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.

1. OECD. (2026). OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education. Ed. by S. Vincent-Lancrin. Paris: OECD Publishing, 247 p., doi: 10.1787/062a7394-en.

2. Ananin, D.P., Komarov, R.V., Remorenko, I.M. (2025). “When It Is Honest, It Is Good; for Imitation, It Is Bad”: Strategies of Using Generative AI in a Russian University. Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 34, no. 2, pp. 31-50, doi: 10.31992/0869-3617-2025-34-2-31-50 (In Russ., abstract in Eng.).

3. Kuzminov, Ya.I., Kruchinskaya, E.V., Gruzdev, I.A., Naumov, A.A. (2025). Lagging Behind and Moving Ahead: How Students Use Generative AI for Educational Purposes. Vysshee obrazovanie

4. v Rossii = Higher Education in Russia. Vol. 34, no. 6, pp. 9-35, doi: 10.31992/0869-3617-2025-34-6-9-35 (In Russ., abstract in Eng.).

5. Doshi, A.R., Bell, J.J., Mirzayev, E., Vanneste, B.S. (2025). Generative Artificial Intelligence andEvaluating Strategic Decisions. Strategic Management Journal. Vol. 46, no. 3, pp. 583-610, doi: 10.1002/smj.3677.

6. Sidorchuk, R.R. (2025). Usovershenstvovannaya struktura modeli PAIR-PPAIR dlya vnedreniya generativnogo iskusstvennogo intellekta v obrazovatel’nuyu praktiku podgotovki studentovmarketologov [An Improved PAIR-PPAIR Model Structure for Integrating Generative Artificial Intelligence into the Educational Practice of Training Marketing Students]. Marketing MBA. Marketingovoe upravlenie predpriyatiem [Marketing MBA. Marketing Management of Enterprise]. Vol. 16, no. 1, pp. 50-59. Available at: https://www.elibrary.ru/download/elibrary_80597102_82115304.pdf (accessed 07.03.2026). (In Russ.).

7. Whittemore, R., Knafl, K. (2005). The Integrative Review: Updated Methodology. Journal of Advanced Nursing. Vol. 52, no. 5, pp. 546-553, doi: 10.1111/j.1365-2648.2005.03621.x.

8. Snyder, H. (2019). Literature Review as a Research Methodology: An Overview and Guidelines. Journal of Business Research. Vol. 104, pp. 333-339, doi: 10.1016/j.jbusres.2019.07.039.

9. Thomas, J., Harden, A. (2008). Methods for the Thematic Synthesis of Qualitative Research in Systematic Reviews. BMC Medical Research Methodology. Vol. 8, article no. 45, doi: 10.1186/1471-2288-8-45.

10. Vygotsky, L.S. (1982). Myshlenie i rech’ [Thinking and Speech]. In: Sobranie sochinenii: v 6 t. T. 2: Problemy obshchei psikhologii [Collected Works: in 6 vols. Vol. 2: Problems of General Psychology]. Ed. by V.V. Davydov. Moscow: Pedagogika Publ. Pp. 5-361. (In Russ.).

11. Lotman, Yu.M. (1992). Ustnaya rech’ v istoriko-kul’turnoi perspektive [Oral Speech in Historical and Cultural Perspective]. In: Izbrannye stat’i: v 3 t. T. 1 [Selected Articles: in 3 vols. Vol. 1]. Tallinn: Aleksandra Publ. Pp. 184-190. ISBN: 5-450-01551-8. (In Russ.).

12. Olson, D.R. (1994). The World on Paper: The Conceptual and Cognitive Implications of Writing and Reading. Cambridge; New York: Cambridge University Press, 318 p. ISBN: 0-521-44311-3.

13. Donald, M. (1991). Origins of the Modern Mind: Three Stages in the Evolution of Culture and Cognition. Cambridge, MA: Harvard University Press, 413 p. ISBN: 978-0-674-64484-7.

14. Goody, J. (1977). The Domestication of the Savage Mind. Cambridge: Cambridge University Press, 184 p. ISBN: 0-521-29242-5.

15. Olson, D.R., Torrance, N. (Eds.). (2009). The Cambridge Handbook of Literacy. Cambridge: Cambridge University Press, 624 p., doi: 10.1017/CBO9780511609664.

16. Ong, W.J. (1982). Orality and Literacy: The Technologizing of the Word. London; New York: Methuen, 201 p. ISBN: 0-203-42625-8.

17. Overmann, K.A. (2025). Writing and Executive Function: A New Perspective on Cognitive Externalization. Current Anthropology. Vol. 66, no. 2, pp. 133-145, doi: 10.1086/733185.

18. Overmann, K.A. (2023). Material Scaffolds in Numbers and Time. Doi: 10.31235/osf.io/m65z4.

19. Plato. (1993). Fedr [Phaedrus]. Translated from Ancient Greek by A.N. Egunov. In: Sobranie sochinenii: v 4 t. [Collected Works: in 4 vols.]. Ed. by A.F. Losev, V.F. Asmus, A.A. Takho-Godi. Vol. 2. Moscow: Mysl’ Publ., 528 p. ISBN: 5-244-00471-9. (In Russ.).

20. Havelock, E.A. (1982). Preface to Plato. Cambridge, MA: Belknap Press of Harvard University Press, 328 p. ISBN: 0-674-69906-8.

21. Kalantzis, M., Cope, B. (2025). Literacy in the Time of Artificial Intelligence. Reading Research Quarterly. Vol. 60, no. 1, article no. e591, doi: 10.1002/rrq.591.

22. Clark, A. (2025). Extending Minds with Generative AI. Nature Communications. Vol. 16, article no. 4627, doi: 10.1038/s41467-025-59906-9.

23. Clark, A., Chalmers, D. (1998). The Extended Mind. Analysis. Vol. 58, no. 1, pp. 7-19, doi: 10.1093/analys/58.1.7.

24. Hutchins, E. (1995). Cognition in the Wild. Cambridge, MA: MIT Press, 381 p. ISBN: 0-262-58146-9.

25. Wu, Y., Guo, W., Liu, Z., Ji, H., Xu, Z., Zhang, D. (2025). How Large Language Models Encode Theory-Of-Mind: A Study on Sparse Parameter Patterns. npj Artificial Intelligence. Vol. 1, article no. 20, doi: 10.1038/s44387-025-00031-9.

26. Dvořaček, J., Kodrik, D. (2024). Brain and Cognition: The Need for a Broader Biological Perspective to Overcome Old Biases. Neuroscience & Biobehavioral Reviews. Vol. 167, article no. 105928, doi: 10.1016/j.neubiorev.2024.105928.

27. Gatti, A. (2024). Alfabetizaciуn e Inteligencia Artificial. Journal of Neuroeducation. Vol. 5, no. 1, pp. 52-58, doi: 10.1344/joned.v5i1.46108.

28. Hooper, V.J. (2025). Cognitive Offloading and the Reshaping of Human Thought: The Subtle Influence of Artificial Intelligence. Colloquia. Vol. 12, pp. 1-14, doi: 10.31207/colloquia.v12i1.185.

29. Leon-Dominguez, U. (2024). Potential Cognitive Risks of Generative Transformer-Based AI Chatbots on Higher Order Executive Functions. Neuropsychology. Vol. 38, no. 4, pp. 293-308, doi: 10.1037/neu0000948.

30. Sweller, J. (1988). Cognitive Load during Problem Solving: Effects on Learning. Cognitive Science. Vol. 12, no. 2, pp. 257-285, doi: 10.1207/s15516709cog1202_4.

31. Linden, K., Hachem, H.H., Kondyli, V. (2025). Homo Promptus: Predicting the Impact of Generative AI on Human Memory and Creativity. Memory, Mind & Media. Vol. 4, article no. e15, doi: 10.1017/mem.2025.10012.

32. Jadhav, A. (2025). Distributed Atrophy: How AI Shapes and Shrinks Our Cognitive Habits. OSF Preprints. Doi: 10.31219/osf.io/hzuw7_v1.

33. Jain, N., Kiran, M. (2025). Rethinking Education in the Age of Generative AI: Cognitive Offloading, Assessment Reform, and Institutional Adaptation. OSF Preprints. Doi: 10.35542/osf.io/zakwe_v2.

34. Tao, Y., Viberg, O., Baker, R.S., Kizilcec, R.F. (2024). Cultural Bias and Cultural Alignment of Large Language Models. PNAS Nexus. Vol. 3, no. 9, article no. pgae346, doi: 10.1093/pnasnexus/pgae346.

35. Ma, Y., Su, Y., Li, M., Zhang, Y., Chai, W., et al. (2025). Preparing Students for an AI-Driven World: Generative AI and Curriculum Reform in Higher Education. Frontiers of Digital Education. Vol. 2, no. 4, article no. 30, doi: 10.1007/s44366-025-0067-6.

36. Mukhlaeva, T.V. (2025). Generativnyi iskusstvennyi intellekt: transformatsii v obrazovanii, perspektivy i dinamika [Generative Artificial Intelligence: Transformations in Education, Prospects, and Dynamics]. Chelovek i obrazovanie [Human and Education]. No. 2 (83), pp. 142-153, doi: 10.54884/1815-7041-2025-83-2-142-153. (In Russ.).

37. Tillmanns, T., Salomao Filho, A., Rudra, S., Weber, P., Dawitz, J., et al. (2025). Mapping Tomorrow’s Teaching and Learning Spaces: A Systematic Review on GenAI in Higher Education. Trends in Higher Education. Vol. 4, no. 1, article no. 2, doi: 10.3390/higheredu4010002.

38. Lyu, W., Zhang, S., Chung, T., Sun, Y., Zhang, Y. (2025). Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-Methods Study in U.S. Higher Education. arXiv, arXiv:2502.05770. Doi: 10.48550/arXiv.2502.05770.

39. Louca, J. (2025). The AI-Pocalypse in Academia: Challenges and Opportunities in the Age of Generative AI. In: 1st AI-Pocalypse Meeting: How AI is Turning Academia Upside Down. Lisbon: Zenodo, pp. 1-13, doi: 10.5281/zenodo.15780186.

40. Bobula, M. (2024). Generative Artificial Intelligence (AI) in Higher Education: A Comprehensive Review of Challenges, Opportunities, and Implications. Journal of Learning Development in Higher Education. No. 30, doi: 10.47408/jldhe.vi30.1137.

41. Jensen, L.X., Buhl, A., Sharma, A., Bearman, M. (2025). Generative AI and Higher Education: A Review of Claims from the First Months of ChatGPT. Higher Education. Vol. 89, pp. 1145-1161, doi: 10.1007/s10734-024-01265-3.

42. Thomas, G.A., Abu, S., Ajayi, A.B., Frempong, P.A., Mensah, D., et al. (2025). Navigating Generative AI in Education: A Narrative Review of Teaching Practices and Ethical Challenges. Asian Journal of Education and Social Studies. Vol. 51, no. 11, pp. 507-521, doi: 10.9734/ajess/2025/v51i112630.

43. Hong, H., Vate-U-Lan, P., Viriyavejakul, C. (2025). Perceptions of the Generative AI-Enabled Cognitive Offload Instruction in English Writing. International Journal of Evaluation and Research in Education. Vol. 14, no. 3, pp. 1761-1769, doi: 10.11591/ijere.v14i3.33138.

44. Gee, J.P., Zhang, Q.A. (2024). Cybersapien Literacy: Integrating AI and Human. Phi Delta Kappan. Vol. 106, no. 3, pp. 32-38, doi: 10.1177/00317217241295428.

45. Sysoev, P.V. (2024). Ethics and AI Plagiarism in the Academic Environment: Students’ Understanding of Authorship Ethics and Plagiarism in the Process of Interaction with Generative Artificial Intelligence. Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 33, no. 2, pp. 31-53, doi: 10.31992/0869-3617-2024-33-2-31-53. (In Russ., abstract in Eng.).

46. Tikhonova, N.V., Pomortseva, N.P. (2025). Graduate Qualification Thesis in the Context of the Spread of Artificial Intelligence: Students’ Perspective. Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 34, no. 6, pp. 112-135, doi: 10.31992/0869-3617-2025-34-6-112-135. (In Russ., abstract in Eng.).

47. El Moussaddar, J., Habiballah, S., Tadist, K. (2025). Drawing the Line: A Review on Generative AI Use in Higher Education. The International Journal of Technology, Innovation, and Education. Vol. 3, no. 3, pp. 102-116, doi: 10.5281/zenodo.17442962.

48. Davlatova, M.A., Speranskaya, M.V. (2026). Prompt Engineering as a Key Competence in Education: Essence, Features and Assessment Approaches. Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 35, no. 2, pp. 53-73, doi: 10.31992/0869-3617-2026-35-2-53-73. (In Russ., abstract in Eng.).

49. Sidorchuk, R.R. (2026). Marketingovoe planirovanie. Iskusstvennyi intellekt, instrumenty, metriki, pokazateli [Marketing Planning. Artificial Intelligence, Tools, Metrics, Indicators]. Moscow: KnoRus, 239 p. ISBN: 978-5-406-14846-4. (In Russ.).

50. Sidorchuk, R.R. (2024). Vliyanie iskusstvennogo intellekta na marketingovoe obrazovanie [The Impact of Artificial Intelligence on Marketing Education]. Marketing v Rossii i za rubezhom [Marketing in Russia and Abroad]. No. 3, pp. 74-79. Available at: https://www.elibrary.ru/item.asp?id=68455377 (accessed 07.03.2026). (In Russ.).

51. Sysoev, P.V. (2023). Artificial Intelligence in Education: Awareness, Readiness, and the Practice of Using Artificial Intelligence Technologies by Higher School Teachers in Their Professional Activities. Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 32, no. 10, pp. 9-33, doi: 10.31992/0869-3617-2023-32-10-9-33. (In Russ., abstract in Eng.).