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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Samara Journal of Science</journal-id><journal-title-group><journal-title xml:lang="en">Samara Journal of Science</journal-title><trans-title-group xml:lang="ru"><trans-title>Самарский научный вестник</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2309-4370</issn><issn publication-format="electronic">2782-3016</issn><publisher><publisher-name xml:lang="en">Samara State University of Social Sciences and Education</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">716827</article-id><article-id pub-id-type="doi">10.55355/snv2026152302</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Pedagogical Sciences</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Педагогические науки</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Epistemic transformation of social experience in the age of generative artificial intelligence: philosophical foundations of education content</article-title><trans-title-group xml:lang="ru"><trans-title>Эпистемическая трансформация социального опыта в эпоху генеративного искусственного интеллекта: философские основания содержания образования</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Berezhnova</surname><given-names>Elena Victorovna</given-names></name><name xml:lang="ru"><surname>Бережнова</surname><given-names>Елена Викторовна</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>doctor of pedagogical sciences, professor of World Literature and Culture Department</p></bio><bio xml:lang="ru"><p>доктор педагогических наук, профессор кафедры мировой литературы и культуры</p></bio><email>e.v.berezhnova@inno.mgimo.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Gulov</surname><given-names>Artem Petrovich</given-names></name><name xml:lang="ru"><surname>Гулов</surname><given-names>Артем Петрович</given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>doctor of pedagogical sciences, associate professor of English Language Department</p></bio><bio xml:lang="ru"><p>доктор педагогических наук, доцент кафедры английского языка № 6</p></bio><email>gulov@tea4er.org</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Moscow State Institute of International Relations (University)</institution></aff><aff><institution xml:lang="ru">Московский государственный институт международных отношений (университет)</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2026-06-01" publication-format="electronic"><day>01</day><month>06</month><year>2026</year></pub-date><volume>15</volume><issue>2</issue><issue-title xml:lang="ru"/><fpage>165</fpage><lpage>174</lpage><history><date date-type="received" iso-8601-date="2026-07-28"><day>28</day><month>07</month><year>2026</year></date><date date-type="accepted" iso-8601-date="2026-07-28"><day>28</day><month>07</month><year>2026</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2026, Berezhnova E.V., Gulov A.P.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2026, Бережнова Е.В., Гулов А.П.</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="en">Berezhnova E.V., Gulov A.P.</copyright-holder><copyright-holder xml:lang="ru">Бережнова Е.В., Гулов А.П.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://snv63.ru/2309-4370/article/view/716827">https://snv63.ru/2309-4370/article/view/716827</self-uri><abstract xml:lang="en"><p> </p><p>This article presents a philosophical analysis of the transformation of social experience – the key source of education content – in the context of algorithmically mediated culture and the proliferation of generative artificial intelligence (GenAI). The central thesis holds that GenAI systems, acting as non-human agents of knowledge production, alter the epistemic conditions under which social experience is created, validated, and circulated, thereby qualitatively transforming the very source of education content. Drawing on the culturological theory of education content (M.N. Skatkin, I.Ya. Lerner, V.V. Kraevskii) and engaging with the philosophy of algorithmic culture and the philosophy of higher education, the authors employ conceptual framework analysis and theoretical synthesis to develop the DAASE model (Digitally-Algorithmically Augmented Social Experience). The model identifies five analytical dimensions of transformed social experience: algorithmic filtering of the information space, delegation of cognitive operations, knowledge production by non-human agents, displacement of agentic creativity by algorithmic generativity, and distortion of value orientations. Each dimension corresponds to a principle of education content formation: critical algorithmic literacy, epistemic agency, integrativity, human-centredness, and ethical reflexivity. The article demonstrates that DAASE does not replace the classic four-component structure of education content but undertakes its conceptual reconstruction for the conditions of higher education in the age of algorithmic mediation. The limitations of the model and directions for empirical validation are outlined.</p> </abstract><trans-abstract xml:lang="ru"><p> </p><p>Статья посвящена философскому анализу трансформации социального опыта – ключевого источника содержания образования – в условиях алгоритмически опосредованной культуры и распространения генеративного искусственного интеллекта (GenAI). Исходным тезисом исследования служит положение о том, что GenAI-системы, выступая в роли нечеловеческих агентов производства знания, изменяют эпистемические условия, при которых социальный опыт создаётся, валидируется и циркулирует, что ведёт к качественной трансформации самого источника содержания образования. Опираясь на культурологическую теорию содержания образования (М.Н. Скаткин, И.Я. Лернер, В.В. Краевский) и привлекая ресурсы философии алгоритмической культуры и философии высшего образования, авторы посредством концептуального анализа рамок и теоретического синтеза разрабатывают модель DAASE (Digitally-Algorithmically Augmented Social Experience), описывающую пять аналитических измерений трансформированного социального опыта: алгоритмическую фильтрацию информационного пространства, делегирование когнитивных операций, производство знания нечеловеческими агентами, замещение субъектного творчества алгоритмической генеративностью и деформацию ценностных ориентиров. Каждому измерению соответствует принцип формирования содержания образования: критическая алгоритмическая грамотность, эпистемическая агентность, интегративность, человекоцентрированность и этическая рефлексивность. Показано, что модель DAASE не заменяет классическую четырёхкомпонентную структуру содержания образования, а осуществляет её концептуальную реконструкцию для условий высшей школы в эпоху алгоритмической медиации. Обозначены ограничения модели и направления эмпирической валидации.</p> </trans-abstract><kwd-group xml:lang="en"><kwd>education content</kwd><kwd>social experience</kwd><kwd>generative artificial intelligence</kwd><kwd>algorithmic culture</kwd><kwd>culturological theory of education content</kwd><kwd>epistemic agency</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>содержание образования</kwd><kwd>социальный опыт</kwd><kwd>генеративный искусственный интеллект</kwd><kwd>алгоритмическая культура</kwd><kwd>культурологическая теория содержания образования</kwd><kwd>эпистемическая агентность</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Краевский В.В. Общие основы педагогики: учебник. М.: Академия, 2003. 256 с.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Теоретические основы содержания общего среднего образования / под ред. В.В. Краевского, И.Я. Лернера. М.: Педагогика, 1983. 352 с.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Holmes W., Tuomi I. State of the art and practice in AI in education // European Journal of Education. 2022. Vol. 57, iss. 4. P. 542–570. DOI: 10.1111/ejed.12533.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Zawacki-Richter O., Marín V.I., Bond M., Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? // International Journal of Educational Technology in Higher Education. 2019. Vol. 16. Art. 39. DOI: 10.1186/s41239-019-0171-0.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Crompton H., Burke D. Artificial intelligence in higher education: the state of the field // International Journal of Educational Technology in Higher Education. 2023. Vol. 20. Art. 22. DOI: 10.1186/s41239-023-00392-8.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Yan L., Sha L., Zhao L., Li Y., Martinez-Maldonado R., Chen G., Li X., Jin Y., Gašević D. Practical and ethical challenges of large language models in education: a systematic scoping review // British Journal of Educational Technology. 2024. Vol. 55, iss. 1. P. 90–112. DOI: 10.1111/bjet.13370.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Kasneci E., Sessler K., Küchemann S., Bannert M., Dementieva D., Fischer F., Gasser U., Groh G., Günnemann S., Hüllermeier E., Krusche S., Kutyniok G., Michaeli T., Nerdel C., Pfeffer J., Poquet O., Sailer M., Schmidt A., Seidel T., Stadler M., Weller J., Kuhn J., Kasneci G. ChatGPT for good? On opportunities and challenges of large language models for education // Learning and Individual Differences. 2023. Vol. 103. Art. 102274. DOI: 10.1016/j.lindif.2023.102274.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Jose B., Cleetus A., Joseph B., Joseph L., Jose B., John A.K. Epistemic authority and generative AI in learning spaces: rethinking knowledge in the algorithmic age // Frontiers in Education. 2025. Vol. 10. P. 1647687. DOI: 10.3389/feduc.2025.1647687.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Wu J.-Yu., Lee Yu.-H., Chai Ch.S., Tsai Ch.-Ch. Strengthening human epistemic agency in the symbiotic learning partnership with generative artificial intelligence // Educational Researcher. 2025. Vol. 54, iss. 6. P. 358–368. DOI: 10.3102/0013189x251333628.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Jandrić P., Knox J. The postdigital turn: philosophy, education, research // Policy Futures in Education. 2022. Vol. 20, iss. 7. P. 780–795. DOI: 10.1177/14782103211062713.</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Лернер И.Я. Дидактические основы методов обучения. М.: Педагогика, 1981. 186 с.</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Скаткин М.Н. Проблемы современной дидактики. 2-е изд. М.: Педагогика, 1984. 96 с.</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Краевский В.В., Бережнова Е.В. Методология педагогики: новый этап: учеб. пособие. 2-е изд., стер. М.: Академия, 2008. 393 с.</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Левицкий М.Л., Осмоловская И.М. Теоретические основания развития дидактики общего образования в современных условиях // Ценности и смыслы. 2025. № 6 (100). C. 39–53. DOI: 10.24412/2071-6427-2025-6-39-53.</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Акимова Н.В. Риски образования в эпоху искусственного интеллекта // Rhema. Рема. 2025. № 2. С. 107–117. DOI: 10.31862/2500-2953-2025-2-107-117.</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Макарова (Сай) Т.А. Современные ориентиры обновления содержания образования в высшей школе // Педагогическое образование в России. 2015. № 1. С. 36–41.</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Biesta G. What is education for? On good education, teacher judgement, and educational professionalism // European Journal of Education. 2015. Vol. 50, iss. 1. P. 75–87. DOI: 10.1111/ejed.12109.</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Barnett R. The philosophy of higher education: A critical introduction. London: Routledge, 2022. 290 p.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Knox J. Posthumanism and the massive open online course: Contaminating the subject of global education. New York; London: Routledge, 2016. 238 p.</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Striphas T. Algorithmic culture // European Journal of Cultural Studies. 2015. Vol. 18, iss. 4–5. P. 395–412. DOI: 10.1177/1367549415577392.</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Bucher T. If…then: Algorithmic power and politics. New York: Oxford University Press, 2018. DOI: 10.1093/oso/9780190493028.001.0001.</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Guidance for generative AI in education and research. Paris: UNESCO, 2023. 44 p. DOI: 10.54675/ewzm9535.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Empowering learners for the age of AI: launch of the draft AI literacy framework and stakeholder consultations [Internet] // European Education Area. https://education.ec.europa.eu/event/empowering-learners-for-the-age-of-ai-launch-of-the-draft-ai-literacy-framework-and-stakeholder-consultations.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Baker R.S., Hawn A. Algorithmic bias in education // International Journal of Artificial Intelligence in Education. 2022. Vol. 32, iss. 4. P. 1052–1092. DOI: 10.1007/s40593-021-00285-9.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Selwyn N. Should robots replace teachers? AI and the future of education. Cambridge: Polity Press, 2019. 160 p.</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Molenaar I. The concept of hybrid human-AI regulation: exemplifying how to support young learners’ self-regulated learning // Computers and Education: Artificial Intelligence. 2022. Vol. 3. Art. 100070. DOI: 10.1016/j.caeai.2022.100070.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Couldry N., Mejias U.A. The costs of connection: how data is colonizing human life and appropriating it for capitalism. Stanford: Stanford University Press, 2019. 323 p.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>Щедровицкий Г.П. Мышление – Понимание – Рефлексия. М.: Наследие ММК, 2005. 800 с.</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Зинченко В.П. Психологические основы педагогики (психолого-педагогические основы построения системы развивающего обучения Д.Б. Эльконина – В.В. Давыдова): учеб. пособие. М.: Гардарики, 2002. 431 с.</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Jabareen Y. Building a conceptual framework: philosophy, definitions, and procedure // International Journal of Qualitative Methods. 2009. Vol. 8, iss. 4. P. 49–62. DOI: 10.1177/160940690900800406.</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Shneiderman B. Human-centered AI. Oxford: Oxford University Press, 2022. 377 p.</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Markauskaite L., Goodyear P. Epistemic fluency and professional education: innovation, knowledgeable action and actionable knowledge. Dordrecht: Springer, 2018. 636 p. DOI: 10.1007/978-94-007-4369-4.</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Alfredo R., Echeverria V., Jin Y., Yan L., Swiecki Z., Gašević D., Martinez-Maldonado R. Human-centred learning analytics and AI in education: a systematic literature review // Computers and Education: Artificial Intelligence. 2024. Vol. 6. Art. 100215. DOI: 10.1016/j.caeai.2024.100215.</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Tayie S.S. Fostering algorithmic literacy in education: navigating news ecosystems for critical media understanding // Comunicar. 2025. Vol. 33, № 82. P. 127–137. 10.5281/zenodo.16122006.</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Thumlert K., McBride M., Tomin B. Nolan J., Lotherington H., Boreland T. Algorithmic literacies: identifying educational models and heuristics for engaging the challenge of algorithmic culture // Digital Culture &amp; Education. 2022. Vol. 14, iss. 4. P. 19–35.</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Williamson B., Eynon R. Historical threads, missing links, and future directions in AI in education // Learning, Media and Technology. 2020. Vol. 45, iss. 3. P. 223–235. DOI: 10.1080/17439884.2020.1798995.</mixed-citation></ref></ref-list></back></article>
