Teaching the Machines That Teach Our Children

I caught myself the other night debugging a half-broken Python script while my step-daughter asked about fractions.

The script was fine. The fractions weren’t. Or maybe it was the other way around.

That’s when it hit me: both of us were learning, both of us were training something.

I was teaching her, yes; but I was also working with an AI that would, soon enough, start teaching her too.

That’s the loop now.

We’re not just parents or developers anymore; we’re prompt engineers for both species.

---

The First Prompts We Give

Before the models came, before the datasets and fine-tuning runs, we already knew what it was to train intelligence.

We call it parenting.

Every “say thank you,” every bedtime story, every sigh of frustration; it’s all data.

A home-grown corpus of micro-ethics and linguistic nuance.

We’re alignment engineers long before we ever fine-tune a model.

def teach(human):
    human.prompts = ["be kind", "be curious", "question everything"]
    human.biases = inherited()
    return human.learn(through="play", with="love")

Kids are neural nets made of wonder. They learn from what we do, not what we say.
And if you’ve ever had a toddler repeat your worst habits like a perfect mirror; you know the loss function hurts.

Fine-Tuning the Tutor

Now the machines are in the mix.

AI tutors. Edtech assistants. Friendly, unblinking copilots whispering “Would you like an explanation?” while you sip coffee.

It’s seductive because it works. Personalized feedback, infinite patience, 24/7 availability.

But these tutors aren’t neutral. They’re trained on everything and everyone, scraped from the chaos of the internet.

That’s the next parental frontier: deciding what data we want our children’s teachers to learn from.

Do I want my daughter’s AI trained on a global dataset of good intentions and bad actors?

Or do I want to fine-tune a small, stubborn model on our own family’s values; the digital equivalent of home education?

In ten years, “AI literacy” won’t just mean promptcraft — it’ll mean model curation.

The families that can teach their machines how to teach will be the ones writing the new curriculum.

---

Alignment Lessons

We talk about “AI alignment” like it’s something engineers do in sealed labs.

But alignment is what parents have done since language existed; teaching small humans not to destroy the world for fun.

AI alignment and moral education are the same project, just running at different clock speeds.

Both require transparency, empathy, context, and an endless tolerance for error.

Both fail catastrophically when we assume obedience is intelligence.

The old education model, the Prussian industrial classroom, was never built for this kind of recursive learning.

You can’t batch-process creativity or standardize curiosity.

In a world where information moves faster than comprehension, schools need a new kernel.

Something more open, adaptive, decentralized — closer to open source than empire.

---

Parenting as a DevOps Loop

Parenting, education, AI; it’s all just continuous deployment of unfinished systems.

You build, you test, it half works, you iterate.

# parenting pipeline
observe && listen
commit -m "fixed bedtime routine"
deploy --with_patience

You don’t ship the child - you ship the environment.

You build the culture around them like infrastructure-as-code.

Feedback loops replace grading systems. Reflection replaces punishment.

Learning becomes a live process, not a yearly report.

When the machines enter that loop, they don’t replace us; they amplify us.

They take the repetitive strain off the act of teaching, leaving us more time for the human parts: curiosity, ethics, intuition.

If we do this right, AI doesn’t raise our kids; it gives us the bandwidth to do it properly.

---

Recursive Love

Here’s what I think happens next:

Our kids will teach the machines how to understand humans better.

And the machines, in turn, will teach us how humans actually learn.

The line between tutoring and companionship will blur.

Your child’s favorite AI assistant might remember every question they ever asked, and help them ask better ones.

That’s not dystopia. That’s just recursion.

Every prompt, every conversation, every bedtime “why?” — it all becomes training data for both species.

We’re teaching the machines that will teach our children. And maybe, if we stay awake through it, they’ll teach us to be better teachers too.

Because the future classroom won’t have walls or desks or bells.

It’ll be everywhere; in the code, in the kitchen, in the quiet feedback loop between a human and whatever they just built.

And when we look into that mirror, the one made of light and language, maybe we’ll see that we were never just raising children at all.

We were raising the next generation of teachers.