Artificial intelligence in education: A tool for equity or a barrier to inclusion

Abstract

This systematic literature review critically examines the dual role of Artificial Intelligence (AI) in education as a catalyst for equity and as a potential barrier to inclusion. Drawing on 29 peer-reviewed studies from 2020 to 2025, the review investigates how AI technologies such as personalized learning systems, intelligent tutoring, and automated grading can support equitable educational outcomes, particularly for underserved and marginalized learners. At the same time, it highlights significant challenges, including digital access disparities, algorithmic bias, and insufficient teacher training, which risk reinforcing existing educational inequities. The review is guided by the PICO framework (Population, Intervention, Context, Outcome) to define the scope of the research focus and the SPIDER framework (Sample, Phenomenon of Interest, Design, Evaluation, Research type) to structure the selection and synthesis of qualitative and mixed-method studies. The study synthesizes thematic findings using mixed-methods analysis and explores the alignment of AI integration with Sustainable Development Goal 4 (SDG 4). The review identifies critical research gaps in longitudinal impact, geographic representation, learner voice, and ethical governance. It concludes that while AI holds transformative potential for inclusive education, realizing this promise requires intentional design, ethical oversight, infrastructure investment, and cross-sector collaboration. These findings offer actionable insights for educators, policymakers, and AI developers aiming to promote fairness and inclusivity in educational innovation.

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