Ontology-Based Layered Hybrid AI-Driven Knowledge Model for Personalized E-Learning

An ontology-based, layered hybrid knowledge model for personalized e-learning — a conceptual architecture paper by Tatyana Ivanova (2026) that classifies the knowledge required for personalization and proposes a layered knowledge-base architecture grounded in description logic. Its central move is replacing the classic ITS four-model architecture (domain, student, tutoring, interface) with systems of mapped ontologies, while adding procedural knowledge (rules), probabilistic/fuzzy knowledge (via fuzzy/probabilistic description-logic extensions), and implicit knowledge extracted through learning analytics and machine learning. Because ontologies alone are static and handle uncertainty poorly, the paper argues personalization requires combining them with teaching-strategy sequencing rules, analytics, and ML — and it contributes a metadata framework for describing, discovering, and reusing educational ontologies.

Key Findings

• Personalization needs more than ontologies. Ontologies are mostly static or slowly evolving and have limited handling of uncertainty, so effective personalized learning must combine them with teaching-strategy sequencing rules, learning analytics (to infer and predict learner needs), and machine learning for extracting implicit knowledge from educational data.
• Systems of mapped ontologies extend the ITS architecture. The paper differs from most prior work by proposing a system of mapped ontologies in place of each single ontology across the four classic ITS knowledge-base models (domain, student/tutoring, pedagogical, interface), together with storage for procedural knowledge (as rules) and imprecise/probabilistic knowledge (fuzzy or probabilistic description-logic extensions).
• ITS vs. IES distinction. An Intelligent Tutoring System is a specialized AI system focused on individual personalized tutoring; an Intelligent Educational System (IES) is a broader, multi-module ecosystem integrating tutoring, analytics, recommendation, and administrative decision-making. Ontologies in ITS enable cognitive tutoring; ontologies in IES mainly enable semantic integration.
• Ontology classification for reuse. The paper proposes a purpose-based classification of educational ontologies driven by the ITS model (tutoring-domain, pedagogy, or learner-profile), and a metadata framework with eight upper-level classes — ontologies, rules, mappings, data-driven (ML/analytics), ontology management tools, core descriptors, technical descriptors, and usage contexts — to support semantic search, evaluation, recommendation, and mapping, thereby reducing the time, effort, and cost of ontology development and evolution.

Connected Concepts

• Knowledge Graph
• Intelligent Tutoring
• Personalized Learning
• Adaptive Learning
• Student Modeling
• Instructional Design
• Edtech Platform
• Learning Analytics
• LLM

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Citation

Ivanova, T. (2026). Ontology-based layered hybrid AI-driven knowledge model for personalized e-learning. Mathematics, 14(5), 808.