What Shapes Nurses’ Attitudes Toward AI? A Review of 30 Studies Points to Training and Experience

A systematic review of 30 studies covering 12,958 registered nurses has identified the factors most consistently associated with how nurses view artificial intelligence and how literate they report themselves to be in using it. The review, conducted by researchers at The Second Affiliated Hospital of Chongqing Medical University in China and published in Frontiers in Public Health, followed PRISMA guidelines and searched seven English-language databases—PubMed, Web of Science, Medline, Embase, The Cochrane Library, IEEE Xplore and CINAHL—alongside three Chinese databases: CNKI, Wanfang Data and the VIP Database. The search extended from database inception through May 30, 2026, and the team extracted independent factors influencing attitudes and literacy, then synthesized them narratively across thematic domains.

The studies included came predominantly from China and Turkey, a geographic concentration that itself frames how broadly the findings can be generalized. For nurses’ attitudes toward artificial intelligence, the review identified a wide set of independent factors: educational attainment, age, length of service, gender, professional title or nursing level, hospital tier or category, AI awareness, understanding and knowledge, AI training experience, AI usage experience, artificial intelligence anxiety, and digital leadership. For nurses’ artificial intelligence literacy, the picture was narrower. The factors independently affecting literacy were leadership style, AI training experience, and usage experience or frequency of use.

The authors’ conclusion draws a distinction between what can be changed and what cannot. Demographic characteristics such as age, gender and years of service are fixed attributes of the workforce, whereas training, hands-on experience and organizational leadership are modifiable. Current evidence, they write, indicates that the key modifiable factors for improving nurses’ attitudes toward and literacy in artificial intelligence are AI-related training, practical hands-on experience and organizational leadership, with technical anxiety flagged as a factor that should not be overlooked. Compared with demographic characteristics, educational and experiential interventions appear more effective in enhancing nurses’ AI literacy levels—a notable claim, though the review’s narrative synthesis of cross-sectional studies cannot establish that such interventions cause literacy gains; it can only report consistent associations across the included studies.

Because every included study was cross-sectional, the review captures snapshots of associations rather than tested interventions. The reported factors describe which nurse and organizational characteristics travel together with more favorable attitudes or higher self-assessed literacy, not demonstrations that changing one variable changes the other. The authors nevertheless translate their synthesis into recommendations for nursing managers: implement smart training programs, increase opportunities for nurses to apply new technologies in clinical practice, and formulate policies on data privacy protection and accountability division to support the safe and effective integration of AI into patient care. Those recommendations rest on the same associative evidence base, and their effectiveness as interventions remains to be tested in future studies.

Subject of Research: Factors influencing registered nurses' attitudes toward and literacy in artificial intelligence

Article Title: Factors influencing registered nurses’ attitudes toward and literacy in artificial intelligence: a cross-sectional systematic review

Article References: Dai, Y., Zhao, X., Pan, X., & Wei, J. (2026). Factors influencing registered nurses’ attitudes toward and literacy in artificial intelligence: a cross-sectional systematic review. Frontiers in Public Health, 14, Article 1997701. https://doi.org/10.3389/fpubh.2026.1997701

Image Credits: AI Generated

DOI: 10.3389/fpubh.2026.1997701

Keywords: artificial intelligence, nursing, systematic review, nurses' attitudes, AI literacy, health workforce, training, digital leadership, cross-sectional studies, health policy, Factors, influencing