Sociodemographic differences in health sciences students’ engagement with artificial intelligence: a cross-sectional study
TL;DR
Students’ engagement with AI was multidimensional, with sociodemographic and academic differences varying across specific dimensions, and these findings support structured and equitable AI education across health sciences curricula to strengthen practical skills, knowledge, critical appraisal, and responsible use.
Abstract
Artificial intelligence (AI) is increasingly transforming higher education and health sciences training. However, evidence regarding sociodemographic differences in students’ engagement with AI remains limited, particularly in Latin America. Objectives This study aimed to examine sociodemographic differences in health sciences students’ engagement with AI across three dimensions—Critical Perceptions of AI in Health Sciences Education, Practical Use of AI Tools, and AI Knowledge and Readiness—according to sex, age, academic program, and ethnic self-identification. Methods A quantitative, cross-sectional study was conducted with 668 undergraduate students from four health sciences programs at a public university in Ecuador. A 31-item Likert-type questionnaire was administered. Exploratory factor analysis supported a final 30-item structure comprising three dimensions: Critical Perceptions of AI in Health Sciences Education, Practical Use of AI Tools, and AI Knowledge and Readiness, with high internal consistency (Cronbach’s α = 0.905–0.911). Non-parametric tests, adjusted general linear models, and Multiple Correspondence Analysis were performed. Results Adjusted analyses showed that sex was associated with Critical Perceptions ( p = 0.013) and Practical Use ( p = 0.043), with higher Critical Perceptions among female students and higher Practical Use among male students. Academic program was associated with Practical Use ( p < 0.001) and AI Knowledge and Readiness ( p = 0.003), while ethnic self-identification was associated with AI Knowledge and Readiness ( p = 0.029). Age was not independently associated with any dimension. Significant adjusted effects were small (partial η² = 0.006–0.028). Conclusions Students’ engagement with AI was multidimensional, with sociodemographic and academic differences varying across specific dimensions. These findings support structured and equitable AI education across health sciences curricula to strengthen practical skills, knowledge, critical appraisal, and responsible use.