Unpacking the heterogeneity of AI literacy among nursing undergraduates: A latent profile analysis

Artificial intelligence (AI) is increasingly transforming healthcare, making AI literacy an essential competency for future nurses. However, variable-centered studies may overlook heterogeneity in nursing students' AI literacy.

To identify latent profiles of AI literacy among nursing undergraduates and examine demographic and psychological factors associated with profile membership.

A cross-sectional study was conducted among 342 nursing undergraduates recruited by convenience sampling. AI literacy was measured using the Artificial Intelligence Literacy Scale. Latent Profile Analysis was performed based on 12 scale items, and multinomial logistic regression was used to examine associated factors.

Three AI literacy profiles were identified: Moderate AI Literacy Profile (48.0%), Passive Adopters with Low Ethical Vigilance Profile (36.8%), and High AI Literacy Profile (15.2%). Critical thinking positively predicted membership in the High AI Literacy Profile (OR = 5.196, p < 0.001), whereas lower family income (<2000 CNY) reduced the likelihood of belonging to this profile (OR = 0.073, p = 0.018). Higher learning motivation decreased the likelihood of membership in the Passive Adopters with Low Ethical Vigilance Profile (OR = 0.484, p < 0.001), while higher professional identity increased the likelihood of belonging to this profile (OR = 1.484, p = 0.017).

Nursing undergraduates showed heterogeneous AI literacy profiles rather than a simple low-to-high pattern. Critical thinking and socioeconomic resources were associated with higher AI literacy, while professional identity alone may not ensure balanced AI literacy development. Tailored educational strategies are needed to strengthen AI knowledge, critical thinking, autonomous learning motivation, ethical awareness, and equitable access to AI-related learning resources.