Modern Adaptive and Intelligent Digital Learning Systems: Mechanisms and Potential

Abstract

Adaptive educational systems are becoming increasingly promising in the modern world, as they allow students to receive education that meets their individual needs and abilities. Effective learning requires not only high-quality content, but also a personalized approach, which adaptive systems are capable of providing. Additionally, digital tools are able to mitigate the shortage of personnel at all levels of the education market. This article examines the necessary elements of an adaptive system — the knowledge domain model, the user model, adaptivity mechanism, and explanation model — and the impact of each on the potential effectiveness of existing and potentially possible systems. Special attention is paid to the individual characteristics that creators of adaptive systems use to build a user model. These characteristics can be grouped into 4 categories corresponding to cognitive, affective, behavioral/psychomotor, and mixed domains. The article analyzes methods for determining user characteristics and possible ways to identify them more accurately. The article also proposes currently unused adaptivity mechanisms that focus more on mastering new tools and instruments rather than knowledge per se. In particular, it explores human-computer interaction in both individual and group formats, involving both students and teachers. In conclusion, the prospects of using artificial intelligence and collaborative tools in creating and improving adaptive systems are described, emphasizing the need for interdisciplinary collaboration and consideration of complex cognitive process models while creating and testing the systems.