AI Anxiety Emerges as Hidden Barrier Shaping Nursing Students’ Attitudes Toward Artificial Intelligence

Artificial intelligence is rapidly reshaping hospitals, clinics, and the daily workflows of healthcare professionals, yet the people who will soon be running those wards—nursing students—remain surprisingly understudied when it comes to how they actually feel about the technology. A new cross-sectional study from researchers at The Second Xiangya Hospital of Central South University in Changsha, China, published in BMC Medical Education, offers one of the most detailed portraits to date of how future nurses think, worry, and feel about AI. The central finding is striking: the emotional response known as AI anxiety acts as a statistical bridge between what students know about artificial intelligence and how positively they view it, suggesting that teaching technical skills alone may not be enough to build a workforce ready for intelligent healthcare.

The research team, led by Liping Wang, Manhua Nie, Yanchao Xiao, and corresponding author Sai Liu, recruited nursing students from ten universities spanning seven provinces in China between May and June 2025. Using convenience sampling, they collected data through a battery of validated instruments: a self-designed demographic questionnaire, the General Attitudes Towards Artificial Intelligence Scale, the Artificial Intelligence Literacy Scale, and the Artificial Intelligence Anxiety Scale. The final analytical sample comprised 455 nursing students, a substantial cohort drawn from a wide geographic spread that lends the findings considerable weight within the constraints of the study design. All data were analyzed using SPSS Statistics 26.0 and AMOS 29.0, the latter allowing the team to construct and test structural equation models that could probe the relationships among literacy, anxiety, and attitudes simultaneously rather than one pair at a time.

The results paint a nuanced picture of a generation caught between enthusiasm and unease. Nursing students in the sample reported moderate levels of both AI literacy and AI anxiety, while generally maintaining positive attitudes toward artificial intelligence overall. That combination—middling knowledge, middling worry, and a broadly favorable outlook—may reflect the reality of contemporary nursing education, where AI is increasingly visible in coursework and clinical discussion but has not yet become a fully integrated pillar of the curriculum. The students, in other words, are neither technophiles nor technophobes; they occupy a middle ground where their attitudes remain open but their confidence and their fears are both very much alive.

The most consequential finding, however, lies in the mediation analysis. When the researchers modeled the pathways between variables, they found that AI anxiety statistically mediated the association between AI literacy and attitudes toward AI. In practical terms, this means the relationship between knowing about AI and feeling good about AI is not a simple straight line. Higher AI literacy was significantly associated with more positive attitudes, but part of that association appeared to flow through reduced anxiety. Students who understood more about how artificial intelligence works tended to fear it less, and that lower anxiety, in turn, contributed to a more welcoming stance toward the technology. Anxiety, in this model, is not merely a background emotion—it is a mechanism through which education exerts part of its effect.

This mediation structure carries real theoretical weight. In psychological research, a mediator explains how or why one variable influences another, and identifying one changes the design of interventions. If literacy influenced attitudes only directly, the prescription would be straightforward: teach more AI content. But because anxiety sits in the pathway, the study’s authors argue that nursing education may benefit from a dual-path approach—one designed to simultaneously enhance cognitive competencies, meaning AI literacy, and to manage affective responses, meaning AI anxiety. A curriculum that builds technical understanding while explicitly addressing students’ fears about job displacement, clinical error, or professional obsolescence could, according to this model, produce more durable gains in acceptance than either strategy alone.

The technical machinery behind the conclusion deserves attention. Structural equation modeling, implemented in AMOS 29.0, allows researchers to test whether a hypothesized network of causal-style pathways is consistent with observed data, and the team supplemented their primary analysis with a stepwise model modification procedure documented in their supplementary materials, including fit indices and the rationale for each adjustment. The study also reported complete subdimension and total score results for the full sample of 455 participants, and the authors followed the STROBE checklist for reporting cross-sectional research. These methodological guardrails matter because mediation claims are easy to overstate; the authors are careful to describe the mediation as statistical, which is the appropriate caution for cross-sectional data collected at a single point in time.

That caution points to the study’s principal limitation. Because the data are cross-sectional, the direction of the pathways cannot be definitively established. It is plausible that students with more positive attitudes seek out AI knowledge, or that anxiety suppresses the motivation to learn about AI, creating a feedback loop rather than a one-way street. Convenience sampling also limits generalizability, since students who volunteer for surveys about AI may differ systematically from those who decline. The authors acknowledge these constraints implicitly through their careful language, and the field will need longitudinal and experimental designs—perhaps curricular interventions that track literacy, anxiety, and attitudes across an entire degree program—to confirm the causal architecture the mediation model implies.

Even with those caveats, the stakes for nursing are high. Artificial intelligence is widely described as a key driver of the Fourth Industrial Revolution, and its impact on healthcare is accelerating through diagnostic support tools, predictive analytics, intelligent monitoring systems, and administrative automation. Nursing students’ acceptance and readiness to use these systems are crucial for successful integration, because nurses are the professionals who will interact with AI-enabled tools most frequently at the bedside. A workforce that understands AI but fears it may hesitate at the point of care; a workforce that neither understands nor trusts it may resist adoption outright. The study’s framing of perceptions as vital preparation for the intelligent healthcare era reflects this operational reality.

The emotional dimension of technological adoption has often been treated as secondary in health professional education, where competencies and knowledge checklists dominate. This study adds to a growing body of evidence that affect is not noise in the adoption equation but a signal worth measuring and addressing. AI anxiety among students may stem from many sources—uncertainty about future employment, unfamiliarity with algorithmic decision-making, high-profile narratives about automation replacing human work—and the finding that it channels the relationship between literacy and attitudes suggests that ignoring it could undercut even well-designed technical training. Educators who pair AI content with opportunities to discuss fears openly, practice with real tools, and build confidence through supervised exposure may be acting directly on the mechanism the study identified.

The research, conducted by a team based in the Clinical Nursing Teaching and Research Section and the Department of Kidney Transplantation at The Second Xiangya Hospital of Central South University, received ethical approval from the hospital’s Ethics Committee and was funded in part by the Natural Science Foundation of Hunan Province and the Hunan Nursing Association’s Young Talent Initiative. Published open access in BMC Medical Education, it offers educators across nursing and the wider health professions a concrete, testable framework: measure literacy, measure anxiety, and design curricula that move both. As AI continues its advance into clinical medicine, the study suggests that the battle for acceptance will be fought not only in lecture halls and skills labs but in the emotional lives of the students training to become the next generation of caregivers.

Subject of Research: The mediating role of AI anxiety in the relationship between AI literacy and attitudes toward artificial intelligence among nursing students

Article Title: The mediating role of AI anxiety between AI literacy and attitudes towards AI in nursing students: a cross-sectional study

Article References: The mediating role of AI anxiety between AI literacy and attitudes towards AI in nursing students: a cross-sectional study. (n.d.). https://doi.org/10.1186/s12909-026-10522-1

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10522-1

Keywords: artificial intelligence, AI literacy, AI anxiety, nursing students, nursing education, mediation analysis, cross-sectional study, structural equation modeling, healthcare technology, medical education, China, attitudes toward AI