The article is devoted to the issue of the construction of an intelligent tutoring system which was created by our university for implementing distance learning and combined forms of studies. Significantly higher demand for such tools occurred during the COVID-19 pandemic when distance learning was used by students in their full-time studies. Current Learning Management Systems (LMS) do not address students’ individuality regarding their various levels of input knowledge and skills or their different learning styles, which, in our case, are based on sensory preferences. Therefore, this article proposes a model of an intelligent tutoring system to control learning by accentuating the individual needs of a student. The foundation stones of this system are an expert system and adaptation mechanisms. The expert system acts as a tool for the identification of students’ needs from the point of view of input knowledge and sensory preferences. Sensory preferences influence the student’s learning style. The implemented adaptation mechanisms control the progress of the student through a study unit. The model was implemented in the LMS Moodle environment. Regarding the focus of the research content, our model is oriented on the study of the English language, where each student receives a unique study plan, which is continuously adapted based on achieved results. We consider the focus on the individuality of the student to be an innovative approach that can be achieved automatically on a mass scale.

The study focuses on evaluating the performance of AI-based tools, specifically ChatGPT versions 3.5 and 4.0, in comparison to human students in the field of Theoretical Computer Science Education. The experiment aims to assess the capabilities of both AI and human subjects in solving learning tasks based on Bloom’s Taxonomy. The primary objectives of the study are to determine the educational performance of AI and students, identify areas where students may outperform AI in learning tasks, and evaluate the normalized overall results of educational performance with equal assignment weights. The assessment included testing on various types of Bloom’s Learning Task Taxonomy (LTT) based on specific Learning Objectives (LOs). Hypotheses were formulated for quantitative and qualitative analysis to compare the performance of AI and human subjects. Quantitative analysis revealed engaging results regarding the educational performance evaluation with AI-based tools. While some differences were observed in the performance of AI and students, the normalized overall results with equal assignment weights did not show statistically significant differences. The study highlighted the advantages and disadvantages of both humans and AI bots in solving learning tasks. The study concludes that there are areas where students may have an advantage over AI in tasks requiring understanding, evaluation, and creative thinking. Recommendations are provided for educators on utilizing AI-based tools in education, emphasizing the coexistence of AI possibilities with well-designed assignments. The findings suggest that AI has a valuable role in education, and a thorough analysis of teaching approaches and student evaluation is essential in leveraging artificial intelligence effectively.

The importance of intelligent tutoring systems has rapidly increased in past decades. There has been an exponential growth in the number of ends users that can be addressed as well as in technological development of the environments, which makes it more sophisticated and easily implementable. In the introduction, the paper offers a brief overview of intelligent tutoring systems. It then focuses on two types that have been designed for education of students in the tertiary sector. The systems use elements of adaptivity in order to accommodate as many users as possible. They serve both as a support of presence lessons and, primarily, as the main educational environment for students in the distance form of studies – e-learning. The systems are described from the point of view of their functionalities and typical features that show their differences. The authors conclude with an attempt to choose the best features of each system, which would lead to creation of an even more sophisticated intelligent tutoring system for e-learning.