ChatGPT to generate clinical vignettes for teaching and multiple-choice questions for assessment: A randomized controlled experiment.

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

None of the scores in 10 statements regarding the cases showed a significant difference between the ChatGPT group and the control group ( > .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.