Agent-Based Improvement of Multiple-Choice Question Quality in Medical Education

High-quality multiple-choice questions (MCQs) are essential for medical education, but costly to design. Large language models (LLMs) can generate MCQs automatically, yet their outputs often fail to meet core quality criteria. This paper presents a self-optimizing multi-agent approach that refines LLM-generated MCQs by aligning them with predefined quality standards. Expert evaluation shows significant improvements for question stems, producing clearer and more answerable items. For answer options, however, improvements remain inconsistent. The findings indicate that automated refinement by self-optimizing agents can reduce manual workload for educators and accelerate the creation of high-quality question stems, while highlighting open challenges for future research.