Artificial Intelligence Literacy Among Nursing Students: A Scoping Review of Status, Influencing Factors, and Educational Implications (Preprint)

BACKGROUND Smart healthcare is evolving at a rapid pace, setting new requirements for nursing students: mastering artificial intelligence (AI) literacy has quickly become a core competency for them to establish their future careers. OBJECTIVE To conduct a scoping review of studies on AI literacy among nursing students, clarifying its current status, dimensional characteristics, core influencing factors, mechanisms linking to core nursing competencies, and educational intervention effectiveness, to provide evidence for nursing education digital transformation and AI literacy cultivation. METHODS Following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines, we searched eight electronic databases: PubMed, Embase, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, China Biomedical Literature Database (CBM), and VIP Chinese Scientific Journal Database. Two reviewers independently performed literature screening, full-text review, and data extraction, with discrepancies resolved by a third reviewer. RESULTS A total of 47 studies (2023–2026) from 18 countries/regions were included, dominated by China (30.0%), South Korea (8.3%), and Saudi Arabia (11.7%). The majority of studies (n=30, 63.8%) employed a cross-sectional design, and 71.4% of participants were undergraduate nursing students. Overall, nursing students' AI literacy was reported to be at a moderate level, with uneven development across dimensions. While AI tools were widely utilized for basic learning support, their deep integration with clinical practice remained limited. Key weaknesses were identified in technical understanding, critical evaluation, and ethical cognition. AI literacy was influenced by a multifaceted set of individual (education level, AI training experience), educational (lack of structured curriculum), environmental (tool defects, resource inequality), psychological (AI self-efficacy, anxiety), and ethical factors. It demonstrated consistent positive correlations with multiple nursing core competencies, including clinical performance (β=0.432, P