• Viktor A. Uglev, Siberian Federal University (Zheleznogorsk, Russia)
• Georgy A. Smirnov, Moscow University of the Ministry of Internal Affairs of Russia (Moscow, Russia)

The problem of a systematic and scientifically-based approach to the organization of e-learning is becoming increasingly relevant in the context of the widespread use of large language models. This article explores the issue of conceptual and methodological support for the operation of Intelligent Tutoring Systems (ITS). It highlights the challenge of describing and explaining the internal mechanisms of these systems in scientific publications, allowing for the verification and automatic explanation of their decisions. As an example, the author's experimental learning system, AESU, is examined. The article focuses on the fundamental concepts, mechanisms, and methodological solutions employed in this system. The main concepts include P.K. Anokhin's theory of functional systems (the model of afferent synthesis), D.A. Pospelov's applied semiotics and V.A. Lefebvre's theory of reflexive control. The role of cognitive visualization (mapping) in the synthesis of solutions is demonstrated. This involves combining the logic of production expert systems, fuzzy logic, and Shortliffe's hypothesis testing, which are the intellectual mechanisms of the learning system's solver. The emergent effect arising from the use of the above-mentioned concepts and mechanisms allows us to propose an author's approach to the implementation of modular data concentration and cross-cutting approach of the learning situation. The structural and functional notation of maps becomes the system-forming element in the synthesis of ITS solutions: the method of Cognitive Maps of Knowledge Diagnosis and the method of Unified Graphic Visualization of Activity. The article demonstrates the possibility of mutual transition between the parametric and visual representation of the learning situation between these types of maps and their use in the development of solutions by both the solver of the learning system and the student during their work in the electronic learning environment. In conclusion, the architectural features of the AESU ITS are noted. In addition to the typical functions of learning management systems, the proposed ITS implements a dialogic mechanism for interacting with students in a natural language form without using the capabilities of large language models. The flexible feedback mechanism allows for interactive dialogue based on methodologically sound conclusions, which increases the level of trust between the student and the learning system and promotes engagement in the educational process based on the ITS.

electronic learning, Intelligent Tutoring Systems, cognitive visualization, decision-making, method

2026-09-03