用大模型模拟学生学习，解决错误模式不真实的问题
Towards Valid Student Simulation with Large Language Models
- 通过显式定义知识状态约束生成更真实的错误模式
- 提出'认知状态规范'框架，确保模拟学生行为合理
- 适合教育研究者和智能教学系统开发者参考
本文提出一种基于大语言模型的教育场景学生模拟概念与方法框架。作者指出，通用大模型在模拟部分知识学习者时存在‘能力悖论’，导致错误模式与学习动态不真实。为此，论文将学生模拟重构为受约束的生成问题，引入显式的认知状态规范（ESS），明确模拟学生可访问内容、错误结构及状态演化机制。同时提出目标-环境框架，根据行为目标与部署场景定位模拟系统。本文不提供新系统或基准，而是综述已有文献，形式化关键设计维度，并阐述有效性的评估与伦理风险等开放挑战。核心主张是：使用大模型模拟学生需以认知保真度优先于表面逼真度，方能作为可靠的科学与教学工具。
原文摘要 · Abstract (English)
This paper presents a conceptual and methodological framework for large language model (LLM) based student simulation in educational settings. The authors identify a core failure mode, termed the "competence paradox" in which broadly capable LLMs are asked to emulate partially knowledgeable learners, leading to unrealistic error patterns and learning dynamics. To address this, the paper reframes student simulation as a constrained generation problem governed by an explicit Epistemic State Specification (ESS), which defines what a simulated learner can access, how errors are structured, and how learner state evolves over time. The work further introduces a Goal-by-Environment framework to situate simulated student systems according to behavioral objectives and deployment contexts. Rather than proposing a new system or benchmark, the paper synthesizes prior literature, formalizes key design dimensions, and articulates open challenges related to validity, evaluation, and ethical risks. Overall, the paper argues for epistemic fidelity over surface realism as a prerequisite for using LLM-based simulated students as reliable scientific and pedagogical instruments.
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