Enhancing sustainable academic course delivery using AI in technical universities: An empirical analysis using adaptive learning theory

Abstract

The educational landscape is undergoing a significant transformation driven by the rapid advancements in Artificial Intelligence (AI) that hold immense potential for enhancing sustainable academic course delivery, fostering deeper understanding, and improving student-learning outcomes. However, while AI applications promise individualized learning experiences and more efficient instructional methods, their integration into Technical Universities, particularly in developing countries, remains limited. Few studies address the unique challenges and opportunities of deploying AI in this context, leaving educators and policymakers without clear, empirically-backed strategies for implementation. This study seeks to bridge this gap by analyzing the impact of AI integration on academic course delivery in Technical Universities, guided by Adaptive Learning Theory. A mixed-method approach was adopted, combining qualitative interviews with 8 students and 8 lecturers and structured surveys from 124 randomly selected students and lecturers, achieving an 81 % response rate. Structural equation modeling was employed to examine the relationships between AI-driven parameters and academic course delivery. It was found that personalized learning, natural language processing, intelligent tutoring systems, and data-driven insights significantly enhance course delivery, while virtual and augmented reality showed limited impact in this setting. The results highlight AI’s potential to transform course design and delivery in Technical Universities, leading to improved learning outcomes. The study presents exciting possibilities that AI presents for educators and policymakers.