  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.
