AI in Education Reimagining Learning or Reinforcing Exclusion?

Abstract

Artificial Intelligence (AI) is becoming increasingly integrated into education, offering potential benefits such as improved personalization, efficiency, and accessibility. Yet, its capacity to reshape learning or reinforce exclusionary structures remains a subject of debate. This literature review critically explores whether AI acts as a catalyst for educational transformation or merely reinforces existing linguistic, cultural, and epistemological inequalities. While supporters claim AI enables innovation and learner-centered approaches, critics emphasize its reproduction of Western-dominant knowledge systems, often neglecting low-resource languages and diverse ways of knowing. The review identifies two dominant paradigms: incremental adaptation, where AI is incorporated into traditional educational models, and epistemic transformation, where AI is envisioned as a means to radically reconfigure how knowledge is created and shared. The findings suggest that although AI-enhanced tools improve engagement and evaluation, they often fail to address issues of linguistic justice, cultural inclusion, and the threat of digital neocolonialism. The review calls for a critical reorientation of AI’s role in education, emphasizing the need for equitable access to knowledge, multilingual AI infrastructures, and ethically grounded pedagogical practices.