Professional Values and Ethical Sensitivity in Digitalized Care: Nursing Students’ Approaches to Artificial Intelligence and Robot Nurses

Objective: This study aimed to examine the relationship between nursing students’ professional values (PVs) and their ethical sensitivities toward artificial intelligence (AI) and robot nurses, and to identify the factors influencing these variables.

Methods: This cross-sectional, correlational study was conducted between January and February 2026 with 400 nursing students at a public university in Türkiye, in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. Data were collected using a Descriptive Information Form, the Revised Nurses’ Professional Values Scale, and the Ethical Sensitivity Scale for AI and Robot Nurses.

Results: The mean Revised Nurses’ Professional Values Scale score was 101.24 ± 17.21 and the mean ethical sensitivity score was 54.74 ± 7.61. The scale scores differed significantly according to grade level and knowledge about AI (P < .05), whereas Ethical Sensitivity Toward AI and Robot Nurses Scale score differed according to perceptions of the impact of AI on the profession and potential ethical problems (P < .01). Professional values were positively correlated with ethical sensitivity (r = 0.156, P < .01). Hierarchical regression analysis showed that PVs, gender, and perceptions of the impact of AI on the profession were significant predictors of ethical sensitivity, explaining 14% of the variance.

Conclusion: Nursing students exhibited high PVs and moderate-to-high ethical sensitivity toward AI and robot nurses. Although PVs were positively associated with ethical sensitivity, the relatively low explained variance suggests that ethical sensitivity is influenced by multiple factors. Integrating AI-related ethical content into nursing education may support ethical awareness and preparedness for technology-integrated care.

Cite this article as: Aktan GG, Palaz SC. Professional values and ethical sensitivity in digitalized care: nursing students’ approaches to artificial intelligence and robot nurses. Arch Health Sci Res. 2026, 52, 0107, doi: 10.5152/ArcHealthSciRes.2026.26107.