The role of artificial intelligence in assessing the quality of learning and student development

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Abstract

the article considers the role of artificial intelligence (AI) in assessing the quality of learning and student development in the education system. An analysis of existing approaches to assessment, traditional and modern methods is conducted, and the possibilities and advantages of using AI in this area are considered. The results of the analysis were the identification of areas of AI use in assessment (automatic checking of assignments, personalization of learning, predicting academic performance, analyzing behavior in the online environment, virtual assistants), a comparative assessment of the effectiveness of various AI methods based on the OULAD dataset, as well as an overview of the current situation with the introduction of AI into educational practice in Russia. The importance of an integrated approach to the introduction of AI in education, taking into account both technical and ethical aspects, is emphasized.

About the authors

V. V Aleshov

Kherson State Pedagogical University

Email: aleshovvladimyr@yandex.ru
ORCID iD: 0009-0006-9757-9013

Ya. B Samchinskaya

Kherson State Pedagogical University

M. I Sherman

Kherson State Pedagogical University

E. V Aleshov

Kherson State Pedagogical University

ORCID iD: 0009-0006-9757-9013

Yu. A Kazantsev

Kherson State Pedagogical University

References

  1. Choi Y., McClenen C. Development of adaptive formative assessment system using computerized adaptive testing and dynamic bayesian networks // Appl. Sci. 2020. Vol. 10. P. 81 – 96.
  2. Goel A.K.,Joyner D.A. Using AI to Teach AI: Lessons from an Online AI Class // AI Mag. 2017. Vol. 38. P. 48 – 59.
  3. Gonz?lez-Calatayud V., Prendes-Espinosa P., Roig-Vila R. Artificial Intelligence for Student Assessment: A Systematic Review // Appl. Sci. 2021. Vol. 11. P. 54 – 67. https://doi.org/10.3390/app11125467
  4. Grivokostopoulou F., Perikos I., Hatzilygeroudis I. An Educational System for Learning Search Algorithms and Automatically Assessing Student Performance // Int. J. Artif. Intell. Educ. 2017. Vol. 27. P. 207 – 240.
  5. Hooda M., Rana C., Dahiya O., Rizwan A., Hossain M.S. Artificial Intelligence for Assessment and Feedback to Enhance Student Success in Higher Education // Mathematical Problems in Engineering. 2022. Vol. 2022. P. 1 – 19.
  6. Jani K.H., Jones, K.A., Jones G.W., Amiel J., Barron B., Elhadad N. Machine learning to extract communication and history-taking skills in OSCE transcripts // Med. Educ. 2020. № 54. P. 1159 – 1170.
  7. Kaila E., Kurvinen E., Lokkila E., Laakso M.-J. Redesigning an Object-Oriented Programming Course // ACM Trans. Comput. Educ. 2016. № 16. P. 1 – 21.
  8. Liu M., Wang Y., Xu W., Liu L. Automated Scoring of Chinese Engineering Students’ English Essays // Int. J. Distance Educ. Technol. 2017. Vol. 15. P. 52 – 68.
  9. Maicher K.R., Zimmerman L., Wilcox B., Liston B., Cronau H., Macerollo A., Jin L., Jaffe E., White M., Fosler-Lussier E., et al. Using virtual standardized patients to accurately assess information gathering skills in medical students // Med. Teach. 2019. Vol. 41. P. 1053 – 1059.
  10. Ouguengay Y.A., El Faddouli N.-E., Bennani S. A neuro-fuzzy inference system for the evaluation of reading/writing competencies acquisition in an e-learning environnement // J. Theor. Appl. Inf. Technol. 2015. Vol. 81. P. 600 – 608.
  11. Owan V.J., Abang K.B., Idika D.O., Etta E.O., Bassey B.A. Exploring the potential of artificial intelligence tools in educational measurement and assessment // Eurasia Journal of Mathematics, Science and Technology Education, 2023. Vol. 19 (8). P. em2307. https://doi.org/10.29333/ejmste/13428
  12. Rhienmora P., Haddawy P., Suebnukarn S., Dailey M.N. Intelligent dental training simulator with objective skill assessment and feedback // Artif. Intell. Med. 2011. Vol. 52. P. 115 – 121.
  13. Swiecki Z, Khosravi H, Chen G, Martinez-Maldonado R, Lodge JM, Milligan S et al. Assessment in the age of artificial intelligence // Computers and Education: Artificial Intelligence. 2022. Vol. 3. P. 100075. doi: 10.1016/j.caeai.2022.100075
  14. Ulum ?.G. A critical deconstruction of computer-based test application in Turkish State University // Educ. Inf. Technol. 2020. № 25. P. 4883 – 4896.

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