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If you teach and you read the research on generative AI, you have probably noticed that it gives you two sets of instructions. One set of studies reports that language models personalize instruction and improve performance. Another reports cognitive offloading, weakened memory traces, eroded critical thinking, and what Fan and colleagues called “metacognitive laziness.”
A teacher reading current research in good faith is told both to adopt the technology and to restrict it. I have started calling this the Practitioner’s Paradox, and I think it is the most consequential unresolved problem in educational technology today. That paradox is not a result of flawed research. Instead, it points to a mistake in how the research question is being asked in the first place.
Kestin and colleagues ran a crossover trial in undergraduate physics and found that a purpose-built AI tutor produced larger immediate learning gains than an in-class active-learning lesson. Similarly, Bastani and colleagues ran a field experiment in high school mathematics and found that access to a general GPT interface improved practice performance and then lowered performance on the unaided test that followed. A second tutor that withheld answers and gave teacher-designed hints largely erased that loss. And Fan’s writing study found that ChatGPT improved essay revision scores while producing no advantage in transfer.
All of these results are academically sound. They just do not add up to a single consolidated answer about “the effect of AI on learning.”
I do not think such an answer exists. To address this properly, I have been developing a theory I call Synthetic Constructivism, which grew out of my earlier work on game-based learning. Its main claim is that the contradiction dissolves once you stop asking what the tool does to the learner and start asking what the learner and the tool do together.
An academic paper on the theory has just been accepted for ICERI2026, the 19th International Conference of Education, Research and Innovation, in Seville this November, and I have been invited to give a keynote about it at LLM2026, the International Conference on Large Language Models, in Las Vegas in December. Both of those are targeted at academic researchers and are fairly technical. So in today’s post, I want to lay out the core of that theory in plain terms and then state what it asks of teachers.
What the games debate taught me
Game-based learning spent about twenty years stuck in the same kind of argument. The General Aggression Model (Anderson & Bushman) held that violent games train players in aggressive patterns of thought. The Catalyst Model (Ferguson et al.), on the other hand, held that the medium is a negligible trigger for aggression driven by biology and environment. Neither could move the other, because both assumed a linear media-effects model in which a stimulus produces an effect in a passive recipient.
In a keynote given at the 2011 Clash of Realities conference in Cologne, Germany, I argued that the way out was to drop that model completely and treat a digital game as a closed feedback loop that the player helps constitute. With that framing, learning is a stabilization of action inside the loop rather than an effect transmitted by a medium. And once learning belongs to the loop, contradictory findings stop being anomalies. They are what you should expect from a system that is sensitive to who the learner was before they started to play.
Generative AI belongs to the same class of media, but while a game constrains you with fixed rules, a language model constrains you with probabilities over an open-ended space of text. Every prompt and correction reshapes what comes next. Consequently, expressive power goes up, and so does path dependence. That is why I think the cybernetic frame fits AI-assisted learning even better than it fit games.
You are the one watching the loop
Radical constructivism, in the version developed by Ernst von Glasersfeld, holds that what we call knowledge is a construction, and that the test of a construction is whether it works. Von Glasersfeld’s image for this is a key in a lock. A key that opens a lock tells you nothing about what the lock looks like inside. It only tells you that it fits. Knowledge, on this view, is a key that fits, and there is no way to check it against the lock directly. The constructing itself is unified and internal, the mind watching and revising its own operations mostly below awareness.
When you work with a language model as a learner, that process splits in two. The model executes the symbolic operations of the task. It drafts, summarizes, restructures, and explains. You stand outside that work, directing and monitoring it, and you decide whether to accept what it produced. In the vocabulary of Heinz von Foerster, the model is a first-order cybernetic system and you are the second-order observer regulating it.
Together, the two positions form a feedback loop. Projection is the feed-forward half: you push a mental model or a set of constraints into the system as a prompt. Back-Projection is the feedback half: the output the system returns to you for evaluation. Because the loop is externalized, the thinking that is normally silent becomes visible. You can read your own half-formed idea back off the screen and revise it. I call this an Epistemological Laboratory.
But this is also the trap. The first-order system keeps producing fluent output whether or not anyone is judging it, and a fast run of completion requests just externalizes action while bypassing monitoring entirely. Which raises the question of what makes a good second-order observer, and here a sixty-year-old theorem in cybernetics can provide the answer. Conant and Ashby showed that a regulator can only be as good as its understanding of what it regulates.
In other words, a learner who does not understand how the model fails, or what counts as evidence in the discipline, cannot regulate the loop. Prior knowledge and metacognitive skill are the controls. This means that machine capability is not the binding constraint on educational outcomes. Learner-side model quality is.
Why identical tools produce opposite results
It is that last point that causes the practitioner’s paradox to fall apart. Mathematically speaking, the loop runs in a high-dimensional space of possible text. Systems like that are highly sensitive to the initial conditions the learner supplies. Prior knowledge decides what a student can ask and which errors they can see. Metacognitive habit decides whether they check or accept.
The result is that small differences get amplified across iterations. This is effectively a butterfly effect, applied to learning. And so, two students with the same tool and the same assignment end up on completely different trajectories. The scaffolding literature and the erosion literature are therefore both accurate. They are just describing different states of the same system.
I call the most critical consequence of this the Non-Prompter Disadvantage. An experienced prompt writer projects viable prompts and reads the output critically, so the recursion settles fast onto something useful. A novice struggles to get the loop going at all. Their sessions drift, or they converge early on a fluent output that encodes no durable understanding. And that disadvantage compounds, quickly, because every failed loop deprives the novice of the practice that would have built the competence.
