AI-enabled adaptive learning systems: A systematic mapping of the literature

TLDR

Mobile internet, cloud computing, big data, and AI breakthroughs have transformed education, leading to advanced AI‑enabled learning systems that adapt to individual student needs, yet few implementations address many students’ concerns. This study systematically maps the literature on AI‑enabled adaptive learning systems to identify research gaps and guide future design. The authors analyzed 147 studies published between 2014 and 2020 to conduct the mapping. The mapping identified AI‑enabled learning intervention types, visualized author co‑occurrence with major research themes, reviewed common analytical methods, and offers a guide for designing systems that address specific learning problems.

Abstract

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students' needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users' learning experiences.