Simulating Classroom Education with LLM-Empowered Agents
<|reference_start|>Simulating Classroom Education with LLM-Empowered Agents: 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.<|reference_end|>
@article{zhang2024simulating,
    title={Simulating Classroom Education with LLM-Empowered Agents},
    author={Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou,
  Zhiyuan Liu, Lei Hou, Juanzi Li},
    journal={arXiv preprint arXiv:2406.19226},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19226},
    primaryClass={cs.CL cs.HC}
}