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

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 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.