The aim of the study is to develop and substantiate an approach for detecting and classifying unacceptable information security events in critical information infrastructure systems using machine learning methods. The proposed approach is focused on improving the effectiveness of identifying unacceptable events under conditions of large-scale heterogeneous data processing and strict time constraints for response.
The increasing number and complexity of cyberattacks targeting critical information infrastructure, as well as the need for timely detection of information security events that may lead to significant negative consequences for the stability of critical systems determine the relevance of the study. The limitations of traditional signature-based and expertdriven methods, caused by the high dynamics of security events and data noise, necessitate the use of intelligent data processing techniques.
Materials and methods. The study employs machine learning methods, statistical analysis, and processing of information security event data. Event logs and network traffic of a real object of critical information infrastructure of the energy sector are used as initial data. A methodology for forming a training sample has been developed, including data preprocessing, expert labeling, informative features’ selection, and class balancing. A Random Forest algorithm was used to classify unacceptable events.
Results. Experimental results demonstrate the effectiveness of the proposed model in terms of precision, recall, and F1-score with a minimal level of false positives. The findings confirm the practical applicability of the proposed approach for automating monitoring and detection of unacceptable information security events in critical information infrastructure systems.

The purpose of the study is to develop a model of a hybrid intelligent educational environment according to the intellectual partnership between humans and artificial intelligence, based on an analysis of the practice of using artificial intelligence in school and university education. To achieve this goal, it was necessary to solve the following tasks: to assess the opinions of participants in educational relations about the use of artificial intelligence in education, the associated threats and opportunities; to identify the features of using artificial intelligence in higher education; and to develop methodological approaches to forming an intellectual partnership between students and artificial intelligence as the basis for a model of a hybrid intelligent educational environment.
Materials and methods. The study used a range of methods, including inductive-deductive, statistical and comparative analysis, modeling, and visualization of graphical data.
The information base was compiled from scientific publications, materials of the All-Russian Public Opinion Research Center, research results of the scientists of the Higher School of Economics, open sources of the Internet.
Results. The integration of artificial intelligence into the educational environment is accompanied by a number of problems (technological, social, psychological, ethical), which requires the management of educational organizations to take them into account when creating a new pedagogical ecosystem, in which artificial intelligence acts as a new tool of the educational process. An analysis of the parental community’s opinions regarding the use of artificial intelligence in children’s education revealed diverse assessments: while acknowledging the high educational potential of the technology, there were also concerns about the associated threats, with the main concern being the potential decline in the quality of knowledge. The article identifies the broad opportunities for using artificial intelligence by students and teachers in school and university education. It also highlights the fundamental differences in the use of artificial intelligence in higher education, which are related to the depth and complexity of solving scientific and educational tasks. The article proposes methodological approaches to creating an intellectual partnership between students and artificial intelligence, aimed at preventing the passive use of technology that leads to intellectual stagnation. The model of a hybrid intellectual educational environment is developed, which requires not only the modernization of technological infrastructure, but also pedagogical and cultural transformation based on ethical principles. The tasks have been formulated to train new qualified personnel who can use artificial intelligence technology as a tool for their professional activities.
Conclusion. In the era of unprecedented growth in technological achievements and the widespread and deep penetration of artificial intelligence into all areas of life, including the educational environment, the modern education system is in the process of reevaluating the role and place of artificial intelligence in the learning, development, and upbringing of young people. Recognizing the need to adapt to new technological trends, educational organizations are carefully assessing the potential and threats associated with the implementation of artificial intelligence in the educational process, as well as evaluating their own resources to implement the concept of “collaborative intelligence”, which combines human and artificial intelligence. The intellectual partnership of participants in educational relationships with artificial intelligence has a high potential for solving pedagogical tasks: artificial intelligence, by solving routine tasks, frees up the teacher for creative work, emotional support, and the development of new methods for forming meta-cognitive competencies among students; artificial intelligence expands scientific and educational opportunities for students in a proactive position of using technology.

Purpose of the study. The aim of this study is to substantiate the criteria and factors for the quality of screen interface design for digital educational resources from the perspective of visual complexity and cognitive load, as well as to identify their impact on learning outcomes in a digital and bilingual educational environment. Particular attention is paid to establishing the relationship between the structure of the visual presentation of educational material, the level of learners’ cognitive load, and the effectiveness of knowledge acquisition. This goal is aimed at developing scientifically based recommendations for optimizing the design of digital educational resources, ensuring that the visual structure of educational content matches the cognitive capabilities of learners and improving learning effectiveness in a multilingual educational environment.
Materials and methods. The study is based on an analysis of domestic and international scientific works in the field of perception psychology, cognitive ergonomics, cognitive load theory, instructional design, and bilingual education. The methodological framework utilized concepts of visual complexity, cognitive load theory, adaptive cognitive control principles, as well as the results of empirical studies conducted using behavioral methods and eye tracking. For the analytical section, typical screen pages of digital educational resources, varying in visual complexity, were examined. Interfaces were evaluated based on criteria such as quantitative richness, structural organization, color and graphic complexity, semantic richness, and dynamic characteristics.
Results. The study found that the visual complexity of a screen interface directly impacts learners’ cognitive load. Increasing the number of elements, semantic density, color variability, and dynamic components leads to the growth of the external cognitive load, a decrease in the information retrieval speed, and an increase in the likelihood of errors. In bilingual learning, cognitive load becomes complex, integrating subject-specific and linguistic information processing. It has been shown that high proficiency in a second language reduces the load on working memory and executive control, while frequent code-switching simultaneously increases linguistic complexity and develops cognitive flexibility. A “visual complexity – cognitive load – performance” model is proposed, describing the cause-and-effect relationship between interface design and learning outcomes.
Conclusion. The obtained results confirm the need for a systematic assessment of visual complexity and cognitive load in the design of digital educational interfaces. Well-founded evaluation criteria enable comparable and reproducible analysis of interface quality, as well as the development of design solutions that align with learners’ cognitive abilities. The proposed approach has high practical significance, as it can be used in the creation, examination, and adaptation of e-learning courses, multimedia lectures, and interactive educational platforms, ensuring improved e-learning effectiveness and the sustainability of educational outcomes in the digital environment.

The purpose of research is to develop a methodological tool for assessing the commitment of higher school lecturers to professional activities based on the concept of well-being. The study systematized key motivation factors and occupational stressors, adapted the fivecomponent well-being model (professional, emotional, physical, social and financial well-being) to the conditions of higher education, and developed the Index of Teacher’s Commitment to Professional Activity (ITCPA) to monitor the risks of reduced professional involvement and staff outflow in a dynamic educational environment.
Materials and methods. The paper uses the works of domestic and foreign authors on motivation, organizational stress management and the formation of a favorable working environment. The method of comparative analysis is applied, which includes a comparison of theoretical models of well-being, commitment assessment tools, and approaches to stress management in an academic environment. Additionally, analytical, conceptual and retrospective analysis, a survey of the Faculty and formalized modeling were used to develop the calculated structure of the Index of Teacher’s Commitment to Professional Activity (ITCPA).
Results. The main results of the study are the construction of an original neurocognitive typology of higher school lecturers, the development of the Index of Teacher’s Commitment to Professional Activity (ITCPA), structured in accordance with five well-being components: professional well-being (influence, autonomy, career opportunities), emotional well-being (level of anxiety, burnout, emotional stability), physical well-being (health, sleep, level of chronic fatigue), social well-being (quality of communication, support of colleagues), financial well-being (income satisfaction, financial planning, stability). Indicators, assessment methods and measurement scales adapted to the conditions of higher education are defined for each component. To ensure comparability of the indexes, minimax normalization was applied, and the weights of the components were determined expertly. An illustrative and conceptual demonstration of the index calculation was carried out and measures were proposed to create an environment for the well-being of a lecturer at a university based on the well-being approach.
Conclusion. The results obtained make it possible to move from reactive practices of personnel risk management to proactive monitoring of the well-being of the Faculty. The proposed neurocognitive typology creates the basis for personalization by targeting existing resources. The Index of Teacher’s Commitment to Professional Activity (ITCPA) not only diagnoses the current condition, but also identifies “risk points” in the early stages of professional burnout, especially among lecturers with profiles of “resource-deficient” and “cognitively overloaded”. In addition, the need to develop management solutions aimed not only at reducing stress, but also at developing autonomy, influence and collegiality as intangible resources that determine commitment to the profession in the context of the transformation of higher education is emphasized. The prospects for further work are related to the empirical calibration of ITCPA weights based on criterion variables (publication activity, students’ satisfaction) and the introduction of personalized support strategies for lecturers based on their cognitiveemotional profile.

The aim of this study is to develop, test, and evaluate the effectiveness of a pedagogical model integrating artificial intelligence tools and “the flipped classroom” blended learning technology for developing key management competencies of students of an economic university.
Materials and methods. The study utilized a combination of methods: theoretical analysis, a pedagogical experiment, expert assessment, and statistical data processing. The experiment was conducted at the Minsk branch of the Plekhanov Russian University of Economics during the 2021-2025 academic years with students majoring in “Business Informatics”, “Management”, and “Economics”, divided into control and experimental groups.
Results. A comparison of the results of educational activities of students in the experimental and control groups demonstrates the high potential of a pedagogical model integrating artificial intelligence tools and “flipped classroom” technology for developing students’ management competencies, which in turn has a positive impact on their other educational and academic achievements.
Conclusion. The integration of artificial intelligence technologies and “the flipped classroom” model creates a powerful educational foundation for the targeted development of management competencies of economic university’s students. This approach transforms the educational process from passive information acquisition into an active, practical learning environment, closely aligned with the realities of digital business. The key success lies in the synergy: artificial intelligence takes over the routine personalization and training of basic knowledge, while students and faculty focus on developing the unique qualities needed by future economic leaders — competence, communication skills, critical thinking, creativity, emotional intelligence, leadership potential, and self-confidence. The conclusion confirms the high effectiveness of the proposed integration, which ensures personalization, interactivity, and a practice-oriented educational process. The model can be scaled to other areas of management training.

Purpose. The purpose of this article is to develop a methodological framework for the creation and use of training simulators new teaching tools actively used in higher education institution of economics. The relevance of this study lies in the need to improve the quality of professional training for future economists in the context of the implementation of a competency-based approach, the digitalization of teaching methods, and the use of real economic data.
Research materials and methods. To achieve this objective, a systems approach was used to analyze the pedagogical category of “Training simulator”. The study relies on a system of complementary methods: qualitative and quantitative. Specifically, a theoretical approach was employed, including an analysis of regulatory and methodological documents for higher economic education, teaching materials on the use of digital technologies, and training simulators already in use for training future economists. An empirical (practical) method was employed: a survey of practicing lecturers and students in the undergraduate economics program, followed by preliminary processing of the obtained results. The methodological basis of the study was based on works on the theory of digital and pedagogical technologies by Russian and foreign authors, as well as comparative analysis techniques for the methodological characteristics of training simulators.
Results. The study established a basic set of characteristics of training simulators, including “Interactivity”, “Simulation”, “Feedback”, “Repeatability”, and “Gradual complexity”. These characteristics are used in most pedagogical studies to describe the methodological and research potential of training simulators. A summary of pedagogical experience allowed us to present an expanded set of characteristics of the training simulator as a pedagogical object, which are relevant for the development of methods for the elaboration and use of training simulators in higher education institutions.
New characteristics of training simulators during the research process include “Modularity”, “Visualization and clarity”, “Adaptability”, “Motivation”, and “Intellectual student support”, utilizing the capabilities of artificial intelligence technologies. Particular attention is paid to revealing the pedagogical significance of characteristics of training simulators not included in the set of basic characteristics, with an emphasis on classical didactic principles used in higher institution of economics. Attitude toward training simulators in the teaching of mathematical disciplines in higher institution of economics are clarified. The obtained results of assessing the usefulness of training simulators by subgroups of respondents, as well as assessing the readiness and need for using training simulators in the teaching of mathematical disciplines in higher institution of economics, allow us to determine optimal didactic conditions.
Conclusion. The components of the methodology for developing and using training simulators presented in the article (basic and extended sets of characteristics of the training simulator; specific attitude toward training simulators; assessments of readiness and need for using training simulators; assessments of the usefulness of training simulators; and assessments of perceptions of the risks of using training simulators by various subgroups of respondents, etc.) contribute to unlocking the didactic potential of training simulators as a teaching tool. The article’s material opens new avenues for improving the application of both existing training simulators and the creation of new training simulators in line with the professional training goals of future economists and modern advances in pedagogical science.

The purpose of the study is to develop, theoretically substantiate, and experimentally test a teaching methodology aimed at effectively developing algorithmic thinking in schoolchildren during the process of mastering the basics of programming in the C++ language. The relevance of this paper is due to the need to improve existing approaches to teaching programming, taking into account the current requirements for the quality of training specialists in the field of information technology.
Materials and methods. To achieve the stated research goal, a set of complementary methods was used to ensure a comprehensive study of the problem and the development of effective recommendations. Both theoretical and empirical approaches were applied. Among the theoretical methods, special attention was paid to analyzing existing scientific literature on the development of algorithmic thinking, specifying the initial data, identifying general patterns, and conducting a comparative analysis of known approaches. The most important stages were the processes of deduction and meaningful interpretation of the obtained results. The empirical component of the study included a questionnaire survey of students of a general education institution, followed by testing the level of formation of algorithmic thinking. This allowed for an objective assessment of the respondents’ level of preparedness and the identification of key factors affecting the success of mastering the basic concepts of programming.
Results. The article presents a methodology aimed at the purposeful development of algorithmic thinking in the conditions of the educational environment. The structure of the methodology is a clearly organized set of interrelated components, including: the principles of creating an effective educational process; the goals and objectives of the course aimed at the formation of the necessary skills and abilities; learning strategies that provide for an optimal combination of traditional and innovative formats of presenting the material, the specified content of the educational material focused on solving typical and non-standard tasks, organizational forms of conducting classes that promote the active involvement of students in the learning process. Special attention was paid to identifying criteria for assessing the levels of algorithmic thinking development and creating a specialized set of tasks aimed at the consistent formation of each individual element of the mental structure. The set of tasks was designed to gradually increase the complexity of the problems being solved, ensuring a progressive movement from simple examples to solving complex problem situations.
Conclusion. The implemented pedagogical experiment has shown the effectiveness of the proposed methodology. Students trained according to the developed course demonstrated significant progress in developing algorithmic thinking, which was expressed in improving the ability to solve practical problems, increasing the level of independence, and a more creative approach to the implementation of projects. The practical significance of the research lies in the possibility for widespread dissemination of experience in the educational environment, providing an effective tool for training qualified computer science teachers and improving the additional education program for the gifted children, interested in programming. The developed approach can significantly improve the quality of professional training for information technology specialists, facilitating the integration of the latest scientific and practical achievements into the educational process.

The purpose of the study is to identify priority areas for the modernization of university education in the field of digital skills development based on a comparison of international approaches to measuring digital competencies, data of official Rosstat statistics for 2025, and modern online learning models. Special attention is paid to identifying the gap between normative models of digital competencies and actual practices of their use by the population, as well as finding effective educational mechanisms to bridge this gap.
Methods of research. The methodological basis of the study includes the analysis and comparison of international frameworks for digital competencies (DigComp 2.2, ITU Digital Skills Toolkit, PIAAC), which make it possible to structure digital skills by levels, as well as official statistical data from the Federal State Statistics Service (Rosstat) for 2022–2025, reflecting the prevalence of various types of online activities among the population aged 15 years and older. Additionally, a systematic review of modern online education models as an environment for developing applied digital competencies was conducted. The study employs methods of comparative analysis, systematization, generalization, and interpretation of empirical data.
Results. The structure of the population’s digital skills is found to be markedly heterogeneous: despite the widespread adoption of basic communication and information practices (calls, video calls – 80.5%; file sharing via messaging – 75.9%), more complex and professionally significant competencies remain poorly represented (use of neural networks – 6.4%; programming – 1.5%). A steady increase has been recorded in the use of cloud technologies (from 16.5% in 2022 to 20.5% in 2025) and information verification skills (from 12.3% to 15.0%). It is shown that international models of digital competencies cover a wide range of skills, including data processing, digital content and artificial intelligence technologies; however, the actual level of these skills among the population lags significantly behind. It is argued that online education, characterized by high adaptability, a modular structure, project-based orientation, and a close connection with practical labor market needs, serves as an effective environment for developing in-demand digital skills and can be considered a reference model for updating university curricula.
Conclusion. The study concludes that systemic modernization of university education is necessary, considering the identified imbalances in the structure of the population’s digital skills. Key priorities include integrating universal modules on generative artificial intelligence (prompt engineering, generation of analytical materials, automation of routine tasks), developing cloud-related competencies (collaborative work, data processing in digital environments, the use of BI systems), and implementing project-based learning methods. Mechanisms for improving the assessment of educational outcomes are proposed, including the use of digital portfolios and the recognition of online learning outcomes through crediting the results of mastering individual disciplines and the recognition of learning outcomes, as well as a phased roadmap has been developed for integrating digital modules into bachelor’s degree programs.