Enhancing multicultural education models through artificial intelligence: the role of teacher competence, technological readiness, and institutional support in early childhood education

Introduction Indonesia's extraordinary cultural diversity, which encompasses more than 1,340 ethnic groups and 718 regional languages, presents both opportunities and challenges for early childhood education. Although multicultural education is recognized as essential during the formative years, the integration of artificial intelligence (AI) models with culturally responsive pedagogy remains largely unexplored, particularly in early childhood education. This study investigates the structural relationships among teachers’ multicultural competencies (TMC), AI technology readiness (ATR), institutional support systems (ISS), the implementation of AI-based multicultural learning media (AMLMI), and the effectiveness of multicultural education management (MEME). Method A cross-sectional survey was conducted among 500 kindergarten teachers in Central Java Province, Indonesia, using stratified random sampling. Data were collected using a structured questionnaire comprising 48 items, rated on a five-point Likert scale. The proposed structural model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0, including bootstrapping with 5,000 subsamples for hypothesis testing. Results The measurement model demonstrated adequate convergent validity (all external loadings >.70; AVEs > .50) and strong construct reliability (Cronbach's Alpha > 0.88; CR > 0.91). The structural model showed that ISS was the strongest predictor of AMLMI ( β = .571, p < .001), followed by TMC ( β = .219, p < .001) and ATR ( β = .163, p = .001), which collectively explain 77.1% of the variance. AMLMI, in turn, is strongly associated with MEME ( β = .832, p < .001; R² = .692). Full mediation was confirmed for all indirect paths (VAF=100%), with ISS providing the largest indirect effect on MEME through AMLMI ( β = .475, p < .001). Discussion The findings indicate that institutional support is the strongest predictor of AI-based multicultural education outcomes, while teachers’ multicultural competence and AI technology readiness serve as significant complementary predictors.The pattern of full mediation suggests that these factors must be implemented through the effective application of AI-based media to enhance multicultural education management. This study contributes an integrated empirical model that bridges the fragmented literature on multicultural pedagogy, AI readiness, and institutional support, offering actionable insights for policymakers and practitioners in culturally diverse early childhood education.