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• The SLOW framework significantly improves personalization, emotional sensitivity, and clarity in AI tutoring compared to standard LLM-based approaches.result 85%
• SLOW's architecture explicitly separates learner-state inference from instructional action selection, inspired by dual-process accounts of human tutoring.method 90%
• The framework integrates causal evidence parsing from learner language, fuzzy cognitive diagnosis with counterfactual stability analysis, and prospective affective reasoning.method 90%
• Ablation studies confirm the necessity of each module (evidence parsing, cognitive validation, affective prediction) within the SLOW framework.result 85%
• Most generative tutors primarily operate through intuitive, single-pass generation, which precludes a dedicated reasoning workspace and conflates diagnostic and strategic signals.limitation 90%
• SLOW enables interpretable and reliable intelligent tutoring through a visualized decision-making process.result 90%
• The cognitive validation module uses counterfactual simulation and fuzzy tools to iteratively refine a diagnostic score, enhancing transparency and interpretability.technical 80%
• This work advances the interpretability and educational validity of LLM-based adaptive instruction.result 90%

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