Development and structural validation of the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE): a three-domain measure of perceived AI competency

Abstract

The rapid integration of artificial intelligence (AI) into healthcare and nursing education has created new competency requirements for nurse educators. Despite this growing demand, there is a notable lack of rigorously developed and psychometrically validated instruments specifically designed to assess AI-related competencies among nursing faculty. This gap limits the ability to systematically evaluate nurse educators’ perceived preparedness for AI integration and hinders evidence-based curriculum development in nursing education. This study aimed to develop and psychometrically validate the Artificial Intelligence Competency Scale for Nurse Educators (AI-CSNE). A methodological cross-sectional study was conducted among 470 nurse educators from multiple nursing faculties in Egypt between mid-October 2025 and February 2026. The scale was developed based on the UNESCO Artificial Intelligence Competency Framework for Teachers and relevant literature. Content validity was established through expert evaluation and pilot testing. The total sample ( N = 470) was randomly divided into two independent subsamples. Exploratory factor analysis (EFA) was conducted using the first subsample ( n = 235) to identify the underlying factor structure, followed by confirmatory factor analysis (CFA) using the second independent subsample ( n = 235) to validate the proposed measurement model. Internal consistency reliability was assessed using Cronbach’s alpha coefficients. Exploratory factor analysis supported a three-factor structure representing Foundational AI Knowledge and Instructional Application, Ethical and Human-Centered AI Use, and AI-Supported Professional Growth. Factor loadings ranged from 0.661 to 0.822, and the three factors explained 56.10% of total variance. Internal consistency reliability was satisfactory, with Cronbach’s alpha coefficients of 0.842 for Foundational AI Knowledge and Instructional Application, 0.810 for Ethical and Human-Centered AI Use, and 0.697 for AI-Supported Professional Growth in the EFA subsample, with comparable estimates in the CFA subsample. Confirmatory factor analysis supported the three-factor structure of the AI-CSNE, with excellent model fit (CFI = 0.989, TLI = 0.986, RMSEA = 0.023, SRMR = 0.046). The Foundational AI Knowledge and Instructional Application domain demonstrated the highest standardized factor loadings (ranging from 0.626 to 0.785), while the Ethical and Human-Centered AI Use domain showed comparatively lower but still acceptable loadings (0.570 to 0.734). The near-zero inter-domain correlations indicate that the three subscales are empirically distinct and should be interpreted separately. The AI-CSNE demonstrates initial evidence of structural validity and internal consistency as an instrument comprising three distinct subscales for assessing perceived AI competency among nurse educators. The near-zero inter-domain correlations indicate that the three subscales should be interpreted separately rather than combined into a single total score. As the scale measures perceived rather than objectively demonstrated competency, it is best suited for exploratory group-level needs assessment, research comparisons, program evaluation, and self-reflective development rather than for individual appraisal, certification, or high-stakes evaluation. Convergent, criterion, and known-groups validity were assessed and demonstrated no significant differences across subgroups, though further validation is required. The scale provides a foundation for research and self-reflective faculty development but should be used with appropriate caution until comprehensive validation is established. Not applicable.