Student simulation presents a transformative approach to enhance learning outcomes, advance educational research, and ultimately shape the future of effective pedagogy. We explore the feasibility of using large language models (LLMs), a remarkable achievement in AI, to simulate student learning behaviors. Unlike conventional machine learning based prediction, we leverage LLMs to instantiate virtual students with specific demographics and uncover intricate correlations among learning experiences, course materials, understanding levels, and engagement. Our objective is not merely to predict learning outcomes but to replicate learning behaviors and patterns of real students. We validate this hypothesis through three experiments. The first experiment, based on a dataset of N = 145, simulates student learning outcomes from demographic data, revealing parallels with actual students concerning various demographic factors. The second experiment (N = 4524) results in increasingly realistic simulated behaviors with more assessment history for virtual students modelling. The third experiment (N = 27), incorporating prior knowledge and course interactions, indicates a strong link between virtual students' learning behaviors and fine-grained mappings from test questions, course materials, engagement and understanding levels. Collectively, these findings deepen our understanding of LLMs and demonstrate its viability for student simulation, empowering more adaptable curricula design to enhance inclusivity and educational effectiveness.

翻译：学生模拟提供了一种变革性方法，用于提升学习效果、推动教育研究，并最终塑造有效教学法的未来。我们探索了利用大型语言模型（LLM，人工智能的一项显著成就）模拟学生学习行为的可行性。与传统基于机器学习的预测不同，我们利用LLM实例化具有特定人口统计特征的虚拟学生，并揭示学习经历、课程材料、理解水平和参与度之间错综复杂的关联。我们的目标不仅是预测学习结果，更是复现真实学生的学习行为与模式。我们通过三项实验验证了这一假设。第一项实验基于N = 145的数据集，从人口统计特征数据模拟学生学习结果，揭示了虚拟学生在多种人口统计因素上与真实学生的相似之处。第二项实验（N = 4524）的结果表明，随着用于建模虚拟学生的评估历史数据增加，模拟行为变得更加真实。第三项实验（N = 27）结合先验知识与课程互动信息，表明虚拟学生的学习行为与试题、课程材料、参与度和理解水平的细粒度映射之间存在强关联。综合而言，这些发现深化了我们对LLM的理解，并证明了其用于学生模拟的可行性，从而助力设计更具适应性的课程体系，以增强包容性与教育效果。