[2603.28062] SLOW: Strategic Logical-inference Open Workspace for Cognitive Adaptation in AI Tutoring

Computer Science > Artificial Intelligence arXiv:2603.28062 (cs) [Submitted on 30 Mar 2026] Title:SLOW: Strategic Logical-inference Open Workspace for Cognitive Adaptation in AI Tutoring Authors:Yuang Wei, Ruijia Li, Bo Jiang View a PDF of the paper titled SLOW: Strategic Logical-inference Open Workspace for Cognitive Adaptation in AI Tutoring, by Yuang Wei and 2 other authors View PDF HTML (experimental) Abstract:While Large Language Models (LLMs) have demonstrated remarkable fluency in educational dialogues, most generative tutors primarily operate through intuitive, single-pass generation. This reliance on fast thinking precludes a dedicated reasoning workspace, forcing multiple diagnostic and strategic signals to be processed in a conflated manner. As a result, learner cognitive diagnosis, affective perception, and pedagogical decision-making become tightly entangled, which limits the tutoring system's capacity for deliberate instructional adaptation. We propose SLOW, a theory-informed tutoring framework that supports deliberate learner-state reasoning within a transparent decision workspace. Inspired by dual-process accounts of human tutoring, SLOW explicitly separates learner-state inference from instructional action selection. The framework integrates causal evidence parsing from learner language, fuzzy cognitive diagnosis with counterfactual stability analysis, and prospective affective reasoning to anticipate how instructional choices may influence learners' emoti...