The article addresses issues related to the use of artificial intelligence for the evaluation of the quality of study programmes and educational institutions in terms of accreditation. Ongoing changes in the structure and content of higher education are due to the state educational policy; however, technological challenges that higher education is facing should not be disregarded, especially since higher education quite effectively applies technological innovations in the educational process (teaching, learning, management). Accreditation, as a procedure of evaluation, recognition, and quality assurance of education, on the one hand, should respond to changes in the educational process. On the other hand, it should use modern available digital technologies, including AI tools.

   This research aims to analyze and describe the testing of digital and AI tools in the pilot project TOP accreditation, as well as to discuss the prospects for developing such approaches in Russia, given the international trends.

   A hypothesis was developed that natural intelligence, an expert, cannot be replaced by artificial intelligence when evaluating the relevance and quality of information sources in use, as well as when making a final decision about education quality. The analysis of international practices showed that most initiatives on using AI in accreditation are at the stage of academic studies or early pilot projects. The findings can be used when developing a strategy and procedures for education quality evaluation, and making decisions on accreditation.

   The article presents a systematic review of the international policies of the world’s leading universities on regulating the use of generative AI technologies in higher education.

   The purpose is to analyze the attitude of leading universities towards the use of generative AI and to identify key areas of the educational process that need to be transformed for technology integration to be effective.

   The regulatory policies of the world’s leading universities are highlighted and compared, some of which convey an extremely negative attitude towards new technologies, while others are progressive. The compliance of regulatory policies with the challenges facing the academic community is analyzed. The main focus is on reviewing best practices in developing regulations for the use of generative AI. The article will be of interest to the academic community as a whole, but above all, to the management corps and researchers involved in the integration of generative AI into higher education.

   The authors propose reconsidering the situation of the spread of artificial intelligence technologies in higher  education and developing a pedagogy of hybrid intelligence. In the future, the intellectual workforce will be organized in teams formed by people and AI models and agents. New “entities” will study at the university – students equipped with clusters of artificial intelligence. University education will not be adequate for the present day if it does not take into account the capabilities of AI as a partner of a person in educational and professional activities. Pedagogy will work with a “hybrid” subject, which includes people and AI. The field of questions has been outlined that need to be answered to build a hybrid intelligence pedagogy and deploy it in universities. The authors seek to initiate discussions, trial actions, and experiments in the field of pedagogy of hybrid intelligence.

   The article examines universities’ strategic responses to the key challenges of the digital era, including rising stakeholder expectations, competition with EdTech companies, misalignment with labor market demands, and the need to rethink the university mission in the context of generative AI.

   The relevance of the study stems from the need of universities at an early stage of transformation for reference points to inform long-term strategy development.

   The methodology combines an analysis of academic literature (25 selected articles) with 30 in-depth interviews conducted with internationally recognized experts from leading universities (Stanford University, the University of Arizona, HSE University, the University of Reading, among others), as well as representatives of EdTech companies and the business sector. The study identifies five priority thematic areas for development, reflecting a consolidated expert consensus. The findings demonstrate that digital transformation has become a necessary condition for closing the gap with labor market requirements and requires close cooperation with business and EdTech providers. Practical recommendations for university leadership are formulated based on global best practices, including the transition to hybrid models, the implementation of project-based learning, the development of systems for recognizing micro-credentials, and a focus on fostering a new type of student mindset. Specific solutions are also proposed for resource-constrained universities.

   Training personnel for the development of artificial intelligence technologies is one of the strategic tasks facing Russian universities.

   This article aims to analyze the employment of graduates from Russian universities in educational programs related to artificial intelligence.

   The study tests the hypothesis about the influence of university status on the Alliance ranking in the field of AI and the presence of specialized educational programs in AI at the university on the effectiveness of graduate employment in these programs.

   The study is based on the monitoring of universities implementing educational programs in the field of artificial intelligence (n = 191).

   As part of the analysis of the employment of graduates with competencies in the field of artificial intelligence, the specifics of their distribution among employment channels were determined. The authors identified key universities in terms of the effectiveness of training AI personnel, the amount of wages, and the most popular positions in employment. Based on the comparison of the employment rates of university graduates, as well as alternative sources of AI personnel training (self-education and professional retraining) with the indicators of personnel needs in the field of artificial intelligence, a conclusion was made about the provision of this need by 43.9 %. The obtained data allow us to conclude that universities have successfully implemented the Russian Government’s order to increase the volume of training of AI specialists.

   The value of the article lies in the presentation of unique factual materials that, for the first time, describe the employment of university graduates in the field of AI.

   In particular, the article details the losses of human resources in the AI field on the way from the admission of applicants to universities to the direct employment of graduates. The target audience of the article is researchers, experts, analysts, employees, and managers of universities, as well as government representatives involved in the process of developing artificial intelligence in Russia.

   This article examines the dynamics of the development of the system of continuing professional education (CPE) in the Russian Federation over the period 2020–2023, based on official statistical data. The findings indicate that during the period under review the CPE system demonstrated устойчивый growth: the number of individuals completing CPE programmes increased from 6.6 to 8.3 million, while the average frequency of participation in CPE among the employed population in 2023 was approximately once every nine years. At the same time, significant intersectoral disparities in CPE coverage were identified. In manufacturing industries, the average training frequency reached nearly once every 80 years; in construction, once every 21 years; and in agriculture, forestry, hunting, fishing, and aquaculture, once every 29 years. The study also reveals emerging trends in the CPE services market, including a growing share of training delivered directly at enterprises without the involvement of specialized CPE providers, as well as a sharp decline in the proportion of employees with secondary vocational education participating in CPE programmes. An analysis of regional differences made it possible to identify federal districts and constituent entities acting as “attracting” and “sending” centers of training, based on a comparison between their share of CPE participants nationwide and their contribution to the country’s gross domestic product.

   The article concludes that the CPE market is likely to continue transforming in the coming years, and that organizations that take these trends into account may enhance the effectiveness of their continuing professional education services.

   As project-based learning becomes increasingly integrated into higher education, the demand for new tools to monitor collaborative work has grown significantly. This article presents an approach to generating feedback based on the analysis of digital traces produced within professional collaboration tools – specifically, task trackers (e. g., Trello, Wekan) and code repositories (e.g., GitLab). The study reviews current frameworks for feedback provision and digital trace analysis, identifies the sources and structure of digital traces in project-based work, and proposes a method for extracting and processing student activity logs. Social Network Analysis (SNA) techniques are applied to assess the interaction structure within teams, and an interpretive model is introduced to generate feedback based on network metrics. The empirical basis of the study consists of 253 student projects implemented between 2019 and 2023 at MIEM, National Research University Higher School of Economics. The findings demonstrate that visualizing network characteristics and role dynamics within teams significantly enhances the ability to detect key interaction patterns and atypical behaviors that may require pedagogical intervention. Integrating such mechanisms into digital learning environments offers a unique opportunity for continuous monitoring of team processes and for improving the quality management of project-based learning.

   The article analyzes the effectiveness of education based on the results of admission to state-funded places in engineering and technical specialties at Russian universities. The retention rate is considered as an indicator that complements the universal indicators of the Russian Ministry of Higher Education and Science’s Monitoring, and characterizes the quality of admission, the educational process, and educational outcomes.

   The aim of the study is to develop a typology of training programs based on admission and completion indicators, taking into account the significance of the retention rate indicators.

   Based on data from the VPO-1 form of the Ministry for the period from 2014 to 2024, the influence of the dynamics of budget place redistribution in favor of engineering and technical specialties on changes in the retention rate is analyzed. It is concluded that an increase in the number of state-funded places in engineering and technical fields generally does not lead to a corresponding increase in the number of graduates. It is revealed that the gap between admission and graduation is particularly pronounced in regional universities, where the retention rate is significantly lower than in metropolitan universities. The analysis was conducted both for the Russian Federation as a whole and for individual subjects of the Russian Federation, including the Krasnoyarsk Region and the city of Moscow. It is shown that engineering and technical fields form a separate type of training, characterized by a decrease in the retention rate when budget places are redistributed in their favor. Based on the conducted research, recommendations are formulated on improving the management of budget place allocation for engineering and technical specialties for executive authorities and founders of higher education institutions in the Russian Federation.

   The aim of this article is to analyze the institutional barriers to implementing transformation in universities, as well as the factors enabling their overcoming, through the lens of those leading these processes.

   To this end, the author conducted 22 in-depth expert interviews with university leaders (primarily rectors and vice-rectors) participating in the “Priority 2030” program, as well as with experts in higher education management. The interpretation of this qualitative study was grounded in an institutional theoretical framework and employed thematic analysis methodology, which enabled a structured identification of key themes and meanings within managerial discourse. University leaders identified the main barriers to change implementation as: misalignment between new objectives and existing practices, fragmentation and siloed nature of university structures, staff rigidity, symbolic compliance with required activities, and increasing bureaucratization. The factors used to overcome these barriers include: the creation and maintenance of internal communication channels, the managerial resource of rectoral leadership, the legitimization and adoption of project-based management practices, and strategic personnel changes. Particular attention is given to the role of the management
team in strategic stakeholder engagement, where the importance of simultaneously meeting external expectations and articulating an autonomous institutional agenda is emphasized. Thus, this study contributes to the ongoing discourse on contemporary university transformation by offering the perspective of those directly responsible for its implementation. The findings may be valuable to higher education researchers and university administrators, as well as to those involved in designing organizational change programs and providing educational or consulting support for their implementation.

   The paper proposed focuses both leaders and academics of the Russian universities on phenomenon of the university corporate culture. Special importance of this phenomenon for the university advancing is explained by value-oriented nature of the university activity firstly, and by the fact that the corporate culture is the main factor ensuring unity and uniqueness of a university, secondly. Research of the web-sites of 237 Russian universities administrated by the Russian Ministry of Science and Higher Education showed that it is very difficult or even impossible to get any information about corporate culture of these universities. It caused not because both rectors and academics do not pay enough attention to corporate culture, but because the evolution of the corporate culture in the main part of the universities is carried out spontaneously, but not on the system bases. Current condition of the corporate culture is not described in the terms ensuring the shared by the university community picture of this culture. Challenges to the culture from external environment as well as impact of the environment on the culture are not analyzed. An example of influence of these shortcomings on prestige of both Russian universities and Russia as a country is given. It is shown that experimentally found trend to increasing hierarchical component in the Russian university’s corporate culture can become an obstacle on the way of these universities to the technological leadership.