Characteristics and determinants of artificial intelligence (AI) literacy in Chinese nursing students: A cross-sectional study

Overview

This cross-sectional study examined artificial intelligence literacy among nursing students in China, focusing on both the current levels of competency and the factors that predict literacy outcomes. The investigation was motivated by the rapid integration of artificial intelligence technologies into clinical practice, nursing education, and healthcare research, which necessitates immediate adaptation to address ethical risks and ensure patient safety. The study employed a comprehensive assessment framework that evaluated multiple dimensions of artificial intelligence literacy, including awareness, operational usage, evaluation capabilities, and ethical considerations. The research targeted nursing students enrolled in a Master of Science program at a public higher education institution, utilizing convenience sampling to recruit participants over a three-week period in April 2025.

Methods and approach

The study utilized a cross-sectional design with data collection conducted through anonymous, self-administered online questionnaires distributed to 423 nursing students between April 1 and April 20, 2025. Participants were recruited via convenience sampling from a single public institution offering graduate-level nursing education. The assessment instrument measured artificial intelligence literacy across multiple dimensions, yielding a composite scale score as well as domain-specific subscores. Analytical procedures included computation of descriptive statistics such as means, standard deviations, frequencies, and percentages to characterize the sample distribution. Multivariable linear regression analysis was performed to identify determinants of artificial intelligence literacy, with adjustment for relevant covariates to control for confounding variables. The regression modeling examined both overall literacy scores and dimension-specific outcomes to detect differential associations across competency domains.

Results

The nursing student cohort demonstrated moderate artificial intelligence literacy, with a mean scale score of 59.67 (SD = 8.52), though performance varied substantially across domains. The ethics dimension emerged as the least developed area of competency, contrasting with relatively stronger performance in operational usage skills. Multivariable regression analysis identified frequency of artificial intelligence use, attitudes toward artificial intelligence, and digital literacy as significant predictors of overall artificial intelligence literacy. Dimension-specific analyses revealed distinct patterns of association: awareness was correlated with gender, attitudes toward artificial intelligence, interest in artificial intelligence, and digital literacy; usage was associated with age, frequency of artificial intelligence use, and attitudes toward artificial intelligence; evaluation competency was linked to attitudes toward artificial intelligence; and ethics showed association with gender. These findings indicate that artificial intelligence literacy in nursing students is shaped by a combination of exposure-related factors, attitudinal variables, and foundational digital competencies, with different predictors exerting influence on specific literacy dimensions.

Implications

The identification of artificial intelligence ethics as the most deficient domain among Chinese nursing students indicates a critical gap in preparation for technology-integrated healthcare delivery. The study demonstrates that artificial intelligence literacy is not a unitary construct but rather comprises distinct dimensions requiring targeted educational interventions. The finding that frequency of use, attitudes, interest, and digital literacy collectively shape literacy profiles suggests that educational strategies must address both technical skill development and attitudinal formation. Practical implications include the need for curriculum reforms incorporating specialized artificial intelligence ethics workshops and enhanced digital literacy instruction to address identified deficiencies. The dimension-specific patterns of association suggest that interventions should be tailored to target particular competency areas, with ethics education requiring specific attention given its underdevelopment relative to operational skills. These findings inform policy development in nursing education, indicating that preparation for artificial intelligence-integrated healthcare environments requires comprehensive approaches that extend beyond technical training to encompass ethical reasoning, critical evaluation capabilities, and foundational digital competencies. The results underscore the urgency of educational adaptation to ensure that nursing graduates possess the literacy required for safe and ethical practice in technology-driven healthcare contexts.

Disclosure

• Research title: Characteristics and determinants of artificial intelligence (AI) literacy in Chinese nursing students: A cross-sectional study
• Authors: Xuefen Lan, M. Li, Yu Wang, Miaoqin Chen, Heyun Jiang, Shunfei Lu, Ying Zhou
• Publication date: 2026-01-07
• DOI: https://doi.org/10.1016/j.ijnsa.2026.100482
• OpenAlex record: View
• Image credit: Photo by EqualStock on Unsplash (Source • License)
• Disclosure: This post was generated by artificial intelligence. The original authors did not write or review this post.