Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
This addresses the labor-intensive task for educators in creating math MCQs, but it is incremental as it builds on existing LLM automation efforts with limited new breakthroughs.
The paper tackled the problem of automating high-quality math multiple choice question generation by introducing a prototype tool for human-LLM collaboration, finding that LLMs generate good question stems but struggle with creating distractors that capture student errors, yet collaboration can improve efficiency.
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.