Artificial Intelligence Perceptions and Readiness Among Clinical Nurses: A Latent Profile Analysis With Implications for Nursing Management.

AIMS: This study aimed to identify latent profiles of artificial intelligence (AI) perceptions and readiness among clinical nurses based on their AI-related attitudes, literacy, self-efficacy, and anxiety, and to explore the influencing factors associated with each profile. MATERIALS AND METHODS: Nurses were recruited via convenience sampling from public hospitals in China. A total of 1018 valid questionnaires were included for final analysis. Latent profile analysis was performed to classify subgroups. Multivariate logistic regression was adopted to explore the influencing factors of different profiles. RESULTS: Three profiles were identified: the Low AI Perceptions and Readiness Profile (66.8%), characterized by the lowest AI attitudes, literacy, and self-efficacy with moderate anxiety; the Ambivalent AI Perceptions and Readiness Profile (20.8%), exhibiting moderate attitudes and literacy but the highest self-efficacy and anxiety; and the High AI Perceptions and Readiness Profile (12.4%), demonstrating the highest attitudes, literacy, and self-efficacy with the lowest anxiety. Significant differences across profiles were found in education level, work experience, preceptor role, AI training experience, region, hospital level, and teaching hospital status. CONCLUSIONS: Clinical nurses exhibit substantial heterogeneity in AI perceptions and readiness patterns. Most are in the Low AI Perceptions and Readiness group due to insufficient training and limited resources. The Ambivalent AI Perceptions and Readiness group consists of nurses with mixed AI perceptions and high anxiety levels who require targeted support, while the High AI Perceptions and Readiness group can serve as peer mentors. These findings highlight the necessity of profile-based training to enhance AI integration in nursing education and practice. IMPLICATIONS FOR NURSING MANAGEMENT: The three profiles identified in this study provide nurse administrators with an empirically based reference for differentiating educational approaches across learner groups. This classification can assist in designing targeted content, structuring peer support, and planning curricula to facilitate AI integration in nursing practice.