Despite the rapid deployment of LLMs into classrooms, validating educational AI remains uniquely intractable: interventions act on developing learners whose cognitive and social trajectories are irreversibly shaped, while real-world trials are slow, ethically constrained, and institutionally locked. LLM-based educational simulators have emerged as a potential remedy, but many still collapse learning into persona-conditioned role-play and, when optimized only to reproduce existing classrooms, can structurally penalize the institutional novelty that pedagogical reform requires. In this work, we introduce AgentSchool, an LLM-driven multi-agent simulator that models learning as state transition rather than prompted behavior. AgentSchool couples cognitively growable student agents -- equipped with weighted subject knowledge graphs, thinking-workflow pools, and explicit misconceptions -- with adaptive teacher agents that plan, scaffold, and reflect along the Zone of Proximal Development, embedded in a configurable scenery generator that situates instruction within both formal and informal learning fields, and a multi-scale simulator that decouples interaction scale, temporal granularity, and simulation duration. Experiments show that structured student agents produce more differentiated mastery and misconception traces than a baseline simulator, while teacher-agent comparisons show backbone-dependent patterns consistent with ZPD-informed adaptation. Further, AgentSchool generates plausible traces of peripheral participation, clique formation, aggressor-induced cohesion, and opinion-leader emergence consistent with classroom social theories. Beyond its role as an educational research instrument, AgentSchool frames education as a socially meaningful testbed for long-horizon memory, multi-agent coordination, and future institutional reasoning under organizational pressure.

翻译：尽管大语言模型已迅速部署至课堂教学中，教育人工智能的验证仍面临独特的复杂性：作用于发展中的学习者时，其认知与社会发展轨迹将产生不可逆的塑形效应；而现实世界的实验周期漫长、受伦理约束且受制于制度壁垒。基于大语言模型的教育仿真系统虽应运而生，但大多数方法仍将学习简化为基于人格特征的角色扮演，且当其仅以复现现有课堂生态为优化目标时，会从结构层面抑制教学改革所需的制度创新。本研究提出AgentSchool——一种由大语言模型驱动的多智能体仿真系统，将学习建模为状态迁移过程而非提示行为。该系统由可认知成长的学生智能体（配备带权重的学科知识图谱、思维工作流池与显式错误概念）与自适应教师智能体（在最近发展区框架下实施教学规划、支架搭建与反思调整）构成，内嵌于可配置的场景生成器中，实现正式与非正式学习场域的教学情境构建，并通过多尺度仿真器解耦互动规模、时间粒度和仿真时长。实验表明，结构化学生智能体相较于基准仿真系统能产生更差异化的知识掌握度与错误概念踪迹；教师智能体对比分析显示其行为模式与基于最近发展区理论的适应性调整存在依赖骨干网络的规律性特征。此外，AgentSchool可生成与课堂社会学理论一致的外围参与、小团体形成、攻击者引发凝聚力及意见领袖涌现等可信行为轨迹。作为教育研究工具之外，AgentSchool将教育领域构建为具有社会意义的测试基准，用于研究组织压力下的长时记忆、多智能体协同及制度推理机制。