Application of Generative Artificial Intelligence-Assisted Pedagogy in Clinical Ophthalmology Education

Corresponding author: linchengfei, linchengfeidyyy@163.com

DOI: 10.12201/bmr.202603.00124

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  Abstract: 【】Objective To investigate the application of generative artificial intelligence-assisted pedagogy in clinical ophthalmology education. Methods This randomized controlled study divided 27 resident and intern physicians into an experimental group (generative AI-assisted teaching group, n=14) and a control group (traditional teaching group, n=13) using stratified random number table method. Teaching effectiveness was evaluated by comparing theoretical knowledge assessment scores, simulated patient clinical reasoning assessment scores, and questionnaire results. Results The total score of the clinical reasoning assessment for the experimental group was significantly higher than that of the control group (P<0.05). Specifically, scores in history summarization, preliminary diagnosis and differential diagnosis, and medical humanities showed significant improvement compared to the control group (P<0.05). The theoretical assessment scores, as well as the scores for history taking communication and treatment management within the clinical reasoning assessment, were higher in the experimental group than in the control group, but the differences were not statistically significant (P>0.05). Regarding learning interest, self-evaluated improvement in clinical reasoning ability, and satisfaction with the teaching method, the experimental groups ratings were significantly higher than those of the control group (P<0.05). There was no significant difference between the two groups in self-rated scores for learning efficiency or increased learning burden (a negative indicator) (P>0.05). Conclusion Compared to the traditional lecture-based model, the generative AI-assisted teaching model demonstrates superior effectiveness in clinical ophthalmology education. It can enhance the clinical competency of ophthalmology interns and resident physicians without increasing their learning burden or diminishing their learning motivation.

  Key words: Ophthalmology; Clinical Education; Generative Artificial Intelligence

  Submit time: 31 March 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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