Machine learning is a subset of artificial intelligence focused on developing algorithms that enable computers to learn and make decisions from data without being explicitly programmed. It involves training models on large datasets, allowing them to discover patterns and relationships. These models can then make inferences, decisions or predictions or generate outputs consistent with and contingent on the provided input even in cases where no such inputs were present during the learning process.

Human learning is a complex, multifaceted process that encompasses the acquisition, understanding, and application of knowledge and skills. It involves various cognitive, emotional, and environmental interactions that lead to changes in an individual's knowledge, behaviors, and attitudes.

On the material level, human biochemistry-based learning is fundamentally different from machine learning taking place on silicon-based transistors.

A human-machine peer learning curriculum (i.e., a HMPL-C) is a planned sequence of educational instructions–i.e., a curriculum–which involves:
1. At least one human learner G, H, I, ... which gradually develops her/his/their skill Γ.
2. At least one artificial learner a, b, c, ... which gradually develops its/her/his/their skill σ.
3. Activities by means of which G (resp. H, I, etc.) develops her/his/their skill Γ, which directly involve knowledge and competence exhibited by a (resp. b, c, etc.).
4. Activities by means of which a (resp. b, c, etc.) develops her/his/their skill σ, which directly involve knowledge and competence exhibited by G (resp. H, I, etc.).