用动态AI导师提升协作学习，让讨论更深入、参与更公平。
Dynamic Framework for Collaborative Learning: Leveraging Advanced LLM with Adaptive Feedback Mechanisms
- 用先进大模型实时调节讨论，自适应学生需求
- 实测显著提升协作效率与理解深度，支持多学科推广
- 适合教育科技开发者和希望个性化教学的教师
本文提出一种将大语言模型整合到协作学习平台的动态框架，以增强学生参与度、批判性思维与包容性。该框架利用先进的LLM作为动态主持人，实时引导讨论并根据学习者需求变化自适应调整，确保多元包容的学习体验。核心创新包括强化反馈机制，优化AI主持效果，促进反思性学习，并平衡用户间参与度。系统采用模块化架构，前端基于ReactJS，后端使用Flask，结合高效问题检索技术，实现提示词与讨论流程的动态调整，支持个性化互动。测试表明，该框架显著提升学生协作质量，加深知识理解，并可有效扩展至不同学科与用户群体。相比现有系统在静态调控与个性化方面的局限，本工作为下一代AI驱动教育工具奠定了坚实基础，推动更公平、更有效的学习成果。
原文摘要 · Abstract (English)
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.
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