Effect of Nursing Students' Artificial Intelligence Self-Efficacy on Innovative Behavior.
Abstract
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.