Evidence map›Paper›PMID 42332704›Full record

ArticleBMC nursing2026

Cross-cultural adaptation and psychometric evaluation of the Shinners Artificial Intelligence Perception Scale (SAIP): evidence from Turkish nursing students.

Meltem Özduyan Kiliç, Ergün Kaplan, Fatoş Korkmaz

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Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Meltem Özduyan KiliçFaculty of Nursing, Ankara University, Ankara, 06080, Turkey. meltem.ozduyan17@gmail.com.ORCID http://orcid.org/0000-0001-6881-0892
Ergün KaplanFaculty of Nursing, Hacettepe University, Ankara, 06100, Turkey.ORCID http://orcid.org/0000-0003-0180-4417
Fatoş KorkmazFaculty of Nursing, Hacettepe University, Ankara, 06100, Turkey.ORCID http://orcid.org/0000-0003-4457-8691

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No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntegrating artificial intelligence (AI) technologies into nursing practice is expanding rapidly, yet validated instruments to assess nursing students' perceptions of AI remain scarce in non-English-speaking contexts. The Shinners Artificial Intelligence Perception (SAIP) Scale was developed to measure healthcare professionals' perceptions of AI; however, its applicability in Turkish culture has not been tested.

methodsThis methodological study was conducted between February and July 2025 and involved 412 nursing students from three public nursing faculties in Ankara, Türkiye. The cross-cultural adaptation process followed forward-backward translation, expert review, and content validity assessment using the Davis technique. Construct validity was examined through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was assessed using item-total correlations, Cronbach's alpha, and test-retest analysis with a subgroup of 32 participants.

resultsContent validity index (CVI) was 0.88. The KMO sample adequacy was 0.802, and Bartlett's test yielded χ

conclusionsIn this study, psychometric evaluation yielded an 8-item Turkish version of the SAIP (SAIP-TR); thus, the adapted instrument should be considered a shortened form of the original scale. The SAIP-TR may provide a useful framework for assessing AI-related perceptions among nursing students and may support educators and researchers in understanding students' readiness for AI integration into nursing practice and in informing future educational strategies. However, further validation studies in more diverse populations are recommended to strengthen the scale's generalizability and conceptual stability. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial intelligenceCross-cultural validationNursing educationNursing studentsPsychometricsScale adaptation

Identifiers

PMID42332704
PMCPMC13536636

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