Sep 2026· Automation, Control, and Information Technology· pp. 1651-1657· 0 citations· 31 references
Abstract
This paper proposes a method for controlled educational content generation based on knowledge-oriented models and Large Language Models (LLMs). The method involves constructing a semantic context according to a specified learning objective, identifying mandatory concepts, semantic relationships, and pedagogical constraints, and using them to guide the generation process. To ensure the quality of the generated content, an evaluation framework is proposed based on semantic coverage, factual correctness, pedagogical alignment, difficulty compliance, and content completeness. An iterative “generation → validation → evaluation → feedback → regeneration” mechanism is implemented to automatically refine the generated material when it fails to satisfy the predefined requirements. The proposed approach provides a foundation for improving the controllability and quality of automatically generated educational materials and facilitates their subsequent integration into intelligent and adaptive learning environments.
The Conceive–Design–Implement–Operate (CDIO) Syllabus expresses graduate competencies in natural language developed through project-based learning, yet the link between declared competencies and delivered content is maintained manually. This study proposes the CDIO Competency–Knowledge Ontology (CDIO–CKO): a decidable...
Yenglik Kadyr, B. Kubekov, A. Kassekeyeva et al.· Frontiers of Computer Scienc...· 0 citations
The article presents a systematic review of artificial intelligence methods for educational content generation in multi-agent systems. Modern approaches to the automatic generation of educational questions, lesson plans, and materials were analyzed using large language models. Four main groups of generation methods wer...
Y. Melnyk, S. Otrokh, Anton Pasichniuk et al.· Electronics and Control Syst...· 0 citations
Traditional e-learning systems often provide limited personalization and adaptation to individual student needs. This paper presents a competence-based architecture for personalized adaptive learning in higher education that integrates an ontology-based content model, practical-skill verification, a learning-analytics-...
J. Jović, D. Domazet, Nemanja Zdravković· Applied Sciences· 0 citations
The rapid development of Artificial Intelligence (AI) and Large Language Models (LLMs) has created new opportunities for providing personalized and context-aware learning support to university students. This study presents a Smart Educational Agent designed to support learning through the integration of Large Language...
B. Deepak, S. Ramya, K. G et al.· International journal of com...· 0 citations
Artificial Intelligence (AI) is changing software engineering by enabling autonomous software agents. This shift requires new educational approaches that prepare students to design and realise such systems. We present first experiences from an exploratory seminar in which students developed software agents using three...
L. Hofmann, José Ignacio Requeno Jarabo, Elena Gómez-Martínez et al.· Proceedings of the ACM/IEEE...· 0 citations
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