Building a predictive model for nursing students’ use of artificial intelligence: advancing technology acceptance and application in nursing education

BMC Nursing https://doi.org/10.1186/s12912-026-04853-z Article in Press Building a predictive model for nursing students’ use of artificial intelligence: advancing technology acceptance and application in nursing education Huiling Zhang, Qianqian Hu, Shuang Yu, Hui Shi, Zheyuan Xia & Fang Meng Received: 1 September 2025 Accepted: 1 June 2026 Cite this article as: Zhang H., Hu Q., Yu S. et al. Building a predictive model for nursing students’ use of artificial intelligence: advancing technology acceptance and application in nursing education. BMC Nurs (2026). https://doi. org/10.1186/s12912-026-04853-z A S S We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. ACCEPTED ARTICLEMANUSCRIPT IN PRESS Building a Predictive Model for Nursing Students' Use of Artificial Intelligence: Advancing Technology Acceptance and Application in Nursing Education Huiling Zhang1 ,QianqianHu1, ShuangYu1*,Hui Shi1*, Zheyuan Xia1*, FangMeng2* Corresponding Author Huiling Zhang（） ShuangYu(41871521qq.com), HuiShi(), ZheyuanXia (),Fang Meng() 1Key Laboratory of Geriatric Nursing and Health, School of Nursing, Anhui University of Chinese Medicine, Hefei, China 2Xuzhou Medical University, Xuzhou, China Funding Information S S E R P This work was supported by the Anhui Provincial Higher Education Institution Key Project of Natural Science Research (No. 2023AH050774); IN Anhui Provincial Higher Education Quality Engineering Project (No. E L C I T R A 2023jyxm0354); and the General Project of Teaching Research of Anhui University of Chinese Medicine (No. 2024xjjy_yb037). Conflicts of Interest The authors declare that they have no conflicts of interest. 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. Acknowledgements The authors extend their sincere gratitude to the Laboratory of Geriatric Nursing and Health, School of Nursing, Anhui University of Chinese Medicine, for their support and resources. Author Contributions Huiling Zhang contributed to the conceptualization, study design, data ACCEPTED ARTICLEMANUSCRIPT IN PRESS acquisition, analysis, and interpretation. Fang Meng contributed critically to the revision and intellectual enhancement of the manuscript. Both authors reviewed and approved the final version of the manuscript. Abstract Background With 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. Objective S S E R P This 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 IN constructs a predictive model to provide theoretical support and practical E L C I T R A guidance for the application of AI in nursing education. Methods A 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. Results The 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 ACCEPTED ARTICLEMANUSCRIPT IN PRESS influenced by their technical literacy but also by their attitudes and perceptions toward the technology. Conclusion Educational 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. 1. Introduction With the rapid advancement of artificial intelligence (AI) technology, AI has increasingly permeated various industries, particularly healthcare and S S E R P nursing1. In recent years, AI has demonstrated significant potential in enhancing the quality of nursing education, optimizing clinical practice, and improving the overall competencies of nursing students. Studies have IN shown that AI can revolutionize traditional teaching models, boost the E L C I T R A efficiency of clinical decision-making support, and enable nursing students to acquire skills more independently, adapt more effectively, and strengthen their problem-solving capabilities2.In this study, artificial intelligence (AI) primarily refers to AI-based educational and clinical support tools, such as intelligen (...truncated)