dtЦифровая трансформацияDigital Transformation2522-96132524-2822Educational Establishment “Belarusian State University of Informatics and Radioelectronics”10.35596/1729-7648-2025-31-4-5-14dt-966Research ArticleЭКОНОМИЧЕСКИЕ НАУКИ, ОБРАЗОВАНИЕECONOMIC SCIENCES, EDUCATIONИспользование больших языковых моделей для создания учебных материалов по дисциплине «Базы данных»Using Large Language Models to Create Educational Materials for the “Databases” DisciplineАсенчикО. Д.AsenchikA. D.

Асенчик Олег Даниилович, канд. физ.-мат. наук, доц., доц. каф. информационных технологий

Тел.: +375 29 696-75-01

246029, Гомель, просп. Октября, 48

Asenchik Aleh Daniilovich, Cand. Sci. (Phys. and Math.), Associate Professor, Associate Professor at the Department of Information Technology

Tel.: +375 29 696-75-01

246029, Oktyabrya Ave., 48, Gomel

olgasn@tut.byГомельский государственный технический университет имени П. О. СухогоSukhoi State Technical University of Gomel202523122025314514Copyright © Асенчик О.Д., 20252025Асенчик О.Д.Asenchik A.D.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://dt.bsuir.by/jour/article/view/966

Рассмотрена проблема использования больших языковых моделей (LLM) для создания учебных материалов. Предложена и верифицирована практическая методика с целью генерации качественного учебного контента для конкретной дисциплины «Базы данных». Представлена многоступенчатая методика, в которой одна LLM генерирует контент, а вторая, независимая «рассуждающая» модель, верифицирует его на предмет качества и корректности. Для проверки сгенерированных материалов на отсутствие фактических ошибок применялись метод сравнения с авторитетным источником и модифицированный алгоритм «цепочки верификаций». Результаты подтверждают, что данный подход при использовании современных производительных LLM (таких как DeepSeek, Gemini) позволяет создавать высококачественные учебные тексты с низкой вероятностью появления галлюцинаций. Методика способна значительно ускорить разработку надежных учебно-методических материалов и может быть оптимизирована путем сокращения числа итераций при высоком качестве первоначального ответа.

This paper examines the use of large language models (LLM) for creating educational materials. A practical methodology for generating high-quality educational content for the specific discipline of “Databases” is proposed and verified. A multi-stage methodology is presented, in which one LLM generates content, and a second, independent “reasoning” model verifies its quality and correctness. A comparison method with an authoritative source and a modified “verification chain” algorithm was used to check the generated materials for factual errors. The results confirm that this approach, when used with modern, high-performance LLMs (such as DeepSeek and Gemini), enables the creation of high-quality educational texts with a low probability of hallucinations. The methodology can significantly accelerate the development of reliable educational materials and can be optimized by reducing the number of iterations while maintaining a high-quality initial response.

высшее образованиеучебные материалыбольшие языковые модели (LLM)искусственный интеллектбазы данныхhigher educationeducational materialslarge language models (LLM)artificial intelligencedatabasesReferencesLarge Language Models in Education: Vision and Opportunities / W. Gan [et al.] // 2023 IEEE International Conference on Big Data. 2023. P. 4776–4785. https://doi.org/10.1109/BigData59044.2023.10386291.Gan W., Wang Z., Li J., Li Y., Zheng Y., Yu J. (2023) Large Language Models in Education: Vision and Opportunities. 2023 IEEE International Conference on Big Data. 4776–4785. https://doi.org/10.1109/ BigData59044.2023.10386291.ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education / E. Kasneci [et al.] // Learning and Individual Differences. 2023. Vol. 103. https://doi.org/10.1016/j.lindif.2023.102274.Kasneci E., Seßler K., Küchemann S., Bannert M., Dementieva D., Fischer F., et al. (2023) ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences. 103. https://doi.org/10.1016/j.lindif.2023.102274.Bonner, E. Large Language Model-Based Artificial Intelligence in the Language Classroom: Practical Ideas for Teaching / E. Bonner, R. Lege, E. Frazier // Teaching English with Technology. 2023. Vol. 23, No 1. P. 23–41.Bonner E., Lege R., Frazier E. (2023) Large Language Model-Based Artificial Intelligence in the Language Classroom: Practical Ideas for Teaching. Teaching English with Technology. 23 (1), 23–41.Baidoo-Anu, D. Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning / D. Baidoo-Anu, L. Owusu Ansah // Journal of AI. 2023. Vol. 7, No 1. P. 52–62. https://doi.org/10.61969/jai.1337500.Baidoo-Anu D., Owusu Ansah L. (2023) Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI. 7 (1), 52–62. https://doi.org/10.61969/jai.1337500.A Comprehensive Review on Generative AI for Education / U. Mittal [et al.] // IEEE Access. 2024. Vol. 12. P. 142733–142759. https://doi.org/10.1109/ACCESS.2024.3468368.Mittal U., Sai S., Chamola V., Sangwan D. (2024) A Comprehensive Review on Generative AI for Education. IEEE Access. 12, 142733–142759. https://doi.org/10.1109/ACCESS.2024.3468368.Enlarged Education – Exploring the Use of Generative AI to Support Lecturing in Higher Education / D. Hennekeuser [et al.] // International Journal of Artificial Intelligence in Education. 2024. https://doi.org/10.1007/s40593-024-00424-y.Hennekeuser D., Vaziri D. D., Golchinfar D., El-Khatib K. (2024) Enlarged Education – Exploring the Use of Generative AI to Support Lecturing in Higher Education. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00424-y.Generative AI in Education: Perspectives Through an Academic Lens / I. Întorsureanu [et al.] // Electronics. 2025. Vol. 14. https://doi.org/10.3390/electronics14051053.Întorsureanu I., Oprea S.-V., Bâra A., Vespan D. (2025) Generative AI in Education: Perspectives Through an Academic Lens. Electronics. 14. https://doi.org/10.3390/electronics14051053.Evaluating the Quality of AI-Generated Digital Educational Resources for University Teaching and Learning / Q. Huang [et al.] // Systems. 2025. Vol. 13. https://doi.org/10.3390/systems13030174.Huang Q., Lv C., Lu L., Tu S. (2025) Evaluating the Quality of AI-Generated Digital Educational Resources for University Teaching and Learning. Systems. 13. https://doi.org/10.3390/systems13030174.Survey of Hallucination in Natural Language Generation / Z. Ji [et al.] // ACM Computing Surveys. 2023. Vol. 55, No 12. P. 1–38. https://doi.org/10.1145/3571730.Ji Z., Lee N., Frieske R., Yu T., Su D., Xu Y., et al. (2023) Survey of Hallucination in Natural Language Generation. ACM Computing Surveys. 55 (12), 1–38. https://doi.org/10.1145/3571730.Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks / P. Lewis [et al.] // Advances in Neural Information Processing Systems. 2020. Vol. 33. P. 9459–9474.Lewis P., Perez E., Piktus A., Petroni F., Karpukhin V., Goyal N., et al. (2020) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems. 33, 9459–9474.Chain-of-Verification Reduces Hallucination in Large Language Models / S. Dhuliawala [et al.] // arXiv:2309.11495. 2023. https://doi.org/10.48550/arXiv.2309.11495.Dhuliawala S., Komeili M., Xu J., Raileanu R., Weston J., Roller S. (2023) Chain-of-Verification Reduces Hallucination in Large Language Models. arXiv:2309.11495. https://doi.org/10.48550/arXiv.2309.11495.

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