ArticleBMC nursing2026
Artificial intelligence literacy and associations with thriving at work among nurses in Anhui Province, China: a latent profile analysis.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe integration of artificial intelligence (AI) technologies into clinical nursing is changing nursing practice and creating new competency requirements for nurses. Some nurses may still face difficulties in technical adaptation, ethical judgment, and practical use of AI tools in intelligent healthcare environments. Understanding nurses' AI literacy and its relationship with thriving at work may help hospitals design more targeted support strategies.
aimsThis study aimed to investigate the current status of nurses' artificial intelligence literacy, identify latent profiles of self-reported AI literacy, analyze factors associated with profile membership, and examine differences in thriving at work across AI literacy profiles.
designA cross-sectional study.
methodsIn January 2026, 1000 nurses from 62 hospitals in Anhui Province, China were recruited by convenience sampling. Data were collected using a general information questionnaire, the Artificial Intelligence Literacy Scale, the Chinese version of the Thriving at Work Scale, a nine-item measure of attitudes toward AI in nursing, and the General Self-Efficacy Scale. Latent profile analysis was used to classify nurses' AI literacy profiles. Factors associated with profile membership were examined using univariate analysis and multinomial logistic regression. Scores on the Thriving at Work Scale were compared across AI literacy profiles.
resultsThree distinct latent profiles were identified: Ethical Awareness Deficit profile (n = 332, 33.20%), Cognition Practice Gap profile (n = 538, 53.80%), and Comprehensive Literacy Advantage profile (n = 130, 13.00%). Educational level, key nursing position/department management role, years of work experience, AI training experience, AI usage frequency in the past 6 months, attitude toward AI in nursing, and self-efficacy were associated with AI literacy profile membership (all P < 0.05). Scores on the Thriving at Work Scale differed significantly across the three profiles (P < 0.05).
conclusionsThis multicenter cross-sectional study identified three latent profiles of self-reported artificial intelligence literacy among nurses from hospitals in Anhui Province, China. Thriving at work differed significantly across these profiles. The findings may inform stratified educational and managerial strategies to support nurses' AI literacy and thriving at work in similar clinical contexts. CLINICAL TRIAL NUMBER: Not applicable.
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