CoDAE: Adapting Large Language Models for Education via Chain-of-Thought Data Augmentation

CODAE is a framework that fine-tunes open-source LLMs for AI tutoring by augmenting real student-tutor dialogues with Chain-of-Thought prompting to improve pedagogical quality. The paper addresses three key limitations (over-compliance, low response adaptivity, and threat vulnerability) and evaluates models on their ability to provide step-by-step guidance without prematurely revealing answers.

Large Language Models (LLMs) are increasingly employed as AI tutors due to their scalability and potential for personalized instruction. However, off-the-shelf LLMs often underperform in educational settings: they frequently reveal answers too readily, fail to adapt their responses to student uncertainty, and remain vulnerable to emotionally manipulative prompts. To address these challenges, we introduce CoDAE, a framework that adapts LLMs for educational use through Chain-of-Thought (CoT) data

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