From Colonial Educational Inequality to the AI Divide: A Socio-Historical Analysis of Stem Education and Self-Regulated Learning in South and Southeast Asia

Abstract

Inequalities in STEM (Science, Technology, Engineering and Mathematics) education in South and Southeast Asia are understood to be a manifestation of persistent structural educational inequalities rooted in historical educational systems and exacerbated by contemporary technological changes. This study examines the historical context of educational inequalities in STEM education in South and Southeast Asia. In particular, it focuses on the enduring impact of colonial education policies and postcolonial structural dependency. It also examines how the traditional digital divide has transformed into an emerging AI divide as a result of the increasing integration of artificial intelligence (AI) into education systems. Finally, this article analyzes how a learner-centered teaching approach called Self-Regulated Learning (SRL) can serve as an effective pedagogical approach that promotes equitable educational participation for all in AI-supported STEM educational environments. Today’s STEM educational inequalities are not accidental; rather, the study argues that they have their roots in colonial educational policies that prioritized Western scientific traditions and restricted scientific knowledge to socially and economically privileged groups (Macaulay; Wood). These inequalities have persisted through the post-colonial period through continued reliance on knowledge systems, digital infrastructures, and artificial intelligence technologies developed abroad. The rapid spread of AI-based educational platforms has exacerbated these inequalities, creating a new inequality now known as the “AI Divide.” This emerging AI divide reflects not only differences in access to technology; but also inequalities in the acquisition of AI literacy, the critical evaluation and application of the knowledge AI generates, and the opportunities to participate effectively in AI-supported educational environments. Through an analysis of evidence from South and Southeast Asian countries, the study shows that socially and economically advantaged learners benefit most from AI-supported STEM education environments; while students from marginalized communities continue to lag behind academically. In this context, Self-Regulated Learning (SRL) is identified as an important pedagogical approach that enhances learners’ autonomy, responsibility, and learning ability and promotes equal participation in AI-supported STEM education. Finally, the study concludes that technological progress alone cannot eliminate educational inequality. To create true equity in AI-supported STEM education, educational structural reforms, inclusive teaching practices, efforts to strengthen learners’ Self-Regulated Learning skills, and equitable access to technological opportunities are necessary.