Highly developed competencies, including those acquired through targeted self-education, produce marketable specialists, from whom employers expect continuous professional development. Such competencies can be acquired during university or even school education.

The purpose of the article is to examine various aspects of developing a subject domain model in adaptive learning systems that support the development of self-learning skills. To obtain this result, we analyzed and summarized the results of experiments implementing adaptive learning systems in Russia and abroad. A software prototype of the system to confirm the feasibility of the proposed principles was created. An attempt is made to substantiate the need to stimulate the development of self-educational competence and, in this regard, to demonstrate the specifics of constructing a subject domain model. The relationship between the development of self-educational competence and learning in a digital educational platform (DEP) is examined. A diagram of the interaction between the components of the digital educational platform in the adaptive learning process supporting self-learning and a generalized algorithm for the system’s operation are presented. The correspondence of the proposed approach with the principles of L. S. Vygotsky’s zone of proximal development is outlined.

The proposed principles for developing a domain model to support self-learning can be used to expand the functionality of existing adaptive learning systems and digital educational platforms. The authors offer a new perspective on the role of a modern learning automation system, which, in addition to automating typical routine actions, also contributes to the development of a personalized approach to self-learning for each student.

The article presents a study of the process of integrating educational video content into the corporate information system (CIS) of a university. The empirical basis of the research is the implementation of this integration within the framework of teaching the “Data Analysis” course, aimed at overcoming students’ difficulties in mastering: theoretical material (probabilistic and statistical methods), software tools (Microsoft Excel, RStudio). The aim of the study is to develop and test, within the “Data Analysis” course, a model for integrating educational video content into the university’s corporate information system. The model covers: the typology of video content; the technology for its creation; the mechanism for its implementation.

The research methodology is based on a practice-oriented approach and includes: designing a structural model for video content integration into the CIS (educational campus, instructor’s work portal, IT toolkit); developing a typology of educational videos (video lecture, video consultation, guide, case study) using available technologies; empirically testing the model through anonymous student surveys (n = 109) to assess its didactic effectiveness and relevance. The results of the study demonstrate that the proposed model integrates the Educational Campus — the Financial University’s distance learning system implemented on the Moodle platform, IT tools as the technical foundation, and the teacher’s portal module as a centralized repository for educational video content, thereby contributing to improved accessibility, flexibility, and personalization of learning. The survey confirmed: high demand for video materials among students; a positive impact on understanding complex topics; reduced difficulties in working with software tools.

The practical significance of the findings lies in the model’s scalability for digitalising other academic disciplines, thus contributing to the modernisation of the educational process in higher education institutions.

The value of the work is determined by: its target audience (university instructors and instructional designers responsible for educational digitalisation); its novelty — a comprehensive integration model that elevates educational video content from an auxiliary tool to a structural element of the corporate educational system, ensuring flexibility, accessibility, and personalisation in the context of digital transformation in higher education; its international relevance — the approach aligns with global trends in the digitalisation of higher education and can be adapted by universities worldwide.

The article presents the experience of developing and implementing an analytical dashboard “Course monitoring” based on the domestic BI solution Yandex DataLens to support digital management of the effectiveness of online courses within the university’s system of additional professional education (APE). The design process is described in detail — from identifying the target audience (educational program development teams, program coordinators, heads of structural units, and vice-rectors overseeing educational activities, informatization, and information technologies) and selecting key performance metrics (completion rate, net promoter score, customer satisfaction, and practical assignment results) to integrating data from the LMS (Moodle), surveys, and internal databases, followed by prototyping, usability testing, and iterative refinement based on user feedback.

The developed analytical dashboard for evaluating the online courses effectiveness enables timely, data-driven managerial decisions, aligns with the principles of evidence-based education and the learner-centered approach, and complies with Russian data security requirements.

The dashboard was piloted within two APE programs — “Artificial intelligence in education and research” and “Advanced AI technologies in research and development” — involving 1,935 participants from Southern Federal University.

The “Course monitoring” dashboard is implemented on the Yandex DataLens platform and is accessible via a public link, with strict adherence to data confidentiality: all participant data are anonymized, and access to detailed information is restricted in accordance with the university’s internal regulations and the requirements of Federal Law No. 152-FZ “On Personal Data”.

The article demonstrates a practically viable and scalable model for digitizing managerial processes in higher education institutions using domestic technologies, fully aligned with the principles of evidence-based decision-making, data security, and adaptability to the evolving needs of educational organizations.

In the context of the rapid introduction of generative language models into education, prompt engineering is of particular importance as a system of methods for formulating text instructions for AI in order to solve pedagogical problems. The purpose of the study presented in the article is to analyze the pedagogical potential and limitations of prompt engineering in the context of higher education, as well as to determine the directions of its effective use in the professional activity of a teacher.

Key functional areas where prompt engineering can be effectively applied are identified: learning and teaching, methodological, research, project, educational, expert and analytical, communicative. Based on the analysis of theoretical sources and the results of empirical research, a classification of prompts has been developed based on the principles of their construction, with reference to didactic tasks. It is shown that prompts can be used not only to generate educational content, but also to organize reflection, develop critical thinking, and model professional situations. The barriers and risks limiting the implementation of prompt engineering are described: methodological, technological, personnel and organizational. It is noted that the successful integration of technology requires the formation of methodologically sound strategies for teachers, the development of digital competencies and legal regulation of interaction with AI. The presented conclusions were confirmed during the implementation of the program of additional professional education “Artificial intelligence tools and services in the work of a higher education teacher”. The conclusion is made about the high importance of prompt engineering as a component of digital pedagogy and the importance of developing institutional strategies for its implementation.

The development of generative artificial intelligence (GenAI) and its integration into the industrial sector is influencing the range of skills in demand in the labor market. To prepare competitive professionals, higher education systems need to develop students’ skills in interacting with large language models (LLMs) as well as soft skills, such as critical thinking, which enable them to adapt to changing requirements. Prompt engineering, as the process of creating and modifying queries to LLMs, is becoming an important tool in education. However, existing research on the potential of prompt engineering for critical thinking development is primarily theoretical. The aim of this empirical study was to identify opportunities for developing specific aspects of critical thinking through prompt engineering training. The target sample included 93 engineering students who collaboratively completed tasks in the discipline “Legal regulation in the field of communications” using generative AI tools. Thirty-eight student dialogues with LLMs were analyzed using qualitative content analysis. Eight interaction patterns were identified, some of which were associated with the use of prompt engineering techniques. The most common pattern was the addition of information from lectures or other sources, indicating students’ efforts to refine and improve LLM responses. The data analysis revealed the manifestation of critical thinking aspects, such as analysis, comparison, and synthesis of information, in student prompts. A comparison of the results from two coding stages demonstrated a connection between three of the eight patterns and the aforementioned critical thinking aspects. The study suggests that prompt engineering training can become a promising tool for developing critical thinking in students, provided certain educational conditions are met. Additionally, the results indicate the potential of using LLM dialogue analysis to assess student skills.

The article examines how an intelligent decision support system (DSS) becomes part of the learning activity of a management student and under what conditions it develops analytical thinking rather than replacing it. A managerial decision is decomposed into six phases: problem recognition and framing, data collection and analysis, generation of alternatives, evaluation and choice, justification, reflection on the result. Each phase is matched with a component of managerial competency formed within it, and the paper shows how this process changes when part of the operations is taken over by an intelligent system. A model of cognitive load distribution between the learner and the DSS across the decision phases is proposed, with a verification point at which the initiative of analysis must return to the student. The educational effect of working with such systems is described through the opposition of two mechanisms: cognitive amplification, in which the system frees up resources for substantive analysis, and cognitive substitution, in which a ready-made recommendation displaces independent analytical work. It is shown that what switches the system between these modes is not its technical characteristic but the way the learning task is organized and the student’s level of analytical literacy. Instead of a chronological staging, three principles for designing learning situations are proposed: managed incompleteness of the system’s output, mandatory parallel verification, and explicit reflection on the choice procedure. Didactic conditions under which an intelligent DSS works toward competency formation are formulated, and directions for empirical validation of the model are outlined.

Despite the use of automatic text generation by large language models, the skills of technical writing, documentation and justification of technical solutions will always be relevant. But the evolution of software development driven by the rise of agent-based systems requires the search for new educational activities that would stimulate students to work independently. Industrial practices show that the project repository is structured and contains artifacts that document the software project. Therefore, the purpose of the article is to propose methods for maintaining a software code repository in the educational process of training IT specialists.
The article presents an analysis of domestic standards for software engineering, modern practices for managing rapid software development and open source projects to determine the optimal structure of the repository and the artifacts that document it from the point of view of managing the technological process of software development, teamwork and acquiring professional skills.
The analysis allows us to propose the use of a properly designed software code repository. Examples of compiling standard tasks for creating artifacts to support the development of a software product are shown, as well as templates for compiling technical specifications, architectural specifications and user documentation, which allow developing skills in substantiating technical solutions, presenting a software product, systematizing knowledge of development technology, gaining experience working with various tools.

The proposed practices are included in the teaching of the disciplines “Software engineering” and “Software technology” are successfully used in the project activities of the educational process of Voronezh State Technical University. The experience of using a catalog of student projects to develop professional skills of a programmer and an information technology specialist is described.
Based on these results, recommendations are formulated to improve the quality of the educational process in the disciplines of the professional cycle of the direction “Information systems and technologies”.

Computer game development is considered in the article as a technology for teaching programming that links learners’ intrinsic motivation with the mastery of algorithmic constructs. The aim of the study presented in the article is to systematize approaches to using game development in programming education and to propose a didactic model that maps the stages of creating a game onto programming constructs and target competencies. The method is an analytical review of Russian- and English-language sources (constructionism, computational thinking, meta-analyses of the effectiveness of educational games) followed by conceptualization. Two roles of games in learning are distinguished — the game as a means of presenting content and the game as an object of development; the latter is found more productive for programming education. Three levels of tools are identified — block-based environments, scripting and professional engines — and matched to educational levels and the complexity of the constructs involved. A five-stage model is proposed (concept — mechanics — implementation — debugging — release), at each stage of which specific constructs (loops, conditionals, events, data structures) and meta-subject practices (decomposition, debugging, iterative design) are activated. Conditions of effectiveness are formulated in line with the meta-analytic estimate of a positive but design-dependent effect of educational games; risks (entertainment displacing learning goals, uneven cognitive load, assessment difficulty) and ways to mitigate them are outlined. The conclusions are applicable to the design of informatics courses and extracurricular activities; the limitation is the predominance of foreign empirical evidence and the need for local verification of the model.

Relevance. Digital transformation is among the national development goals of the Russian Federation, and the transfer of mass public services to an electronic and proactive format is changing how citizens and organisations interact with tax authorities. Work with tax benefits and preferences is increasingly mediated by digital services, yet the growing complexity and frequent revision of incentive legislation create a gap between the technological availability of benefits and the competence of both their recipients and future tax professionals.
The aims of the study presented in the article are to develop a model for teaching the use of tax benefits in a digital educational environment and to substantiate the composition of the digital competencies to be formed.
Materials and methods. The study draws on analysis of the regulatory framework (the Tax Code of the Russian Federation and acts of strategic state planning), a synthesis of research on taxation and the digitalisation of education. А functional analysis of the digital services of the Federal Tax Service was conducted. On this basis, a modular teaching model and a matrix matching digital tools to learning tasks were designed.
Results. Four components of digital competence in working with tax benefits were identified: legal, analytical-computational, service-technological and critical-evaluative. А modular learning structure relying on an adaptive personalised educational environment and on real tax-administration services was proposed. The social tax deduction for education and the tax benefits of the IT industry were justified as cross-cutting learning cases.
Conclusions. The model is applicable in training economists and financiers, as well as in carrying out activities to improve tax literacy of the population. Its implementation requires linking the learning environment with operating digital services and regularly updating the content following changes in legislation.

The digital transformation of higher education and the rapid spread of artificial intelligence (AI) technologies make it urgent to provide a scientifically grounded description of the criteria and levels of development of foreign language communicative competence (FLCC) in graduates of non-linguistic universities. The aims of the study are to identify and substantively characterise the criteria and levels of FLCC development of students of non-linguistic programmes in line with the Common European Framework of Reference for Languages (CEFR) and to reveal the didactic potential of AI technologies for its formation. The materials comprised regulatory documents (the Federal State Educational Standard 3++, the Council of Europe document “Common European Framework of Reference for Languages: Learning, Teaching, Assessment”, letter of the Ministry of Education of Russia of 04.12.2019 No. 04-1375), the results of Russian and international research, and the authors’ long-term teaching experience; the methods of analysis, synthesis, comparison, and modelling were applied. It is established that the content of FLCC is formed by three qualitatively distinct competencies — linguistic, speech, and sociocultural — regarded as criteria of its development; their components and measurable indicators are described. Three levels of FLCC development (Elementary — A1–A2, Intermediate — B1–B2, Advanced — C1–C2) are identified and mapped onto the CEFR scale; it is shown that B1–B2 are the target levels for bachelor’s and specialist programmes, and B2–C1 for master’s programmes. The didactic potential of AI technologies (adaptive platforms, conversational chatbots, generative language models) is systematised with respect to the formation of each FLCC component and the implementation of individual learning trajectories. The results make it possible to unify the content, objectives, and monitoring of foreign language training at a non-linguistic university.

In the context of the rapid digitalization of Chinese schools, providing programmatic-methodological and technological support for moral education has become one of the priority tasks of education. The aim of the study presented in the article is a comprehensive analysis of current strategies, programmatic-methodological solutions, and technological tools for the digitalization of moral education in the educational system of the People’s Republic of China. The research is aimed at identifying the mechanisms for integrating traditional Confucian values into the digital environment of modern China.
The research methodology is based on a systemic and regional analysis of practices in main Chinese provinces and cities. The work employs a content analysis of the regulatory and legal framework of the PRC, as well as a synthesis of pedagogical experience in implementing intelligent monitoring systems and “smart” educational platforms.
The study describes a unique precedent of technological support for ethical education, which could serve as a benchmark model for modernizing national education systems in the context of global digitalization. In particular, it analyzes the experience of using digital platforms and resources for modeling the moral choices of young people and creating systems for process-based assessment (“digital portraits”) of students. The proposed tools for personalizing educational trajectories make it possible to increase the effectiveness of forming ethical norms and social responsibility among youth.
The results of the study can be used to develop innovative models of education in the digital environment and to adapt the Chinese experience of creating a large-scale information infrastructure aimed at realizing the educational potential of digital technologies. The value of the work is determined by the novelty of its analysis of data from the period 2022–2025.
The work is intended for specialists in the field of comparative pedagogy, heads of educational organizations, and educational methodologists.

Relevance. The digitalization of education enables the application of machine learning methods and neural network models to monitoring and decision support in the educational environment. One of the most socially significant tasks is the early automated identification of adolescents’ presuicidal risk, the effectiveness of which critically depends on a well-grounded set of model input features.
The aims of the study presented in the article are to develop the architecture of a neural network model for recognizing indicators of adolescents’ presuicidal risk in the digital educational environment and to empirically substantiate its set of input features based on self-attitude indicators and the riskogenity of the educational environment.

Materials and methods. The empirical basis for feature selection consisted of data from 599 students aged 13–17 years (M = 14.34; SD = 0.83). The Self-Attitude Inventory by S. R. Pantileev, the Piers—Harris Self-Concept Scale, the inventory of the propensity for victim behavior, the personal differential and the “Index of sociocultural safety of a schoolchild” were used. Feature selection and validation were performed using correlation analysis, Student’s t-test and one-way analysis of variance (ANOVA); a fully connected neural network architecture with a three-class output was proposed.
Results. A compact, empirically substantiated set of input features was formed: self-worth, self-acceptance, withdrawal, self-blame, behavior, family and the riskogenity of the educational environment. A high level of propensity for self-injurious and self-destructive behavior, corresponding to the target high-risk class, was found in 29 % of adolescents.
Conclusions. The proposed neural network model with an empirically grounded feature set can be integrated into the digital educational environment as a tool for scalable early identification of presuicidal risk, subject to ethical constraints and confirmation by a specialist.