Artificial Intelligence Adoption, Perceived Academic Administration Effectiveness And Perceived Student Engagement In Higher Education Institutions Of Punjab, India: An Integrated Technology Acceptance Perspective

Abstract

This study examines Artificial Intelligence (AI) adoption in higher education institutions (HEIs) in Punjab, India and its relationship with perceived academic administration effectiveness and perceived student engagement. It is grounded in Technology Acceptance Model (TAM) and extends it through incorporating institutional support as a contextual facilitating condition. Quantitative, cross-sectional survey design was used and Data were obtained from 370 respondents, including students, faculty, and administrative personnel from public and private HEIs. The questionnaire measured Adoption of AI, self-reported usefulness, self-reported ease of use, institutional support, self-reported academic administration effectiveness and perceived student engagement on a five-point Likert scale. Analysis used descriptive statistics, reliability analysis, exploratory factor analysis, correlation, regression, controlled regression robustness checks and hierarchical moderation analysis. Findings show that Adoption of AI is positively related with both perceived academic administration effectiveness and self-reported student engagement. Perceived usefulness and self-reported ease of use significantly predicted adoption of AI with usefulness showing stronger effect. Institutional support positively moderated Adoption of AI-administration relationship, signifying that AI tools create greater administrative value when institutions provide infrastructure, training, technical assistance, leadership support and clear digital governance. This paper contributes to AI-in-education studies by linking adoption factors with both administrative and student-centered outcomes in one integrated model. It also suggests that Adoption of AI in Punjab-based HEIs should be treated as a planned institutional strategy rather than a fragmented technological intervention.