Background: Traditional research on collaborative learning scaffolding is often time-consuming and resource-heavy, which hinders the rapid iteration and optimization of instructional strategies. LLM-based multi-agent systems have recently emerged as a powerful tool to simulate complex social interactions and provide a novel paradigm for educational research. Objectives: This study proposes an LLM-based multi-agent simulation approach to investigate collaborative learning processes and the effectiveness of instructional scaffolds prior to actual classroom deployment. The research specifically examines the feasibility of simulating group discussions and the alignment of these simulations with established learning science theories. Methods: The simulation system was implemented using the MetaGPT framework and GPT-4o, comprising one teacher agent and five distinct student roles (Leader, Supporter, Expounder, Rebutter, and Summarizer). Two scaffolding strategies, "Deep Think before Speak" and "Direct Speak", were compared across ten classical Chinese poetry appreciation tasks. Evaluation was conducted through discourse analysis of quality and behavior. Results and Conclusions: The introduction of the "Deep Think before Speak" scaffold significantly improved the agents' discourse diversity and interaction depth while notably reducing content repetitiveness. Behavioral analysis showed that the scaffold encouraged more complex interaction patterns, such as reflecting, rebutting, and explaining. These findings align with the ICAP framework, as the scaffold prompted agents to move from simple "Active" participation to "Constructive" and "Interactive" knowledge co-construction. This study demonstrates the feasibility and ecological validity of using LLM-based multi-agent systems to simulate authentic collaborative learning dynamics.

翻译：背景：传统的协作学习支架研究往往耗时且资源密集，阻碍了教学策略的快速迭代与优化。基于大语言模型的多智能体系统近期作为模拟复杂社会交互的强大工具出现，为教育研究提供了新范式。目标：本研究提出一种基于大语言模型的多智能体仿真方法，旨在实际课堂部署前探究协作学习过程及教学支架的有效性。研究具体考察了模拟小组讨论的可行性，以及这些模拟与现有学习科学理论的一致性。方法：仿真系统基于MetaGPT框架和GPT-4o实现，包含一个教师智能体和五个不同的学生角色（领导者、支持者、阐述者、反驳者、总结者）。在十项中国古典诗词赏析任务中，比较了两种支架策略：“深思而后言”与“直接发言”。通过话语分析对质量和行为进行评估。结果与结论：“深思而后言”支架的引入显著提升了智能体的话语多样性和交互深度，同时明显减少了内容重复性。行为分析表明，该支架促进了更复杂的交互模式，如反思、反驳和解释。这些发现与ICAP框架一致，因为该支架促使智能体从简单的“主动”参与转向“建构性”和“交互性”知识共建。本研究证明了利用大语言模型多智能体系统模拟真实协作学习动态的可行性与生态效度。