Между интересом и умением: как студенты воспринимают и применяют ИИ

По мере того как ИИ становится неотъемлемой частью образования и будущего рынка труда, важно понимать, как студенты воспринимают и используют его. В этом исследовании рассматривается проблема отношения российских студентов вузов к ИИ и изучается связь между их установками и реальными навыками эффективного использования ИИ. Для оценки представлений студентов об ИИ использован специально разработанный опросник, охватывающий четыре аспекта: интерес к ИИ, субъективный опыт использования ИИ, восприятие ценности ИИ в будущем и риски, связанные с ИИ. Навыки использования ИИ измерялись с помощью практического задания – создания эффективного запроса для большой языковой модели в рамках решения аутентичной рабочей задачи. Результаты показали, что многим студентам было трудно составлять такие запросы. Те, кто считал себя более опытным в использовании ИИ, показали лишь немногим лучшие результаты (r = 0,20), как и студенты с более выраженным интересом к ИИ (r = 0,12). Однако в целом связь между установками и реальными навыками была слабой. Студенты, рассматривающие ИИ как риск, давали более низкую его оценку как ценности для будущего (r = –0,09), но это не влияло на их интерес к ИИ или ощущение собственного опыта. В конечном итоге, несмотря на высокий интерес студентов к ИИ, их умение эффективно использовать его остаётся ограниченным.

1. Almatrafi O., Johri A., Lee H. A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019-2023) // Computers and Education Open. 2024. Article no. 100173. DOI: 10.1016/j.caeo.2024.100173

2. Mills K., Ruiz P., Lee K.W., Coenraad M., Fusco J. et al. AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. Digital Promise, 2024. DOI: 10.51388/20.500.12265/218

3. Sindermann C., Sha P., Zhou M., Wernicke J., Schmmitt H.S. et al. Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language // Künstliche Intelligenz. 2021. Vol. 35. P. 109–118. DOI: 10.1007/s13218-020-00689-0

4. Ibrahim F., Münscher J.C., Daseking M., Telle N.T. The technology acceptance model and adopter type analysis in the context of artificial intelligence // Frontiers in Artificial Intelligence. 2025. Vol. 7. Article no. 1496518. DOI: 10.3389/frai.2024.1496518

5. Wang J., Fan W. The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis // Humanit Soc Sci Commun. 2025. No. 12. Article no. 621. DOI: 10.1057/s41599-025-04787-y

6. Ali D., Fatemi Y., Boskabadi E., Nikfar M., Ugwuoke J., Ali H. ChatGPT in teaching and learning: a systematic review // Education Sciences. 2024. Vol. 14. No. 6. Article no. 643. DOI: 10.3390/educsci14060643

7. Laupichler M.C., Aster A., Schirch J., Raupach T. Artificial intelligence literacy in higher and adult education: a scoping literature review // Computers and Education. 2022. Vol. 3. Article no. 100101. DOI: 10.1016/j.caeai.2022.100101

8. Zhou X., Schofield L., Zhang J.J., Abuelma- atti A., Howell L. Building bridges in AI: Enhancing AI literacy for students and staff across disciplines // PGR Student Partners – Empowering doctoral students through partnership and co-creation in institutional equality, diversity and inclusion change projects. 2024. Vol. 20. URL: https://www.researchgate.net/publication/384291235_Building_bridges_in_AI_Enhancing_AI_literacy_for_students_and_staff_across_disciplines (дата обращения: 23.06.2025).

9. Lintner T. A systematic review of AI literacy scales // NPJ Science of Learning. 2024. Vol. 9. No. 1. Article no. 50. DOI: 10.1038/s41539-024-00264-4

10. Mansoor H.M., Bawazir A., Alsabri M.A., Alharbi A., Okela A.H. Artificial intelligence literacy among university students – a comparative transnational survey // Frontiers in Communication. 2024. Vol. 9. Article no. 1478476. DOI: 10.3389/fcomm.2024.1478476

11. Fernandes D., Villa S., Nicholls S., Haavisto O., Buschek D. et al. Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction // arXiv preprint. 2024. Article no. 2409.16708. DOI: 10.48550/arXiv.2409.16708

12. Mousavi Baigi S.F., Sarbaz M., Ghaddari- pouri K., Ghaddaripouri M., Mousavi A.S. et al. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review // Health Science Reports. 2023. Vol. 6. No. 3. Article no. e1138. DOI: 10.1002/hsr2.1138

13. Klar M. Using ChatGPT is easy, using it effectively is tough? A mixed-methods study on K-12 students’ perceptions, interaction patterns, and support for learning with generative AI chatbots // Smart Learning Environments. 2025. Vol. 12. No. 1. P. 1–19. DOI: 10.1186/s40561025-00385-2

14. Mahmood K. Do people overestimate their information literacy skills? A systematic review of the Dunning-Kruger effect // Communications in Information Literacy. 2016. Vol. 10. No. 2. P. 3–24. DOI: 10.15760/comminfolit.2016.10.2.2

15. Giray L. Prompt engineering with ChatGPT: A guide for academic writers // Annals of Biomedical Engineering. 2023. Vol. 51. No. 6. P. 2692–2633. DOI: 10.1007/s10439-023-03272-4

16. Yang M., Jiang Sh., Li B., Herman K. Analysing Nontraditional Students’ ChatGPT Interaction, Engagement, Self-Efficacy and Performance: A Mixed-Methods Approach // British Journal of Educational Technology. Vol. 56. No. 5. P. 1973–2000. DOI: 10.1111/bjet.13588

17. Ekin S. Prompt engineering for ChatGPT: A quick guide to techniques, tips, and best practices // TechRxiv. 2023. DOI: 10.36227/techrxiv.22683919.v2

18. Nori H., King N., McKinney S.M., Carig- nan D., Horvitz E. Capabilities of GPT-4 on medical challenge problems // arXiv preprint. 2023. Article no. 2303.13375. DOI: 10.48550/arXiv.2303.13375

19. Lee D., Palmer E. Prompt engineering in higher education: a systematic review to help inform curricula // International Journal of Educational Technology in Higher Education. 2025. Vol. 22. No. 1. Article no. 7. DOI: 10.1186/s41239-025-00503-7

20. Krupp S., Rinas R., Klieme E., Nagel M. Large language models in the classroom: Opportunities and challenges of ChatGPT for higher education // Zeitschrift für Erziehungswissenschaft. 2023. Vol. 26. No. 3. P. 573–594. DOI: 10.1007/s11618023-01128-6

21. Federiakin D., Molerov D., Zlatkin-Troitschan-skaia O., Maur A. Prompt engineering as a new 21st century skill // Frontiers in Education. 2024. Vol. 9. Article no. 1366434. DOI: 10.3389/feduc.2024.1366434

22. Maharjan J., Garikipati A., Singh N.P., Cyrus L.,Sharmma M. et al. OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models // Scientific Reports. 2024. Vol. 14. No. 2. DOI: 10.1038/s41598-024-64827-6

23. Hwang G.J., Chiu L.Y., Tseng J.C.R. A framework of prompt literacy for supporting AI learning: Conceptualization and application // Computers and Education: Artificial Intelligence. 2023. Vol. 4. Article no. 100160. DOI: 10.1016/j.caeai.2023.100160

24. Busch K., Rochlitzer A., Sola D., Leopold H. Just tell me: Prompt engineering in business process management // Enterprise, Business-Process and Information Systems Modeling. 2023. P. 3–11. DOI: 10.1007/978-3-031-34241-7_1

25. Brown T., Mann B., Ryder N., Subbiah M., Kaplan J. et al. Language models are few-shot learners // Advances in Neural Information Processing Systems. 2020. Vol. 33. P. 1877–1901. DOI: 10.48550/arXiv.2005.14165

26. Kojima T., Gu S.S., Reid M., Matsuo Yu., Iwasawa Yu. Large language models are zeroshot reasoners // Advances in Neural Information Processing Systems. 2022. Vol. 35. DOI: 10.48550/arXiv.2205.11916

27. Wang X., Wei J., Schuurmans D., Lee Q., Chi E. et al. Self-consistency improves chain of thought reasoning in language models // arXiv preprint. 2022. Article no. 2203.11171. DOI: 10.48550/arXiv.2203.11171

28. Walter S. Integrating LLMs in higher education: Implications for assessment and academic integrity // Journal of Learning Analytics and AI in Education. 2024. Vol. 5. No. 1. P. 45–61. DOI: 10.61669/001c.131915

29. Semnani S.H., Li X., Ye Q., Callison-Burch C. SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models // arXiv preprint. 2023. Article no. 2303.08896. DOI: 10.48550/arXiv.2303.08896

30. Gattupalli S., Maloy R.W., Edwards S.A. Prompt Literacy: A Pivotal Educational Skill in the Age of AI // College of Education Working Papers and Reports Series. 2023. DOI: 10.7275/3498-wx48

31. Carolus A., Koch M.J., Straka S., Latoschik M.E., Wienrich C. MAILS-Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies // Computers in Human Behavior: Artificial Humans. 2023. Vol. 1. No. 2. Article no. 100014. DOI: 10.1016/j.chbah.2023.100014

32. Katsantonis A., Katsantonis I.G. University students’ attitudes toward artificial intelligence: an exploratory study of the cognitive, emotional, and behavioural dimensions of AI attitudes // Education Sciences. 2024. Vol. 14. No. 9. Article no. 988. DOI: 10.3390/educsci14090988

33. Roberts-Tyler E.J., Roberts S., Watkins R., Gillespie D. Effects of implementation support on children’s reading outcomes following an online early reading programme: a clusterrandomised controlled trial // British Journal of Educational Technology. 2023. Vol. 54. No. 5. P. 1373–1396. DOI: 10.1111/bjet.13312

34. Long D., Magerko B. What is AI literacy? Competencies and design considerations // Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. P. 1–16. DOI: 10.1145/3313831.3376727

35. Yang W. Artificial intelligence education for young children: why, what, and how in curriculum design and implementation // Computers and Education. 2022. Vol. 3. Article no. 100061. DOI: 10.1016/j.caeai.2022.100061

36. Pedro F., Subosa M., Rivas A., Valverde P. Artificial intelligence in education: challenges and opportunities for sustainable development // UNESCO. 2019. 46 p. URL: https://unesdoc.unesco.org/ark:/48223/pf0000366994 (дата обращения: 23.06.2025).

37. Miao F., Holmes W., Huang R., Zhang H. AI and education: A guidance for policymakers. UNESCO Publishing, 2021. 45 p. DOI: 10.54675/PCSP7350

38. Casal-Otero L., Catala A., Fernández-Morante C. Taboada M., Cebreiro B. et al. AI literacy in K-12: a systematic literature review // International Journal of STEM Education. 2023. Vol. 10. Article no. 29. DOI: 10.1186/s40594-023-00418-7

39. Hwang Y., Lee J.H., Shin D. What is prompt literacy? An exploratory study of language learners’ development of new literacy skill using generative AI // arXiv preprint. Article no. 2311.05373. 2023. DOI: 10.48550/arXiv.2311.05373

40. Li X., Jiang M.Y., Jong M.S., Zhang X., Chai Ch. Understanding medical students’ perceptions of and behavioral intentions toward learning artificial intelligence // International Journal of Environmental Research and Public Health. 2022. Vol. 19. Article no. 8733. DOI: 10.3390/ijerph19148733

41. Morales-García W.C., Sairitupa-Sanchez L.Z., Morales-García S.B., Morales-García M. Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students // Frontiers in Education. 2024. Vol. 9. Article no. 1293437. DOI: 10.3389/feduc.2024.1293437

42. Wang Y.Y., Chuang Y.W. Artificial intelligence self-efficacy: scale development and validation // Education and Information Technologies. 2023. Vol. 28. P. 1–24. DOI: 10.1007/s10639-023-12001-y

43. Soto-Sanfiel M.T., Angulo-Brunet A., Lutz C. The scale of artificial intelligence literacy for all (SAIL4ALL): a tool for assessing knowledge on artificial intelligence in all adult populations and settings // OSF preprint. 2024. DOI: 10.31235/osf.io/bvyku

44. Ng D.T.K., Wu W., Leung J.K.L., Chiu T.K.F., Chu S.K.W. Design and validation of the AI literacy questionnaire: the affective, behavioural, cognitive and ethical approach // British Journal of Educational Technology. 2023. Vol. 54. P. 1–23. DOI: 10.1111/bjet.13411

45. Hwang H.S., Zhu L.C., Cui Q. Development and validation of a digital literacy scale in the AI era for college students // KSII Transactions on Internet and Information Systems. 2023. Vol. 17. No. 8. P. 2241–2258. DOI: 10.3837/tiis.2023.08.016

46. Eccles J.S., Wigfield A. From expectancyvalue theory to situated expectancy-value theory: a developmental, social cognitive, and sociocultural perspective on motivation // Contemporary Educational Psychology. 2020. Vol. 61. Article no. 101859. DOI: 10.1016/j.cedpsych.2020.101859

47. Zhai X., Chu X., Chai C.S., Jong M.S.Y., Istenic A. et al. A review of artificial intelligence in education from 2010 to 2020 // Complexity. 2021. Vol. 2021. DOI: 10.1155/2021/8812542

48. Chan C.K.Y., Hu W. Students’ voices on generative AI: perceptions, benefits, and challenges in higher education // International Journal of Educational Technology in Higher Education. 2023. Vol. 20. No. 1. Article no. 43. DOI: 10.1186/s41239-023-00411-8

49. Venkatesh V., Davis F.D. A theoretical extension of the technology acceptance model: Four longitudinal field studies // Management Science. 2000. Vol. 46. No. 2. P. 186–204. DOI: 10.1287/mnsc.46.2.186.11926

50. Zhang B., Dafoe A. Artificial intelligence: American attitudes and trends. Oxford, UK: Centre for the Governance of AI; Future of Humanity Institute, University of Oxford; 2019. DOI: 10.2139/ssrn.3312874

51. de Ayala R.J. The Theory and Practice of Item Response Theory. 2nd ed. New York: The Guilford Press, 2022. Methodology in the Social Sciences. 643 p. URL: https://www.guilford.com/books/The-Theory-and-Practice-of-ItemResponse-Theory/R-de-Ayala/9781462547753 (дата обращения: 23.06.2025).

52. Robitzsch A., Kiefer T., Wu M., Robitzsch A.C. TAM: Test Analysis Modules. R package, ver. 4.1-0. 2022. DOI: 10.32614/CRAN.package.TAM

53. Revelle W. Psych: Procedures for Psychological, Psychometric, and Personality Research. R package, ver. 2.3-6. 2023. DOI: 10.32614/CRAN.package.psych

54. Smith E.V., Conrad K.M., Chang K., Piazza J. An introduction to Rasch measurement for scale development and person assessment // Journal of Nursing Measurement. 2002. Vol. 10. No. 3. P. 189–206. DOI: 10.1891/jnum.10.3.189.52562

55. Reckase M.D. Unidimensional IRT models // Multidimensional Item Response Theory. New York: Springer, 2009. P. 17–30. DOI: 10.1007/978-0-387-89976-3

56. Dunning D., Heath C., Suls J.M. Flawed self‐assessment: implications for health, education, and the workplace // Psychological Science in the Public Interest. 2004. Vol. 5. No. 3. P. 69–106. DOI: 10.1111/j.1529-1006.2004.00018.x

57. Brantmeier C., Vanderplank R., Strube M. What about me?: Individual self-assessment by skill and level of language instruction // System. 2012. Vol. 40. No. 1. P. 144–160. DOI: 10.1016/j.system.2012.01.003

58. Fošner A. University students’ attitudes and perceptions towards AI tools: implications for sustainable educational practices // Sustainability. 2024. Vol. 16. No. 19. Article no. 8668. DOI: 10.3390/su16198668

59. Ананин Д.П., Комаров Р.В., Реморенко И.М. «Когда честно – хорошо, для имитации – плохо»: стратегии использования генеративного искусственного интеллекта в российском вузе // Высшее образование в России. 2025. Т. 34. № 2. С. 31–50. DOI: 10.31992/0869-3617-2025-34-2-31-50

60. Wu W., Zhang B., Li S., Liu H. Exploring factors of the willingness to accept AI-assisted learning environments: An empirical investigation based on the UTAUT model and perceived risk theory // Frontiers in Psychology. 2022. Vol. 13. Article no. 870777. DOI: 10.3389/fpsyg.2022.870777

61. Буякова К.И., Дмитриев Я.А., Иванова А.С., Фещенко А.В., Яковлева К.И. Отношение студентов и преподавателей к использованию инструментов с генеративным искусственным интеллектом в вузе // Образование и наука. 2024. Т. 26. № 7. С. 160–193. DOI: 10.17853/1994-5639-2024-7-160-193

62. Начало конца или новой эпохи? Эффекты генеративного искусственного интеллекта в высшем образовании / Я.И. Кузьминов (научая редакция), М.А. Кирюшина, А.П. Ворочков, Е.В. Кручинская, Е.А. Терентьев, И.Д. Фрумин; Национальный исследовательский университет «Высшая школа экономики», Институт образования. М.: НИУ ВШЭ, 2024. 64 с. 100 экз. Современная аналитика образования. № 8 (82). URL: https://ioe.hse.ru/pubs/share/direct/991726366.pdf (дата обращения: 23.06.2025).