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Agent-Based Improvement of Multiple-Choice Question Quality in Medical Education
Lilly Marie Düsterbeck1, Michael Grössler1, Graziella Credidio1
1Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf (UKE), Germany.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
This study introduces a self-optimizing multi-agent approach to improve large language model (LLM)-generated medical education questions. The method enhances question stems but shows inconsistent results for answer options, reducing educator workload.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Education
- Natural Language Processing
Background:
- High-quality multiple-choice questions (MCQs) are crucial for medical education but expensive to create.
- Large language models (LLMs) can automate MCQ generation, but quality often falls short of educational standards.
Purpose of the Study:
- To present a novel self-optimizing multi-agent framework for refining LLM-generated MCQs.
- To enhance the quality and alignment of MCQs with predefined educational criteria.
Main Methods:
- A self-optimizing multi-agent system was developed to refine LLM-generated MCQs.
- The system aligned generated questions with established quality standards through iterative refinement.
- Expert evaluation was employed to assess the quality improvements.
Main Results:
- Significant improvements were observed in the clarity and answerability of question stems.
- Refinement of answer options yielded inconsistent results, requiring further development.
- The automated approach demonstrated potential in reducing manual workload for question design.
Conclusions:
- Self-optimizing agents can effectively improve LLM-generated question stems for medical education.
- Further research is needed to address challenges in refining answer options for MCQs.
- This approach accelerates the creation of high-quality educational assessment items.
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