Preliminary Psychometric Evaluation of the Arabic Version of the NURS-DIAL Questionnaire Among Nursing Students in Saudi Arabia: Cross-Sectional Study
ABSTRACT
Background:
The rapid progress in digital transformation and the increasing reliance on of Artificial Intelligence (AI) in healthcare have created an urgent need for validated assessments of digital and AI literacy among nursing students. However, there is a substantial gap in culturally adapted assessment tools for Arabic-speaking populations.
Objective:
The aim of the study was to translate, culturally adapt, and psychometrically validate the Arabic version of the NURSing Digital and AI Literacy Assessment (NURS-DIAL).
Methods:
This study followed cross-sectional design.Standardized methods were used to translate the NURS-DIAL questionnaire on information literacy, mHealth literacy, AI literacy, digital well-being, and AI education topics, including forward-backward translation, expert content validity testing, pilot testing, and confirmatory factor analysis using diagonally weighted least squares estimation.
Results:
Content validity indices met the recommended standards (I-CVI = 0.83–1.00, S-CVI/Ave = 0.94), and confirmatory factor analysis supported a five-factor, 52-item model with acceptable fit (CFI = 0.930, TLI = 0.926, RMSEA = 0.055, SRMR = 0.103, GFI = 0.947). All subscales showed good to excellent internal consistency (Cronbach's α = 0.856–0.919), with inter-subscale correlations and group differences by gender and year of study supporting convergent, discriminant, and known-groups validity.
Conclusions:
Although limited, the available data provide initial support for the validity and reliability of the Arabic NURS-DIAL for measuring digital and AI literacy among nursing students in Saudi Arabia, serving as an important tool for curriculum development, competency assessment, and further research in AI-enabled nursing education. Clinical Trial: n/a
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