Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Dynamic Framework for Collaborative Learning: Leveraging Advanced LLM with Adaptive Feedback Mechanisms

Hassam Tahir, Faizan Faisal, Fady Alnajjar, Muhammad Imran Taj, Lucia Gordon, Aila Khan, Michael Lwin, Omar Mubin

TL;DR

The paper tackles the challenge of scalable, inclusive, and adaptive AI-driven moderation for collaborative learning. It introduces a GPT-4o-based framework with retrieval-augmented generation that dynamically adjusts prompts, questions, and feedback to evolving group dynamics. Key contributions include a multi-level feedback architecture, dataset-agnostic design, real-time facilitation, and a modular implementation with Flask, React, and LangChain. Experimental results from simulated groups demonstrate responsive moderation, balanced participation, and personalized feedback, underscoring potential for broad adoption across subjects and learner profiles. The work advances equitable, scalable AI-enabled learning tools with practical implications for modern classrooms and online platforms.

Abstract

This paper presents a framework for integrating LLM into collaborative learning platforms to enhance student engagement, critical thinking, and inclusivity. The framework employs advanced LLMs as dynamic moderators to facilitate real-time discussions and adapt to learners' evolving needs, ensuring diverse and inclusive educational experiences. Key innovations include robust feedback mechanisms that refine AI moderation, promote reflective learning, and balance participation among users. The system's modular architecture featuring ReactJS for the frontend, Flask for backend operations, and efficient question retrieval supports personalized and engaging interactions through dynamic adjustments to prompts and discussion flows. Testing demonstrates that the framework significantly improves student collaboration, fosters deeper comprehension, and scales effectively across various subjects and user groups. By addressing limitations in static moderation and personalization in existing systems, this work establishes a strong foundation for next-generation AI-driven educational tools, advancing equitable and impactful learning outcomes.

Dynamic Framework for Collaborative Learning: Leveraging Advanced LLM with Adaptive Feedback Mechanisms

TL;DR

The paper tackles the challenge of scalable, inclusive, and adaptive AI-driven moderation for collaborative learning. It introduces a GPT-4o-based framework with retrieval-augmented generation that dynamically adjusts prompts, questions, and feedback to evolving group dynamics. Key contributions include a multi-level feedback architecture, dataset-agnostic design, real-time facilitation, and a modular implementation with Flask, React, and LangChain. Experimental results from simulated groups demonstrate responsive moderation, balanced participation, and personalized feedback, underscoring potential for broad adoption across subjects and learner profiles. The work advances equitable, scalable AI-enabled learning tools with practical implications for modern classrooms and online platforms.

Abstract

This paper presents a framework for integrating LLM into collaborative learning platforms to enhance student engagement, critical thinking, and inclusivity. The framework employs advanced LLMs as dynamic moderators to facilitate real-time discussions and adapt to learners' evolving needs, ensuring diverse and inclusive educational experiences. Key innovations include robust feedback mechanisms that refine AI moderation, promote reflective learning, and balance participation among users. The system's modular architecture featuring ReactJS for the frontend, Flask for backend operations, and efficient question retrieval supports personalized and engaging interactions through dynamic adjustments to prompts and discussion flows. Testing demonstrates that the framework significantly improves student collaboration, fosters deeper comprehension, and scales effectively across various subjects and user groups. By addressing limitations in static moderation and personalization in existing systems, this work establishes a strong foundation for next-generation AI-driven educational tools, advancing equitable and impactful learning outcomes.
Paper Structure (21 sections, 5 figures, 3 tables)

This paper contains 21 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Dataset Investigation & Selection
  4. Proposed Framework
  5. Implementation
  6. Technical Implementation
  7. Enhancing User Engagement through Adaptive AI Facilitation
  8. Experimentation
  9. Experimentation Process
  10. Experimental Setup
  11. Persona Design and Implementation
  12. Procedure
  13. Results
  14. Latency and System Performance
  15. Conversation Dynamics
  16. ...and 6 more sections

Figures (5)

  • Figure 1: PeerGpt architecture Liu_2024
  • Figure 2: User Journey: Sequential Interaction Flow with the System
  • Figure 3: System Architecture Diagram Illustrating the Integration of Frontend, Backend, and AI Components.
  • Figure 4: LLM-based Feedback Generation.
  • Figure 5: Detailed UML Sequence Diagram showing the complete interaction flow between system components, users, and AI moderator.