Transactions on Machine Intelligence 2821-1693 9 2 2026 06 01 A Knowledge Graph-Based Neuro-Symbolic Conceptual Architecture for Semantic Learner Modeling in Intelligent Tutoring Systems 124 141 252077 10.22034/tmi.2026.252077 EN M. Bayatani PhD Student, Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran N. Osati Eraghi Assistant Professor, Department of Computer Engineering, Arak Branch, Islamic Azad University, Arak, Iran 0000-0002-6830-7290 Journal Article 2026 01 27 Intelligent Tutoring Systems (ITSs) require accurate and semantic modeling of learner states to deliver personalized learning experiences. Despite advances in the application of ontologies and knowledge graphs in educational systems, existing approaches are generally based either on symbolic methods, which offer high interpretability but have limited flexibility in handling dynamic data and complex learner interactions, or on data-driven approaches, which are capable of extracting behavioral patterns but often lack sufficient semantic transparency and explainability. This paper proposes a knowledge graph-based neuro-symbolic conceptual architecture for semantic learner modeling in Intelligent Tutoring Systems. In the proposed architecture, the data-driven component is responsible for analyzing learner interactions and extracting patterns related to learning states, whereas the symbolic component employs ontologies and knowledge graphs to organize educational knowledge, maintain semantic consistency, and support interpretable reasoning. The architecture's integration mechanism is designed to use the results of learner data analysis to update and enrich the learner model within the knowledge graph. Subsequently, the symbolic layer employs semantic rules and conceptual relationships to generate adaptive instructional recommendations. The proposed architecture is organized as a five-layer framework, providing a conceptual foundation for developing Intelligent Tutoring Systems with enhanced explainability, semantic consistency, and adaptability. By combining the strengths of data-driven and symbolic approaches, this framework seeks to reduce the gap between learner data analytics and semantic reasoning in intelligent educational environments. https://www.tmachineintelligence.ir/article_252077_ebe366c14eac998595e9fa23f2a7a278.pdf