Algorithmic Governance in Education: A Framework for AI-Driven Decision Systems, Inequality and Policy Accountability - Kamal Singh Kunwar (PT. Academic Bright Collaboration, 2026)
Purpose – This article develops a mechanism-based conceptual framework to explain how artificial intelligence (AI)-enabled decision systems are reshaping governance processes and distributive outcomes in contemporary education systems. It addresses a key gap in the existing scholarship: the absence of an integrated analytical lens linking algorithmic decision-making with institutional accountability and inequality in education. Methods – This study adopts a theory-building approach grounded in cross-disciplinary synthesis, drawing on insights from Artificial Intelligence, Decision Science, and Public Policy. Through a structured analytical method, it advances a multilevel framework that explains how AI-driven decisions are produced, interpreted, and implemented within institutional contexts. The analysis focuses on causal mechanisms rather than technical system design, positioning the contributions within governance and policy analysis.Findings – Four interrelated mechanisms are identified: (1) algorithmic bias transmission rooted in data and model construction; (2) institutional mediation shaping the interpretation and use of algorithmic outputs; (3) policy distortion arising from uneven or selective implementation; and (4) the reproduction or amplification of inequality across educational settings. Together, these mechanisms illustrate how AI systems interact with institutional structures to influence their outcomes. Research implications – This study provides a structured basis for analyzing accountability and inequality in AI-enabled education, while offering indicative directions for improving transparency and governance in policy contexts.Originality – This study introduces the Unified Algorithmic Governance Framework (UAGF), which integrates data processes, algorithmic decision-making, institutional dynamics, and socio-educational outcomes into a single analytical model. Unlike existing work on AI ethics and educational data governance, the framework emphasizes the interaction between technical systems and institutional processes in producing distributive effects.