THE POSSIBILITIES OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE TRADITIONAL METHODOLOGICAL SYSTEM OF TEACHING DISCRETE MATHEMATICS IN HIGHER EDUCATION INSTITUTIONS

Keywords: discrete mathematics; methodological system; artificial intelligence; generative AI; higher education; assessment; verification of reasoning; academic integrity

Abstract

Abstract. This article examines how artificial intelligence (AI) tools can be methodically integrated intoteaching discrete mathematics in higher education. The focus is on moving from isolated AI-use scenariosto a systemic approach in which AI functions are aligned with learning goals, content, teaching methods,and assessment of learning outcomes.Purpose. To substantiate the possibilities of integrating AI into the traditional methodological systemof teaching discrete mathematics in higher education by aligning AI functions with course topics, task types,usage regulations, and criteria for verifying learning outcomes.Methods. The study analyses and synthesises scholarly sources on AI integration in mathematics education,the use of intelligent tutoring systems in mathematics learning, and the application of generative AI in educationalassessment. Based on the reviewed literature, a conceptual and methodological design approach is used to developan AI integration matrix following the scheme “topic – function – task – regulation – verification”. Results. The findings show that most recent studies describe local AI-use practices, while systemic modelsthat transform the methodological system as a whole are relatively limited. The paper argues for viewingAI as a structural component of the methodological system in higher education. An AI integration matrix isproposed for key discrete mathematics topics (logic, proof methods, combinatorics, recurrences/recursion,graphs, number theory, formal languages/automata), including permissible modes of AI use and criteria forverifying reasoning. Conclusions. Systemic AI integration in teaching discrete mathematics should involvenot only the use of tools but also clear rules and verification procedures for learning outcomes. Assessment isthe most sensitive component: in discrete mathematics, priority should be given to verifying the correctnessof reasoning and proofs rather than only the final answer.

References

1. Гуревич Р., Коношевський Л., Коношевський О., Воєвода А., Люльчак С. Інтеграція штучного інтелекту в сферу освіти: проблеми, виклики, загрози, перспективи. Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems. 2024. 72. С. 170–186. https://doi.org/10.31652/2412-1142-2024-72-170-186
2. Мар’єнко М., Коваленко В. Штучний інтелект та відкрита наука в освіті. Фізико-математична освіта. 2023. Вип. 38, № 1. С. 48–53. DOI: 10.31110/2413-1571-2023-038-1-007
3. Наливайко О. О. Перспективи використання нейронних мереж у вищій освіті України. Інформаційні технології і засоби навчання. 2023. Т. 97, № 5.C. 1–17. DOI: 10.33407/itlt.v97i5.5322
4. Саган О. В., Блах В. С. Штучний інтелект як структурна складова методичної системи викладання освітньої компоненти у вищій освіті. Педагогічні науки: збірник наукових праць. 2025. Вип. 112. С. 20–25. DOI: 10.32999/ksu2413-1865/2025-112-3
5. Співаковський О. В., Омельчук С. А., Кобець В. В., Валько Н. В., Мальчикова Д. С. Інституційна політика щодо штучного інтелекту в університетському навчанні, викладанні та дослідженнях. Інформаційні технології і засоби навчання. 2023. Т. 97, № 5. C. 181–202. DOI: 10.33407/itlt.v97i5.5395
6. Ilieva G., Yankova T., Ruseva M., Kabaivanov S. A framework for generative AI-driven assessment in higher education. Information. 2025. Vol. 16, No. 6. Art. 472. https://doi.org/10.3390/info16060472
7. Nguyen, D. T., & Pham, Q. V. The evolving landscape of AI integration in mathematics education: A systematic review of trends (2015–2025). Eurasia Journal of Mathematics, Science and Technology Education. 2025. Т. 21, № 10. em2714. DOI: 10.29333/ejmste/17078
8. Panqueban D., Huincahue M. Artificial intelligence in mathematics education: a systematic review. Uniciencia. 2024. Vol. 38(1). pp. 1–17. https://dx.doi.org/10.15359/ru.38-1.20
9. Pepin B., Buchholtz N., Salinas-Hernández U. A scoping survey of ChatGPT in mathematics education. Digital Experiences in Mathematics Education. 2025. Vol. 11. PP. 9–41. DOI: 10.1007/s40751-025-00172-1
10. Pepin B., Buchholtz N., Salinas-Hernández U. Mathematics education in the era of ChatGPT: investigating its meaning and use for school and university education-editorial to special issue. Digital Experiences in Mathematics Education. 2025. Vol.11. P. 1–8. DOI: 10.1007/s40751-025-00173-0
11. Son T. Intelligent tutoring systems in mathematics education: a systematic review (2003–2023). Computers. 2024. Vol. 13, No. 10. Art. 270.
12. Udias A., Alonso-Ayuso A., Alfaro C., Algar M. J., Cuesta M., Fernández-Isabel A., Gómez J., Lancho C., Cano E. L., Martín de Diego I., & Ortega F.ChatGPT’s performance in university admissions tests in mathematics. International Electronic Journal of Mathematics Education. 2024. 19(4), em0795. https://doi.org/10.29333/iejme/15517
13. Zhao Jian, Chapman Elaine, G.P. Sabet Peyman. Generative AI and Educational Assessments: A Systematic Review. Education Research and Perspectives. 2024. Vol. 51. P. 124–155. https://doi.org/10.70953/ERPv51.2412006
Published
2026-04-29
Pages
27-31
Section
СЕКЦІЯ 2. ТЕОРІЯ І ПРАКТИКА НАВЧАННЯ