ArticleBMC nursing2026
Building a predictive model for nursing students' use of artificial intelligence: advancing technology acceptance and application in nursing education.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundWith the rapid development of artificial intelligence (AI) technology, its application across various industries, particularly in healthcare and nursing, has been expanding. However, the factors influencing nursing students' acceptance and use of AI tools have not been fully explored. Understanding the key factors that affect nursing students' use of AI tools is crucial to enhancing AI integration into nursing education.
objectiveThis study aims to analyze the multidimensional factors influencing nursing students' use of AI tools during their studies and internships. Using the Technology Acceptance Model (TAM) and nomograms, the study constructs a predictive model to provide theoretical support and practical guidance for the application of AI in nursing education.
methodsA survey-based research design was employed, collecting data from 178 full-time undergraduate and graduate nursing students at Anhui University of Chinese Medicine. The questionnaire addressed variables including students' educational background, attitudes toward artificial intelligence, and AI literacy. Multivariate regression analysis was conducted to establish a predictive model for nursing students' use of AI tools.
resultsThe results indicate that educational background, attitudes toward artificial intelligence, and AI literacy significantly influence nursing students' intention to use AI tools. The predictive model, built on these factors, achieved an AUC value of 0.79, demonstrating strong discriminatory power and predictive accuracy. The study reveals that nursing students' acceptance of AI in nursing education is not only influenced by their technical literacy but also by their attitudes and perceptions toward the technology.
conclusionEducational background, attitudes toward artificial intelligence, and AI literacy are key determinants of nursing students' intention to use AI tools. The predictive model demonstrated good performance and provides practical guidance for integrating AI into nursing education. Targeted educational interventions focusing on improving AI literacy and fostering positive attitudes may enhance AI adoption among nursing students. CLINICAL TRIAL REGISTRATION NUMBER: Not applicable. This study is an observational investigation focusing on nursing students' utilization of artificial intelligence and does not involve any interventions or treatments. Therefore, it does not meet the criteria for clinical trial registration.
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