Ontology-grounded Neuro-symbolic Intelligent Tutoring and Knowledge-assessment System Supporting Smart Digital Education
Year: 2026
Type: article
Abstract: Intelligent tutoring systems (ITS) for elementary algebra face a persistent tension between the interpretability of symbolic, knowledge-engineered models and the predictive power of purely neural learner models, while recent large-language-model (LLM) assistants add fluency at the cost of factual and pedagogical reliability. This paper describes an algebra tutoring system organised around a formal ontology. The ontology is an OWL knowledge base: it lists the algebraic concepts, records which concepts are prerequisites for which, and states axioms for the misconceptions students commonly hold. Both the reasoning and the assessment components read from it. Reasoning is handled by a rule-based computer-algebra solver paired with a neural knowledge-tracing model. The prerequisite relations enter the training objective as a regularisation term: the tracer is penalised when it claims a student has mastered a concept while that student still fails the prerequisites for it. Assessment uses an item-response model with ontology-weighted item parameters. Because the weights are tied to concepts, the model returns a separate mastery estimate for each concept, which is what makes the output legible to a teacher. A retrieval-augmented, ontology-constrained LLM pedagogical assistant generates hints and explanations whose vocabulary and inferences are restricted to entailments of the knowledge base, sharply reducing hallucination. The study ran for eight weeks with 428 learners. On held-out interactions the knowledge-tracing model reached an AUC of 0.851 and an RMSE of 0.369, ahead of both the Bayesian and the deep knowledge-tracing baselines. Learning gains separated the conditions more sharply than the tracing metrics did: normalized gain was (g) = 0.58 for the full system, 0.41 for a generic LLM tutor, and 0.24 for the textbook condition. Expert raters also preferred the ontology-grounded hints when asked to judge faithfulness. What this shows is narrower than it may look. A single cohort over eight weeks cannot establish that ontological grounding is what produced the gains, since the grounded system also differs from the LLM tutor in its solver and its hint templates. It does show that the combination is workable at the scale of a real course, and that grounding the hints in an explicit knowledge base does not cost accuracy in exchange for transparency.
Source: Вестник КазАТК
Institution: L. N. Gumilyov Eurasian National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7225110716