Potential categories and associated factors of artificial intelligence literacy in Chinese nurses: a multicenter cross-sectional study

Artificial intelligence is reshaping the global healthcare service model, placing new demands on the capabilities of nurses. Artificial intelligence literacy is a multi-dimensional concept encompassing awareness, application, evaluation, and ethics. Although artificial intelligence literacy is crucial for integrating artificial intelligence into nursing practice, existing studies mostly adopt a variable-centered approach, which may overlook the heterogeneity of clinical nurses’ artificial intelligence literacy. The purpose of this study was to identify the potential profile of Chinese nurses ' artificial intelligence literacy and to explore the related factors of profile attribution. This study adopted a multi-center cross-sectional design and was conducted in six hospitals in three provinces of China from January to April 2026. A total of 1160 registered nurses completed the questionnaire survey. The study used general data questionnaire, artificial intelligence literacy scale, artificial intelligence work attitude scale and organizational support scale for data collection. Latent profile analysis was used to identify the heterogeneity of nurses ' artificial intelligence literacy, and multivariate logistic regression analysis was used to explore the influencing factors of different artificial intelligence literacy profiles. This study identified four potential profiles of artificial intelligence literacy: the actively embracing type, the knowledge-application dissociation type, the skill-oriented type, and the weak literacy type. Among them, the actively embracing type accounted for the lowest proportion, and the majority of nurses belonged to the knowledge-application dissociation type and the skill-oriented type. Age, education level, artificial intelligence training experience, frequency of artificial intelligence use, attitude towards artificial intelligence, and organizational support were significantly associated with profile attribution. Most clinical nurses are classified as either knowledge-application dissociated type or skill-oriented type. Special attention should be paid to nurses with lower educational attainment and those without prior experience in artificial intelligence training, as these nurses may encounter greater obstacles in developing artificial intelligence literacy. Additionally, enhancing nurses’ positive attitudes towards artificial intelligence and improving organizational support may be associated with improved nurses’ artificial intelligence literacy. Not applicable.