ABSTRACT Background While cataract surgical simulation is known to improve trainee performance, the mechanisms underpinning individual variation in learning trajectories remain poorly understood. This study aimed to investigate the associations between early performance on the Eyesi surgical simulator and subsequent learning efficiency, proficiency and progression across a structured cataract simulation curriculum. Methods Twenty medical students without prior surgical simulation experience completed a structured 17‐module Eyesi cataract simulation curriculum. Relationships between early simulator performance and learning trajectory were analysed at the intra‐module and inter‐module levels using Spearman's rank correlation coefficients. Exploratory analyses examined learner phenotypes and associations with extracurricular activities. Results Higher baseline performance was associated with fewer attempts required for module completion across the curriculum, particularly for total score ( ρ = −0.834, p < 0.001), odometer ( ρ = −0.773, p < 0.001), instrument slipped out of sphere ( ρ = −0.715, p < 0.001) and out‐of‐tolerance percentage ( ρ = −0.698, p < 0.001). Early performance in the first curriculum module was associated with higher course‐level baseline performance ( ρ = 0.512, p = 0.021), greater course completion ratio ( ρ = 0.449, p = 0.047) and fewer mean attempts per module ( ρ = −0.500, p = 0.025). Participants were classified into distinct learner phenotypes characterised by differences in movement efficiency, consistency and progression. Exploratory analyses identified hypothesis‐generating differences according to prior musical experience. Conclusion Eyesi‐derived performance metrics captured early in cataract simulation training were associated with subsequent learning efficiency, proficiency and progression within a structured simulated curriculum. Future research may consider investigating the use of such data to inform individualised training strategies in early cataract simulation training. These findings should be considered exploratory and require validation in larger cohorts.
