ArticleBMC nursing2026
Artificial intelligence literacy and influencing factors among clinical nurses in southeastern 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. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence literacy and associations with thriving at work among nurses in Anhui Province, China: a latent profile analysis.BMC nursing · 2026Article
- Hospital Digital Transformation, Nursing Data Burden, and Leadership and Governance Capacity: A Critical Narrative Review.Journal of nursing management · 2026Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe rapid integration of artificial intelligence (AI) in clinical settings presents unprecedented opportunities, yet it concurrently triggers significant challenges, including ethical dilemmas, technology-induced anxiety, and care quality. Although clinical nurses are the primary users of healthcare AI, their literacy levels and cognitive profiles remain largely unexplored. This knowledge gap hinders the development of targeted interventions necessary to ensure the safe and effective deployment of AI in patient care.
objectiveTo identify the latent profiles of AI literacy among clinical nurses, explore the factors influencing profile membership, and provide insights for clinical nursing management.
methodsA cross-sectional study was conducted using a convenience sample of clinical nurses from 3 tertiary hospitals in southeastern China between December 2025 and February 2026. Data were collected using a sociodemographic questionnaire, the AI Literacy Scale, the Attitude Towards the Use of AI Technologies in Nursing Scale, and the Innovative Behavior Inventory. Latent profile analysis (LPA) was utilized to identify distinct AI literacy profiles, followed by univariate analysis and multivariate logistic regression to determine the factors associated with profile membership.
resultsOf the 375 distributed questionnaires, 366 valid responses were obtained (response rate: 97.6%). The mean AI literacy score of the participants was 57.61 ± 11.27. LPA revealed three distinct latent profiles: the "low AI literacy-low use" group (21.00%), the "moderate AI literacy-high evaluation" group (71.60%), and the "high AI literacy-high ethics" group (7.40%). Multivariate logistic regression indicated that educational attainment, working experience, professional position, prior AI-related training, attitudes toward AI application, and innovative behavior scores were significant predictors of AI literacy profile membership (P < 0.05).
conclusionWhile the overall AI literacy of clinical nurses in southeastern China is at a moderate-to-high level, significant heterogeneity exists within the population. Understanding these distinct profiles and their associated factors may inform the development of tailored educational strategies. Providing foundational support for low-literacy groups and encouraging advanced ethical discussions among highly literate groups could potentially assist nurses in adapting to the increasing integration of AI in clinical practice.
trial registrationThis study does not involve clinical trials or interventional procedures and therefore does not meet the criteria for clinical trial registration.
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