GeoDial：面向几何问题求解的多模态对话式辅导数据集，包含可视化辅导轮次
GeoDial: A Multimodal Conversational Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns
AI总结：
提出GeoDial数据集，包含1300+几何师生对话，通过可扩展标注协议整合对话行为、视觉高亮和反馈，微调视觉语言模型发现其难以生成准确图解高亮。
摘要：
几个教育领域严重依赖图表和视觉线索，但现有的大多数辅导数据集仅限于纯文本交互。这限制了AI辅导者的发展，使其无法像人类教师那样以视觉为基础的方式进行教学。因此，我们引入了GeoDial，这是一个多模态辅导数据集，包含来自经验丰富的数学教师的1300多个几何领域的师生对话，其中教学轮次明确地基于图表高亮。我们提出了一种可扩展的标注协议，该协议整合了对话行为、视觉高亮和反馈，从而能够对语言和视觉辅导行为进行细粒度监督。为了说明这一设置带来的挑战，我们在GeoDial上微调了几个视觉语言模型，并评估它们生成辅导话语和图表高亮的能力。虽然监督微调显著提高了生成对话的质量，但它难以生成准确的图表高亮，揭示了当前方法的一个关键局限性，并强调了需要更有效地将视觉推理与教学互动相结合的方法。
英文摘要：
Several educational domains rely heavily on diagrams and visual cues, yet most existing tutoring datasets are limited to text-only interactions. This limits the development of AI tutors that can teach in visually grounded ways used by human instructors. Thus, we introduce GeoDial, a multimodal tutoring dataset of over 1.3K teacher-student dialogs in the domain of geometry collected from experienced math teachers, where instructional turns are explicitly grounded in diagram highlights. We propose a scalable annotation protocol that integrates dialog acts, visual highlighting, and feedback, enabling fine-grained supervision of both language and visual tutoring behavior. To illustrate the challenges posed by this setting, we fine-tune several vision-language models on GeoDial and evaluate their ability to generate tutoring utterances and diagram highlights. While supervised fine-tuning substantially improves the quality of generated dialog, it struggles to produce accurate diagram highlights, revealing a key limitation of current methods and highlighting the need for approaches that more effectively integrate visual reasoning with pedagogical interaction.
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