We describe a pilot study on generating synthetic
explanatory dialogues for the medical domain,
based on a pre-existing medical dataset of multiplechoice questions with human-written explanations.
We use an instruction-tuned large language model
(LLM) to generate dialogues between a medical student and a teacher/doctor helping answer questions
about clinical cases. We inject varying degrees
of background knowledge into the teacher prompt
and analyze the effectiveness of these dialogues
in terms of whether the student is able to get to
the correct answer and in how many turns. This
method has potential applications in developing
and evaluating argument-based explanations
