Human-Machine Peer Learning (HMPL): What and Why? [CC BY-NC-SA] by Prof. Dr. Dr. Daniel Devatman Hromada /


In this Boardroom Dialogue we will discuss the possibility of humans and AIs becoming "peers" in helping each other to acquire skills and competences. 

The dialogue will start with introducing the concept of Human-Machine Peer Learning (HMPL) and exploring its potential to provide a paradigm for constructing human-machine learning curricula from which both humans as well as machines benefit. 

Participants will acquire both the theoretical concept of human-machine peer learning and concrete insights into how HMPL is implemented through the illustration of two prototypical learning scenarios where HMPL is already deployed.

Why ? (my personal narrative)

"obsessed" by human-robot relations from early childhood onwards (hello Johny 5!)

Čapek, Asimov, Turing, Cyberpunk ...

since beginning of my academic life I try to transpose main elements of ontogeny of human intelligence into computational domain (Master: Facial expression recognition; PhD: Computational models of language acquisition)

2011 Paper "Central Problem of Roboethics: from defintion towards solution" provides a potential solution to what AI folks label these days as "Alignment problem"

the solution is: To raise machines as we raise our (own) children.

one of the last pieces of puzzles is "peer learning"

Why and why not ?

To prepare for the discussion which will ensue, please think about the role which machines, AIs - and associated concepts, myths and archetypes - played (until this day) in Your own personal narrative.



dall-e 3: "illustration on black background of concept human-machine peer learning where machine learns from human and human learns from machine"

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:

Humans and machines can learn together.
Humans and machines can learn from each other.

Human learning


dall-e 3: "illustration on black background of essence of human learning"

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


dall-e 3: "illustration on black background of peer learning"

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.



dall-e 3: "illustration on black background of peer learning, person A teaching X to B and B teaching Y to A , A is in the center of the first circle of one color and in periphery of the second circle, B is in the center of the second circle and on the periphery of the first circle"

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.



dall-e 3: "illustration on black background of essence of montessori education, no text"

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


dall-e 3: "illustration on black background of essence of machine learning"

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.

Caution !


dall-e 3: "illustration on black background elucidating difference between carbon-based process of human/organic learning and silicon-based process of machine learning. do not use light bulbs"

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



Table 1 from article Hromada & Kim (2023) Frontiers in Education

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.).


Note that HMPL is more than a theoretical concept. It is happening. Here. Now.
Humanity teaches the big & mighty ones (e.g. GPT4, DALL-e 3) and big & mighty ones provide educational & cognitive services in return. 
But what about the "small" ones, the "adaptive" ones, the "personalized" ones, the embedded (or embooked ?) ones ?

Vocabulary learning

Front. Educ., 2023
Sec. Digital Education
Volume 8 - 2023 |
Proof-of-concept of feasibility of human–machine peer learning for German noun vocabulary learning

Reading acquisition

In the second Hromada & Kim (2023) article, we describe first, syllable-oriented exercise by means of which the Primer aimed to assist one 5-year-old pre-schooler in increase of her reading competence. The pupil went through sequence of exercises composed of evaluation and learning tasks. Consistently with previous HMPL study, we observe increase of both child's reading skill as well as of machine's ability to accurately process child's speech.


0. Can an entity E be considered a "peer" even if it does not have a physical body ?

1. Can AI ever be a "peer" to a human being or is it a fallacy to believe so ?

2. Should an AI communicate what it is doing (e.g. showing 👂 when listening) or what it expects the human peer to do (e.g. show 👄 when H should speak) ?

3. Can You imagine a competence X which is being simultaneously acquired by a human H and a machine M within the context of their common mutual encounter ?

4. Can You imagine a competence A transmitted from H to M during the same encounter whereby competence B is transferred from M to H ?

5. What other question concerning the human-machine education should also be asked ?

Where do we go from here ?

Someone willing to join me on a journey towards
"Encounter": a journal on human-machine education


Join me & Zihern Lee and our two HMPL-positive Primers at "Personal Primer" how to workshop between 14:30 - 15:30 in the "Bishop" room.