意图至关重要：利用细粒度教学意图标注增强AI辅导
Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation
AI总结：
本文针对LLM辅导回复与教学策略不一致的问题，基于MathDial构建11类细粒度教学意图标注并微调模型，证明其优于原有四分类方案。
摘要：
大型语言模型（LLM）在教育应用中，尤其是在智能辅导系统中，展现出巨大前景。然而，有效的辅导需要与教学策略保持一致；如果没有针对特定任务的适配，当前LLM尚缺乏这一点。在本研究中，我们探讨对教师意图进行细粒度标注是否能够提升LLM生成辅导回复的质量。我们聚焦于面向数学教学的对话数据集MathDial，并采用一个自动化标注框架，利用包含11种教学意图的详细分类体系对该数据集的一部分进行重新标注。随后，我们使用这些新标注对一个LLM进行微调，并将其性能与基于原有四分类体系训练的模型进行比较。自动评估和定性评估均表明，细粒度模型能够生成更符合教学要求且更有效的回复。我们的发现凸显了意图具体性对于教育场景中受控文本生成的价值；同时，我们发布了标注数据和代码，以促进进一步研究。
英文摘要：
Large language models (LLMs) hold great promise for educational applications, particularly in intelligent tutoring systems. However, effective tutoring requires alignment with pedagogical strategies - something current LLMs lack without task-specific adaptation. In this work, we explore whether fine-grained annotation of teacher intents can improve the quality of LLM-generated tutoring responses. We focus on MathDial, a dialog dataset for math instruction, and apply an automated annotation framework to re-annotate a portion of the dataset using a detailed taxonomy of eleven pedagogical intents. We then fine-tune an LLM using these new annotations and compare its performance to models trained on the original four-category taxonomy. Both automatic and qualitative evaluations show that the fine-grained model produces more pedagogically aligned and effective responses. Our findings highlight the value of intent specificity for controlled text generation in educational settings, and we release our annotated data and code to facilitate further research.
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