With the rise of generative artificial intelligence (AI), the relationship between these emerging technologies and education, as well as educational practices, has become a central topic of scholarly debate. Research in this area is rapidly expanding, particularly regarding the potential benefits and drawbacks of AI use by students in education. However, despite the growing interest, certain gaps remain. Firstly, research often lacks a strong empirical foundation with rigorous hypothesis testing using validated methodologies, especially within the Russian context. Secondly, existing studies tend to focus primarily on opportunities for development rather than potential challenges. The authors believe that identifying these challenges is crucial for effectively managing the integration of AI into education, and this serves as the primary goal of this study. The core objective of this research is to provide empirical evidence supporting the existence of such challenges and to delineate their specific nature. To achieve this, we analyze data from a survey conducted by the authors in 2025, involving students from leading Russian universities (N=4207). One of the most significant challenges identified by the study is the exacerbation of inequality within the educational landscape. This is particularly evident in the disparate AI usage patterns between students in STEM fields and those in non-STEM disciplines. Furthermore, significant heterogeneity exists among students with varying academic performance (GPA). For highachieving students, AI tends to serve as a tool for enhancement, whereas for others, the opposite effect is observed. These findings are partially consistent with existing literature reviews, both domestic and international, as well as other surveys conducted on the topic. However, they contribute to a more defined understanding of the challenges associated with increasing educational inequality due to AI. Addressing the divisions within the educational sphere resulting from unequal levels of AI integration and utilization represents a crucial first step toward developing appropriate educational strategies that leverage AI as a tool to empower students, rather than the contrary.

The article presents the results of summarizing and systematizing scientific and practical research on the main directions of implementing generative artificial intelligence (GenAI) tools into the higher educational process by national scientific and pedagogical community, and the problems accompanying this process. The technological breakthrough in machine learning has significantly expanded the areas of artificial intelligence application. It has also necessitated a well-grounded assessment of the GenAI technologies potential for their most expedient integration into higher education, as well as the search for optimal and safe ways for teachers and students to interact with them.

The study is based on an analytical review of the publications devoted to the theoretical and applied problems of introducing GenAI tools in Russian universities. The total of 270 articles published between 2020 and 2024 and included in the List of peer-reviewed scientific publications approved by the Higher Attestation Commission (HAC) were analyzed. Methods of qualitative and quantitative content analysis, contextual analysis, and analytical grouping, as well as various mathematical methods, were applied.

The analysis showed that authors are mainly focused on summarizing the experience of using GenAI in the educational process within the scope of a specific academic discipline, educational program, or a separate area of a university’s activities. High research activity is evident in relation to problems associated with assessing the capabilities of GenAI and its specific services in teaching different disciplines. At the same time, scientists are primarily interested in such functions as the personalization of the educational process, educational outcomes assessment, and the training courses and individual classes design.

Particular attention is paid by researchers both to assessing the GenAI tools prevalence in educational practice and to the theoretical and empirical justification of a set of psychological, organizational, and pedagogical conditions that ensure the successful GenAI integration. Finally, the most intensive GenAI implementation is carried out in foreign languages teaching.

The current stage of integration of artificial intelligence (AI) technologies into Educa- tion is characterized by a gradual transition to the triad “teacher – student – artificial intelligence”. 
AI is gradually beginning to take on many functions previously associated with the teacher, and this brings changes to the traditional learning process, transferring it to a new, more complex level in terms of solving cognitive problems. In turn, it creates a need for teachers and lecturers to solve new didactic objectives, which requires a revision of some of the teacher’s functions and requirements for his competence in the field of AI. The purpose of the study is to develop the structure and content of a teacher’s competence in the field of AI and to determine which of the structural components of this type of competence higher education teachers are able to implement at the present stage. 
Based on the analysis of academic literature, the following structural components of a teacher’s competence in the field of AI were proposed: 1) motivational; 2) normative and legal; 3) information security; 4) ethical; 5) prompt engineering; 6) teaching and assessment; 7) management of the edu- cational process; 8) professional development. As part of the empirical component of the study, an online survey was conducted to determine the structural components of competence in the field of AI of higher education teachers, which they are able to implement. The respondents were 219 teach- ers of specialized disciplines from 17 universities of the Russian Federation. The results of the study showed that among the substantive components of competence in the field of AI, teachers are more proficient in such aspects as teaching and assessment (x̄ = 3,35–3,71, М_о = 4), information security (x̄ = 3,56–3,88, М_о = 4), management of the educational process (x̄ = 3,41–3,84, М_о = 4). 
The most difficulties for teachers at the present stage are caused by the normative and legal component  (x̄ = 3,35–3,47, М_о = 3) and prompt engineering (x̄ = 2,97–3,21, М_о = 3). The structure and content of the teacher’s competence in the field of AI proposed in the paper are of a recommendatory and framework nature. Based on them, depending on the specifics of the subject area and the availability of AI technical solutions, it is possible to develop the content of the competence in the field of using AI by teachers of specific academic disciplines or specialties.

The paper explores the fundamental characteristics of the socio-institutional paradigm used to study “social intercourse” (“obschenie”) in social philosophy. It also discusses the implications of this paradigm for higher education studies. Within this context, the authors view higher education as a process of social interactions between faculty and students. The starting point of the paper’s argument is an understanding of social intercourse (“obschenie”) as a multilevel social phenomenon, an adequate philosophical reflection of which implies three research paradigms: information-instrumental, existential-phenomenological, and social-institutional. Taking into account the impulsive expansion of AI instruments into everyday life, the authors promote the social-institutional paradigm for higher education social analytics, highlighting its emphasis on the transformation of university social relations. After a rather short overview of the socio-institutional paradigm within philosophy, the authors then examine promising avenues of contemporary social science research relevant to this paradigm. The authors identify three most promising areas: the development of microsociology and sociology of emotions toward the analysis of social structures and relations (R. Collins, J. Barbalet), institutional ethnography (D. Smith), and philosophical and anthropological rethinking of Marx’s heritage (D. Graeber, P. Virno). For each of the conceptual frames, the authors formulate research questions about higher education in the age of AI. These questions specify further directions for the social analytics of higher education that avoid both techno-optimism and techno-pessimism.

The purpose of this article is to answer the following research questions: 1) Is it legal to use educational materials created using AI in educational practice? 2) Is it possible to use educational materials developed using AI for commercial purposes? 3) Is it possible to protect the copyright to educational materials created using AI? The authors conduct a comparative literature analysis in the fields of education and law, as well as relevant legal frameworks and judicial practices in Russia and abroad. In the article authors also provides answers to three key questions mentioned above faced by modern educators when designing educational content. Is it possible to protect one’s copyright for educational content created using AI? In conclusion, the article presents some fundamental principles that should be followed when developing educational materials involving artificial intelligence technologies.

Final qualifying paper is the important part of university studies, demonstrating the quality of students’ mastering of the educational program. However, in the context of the spread of artificial intelligence (AI), the question arises about the objectivity and appropriateness of using this form of assessment. In order to make informed decisions in this area, it is necessary to analyze the opinion of the main educational stakeholders. Despite the numerous surveys of students regarding the use of artificial intelligence in the learning process, there are practically no works aimed at identifying the practices and strategies of students’ use of AI in research activities. This article aims to fill the existing gap. The aim of the study is to identify the attitude of senior undergraduates of the Higher School of Foreign Languages and Translation Studies of Kazan Federal University to the process of writing and defending the final qualifying paper in the context of AI proliferation. The results of interpreting the survey of graduates allow the authors to determine a positive trend in the undergraduate’s interaction with the supervisor as compared to the similar surveys conducted before. An obvious challenge to the system of higher education appeared the critical attitude of students to “Antiplagiarism” system and a tendency to a positive perception of the AI role in writing a thesis (only 10,7% of respondents consider the use of AI in writing a final paper unethical). At the same time, there is a direct link between the use of AI and ethical approval of its use. However, the authors predict the improvement in the situation after the introduction of lecture and practical disciplines and research practice during the senior year of the bachelor’s degree. The assumption is based, as provided in the article, on the best practice of organizing research work of the students majoring in Linguistics. It is implemented in various forms: lecture courses, practical classes, field practice, final paper draft defense as well as mock defense of the bachelor’s qualifying paper. The authors come to the conclusion that an increase in the quality of training a university graduate is possible with the implementation of a comprehensive differentiated approach to the organization of research work combined with effective tackling of ethical problems of scientific activity of a university student.

The article examines the transformation of subjectivity in the educational process under conditions of neural network integration. The author proposes a post-non-classical approach to understanding human-artificial intelligence interaction through the concept of “subject reassembly”, complemented by the introduced concept of “subjective membrane”. This concept characterizes the selective permeability of subject boundaries while maintaining internal homeostasis. The paper traces the evolution of educational paradigms: from subject-object, through subject-subject, to a network model of distributed pedagogy. The author demonstrates how neural networks transform not only teaching methods but also the very ontology of educational space, creating a new form of distributed subjectivity. It is noted that interaction with neural networks unfolds in a mode of “being between”, forming a special diffractive space where traditional epistemological boundaries become blurred. Special attention is given to the productive paradox of “educational autoimmunity”, where interaction with neural networks requires partial reconsideration of established cognitive schemes to form new, more adaptive thinking structures. The article also proposes a new understanding of the teacher’s role as a mediator between human and machine intelligence in a hybrid educational space. Ethical issues related to AI integration in education are considered, and conceptual foundations for forming a new educational ethics in the context of digital transformation are proposed.

This article explores the impact of artificial intelligence (AI) on the field of education. We propose the concept of “Communicative Artificial Intelligence” (CAI) to more accurately describe the new reality in education, where AI becomes not just a tool, but an active participant in communication. The article examines academic discussions about AI as a social actor, proposes a theoretical framework of communicative constructivism for interpreting CAI, discusses arguments in favour of introducing a new concept, and suggests key questions for further research in this area. Particular attention is paid to the transformation of assessment and reflective practices, as well as the value aspects of human-AI interaction in the educational process. The active and large-scale development of communicative artificial intelligence leads to the need to identify a specific subject area of research in education.