构建对话式教学数据集，让AI助教更像真人教师。
ConvoLearn: A Learning Sciences Grounded Dataset for Fine-Tuning Dialogic AI Tutors
- 基于学习科学设计2134组师生对话，覆盖6大教学维度。
- 用该数据训练的模型在真实课堂中获教师认可，效果接近商用系统。
- 适合教育AI研究者、智能辅导系统开发者使用。
尽管大型语言模型在教育领域日益普及，但仍与有效教学的核心原则——知识的对话建构不一致。我们提出ConvoLearn，一个包含2,134组半合成师生对话的数据集，基于知识建构理论，涵盖六个对话式教学维度，场景设定为中学地球科学课程。实验表明，带有维度标签的对话训练数据能捕捉到有意义的教学信号，并在真实课堂中泛化：基于ConvoLearn训练的分类器得分与专家编码的课堂教学质量在多个子维度上显著相关。作为概念验证，我们将Mistral-7B模型在ConvoLearn上进行微调，结果显示，在维度级微调下，该70亿参数开源模型可生成具有对话式教学特征的行为，经认证教师评估，其表现可媲美强效专有基线。本工作为开发更具对话性的AI助教提供了支持。
原文摘要 · Abstract (English)
Despite their growing adoption in education, LLMs remain misaligned with the core principle of effective tutoring: the dialogic construction of knowledge. We introduce ConvoLearn, a dataset of 2,134 semi-synthetic tutor-student dialogues operationalizing six dimensions of dialogic tutoring grounded in knowledge-building theory, situated in a middle school Earth Science curriculum. We show that dimension-labeled dialogic training data captures meaningful pedagogical signal that generalizes beyond its semi-synthetic domain: scores from a classifier trained on ConvoLearn correlate significantly with expert-coded instructional quality in authentic classrooms across multiple subscales. As a proof of concept, we fine-tune Mistral-7B on ConvoLearn and show that dimension-level fine-tuning can steer a 7B open-weight model toward dialogic tutoring behavior that credentialed teachers rate as competitive with a strong proprietary baseline. With this work, we support the development of AI tutors capable of more dialogic interactions.
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