The Determinants of Artificial Intelligence Integration in Nursing: A Systematic Review

Abstract

Artificial intelligence is increasingly entering nursing education and clinical work, yet evidence on the factors shaping its integration remains fragmented across populations, settings, and outcome measures. This review investigates and organises the determinants associated with nursing readiness, intention, acceptance, adoption, and use of artificial intelligence. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses informed review synthesised 53 English-language primary empirical studies involving nurses, nursing students, nursing faculty, and nurse leaders published from January 2015 through December 2025. Database searching, citation tracking, targeted updating, critical appraisal, binary determinant mapping, and thematic narrative synthesis were applied. Fourteen determinants were identified, with attitude and demographics examined in 39 studies, artificial intelligence literacy in 37, training in 35, and usefulness in 28. Artificial intelligence literacy, attitude, usefulness, training, and enabling organisational conditions showed the most consistent support, whereas anxiety, ethical trust, and role concerns were context dependent. Social influence and innovativeness were examined in only three and five studies, respectively. The review contributes a staged sociotechnical taxonomy that separates research frequency from evidential influence. Future responsible implementation should align competency development, usability, ethical governance, infrastructure, and organisational support while evaluating sustained real-world use.