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Background Artificial intelligence (AI) is increasingly integrated into nursing practice and nursing education, and understanding nurses’ and nursing students’ attitudes toward AI is essential for its successful adoption. However, to our knowledge, no scoping review has systematically evaluated the psychometric properties of instruments designed to assess these attitudes in nursing populations, including the foundational source instruments on which some nursing-specific adaptations are based. Methods This scoping review followed the PRISMA-ScR guidelines. Nine databases were searched from inception to September 8, 2026. Eligible studies included nursing-specific studies (registered nurses or nursing students) and foundational source studies from non-nursing populations that developed instruments subsequently validated or adapted in nursing populations. Data on instrument characteristics and reported psychometric properties were extracted and synthesized descriptively. Results Twenty-four studies were included, comprising 18 nursing-specific studies and 6 foundational source studies, covering 11 tool families and 24 instrument versions. Content validity and structural validity were commonly reported, whereas convergent validity, discriminant validity, and cross-cultural measurement invariance were not consistently reported. Criterion-related validity was not reported in any included study. Internal consistency varied across language versions and instruments. Conclusions Although a substantial number of instruments have been developed to assess attitudes toward AI among nurses and nursing students, cross-cultural measurement invariance and criterion-related validity were not reported. Future research may benefit from prioritizing cross-cultural measurement invariance testing, further validation of culturally adapted Chinese versions, and multicenter validation studies. This review maps reported psychometric findings and does not establish comparative psychometric superiority among the included instruments. Protocol registration Open Science Framework (OSF) (March 15, 2026; DOI: https://doi.org/10.17605/OSF.IO/MRYHT).
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Huang et al. (2026) studied this question.
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