Education is a complex and multidisciplinary field. Effective personalization in education is grounded in both educational theory and hybrid AI practice. Personalization is typically driven by explicit, structured knowledge; however, the effective automated extraction of implicit knowledge from educational data is also of great importance. This research analyzes and classifies the knowledge required for personalization, as well as the effective technologies for its representation and storage in both human-readable and machine-processable forms. As a result, we propose a conceptual model of a layered, hybrid knowledge base architecture grounded in mathematical logic, designed to structure knowledge for supporting personalization in intelligent educational systems. Systems of mapped ontologies constitute a core component of the proposed architecture. The proposed architecture extends the well-known intelligent tutoring systems architecture by incorporating new types of knowledge as well as structural and organizational elements and by providing a detailed description of their interrelationships and integration mechanisms. It is important to make easier and effective development of ontologies for usage in knowledge models, integrated in practical e-learning systems. The proposed conceptual model also promotes ontology reuse, thereby reducing the time, effort, and cost associated with ontology development and evolution. To enhance ontology development and usage through effective reuse, we propose a structured organization of metadata for describing all components of hybrid AI-driven knowledge bases. This metadata framework can support the development of an ontology that facilitates the discovery, selection, and reuse of appropriate ontologies, rules, mappings, and tools stored in specialized knowledge repositories for educational purposes.
Tatyana Ivanova (Fri,) studied this question.