ConvoLearn：一种基于学习科学的对话式AI辅导教师数据集
ConvoLearn: A Learning Sciences Grounded Dataset for Fine-Tuning Dialogic AI Tutors
AI总结：
ConvoLearn通过六个对话教学维度构建了2134个半合成对话数据集，展示了基于知识建构理论的对话教学信号，证明了其在真实课堂中的有效性。
摘要：
尽管LLM在教育领域日益普及，但其仍与有效辅导的核心原则——知识的对话建构相偏离。我们介绍了ConvoLearn，一个包含2134个半合成辅导-学生对话的数据集，该数据集基于知识建构理论，应用于中学地球科学课程。我们证明了带有维度标记的对话训练数据能捕捉到有意义的教学信号，并在半合成领域外泛化：在ConvoLearn上训练的分类器在多个子尺度上与专家编码的教学质量显著相关。作为概念验证，我们使用ConvoLearn对Mistral-7B进行微调，并展示出在维度层面的微调可以引导7B开放模型朝向对话式辅导行为，这种行为被认证教师评为与强私有基准线相竞争。通过这项工作，我们支持了能够进行更对话式互动的AI辅导教师的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
