Territorial Inequality: Access to Teacher Training in Artificial Intelligence, Maule Region, Chile

Abstract

This study examines territorial inequalities in access to formal Artificial Intelligence (AI) training among primary education teachers in Chile’s Maule Region through multivariate predictive analysis. Using data from 150 teachers across 15 communes, we have identified geographic, institutional, and professional factors predicting access to professional development opportunities in emerging technologies. Results reveal significant urban concentration of training (71% of trained teachers in urban contexts versus 29% in rural), with five communes showing complete absence of access, configuring what we conceptualize as “training deserts”. Multivariate logistic regression analysis identifies geographic context as the main predictive factor (Wald = 8.42, p < 0.01), with rural teachers showing approximately 43% less probability of accessing specialized training compared to their urban counterparts (OR = 0.57, 95% CI: 0.42-0.78). The calculated Urban Concentration Index reaches 1.21 and the Urban-Rural Access Ratio is 1.72, evidencing substantial territorial gaps. These findings fundamentally challenge homogeneous teacher professional development policies and suggest the urgent need to implement territorially differentiated strategies that guarantee substantive equity in access to training on emerging technologies, particularly in Latin American rural contexts characterized by historical institutional fragmentation and structural geographic isolation.