Generative AI in medical education: feasibility and educational value of LLM-generated clinical cases with MCQs.

ABSTRACT:

Objective

To evaluate the feasibility and educational value of employing large language models (LLMs) to generate clinical case scenario with multiple-choice questions (MCQs) for undergraduate medical education.

Methods

Twelve ophthalmology clinical case scenarios with MCQs generated by ChatGPT 4.0 were assessed for quality by eight teachers. High-scoring cases with MCQs were selected for review classes to test students' learning. Student perceptions were collected via in-class and after-class questionnaires using a 5-point Likert scale.

Results

The average quality score of the 12 cases with MCQs was 52.33 ± 5.44 (range: 48-54.25; max = 60). There were statistical differences in the teachers' scores for identical clinical cases (F = 16.050, P < 0.001). Among 20 studen

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC12560302 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature