Researchers collected responses from 20 April to 1 May 2026. Of 375 questionnaire submissions, 362 met the study’s quality-control criteria, producing an analytic retention rate of 96.5%. Separate item-level confirmatory factor analyses evaluated a four-factor structure of AI literacy comprising knowledge, skills, values and ethics. The model showed good fit and strong item loadings, and skills and values were highly correlated. Additional measurement analyses supported perceived usefulness, perceived ease of use and attitudes toward artificial intelligence as correlated but distinguishable constructs.

The fully adjusted multivariable regression associated three characteristics with mean AI-literacy scores: more frequent AI use, previous AI education and interest in AI. These findings show positive statistical relationships after adjustment for the variables included in the model, rather than experimental effects. The population was limited to first-year nursing students, and the geographically narrow, cross-sectional design prevents conclusions about causality, changes over time or how the pattern might differ in other provinces, countries, educational programmes or later stages of nursing training.

A regularised partial correlation network was then used to map conditional relationships among seven dimension-mean nodes: knowledge, skills, values, ethics, perceived usefulness, perceived ease of use and attitudes toward AI. Of 21 potential network edges, 17 were non-zero. Skills had the highest expected influence, while perceived usefulness had the highest strength. Bootstrap diagnostics supported strong stability of those two network measures under case removal, with a case-dropping stability coefficient of 0.751 for both. Near-zero intraclass correlation estimates indicated little clustering by institution, and institution-adjusted analyses preserved the main regression and network findings.

The ranking of one-step bridge connectivity depended on the network partition used. Values ranked first under the primary two-community partition, whereas perceived usefulness ranked first under the alternative four-community partition; perceived ease of use ranked second in both. The researchers concluded that AI-use frequency, prior AI education and interest were positively associated with AI literacy, while skills, perceived usefulness, values and perceived ease of use may help identify questions for prospective evaluation of AI education in undergraduate nursing programmes. The network rankings and regression associations describe the observed sample and do not prove that any dimension should be prioritised in a curriculum or that an educational intervention will improve literacy or technology acceptance.

Subject of Research: Cross-sectional analysis of AI literacy and technology-acceptance constructs among first-year nursing students in China

Article Title: Artificial intelligence literacy and technology acceptance model-related constructs among first-year nursing students: a multi-institutional cross-sectional network analysis

Article References: Wang, Y., Zhao, Y., Li, L., Yu, Y., Zhu, L., Ma, S., & Yang, Z. (2026). Artificial intelligence literacy and technology acceptance model-related constructs among first-year nursing students: a multi-institutional cross-sectional network analysis. BMC Nursing. https://doi.org/10.1186/s12912-026-05449-3

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05449-3

Keywords: artificial intelligence literacy, nursing education, first-year students, network analysis, technology acceptance, perceived usefulness, perceived ease of use, AI skills, AI ethics, China, Artificial, intelligence