ONTOLOGICAL MODEL OF THE DOMAIN “INTELLIGENT ASSISTANT OF AN IT TEACHER”
DOI:
https://doi.org/10.31110/2616-650X-vol14i6-017Keywords:
ontological model, intelligent teacher assistant, IT education, knowledge representation, intelligent educational systems, large language modelsAbstract
The article addresses the problem of formalized knowledge representation in the domain of an intelligent assistant for an IT teacher, taking into account the specifics of modern educational technologies and the dynamic development of information technologies. The expediency of applying the ontological approach to structuring knowledge, ensuring its consistency, and enabling integration with intelligent systems is substantiated. Based on a systemic analysis of the teacher’s professional activity and the structure of the educational process, key groups of domain concepts are identified, including learning actors, structural components of the educational process, learning activities, domain objects of the IT field, functional capabilities of the intelligent assistant, and educational data. An ontological model is proposed to integrate pedagogical and technical components within a unified, formalized structure. A distinctive feature of the model is the consideration of the specifics of IT education, in particular, the formalized nature of learning outcomes in the form of source code, as well as the need to analyze, test, and evaluate them. The developed model includes a system of semantic relations between concepts, enabling it to represent both the structure of the educational process and the processes of programming activity. In addition, a formalized representation of the intelligent assistant is proposed as a system for data and knowledge processing that supports the generation of educational content, learning analytics, and recommendation formation. The expediency of integrating ontological models with modern artificial intelligence technologies, particularly large language models, is substantiated, enabling improvements in the accuracy, interpretability, and adaptability of intelligent educational systems. The obtained results have theoretical significance for the development of knowledge representation methods in educational systems and practical value for the design of intelligent teacher assistants in IT education
References
Abbasnejad B., Soltani S., Taghizadeh F., Zare A. Developing a multilevel framework for AI integration in technical and engineering higher education: insights from bibliometric analysis and ethnographic research. Interactive Technology and Smart Education. 2026. Vol. 23, No. 1. P. 49–79. https://doi.org/10.1108/ITSE-12-2024-0314
Alam A., Mohanty A. Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering. 2023. Vol. 10, No. 2. P. 2283282. https://doi.org/10.1080/23311916.2023.2283282
Chen G., et al. Knowledge graph and large language model integration with focus on educational applications: A survey. Neurocomputing. 2025. Vol. 654. P. 131230. https://doi.org/10.1016/j.neucom.2025.131230
Chen Y., Ding N., Zheng H.-T., Liu Z., Sun M., Zhou B. Empowering private tutoring by chaining large language models. 2023. https://doi.org/10.48550/arXiv.2309.08112
Gan W., Qi Z., Wu J., Lin J. C.-W. Large language models in education: Vision and opportunities. 2023. https://doi.org/10.48550/arXiv.2311.13160
Ivanova T. Ontology-Based Layered Hybrid AI-Driven Knowledge Model for Personalized E-Learning. Mathematics. 2026. Vol. 14, No. 5. P. 808. https://doi.org/10.3390/math14050808
Kamalov F., Santandreu Calonge D., Gurrib I. New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability. 2023. Vol. 15, No. 16. P. 12451. https://doi.org/10.3390/su151612451
Oyetade K., Zuva T., Harmse A. Integrating Industry 4.0 technologies into IT education. Cogent Education. 2025. Vol. 12, No. 1. P. 2479195. https://doi.org/10.1080/2331186X.2025.2479195
Pasichnyk V., Kunanets N., Yunchyk V., Khomyak M., Fedonyuk A., Knysh Y. Expert assessment of educational content in IT specialists training process. Proceedings of the Modern Data Science Technologies Workshop (MoDaST-2024). 2024. Vol. 3723. P. 121–132. https://ceur-ws.org/Vol-3723/paper8.pdf
Pasichnyk V., et al. Model of the recommender system for the selection of electronic learning resources. MoMLeT+DS 2023. 2023. Vol. 3426. P. 344–355. https://ceur-ws.org/Vol-3426/paper28.pdf
Raihan N., Siddiq M. L., Santos J. C. S., Zampieri M. Large language models in computer science education: A systematic literature review. 2024. https://doi.org/10.48550/arXiv.2410.16349
Shi Y., Yu K., Dong Y., Chen F. Large language models in education: A systematic review of empirical applications, benefits, and challenges. Computers and Education: Artificial Intelligence. 2026. Vol. 10. P. 100529. https://doi.org/10.1016/j.caeai.2025.100529
Wang S., Wang F., Zhu Z., Wang J., Tran T., Du Z. Artificial intelligence in education: A systematic literature review. Expert Systems with Applications. 2024. Vol. 252. P. 124167. https://doi.org/10.1016/j.eswa.2024.124167
Yunchyk V., Fedonyuk A., Khomyak M., Yatsyuk S. Cognitive modeling of the learning process of training IT specialists. CEUR Workshop Proceedings. 2021. Vol. 2917. P. 141–150. https://ceur-ws.org/Vol-2917/paper13.pdf
Zerkouk M., Mihoubi M., Chikhaoui B. A comprehensive review of AI-based intelligent tutoring systems: Applications and challenges. 2025. https://doi.org/10.48550/arXiv.2507.18882
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