Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice and to explore the factors associated with profile membership with type affiliation.
Abstract
Artificial intelligence (AI) is being gradually integrated into clinical nursing practice, where it plays an important role in improving nursing quality, reducing nurses’ workload, and promoting the intelligent transformation of nursing. Nurses’ attitudes toward AI applications in nursing directly affect the promotion and implementation of this technology. Understanding these attitudes and their heterogeneity is crucial for the successful implementation of AI technology. This study aimed to identify potential types of nurses’ attitudes toward the use of AI in nursing and to explore the factors associated with profile membership with type affiliation. A cross-sectional survey was conducted among 206 clinical nurses in Anhui Province, China, in July 2025. Data were collected using a general information questionnaire, the Attitudes Toward the Application of AI Technology in Nursing Scale, and the Multidimensional Nursing Generations Questionnaire. Latent profile analysis(LPA) was used to identify distinct attitude profiles. Univariate analyses and multinomial logistic regression were performed to explore associated factors. Three profiles were identified: positive acceptance (16.02%), ambivalent balance (9.71%), and cautious skepticism (74.27%). Multinomial logistic regression showed that educational level, computer proficiency, English proficiency, and generational characteristics were significantly associated with profile membership (all P < 0.05). Nurses showed moderate attitudes toward AI in nursing with substantial heterogeneity. Profile-tailored strategies may help nursing managers facilitate the effective and sustainable implementation of AI technologies in clinical practice.
Objectives With the rapid advancement of artificial intelligence (AI), it has exerted a profound influence on the medical field. Currently, AI applications in nursing remain nascent in China. This study aimed to investigate nurses’ attitudes and anxiety levels toward AI in general hospitals in western China, and to analyze the association of these psychological factors with their AI training acceptance and clinical AI tool usage behavior. Methods A multicenter cross-sectional design was employed. In February 2026, a questionnaire survey was conducted among 620 registered nurses in public general hospitals across western China (Sichuan, Guizhou, and Qinghai provinces). The questionnaire collected data on nurses’ demographic information, whether they had received AI training and whether they had used AI tools in the workplace, as well as their responses to the General Attitudes Toward AI Scale (GAAIS) and the AI Anxiety Scale (AIAS). Participation was voluntary and anonymous. Descriptive statistics, reliability and validity testing, correlation analysis, and structural equation modeling were performed using R software (version 4.5.1). Results Of 603 returned questionnaires (97.3% response rate), 591 were valid. Negative attitudes were significantly associated with lower training acceptance (β = −0.140, p = 0.015) and lower tool usage (β = −0.141, p = 0.003). Positive attitudes and AI anxiety showed no significant associations with either outcome. Older age and longer work experience were associated with higher rates of both outcomes, with work experience showing the strongest association with tool usage. No significant association was found between training acceptance and tool usage after covariate adjustment (β = 0.070, p = 0.356). Conclusion Hospital-level resources, older age, and longer work experience were the factors most strongly associated with nurses’ AI training acceptance and AI tool usage, with work experience showing the strongest association with tool usage. Negative attitudes were associated with lower engagement in both outcomes, whereas positive attitudes and anxiety showed no significant independent associations. These findings suggest that institutional support, experience-based peer learning, and targeted reduction of negative perceptions may be key priorities for AI integration in nursing practice.
Linlin Guo, Qin Ding, M. Tang et al.· Frontiers in Public Health· 0 citations
Optimists and Realists appear to actively integrate AI tools into clinical practice and examination preparation and generally perceive them as beneficial for learning outcomes, highlighting the importance of adopting differentiated pedagogical approaches rather than a one-size-fits-all curriculum.
Pelin Karataş, Demet Öztürk· Journal of Education and Res...· 0 citations
Objective To investigate the current status of artificial intelligence (AI) anxiety among newly recruited nurses, explore its latent categories and characteristics, and analyze related influencing factors, thereby providing a scientific basis for promoting the acceptance of AI technology among newly recruited nurses and enhancing the nursing workforce’s adaptability to AI applications. Methods Convenience sampling was used to select 715 newly recruited nurses from four Grade A tertiary hospitals in Liaoning, Shandong, and Jilin, China. Data were collected using a demographic questionnaire, the Artificial Intelligence Anxiety Scale, the Attitude Scale toward the Use of Artificial Intelligence Technology in Nursing, and the General Self-Efficacy Scale. Latent profile analysis was conducted using Mplus 8.3 software to explore the categories and characteristics of AI anxiety among newly recruited nurses, and univariate analysis and multivariate logistic regression analysis were performed using SPSS 26.0 software to investigate the influencing factors of different categories. Results AI anxiety among newly recruited nurses was classified into three categories: low anxiety-technology acceptance (47.6%), moderate anxiety-ambivalent watch and wait (37.5%), and high anxiety-technology rejection (15.0%). Educational level, income level, experience with AI training, proficiency in AI technology, attitude toward AI, and self-efficacy were identified as significant predictors of the latent dimensions of AI anxiety among newly recruited nurses. Conclusion Heterogeneity exists in AI anxiety among newly recruited nurses, suggesting that nursing managers should focus on the “high anxiety-technology rejection” group by providing targeted AI training and psychological support to enhance these nurses’ acceptance and application of AI technology.
Xin-Ru Ma, Huan Wang· Frontiers in Public Health· 0 citations
The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use, and further educational initiatives are needed to enhance nurses’ preparedness for AI integration in clinical practice.
R. Elsayed, A. Nagy, Eman Sobhy El-Said Hussein et al.· BMC Nursing· 1 citation
The integration of artificial intelligence (AI) technologies into clinical nursing is changing nursing practice and creating new competency requirements for nurses. Some nurses may still face difficulties in technical adaptation, ethical judgment, and practical use of AI tools in intelligent healthcare environments. Understanding nurses’ AI literacy and its relationship with thriving at work may help hospitals design more targeted support strategies.
This study aimed to investigate the current status of nurses’ artificial intelligence literacy, identify latent profiles of self-reported AI literacy, analyze factors associated with profile membership, and examine differences in thriving at work across AI literacy profiles.
A cross-sectional study.
In January 2026, 1000 nurses from 62 hospitals in Anhui Province, China were recruited by convenience sampling. Data were collected using a general information questionnaire, the Artificial Intelligence Literacy Scale, the Chinese version of the Thriving at Work Scale, a nine-item measure of attitudes toward AI in nursing, and the General Self-Efficacy Scale. Latent profile analysis was used to classify nurses’ AI literacy profiles. Factors associated with profile membership were examined using univariate analysis and multinomial logistic regression. Scores on the Thriving at Work Scale were compared across AI literacy profiles.
Three distinct latent profiles were identified: Ethical Awareness Deficit profile (
n
= 332, 33.20%), Cognition Practice Gap profile (
n
= 538, 53.80%), and Comprehensive Literacy Advantage profile (
n
= 130, 13.00%). Educational level, key nursing position/department management role, years of work experience, AI training experience, AI usage frequency in the past 6 months, attitude toward AI in nursing, and self-efficacy were associated with AI literacy profile membership (all
P
< 0.05). Scores on the Thriving at Work Scale differed significantly across the three profiles (
P
< 0.05).
This multicenter cross-sectional study identified three latent profiles of self-reported artificial intelligence literacy among nurses from hospitals in Anhui Province, China. Thriving at work differed significantly across these profiles. The findings may inform stratified educational and managerial strategies to support nurses’ AI literacy and thriving at work in similar clinical contexts.
Not applicable.
The findings highlight the need for a supportive educational environment with guidance to enable nursing students to use artificial intelligence appropriately and responsibly when needed, particularly among vocational college students and those from socioeconomically disadvantaged backgrounds.
Hui-Ying Fan, Qing Zhou, Lili Deng et al.· BMC Nursing· 0 citations