Artificial intelligence literacy and influencing factors among clinical nurses in southeastern China: a latent profile analysis

BMC Nursing https://doi.org/10.1186/s12912-026-04872-w Article in Press Artificial intelligence literacy and influencing factors among clinical nurses in southeastern China: a latent profile analysis Jingyi Li, Yousheng Liu, Liting Xiang & Guanmian Liang Received: 9 April 2026 Accepted: 8 June 2026 Cite this article as: Li J., Liu Y., Xiang L. et al. Artificial intelligence literacy and influencing factors among clinical nurses in southeastern China: a latent profile analysis. BMC Nurs (2026). https://doi.org/10.1186/ s12912-026-04872-w A We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. E R P S S If this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article. I T R E L C IN © The Author(s) 2026. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. ACCEPTED ARTICLEMANUSCRIPT IN PRESS Artificial intelligence literacy and influencing factors among clinical nurses in southeastern China: A latent profile analysis Jingyi Lia, Yousheng Liua, *, Liting Xiangb, Guanmian Liangb a Urology Department, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China. b Nursing Department, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China. *Correspondence author. Urology Department, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China. (e-mail: ) S S E Background: The rapid integration of artificial intelligence (AI) in PR clinical settings presents unprecedented opportunities, yet it E L C IN concurrently triggers significant challenges, including ethical I T AR 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. ACCEPTED ARTICLEMANUSCRIPT IN PRESS 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 S S E regression to determine the factors associated with profile membership. IN PR Results: Of the 375 distributed questionnaires, 366 valid E L C responses were obtained (response rate: 97.6%). The mean AI I T AR 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 AIrelated training, attitudes toward AI application, and innovative behavior scores were significant predictors of AI literacy profile membership (P < 0.05). ACCEPTED ARTICLEMANUSCRIPT IN PRESS 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 S S E AI in clinical practice. PR Trial registration This study does not involve clinical trials or IN interventional procedures and therefore does not meet the criteria E L C for clinical trial registration. I T AR Keywords Latent profile analysis, Clinical nurses, Artificial intelligence literacy, Influencing factors Introduction Artificial intelligence (AI) refers to the application of computers and technology to replicate intelligent behavior and problemsolving akin to that of a human being [1]. AI has been applied across diverse sectors such as finance, commerce, entertainment, social media, and transportation[2]. The transformative impact of AI in nursing is manifested across a broad spectrum, including ACCEPTED ARTICLEMANUSCRIPT IN PRESS predictive analytics[3], decision-support systems[4], and robotic caregivers[5]. These technologies are fundamentally reshaping care delivery and patient engagement. Their integration into both practice and education is crucial for advancing health outcomes, personalizing care, and preparing nurses for an increasingly digital healthcare environment[6]. The accelerating adoption of AI in nursing practice necessitates a thorough investigation into the perceptions, competencies, and S S E readiness of nurses to effectively interact with and leverage this PR transformative technology. AI literacy is the ability to understand, IN use, and critically evaluate AI applications. It involves recognizing E L C AI, grasping how it works, using AI tools responsibly, and assessing I T AR their implications[7]. Higher levels of AI literacy present significant opportunities for success in technologically advanced settings. Conversely, limited skills in this area may pose substantial challenges to effective adaptation and engagement [8]. Current research indicates a notable gap bet (...truncated)