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

Yuang Wei, Ruijia Li, Bo Jiang

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' 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.

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

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' 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.

Paper Structure

This paper contains 20 sections, 5 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dual-System Theory and Test-Time Scaling
  4. LLM-based ITS
  5. Dialogue-based Learner Modeling
  6. Methods
  7. Parsing Evidence
  8. Cognitive Validation
  9. Affective Prediction
  10. Strategy Integration
  11. Experimental Design
  12. Dataset
  13. Baselines
  14. Metrics
  15. Results
  16. ...and 5 more sections

Figures (3)

  • Figure 1: An illustrative case motivating the need for deliberate learner-state reasoning. General LLMs (Left) often provides abstract Metaphors because their "fast-thinking" nature leads to the entanglement of diagnosis and decision-making. Our SLOW framework (Right) introduces a transparent open workspace to decouple these processes. By performing counterfactual cognitive validation and prospective affective simulation, SLOW explicitly weighs cognitive gains against affective risks to generate guidance that is more aligned with the student's needs.
  • Figure 2: Overview of the Strategic Logical-inference Open Workspace (SLOW) framework. The architecture illustrates the transition from (1) Evidence Parsing, where dialogue is deconstructed into cognitive and affective primitives, to (2) Cognitive Validation and (3) Affective Prediction, which utilize counterfactual simulation and prospective simulation to refine the internal state. Finally, (4) Strategy Integration balances these signals to execute a calibrated tutoring action.
  • Figure 3: A case of SLOW Reasoning Workspace demonstrating interpretability.