Simulating Classroom Education with LLM -Empowered Agents (2025)

Abstract: 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. Our implementation and service can be found at https://github.com/THU-MAIC/SimClass.

## introduction the pursuit of utilizing artificial intelligence to provide immediate and customized teaching for students origins from the era of intelligent tutoring systems (its) @xcite . following this enthusiasm, from personalized educational recommendation systems @xcite to teaching assistants @xcite and even llm-driven ai teacher @xcite , researchers have conducted enormous technological explorations and achieved impressive performance in specific educational tasks. as technology advances, intense discussions have also emerged around this topic concerning methodologies @xcite . one of the most central directions is how to fully leverage the capabilities of large models to simulate real classrooms with multiple agents for automated teaching and collaborative learning. from an educational perspective, this approach allows large models to move beyond their instrumental use, delve deeper into educational paradigms, and support students cognitively and socially @xcite @xcite . from a technical standpoint, multi-agent collaboration technologies @xcite could further stimulate the latent knowledge of large models in education, leading to the emergence of richer capabilities @xcite . however, several fundamental research questions for llm-empowered multi-agent systems with real user participation remain: (1) simulation performance: how well can a multi-agent classroom simulate real-time teacher-student interactions? (2) learning experience: can students in such environment experience a high sense of presence and learn effectively? (3) group behavior observation: what behaviors may arise spontaneously in multi-agent scenarios? in response to the research questions above, we present simclass, a multi-agent classroom simulation framework, and conduct real-world experiments and analysis on it. for better simulation, we identify representative class roles and design a novel class control mechanism (figure 1 ). we deploy two different courses with prepared slides and teaching scripts as the foundation. we conduct online experiments with over 400 students, who participate in the courses, interact with the system, and take quizzes after each section, with all the behavioral data carefully recorded. additionally, we construct ablation systems and invite another 48 students to experience different classroom settings. our research addresses the following parts: the experimental results demonstrate the effectiveness of the class roles and control mechanism design in the following aspects: (1) performance: simclass fosters a vivid learning environments with lively teacher-student and student-student interactions; (2) experience: students retain the knowledge gained in the simclass, with increased interactions contributing to improved learning outcomes. the presence of multiple classroom agents enhances user engagement and strengthens their sense of presence; (3) behavior: the control mechanism elicits group behaviors in the multi-agent classroom, including collaborative teaching, discussions, emotional company and discipline management. in summary, the llm-based multi-agent system shows great potential for simulating classroom for educational purposes. we hope our work serves as a pioneering effort in this area. ## experiments we focus on three research questions to evaluate and understand simclass as a multi-agent learning environment: (1) what is the performance of simclass? (2) what are the impacts of various interaction types within simclass (e.g. the roles of student agents)? (3) how do agents behave in simclass? to address these questions, we deploy simclass online and invite a group of university students to use the system. with the institutional review board (irb) approval from our institution, we design quizzes and collected interaction data for further analysis. additionally, we develop ablation systems to investigate the influence of class roles and interactions in the environment. ## conclusion we introduce simclass, a novel multi-agent classroom framework leveraging llms for teaching. our experiments across two courses with real users demonstrate its effectiveness in simulating dynamic teaching environments, where agents collaborate to enhance user experience. increased interaction with the system results in better learning outcomes, and the multi-agent setup encourages students to engage more. we hope our efforts can advance the explorations of llm-empowered education systems for researchers, practitioners, and pedagogues.