Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: a latent profile analysis

BMC Nursing https://doi.org/10.1186/s12912-026-04860-0 Article in Press Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: a latent profile analysis Binmi Tang, Yali He, Yuxia Xiang & Yufang Chen Received: 20 April 2026 Accepted: 4 June 2026 Cite this article as: Tang B., He Y., Xiang Y. et al. Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: a latent profile analysis. BMC Nurs (2026). https:// doi.org/10.1186/s12912-026-04860-0 A S S 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. IN E R P If this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. ACCEPTED ARTICLEMANUSCRIPT IN PRESS Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: a latent profile analysis Binmi Tang1*, Yali He1, Yuxia Xiang2, Yufang Chen3 1 Department of Nursing, Traditional Chinese Medicine Hospital of Qingbaijiang District Chengdu, China 2 Department of Cardiology and Nephrology, Traditional Chinese Medicine Hospital of Qingbaijiang District Chengdu China 3 Department of Hemodialysis, Traditional Chinese Medicine Hospital of S S E R P Qingbaijiang District Chengdu, China Correspondence author: Binmi Tang, Abstract E L C I T R A IN Aims: To understand the current state of nurses’ artificial intelligence (AI) literacy. This study employs latent profile analysis to examine the relationship between different profile categories of AI literacy and evidence-based practice competence (EBPC) among nurses. Methods: From January to February 2026, nurses from Qingbaijiang District in Sichuan Province were selected by a self-designed general information questionnaire, the Artificial Intelligence Literacy Scale and the Questionnaire to Evaluate the Competency in Evidence-Based Practice of Registered Nurses. Latent profile analysis was performed to explore the profile categories of nurses’ AI literacy, the single-factor analysis and multivariate logistic regression analysis were employed to investigate the ACCEPTED ARTICLEMANUSCRIPT IN PRESS relevant influencing factors. Results: The AI literacy of nurses could be divided into three categories: the low AI literacy group (48.8%), the moderate AI literacy group (37.1%) and the high AI literacy group (14.1%). AI training and EBPC were the influencing factors of different profile categories (P < 0.001). These profile categories had significant effects on the nurses’ EBPC, as well as its four dimensions: attitude, knowledge, skill and application. Conclusion: Nursing administrators should effectively identify nurses with low AI literacy and develop personalized intervention plans to promote the development of AI literacy and EBPC. Clinical trial number: Not applicable. IN S S E R P Keywords: Artificial intelligence literacy, Evidence-based practice, Latent E L C I T R A profile analysis, Nurses, Nurse administrators Introduction The rapid development of artificial intelligence (AI) is reshaping traditional medical work models, with its application scope expanding from assisting disease diagnosis and providing personalized treatment plans to home robotic care [1]. In nursing practice, AI plays a significant role in optimizing patient care processes, improving patient health outcomes, enhancing nursing work efficiency, and reducing work burden [2-4]. In order to better cope with the wave of AI impact, the World Health Organization (WHO) has put forward new capability requirements for medical staff [5]. AI literacy is increasingly recognized as a crucial competency that nurses need to ACCEPTED ARTICLEMANUSCRIPT IN PRESS possess [6]. AI literacy refers to the comprehensive competence required for nursing professionals (including educators, students, and clinical practitioners) to correctly understand and evaluate AI technologies, make sound ethical judgments, and engage in innovative human-AI collaboration within an AI-driven healthcare environment [7, 8]. Kahraman et al. [9] reported that operating room nurses’ AI literacy was at a low-to-moderate level, with limited practical application of AI. Similarly, Ronquillo et al. [10] identified that nurses’ insufficient understanding of AI concepts and S S E R P application contexts constrain effective AI use in nursing practice. Although nurses generally hold positive attitudes toward AI, their actual use of AI IN technologies remains limited [11]. Previous studies further indicate that E L C I T R A nurses exhibit inadequate technical competence in applying AI tools, as well as limited awareness and capacity to address ethical concerns associated with AI applications [12]. The level of AI literacy has critical implications for nursing practice. Higher AI literacy enhances nurses’ ability to apply AI tools, thereby improving patient care quality [13, 14]. Conversely, low AI literacy may lead to misuse of AI tools, misinterpretation of AI-generated outputs, and erosion of patient trust [13]. However, current AI literacy assessment scales tend to focus mainly on technical operation, with limited exploration of ethical and cognitive dimensions [9]. This indicates that nurses’ AI capabilities in ethical and cognitive dimensions have not received sufficient attention. Therefore, it is necessary to strengthen their AI literacy ACCEPTED ARTICLEMANUSCRIPT IN PRESS from more comprehensive dimensions to fully realize the potential of AI in nursing practice [15]. Evidence-based comprehensive practice ability of competence nurses to (EBPC) acquire, refers screen, a (...truncated)