SLOW：面向AI辅导的认知适应战略逻辑推理开放工作区
SLOW: Strategic Logical-inference Open Workspace for Cognitive Adaptation in AI Tutoring
AI总结：
SLOW框架通过透明决策工作区实现学习者状态推理与教学决策分离，提升个性化、情感敏感性和清晰度。
摘要：
尽管大型语言模型在教育对话中表现出色，但大多数生成辅导系统依赖于单次生成，限制了推理能力。本文提出SLOW框架，结合因果证据解析、模糊认知诊断和前瞻性情感推理，通过可视化决策过程实现可解释的智能辅导，提升教育有效性和适应性。
英文摘要：
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' emotional trajectories. These signals are jointly considered to guide pedagogically and affectively aligned tutoring strategies. Evaluation using hybrid human-AI judgments demonstrates significant improvements in personalization, emotional sensitivity, and clarity. Ablation studies further confirm the necessity of each module, showcasing how SLOW enables interpretable and reliable intelligent tutoring through a visualized decision-making process. This work advances the interpretability and educational validity of LLM-based adaptive instruction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
