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Artificial intelligence attitudes and ethical sensitivity in patient care among nursing students: A cross-sectional study.

Jul 2026 · Applied Nursing Research · Vol 90, pp. 152120 · 0 citations · 34 references
Medicine

Abstract

Background

The growing integration of artificial intelligence (AI) into healthcare is transforming nursing practice and clinical decision-making. However, evidence regarding the relationship between nursing students' attitudes toward AI and ethical sensitivity remains limited.

Aim

This study examined the relationship between nursing students' attitudes toward AI and their ethical sensitivity in patient care and identified factors associated with these variables.

Methods

This descriptive, cross-sectional correlational study was conducted with 278 nursing students at a public university in Türkiye. Data were collected through an online survey using the Ethical Sensitivity Questionnaire in Nurses (ESQN), the Artificial Intelligence Attitude Scale-Short Form (AIAS-4), and a sociodemographic form. Data were analyzed using non-parametric tests, Spearman's correlation, and multiple linear regression analyses.

Results

The mean ESQ-N score was 39.82 ± 5.41, and the mean AIAS-4 score was 27.16 ± 6.82. Ethical sensitivity scores differed significantly according to AI ethics education (p = 0.008). AI attitudes differed significantly by academic year (χ2(3) = 8.04, p = 0.018), with fourth-year students reporting higher scores than first-year students (p_adj < 0.05). No significant correlation was found between ESQ-N and AIAS-4 scores (p > 0.05). Regression analyses showed that ethical sensitivity was predicted by academic year and AI ethics education (Adj. R2 = 0.13), whereas AI attitudes were predicted only by academic year (Adj. R2 = 0.15).

Conclusions

Attitudes toward AI and ethical sensitivity appear to represent distinct dimensions of professional competence. Integrating AI-related ethical content into nursing curricula may support the development of both digital and ethical competencies in future nurses.

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