Cognitive system design for motivation and self-regulation - A proposed theoretical framework for intelligent tutoring systems

Abstract

The development of Intelligent Tutoring Systems has been a sought-after and rapidly growing field for research, especially now, with the emergence of Generative Artificial Intelligence. This study aspires to contribute to the topic of cognitive systems for education by proposing a Cognitive Architecture which encapsulates three contemporary psychological theories and constructs for learning, motivation, and self-regulation, namely the Zone of Proximal Development, Self-Determination Theory and Self-Regulated Learning by providing a theoretical blueprint for an Intelligent Tutoring Systems for children of 8 to12 years of age. The study employs surveys and experimental designs to preliminary tap into correlations between constructs of the three theories of contemporary approaches. By extracting data using instruments and cognitive tasks to the relevant population and their parents and teachers, the researcher attempts to identify any associations between items and factors and converge on a minimal set of variables and predictors, which in turn may lead to an efficient computational design model for a cognitive assistant that will employ optimal strategies for learning. However, the results suggest that more complex experimental designs may be needed to tap into the nuances of self-regulation and motivation. Finally, the study attempts to converge findings from the literature and offer a well-informed summarisation to psychologists, cognitive scientists, software architects and developers for future designs.