AI in the Classroom, Inequality in the Background: The Dual Effects of Generative AI on Educational Equity Across Linguistic and Cultural Lines in Higher Education—A Systematic Review and Critical Synthesis

Vollständiger Abstract

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Amid the rapid proliferation of GenAI in higher education, claims about its democratizing potential have outpaced empirical scrutiny of its equity consequences. This study critically reviews generative artificial intelligence (GenAI) and its implications for equity in higher education. Drawing on literature published between 2023 and 2026, it develops a three-dimensional analytical framework encompassing opportunity, process, and outcome equity to examine the dual role of GenAI as both an inclusion-enabling and inequality-reproducing force. The findings suggest that GenAI enhances educational inclusion through multilingual support, personalized learning, and expanded participation opportunities. However, these benefits are accompanied by structural risks, including algorithmic bias, unequal digital literacy, and systematic misclassification in AI-driven assessment systems. These dynamics collectively reinforce existing educational inequalities rather than eliminate them. The study further demonstrates that current governance frameworks remain conceptually fragmented, lacking operational specificity, contextual adaptability, and empirical validation. As a result, the equity outcomes of GenAI remain highly uneven across institutional and regional contexts. Localized governance, informed by sustained empirical tracking, is recommended over generic institutional frameworks for mitigating the uneven impacts of GenAI.