Commonsense Reasoning Cues and Explanation Quality in Educational Tutoring Agents: A Simulation Study
Keywords:
Educational Tutoring Agents, Commonsense Reasoning, Explanation Quality, Simulation Study, Pedagogical Artificial IntelligenceAbstract
Educational tutoring agents have increasingly relied on advanced natural language processing capabilities to provide personalized instruction and adaptive feedback. However, a persistent challenge in deploying these artificial intelligence systems lies in their ability to generate explanations that align with human cognitive expectations and foundational world knowledge. This paper presents a comprehensive simulation study investigating the role of commonsense reasoning cues in enhancing the explanation quality of educational tutoring agents. By systematically varying the presence, density, and type of commonsense reasoning cues integrated into the generative pipelines of simulated tutoring systems, we evaluate the resulting pedagogical outputs across multiple dimensions of explanation quality, including clarity, coherence, relevance, and pedagogical efficacy. The simulation environment models complex agent-student interactions across diverse academic domains, enabling a controlled yet expansive analysis of conversational dynamics. Findings from this investigation reveal that the strategic injection of commonsense reasoning cues significantly mitigates cognitive dissonance in simulated learners and drastically reduces instances of logical hallucinations. Furthermore, different categories of commonsense knowledge, specifically temporal, spatial, and causal constructs, demonstrate varying degrees of impact depending on the subject matter being tutored. This research provides a robust theoretical and empirical foundation for the future design of pedagogically sound artificial intelligence tutors, emphasizing the critical necessity of grounding automated instructional discourse in shared human understanding.References
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