Math Multiple Choice Question Generation via Human-Large Language Model Collaboration

TL;DR

This paper presents a collaborative tool leveraging large language models to assist educators in generating high-quality math multiple choice questions, aiming to improve efficiency while addressing challenges in distractor quality and mathematical accuracy.

Contribution

It introduces a prototype tool for human-AI collaboration in math MCQ creation and evaluates its potential through a pilot study with educators.

Findings

LLMs can generate well-formulated question stems

Distractor quality and capturing misconceptions remain challenging

Human-AI collaboration can improve MCQ generation efficiency

Abstract

Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation, but challenges persist in ensuring mathematical accuracy and addressing student errors. This paper introduces a prototype tool designed to facilitate collaboration between LLMs and educators for streamlining the math MCQ generation process. We conduct a pilot study involving math educators to investigate how the tool can help them simplify the process of crafting high-quality math MCQs. We found that while LLMs can generate well-formulated question stems, their ability to generate distractors that capture common student errors and misconceptions is limited. Nevertheless, a human-AI collaboration has the potential to enhance the efficiency and effectiveness of MCQ generation.