ArticleBMC nursing2026
Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: 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. Not yet cited in PubMed.
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Abstract
aimsTo 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.
methodsFrom 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.
resultsThe 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.
conclusionNursing 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.
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