Аңдатпа
Personalized learning remains only partially achieved in current educational technologies, as many systems rely on opaque predictive models or static curriculum representations that lack explainable process planning. This study proposes an ontology-based framework that integrates learner profiles, competency structures, and educational content into a unified semantic model for automated and transparent learning path generation. Using Design Science Research Methodology, a four-layer OWL 2 DL ontology (learner, competency, content, and process) was developed with hybrid reasoning that combines OWL inference, SWRL rules, and SPARQL queries. The framework is aligned with Bloom’s Taxonomy, the European Qualifications Framework (EQF), and IEEE Learning Object Metadata (LOM). A web-based prototype demonstrates the generation of personalized learning trajectories and explainable recommendations through interactive competency graphs. The proposed approach provides a standards-aligned and interpretable solution for automated educational planning and semantic personalization.