  Machine Teaching (MT) is an interactive process where a human and a machine
interact with the goal of training a machine learning model (ML) for a
specified task. The human teacher communicates their task expertise and the
machine student gathers the required data and knowledge to produce an ML model.
MT systems are developed to jointly minimize the time spent on teaching and the
learner's error rate. The design of human-AI interaction in an MT system not
only impacts the teaching efficiency, but also indirectly influences the ML
performance by affecting the teaching quality. In this paper, we build upon our
previous work where we proposed an MT framework with three components, viz.,
the teaching interface, the machine learner, and the knowledge base, and focus
on the human-AI interaction design involved in realizing the teaching
interface. We outline design decisions that need to be addressed in developing
an MT system beginning from an ML task. The paper follows the Socratic method
entailing a dialogue between a curious student and a wise teacher.
