Artificial Intelligence and Bias in Education: A Mini-Review

Abstract

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d529076e114">Artificial intelligence (AI) is reshaping the educational landscape throughadaptive learning systems, automated assessment, and predictive analytics. While theseinnovations promise personalization, efficiency, and scalability, they also raise pressingethical concerns regarding bias and equity. This review examines the multifaceted natureof AI bias in education, exploring how data, algorithmic design, and human interactioncontribute to unequal outcomes. Biased AI systems can inadvertently create or reinforceexisting social and educational disparities, disadvantaging marginalized groups moreand undermining the growth of trust in educational technologies. While existing reviewarticles in the education field mostly just mention the ethical concerns and relatedchallenges, this work aims to highlight key sources of bias (e.g. data imbalance, modeldesign, and contextual misalignment), the existing AI tools, and possible datasets, toevaluate their impact on fairness and inclusivity in learning environments. The mostrecent relevant works show that by integrating technical innovation with educationalequity, AI can evolve from a potential amplifier of bias into a powerful tool for inclusivelearning.

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