An AI Governance Framework to Address Algorithmic Bias and Educational Equity: A Systematic Literature Review - Muhammad Ruslan Maulani; Saripudin Saripudin; Mumu Komaro (Universitas Amikom Purwokerto, 2026)
The rapid integration of artificial intelligence (AI) in educational systems has generated unprecedented opportunities for personalized learning and operational efficiency. However, it has also exposed deep-rooted concerns regarding algorithmic bias and educational equity. Employing the PRISMA 2020 protocol, this systematic literature review synthesizes evidence from 46 peer-reviewed studies (2021–2026) selected from an initial pool of 102 Scopus-indexed records to examine the sources and manifestations of algorithmic bias, its differential impact on marginalized students, and the adequacy of existing AI governance frameworks in education. Findings show that bias operates through interlocking channels—biased training data, opaque model architectures, and inequitable institutional deployment—that disproportionately disadvantage students from low-income, racially minoritized, and geographically remote backgrounds, while existing governance frameworks remain largely generic and fail to address education's distinctive pedagogical and institutional dynamics. To address this gap, the study's principal contribution is the Educational AI Governance Framework (EAGF), a novel five-layer model—spanning technical accountability, institutional policy, pedagogical integration, participatory governance, and regulatory alignment—grounded in the FAIR principles (Fairness, Accountability, Inclusivity, and Responsiveness) and aligned with UNESCO, OECD, and EU AI governance standards. Unlike prior general-purpose AI ethics frameworks, the EAGF integrates these dimensions into a single coherent architecture, offering both a theoretical advance in intersectionality-aware AI governance and a practical roadmap for policymakers, educational institutions, EdTech developers, and vocational education and training (VET) systems seeking to operationalize equitable AI governance.