Abstract
AIM: This study aimed to explore the formation mechanism of artificial intelligence (AI) usage intention among nurses in public hospitals in Beijing, Sichuan, and Yunnan, China, analyzing the influence of AI literacy on usage intention through AI self-efficacy and general attitudes. METHODS: A multi-center cross-sectional design was adopted, surveying 901 registered nurses via the Wenjuanxing platform from December 26, 2024, to February 25, 2025, with 878 valid questionnaires returned (effective rate 97.45%). Data were collected using the AI Literacy Scale (AILS), General Attitudes toward AI Scale (GAAIS), AI Self-Efficacy Scale (AISES), and AI Usage Intention Scale. Descriptive statistics, correlation analysis, and structural equation modeling (SEM) analysis were conducted using SPSS 26.0 and AMOS 26.0, with case weighting adjustments based on the total number of nurses in each region. RESULTS: Of the respondents, females accounted for 94.08%, those aged 40 and below accounted for 84.03%, and only 14.24% of nurses had received AI training. The average scores for GAAIS, AILS, and AISES were 69.33 ± 10.31, 56.27 ± 8.60, and 107.92 ± 22.35, respectively, with higher scores observed among nurses with master's degrees or above, preceptors, and those in Beijing. GAAIS showed strong positive correlations with AILS (r = 0.549), GAAIS with AISES (r = 0.567), and AILS with AISES (r = 0.684, p < 0.001), and AI usage intention was closely correlated with all three (p < 0.001). Structural equation modeling analysis indicated that the direct effect of AI literacy on usage intention accounted for 30.51%, with indirect effects through AI self-efficacy (21.41%) and general attitudes (14.58%), resulting in a total effect of 0.967 (p < 0.001). CONCLUSION: AI literacy effectively promotes nurses' AI usage intention by enhancing their self-efficacy and improving their attitudes toward AI, with self-efficacy being particularly crucial. This mechanism, combining both direct and indirect effects, suggests that enhancing confidence and knowledge is key to promoting AI acceptance. Given the low training participation rate (14.24%) and regional disparities (Beijing outperforming Yunnan), it is recommended that hospitals implement systematic AI training, prioritizing groups with low training exposure and underdeveloped regions, while simultaneously improving attitudes through promotional activities to advance the widespread adoption of AI in nursing and elevate patient care standards.