It is crucial to explore the impact of different teaching methods on student learning in educational research. However, real-person experiments face significant ethical constraints, and we cannot conduct repeated teaching experiments on the same student. LLM-based generative agents offer a promising avenue for simulating student behavior. Before large-scale experiments, a fundamental question must be addressed: are student agents truly credible, and can they faithfully simulate human learning? In this study, we built a Big Five Personality-based student agent model with a full pipeline of student-teacher interaction, self-study, and examination. To evaluate behavioral fidelity, we collected 13 empirical studies on Big Five traits and learning, and distilled them into 14 criteria. We found that the 71.4% of the student agents' behavior was aligned with human learners.

翻译：在教育研究中，探索不同教学方法对学生学习的影响至关重要。然而，真实人体实验面临显著的伦理约束，我们无法对同一名学生进行重复教学实验。基于大语言模型的生成式智能体为模拟学生行为提供了有前景的途径。在大规模实验之前，必须解决一个根本性问题：学生智能体是否真正可信，能否忠实地模拟人类学习？在本研究中，我们构建了基于大五人格的学生智能体模型，具备师生互动、自主学习和考试的全流程。为评估行为保真度，我们收集了13项关于大五人格与学习的实证研究，并将其提炼为14条标准。研究发现，71.4%的学生智能体行为与人类学习者一致。