ArticleBMC nursing2026
A latent profile analysis of artificial intelligence literacy among undergraduate nursing students: a cross-sectional study.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI Dependence and Self-Reported Creativity Among Chinese College Students: The Roles of Motivation for AI Use and Perceived AI Literacy.Behavioral sciences (Basel, Switzerland) · 2026Article
- AI literacy and ethical attitudes toward AI in medical students: evidence from a structural model.BMC medical education · 2026Article
- Artificial intelligence literacy and associations with thriving at work among nurses in Anhui Province, China: a latent profile analysis.BMC nursing · 2026Article
- Profiles of artificial intelligence literacy and associations with evidence-based practice competence among nurses: a latent profile analysis.BMC nursing · 2026Article
- Clinical nursing interns' perceptions of artificial intelligence-assisted tools in human-AI collaboration: a qualitative persona-based study.Frontiers in public health · 2026Article
- Latent profiles of college students' AI literacy and their association with AI acceptance: the sequential indirect role of perceived affordances and growth mindset.Frontiers in psychology · 2026Article
- The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity.Frontiers in psychology · 2026Article
- How does artificial intelligence literacy affect university students' innovative behavior? A serial mediation analysis based on latent profiles.Frontiers in psychology · 2026Article
- Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis.Frontiers in public health · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
aimsTo understand the current status of artificial intelligence (AI) literacy among undergraduate nursing students through latent profile analysis, identify potential subgroups and their population characteristics, and analyze the influencing factors of different profile categories.
designA cross-sectional study.
methodsThe study utilized 686 undergraduate nursing students from an undergraduate college in Guangdong Province as research subjects. The study employed a number of research tools, including demographic characteristics, the Artificial Intelligence Literacy Scale (AILS), and the Artificial Intelligence Self-Efficacy Questionnaire. A latent profile model of undergraduate nursing students’ AI literacy was analyzed using Mplus 8.3. The influencing factors of each profile model were analyzed by multiple logistic regression analysis.
results686 nursing students were finally included. Undergraduate nursing students’ AI literacy score was (64.69 ± 11.00). Undergraduate nursing students’ AI literacy could be categorized into three latent profile analysis: low AI literacy group (18.80%), moderate AI literacy group (58.50%) and high AI literacy group (22.70%). Logistic regression analysis showed that grade, only child status, mother’s education level, whether interested in AI technology, frequency of AI technology use in the past 3 months, whether used AI tools and AI self-efficacy were the influencing factors of potential categories among undergraduate nursing students’ AI literacy (P < 0.05).
conclusionThe findings revealed the heterogeneity of AI literacy among undergraduate nursing students and could guide the identification and early intervention of undergraduate nursing students with low AI literacy. CLINICAL TRIAL NUMBER: Not applicable.
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