J. Teach. Writ. Commun.
PD OCT
PY 2024
VL 54
IS 4
SI SI
BP 439
EP 462
DI 10.1177/00472816241260043
PG 24
WC Communication
WE Emerging Sources Citation Index (ESCI)
SC Communication
GA M0J3X
UT WOS:001354491500006
DA 2024-12-25
ER

PT J
AU Oddone, K
   Garrison, K
   Gagen-Spriggs, K
AF Oddone, Kay
   Garrison, Kasey
   Gagen-Spriggs, Krystal
TI Navigating Generative AI: The Teacher Librarian's Role in Cultivating
   Ethical and Critical Practices
SO JOURNAL OF THE AUSTRALIAN LIBRARY AND INFORMATION ASSOCIATION
LA English
DT Article
DE Teacher librarians; generative artificial intelligence; AI in education;
   ethical information use; CATWOE
ID ARTIFICIAL-INTELLIGENCE
AB The recent evolution of Generative Artificial Intelligence (GAI) has already made a tangible impact within the realm of education. Positioned at the forefront of information and digital literacies, teacher librarians (TLs) can be leaders in exploring the potential for AI platforms to transform learning and teaching. This research is based on an exploratory methodology, to investigate how TLs might teach with and about GAI by applying the CATWOE analysis technique [Checkland, P., & Poulter, J. (2006). Learning for action: A short definitive account of soft systems methodology, and its use for practitioners, teachers and students. Wiley.] within the context of three specific GAI platforms. Findings highlight the multifaceted role that TLs can assume, extending beyond the pedagogical integration of GAI to encompass a broader educational mandate: to scaffold students' capabilities as critical and ethical users of these tools in the creation and use of information.
C1 [Oddone, Kay; Garrison, Kasey; Gagen-Spriggs, Krystal] Charles Sturt Univ, Fac Arts & Educ, Sch Informat & Commun Studies, Wagga Wagga, NSW, Australia.
   [Oddone, Kay] Charles Sturt Univ, Fac Arts & Educ, Sch Informat & Commun Studies, Locked Bag 588, Wagga Wagga, NSW 2678, Australia.
C3 Charles Sturt University; Charles Sturt University
RP Oddone, K (corresponding author), Charles Sturt Univ, Fac Arts & Educ, Sch Informat & Commun Studies, Locked Bag 588, Wagga Wagga, NSW 2678, Australia.
EM koddone@csu.edu.au
RI Garrison, Kasey/LKK-6323-2024; Oddone, Kay/LUZ-4168-2024
OI Oddone, Kay/0000-0002-6814-3783; Garrison, Kasey/0000-0002-2495-0233;
   Gagen-Spriggs, Krystal/0000-0001-9046-5327
CR Altman S., 2023, Governance of superintelligence
   [Anonymous], 2006, Learning for Action: A Short Definitive Account of Soft Systems Methodology and Its Use for Practitioners, Teachers and Students
   appengine.ai, QUILLBOT
   Australian Library and Information Association & Australian School Library Association, 2016, STATEMENT TEACHER LI
   Baer A., 2023, Teaching Critical Reading Skills: Strategies for Academic Librarians, P275
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Bergvall-Kåreborn B, 2004, SYST PRACT ACT RES, V17, P55, DOI 10.1023/B:SPAA.0000018903.18767.18
   Bozkurt A., 2023, Asian J. Dist. Educ, V18, P53, DOI DOI 10.5281/ZENODO.7636568
   Branch-Mueller J., 2016, LIBRARIANS EDUCATORS, P113
   Burroughs A., 2022, EDTECH FOCUS K  0708
   Cassidy C., 2023, THE GUARDIAN    0123
   Chesterman S., 2023, SSRN Electronic Journal, DOI DOI 10.2139/SSRN.4321596
   Chia O., 2023, STRAITS TIMES   0102
   Chiu TKF, 2023, INTERACT LEARN ENVIR, DOI 10.1080/10494820.2023.2253861
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Crawford J, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.3.02
   de Sousa EBG, 2021, FRONT ARTIF INTELL, V4, DOI 10.3389/frai.2021.737891
   Dobrin S. I., 2023, TALKING GENERATIVE G
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Elsen-Rooney M., 2023, NYC ED DEP BLOCKS CH
   Faezirad M., 2023, J SYSTEMS THINKING P, V2, P56
   Forsyth O., 2022, Antler
   Francisco M, 2023, CURR OPIN ENV SUST, V61, DOI 10.1016/j.cosust.2022.101250
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Furze L., 2023, SYNERGY, V21
   Gallant TB, 2017, THEOR PRACT, V56, P88, DOI 10.1080/00405841.2017.1308173
   Garrison K. L., 2019, 48 ANN C INT ASS SCH
   Gavigan K., 2021, CONVERSATION    1106
   Groes Kasper, 2023, The Carbon Footprint of ChatGPT
   Hanff A., 2023, REGISTER
   Hossain Z, 2022, INT J EDUC INTEGR, V18, DOI 10.1007/s40979-021-00096-4
   Hosseini M, 2024, ACCOUNT RES, V31, P715, DOI 10.1080/08989621.2023.2168535
   Hutchinson E., 2023, ELIZABETH HUTCH 0403
   Illia L, 2023, BUS ETHICS ENV RESP, V32, P201, DOI 10.1111/beer.12479
   International Federation of Library Associations and Institutions, 2021, IFLA SCH LIB MANIFES
   Ishaq S, 2022, MSYSTEMS, V7, DOI 10.1128/msystems.00806-22
   Jaeger C., 2023, THE AGE         0201
   Jamieson S., 2018, STUDENT PLAGIARISM H, P105, DOI [https://doi.org/10.4324/9781315166148, DOI 10.4324/9781315166148]
   Jensen M. H., 2022, AALBORG UNIVERSITETS, DOI [10.54337/aau510574702, DOI 10.54337/AAU510574702]
   Ji ZW, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3571730
   Jimenez K., 2023, USA Today
   Jovanovic M, 2022, COMPUTER, V55, P107, DOI 10.1109/MC.2022.3192720
   Keegin Joseph M., 2023, CHRON HIGHER EDUC
   Khaddage F., 2023, SOC INFORM TECHNOLOG
   Kidd C, 2023, SCIENCE, V380, P1222, DOI 10.1126/science.adi0248
   Kovanovic V., 2022, The Conversation
   Lambert J, 2024, COMPUT SCH, V41, P559, DOI 10.1080/07380569.2023.2256710
   Lance KC, 2018, PHI DELTA KAPPAN, V99, P15, DOI 10.1177/0031721718767854
   Laretive J, 2019, J AUST LIB INF ASSOC, V68, P225, DOI 10.1080/24750158.2019.1649795
   Lauriola I, 2022, NEUROCOMPUTING, V470, P443, DOI 10.1016/j.neucom.2021.05.103
   Lee M., 2011, ACCESS, V25, P10
   Liang WX, 2023, PATTERNS, V4, DOI 10.1016/j.patter.2023.100779
   Lodge JM, 2023, Assessment reform for the age of Artificial Intelligence
   Luckin R., 2022, COMPUTERS ED ARTIFIC, V3, DOI [DOI 10.1016/J.CAEAI.2022.100076, 10.1016/J.CAEAI.2022.100076]
   Luckin R., 2016, Intelligence unleashed: An argument for AI in education
   Lund BD, 2023, J ASSOC INF SCI TECH, V74, P570, DOI 10.1002/asi.24750
   Malik G, 2019, ADV INTELL SYST, V707, P407, DOI 10.1007/978-981-10-8639-7_42
   Matulionyte Rita, 2023, Law, Innovation and Technology, P124, DOI 10.1080/17579961.2023.2184138
   Mayes C., 2023, SCH LIBR J
   McCarthy PM, 2009, BEHAV RES METHODS, V41, P682, DOI 10.3758/BRM.41.3.682
   McGrew S, 2022, COMPUT EDUC, V185, DOI 10.1016/j.compedu.2022.104519
   Mercer K., 2023, TEACHING SCI STUDENT, P49, DOI [https://doi.org/10.1007/978-3-030-91628-2_6, DOI 10.1007/978-3-030-91628-2_6]
   Merga M. K., 2021, SCAN J EDUCATORS, V40, P12
   Mitchell M., 2019, Artificial Intelligence
   Monett D, 2018, STUD APPL PHILOS, V44, P212, DOI 10.1007/978-3-319-96448-5_21
   Naughton J., 2023, THE GUARDIAN    0604
   Nayyer K., 2022, The rise of AI: Implications and applications of artificial intelligence in academic libraries, P165
   New South Wales Department of Education, 2023, AUSTR FRAM GEN ART I
   New South Wales Department of Education, 2022, INFORM PROCESS
   Nolan B., 2023, Business Insider
   Oddone K., 2022, ACCESS, V36, P15
   OpenAI, 2023, Terms of use
   Ortiz S., 2023, What is ChatGPT and why does it matter? Here's what you need to know
   Pavey S., 2023, SYNERGY, V21
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Perrigo B, 2023, TIME
   Potter W., 2023, CONVERSATION    0426
   Puthiyedath A. R., 2023, REFLECTIONS
   Quillbot, 2023, TERMS
   Raicu I., 2023, CAN BIASED HUMANS DE
   Raval Noopur, 2021, Interactions, V28, P27, DOI DOI 10.1145/3469257
   Reiter B., 2017, IJSRMHUMAN, V5, P129
   Ribble M., 2015, DIGITAL CITIZENSHIP
   Rossi S. L., 2022, ACAD INTEGRITY CANAD, P411, DOI [10.1007/978-3-030-83255-1, DOI 10.1007/978-3-030-83255-1_21]
   Sadasivan Vinu Sankar, 2023, arXiv
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Shimony E., 2023, CHATGPT CAN BE USED
   Siemens G., 2022, CONVERSATION    1222
   Sinatra Gale, 2023, The Conversation24 May
   Somoye F. L., 2023, WHAT IS PERPLEXITY W
   Song S., 2020, EFL MAGAZINE     MAR
   Southgate E., 2018, Artificial intelligence and emerging technologies (virtual, augmented and mixed reality) in schools: A research report
   Stebbins R.A., 2001, Qualitative research methods: Exploratory research in the social sciences, DOI DOI 10.4135/9781412984249
   Sullivan M., 2023, FAST CO
   Sye D., 2023, Journal of New Librarianship, V8, P76, DOI DOI 10.33011/NEWLIBS/13/9
   Tlili A, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00237-x
   UNESCO, 2023, INT FORUM ED STEERIN
   Wach K, 2023, ENTREPR BUS ECON REV, V11, P7, DOI 10.15678/EBER.2023.110201
   Wall J., 2022, ACCESS, V36, P15
   Wang Tianchong, 2022, Artificial Intelligence in Education: Emerging Technologies, Models and Applications: Proceedings of 2021 2nd International Conference on Artificial Intelligence in Education Technology. Lecture Notes on Data Engineering and Communications Technologies (104), P3, DOI 10.1007/978-981-16-7527-0_1
   Wiggers K., 2023, TechCrunch
   Wineburg S, 2022, J EDUC PSYCHOL, V114, P893, DOI 10.1037/edu0000740
   Wodecki B., 2023, BUSINESS
   Ziarko J., 2022, SECURITY THEORY PRAC, V47, P75
NR 104
TC 5
Z9 5
U1 33
U2 93
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 2475-0158
EI 2475-0166
J9 J AUST LIB INF ASSOC
JI J. Aust. Libr. Inf. Assoc.
PD JAN 2
PY 2024
VL 73
IS 1
BP 3
EP 26
DI 10.1080/24750158.2023.2289093
EA DEC 2023
PG 24
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA NB2T4
UT WOS:001115058400001
DA 2024-12-25
ER

PT J
AU Xu, QF
   Zhou, GH
   Zhang, C
   Chang, FT
   Cao, Y
   Zhao, D
AF Xu, Qingfeng
   Zhou, Guanghui
   Zhang, Chao
   Chang, Fengtian
   Cao, Yan
   Zhao, Dan
TI Generative AI and DT integrated intelligent process planning: a
   conceptual framework
SO INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Intelligent process planning; CAPP; Generative AI; Transformer; DT
ID DIGITAL TWIN; MULTIOBJECTIVE OPTIMIZATION; PRODUCTION SYSTEM
AB Process planning serves as a critical link between design and manufacturing, exerting a pivotal influence on the quality and efficiency of production. However, current intelligent process planning systems, like computer-aided process planning (CAPP), still contend with the challenge of realizing comprehensive automation in process decision-making. These obstacles chiefly involve, though are not confined to, issues like limited intelligence, poor flexibility, low reliability, and high usage thresholds. Generative artificial intelligence (AI) has attained noteworthy accomplishments in natural language processing (NLP), offering new perspectives to address these challenges. This paper summarizes the limitations of current intelligent process planning methods and explores the potential of integrating generative AI into process planning. With synergistically incorporating digital twin (DT), this paper introduces a conceptual framework termed generative AI and DT-enabling intelligent process planning (GIPP). The paper elaborates on two supporting methodologies: process generative pre-trained transformer (GPT) modelling and DT-based process verification method. Moreover, a prototype system is established to introduce the implementation and machining execution mechanism of GIPP for milling a specific thin-walled component. Three potential application scenarios and a comparative analysis are employed to elucidate the practicality of GIPP, providing new insights for intelligent process planning.
C1 [Xu, Qingfeng; Zhou, Guanghui; Zhang, Chao; Zhao, Dan] Xi An Jiao Tong Univ, Sch Mech Engn, 28 Xianning West Rd, Xian 710049, Peoples R China.
   [Zhou, Guanghui; Zhang, Chao; Zhao, Dan] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710054, Peoples R China.
   [Chang, Fengtian] Changan Univ, Sch Construct Machinery, Xian 710064, Peoples R China.
   [Cao, Yan] Xian Technol Univ, Sch Mechatron Engn, Xian 710021, Peoples R China.
C3 Xi'an Jiaotong University; Xi'an Jiaotong University; Chang'an
   University; Xi'an Technological University
RP Zhou, GH; Zhang, C (corresponding author), Xi An Jiao Tong Univ, Sch Mech Engn, 28 Xianning West Rd, Xian 710049, Peoples R China.; Zhou, GH; Zhang, C (corresponding author), Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710054, Peoples R China.
EM ghzhou@mail.xjtu.edu.cn; superzc@mail.xjtu.edu.cn
OI Zhang, Chao/0000-0001-8260-1210
FU National Natural Science Foundation of China
FX No Statement Available
CR Ahmad N, 2001, P 7 ANN PAP MEET 2 I, P25
   Aivaliotis P, 2019, INT J COMPUT INTEG M, V32, P1067, DOI 10.1080/0951192X.2019.1686173
   Al-wswasi M, 2018, INT J ADV MANUF TECH, V97, P809, DOI 10.1007/s00170-018-1966-1
   [Anonymous], 2021, arXiv
   [Anonymous], 2014, The evaluation of a novel haptic machining VR-based process planning system using an original process planning usability method
   Behandish M, 2018, COMPUT AIDED DESIGN, V102, P115, DOI 10.1016/j.cad.2018.04.022
   Blasek N, 2023, LANGUAGE MODELS REQU
   Brown TB., 2020, ADV NEURAL INFORM PR, V2020, P1877, DOI [10.48550/ARXIV.2005.14165, DOI 10.48550/ARXIV.2005.14165]
   Cao Y., 2023, J. ACM, V37, P1
   Choi K., 2020, ARXIV
   DebRoy T, 2017, SCRIPTA MATER, V135, P119, DOI 10.1016/j.scriptamat.2016.12.005
   Denkena B, 2007, CIRP ANN-MANUF TECHN, V56, P175, DOI 10.1016/j.cirp.2007.05.042
   Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
   Dhariwal P., 2020, JUKEBOX GENERATIVE M
   Ding K, 2019, INT J PROD RES, V57, P6315, DOI 10.1080/00207543.2019.1566661
   Elizalde B., 2022, arXiv
   Fan HL, 2024, J INTELL MANUF, DOI 10.1007/s10845-023-02294-y
   Gao X, 2016, PROC CIRP, V56, P585, DOI 10.1016/j.procir.2016.10.115
   Glaessgen E., 2012, 53 AIAAASMEASCEAHSAS, P1818, DOI [DOI 10.2514/6.2012-1818, 10.2514/6.2012-1818]
   Gozalo-Brizuela R., 2023, ARXIV
   Halevi G, 2014, LECT NOTE MANAGE IND, V1, P1, DOI 10.1007/978-3-319-03470-6
   Han Song, 2015, ARXIV151000149
   Heng JN, 2017, APPL ENERG, V208, P845, DOI 10.1016/j.apenergy.2017.09.063
   Hinton G., 2015, arXiv
   Hochhalter J., 2014, COUPLING DAMAGE SENS
   Houlsby N, 2019, PR MACH LEARN RES, V97
   Hu E. J., 2021, ARXIV
   Kong YN, 2021, INT CONF E BUS ENG, P82, DOI 10.1109/ICEBE52470.2021.00032
   Kumar SPL, 2017, ENG APPL ARTIF INTEL, V65, P294, DOI 10.1016/j.engappai.2017.08.005
   Lewkowycz A., 2022, Advances in Neural Information Processing Systems, V35, P3843
   Li BM, 2011, KNOWL-BASED SYST, V24, P1108, DOI 10.1016/j.knosys.2011.05.005
   Li JJ, 2022, INT J PROD RES, V60, P5217, DOI 10.1080/00207543.2021.1951869
   Li Lei, 2023, ARXIV
   Li MH, 2023, PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023, P208, DOI 10.1145/3600100.3623719
   Li Y, 2023, CHATDOCTOR MEDICAL C, DOI [10.7759/cureus.40895.Cureus, DOI 10.7759/CUREUS.40895.CUREUS]
   Liang PP., 2023, ARXIV
   Liu SM, 2023, J MANUF SYST, V67, P361, DOI 10.1016/j.jmsy.2023.02.010
   Löcklin A, 2020, IEEE INT C EMERG, P851, DOI 10.1109/ETFA46521.2020.9212051
   Lu YQ, 2020, J MANUF SYST, V56, P312, DOI 10.1016/j.jmsy.2020.06.010
   Luo RQ, 2022, BRIEF BIOINFORM, V23, DOI 10.1093/bib/bbac409
   Ma SY, 2022, APPL ENERG, V326, DOI 10.1016/j.apenergy.2022.119986
   Makatura L, 2023, ARXIV
   Markel JM, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P226, DOI 10.1145/3573051.3593393
   Mikolov T, 2010, 11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, P1045
   Min QF, 2019, INT J INFORM MANAGE, V49, P502, DOI 10.1016/j.ijinfomgt.2019.05.020
   Mukherjee T, 2019, APPL MATER TODAY, V14, P59, DOI 10.1016/j.apmt.2018.11.003
   Murphy K. P., 2022, Probabilistic Machine Learning: An Introduction
   Niebel BW, 1965, ASME Paper 737, P737
   Ouyang L, 2022, ADV NEUR IN
   Pang JZ, 2023, J MANUF SYST, V68, P477, DOI 10.1016/j.jmsy.2023.05.008
   Papanikolaou Y, 2020, ARXIV
   Psarommatis F, 2023, INT J PROD RES, V61, P5723, DOI 10.1080/00207543.2022.2101960
   Qi QL, 2018, IEEE ACCESS, V6, P3585, DOI 10.1109/ACCESS.2018.2793265
   Radford A., 2018, IMPROVING LANGUAGE U
   Radford A., 2019, OPENAI BLOG
   Razavi A, 2019, ADV NEUR IN, V32
   Rombach R, 2022, PROC CVPR IEEE, P10674, DOI 10.1109/CVPR52688.2022.01042
   Scarlatos A, 2023, ARXIV
   Sennrich R., 2016, arXiv
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Sohl-Dickstein J, 2015, PR MACH LEARN RES, V37, P2256
   Stefanini M, 2021, ARXIV
   Talkhestani BA, 2018, PROC CIRP, V67, P13, DOI 10.1016/j.procir.2017.12.168
   Tao F, 2019, INT J PROD RES, V57, P3935, DOI 10.1080/00207543.2018.1443229
   Tao F, 2018, INT J ADV MANUF TECH, V94, P3563, DOI 10.1007/s00170-017-0233-1
   Touvron H., 2023, arXiv
   Uhlemann THJ, 2017, PROC CIRP, V61, P335, DOI 10.1016/j.procir.2016.11.152
   Vaswani A, 2017, ADV NEUR IN, V30
   Wang H., 2023, ARXIV
   Wang Xingzhi, 2023, Procedia CIRP, P7, DOI 10.1016/j.procir.2023.04.001
   Wang Y, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2305.18339
   Wen QS, 2021, PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021, P4653
   Wu Q, 2022, DISTILLING TEXT IMAG
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Xiao YZ, 2023, J MANUF SYST, V70, P417, DOI 10.1016/j.jmsy.2023.08.006
   Xiong H., 2023, ARXIV
   Xu M, 2023, ARXIV, DOI DOI 10.48550/ARXIV.2303.16129
   Xu X, 2011, INT J COMPUT INTEG M, V24, P1, DOI 10.1080/0951192X.2010.518632
   Yang H, 2024, ARXIV
   Yang L., 2023, arXiv
   Yu Gu, 2022, ACM Transactions on Computing and Healthcare, V3, DOI 10.1145/3458754
   Zhang C, 2018, HEKM HIGH END EQUIPM, DOI [10.1115/DETC2018-85151, DOI 10.1115/DETC2018-85151]
   Zhang C, 2023, ADV ENG INFORM, V57, DOI 10.1016/j.aei.2023.102121
   Zhang C, 2023, INT J ADV MANUF TECH, V124, P2847, DOI 10.1007/s00170-022-10667-5
   Zhang C, 2022, IEEE ACCESS, V10, P80784, DOI 10.1109/ACCESS.2022.3195905
   Zhang C, 2020, KNOWL-BASED SYST, V191, DOI 10.1016/j.knosys.2019.105247
   Zhang H, 2023, ARXIV
   Zhang H, 2017, IEEE ACCESS, V5, P26901, DOI 10.1109/ACCESS.2017.2766453
   Zhao P, 2020, IEEE ACCESS, V8, P41229, DOI 10.1109/ACCESS.2020.2974241
   Zhou GH, 2020, INT J PROD RES, V58, P1034, DOI 10.1080/00207543.2019.1607978
NR 90
TC 3
Z9 3
U1 45
U2 45
PU SPRINGER LONDON LTD
PI LONDON
PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND
SN 0268-3768
EI 1433-3015
J9 INT J ADV MANUF TECH
JI Int. J. Adv. Manuf. Technol.
PD JUL
PY 2024
VL 133
IS 5-6
BP 2461
EP 2485
DI 10.1007/s00170-024-13861-9
EA JUN 2024
PG 25
WC Automation & Control Systems; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Automation & Control Systems; Engineering
GA YP3D3
UT WOS:001243282000005
DA 2024-12-25
ER

PT J
AU Spennemann, DHR
   Biles, J
   Brown, L
   Ireland, MF
   Longmore, L
   Singh, CL
   Wallis, A
   Ward, C
AF Spennemann, Dirk H. R.
   Biles, Jessica
   Brown, Lachlan
   Ireland, Matthew F.
   Longmore, Laura
   Singh, Clare L.
   Wallis, Anthony
   Ward, Catherine
TI ChatGPT giving advice on how to cheat in university assignments: how
   workable are its suggestions?
SO INTERACTIVE TECHNOLOGY AND SMART EDUCATION
LA English
DT Article
DE Academic misconduct; ChatGPT; Contract cheating; Ethics of AI;
   Generative AI
AB PurposeThe use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.Design/methodology/approachAlthough ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT's can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT's suggestions would be successful in avoiding detection by markers when submitting contract-written work.FindingsWhile the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student's ability to distinguish between genuinely viable options and those that appear to be workable but are not.Originality/valueThis paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.
C1 [Spennemann, Dirk H. R.] Charles Sturt Univ, Sch Agr Environm & Vet Sci, Albury Wodonga Campus, Albury, NSW, Australia.
   [Biles, Jessica] Charles Sturt Univ, Sch Nursing Paramed & Healthcare Sci, Albury Wodonga Campus, Albury, NSW, Australia.
   [Brown, Lachlan] Charles Sturt Univ, Sch Social Work & Arts, Wagga Wagga Campus, Wagga Wagga, NSW, Australia.
   [Ireland, Matthew F.] Charles Sturt Univ, Sch Dent & Med Sci, Orange Campus, Orange, NSW, Australia.
   [Longmore, Laura; Wallis, Anthony; Ward, Catherine] Charles Sturt Univ, Fac Business Justice & Behav Sci, Bathurst Campus, Bathurst, NSW, Australia.
   [Singh, Clare L.] Charles Sturt Univ, Sch Dent & Med Sci, Wagga Wagga Campus, Wagga Wagga, NSW, Australia.
C3 Charles Sturt University; Charles Sturt University; Charles Sturt
   University; Charles Sturt University; Charles Sturt University; Charles
   Sturt University
RP Spennemann, DHR (corresponding author), Charles Sturt Univ, Sch Agr Environm & Vet Sci, Albury Wodonga Campus, Albury, NSW, Australia.
EM dspennemann@csu.edu.au
RI Spennemann, Dirk/J-4199-2016; Singh, Clare/AAU-5278-2020; Biles,
   Jessica/JBI-8412-2023
OI Biles, Jessica/0000-0002-0107-7435; Spennemann,
   Dirk/0000-0003-2639-7950; Singh, Clare Louise/0000-0001-6632-0427
CR Ali K., 2023, THRILLS CHILLS CHATG, V2023020513
   Amigud A, 2020, ASSESS EVAL HIGH EDU, V45, P541, DOI 10.1080/02602938.2019.1670780
   Bishop L., 2023, COMPUTER WROTE THIS
   Chan Colleen, 2023, arXiv
   Chang K-L, 2023, ARXIV
   Chaudhry IS, 2023, COGENT EDUC, V10, DOI 10.1080/2331186X.2023.2210461
   Chechitelli A., 2023, UNDERSTANDING FALSE
   Cotton DRE, 2024, INNOV EDUC TEACH INT, V61, P228, DOI 10.1080/14703297.2023.2190148
   Currie G, 2023, RADIOGRAPHY, V29, P792, DOI 10.1016/j.radi.2023.05.011
   Dalalah D, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100822
   David Baidoo-Anu, 2023, ED ERA GENERATIVE AR, DOI [10.2139/ssrn.4337484, DOI 10.2139/SSRN.4337484]
   de Winter JCF, 2024, INT J ARTIF INTELL E, V34, P915, DOI 10.1007/s40593-023-00372-z
   Derner E., 2023, arXiv, P1
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Edwards B., 2023, OPENAI CONFIRMS AI W
   Edwards Benj, 2023, Why AI Detectors Think the US Constitution Was Written by AI
   Fowler S., 2023, 1 100 DAYS CHATGPT A
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Gravel J., 2023, Mayo Clinic Proceedings: Digital Health, V1, P226
   Hartmann J., 2023, ARXIV
   Hassoulas A., 2023, J APPL LEARNING TEAC, V6
   Heng JJY, 2023, POSTGRAD MED J, V99, P1125, DOI 10.1093/postmj/qgad058
   Jaybird, 2022, CHATGPT HAS HANDF ET
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Khalil M., 2023, arXiv
   Krugel S., 2023, ARXIV
   Lancaster T, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00131-6
   Lancaster T, 2019, J INF COMMUN ETHICS, V17, P72, DOI 10.1108/JICES-04-2018-0040
   Li H., 2023, ARXIV
   Ma P., 2023, ARXIV
   Markov T., 2023, NEW IMPROVED CONTENT
   McGee R.W., 2023, CAN TAX EVASION EVER
   McGee R.W., 2023, ETHICS COMMITTEES CA
   OpenAI, 2023, CHATGPT OPT LANG MOD
   OpenAI, 2023, PREPRINT
   OpenAI, 2023, OpenAI-Educator FAQ
   Perkins M., 2023, ARXIV
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Rigby D, 2015, J ECON BEHAV ORGAN, V111, P23, DOI 10.1016/j.jebo.2014.12.019
   Romig J.M., 2023, ETHICS CHATGPT LEGAL
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Spennemann D. H., 2023, Knowledge, V3, DOI DOI 10.3390/KNOWLEDGE3030032
   Spennemann D.H.R., 2023, ARXIV
   Spennemann D. H. R., 2023, arXiv, P1
   Spennemann DHR, 2023, PUBLICATIONS, V11, DOI 10.3390/publications11030045
   Spennemann DHR, 2023, HERITAGE-BASEL, V6, P5732, DOI 10.3390/heritage6080302
   Susnjak T., 2022, ARXIV
   Tindle R., 2023, ACAD MISCONDUCT GENE
   Weber-Wulff D., 2023, ARXIV
   Yahoo!Finance News Direct, 2022, YAHOO FINANCE NEWS D
   Zhao I., 2023, INT STUDENTS RES CON
   Zhuo Terry Yue, 2023, Red teaming chatgpt via jailbreaking: Bias, robustness, reliability and toxicity
NR 52
TC 3
Z9 3
U1 23
U2 39
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 1741-5659
EI 1758-8510
J9 INTERACT TECHNOL SMA
JI Interact. Technol. Smart Educ.
PD OCT 30
PY 2024
VL 21
IS 4
SI SI
BP 690
EP 707
DI 10.1108/ITSE-10-2023-0195
EA APR 2024
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA K4N9Y
UT WOS:001202841300001
OA Green Submitted
DA 2024-12-25
ER

PT J
AU Lucchi, N
AF Lucchi, Nicola
TI ChatGPT: A Case Study on Copyright Challenges for Generative Artificial
   Intelligence Systems
SO EUROPEAN JOURNAL OF RISK REGULATION
LA English
DT Article
DE Artificial intelligence; ChatGPT; copyright; data sharing; intellectual
   property; language models; training data
ID PROTECTION
AB This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems, with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate high-quality outcomes, generative AI systems require substantial quantities of training data, which may frequently comprise copyright-protected information. This prompts inquiries into the legal principles of fair use, the creation of derivative works and the lawfulness of data gathering and utilisation. The utilisation of input data for the purpose of training and enhancing AI models presents significant concerns regarding potential violations of copyright. This paper offers suggestions for safeguarding the interests of copyright holders and competitors, while simultaneously addressing legal challenges and expediting the advancement of AI technologies. This study analyses the ChatGPT platform as a case example to explore the necessary modifications that copyright regulations must undergo to adequately tackle the intricacies of authorship and ownership in the realm of AI-generated creative content.
C1 [Lucchi, Nicola] Univ Pompeu Fabra, Dept Law, Barcelona, Spain.
C3 Pompeu Fabra University
RP Lucchi, N (corresponding author), Univ Pompeu Fabra, Dept Law, Barcelona, Spain.
EM Nicola.Lucchi@upf.edu
RI Lucchi, Nicola/AAC-3132-2021; Lucchi, Nicola/A-9678-2015
OI Lucchi, Nicola/0000-0001-8611-0072
CR Abbamonte G, 2021, EIPR, V43, P702
   Abbot R, 2020, REASONABLE ROBOT
   Abbott R, IN PRESS
   Abbott R, 2022, RES HDB INTELLECTUAL
   Adamopoulou E, 2020, Artificial intelligence applications and innovations 2020, DOI [10.1007/978-3-030-49186-4_31, DOI 10.1007/978-3-030-49186-4_31]
   Alpaydin E, 2004, INTRO MACHINE LEARNI, P2
   Anderson K, 2023, GEYSER          0113
   [Anonymous], 2019, COFEMEL SOC VESTUARI
   [Anonymous], 1994, Campbell v. Acuff-Rose Music, Inc
   [Anonymous], 2016, DIR 95 46 EC GEN DAT
   [Anonymous], 2023, SILVERMAN ET AL V OP
   [Anonymous], 2023, TREMBLAY ET AL V OPE
   [Anonymous], 2021, 20210106COD EUR COMM
   [Anonymous], 1884, Burrow-Giles Lithographic Co. v. Sarony
   [Anonymous], 2018, SIMILARLY US SHORTLY
   [Anonymous], 2022, 2 REQ REC REF REG RE
   [Anonymous], 2016, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April2016on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation)
   [Anonymous], 2009, INFOPAQ INT V DANSKE
   [Anonymous], 2019, OJ L, V130, P92
   [Anonymous], 2023, Andy Warhol Found. for the Visual Arts, Inc., v. Goldsmith, 598 U.S. __, 143 S. Ct. 1258, 1282
   [Anonymous], 2009, INFOPAQ INT A S V DA
   [Anonymous], 2019, Funke Medien NRW GmbH v. Federal Republic of Germany 2019
   [Anonymous], 2020, SI BROMPTON BICYCLE
   [Anonymous], 2019, Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC (Text with EEA relevance.)
   [Anonymous], 2019, DIRECTIVES 969EC 200
   [Anonymous], 1988, 9 3 UK COPYRIGHT DES
   [Anonymous], 2015, European Parliament
   [Anonymous], 2023, THALER V PERLMUTTER
   [Anonymous], 1991, Feist Publications Inc v Rural Telephone Service Co
   [Anonymous], 2023, RACC ILL DAT PERS AS
   [Anonymous], 2023, GETTY IMAGES US INC
   [Anonymous], 2015, Authors Guild v Google
   Bender EM, 2020, P 58 ANN M ASS COMP, P5185, DOI DOI 10.18653/V1/2020.ACL-MAIN.463
   Bonadio E, 2022, RES HDB INTELLECTUAL
   Bonadio E, 2022, INT REV INTELLECTUAL, V53, P1187
   Bonadio E., 2020, Intellect Prop Q, V2020, P112
   Bonadio E, 2022, IIC-INT REV INTELL P, V53, P1174, DOI 10.1007/s40319-022-01213-7
   Bonadio E, 2018, NON-CONVENTIONAL COPYRIGHT: DO NEW AND ATYPICAL WORKS DESERVE PROTECTION?, P83
   Bonadio E, 2018, NON-CONVENTIONAL COPYRIGHT: DO NEW AND ATYPICAL WORKS DESERVE PROTECTION?, P1
   Bonadio E, 2018, EUR J RISK REGUL, V9, P655, DOI 10.1017/err.2018.58
   Bridy A., 2012, STANFORD TECHNOLOGY, V5, P1
   Brown TB, 2020, ADV NEUR IN, V33
   Burk Daniel L, 2020, HOUS L REV, V58, P263
   Carroll MW, 2019, UC DAVIS LAW REV, V53, P894
   Copyright Office, 2023, COP REG GUID WORKS C, P37
   Craig, 2021, OSGOODE LEGAL STUDIE
   Dale R, 2016, NAT LANG ENG, V22, P811, DOI 10.1017/S1351324916000243
   Denicola RC, 2016, RUTGERS U LAW REV, V69, P251
   Department of Commerce's National Institute of Standards and Technology, 2023, ARTIF INTELL
   Devlin J., 2018, ARXIV
   Dornis Tim W., 2020, Yale JL Tech, V22, P1
   EC, 2019, 640163 EC PE
   European Parliament, 2019, RES COMPR EUR IND PO
   Floridi L, 2023, PHILOS TECHNOLOGY, V36, P6
   Foster D, 2019, GENERATIVE DEEP LEAR, P1
   Franceschelli G, 2022, DATA POLICY, V4, DOI 10.1017/dap.2022.10
   Ginsburg JC., 2019, BERKELEY TECHNOLOGY, V34, P343, DOI DOI 10.2139/SSRN.3233885
   Goldberg Y, 2017, NEURAL NETWORK METHO, P105
   Goodfellow I, 2016, ADAPT COMPUT MACH LE, P1
   Grimmelmann J, 2016, COLUM JL ARTS, V39, P403
   Guadamuz A, 2017, WORLD INTELLECTU OCT
   Guadamuz A, 2023, TECHNOLLAMA     0708
   Guadamuz Andres., 2017, Intellectual Property Quarterly, V2, P169
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Helberger N, 2023, INTERNET POLICY REV, V12, P28, DOI 10.14763/2023.1.1682
   Henderson P, 2018, PROCEEDINGS OF THE 2018 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY (AIES'18), P123, DOI 10.1145/3278721.3278777
   Hugenholtz PB, 2021, IIC-INT REV INTELL P, V52, P1190, DOI 10.1007/s40319-021-01115-0
   Intellectual Property Office (UK), 2020, ARTIF INTELL
   International Confederation of Societies of Authors and Composers, GLOB CREAT PERF DEM
   International Telecommunication Union, GLOB IN AI DAT COMM
   Jurafsky Dan, 2009, Speech and Language Processing, V2nd
   Khoury AH, 2017, CARDOZO ART ENTERT L, V35, P635
   Kop M, 2020, MACHINE LEARNING EU
   Kop M, 2021, HARVARD J LAW TECHNO, V34, P1
   Leistner M, 2022, IPR USE OPEN DATA DA
   Lemley M. A., 2020, Texas Law Rev, V99, P447, DOI [10.2139/ssrn.3528447, DOI 10.2139/SSRN.3528447]
   Lemley MA, GENERATIVE AI TURNS
   Lemley MA, 2021, BOSTON U LAW REV, V101, P68
   Lemley MA, 2019, U CHICAGO LAW REV, V86, P1311
   Lim D., 2018, Akron Law Review, V52, P813
   Manning CD, 2008, INTRO INFORM RETRIEV, P238
   Manning CD, 1999, FDN STAT NATURAL LAN, P3
   Margoni Thomas, 2022, GRUR International, V71, P685, DOI DOI 10.1093/GRURINT/IKAC054
   OECD, 2024, International Journal of Legal Science and Innovation
   OECD Observatory of Public Sector Information, 2019, ALG IMP ASS
   On the importance of data sharing, 2020, EUROPEAN STRATEGY BU
   OpenAI, US
   OpenAI, Introducing ChatGPT
   Patel F, 2023, PERILS PROMISE AI RE
   Patry W, 2023, J INTELLET PROP LAW, DOI 10.1093/jiplp/jpad060
   Quang Jenny, 2021, Berkeley Technology Law Review, V36, P1407
   Radford A., 2018, IMPROVING LANGUAGE U
   Radford A., 2019, OPENAI BLOG
   Reese RA, 2008, COLUM JL ARTS, V31, P485
   SAMUELSON P, 1986, U PITT LAW REV, V47, P1185
   Senftleben M, 2022, TAX MACHINES PURPOSE
   Senftleben M, 2023, GENERATIVE AI AUTHOR
   Sherman B, 2012, COPYRIGHT CHALLENGE, P1
   Sobel B, 2021, ARTIF INTELL, P221
   Strowel A, 2023, IIC-INT REV INTELL P, DOI 10.1007/s40319-023-01321-y
   The European AI Alliance, EUROPEAN COMMISSION
   Tobin S, 2023, REUTERS         0601
   US Copyright Office, 2021, COMP US COP OFF PRAC
   Vesala J, 2023, IIC-INT REV INTELL P, V54, P351, DOI 10.1007/s40319-023-01301-2
   Vincent J, 2022, VERGE           1115
   whitehouse.gov, BLUEPR AI BILL RIGHT
   WIPO, 2020, WIPOIPAI2GE201REV
   WIPO, WIPO TECHN TRENDS 20
   Yoo C S., 2020, Journal of Law and Economic Regulation, V13, P7
   Yu P. K., 2020, Florida Law Review, V72, P331
   Yu R, 2017, U PENN LAW REV, V165, P1245
NR 111
TC 24
Z9 26
U1 50
U2 175
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 1867-299X
EI 2190-8249
J9 EUR J RISK REGUL
JI Eur. J. Risk Regul.
PD SEP
PY 2024
VL 15
IS 3
SI SI
BP 602
EP 624
DI 10.1017/err.2023.59
EA AUG 2023
PG 23
WC Law
WE Emerging Sources Citation Index (ESCI)
SC Government & Law
GA N0U7J
UT WOS:001068213000001
OA hybrid
DA 2024-12-25
ER

PT J
AU Giorgi, S
   Isman, K
   Liu, TT
   Fried, Z
   Sedoc, J
   Curtis, B
AF Giorgi, Salvatore
   Isman, Kelsey
   Liu, Tingting
   Fried, Zachary
   Sedoc, Joao
   Curtis, Brenda
TI Evaluating generative AI responses to real-world drug-related questions
SO PSYCHIATRY RESEARCH
LA English
DT Article
DE Large language models; Generative AI; Substance use; Alcohol; Marijuana;
   Opioids
ID SUBSTANCE USE
AB Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse in real time, hold potential for large-scale deployment in clinical settings such as substance use treatment. Treatment for substance use disorders (SUDs) is particularly high stakes, requiring evidence-based clinical treatment, mental health expertise, and peer support. Thus, promises of AI systems addressing deficient healthcare resources and structural bias are relevant within this domain, especially in an anonymous setting. This study explores the effectiveness of generative AI in answering real-world substance use and recovery questions. We collect questions from online recovery forums, use ChatGPT and Meta's LLaMA-2 for responses, and have SUD clinicians rate these AI responses. While clinicians rated the AI-generated responses as high quality, we discovered instances of dangerous disinformation, including disregard for suicidal ideation, incorrect emergency helplines, and endorsement of home detox. Moreover, the AI systems produced inconsistent advice depending on question phrasing. These findings indicate a risky mix of seemingly high-quality, accurate responses upon initial inspection that contain inaccurate and potentially deadly medical advice. Consequently, while generative AI shows promise, its real-world application in sensitive healthcare domains necessitates further safeguards and clinical validation.
C1 [Giorgi, Salvatore; Isman, Kelsey; Liu, Tingting; Fried, Zachary; Curtis, Brenda] Natl Inst Drug Abuse, Baltimore, MD USA.
   [Giorgi, Salvatore] Univ Penn, Philadelphia, PA USA.
   [Sedoc, Joao] NYU, New York, NY USA.
C3 National Institutes of Health (NIH) - USA; NIH National Institute on
   Drug Abuse (NIDA); University of Pennsylvania; New York University
RP Curtis, B (corresponding author), Biomed Res Ctr, 251 Bayview Blvd,Suite 200, Baltimore, MD 21224 USA.
EM brenda.curtis@nih.gov
RI Liu, Tingting/GYD-4727-2022; Curtis, Brenda/AAU-2837-2021
OI Curtis, Brenda/0000-0002-2511-3322; Isman, Kelsey/0009-0004-3414-9337;
   Sedoc, Joao/0000-0001-6369-3711; Giorgi, Salvatore/0000-0001-7381-6295
FU NIH, National Institute on Drug Abuse (NIDA)
FX We would like to thank the clinical annotators for their expert opinions
   when rating the AI-generated responses. This research was supported in
   part by the Intramural Research Program of the NIH, National Institute
   on Drug Abuse (NIDA) .
CR Abercrombie Gavin, 2023, P 2023 C EMPIRICAL M, P4776, DOI [DOI 10.18653/V1/2023.EMNLP, 10.18653/v1/2023.emnlp-main.290]
   Achiam OJ, 2023, Arxiv, DOI [arXiv:2303.08774, 10.48550/arXiv.2303.08774]
   Amin S, 2023, TOB CONTROL, DOI 10.1136/tc-2023-058009
   [Anonymous], 2021, National survey on drug use and health"
   Antoniak M, 2024, Arxiv, DOI [arXiv:2312.11803, DOI 10.48550/ARXIV.2312.11803]
   Ashford RD, 2019, ALCOHOL TREAT Q, V37, P257, DOI 10.1080/07347324.2018.1513777
   Ashford RD, 2018, DRUG ALCOHOL DEPEN, V189, P131, DOI 10.1016/j.drugalcdep.2018.05.005
   Yeung JA, 2023, FRONT DIGIT HEALTH, V5, DOI 10.3389/fdgth.2023.1161098
   Ayers JW, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.17517
   Baumgartner J., 2020, ICWSM, P830, DOI DOI 10.1609/ICWSM.V14I1.7347
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Berglund L, 2024, Arxiv, DOI [arXiv:2309.12288, DOI 10.48550/ARXIV.2309.12288]
   Bian N., 2024, P 2024 JOINT INT C C, P3098
   Boettcher N, 2021, JMIR MENT HEALTH, V8, DOI 10.2196/29487
   Brown TB, 2020, ADV NEUR IN, V33
   Chen AT, 2022, DRUG ALC DEPEND REP, V3, DOI 10.1016/j.dadr.2022.100061
   De Choudhury M., 2014, P INT AAAI C WEB SOC, V8, P71
   Demszky D, 2023, NAT REV PSYCHOL, V2, P688, DOI 10.1038/s44159-023-00241-5
   Diaz-Asper C., 2023, Am. Psychol.
   Donovan DM, 2013, SOC WORK PUBLIC HLTH, V28, P313, DOI 10.1080/19371918.2013.774663
   Farahmand P, 2020, PSYCHIAT ANN, V50, P494, DOI 10.3928/00485713-20201008-01
   Giorgi S., 2024, P INT AAAI C WEB SOC
   Giorgi S, 2023, FRONT PUBLIC HEALTH, V11, DOI 10.3389/fpubh.2023.1275975
   He N, 2023, J TELEMED TELECARE, DOI 10.1177/1357633X231181922
   Heston T.F., 2023, medRxiv
   Himmelstein G, 2022, JAMA NETW OPEN, V5, DOI 10.1001/jamanetworkopen.2021.44967
   Hussain Shafquat, 2019, Web, Artificial Intelligence and Network Applications. Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019). Advances in Intelligent Systems and Computing (AISC 927), P946, DOI 10.1007/978-3-030-15035-8_93
   Kennedy-Hendricks A, 2016, DRUG ALCOHOL DEPEN, V165, P61, DOI 10.1016/j.drugalcdep.2016.05.010
   Kiang MV, 2021, DRUG ALCOHOL DEPEN, V228, DOI 10.1016/j.drugalcdep.2021.109081
   Kjell O.N., 2023, Psychiatry Research
   Korngiebel DM, 2021, NPJ DIGIT MED, V4, DOI 10.1038/s41746-021-00464-x
   Liedke J., 2022, Social media and news fact sheet
   Lord SP, 2015, BEHAV THER, V46, P296, DOI 10.1016/j.beth.2014.11.002
   Manson JH, 2013, EVOL HUM BEHAV, V34, P419, DOI 10.1016/j.evolhumbehav.2013.08.001
   Mathet Y, 2015, COMPUT LINGUIST, V41, P437, DOI 10.1162/COLI_a_00227
   Matthews S., 2019, The Stigma of Addiction, P5
   Miller-Rosales C, 2023, JAMA NETW OPEN, V6, DOI 10.1001/jamanetworkopen.2023.23741
   National Academies of Sciences, 2016, Division of Behavioral, Social Sciences, Board on Behavioral, Sensory Sciences, and Committee on the Science of Changing Behavioral Health Social Norms. Ending discrimination against people with mental and substance use disorders: The evidence for stigma change
   Ogilvie L, 2022, EUR ADDICT RES, V28, P405, DOI 10.1159/000525959
   Omiye Jesutofunmi A, 2023, NPJ Digit Med, V6, P195, DOI 10.1038/s41746-023-00939-z
   OpenAI, Introducing ChatGPT
   Proferes N., 2021, Social Media + Society, V7, DOI [DOI 10.1177/20563051211019004, 10.1177/20563051211019004]
   Scissors LE, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P277
   Sharma A, 2023, NAT MACH INTELL, V5, P46, DOI 10.1038/s42256-022-00593-2
   Singhal K, 2023, NATURE, V620, P172, DOI 10.1038/s41586-023-06291-2
   Snell-Rood C, 2021, PSYCHIAT SERV, V72, P935, DOI 10.1176/appi.ps.202000312
   Stade Elizabeth C, 2024, Npj Ment Health Res, V3, P12, DOI 10.1038/s44184-024-00056-z
   Stull SW, 2022, J ADDICT MED, V16, P135, DOI 10.1097/ADM.0000000000000867
   Touvron H, 2023, Arxiv, DOI [arXiv:2302.13971, 10.48550/arXiv.2302.13971]
   Valdez Danny, 2022, PLOS Digit Health, V1, pe0000143, DOI 10.1371/journal.pdig.0000143
   Varghese J., 2023, J. Hepatol
   Volkow ND, 2020, NEW ENGL J MED, V382, P1289, DOI 10.1056/NEJMp1917360
   Wakeman SE, 2018, SUBST USE MISUSE, V53, P330, DOI 10.1080/10826084.2017.1363238
   Zack Travis, 2024, Lancet Digit Health, V6, pe12, DOI 10.1016/S2589-7500(23)00225-X
   Zhang P, 2023, FUTURE INTERNET, V15, DOI 10.3390/fi15090286
NR 55
TC 0
Z9 0
U1 16
U2 16
PU ELSEVIER IRELAND LTD
PI CLARE
PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND
SN 0165-1781
EI 1872-7123
J9 PSYCHIAT RES
JI Psychiatry Res.
PD SEP
PY 2024
VL 339
AR 116058
DI 10.1016/j.psychres.2024.116058
EA JUL 2024
PG 8
WC Psychiatry
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Psychiatry
GA A2V2V
UT WOS:001281151900001
PM 39059040
OA hybrid
DA 2024-12-25
ER

PT J
AU Elim, EHSY
AF Elim, Emily Hui Sein Yue
TI Promoting cognitive skills in AI-supported learning environments: the
   integration of bloom's taxonomy
SO EDUCATION 3-13
LA English
DT Article; Early Access
DE Bloom's taxonomy; cognitive thinking; reflective practice; generative
   artificial intelligence; experimental learning
ID INTERNATIONAL BACCALAUREATE; ARTIFICIAL-INTELLIGENCE
AB This study introduces a model of Bloom's taxonomy aimed at deepening learners' cognitive thinking. It investigates the process of questioning and reflection within the structured framework of Bloom's taxonomy and explores how these insights can be applied in students' utilisation of generative Artificial Intelligence. An experiment was conducted in a Year 5 class at an International Baccalaureate Primary school in Hong Kong, involving 25 students. The findings indicate that Creating and Evaluating were the dominant aspects in the students' questioning and answering process. However, the skill of 'Applying' showed a significantly low influence, suggesting a lack of proficiency in applying AI conversations to other learning areas. This research contributes to the field by providing insights into the integration of generative AI within Bloom's taxonomy. Educators and researches can utilise these findings to enhance critical thinking and learning outcomes through AI integration.
C1 [Elim, Emily Hui Sein Yue] UCL, Inst Educ, London, England.
C3 University of London; University College London; UCL Institute of
   Education
RP Elim, EHSY (corresponding author), UCL, Inst Educ, London, England.
EM emilyhuiseinyue@gmail.com
OI Hui, Emily Sein Yue Elim/0000-0001-8681-0802
CR Adamopoulou E, 2020, MACH LEARN APPL, V2, DOI 10.1016/j.mlwa.2020.100006
   Biswas S., 2023, ISO: 690
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Chai CS, 2021, EDUC TECHNOL SOC, V24, P89
   Chen LJ, 2020, IEEE ACCESS, V8, P75264, DOI 10.1109/ACCESS.2020.2988510
   Creely E., 2022, Creative Provocations: Speculations on the Future of Creativity, V7, DOI [https://doi.org/10.1007/978-3-031-14549-0_3, DOI 10.1007/978-3-031-14549-0_3]
   Das S, 2022, J EDUC-US, V202, P554, DOI 10.1177/00220574211002199
   Dulfer N, 2019, J RES INT EDUC, V18, P142, DOI 10.1177/1475240919865654
   Fu ZY, 2020, CCF T PERVAS COMPUT, V2, P33, DOI 10.1007/s42486-020-00028-0
   Fuchs K, 2023, FRONT EDUC, V8, DOI 10.3389/feduc.2023.1166682
   Glanville M., 2023, International Baccalaureate
   Harel I., 1990, INTERACTIVE LEARNING, V1, P1, DOI [https://doi.org/10.1080/1049482900010102, DOI 10.1080/1049482900010102]
   IBO, 2023, Artificial Intelligence (AI) in Learning, Teaching, and Assessment
   Ji H, 2023, J RES TECHNOL EDUC, V55, P48, DOI 10.1080/15391523.2022.2142873
   Kalla D., 2023, International Journal of Innovative Science and Research Technology, V8, P7
   Kubota S., 2021, INTED2021 P, P5658, DOI [10.21125/inted.2021.1141, DOI 10.21125/INTED.2021.1141]
   Lai C, 2014, EDUC RES-UK, V56, P77, DOI 10.1080/00131881.2013.874159
   Lee M, 2022, EDUC REV, V74, P131, DOI 10.1080/00131911.2021.1891023
   Legislative Council (LEGCO), 2019, Study of Development blueprints and Growth Drivers of Artificial Intelligence in Selected Places
   Liu CC, 2023, INTERACT LEARN ENVIR, V31, P5614, DOI 10.1080/10494820.2021.2012812
   Maboloc CR, 2023, J PUBLIC HEALTH-UK, V46, pe152, DOI 10.1093/pubmed/fdad125
   Mhlanga D., 2023, SSRN Electric Journal
   Muhayimana T., 2022, Curriculum Perspectives, V42, P51, DOI [10.1007/s41297-021-00156-2, DOI 10.1007/S41297-021-00156-2]
   Osbeck L.M., 2011, Mind Society, V10, P57, DOI [DOI 10.1007/S11299-010-0074-1, DOI 10.1177/01461672211023652]
   Ottenbreit-Leftwich Anne, 2021, SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, DOI 10.1145/3408877.3439642
   Pappas P., 2020, A Taxonomy of Reflection: Critical Thinking for Students, Teachers
   Pask Gordon., 1975, CONVERSATION COGNITI
   Rospigliosi PA, 2023, INTERACT LEARN ENVIR, V31, P1, DOI 10.1080/10494820.2023.2180191
   Seungki Shin, 2021, International Journal of Information and Education Technology, P392, DOI 10.18178/ijiet.2021.11.9.1540
   Sit C, 2020, INSIGHTS IMAGING, V11, DOI 10.1186/s13244-019-0830-7
   Spector JM, 2019, SMART LEARN ENVIRON, V6, DOI 10.1186/s40561-019-0088-z
   Syamsuddin A., 2020, International Journal of Scientific Technology Research, V9, P4418
   UNESCO, 2019, Artificial intelligence in education: challenges and opportunities for sustainable development
   Wang T., 2020, INT C ED ARTIFICIAL
NR 34
TC 1
Z9 1
U1 37
U2 53
PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
PI ABINGDON
PA 2-4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND
SN 0300-4279
EI 1475-7575
J9 EDUC 3-13
JI Educ. 3-13
PD 2024 APR 2
PY 2024
DI 10.1080/03004279.2024.2332469
EA APR 2024
PG 11
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA MQ2Q6
UT WOS:001195031700001
OA hybrid
DA 2024-12-25
ER

PT J
AU De Silva, D
   Jayatilleke, S
   El-Ayoubi, M
   Issadeen, Z
   Moraliyage, H
   Mills, N
AF De Silva, Daswin
   Jayatilleke, Shalinka
   El-Ayoubi, Mona
   Issadeen, Zafar
   Moraliyage, Harsha
   Mills, Nishan
TI The Human-Centred Design of a Universal Module for Artificial
   Intelligence Literacy in Tertiary Education Institutions
SO MACHINE LEARNING AND KNOWLEDGE EXTRACTION
LA English
DT Article
DE artificial intelligence; generative AI; AI literacy
AB Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups.
C1 [De Silva, Daswin; Jayatilleke, Shalinka; Issadeen, Zafar; Moraliyage, Harsha; Mills, Nishan] La Trobe Univ, Res Ctr Data Analyt & Cognit, Melbourne 3083, Australia.
   [El-Ayoubi, Mona] La Trobe Univ, Educ Serv, Melbourne 3086, Australia.
C3 La Trobe University; La Trobe University
RP De Silva, D (corresponding author), La Trobe Univ, Res Ctr Data Analyt & Cognit, Melbourne 3083, Australia.
EM d.desilva@latrobe.edu.au
OI Mills, Nishan/0000-0003-2157-3767; Moraliyage,
   Harsha/0000-0002-6212-8312
CR Askeroth JH, 2019, ONLINE LEARN, V23, P135, DOI 10.24059/olj.v23i4.2043
   Bejakovic P, 2020, EMPL RELAT, V42, P921, DOI 10.1108/ER-07-2019-0274
   Bloom BS, 2020, Taxonomy of educational objectives: The classification of educational goals
   Brophy E.J., 1999, Allyn Bacon, P3, DOI [10.5860/choice.37-3455, DOI 10.5860/CHOICE.37-3455]
   Cao YH, 2023, Arxiv, DOI [arXiv:2303.04226, 10.48550/arXiv.2303.04226, DOI 10.48550/ARXIV.2303.04226]
   Casal-Otero L, 2023, INT J STEM EDUC, V10, DOI 10.1186/s40594-023-00418-7
   Chamishka S, 2022, MULTIMED TOOLS APPL, V81, P35173, DOI 10.1007/s11042-022-13363-4
   Chassignol M, 2018, PROCEDIA COMPUT SCI, V136, P16, DOI 10.1016/j.procs.2018.08.233
   Chen XL, 2022, EDUC TECHNOL SOC, V25, P28
   De Silva D., IEEE Ind. Electron. Mag, P2024
   De Silva D., 2023, P 2023 IEEE INT C IN, P1
   De Silva D., 2011, P 2011 INT C EL MACH, P1
   De Silva D, 2022, PATTERNS, V3, DOI 10.1016/j.patter.2022.100489
   De Silva D, 2015, AUSTRALAS J INF SYST, V19, pS99
   Dhanarajan G., 2009, P 19 ANN C ASS AS OP
   Duri Long, 2021, Proceedings of the ACM on Human-Computer Interaction, V5, DOI 10.1145/3476034
   Ehlers U.D., 2006, Handbook on Quality and Standardisation in e-Learning, DOI [10.1007/3-540-32788-61, DOI 10.1007/3-540-32788-61]
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Espey M, 2018, HIGH EDUC RES DEV, V37, P15, DOI 10.1080/07294360.2017.1344196
   Garrison DR, 2005, AM J DISTANCE EDUC, V19, P133, DOI 10.1207/s15389286ajde1903_2
   Gasevic D., 2023, Computers and Education: Artificial Intelligence, V4, P100130, DOI [10.1016/j.caeai.2023.100130 10.1016/j.caeai.2023.100130, DOI 10.1016/J.CAEAI.2023.100130, 10.1016/j.caeai.2023.100130]
   Gilster P., 1997, Digital Literacy
   Government A., 2023, Australian Universities Accord, Interim Report
   Guan C., 2020, International Journal of Innovation Studies, V4, P134, DOI [10.1016/j.ijis.2020.09.001, DOI 10.1016/J.IJIS.2020.09.001]
   Hagendorff T, 2020, MIND MACH, V30, P99, DOI 10.1007/s11023-020-09517-8
   Hmelo-Silver CE, 2007, EDUC PSYCHOL-US, V42, P99, DOI 10.1080/00461520701263368
   Honebein PeterC., 1996, Constructivist learning environments: case studies in instructional design
   Hu K., ChatGPT Sets Record for Fastest-Growing User Base-Analyst Note
   Johnson R. B., 2017, ED RES QUANTITATIVE, DOI DOI 10.3102/0013189X033007014
   Jyh-An Lee, 2022, Law, Innovation and Technology, V14, P95, DOI 10.1080/17579961.2022.2047520
   Kandlhofer M, 2016, PROC FRONT EDUC CONF
   Kleyko D., 2019, INT JOINT C NEUR NET, P1, DOI [DOI 10.1109/ijcnn.2019.8852471, DOI 10.1109/IJCNN.2019.8852471]
   Kuka Lisa, 2022, Learning with Technologies and Technologies in Learning: Experience, Trends and Challenges in Higher Education. Lecture Notes in Networks and Systems (456), P551, DOI 10.1007/978-3-031-04286-7_26
   Laupichler M.C., 2022, Comput. Educ. Artif. Intell, V3, P100101, DOI DOI 10.1016/J.CAEAI.2022.100101
   Long DR, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376727
   Lu H., 2020, Education Journal, V9, P73
   Lyu WJ, 2021, APPL ENERG, V303, DOI 10.1016/j.apenergy.2021.117615
   Maguire M, 2001, INT J HUM-COMPUT ST, V55, P587, DOI 10.1006/ijhc.2001.0503
   Martin F, 2018, ONLINE LEARN, V22, P205, DOI 10.24059/olj.v22i1.1092
   Matharaarachchi A, 2024, ENERGIES, V17, DOI 10.3390/en17081935
   MCTAGGART R, 1991, ADULT EDUC QUART, V41, P168, DOI 10.1177/0001848191041003003
   Nallaperuma D, 2018, IEEE IND ELEC, P3120, DOI 10.1109/IECON.2018.8591357
   Nawaratne R, 2020, IEEE T IND INFORM, V16, P7756, DOI 10.1109/TII.2019.2957454
   Nawaratne R, 2017, IEEE IND ELEC, P4790, DOI 10.1109/IECON.2017.8216826
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   Peng DH, 2022, EDUC RES INT, V2022, DOI 10.1155/2022/2533413
   Reddy P, 2020, INT J TECHNOETHICS, V11, P65, DOI 10.4018/IJT.20200701.oa1
   Roddy C., 2017, Frontiers in Education, V2, DOI [DOI 10.3389/FEDUC.2017.00059, 10.3389/feduc.2017.00059]
   Styers ML, 2018, CBE-LIFE SCI EDUC, V17, DOI 10.1187/cbe.16-11-0332
   Su J., 2023, COMPUTERS ED ARTIFIC, V4, P100124, DOI [DOI 10.1016/J.CAEAI.2023.100124, 10.1016/j.caeai.2023.100124 10.1016/j.caeai.2023.100124]
   Sun PC, 2008, COMPUT EDUC, V50, P1183, DOI 10.1016/j.compedu.2006.11.007
   Veale M, 2021, COMPUTER LAW REV INT, V22, P97, DOI [DOI 10.9785/CRI-2021-220402, 10.9785/cri-2021-220402]
   Wei Jason, 2022, arXiv
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhang H, 2023, INT J ARTIF INTELL E, V33, P290, DOI 10.1007/s40593-022-00293-3
NR 55
TC 1
Z9 1
U1 36
U2 36
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2504-4990
J9 MACH LEARN KNOW EXTR
JI Mach. Learn. Knowl. Extr.
PD JUN
PY 2024
VL 6
IS 2
BP 1114
EP 1125
DI 10.3390/make6020051
PG 12
WC Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Engineering, Electrical & Electronic
WE Emerging Sources Citation Index (ESCI)
SC Computer Science; Engineering
GA WO6A3
UT WOS:001255844700001
OA gold
DA 2024-12-25
ER

PT J
AU Dubey, R
   Gunasekaran, A
   Papadopoulos, T
AF Dubey, Rameshwar
   Gunasekaran, Angappa
   Papadopoulos, Thanos
TI Benchmarking operations and supply chain management practices using
   Generative AI: Towards a theoretical framework
SO TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
LA English
DT Article
DE Generative Artificial Intelligence (Gen AI); Artificial Intelligence
   Supply Chain; Management; Benchmarking; Organisational Theories
ID RESOURCE-BASED VIEW; DYNAMIC CAPABILITIES; BIG DATA;
   PERFORMANCE-MEASUREMENT; COMPETITIVE ADVANTAGE; ARTIFICIAL-INTELLIGENCE;
   INSTITUTIONAL THEORY; ORGANIZATION DESIGN; STAKEHOLDER THEORY; FIRM
   PERFORMANCE
AB Generative Artificial Intelligence (Gen AI) is an up-and-coming technological innovation that has the potential to revolutionise businesses and create significant value. Despite garnering excitement from some quarters, there are still people who are sceptical about its benefits and even fearful of its impact, particularly in the supply chain context, where it is not yet fully understood. To help academics and practitioners better understand the practical implications of Gen AI in benchmarking supply chain management practices, we propose a theoretical toolbox. This toolbox draws from ten popular organisational theories and provides a comprehensive framework for evaluating the usefulness of Gen AI. By expanding theoretical boundaries, the toolbox provides a deeper understanding of the practical applications of Gen AI for researchers and practitioners in supply chain management.
C1 [Dubey, Rameshwar] Montpellier Business Sch, 2300 Ave Moulins, F-34185 Montpellier, France.
   [Dubey, Rameshwar] Liverpool John Moores Univ, Liverpool Business Sch, Liverpool L3 5UG, England.
   [Gunasekaran, Angappa] Sch Business Adm, E356T Olmsted Bldg,Penn State Harrisburg,777 West, Middletown, PA 17057 USA.
   [Papadopoulos, Thanos] Univ Kent Chatham Maritime, Kent Business Sch, Gillingham ME4 4AG, Kent, England.
C3 Montpellier Business School; Liverpool John Moores University;
   University of Liverpool; Pennsylvania Commonwealth System of Higher
   Education (PCSHE); Pennsylvania State University; University of Kent
RP Gunasekaran, A (corresponding author), Sch Business Adm, E356T Olmsted Bldg,Penn State Harrisburg,777 West, Middletown, PA 17057 USA.
EM r.dubey@ljmu.ac.uk; aqg6076@psu.edu; A.Papadopoulos@kent.ac.uk
RI DUBEY, Rameshwar/U-7022-2018; Gunasekaran, Angappa/AEZ-0121-2022;
   Papadopoulos, Thanos/ABD-5724-2021
FX We thank Professor Choi and three anonymous reviewers for their valuable
   insights, which strengthened our draft.
CR Abu Huson Y, 2024, TOTAL QUAL MANAG BUS, V35, P91, DOI 10.1080/14783363.2023.2256260
   Adebanjo D, 2010, INT J OPER PROD MAN, V30, P1140, DOI 10.1108/01443571011087369
   Akhtar P, 2024, INT J PROD ECON, V273, DOI 10.1016/j.ijpe.2024.109283
   Akter S, 2016, INT J PROD ECON, V182, P113, DOI 10.1016/j.ijpe.2016.08.018
   Akyuz GA, 2010, INT J PROD RES, V48, P5137, DOI 10.1080/00207540903089536
   Al-Shboul MA, 2018, INT J PRODUCT PERFOR, V67, P1482, DOI 10.1108/IJPPM-11-2016-0257
   Almazmomi N, 2022, BENCHMARKING, V29, P1264, DOI 10.1108/BIJ-01-2021-0021
   Alsharhan A, 2024, IEEE T ENG MANAGE, V71, P10232, DOI 10.1109/TEM.2023.3298360
   Amaya J, 2024, J OPER MANAG, V70, P482, DOI 10.1002/joom.1296
   Anand G, 2008, BENCHMARKING, V15, P257, DOI 10.1108/14635770810876593
   Andersen B, 1999, J BUS IND MARK, V14, P378, DOI 10.1108/08858629910290139
   [Anonymous], 2023, Toyota Newsroom
   [Anonymous], 1992, Total Quality Management, DOI DOI 10.1080/09544129200000019
   Aragón-Correa JA, 2003, ACAD MANAGE REV, V28, P71
   Arsenyan J, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2022.3229821
   Asrofah T, 2010, BENCHMARKING, V17, P115, DOI 10.1108/14635771011022343
   ASTLEY WG, 1983, ADMIN SCI QUART, V28, P245, DOI 10.2307/2392620
   Avdiji H, 2020, J ASSOC INF SYST, V21, P695, DOI 10.17705/1jais.00617
   Bag S., 2024, Transportation Research Part e: Logistics and Transportation Review, V188
   Bag S, 2023, INT J PROD ECON, V266, DOI 10.1016/j.ijpe.2023.109059
   Bag S, 2021, TECHNOL FORECAST SOC, V163, DOI 10.1016/j.techfore.2020.120420
   Bagchi P. K., 1996, International Journal of Physical Distribution & Logistics Management, V26, P4, DOI 10.1108/09600039610113173
   Banh L, 2023, ELECTRON MARK, V33, DOI 10.1007/s12525-023-00680-1
   BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108
   BARTLETT CA, 1994, HARVARD BUS REV, V72, P79
   Beamon BM, 1999, INT J OPER PROD MAN, V19, P275, DOI 10.1108/01443579910249714
   Bendoly E, 2024, DECISION SCI, V55, P325, DOI 10.1111/deci.12619
   BENSAOU M, 1995, MANAGE SCI, V41, P1471, DOI 10.1287/mnsc.41.9.1471
   Brandon-Jones E, 2014, J SUPPLY CHAIN MANAG, V50, P55, DOI 10.1111/jscm.12050
   Butt A, 2024, LONG RANGE PLANN, V57, DOI 10.1016/j.lrp.2024.102415
   Cannas VG, 2024, INT J PROD RES, V62, P3333, DOI 10.1080/00207543.2023.2232050
   Carpenter MA, 2004, J MANAGE, V30, P749, DOI 10.1016/j.jm.2004.06.001
   CDO Magazine Bureau, 2024, Volkswagen Unveils New AI Lab to Make Cars Smarter
   Chakraborty D, 2023, J BUS RES, V166, DOI 10.1016/j.jbusres.2023.114140
   Chen SK, 2022, TRANSPORT RES E-LOG, V161, DOI 10.1016/j.tre.2022.102709
   Choi TM, 2022, PROD OPER MANAG, V31, P9, DOI 10.1111/poms.13622
   Choi TM, 2021, TRANSPORT RES E-LOG, V145, DOI 10.1016/j.tre.2020.102190
   Choi TM, 2021, INT J PROD RES, V59, P286, DOI 10.1080/00207543.2020.1722861
   Choi TM, 2020, TRANSPORT RES E-LOG, V135, DOI 10.1016/j.tre.2020.101860
   Choi TM, 2018, PROD OPER MANAG, V27, P1868, DOI 10.1111/poms.12838
   Choi TM, 2016, PROD OPER MANAG, V25, P379, DOI 10.1111/poms.12534
   Craighead CW, 2020, DECISION SCI, V51, P838, DOI 10.1111/deci.12468
   Csaszar FA, 2020, ORGAN SCI, V31, P1198, DOI 10.1287/orsc.2019.1346
   Cushman R., 2023, How generative AI is revolutionizing supply chain operations
   Dash A., 2022, Ann. Data Sci, V11, P1545, DOI [10.1007/s40745-022-00436-2, DOI 10.1007/S40745-022-00436-2]
   Dhamija P, 2020, TQM J, V32, P869, DOI 10.1108/TQM-10-2019-0243
   DiMaggio PJ, 2000, ADV STRATEG MANAGE, V 17, P143, DOI 10.2307/2095101
   Doh JP, 2014, ACAD MANAGE PERSPECT, V28, P255, DOI 10.5465/amp.2014.0013
   Dubey R, 2020, INT J PROD RES, V58, P3381, DOI 10.1080/00207543.2020.1722860
   Dubey R, 2021, INT J PROD RES, V59, P110, DOI 10.1080/00207543.2019.1582820
   Dubey R, 2019, BRIT J MANAGE, V30, P341, DOI 10.1111/1467-8551.12355
   Dutta P, 2020, TRANSPORT RES E-LOG, V142, DOI 10.1016/j.tre.2020.102067
   Dutta S., 2023, How Supply Chain Benefits from using generative AI
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dyer JH, 1998, ACAD MANAGE REV, V23, P660, DOI 10.2307/259056
   Eisenhardt KM, 2000, STRATEGIC MANAGE J, V21, P1105, DOI 10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-E
   Fainshmidt S, 2016, J MANAGE STUD, V53, P1348, DOI 10.1111/joms.12213
   Fawcett SE, 2009, BENCHMARKING, V16, P222, DOI 10.1108/14635770910948231
   Feuerriegel S, 2024, BUS INFORM SYST ENG+, V66, P111, DOI 10.1007/s12599-023-00834-7
   Fiorini PD, 2018, INT J INFORM MANAGE, V43, P112, DOI 10.1016/j.ijinfomgt.2018.07.005
   Freeman R.E., 2010, Strategic management: A stakeholder approach
   GALBRAITH JR, 1974, INTERFACES, V4, P28, DOI 10.1287/inte.4.3.28
   Garcia-Buendia N, 2024, PROD PLAN CONTROL, V35, P618, DOI 10.1080/09537287.2022.2114960
   Gattiker TF, 2007, INT J PROD RES, V45, P2895, DOI 10.1080/00207540600690511
   Geletkanycz MA, 1997, STRATEGIC MANAGE J, V18, P615, DOI 10.1002/(SICI)1097-0266(199709)18:8<615::AID-SMJ889>3.0.CO;2-I
   Ghobakhloo M, 2024, J MANUF TECHNOL MANA, V35, P94, DOI 10.1108/JMTM-12-2023-0530
   Grimes M, 2023, ACAD MANAGE J, V66, P1617, DOI 10.5465/amj.2023.4006
   GROVER V, 1993, DECISION SCI, V24, P603, DOI 10.1111/j.1540-5915.1993.tb01295.x
   Guan JC, 2006, EUR J OPER RES, V170, P971, DOI 10.1016/j.ejor.2004.07.054
   Guillot R, 2024, J BUS IND MARK, V39, P553, DOI 10.1108/JBIM-06-2023-0361
   Gunasekaran A, 2004, INT J PROD ECON, V87, P333, DOI 10.1016/j.ijpe.2003.08.003
   Gunasekaran A, 2007, INT J PROD RES, V45, P2819, DOI 10.1080/00207540600806513
   Gunasekaran A, 2018, INT J PROD RES, V56, P6735, DOI 10.1080/00207543.2018.1551958
   Gupta M, 2016, INFORM MANAGE-AMSTER, V53, P1049, DOI 10.1016/j.im.2016.07.004
   Gupta S, 2024, IEEE T ENG MANAGE, V71, P10496, DOI 10.1109/TEM.2021.3116770
   HAMBRICK DC, 1984, ACAD MANAGE REV, V9, P193, DOI 10.2307/258434
   Hardy C, 2005, ACAD MANAGE REV, V30, P58, DOI [10.5465/AMR.2005.15281426, 10.2307/20159095]
   Helo P, 2022, PROD PLAN CONTROL, V33, P1573, DOI 10.1080/09537287.2021.1882690
   Hitt MA, 2016, J OPER MANAG, V41, P77, DOI 10.1016/j.jom.2015.11.002
   Hughes P, 2018, INT J OPER PROD MAN, V38, P1125, DOI 10.1108/IJOPM-10-2016-0634
   Ivanov D, 2024, TRANSPORT RES E-LOG, V185, DOI 10.1016/j.tre.2024.103526
   Ivanov D, 2024, IEEE T ENG MANAGE, V71, P10485, DOI 10.1109/TEM.2021.3095193
   Jackson I, 2024, INT J PROD RES, V62, P6120, DOI 10.1080/00207543.2024.2309309
   Jackson I, 2023, TRANSPORT RES E-LOG, V180, DOI 10.1016/j.tre.2023.103360
   Jones TM, 2007, ACAD MANAGE REV, V32, P137, DOI 10.2307/20159285
   Kala K, 2023, TRANSPORT RES E-LOG, V176, DOI 10.1016/j.tre.2023.103219
   Kamble SS, 2020, INT J PROD RES, V58, P65, DOI 10.1080/00207543.2019.1630770
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   KAPLAN RS, 1992, HARVARD BUS REV, V70, P71
   Kar A. K., 2023, Global Journal of Flexible Systems Management, V24, P659, DOI [DOI 10.1007/S40171-023-00356-X, https://doi.org/10.1007/s40171-023-00356-x]
   Kauppi K, 2013, INT J OPER PROD MAN, V33, P1318, DOI 10.1108/IJOPM-10-2011-0364
   Ketchen DJ, 2007, J OPER MANAG, V25, P573, DOI 10.1016/j.jom.2006.05.010
   Ketter W, 2016, MIS QUART, V40, P1057, DOI 10.25300/MISQ/2016/40.4.12
   Kumar S, 2018, PROD OPER MANAG, V27, P1893, DOI 10.1111/poms.12961
   Kuo HT, 2024, TRANSPORT RES E-LOG, V185, DOI 10.1016/j.tre.2024.103496
   Lambert DM, 2017, IND MARKET MANAG, V62, P1, DOI 10.1016/j.indmarman.2016.12.002
   Latunreng W., 2019, J. Supply Chain Manag., V8, P404
   Le TT, 2024, IEEE T ENG MANAGE, V71, P8577, DOI 10.1109/TEM.2023.3348274
   Li SH, 2006, OMEGA-INT J MANAGE S, V34, P107, DOI 10.1016/j.omega.2004.08.002
   Lim A, 2022, SUPPLY CHAIN MANAG, V27, P611, DOI 10.1108/SCM-03-2021-0129
   Lima FR, 2019, INT J PROD ECON, V212, P19, DOI 10.1016/j.ijpe.2019.02.001
   Liu HF, 2016, J OPER MANAG, V44, P13, DOI 10.1016/j.jom.2016.03.009
   Liu W, 2024, IEEE T ENG MANAGE, V71, P10427, DOI 10.1109/TEM.2022.3187986
   Lockamy A, 2004, INT J OPER PROD MAN, V24, P1192, DOI 10.1108/01443570410569010
   Maestrini V, 2017, INT J PROD ECON, V183, P299, DOI 10.1016/j.ijpe.2016.11.005
   Magd H., 2003, Benchmarking, V10, P261, DOI [10.1108/14635770310477780, DOI 10.1108/14635770310477780]
   Mariani M, 2024, J BUS RES, V175, DOI 10.1016/j.jbusres.2024.114542
   Menhat M, 2023, BENCHMARKING, V30, P3168, DOI 10.1108/BIJ-11-2021-0704
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Miles R E, 1978, Acad Manage Rev, V3, P546, DOI 10.2307/257544
   Mithas S, 2022, PROD OPER MANAG, V31, P4475, DOI 10.1111/poms.13864
   Mithas S, 2011, MIS QUART, V35, P237
   MORGAN RM, 1994, J MARKETING, V58, P20, DOI 10.2307/1252308
   Moshtari M, 2016, PROD OPER MANAG, V25, P1542, DOI 10.1111/poms.12568
   Munir M, 2022, INT J OPER PROD MAN, V42, P1576, DOI 10.1108/IJOPM-11-2021-0677
   Norton S, 2024, General Mills Lays The Foundation For An AI-Driven Future
   Olan F, 2024, IEEE T ENG MANAGE, V71, P13296, DOI 10.1109/TEM.2021.3133104
   Oliver C, 1997, STRATEGIC MANAGE J, V18, P697, DOI 10.1002/(SICI)1097-0266(199710)18:9<697::AID-SMJ909>3.0.CO;2-C
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Parmar BL, 2010, ACAD MANAG ANN, V4, P403, DOI 10.1080/19416520.2010.495581
   Partington D, 2000, BRIT J MANAGE, V11, P91, DOI 10.1111/1467-8551.00153
   Patterson KA, 2003, TRANSPORT RES E-LOG, V39, P95, DOI 10.1016/S1366-5545(02)00041-8
   Peng MW, 2009, ACAD MANAGE PERSPECT, V23, P63, DOI 10.5465/AMP.2009.43479264
   PFEFFER J, 1977, ORGAN DYN, V6, P15, DOI 10.1016/0090-2616(77)90043-2
   Premkumar G, 1995, DECISION SCI, V26, P303, DOI 10.1111/j.1540-5915.1995.tb01431.x
   Queiroz MM, 2022, INT J PROD ECON, V245, DOI 10.1016/j.ijpe.2021.108405
   Reynolds SJ, 2006, J BUS ETHICS, V64, P285, DOI 10.1007/s10551-005-5493-2
   Richey RG Jr, 2023, J BUS LOGIST, V44, P532, DOI 10.1111/jbl.12364
   Rolf B, 2023, INT J PROD RES, V61, P7151, DOI 10.1080/00207543.2022.2140221
   Russell J.A., 1974, APPROACH ENV PSYCHOL
   Saetra HS, 2023, TECHNOL SOC, V75, DOI 10.1016/j.techsoc.2023.102372
   Sarkis J, 2011, INT J PROD ECON, V130, P1, DOI 10.1016/j.ijpe.2010.11.010
   Schilke O, 2018, ACAD MANAG ANN, V12, P390, DOI 10.5465/annals.2016.0014
   Schulze J, 2017, ORGAN PSYCHOL REV, V7, P66, DOI 10.1177/2041386616675522
   Sellitto MA, 2015, INT J PROD RES, V53, P4917, DOI 10.1080/00207543.2015.1005251
   Sharma P, 2024, IEEE T ENG MANAGE, V71, P10585, DOI 10.1109/TEM.2022.3209786
   Shore A, 2024, TECHNOVATION, V135, DOI 10.1016/j.technovation.2024.103063
   Sirmon DG, 2011, J MANAGE, V37, P1390, DOI 10.1177/0149206310385695
   Sodhi MS, 2021, J SUPPLY CHAIN MANAG, V57, P7, DOI 10.1111/jscm.12255
   Sodhi MS, 2021, PROD OPER MANAG, V30, P625, DOI 10.1111/poms.13304
   Sodhi MS, 2015, PROD OPER MANAG, V24, P1375, DOI 10.1111/poms.12393
   Sodhi MS, 2009, TRANSPORT RES E-LOG, V45, P937, DOI 10.1016/j.tre.2009.05.004
   Soni G, 2010, BENCHMARKING, V17, P44, DOI 10.1108/14635771011022316
   Sousa R, 2008, J OPER MANAG, V26, P697, DOI 10.1016/j.jom.2008.06.001
   Srinivasan R, 2018, PROD OPER MANAG, V27, P1849, DOI 10.1111/poms.12746
   Stark A, 2023, J OPER MANAG, V69, P890, DOI 10.1002/joom.1231
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   Teece DJ, 2007, STRATEGIC MANAGE J, V28, P1319, DOI 10.1002/smj.640
   Teece DJ, 1997, STRATEGIC MANAGE J, V18, P509, DOI 10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
   Tiwari M, 2024, TRANSPORT RES E-LOG, V188, DOI 10.1016/j.tre.2024.103613
   Tiwari M, 2024, INT STUD MANAG ORG, V54, P380, DOI 10.1080/00208825.2024.2324245
   Tushman M.L., 1978, Academy of Management Review, V3, P613, DOI [DOI 10.5465/AMR.1978.4305791, 10.2307/257550, DOI 10.2307/257550]
   Upadhyay N, 2023, TECHNOL FORECAST SOC, V189, DOI 10.1016/j.techfore.2023.122344
   Volberda HW, 2012, ORGAN SCI, V23, P1040, DOI 10.1287/orsc.1110.0687
   Wamba SF, 2024, INT J PROD RES, V62, P5676, DOI 10.1080/00207543.2023.2294116
   Wamba SF, 2023, INT J PROD ECON, V265, DOI 10.1016/j.ijpe.2023.109015
   Wang LL, 2023, PROD OPER MANAG, V32, P1002, DOI 10.1111/poms.13953
   Warschauer M, 2003, TECHNOLOGY AND SOCIAL INCLUSION - RETHINKING THE DIGITAL DIVIDE, P1
   WEICK KE, 1989, ACAD MANAGE REV, V14, P516, DOI 10.2307/258556
   WHETTEN DA, 1989, ACAD MANAGE REV, V14, P490, DOI 10.2307/258554
   Willmington C, 2022, BMC HEALTH SERV RES, V22, DOI 10.1186/s12913-022-07467-8
   Wong WP, 2008, BENCHMARKING, V15, P25, DOI 10.1108/14635770810854335
   Xu J, 2021, INT J PHYS DISTR LOG, V51, P656, DOI 10.1108/IJPDLM-04-2020-0129
   Xu XY, 2023, PROD OPER MANAG, V32, P524, DOI 10.1111/poms.13885
   Xu XY, 2023, DECISION SCI, V54, P375, DOI 10.1111/deci.12552
   Yan YM, 2022, TRANSPORT RES E-LOG, V162, DOI 10.1016/j.tre.2022.102712
   Yanow D, 2012, J ORGAN ETHNOGR, V1, P31, DOI 10.1108/202466741211220633
   Yu YS, 2023, IEEE T ENG MANAGE, DOI 10.1109/TEM.2023.3268340
   Yusuf YY, 2014, INT J PROD ECON, V147, P531, DOI 10.1016/j.ijpe.2012.10.009
   Zhu QY, 2022, TRANSPORT RES E-LOG, V164, DOI 10.1016/j.tre.2022.102824
NR 170
TC 3
Z9 3
U1 83
U2 83
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1366-5545
EI 1878-5794
J9 TRANSPORT RES E-LOG
JI Transp. Res. Pt. e-Logist. Transp. Rev.
PD SEP
PY 2024
VL 189
AR 103689
DI 10.1016/j.tre.2024.103689
EA JUL 2024
PG 19
WC Economics; Engineering, Civil; Operations Research & Management Science;
   Transportation; Transportation Science & Technology
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Engineering; Operations Research & Management
   Science; Transportation
GA A2N6B
UT WOS:001280951000001
DA 2024-12-25
ER

PT J
AU Tosi, D
AF Tosi, Davide
TI Studying the Quality of Source Code Generated by Different AI Generative
   Engines: An Empirical Evaluation
SO FUTURE INTERNET
LA English
DT Article
DE generative artificial intelligence; source code generation; software
   quality; software metrics
AB The advent of Generative Artificial Intelligence is opening essential questions about whether and when AI will replace human abilities in accomplishing everyday tasks. This issue is particularly true in the domain of software development, where generative AI seems to have strong skills in solving coding problems and generating software source code. In this paper, an empirical evaluation of AI-generated source code is performed: three complex coding problems (selected from the exams for the Java Programming course at the University of Insubria) are prompted to three different Large Language Model (LLM) Engines, and the generated code is evaluated in its correctness and quality by means of human-implemented test suites and quality metrics. The experimentation shows that the three evaluated LLM engines are able to solve the three exams but with the constant supervision of software experts in performing these tasks. Currently, LLM engines need human-expert support to produce running code that is of good quality.
C1 [Tosi, Davide] Univ Insubria, Dept Theoret & Appl Sci, I-21100 Varese, Italy.
C3 University of Insubria
RP Tosi, D (corresponding author), Univ Insubria, Dept Theoret & Appl Sci, I-21100 Varese, Italy.
EM davide.tosi@uninsubria.it
RI TOSI, DAVIDE/AAI-1310-2020
OI TOSI, DAVIDE/0000-0003-3815-2512
FU NRRP MUR program - EU-NGEU [PE00000014]
FX This work was supported in part by project SERICS (PE00000014) under the
   NRRP MUR program funded by the EU-NGEU.
CR aws, 2023, Amazon Amazon CodeWhisperer
   Beganovic A., 2023, Southeast Eur. J. Soft Comput., V12, P8
   Chen E., 2023, P INT C ART INT ED T
   claude, 2023, Anthropic Claude
   Cordasco I.S., 2010, Flake8
   EvalPlus Team, 2023, EvalPlus
   Feng YH, 2023, P INT COMP SOFTW APP, P876, DOI 10.1109/COMPSAC57700.2023.00117
   github, 2021, GitHub GitHub Copilot
   github, 2005, GitHub CodeQL
   Google, 2023, Bard
   Guo Q., 2024, P 2024 IEEE ACM INT, DOI DOI 10.1145/3597503.3623306
   Jamdade M, 2024, PROCEEDINGS OF THE 2024 ACM SOUTHEAST CONFERENCE, ACMSE 2024, P229, DOI 10.1145/3603287.3651194
   Jeuring J., 2023, P 23 KOL CALL INT C, DOI [10.1145/3631802.3631807, DOI 10.1145/3631802.3631807]
   Khoury R, 2023, Arxiv, DOI arXiv:2304.09655
   leetcode, 2015, LeetCode
   Liu JW, 2023, Arxiv, DOI arXiv:2305.01210
   Liu Y, 2024, ACM T SOFTW ENG METH, V33, DOI 10.1145/3643674
   Liu ZJ, 2024, IEEE T SOFTWARE ENG, V50, P1548, DOI 10.1109/TSE.2024.3392499
   meta, 2023, Meta Llama
   microsoft, 2023, Microsoft Bing Copilot AI
   OpenAI, 2022, ChatGPT: Optimizing language models for dialogue
   Sakib Fardin Ahsan, 2023, arXiv, DOI 10.48550/arXiv.2307.08260
   sonarsource, 2006, SonarSource SonarQube
   sonarsource, 2006, SonarSource SonarCloud
   Tian HY, 2023, Arxiv, DOI arXiv:2304.11938
   Yetistiren B, 2023, Arxiv, DOI arXiv:2304.10778
NR 26
TC 0
Z9 0
U1 15
U2 15
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 1999-5903
J9 FUTURE INTERNET
JI Future Internet
PD JUN
PY 2024
VL 16
IS 6
AR 188
DI 10.3390/fi16060188
PG 19
WC Computer Science, Information Systems
WE Emerging Sources Citation Index (ESCI)
SC Computer Science
GA WQ1N5
UT WOS:001256248100001
OA gold
DA 2024-12-25
ER

PT J
AU Pellas, N
AF Pellas, Nikolaos
TI The Effects of Generative AI Platforms on Undergraduates' Narrative
   Intelligence and Writing Self-Efficacy
SO EDUCATION SCIENCES
LA English
DT Article
DE artificial intelligence; digital platforms; narrative intelligence;
   writing self-efficacy; storytelling
AB Digital storytelling and generative artificial intelligence (AI) platforms have emerged as transformative tools that empower individuals to write with confidence and share their stories effectively. However, a research gap exists in understanding the effects of using such web-based platforms on narrative intelligence and writing self-efficacy. This study aims to investigate whether digital story creation tasks on web-based platforms can influence the narrative intelligence and writing self-efficacy of undergraduate students. A pretest-posttest comparison study between two groups was conducted with sixty-four undergraduate students (n = 64), majoring in Primary Education. More specifically, it compares the effects of the most well-known conventional platforms, such as Storybird, Storyjumper, and ZooBurst (control condition), and generative AI platforms, such as Sudowrite, Jasper, and Shortly AI (experimental condition) on undergraduate students, with an equal distribution in each group. The findings indicate that the utilization of generative AI platforms in the context of story creation tasks can substantially enhance both narrative intelligence scores and writing self-efficacy when compared to conventional platforms. Nonetheless, there was no significant difference in the creative identity factor. Generative AI platforms have promising implications for supporting undergraduates' narrative intelligence and writing self-efficacy in fostering their story creation design and development.
C1 [Pellas, Nikolaos] Univ Western Macedonia, Dept Primary Educ, Florina 53100, Greece.
C3 University of Western Macedonia
RP Pellas, N (corresponding author), Univ Western Macedonia, Dept Primary Educ, Florina 53100, Greece.
EM aff00192@uowm.gr
RI Pellas, Nikolaos/K-6578-2013
OI Pellas, Nikolaos/0000-0002-3071-6275
CR Bender SM., 2023, MEDIA PRACTICE ED, V24, P351, DOI [10.1080/25741136.2023.2204203, DOI 10.1080/25741136.2023.2204203]
   Campbell C. W., 2023, Int. J. Educ. Res. Open, V4, P100218, DOI [10.1016/j.ijedro.2022.100218, DOI 10.1016/J.IJEDRO.2022.100218]
   Cennamo K.S., 2010, TECHNOLOGY INTEGRATI
   Cha ES, 2007, J ADV NURS, V58, P386, DOI 10.1111/j.1365-2648.2007.04242.x
   Chaudhary S., 2023, 15 Best Free AI Content Generator & AI Writers for 2023
   Cohen L., 2005, RES METHODS ED, DOI 10.4324/9781315456539-14
   Collins A., 2016, Design Research: Theoretical and Methodological Issues
   CORTINA JM, 1993, J APPL PSYCHOL, V78, P98, DOI 10.1037/0021-9010.78.1.98
   Dai JC, 2023, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.1069832
   Fang XX, 2023, EDUC INF TECHNOL, V28, P14361, DOI 10.1007/s10639-023-11741-5
   Fitriyani R., 2023, J. Engl. Educ. List, P4
   Garth A., 2008, Analysis data using SPSS
   Ge J, 2023, HEPATOL COMMUN, V7, DOI 10.1097/HC9.0000000000000097
   Golabi A., 2019, Iran. J. Engl. Acad. Purp, V8, P1
   Guvey A., 2020, Educ. Policy Anal. Strateg. Res, V15, P159, DOI [10.29329/epasr.2020.270.8, DOI 10.29329/EPASR.2020.270.8]
   Han A, 2023, 22ND ANNUAL ACM INTERACTION DESIGN AND CHILDREN CONFERENCE, IDC 2023, P470, DOI 10.1145/3585088.3593867
   Im Hyeonjeong, 2023, GROUP '23: The 2023 ACM International Conference on Supporting Group Work (GROUP '23) Companion, P6, DOI 10.1145/3565967.3570973
   Ispir B, 2023, J QUAL RES EDUC, P187, DOI 10.14689/enad.35.1714
   Lambert J, 2024, COMPUT SCH, V41, P559, DOI 10.1080/07380569.2023.2256710
   Lee JH, 2023, RELC J, V54, P508, DOI 10.1177/00336882231165060
   Mitchell KM, 2021, ASSESS WRIT, V48, DOI 10.1016/j.asw.2021.100524
   Nurlaela Ilham M.J., 2022, J. Educ. FKIP UNMA, V8, P1641
   O'Meara J, 2023, CONVERGENCE-US, V29, P1070, DOI 10.1177/13548565231185865
   Pellas N, 2023, SMART LEARN ENVIRON, V10, DOI 10.1186/s40561-023-00276-4
   Pishghadam R., 2012, Lang. Test. Asia, V2, P1, DOI [10.1186/2229-0443-2-3-53, DOI 10.1186/2229-0443-2-3-53]
   Pishghadam R., 2011, The International Journal of Educational and Psychological Assessment, V8, P75
   Randall WL, 1999, J AGING STUD, V13, P11, DOI 10.1016/S0890-4065(99)80003-6
   Rodriguez C.C., 2023, Digital Storytelling in Primary Education: A Comparison of Already Made Resources and Story Creation Tools to Improve English as a Foreign Language, P387
   Rogers J, 2020, ROUT HANDB APPL, P133
   Sun T, 2020, SYSTEM, V90, DOI 10.1016/j.system.2020.102221
   Teng MF, 2023, FOREIGN LANG ANN, V56, P144, DOI 10.1111/flan.12638
   Yuan A, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P841, DOI 10.1145/3490099.3511105
   Zakaria SM., 2016, Creative Education, V7, P2107, DOI [DOI 10.4236/CE.2016.715210, 10.4236/ce.2016, DOI 10.4236/CE.2016]
   Ziaei S., 2019, Issues Lang. Teach, V8, P163
NR 34
TC 6
Z9 6
U1 48
U2 119
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2227-7102
J9 EDUC SCI
JI Educ. Sci.
PD NOV
PY 2023
VL 13
IS 11
AR 1155
DI 10.3390/educsci13111155
PG 18
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA AK4S8
UT WOS:001118353400001
OA gold
DA 2024-12-25
ER

PT J
AU Farah, MF
   Ramadan, Z
   Nassereddine, Y
AF Farah, Maya F.
   Ramadan, Zahy
   Nassereddine, Yaman
TI When digital spaces matter: The influence of uniqueness and place
   attachment on self-identity expression with brands using generative AI
   on the metaverse
SO PSYCHOLOGY & MARKETING
LA English
DT Article
DE brands; generative AI; metaverse; need for uniqueness; place attachment;
   self-identity expression; virtual worlds
ID VIRTUAL-REALITY; CONSUMERS NEED; FASHION
AB Generative artificial intelligence technologies are transforming brand-customer interactions by offering highly personalized and dynamic virtual experiences. This underscores the importance of studying their significant impact on customer experiences in the Metaverse. Accordingly, the study aims to explore how users' attachment to the Metaverse relates to the underlying psychological drivers of customer engagement with brands using generative AI in the digital realm. A mixed-method approach was adopted, beginning with an exploratory study that involved in-depth interviews with 24 participants to gain an initial understanding of consumers' psychological and behavioral reactions to brands in the Metaverse. An empirical study was then conducted, surveying 407 UK-based users of virtual worlds who interacted with brands using generative AI on these platforms. The goal was to understand how the need for uniqueness influences self-identity expression and to examine how these factors are affected by attachment to virtual places. The research highlights the nuanced relationship between the pursuit of uniqueness and the integration of brand narratives into one's identity particularly when supported by highly personalized, adaptive AI technologies.
C1 [Farah, Maya F.; Ramadan, Zahy; Nassereddine, Yaman] Lebanese Amer Univ, POB 13-5053, Beirut 11022801, Lebanon.
C3 Lebanese American University
RP Farah, MF (corresponding author), Lebanese Amer Univ, POB 13-5053, Beirut 11022801, Lebanon.
EM mfarah@lau.edu.lb
RI Ramadan, Zahy/O-2521-2016; Farah, Maya/L-2322-2013
OI Ramadan, Zahy/0000-0001-8368-3617; Farah, Maya/0000-0002-6251-4096
FU Lebanese American University
FX Lebanese American University
CR Ameen N, 2022, PSYCHOL MARKET, V39, P2110, DOI 10.1002/mar.21715
   Ameen N, 2022, PSYCHOL MARKET, V39, P1802, DOI 10.1002/mar.21699
   Atzeni M, 2022, INT J TOUR RES, V24, P240, DOI 10.1002/jtr.2497
   Batra R, 2012, J MARKETING, V76, P1, DOI 10.1509/jm.09.0339
   Boley BB, 2021, J ENVIRON PSYCHOL, V74, DOI 10.1016/j.jenvp.2021.101577
   Brown M., 1993, TESTING STRUCTURAL E, P136, DOI DOI 10.1177/0049124192021002005
   Brüns JD, 2024, J RETAIL CONSUM SERV, V79, DOI 10.1016/j.jretconser.2024.103790
   Caporusso N., 2023, Res. Directs Psychol. Behav, V3, P10785, DOI [10.53520/rdpb2023.10795, DOI 10.53520/RDPB2023.10795]
   Chaffey D  ..., 2024, Global social media statistics research summary
   Chen C, 2022, PSYCHOL MARKET, V39, P524, DOI 10.1002/mar.21630
   Chen Y, 2024, J MED INTERNET RES, V26, DOI [10.2024/1/e53008, 10.2196/53008]
   Claffey E, 2017, PSYCHOL MARKET, V34, P356, DOI 10.1002/mar.20994
   Clark RA, 2005, PSYCHOL MARKET, V22, P289, DOI 10.1002/mar.20060
   Creswell JW, 2000, THEOR PRACT, V39, P124, DOI 10.1207/s15430421tip3903_2
   Daryanto A, 2021, J BUS RES, V123, P208, DOI 10.1016/j.jbusres.2020.09.045
   De Freitas J, 2024, J CONSUM PSYCHOL, V34, P481, DOI 10.1002/jcpy.1393
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Dwivedi YK, 2023, PSYCHOL MARKET, V40, P750, DOI 10.1002/mar.21767
   Dwivedi YK, 2022, INT J INFORM MANAGE, V66, DOI 10.1016/j.ijinfomgt.2022.102542
   Eastman JK, 2021, PSYCHOL MARKET, V38, P1881, DOI 10.1002/mar.21546
   Fan XJ, 2022, TOURISM MANAGE, V91, DOI 10.1016/j.tourman.2022.104534
   Farah MF, 2024, SPR PROC BUS ECON, P23, DOI 10.1007/978-3-031-62135-2_4
   Farah MF, 2024, J RES INTERACT MARK, V18, P741, DOI 10.1108/JRIM-01-2024-0051
   Farah MF, 2019, J RETAIL CONSUM SERV, V48, P136, DOI 10.1016/j.jretconser.2019.02.016
   Fromkin H.L., 1980, SOCIAL EXCHANGE ADV, P57, DOI DOI 10.1007/978-1-4613-3087-5_3
   Geng LH, 2019, PSYCHOL MARKET, V36, P188, DOI 10.1002/mar.21169
   Giakoumaki C, 2020, PSYCHOL MARKET, V37, P457, DOI 10.1002/mar.21312
   Giri C, 2019, IEEE ACCESS, V7, P95376, DOI 10.1109/ACCESS.2019.2928979
   Guan M., 2022, INT ENCY HLTH COMMUN, DOI [10.1002/9781119678816.iehc0667, DOI 10.1002/9781119678816.IEHC0667]
   Gunduz U., 2017, Mediterranean Journal of Social Sciences, V8, P85, DOI DOI 10.1515/MJSS-2017-0026
   Harreis H., 2023, Tech. Rep.
   Hilken T, 2022, PSYCHOL MARKET, V39, P1660, DOI 10.1002/mar.21678
   Hilken T, 2022, PSYCHOL MARKET, V39, P495, DOI 10.1002/mar.21600
   HOELTER JW, 1983, SOCIOL METHOD RES, V11, P325, DOI 10.1177/0049124183011003003
   Hollebeek LD, 2024, PSYCHOL MARKET, V41, P880, DOI 10.1002/mar.21957
   Hosany S, 2022, PSYCHOL MARKET, V39, P1467, DOI 10.1002/mar.21665
   Huang MH, 2024, J MARKETING, V88, P1, DOI 10.1177/00222429231224748
   Huang MH, 2018, J SERV RES-US, V21, P155, DOI 10.1177/1094670517752459
   IKEA, 2017, IKEA PLACE APP LAUNC
   Insight Partners, 2023, VIRTUAL REALITY MARK
   Koles B, 2021, CURR OPIN PSYCHOL, V39, P60, DOI 10.1016/j.copsyc.2020.07.017
   Koslow S, 2022, INT J ADVERT, V41, P827, DOI 10.1080/02650487.2021.1954804
   Kyriazos T., 2018, Psychology, V9, P2207, DOI [10.4236/psych.2018.98126, DOI 10.4236/PSYCH.2018.98126]
   Ladhari R, 2020, J RETAIL CONSUM SERV, V54, DOI 10.1016/j.jretconser.2019.102027
   Leveau PH, 2023, PSYCHOL MARKET, V40, P1329, DOI 10.1002/mar.21822
   Liu YC, 2024, SOC INDIC RES, V171, P701, DOI 10.1007/s11205-023-03289-1
   Loh HS, 2021, PSYCHOL MARKET, V38, P537, DOI 10.1002/mar.21452
   Matz SC, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-53755-0
   McDowell M., 2024, ULTIMATE RESOURCE FA
   Mehta P, 2022, PSYCHOL MARKET, V39, P2013, DOI 10.1002/mar.21716
   Mir M, 2023, PSYCHOL MARKET, V40, P2060, DOI 10.1002/mar.21864
   Mishra A, 2021, PSYCHOL MARKET, V38, P385, DOI [10.1016/j.procir.2020.04.049, 10.1002/mar.21436]
   Nunnally J. C., 1978, PSYCHOMETRIC THEORY
   Oleksy T, 2023, COMPUT HUM BEHAV, V141, DOI 10.1016/j.chb.2022.107642
   Ooi KB, 2023, J COMPUT INFORM SYST, DOI 10.1080/08874417.2023.2261010
   Pala E, 2022, PSYCHOL MARKET, V39, P483, DOI 10.1002/mar.21582
   Pantelidis C, 2024, INT J CONTEMP HOSP M, V36, P3704, DOI 10.1108/IJCHM-09-2023-1489
   Park SM, 2022, IEEE ACCESS, V10, P4209, DOI 10.1109/ACCESS.2021.3140175
   Pentina I, 2018, J ADVERTISING, V47, P55, DOI 10.1080/00913367.2017.1405756
   Pfaff A, 2023, PSYCHOL MARKET, V40, P2413, DOI 10.1002/mar.21874
   Pizzi G, 2020, J BUS RES, V119, P502, DOI 10.1016/j.jbusres.2019.11.063
   Pizzi G, 2019, COMPUT HUM BEHAV, V96, P1, DOI 10.1016/j.chb.2019.02.008
   PODSAKOFF PM, 1986, J MANAGE, V12, P531, DOI 10.1177/014920638601200408
   Pozharliev R, 2021, PSYCHOL MARKET, V38, P881, DOI 10.1002/mar.21475
   Rahman MS, 2023, J RETAIL CONSUM SERV, V72, DOI 10.1016/j.jretconser.2023.103273
   Ramadan Z, 2024, QUAL MARK RES, V27, P921, DOI 10.1108/QMR-02-2023-0028
   Ramadan Z, 2023, QUAL MARK RES, V26, P473, DOI 10.1108/QMR-12-2021-0148
   Ramadan Z, 2023, VIRTUAL REAL-LONDON, V27, P1905, DOI 10.1007/s10055-023-00783-2
   Rauschnabel PA, 2024, PSYCHOL MARKET, V41, P819, DOI 10.1002/mar.21953
   Rauschnabel PA, 2018, PSYCHOL MARKET, V35, P557, DOI 10.1002/mar.21106
   Reed A, 2002, PSYCHOL MARKET, V19, P235, DOI 10.1002/mar.10011
   Relph E., 1976, Place and Placelesness
   Rogova N., 2023, AMS REV, V13, P55
   Scarpi D, 2022, PSYCHOL MARKET, V39, P1687, DOI 10.1002/mar.21692
   Scholdra TP, 2023, J RETAILING, V99, P563, DOI 10.1016/j.jretai.2023.11.001
   Spotify, 2023, SPOTIFY DEBUTS NEW A
   Statista, 2023, AR VRUNITED KINGDOM
   Steenkamp JBEM, 2000, INT J RES MARK, V17, P195, DOI 10.1016/S0167-8116(00)00016-1
   Sung E, 2023, PSYCHOL MARKET, V40, P2306, DOI 10.1002/mar.21854
   Susarl A, 2023, INFORM SYST RES, V34, P399, DOI 10.1287/isre.2023.ed.v34.n2
   TABACHNICK BG, 2001, USING MULTIVARIATE S
   Thorbjornsen H, 2007, PSYCHOL MARKET, V24, P763, DOI 10.1002/mar.20183
   Tian KT, 2001, J CONSUM RES, V28, P50, DOI 10.1086/321947
   TINSLEY HEA, 1987, J COUNS PSYCHOL, V34, P414, DOI 10.1037/0022-0167.34.4.414
   Tuan Y.-F., 1977, SPACE PLACE PERSPECT
   Wang F, 2022, ASIA PAC J TOUR RES, V27, P274, DOI 10.1080/10941665.2022.2061363
   Wiedmann KP, 2009, PSYCHOL MARKET, V26, P625, DOI 10.1002/mar.20292
   Wong IA, 2023, J HOSP TOUR MANAG, V56, P253, DOI 10.1016/j.jhtm.2023.06.022
   Zhao SY, 2008, COMPUT HUM BEHAV, V24, P1816, DOI 10.1016/j.chb.2008.02.012
NR 89
TC 0
Z9 0
U1 74
U2 74
PU WILEY
PI HOBOKEN
PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN 0742-6046
EI 1520-6793
J9 PSYCHOL MARKET
JI Psychol. Mark.
PD DEC
PY 2024
VL 41
IS 12
BP 2965
EP 2976
DI 10.1002/mar.22097
EA AUG 2024
PG 12
WC Business; Psychology, Applied
WE Social Science Citation Index (SSCI)
SC Business & Economics; Psychology
GA L4Y1C
UT WOS:001284879300001
DA 2024-12-25
ER

PT J
AU Voss, S
AF Voss, Stefan
TI Bus Bunching and Bus Bridging: What Can We Learn from Generative AI
   Tools like ChatGPT?
SO SUSTAINABILITY
LA English
DT Article
DE generative artificial intelligence; ChatGPT; Bing; computational
   logistics; artificial intelligence; public transport; bus bunching; bus
   bridging
ID CHALLENGES
AB Regarding tools and systems from artificial intelligence (AI), chat-based ones from the area of generative AI have become a major focus regarding media coverage. ChatGPT and occasionally other systems (such as those from Microsoft and Google) are discussed with hundreds if not thousands of academic papers as well as newspaper articles. While various areas have considerably gone into this discussion, transportation and logistics has not yet come that far. In this paper, we explore the use of generative AI tools within this domain. More specifically, we focus on a topic related to sustainable passenger transportation, that is, the handling of disturbances in public transport when it comes to bus bunching and bus bridging. The first of these concepts is related to analyzing situations where we observe two or more buses of the same line following close to each other without being planned deliberately and the second is related to the case where buses are used to replace broken connections in other systems, such as subways. Generative AI tools seem to be able to provide meaningful entries and a lot of food for thought while the academic use may still be classified as limited.
C1 [Voss, Stefan] Univ Hamburg, Inst Informat Syst, D-20146 Hamburg, Germany.
C3 University of Hamburg
RP Voss, S (corresponding author), Univ Hamburg, Inst Informat Syst, D-20146 Hamburg, Germany.
EM stefan.voss@uni-hamburg.de
RI Voß, Stefan/K-6655-2019
OI Voss, Stefan/0000-0003-1296-4221
FU  [1683525900-UHH-OAF]
FX This research received no external funding. The APC was funded by Open
   Access Fund Universitat Hamburg, No 1683525900-UHH-OAF.
CR Aboudina A, 2021, PUBLIC TRANSPORT, V13, P457, DOI 10.1007/s12469-020-00238-w
   Aemmer Z, 2022, PUBLIC TRANSPORT, V14, P263, DOI 10.1007/s12469-022-00291-7
   AI HLEG, 2019, HIGH LEV EXP GROUP A
   Arriagada J, 2019, J INTELL TRANSPORT S, V23, P332, DOI 10.1080/15472450.2018.1494596
   Bartholdi JJ, 2012, TRANSPORT RES B-METH, V46, P481, DOI 10.1016/j.trb.2011.11.001
   Chan A., 2022, ETHICS, V3, P53, DOI [DOI 10.1007/S43681-022-00148-6, 10.1007/s43681-022-00148-6]
   Chen GJ, 2022, TRANSPORT RES C-EMER, V143, DOI 10.1016/j.trc.2022.103828
   Chen Y, 2021, EUR J OPER RES, V295, P484, DOI 10.1016/j.ejor.2021.03.014
   Daduna J, 2000, INFORMATIONSMANAGEME, DOI [10.1007/978-3-642-57682-9, DOI 10.1007/978-3-642-57682-9]
   Daganzo CF, 2009, TRANSPORT RES B-METH, V43, P913, DOI 10.1016/j.trb.2009.04.002
   Degeler V, 2021, PUBLIC TRANSPORT, V13, P533, DOI 10.1007/s12469-020-00251-z
   Deguchi A., 2020, Society 5.0: A peoplecentric super-smart society, P1
   Dehouche N., 2021, Ethic in Science and Environmental Politics, V21, P17, DOI [DOI 10.3354/ESEP00195, 10.3354/esep00195]
   Deng YJ, 2018, SUSTAINABILITY-BASEL, V10, DOI 10.3390/su10124427
   Du HP, 2023, IEEE T INTELL VEHICL, V8, P2020, DOI 10.1109/TIV.2023.3253281
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eysenbach Gunther, 2023, JMIR Med Educ, V9, pe46885, DOI 10.2196/46885
   Frederico GF, 2023, LOGISTICS-BASEL, V7, DOI 10.3390/logistics7020026
   Ge L., 2022, P 5 DATA SCI MEETS O
   Ge LP, 2022, PUBLIC TRANSPORT, V14, P191, DOI 10.1007/s12469-022-00301-8
   Ge LP, 2022, PUBLIC TRANSPORT, DOI 10.1007/s12469-022-00292-6
   Ge LP, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su132011450
   Gong ZY, 2020, IEEE IC COMP COM NET
   Grant MJ, 2009, HEALTH INFO LIBR J, V26, P91, DOI 10.1111/j.1471-1842.2009.00848.x
   Guo BY, 2023, Arxiv, DOI [arXiv:2301.07597, DOI 10.48550/ARXIV.2301.07597]
   Gutenschwager K., 2001, Informationsmanagement, DOI [10.1007/978-3-642-56878-7, DOI 10.1007/978-3-642-56878-7]
   Hagan C., 2014, WHY BUSES ARRIVE BUN
   Itani A, 2021, TRANSPORT RES REC, V2675, P1410, DOI 10.1177/03611981211007836
   Itani A, 2020, TRANSPORT RES REC, V2674, P600, DOI 10.1177/0361198120917399
   Kasneci E, 2023, LEARN INDIVID DIFFER, V103, DOI 10.1016/j.lindif.2023.102274
   Kepaptsoglou K, 2009, PUBLIC TRANSPORT, V1, P275, DOI 10.1007/s12469-010-0017-6
   Kim J, 2023, Findings, DOI DOI 10.32866/001C.72634
   Kooli C, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15075614
   Liang JP, 2019, TRANSPORT RES E-LOG, V132, P97, DOI 10.1016/j.tre.2019.10.008
   Lin CC, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15054012
   Liu T., 2022, J ADV TRANSP, V2022, P2113311, DOI [10.1155/2022/2113311, DOI 10.1155/2022/2113311]
   Luo CL, 2021, COMPUT OPER RES, V131, DOI 10.1016/j.cor.2021.105284
   McGee R.W., 2023, SSRN ELECT J, DOI [10.2139/ssrn.4413418, DOI 10.2139/SSRN.4413418]
   Mollick E. R., 2023, The Wharton School Research Paper, DOI DOI 10.2139/SSRN.4391243
   Moreira-Matias L, 2016, APPL SOFT COMPUT, V47, P460, DOI 10.1016/j.asoc.2016.06.031
   NACTO, 2016, Transit Street Design Guide
   O'Leary DE, 2023, INTELL SYST ACCOUNT, V30, P41, DOI 10.1002/isaf.1531
   Olvera-Toscano CM, 2023, PUBLIC TRANSPORT, V15, P595, DOI 10.1007/s12469-023-00326-7
   Otero I, 2022, INTELLIGENCE, V90, DOI 10.1016/j.intell.2021.101614
   Pahl J., 2022, Working Paper, Tech. rep.
   Pelillo M., 2021, Machines We Trust: Perspectives on Dependable AI
   Ramamonjison R, 2023, Arxiv, DOI arXiv:2303.08233
   Rieder G, 2021, MACHINES WE TRUST, P27
   Sajikumar S, 2022, PUBLIC TRANSPORT, V14, P655, DOI 10.1007/s12469-021-00273-1
   Santos VB, 2022, COMPUT J, V65, P2044, DOI 10.1093/comjnl/bxab045
   Sharp, 2020, Impact, V2020, P2, DOI [10.21820/23987073.2020.2.4, DOI 10.21820/23987073.2020.2.4]
   Soltysik-Piorunkiewicz A, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13020917
   Stokel-Walker C, 2023, NATURE, V614, P214, DOI 10.1038/d41586-023-00340-6
   Sun Y, 2019, Arxiv, DOI [arXiv:1904.09223, DOI 10.48550/ARXIV.1904.09223]
   Voβ S., 2023, SUCCESSFULLY USING C
   Voss Stefan, 2020, HCI International 2020 - Late Breaking Papers. User Experience Design and Case Studies. 22nd HCI International Conference, HCII 2020. Proceedings. Lecture Notes in Computer Science (LNCS 12423), P527, DOI 10.1007/978-3-030-60114-0_36
   Voss S, 2014, BUS INFORM SYST ENG+, V6, P181, DOI 10.1007/s12599-014-0328-2
   Wang DJ, 2023, Arxiv, DOI arXiv:2304.03892
   Wang FY, 2023, IEEE-CAA J AUTOMATIC, V10, P831, DOI 10.1109/JAS.2023.123552
   Wang JW, 2020, TRANSPORT RES C-EMER, V116, DOI 10.1016/j.trc.2020.102661
   Wang Y, 2023, TRANSPORT RES C-EMER, V150, DOI 10.1016/j.trc.2023.104098
   Winston P.H.., 1992, Artificial intelligence
   Wu ZJ, 2022, TRANSP SAFETY ENV, V4, DOI 10.1093/tse/tdac003
   Yanik S, 2023, PUBLIC TRANSPORT, V15, P169, DOI 10.1007/s12469-022-00303-6
   Yu HY, 2016, TRANSPORT RES C-EMER, V72, P45, DOI 10.1016/j.trc.2016.09.007
   Zhan Yu, 2023, Proceedings of SPIE, DOI 10.1117/12.2668744
   Zhang C, 2023, Arxiv, DOI arXiv:2304.06488
   Zhang JF, 2023, PLOS ONE, V18, DOI 10.1371/journal.pone.0277577
   Zheng O, 2023, Arxiv, DOI arXiv:2303.05382
   Zhou C, 2022, TRANSPORT POLICY, V123, P1, DOI 10.1016/j.tranpol.2022.04.022
   Zhu JJ, 2023, ENVIRON SCI TECHNOL, DOI 10.1021/acs.est.3c01818
NR 71
TC 8
Z9 8
U1 8
U2 56
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUN
PY 2023
VL 15
IS 12
AR 9625
DI 10.3390/su15129625
PG 19
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA K3ZI0
UT WOS:001015849300001
OA gold
DA 2024-12-25
ER

PT J
AU Karpouzis, K
AF Karpouzis, Kostas
TI Plato's Shadows in the Digital Cave: Controlling Cultural Bias in
   Generative AI
SO ELECTRONICS
LA English
DT Article
DE ethics; bias; culture; diversity; fairness; societal impact; generative
   AI; training data
AB Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato's Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.
C1 [Karpouzis, Kostas] Panteion Univ Social & Polit Sci, Dept Commun Media & Culture, Athens 17671, Greece.
C3 Panteion University
RP Karpouzis, K (corresponding author), Panteion Univ Social & Polit Sci, Dept Commun Media & Culture, Athens 17671, Greece.
EM kkarpou@panteion.gr
RI Karpouzis, Kostas/AAQ-8018-2020
OI Karpouzis, Kostas/0000-0002-4615-6751
CR [Anonymous], 2010, The allegory of the cave
   [Anonymous], 1984, Elbow room: The varieties of free will worth wanting, DOI DOI 10.1017/CBO9781139172714
   Austin N, 1996, CLASSICAL WORLD, V89, P493, DOI 10.2307/4351858
   Barocas S, 2016, CALIF LAW REV, V104, P671, DOI 10.15779/Z38BG31
   Bellamy RKE, 2019, IBM J RES DEV, V63, DOI 10.1147/JRD.2019.2942287
   Bostrom N, 2014, SUPERINTELLIGENCE PA, DOI DOI 10.1080/01402390.2013.844127
   Chakraborty J, 2021, PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), P429, DOI 10.1145/3468264.3468537
   Crisp R, 2014, Aristotle, Nicomachean ethics
   Eubanks V, 2018, AUTOMATING INEQUALIT
   Fairclough N., 1995, CRITICAL DISCOURSE A, DOI DOI 10.4324/9781315834368
   GAGARIN M, 1987, CLASSICAL WORLD, V80, P452, DOI 10.2307/4350105
   Garcia Megan, 2016, WORLD POLICY J, V33, P111, DOI DOI 10.1215/07402775-3813015
   Harari YN  ..., 2017, Homo Deus: A brief history of tomorrow, DOI DOI 10.17104/9783406704024
   Hutto C, 2014, P INT AAAI C WEB SOC, V8, P216, DOI DOI 10.1609/ICWSM.V8I1.14550
   Jurgens D, 2017, PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 2, P51, DOI 10.18653/v1/P17-2009
   Karpouzis K., 2023, P INT C FRONTIERS AR, P59
   Karpouzis K., 2024, arXiv
   Karpouzis K, 2024, Arxiv, DOI arXiv:2403.12071
   Lauer David, 2021, AI Ethics, V1, P395, DOI 10.1007/s43681-021-00068-x
   Lee NT, 2018, J INF COMMUN ETHICS, V16, P252, DOI 10.1108/JICES-06-2018-0056
   Noble SU, 2018, ALGORITHMS OF OPPRESSION, P1
   Obermeyer Z, 2019, SCIENCE, V366, P447, DOI 10.1126/science.aax2342
   Palmini O, 2024, ETHICS INF TECHNOL, V26, DOI 10.1007/s10676-024-09752-y
   Patterson L, 2008, GEND MANAG, V23, P458, DOI 10.1108/17542410810897562
   Smith N., 1999, J ETHICS, V3, P31, DOI DOI 10.1023/A:1026402630245
   Tramèr F, 2017, 2017 IEEE EUROPEAN SYMPOSIUM ON SECURITY AND PRIVACY (EUROS&P), P401, DOI 10.1109/EuroSP.2017.29
   Turing AM., 1950, MIND, VLIX, P433, DOI [DOI 10.1093/MIND/LIX.236.433, 10.1093/mind/LIX.236.433]
   Wang TL, 2019, IEEE I CONF COMP VIS, P5309, DOI 10.1109/ICCV.2019.00541
   Wolf S, 2007, RATIO, V20, P145, DOI 10.1111/j.1467-9329.2007.00354.x
   Zehlike M, 2017, CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, P1569, DOI 10.1145/3132847.3132938
NR 30
TC 0
Z9 0
U1 20
U2 28
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
EI 2079-9292
J9 ELECTRONICS-SWITZ
JI Electronics
PD APR
PY 2024
VL 13
IS 8
AR 1457
DI 10.3390/electronics13081457
PG 13
WC Computer Science, Information Systems; Engineering, Electrical &
   Electronic; Physics, Applied
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science; Engineering; Physics
GA OX3P3
UT WOS:001210540500001
OA gold
DA 2024-12-25
ER

PT J
AU Ross, EAS
   Baines, J
AF Ross, Edward A. S.
   Baines, Jackie
TI Treading water: new data on the impact of AI ethics information sessions
   in classics and ancient language pedagogy
SO JOURNAL OF CLASSICS TEACHING
LA English
DT Article
DE artificial intelligence; conversational AI tools; new teaching tools;
   Ancient Greek; Classical Latin
AB Over 2023, many universities and policy organisations in the higher education (HE) sector are working to create guiding principles and guidelines for the use of generative artificial intelligence (AI) in HE Teaching and Learning (T&L). Despite these guidelines, students remain unsure if and how they should use AI. This article discusses the AI information sessions held over the Autumn 2023 term in the Department of Classics at the University of Reading, which aimed to provide students with the knowledge and tools to make informed judgements about using AI in their studies. These sessions discussed the benefits and drawbacks of generative AI, highlighting training data, content policy, environmental impact, and examples of potential uses. Staff and student participants were surveyed before and after these information sessions to gather their opinions surrounding AI use. Although at least 60% of participants had previously used generative AI, 80% of participants were apprehensive of or against using generative AI tools for learning purposes following the AI information sessions. By providing staff and students with the ethical considerations surrounding generative AI, they can make an informed judgement about using AI in their work without misplaced faith or excessive fear.
C1 [Ross, Edward A. S.; Baines, Jackie] Univ Reading, Dept Class, Reading, England.
C3 University of Reading
RP Ross, EAS (corresponding author), Univ Reading, Dept Class, Reading, England.
EM edward.ross@reading.ac.uk
RI Ross, Edward/KVB-8402-2024
OI Ross, Edward/0000-0003-4174-835X
FU Teaching and Learning Enhancement Project (TLEP) grant from the
   University of Reading; Council of University Classics Departments
FX This article is part of the wider 'ChatGPT: A Conversational Language
   Study Tool' project at the University of Reading. This project has been
   reviewed by the University of Reading University Research Ethics
   Committee and has been given a favourable ethical opinion for conduct.
   The project is supported by a Teaching and Learning Enhancement Project
   (TLEP) grant from the University of Reading and an Education Grant from
   the Council of University Classics Departments. Special thanks to
   Jacinta Hunter, Fleur McRitchie Pratt, Nisha Patel, Luke Edwards, and
   Rikard Roitto for bringing changes in AI outputs to our attention.
CR Anguiano D., 2023, GUARDIAN        1001
   Anthropic, 2023, CLAUDE 2 JULY 11 202
   Appel G., 2023, Harvard Business Review
   Atlas S., 2023, CHATGPT HIGHER ED PR
   Bikbaeva D., 2023, FASHION LAW     0201
   Bloomsbury Publishing, GENERATIVE AI APA 7
   Bloomsbury Publishing, GENERATIVE AI OSCOLA
   Bloomsbury Publishing, GENERATIVE AI MLA 9
   Bloomsbury Publishing, GENERATIVE AI MHRA
   Bloomsbury Publishing, GENERATIVE AI CHICAG
   Bloomsbury Publishing, GENERATIVE AI IEEE
   Bloomsbury Publishing, GENERATIVE AI HARVAR
   Bloomsbury Publishing, GENERATIVE AI VANCOU
   Brown TB, 2020, ADV NEUR IN, V33
   Cassette, 2020, CASSETTEAI V1 AUGUST
   Clackson J., 2011, A Companion to the Latin Language, P236, DOI [https://doi.org/10.1002/9781444343397.ch15, DOI 10.1002/9781444343397.CH15]
   Coffey L., 2023, Inside Higher Ed
   Creamer E., 2023, GUARDIAN        0705
   Cullen H., 2016, LATIN GCSE PART 2
   de Vries A, 2023, JOULE, V7, P2191, DOI 10.1016/j.joule.2023.09.004
   Diffusers Team, 2023, Stable Diffusion XL Base 1.0: A Diffusion-based Text-to-Image Generative Model
   EssayAIGroup, 2020, ESSAYAILAB JUNE 18 2
   Ewans M, 2019, BLOOMSBURY STUD CLAS, P73
   Google, 2023, BARD NOVEMBER 16 202
   Google, 2023, EXPT UPDATES
   Hao K., 2019, TECHNOLOGY REV
   Heikkila M., 2023, MIT Technology Review
   HeyGen, 2023, HEYGEN 30 APRIL 10 2
   Jisc, 2023, GENERATIVE AI PRIMER
   Katz L., 2023, FORBES          1026
   Lucchi N, 2024, EUR J RISK REGUL, V15, P602, DOI 10.1017/err.2023.59
   Magical Tome, 2023, TOME V2 NOVEMBER 20
   Metz R., 2023, CNN BUSINESS    1021
   Microsoft, 2023, BING CHAT NOVEMBER 1
   Midjourney, 2023, MIDJOURNEY BOT 52 JU
   Morpurgo Davies A., 2015, PRONUNCIATION GREEK, DOI [10.1093/acrefore/9780199381135.013.5365, DOI 10.1093/ACREFORE/9780199381135.013.5365]
   Nam J., 2023, BESTCOLLEGES    1122
   OpenAI, 2023, GPT-4 technical report
   OpenAI, 2023, CHATGPT RELEASE NOTE
   OpenAI, 2023, DALL E 3 AUGUST 20 2
   OpenAI, 2023, INTRO GPTS
   OpenAI, 2023, CHATGPT 4 NOVEMBER 2
   OpenAI, 2023, Usage policies
   Perplexity, 2023, PERPLEXITY IMAGE UPL
   Perplexity, 2023, PERPLEXITY COPILOT A
   Quality Assurance Agency for Higher Education, 2023, RECONSIDERING ASSESS
   Ross EAS, 2023, J CLASS TEACH, DOI 10.1017/S2058631023000430
   Russell Group, 2023, NEW PRINCIPLES USE A
   Shan S, 2023, PROCEEDINGS OF THE 32ND USENIX SECURITY SYMPOSIUM, P2187
   St. George's University of London Library, 2023, REFERENCE AI ASSIGNM
   Strubell E, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P3645
   Taylor J., 2016, GREEK GCSE PART 2 RE
   Taylor J., 2023, GUARDIAN        0809
   UNESCO International Institute for Higher Education in Latin America and the Caribbean, 2023, CHATGPT ARTIFICIAL I
   University of Queensland Australia Library, 2023, USING CHATGPT OTHER
   University of Reading Sustainability Services, 2020, OUR FUTURE 1
   urns P., 2023, How Much Latin Does ChatGPT 'Know'?
   veekaybee, 2022, CHATGPTMD GITHUB GIS
   Vincent J., 2023, VERGE           0428
   Watercutter A., 2023, Wired
   Wong M., 2023, ATLANTIC
   Zahn M., 2023, AUTHORSLAWSUIT OPENA
NR 62
TC 0
Z9 0
U1 12
U2 12
PU CAMBRIDGE UNIV PRESS
PI CAMBRIDGE
PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND
SN 1741-7627
EI 2058-6310
J9 J CLASS TEACH
JI J. CLASS. TEACH.
PD FAL
PY 2024
VL 25
IS 50
BP 181
EP 190
DI 10.1017/S2058631024000412
EA MAY 2024
PG 10
WC Classics
WE Emerging Sources Citation Index (ESCI)
SC Classics
GA L7L3M
UT WOS:001230550300001
OA gold
DA 2024-12-25
ER

PT J
AU Harrison, RM
   Lapteva, E
   Bibin, A
AF Harrison, Rachel M.
   Lapteva, Ekaterina
   Bibin, Anton
TI Behavioral Nudging With Generative AI for Content Development in SMS
   Health Care Interventions: Case Study
SO JMIR AI
LA English
DT Article
DE generative artificial intelligence; generative AI; prompt engineering;
   large language models; GPT; content design; brief message interventions;
   mHealth; behavior change techniques; medication adherence; type 2
   diabetes
ID MEDICATION ADHERENCE; TEXT; DISEASE; MHEALTH; TRIAL
AB Background: Brief message interventions have demonstrated immense promise in health care, yet the development of these messages has suffered from a dearth of transparency and a scarcity of publicly accessible data sets. Moreover, the researcher-driven content creation process has raised resource allocation issues, necessitating a more efficient and transparent approach to content development. Objective: This research sets out to address the challenges of content development for SMS interventions by showcasing the use of generative artificial intelligence (AI) as a tool for content creation, transparently explaining the prompt design and content generation process, and providing the largest publicly available data set of brief messages and source code for future replication of our process. Methods: Leveraging the pretrained large language model GPT-3.5 (OpenAI), we generate a collection of messages in the context of medication adherencefor individuals with type 2 diabetes using evidence-derived behavior changetechniques identified in a prior systematic review. We create an attributed prompt designed to adhere to content (readability and tone) and SMS (character count and encoder type) standards while encouraging message variability to reflect differences in behavior change techniques. Results: We deliver the most extensive repository of brief messages for a singular health care intervention and the first library of messages crafted with generative AI. In total, our method yields a data set comprising 1150 messages, with 89.91% (n=1034) meeting character length requirements and 80.7% (n=928) meeting readability requirements. Furthermore, our analysis reveals that all messages exhibit diversity comparable to an existing publicly available data set created under the same theoretical framework for a similar setting. Conclusions: This research provides a novel approach to content creation for health care interventions using state-of-the-art generative AI tools. Future research is needed to assess the generated content for ethical, safety, and research standards, as well as to determine whether the intervention is successful in improving the target behaviors. (JMIR AI 2024;3:e52974) doi: 10.2196/52974
C1 [Harrison, Rachel M.] Ophiuchus LLC, GenAI Lab, 1111B S Governors Ave,STE 7359, Dover, DE 19904 USA.
   [Lapteva, Ekaterina] Russian Acad Sci, Inst Psychol, Moscow, Russia.
   [Bibin, Anton] Skoltech AI Ctr Res Educ & Innovat, Skolkovo Inst Sci & Technol, Moscow, Russia.
C3 Institute of Psychology of Russian Academy of Sciences; Russian Academy
   of Sciences; Skolkovo Institute of Science & Technology
RP Harrison, RM (corresponding author), Ophiuchus LLC, GenAI Lab, 1111B S Governors Ave,STE 7359, Dover, DE 19904 USA.
EM rae@ophiuchus.ai
CR Abdi H, 2010, WIRES COMPUT STAT, V2, P433, DOI 10.1002/wics.101
   Abroms LC, 2015, JMIR MHEALTH UHEALTH, V3, DOI [10.2196/mhealth.4917, 10.2196/mhealth.3846]
   Anaby-Tavor A, 2020, AAAI CONF ARTIF INTE, V34, P7383
   [Anonymous], 2017, Text messaging in healthcare research toolkit
   [Anonymous], CODE FEDERAL REGULAT
   API reference, OpenAI Platform
   Arambepola C, 2016, J MED INTERNET RES, V18, DOI 10.2196/jmir.5425
   Armanasco AA, 2017, AM J PREV MED, V52, P391, DOI 10.1016/j.amepre.2016.10.042
   Arora S, 2014, ANN EMERG MED, V63, P745, DOI 10.1016/j.annemergmed.2013.10.012
   Bartlett YK, 2020, J MED INTERNET RES, V22, DOI 10.2196/15989
   Bensoussan BE, 2012, Analysis Without Paralysis: 12 Tools to Make Better Strategic Decisions
   BERT base model (uncased), Hugging Face
   Bommakanti KK, 2020, BMC PUBLIC HEALTH, V20, DOI 10.1186/s12889-019-7892-9
   Bostrom N, 2014, CAMBRIDGE HANDBOOK OF ARTIFICIAL INTELLIGENCE, P316
   Brath H, 2013, BRIT J CLIN PHARMACO, V76, P47, DOI 10.1111/bcp.12184
   Chatterjee S, 2017, LANCET, V389, P2239, DOI 10.1016/S0140-6736(17)30058-2
   Cole-Lewis H, 2010, EPIDEMIOL REV, V32, P56, DOI 10.1093/epirev/mxq004
   Cramer JA, 2008, INT J CLIN PRACT, V62, P76, DOI 10.1111/j.1742-1241.2007.01630.x
   Cramer JA, 2004, DIABETES CARE, V27, P1218, DOI 10.2337/diacare.27.5.1218
   Creative Commons, Attribution 4.0 International (CC BY 4.0)
   De Leon E, 2014, J MED INTERNET RES, V16, P119, DOI 10.2196/jmir.2837
   Dobson R, 2017, DIGIT HEALTH, V3, DOI 10.1177/2055207617740315
   Dogru OC, 2022, TRANSL BEHAV MED, V12, P979, DOI 10.1093/tbm/ibac058
   Eakin EG, 1998, HEALTH EDUC RES, V13, P519, DOI 10.1093/her/13.4.519
   Embeddings, OpenAI Platform
   FAQ, OpenAI Platform
   Farmer AJ, 2016, DIABETIC MED, V33, P565, DOI 10.1111/dme.12987
   Faulkner X, 2005, INTERACT COMPUT, V17, P167, DOI 10.1016/j.intcom.2004.11.002
   Finitsis DJ, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0088166
   Fjeldsoe BS, 2009, AM J PREV MED, V36, P165, DOI 10.1016/j.amepre.2008.09.040
   Frantz RS, 2015, EDUC RESEARCHER, V44, P387, DOI 10.3102/0013189X15603980
   Froisland DH, 2012, J MED INTERNET RES, V14, P113, DOI 10.2196/jmir.2155
   Gellman MD, 2020, Encyclopedia of Behavioral Medicine
   Ghersi Davina, 2009, J Evid Based Med, V2, P1, DOI 10.1111/j.1756-5391.2009.01014.x
   GitHub, google-research / bert
   Graesser AC, 2011, EDUC RESEARCHER, V40, P223, DOI 10.3102/0013189X11413260
   Green SM, 2023, J Med Internet Res, V25, DOI [10.2196/38073, DOI 10.2196/38073]
   Hall AK, 2015, ANNU REV PUBL HEALTH, V36, P393, DOI 10.1146/annurev-publhealth-031914-122855
   Harrison RM, 2023, INT CONF DAT MIN WOR, P1535, DOI 10.1109/ICDMW60847.2023.00195
   Head KJ, 2013, SOC SCI MED, V97, P41, DOI 10.1016/j.socscimed.2013.08.003
   Hoermann S, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7023
   Hu K., 2023, REUTERS         0202
   Kamal AK, 2015, BMC NEUROL, V15, DOI 10.1186/s12883-015-0471-5
   Kebede MM, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7135
   Khan Arif Ali, 2022, EASE 2022: The International Conference on Evaluation and Assessment in Software Engineering 2022, P383, DOI 10.1145/3530019.3531329
   Koivusilta LK, 2007, SCAND J PUBLIC HEALT, V35, P95, DOI 10.1080/14034940600868721
   Krishna S, 2009, TELEMED J E-HEALTH, V15, P231, DOI 10.1089/tmj.2008.0099
   Kumar V, 2020, P 2 WORKSH LIF LONG
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Liu YH, 2023, Arxiv, DOI [arXiv:2304.01852, DOI 10.1016/J.METRAD.2023.100017]
   Long H, 2019, J MED INTERNET RES, V21, DOI 10.2196/10421
   Lu Y, 2022, PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS), P8086
   Lugosi G., 2023, PREPRINT
   Maar MA, 2016, JMIR MHEALTH UHEALTH, V4, P266, DOI 10.2196/mhealth.4994
   MacPherson MM, 2021, TRANSL BEHAV MED, V11, P1585, DOI 10.1093/tbm/ibab058
   Marcolino MS, 2018, JMIR MHEALTH UHEALTH, V6, DOI 10.2196/mhealth.8873
   Michie S, 2013, ANN BEHAV MED, V46, P81, DOI 10.1007/s12160-013-9486-6
   Michie S, 2011, IMPLEMENT SCI, V6, DOI 10.1186/1748-5908-6-10
   Militello LK, 2012, WORLDV EVID-BASED NU, V9, P66, DOI 10.1111/j.1741-6787.2011.00239.x
   Nelligan RK, 2019, JMIR MHEALTH UHEALTH, V7, DOI 10.2196/14619
   Nelson J., 2012, Measures of text difficulty
   Nieuwlaat R, 2014, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD000011.pub4
   openai, New and improved embedding model
   Orr JA, 2015, HEALTH PSYCHOL REV, V9, P397, DOI 10.1080/17437199.2015.1022847
   Park JS, 2023, P 36 ANN ACM S US IN, DOI DOI 10.1145/3586183.3606763
   Park LG, 2014, PATIENT EDUC COUNS, V94, P261, DOI 10.1016/j.pec.2013.10.027
   Patrick K, 2009, J MED INTERNET RES, V11, DOI 10.2196/jmir.1100
   Perera AI, 2014, AIDS PATIENT CARE ST, V28, P579, DOI 10.1089/apc.2014.0156
   pewresearch, Mobile fact sheet
   Puri R, 2020, PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), P5811
   Radford A., 2019, OPENAI BLOG
   Ranney ML, 2014, J ADOLESCENT HEALTH, V55, P33, DOI 10.1016/j.jadohealth.2013.12.017
   Rathbone AL, 2017, J MED INTERNET RES, V19, DOI 10.2196/jmir.7740
   Reynolds L, 2021, EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21), DOI 10.1145/3411763.3451760
   Shetty Ananth Samith, 2011, J Assoc Physicians India, V59, P711
   Smith V, 2011, BMC MED RES METHODOL, V11, DOI 10.1186/1471-2288-11-15
   Stowell E, 2018, PROCEEDINGS OF THE 2018 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2018), DOI 10.1145/3173574.3173589
   Vaithilingam P, 2022, EXTENDED ABSTRACTS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2022, DOI 10.1145/3491101.3519665
   Valueva EA, 2017, PSIKHOL ZH, V38, P18, DOI 10.7868/S0205959217050026
   van der Maaten L, 2008, J MACH LEARN RES, V9, P2579
   van Heerden A, 2012, B WORLD HEALTH ORGAN, V90, P393, DOI 10.2471/BLT.11.099788
   Vaswani A, 2017, ADV NEUR IN, V30
   Vogels EA, 2023, Pew Research Center
   Webb TL, 2010, J MED INTERNET RES, V12, DOI 10.2196/jmir.1376
   Whittaker R, 2008, J MED INTERNET RES, V10, DOI 10.2196/jmir.1007
   Willoughby JF, 2018, COMPUT HUM BEHAV, V79, P75, DOI 10.1016/j.chb.2017.10.031
   Wu TY, 2023, IEEE-CAA J AUTOMATIC, V10, P1122, DOI 10.1109/JAS.2023.123618
   Ybarra ML, 2012, J MED INTERNET RES, V14, P103, DOI 10.2196/jmir.2061
   Zhang ZY, 2021, AI OPEN, V2, P216, DOI 10.1016/j.aiopen.2021.12.003
   Zhou B, 2016, LANCET, V387, P1513, DOI 10.1016/S0140-6736(16)00618-8
NR 90
TC 0
Z9 0
U1 1
U2 1
PU JMIR PUBLICATIONS, INC
PI TORONTO
PA 130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA
EI 2817-1705
J9 JMIR AI
JI JMIR AI
PY 2024
VL 3
AR e52974
DI 10.2196/52974
PG 19
WC Health Care Sciences & Services; Medical Informatics
WE Emerging Sources Citation Index (ESCI)
SC Health Care Sciences & Services; Medical Informatics
GA P0H0G
UT WOS:001374817100001
PM 39405108
OA gold
DA 2024-12-25
ER

PT J
AU Estaiteyeh, M
   McQuirter, R
AF Estaiteyeh, Mohammed
   McQuirter, Ruth
TI Generative or Degenerative?! Implications of AI Tools in Pre-Service
   Teacher Education and Reflections on Instructors' Professional
   Development
SO BROCK EDUCATION-A JOURNAL OF EDUCATIONAL RESEARCH AND PRACTICE
LA English
DT Article
DE artificial intelligence (AI); artificial intelligence in education
   (AIEd); teacher education; professional development; generative AI
   (GenAI)
ID NARRATIVE INQUIRY
AB Despite existing research on AI applications in education (AIEd), the release of ChatGPT has disrupted the status quo in the educational landscape. Although this technology can personalize learning, decrease teacher workload, and offer access to a wealth of information, concerns around generative AI (GenAI) tools have emerged, including academic integrity, data accuracy, and bias in information. Given research highlights and acknowledging educators' varied levels of awareness and conflicting views toward AIEd, two teacher educators (also authors of this paper) in the Faculty of Education at Brock University facilitated three workshops among different groups of teacher educators. The workshops focused on the emerging nature of GenAI tools, their affordances, and their implications for educators' practices. Adopting a narrative inquiry approach, the authors describe the details of these workshops and present their reflections on the process of preparing for and facilitating them. Implications for teacher education research and practice are also presented and discussed.
C1 [Estaiteyeh, Mohammed; McQuirter, Ruth] Brock Univ, Fac Educ, St Catharines, ON, Canada.
C3 Brock University
RP Estaiteyeh, M (corresponding author), Brock Univ, Fac Educ, St Catharines, ON, Canada.
EM mestaiteyeh@brocku.ca
RI Estaiteyeh, Mohammed/LFV-0978-2024
OI Estaiteyeh, Mohammed/0000-0001-8925-3108
CR Anders B., 2023, The AI literacy imperative: Empowering instructors & students
   [Anonymous], 2023, Times ColonistJanuary 8
   Barrett A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00427-0
   Bell JS, 2002, TESOL QUART, V36, P207, DOI 10.2307/3588331
   Brock University, 2024, Guidance on generative AI
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00408-3
   Chiu TKF, 2021, TECHTRENDS, V65, P796, DOI 10.1007/s11528-021-00637-1
   Clandinin DJ, 2006, RES STUD MUSIC EDUC, V27, P44, DOI 10.1177/1321103X060270010301
   Couturier C., 2023, University AffairsSeptember 20
   Dawson P, 2023, Don't fear the robot: Future-authentic assessment and generative artificial intelligence
   Dewey J., 1986, EDUC FORUM, V50, P241, DOI [10. 1080/00131728609335764. eprint, DOI 10.1080/00131728609335764.EPRINT]
   Farrokhnia M, 2024, INNOV EDUC TEACH INT, V61, P460, DOI 10.1080/14703297.2023.2195846
   Fullan M., 2006, Centre for Strategic Education Seminar Series Paper No. 157
   Haggart B., 2023, The ConversationJanuary 30
   Halaweh M, 2023, CONTEMP EDUC TECHNOL, V15, DOI 10.30935/cedtech/13036
   Hussain ST, 2018, J INNOV KNOWL, V3, P123, DOI 10.1016/j.jik.2016.07.002
   Hwang GJ., 2020, COMPUTERS ED ARTIFIC, V1, P100001, DOI [DOI 10.1016/J.CAEAI.2020.100001, 10.1016/j.caeai.2020.100001]
   Kohnke Kohnke L. L., 2023, Computers and Education: Artificial Intelligence, V5 5, P100156, DOI [10.1016/j.caeai.2023.100156 10.1016/j.caeai.2023.100156, DOI 10.1016/J.CAEAI.2023.100156]
   Kumar R., 2023, HDB ACAD INTEGRITY, DOI [10.1007/978-981-287-079-7_153-1, DOI 10.1007/978-981-287-079-7_153-1]
   Kumar R, 2023, INT J EDUC INTEGR, V19, DOI 10.1007/s40979-023-00130-7
   Kurz TL., 2022, J INTERACT LEARN RES, V33, P225
   Lameras P, 2022, INFORMATION, V13, DOI 10.3390/info13010014
   Lang R., 2022, UniladDecember 29
   Lo CK, 2023, EDUC SCI, V13, DOI 10.3390/educsci13040410
   Merriam S.B., 2015, QUALITATIVE RES GUID
   Morrison L., 2024, ART INT ED C SHAP FU
   Murray G, 2009, QUALITATIVE RESEARCH IN APPLIED LINGUISTICS: A PRACTICAL INTRODUCTION, P45
   Ng D. T. K., 2021, COMPUTERS ED ARTIFIC, V2, P100041, DOI DOI 10.1016/J.CAEAI.2021.100041
   OpenAI, 2022, CHATGPT OPT LANG MOD
   Panjwani-Charani S., Uses of artificial intelligence in STEM Education
   Rahman MM, 2023, APPL SCI-BASEL, V13, DOI 10.3390/app13095783
   Ross E., 2023, Embracing artificial intelligence in the classroom
   Salas-Pilco SZ, 2022, EDUC SCI, V12, DOI 10.3390/educsci12080569
   Savin-Baden M, 2007, J GEOGR HIGHER EDUC, V31, P459, DOI 10.1080/03098260601071324
   Schulten K., 2023, The New York TimesJanuary 24
   Seo K, 2021, INT J EDUC TECHNOL H, V18, DOI 10.1186/s41239-021-00292-9
   Starr L.J., 2010, CANADIAN J NEW SCHOL, V3, P1
   studyonline.ca, My digital companion: Making sense of ChatGPT
   Trust T., 2023, Contemporary Issues in Technology and Teacher Education, V23
   Volante L., 2023, KappanAugust 28
   Volante L., 2023, The ConversationFebruary 27
   Williamson B, 2023, Code acts in education: Degenerative AI in education
   Yan LX, 2023, Arxiv, DOI [arXiv:2303.13379, DOI 10.48550/ARXIV.2303.13379]
   Young J. R., 2023, EdSurgeJanuary 19
   Zawacki-Richter O, 2019, INT J EDUC TECHNOL H, V16, DOI 10.1186/s41239-019-0171-0
   Zhu CJ, 2023, KNOWL MANAG E-LEARN, V15, P133, DOI 10.34105/j.kmel.2023.15.008
NR 46
TC 0
Z9 0
U1 7
U2 7
PU BROCK UNIV, FAC EDUCATION
PI CATHARINES
PA 500 GLENRIDGE AVE, ST, CATHARINES, ON L2S 3A1, CANADA
SN 1183-1189
J9 BROCK EDUC
JI Brock Educ.
PY 2024
VL 33
IS 3
SI SI
BP 75
EP 98
PG 24
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA D6C5J
UT WOS:001297037800008
DA 2024-12-25
ER

PT J
AU Schmitt, RH
   Wolfschla, D
   Woltersmann, JH
   Stohrer, L
AF Schmitt, Robert H.
   Wolfschla, Dominik
   Woltersmann, Jan-Henrik
   Stohrer, Lennart
TI Measurability of quality characteristics identified fi ed in latent
   spaces of Generative AI Models
SO CIRP ANNALS-MANUFACTURING TECHNOLOGY
LA English
DT Article
DE Metrology; Artificial intelligence; Generative artificial intelligence
ID FEATURE-EXTRACTION
AB Deep Learning can learn complex properties from image datasets, which are difficult to model with traditional machine vision algorithms, inherently in the form of disentangled latent spaces. With latent spaces of Generative AI models, a feature extraction method to access these properties can be implemented. This work evaluates whether the learned properties can be measured in the latent space. Quantity and quantity-value scale properties and the measurability of the dimensional quality characteristic 'filling degree' using a linear calibration function are demonstrated for an industrial machine vision application. An uncertainty indicator between 0.4-0.9 mm is estimated for the latent space measurements. (c) 2024 The Authors. Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
C1 [Schmitt, Robert H.; Woltersmann, Jan-Henrik; Stohrer, Lennart] WZL RWTH Aachen Univ, Aachen, Germany.
   [Schmitt, Robert H.] Fraunhofer Inst Prod Technol IPT, Aachen, Germany.
C3 Fraunhofer Gesellschaft
RP Schmitt, RH (corresponding author), WZL RWTH Aachen Univ, Aachen, Germany.; Schmitt, RH (corresponding author), Fraunhofer Inst Prod Technol IPT, Aachen, Germany.
EM robert.schmitt@wzl-iqs.rwth-aachen.de
OI Wolfschlager, Dominik/0000-0003-2399-4856; Schmitt,
   Robert/0000-0002-0011-5962
FU Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
   [-507911127]; Ministry of Science and Education (BMBF) [01IS22094D]
FX Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research
   Foundation) -507911127. Partially funded by the Ministry of Science and
   Education (BMBF) with the project WestAI-AI Service Center West under
   grant id 01IS22094D.
CR Akay H, 2021, CIRP ANN-MANUF TECHN, V70, P139, DOI 10.1016/j.cirp.2021.04.021
   Chen NT, 2018, PR MACH LEARN RES, V84
   Cramer S., 2024, Measurement Uncertainty: Relating The Uncertainties Of Physical And Virtual Measurements
   Goodfellow I, 2020, COMMUN ACM, V63, P139, DOI 10.1145/3422622
   Harkonen E, 2020, NeurIPS, V34
   Huang L, 2022, CIRP ANN-MANUF TECHN, V71, P121, DOI 10.1016/j.cirp.2022.04.053
   Jesche F, 2021, Vehicles of Tomorrow 2019, P101
   Joint Committee for Guides in Metrology, 2012, International Vocabulary of Metrology-Basic and General Concepts and Associated Terms (VIM)
   Karras T, 2019, PROC CVPR IEEE, P4396, DOI 10.1109/CVPR.2019.00453
   Nitzan Y., 2021, LARGE: Latent-Based Regression Through GAN Semantics
   Pidhorskyi S, 2020, PROC CVPR IEEE, P14092, DOI 10.1109/CVPR42600.2020.01411
   Schleich B, 2022, CIRP ANN-MANUF TECHN, V71, P133, DOI 10.1016/j.cirp.2022.03.021
   Schmitt RH, 2022, CIRP ANN-MANUF TECHN, V71, P433, DOI 10.1016/j.cirp.2022.03.016
   Schutte K, 2021, NeurIPS, V35
   Weimer D, 2016, CIRP ANN-MANUF TECHN, V65, P417, DOI 10.1016/j.cirp.2016.04.072
   Zuo C, 2022, LIGHT-SCI APPL, V11, DOI 10.1038/s41377-022-00714-x
NR 16
TC 0
Z9 0
U1 6
U2 6
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-8506
EI 1726-0604
J9 CIRP ANN-MANUF TECHN
JI CIRP Ann-Manuf. Technol.
PY 2024
VL 73
IS 1
BP 389
EP 392
DI 10.1016/j.cirp.2024.04.073
EA JUL 2024
PG 4
WC Engineering, Industrial; Engineering, Manufacturing
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering
GA ZV2T4
UT WOS:001278004100001
OA hybrid
DA 2024-12-25
ER

PT J
AU Korinek, A
AF Korinek, Anton
TI Generative AI for Economic Research: Use Cases and Implications for
   Economists†
SO JOURNAL OF ECONOMIC LITERATURE
LA English
DT Article
AB Generative artificial intelligence (AI) has the potential to revolutionize research. I analyze how large language models (LLMs) such as ChatGPT can assist economists by describing dozens of use cases in six areas: ideation and feedback, writing, back-ground research, data analysis, coding, and mathematical derivations. I provide gen-eral instructions and demonstrate specific examples of how to take advantage of each of these, classifying the LLM capabilities from experimental to highly useful. I argue that economists can reap significant productivity gains by taking advantage of gener-ative AI to automate micro-tasks. Moreover, these gains will grow as the performance of AI systems continues to improve. I also speculate on the longer-term implications of AI-powered cognitive automation for economic research. The online resources asso-ciated with this paper explain how to get started and will provide regular updates on the latest capabilities of generative AI in economics. (JEL A11, C45, D83, I23, O33)
C1 [Korinek, Anton] Univ Virginia, Charlottesville, VA 22903 USA.
   [Korinek, Anton] Brookings Inst, Washington, DC 20036 USA.
   [Korinek, Anton] Ctr Governance AI GovAI, Oxford, England.
   [Korinek, Anton] NBER, Cambridge, MA 02138 USA.
   [Korinek, Anton] CEPR, London, England.
C3 University of Virginia; Brookings Institution; National Bureau of
   Economic Research; Centre for Economic Policy Research - UK
RP Korinek, A (corresponding author), Univ Virginia, Charlottesville, VA 22903 USA.; Korinek, A (corresponding author), Brookings Inst, Washington, DC 20036 USA.; Korinek, A (corresponding author), Ctr Governance AI GovAI, Oxford, England.; Korinek, A (corresponding author), NBER, Cambridge, MA 02138 USA.; Korinek, A (corresponding author), CEPR, London, England.
EM akorinek@virginia.edu
CR Agrawal A, 2018, PREDICTION MACHINES
   Anderljung M, 2023, Arxiv, DOI [arXiv:2307.03718, 10.48550/arXiv.2307.03718]
   Ardekani Aref Mahdavi, 2023, EconSentGPT: A Universal Economic Sentiment Engine?, DOI [10.2139/ssrn.4405779, DOI 10.2139/SSRN.4405779]
   Argyle LP, 2023, POLIT ANAL, V31, P337, DOI 10.1017/pan.2023.2
   Bai YT, 2022, Arxiv, DOI arXiv:2212.08073
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Bommasani R., 2021, arXiv
   Bubeck S, 2023, Arxiv, DOI arXiv:2303.12712
   Buchanan Joy, 2023, GPT-3.5 Hallucinates Nonexistent Citations: Evidence from Economics, DOI [10.2139/ssrn.4467968, DOI 10.2139/SSRN.4467968]
   Carlsmith Joseph, 2020, Open PhilanthropySeptember 11
   Charness Gary, 2023, NBER Working Paper 31679
   Cowen Tyler, 2023, GMU Working Paper in Economics, P23
   Dowling M, 2023, FINANC RES LETT, V53, DOI 10.1016/j.frl.2023.103662
   Dunn A., 2022, arXiv
   Dziri N, 2023, Arxiv, DOI [arXiv:2305.18654, 10.48550/arXiv.2305.18654]
   Eloundou T, 2023, Arxiv, DOI [arXiv:2303.10130, DOI 10.48550/ARXIV.2303.10130]
   Felten E W., 2023, How will Language Modelers like ChatGPT Affect Occupations and Industries? (SSRN Scholarly Paper 4375268), DOI DOI 10.2139/SSRN.4375268
   Frank R.H., 1991, The strategy of choice, P25
   Frieder S, 2023, Arxiv, DOI [arXiv:2301.13867, DOI 10.48550/ARXIV.2301.13867]
   Ganguli Deep., 2022, 2022 ACM C FAIRN ACC, P1747, DOI [DOI 10.1145/3531146.3533229, 10.1145/3531146.3533229]
   Gentzkow M, 2019, J ECON LIT, V57, P535, DOI 10.1257/jel.20181020
   Girotra K, 2010, MANAGE SCI, V56, P591, DOI 10.1287/mnsc.1090.1144
   Girotra Karan, 2023, Ideas Are Dimes a Dozen: Large Language Models for Idea Generation in Innovation, DOI DOI 10.2139/SSRN.4526071
   Hoffmann J, 2022, Arxiv, DOI [arXiv:2203.15556, 10.48550/arXiv.2203.15556]
   Horton John J, 2023, NBER Working Paper 31122
   Jiao WX, 2023, Arxiv, DOI [arXiv:2301.08745, DOI 10.48550/ARXIV.2301.08745, 10.48550/ARXIV.2301.08745]
   Kaplan J, 2020, Arxiv, DOI [arXiv:2001.08361, 10.48550/arXiv.2001.08361]
   Kasparov G., 2017, Deep thinking: Where machine intelligence ends and human creativity begins
   Keynes J.M., 1936, GEN THEORY EMPLOYMEN
   Knight W, 2023, WIREDApr. 17
   Korinek Anton, 2023, The Oxford Handbook of AI Governance
   Korinek Anton., 2023, NBER Working Paper 30957
   Li K, 2024, Arxiv, DOI arXiv:2210.13382
   Lopez-Lira A, 2024, Arxiv, DOI arXiv:2304.07619
   Mollick E. R., 2023, USING IMPLEMENT EFFE, DOI [10.2139/ssrn.4391243, DOI 10.2139/SSRN.4391243]
   Noorbakhsh K, 2021, Arxiv, DOI arXiv:2110.03501
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Peng S., 2023, arXiv, DOI DOI 10.48550/ARXIV.2302.06590
   Ricardo D., 1817, Readings in the economics of the division of labor: The classical tradition, V1
   Sevilla J, 2022, IEEE IJCNN, DOI 10.1109/IJCNN55064.2022.9891914
   Silver D, 2017, Arxiv, DOI arXiv:1712.01815
   Sutton R, 2019, Incomplete Ideas (blog)March 13
   Thompson Alan D, 2023, GPT-3.5 and ChatGPT: An Illustrated Overview
   Turing AM., 1950, MIND, VLIX, P433, DOI [DOI 10.1093/MIND/LIX.236.433, 10.1093/mind/LIX.236.433]
   Vaswani A, 2017, ADV NEUR IN, V30
   Wei J., 2022, Advances in neural information processing systems, V35, p24 824
   Wei JS, 2022, Arxiv, DOI [arXiv:2206.07682, DOI 10.48550/ARXIV.2206.07682]
   Wolfram S., 2023, WHAT IS CHATGPT DOIN
   Zou A, 2023, Arxiv, DOI arXiv:2307.15043
NR 49
TC 21
Z9 21
U1 115
U2 173
PU AMER ECONOMIC ASSOC
PI NASHVILLE
PA 2014 BROADWAY, STE 305, NASHVILLE, TN 37203 USA
SN 0022-0515
EI 2328-8175
J9 J ECON LIT
JI J. Econ. Lit.
PD DEC
PY 2023
VL 61
IS 4
BP 1281
EP 1317
DI 10.1257/jel.20231736
PG 37
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA FE7L3
UT WOS:001144150800005
DA 2024-12-25
ER

PT J
AU Qiu, T
   Yang, D
   Zeng, H
   Chen, XH
AF Qiu, Ting
   Yang, Di
   Zeng, Hui
   Chen, Xinghao
TI Understanding graphic designers' usage behavior of generative artificial
   intelligence tools
SO KYBERNETES
LA English
DT Article; Early Access
DE Graphic designer; Innovation diffusion theory; UTAUT2 model; Usage
   behavior
ID COMPUTER ANXIETY; INFORMATION-TECHNOLOGY; USER ACCEPTANCE; PLS-SEM;
   INNOVATIVENESS; COLLINEARITY; EXPERIENCE; IMPACT; FUTURE
AB PurposeThe rapid development of generative artificial intelligence has witnessed its widespread integration across various industries, contributing to enhanced productivity. However, a comprehensive exploration of the underlying factors influencing the behavior of graphic designers in employing such tools remains incomplete. This research aims to amalgamate the IDT theory with the UTAUT2 model to construct a structural model, delving into the factors affecting graphic designers' behavior in using GenAI tools.Design/methodology/approachA survey was conducted with 394 respondents, and the results were analyzed using PLS-SEM.FindingsThe findings reveal that most factors proposed in both the UTAUT2 and IDT theories exert positive influences. Notably, the study highlights that AI anxiety significantly influences designers' usage behavior.Originality/valueThis research provides a theoretical foundation and practical guidance for both graphic designers and AI developers.
C1 [Qiu, Ting] Inner Mongolia Normal Univ, Sch Design, Hohhot, Peoples R China.
   [Yang, Di] Dalian Univ, Acad Fine Arts, Dalian, Peoples R China.
   [Zeng, Hui] Jiangnan Univ, Sch Design, Wuxi, Peoples R China.
   [Chen, Xinghao] Tianjin Acad Fine Arts, Sch Art & Design, Tianjin, Peoples R China.
C3 Inner Mongolia Normal University; Dalian University; Jiangnan
   University; Tianjin Academy of Fine Arts
RP Zeng, H (corresponding author), Jiangnan Univ, Sch Design, Wuxi, Peoples R China.
EM qiuting2022@gmail.com; lydiayang11@163.com;
   7230306023@stu.jiangnan.edu.cn; cxhzjy@foxmail.com
OI Zeng, Hui/0009-0000-3792-9815
CR Adikoeswanto D, 2022, HELIYON, V8, DOI 10.1016/j.heliyon.2022.e10027
   Agarwal R, 1998, INFORM SYST RES, V9, P204, DOI 10.1287/isre.9.2.204
   Ajamieh A, 2016, J BUS RES, V69, P4667, DOI 10.1016/j.jbusres.2016.03.056
   Ajjan H, 2008, INTERNET HIGH EDUC, V11, P71, DOI 10.1016/j.iheduc.2008.05.002
   Ajzen I., 1985, UNDERSTANDING ATTITU, P11
   Alenezi AR, 2010, TURK ONLINE J EDUC T, V9, P22
   Alhwaiti M, 2023, APPL ARTIF INTELL, V37, DOI 10.1080/08839514.2023.2175110
   Aliu F, 2024, J SCI TECHNOL POLICY, DOI 10.1108/JSTPM-01-2024-0013
   Almaiah MA, 2022, ELECTRONICS-SWITZ, V11, DOI 10.3390/electronics11223662
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Becker JM, 2015, MARKET LETT, V26, P643, DOI 10.1007/s11002-014-9299-9
   Bozionelos N, 2001, COMPUT HUM BEHAV, V17, P213, DOI 10.1016/S0747-5632(00)00039-X
   Budhathoki T, 2024, STUD HIGH EDUC, V49, P831, DOI 10.1080/03075079.2024.2333937
   Casey T, 2012, COMPUT HUM BEHAV, V28, P2034, DOI 10.1016/j.chb.2012.05.022
   Chang A., 2023, Digital health entrepreneurship, P75
   Chatterjee S, 2023, INFORM SYST FRONT, V25, P1299, DOI 10.1007/s10796-021-10181-1
   Chatterjee S, 2019, BOTTOM LINE, V32, P144, DOI 10.1108/BL-02-2019-0069
   Chemnad K, 2024, FRONT ARTIF INTELL, V7, DOI 10.3389/frai.2024.1349668
   Chen JX, 2023, FRONT PUBLIC HEALTH, V10, DOI 10.3389/fpubh.2022.1035398
   Cillo P, 2024, J ACAD MARKET SCI, DOI 10.1007/s11747-024-01044-7
   Cronbach LJ, 1951, PSYCHOMETRIKA, V16, P297
   Dabija DC, 2023, OECON COPERNIC, V14, P1053, DOI 10.24136/oc.2023.031
   Dabner D.Stewart., 2017, Graphic Design School: The Principles and Pracice of Graphic Design
   David Y., 2023, CERN IdeaSquare Journal of Experimental Innovation, V7, P43
   DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008
   DEUTSCH MORTON, 1955, JOUR ABNORMAL AND SOCIAL PSYCHOL, V51-31, P629, DOI 10.1037/h0046408
   Dove G, 2017, PROCEEDINGS OF THE 2017 ACM SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'17), P278, DOI 10.1145/3025453.3025739
   EYSENCK MW, 1992, COGNITION EMOTION, V6, P409, DOI 10.1080/02699939208409696
   Formosa P, 2021, MIND MACH, V31, P595, DOI 10.1007/s11023-021-09579-2
   FORNELL C, 1981, J MARKETING RES, V18, P39, DOI 10.2307/3151312
   Ghazizadeh M, 2012, COGN TECHNOL WORK, V14, P39, DOI 10.1007/s10111-011-0194-3
   Hacker P, 2023, PROCEEDINGS OF THE 6TH ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2023, P1112, DOI 10.1145/3593013.3594067
   Hair J.F., 2021, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Cham, DOI [10.1007/978-3-030-80519-7, DOI 10.1007/978-3-030-80519-7, 10.3926/oss.407, DOI 10.1007/978-3-030-80519-71]
   Hair JF, 2014, PRIMER PARTIAL LEAST
   Hair JF, 2011, J MARKET THEORY PRAC, V19, P139, DOI 10.2753/MTP1069-6679190202
   Hair JF, 2014, EUR BUS REV, V26, P106, DOI 10.1108/EBR-10-2013-0128
   Hair JF, 2019, EUR BUS REV, V31, P2, DOI 10.1108/EBR-11-2018-0203
   Hasani N, 2022, PET CLIN, V17, P13, DOI 10.1016/j.cpet.2021.09.009
   Hopcan S, 2024, EDUC INF TECHNOL, V29, P7281, DOI 10.1007/s10639-023-12086-9
   Hosny A, 2018, NAT REV CANCER, V18, P500, DOI 10.1038/s41568-018-0016-5
   Tran HTT, 2016, INT J BANK MARK, V34, P78, DOI 10.1108/IJBM-06-2014-0073
   Khosrowi D, 2023, PROCEEDINGS OF THE 2023 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2023, P890, DOI 10.1145/3600211.3604716
   Kim J, 2023, INT J DES CREAT INNO, V11, P81, DOI 10.1080/21650349.2023.2167124
   Kim SC, 2019, JAMA NETW OPEN, V2, DOI 10.1001/jamanetworkopen.2019.8061
   Kliestik T., 2023, Analysis and Metaphysics, V22, P43
   Kristiansen TB, 2022, FRONT DIGIT HEALTH, V4, DOI 10.3389/fdgth.2022.862095
   Kuberkar Sachin., 2020, International Journal on Emerging Technologies, V11, P948
   Langer M, 2021, COMPUT HUM BEHAV, V123, DOI 10.1016/j.chb.2021.106878
   Lazaroiu G, 2023, OECON COPERNIC, V14, P703, DOI 10.24136/oc.2023.020
   Lee Chien-Ching, 2013, Acta Anaesthesiol Taiwan, V51, P22, DOI 10.1016/j.aat.2013.03.013
   Li L, 2024, TELEMED E-HEALTH, V30, P722, DOI 10.1089/tmj.2023.0313
   Li TJJ, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445049
   Li WY, 2024, INT J HUM-COMPUT INT, DOI 10.1080/10447318.2024.2310354
   Lin P., 2013, Journal of Theoretical and Applied Information Technology, V47, P1120
   Lin YJ, 2020, PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20), DOI 10.1145/3313831.3376421
   Liu Y, 2023, J MATERIOMICS, V9, P798, DOI 10.1016/j.jmat.2023.05.001
   Manickam P, 2022, BIOSENSORS-BASEL, V12, DOI 10.3390/bios12080562
   MASON CH, 1991, J MARKETING RES, V28, P268, DOI 10.2307/3172863
   McGrath C, 2001, J MANAGE INQUIRY, V10, P386, DOI 10.1177/1056492601104012
   McQuiggan S., 2015, Mobile Learning: A Handbook for Developers, Educators and Learners
   Milne N, 2019, BMC MED EDUC, V19, DOI 10.1186/s12909-019-1825-2
   Mitre-Ortiz A, 2023, UNIVERSAL ACCESS INF, V22, P825, DOI 10.1007/s10209-022-00882-y
   Nadella G.S., 2023, International Journal of Sustainable Development in Computing Science, V5, P1
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Qiu QR, 2022, COMPANION PROCEEDINGS OF THE 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, IUI 2022 COMPANION, P26, DOI 10.1145/3490100.3516450
   Renko M, 2009, J SMALL BUS MANAGE, V47, P331, DOI 10.1111/j.1540-627X.2009.00274.x
   Rogers EM, 2009, COMMUN SER, P418
   Saharia C, 2022, ADV NEUR IN
   Sair S., 2018, Pakistan Journal of Commerce and Social Sciences (PJCSS), V12, P501
   Shamsi M, 2021, INT J ENV RES PUB HE, V18, DOI 10.3390/ijerph182211888
   Sharma Devansh, 2023, Environ Sci Pollut Res Int, V30, P90088, DOI 10.1007/s11356-023-26856-y
   Shi JG, 2023, COMPUT HUM BEHAV, V139, DOI 10.1016/j.chb.2022.107538
   Shi Yang, 2023, Proceedings of the ACM on Human-Computer Interaction, DOI 10.1145/3610217
   Silverman JA, 2023, J PEDIATR GASTR NUTR, V77, P573, DOI 10.1097/MPG.0000000000003931
   SIMONSON MR, 1987, J EDUC COMPUT RES, V3, P231, DOI 10.2190/7CHY-5CM0-4D00-6JCG
   Strzelecki A, 2024, INTERACT LEARN ENVIR, V32, P5142, DOI 10.1080/10494820.2023.2209881
   Sun J, 2022, IUI'22: 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P212, DOI 10.1145/3490099.3511119
   Takaffoli M., 2024, P 2024 ACM DES INT S, P1579, DOI [10.1145/3643834.3660720, DOI 10.1145/3643834.3660720]
   Tasheva Z., 2024, American Journal of Applied Science and Technology, V4, P24, DOI [10.37547/ajast/volume04issue02-05, DOI 10.37547/AJAST/VOLUME04ISSUE02-05]
   TINSLEY HEA, 1987, J COUNS PSYCHOL, V34, P414, DOI 10.1037/0022-0167.34.4.414
   Uddin M.A., 2020, Journal of Open Innovation: Technology, Market, and Complexity, V6, P2, DOI [10.3390/joitmc6010002, DOI 10.3390/JOITMC6010002]
   Urbach N., 2010, J INFORM TECHNOLOGY, V11, P5
   Venkatesh V, 2003, MIS QUART, V27, P425, DOI 10.2307/30036540
   Venkatesh V, 2012, MIS QUART, V36, P157
   Wang SM, 2011, ONLINE INFORM REV, V35, P50, DOI 10.1108/14684521111113588
   Wang YY, 2023, IEEE ACCESS, V11, P143272, DOI 10.1109/ACCESS.2023.3342055
   Wang YY, 2022, INTERACT LEARN ENVIR, V30, P619, DOI 10.1080/10494820.2019.1674887
   Warner J., 2023, P 36 ANN ACM S US IN, P1, DOI [10.1145/3586183.3606751, DOI 10.1145/3586183.3606751]
   Wen F., 2024, Journal of Educational Technology Development and Exchange (JETDE), V17, P130, DOI [10.18785/jetde.1701.07, DOI 10.18785/JETDE.1701.07]
   White C., 2011, Social Media, Crisis Communication, and Emergency Management
   Wu WT, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.870777
   Wu YH, 2016, Arxiv, DOI arXiv:1609.08144
   Yin M, 2023, LECT NOTES COMPUT SC, V14059, P288, DOI 10.1007/978-3-031-48057-7_18
   Yu LR, 2022, BEHAV SCI-BASEL, V12, DOI 10.3390/bs12050127
   Zhang LY, 2012, COMPUT HUM BEHAV, V28, P1902, DOI 10.1016/j.chb.2012.05.008
   Zhu QY, 2022, SOCIO-ECON PLAN SCI, V83, DOI 10.1016/j.seps.2021.101011
NR 96
TC 0
Z9 0
U1 8
U2 8
PU EMERALD GROUP PUBLISHING LTD
PI Leeds
PA Floor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE,
   ENGLAND
SN 0368-492X
EI 1758-7883
J9 KYBERNETES
JI Kybernetes
PD 2024 NOV 28
PY 2024
DI 10.1108/K-05-2024-1159
EA NOV 2024
PG 24
WC Computer Science, Cybernetics
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Computer Science
GA N4O0D
UT WOS:001364138300001
DA 2024-12-25
ER

PT J
AU Robertson, J
   Ferreira, C
   Botha, E
   Oosthuizen, K
AF Robertson, Jeandri
   Ferreira, Caitlin
   Botha, Elsamari
   Oosthuizen, Kim
TI Game changers: A generative AI prompt protocol to enhance human-AI
   knowledge co-construction
SO BUSINESS HORIZONS
LA English
DT Article
DE Large language models; Generative AI; ChatGPT; Prompt engineering;
   Constructivism
ID ARTIFICIAL-INTELLIGENCE
AB The democratization of powerful artificial intelligence (AI) tools, including ChatGPT, has sparked the interest of business practitioners given their ability to fundamentally change the way we work. While AI tools are positioned to augment human capabilities, their effective implementation requires the skill to understand where, when and how to best utilize them efficiently. Furthermore, meaningful engagement with the content produced by generative AI (GenAI) necessitates the intricacy of appropriate prompt engineering to optimize the learning process. As the field of GenAI continues to advance, the art of developing impactful prompts has become a necessary skill for harnessing its full potential. This research develops an AI prompting protocol through a constructivist theory lens. Based on the principles of constructivism, where individuals assimilate new knowledge by bridging it with their existing understanding, this research suggests an active engagement process in the human-AI co-construction of knowledge through GenAI. The goal is to empower business managers and their teams to construct effective AI prompts and validate responses, thereby enhancing user interaction, optimizing workflows, and maximizing the potential outcomes of AI chatbots. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).
C1 [Robertson, Jeandri] Lulea Univ Technol, Lulea, Sweden.
   [Robertson, Jeandri] Univ Cape Town, Cape Town, South Africa.
   [Ferreira, Caitlin] Univ Cape Town, Grad Sch Business, Cape Town, South Africa.
   [Botha, Elsamari] Univ Canterbury, UC Business Sch, Christchurch, New Zealand.
   [Oosthuizen, Kim] Univ Stellenbosch, Business Sch, Cape Town, South Africa.
C3 Lulea University of Technology; University of Cape Town; University of
   Cape Town; University of Canterbury; Stellenbosch University
RP Robertson, J (corresponding author), Lulea Univ Technol, Lulea, Sweden.; Robertson, J (corresponding author), Univ Cape Town, Cape Town, South Africa.
EM jeandri.robertson@ltu.se; caitlin.ferreira@uct.ac.za;
   elsamari.botha@canterbury.ac.nz; kim.botes03@icloud.com
OI Ferreira, Caitlin/0000-0001-9575-6676; Robertson,
   Jeandri/0000-0002-3486-8292
CR Alon-Barkat S, 2023, J PUBL ADM RES THEOR, V33, P153, DOI 10.1093/jopart/muac007
   [Anonymous], 1955, J CONSULT PSYCHOL, V19, P77
   Ausubel D. P., 2012, The acquisition and retention of knowledge: A cognitive view
   Arrieta AB, 2020, INFORM FUSION, V58, P82, DOI 10.1016/j.inffus.2019.12.012
   Berente N, 2021, MIS QUART, V45, P1433, DOI DOI 10.25300/MISQ/2021/16274
   Black JS, 2020, BUS HORIZONS, V63, P215, DOI 10.1016/j.bushor.2019.12.001
   BRUNER JS, 1961, HARVARD EDUC REV, V31, P21
   Budhwar P, 2023, HUM RESOUR MANAG J, V33, P606, DOI 10.1111/1748-8583.12524
   Bulat A, 2024, INT J COMPUT VISION, V132, P1108, DOI 10.1007/s11263-023-01904-9
   Cabrera AA, 2023, ACM T COMPUT-HUM INT, V30, DOI 10.1145/3542921
   Campbell C, 2020, BUS HORIZONS, V63, P227, DOI 10.1016/j.bushor.2019.12.002
   Canhoto AI, 2020, BUS HORIZONS, V63, P183, DOI 10.1016/j.bushor.2019.11.003
   Cooper G, 2023, J SCI EDUC TECHNOL, V32, P444, DOI 10.1007/s10956-023-10039-y
   Crosthwaite P., 2023, Applied Corpus Linguistics, V3, DOI [DOI 10.1016/J.ACORP.2023.100066, https://doi.org/10.1016/j.acorp.2023.100066]
   De Bruyn A, 2020, J INTERACT MARK, V51, P91, DOI 10.1016/j.intmar.2020.04.007
   De Cremer D., 2023, Harvard Business Review
   Desouza KC, 2020, BUS HORIZONS, V63, P205, DOI 10.1016/j.bushor.2019.11.004
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Eriksson T, 2020, TQM J, V32, P795, DOI 10.1108/TQM-12-2019-0303
   Ferraro C, 2024, BUS HORIZONS, V67, P549, DOI 10.1016/j.bushor.2024.04.013
   Frey B. B., 2018, SAGE ENCY ED RES MEA, DOI [DOI 10.4135/9781506326139, 10.4135/9781506326139.n151, 10.4135/9781506326139]
   Hashem R, 2024, RES PRACT TECH ENHAN, V19, DOI 10.58459/rptel.2024.19023
   Jarrahi MH, 2023, BUS HORIZONS, V66, P87, DOI 10.1016/j.bushor.2022.03.002
   Jarrahi MH, 2018, BUS HORIZONS, V61, P577, DOI 10.1016/j.bushor.2018.03.007
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kaplan A, 2020, BUS HORIZONS, V63, P37, DOI 10.1016/j.bushor.2019.09.003
   Kaplan A, 2019, BUS HORIZONS, V62, P15, DOI 10.1016/j.bushor.2018.08.004
   Kietzmann J., 2024, Business Horizons, V67, P453
   Kim M, 2024, TECHTRENDS, V68, P37, DOI 10.1007/s11528-023-00899-x
   Krakowski S, 2023, STRATEGIC MANAGE J, V44, P1425, DOI 10.1002/smj.3387
   Liu PF, 2023, ACM COMPUT SURV, V55, DOI 10.1145/3560815
   Marinucci L, 2023, AI SOC, V38, P747, DOI 10.1007/s00146-022-01474-3
   Mcintosh Timothy R., 2024, IEEE Transactions on Artificial Intelligence, V5, P2739, DOI 10.1109/TAI.2023.3332837
   Metze K, 2024, J PEDIATR SURG, V59, P158, DOI 10.1016/j.jpedsurg.2023.08.018
   Nickerson Raymond S., 1998, REV GEN PSYCHOL, V2, P175, DOI [10.1037/1089-2680.2.2.175, DOI 10.1037/1089-2680.2.2.175]
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Oosthuizen K, 2021, AUSTRALAS MARK J, V29, P264, DOI 10.1016/j.ausmj.2020.07.007
   Paschen U, 2020, BUS HORIZONS, V63, P147, DOI 10.1016/j.bushor.2019.10.004
   Peres R, 2023, INT J RES MARK, V40, P269, DOI 10.1016/j.ijresmar.2023.03.001
   Peters LD, 2013, IND MARKET MANAG, V42, P336, DOI 10.1016/j.indmarman.2013.02.003
   Przegalinska A, 2019, BUS HORIZONS, V62, P785, DOI 10.1016/j.bushor.2019.08.005
   Rai A, 2020, J ACAD MARKET SCI, V48, P137, DOI 10.1007/s11747-019-00710-5
   Raisch S, 2021, ACAD MANAGE REV, V46, P192, DOI 10.5465/amr.2018.0072
   Ramaul L, 2024, BUS HORIZONS, V67, P615, DOI 10.1016/j.bushor.2024.05.006
   Ray P.P., 2023, INTERNET THINGS CYBE, V3, P121, DOI [10.1016/j.iotcps.2023.04.003, DOI 10.1016/J.IOTCPS.2023.04.003]
   Short C. E., 2023, Journal of Business Venturing Insights, V19
   Stojanov A, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00404-7
   Suh M, 2021, CHI '21: PROCEEDINGS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, DOI 10.1145/3411764.3445219
   Sundberg L, 2023, BUS HORIZONS, V66, P777, DOI 10.1016/j.bushor.2023.04.003
   Ültanir E, 2012, INT J INSTR, V5, P195
   Vygotsky L.S., 1979, MIND SOC
   Wang XR, 2021, IUI '21 - 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES, P318, DOI 10.1145/3397481.3450650
   Zamfrescu-Pereira JD, 2023, PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023), DOI 10.1145/3544548.3581388
NR 53
TC 16
Z9 16
U1 95
U2 95
PU ELSEVIER
PI AMSTERDAM
PA RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
SN 0007-6813
EI 1873-6068
J9 BUS HORIZONS
JI Bus. Horiz.
PD SEP-OCT
PY 2024
VL 67
IS 5
BP 499
EP 510
DI 10.1016/j.bushor.2024.04.008
EA AUG 2024
PG 12
WC Business
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA E2L5M
UT WOS:001301369500001
OA hybrid, Green Published
DA 2024-12-25
ER

PT J
AU Sorenson, B
   Hanson, K
AF Sorenson, Benjamin
   Hanson, Kenneth
TI Identifying Generative Artificial Intelligence Chatbot Use on
   Multiple-Choice, General Chemistry Exams Using Rasch Analysis
SO JOURNAL OF CHEMICAL EDUCATION
LA English
DT Article
DE First-Year Undergraduate/General; Curriculum; Testing/Assessment;
   Professional Development
AB Generative artificial intelligence (AI) technology is expected to have a profound impact on chemical education. While there are certainly positive uses, some of which are being actively implemented even now, there is a reasonable concern about its use in cheating. Efforts are underway to detect generative AI usage on open-ended questions, lab reports, and essays, but its detection on multiple choice exams is largely unexplored. Here we propose the use of Rasch analysis to identify the unique behavioral pattern of ChatGPT on General Chemistry II, multiple choice exams. While raw statistics (e.g., average, ability, outfit) were insufficient to readily identify ChatGPT instances, a strategy of fixing the ability scale on high success questions and then refitting the outcomes dramatically enhanced its outlier behavior in terms of Z-standardized out-fit statistic and ability displacement. Setting the detection threshold to a true positive rate (TPR) of 1.0, a false positive rate (FPR) of <0.1 was obtained across a majority of the 20 exams investigated here. Furthermore, the receiver operating characteristic curve (i.e., FPR vs TPR) exhibited outstanding areas under the curve of >0.9 for nearly all exams. While limitations of this method are described and the analysis is by no means exhaustive, these outcomes suggest that the unique behavior patterns of generative AI chat bots can be identified using Rasch modeling and fit statistics.
C1 [Sorenson, Benjamin; Hanson, Kenneth] Florida State Univ, Dept Chem & Biochem, Tallahassee, FL 32304 USA.
C3 State University System of Florida; Florida State University
RP Hanson, K (corresponding author), Florida State Univ, Dept Chem & Biochem, Tallahassee, FL 32304 USA.
EM hanson@chem.fsu.edu
FU National Science Foundation [DMR-2327754]
FX We would like to thank Florida State University for their support in all
   aspects of the courses delivery and assessment as well as Debbie A.
   Hanson for generating the TOC image with some inspiration from ChatGPT
   4.0. The exam analysis was supported in part by the National Science
   Foundation under Grant No. DMR-2327754.
CR Alasadi EA, 2023, J CHEM EDUC, V100, P2965, DOI 10.1021/acs.jchemed.3c00323
   Atinosukarto I, 2021, IEEE TALE2021: IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND EDUCATION, P838, DOI 10.1109/TALE52509.2021.9678528
   Baker F. B., 1985, BASICS ITEMRESPONSE
   Bond T. G., 2013, APPLYING RASCHMODEL
   Chan JCK, 2023, P NATL ACAD SCI USA, V120, DOI 10.1073/pnas.2302020120
   Chang CY, 2022, EDUC TECHNOL SOC, V25, P15
   Chang CY, 2022, BRIT J EDUC TECHNOL, V53, P171, DOI 10.1111/bjet.13158
   Chen Y, 2023, INFORM SYST FRONT, V25, P161, DOI 10.1007/s10796-022-10291-4
   Cizek G. C., 2016, HDB OFQUANTITATIVE M
   Clark TM, 2023, J CHEM EDUC, V100, P3934, DOI 10.1021/acs.jchemed.3c00500
   Clark TM, 2023, J CHEM EDUC, V100, P1905, DOI 10.1021/acs.jchemed.3c00027
   David Hosmer W., 2000, Applied logistic regression, P160, DOI [DOI 10.1002/0471722146.CH5, 10.1002/0471722146.CH5, 10.1002/0471722146.ch5]
   Duong D, 2024, EUR J HUM GENET, V32, P466, DOI 10.1038/s41431-023-01396-8
   Emenike ME, 2023, J CHEM EDUC, V100, P1413, DOI 10.1021/acs.jchemed.3c00063
   Exintaris B, 2023, J CHEM EDUC, V100, P2972, DOI 10.1021/acs.jchemed.3c00481
   Fergus S, 2023, J CHEM EDUC, V100, P1672, DOI 10.1021/acs.jchemed.3c00087
   Fryer LK, 2019, COMPUT HUM BEHAV, V93, P279, DOI 10.1016/j.chb.2018.12.023
   Gilson Aidan, 2023, JMIR Med Educ, V9, pe45312, DOI 10.2196/45312
   Han S, 2022, COMPUT EDUC, V179, DOI 10.1016/j.compedu.2021.104395
   Herrmann-Abell CF, 2011, CHEM EDUC RES PRACT, V12, P184, DOI 10.1039/C1RP90023D
   Holmes W, 2022, EUR J EDUC, V57, P542, DOI 10.1111/ejed.12533
   Humphry T, 2023, J CHEM EDUC, V100, P1434, DOI 10.1021/acs.jchemed.3c00006
   Hwang GJ, 2023, INTERACT LEARN ENVIR, V31, P4099, DOI 10.1080/10494820.2021.1952615
   Kirtania DK, 2023, J CHEM INF MODEL, V64, P2132, DOI 10.1021/acs.jcim.3c01110
   Korsakova E, 2022, J CHEM EDUC, V99, P1110, DOI 10.1021/acs.jchemed.1c00789
   Kuhail MA, 2023, EDUC INF TECHNOL, V28, P973, DOI 10.1007/s10639-022-11177-3
   Lee DB, 2022, COMPUT EDUC, V191, DOI 10.1016/j.compedu.2022.104646
   Leon AJ, 2023, J CHEM EDUC, V100, P3859, DOI 10.1021/acs.jchemed.3c00288
   Linacre J. M., 2020, WINSTEPS RASCHMEASUR
   Madsen H. S., 1987, UTILIZINGRASCH ANAL
   Mahroof A., 2020, AI BASED CHATBOTTO S, P216
   Man KW, 2021, EDUC PSYCHOL MEAS, V81, P441, DOI 10.1177/0013164420968630
   Meo SA, 2023, HEALTHCARE-BASEL, V11, DOI 10.3390/healthcare11142046
   Okonkwo C. W., 2021, Comput. Educ. Artif. Intell., V2, DOI [10.1016/j.caeai.2021.100033, DOI 10.1016/J.CAEAI.2021.100033]
   openai, OPENAI CHATGPT
   Passby L, 2023, CLIN EXP DERMATOL, V49, P722, DOI 10.1093/ced/llad197
   Rasch G., 1960, STUDIES MATHEMATICAL
   Sorenson B, 2023, J CHEM EDUC, V100, P3454, DOI 10.1021/acs.jchemed.3c00476
   Sorenson B, 2021, J CHEM EDUC, V98, P1529, DOI 10.1021/acs.jchemed.1c00164
   Talanquer V, 2023, J CHEM EDUC, V100, P2821, DOI 10.1021/acs.jchemed.3c00472
   TANNER WP, 1954, PSYCHOL REV, V61, P401, DOI 10.1037/h0058700
   Tyson J, 2023, J CHEM EDUC, V100, P3098, DOI 10.1021/acs.jchemed.3c00361
   Wang YM, 2023, J CHIN MED ASSOC, V86, P653, DOI 10.1097/JCMA.0000000000000942
   Watts FM, 2023, J CHEM EDUC, V100, P3806, DOI 10.1021/acs.jchemed.3c00664
   Yuriev E, 2023, J CHEM EDUC, V100, P3168, DOI 10.1021/acs.jchemed.3c00829
NR 45
TC 1
Z9 1
U1 8
U2 8
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0021-9584
EI 1938-1328
J9 J CHEM EDUC
JI J. Chem. Educ.
PD JUL 9
PY 2024
VL 101
IS 8
BP 3216
EP 3223
DI 10.1021/acs.jchemed.4c00165
EA JUL 2024
PG 8
WC Chemistry, Multidisciplinary; Education, Scientific Disciplines
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Chemistry; Education & Educational Research
GA C6G2X
UT WOS:001279780200001
DA 2024-12-25
ER

PT J
AU Andrei, AG
   Matcu-Zaharia, M
   Mariciuc, DF
AF Andrei, Andreia Gabriela
   Matcu-Zaharia, Mara
   Mariciuc, Dragos Florentin
TI Ready to Grip AI's Potential? Insights from an Exploratory Study on
   Perceptions of Human-AI Collaboration
SO BRAIN-BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE
LA English
DT Article
DE generative AI; Human-AI collaboration; generation Z; Industry 4.0; AI
ID SMES; ONLINE; IMPACT
AB One of the emerging technologies arising with Industry 4.0 is generative artificial intelligence (AI). Despite its disruptive nature and controversies, the effective and ethical use of AI is increasingly preoccupying organizations of all sizes as well as their employees. Focusing on generative AI, this paper presents findings from a qualitative study that provides insights into how Generation Z, the newest workforce, perceives human-AI collaboration. Based on in-depth interviews and a micro-meso-macro approach, the study reveals a dual perspective. Participants recognized the advantages AI brings, such as increased efficiency, productivity, and information availability. However, they were concerned about various risks such as: technology addiction, job loss, data privacy and ethical issues. At the micro level, generative AI was seen as beneficial for providing information and inspiration, but over-reliance could limit people's skills and create dependency. At the meso, organizational level, it could increase efficiency and productivity, but potentially replace jobs. At the macro, societal level, generative AI could support innovation but risks dehumanizing communication and relationships. Data privacy and ethics concerns were expressed at all three levels, indicating that a combination of institutional safeguards and awareness of data privacy and ethics at all levels is required to achieve the full benefits of generative AI. This would help organisations to capitalise on technological advances and support the development of ethical use of AI tools
C1 [Andrei, Andreia Gabriela] Alexandru Ioan Cuza Univ, Fac Econ & Business Adm, Dept Management Mkt & Business Adm, Iasi, Romania.
   [Matcu-Zaharia, Mara; Mariciuc, Dragos Florentin] Alexandru Ioan Cuza Univ, Sch Econ & Business Adm, Iasi, Romania.
C3 Alexandru Ioan Cuza University; Alexandru Ioan Cuza University
RP Andrei, AG (corresponding author), Alexandru Ioan Cuza Univ, Fac Econ & Business Adm, Dept Management Mkt & Business Adm, Iasi, Romania.
EM andrei.andreia@gmail.com; mara.matcu@feaa.uaic.ro;
   mariciuc.dragos@feaa.uaic.ro
RI Andrei, Andreia/I-9722-2017; Matcu-Zaharia, Mara/HCI-9289-2022
OI Matcu, Mara/0000-0002-7656-0886
CR Akhtar P, 2023, ANN OPER RES, V327, P633, DOI 10.1007/s10479-022-05015-5
   Alhayani B, 2022, APPL NANOSCI, DOI 10.1007/s13204-021-02152-4
   Andrei AG, 2021, MANAG MARK, V16, P167, DOI 10.2478/mmcks-2021-0011
   Andrei AG, 2014, STRATEGICA, P593
   Azeem M., 2012, Academic Research International, V2, P262
   Barhate B, 2022, EUR J TRAIN DEV, V46, P139, DOI 10.1108/EJTD-07-2020-0124
   Calabrese A, 2023, TECHNOL FORECAST SOC, V188, DOI 10.1016/j.techfore.2022.122265
   Chan CKY, 2023, INT J EDUC TECHNOL H, V20, DOI 10.1186/s41239-023-00411-8
   Chan L., 2022, Applied Artificial Intelligence in Business: Concepts and Cases (Applied Innovation and Technology Management) (English Edition)
   Cordes C., 2015, International Encyclopedia of the Social Behavioral Sciences, V8, P430
   Cramarenco RE, 2023, OECON COPERNIC, V14, P731, DOI 10.24136/oc.2023.022
   Curado C, 2018, J BUS RES, V89, P206, DOI 10.1016/j.jbusres.2017.12.056
   Dimock M., 2019, PEW RES CTR
   Dopfer K, 2004, J EVOL ECON, V14, P263, DOI 10.1007/s00191-004-0193-0
   Dreyer Christian, 2023, Procedia Computer Science, P155, DOI 10.1016/j.procs.2022.12.211
   Dwivedi YK, 2021, INT J INFORM MANAGE, V57, DOI 10.1016/j.ijinfomgt.2019.08.002
   Einola K, 2023, HUM RESOUR MANAGE-US, V62, P117, DOI 10.1002/hrm.22147
   Febiandini V. V, 2023, IAIC Transactions on Sustainable Digital Innovation (ITSDI), V4, P164, DOI [10.34306/itsdi.v4i2.586, DOI 10.34306/ITSDI.V4I2.586]
   Firat Mehmet, 2023, Journal of Applied Learning and Teaching, V3, P1, DOI DOI 10.37074/JALT.2023.6.1.22
   Franco M, 2015, LONG RANGE PLANN, V48, P168, DOI 10.1016/j.lrp.2013.08.007
   Fui-Hoon Nah F., 2023, Journal of Information Technology Case and Application Research, V25, P277, DOI [10.1080/15228053.2023.2233814, DOI 10.1080/15228053.2023.2233814]
   Gandasari D, 2020, HELIYON, V6, DOI 10.1016/j.heliyon.2020.e05599
   García-Penalvo FJ, 2023, INT J INTERACT MULTI, V8, DOI 10.9781/ijimai.2023.07.006
   Ghauri S, 2021, J CO-OP ORGAN MANAG, V9, DOI 10.1016/j.jcom.2020.100128
   Gilardini Ricci P. A., 2022, Revista De Ciencias Empresariales, V7, P60, DOI [10.37767/2468-9785(2022)005, DOI 10.37767/2468-9785(2022)005]
   Giray L., 2022, International Journal of Sociologies and Anthropologies Science Reviews (IJSASR), V2, P9, DOI DOI 10.14456/JSASR.2022.26
   Hameed S, 2023, FORESIGHT, V25, P287, DOI 10.1108/FS-10-2021-0213
   Hartmann E, 2017, INT J CONFL VIOLENCE, V11, DOI 10.4119/UNIBI/ijcv.623
   Ho MT, 2022, TECHNOL SOC, V70, DOI 10.1016/j.techsoc.2022.102011
   Hoover RS, 2011, IEEE T PROF COMMUN, V54, P68, DOI 10.1109/TPC.2009.2036896
   JARILLO JC, 1988, STRATEGIC MANAGE J, V9, P31, DOI 10.1002/smj.4250090104
   Kanbach DK, 2024, REV MANAG SCI, V18, P1189, DOI 10.1007/s11846-023-00696-z
   Kanwal A., 2023, Gomal University Journal of Research, V39, P250, DOI [10.51380/gujr-39-03-001, DOI 10.51380/GUJR-39-03-001]
   Kraiwanit T., 2023, Advance Knowledge for Executives, V2, P1
   Matcu M, 2022, P 11 ASECU YOUTH INT, P5
   Mijwil M. M., 2023, AL SALAM J ENG TECHN, V2, P116, DOI [DOI 10.55145/AJEST.2023.02.02.015, 10.55145/ajest.2023.02.02.015]
   Moeuf A, 2018, INT J PROD RES, V56, P1118, DOI 10.1080/00207543.2017.1372647
   Mohammad Bushra, 2023, Stud Health Technol Inform, V305, P644, DOI 10.3233/SHTI230580
   Mohseni Sina, 2021, P INT AAAI C WEB SOC, V15, P421, DOI 10.1609/icwsm.v15i1.18072
   Mondal S, 2023, TECHNOLOGIES, V11, DOI 10.3390/technologies11020044
   Mortelmans D., 2019, PALGRAVE HDB METHODS, DOI DOI 10.1007/978-3-030-16065-4_25
   Noy S, 2023, SCIENCE, V381, P187, DOI 10.1126/science.adh2586
   Paduraru T, 2016, ENVIRON ENG MANAG J, V15, P1635, DOI 10.30638/eemj.2016.176
   Pelau C, 2021, COMPUT HUM BEHAV, V122, DOI 10.1016/j.chb.2021.106855
   Reim W, 2020, AI-BASEL, V1, DOI 10.3390/ai1020011
   Schroth H, 2019, CALIF MANAGE REV, V61, P5, DOI 10.1177/0008125619841006
   Siccama CJ, 2008, QUAL RES J, V8, P91, DOI 10.3316/QRJ0802091
   Stock T, 2016, PROC CIRP, V40, P536, DOI 10.1016/j.procir.2016.01.129
   Thorgren S, 2009, J ENG TECHNOL MANAGE, V26, P148, DOI 10.1016/j.jengtecman.2009.06.006
   Treiblmaier H, 2022, INFORMATION, V13, DOI 10.3390/info13060295
   Upadhyay A, 2023, COMPUT IND ENG, V177, DOI 10.1016/j.cie.2023.109072
   Vatamanescu EM, 2022, KYBERNETES, V51, P2193, DOI 10.1108/K-11-2020-0731
   Vatamanescu EM, 2020, J KNOWL MANAG, V24, P1369, DOI 10.1108/JKM-01-2020-0010
   Vatamanescu EM, 2017, INFORM SYST MANAGE, V34, P205, DOI 10.1080/10580530.2017.1329997
   Vatamanescu EM, 2016, PROC EUR CONF KNOWL, P926
   Vinichenko MV, 2021, INT J ADV APPL SCI, V8, P108, DOI 10.21833/ijaas.2021.10.012
   Vitezic V, 2021, SERV IND J, V41, P926, DOI 10.1080/02642069.2021.1974406
   Wankhede VA, 2023, BENCHMARKING, V30, P281, DOI 10.1108/BIJ-08-2021-0505
   Woods M, 2016, SOC SCI COMPUT REV, V34, P597, DOI 10.1177/0894439315596311
   Zhong RY, 2017, ENGINEERING-PRC, V3, P616, DOI 10.1016/J.ENG.2017.05.015
NR 60
TC 0
Z9 0
U1 27
U2 27
PU EDUSOFT PUBLISHING
PI BACAU
PA 9 MAI STR 82, BACAU, 600065, ROMANIA
SN 2068-0473
EI 2067-3957
J9 BRAIN-BROAD RES ARTI
JI BRAIN-Broad Res. Artif. Intellect. Neurosci.
PY 2024
VL 15
IS 2
BP 1
EP 22
DI 10.18662/brain/15.2/560
PG 22
WC Neurosciences
WE Emerging Sources Citation Index (ESCI)
SC Neurosciences & Neurology
GA E4K0E
UT WOS:001302696600001
OA gold
DA 2024-12-25
ER

PT J
AU Shahzad, MF
   Xu, S
   Zahid, H
AF Shahzad, Muhammad Farrukh
   Xu, Shuo
   Zahid, Hira
TI Exploring the impact of generative AI-based technologies on learning
   performance through self-efficacy, fairness & ethics, creativity, and
   trust in higher education
SO EDUCATION AND INFORMATION TECHNOLOGIES
LA English
DT Article; Early Access
DE Generative AI-based technologies; Higher education; LLM models; Learning
   performance; Self-efficacy; Fairness & ethics; Creativity; Trust
ID STUDENTS; GUIDELINES; QUALITY; CHATGPT
AB Artificial Intelligence (AI) technologies have rapidly transformed the education sector and affect student learning performance, particularly in China, a burgeoning educational landscape. The development of generative artificial intelligence (AI) based technologies, such as chatbots and large language models (LLMs) like ChatGPT, has completely changed the educational environment by providing individualized and engaging programs. This study brings forward a model and hypothesis based on social cognitive theory and appropriate research. This investigation centers on how generative AI-based technologies influence students' learning performance in higher education (HE) institutions and the function of self-efficacy, fairness & ethics, creativity, and trust in promoting these connections. Data is collected from 362 students at Chinese universities using purposive sampling. The proposed structural model was evaluated using partial least squares-structural equation modeling (PLS-SEM). The findings reveal that generative AI technologies such as LLM models exemplified by ChatGPT and chatbots significantly influence students' learning performance through self-efficacy, fairness & ethics, and creativity. Furthermore, trust significantly moderates the relationship between fairness & ethics, creativity, and learning performance but negatively moderates the relationship between self-efficacy and learning performance. This study supports the new explanatory potential of social cognitive theory in technological practices. Additionally, this research suggests using generative AI technologies to enhance students' digital learning and boost academic achievement.
C1 [Shahzad, Muhammad Farrukh; Xu, Shuo] Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China.
   [Zahid, Hira] Univ Punjab, Hailey Coll Commerce, Lahore, Pakistan.
C3 Beijing University of Technology; University of Punjab
RP Shahzad, MF (corresponding author), Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China.
EM farrukhshahzad207@gmail.com; xushuo@bjut.edu.cn; hirazahid550@gmail.com
RI Xu, Shuo/KVY-0402-2024; Farrukh Shahzad, Muhammad/JJE-9020-2023
OI Xu, Shuo/0000-0002-8602-1819; Zahid, Hira/0009-0008-9650-2537
FU National Natural Science Foundation of China
FX All authors contributed equally and acknowledged this work.
CR Abuselidze G., 2021, Journal of Physics: Conference Series, V1840, DOI 10.1088/1742-6596/1840/1/012040
   Ajzen I, 2002, J APPL SOC PSYCHOL, V32, P665, DOI 10.1111/j.1559-1816.2002.tb00236.x
   Almulla MA, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15053978
   Artificial Intelligence in Education Market, 2023, Artificial Intelligence in Education Market Size, Share, Growth and Report 2023
   Ashok M, 2022, INT J INFORM MANAGE, V62, DOI 10.1016/j.ijinfomgt.2021.102433
   BAGOZZI RP, 1989, SOC PSYCHOL QUART, V52, P266, DOI 10.2307/2786991
   Baidoo-Anu D., 2023, J. AI, V7, P52, DOI [10.61969/jai.1337500, DOI 10.61969/JAI.1337500, 10.2139/ssrn.4337484]
   Barton H, 2023, Comput Educ Artif Intell, V6, DOI [DOI 10.1016/J.CAEAI.2023.100198, 10.1016/j.caeai.2023.100198]
   Bernabei M, 2023, Comput Educ: Artif Intell, V5, DOI [DOI 10.1016/J.CAEAI.2023.100172, 10.1016/j.caeai.2023.100172]
   Bilquise G, 2024, EDUC INF TECHNOL, V29, P6357, DOI 10.1007/s10639-023-12076-x
   Brem A, 2023, IEEE T ENG MANAGE, V70, P770, DOI 10.1109/TEM.2021.3109983
   Cai YH, 2021, THINK SKILLS CREAT, V41, DOI 10.1016/j.tsc.2021.100854
   Celik V, 2013, COMPUT EDUC, V60, P148, DOI 10.1016/j.compedu.2012.06.008
   Chang W, 2024, J RETAIL CONSUM SERV, V78, DOI 10.1016/j.jretconser.2024.103743
   Charlwood A, 2022, HUM RESOUR MANAG J, V32, P729, DOI 10.1111/1748-8583.12433
   Chen IS, 2017, COMPUT HUM BEHAV, V72, P362, DOI 10.1016/j.chb.2017.02.059
   Chen L., 2004, EUR MANAG J, V22, P74, DOI [10.1016/j.emj.2003.11.014, DOI 10.1016/J.EMJ.2003.11.014]
   Chen X., 2020, Computers and Education: Artificial Intelligence, V1, P100002, DOI [10.1016/j.caeai.2020.100002 10.1016/j.caeai.2020.100002, DOI 10.1016/J.CAEAI.2020.100002]
   Cheng XS, 2022, INFORM PROCESS MANAG, V59, DOI 10.1016/j.ipm.2022.102940
   College of Economics and Management Beijing University of Technology, About us
   COMPEAU DR, 1995, MIS QUART, V19, P189, DOI 10.2307/249688
   Correia AB, 2024, COGENT BUS MANAG, V11, DOI 10.1080/23311975.2024.2374625
   Dwivedi YK, 2023, INT J INFORM MANAGE, V71, DOI 10.1016/j.ijinfomgt.2023.102642
   Ellen P.S., 1991, J ACAD MARKET SCI, V19, P297, DOI [10.1007/BF02726504, DOI 10.1007/BF02726504, 10.1007/bf02726504]
   Ferrara S, 2022, J EDUC MEAS, V59, P288, DOI 10.1111/jedm.12333
   Floridi L, 2020, MIND MACH, V30, P681, DOI 10.1007/s11023-020-09548-1
   FORNELL C, 1982, J MARKETING RES, V19, P440, DOI 10.2307/3151718
   Hailey College of Commerce University of the Punjab Lahore Pakistan, ABOUT US
   Hair JF, 2014, PRIMER PARTIAL LEAST
   Hair JF, 2020, J BUS RES, V109, P101, DOI 10.1016/j.jbusres.2019.11.069
   Haluza D, 2023, SYSTEMS-BASEL, V11, DOI 10.3390/systems11030120
   Hao Q, 2022, FRONT PSYCHOL, V13, DOI 10.3389/fpsyg.2022.899142
   Hartati R., 2024, TALENTA Conference Series, V7, P73, DOI [10.32734/lwsa.v7i2.2055, DOI 10.32734/LWSA.V7I2.2055]
   Hill JR, 2009, AM J DISTANCE EDUC, V23, P88, DOI 10.1080/08923640902857713
   Hussain K, 2024, DIGIT BUS, V4, DOI 10.1016/j.digbus.2023.100071
   Jeon J, 2023, EDUC INF TECHNOL, V28, P15873, DOI 10.1007/s10639-023-11834-1
   Jo H, 2023, TELEMAT INFORM, V85, DOI 10.1016/j.tele.2023.102067
   Kim J, 2013, INT J IND ERGONOM, V43, P450, DOI 10.1016/j.ergon.2013.03.001
   Kim JK, 2023, J PEDIATR UROL, V19, P598, DOI 10.1016/j.jpurol.2023.05.018
   Kurniawan I. A., 2022, Journal of Applied Management (JAM), V20, P117
   Lee YF, 2022, ETR&D-EDUC TECH RES, V70, P1843, DOI 10.1007/s11423-022-10142-8
   Lekwa AJ, 2019, SCHOOL PSYCHOL, V34, P109, DOI 10.1037/spq0000268
   Lim WM, 2023, INT J MANAG EDUC-OXF, V21, DOI 10.1016/j.ijme.2023.100790
   Maheshwari G, 2024, EDUC INF TECHNOL, V29, P12167, DOI 10.1007/s10639-023-12333-z
   McGill TJ, 2009, COMPUT EDUC, V52, P496, DOI 10.1016/j.compedu.2008.10.002
   Meinel M, 2019, J CREATIVE BEHAV, V53, P546, DOI 10.1002/jocb.234
   Memarian B., 2023, Computers and Education: Artificial Intelligence., DOI DOI 10.1016/J.CAEAI.2023.100152
   Mensah C, 2023, COGENT PSYCHOL, V10, DOI 10.1080/23311908.2023.2167503
   Michel-Villarreal R, 2023, EDUC SCI, V13, DOI 10.3390/educsci13090856
   Mikalef P, 2021, INFORM MANAGE-AMSTER, V58, DOI 10.1016/j.im.2021.103434
   Munir Y, 2016, SOC INDIC RES, V126, P1157, DOI 10.1007/s11205-015-0940-7
   Ouyang F, 2022, EDUC INF TECHNOL, V27, P7893, DOI 10.1007/s10639-022-10925-9
   Oviedo-Trespalacios O, 2023, SAFETY SCI, V167, DOI 10.1016/j.ssci.2023.106244
   Paul J, 2017, COMPUT EDUC, V113, P339, DOI 10.1016/j.compedu.2017.05.020
   Pereira T., 2021, Telematics and Informatics Reports, V1-4, P100003, DOI [https://doi.org/10.1016/j.teler.2022.100003, DOI 10.1016/J.TELER.2022.100003]
   Pham HC, 2024, J RETAIL CONSUM SERV, V78, DOI 10.1016/j.jretconser.2024.103758
   Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9010.88.5.879
   Qadir Junaid, 2023, 2023 IEEE Global Engineering Education Conference (EDUCON), P1, DOI 10.1109/EDUCON54358.2023.10125121
   Rahman MS, 2023, AUSTRALAS J EDUC TEC, V39, P51, DOI 10.14742/ajet.8956
   Ranoliya BR, 2017, 2017 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), P1525, DOI 10.1109/ICACCI.2017.8126057
   ROSCOE AM, 1975, J MARKETING, V39, P20, DOI 10.2307/1250111
   Rouis S, 2011, ELECTRON J RES EDUC, V9, P961
   Salloum SA, 2019, IEEE ACCESS, V7, P128445, DOI 10.1109/ACCESS.2019.2939467
   Sandu N, 2019, INT CONF INFO TECH, DOI 10.1109/ithet46829.2019.8937382
   Sellami AL, 2023, SUSTAINABILITY-BASEL, V15, DOI 10.3390/su15097504
   Shahzad MF, 2024, INT J EDUC TECHNOL H, V21, DOI 10.1186/s41239-024-00478-x
   Shahzad MF, 2024, J RETAIL CONSUM SERV, V79, DOI 10.1016/j.jretconser.2024.103867
   Shahzad MF, 2024, HELIYON, V10, DOI 10.1016/j.heliyon.2024.e29523
   Shahzad MF, 2024, HUM SOC SCI COMMUN, V11, DOI 10.1057/s41599-024-02777-0
   Shahzad MF, 2023, SCI REP-UK, V13, DOI 10.1038/s41598-023-50078-4
   Sharma G., 2017, Int. J. Appl. Res, V3, P749
   Singh S. V., 2022, Journal of Higher Education Theory and Practice, V22, P135
   Sohail SS, 2023, J KING SAUD UNIV-COM, V35, DOI 10.1016/j.jksuci.2023.101675
   Stajkovic AD, 1998, ORGAN DYN, V26, P62, DOI 10.1016/S0090-2616(98)90006-7
   Strzelecki A, 2024, INNOV HIGH EDUC, V49, P223, DOI 10.1007/s10755-023-09686-1
   Tarhini A, 2017, J INT EDUC BUS, V10, P164, DOI 10.1108/JIEB-09-2016-0032
   Tsz Kit Ng Davy, 2021, Proceedings of the Association for Information Science and Technology, V58, P504, DOI 10.1002/pra2.487
   Uzir MUH, 2021, TECHNOL SOC, V67, DOI 10.1016/j.techsoc.2021.101780
   Wang J, 2019, Visualization Analysis of Artificial Intelligence Technology in Higher Education Based on SSCI and SCI Journals from 2009 to 2019, P20, DOI [10.3991/ijet.v16i08.18447Ji, DOI 10.3991/IJET.V16I08.18447JI]
   Wang SF, 2023, EDUC INF TECHNOL, V28, P4919, DOI 10.1007/s10639-022-11338-4
   Wang SF, 2021, SUSTAINABILITY-BASEL, V13, DOI 10.3390/su13179923
   Wang YY, 2024, EDUC INF TECHNOL, V29, P4785, DOI 10.1007/s10639-023-12015-w
   WEIZENBAUM J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/357980.357991
   Widiana P., 2021, Jurnal Ilmu Manajemen, V9, P1113, DOI [10.26740/jim.v9n3.p1113-1123, DOI 10.26740/JIM.V9N3.P1113-1123]
   Woschank M, 2020, SUSTAINABILITY-BASEL, V12, DOI 10.3390/su12093760
   Xu S, 2024, SCI REP-UK, V14, DOI 10.1038/s41598-024-66689-4
   Yilmaz R., 2023, COMPUTERS ED ARTIFIC, DOI [10.1016/j.caeai.2023.100147, DOI 10.1016/J.CAEAI.2023.100147]
   Zhang CF, 2022, IEEE GLOB ENG EDUC C, P998, DOI 10.1109/EDUCON52537.2022.9766384
   Zhang H, 2023, INT J ARTIF INTELL E, V33, P290, DOI 10.1007/s40593-022-00293-3
   Zhang K., 2021, COMPUTERS ED ARTIFIC, V2, P100025, DOI [DOI 10.1016/J.CAEAI.2021.100025, https://doi.org/10.1016/j.caeai.2021.100025, 10.1016/j.caeai.2021.100025]
   Zhang XY, 2024, EDUC INF TECHNOL, DOI 10.1007/s10639-023-12407-y
NR 91
TC 7
Z9 7
U1 195
U2 195
PU SPRINGER
PI NEW YORK
PA ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES
SN 1360-2357
EI 1573-7608
J9 EDUC INF TECHNOL
JI Educ. Inf. Technol.
PD 2024 AUG 24
PY 2024
DI 10.1007/s10639-024-12949-9
EA AUG 2024
PG 26
WC Education & Educational Research
WE Social Science Citation Index (SSCI)
SC Education & Educational Research
GA D6X4N
UT WOS:001297592200001
DA 2024-12-25
ER

PT J
AU Formanek, M
AF Formanek, Matus
TI Exploring the potential of large language models and generative
   artificial intelligence (GPT): Applications in Library and Information
   Science
SO JOURNAL OF LIBRARIANSHIP AND INFORMATION SCIENCE
LA English
DT Article; Early Access
DE ChatGPT; generative artificial intelligence; large language models;
   Library and Information Science; use cases
AB The presented study offers a systematic overview of the potential application of large language models (LLMs) and generative artificial intelligence tools, notably the GPT model and the ChatGPT interface, within the realm of library and information science (LIS). The paper supplements and extends the outcomes of a comprehensive information survey on the subject matter with the author's own experiences and examples showcasing possible applications, demonstrated through illustrative instances. This study does not involve testing available LLMs or selecting the most suitable tool; instead, it targets information professionals, specialists, librarians, and scientists, aiming to inspire them in various ways. Within this paper, we explore both well-known and less recognized use cases of generative AI tools, which may prove relevant not only for the target group of information specialists but also for other users. Our analysis demonstrates that apart from merely summarizing or expanding existing textual content, these AI tools hold the potential for performing non-standard yet sophisticated tasks with electronic information resources. They can facilitate interactive engagement with these resources, aid in the extraction and composition of descriptive metadata, indexing, and even possible classification. Nevertheless, it is essential to acknowledge the numerous limitations of current LLMs, which we acknowledge in this study.
C1 [Formanek, Matus] Univ Zilina, Zilina, Slovakia.
   [Formanek, Matus] Univ Zilina, Fac Humanities, Univ 8215-1, Zilina 01026, Slovakia.
C3 University of Zilina; University of Zilina
RP Formanek, M (corresponding author), Univ Zilina, Fac Humanities, Univ 8215-1, Zilina 01026, Slovakia.
EM matus.formanek@fhv.uniza.sk
RI Formanek, Matus/W-1127-2019
OI Formanek, Matus/0000-0002-0611-1794
CR Abid A, 2021, AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, P298, DOI 10.1145/3461702.3462624
   Bender EM, 2021, PROCEEDINGS OF THE 2021 ACM CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY, FACCT 2021, P610, DOI 10.1145/3442188.3445922
   Chan A., 2022, ETHICS, V3, P53, DOI [DOI 10.1007/S43681-022-00148-6, 10.1007/s43681-022-00148-6]
   chatPDF, FAQ
   Chen Y., 2023, A ChatGPT-based model for user book rating prediction
   Cordell R., 2020, The 2020 State of AI and Machine Learning Report
   Coursera, 2023, What is prompt engineering? Definition and examples
   DAIR.AI, 2023, Retrieval augmented generation (RAG)
   DAIR.AI, 2023, Prompt engineering guide
   Dehouche N., 2021, Ethic in Science and Environmental Politics, V21, P17, DOI [DOI 10.3354/ESEP00195, 10.3354/esep00195]
   Dinneen JD., 2021, ASIST '21: Proceedings of the 84th Annual Meeting of the Association for Information Science Technology, V58, P117
   Duarte Fabio, 2023, Number of ChatGPT Users
   Garoufallou E, 2021, COLL RES LIBR, V82, P410
   Grbin L, 2022, J AUST LIB INF ASSOC, V71, P275, DOI 10.1080/24750158.2022.2087954
   Hopkins Paul, 2018, CHATBOTS DIGITAL ASS
   Hu L., 2023, Generative AI and Future
   Kaushal V, 2022, J AUST LIB INF ASSOC, V71, P215, DOI 10.1080/24750158.2022.2106403
   Krawczyk J., 2023, Bard's latest update: more features, languages and countries
   Li ZH, 2023, Arxiv, DOI arXiv:2304.14347
   Luca E, 2022, J AUST LIB INF ASSOC, V71, P185, DOI 10.1080/24750158.2022.2104814
   McCaffrey C., 2021, Planning and Implementing an Automated Storage and Retrieval System at the University of Limerick
   Mckie I, 2022, J AUST LIB INF ASSOC, V71, P233, DOI 10.1080/24750158.2022.2104738
   Mckie IAS, 2019, J AUST LIB INF ASSOC, V68, P268, DOI 10.1080/24750158.2019.1611694
   Meta AI, 2023, Introducing llama: A foundational, 65-billion-parameter large language model
   Open AI, 2023, What are tokens and how to count them?
   Open AI, 2023, GPT-4
   Padilla Thomas., 2019, Responsible Operations: Data Science, Machine Learning, and AI in Libraries, DOI DOI 10.25333/XK7Z-9G97
   Panda Subhajit, 2023, Library Hi Tech News, P22, DOI 10.1108/LHTN-02-2023-0032
   Panda S., 2023, IP Indian Journal of Library Science and Information Technology, V8, P20
   Pavlik J. V., 2023, JOURNALISM MASS COMM, V78, P84, DOI [10.1177/10776958221149577, DOI 10.1177/10776958221149577]
   Reitz JM., 2013, Dictionary for Library and Information Science
   Sajan BP., 2023, The Lancet. Digital health, V5
   Salvagno M, 2023, CRIT CARE, V27, DOI 10.1186/s13054-023-04380-2
   Samarth V., 2023, What is Prompt Engineering, and What is its Significance in Today's World?
   Saracevic T., 2010, Information Science. Encyclopedia of Library and Information Sciences, V3rd edn., P570
NR 35
TC 2
Z9 2
U1 117
U2 195
PU SAGE PUBLICATIONS LTD
PI LONDON
PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND
SN 0961-0006
EI 1741-6477
J9 J LIBR INF SCI
JI J. Libr. Inf. Sci.
PD 2024 MAR 24
PY 2024
DI 10.1177/09610006241241066
EA MAR 2024
PG 23
WC Information Science & Library Science
WE Social Science Citation Index (SSCI)
SC Information Science & Library Science
GA LX6W0
UT WOS:001190159800001
DA 2024-12-25
ER

PT J
AU Crawford, J
   Vallis, C
   Yang, JH
   Fitzgerald, R
   O'Dea, C
AF Crawford, Joseph
   Vallis, Carmen
   Yang, Jianhua
   Fitzgerald, Rachel
   O'Dea, Christine
TI Editorial: Artifiicial Intelligence is Awesome, but Good Teaching Should
   Always Come First.
SO JOURNAL OF UNIVERSITY TEACHING AND LEARNING PRACTICE
LA English
DT Article
DE ChatGPT; Bard; Andragogy; AIED; large language model; higher education
ID HIGHER-EDUCATION; SOCIAL MEDIA
AB The explosion of generative artificial intelligence into the mainstream of society some twelve months ago has seriously challenged learning and teaching practice. Since then, AI companies such as OpenAI are constantly improving their language models and releasing new features to make them more capable and useful. So, given there have been many disruptors in the past and emerging disruptions in the present, what can we learn in this situation, where Generative AI stands poised to challenge the purpose and relevance of assessment models? From our examples, disruptive technologies only have a major impact when they positively transform practice and are informed by pedagogic models and learning theory. GenAI as a disruptor is only likely to have this positive impact when it informs quality learning and teaching practice. We should be focused on the opportunities that GenAI now presents to higher education. It is argued here and elsewhere that the relative weakness of GenAI is that it creates poor quality output, delivering uninformed, incorrect, biased and bland responses. In itself, this offers opportunities for 'teachable moments' (Newell et al, 2023) and gives us room to support students with their capabilities in an AI informed world. Historically, these opportunities enable higher education to grow and progress. What we have learned so far would appears to be that for research to contribute to the literature, they needed to be informed by it. Likewise, need to ensure that pedagogy, andragogy, and heutagogy come first. We also need to remember that people processes happen, artificial intelligence happens around them, and that artificial intelligence comes after human intelligence. Practitioner Notes1. For AI research to contribute to the literature, it needs to be informed by it.2. Scholars need to ensure that pedagogy, andragogy, and heutagogy come before artificial intelligence.3. People processes happen, artificial intelligence happens around them, and that artificial intelligence comes after human intelligence. 4. Artificial Intelligence comes after human intelligence
C1 [Crawford, Joseph] Univ Tasmania, Hobart, Australia.
   [Vallis, Carmen] Univ Sydney, Sydney, Australia.
   [Yang, Jianhua] Univ Warwick, Warwick, England.
   [Fitzgerald, Rachel] Univ Queensland, Brisbane, Australia.
   [O'Dea, Christine] Univ Huddersfield, Huddersfield, England.
C3 University of Tasmania; University of Sydney; University of Warwick;
   University of Queensland; University of Huddersfield
RP Crawford, J (corresponding author), Univ Tasmania, Hobart, Australia.
EM joseph.crawford@utas.edu.au; carmen.vallis@sydney.edu.au;
   jianhua.yang@warwick.ac.uk; rachel.fitzgerald@uq.edu.au;
   x.odea@hud.ac.uk
RI Fitzgerald, Rachel/AAW-9567-2021
CR Abeysekera L, 2015, HIGH EDUC RES DEV, V34, P1, DOI 10.1080/07294360.2014.934336
   Baker J., 2000, CIC INFORM TECHNOLOG
   Baker J., 2016, P 1 ANN HIGH ED FLIP
   Bearman M, 2023, BRIT J EDUC TECHNOL, DOI 10.1111/bjet.13337
   Belland BR, 2013, EDUC PSYCHOL-US, V48, P243, DOI 10.1080/00461520.2013.838920
   Belot H., 2023, The GuardianNovember 3
   Blaschke LM, 2012, INT REV RES OPEN DIS, V13, P56, DOI 10.19173/irrodl.v13i1.1076
   Boussen S, 2023, BRIT J ANAESTH, V131, pE120, DOI 10.1016/j.bja.2023.06.065
   Carvalho L., 2022, Comput. Educ. Artif. Intell, V3, P100053, DOI [10.1016/j.caeai.2022.100053, DOI 10.1016/J.CAEAI.2022.100053]
   Chen JB, 2023, NEURAL NETWORKS, V164, P521, DOI 10.1016/j.neunet.2023.04.045
   Choi-Lundberg DL, 2023, AUSTRALAS J EDUC TEC, V39, P133, DOI 10.14742/ajet.7615
   Chugh R, 2018, EDUC INF TECHNOL, V23, P605, DOI 10.1007/s10639-017-9621-2
   Cowling MA, 2022, J UNIV TEACH LEARN P, V19
   Crawford J., 2023, J UNIV TEACH LEARN P, V20, P1, DOI [10.33327/AJEE-18-6.3-n000319, DOI 10.33327/AJEE-18-6.3-N000319]
   Crawford J, 2023, INT EDUC J, V22, P7
   Crompton H, 2018, COMPUT EDUC, V123, P53, DOI 10.1016/j.compedu.2018.04.007
   Davis CHF, 2015, COMMUNITY COLL J RES, V39, P409, DOI 10.1080/10668926.2013.828665
   Deslauriers L, 2019, P NATL ACAD SCI USA, V116, P19251, DOI 10.1073/pnas.1821936116
   Dumbu E., 2012, European Journal of Business and Management, V4, P79
   Eager B, 2023, J UNIV TEACH LEARN P, V20
   Friesen N, 2012, J COMPUT ASSIST LEAR, V28, P183, DOI 10.1111/j.1365-2729.2011.00426.x
   GEMAN S, 1992, NEURAL COMPUT, V4, P1, DOI 10.1162/neco.1992.4.1.1
   Ifenthaler D, 2020, ETR&D-EDUC TECH RES, V68, P1961, DOI 10.1007/s11423-020-09788-z
   Jones H., 2023, Technology-Enhanced Learning and the Virtual University. University Development and Administration, DOI [10.1007/978-981-19-9438-8_11-1, DOI 10.1007/978-981-19-9438-8_11-1]
   Khosravi H., 2022, Computers and Education: Artificial Intelligence, V3, P100074, DOI DOI 10.1016/J.CAEAI.2022.100074
   Knight S, 2020, J WRIT RES, V12, P141, DOI 10.17239/jowr-2020.12.01.06
   Knowles M., 2013, Boundaries of adult learning, P82
   Koh J., 2022, Collaborating with AIed for better studentteacher reconnection, DOI [10.14742/apubs.2022.126, DOI 10.14742/APUBS.2022.126]
   Kolb D. A., 1984, EXPERIENTIAL LEARNIN, DOI DOI 10.1016/B978-0-7506-7223-8.50017-4
   Lodge JM, 2023, AUSTRALAS J EDUC TEC, V39, P18, DOI 10.14742/ajet.8695
   Neumann M, 2021, 2021 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE 2021), DOI 10.1109/FIE49875.2021.9637344
   Newell S., Using generative AI effectively in higher education: Sustainable and ethical artificial intelligence for the common good
   Perkins M, 2023, J UNIV TEACH LEARN P, V20, DOI 10.53761/1.20.02.07
   Purvis A., 2024, Journal of University Teaching and Learning Practice
   Reimann P., 2016, Learning: Research and Practice, V2, P130, DOI [DOI 10.1080/23735082.2016.1210198, 10.1080/23735082.2016.1210198]
   Riley M, 1999, SPEECH COMMUN, V29, P209, DOI 10.1016/S0167-6393(99)00037-0
   Rudolph J., 2023, J. Appl. Learn. Teach, V6, P1, DOI [DOI 10.37074/JALT.2023.6.1.9, 10.37074/jalt.2023.6.1.9]
   Sadaf A, 2012, COMPUT EDUC, V59, P937, DOI 10.1016/j.compedu.2012.04.001
   Saettler Paul., 2004, The Evolution of American Educational Technology
   Safi M, 2019, AIRCR ENG AEROSP TEC, V91, P1187, DOI 10.1108/AEAT-09-2018-0241
   Sankey M.D., 2023, Technology-Enhanced Learning and the Virtual University. University Development and Administration, DOI [10.1007/978-981-99-4170-4_31, DOI 10.1007/978-981-99-4170-4_31]
   Smolansky A, 2023, PROCEEDINGS OF THE TENTH ACM CONFERENCE ON LEARNING @ SCALE, L@S 2023, P378, DOI 10.1145/3573051.3596191
   Tess PA, 2013, COMPUT HUM BEHAV, V29, pA60, DOI 10.1016/j.chb.2012.12.032
   Texas Standard, 2020, Texas Standard
   Thorp HH, 2023, SCIENCE, V379, P313, DOI 10.1126/science.adg7879
   Vallis C., 2023, Postdigital Science and Education, DOI [10.1007/s42438-023-00407-7, DOI 10.1007/S42438-023-00407-7]
   Yang S.J., 2021, Comput. Educ.: Artif. Intell., V2, DOI [DOI 10.1016/J.CAEAI.2021.100008, 10.1016/j.caeai.2021.100008]
NR 47
TC 4
Z9 4
U1 11
U2 42
PU Open Access Publishing Assoc
PI Launceston
PA 28a Brisbane St, Launceston, Tasmania, AUSTRALIA
SN 1449-9789
J9 J UNIV TEACH LEARN P
JI J. Univ. Teach. Learn. Pract.
PY 2023
VL 20
IS 7
AR 01
DI 10.53761/1.20.7.01
PG 14
WC Education & Educational Research
WE Emerging Sources Citation Index (ESCI)
SC Education & Educational Research
GA Y2RE3
UT WOS:001103781300009
OA Green Submitted, gold
DA 2024-12-25
ER

EF