AUTOMATION OF STUDENT TESTING USING GENERATIVE ARTIFICIAL INTELLIGENCE AND MODERN INTEGRATION PLATFORMS

D. V. Vlasov

FULL TEXT: PDF (Rus)

Abstract

The study focuses on developing an intelligent testing automation system based on generative language models and integration platforms. The scalability problem of knowledge assessment in mass education is solved through a combination of automated verification and multilevel anomaly analysis. The system is implemented on the Albato platform using Yandex.Forms, Google Sheets, and large language model APIs. The novelty lies in two stage anomaly analysis: strict criteria based and intelligent LLM analysis. Practical significance is confirmed by a ready to implement prototype.

Key words

Testing automation; generative models; anomaly analysis; integration platforms; educational analytics; artificial intelligence; knowledge assessment.

Received: 10/06/2025; accepted for publication: 10/14/2025.

For citation:

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Vlasov D.V. Automation of student testing using generative artificial intelligence and modern integration platforms. Electronic scientific and methodological journal “Pedagogy of computer science”. 2025;1-2. Http://pcs.bsu.by/2025_1-2/3ru.pdf

Content is available under license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

About the authors:

D. V. Vlasov
Herzen State Pedagogical University of Russia
Russia
Vlasov Dmitry – Candidate of Technical Sciences, Associate Professor, Department of Information Technology and E-Learning, Dmitry-v-vlasov@mail.ru
Moika River Embankment, 48; 191186, St. Petersburg, Russia

References

1. Rudinsky I.D. Principles of intelligent automated knowledge testing. Proceedings of the International Conference-Exhibition “Information Technologies in Education”; 2001; Moscow, Russia. Moscow; 2001. (In Russian)

2. Crompton H., Burke D. Artificial intelligence in higher education: the state of the field. Int J Educ Technol High Educ. 2023;20:22. doi:10.1186/s41239-023-00392-8.

3. Rudinsky I.D., Kleandrova I.A. Concept of quantitative assessment of objectivity in pedagogical knowledge testing. Informatics and Education. 2003;(12):100–104. (In Russian)

4. Emirtekin E. Large language model-powered automated assessment: a systematic review. Appl Sci. 2025;15:5683. doi:10.3390/app15105683.

5. Kasneci E., Sessler K., Kuchemann S., Bannert M., Dementieva D., Fischer F., et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn Individ Differ. 2023;110:102274. doi:10.1016/j.lindif.2023.102274.

6. Jukiewicz M. The future of grading programming assignments in education: the role of ChatGPT in automating the assessment and feedback process. Think Skills Creat. 2024;53:101522. doi:10.1016/j.tsc.2024.101522.

7. Rudinsky I.D. Structural foundations of testology. 2nd ed., rev. Moscow: Goryachaya Liniya–Telekom; 2015. 244 p. (In Russian)

8. Davydova N.A., Rudinsky I.D. Automated synthesis of test items for systems of pedagogical knowledge control. Informatization of Education and Science. 2013;1(17):77–90. (In Russian)

9. Zawacki-Richter O., Marin V.I., Bond M., Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int J Educ Technol High Educ. 2019;16:39. doi:10.1186/s41239-019-0171-0.

10. Malouff J.M., Thorsteinsson E.B. Bias in grading: a meta-analysis of experimental research findings. Aust J Educ. 2016;60(3):245–256. doi:10.1177/0004944116664618.

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