Artificial Intelligence and the Right to Equal Education: A Legal Framework for Algorithmic Educational Justice

Abstract

The rapid adoption of artificial intelligence (AI) in education is reshaping how learners access knowledge, receive instruction, are assessed, and are classified by educational institutions. Although AI can expand personalization, accessibility, and educational efficiency, its deployment may also generate new forms of inequality through unequal access to advanced technologies, algorithmic bias, opaque profiling, data-intensive surveillance, and differential quality of AI-mediated learning. This article examines whether the traditional legal conception of the right to equal education remains adequate in an increasingly algorithmic educational environment. The study employs normative legal research using statutory, conceptual, doctrinal, and comparative approaches. It examines international human-rights standards, contemporary AI governance frameworks, education law, and emerging national approaches to AI in education. The analysis argues that formal equality is insufficient where algorithmic systems distribute educational opportunities differently according to data, infrastructure, digital competence, socioeconomic status, or model performance. The article develops the concept of Algorithmic Educational Justice and proposes a seven-dimensional framework encompassing equal access, algorithmic non-discrimination, educational autonomy, data dignity, explainable education, institutional accountability, and effective remedy. The article concludes that AI should be governed as an educational justice issue rather than merely as a technological innovation. The right to equal education must extend to the conditions under which algorithmic systems allocate educational opportunities, and states must ensure that technological transformation does not convert existing educational inequalities into durable algorithmic inequalities.