ArticleBMC medical education2026
Personality traits, technology affinity, and artificial intelligence readiness in medical students: a multinational cross-sectional study.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundArtificial intelligence is increasingly embedded in clinical practice and medical education, yet the psychological determinants of students' readiness remain poorly understood. We are aware of no study that has simultaneously examined personality traits, technology affinity and AI readiness in a single cohort.
methodsIn a cross-sectional online survey using convenience and snowball sampling, medical students from six continents completed three self-report instruments: the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Big Five Inventory-10 and the Affinity for Technology Interaction scale. Pearson correlations, multiple linear regression and ANOVA were applied. The protocol was prospectively registered (OSF: osf.io/7s89a).
resultsOf 1,920 respondents, 1,278 (66.5%) completed all instruments and constituted the analytic sample, whilst 642 (33.5%) with incomplete data were excluded by listwise deletion (54.8% female; mean age 19.9 ± 1.56 years; 65.9% European). Openness correlated with the Vision subscale (r = 0.669) and agreeableness with the Ethics subscale (r = 0.602); technology affinity was associated with overall readiness (r = 0.231; all p < 0.001). Male gender (B = 6.698), openness (B = 1.772) and agreeableness (B = 1.518) were independent predictors of overall readiness, whereas technology affinity did not retain an independent effect once personality and gender were accounted for. Men reported higher overall readiness (η² = 0.143); women scored marginally higher on ethical awareness.
conclusionsPersonality traits were independently associated with AI readiness, whereas technology affinity was associated with overall readiness only at the bivariate level. Given the cross-sectional design, these relationships denote associations rather than causal effects. Medical AI curricula should adopt differentiated instructional approaches informed by students' psychological profiles.
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