Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement. Learner simulators play a critical role in simulating scalable learner behavior without the need for continuous involvement of real learners. However, existing methods are predominantly \textbf{individual-centric}, pairing a simulator with each learner to iteratively infer latent knowledge states from dense interaction histories, which is both data- and computation-intensive, and fragile in cold-start scenarios. We propose a \textbf{cohort-aware roll-call simulation paradigm} that first constructs cohort-level proficiency priors and refines individual learner states through a small number of targeted diagnostic queries. Based on this paradigm, we introduce \textbf{Edu-Theater}, an LLM-powered agent system that performs cohort-aware learner simulation via a teacher agent and retrospective roll-call probing over learner logs. Edu-Theater enables scalable future behavior simulation without the need for dense per-learner histories. Experiments on two real-world datasets demonstrate that Edu-Theater achieves higher simulation accuracy with significantly fewer LLM calls, producing synthetic data that enhances downstream applications such as adaptive testing.

翻译：大规模学习者-任务交互数据对智能教育系统至关重要，但收集成本高昂且受隐私和学习者参与度约束。学习者模拟器可在无需真实学习者持续参与的情况下模拟可扩展的学习者行为，现有方法主要采用**个体中心化**范式——为每个学习者配对模拟器，通过密集交互历史迭代推断潜在知识状态，这种方法既需要大量数据和计算资源，又在冷启动场景中表现脆弱。我们提出一种**群体感知点名模拟范式**，首先构建群体层面的能力先验，再通过少量针对性诊断查询细化个体学习者状态。基于该范式，我们提出**Edu-Theater**——一个由大语言模型驱动的智能体系统，通过教师智能体和基于学习者日志的追溯式点名探测实现群体感知的学习者模拟。Edu-Theater无需依赖每个学习者的密集历史记录即可实现可扩展的未来行为模拟。在两个真实世界数据集上的实验表明，Edu-Theater在显著减少大语言模型调用次数的前提下实现了更高的模拟精度，生成的合成数据能有效提升自适应测试等下游应用性能。