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The literature on machine teaching, machine education, and curriculum designfor machines is in its infancy with sparse papers on the topic primarilyfocusing on data and model engineering factors to improve machine learning. Inthis paper, we first discuss selected attempts to date on machine teaching andeducation. We then bring theories and methodologies together from humaneducation to structure and mathematically define the core problems in lessondesign for machine education and the modelling approaches required to supportthe steps for machine education. Last, but not least, we offer anontology-based methodology to guide the development of lesson plans to producetransparent and explainable modular learning machines, including neuralnetworks.

Machine Education: Designing semantically ordered and ontologically guided modular neural networks