Synopsis
HSE University (National Research University Higher School of Economics, Moscow, Russia) regards artificial intelligence (AI) as a tool with the potential to enhance educational, research, expert-analytical, and administrative processes. The question is, how different generations of the university community, namely students and teachers, face the challenges caused by the uneven processes of integrating AI into teaching, learning, and assessment, and how this process may be impacted in a responsible, inclusive, and respectful way.
Key words
Artificial Intelligence, University Teaching and Learning, Intergenerational Communication
Abstract text
It is not the first year that the world`s leading universities have been formulating and revising the rules for the use of AI in education (e.g. Cornell University, 2023). Ethical guidelines on the use of AI in education are adopted at the level of national and international organizations (e.g. the European Commission, Directorate-General for Education, Youth, Sport and Culture, 2022). The HSE University does not prohibit the use of artificial intelligence (AI) by the students university-wide (https://www.hse.ru/en/studyspravka/ai_guidelines/). They may employ generative models (for text, code, images, etc.) in completing academic work (written or oral). However, to comply with academic integrity standards, , the students shall specify the purposes of using generative models and specific AI, commenting on the effectiveness of their use for specific tasks. At the same time faculties, departments, and teachers are searching for and forming their own ethical conventions about the ethics of using AI in written student papers. The search for solutions is complicated by the diversity of opinions within the university academic community, from a total ban to a complete permission to use any kind of AI tools.
Researchers reveal the innovative role of AI in various spheres of social, economic and cultural life of society as an industrial or technological revolution (Damioli et al., 2025; Hinton, 2025). The social and ethical dimensions of this revolution attract much attention in a growing number of academic publications on the experiences and opinions of participants in the educational process (see, for example, Ali et al., 2024; Dwivedi et al., 2023; Gašević et al., 2023; Grájeda, 2024). The authors focus on assessing the risks caused by prejudice against and discrimination based on the use of AI (Frąckiewicz, 2023). In modern universities the representatives of three or four generations study and work side by side. Artificial intelligence enhances the transmission of information from generation to generation and provides richer communication opportunities between them (Puaschunder, 2024). However, in the context of the rapid development of artificial intelligence, the challenges for the intergenerational communication are becoming particularly acute. Research on the benefits and risks of using AI in education rarely touches on intergenerational relationships. Only in few research projects the attitudes of different generations towards AI, the influence of peers on digital practices, the exchange of information innovations between generations, institutional support and ethical aspects are scrutinized (Jesus et al 2025). In our research we are interested to consider the challenges of AI integration into higher education reflected by students and professors, in particular, in the aspects of inter-generational communication.
As a conceptual analytic framework, we use the theory of generationally mediated AI implementation (Jesus et al, 2025), that emphasizes the integration of AI into higher education not only as improved technologies, their accessibility or usefulness, but also as complex mediation between generations, encompassing cognitive, cultural and normative aspects.
Methodology
The study was carried out using a mixed methods strategy. The empirical base included (i) qualitative exploratory inquiry using students’ and teachers’ reflective comments on the challenges of using AI in education (February-March 2026), (ii) statistical analysis of the data base of students’ declarations on using the AI in their graduate works (May-June 2025), (iii) curricula analysis revealing the integration of AI subjects into the educational programmes (2024-2025 educational year). We asked how students and teachers reflect upon the use of AI in teaching and learning, which students use AI more often in preparation of their graduate works, how the academic performance is impacted by the use of AI, which trends in the use of AI in graduate works are determined by the presence of courses in the curricula.
The study was conducted in accordance with the ethical principles of anonymity and confidentiality. Data were analyzed in aggregate form, without identifying specific research participants. When respondents were asked to comment on AI practices, they were informed of the study’s goals and objectives and provided their voluntary consent to participate. In all other cases, open data were used for analysis.
Findings and Conclusions
We analyzed the obtained database on the 10574 graduate works including 1940 (18,4%) entries with declaration on AI. Out of 1445 graduate works at the Faculty of Social Sciences, 278 (19,2%) have declared use of AI. In addition, we have analyzed the information on the formal integration of AI into the educational programs of faculties as well as the statements of teachers and students that we received in online conversations on the ethical and instrumental aspects of using AI in education.
The results of the data analysis showed that the maximum number of students who actively use AI tools when writing graduate works are studying at programs on data analysis and digital technologies. Among the programs whose students are less likely to declare the use of AI, the social studies and humanities prevail. In addition to them, the students of the natural science programs based on partnership with institutes of the Russian Academy of Sciences also less frequently declare the use of AI. At the HSE University, out of 226 educational programs, 169 have integrated courses on AI in their curricula. There is no correlation between the presence of AI disciplines in the program and the use of these tools by students of these programs. Undergraduate graduates of the Faculty of Social Sciences have slightly higher grades for graduate works written using AI tools than the grades for graduate works in these programs as a whole.
Differences in attitudes and practices of AI application reflect generation gap in the context of rapid technological, cognitive, cultural and normative changes. According to their written anonymous comment, the students have consistent and informed positions, they learn from their peers and from AI itself. Some students are proud of their capabilities to see the limitations of AI and control it. They claim to move beyond the copy-paste practices when using AI to for basic services or are capable of elaborating the codes to reach their own goals. The professors vary greatly in their competencies, they have to retrain, some are stuck on the issue of controlling the students. Such pattern was characterized by Margaret Mead (1970) as post–figurative culture, that is, when the younger ones learn from the older ones. However, some academics are attuned to co-figurative thinking; they support the tripartite practices of education: students, professors and AI learning from each other. Regular discussions in chat rooms and offline meetings of departments and faculties, indicate the need to develop effective and reliable ethical principles.
The use of AI reveals pain points of education system: some students cheat their professors while some teachers, in turn, ignore such practices. There is an explicit problem of a technological inter-generation gap. New questions are being raised for further reflection, in particular on ethics and commitment of teaching and learning, rules and regulations on what needs to be declared and why, assessment models.
We have gained an initial understanding of the current situation related to the use of artificial intelligence in university communications, including in the aspect of intergenerational relations in 2025-26, and lays the foundation for further research. The old issues of miscommunication between students and professors take on new dimensions in the frame of a new technological revolution. Higher education is changing as a result of the integration of artificial intelligence (AI), and this process impacts social relations within the university community in connection with the new issues of trust, digital literacy, and ethical considerations.
It is necessary to closely monitor academic discussions and new practices at other universities and develop new guidelines for university policy in the age of AI. Constant ethical reflection can help in the development of mechanisms for the continuous assessment of the social role of artificial intelligence in relation to the impact on generations. By addressing these challenges, universities can harness the transformative potential of artificial intelligence while ensuring the well-being of different generations (Puaschunder, 2024).
It is essential not only to learn how to work with AI tools for solving specific professional tasks but to work with AI thoughtfully, critically evaluating the results obtained and potential risks, balancing the students’ autonomy with assessment integrity. In order to develop proactive university regulation policies and inclusive culture, it is important to research the emerging tensions between the efforts ensuring the academic freedom and commitment to responsible learning.
Limitations
Firstly, the study was conducted in a single institutional context on a small sample, which does not allow us to make any generalizations, but only assumptions. Secondly, the tools were of an exploratory nature, since we did not yet have consolidated measurement methods and understanding angles at our disposal. Thirdly, the study did not touch on substantive and technological aspects, without focusing on the role of specific AI tools. As the situation is changing rapidly as new AI functions and tools become available, it is important to develop and reflect the new opportunities for mutual, including intergenerational, AI learning.
References
Ali S.B., Haider M., Samiullah D., Shamsy Sh. (2024) Assessing students’ understanding of ethical use of artificial intelligence (AI): a focus group study. (2024). International journal of social science & entrepreneurship, 4(3), 65-87. https://doi.org/10.58661/ijsse.v4i3.301
Cornell University (2023) Ethical AI for teaching and learning https://teaching.cornell.edu/generative-artificial-intelligence/ethical-ai-teaching-and-learning
Damioli G., Roy V. Van, Vertesy D., Vivarelli M. (2025) Is artificial intelligence leading to a new technological paradigm?, Structural change and economic dynamics, 72, 347-359, ISSN 0954-349X, https://doi.org/10.1016/j.strueco.2024.12.006.
Dwivedi Y.K. (2023) Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy, International journal of information management, 71, 2023, 102642,ISSN 0268-4012, https://doi.org/10.1016/j.ijinfomgt.2023.102642.
European Commission, Directorate-General for Education, Youth, Sport and Culture, Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators (2024), Publications office of the european union, https://data.europa.eu/doi/10.2766/153756
Frąckiewicz, M. (2023). OpenAI and the risks of ai bias: addressing stereotypes and discrimination. TS2 SPACE. https://ts2.space/en/openai-and-the-risks-of-ai-bias-addressing-stereotypes-and-discrimination
Gašević D., Siemens G., Sadiq S. (2023). Empowering learners for the age of artificial intelligence. Computers & Education: Artificial Intelligence, 4, 100130. https://doi.org/10.1016/j.caeai.2023.100130
Grájeda A., Burgos J., Córdova P., Sanjinés A. (2024). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2023.2287917
Henke J. (2024). ‘Navigating the AI era: university communication strategies and perspectives on generative AI tools’. Journal of science communication, 23(3), A05. https://doi.org/10.22323/2.23030205.
Hinton G. (2025) AI is the next industrial revolution, Time, 11.12.2025 https://time.com/7339628/geoffrey-hinton-ai/
Jesus J.., Basilisco G.., Gemodo K.- c G.., E. Espiloy G.., M. Lucero, C.., M. Sumilhig J. (2025). Exploring generational perspectives on AI use in higher education: a theory. ASEAN Journal of management & innovation, 12(2), 95–110. retrieved from https://so13.tci-thaijo.org/index.php/AJMI/article/view/1699
Mead M. (1970) Culture and commitment: a study of the generation gap. The Natural History Press.
NRU HSE https://www.hse.ru/en/studyspravka/ai_guidelines/