Work

Artificial intelligence literacy in nursing: a concept analysis

Year: 2026

Type: article

Abstract: Background: As artificial intelligence (AI) technologies are increasingly integrated into nursing education and clinical practice, nurses are required to interact with AI systems in complex decision-making contexts. However, the concept of artificial intelligence literacy remains insufficiently defined in nursing, which may limit the development of effective educational strategies and evidence-based practice guidance. Methods: A comprehensive literature search was conducted in China National Knowledge Infrastructure, Wanfang Data, VIP Database, SinoMed, MEDLINE (via PubMed), Web of Science, and CINAHL (via EBSCOhost) from database inception to October 2025. Studies related to artificial intelligence literacy in nursing were included. Rodgers' evolutionary concept analysis method was applied to examine the conceptual evolution, defining attributes, antecedents, consequences, related concepts, assessment tools, and exemplar cases of artificial intelligence literacy. Results: Thirty-four studies were included. Five defining attributes of artificial intelligence literacy in nursing were identified: Theoretical AI Knowledge, Technical Application Proficiency, Critical Evaluation Skills, Awareness of Ethics and Responsibility, and Human-computer collaborative competence. Antecedents were identified at the individual, educational, and organizational levels. Consequences included enhancing the quality of clinical care, strengthening the role of the nursing profession, improving patient experience, enhancing team collaboration, and enhancing educational effectiveness. A conceptual framework of artificial intelligence literacy in nursing was constructed. Conclusion: Artificial intelligence literacy in nursing extends beyond technical proficiency and represents a context-dependent professional competence grounded in nursing judgment. It is important for supporting safe, ethical, and person-centered nursing care in technology-supported environments.

Source: Frontiers in Public Health

DOI: 10.3389/fpubh.2026.1874659

Authors: Xu Li, Xu Hu, Huiting Xu, Hailing Ju, Pin Yu

Institutions: Tongji University, First Affiliated Hospital of Soochow University, Fudan University, Zhongshan Hospital, Shanghai Tenth People's Hospital

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Quality education

Open Access status: gold

Estimated APC paid: $3,801