A latent profile analysis of artificial intelligence literacy among undergraduate nursing students: a cross-sectional study

AIMS: To 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. DESIGN: A cross-sectional study. METHODS: The 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. RESULTS: 686 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). CONCLUSION: The 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.