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

2025-04-21

International Conference on Activity and Behavior Computing

AI Summary

This study addresses the limitations of existing collaborative learning platforms, which often suffer from static mediation, insufficient personalization, and uneven participation, thereby constraining learning outcomes. To overcome these challenges, we propose a dynamic collaborative learning framework that innovatively employs a large language model as a real-time moderator, integrated with an enhanced adaptive feedback mechanism. This system dynamically adjusts discussion flows and prompts based on learners’ evolving states to foster reflective engagement and equitable participation. Built on a modular architecture—featuring a ReactJS frontend, Flask backend, and efficient query retrieval—the platform supports cross-disciplinary scalability. Empirical evaluations demonstrate that our approach significantly enhances the quality of collaboration, depth of understanding, and educational inclusivity, exhibiting strong adaptability and pedagogical effectiveness across diverse disciplines and heterogeneous learner populations.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

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.

Problem

• collaborative learning
• LLM integration
• adaptive feedback
• inclusive education
• AI moderation

Innovation

• Large Language Models
• Adaptive Feedback
• Collaborative Learning
• Dynamic Moderation
• Modular Architecture