Machine Deep Learning or Deep Learning of Humans? Which is Correct: “Machine Deep Learning” or “Deep Learning of Humans”?

The term “deep learning” is one layer of artificial intelligence. In fact, deep learning is a key subfield of AI and machine learning whose structure was directly inspired by the biological neural networks of the human brain. As mentioned, the foundation of AI technology comes from neuroscience—just as the original computers were modeled on human memory.

But today’s advanced digital machines differ greatly from early simple computers. The digital world aims not only to copy human memory but also to implement the structure of neurons and the complex mechanisms of human neurophysiology so that machines do not have to receive data from outside every moment and can hold the information needed to perform a task in an instant.

So far, the design of advanced digital machines seems to have worked well. Where is the problem? Why, as the digital industry advances, does the human–machine interaction still break down? Why does AI remain distant from the real world of human users, when these users—clients, patients, and service recipients—must trust the technology, share information with it, and receive information from it?

As a psychologist in the learning sciences, I believe the problem is the digital industry’s lack of attention to “human deep learning.”

Where does human deep learning come from?

Until now, attempts have focused on defining deep learning for machines, and designers claim that relying on neuroscience finishes the job. But if neuroscience were sufficient, we would see fewer challenges today. The problem is that data scientists often forget that the ultimate goal is the human being—and humans are the most complex creatures. Neuroscience is only a small part of the knowledge about the mind and human development. To design human-like machines (for example, in medicine, therapy, or rehabilitation), we must explore a much broader and more diverse range of dimensions of user learning.

I call this “human deep learning.”

Although the term “deep learning” may not appear explicitly in some learning and psychology literature, its ideas are present throughout rich scholarship in the learning sciences. Cognitivist approaches in learning science—which view learning as meaningful, durable understanding or as deep change in thinking—are aligned with deep learning. These approaches argue that when a human deeply learns a belief or concept, the change endures and transfers to different but similar situations. This kind of learning is purposeful and therefore shows up in learners’ behavior and performance.

Cognitive and learning theories that speak to the idea of deep learning include Gestalt theory, Piaget, Vygotsky, Bandura (cognitive-behavioral approaches), and others.

What should we do?

If we want to design digital machines’ deep learning with these perspectives in mind, we must focus much more on behavioral sciences and learning psychology. Then the problems of mutual understanding and interaction between machines and humans will become more manageable.

As noted earlier, AI layers have been built heavily on data science, and designers claim to mimic neuroscience, but human sciences are not limited to neuroscience. If interaction between humans and machines is to occur, the processes of meaningful human learning—seen through learning science—must be discovered and used to guide machine design.

To what extent have designers taken steps in this direction?

Conclusion: Redefining AI layers

It is time to reconsider the layers of artificial intelligence. In common models, AI foundations rest mainly on data, algorithms, machine learning, neural networks, and deep learning. But if the ultimate purpose of this technology is to serve and effectively interact with humans, we must add another foundational layer: the learning sciences.

In two recent papers I wrote for the HTLH Europe 2026 conference and published in MedikaLife , I addressed the importance of the learning sciences. The first, “Human-Centered AI in Digital Health: Why Learning Sciences Matter,” discussed why learning sciences matter in digital health, a key application area for AI. The second, “Operationalizing Learning Sciences for Human-Centered AI in Digital Health,” explained some practical principles for applying learning sciences in digital health.

In this article I tried to highlight the central connection between learning sciences and AI—what I call “deep learning.” For that reason I reshaped the common pyramids that describe AI layers according to my perspective and the arguments I presented in my papers, and I proposed a new pyramid.

Learning sciences are not just an academic field; they are the foundation for understanding how humans learn, decide, change behavior, and grow cognitively. At the core of this foundation are cognitive psychology, behavioral sciences, motivation, social learning, self-regulation, and meaningful learning.

This set is what I call “human deep learning.”

In this framework, the future evolution of AI (and all AI-linked domains such as digital health) should be modeled by a pyramid in which learning sciences and human deep learning are not a side layer but the foundation of the whole structure. If AI is to work effectively in medicine, education, mental health, and other human-centered fields, it cannot rely only on data and inspiration from neuroscience. The next generation of AI must learn that humans and their learning processes are central and foundational.

The next generation of AI must learn how humans learn.

References

Ferdosipour, A. (2026). How can instruction and learning lead to deep learning in the learner? The answer from Gestalt cognitive psychologists. The Learning Guild (Accepted/In Press, July 2026 publication).

Ferdosipour, A. (2026). Why AI needs Vygotsky: The case for AI-based intentional friction. The Learning Guild.

Ferdosipour, A. (2026). Human-centered AI in digital health: Why learning sciences matter. Medika Life. https://medika.life/human-centered-ai-in-digital-health-why-learning-sciences-matter/

Ferdosipour, A. (2026). Operationalizing learning sciences for human-centered AI in digital health. Medika Life. https://medika.life/operationalizing-learning-sciences-for-human-centered-ai-in-digital-health/

Ferdosipour, A. (2026). Why biological learning demands the friction we seek to delete? Medika Life.

Ferdosipour, A. (2026). The shift from pure modernity to human-centered modernity. Medika Life.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Piaget, J. (1950). The psychology of the child. Basic Books.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Bandura, A. (1989). Social cognitive theory. Annual Review of Psychology, 40, 1–25.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning: A textbook. MIT Press.