Hybrid modeling of science and math teachers' psychological preparedness for AI- integrated teaching environment: EGA perspective of instructional proficiency, professional motivation, technophilia and needs of training

Abstract

This study explored teachers' psychological readiness for integrating AI into teaching, focusing on cognitive, affective, and contextual factors. Using a 27-item Psychological Preparedness for AI-Integrated Teaching scale and single-item measures, science ( n = 691) and mathematics ( n = 231) educators were assessed. A multi-group confirmatory factor analysis (CFA) with WLSMV estimation confirmed factorial structure and invariance across disciplines, ensuring cross-group comparability. Exploratory Graph Analysis (EGA) with Walktrap community detection identified three dimensions: cognitive-affective readiness, technological-professional competence, and institutional support. Bootstrap analyses validated the stability of this structure. The findings offer a psychometrically sound, multidimensional framework for teacher readiness in AI integration, emphasizing individual and systemic influences on adaptive educational practices.