Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background

Background Declining ophthalmology teaching hours necessitate efficient instructional tools. While generative AI frequently produces structural deviations, these variations can be strategically repurposed as valuable stimuli for comparative learning. This study evaluated the effects of an AI-assisted comparative exercise versus conventional anatomical labeling on knowledge acquisition, learner satisfaction, and cognitive workload. Methods We conducted a quasi-experimental 2 × 2 study with 121 sophomores from two universities in Shanghai, China. Following a standardized 20-min ophthalmic anatomy lecture, intact classes were assigned to either a conventional diagram-labeling task or an AI-assisted comparative exercise. The AI condition included three anatomically correct reference images paired with three expert-curated AI-generated anatomical variants, produced through a systematic expert-in-the-loop approach using Gemini 3.0 Pro. Outcomes comprised baseline and post-intervention knowledge tests, a 5-item satisfaction questionnaire, and the NASA Task Load Index. Results After baseline adjustment and correction for planned comparisons, no statistically significant AI-versus-conventional difference in post-test knowledge scores was detected (all p > 0.05). Among non-medical students, the AI-assisted comparative exercise was associated with higher composite satisfaction (9.17 vs. 7.52; Holm-adjusted p < 0.001; r = 0.60) and better self-assessed performance (7.76 vs. 6.03; Holm-adjusted p = 0.003; r = 0.47), whereas composite NASA-TLX scores did not differ between AI and conventional conditions within either background group. These satisfaction and self-assessed performance benefits were not observed among medical students (all p > 0.05). Conclusion A brief AI-assisted comparative exercise did not demonstrate a statistically conclusive advantage in immediate knowledge outcomes, but was associated with higher satisfaction and better self-assessed performance among non-medical students without increasing composite NASA-TLX scores. Carefully curated AI-generated anatomical variants may therefore serve as a structured adjunct for novice ophthalmic anatomy learning.