Bunkin V.I.¹ (Moscow, Russian Federation) – bunkinvi@gmail.com; Ratanova O.V.¹ (Moscow, Russian Federation) – rov75@yandex.ru; Anisimov A.Yu.¹ (Moscow, Russian Federation) – anisimov_au@mail.ru; Chanturia G.T.¹ (Moscow, Russian Federation) – gchanturiia@synergy.ru; Sibirev I.V.¹ (Moscow, Russian Federation) – ivan.sibirev@yandex.ru

¹Synergy University

Abstract. The relevance of this research stems from the widespread adoption of cloud technologies and artificial intelligence in education and their proven potential to improve the accessibility, personalization, and effectiveness of learning in general, and programming in particular. At the same time, existing approaches to using cloud services and artificial intelligence algorithms are often implemented fragmentedly and without well-defined models for managing the learning process, creating a need for their systematization and evaluation of their pedagogical effectiveness. This article examines the possibilities and prospects for using cloud technologies, and, in particular, the use of artificial intelligence in teaching programming. The study places particular emphasis on the need to develop a specialized knowledge base in future developers that meets the requirements of advanced digital systems. As an example, the possibilities for learning using PythonAnywhere and Amazon Web Services are described, and practical aspects of using cloud IDE technologies are outlined. The purpose of this article is to study existing tools used in teaching programming using cloud technologies, as well as to develop a new approach to teaching that will improve its effectiveness and manageability. Machine learning models that can be used in cloud technologies are considered to improve the effectiveness of learning, including distance learning. The proposed approach to teaching programming is based on the use of cloud technologies and the implementation of artificial intelligence to optimize learning management. This approach customizes the learning process for each student and the subsequent development of the learning trajectory, making the collection of data on each student’s progress more personalized. The use of machine learning models will allow for the subsequent use of data for analysis and subsequent optimization of the machine learning model, the learning tools, and the learning process. All of the above defines the scientific novelty of this topic.

Keywords: cloud technologies; programming; learning; code; server; development environment; machine learning model.

References:

1. Alekseevsky, P. I. (2023). Development of the content of teaching programming using mobile programming methods. Current Issues in Teaching Mathematics and Information Technology, 8, 106–111. (In Russian). https://www.elibrary.ru/clqskb
2. Kumar, R. S. D. (2023). Redefining the learning mechanism in teaching-learning-based optimization and its applications for flowtime-aware-cost minimizing of the workflow in the cloud. Concurrency and Computation: Practice and Experience, 35(23), 56–62. https://doi.org/10.1002/cpe.7762
3. Markelov, V. K. (2022). Google Colaboratory cloud environment tools for teaching programming and preparing for the Unified State Exam in computer science. Computer Science at School, 6(179), 68–80. (In Russian).  https://doi.org/10.32517/2221-1993-2022-21-6-68-80
4. Gasanova, R. G., & Alijadzhieva, A. R. (2022). Goals, principles, and content of teaching English-language computer discourse to students majoring in information systems and programming. Scientific Notes. Electronic Scientific Journal of Kursk State University, 2(62), 1–6. (In Russian). https://www.elibrary.ru/mibqvs
5. Kiselev, D. S., & Kuznetsov, A. O. (2020). Using cloud computing technology in teaching computer science. International Journal of Applied Sciences and Technologies Integral, 3, 52. (In Russian). https://elibrary.ru/dumwis
6. Golitsyna, I. N. (2023). Features of training IT specialists in the context of educational transformations. In Professional education and mentoring during educational transformations of the 21st century. IX Makhmutov Readings: Collection of scientific articles from the international scientific and practical conference (pp. 82–86). Kazan: KFU. https://elibrary.ru/dumwis
7. Kobicheva, A. M. (2025). Cloud technologies in higher education: Advantages, risks, and implementation models. Problems of Modern Science and Education, 12(211), 45–47. (In Russian). https://elibrary.ru/mqljti
8. Kozlov, O. A., Barysheva, I. V., Malkina, E. V., & Shestakova, N. V. (2023). Teaching schoolchildren programming as part of the subject “Computer Science”: Problems and possible solutions. Computer Science at School, 5, 67–73. (In Russian). https://doi.org/10.32517/2221-1993-2023-22-5-67-73
9. Makarov, K. S., Ovsyannikov, A. V., & Markeeva, E. E. (2023). Architecture of a recommender system for teaching programming languages. Auditorium, 2(38), 1–7. https://elibrary.ru/kumwef
10. Lysich, I. V. (2024). Cloud services and digital technologies in secondary vocational education institutions. Science and Practice in Education: A State-of-the-Art Bulletin, 5(4), 138–143. https://doi.org/10.54158/27132838_2024_5_4_138
11. Terekhova, N. V., Bogunova, A. A., & Abrosimova, S. A. (2024). Application of cloud computing technologies in the study of higher mathematics in technical universities. Modern Pedagogical Education, 4, 185–189. https://elibrary.ru/rugnwp
12. Radzhabova, F. M. (2023). Formation of students’ software and algorithmic competence in teaching programming. In Current issues of interaction between education, science and society: Proceedings of the IX scientific and practical conference, Makhachkala, April 19–20, 2023 (pp. 41–46). Makhachkala: Limited Liability Company “ALEF Publishing House”. https://elibrary.ru/nnahhy
13. Radzhabova, F. M., & Guseynova, M. M. (2020). Designing a modern information educational environment based on the didactic capabilities of web technologies. Science: Society, Economics, Law, 2, 76–82. https://doi.org/10.34755/IROK.2020.97.16.052
14. Kasyanov, S. N., & Komissarova, S. A. (2023). Online teaching Python programming language to schoolchildren using the Google Colaboratory cloud service. Pedagogical Informatics, 4, 6–14. https://elibrary.ru/cfnkoa
15. Nascimento, A., Silva, V., Paes, A., & de Oliveira, D. (2021). An incremental reinforcement learning scheduling strategy for data-intensive scientific workflows in the cloud. Concurrency and Computation: Practice and Experience, 33(11), 67–73. https://doi.org/10.1002/cpe.6193
16. Oleksiuk, V. P., & Oleksiuk, O. R. (2020). Methodology of teaching cloud technologies to future computer science teachers. CTE Workshop Proceedings, 7, 592–608. https://doi.org/10.55056/cte.415
17. Dolinsky, M. S. (2024). Using generative artificial intelligence programs in the process of initial teaching programming at a university. Cifra. Pedagogy, 1(3), 1–7. https://doi.org/10.18454/PED.2024.3.1
18. Glebova, A. A. (2025). Comparative analysis of cloud platforms for online teaching programming: Selection criteria and examples of use. Science Bulletin, 3(11), 937–947. https://www.elibrary.ru/aezzcu
19. Arifin, O., [et al.]. (2025). Comparative study on the efficiency of deep learning model training in cloud environments: Google Colab vs AWS. DECODE: Jurnal Pendidikan Teknologi Informasi, 5(2), 324–332. https://doi.org/10.51454/decode.v5i2.1197
20. Vidova, T. A., & Romanova, I. N. (2023). Possibilities of applying artificial intelligence technologies in the educational process. Educational Resources and Technologies, 1(42), 27–35. https://doi.org/10.21777/2500-2112-2023-1-27-35
21. Vovk, E. V., & Suprun, A. A. (2022). Artificial intelligence and digital pedagogy as a trend in the modern educational environment of higher education institutions. Problems of Modern Pedagogical Education, 77(2), 84–86. https://www.elibrary.ru/xkzzww
22. Serdarova, G. D. (2025). Adaptive learning: Personalization of the educational process with the help of neural networks. IN SITU, 3, 146–148. https://www.elibrary.ru/qsjeyw
23. Kurbanova, Z. S., & Ismailova, N. P. (2023). Neural networks in the context of digitalization of education and science. World of Science, Culture, Education, 3(100), 309–311. https://doi.org/10.24412/1991-5497-2023-3100-309-311
24. Shobonov, N. A., Bulaeva, M. N., & Zinovieva, S. A. (2023). Artificial intelligence in. Problems of Modern Pedagogical Education, 79(4), 288–290. https://www.elibrary.ru/iprjag
25. Bespalova, N. V. (2024). Cloud technology security. Computational Nanotechnology, 11(S5), 124–132. https://doi.org/10.33693/2313-223X-2024-11-5-124-132

For citation: Bunkin, V. I., Ratanova, O. V., Anisimov, A. Yu., Chanturia, G. T., & Sibirev, I. V. (2026). Application of cloud technologies and artificial intelligence algorithms in teaching programming. CITISE, 2, 394–409. (In Russian).