Abstract
<title>Abstract</title> <p> Background Artificial intelligence (AI) is rapidly transforming healthcare, particularly in clinical decision support, patient monitoring, and personalized care. As future frontline healthcare providers, nursing students’ AI literacy is crucial for their professional development and the delivery of high-quality, safe patient care in intelligent healthcare environments. However, previous studies have primarily adopted variable-centered approaches, often overlooking the heterogeneous nature of AI literacy among nursing students. There is limited evidence regarding the latent profiles of AI literacy and its associated factors, especially in resource-limited regions such as Inner Mongolia. This study aimed to identify latent classes of AI literacy among nursing students and explore influencing factors, providing empirical support for stratified AI education in nursing programs. Methods A convenience sample of 522 nursing students from a university in eastern Inner Mongolia was recruited in December 2025. Data were collected using a general demographic questionnaire, the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS), the Artificial Intelligence Self-Efficacy Scale (AISE), and the Attitude Scale towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN). Latent profile analysis (LPA) was used to identify subgroups, followed by one-way analysis and multinomial logistic regression to examine associated factors. Results Three distinct latent classes emerged: low-level foundational weakness group (42.25%), medium-level development group (42.41%), and high-level comprehensive development group (15.34%). Multinomial logistic regression indicated that AI self-efficacy, attitudes toward AI technologies in nursing, and concern about AI ethical issues were independent predictors of class membership (all <italic>P</italic> < 0.05). Conclusions Nursing students demonstrate significant heterogeneity in AI literacy. Educators should prioritize AI ethical awareness, self-efficacy, and positive technology attitudes. Implementing tailored, stratified interventions according to students’ literacy profiles is recommended to enhance their professional competence in AI-enhanced healthcare settings. </p>