Cross-sectional survey reveals artificial intelligence self-efficacy drives innovative behavior in nursing students, highlighting the role of self-directed learning.
BACKGROUND: Artificial intelligence (AI) technology is increasingly integrated into health care. Developing nursing students' innovation capacity takes center stage in nursing education. PURPOSE: To explore relationships and influence pathways among nursing students' AI self-efficacy, AI reliance, self-directed learning ability, and innovative behavior. METHODS: A cross-sectional survey was conducted with 414 nursing students from 3 Shandong medical colleges. Data were collected using a demographic questionnaire and 4 standardized scales and analyzed using descriptive statistics and correlation. Serial mediation analysis was carried out with the PROCESS macro (Model 6). RESULTS: AI self-efficacy predicted innovative behavior directly (β = .107) and indirectly through AI reliance (β = .063), self-directed learning ability (β = .112), and their serial mediation (β = .073). CONCLUSIONS: AI self-efficacy directly and indirectly influences nursing students' innovative behavior through AI reliance and self-directed learning ability, providing insights into predictors of undergraduate nursing students' innovative behavior.
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Zhang et al. (2026) studied this question.
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