Nursing Students Show Suboptimal AI Readiness, With Education Level and Coursework Emerging as Key Predictors

A survey of 684 nursing students at two universities in Hubei Province, China, has found that their readiness to work with artificial intelligence remains suboptimal, with measurable gaps concentrated in practical competency rather than conceptual understanding. The study, published in Frontiers in Medicine by Hui Qin and Tao Zeng of Jingchu University of Technology, used the Chinese version of the Medical Artificial Intelligence Readiness Scale for Medical Students (C-MAIRS-MS) to quantify how prepared future nurses feel for an increasingly AI-driven healthcare environment. The students recorded an overall mean AI readiness score of 3.47 on the scale, with a standard deviation of 0.31.

Breaking the results down by dimension, the cognitive domain scored highest at 3.57 (± 0.44), suggesting students are relatively comfortable with the basic ideas behind medical artificial intelligence. The competency dimension scored lowest at 3.32 (± 0.48), indicating that confidence in actually applying AI tools lags behind theoretical familiarity. The authors conclude that this pattern leaves room for improvement in nursing education.

To identify what drives these scores, the researchers combined a general demographic questionnaire with descriptive statistics, univariate analysis, and multiple linear regression. Seven factors emerged as significant predictors of AI readiness: education level, self-rated academic performance, serving as a student cadre, participation in innovation and entrepreneurship projects, frequency of large language model use, AI course learning, and programming foundation. Together, these predictors explained 33.3 percent of the total variance in AI readiness scores (R² = 0.333, adjusted R² = 0.311, F = 15.030, P < 0.001).

That figure means roughly two-thirds of the variation in readiness remains unaccounted for by the measured variables, pointing to other influences that the survey design did not capture. The authors frame the model as evidence for targeted interventions rather than a complete explanation of the phenomenon. The authors identify these activities as significant predictors, though the survey design cannot establish why such experiences relate to greater preparedness.

Because the competency dimension lagged behind the cognitive one, they argue that targeted interventions addressing the key predictors identified in the regression are essential to strengthen AI competency among nursing students. The study's scope also warrants attention. The sample was drawn from two universities in a single Chinese province, so generalizing the findings to nursing students elsewhere requires caution, and the reliance on self-reported readiness means the scores reflect perceived preparedness rather than tested performance.

Subject of Research: Status and influencing factors of artificial intelligence readiness among nursing students

Article Title: Status and influencing factors of artificial intelligence readiness among nursing students

Article References: Qin, H., Zeng, T., & Liu, L. (2026). Status and influencing factors of artificial intelligence readiness among nursing students. Frontiers in Medicine, 13, Article 2007016. https://doi.org/10.3389/fmed.2026.2007016

Image Credits: AI Generated

DOI: 10.3389/fmed.2026.2007016

Keywords: Status, influencing, factors, artificial, intelligence, readiness, nursing, students, scientific research