A concept analysis of generative artificial intelligence-based learning support in nursing education - Hyo Hoon Ku (The Journal of Korean Academic Society of Nursing Education, 2026)
Purpose: This study aimed to elucidate the concept of generative artificial intelligence (AI)-based learning support in nursing education using Walker and Avant’s concept analysis method.Methods: A comprehensive review of scholarly articles published between 2020 and 2025 from RISS (Research Information Sharing Service), DBpia, KISS (Korean studies Information Service System), PubMed, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Scopus was conducted. Following Walker and Avant’s framework, the concept’s attributes, model cases, antecedents, consequences, and empirical referents were identified.Results: Generative AI-based learning support was defined as “a reflective teaching-learning process in which learners reconstruct knowledge and refine clinical reasoning competencies through dynamic interaction with AI, reflecting individual needs and clinical contexts”. Five defining attributes were identified: adaptive personalization, contextual presence, cognitive facilitation, conversational interactivity, and reflective instrumentality. Antecedents comprised technological infrastructure and guidelines, learners’ AI and information literacy, ethical awareness and responsibility, and instructors’ educational design competency and willingness to adopt generative AI. Consequences included strengthened critical thinking and clinical reasoning, improved learning outcomes and self-directed learning, enhanced clinical practice readiness, internalized digital literacy and ethical attitudes, and the potential enhancement of nursing care quality and patient safety.Conclusion: This analysis provides a theoretical foundation for generative AI-based learning support in nursing education. It distinguishes this concept from conventional technological assistance by emphasizing cognitive facilitation and reflective learning. The findings can inform curriculum development, instructor training, and future research, with the potential to support nursing care quality and patient safety through clinical reasoning and responsible AI use.