PSYCHOLOGICAL MECHANISMS FOR OPTIMIZING COGNITIVE LOAD THROUGH ARTIFICIAL INTELLIGENCE TOOLS IN THE DIGITAL LEARNING ENVIRONMENT

The purpose of this study is to identify the psychological mechanisms for optimizing students’ cognitive load through artificial intelligence tools in a digital learning environment. A mixed-methods approach was employed, incorporating an analysis of the theoretical frameworks of J. Sweller, R. Mayer, and R. Picard within the scope of Cognitive Load Theory, alongside observation, survey, and in-depth interview methods. The empirical findings demonstrate that artificial intelligence tools significantly reduce the cognitive burden on students’ working memory by filtering, simplifying, and adapting information. In particular, a decrease in mental strain and stress levels was observed, accompanied by improvements in learning efficiency and academic motivation. The results of the study indicate that artificial intelligence can be regarded as an effective instrument for enhancing cognitive and psychological stability in the educational process, as well as a key factor in the development of personalized, adaptive learning systems.