Socio-professional Challenges and Methodological Aspects of AI Implementation in the School Educational Environment
DOI: 10.23951/1609-624X-2025-5-110-121
The article states that the integration of artificial intelligence (AI) into school education faces a contradiction between its transformative potential and social barriers linked to digital competence, the professional identity of teachers, and ethical risks. Despite growing interest in AI tools, their implementation is constrained by the inertia of traditional pedagogical practices and the disconnect between technological determinism and the social construction of innovation. The study aims to identify patterns in the perception of AI by students and teachers, assess the impact of digital literacy, age, and professional experience on readiness to adopt AI technologies. The empirical basis includes data from online surveys of 169 9th–11th grade students and 40 teachers from schools in the Tomsk region. The theoretical framework integrates and evaluates the SCOT, SAMR, TPACK, and Human-AI Collaboration Theory models to analyze social and technological factors influencing AI adoption. Survey results indicate that 57 % of students support AI education, while 27.2 % exhibit technophobia, associated with low algorithmic literacy and fears of AI errors. Among teachers, 65% use digital technologies, but only 37 % employ AI daily. Key risks identified by respondents include the diminishing role of teachers (38.5%), privacy threats (75 % of girls), and passive learning due to automation (33.7 %). Correlations were found between teachers’ age (younger educators more enthusiastically adopt AI), students’ technical orientation (STEM interests enhance AI acceptance), and school digitalization levels. The study confirms that successful AI integration requires a combination of technological infrastructure, ethical standards, and targeted professional development programs. Recommendations include phased AI adoption (from automation to personalization), project-based formats for technically oriented students, and dialogue among all educational stakeholders. The findings contribute to developing strategies for harmonizing AI solutions while preserving human agency in pedagogy.
Keywords: artificial intelligence in education, social construction of technology (SCOT), digital competence of teachers, personalized learning, digital divide, human-AI collaboration
References:
1. Ukaz Prezidenta RF ot 7 maya 2018 g. № 204 “O natsional’nykh tselyakh i strategicheskikh zadachakh razvitiya Rossiyskoy Federatsii na period do 2024 goda” [On National Goals and Strategic Objectives for the Development of the Russian Federation until 2024: Decree of the President of the Russian Federation No. 204 of May 7, 2018]. Informatsionno-pravovaya sistema “Garant” [Garant Information and Legal System] (in Russian). URL: https://base.garant.ru/71937200/ (accessed 27 April 2025).
2. Ponkin I.V., Kupriyanovsky V.P., Moreva S.L., Ponkin D.I. Proryvnye tekhnologicheskiye innovatsii: ponyatiye, znacheniye i ontologiya [Breakthrough Technological Innovations: Concept, Significance, and Ontology]. Mezhdunarodnyy zhurnal otkrytykh informatsionnykh tekhnologiy – International Journal of Open Information Technologies, 2020, vol. 8, no. 8, pp. 60–68 (in Russian).
3. Sysoev P.V. Iskusstvennyy intellekt v obrazovanii: osvedomlennost’, gotovnost’ i praktika primeneniya prepodavatelyami vysshey shkoly tekhnologiy iskusstvennogo intellekta v professional’noy deyatel’nosti [Artificial Intelligence in Education: Awareness, Readiness, and Practice of AI Technology Use by Higher Education Faculty]. Vyssheye obrazovaniye v Rossii – Higher Education in Russia, 2023, no. 10 (in Russian). URL: https://cyberleninka.ru/article/n/iskusstvennyy-intellekt-v-obrazovanii-osvedomlyonnost-gotovnost-i-praktika-primeneniya-prepodavatelyami-vysshey-shkoly-tehnologiy (accessed 27 April 2025).
4. Erkinbek Aknar. Eticheskiye i prakticheskiye aspekty ispol’zovaniya iskusstvennogo intellekta v obrazovanii [Ethical and Practical Aspects of Artificial Intelligence Use in Education]. In the World of Science and Education, 2024, no. 15 (in Russian). URL: https://cyberleninka.ru/article/n/eticheskii-i-prakticheskie-aspekty-ispolzovaniya-iskusstvennogo-intellekta-v-obrazovanii (accessed 27 April 2025).
5. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign, 2021. 320 p.
6. Luckin R. & Cukurova M. Designing educational technologies in the age of AI: A learning sciences‐informed approach. British Journal of Educational Technology, 2019, vol. 50, no. 6, pp. 2824–2838. https://doi.org/10.1111/bjet.12861
7. Hendrycks D., Mazeika M., Woodside T. An Overview of Catastrophic AI Risks, 2023. doi: 10.48550/arXiv.2306.12001
8. Du J., Wen Y., Wang L., Zhang P., Fei M., Pardalos P. An adaptive human learning optimization with enhanced exploration-exploitation balance. Annals of Mathematics and Artificial Intelligence, 2022, no. 91, pp. 1–40. doi: 10.1007/s10472-022-09799-x
9. Professional’noye razvitiye pedagoga v usloviyakh tsifrovizatsii obrazovaniya: uchebno-metodicheskoye posobiye [Professional Development of Teachers in the Context of Digitalization: Educational and Methodological Manual]. Saint Petersburg, GAOU DPO LOIRO Publ., 2020. 135 p. (in Russian).
10. Tikhonova N.V., Ilduganova G.M. “Menya pugayet to, s kakoy skorost’yu razvivaetsya iskusstvennyy intellect”: vospriyatiye studentami iskusstvennogo intellekta v obuchenii inostrannym yazykam [“I’m Scared by the Speed of AI Development”: Students’ Perception of AI in Foreign Language Learning]. Vysshee obrazovaniye v Rossii – Higher Education in Russia, 2024, no. 4 (in Russian) URL: https://cyberleninka.ru/article/n/menya-pugaet-to-s-kakoy-skorostyu-razvivaetsya-iskusstvennyy-intellekt-vospriyatie-studentami-iskusstvennogo-intellekta-v-obuchenii (accessed 27 April 2025).
11. Mishra P., Koehler M. J. Technological Pedagogical Content Knowledge: A framework for teacher knowledge. Teachers College Record, 2006, vol. 108, no. 6, pp. 1017–1054.
12. Davis F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 1989, vol. 13, no. 3, pp. 319–340.
13. Mugo D., Njagi K., Chemwei B., Motanya J. The Technology Acceptance Model (TAM) and its Application to the Utilization of Mobile Learning Technologies. British Journal of Mathematics & Computer Science, 2017, no. 20, pp. 1–8. doi: 10.9734/BJMCS/2017/29015
14. Puentedura R.R. Transformation, Technology and Education, 2006. URL: http://hippasus.com/resources/tte/ (accessed 27 April 2025).
15. Dellermann D., Calma A., Lipusch N., Weber T., Weigel S., Ebel P. The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems, 2019. doi: 10.24251/HICSS.2019.034
16. Hemmer P., Schemmer M., Kühl N., Vössing M., Satzger G. Сomplementarity in Human-AI collaboration: concept, sources, and evidence, 2024. arXiv:2404.00029v1. URL: https://arxiv.org/pdf/2404.00029v1 (accessed 27 April 2025).
17. Bijker W.E., Hughes T.P., Pinch T.J. The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology. Cambridge: MIT Press, 1987. 456 p.
18. Selwyn N. & Hillman T. & Eynon R. & Ferreira G. & Knox J. & Macgilchrist F. & Sancho J. M. What’s next for Ed-Tech? Critical hopes and concerns for the 2020s. Learning, Media and Technology, 2019, no. 45, pp. 1–6. doi: 10.1080/17439884.2020.1694945
19. Garrison D.R., Cleveland-Innes M., Fung T.C. Exploring Causal Relationships among Teaching, Cognitive and Social Presence: student perceptions of the community of inquiry framework. The Internet and Higher Education, 2010, vol. 13(1-2), pp. 31–36. URL: http://dx.doi.org/10.1016/j.iheduc.2009.10.002 (accessed 27 April 2025).
20. Sweller J., Ayres P., Kalyuga S. Cognitive load theory. New York, Springer, 2011. 76 p.
21. Zawacki-Richter et al. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 2019. URL: https://doi.org/10.1186/s41239-019-0171-0 (accessed 27 April 2025).
22. Lizzie Coles-Kemp. Inclusive Security: Digital Security Meets Web Science. Foundations and Trends® in Web Science, 2020, vol. 7, no. 2, pp. 88–241. URL: http://dx.doi.org/10.1561/1800000030 (accessed 27 April 2025).
23. Voogt J., et al. Computational Thinking in Compulsory Education: Towards an Agenda for Research and Practice. Education and Information Technologies, 2015, vol. 20, no. 4, pp. 715–728. URL: https://doi.org/10.1007/s10639-015-9412-6 (accessed 27 April 2025).
24. Sysoyev P.V. Iskusstvennyy intellekt v obrazovanii: osvedomlonnost’, gotovnost’ i praktika primeneniya prepodavatelyami vysshey shkoly tekhnologiy iskusstvennogo intellekta v professional’noy deyatel’nosti [Artificial intelligence in education: awareness, readiness and practice of using artificial intelligence technologies in professional activities by higher education teachers]. Vyssheye obrazovaniye v Rossii – Higher Education in Russia, 2023, vol. 32, no. 10, pp. 9–33 (in Russian).
25. Skvorchevsky K.A., Dyatlova O.V. Sovremennyye adaptivnyye i intellektual’nyye tsifrovyye sistemy obucheniya: mekhanizmy i potentsial [Modern adaptive and intelligent digital learning systems: mechanisms and potential]. Voprosy obrazovaniya – Educational Studies Moscow, 2024, no. 3 (2), pp. 299–337 (in Russian). URL: https://doi.org/10.17323/vo-2024-19751 (https://vo.hse.ru/article/view/19751).
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Issue: 5, 2025
Series of issue: Issue 5
Rubric: THEORY AND METHODS OF TEACHING AND EDUCATION
Pages: 110 — 121
Downloads: 236