Institutional transformations of higher education in Russia are currently being carried out under the influence of geopolitical factors and a set of gaps between the demands of modern society and the established institutional characteristics of higher education. This article defines the essence of institutional transformations of higher education, systematizes the gaps and contradictions that shape their context in modern Russia, and identifies the main trends in the transformation of the educational and scientific practices of universities that determine the direction of institutional changes. The analysis was conducted using both theoretical and empirical methods, including a content analysis of strategic and reporting documents from 62 leading Russian universities, as well as a secondary analysis of data from the Russian Public Opinion Research Center (VCIOM). It is concluded that the main gap in the institutional transformations of domestic higher education currently lies between trust and value, and the main mechanism is the construction of new practices for educational and scientific activities by universities. The analysis of these practices led the authors to conclude that the direction of institutional changes in the field of educational activities is more associated with the transformation of traditional institutional characteristics, while in the field of scientific activities, it is associated with the formation of new institutional foundations, with a general focus on increasing the integration potential of universities and inter-institutional interaction. The prospects for forming a new national system of higher education are tied to the possibility of consolidating these practices at the systemic level as stable regulatory and organizational characteristics.

   Traditional models of vocational education are decreasing their relevance due to rapid technological progress. The need to update skills and competencies leads to the emergence of new educational trajectories, including short programs for acquiring competencies to perform certain work functions and enter the labor market. The article examines the features of microqualification programs, their potential for employee training and their impact on the labor market. The analysis was carried out on the basis of data from the federal project “Employment Promotion”, data on vacancies on recruitment platforms and the results of focus groups with employers. Microqualification is interpreted as a set of highly specialized skills necessary to perform certain work functions that do not require a document on basic education. The authors of the article consider models based on the use of additional professional education and vocational training programs, as well as models that form individual educational trajectories within the framework of basic professional educational programs. An analysis of the salary data of participants in the federal project “Employment Promotion” shows that the passage of programs for the development of digital competencies provides a significant increase in wages after training. The results of the study demonstrate the high potential of microqualification programs for adult education and the formation of competitive specialists. The article offers recommendations on the implementation of microqualification programs in the Russian education system.

   Drawing on the analysis of staff restructuring in universities around the world, the article stresses the growing share of non-academic staff, in particular highly qualified middle and senior administrative staff.

   The purpose of the article is to analyze how a combination of different categories of administrative staff is represented in universities according to their functions.

   Based on the cluster analysis of the Monitoring of education markets and organizations (N = 92), three groups of universities were identified according to administrative profiles: enterprising, educational and infrastructural. The first cluster is characterized by a high proportion of administrative staff engaged in general management, research, financial issues and information and communication technologies. The second cluster has a high proportion of staff engaged in providing educational activities. The third cluster is characterized by a high proportion of personnel engaged in the maintenance and operation of infrastructure and buildings. Universities with an infrastructural administrative profile are characterized by the presence of a larger area of educational and laboratory facilities per student, however, universities in the sample do not differ in terms of scientific, educational and financial activities.

   The rapid development of artificial intelligence (AI) has posed many dilemmas for higher education, one of which is the development of university educators’ competencies in using AI technologies in the educational process.

   The purpose of this study is to present the current state of the problem of university educators’ professional development in the sphere of AI in the theory and practice of education.

   To achieve the goal, theoretical and empirical methods were used. The group of theoretical ones includes the analysis of scientific literature and Internet sources, study and generalization of advanced pedagogical experience, comparative analysis, content analysis, systematization. The group of empirical methods includes document analysis, questionnaire and survey. The first part of the article presents the analysis of international and Russian regulatory documents, which showed the significance of the studied issue for the state and society, and also allowed us to find out that the legal framework regulating AI in higher education is currently undergoing the stage of active formation. The second part of the article presents the review of scientific publications by foreign and Russian scientists, which helped to highlight the theoretical aspects of the current state of the problem of university educators’ professional development in the field of AI, as well as to identify its insufficient
coverage. The third part of the article presents the results of the study of educational practice in the form of systematization of educators’ development programs offered by universities and commercial organizations at the moment. The systematization is made on two bases: by the means of implementation and by the target audience. The fourth part of the article describes the authors’ experience in the development and implementation of a professional development program for educators on the creation of educational content using neural networks which took place in South Ural State University (National Research University). The conclusion states the necessity of systematic study of the problem, coordination of actions of educational organizations and state bodies to develop a supporting regulatory framework, the necessity to create conditions that promote the continuous development of educators’ AI competencies.

   This article presents the results of an study conducted in 2023, examining the historical and cultural foundations of Russian identity among modern youth. The study focuses on how affiliation with different social and professional groups shapes their perception of Russian history and culture. Particular emphasis is placed on the processes through which individuals develop a personal understanding of history, offering an innovative approach to analyzing historical memory in relation to Russia’s present and future. The preliminary conclusions drawn from this exploratory research highlight the need for comprehensive models to study the processes of youth identification with Russia.

   Development of high-tech industries is an important task for the Russian economy, the solution of which requires the joint efforts of educational, research and production organizations with the governmental support. Nowadays significant results have been achieved in the field of cooperation between universities and business. Their systematization and analysis will make it possible to determine both the most effective areas of interaction and those requiring further development.

   The purpose of the research presented in the article is to identify typical models of cooperation between universities and business based on analyzing the results of their scientific and educational interaction.

   To realize it, clustering of Russian universities was carried out. The cluster analysis identified 6 groups of universitates, each of which has specific features. Universities in the first two clusters are focused on commercializing income from R&D. Universities of cluster 1 are distinguished by a high share of extra-budgetary income from R&D, and from cluster 2 are distinguished by high extra-budgetary income from R&D based on the number of academic staff members. Universities from cluster 3 have high results of joint publication activities with business companies. In universities from clusters 4 and 5 educational cooperation with business is more developed. Universities from cluster 4 have an extensive network of partnerships with enterprises to organize internships for students. Universities from cluster 5 actively cooperate with business to train specialists on a contractual basis. Cluster 6 includes universities that have low results in all areas of cooperation with business. The results of the study demonstrate the main approaches of universities to interact with business companies. The research task was implemented for the first time on a representative sample of Russian universities based on an analysis of the quantitatively assessed results of their cooperation with business. The article may be of interest to universities aimed at developing partnerships with business companies, as well as government authorities developing projects to support university-industry interaction. Support measures could become more diversified, considering the specific features of each cluster, and be aimed at stimulating the development of priority areas of cooperation with business for a particular university.

   The authors of the article present a comprehensive analysis of the accounting of students’ academic performance in the management of the educational process of the university. The information about students that affects their academic performance and satisfaction with the educational organization is analyzed and classified. The focus of the study is on the application of predictive models in the management of the educational process in order to adapt the content of disciplines to the current contingent of students. The study used data only on first-year students (2023/24 academic year) of bachelor’s and specialist’s degree levels (n=1549). The information is depersonalized and contains the following data: demographic (age, gender, citizenship), social (socio-cultural environment, place of residence, place of residence during study), academic (previous education, results of entrance tests, current academic performance, faculty, qualification level), economic (scholarship, type of competition – budget/contract). Methods of mathematical statistics were used to analyze the data: determining the type of data distribution using the Shapiro-Wilk test, establishing the presence of multicollinearity in the construction of multiple regression by the Pearson criterion, establishing correlation dependencies by Spearman’s rank correlation method. Machine learning methods are implemented in the Python programming language (v. 3.8) using the freely distributed Keras library.

   The main results. The classification of factors affecting the academic performance and satisfaction of students is presented. Using the methods of mathematical statistics, the importance of each factor for predicting academic performance has been established. An educational process management model based on Agile Learning Design has been developed and presented, which allows adapting a specific discipline to the current contingent of students.

   Over the past thirty years, thanks to government initiatives for the “construction” of world-class universities, a cohort of elite (leading) universities has been created in China, covering about 5 % of universities, which have tangible advantages over other universities in student recruitment, teaching, research, funding, and support from local authorities. Despite the similar possibilities gained by universities when they transition to the status of elite institutions, each of them has a different budget structure and distinct operational results, which are primarily associated with the positions of universities in international rankings.

   Therefore, the aim of the article is to show whether increased investment in development really allows Chinese universities to be successful in the ranking race.

   The article describes the sources of funding for Chinese universities, shows the funding structure of some leading institutions, and presents a model of funding for state universities. Special attention is paid to factors affecting the level of funding for Chinese universities, such as belonging to the elite group of universities, geographical location, and the range of educational programs implemented. Based on a sample of 29 leading universities, it is shown that the diversity of funding sources is an indicator of success: as a rule, the best universities have a higher share of income from entrepreneurial activities. At the same time, the volume of the university budget only 50 % determines advancement in rankings. The second reason for success is the “symbolic capital” obtained by universities from participation in the 211 / 985 / “Double First Class” projects, which allows them to achieve significant competitive advantages. Sources of information for analysis included: data from the Ministry of Education of China, web-sites of leading Chinese universities, publications available in the open access network of the Chinese internet, as well as scientific articles published in Russian and leading world publications.