Open conversations are one of the most engaging forms of teaching. However, creating those conversations in educational software is a complex endeavor, especially if we want to address the needs of different audiences. While language models hold great promise for educational applications, there are substantial challenges in training them to engage in meaningful and effective conversational teaching, especially when considering the diverse needs of various audiences. No official data sets exist for this task to facilitate the training of language models for conversational teaching, considering the diverse needs of various audiences. This paper presents a novel source for facilitating conversational teaching of scientific concepts at various difficulty levels (from preschooler to expert), namely dialogues taken from video transcripts. We analyse this data source in various ways to show that it offers a diverse array of examples that can be used to generate contextually appropriate and natural responses to scientific topics for specific target audiences. It is a freely available valuable resource for training and evaluating conversation models, encompassing organically occurring dialogues. While the raw data is available online, we provide additional metadata for conversational analysis of dialogues at each level in all available videos.

该论文介绍了一种用于各类难度级别的科学概念对话教学的新方法，并通过对视频转录的对话进行分析，展示了其中多样化的例子可以用于为特定目标受众生成感性合适和自然的回应。这是一个对话模型的有价值资源，可以用于训练和评估，涵盖了有机发生的对话。

以对话为基础在不同教育层次教授科学概念