Determinants of Artificial Intelligence Adoption Intention Among Moroccan Nursing Students: An Extended UTAUT-Based Cross-Sectional Study

Background: Artificial Intelligence (AI) is increasingly entering higher and professional education, yet evidence on students’ pre-implementation readiness remains limited in North African health professions education. This study examined factors associated with undergraduate nursing students’ behavioral intention to use AI in Moroccan paramedical education using an extended UTAUT-informed model. Methods: An analytical cross-sectional survey was conducted among 359 second- and third-year nursing students enrolled in public paramedical training institutes in the Casablanca-Settat region, Morocco. The questionnaire assessed performance expectancy, effort expectancy, social influence, facilitating conditions, AI readiness, positive attitude toward AI, AI literacy and behavioral intention. Descriptive statistics, reliability analyses, group comparisons, Spearman correlations and multiple linear regression were performed. Results: The regression model explained 59.6% of the variance in behavioral intention. Performance expectancy was the strongest independent predictor (β = 0.267, p<0.001), followed by social influence (β = 0.217, p<0.001), positive attitude toward AI (β = 0.206, p<0.001), academic level (β = 0.204, p = 0.004), AI readiness (β = 0.109, p = 0.018) and facilitating conditions (β = 0.087, p = 0.041). Effort expectancy and AI literacy were not independent predictors after adjustment. Conclusions: Students' intentions to adopt AI are predominantly shaped by factors such as perceived educational value, social influence, positive attitudes, readiness and conducive institutional conditions. These insights offer preliminary evidence to support the responsible integration of AI in Moroccan health professions education and similar settings.