AI-Enabled Sustainability in Indian Higher Education Institutions: Use-cases, Barriers, and the KPI–Data–Duty (KDD) Framework

Indian higher education institutions (HEIs) operate as “mini-cities” with substantial electricity, water, mobility, and material footprints. This paper synthesizes how artificial intelligence (AI) can accelerate campus sustainability through (i) resource optimization (especially building energy and water), (ii) monitoring and operational reliability (maintenance, waste, and compliance), and (iii) behavior and mobility nudges (transport and paper reduction). Using a mini-review approach, we consolidate high-impact, campus-relevant AI applications and outline the measurement logic that links interventions to auditable sustainability indicators. We then identify key adoption barriers in Indian HEIs, including limited metering and data interoperability, procurement and skills constraints, governance and privacy concerns, and the environmental footprint of AI systems themselves. To move from isolated pilots to measurable outcomes, we propose the KPI–Data–Duty (KDD) framework, which connects a small set of time-bound sustainability KPIs to minimal viable data architecture, pilot design, and a lightweight Responsible/Green AI duty checklist. The paper contributes an implementation- oriented roadmap and use-case mapping that can support HEI leaders in planning, governing, and scaling AI-enabled sustainability initiatives with accountability.