  The literature on machine teaching, machine education, and curriculum design
for machines is in its infancy with sparse papers on the topic primarily
focusing on data and model engineering factors to improve machine learning. In
this paper, we first discuss selected attempts to date on machine teaching and
education. We then bring theories and methodologies together from human
education to structure and mathematically define the core problems in lesson
design for machine education and the modelling approaches required to support
the steps for machine education. Last, but not least, we offer an
ontology-based methodology to guide the development of lesson plans to produce
transparent and explainable modular learning machines, including neural
networks.
