Working nurses trail students on AI skills use, review of 18 studies finds

The averages hide a split. Nursing students scored higher than working nurses on awareness (79% vs 65%) and implementation (78% vs 55%), and the review links the gap to nurses' lack of formal AI training.

The review also found that many nursing programs lack standardized curricula, faculty training and infrastructure, especially in low-resource settings. Variation between studies was very high (I2 above 97%), so the authors treat the pooled figures as directional rather than definitive.

The authors followed PRISMA 2020 guidelines and searched Medline via PubMed, Web of Science, Cochrane, Scopus, CINAHL and other databases using four progressively broader search levels. After screening, 111 articles were included, covering nursing students, faculty and practicing nurses; both traditional and generative AI were counted.

Quality checks found the 16 cross-sectional studies mostly at low to moderate risk of bias, and the single randomized trial at low risk. Random-effects models pooled awareness, attitude and implementation, with Egger's tests showing no strong publication bias. Meta-regression found year of publication significant only for attitudes.

Attitudes among students (74%) were higher than among nursing staff (63%), though that difference was not significant at the 5% level. The thematic synthesis identified five themes: curriculum integration, faculty readiness, student perception, innovative pedagogy and ethics.

The authors tie the weaker implementation to unprepared faculty, limited funding, inadequate digital infrastructure and a resistant academic community, and say students in urban, high-income regions had far better access to AI tools. They call for mandatory AI competencies, faculty development programs and funded simulation labs.