Abstract

Higher education is being increasingly transformed by Artificial Intelligence (AI), particularly through the emergence of Large Language Models (LLMs) that enable personalized learning, support research activities, and automate various administrative processes. However, the adoption of AI in higher education institutions is uneven, contributing to a widening digital divide between resource-rich Tier-1 urban universities and under-resourced Tier-3 rural institutions. This paper examines the structural, technological, pedagogical, and policy-related barriers that influence AI adoption across diverse institutional contexts. Using a qualitative comparative framework supported by global and Indian case studies, the study analyzes infrastructural constraints, faculty preparedness, digital literacy gaps, ethical considerations, and disparities in funding. The findings indicate that infrastructural capacity, institutional readiness, faculty AI literacy, and sustained public–private collaboration are key factors enabling equitable AI integration. This study proposes a multitiered 'AI for All' framework that emphasizes inclusive infrastructure development, curriculum reform, ethical governance, adoption of open-source technologies, and sustainable funding mechanisms.

Graphical Abstract

Novelty Statement

This study compares Tier-1 and Tier-3 institutions and proposes an inclusive AI adoption model addressing infrastructure, faculty readiness, funding, and ethics.