ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: ADOPTION, PEDAGOGICAL IMPACT, AND INSTITUTIONAL READINESS

Abstract

The study focuses on the integration of the Artificial Intelligence (AI) tools into higher education and explores how perceived usefulness, trust in AI systems, ease of use, and contextual factors combine to affect the student outcomes of the learning process and the adoption of the tool in question. The data used in the research were based on the quantitative design, which involved 412 students represented by the participants of universities with diverse courses and institutions types. To study the relationship between the key variables, the structural equation modeling (SEM) and correlation analyses, accompanied by the sentiment analysis in NVivo 14, were used. The findings indicate that perceived usefulness has the strongest influence on the effectiveness of learning (beta = 0.46, p < 0.001) and lastly, trust in AI is a significant parameter in predicting adoption of AI tool (beta = 0.33, p < 0.001). The effect of the two variables is more pivotal in the private institutions as compared to the public institutions. In terms of disciplines, students enrolled in discipline fields STEM scored higher (mean = 4.23), compared to that of students enrolled in humanities (mean = 3.35) on AI adoption. Sentiment analysis consolidated that there was a vast difference in the positive attitudes of high and low users (62 % and 44 % correspondingly). This paper finds that the institutional contexts and individual attitudes shape the way AI integration takes place. The results provide vital insights to policymakers, instructors and designers of technologies interested in improving the use of AI in learning system in higher education.