Keywords
digital learning environment
cognitive load
working memory
adaptive learning
psychological mechanisms
learning motivation
educational effectiveness
How to Cite
Abstract
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.
References
Sweller, J. Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 1988, pp. 257–285.
Mayer, R. E. Computer-Based Multimedia Learning. Cambridge: Cambridge University Press, 2020, 433 p.
Picard, R. W. Affective Computing. Cambridge, MA: MIT Press, 1997. https://doi.org/10.7551/mitpress/1140.001.0001
Luckin, R. Machine Learning and Human Intelligence: The Future of Education for the 21st Century. London: UCL Press, 2018, pp. 18–139.
Roll, I., & Wylie, R. Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 2016.
Verbitsky, A. A. Digital Learning: Problems, Risks, and Prospects. Higher Education in Russia, (1), 2017, pp. 53–65.
Chernobay, E. V. Teacher Preparation for the Development of Electronic Educational Resources. Moscow: State Pedagogical Academy Publishing, 2009, 104 p.
Testov, V. A. On Certain Strategies of Learning in the Networked Environment. Education and Science, 21(3), 2019, pp. 123–139.
Choshanov, M. A. E-Didactics: A New Perspective on the Theory and Practice of Teaching in the Digital Era. 2017.
Begimkulov, U. Sh. Theory and Practice of Organizing and Managing the Informatization of Pedagogical Education Processes. Doctoral dissertation abstract. Tashkent, 2007, 37 p.
Goziev, E. G. General Psychology. Tashkent: National Society of Philosophers of Uzbekistan Publishing House, 2010, 11 p.
Gulomov, S. S., et al. Blockchain Technologies in the Digital Economy. Tashkent: TMI, “Iqtisod-Moliya”, 2019, 414 p.
This work is licensed under a Creative Commons Attribution 4.0 International License.