Artificial Intelligence for Institutional Accreditation in India: A Framework for NAAC's Data-Driven Quality Assurance

Abstract For nearly three decades, the National Assessment and Accreditation Council (NAAC) has been India's main mechanism for assuring quality in higher education. Its traditional model — built around a self-reported Self-Study Report, a data validation stage, and an on-site peer-team visit ending in a letter grade — has come under increasing strain. Critics point to slow turnaround, difficulty in standardizing judgments across assessor teams, and the challenge of scaling the process to the thousands of institutions India now wishes to bring under formal quality review. In February 2025, NAAC announced a major reform: a binary accreditation outcome, an optional five-level Maturity-Based Graded Level (MBGL) scale for institutions seeking continuous improvement, a unified national data platform, and the beginnings of an AI-assisted evaluation engine to reduce the burden of manual verification. This paper builds on that reform and proposes, in concrete technical terms, what such an AI-assisted engine could look like. We outline a system that can read and structure Self-Study Reports, cross-check institutional claims against government data sources such as UGC, AICTE, AISHE and NIRF, benchmark institutions against their peers, and present the results to human assessors without replacing their judgment. The discussion is grounded in India's higher-education context: wide variation in digital readiness across institutions, obligations under the Digital Personal Data Protection Act, 2023, and the well-known risk that metric-driven systems can encourage institutions to "write to the metric" rather than improve underlying quality. We conclude with a phased pilot study to test the framework before any wider rollout.