  The traditional process of building interactive machine learning systems can
be viewed as a teacher-learner interaction scenario where the machine-learners
are trained by one or more human-teachers. In this work, we explore the idea of
using a conversational interface to investigate the interaction between
human-teachers and interactive machine-learners. Specifically, we examine
whether teachable AI agents can reliably learn from human-teachers through
conversational interactions, and how this learning compare with traditional
supervised learning algorithms. Results validate the concept of teachable
conversational agents and highlight the factors relevant for the development of
machine learning systems that intend to learn from conversational interactions.
