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

• URL: http://arxiv.org/abs/2405.00864v1
• Date: Wed, 1 May 2024 20:53:13 GMT
• Title: Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
• Authors: Jaewook Lee, Digory Smith, Simon Woodhead, Andrew Lan,
• Abstract summary: Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation. This paper introduces a prototype tool designed to facilitate collaboration between LLMs and educators.
• Score: 5.081508251092439
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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