Optimizing Artificial Intelligence Usage among Academicians in Higher Education Institutions

Abstract

Artificial Intelligence (AI) is becoming a transformative force in higher education and offers tremendous potential to reshape the academic landscape. With the power of artificial intelligence, educators and researchers can use advanced tools to perform complex tasks such as data analysis, predictive modeling, and breakthrough insights. This study delves into the complex relationships between attitudes, perceived usefulness, perceived ease of use, trust, intentions, and AI usage in academia. The research framework is based on four different variables: attitude, perceived usefulness, perceived ease of use, and trust, with intention as the mediator and AI use as the outcome variable. To collect primary data, a thorough survey was designed and conducted based on previous research. Structural equation modeling, known for its ability to analyze complex interactions between variables, was used to analyze a comprehensive data set of 362 responses, and convergent and discriminant validity was confirmed. Evaluation of the structural model crucially confirmed the hypotheses and revealed nine direct relationships and four mediated relationships. Notably, seven of the nine direct hypotheses and all four mediating hypotheses were supported. These results highlight the profound importance of these factors in shaping user intentions and facilitating the effective integration of AI into academia. In addition to the empirical findings, this study