---
title: "Bidirectional Q&A Chatbots"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Bidirectional Q&A Chatbots

We will be developing chatbots with bidirectional Q&A interface where both the AI and user can ask questions, similar to how human conversations naturally flow. AI can both ask and answer questions, making interactions feel more like a dialogue. This approach is often referred to as **bi-directional context** or **multi-turn conversation**. Both the AI and the user can ask and answer questions seamlessly. These bidirectional flow could greatly enhance applications like customer service, tutoring, or creative brainstorming sessions.

Our interfaces will allow both parties to ask questions, such as for educational tutors, interactive fiction, or decision-making systems. These often use rule-based approaches combined with generative models to allow the AI to decide when to ask questions or seek additional information.

We will be designing chatbots that can simulate human-like conversations by employing techniques like intent recognition, context maintenance, and dialogue management. These chatbots are designed to provide a more engaging and interactive user experience.

**Agentic AI Frameworks** 

These frameworks, like LangGraph, AutoGen, and AI agent orchestration systems, are moving toward interactions where the AI is capable of proactively asking clarifying or follow-up questions. This enhances collaboration with the user by ensuring that the AI has sufficient context to provide better responses. LangChain’s tools for building AI agents are already designed to iterate based on user input, making it feasible for bidirectional Q&A exchanges.

[Exploring Bi-Directional Context for Improved Chatbot Response Generation Using Deep Reinforcement Learning](https://www.mdpi.com/2076-3417/13/8/5041)

[Intelligent Conversational Chatbot: Design Approaches and Techniques](https://link.springer.com/chapter/10.1007/978-3-031-71481-8_2)

**Initiating a conversation**

AI might ask the first question to initiate a conversation, for example to build a user profile.

**Counter Question**

A user might ask, "What's your favorite movie?" and AI could respond with a question, "Have you seen 'Blade Runner 2049'?"

**Educational Tools:**

**Quizzing:** An AI tutor might ask a question to start a quiz or review session.

**Concept explanation:** If the user is struggling with a topic, the AI might ask, "Can you explain what you understand so far?" to gauge their knowledge level.

**E-Learning:** Personalized tutors ask and answer questions to guide learners through courses, adapting to their knowledge level and learning style.  

In fields like education, chatbots are being designed to engage users in meaningful dialogues, which includes asking probing questions to facilitate learning.


## Best Practices Bidirectional Q&A Chatbots

- Clear visual distinction between AI and user
- Explicit question marking
- Response timing and pacing
- Context preservation
- Natural conversation flow

## Message Types

- User messages (right-aligned, blue background)
- AI messages (left-aligned, white background)
- Timestamps and sender icons for each message

## Bidirectional Q&A Flow

- AI can proactively ask questions (randomly simulated in this demo)
- Tracks when waiting for user response to AI questions
- Different input placeholder text based on context


## Question Management

- AI actively asks questions to gather context
- Visual indicators for questions vs statements
- Tracking of unanswered questions
- Follow-up questions based on context
- Question based on state of user

## Conversation Flow

- Natural back-and-forth dialogue
- AI asks clarifying questions
- Context-aware responses
- Follow-up suggestions
