Beyond Adoption: A Predictive Explanatory Model of AI-Driven Pedagogical Transformation in Elementary Schools

Abstract

The Implementation of Artificial Intelligence (AI) in elementary education has grown significantly, yet most studies remain focused on technology adoption rather than pedagogical transformation. This study proposes a predictive model to explain how individual factors and institutional support simultaneously influence AI-Driven Pedagogical Transformation (AIDPT). A quantitative approach was employed through a survey of 235 elementary school teachers in Indonesia, using multiple linear regression analysis. The results indicate that the model demonstrates strong predictive capacity (Adjusted R2 = 0.689). Innovative Teaching Practices (ITP) emerged as the dominant predictor, followed by Cognitive Flexibility (CF), while Teaching Experience (TE) was not significant in the simultaneous model. These findings confirm a shift from seniority toward adaptive competence as the key driver of AI-based transformation. This study contributes to the development of a predictive readiness framework as a basis for decision-making in teacher professional development.