Digital Marginalization and Educational Inequality in the Age of Artificial Intelligence

Abstract

The rapid integration of artificial intelligence (AI) into educational systems is generally presented as a means of enhancing efficiency and access to learning. However, these technological developments also risk intensifying existing social inequalities. This study examines digital marginalization and educational inequality in the age of artificial intelligence, arguing that AI-driven educational tools can function as mechanisms of exclusion rather than inclusion. Drawing on critical pedagogy and digital studies, the research addresses digital marginalization evident in an unequal access to technology. There appear to be structural and cultural exclusions embedded within algorithmic systems. The study also explores how data disparities in digital education contribute to algorithmic discrimination and unequal learning outcomes. Through discussion of marginalized learner groups, the educational consequences of AI-mediated instruction and assessment are highlighted. The analysis also addresses the ethical implications of surveillance and institutional responsibility in AI-driven education. Ultimately, the study proposes pedagogical strategies aimed at fostering more inclusive and socially just AI design and implementation. Situating AI within broader educational relations, students of different social and cultural background can benefit and better use AI tools.