Governing Artificial Intelligence in Indian Education: A Doctrinal Analysis of NEP 2020, Constitutional Rights, and Data Protection Law

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Purpose – This study examines the governance of artificial intelligence (AI) in Indian education by assessing whether existing constitutional, statutory, judicial, and policy frameworks adequately regulate AI-assisted educational decision-making under the National Education Policy (NEP) 2020 and by identifying appropriate directions for rights-based and risk-sensitive reform.Methods – The study employs doctrinal legal research supplemented by comparative legal analysis. It examines the Constitution of India, the Information Technology Act 2000, the Digital Personal Data Protection Act 2023, relevant copyright provisions, judicial principles, NEP 2020, NITI Aayog’s AI strategy, and selected international frameworks, including UNESCO, OECD, and the European risk-based approach. The analysis focuses on privacy, transparency, fairness, accountability, and human oversight.Findings – India’s Educational AI governance problem is characterized by regulatory fragmentation rather than a complete absence of law. Existing protections remain partial and do not provide an integrated framework for algorithmic educational decision-making. Data-protection compliance alone does not ensure fairness, explainability, contestability, or accountability. The study also identifies gaps in institutional coordination and legal accountability. Its proposed Governance Matrix treats admissions and automated grading as high risk, learning analytics as medium risk, and supplementary AI chatbots and tutors as lower risk.Research implications – India should combine existing constitutional and statutory protections with sector-specific, risk-sensitive obligations, including meaningful human review, explanation, contestation, audits, clear institutional responsibility, grievance mechanisms, AI governance capacity, and attention to digital inclusion.Originality – The study integrates constitutional rights, data protection, educational policy, institutional accountability, and international AI governance into a context-specific framework for Indian education. It also distinguishes data governance from algorithmic accountability and proposes a normative, non-empirical Governance Matrix based on the consequences of AI use rather than fixed technology categories

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