Vysshee Obrazovanie v Rossii = Higher Education in Russia
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Role-Based Competency Models as a Tool for Curriculum Design: Application in Artificial Intelligence Education
Role-Based Competency Models (RBCM) are considered as a promising mechanism for designing educational programs aimed at reducing the gap between industry needs and what universities offer. The research methodology combines literature review, mathematical formalization to define RBCM entities, as well as experimental studies involving 22 universities implementing 72 educational programs in the field of Artificial Intelligence. The paper systematizes approaches to the development and implementation of RBCMs in higher education based on an analysis of the global educational landscape and describes the process of RBCM development from a generalized graduate profile to the definition of professional roles, competencies, indicators of proficiency levels, and a system of practice-oriented tasks. Using the example of the AI RBCM developed under the supervision of the Ministry of Digital Development, Communications and Mass Media of Russia, the paper presents the results of implementing the model in the design and evaluation of educational programs for training top-level specialists in artificial intelligence are demonstrated.
Generative Artificial Intelligence in the Publication Activities of Postgraduate Students in Russia, Belarus, and Kyrgyzstan
Publishing research papers is one of the essential components of a modern scientist’s activity. In the post-Soviet countries, defending a dissertation is impossible without publishing the main research findings. The “publish or perish” paradigm may lead researchers, especially those at the start of their careers, to disregard scientific ethos in pursuit of research results. The spread of generative artificial intelligence (GenAI) tools intensifies this problem, particularly in the absence of widely accepted standards for their use during manuscript preparation. The article examines how postgraduate students in Russia, Belarus, and Kyrgyzstan perceive of acceptable and unacceptable AI usage in the publication activities. The empirical basis consists of in-depth interviews with 27 postgraduate students at leading universities, conducted between April and December 2025. It was revealed that the informants have experience if using generative AI and actively incorporate it into their professional activities. However, they lack awareness of the norms governing these processes and feel a need for specific, clear standards and rules, as their moral in- tuitions in this area remains insufficiently developed. The current situation reflects changes in the scientific ethos that call for rethinking concepts such as authorship, responsibility, and plagiarism, as well as the practices associated with them.
Cognitive Externalization and Generative AI in Higher Education
The spread of generative artificial intelligence (GenAI) in higher education calls into question the adequacy of earlier conceptions of the digitalization of learning, since it affects not only educational infrastructure but also the cognitive operations underlying learning activity itself. The aim of the article is to interpret GenAI as a new wave of cognitive externalization and to determine the implications of this interpretation for learning design, process-based assessment, and learner agency in higher education. Cognitive externalization is defined as the transfer of some operations of memory, fixation, semantic processing, structuring, and preparation of decisions into an external symbolic or socio-technical environment while preserving the need for human control, interpretation, and responsibility or, in some cases, without actual human control when delegated operations are not consciously verified. The study employs an integrative problem-oriented analytical literature review based on a comparison of scholarship on writing as a cognitive technology and research on the impact of GenAI on university teaching and learning. The scientific novelty of the article lies in interpreting GenAI as a new wave of cognitive externalization and in theoretically grounding the previously proposed PPAIR model as a didactic response to the transformation of learning activity. It is shown that the status of a “wave” is determined not by the mere emergence of a new technology but by changes in basic cognitive operations, the object of pedagogical control, and forms of assessment. Unlike writing, which historically enabled the external fixation of memory and the accumulation of knowledge, GenAI externalizes operations of linguistic and semantic processing, the generation of alternatives, and the preparation of decisions. This changes the architecture of learning activity, reduces the diagnostic reliability of text- centric forms of assessment, and intensifies the problem of learner agency. As a process-oriented didactic response, the article theoretically grounds the PPAIR model – Problem – Prompt – AI Generation – Iterate – Reflect – which is aimed at capturing not only the final product but also the student’s trajectory of working with a generative model. It is concluded that the integration of GenAI into university education requires reconsideration of teaching tools, criteria for validating learning outcomes, and institutional foundations of academic responsibility. The practical significance of the article lies in substantiating a shift from a prohibitive logic toward the managed integration of GenAI into the educational process.
Career Trajectories of Graduates of Educational Programs in the Field of Artificial Intelligence: Results of a Qualitative Study of a Regional University
This article examines the training of specialists in the field of artificial intelligence (AI) by universities based on an analysis of the initial career trajectories of graduates of specialized master’s educational programs in the field of artificial intelligence. The relevance of the study is determined by the priority importance of human resources for the development of AI technologies. The scientific novelty of the study is determined by its object: using the method of in-depth interviews, the authors analyzed the effectiveness of training personnel for the field of AI. The objective of the study is to identify the main barriers to employment of graduates of the specialized educational program in AI in the field of their education using the example of a regional university. The empirical base of the study is transcripts of interviews with graduates of master’s educational programs in the field of artificial intelligence, graduating in 2024 and 2025 (N = 25) and transcripts of focus groups with undergraduate (N = 2) and master’s (N = 2) students of the same program. In addition, the curricula and syllabi of the disciplines of the analyzed educational programs are considered. The study revealed that 7 out of 25 graduates (28%) are employed in their field of study. This is primarily influenced by a lack of relevant experience and practical skills; employment in the IT field of interest at the time of enrollment in master’s program; and a lack of initial interest in working with AI technologies. A more detailed analysis of these factors revealed that half of the graduates viewed enrollment in a master’s program as a continuation of their bachelor’s degree, while only 15 out of 25 identified an educational program with AI technologies. Respondents also noted the educational programs’ insufficient practical focus. Among well-known factors contributing to employment in their field of study, such as gaining practical experience with AI during training, a graduate’s professional identity as a researcher significantly influences employment in the AI field. The practical significance of the work lies in the formation of recommendations based on the obtained results that contribute to the modernization of existing practices in domestic higher education in the field of AI.
Digital Capital of Students: a Сomparative Аnalysis by Fields of Study
This article analyzes the digital capital of students in various fields of study in the context of the digital transformation of higher education. The relevance of the study is determined by the increasing role of digital competencies as a factor in academic success, professional mobility, and overcoming new forms of educational inequality. Despite the growing number of studies on digital literacy and competencies, comprehensive empirical assessments of students’ digital capital in Russian scientific practice are limited. The aim of the study is to identify the types of digital capital in students of different educational profiles, as well as to analyze the relationship between the type of digital capital and its components (digital competencies, motivation, attitude towards digital technologies, distance learning), as well as the characteristics of educational activities. Based on P. Bourdieu’s concept of capital, M. Ragnedda’s digital capital, and S. Park’s digital ecosystem, the author’s concept of digital capital was proposed. It includes parameters that take into account educational specifics, namely: material; cultural; social; motivational and institutional. The empirical base of the study consisted of data from an online survey of students from five St. Petersburg universities (N = 458). Data processing was carried out using statistical analysis in the SPSS package. As a result, four types of digital capital were identified among students: deficient, socially oriented, high, and partially formed. The results of the study showed the absence of a pronounced basic digital divide in access to technology, although there are significant differences in the structure of digital competencies and the motivation for their use between the fields of study. Students majoring in technical specialties have a relatively high level of digital capital, demonstrating a wide range of digital skills and a positive attitude toward online learning, while students in the humanities and pedagogical fields are characterized by relatively low indicators of digital capital. According to the study’s findings, the type of digital capital is associated with educational parameters such as academic performance, attitude toward learning, class attendance, length of study, and readiness for additional learning. The findings suggest that developing students’ digital capital is an important area of university education policy.
Assessment of the (Non-)Academic Environment in Higher Education
Contemporary state policy of Russia in the sphere of higher education orients toward encouraging educational organizations to achieve national interests in scientific and technological development. The measures take an ambiguous and multidirectional impact on the academic environment. This paper presents the results of its assessment. The analysis focuses on three parameters of the academic environment: staff structure, distribution of academic workload, and remuneration. It uses data for 2014-2024 on organizations in the higher education system as a whole, as well as on four groups of universities selected according to their degree of participation in state incentive programs. Based on a comparative analysis, the paper identifies the characteristics and trends in the formation of the academic environment. The results obtain indicate a growing stratification of staff into academic and non-academic workers, accompanied by an extensive increase in the number of the latter and a shift in priorities in the distribution of internal resources in their favor. They show that organizations participating in incentive programs achieve the best results in the individual research productivity of academic staff with a lower academic workload and more decent remuneration compared to other universities. The proposed assessment approach views the organization of higher education as a socio-economic system focused on achieving planned results, including in the field of scientific activity. The operationalization of the academic environment through three parameters makes it possible to assess the conditions and limitations of individual research productivity, as well as to track the dynamics of institutional changes. This makes it possible to identify the directions of its transformation – both those that contribute to the development of research potential and those that indicate its institutional constraints.
Factors of Sustainable Integrating University Museums into Educational Process
This article analyzes educational activities of university museums in Russia. The relevance of the study stems from the contradiction between the high educational potential of museums and the fragmented nature of its implementation, as well as the lack of systematic analysis in the literature regarding the conditions for integrating museums into the higher education academic process. Despite the recognition of museums as important centers for heritage preservation and science popularization, the question of their actual involvement in the everyday educational life of universities remains understudied. The scientific novelty lies in conceptualizing the factors that determine the sustainability of a museum within a university’s educational space. The empirical basis consists of 15 semi-structured interviews with heads of museums at classical universities from all federal districts of the Russian Federation, selected using an activity rating developed by the authors that considered the diversity of educational practices and the availability of contact information. Thematic analysis identified, for the first time, four groups of conditions for successful museum integration into the educational process: institutional (formal status and authority), communicative (stable links with departments and faculty), subjective (student engagement), and material (spatial and financial resources). A key scientific finding is the substantiation of the crucial role played by the “museum – faculty – students” triad and the identification of the “paradox of invisibility”, where a museum oriented towards external communications remains unseen by its internal university audience. It is established that under conditions of institutional uncertainty and staff shortages, museums primarily operate under a “running on enthusiasm” model, rendering them highly vulnerable to changes in leadership or departmental personnel. An original trajectory for engaging students (from excursions through internships to volunteering) is proposed as a tool for forming a sustainable student core and overcoming staff shortages. The results contribute to the theory of museum pedagogy and the sociology of education, and can serve as a diagnostic tool for assessing the situation of specific museums and developing strategies for their development.