Artificial Intelligence in Higher Education: Shaping the Future of University Teaching Through Adaptive Learning, Intelligent Tutoring, and Academic Analytics

Abstract

This paper aims to discuss the positive changes that can be brought by the use of Artificial Intelligence (AI) in higher learning including adaptive learning, intelligent tutoring systems, and academic analytics. Artificial Intelligence technologies are a promising yet untapped source of solutions for universities that are eager to adopt and develop new approaches to personalize learning, improve quality of instruction, and boost institutional effectiveness. The cross-sectional online survey targeted 300 students and lecturers in four world regions and used a quantitative approach, structured questionnaires and data analysis via correlation, multiple regression, and exploratory factor analysis to determine the perceived effects of these tools. The findings of this study described moderate to favorable attitudes towards the use of AI, specifically with ITS and adaptive learning. In contrast, the analytic results revealed relatively higher variance. However, the findings of the current study showed less significant and not substantially positive correlation between the usage of AI and outcomes like learning achievement and instructional quality, indicating that these innovations can be overestimated or influenced by factors including institutional preparedness, faculty development, and student motivation. The findings are then discussed within the context of both constructivism, cognitive load, and activity theories, suggesting that without comprehensive integration plans AI might not realize its didactic possibilities. This paper adds to this literature and offers a framework of how AI can be integrated into university teaching and learning practices by combining adaptivity and Intelligent Tutoring Systems and analytics implemented in educational innovation, inclusiveness as well as effectiveness for teaching and learning in the digital age.