Evaluating the quality of automatically generated question items has been a long standing challenge. In this paper, we leverage LLMs to simulate student profiles and generate responses to multiple-choice questions (MCQs). The generative students' responses to MCQs can further support question item evaluation. We propose Generative Students, a prompt architecture designed based on the KLI framework. A generative student profile is a function of the list of knowledge components the student has mastered, has confusion about or has no evidence of knowledge of. We instantiate the Generative Students concept on the subject domain of heuristic evaluation. We created 45 generative students using GPT-4 and had them respond to 20 MCQs. We found that the generative students produced logical and believable responses that were aligned with their profiles. We then compared the generative students' responses to real students' responses on the same set of MCQs and found a high correlation. Moreover, there was considerable overlap in the difficult questions identified by generative students and real students. A subsequent case study demonstrated that an instructor could improve question quality based on the signals provided by Generative Students.

通过利用LLMs模拟学生档案并生成多项选择题的回答，本文提出了一种基于KLI框架设计的生成学生提示结构，该结构可用于评估问题实例的质量，并发现生成学生的回答与真实学生的回答具有高度相关性。

生成学生：利用LLM模拟的学生档案支持问题项目评估