Artificial intelligence (AI) in nursing education: An evolutionary concept analysis.

BACKGROUND: Although Artificial Intelligence (AI) has been explored for decades in nursing education, the existing body of knowledge provides no clear definition of the phenomenon. As technological advancements continue to shape teaching and learning environments, establishing a clear and robust definition of AI in nursing education is critical. AIM: To clarify the concept of AI in nursing education from an evolutionary perspective. METHODS: We conducted a concept analysis following Rodgers' evolutionary method. The searches were performed in September 2024 in MEDLINE/PubMed, CINAHL, Scopus, Web of Science, and ProQuest. Peer-reviewed English literature was included without time frame specifications. The components of Rodgers' evolutionary method (attributes, antecedents, consequences, surrogate terms, and related concepts) were identified and analysed using a deductive-inductive approach. Findings were synthesised into a conceptual model, supported by an illustrative exemplar of AI in nursing education. RESULTS: Out of 1559 records retrieved, a total of 33 articles were included. Key attributes of AI in nursing education included computer systems and interdisciplinary knowledge. Antecedents included curriculum integration, workforce preparation, faculty preparedness, and ethical governance. Consequences were personalised learning and individualised feedback. Surrogate terms were generative AI models and machine learning systems. Related concepts included clinical simulation, and nursing informatics. AI in nursing education is defined as the application of intelligent computational systems to the teaching and learning process, augmenting the intelligence of nursing students and educators and enabling personalised teaching and learning experiences within technology-enhanced educational ecosystems. CONCLUSIONS: Our definition may contribute to a paradigm shift by embedding computational intelligence into nursing education. This is important in moving teaching and learning beyond the transmission of static content and toward adaptive and intelligent learning systems. Further research is warranted to advance conceptual development that can guide the evidence-informed integration of AI into nursing education.