AI-DRIVEN ADAPTIVE LEARNING IN HIGHER EDUCATION: PERSONALIZING UNIVERSITY TEACHING METHODS

Abstract

Without a doubt there is a transformation in higher education as a result of Artificial intelligence (AI) which is due to adaptive learning systems that is tailored to students needs and even beyond that. The traditional approaches often seem homogenous and leads to a disjointed form of learning within students’ populace. In contrast, adaptive learning platforms have real time analyzer which can adjust pace, regulate modality and also temper down the difficulty in content. In recent studies from articles, shows that engagement, knowledge acquisition and motivation especially for students comes with ability for learning to be diverse. (Du Plooy, 2024; Wang et al., 2024; Ipinnaiye et al., 2024). Analysis show that adaptive learning improves outcomes cognitively by at least 10 -15% while this also helps in retention and satisfaction (Hooshyar, D., 2024). There are still challenges that remain such as data risks, faulty workload all of which may hinder institutional adoption of this new approach (Camilleri, 2024). More so, there are varying outcomes such as critical analysis and creativity, which shows reduction in consistent improvements (Chernikova, 2024). In a nutshell, this paper seeks to synthesize an AI-driven adaptive approach towards learning within a multi-layered framework in universities. This analysis shows that AI is here to assist or complement to the already instructor led pedagogy as long as there a form of implementation by the institutions with a robust performance, professional and ethical standards.