TensorX
返回文献探索

Paper · arXiv 2406.19226

Simulating Classroom Education with LLM-Empowered Agents

Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhiyuan Liu, Lei Hou, Juanzi Li

32 upvotesJune 27, 2024arXiv 预印本
AI 摘要

SimClass, a multi-agent framework with LLM-empowered agents, effectively simulates classroom interactions, enhancing user experience and learning processes through collaboration.

large language modelsLLMsmulti-agent collaborative frameworkclassroom simulationuser participationclass rolesclass control mechanismFlanders Interactive Analysis SystemCommunity of Inquiryvirtual classroom teachingemergent group behaviors

Abstract

Large language models (LLMs) have been employed in various intelligent educational tasks to assist teaching. While preliminary explorations have focused on independent LLM-empowered agents for specific educational tasks, the potential for LLMs within a multi-agent collaborative framework to simulate a classroom with real user participation remains unexplored. In this work, we propose SimClass, a multi-agent classroom simulation framework involving user participation. We recognize representative class roles and introduce a novel class control mechanism for automatic classroom teaching, and conduct user experiments in two real-world courses. Utilizing the Flanders Interactive Analysis System and Community of Inquiry theoretical frame works from educational analysis, we demonstrate that LLMs can simulate traditional classroom interaction patterns effectively while enhancing user's experience. We also observe emergent group behaviors among agents in SimClass, where agents collaborate to create enlivening interactions in classrooms to improve user learning process. We hope this work pioneers the application of LLM-empowered multi-agent systems in virtual classroom teaching.

北京市昌平区探索星信息技术及软件开发工作室

京ICP备2026059466号
Simulating Classroom Education with LLM-Empowered Agents | TensorX