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

Abstract

BACKGROUND: 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. OBJECTIVE: To examine the mediating role of AI acceptance in the relationship between self-directed learning ability and perceived clinical competence among nursing students. METHODS: 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. RESULTS: 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. CONCLUSION: 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.