Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling

This paper presents Logic-Muse, an Intelligent Tutoring System (ITS) that teaches logical reasoning skills through adaptive activities, using a hybrid student modeling approach combining Bayesian Knowledge Tracing and Deep Knowledge Tracing evaluated with ~300 students across 48 reasoning tasks. The system tracks student acquisition of reasoning skills (inhibition, counterexample generation, model management) across familiar, counterfactual, and abstract contexts using expert-validated Bayesian networks and neural architectures.

In our previous works, we presented Logic-Muse as an Intelligent Tutoring System that helps learners improve logical reasoning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians, and reasoning psychologists). A catalog of reasoning errors (syntactic and semantic) has been established, in addition to an explicit representation of semantic knowledge and the structures and meta-structures underlying co

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