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Artificial intelligence literacy and influencing factors among clinical nurses in southeastern China: a latent
Jingyi Li1, Yousheng Liu2, Liting Xiang3
1Urology Department, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Background:
The 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.
Objective:
To identify the latent profiles of AI literacy among clinical nurses, explore the factors influencing profile membership, and provide insights for clinical nursing management.
Methods:
A 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.
Results:
Of 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).
Conclusion:
While 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 Registration:
This study does not involve clinical trials or interventional procedures and therefore does not meet the criteria for clinical trial registration.
