Teaching Humans, Learning with Machines
This was a new challenge for me. A classroom full of adults all with that look -- the drift between eager to learn and anxious about learning. I was the instructor in a reskilling program at the Carnegie Library in New Jersey. My job was to teach web development to displaced workers who had spent years in jobs in factories and retail. Their faces told that displacement part well. They were all sitting in left seat on the runway and they had no idea how to fly an airplane.
I've taught across the learning spectrum — computer newbies who couldn't find the power button, college sophomores writing their first iOS app, certification candidates cramming Open Source Unix for a credential, and mid-career professionals trying to recompile their jpob opportunities through code. Each group arrives on their stairway landing with different fears and different metaphors they use to make sense of the landscape. The fundamental transaction is always the same: a person on the edge of their competence, looking st a gap, and asking someone they barely know to help them build a bridge
When you teach a true beginner, you can't start with abstractions. You have to find the concrete thing they already understand and build from there. In a Basic Computer Literacy course, I explained differences in database servers by comparing them to a Diner during the breakfast rush — requests come in, the cook (server) handles them in turn, things get backed up (queues), the grill needs more people cooking (scaling and replication). The metaphor clicked because the Diner experience was shared among them all. I wasn't just teaching them something new; I was using language and examples they all knew fluently.
AI does something remarkably similar. When I ask it to explain a Kubernetes deployment strategy, it doesn't launch into a whitepaper. It asks what I already know, or it guesses based on context, and it finds the analogical handhold. It's not always right — sometimes the metaphor is strained, sometimes it assumes I know Docker when I barely know what a container is. But the impulse is identical: meet the learner where they are, and move forward.
The intermediate learner is a different creature entirely. They've built something that works, which gives them confidence, but they haven't yet built something that fails spectacularly. Teaching them means letting them wander into the goo of complexity and then helping them get unstuck so they can find their way out. I taught iOS development to students who could get a button to change color but couldn't yet reason about memory management or delegation patterns. The temptation is to hand them the answer, but the actual teaching happens in the struggle —in the moment where they stare at structs and classes and realize the Xcode tool they used to create them now demands the language be understood.
AI struggles here. Not because it lacks the knowledge, but because it lacks the patience for productive frustration. When a student is circling a concept, a human teacher reads the confusion and lets the silence hang. AI, optimized for helpfulness, tends to fill that silence with the answer. It's like having a tutor who can't stand to watch you squirm and teaches very little. Squirming is often where the learning happens.
The advanced learner is easier in some ways and harder in others. By the time someone is studying for an Open Source Unix certification or architecting a web application, they know what they don't know. The teaching relationship becomes collaborative. You're not handing down knowledge from Olympus; you're comparing notes with someone who has a different vantage on the same mountain.
Here AI shines. It has read more documentation than any human could memorize. It can rattle off the difference between FreeBSD's jail system and Linux containers, cite the relevant man pages, and suggest edge cases I hadn't considered. It's like having a colleague who never sleeps and has perfect recall of every technical manual ever written. There is an incompleteness to it. The advanced learner isn't just looking for information; they're looking for judgment. They want to know whether a particular architectural choice is wise, not just possible. They want someone to say, "We tried that in 2019 and it was a disaster." AI has no 2019. It has no disaster. It has only patterns and probabilities, a knowledge structure without synthesis --a long way away from wisdom.
The reskilling audience taught me the most about what learning actually requires. These were people who had been told by the economy that their skills no longer mattered, and now they were being asked to bet on themselves one more time. The emotional stakes were enormous. A syntax error wasn't just a syntax error; it was confirmation of their fear that they weren't smart enough for this. A working script wasn't just a working script; it was evidence that they could still grow, still adapt, still matter.
No AI can see that fear. No AI can offer the look across a desk that says, "I know this is hard, and I know you're scared, and I'm not going anywhere until you get it." The human teacher is not just a source of information; they're a witness. They bear testimony to the student's struggle and eventual triumph. They create a context in which failure is survivable because someone else is watching and believes it will be okay.
And yet. AI is always there. It doesn't get tired, doesn't judge, doesn't carry the baggage of its own bad day into the interaction. A student who is embarrassed to ask a "stupid question" in front of a human will happily ask AI the same thing at 2 AM, phrasing it awkwardly, getting a patient answer, and trying again. There is no shame in the machine. For learners who have been corralled by shame before — by classrooms where they fell behind, by colleagues who made them feel small — AI is a sanctuary.
I've come to think of it this way: human teaching is like running with a partner. They set the pace, they encourage you when you flag, they notice when you're about to redline and suggest a slower target. They share the struggle. AI teaching is like a treadmill. It's always ready, always consistent, never judgmental. It will run with you for as long as you want, at whatever pace you choose. But if you trip, it won't catch you. If you need to stop and cry, it won't hand you a towel and tell you about the time it felt the same way.
The best learning probably happens in the space between — where AI handles the informational heavy lifting, the patient repetition, the infinite availability, and the human handles the witness, the belief, the judgment born of scars. I taught a zillion students over the years, and I remember the ones who were struggling more vividly than the ones who breezed through. I remember the looks of "Aha" in the BCL class when the diner metaphor clicked. I remember the woman in the reskilling program who cried when her first PHP form actually submitted a POST. AI will never remember any of it. But it will be there for the next one, and the next, and the next, patient and tireless and empty.
Perhaps that's the real comparison. We teach because we were taught. The chain of human knowledge is also a chain of human care. AI is a tool — an extraordinary one, a transformative one — but it's not a link in that chain. It's a convenience that returns what we ask of it, without ever understanding why the question mattered.
