Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted

TL;DR

This study introduces AnaQuest, a prompting technique that leverages student responses to generate valid multiple-choice questions, with AI-generated items matching human quality and better resembling human items in difficulty and discrimination.

Contribution

The paper presents AnaQuest, an innovative method that uses student responses to generate high-quality MCQs, integrating formative and summative assessments with empirical validation.

Findings

AI-generated MCQs are rated as valid as human-created ones by experts.

AnaQuest-generated questions, especially foils, closely match human items in difficulty and discrimination.

The technique effectively incorporates student understanding to improve question quality.

Abstract

The primary goal of this study is to develop and evaluate an innovative prompting technique, AnaQuest, for generating multiple-choice questions (MCQs) using a pre-trained large language model. In AnaQuest, the choice items are sentence-level assertions about complex concepts. The technique integrates formative and summative assessments. In the formative phase, students answer open-ended questions for target concepts in free text. For summative assessment, AnaQuest analyzes these responses to generate both correct and incorrect assertions. To evaluate the validity of the generated MCQs, Item Response Theory (IRT) was applied to compare item characteristics between MCQs generated by AnaQuest, a baseline ChatGPT prompt, and human-crafted items. An empirical study found that expert instructors rated MCQs generated by both AI models to be as valid as those created by human instructors. However, IRT-based analysis revealed that AnaQuest-generated questions - particularly those with incorrect assertions (foils) - more closely resembled human-crafted items in terms of difficulty and discrimination than those produced by ChatGPT.