Profiles of AI literacy and educational needs of nursing students: a mixed‑method study

Artificial intelligence (AI) is increasingly being integrated into healthcare. This integration provides new opportunities for nursing but also creates challenges. AI literacy is now regarded as an essential competency for nursing students. However, its levels are heterogeneous, and systematic educational integration remains limited. Understanding the distinct profiles of AI literacy and associated educational needs is essential for developing targeted interventions. A mixed-methods cross-sectional study was conducted among nursing students in Zibo. Quantitative data were collected from 403 participants. Latent profile analysis was used to identify subgroups of AI literacy. Multivariate logistic regression was performed to examine factors associated with profile membership. Qualitative data were collected through 15 semi-structured interviews. Educational needs were explored using conventional content analysis. A three-profile model provided the best fit for nursing students’ AI literacy. The three profiles were classified as low literacy (24.57%), moderate literacy (37.22%), and high literacy (38.21%). Internship experience, AI-related training, frequency of daily AI use, participation in innovation and entrepreneurship activities, critical thinking, and ethical sensitivity were identified as significant predictors of profile membership. Qualitative findings showed that educational needs involved both teaching formats and learning content. Interactive and case-based learning was preferred. Virtual simulation and generative AI were also considered useful teaching tools. Content needs included basic AI principles, clinical application skills, and legal and ethical guidelines. Professional identity formation was also emphasised by students with high AI literacy. Nursing students’ AI literacy is heterogeneous and influenced by multiple factors. Educational strategies should be tailored to different literacy profiles, incorporating practical experience, critical thinking and ethics training, and diverse teaching formats aligned with students’ specific needs to effectively enhance AI competency.