Self-Perceived Ethical Knowledge in AI-Enhanced Teacher Education: Adaptation and Validation of a Scale for Chinese Preservice Preschool Teachers

Abstract

The increasing integration of artificial intelligence (AI) into early childhood education has led to ethical concerns regarding children’s privacy, fairness, and developmental appropriateness. While ethical knowledge is recognised as an important component of teachers’ professional knowledge, existing instruments such as the Technological Pedagogical Content Ethical Knowledge (TPCEK) framework were developed for preservice teachers in general and do not fully capture the ethical issues specific to AI-supported preschool education. In this study, we adapted and validated a Self-Perceived Ethical Knowledge Scale for Chinese preservice preschool teachers based on the eight ethics-related dimensions of TPCEK. A two-phase sequential design was employed. In Phase 1, a three-round modified Delphi study was conducted, involving 20 experts who refined an initial 40-item pool into a 33-item scale. In Phase 2, we examined the scale’s psychometric properties by performing an exploratory factor analysis (EFA) on a pilot sample (n = 289)—with factor retention corroborated by principal-axis parallel analysis—and confirmatory factor analysis (CFA) on a separate main-study sample (n = 583). The final 31-item, eight-factor scale demonstrated satisfactory model fit (CFI = 0.943, TLI = 0.932, RMSEA = 0.047, SRMR = 0.048), internal consistency (Cronbach’s α = 0.776–0.887; CR = 0.784–0.891), convergent validity (AVE = 0.549–0.622), and discriminant validity (Fornell–Larcker criterion and HTMT < 0.85). A second-order CFA also showed acceptable fit, although it fitted the data significantly less well than the correlated first-order model. The validated scale provides a context-specific instrument for assessing self-perceived ethical knowledge among preservice preschool teachers in AI-enhanced teacher education.