Previously submitted to: JMIR Medical Education (no longer under consideration since May 06, 2025)
Date Submitted: Nov 5, 2024
Open Peer Review Period: Nov 7, 2024 - Jan 2, 2025
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Reducing Faculty Burden with Question Creation: A randomized control trial comparing AI-generated versus expert-generated questions through student feedback and performance
ABSTRACT
Background:
Developing high quality formative and summative assessment questions can take considerable time and effort, which in turn can contribute to burnout among medical school teaching faculty. Generative AI can decrease faculty burden by reducing the time needed to develop assessment materials.
Objective:
This study compares students’ performance on and subjective assessment of AI- versus expert-generated questions. We hypothesized that there would be no significant difference in student performance or subjective assessment of AI vs expert-generated questions.
Methods:
We designed a single-center, prospective, double-blind randomized controlled trial where student participants randomly received one AI or expert-generated question per day over the course of four weeks.
Results:
We found no significant difference between the distributions of the proportion of correct responses to AI- versus expert-generated questions (p=0.1809). Interestingly, the cumulative proportion of questions answered correctly over 28 days increased slightly for human-generated questions but stayed constant for AI-generated questions. There was a significant difference (p=0.0051) between the distribution of student difficulty ratings for AI- versus expert-generated questions.
Conclusions:
Generative AI can reduce faculty burden by creating effective formative and summative assessments.
Citation
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