Simulating Classroom Education with LLM-Empowered Agents
On the map: LLM agents and tool use → Educational agents and math problem solving
Abstract (arXiv)
Large language models (LLMs) have been applied across various intelligent educational tasks to assist teaching. While preliminary studies have focused on task-specific, independent LLM-empowered agents, the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. In this work, we propose SimClass, a multi-agent classroom simulation teaching framework. 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. Using the Flanders Interactive Analysis System and Community of Inquiry theoretical frameworks from educational analysis, we demonstrate that LLMs can simulate a dynamic learning environment for users with active teacher-student and student-student interactions. We also observe 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.
Key facts. Cited by 127 papers in the map (data through 14 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 19 of its 42 references that are in the map. First posted on arXiv 27 Jun 2024.
Builds on
19 of 42 references.
References · 19
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- 2023CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principlescited by 48
- 2019Exploiting Cognitive Structure for Adaptive Learningcited by 37
- 2024MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Educationcited by 37
- 2023COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goalcited by 18
- 2023LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Promptscited by 9
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Citing papers per year: 2024: 20; 2025: 63; 2026: 44.
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