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.
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.
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.
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.
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.
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.
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.
The article develops the concept of the epistemic centaur – an emergent alliance between human consciousness and Communicative Artificial Intelligence (ComAI). ComAI denotes a new generation of language models functioning according to the logic of communication: they sustain the context of dialogue, read the pragmatics of discourse, respond to the interlocutor’s intention, and initiate new semantic moves – as distinct from generative AI as a purely technical characteristic. The article demonstrates that ComAI does not satisfy any of the four criteria of the extended mind proposed by A. Clark and D. Chalmers, for structural reasons: it does not store but generates knowledge; it is not stable but semantically plastic; it requires verification rather than uncritical acceptance; and it produces meaning without a history of authorship. A critical analysis of distributed cognition theory (E. Hutchins) and Actor-Network Theory (B. Latour), together with their contemporary applications to AI, reveals both their explanatory potential and their limitations. On this basis, five principles of the epistemic centaur are formulated: communicative agency, emergence of meaning, dialogic maieutics, indivisibility of responsibility, and epistemic locality. In dialogue with the post-structuralist tradition (R. Barthes, M. Foucault), the article shows that the centaur situation does not abolish but revives the figure of the Author. A distinction is drawn between the functional and dysfunctional centaur, and the implications of the concept for the epistemology of science, academic authorship, and higher education pedagogy are articulated.
The relevance of this study stems from a contradiction between the widespread de facto use of artificial intelligence (AI) in Russian universities and the lack of formal rules governing this process. The aim of this work is to comprehensively analyze official documents from Russian universities that regulate the use of AI in education, including ethical aspects and risks. The methodology is based on a two-stage content analysis of publicly available university policy documents. The theoretical framework draws on neo-institutional theory, particularly the concepts of institutional isomorphism and the stages of institutionalization. The scientific novelty of the study lies not only in expanding the geography of AI policy research by including the Russian case but also in advancing neo-institutional analysis of regulation under conditions of technological uncertainty. The Russian material allows us to refine the dynamics of early-stage institutionalization: we show that the formation of AI policies is nonlinear, combining the borrowing of discursive formulas with the persistence of an “institutional vacuum” at the procedural level. We introduce a typology of adaptive and restrictive regulatory modes, interpreted as two stable patterns of institutional response to digital innovation, and demonstrate their simultaneous coexistence within a single national system. Ethical principles are interpreted as a specific mechanism for the symbolic legitimation of universities in the absence of detailed regulations. This complements existing approaches to analyzing institutional isomorphism and competitive differentiation among universities. Future research directions include analyzing how participants in the educational process internalize these rules and conducting longitudinal observations of the evolution of institutional forms.
One of the main contradictions in the integration of artificial intelligence (AI) technologies into higher education is that, while providing unprecedented didactic opportunities for adapting and personalizing learning, AI simultaneously creates a complex set of systemic challenges and risks that require a rethinking of educational content, traditional teaching and assessment methods, and the role of the teacher in the educational process. Moreover, the effectiveness of countering emerging threats largely depends on the extent to which faculty understand these challenges and are prepared to respond to them. The purpose of this study is to identify key challenges and risks facing the higher education system in the context of AI, as well as to determine the extent to which faculty understand these challenges and risks and their readiness to take practical steps to overcome them. Based on an analysis of the scientific literature, three key challenges were identified (changes in educational content, changes in traditional approaches and methods of teaching and assessment, and the changing role of the teacher in the “teacher-AI-student” triad) and four risk groups (the development of clip-based thinking and a decrease in students’ cognitive activity, the spread of AI plagiarism, hallucinations and bias in AI, and the strengthening of digital and social inequality). An online survey was conducted to determine lecturers’ understanding of these challenges and risks, as well as their preparedness to respond to them. Respondents included 1,272 lecturers from 43 Russian universities. The survey results reveal a systemic gap between the stated understanding of challenges and risks (about 70% of teachers recognize the threat of AI plagiarism and the decline in students’ cognitive abilities) and the actual practical readiness for organizational and methodological changes (only 20-30% of teachers are actually changing their teaching and monitoring methods, integrating AI into the educational process). Approximately 30-40% of respondents expressed a neutral attitude toward most issues related to the implementation of new methods and forms of assessment, while 15-25% expressed a negative attitude. The data obtained indicate that, at the current stage, the systemic integration of AI into higher education cannot be carried out on a proactive basis by individual faculty. The majority of Russian university faculty lack systemic support (regulatory, methodological, time-based, and financial) for the transition to new educational models in the context of the spread of AI. Addressing the challenges and risks of AI is possible only at the institutional level through the development and implementation of regulatory guidelines for the use of AI, the introduction of universal AI competencies in the Federal State Educational Standards of Higher Education, the revision of faculty workloads, and the creation of a resource base for personalized student engagement with AI tools.
Today, there is a growing anxiety about artificial intelligence in the educational space, which greatly affects the willingness to make technological decisions and plan for one’s professional future. In empirical studies, AI anxiety is defined as a complex phenomenon including a range of negative emotional responses that shapes in people due to development and potential impact of artificial intelligence on various spheres of life. Given the impact of AI anxiety on learning, career choices, participation in the digital economy, and mental well-being, there is a need for its holistic conceptual description and reliable diagnosis. The aim of the study is to present a reliable and valid Russian-language tool for diagnosing AI anxiety in students. The adopted basis and modified for a student sample (N = 521) “Artificial Intelligence Anxiety Scale” (M.H. Alkhaibani et al.) showed an acceptable level of the external and internal validity as well as reliability on internal consistency. The factor analysis confirmed the five-factor structure of the questionnaire: concerns related to AI accuracy and reliability; concerns related to academic integrity (plagiarism); and concerns stemming from regulatory uncertainty (guidelines), fears of loss of learning and cognitive skills, as well as a decrease in motivation. External validity is confirmed by the correlations with neuroticism and conscientiousness as personality traits, image of the future, attitude towards artificial intelligence, and some socio-demographic characteristics. Adapted and modified psychodiagnostic tool for studying AI anxiety opens up opportunities for research on a Russian-speaking student sample.
To understand the factors of successful integration of artificial intelligence into research process, it is necessary to examine the existing experience of scientific collectives, analyze key aspects of their work and the experience of implementing artificial intelligence (AI), whether successful or not. The purpose of this study is to identify the key factors that determine the success of artificial intelligence integration into the research activities of scientific collectives.To achieve the research objectives, we employ a multiple case study approach and conduct a series of semi-structured in-depth interviews. The focus of this study is four research collectives from Siberian scientific institutions. The scientific groups have been working or have worked on projects from different disciplines: physics, history, and medicine.We identified three interconnected conditions for successful artificial intelligence implementation. First, the possibility of harmonizing the research task and data format. Active integration of AI into the work of research collectives started when traditional methods had become inefficient from technological standpoint. Second, active organizational policy allowed research collectives to establish collaborations with programmers, acquiring necessary competencies. Third, two-way communication between researchers and AI specialists enabled consolidation of experience in applying the technology, reducing costs, and integrating AI specialists into the scientific group. Thus, the implementation of AI proves to be not so much a linear, technologically determined process, but rather a coordination of the research task, available resources, competencies, organizational culture, and disciplinary norms for validating acquired knowledge.
Three years ago, we published an article examining higher education transformation in the era of artificial intelligence technology expansion. The paper’s focus was on LLMs such as ChatGPT.The purpose of the present paper is to compare the expectations for 2023 with the realities observed in 2026. We aim to assess the current state of higher education and to propose new perspectives for its progress. To begin with, we present and examine the findings from a content analysis of articles about AI in education published in leading Russian-language journals between 2023 and 2025. This helps us to identify prevailing trends and gaps in professional literature. Based on this analysis, we discuss both realized and unrealized expectations for 2023 and subsequently observe the risks currently facing participants in higher education. The article then outlines seven propositions for Russian higher education in the AI era, which may serve as the basis for research questions in future studies. We conclude with a suggestion regarding the long-term transformation of higher education.
The article examines the relationship between the use of generative artificial intelligence in learning and several AI-related characteristics of students at Russian universities. The study is based on the assumption that the frequency of AI use should not be treated as a direct indicator of AI literacy or competence in working with generative systems. Four characteristics are considered separately: functional reliance on AI in decision-making, anthropomorphization of AI, ethical sensitivity to its use, and prompting skill assessed through a performance-based task. The empirical analysis draws on data from 1,647 thirdand fourth-year undergraduate students from 10 Russian universities, representing STEM, humanities, and social science fields. The results show that the overall frequency of generative AI use is positively associated with functional reliance on AI and anthropomorphization, but is not significantly related to ethical sensitivity or prompting skill after adjustment for multiple comparisons. The use of AI for preparing course papers and project work is associated with both functional reliance on AI and anthropomorphization. More instrumental scenarios, including programming and searching for learning materials, are associated with lower levels of anthropomorphization. Ethical sensitivity is more strongly related to field of study: students in the humanities and social sciences demonstrate higher levels of ethical concern than students in STEM fields. No robust predictors of prompting skill were identified in the final model. The findings suggest that the frequency of generative AI use cannot serve as a sufficient indicator of students’ AI literacy. The article argues for a more differentiated approach to teaching, assessment, and institutional regulation of AI use in higher education.
In the context of the trend towards personalization in higher education and the spread of individual educational trajectories (IET), this article aims at a theoretical analysis of the value and mechanisms of group learning within a permanent student group. Based on a review of theoretical and methodological approaches (social constructivism, activity theory, the concept of transactive memory, social learning theory, etc.), an attempt is made to identify the functions and effects of a permanent student collective. These include the formation of a distributed cognitive system, the creation of conditions for educational collaboration and co-construction of knowledge, as well as ensuring psychological safety and group cohesion. It is hypothesized that the transition to an IET model, associated with the replacement of permanent groups with temporary collectives, creates a risk of losing these effects, which could negatively impact the depth of knowledge acquisition, the development of soft skills, and the socio-psychological adaptation of students. The study conceptualizes the key contradictions and forms a theoretical basis for further research into the social aspects of learning in the context of personalization.
The article presents a scoping review (PRISMA-ScR protocol) of research on the “service learning” approach in the Russian higher education system. A total of 95 peer-reviewed articles from 2024–2026 were selected for qualitative synthesis from the Russian Science Citation Index (RSCI). The aim of the study is to conduct a comprehensive audit of the current state of the field: to identify thematic trends, assess the methodological maturity of publications, systematise the documented effects, and determine knowledge gaps.It was established that service learning has consolidated its position in Russian academic and pedagogical discourse as an independent interdisciplinary phenomenon with a distinctive national character, drawing on traditions of labour education and the concept of the university’s third mission. Three key thematic trends were identified: conceptual differentiation of service learning from related concepts (volunteering, social project design); the emergence of sector-specific implementation methods in medicine, engineering, and ecology; and the integration of digital platforms (DOBRO.RF) into programme coordination.Methodological analysis shows that the field is transitioning from descriptive case narration to evidence-based pedagogy: quantitative methods are gaining ground (nationwide surveys, non-parametric statistics), alongside narrative and discourse analysis. At the same time, several “academic gaps” were identified: a shortage of longitudinal studies on long-term effects; insufficient representation of the “beneficiary voice” in empirical data; limited sector-specific methodologies for STEM disciplines; and inadequate study of mentor-teacher motivation and burnout risks. Promising research questions are formulated, the answers to which will make it possible to build a national evidence base for service learning and integrate it into international academic dialogue.
The article examines the development of higher environmental education in Kazakhstan and Russia in the context of growing environmental challenges and recent changes in educational approaches. A review of academic literature suggests that, despite increasing attention to education for sustainable development, comparative studies of post-Soviet educational systems remain limited. At the same time, the pedagogical aspects of integrating project-based learning into the development of students’ environmental competence have not been sufficiently addressed. The aim of the study is to analyze the development of higher environmental education in Kazakhstan and Russia and to clarify the role of project-based learning in fostering students’ environmental competence. The research is based on comparative-historical analysis, content analysis of scientific publications and educational programs, as well as elements of a systems approach. The results show that environmental education in both countries has developed gradually and is currently shifting from predominantly theoretical instruction to more practice-oriented models. The Russian system is characterized by a higher level of institutional regulation, whereas the Kazakhstani system shows greater flexibility in the design of educational programs. The findings also indicate that the use of project-based learning supports the development of systems thinking, critical reasoning, and practical skills among students.The scientific contribution of the study lies in refining the comparative understanding of environmental education development in Kazakhstan and Russia and in identifying pedagogical conditions that support the effective use of project-based learning in developing students’ environmental competence.
In a rapidly changing society, the skills required to perform specific job functions, known as micro-credentials, are becoming increasingly important for adult learners. This article aims to identify the specific features of micro-credentials, based on international and Russian experiences with implementing micro-credential programs, assess their impact on lifelong learning, and explore the role of universities in providing these programs. The research relies on content analysis of academic literature, comparative analysis, data collection, and parsing. The authors have conducted a discourse analysis on the concept “microcredentials” and formulated a comprehensive definition which is based on four key criteria: training duration, number of credits, learning environment, and certification. Benefits and limitations of micro-credentials in different countries have been identified, including the European Union, the United States, the BRICS countries, and Kazakhstan. The role of microcredentials within the national qualification framework is demonstrated. Special attention has been given to the functions of Russian universities, educational technology companies, and their collaboration in implementing microcredential programs, as well as the importance of their seamless integration into national higher education systems and their official recognition. The authors emphasize the significance of microcredentials in addressing labor market imbalances and bridging skill gaps, as well as aligning university curricula with the needs of prospective employers. These findings may be beneficial for the academic community, human resource (HR) professionals, and HR development experts. The research contributes to the establishment of an efficient adult education ecosystem in the country.
The article examines practices for fostering a culture of interethnic and interreligious consent in higher pedagogical education in Russia. This process requires particular attention in the training of future teachers, whose values, agency, and competencies shape the development of a culture of civic harmony in new generations of Russians. Using a comprehensive methodology that includes cognitive mapping of digital content, social media analysis, and an expert survey, the dominant formats, substantive emphases, and institutional deficiencies in the activities of pedagogical universities are identified. A predominance of event-based, episodic approaches is established, with a lack of systemic and longterm projects aimed at fostering a sustainable culture and corresponding attitudes among students. Significant regional differences, underdeveloped monitoring mechanisms, and their replacement with preventive measures are identified. The authors conclude that declared assessments of the state of interethnic and interreligious harmony do not correspond to the actual level of methodological and organizational development at universities. The need for institutionalizing best practices, developing digital formats for educational work, and improving teacher training as a key resource for fostering a culture of harmony based on all-Russian civic identity is substantiated. The findings are particularly significant during the 2026 Year of Unity of the Peoples of Russia, providing scientifically based guidelines for adjusting upbringing policy in the national pedagogical education system.
The massification of higher education has made a university degree a widespread and socially expected goal, while simultaneously intensifying stratification within higher education itself. To explain educational inequality, it is increasingly important to examine not only whether students enter higher education, but also which segments of the higher education system they enter. Against this background, the college-to-university pathway constitutes a significant route into higher education and a mechanism for redistributing students within a hierarchically structured university field. This article presents a systematic literature review and analyses studies that conceptualize the college-to-university transition as a process of educational choice: how educational aspirations are formed, how risks and costs are assessed, how the receiving university is selected, and how plans are reshaped after enrolment. The review is based on an analysis of 117 publications selected from the international and Russian literature according to predefined inclusion and exclusion criteria. The review shows that this transition is socially stratified and often mediated by logics of risk avoidance. For students with fewer resources, it serves as a way to reduce the likelihood of educational failure while preserving access to a degree; for more privileged groups, it can in some cases function as a compensatory strategy for bypassing selective barriers. The prior institutional environment also plays a substantial role. Colleges institutionally affiliated with universities are more likely to provide an infrastructure that supports transition, including access to information, advising, and curricular alignment, whereas sectors less connected to higher education amplify uncertainty and increase risks at both the admission and adaptation stages. Within higher education, the choice of the receiving university generally reproduces the hierarchy of the system and is often accompanied by the underutilization of educational opportunities, thereby reinforcing inequality. The article’s central conclusion is that the literature still retains a “black box” between social origin and the concrete choice of universities. A promising direction for future research is to examine how students interpret the institutional landscape and translate these interpretations into actual decisions.
