Front. Educ., 2023
Sec. Digital Education
Volume 8 - 2023 | https://doi.org/10.3389/feduc.2023.1063337

Human–Machine Peer Learning (HMPL) is a proposal that is positioned at the very frontier between educational, cognitive, and computer sciences. HMPL's core precepts which I introduced in my 2022 and 2023 papers are simple:

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.

Peer learning is an educational approach where individuals learn from and with each other. This concept is rooted in the idea that learning is a social and collaborative process. Unlike traditional teacher-student models, peer learning involves individuals of similar status sharing knowledge, ideas, and experiences. Both participants in the process can act as both teacher and learner.

In Vygotsky's view, peer learning is crucial. Peers can act as the more knowledgeable other within the Zone of Proximal Development, facilitating learning and skill development. He believed that interaction with peers leads to the internalization of knowledge.

The notion of "Zone of Proximal Development" refers to the difference between what a learner can do without help and what they can achieve with guidance and encouragement from a slightly more skilled partner.

In Montessori classrooms, children of different ages are often grouped together. This setting naturally facilitates peer learning, where older children can teach and model for younger ones, enhancing understanding and empathy. Montessori saw peer interactions as a key component of learning. In her view, children learn from each other in a collaborative, less competitive environment. This method encourages independence, responsibility, and social development.

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.

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.).