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An artificial intelligence-driven platform for practice question generation.

Andrew Zahn1, Seth Overla2, D J Lowrie3

  • 1University of Cincinnati College of Medicine, Cincinnati, OH, United States.

Academic Medicine : Journal of the Association of American Medical Colleges
|January 25, 2026
PubMed
Summary
This summary is machine-generated.

AI-powered tools can create high-quality practice questions for medical licensing exams like the USMLE, improving access to study resources for all students. This technology aims to enhance medical education and trainee performance.

Keywords:
artificial intelligenceautomated item generationdesign-based researchlarge language modelsmedical education technology

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Assessment and Evaluation

Background:

  • High-stakes medical licensing exams, such as the United States Medical Licensing Examination (USMLE), are crucial for medical education and patient care.
  • Unequal access to quality board preparation materials disadvantages students from underrepresented and financially struggling backgrounds.

Purpose of the Study:

  • To develop and pilot an AI-driven system for generating USMLE-style practice questions.
  • To assess the feasibility and effectiveness of using Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for medical question creation.

Main Methods:

  • An AI system utilizing LLMs, RAG, and few-shot prompting generated 565 USMLE-style questions from preclinical hematology lectures.
  • A faculty course director oversaw a human-in-the-loop process to ensure content validity and adherence to National Board of Medical Examiners (NBME) guidelines.
  • Validated questions were deployed via a mobile app for student practice and feedback.

Main Results:

  • 87% of generated questions (490/565) were accurate and NBME-compliant.
  • Eighty medical students utilized the question bank, with usage trending towards improved performance on related exam questions.
  • Qualitative feedback indicated strong student enthusiasm for AI-assisted study tools.

Conclusions:

  • Large Language Models can effectively generate high-quality, guideline-adherent practice questions for medical licensing exams.
  • Future development will focus on AI-driven content review for scalability and reduced faculty workload.
  • The platform's expansion to more courses and health professions is planned to broaden access and support ongoing refinement.