用LLM打造实时协作学习平台，支持多人互动与情境化辅导。
LLM Bazaar: A Service Design for Supporting Collaborative Learning with an LLM-Powered Multi-Party Collaboration Infrastructure
- 基于开源Bazaar架构，嵌入LLM代理实现实时协作支持。
- 可动态响应学习场景，提升小组讨论的深度与参与度。
- 适合教育科技研究者及在线协作系统开发者参考。
近二十年来，对话式智能体在协作学习中扮演关键角色，影响小组互动、塑造群体动态并促进学生参与。近年来，将大语言模型（LLMs）融入这些智能体，为培养批判性思维和协作解决问题提供了新可能。本文以开源协作支持架构Bazaar为基础，引入一个具备LLM能力的代理外壳，实现了面向小组学习的实时、上下文敏感的智能化协作支持。该设计与基础设施为探索定制化LLM驱动环境如何重塑协作学习结果与交互模式奠定了基础。
原文摘要 · Abstract (English)
For nearly two decades, conversational agents have played a critical role in structuring interactions in collaborative learning, shaping group dynamics, and supporting student engagement. The recent integration of large language models (LLMs) into these agents offers new possibilities for fostering critical thinking and collaborative problem solving. In this work, we begin with an open source collaboration support architecture called Bazaar and integrate an LLM-agent shell that enables introduction of LLM-empowered, real time, context sensitive collaborative support for group learning. This design and infrastructure paves the way for exploring how tailored LLM-empowered environments can reshape collaborative learning outcomes and interaction patterns.
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