Enhancing perceived clinical competence of nursing students in the AI era: the role of AI acceptance and self-directed learning

Abstract

In the context of AI-driven transformation in healthcare education, preparing nursing students to effectively engage with generative artificial intelligence tools has become increasingly important. While self-directed learning (SDL) has been consistently associated with clinical competence, the role of AI acceptance in this relationship remains underexplored. To examine the mediating role of AI acceptance in the relationship between self-directed learning ability and perceived clinical competence among nursing students. A cross-sectional, correlational design was employed. Data were collected from 550 nursing students at Alexandria University, Egypt, using validated self-report instruments measuring self-directed learning ability, AI acceptance, and perceived clinical competence. Structural equation modeling was conducted to test the hypothesized relationships and examine the mediating effects. Self-directed learning ability was significantly associated with clinical competence (β = 0.452, p < 0.001), and AI acceptance was positively associated with perceived clinical competence (β = 0.489, p < 0.001). AI acceptance partially mediated the relationship between self-directed learning and perceived clinical competence (indirect effect: β = 0.206, p < 0.001). The model accounted for 51.0% of the variance in clinical competence. The findings indicate that both self-directed learning and AI acceptance are associated with perceived clinical competence, with AI acceptance acting as a mediating factor. These results highlight the relevance of integrating learner-centered approaches with supportive AI-enabled learning environments in nursing education.