Educational chatbots are a promising tool for assisting student learning.
However, the development of effective chatbots in education has been
challenging, as high-quality data is seldom available in this domain. In this
paper, we propose a framework for generating synthetic teacher-student
interactions grounded in a set of textbooks. Our approaches capture one aspect
of learning interactions where curious students with partial knowledge
interactively ask a teacher questions about the material in the textbook. We
highlight various quality criteria that such dialogues should fulfill and
compare several approaches relying on either prompting or fine-tuning large
language models. We use synthetic dialogues to train educational chatbots and
show benefits of further fine-tuning in different educational domains. However,
human evaluation shows that our best data synthesis method still suffers from
hallucinations and tends to reiterate information from previous conversations.
Our findings offer insights for future efforts in synthesizing conversational
data that strikes a balance between size and quality. We will open-source our
data and code.

我们提出了一个用于生成基于教科书的综合教师 - 学生互动的框架，通过借助合适的数据合成方法来训练教育聊天机器人，同时对数据的大小和质量进行平衡以解决幻觉和重复信息的问题。

Book2Dial: 从课本中生成师生互动，实现高性价比的教育聊天机器人开发

Book2Dial: Generating Teacher-Student Interactions from Textbooks for  Cost-Effective Development of Educational Chatbots

In conversation, uptake happens when a speaker builds on the contribution of
their interlocutor by, for example, acknowledging, repeating or reformulating
what they have said. In education, teachers' uptake of student contributions
has been linked to higher student achievement. Yet measuring and improving
teachers' uptake at scale is challenging, as existing methods require expensive
annotation by experts. We propose a framework for computationally measuring
uptake, by (1) releasing a dataset of student-teacher exchanges extracted from
US math classroom transcripts annotated for uptake by experts; (2) formalizing
uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next
utterance classification; (3) conducting a linguistically-motivated comparison
of different unsupervised measures and (4) correlating these measures with
educational outcomes. We find that although repetition captures a significant
part of uptake, pJSD outperforms repetition-based baselines, as it is capable
of identifying a wider range of uptake phenomena like question answering and
reformulation. We apply our uptake measure to three different educational
datasets with outcome indicators. Unlike baseline measures, pJSD correlates
significantly with instruction quality in all three, providing evidence for its
generalizability and for its potential to serve as an automated professional
development tool for teachers.

通过计算分析，我们提出了一个框架来衡量教师 - 学生交互式对话中的引用行为，并通过应用到不同领域的教育数据集中验证了该方法的有效性，为教师的自动化专业发展提供了证据。