ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment
Abstract
AIM: This study aimed to evaluate the real-life performance of clinical vignettes and multiple-choice questions generated by using ChatGPT. METHODS: This was a randomized controlled study in an evidence-based medicine training program. We randomly assigned seventy-four medical students to two groups. The ChatGPT group received ill-defined cases generated by ChatGPT, while the control group received human-written cases. At the end of the training, they evaluated the cases by rating 10 statements using a Likert scale. They also answered 15 multiple-choice questions (MCQs) generated by ChatGPT. The case evaluations of the two groups were compared. Some psychometric characteristics (item difficulty and point-biserial correlations) of the test were also reported. RESULTS: > .05). In the test, only six MCQs had acceptable levels (higher than 0.30) of point-biserial correlation, and five items could be considered acceptable in classroom settings. CONCLUSIONS: The results showed that the quality of the vignettes are comparable to those created by human authors, and some multiple-questions have acceptable psychometric characteristics. ChatGPT has potential in generating clinical vignettes for teaching and MCQs for assessment in medical education.
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A significant breakthrough occurred in November 2022 with the public release of large language models driven by generative AI, signifying a qualitative shift in capability and accessibility.
“Since then, a significant breakthrough occurred in November 2022 with the public release of large language models (LLMs) driven by generative AI, signifying a qualitative shift in both capability and accessibility [6].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [6]
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Klausner, E. A., & Mark, K. S. (2026) · Pharmacy · doi:10.3390/pharmacy14070142
Bantidos, M. G., Dafli, E., Karagiannidis, E., Fyntanidou, B., Gilou, S. C., Nasoufidou, A., & Bamidis, P. D. (2026) · JMIR Medical Education · doi:10.2196/100839
Lettow, N., Pantani, K., & Littkemann, J. (2026) · Journal of Digital Educational Technology · doi:10.29333/jdet/19699
- Authors
- Coşkun, Ö., Kıyak, Y. S., & Budakoğlu, I. İ.
- Affiliations
- Department of Medical Education and Informatics, Gazi University
- Gazi University (TR)
- Venue
- Medical Teacher
- Publisher
- Informa UK Limited
- Published
- 2024-03-13
- Volume
- 47
- Issue
- 2
- Pages
- 268-274
- ISSN
- 0142-159X (ISSN-L)
- 1466-187X
- OpenAlex
- W4392744531
- Cited by
- 64
- References
- 39
- Citations by year
- 2026: 22 · 2025: 31 · 2024: 11
- FWCI
- 6.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Teacher (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- ChatGPT, clinical vignettes, multiple-choice questions, artificial intelligence in medical education, evidence-based medicine training, medical students, item difficulty, point-biserial correlation, psychometric properties, automated test generation
- MeSH terms
- Generative Artificial Intelligence, Adult, Education, Medical, Undergraduate, Educational Measurement, Female, Humans, Male, Psychometrics, Students, Medical, Teaching, Evidence-Based Medicine, Young Adult
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