APPLICATION OF ARTIFICIAL INTELLIGENCE TO AUTOMATICALLY VERIFY STUDENT CALCULATIONS IN HIGHER EDUCATION INSTITUTIONS
DOI:
https://doi.org/10.17770/etr2025vol3.8551Keywords:
artificial intelligence, check automation, higher education, pedagogical experimentAbstract
Artificial Intelligence (AI) is becoming a key tool in education, automating the verification of students' calculations, which is especially important for disciplines involving mathematical and technical calculations. The article focuses on improving the digital literacy of students and teachers through the integration of innovative technologies. The use of AI transforms learning by promoting individualization of the process, instant feedback and in-depth mastering of the material. The perspectives of adaptive educational platforms that analyze the level of preparation of students and build personalized learning trajectories are discussed. The challenges of AI implementation are also discussed, including algorithm improvement, ethical issues of automated assessment, and integration with existing educational systems. The study evaluated the effectiveness of implementing AI systems to check students' calculation papers. The results showed that the use of artificial intelligence allowed to reduce the time of work verification by 60-70%, to increase the accuracy of assessment by 25-30% due to minimization of the human factor, and to increase student performance by 15-20% due to individualized recommendations. It is concluded that the use of artificial intelligence in the verification of calculations not only increases the efficiency of the educational process, but also develops students' skills in working with digital technologies, which are necessary in modern conditions. The introduction of artificial intelligence into the educational environment contributes to the creation of adaptive, interactive and innovative approaches to learning, improving the quality of education and its accessibility.
References
B. Brendan, “OCED Framework for Classifying of AI Systems.” (2022, March 14). [Online] Available: https://oralytics.com/2022/03/14/oced-framework-for-classifying-of-ai-systems/ [Accessed April 25, 2025].
N. Annus, “Educational software and artificial intelligence: Students' experiences and innovative solutions.” Information Technologies and Learning Tools, 101(3), 200. 2024 Available: journal.iitta.gov.ua , https://journal.iitta.gov.ua/index.php/itlt/index [Accessed April 25, 2025] https://doi.org/10.33407/itlt.v101i3.5479
A. Çınar, E. Ince, M. Gezer, et al. “A machine learning algorithm for evaluating open-ended physics questions in Turkish.” Education and Information Technologies, 25, pp. 3821–3844. 2020 Available: link.springer.com https://link.springer.com/ [Accessed April 25, 2025] https://doi.org/10.1007/s10639-020-10128-0
R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate student performance using educational data mining.” Computers & Education, 113, 177–194. 2017 Available: sciencedirect.com https://www.sciencedirect.com/ [Accessed April 25, 2025] https://doi.org/10.1016/j.compedu.2017.05.007
X. Chen, D. Zou, H. Xie, and G. Chen, “Artificial Intelligence in Education: A Review.” International Journal of Educational Technology in Higher Education, 18, 41. 2021.
O. Zawacki-Richter, , V. I. Marín, M. Bond, and Gouverneur, F. “Systematic Review of Artificial Intelligence in Education: Challenges and Opportunities.” Educational Research Review, 37, 100491. 2022.
L. Chen, , P. Chen, and Z. Lin, “Artificial Intelligence in Education: A Review.” IEEE Access, 8, 75264–75278. 2020.
K. Crawford, “Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence.” Yale University Press. ISBN 9780300230548. 2021.
A. Garcia, J. Smith, and K. Lee, “Artificial intelligence in engineering: Recent advances and future directions.” Engineering Applications of Artificial Intelligence, 103, Article 104235. 2021
I. Roll, and R. Wylie, “Artificial Intelligence in Education: Emerging Approaches and Opportunities.” International Journal of Artificial Intelligence in Education, 31(2), 315–330. 2021.
V. V. Osadchyi, O. P. Pinchuk, and T. A. Vakaliuk, “From the digital transformation strategy to the productive integration of technologies in education and training: Report 2023.” CEUR Workshop Proceedings, 3553, 1–8. 2023
S. G. Litvinova, and O. M. Sokoliuk, “Criteria and indicators for assessing the quality of educational objects of augmented reality in physics textbooks.” Information technologies and learning tools. 88(2), 23–37. 2022 Available: journal.iitta.gov.ua https://journal.iitta.gov.ua/index.php/itlt/article/view/4870 [Accessed April 25, 2025] https://doi.org/10.33407/itlt.v88i2.4870
O. Hrytsenchuk, “The use of artificial intelligence in education: Trends and perspectives in Ukraine and abroad.” UNESCO Chair Journal “Lifelong Professional Education in the XXI Century”, (10). 2024 Available: unesco-journal.com.ua https://unesco-journal.com.ua/index.php/journal/article/view/123 [Accessed April 25, 2025] https://doi.org/10.35387/ucj.2(10).2024.0012
O. V. Ovcharuk, I.V. Ivanyuk, O.O. Hrytsenchuk, and I..D. Malytska, “Results of the online survey ‘Readiness and needs of teachers to use digital tools and ICT in wartime: 2023.”/”Результати онлайн-опитування «Готовність і потреби вчителів щодо використання цифрових засобів та ІКТ в умовах війни: 2023» .”Analytical report. IEC of NAPS of Ukraine. (In Ukrainian) 2023. Available: researchgate.net https://www.researchgate.net/ [Accessed April 25, 2025] https://doi.org/10.13140/RG.2.2.25529.34402
V. Bulgakov, , S. Pascuzzi, , S. Ivanovs, , F. Santoro, , A.S. Anifantis, I. Ihnatiev, “Performance assessment of front-mounted beet topper machine for biomass harvesting.” Energies, 13(14), 3524. 2020
V. Bulgakov, V. Adamchuk, S. Ivanovs, Y. Ihnatiev, “Theoretical investigation of aggregation of top removal machine frontally mounted on wheeled tractor.” Engineering for Rural Development, 16, pp. 273–280. 2017.
S. Ivanovs, V. Bulgakov, V. Nadykto, Ye. Ihnatiev, S. Smolinskyi, Z. Kiernicki, “Experimental study of the movement controllability of a machine-and-tractor aggregate of the modular type.” INMATEH – Agricultural Engineering, 2020, 61(2), pp. 9–16. 2020.
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Copyright (c) 2025 Іnna Savytska, Oksana Bulgakova, Lesya Zbaravska, Ādolfs Ruciņš, Aivars Aboltins, Iryna Mushenyk, Valentina Vasileva, Valentyna Kultenko
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