Personalized Learning Based on Artificial Intelligence: How Ready Are Modern Students for New Educational Opportunities

One of the key advantages of integrating artificial intelligence (AI) technologies into education is the creation of conditions for the implementation of a personalized learning model – a system developing individual potential, in which the learner is the main subject of the educational process and, in accordance with individual abilities, interests and needs, selects the educational content and chooses methods, techniques, and means of teaching, determines the pace of mastering the educational material and takes responsibility for the process and outcome of the learning. At the same time, the readiness of students to use a personalized form of learning will largely determine its effectiveness. The goal of the study is to determine the readiness of Russian university students for personalized learning based on AI tools. Based on the analysis of academic literature, the following semantic components of personalized learning were proposed: a) student subjectivity; b) partnership; c) dominance of problem-solving assignment; d) pace; e) adaptability, and f) An online survey was conducted to determine the students’ readiness for personalized learning. The respondents were 1,211 students from 38 Russian universities. The results of the questionnaire survey indicate that at the present stage about 50% of students use AI to solve various educational tasks. The opinions of students were divided regarding their readiness for personalized learning. About 45–60% of respondents expressed their readiness for such learning, 25–30% of respondents were neutral and 5–10% had a negative attitude on most issues. The data obtained indicate that at the present time, personalized learning cannot be widespread. Not all students of Russian universities fully understand the essence and potential of personalized learning, are ready and willing to act as subjects of the educational process, bearing full responsibility for the process and outcome of learning. Personalized learning places new demands on teachers, whose function is to prepare students for interaction with AI, adequately assess their abilities, formulate the goal of learning, check feedback materials from generative AI, build an individual learning trajectory, determine the pace of learning, select the content, means and methods of teaching, reflection, etc.

1. Sysoyev, P.V. (2024). Didactic Properties and Learning Functions of Neural Networks. Perspektivy nauki i obrazovania = Perspectives of Science and Education. No. 6 (72), pp. 672-690, doi: 10.32744/pse.2024.6.42 (In Russ., abstract in Eng.).

2. Ivakhnenko, E.N., Nikolskiy, V.S. (2023). ChatGPT in Higher Education and Science: A Threat or a Valuable Resource? Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 32, no. 4, pp. 9-22, doi: 10.31992/0869-3617-2023-32-4-9-22 (In Russ., abstract in Eng.).

3. Sysoyev, P.V. (2024). Ethics and AI-Plagiarism in an Academic Environment: Students’ Understanding of Compliance with Author’s Ethics and the Problem of 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.).

4. Filatov, E.M. (2024). Development of Students’ Foreign Language Communicative Skills Based on the Character.ai Web Application. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki = Tambov University Review. Series: Humanities. Vol. 29, no. 5, pp. 1248-1260, doi: 10.20310/1810-0201-2024-29-5-1248-1260 (In Russ., abstract in Eng.).

5. Titova, S.V. (2024). Technological Solutions Based on Artificial Intelligence in Teaching Foreign Languages: An Analytical Review. Vestnik Moskovskogo universiteta. Seriya 19: Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series 19. Linguistics and Intercultural Communication. Vol. 27, no. 2, pp. 18-37, doi: 10.55959/MSU-2074-1588-19-27-2-2 (In Russ., abstract in Eng.).

6. Sysoyev, P.V., Filatov, E.M. (2024). Method of Teaching Students’ Foreign Language Creative Writing Based on Evaluative Feedback from Artificial Intelligence. Perspektivy nauki I obrazovania = Perspectives of Science and Education. No. 67 (1), pp. 115-135, doi: 10.32744/ pse.2024.1.6 (In Russ., abstract in Eng.).

7. Itinson, K.S. (2020.) Informatization of Medical Education: Artificial Intelligence Systems in Training Students and Doctors. Baltiyskiy gumanitarnyy zhurnal = Baltic Humanitarian Journal. Vol. 9, no. 3 (32), pp. 91-93, doi: 10.26140/bgz3-2020-0903-0021 (In Russ., abstract in Eng.).

8. Yagudina, A.R., Tsilitsky, V.S., Vinogradova, I.V., Kuznetsova, S.B., Zharina, N.A. (2022). Artificial Intelligence and Its Role in the Teaching of Economic Disciplines at the University. Moskovskiy ekonomicheskiy zhurnal = Moscow Economic Journal. No. 2, pp. 634-642, doi: 10.55186/2413-046X_2022_7_2_104 (In Russ., abstract in Eng.).

9. Ivanova, A.P. (2021). Artificial Intelligence in the Field of Law and Legal Practice: The Main Problems and Prospect of Development. Sotsialnyye i gumanitarnyye nauki. Otechestvennaya i zarubezhnaya literatura. Seriya 4: Gosudarstvo i pravo = Social Sciences and Humanities. Domestic and Foreign Literature. Series 4: State and Law. No. 1, pp. 90-98, doi: 10.31249/rgpravo/2021.01.09 (In Russ., abstract in Eng.).

10. Parshina, K.V., Saltykova, G.M. (2021). Modern Technologies in Teaching Students of the Direction of Training “Design”. Pedagogicheskiy zhurnal = Pedagogical Journal. Vol. 11, no. 1-1, pp. 263-270, doi: 10.34670/AR.2021.47.77.032 (In Russ., abstract in Eng.).

11. Levin, B.A., Piskunov, A.A., Poliakov, V.Yu., Savin, A.V. (2022). Artificial Intelligence in Engineering Education. Vysshee obrazovanie v Rossii = Higher Education in Russia. Vol. 31, no. 7, pp. 79-95, doi: 10.31992/0869-3617-2022-31-6-79-95 (In Russ., abstract in Eng.).

12. Azbel, A.A., Ilyushin, L.S., Kazakova, E.I., Morozova, P.A. (2022). Teachers’ and Students’ Attitudes towards Feedback: Contradictions and Development Trends. Obrazovaniye i nauka = The Education and Science Journal. Vol. 24, no. 7, pp. 76-109, doi: 10.17853/1994-5639-2022-7-76-10 (In Russ., abstract in Eng.).

13. Sysoyev, P.V., Filatov, E.M., Sorokin, D.O. (2024). Feedback in Foreign Language Teaching: From Information Technologies to Artificial Intelligence. Yazyk i kultura = Language and Culture. Vol. 65, рр. 242-261, doi: 10.17223/19996195/65/11 (In Russ., abstract in Eng.).

14. Ayeni, O.O., Hamad, N.M.A., Chisom, O.N., Osawaru, B., Adewusi, O.E. (2024). AI in Education: A Review of Personalized Learning and Educational Technology. GSC Advanced Research and Reviews. No. 18 (02), pp. 261-271, doi: 10.30574/gscarr.2024.18.2.0062

15. Peters, M.A., Araya, D. (2011). Transforming American Education: Learning Powered by Technology. E–Learn. Digit. Media. No. 8., pp. 102-105, doi: 10.2304/elea.2011.8.2.102

16. Jegede, O.O. (2024). Artificial Intelligence and English Language Learning: Exploring the Roles of AI-Driven Tools in Personalizing Learning and Providing Instant Feedback. Universal Library of Languages and Literatures. No. 1 (2), pp. 06-19, doi: 10.70315/uloap.ullli.2024.0102002

17. Katiyar, N., Awasthi, V., Pratap, R., Mishra, K., Shukla, N., Singh, R., Tiwari, M. (2024). AiDriven Personalized Learning Systems: Enhancing Educational Effectiveness. Educational Administration: Theory and Practice. No. 30 (5), pp. 11514-11524, doi: 10.53555/kuey.v30i5.4961

18. Konobeev, A.V., Yukhimuk, Y.A., Voytsekhovskaya, V.D., Shchekich, M. (2020). Personalization as an Approach to Education. Diskurs professionalnoy kommunikatsyii = Professional Discourse & Communication. Vol. 2, no. 3, pp. 118-138., doi: 10.24833/2687-0126-2020-2-3118-138 (In Russ.).

19. Kalina, I.I., Polozhevets, P.G., Kazakov, E.I., Kalimullin, A.M., Remorenko, I.M. (2022). Challenges and Prospects of Personal Development Education. Obrazovatelnaya politika = Educational Policy. No. 2 (90), pp. 12-27. Available at: https://www.elibrary.ru/item.asp?id=49370883 (accessed 12.12.2024). (In Russ.).

20. Bratash, V.S., Turchin, А.P., Yermakov, D.S., Nesterenko, D.P., Vysotskaya, Е.N., Lupanova, Т.N. (2023). Personalizirovannoye obrazovaniye: prosto i naglyadno [Personalized Education: Simple and Clear]. Saint Petersburg: Svoye izdatelstvo, 52 p. Available at: https://www.elibrary.ru/item.asp?id=60036878 (accessed 12.12.2024). (In Russ.).

21. Kravchenko, D.A., Bleskina, I.A., Kalyaeva, E.N., Zemlyakova, E.A., Abbakumov, D.F. (2020). Personalization in Education: From Programmed to Adaptive Learning. Sovremennaya zarubezhnaya psikhologiya = Journal of Modern Foreign Psychology. Vol. 9, no. 3, pp. 34-46, doi: 10.17759/jmfp.2020090303 (In Russ., abstract in Eng.).

22. Bernard, R.M., Borokhovski, E., Schmid, R.F., Pickup, D.I., Upitis, R., Abrami, P., Jungert, T. (2019). Twenty-first Century Adaptive Teaching and Individualized Learning Pperationalized as Specific Blends of Student-centered Instructional Events: A Systematic Review and Meta-analysis. Campbell Systematic Reviews. Vol. 15, no. 1-2, doi: 10.1002/cl2.1017

23. Khramova, M.V., Aleksandrova, H.A. (2020). Adaptivnyye platformy – kak osnovnoy trend sistemy distantsionnogo obucheniya v tsifrovom mire [Adaptive Platforms – as the Main Trend of Distance Learning Systems in the Digital World]. Informatsionnyye tekhnologii v obrazovanii = Information Technology in Education. No. 3, pp. 308-312. Available at: https://www.elibrary.ru/item.asp?id=44637448 (accessed 12.12.2024). (In Russ.).

24. Matnurova, A.A. (2024). Feedback as a Tool for Personilized Corporate Training. Flagman nauki = Flagship of Science. No. 6 (17), pp. 572-577, doi: 10.37539/2949-1991.2024.17.6.006 (In Russ., abstract in Eng.).

25. Korenev, A.A. (2024). Strategies of Using Artificial Intelligence for Written Corrective Feedback in Language Education. Vestnik Moskovskogo universiteta. Seriya 19. Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series 19. Linguistics and Intercultural Communication. No. 2, pp. 68-77, doi: 10.55959/MSU-2074-1588-19-27-2-5 (In Russ., abstract in Eng.).

26. Xi, X. (2010). Automated Scoring and Feedback Systems: Where Are We and Where Are We Heading? Language Testing. No. 27 (3), pp. 291-300, doi: 10.1177/0265532210364643

27. Titova, S.V., Barinova, K.V. (2021). Feedback and Reflection in Foreign Language Teaching. Vestnik Moskovskogo universiteta. Seriya 19: Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series 19. Linguistics and Intercultural Communication. Vol. 2, pp. 200-214. Available at: https://www.elibrary.ru/item.asp?id=46505018 (accessed 12.12.2024). (In Russ., abstract in Eng.).

28. Hattie, J., Timperley, H. (2007). The Power of Feedback. Review of Educational Research. No. 77 (1), pp. 81-112, doi: 10.3102/003465430298487

29. Sysoyev, P.V. (2023). Artificial Intelligence in Education: Awareness, Readiness and Practice of Using Artificial Intelligence Technologies in Professional Activities by University Faculty. 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.).

30. Titova, S.V. (2024). Intelligent Learning Systems for Personalizing and Adapting Language Courses. Vestnik Moskovskogo universiteta. Seriya 19. Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series19. Linguistics and Intercultural Communication. Vol. 27, no. 4, pp. 84-99, doi: 10.55959/MSU-2074-1588-19-27-4-6 (In Russ.).

31. Evstigneev, M.N. (2023). Twee Neural Network as a New Tool for English Language Teacher. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki. = Tambov University Review. Series: Humanities. Vol. 28, no. 6, pp. 1428-1442, doi: 10.20310/1810-0201-2023-28-6-14281442 (In Russ., abstract in Eng.).

32. Park, J. (2019). An AI-Based English Grammar Checker vs. Human Raters in Evaluating EFL learners’ Writing. Multimedia-Assisted Language Learning. Vol. 22, no. 1, pp. 112-131, doi: 10.15702/mall.2019.22.1.112

33. Perdana, I., Farida, M. (2019). Online Grammar Checkers and Their Use for EFL Writing. Journal of English Teaching, Applied Linguistics, and Literatures. Vol. 2, no. 2, pp. 67-76, doi:10.20527/jetall.v2i2.7332

34. Almusharraf, N., Alotaibi, H. (2023). An Error-Analysis Study from an EFL Writing Context: Human and Automated Essay Scoring Approaches. Technology, Knowledge and Learning. Vol. 28, pp. 1015-1031, doi: 10.1007/s10758-022-09592-z

35. Tormyshova, T.Yu., Ryazantseva, T.Yu., Sukhanova, N.I. (2024). Teaching Students of Linguistics to Write Essays in a Foreign Language Based on Working with the Criterion Automated Assessment System. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki = Tambov University Review. Series: Humanities. Vol. 29, no. 1, pp. 99-108, doi: 10.20310/1810-0201-2024-29-1-99-108 (In Russ., abstract in Eng.).

36. Pribytkova, A.A., Tormyshova, T.Yu., Khaustov, O.N. (2024). The Use of the Criterion Automated Assessment System in Teaching Students of Language Specialties to Write Essays in a Foreign Language: The Results of an Experimental Test. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki = Tambov University Review. Series: Humanities. Vol. 29, no. 2, pp. 378-389, doi: 10.20310/1810-0201-2024-29-2-378-389 (In Russ., abstract in Eng.).

37. Manap, M.R., Ramli, N.F., Kassim, A.A.M. (2019). Web 2.0 Automated Essay Scoring Application and Human ESL Essay Assessment: A Comparison Study. European Journal of English Language Teaching. Vol. 5, no. 1, pp. 146-162, doi: 10.5281/zenodo.3461784

38. Sysoyev, P.V., Filatov, E.M., Khmarenko, N.I., Murunov, S.S. (2024). Teacher vs Artificial Intelligence: A Comparison of the Quality of Feedback Provided by a Teacher and Generative Artificial Intelligence in Assessing Students’ Creative Writing. Perspektivy nauki i obrazovania = Perspectives of Science and Education. No. 71 (5), pp. 694-712, doi: 10.32744/pse.2024.5.41 (In Russ., abstract in Eng.).

39. Sysoyev, P.V., Filatov, E.M., Evstigneev, M.N., Polyakov, O.G., Evstigneeva, I.A., Sorokin, D.O. (2024). A Matrix of Artificial Intelligence Tools in Pre-Service Foreign Language Teacher Training. Vestnik Tambovskogo universiteta. Seriya: Gumanitarnye nauki = Tambov University Review. Series: Humanities. Vol. 29, no. 3, pp. 559-588, doi: 10.20310/1810-0201-2024-29-3-559-588 (In Russ., abstract in Eng.).

40. Avramenko, A.P., Fadeeva, V.A., Ternovsky, V.V. (2024). Integrating Artificial Intelligence Technologies in Foreign Language Higher Education: From Digitalization to Automation. Vestnik Moskovskogo universiteta. Seriya 19. Lingvistika i mezhkul’turnaya kommunikatsiya = Moscow State University Bulletin. Series 19. Linguistics and Intercultural Communication. Vol. 27, no. 2, pp. 55-67, doi: 10.55959/MSU-2074-1588-19-27-2-4 (In Russ.).

41. Sysoyev, P.V., Filatov, E.M. (2024). Artificial Intelligence in Teaching Russian as a Foreign Language. Russian Language Studies. Vol. 22, no. 2, pp. 300-317, doi: 10.22363/2618-8163-2024-222-300-317

42. Aleynikova, D.V. (2022). Features of Teaching Lawyers Argumentative Discourse in the Context of Digitalization. Vestnik of Moscow State Linguistic University. Education and Teaching. No. 2 (843), pp. 14-19, doi: 10.52070/2500-3488_2022_2_843_14 (In Russ., abstract in Eng.).

43. Han, D. (2022). The Effects of Voice-based AI Chatbots on Korean EFL Middle School Students’ Speaking Competence and Affective Domains. Asia-Pacific Journal of Convergent Research Interchange. No. 6, pp. 71-80, doi: 10.47116/apjcri.2020.07.07