Classification and Integrated Assessment of AIS Architectures: A Reproducible Decision-making Methodology for Designing Adaptive Systems of Additional Education
( Pp. 202-211)
More about authors
Prikhodko Nikita A.
postgraduate student, Department of Applied Mathematics; MIREA – Russian Technological University
MIREA – Russian Technological University
Moscow, Russian Federation
MIREA – Russian Technological University
Moscow, Russian Federation
Abstract:
The article offers a reproducible methodology for selecting the architecture of intelligent information systems (AIS) for additional professional education (APE). The classification of architectures (centralized, distributed/microservice, cloud, multi-agent) was performed, six evaluation criteria (adaptability, scalability, interoperability, reliability, data security, complexity of implementation) were formalized, and an integrated metric was developed that takes into account external constraints (technical, legal, financial) and the phase aspect of the application (entry, mass training, individual support). The methodology is based on an analytical review of publications, comparative analysis and case analysis of typical platforms (Moodle, Open edX, Stepik), as well as the calculation of a summary indicator of the suitability of architectures. The scientific novelty consists in the integration of architectural, technical, pedagogical and technological requirements into a single model with explicit consideration of limitations and phases of use; the practical significance lies in the possibility of adjusting weights and the applicability of the model for decision-making in vocational training organizations. The work forms a methodological basis and serves as a starting point for subsequent scientific research and empirical validation of the proposed model.
How to Cite:
Prikhodko N.A. Classification and integrated assessment of AIS architectures: A reproducible decision-making methodology for designing adaptive systems of additional education. Computational Nanotechnology. 13, 1 (2026), 202–211. DOI: 10.33693/2313-223X-2026-13-1-202-211. EDN: MIFYBI
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Komleva N.V., Vilyavin D.A. Digital platform for creating personalized adaptive online courses. Open Education. 2020. Vol. 24. No. 2. Pp. 65–72. (In Rus.). DOI: 10.21686/1818-4243-2020-2-65-72. EDN: CWBDOO.
Lavrinenko I.Yu. Prospects for using LMS within the framework of modern higher education. Scientific-Methodological Electronic Journal “Koncept”. 2023. No. 1. Pp. 17–35. (In Rus.). DOI: 10.24412/2304-120X-2023-11002. EDN: JBMUHS.
Meretskov O.V. E-learning courses as a tool for digital transformation of additional professional education. Kompetentnost. 2023. No. 5. Pp. 17–25. (In Rus.). DOI: 10.24412/1993-8780-2023-5-17-25. EDN: FVYLEP.
Sidorova A.D., Gvozdeva T.V. Models and principles of building an adaptive learning system. System Analysis in Science and Education. 2024. No. 3. Pp. 131–139. (In Rus.). EDN: SJHGAS.
Skvorchevsky K.A., Dyatlova O.V. Modern adaptive and intelligent digital learning systems: mechanisms and potential. Educational Studies Moscow. 2024. No. 3 (2). Pp. 299–337. (In Rus.). DOI: 10.17323/vo-2024-19751. EDN: RCYMEL.
Shcherbakov N.V., Rezedinova E.Yu., Shchukin A.V. Adaptive architecture for a modern learning management system. In: Systems Analysis in Design and Management. Proceedings of the XXVIII International Scientific and Practical Conference. St. Petersburg: Polytech-Press, 2024. Pp. 443–452. DOI: 10.18720/SPBPU/2/id24-482. EDN: KTDZOJ.
Yakubov M., Rasulova N. Technology of personally oriented adaptive learning systems. Universum: Technical Sciences. 2021. No. 4-5 (85). Pp. 28–33. (In Rus.). DOI: 10.32743/UniTech.2021.85.4-5.28-33. EDN: BZDDJH.
Abid A., Tufail A.H., Salem O.M.A. et al. An evaluation framework and comparative analysis of the widely used learning management systems. PLoS ONE. 2024. Vol. 19. No. 12. Art. e0311111. DOI: 10.1371/journal.pone.0311111. EDN: JALORY.
Ayouni S., Menzli L.J., Hajjej F. et al. Fuzzy vikor application for learning management systems evaluation in higher education. International Journal of Information and Communication Technology Education. 2021. Vol. 17. No. 2. Pp. 17–35. DOI: 10.4018/ijicte.2021040102. EDN: NZQAOX.
El Fazazi H., Elgarej M., Qbadou M., Mansouri K. Design of an adaptive e-learning system based on multi-agent approach and reinforcement learning. Engineering, Technology and Applied Science Research. 2021. Vol. 11. No. 1. Pp. 6637–6644. DOI: 10.48084/etasr.3905. EDN: OWBPQM.
Kabudi T., Pappas I., Olsen D.H. AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence. 2021. Vol. 2. Art. 100017. DOI: 10.1016/j.caeai.2021.100017. EDN: LZDWGW.
Mohamedhen A.S., Alfazi A., Arfaoui N. et al. Towards multi-agent system for learning object recommendation. Heliyon. 2024. Vol. 10. No. 20. Art. e39088. DOI: 10.1016/j.heliyon.2024.e39088. EDN: LBBDCY.
Viswanathan N., Meacham S., Adedoyin F.F. Enhancement of online education system by using a multi-agent approach. Computers and Education: Artificial Intelligence. 2022. Vol. 3. Art. 100057. DOI: 10.1016/j.caeai.2022.100057. EDN: FLTZWU.
Keywords:
intelligent information systems, additional professional education, architecture of intelligent information systems, adaptive learning, multi-agent systems, cloud architecture, microservice architecture, integrated assessment, decision support.
