Artificial Intelligence Attitudes and Readiness Among Undergraduate Nursing Students: A Cross-Sectional Study

Purpose:This study examined undergraduate nursing students’ attitudes toward artificial intelligence and their readiness for medical artificial intelligence, as well as the factors associated with AI readiness.Methods: A descriptive, cross-sectional design was employed. Two hundred and fifty undergraduate nursing students at a nursing faculty in western Türkiye participated during the 2025–2026 academic year. Three instruments were administered: a Participant Information Form, the General Attitudes toward Artificial Intelligence Scale, and the Medical Artificial Intelligence Readiness Scale for Medical Students.Results: The Positive General Attitudes toward Artificial Intelligence Scale yielded a mean of 3.88 ± 0.66, the Negative General Attitudes toward Artificial Intelligence Scale 3.26 ± 0.87, and the Medical Artificial Intelligence Readiness Scale for Medical Students 74.29 ± 13.43. Academic year was significantly associated with AI readiness. One-way ANOVA showed significant differences across year groups (F = 3.450; p = 0.012; η² = 0.040), with fourth-year students scoring higher than second-year students. In the multiple linear regression analysis, both second-year (β = −0.156, p = 0.014) and third-year (β = −0.143, p = 0.024) students had significantly lower readiness scores compared with fourth-year students. A moderate positive correlation was observed between positive AI attitudes and readiness scores(r = 0.430; p < 0.01). Positive attitudes toward AI and self-reported computer skills emerged as the strongest independent factors associated with readiness.Conclusion: Nursing students displayed generally favorable attitudes toward AI, yet readiness levels varied across academic years. Attitude and readiness, while correlated, appear to be distinct constructs. These findings highlight the need for intentional, practice-oriented integration of AI competencies into undergraduate nursing curricula.