vovrВысшее образование в России (Vysshee obrazovanie v Rossii = Higher Education in Russia)Vysshee Obrazovanie v Rossii = Higher Education in Russia0869-36172072-0459Moscow Polytechnic University10.31992/0869-3617-2026-35-6-127-148vovr-6327Research ArticleСтатьиКогда чаще не значит лучше: сценарии использования генеративного ИИ в высшем образованииWhen Greater Frequency Is Not Better: Patterns of Generative AI Use in Higher Educationhttps://orcid.org/0000-0002-3915-3165ТарасоваК. В.TarasovaK. V.

Тарасова Ксения Вадимовна – канд. пед. наук, директор Центра психометрики и измерений в образовании Института образования

Researcher ID: ABD-3327-2020  

101000, г. Москва, Потаповский пер., 16, стр. 10

Ksenia V. Tarasova – Cand.Sci. (PSciences), Director of the Center for Psychometrics and Measurement in Education, Institute of Education

Researcher ID: ABD-3327-2020  

16, bldg. 10, Potapovsky lane, Moscow, 101000 

ktarasova@hse.ruhttps://orcid.org/0000-0002-1682-0578ТаловД. П.TalovD. P.

Талов Даниил Павлович – аспирант, младший научный сотрудник Центра психометрики и измерений в образовании Института образования

Researcher ID: MDS-9225-2025  

101000, г. Москва, Потаповский пер., 16, стр. 10

Daniil P. Talov – PhD student, Junior Research Fellow, Center for Psychometrics and Measurement in Education, Institute of Education

Researcher ID: MDS-9225-2025  

16, bldg. 10, Potapovsky lane, Moscow, 101000 

dtalov@hse.ruhttps://orcid.org/0000-0003-3340-7651ИвановаА. Е.IvanovaA. E.

Иванова Алина Евгеньевна – канд. наук об образовании, заместитель директора Центра психометрики и измерений в образовании Института образования

Researcher ID: I-4300-2015  

101000, г. Москва, Потаповский пер., 16, стр. 10

Alina E. Ivanova – Cand.Sci. (Education), Senior Research Fellow, Center for Psychometrics and Measurement in Education, Institute of Education  

Researcher ID: I-4300-2015  

16, bldg. 10, Potapovsky lane, Moscow, 101000 

aeivanova@hse.ruНациональный исследовательский университет «Высшая школа экономики»РоссияNational Research University Higher School of EconomicsRussian Federation202624062026356127148Copyright © Тарасова К.В., Талов Д.П., Иванова А.Е., 20262026Тарасова К.В., Талов Д.П., Иванова А.Е.Tarasova K.V., Talov D.P., Ivanova A.E.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vovr.elpub.ru/jour/article/view/6327

Статья посвящена анализу связи между использованием генеративного искусственного интеллекта (ИИ) в учебной деятельности и ИИ-связанными характеристиками студентов российских вузов. Исходной предпосылкой исследования является необходимость отказаться от упрощённого отождествления частоты использования ИИ с ИИ-грамотностью или компетентностью взаимодействия с генеративными системами. В статье отдельно рассматриваются четыре характеристики: функциональная опора на ИИ при принятии решений, антропоморфизация ИИ, этическая чувствительность к его использованию и навык промптинга как показатель, оцениваемый через задание с наблюдаемым продуктом. Эмпирическую базу составили данные 1647 студентов 3–4-го курсов бакалавриата из 10 российских университетов, обучающихся по ST EM-дисциплинам, гуманитарным и социальным направлениям. Анализ показал, что общая частота использования генеративного ИИ положительно связана с функциональной опорой на ИИ и антропоморфизацией, но не имеет статистически значимой связи с этической чувствительностью и навыком промптинга после учёта множественных сравнений. Использование ИИ при подготовке курсовых и проектных работ оказалось связано как с функциональной опорой на ИИ, так и с антропоморфизацией. Более инструментальные сценарии, включая программирование и поиск учебных материалов, напротив, ассоциированы с более низкой антропоморфизацией. Этическая чувствительность в большей степени связана с направлением подготовки: студенты гуманитарных и социальных наук демонстрируют более высокие значения по сравнению со студентами ST EM-направлений. Для навыка промптинга устойчивых предикторов в итоговой модели не выявлено. Полученные результаты показывают, что частота использования генеративного ИИ не может рассматриваться как достаточный индикатор ИИ-грамотности студентов. Статья обосновывает необходимость более дифференцированного подхода к обучению, оцениванию и институциональному регулированию использования ИИ в высшем образовании.

The article examines the relationship between the use of generative artificial intelligence in learning and several AI-related characteristics of students at Russian universities. The study is based on the assumption that the frequency of AI use should not be treated as a direct indicator of AI literacy or competence in working with generative systems. Four characteristics are considered separately: functional reliance on AI in decision-making, anthropomorphization of AI, ethical sensitivity to its use, and prompting skill assessed through a performance-based task. The empirical analysis draws on data from 1,647 thirdand fourth-year undergraduate students from 10 Russian universities, representing STEM, humanities, and social science fields. The results show that the overall frequency of generative AI use is positively associated with functional reliance on AI and anthropomorphization, but is not significantly related to ethical sensitivity or prompting skill after adjustment for multiple comparisons. The use of AI for preparing course papers and project work is associated with both functional reliance on AI and anthropomorphization. More instrumental scenarios, including programming and searching for learning materials, are associated with lower levels of anthropomorphization. Ethical sensitivity is more strongly related to field of study: students in the humanities and social sciences demonstrate higher levels of ethical concern than students in STEM fields. No robust predictors of prompting skill were identified in the final model. The findings suggest that the frequency of generative AI use cannot serve as a sufficient indicator of students’ AI literacy. The article argues for a more differentiated approach to teaching, assessment, and institutional regulation of AI use in higher education.

генеративный ИИИИ-грамотностьвысшее образованиепромптингантропоморфизация ИИэтика ИИgenerative AIAI literacyhigher educationpromptinganthropomorphization of AIAI-related ethicsИсследование осуществлено в рамках Программы фундаментальных исследований НИУ ВШЭ (HSE-BR-2025-009).The study was implemented in the framework of the Basic Research Program at HSE University in 2026 (HSE-BR-2025-009).ReferencesMiao F., Holmes W. Guidance for generative AI in education and research. – Paris: UNESCO, 2023. – 44 p. – DOI: 10.54675/EWZM9535.Miao, F., Holmes, W. (2023). Guidanc4 for G4n4rativ4 AI in Education and R4s4arch. Paris : UNESCO, 44 p., doi: 10.54675/EWZM9535.OECD. Trends shaping education 2025. – Paris: OECD Publishing, 2025. – 101 p. – DOI: 10.1787/ee6587fd-enOECD. (2025). Tr4nds Shaping Education /0/5. Paris : OECD Publishing, 101 p., doi: 10.1787/ee6587fd-en.Sousa A.E., Cardoso P. Use of generative AI by higher education students // Electronics. – 2025. – Vol. 14, no. 7. – Article no. 1258. – DOI: 10.3390/electronics14071258.Sousa, A.E., Cardoso, P. (2025). Use of Generative AI by Higher Education Students. El4ctronics. Vol. 14, no. 7, article no. 1258, doi: 10.3390/electronics14071258.Wu F., Dang Y., Li M. A systematic review of responses, attitudes, and utilization behaviors on generative AI for teaching and learning in higher education // Behavioral Sciences. – 2025. – Vol. 15, no. 4. – Article no. 467. – DOI: 10.3390/bs15040467.Wu, F., Dang, Y., Li, M. (2025). A Systematic Review of Responses, Attitudes, and Utilization Behaviors on Generative AI for Teaching and Learning in Higher Education. B4havioral Sci4nc4s. Vol. 15, no. 4, article no. 467, doi: 10.3390/bs15040467.Bittle K., El-Gayar O. Generative AI and academic integrity in higher education: A systematic review and research agenda // Information. – 2025. – Vol. 16, no. 4. – Article no. 296. – DOI: 10.3390/info16040296.Bittle, K., El-Gayar, O. (2025). Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. Information. Vol. 16, no. 4, article no. 296, doi: 10.3390/info16040296.Çerkini B., Bajraktari K., Çibuçiu B., Ramadani F., Zejnullahu F., Hajdini L. Artificial intelligence in higher education: Student perspectives on practices, challenges, and policies in a transitional context // Frontiers in Education. – 2025. – Vol. 10. – Article no. 1700056. – DOI: 10.3389/feduc.2025.1700056.Çerkini, B., Bajraktari, K., Çibuçiu, B., Ramadani, F., Zejnullahu, F., Hajdini, L. (2025). Artificial Intelligence in Higher Education: Student Perspectives on Practices, Challenges, and Policies in a Transitional Context. Fronti4rs in Education. Vol. 10, article no. 1700056, doi: 10.3389/feduc.2025.1700056.Jin Y., Yan L., Echeverria V., Gašević D., Martinez-Maldonado R. Generative AI in higher education: A global perspective of institutional adoption policies and guidelines // Computers and Education: Artificial Intelligence. – 2025. – Vol. 8. – Article no. 100348. – DOI: 10.1016/j.caeai.2024.100348.Jin, Y., Yan, L., Echeverria, V., Gašević, D., Martinez-Maldonado, R. (2025). Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines. Comput4rs and Education: Artificial Int4llig4nc4. Vol. 8, article no. 100348, doi: 10.1016/j.caeai.2024.100348.Aristombayeva M., Satybaldiyeva R., Maung B.M., Lee D. Guiding the uncharted: The emerging (and missing) policies on generative AI in higher education // Frontiers in Education. – 2025. – Vol. 10. – Article no. 1644081. – DOI: 10.3389/feduc.2025.1644081.Aristombayeva, M., Satybaldiyeva, R., Maung, B.M., Lee, D. (2025). Guiding the Uncharted: The Emerging (and Missing) Policies on Generative AI in Higher Education. Fronti4rs in Education. Vol. 10, article no. 1644081, doi: 10.3389/feduc.2025.1644081.Tsao J. Trajectories of AI policy in higher education: Interpretations, discourses, and enactments of students and teachers // Computers and Education: Artificial Intelligence. – 2025. – Vol. 9. – Article no. 100496. – DOI: 10.1016/j.caeai.2025.100496.Tsao, J. (2025). Trajectories of AI Policy in Higher Education: Interpretations, Discourses, and Enactments of Students and Teachers. Comput4rs and Education: Artificial Int4llig4nc4. Vol. 9, article no. 100496, doi: 10.1016/j.caeai.2025.100496.Miao F., Shiohira K., Lao N. AI competency framework for students. – Paris: UNESCO, 2024. – 80 p. – DOI: 10.54675/JKJB9835.Miao, F., Shiohira, K., Lao, N. (2024). AI Comp4t4ncy Fram4work for Stud4nts. Paris : UN-ESCO, 80 p., doi: 10.54675/JKJB9835.Алешковский И.А., Гаспаришвили А.Т., Нарбут Н.П., Крухмалева О.В., Савина Н.Е. Российские студенты о возможностях и ограничениях искусственного интеллекта в обучении // Вестник РУДН. Серия: Социология. – 2024. – Т. 24, № 2. – С. 335–353. – DOI: 10.22363/2313-2272-2024-24-2-335-353.Aleshkovskiy, I.A., Gasparishvili, A.T., Narbut, N.P., Krukhmaleva, O.V., Savina, N.E. (2024). Russian Students on the Opportunities and Limitations of Artificial Intelligence in Learning. V4stnik RUDN. S4riya: Sotsiologiya = RUDN Journal of Sociology. Vol. 24, no. 2, pp. 335-353, doi: 10.22363/2313-2272-2024-24-2-335-353 (In Russ., abstract in Eng.).Буякова К.И., Дмитриев Я.А., Иванова А.С., Фещенко А.В., Яковлева К.И. Отношение студентов и преподавателей к использованию инструментов с искусственным интеллектом в вузе // Образование и наука. – 2024. – Т. 26, № 7. – С. 160–193. – DOI: 10.17853/1994-5639-2024-7-160-193.Buyakova, K.I., Dmitriev, Ya.A., Ivanova, A.S., Feshchenko, A.V., Yakovleva, K.I. (2024). Attitudes of Students and Teachers Toward the Use of Artificial Intelligence Tools in a University]. Obrazovani4 i nauka = Th4 Education and Sci4nc4 Journal. Vol. 26, no. 7, pp. 160-193, doi: 10.17853/1994-5639-2024-7-160-193 (In Russ., abstract in Eng.).Кошкина Е.А., Бордовская Н.В., Гнедых Д.С., Хромова М.А., Демьянчук Р.В., Исхакова М.П., Балышев П.А. Генеративный искусственный интеллект в высшем образовании: обзор теоретических подходов и практик применения // Высшее образование в России. – 2025. – Т. 34, № 6. – С. 36–57. – DOI: 10.31992/0869-3617-2025-34-6-36-57.Koshkina, E.A., Bordovskaya, N.V., Gnedykh, D.S., Khromova, M.A., Dem’yanchuk, R.V., Iskhakova, M.P., Balyshev, P.A. (2025). Generative Artificial Intelligence in Higher Education: A Review of theoretical Approaches and Application Practices. Vyssh44 obrazovani4 v Rossii = High4r Education in Russia. Vol. 34, no. 6, pp. 36-57, doi: 10.31992/0869-3617-2025-34-6-36-57 (In Russ., abstract in Eng.).Кузьминов Я.И., Кручинская Е.В., Груздев И.А., Наумов А.А. Отстающие и опережающие: как студенты используют генеративный искусственный интеллект в образовательных целях // Высшее образование в России. – 2025. – Т. 34, № 6. – С. 9–35. – DOI: 10.31992/0869-3617-2025-34-6-9-35.Kuz’minov, Ya.I., Kruchinskaya, E.V., Gruzdev, I.A., Naumov, A.A. (2025). Laggards and Frontrunners: How Students Use Generative Artificial Intelligence for Educational Purposes. Vyssh44 obrazovani4 v Rossii = High4r 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.).Тихонова Н.В., Поморцева Н.П. Выпускная квалификационная работа в вузе в условиях распространения искусственного интеллекта: взгляд студентов // Высшее образование в России. – 2025. – Т. 34, № 6. – С. 112–135. – DOI: 10.31992/0869-3617-2025-34-6-112-135.Tikhonova, N.V., Pomortseva, N.P. (2025). Final Qualifying Work at a University in the Context of the Spread of Artificial Intelligence: Students’ Perspective. Vyssh44 obrazovani4 v Rossii = High4r 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.).Земцов Д.И., Груздев И.А. «Цифровой кен-тавр»: совместное обучение человека и ИИ в университете // Высшее образование в России. – 2025. – Т. 34, № 10. – С. 47–62. – DOI: 10.31992/0869-3617-2025-34-10-47-62.Zemtsov, D.I., Gruzdev, I.A. (2025). “Digital Centaur”: Human-AI Collaborative Learning at the University. Vyssh44 obrazovani4 v Rossii = High4r Education in Russia. Vol. 34, no. 10, pp. 47-62, doi: 10.31992/0869-3617-2025-34-10-47-62 (In Russ., abstract in Eng.).Oncioiu I., Bularca A. R. Artificial intelligence governance in higher education: The role of knowledge-based strategies in fostering legal awareness and ethical artificial intelligence litera-cy // Societies. – 2025. – Vol. 15, no. 6. – Article no. 144. – DOI: 10.3390/soc15060144.Oncioiu, I., Bularca, A.R. (2025). Artificial Intelligence Governance in Higher Education: The Role of Knowledge-Based Strategies in Fostering Legal Awareness and Ethical Artificial Intelligence Literacy. Soci4ti4s. Vol. 15, no. 6, article no. 144, doi: 10.3390/soc15060144.Hackl V., Müller A.E., Sailer M. The AI literacy heptagon: A structured approach to AI literacy in higher education // Computers and Education: Artificial Intelligence. – 2026. – Vol. 10. – Article no. 100540. – DOI: 10.1016/j.caeai.2026.100540.Hackl, V., Müller, A.E., Sailer, M. (2026). The AI Literacy Heptagon: A Structured Approach to AI Literacy in Higher Education. Comput4rs and Education: Artificial Int4llig4nc4. Vol. 10, article no. 100540, doi: 10.1016/j.caeai.2026.100540.Zhang C., Magerko B. Generative AI literacy: A comprehensive framework for literacy and responsible use. – arXiv:2504.19038. – 2025. – DOI: 10.48550/arXiv.2504.19038.Zhang, C., Magerko, B. (2025). Generative AI Literacy: A Comprehensive Framework for Literacy and Responsible Use. arXiv:/504.190S8, doi: 10.48550/arXiv.2504.19038.Zamfirescu-Pereira J.D., Wong R.Y., Hartmann B., Yang Q. Why Johnny can’t prompt: How non AI experts try (and fail) to design LLM prompts // Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. – New York: Association for Computing Machinery, 2023. – Article no. 437. – P. 1–21. – DOI: 10.1145/3544548.3581388.Zamfirescu-Pereira, J.D., Wong, R.Y., Hartmann, B., Yang, Q. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. In: Proc44dings of th4 /0/S CHI Conf4r4nc4 on Human Factors in Computing Syst4ms. New York : Association for Computing Machinery, article no. 437, pp. 1-21, doi: 10.1145/3544548.3581388.Federiakin D., Molerov D., Zlatkin-Troitschanskaia 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.Federiakin, D., Molerov, D., Zlatkin-Troitschanskaia, O., Maur, A. (2024). Prompt Engineering as a New 21st Century Skill. Fronti4rs in Education. Vol. 9, article no. 1366434, doi: 10.3389/feduc.2024.1366434.Carrasco-Sáez J.L., Contreras-Saavedra C., SanMartín-Quiroga S., Contreras-Saavedra C.E., Viveros-Muñoz R. Analyzing higher education students’ prompting techniques and their impact on ChatGPT’s performance: An exploratory study in Spanish // Applied Sciences. – 2025. – Vol. 15, no. 14. – Article no. 7651. – DOI: 10.3390/app15147651.Carrasco-Sáez, J.L., Contreras-Saavedra, C., San-Martín-Quiroga, S., Contreras-Saavedra, C.E., Viveros-Muñoz, R. (2025). Analyzing Higher Education Students’ Prompting Techniques and their Impact on ChatGPT’s Performance: An Exploratory Study in Spanish. Appli4d Sci4nc4s. Vol. 15, no. 14, article no. 7651, doi: 10.3390/app15147651.Qu Y., Tan M.X.Y., Wang J. Disciplinary differences in undergraduate students’ engagement with generative artificial intelligence // Smart Learning Environments. – 2024. – Vol. 11. – Article no. 51. – DOI: 10.1186/s40561-024-00341-6.Qu, Y., Tan, M.X.Y., Wang, J. (2024). Disciplinary Differences in Undergraduate Students’ Engagement with Generative Artificial Intelligence. Smart L4arning Environm4nts. Vol. 11, article no. 51, doi: 10.1186/s40561-024-00341-6.Ferdman A., Ratti E. What do we teach to engineering students: Embedded ethics, morality, and politics // Science and Engineering Ethics. – 2024. – Vol. 30. – Article no. 7. – DOI: 10.1007/s11948-024-00469-1.Ferdman, A., Ratti, E. (2024). What Do We Teach to Engineering Students: Embedded Ethics, Morality, and Politics. Sci4nc4 and Engin44ring Ethics. Vol. 30, article no. 7, doi: 10.1007/s11948-024-00469-1.Nass C., Moon Y. Machines and mindlessness: Social responses to computers // Journal of Social Issues. – 2000. – Vol. 56, no. 1. – P. 81–103. – DOI: 10.1111/0022-4537.00153.Nass, C., Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issu4s. Vol. 56, no. 1, pp. 81-103, doi: 10.1111/0022-4537.00153.Epley N., Waytz A., Cacioppo J.T. On seeing human: A three-factor theory of anthropomorphism // Psychological Review. – 2007. – Vol. 114, no. 4. – P. 864–886. – DOI: 10.1037/0033-295X.114.4.864.Epley, N., Waytz, A., Cacioppo, J.T. (2007). On Seeing Human: A Three-Factor Theory of Anthropomorphism. Psychological R4vi4w. Vol. 114, no. 4, pp. 864-886, doi: 10.1037/0033-295X.114.4.864.Gong L. How social is social responses to computers? The function of the degree of anthropomorphism in computer representations // Computers in Human Behavior. – 2008. – Vol. 24, no. 4. – P. 1494–1509. – DOI: 10.1016/j.chb.2007.05.007.Gong, L. (2008). How Social Is Social Responses to Computers? The Function of the Degree of Anthropomorphism in Computer Representations. Comput4rs in Human B4havior. Vol. 24, no. 4, pp. 1494-1509, doi: 10.1016/j.chb.2007.05.007.Waytz A., Heafner J., Epley N. The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle // Journal of Experimental Social Psychology. – 2014. – Vol. 52. – P. 113–117. – DOI: 10.1016/j.jesp.2014.01.005.Waytz, A., Heafner, J., Epley, N. (2014). The Mind in the Machine: Anthropomorphism Increases Trust in an Autonomous Vehicle. Journal of Exp4rim4ntal Social Psychology. Vol. 52, pp. 113-117, doi: 10.1016/j.jesp.2014.01.005.Gambino A., Fox J., Ratan R.A. Building a stronger CASA: Extending the computers are social actors paradigm // Human-Machine Communication. – 2020. – Vol. 1. – P. 71–86. – DOI: 10.30658/hmc.1.5.Gambino, A., Fox, J., Ratan, R.A. (2020). Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm. Human-Machin4 Communication. Vol. 1, pp. 71-86, doi: 10.30658/hmc.1.5.Yankouskaya A., Babiker A.B., Rizvi S.W.F., Alshakhsi S., Liebherr M., Ali R. LLM-D12: A dual-dimensional scale of instrumental and relational dependencies on large language models // ACM Transactions on the Web. – 2025. – Advance online publication. – DOI: 10.1145/3765895.Yankouskaya, A., Babiker, A.B., Rizvi, S.W.F., Alshakhsi, S., Liebherr, M., Ali, R. (2025). LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models. ACM Transactions on th4 W4b. Advance online publication, doi: 10.1145/3765895.Pikhart M., Al-Obaydi L.H. Reporting the potential risk of using AI in higher education: Subjective perspectives of educators // Com-puters in Human Behavior Reports. – 2025. – Vol. 18. – Article no. 100693. – DOI: 10.1016/j.chbr.2025.100693.Pikhart, M., Al-Obaydi, L.H. (2025). Reporting the Potential Risk of Using AI in Higher Education: Subjective Perspectives of Educators. Comput4rs in Human B4havior R4ports. Vol. 18, article no. 100693, doi: 10.1016/j.chbr.2025.100693.Huang D., Hash N., Cummings J.J., Prena K. Academic cheating with generative AI: Exploring a moral extension of the theory of planned behavior // Computers and Education: Artificial Intelligence. – 2025. – Vol. 8. – Article no. 100424. – DOI: 10.1016/j.caeai.2025.100424.Huang, D., Hash, N., Cummings, J.J., Prena, K. (2025). Academic Cheating with Generative AI: Exploring a Moral Extension of the Theory of Planned Behavior. Comput4rs and Education: Artificial Int4llig4nc4. Vol. 8, article no. 100424, doi: 10.1016/j.caeai.2025.100424.Pangestu F.N.N., Hamidah I., Widaningsih L., Abdullah A.G., Saputra N.A. A scoping literature review of prompt engineering for bridging students’ AI literacy in higher education // Computers and Education: Artificial Intelligence. – 2026. – Vol. 10. – Article no. 100581. – DOI: 10.1016/j.caeai.2026.100581.Pangestu, F.N.N., Hamidah, I., Widaningsih, L., Abdullah, A.G., Saputra, N.A. (2026). A Scoping Literature Review of Prompt Engineering for Bridging Students’ AI Literacy in Higher Education. Comput4rs and Education: Artificial Int4llig4nc4. Vol. 10, article no. 100581, doi: 10.1016/j.caeai.2026.100581.Иванова А.Е., Тарасова К.В., Талов Д.П. Между интересом и умением: как студенты воспринимают и применяют ИИ // Высшее образование в России. – 2025. – Т. 34, № 8–9. – С. 9–32. – DOI: 10.31992/0869-3617-2025-34-8-9-9-32.Ivanova, A.E., Tarasova, K.V., Talov, D.P. (2025). Between Interest and Skill: How Students Perceive and Use AI. Vyssh44 obrazovani4 v Rossii = High4r Education in Russia. Vol. 34, no. 8-9, pp. 9-32, doi: 10.31992/0869-3617-2025-34-8-9-9-32 (In Russ., abstract in Eng.).Consoli T., Petko D. Which educational approaches predict students’ generative AI confidence and responsibility? // Computers and Education: Artificial Intelligence. – 2025. – Vol. 9. – Article no. 100431. – DOI: 10.1016/j.caeai.2025.100431.Consoli, T., Petko, D. (2025). Which Educational Approaches Predict Students’ Generative AI Confidence and Responsibility? Comput4rs and Education: Artificial Int4llig4nc4. Vol. 9, article no. 100431, doi: 10.1016/j.caeai.2025.100431.Schutte N.S., Li H. The role of self-efficacy and curiosity in student use of artificial intelligence (AI) // International Journal of Educational Technology in Higher Education. – 2025. – Vol. 22. – Article no. 73. – DOI: 10.1186/s41239-025-00574-6.Schutte, N.S., Li, H. (2025). The Role of Self-Efficacy and Curiosity in Student Use of Artificial Intelligence (AI). Int4rnational Journal of Educational T4chnology in High4r Education. Vol. 22, article no. 73, doi: 10.1186/s41239-025-00574-6.Jongkind R., Bikker Y., Meinema J., Broens T. Studying with GenAI: Cross-sectional study on usage patterns, needs, competencies, and ethical perspectives of medical informatics students // Frontiers in Education. – 2025. – Vol. 10. – Article no. 1658415. – DOI: 10.3389/feduc.2025.1658415.Jongkind, R., Bikker, Y., Meinema, J., Broens, T. (2025). Studying with GenAI: Cross-Sectional Study on Usage Patterns, Needs, Competencies, and Ethical Perspectives of Medical Informatics Students. Fronti4rs in Education. Vol. 10, article no. 1658415, doi: 10.3389/feduc.2025.1658415.Hadinejad N., Sperling K., McGrath C. Generative AI chatbots in higher education: Student experiences and perceived ethical challenges // Computers and Education Open. – 2025. – Vol. 9. – Article no. 100311. – DOI: 10.1016/j.caeo.2025.100311.Hadinejad, N., Sperling, K., McGrath, C. (2025). Generative AI Chatbots in Higher Education: Student Experiences and Perceived Ethical Challenges. Comput4rs and Education Op4n. Vol. 9, article no. 100311, doi: 10.1016/j.caeo.2025.100311.

The authors declare that there are no conflicts of interest present.