The Artificial Intelligence in Healthcare Scale: Development and validation of a tool to assess nursing students’ competencies

Abstract

• Developed and validated a novel scale assessing AI competencies in nursing students. • Psychometric testing confirmed robust validity and reliability across three domains. • The AIHS supports evidence-based curriculum design and evaluation in digital health. • Offers a practical framework for integrating AI literacy into healthcare education. Artificial intelligence (AI) is increasingly used in healthcare to improve decision-making, enhance patient outcomes, and support clinical workflows. However, validated instruments to assess AI-related competencies among healthcare professionals remain limited. To develop and evaluate the psychometric properties of the AI in Healthcare Scale (AIHS). A cross-sectional web-based study was conducted with 1,713 nursing students from 10 Arab countries. The AIHS was developed through literature review, expert validation, and pilot testing. The sample was divided for exploratory factor analysis and confirmatory factor analysis (CFA). Reliability was assessed using Cronbach’s alpha and item–total correlations, while convergent and discriminant validity were evaluated using composite reliability and average variance extracted. The final 17-item scale demonstrated a three-factor structure, accounting for 55.23% of the variance. CFA indicated excellent fit ( χ ²/df = 2.689, root mean square error of approximation = 0.045, comparative fit index = 0.97). Internal consistency was strong across subscales ( α = 0.70–0.903). The AIHS is a valid and reliable instrument for assessing AI competencies among nursing students and can support learning needs assessment, curriculum development, and the evaluation of AI education programs.

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