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Xuefen Lan

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Review Aug 2026

Artificial intelligence (AI) literacy educational interventions among nursing students: A systematic review and narrative synthesis.

AIM To systematically synthesize evidence on AI literacy educational interventions among nursing students. BACKGROUND Artificial Intelligence (AI) literacy is increasingly recognized as an important educational priority for nursing students. However, evidence regarding educational interventions designed to enhance AI literacy remains limited and fragmented. DESIGN A systematic review. METHODS Seven databases were searched from inception to March 24, 2026. Studies were included if they: (1) enrolled nursing students as participants; (2) evaluated AI literacy educational interventions; (3) measured AI literacy as an outcome; and (4) employed quasi-experimental or randomized controlled trial designs. Methodological quality was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies and findings were synthesized narratively. RESULTS Three quasi-experimental studies involving 443 nursing students were included. Two studies included comparison groups, whereas one used a single-group pre-test/post-test design. All three studies reported statistically significant improvements in AI literacy following the interventions. One study reported significant improvements in higher-order thinking skills, another reported a significant reduction in AI anxiety and the third demonstrated enhanced performance in academic writing. Methodological quality was rated as high in two studies and moderate in one study. Heterogeneity in study designs and measurement instruments precluded meta-analysis. CONCLUSIONS Preliminary evidence suggests that AI literacy educational interventions may improve nursing students' AI literacy. However, the current evidence base remains limited, with only three quasi-experimental studies identified and no randomized controlled trials available. Future research should prioritize well-designed randomized controlled trials, the development of nursing-specific AI literacy assessment instruments and investigations of long-term outcomes to inform educational practices.

Min-Qi Xia, Qingqing Zhu, Yaxuan Luo et al. · 0 citations
Open access Aug 2026

Artificial intelligence literacy among nursing students and its association with learning engagement

Background Current research does not examine how distinct AI literacy profiles are differentially associated with learning engagement, thereby impeding the development of stratified and precise training plans for nursing students. Objective To identify latent profiles of artificial intelligence literacy among undergraduate nursing students, characterize their distributional features, and examine the relationship between distinct AI literacy profiles and learning engagement. Methods The study included 479 Chinese undergraduate nursing students who finished the Utrecht Work Engagement Scale-Student Version and the Artificial Intelligence Literacy Scale. Latent profile analysis was conducted using item-level AI literacy scores as manifest indicators. Results Three distinct profiles of AI literacy were identified: low literacy—ethically cautious, medium literacy—balanced development, and high literacy—fully mature. Non-parametric test results demonstrated significant differences in learning engagement and its dimensions across the three AI literacy profiles. After controlling for relevant confounding factors in multilevel linear regression analyses, AI literacy profile remained significantly associated with learning engagement, accounting for an additional 31.2% of the variance. Students in the medium and high AI literacy groups demonstrated significantly higher levels of learning engagement compared to those in the low literacy group. Conclusion Undergraduate nursing students’ AI literacy is heterogeneous and markedly related to learning engagement. These findings provide valuable insights for improving student engagement in AI-supported learning environments.

Min Li, Yue Cao, Rui-Lin Zhang et al. · 0 citations