Gen-AI Integration in Higher Education: Predicting Intentions Using SEM-ANN Approach

This research delves into the multifaceted landscape of various factors that influence the adoption of Generation-Artificial Intelligence (Gen-AI) in Higher Education. By employing a comprehensive framework that includes perceived risk, perceived ease of use, usefulness, Technological Pedagogical Content Knowledge (TPACK), and trust, the study aims to reveal the crucial roles that these factors play in shaping the intention to embrace Gen-AI. Through the utilization of a hybrid approach that combines Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN), the research examines the complex relationships among these predictors and their overall impact on the adoption of Gen-AI. By examining a diverse cohort of 242 participants, including undergraduate and postgraduate students, as well as faculty members from Indian Higher Education Institutions, the research uncovers significant determinants such as perceived ease of use, usefulness, TPACK, and trust. Interestingly, perceived usefulness does not emerge as a significant factor in influencing the intention to adopt AI in Higher Education. Through demographic analysis using the SPSS statistical tool package, the study reveals non-compensatory and nonlinear relationships between age, gender, and the intention to use AI. By leveraging the significant predictors identified through SEM, the developed ANN model accurately predicts the intention to use AI with a 71% accuracy rate. This study not only provides valuable insights into the theoretical foundations but also offers practical implications for the seamless integration of Gen-AI in higher education.