Development of an artificial intelligence competency scale for early childhood educators (ACES)

This study aims to develop an AI competency scale for Early Childhood Education (ECE) teachers and to conduct its validity and reliability analyses. In the study, the deductive method was used to develop a scale. The sample consisted of 508 ECE teachers. A 31-item draft form was created based on a literature review and expert opinions. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Cronbach’s Alpha reliability analysis were conducted for validity and reliability. Item-total test correlations, item discrimination power for the lowest and highest 27% groups, convergent validity, content validity, discriminant validity, and test-retest reliability were examined. As a result of validity and reliability analyses, 6 items were removed from the scale. The findings supported a 25-item, four-factor structure of the ACES scale, including AI Knowledge, AI Skills, Attitude towards AI, and Ethics in AI Usage. This structure explains 77.03% of the variance. The Cronbach’s Alpha internal consistency coefficients were calculated as 0.91 for AI Knowledge, 0.94 for AI Skills, 0.92 for Attitude towards AI, 0.84 for Ethics in AI Usage, and 0.93 for the scale overall. The findings provide initial psychometric evidence for the self-report-based ACES scale as a tool for assessing ECE educators’ perceived AI competencies.