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.

翻译：本文提出了一种将大语言模型（LLM）整合到协作学习平台中的框架，旨在提升学生的参与度、批判性思维和包容性。该框架采用先进的大语言模型作为动态协调者，以促进实时讨论并适应学习者不断变化的需求，从而确保多样化和包容性的教育体验。其核心创新包括：稳健的反馈机制——该机制能够优化人工智能协调、促进反思性学习并平衡用户间的参与度。系统采用模块化架构，前端使用ReactJS，后端操作基于Flask，并辅以高效的问题检索功能，通过动态调整提示词和讨论流程来支持个性化且富有吸引力的互动。测试表明，该框架显著提升了学生的协作能力，促进了更深入的理解，并能有效扩展到不同学科和用户群体。通过解决现有系统中静态协调和个性化方面的局限，本研究为下一代人工智能驱动的教育工具奠定了坚实基础，推动了公平且富有成效的学习成果。