  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.
