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.

翻译：本文提出了一个基于大型语言模型（LLM）的教育场景学生模拟的概念与方法论框架。作者识别出一种核心失效模式，称为“能力悖论”，即能力宽泛的LLM被要求模拟知识不完全的学习者，从而导致不现实的错误模式与学习动态。为解决此问题，本文重新将学生模拟定义为一个受显式认知状态规范（Epistemic State Specification, ESS）约束的生成问题。ESS定义了模拟学习者能够访问的知识、错误的结构方式以及学习者状态随时间演变的规律。研究进一步引入了“目标-环境”框架，依据行为目标和部署情境来定位模拟学生系统。本文并未提出新的系统或基准，而是综合了现有文献，形式化了关键设计维度，并阐明了与有效性、评估及伦理风险相关的开放挑战。总体而言，本文主张将认知保真度置于表面真实感之上，以此作为将基于LLM的模拟学生用作可靠的科学与教学工具的前提条件。