用细粒度教学意图标注提升AI助教的辅导质量
Intent Matters: Enhancing AI Tutoring with Fine-Grained Pedagogical Intent Annotation
- 用11种教学意图细化标注数学对话数据
- 新模型生成的辅导回复更符合教学策略
- 适合教育AI研究者与智能辅导系统开发者
大型语言模型在智能辅导系统中前景广阔，但有效辅导需匹配教学策略，而现有模型缺乏此类适配。本文以MathDial数学教学对话数据集为基础，采用自动化标注框架，使用包含11种教学意图的详细分类体系对部分数据重新标注。基于新标注数据微调语言模型后，与原四类意图训练的模型相比，自动评估和人工评价均显示新模型生成的回应更具教学一致性与有效性。结果表明，意图细化对教育场景中的可控文本生成至关重要。相关标注数据与代码已开源，促进后续研究。
原文摘要 · Abstract (English)
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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