The purpose of the paper is to evaluate the effectiveness of the “Vznaniya” educational platform in preparation for the centralized testing in English, and to identify the correlation between the work on the platform and the exam result. The objectives included the development of a course on the platform, analysis of the system’s didactic potential, and comparison of the platform’s analytics with the exam results. The relevance of the paper is determined by the need to integrate effective digital tools into pre-university preparation practice, given the digital transformation of education and high competition in entrance exams. Materials and methods. The research was based on a comprehensive approach, including theoretical analysis of the pedagogical potential of digital platforms and practical testing of the course. To achieve the goal, a specialized preparatory course for the centralized exam was developed and implemented on the “Vznaniya” platform at the Intensivkurs educational center in Minsk within a blended learning model. The methodological basis for the course development was the analysis of the centralized testing specification, as well as the creation of authentic task types on the “Vznaniya” platform that simulate exam tasks. The main methods were quantitative analysis of the platform’s educational analytics, comparative analysis, and statistical data processing. The key metrics analyzed were the average task completion rate, the percentage of material covered, and the final exam score. Results. A stable positive correlation was established between activity on the platform and test results. The obtained results demonstrate that the use of the digital platform not only confirms its role as an effective training and diagnostic tool but also contributes to increasing student motivation, improving the dynamics of subject mastery, and fostering sustainable self-study skills. The analysis showed that the digital environment provides a more flexible organization of the educational process, allows for consideration of individual learning trajectories, and enhances interaction between the teacher and the student. Conclusion. The research proves the high effectiveness of the “Vznaniya” platform for targeted preparation for a standardized exam. Systematic use of its resources ensures personalization, objective monitoring, and the formation of study discipline, leading to improved results. The obtained data opens prospects for integrating such digital environments into a continuous educational trajectory. Further research is required on the development of adaptive algorithms based on platform data and the assessment of the long-term impact of such preparation on success in university studies.
The purpose of the study is the development, theoretical substantiation, and empirical testing of a cognitive-digital model for teaching foreign language phraseology. This model is based on the integration of metaphorical modeling, generative artificial intelligence (AI), and virtual reality (VR). It aims to overcome the cognitive gap between the form and the culturally determined meaning of set expressions, which poses a significant challenge for learners. The research was carried out on the example of Spanish idioms in a university classroom. Materials and methods. The theoretical foundation is a synthesis of cognitive linguistics (conceptual metaphor theory), principles of embodied cognition, and social constructivism. A four-stage methodological model was developed, including: 1) semantic-cultural deconstruction using generative AI (ChatGPT-4, DeepSeek) and specialized prompt templates; 2) immersive visualization of the historical-cultural context in accessible VR environments (CoSpaces Edu, Google Cardboard); 3) contextualized practice of idiom usage with adaptive AI tools (Character.AI for dialogues, DeepL Write for text analysis, ELSA Speak for phonetic practice); 4) socioreflective transfer through a theatrical method with digital reflection tools (Padlet, Mentimeter). Empirical testing was conducted via a pedagogical experiment involving 60 A2-level linguistics students divided into control and experimental groups. Methods included preand post-testing, comparative and correlation analysis, qualitative analysis of the digital footprint (logs of interaction with AI and VR), expert assessment of video recordings of theatrical performances, and data triangulation. Results. A statistically significant superiority of the experimental group over the control group was established across all measured parameters: recognition and understanding of idioms (p < 0.001, Cohen’s effect size d = 1.80), pragmatic choice of appropriate usage (p < 0.001, d = 1.45), and, especially, the task of explaining the conceptual foundations of idioms (p < 0.001, d = 2.30). Qualitative analysis revealed positive correlations between the structure of AI prompts (r = 0.65), active exploratory behavior in the VR environment (r = 0.71), and the depth of competence, facilitating a shift from rote memorization to meaningful mastery of phraseology. Conclusion. The proposed cognitive-digital model, which implements a sequential integration of metaphorical modeling, generative AI, and VR, is an effective tool for the deep acquisition of foreign language phraseology, as demonstrated by the example of Spanish. The scientific novelty lies in the theoretical and methodological synthesis of cognitive linguistics and digital pedagogy, as well as in the development of a comprehensive assessment model based on data triangulation. The practical significance consists in creating teaching modules, prompt templates, and VR scenarios adapted for A2–B1 levels, ready for the implementation in university practice. The identified limitations (digital fatigue, dependence on prompt quality) indicate the need for pedagogical support and preliminary development of AI interaction skills. Research prospects are associated with the development of an integrated software platform, adaptation of the model for other languages, and an in-depth study of digital load optimization.
Problem and purpose. Bilingual teaching aids, presented in two languages, are becoming widely used in bilingual education. The design of these aids can significantly impact the learner’s cognitive load and their comfort with learning material. In this regard, the question of what textbook design can influence the quality of bilingual learning for students is of interest. The goal of this study is to identify suitable formats for presenting computer science material in bilingual textbooks that can contribute to improving the quality of bilingual learning for students (by quality, we mean academic performance in computer science and the development of bilingualism among students from Russia’s ethnic regions). Materials and methods. To achieve this goal, the following methods were used: D. Barkhatova’s inversion approach to developing tools and methods for bilingual computer science education; a competency-based approach to designing and processing questionnaire questions; N. Pak’s mental approach to substantiating the need for bilingual education for students living in ethnic regions; and an ethno-pedagogical approach based on the use of the language of a particular ethnic group in the development of bilingual teaching aids. A combination of complementary research methods was also employed: theoretical (analysis of sources on the research problem, data specification, generalization of psychological and pedagogical literature, data comparison, deduction, substantive interpretation, and analysis of results) and empirical (questionnaires, testing, processing, and analysis of the obtained results). Results. Suitable formats for presenting computer science educational material in bilingual textbooks that contribute to improving the quality of bilingual education for students were identified using specially developed questionnaires. An analysis of the results of a questionnaire survey of experts and students allowed us to determine the most appropriate format for presenting educational information in bilingual textbooks. Synchronous (63% of experts) and mirror (40% of students) bilingual presentation of information are preferred for presenting theory in flipped textbooks; horizontally sequential (48% of experts and 35% of students) presentation is preferred for the practical block; and mirror (57% of experts) and synchronous (37% of students) formats are preferred for the control block. Thus, the most appropriate formats for presenting computer science educational material in bilingual textbooks have been identified, contributing to the improvement of the quality of bilingual education for students. Conclusion. The statistical data from the questionnaire survey of experts and students allowed us to determine the most appropriate format for presenting educational information in bilingual textbooks. Research to determine the appropriate format for presenting educational information in bilingual textbooks has shown that continuous and inverted textbooks are the most suitable and all three formats, we developed, turned out to be preferable in one or another type of educational activity. Promising areas for further work include research to assess the effectiveness and quality of bilingual education for students, as well as the development of electronic versions of bilingual computer science textbooks, pilot testing at universities and schools, and studying the design of electronic bilingual computer science textbooks.
The purpose of this study is to examine the theoretical foundations and pedagogical practices of using artificial intelligence in the educational activities of university students, and to identify, analyze, and compare the motivational focus of using artificial intelligence in the education of students in technical and humanitarian fields. Materials and methods of research. 145 students from Kazan (Volga region) federal university participated in the research. 73 first-year students are studying at the Institute of Psychology and Education in the humanities, and 72 first-year students are studying at the Institute of Physics in the natural sciences. The following research methods were used in the course of this paper: systematization and theoretical analysis of pedagogical literature on the research problem; interview “How I see the advantages and disadvantages of using AI in educational activities”; questionnaire “Self-assessment of the level of students’ motivational focus on using AI in learning”; modified methodology for identifying the motives for studying the factors of attractiveness and negative attitude in students’ use of artificial intelligence in educational activities (V. Yadov, modified by N. Kuzmina and A. Rean). This method has been repeatedly used in pedagogical research affecting the problems of a professional school and has proven itself positively. The modified method “Studying the motives of students’ use of artificial intelligence in educational activities” (A. Rean, V. Yakunin) was used to identify the dominant motives. A comparative analysis of the results was also conducted, and the Student’s t-test was used for statistical data processing. The novelty of the study is due to the insufficient development of the problem of the motivational component of the use of artificial intelligence by students of higher educational institutions, the presented review of pedagogical literature on the use of artificial intelligence in education, the comparative analysis of approaches to defining and the role of artificial intelligence in educational activities, and the identified motives for the use of artificial intelligence by students of technical and humanitarian fields. Results. The article presents an analysis of scientific approaches to the theoretical justification and pedagogical practice of using artificial intelligence in the educational activities of university students. The practical significance of the study lies in the fact that the author’s questionnaire and modified methods can be practically used in pedagogical research. The article provides a quantitative analysis of the results of diagnosing the students’ motivational orientation towards using artificial intelligence in their educational activities. The obtained results are valuable both from a theoretical and practical perspective of using information technologies in the education of students in the humanities and technical fields. Conclusion. The analysis of the results obtained allowed us to draw conclusions about the comparability of the level of motivation of university students of the humanities and natural sciences to the use of artificial intelligence in education at this stage. It is necessary to carry out pedagogical work to inform students about AI technologies, improve the skills of using the advantages, and increase the level of awareness of the risks of unlimited use of artificial intelligence in education. Thus, the use of artificial intelligence should be auxiliary in nature. Students should approach its use with caution, combining its capabilities with their personal experience and knowledge, in order to maintain the quality of education and develop their skills. The results of the paper can be used by teachers and researchers in the field of innovative technologies in higher education to expand and deepen the content of various courses at universities, in particular the course on information technologies in education.
The purpose of the study - the analysis of the essence of the components of artificial intelligence systems and their use in educational processes, as well as their impact on human cognitive abilities. Research materials and methods - the analysis and systematization of information, as well as system-information modeling of processes that ensure the formation of competencies as a target result of educational activities within the framework of an integrated system of reproduction-the use of knowledge and human development. Results. An information model of the processes of perception and cognition occurring in consciousness is presented. Education is considered as a system of complex interrelated processes of activity for the formation of knowledge, skills, and competencies. Moreover, motives, incentives, and emotions play an important role in cognitive and educational activities. Perception, understanding, and cognition are basically information processes characterized by stochasticity and directionality, which ensures transcending the known and synthesizing new knowledge. Explicit and implicit knowledge is represented as a set of models of reality, with varying degrees corresponding to this reality, obtained and used in various circumstances. From the point of view of the form of knowledge existence, in contrast to data, it is a set of facts that are systematized, attributed in accordance with the properties of the subject area, qualified in terms of consistency and identified. Attention is focused on the fact that data, information, knowledge are the role names of a specific information object (messages, records, thoughts, etc.) their common potentially effective component, having different purposes (storage/transmission; search/analysis/ synthesis; specific application) and, accordingly, different contexts (meta-components and methods). The problems related to the information and cognitive features of the “digital” generation are discussed, as well as some properties of artificial intelligence systems that determine their unconstructive role in educational processes. Conclusion. The use of digital media as an educational medium does not allow us to identify (and, consequently, develop) motives and incentives as a driving force for education. In addition, such an individual-oriented environment increases alienation from society, reduces the effectiveness of emotions and incentives for the development of volitional and organizational qualities. It is concluded that the features of the zoomers’ generation are not the laws of nature “in action” and not heredity, but the natural realization of the most important property of adaptability and learning, which actually ensured the accelerated development of humankind. Therefore, it is necessary to change the goals and values, and not to create technological “crutches”. The use of AI in education must be strictly limited and controlled, since it, replacing humans in the fundamental operations of thinking, will thereby contribute to the degradation of not only students, but also teachers, including in terms of human ability to memorize and choose, to create hypotheses and conclusions.
Purpose of the research. The relevance of this study is driven by the steady growth of disinformation in the digital environment, which makes the development of automated content verification systems a critical tool for ensuring national information security. In addition, such systems serve as important digital technologies for educational purposes, helping to improve the digital literacy of modern users. The aim of this paper is to develop and substantiate the architecture of an anti-fake service as a specialized digital tool for education. The study addresses the following objectives: a comparative assessment of the effectiveness of neural network models for analyzing news in Russian and the development of methodological recommendations for integrating digital services into the educational process to foster a critical attitude toward information in the contemporary Russian-language information space. Materials and methods. The anti-fake service is based on an ensemble method for training neural network models, combining recurrent neural networks (including long short-term memory architecture) and convolutional neural networks. The training was conducted on the public data set of Russian-language news “Fake and real Russian news”. A specialized preprocessing pipeline, including lemmatization and stop word removal, was implemented for Russian-language text. The quality of binary classification for the Russian-language news was assessed using a set of metrics: Accuracy, Precision, Recall, and F1-score. The digital service architecture is implemented using a microservices approach based on the FastAPI/ React technology stack, ensuring system scalability and flexibility. The results. It is developed a working prototype of an intelligent antifake service for analyzing Russian-language news texts. A key feature of the proposed digital solution is that it allows users to interactively select and combine deep learning models in real time via an intuitive web interface. This functionality not only enables news classification as reliable or fake, but also clearly demonstrates the inner workings of deep learning algorithms in an educational context, helping users to develop practical information verification skills. Experimental data confirm the effectiveness of the proposed approach to developing an anti-fake service: the highest accuracy (Accuracy 93.3%) was achieved using a convolutional neural network, owing to its ability to identify local semantic patterns in texts. The use of a probabilistic ensemble further improved the reliability and robustness of binary classification of Russian-language news. Conclusion. The conducted research solves a pressing problem at the intersection of information security and pedagogy countering disinformation through the development of digital literacy. The novelty and key advantage of the developed anti-fake service lie in its functional duality: it serves both as a tool for the automatic binary classification of Russian-language news texts and at the same time, as a service for developing users’ practical information verification skills through interactive engagement with neural network models. The anti-fake service has significant didactic potential and can be integrated into educational programs for courses to develop students’ digital literacy, including its use in courses on computer science, information security, machine learning, and other disciplines. Thus, the developed anti-fake service functions both as a tool for automatic verification of Russian-language content and as a digital educational tool that promotes the formation of a critical attitude toward information in the digital media environment.
The purpose of the study. The discussion and adoption of the “Strategy for the development of education in the Russian Federation for the period up to 2036 with a perspective until 2040” sets the task of appropriate personal and professional development of employees at all levels of the education system. In this regard, the analysis of the content of the concept of “space of teacher’s development” and its categorical clarification within the framework of the model representation by analogy with the mathematical interpretation of physical space becomes relevant. The inextricable connection between the personal development of the current and future teacher with the emotional, social, cultural, intellectual and professional development of his human potential is indicated. The center of this space is the personality of the teacher as the author of his creation in the general educational context. The need for the formation of longitudinal professional and personal development goals in the development of educational programs of advanced training institutes for educators and pedagogical universities is actualized. Materials and methods. Scientific publications in the field of personality-oriented, culturological, competence approaches in the analysis of the processes of professional formation of a teacher are used. The method of comparative analysis of the content of publications and the method of analogy in constructing a model of the teacher’s development space are applied. The experimental part of the paper used a survey method of experts in focus groups, members of dissertation councils and school teachers who had completed advanced training courses. The results of the surveys are compared with the theory of the development of the human thinking proposed by R. Keegan. The results of the study. Based on the comparison of the converging results of the survey of focus group experts and teachers with the provisions of the theory of human thinking development, it is shown that the phenomenon of personal and professional development includes a hierarchy of forms of emotional, social, cultural, intellectual and professional development in a conditional (configurational) space, the coordinate axes (vectors) of which are the categories of subjectivity, competence and skill. These forms arise in the process of evolution of a human personality in the education system in such a way that the path of development is realized along a nonlinear trajectory with inflection points and an exit to a plateau of achieving mastery. The image of a conical spiral of the teacher’s professional development is proposed, immanently including emotional, social, cultural and intellectual development, the phases of which are determined by the totality of coordinates of subjectivity, competence and skill. The development spaces of the teacher and the student intersect in the educational context, forming the zone of proximal development according to L. Vygotsky. In the general educational space (goal content structure), the teacher is able to form a personal educational environment as an area of professional and personal development space. Conclusion. It is concluded that the proposed spatial-like model implements the gnostic, communicative, constructive and evaluativereflective functions of personality development, takes into account the individualization of the development trajectory, personal involvement and direct participation in the processes (forms) of development, duration, continuity of the development process, covering the entire career of the teacher.
As part of operational risk management, business processes are considered as one of the generators of risk events. Empirical data show that a significant share of operational losses in credit institutions is associated with the human factor, namely, the professional unpreparedness of employees. In this context, a promising area of risk management is the development and implementation of tools based on artificial intelligence, designed for automated assessment of the criticality level of operations, which allows mitigating the risks generated by personnel. The aim of the paper is to develop an intelligent system for preventive monitoring of the occurrence of a critical state of a business process due to actions or omissions of personnel to prevent an operational risk event. To achieve this goal, professional and personal criteria for evaluating personnel, criteria for assessing their impact on the business process, as well as accumulated statistical indexes were analyzed. The general structure of the business process status indication system is proposed, organized according to the modular principle. It is proposed to use artificial neural networks (ANN) of direct propagation as composite modules. The paper describes the main data flows coming to the ANN inputs and compares different ANN models for each of the system modules. The results obtained can be used in various areas of activity related to personnel actions to prevent the negative consequences of the critical state of the business process.
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 of the study. This article aims to identify and systematically analyze the dual effects and risks associated with the use of generative artificial intelligence (GenAI) in higher education, with a specific focus on its impact on students’ metacognitive skills. The study seeks to define the pedagogical conditions under which GenAI functions not as a replacement for cognitive engagement, but as a purposeful instrument for supporting metacognitive regulation – encompassing its three core components: planning, monitoring, and evaluation of one’s own learning.Materials and methods. The study is grounded in a systematic review of empirical research published between 2023 and 2025, including both quantitative and qualitative studies from the fields of pedagogy, cognitive psychology, and educational technologies. Theoretical generalization, synthesis, and structural analysis of scholarly literature were employed in the paper. The theoretical framework draws on J. Flavell’s model of metacognitive regulation and G. Zimmerman’s theory of self-regulated learning, further enriched by insights from distributed cognition theory. Results. The analysis revealed a fundamental duality in GenAI’s influence: on the one hand, it can foster metacognitive development through structured support, socratical interaction, and reflective dialogue; on the other hand, it can provoke such negative phenomena as “metacognitive laziness,” false self-efficacy, and cognitive passivity. Based on the conducted synthesis, an analytical framework is proposed that includes four key conditions for the effective integration of GenAI: (1) explicit definition of the AI’s functional role (coach, opponent, mentor, facilitator, etc.); (2) linking interaction to a specific component of metacognitive regulation; (3) a mandatory reflective component; and (4) teaching prompt engineering as a metacognitive skill. A typology of possible pedagogical strategies is developed, differentiated by three components of regulation: (1) planning – AI as a training coach; (2) monitoring – AI as a “mirror of understanding” or a tool for comparative analysis; and (3) evaluation – AI as a counterargument generator or a facilitator of meta-discussion.Conclusion. Generative AI itself does not carry an unequivocal risk or benefit for metacognitive development; its effect is determined solely by the pedagogical context of integration. The crucial condition for productive use is a reconceptualization of AI’s role – not as a tool for automating thought, but as a dynamic learning partner. The author advocates for cultivating an experimental, reflective, and dialogic culture of GenAI implementation, one centered on preserving and strengthening students’ cognitive autonomy as a foundational goal of contemporary higher education. This approach entails more than mere technical proficiency with AI; it requires fostering students’ critical awareness, readiness for meta-level analysis of their own cognitive strategies, and a sense of responsibility for their learning process.
The purpose of the study. The digitalization of education implies large-scale transformations encompassing the implementation of digital technologies at every level of general and vocational education, as well as additional education, as well as changes in the interactions of all participants in the educational process. One of the six key challenges addressed by the Education Development Strategy to 2036 is the rapid spread of digital technologies and artificial intelligence. The aim of this study is to determine the role of digital traces generated during the educational process as a tool for digital transformation in educational management, as well as to demonstrate the practical implementation of digital trace processing using the VKontakte social network API and data analysis methods (Educational Data Mining).Materials and Methods. The theoretical and methodological basis for the study was formed by the work of Russian and international researchers in the field of digital data analysis arising during the educational process. The study utilized data analysis methods, natural language processing techniques, and Python libraries such as pandas, numpy, mathplotlib, and others. The empirical portion of the study is based on the analysis of the digital footprint of the educational organization's communities on the social network VKontakte, represented as unstructured text.Results. Research shows that large volumes of heterogeneous digital trace data, including those presented in the form of semi-structured data, inevitably arise in the context of the digitalization of education and ensuring the information openness of educational organizations. This data is of interest for educational analytics used to address issues related to the digital transformation of the educational process, the digital transformation of educational management, and the continuity and integration of educational levels. The digital trace generated through interactions with the electronic information and educational environment and other digital resources of educational organizations on the internet (websites, social media pages, and instant messaging apps) opens up opportunities for analyzing data on the educational process and participants in educational relationships. However, systematic approaches to its analysis and use in the context of the digital transformation of education are required, including those that take into account legal requirements for personal data, ethical aspects, and security aspects. This article examines the prospects for analyzing digital data in educational organization communities on social media using data analysis and machine learning methods and presents a practical example of data analysis in such communities on the social network VKontakte using an API.Conclusion. The obtained results can be used both for initial studies of digital footprint analysis and as a basis for developing a system for generating educational analytics. Practical application of the results will facilitate the digital transformation of educational management.
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.
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 the study is to develop methods for optimal estimation of unknown non-random factors affecting the quality of production equipment by the criterion of maximum likelihood, as well as random factors by the criterion of minimum standard error based on the processing of information related to claims received using the mathematical theory of random point processes and the theory of statistical solutions.The research method consists in applying the well-known hypothesis about the distribution of operating time for failure of technical systems in the form of an exponential distribution depending on the failure rate function. The fact is used that the corresponding distribution of the number of failures is distributed according to the Poisson law with the same function of failure rates. It is assumed that the intensity function depends not only on time, but also on a set of unknown non-random parameters, or on random parameters. It is emphasized that such factors may reflect the generalized state of the technical system, and information about this may be contained in the facts of product claims. The task of optimal estimation of the parameters on which the failure rate function depends is set. Since in this formulation of the problem, only the facts of filing claims, as well as the times of their presentation, are available for processing, the maximum likelihood function method is used for optimal estimation of nonrandom parameters, and the optimal Kalman filter is used for random parameters. The problem of optimal estimation of unknown parameters from a multiplicatively separable failure rate function, i.e. one that is representable as a product of a separate function of time and a vector function of unknown parameters, is considered. It is shown that for such a function, the optimal estimation problem is reduced to the problem of estimating a single scalar parameter that scales the time function. The well-known Kalman algorithm for continuous parameters is applied to the case of the observed process in the form of the number of claims’ events and the time of their occurrence. Examples of evaluation of both unknown and random factors are given for unified real data on tissue defects, and confirm the operability of the algorithms and their applicability for the simplest assessments of the condition of production equipment.The new results include the formulation of the problem of studying a failure intensity function that depends on a set of unknown nonrandom parameters, the application of the maximum likelihood method and Kalman algorithm for optimal estimation of these parameters, and the proof that for a separable failure intensity function, the optimal estimation reduces to the estimation of a scalar quantity that scales the time-dependent intensity function.The conclusion states that examples of assessment of factors affecting the function of the failure rate confirm the operability of the algorithm and its applicability for the simplest assessments of the condition of production equipment. A separate task is to develop analytical expressions for the failure rate function that depends on parameters, as well as methods for comparing estimates obtained by different methods. Solving these tasks will make it possible to develop methods for clarifying the condition of production equipment.
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.
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 research. Development of a set of business requirements for the software of digital educational platforms, intended for the formation and implementation of networked educational programs in continuing professional education.Research methods. The study employed methods of comparative analysis and decomposition. Analysis of the regulatory framework and scientific literature resulted in the identification of key characteristics of the network form of educational program implementation. A comparative analysis of seven russian-developed educational platforms was conducted to assess their support for these characteristics. Based on the identified limitations of existing solutions, a set of business requirements was formulated.Results. Five key characteristics of the network form of educational program implementation were identified (shared resource pool, heterogeneous modules, flexible organizational structure, integration with the labor market, personalized learning), distinguishing it from traditional models of network education organization. The comparative analysis revealed incomplete support for these characteristics by modern platforms, particularly regarding the flexible organization structure. Based on these characteristics, a set of five business requirements (business requirements - 1 – business requirements - 5) for the architecture of network digital educational platforms was formulated, aimed at ensuring horizontal network interaction, modular program assembly, and dynamic adaptation of educational trajectories.Conclusion. The developed set of business requirements provides a methodological foundation for designing a new class of information systems – network digital educational platforms – that overcome the technological gap between existing solutions and the needs of networked continuing professional education organization. The requirements have an engineering nature and can be used for drafting technical specifications when creating specific platforms that realize the advantages of network form of educational program implementation.
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 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 the paper is to conduct a comprehensive analysis and systematization of the legal and regulatory framework governing the development of digital literacy among future foreign language teachers, identify key contradictions and deficiencies in its content, and provide a theoretical justification and develop a concept of professional digital literacy for foreign language teachers, correlated with current strategic documents, professional standards, and educational standards, and focused on the subsequent design of educational programs and modules for the development of digital competencies. The study clarifies the content of the concept of “professional digital literacy of foreign language teachers” and its connection with the requirements of current strategic documents, professional standards, and educational standards of higher education.Materials and methods: the study used a systematic analysis of current legal and regulatory documents in the field of education, including the Federal law “On Education in the Russian Federation”, the professional standard “Teacher,” the Federal state educational standard of higher education in the field of training 44.03.05 “Pedagogical education,” as well as national projects and strategies for digital development. Methods of comparative analysis, content analysis of scientific publications, synthesis, and systematization of the data obtained were used. The empirical base consisted of twenty-four sources, including scientific articles, monographs, and legal regulatory acts, selected according to criteria of relevance to the topic of digital literacy of teachers and the specifics of training foreign language teachers.Results: a multi-level system of legal and regulatory support for teachers’ digital literacy has been identified, including strategic documents (the national projects “Data economy and digital transformation of the state” and “Education”), sectoral standards (the Federal state educational standard of higher education and the professional standard for teachers) and departmental regulations. It is shown that existing regulatory requirements, while providing a general framework for the digital transformation of education, only partially consider the specifics of the professional activities of a foreign language teacher, associated with the intensive use of digital language resources, online platforms and artificial intelligence tools. Key contradictions have been identified between the dynamic development of digital technologies and the relatively static character of the regulatory framework, as well as between the integral nature of professional digital literacy and the fragmentarity of its normative consolidation in different documents.Conclusion: a concept of professional digital literacy for foreign language teachers has been developed, including linguo-didactic, technological, communicative, and ethical-legal components. The following areas for improving the legal and regulatory framework have been proposed: the development of industry recommendations on digital literacy for foreign language teachers, the creation of a system for the continuous development of digital competencies at the stages of basic training and professional development, and the integration of requirements for working with artificial intelligence and large language models into educational standards and model basic professional educational programs. The practical significance of the results lies in the possibility of using the developed concept and proposed directions when updating curricula, forming modules on digital literacy in pedagogical universities, and designing programs for additional professional education.
