AI as a Translation Layer for Learning

I. On Shattering

Two weeks before my machine learning exam, I ran into a wall.

Take this formal definition of shattering found in my textbook:

Definition 6.3 (Shattering) A hypothesis class $\mathcal{H}$ shatters a finite set $C \subset \mathcal{X}$ if the restriction of $\mathcal{H}$ to $C$ is the set of all functions from $C$ to $\{0, 1\}$. That is, $|\mathcal{H}_C| = 2^{\lvert C \rvert}$.

From this definition it was unclear to me what exactly shattering stood for. I could parse the symbols and understand what each variable represented, yet, I could not tell you what it meant for $\mathcal{H}$ to shatter $\mathcal{C}$.

So I sent it to an AI.

The response came back:

A hypothesis class $\mathcal{H}$ (think of this as your "model," like a straight line) shatters a set of data points $\mathcal{C}$ if it is flexible enough to classify those points correctly, no matter how you label them.

Fifteen minutes of circular confusion for one sentence reframed slightly differently to make the whole thing... obvious. Interestingly this kept happening over and over again. I kept studying, kept hitting walls, kept sending things to the AI, and kept getting the same experience: from confusion to clarity.

By the end of the week I had started to think the AI was doing something genuinely important, something that wasn't just "answering questions." It was, I decided, acting as a universal translation layer for complex ideas.

II. But Translation Isn't New

Let me not oversell this, because the obvious objection is: teachers have been doing this for centuries.

That's true. A good professor can take a dense formal definition and translate it into plain English. That's basically what a lecture is. And before AI, you could ask your professor for clarification during office hours, or ask a TA, or beg a study group partner who happened to understand it better than you. Translation of complex ideas into accessible language isn't new. It's the job description of anyone in education.

So what is new?

Here's what I think: it's not the translation. It's the number of attempts you can demand before the translator gives up on you.

When a professor explains shattering in lecture, they give you one explanation, maybe two, and then they move on. They have thirty other students and fifteen more topics to cover before the exam. Even in office hours, there's a soft social ceiling on how many times you can ask the same question in different forms before you start feeling like a burden, or worse, like you're revealing a kind of fundamental incomprehension you would rather not advertise. I don't know how many times I've sat in office hours thinking I still don't get it but said nothing, because everyone else seemed to have moved on and asking a fourth time felt like announcing that I maybe shouldn't be in the course.

With AI, that ceiling doesn't exist.

I asked about shattering three different ways. "What does shattering mean?" Then: "I understand the definition but not why we care about it." Then: "Can you show me with actual numbers?" Each time I got a new angle, a new analogy, a new concrete example, each one calibrated to the specific gap I had just revealed by asking.

This is, I think, AI's real contribution to learning. Not that it knows more than a professor (it sometimes doesn't). Not that it's available at 2am (though it is). It's that it can give you the next explanation after the one that didn't work, and then the one after that, indefinitely, without any hint of frustration or impatience, until something finally clicks.

III. What's Actually Happening

Now here is where it gets interesting (at least to me, and hopefully to you, since you've read this far).

What is actually happening when AI does this? It's not just "more patient teacher." Something more specific is going on. When I said "I understand X but not how it connects to Y," the AI responded to that exact gap. It was maintaining a running model of where I was in my understanding, and generating each explanation from that model.

Textbooks can't do this. A textbook gives you exactly one explanation per concept, written for some imagined average reader who has certain assumed prerequisites and learns in a certain assumed way. If you're not that reader (if you find concrete examples more clarifying than abstract definitions, or if you happen to carry a specific misconception the author didn't anticipate, or if you just need the thing explained three different ways before any of them stick) you're on your own.

Lectures are better, because a good lecturer reads the room. They notice the confused expressions and slow down. They try a different analogy. But there are thirty of you, and the lecturer is optimizing for the class as a whole. They can adjust for the average confusion in the room, not for your specific confusion about your specific misunderstanding.

What AI can do, when it's working well, is adjust the explanation for you specifically, based on what you've told it you don't understand. This sounds obvious when you say it, but it's genuinely different from how almost all educational content works. "Personalized tutoring" is the marketing phrase, but that doesn't capture it quite right either. It's more like: AI can provide explanation on demand, customized to the exact shape of your current confusion.

The demand part matters. You have to ask. You have to be specific about where you're lost. The AI isn't reading your mind. But the payoff, when it works, is that you don't have to receive an explanation designed for someone else and try to extract what you need from it. You can just say "I don't get this part" and get an explanation designed for the gap you actually have.

IV. In Defense of Confusion

It's worth acknowledging the objection here, because it's a real one.

There's a view (I find it genuinely compelling) that struggling with something is part of how you internalize it. When you sit with a confusing definition for twenty minutes, turning it over, trying different interpretations, making mistakes and backtracking. You're building the cognitive infrastructure for understanding it deeply. You're mapping out the space of wrong interpretations before you learn the right one. The confusion is productive. The struggle is where the learning actually happens.

If AI makes it too easy to skip the confusion (if you can just hit a wall and immediately receive a clean explanation) maybe what you end up with is surface-level understanding rather than the real thing. You know what shattering means when someone explains it to you. But you don't know it in your bones the way you might if you would wrestled with it for an hour. You can recognize the definition. You can't yet feel when it should apply.

I think there's something to this. I'm genuinely uncertain how much depth I traded away by outsourcing my confusion to the AI rather than sitting with it longer.

But here's the counterpoint: a lot of confusion isn't productive. It just feels productive because you're working hard.

When I stared at the shattering definition for fifteen minutes, I wasn't building intuition. I was going in circles, getting more frustrated, and accumulating the vague sense that maybe I was just bad at this. The struggle only becomes productive once you have enough context to interpret it correctly. Before that, you're not failing in an educational way; you're just failing. You're a person banging on a door in the dark without knowing there's a handle.

A good explanation (the right explanation, at the right moment) doesn't short-circuit the struggle. It relocates it. Now that I know what shattering means, I can genuinely wrestle with why it implies what it implies about generalization, rather than burning time trying to figure out what the words mean. The AI moved me to a harder, better problem. The confusion is still there; it's just more useful confusion.

V. All the Locked Knowledge

All of which makes me think the real question isn't about studying for machine learning exams. It's about something much larger.

There is an enormous amount of human knowledge that is, in practice, locked. Not locked deliberately. It's all in libraries and on the internet, technically available to anyone. But locked in the sense that to understand a paper on options pricing, you need the mathematical prerequisites, which require their own prerequisites, which require yet more, and acquiring all of them takes years of specialized training. Same with medical literature. Legal documents. Monetary policy papers. Environmental impact assessments. Building codes. The terms of your own health insurance.

These things affect everyone's lives, and almost no one outside those fields can actually read them, not because people aren't smart enough, but because the translation layer is missing.

If AI can really act as that layer (not just for shattering, but for all of it) then the minimum viable literacy required to engage with hard ideas drops significantly. You still have to ask the right questions. You still have to think. You still have to do the work of understanding rather than just consuming explanations. But the years of prerequisite-gathering that used to gatekeep entry into complicated topics? Those compress dramatically.

I don't know what happens when that barrier falls. I'm not sure anyone does. Maybe it just means more people feel vaguely informed about things they still don't really understand, which might be worse than before. Maybe it means a much wider range of people can meaningfully engage with ideas that used to require professional training to even read. Maybe both are true at once in ways that are hard to disentangle.

The landscape of learning is changing. For the first time, the tools of understanding might truly be within reach, not just for specialists, but for anyone who wants to ask and is willing to keep asking.