Artificial Intelligence Adoption and Employee Engagement among Faculty and Staff in Private Higher Education Institutions: Evidence from Hyderabad

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed organizational practices across sectors, including higher education. AI-enabled technologies, such as predictive analytics, automated feedback systems, machine learning, and intelligent decision-support tools, are increasingly integrated into Human Resource Management (HRM) functions to improve workforce effectiveness and institutional performance. In higher education institutions, employee engagement is a critical determinant of teaching quality, research productivity, innovation, and organizational sustainability. Despite the growing adoption of AI technologies, there is limited empirical evidence on their influence on employee engagement in private higher education institutions in India. Therefore, the present study examines the relationship between AI adoption and employee engagement among faculty and administrative staff in private higher education institutions located in Hyderabad. A quantitative research design was adopted, and primary data were collected using a structured questionnaire administered to 410 respondents from selected private higher education institutions. The study evaluated employee perceptions of AI adoption, perceived usefulness, organizational readiness, trust in AI systems, ethical concerns, and employee engagement. Statistical analyses, including descriptive, correlation, regression, and structural equation modeling, were employed to examine the relationships among the study variables. The findings indicate that AI adoption positively influences employee engagement by improving communication, feedback mechanisms, decision-making efficiency, and workforce analytics. Employees generally perceived AI-enabled systems as useful and supportive of institutional operations. Trust and organizational readiness emerged as significant determinants of successful AI implementation and engagement outcomes. Conversely, ethical concerns related to privacy, transparency, and algorithmic bias were found to negatively affect employee perceptions and reduce engagement levels. The study further demonstrates that the effectiveness of AI-driven engagement initiatives depends not only on technological capability but also on leadership support, employee training, and responsible governance practices. The study contributes to the emerging literature on AI-enabled HRM by providing empirical evidence from the higher education sector and proposing a practical framework linking AI adoption, trust, and employee engagement. The findings offer valuable insights for academic leaders, policymakers, and HR professionals seeking to leverage AI technologies to enhance employee engagement, institutional effectiveness, and long-term organizational sustainability.