Development and Preliminary Psychometric Evaluation of a Digital-Intelligence Literacy Self-Report Scale Among Students in Medical and Health-Related Programs

Medical students increasingly engage with interconnected digital systems, health data, and artificial intelligence (AI), but existing instruments vary in the aspects of digital, data, and AI literacy they assess in medical contexts. This study developed and initially evaluated the Chinese-language Digital-Intelligence Literacy Scale for Medical Students (DIL-MS). Following two rounds of consultation with 16 experts, a 34-item questionnaire was field-tested at one Chinese university. Of 1118 returned questionnaires, 710 were analyzed in non-overlapping exploratory (n = 344) and confirmatory (n = 366) subsamples. Full-pool exploratory factor analysis and confirmatory factor analysis for ordinal data informed a revised 32-item scale across six domains: readiness, theoretical literacy, application skills, health data practices and interpretation, critical thinking, and ethical literacy. Parallel analysis suggested six factors, with some item assignments differing from the proposed domains. The correlated six-factor model fit better than the tested five-, four-, and single-factor alternatives, but absolute fit was limited (robust comparative fit index = 0.802; robust root mean square error of approximation = 0.090). Overall Cronbach’s α was 0.938, and domain α ranged from 0.726 to 0.871; convergent and discriminant validity evidence varied across domains. The DIL-MS provides medically contextualized self-report content for exploring perceived learning needs and mapping curricula.