  We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods.
