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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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LLM-Generated multiple choice practice quizzes for preclinical medical students.

Troy Camarata¹, Lise McCoy², Robert L Rosenberg³

¹Baptist University College of Osteopathic Medicine, Memphis, Tennessee, United States.

Advances in Physiology Education

|June 14, 2025

Summary

Artificial intelligence can create medical exam questions, but expert review is crucial. Large language models show promise for generating practice quizzes when supervised by subject matter experts.

Keywords:

artificial intelligence large language model medical education multiple choice questions practice questions

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Area of Science:

Medical Education
Artificial Intelligence in Education

Background:

Multiple choice questions (MCQs) are essential for medical education assessment.
Automated MCQ generation can reduce faculty workload and expand student practice resources.

Purpose of the Study:

To assess the feasibility of using ChatGPT for generating USMLE/COMLEX-USA-style medical practice questions.
To evaluate the quality and accuracy of AI-generated medical assessment items.

Main Methods:

Second-year medical students used ChatGPT-generated renal physiology quizzes.
Independent experts evaluated question quality against NBME/NBOME guidelines.

Main Results:

49% of AI-generated questions had writing flaws; 22% had factual/conceptual errors.
91% of questions were deemed suitable as a revision starting point.

Conclusions:

Large language models (LLMs) are feasible for generating medical education practice questions.
Expert supervision and training in item writing are essential for effective LLM use in medical assessment.