Artificial Intelligence in Nursing Education: A Scoping Review of Academic Perspectives.

AIM: To examine nursing academics' perceptions and experiences of artificial intelligence (AI) integration in nursing education. DESIGN: Scoping review. DATA SOURCES: MEDLINE, CINAHL, ERIC, Scopus, and Web of Science were searched in August 2025. METHODS: A scoping review using Joanna Briggs Institute methodology. Peer-reviewed original research and reviews published in English (2019-2025) were included if they examined nursing educators' perspectives, attitudes, or experiences with AI in nursing education across undergraduate, postgraduate, and professional contexts. The Substitution, Augmentation, Modification, Redefinition (SAMR) framework was used to classify pedagogical integration levels. RESULTS: Fifteen studies from eight countries, encompassing 2004 nursing academics, were included. A pattern described as an "adoption paradox" was identified: whilst most academics believe AI will revolutionise nursing education, implementation remains conservative. Two-thirds of applications operate at the augmentation level, with none achieving transformative redefinition. Nursing academics use AI selectively, predominantly for academic productivity and research writing but rarely for student assessment. Primary barriers included knowledge gaps, institutional policy vacuums, and pronounced global access inequities. Academics expressed concerns regarding critical thinking erosion and professional identity threats whilst acknowledging efficiency benefits. CONCLUSIONS: Nursing academics appear to adopt AI selectively, prioritising preservation of core professional values while embracing applications perceived to enhance, rather than replace, educational practice. The absence of transformative integration suggests perceived incompatibilities between artificial intelligence and nursing's relational foundations, signalling a need for more active pedagogical engagement to bridge this widening gap. IMPACT: This review addresses the critical gap in understanding how nursing academics integrate artificial intelligence while maintaining professional values. Despite high optimism, actual implementation remains basic, with multiple barriers limiting transformative adoption. Findings provide evidence for nursing education programs globally regarding faculty development, institutional policy frameworks, and curriculum design strategies integrating technological advancement whilst maintaining person-centred values. NO PATIENT OR PUBLIC CONTRIBUTION: Not applicable, as no patients or public were involved.