AI-Driven Transformation in Higher Education: A Quantitative Analysis Using Sem

The rapid proliferation of Articial Intelligence (AI) is fundamentally reshaping the Indian higher education landscape, moving institutional adoption from a strategic advantage to a systemic necessity. This study investigates the intricate socio-technical dynamics governing AI integration by utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyse data from 582 stakeholders across diverse Indian HEIs. The research evaluates a multi-stage causal framework, emphasizing how Performance Expectancy (PE) and Effort Expectancy (EE) signiantly cultivate positive stakeholder attitudes. Findings reveal that while Attitude (ATT) and Behavioural Intention (BI) serve as powerful determinants for the actual Adoption of Academic Higher Education technology (AAHE), Perceived Risk (PR) and Facilitating Conditions (FC) exert surprisingly minimal direct inuence on user sentiment. Furthermore, the analysis identis Age and Job Description as critical moderators, suggesting that targeted professional development and demographic-speciinterventions are essential for scaling AI. These empirical insights offer a strategic roadmap for policymakers to bridge the gap between AI's theoretical potential and its practical realization.