Congress Couldn’t Write AI Rules for Schools. 98 Teenagers Did.

There’s no national policy for how AI should be used in schools. Not one. States are passing their own patchwork of rules, districts are writing their own, and most families are left guessing.

So a group of teenagers did what Congress hasn’t: they wrote the bill themselves.

On a late-July weekend, 98 teens from all 50 states gathered at the Edward M. Kennedy Institute for the United States Senate — inside a full-scale replica of the actual Senate chamber — and spent the weekend debating, amending, and voting on legislation to govern AI in K-12 schools. They called it S 2026, the Students First Act. It passed 83 to 15.

Here’s the part worth sitting with: the student-focused section of that bill has 15 provisions, and several of them circle back to the same worry, again and again, that AI is doing their thinking for them.

That’s not a rule an ed-tech company would write. It’s not a rule most school boards have written either. It’s a rule from the people actually sitting in the classroom, who can apparently tell the difference between AI that helps them think and AI that thinks for them, even when the adults writing policy around them mostly can’t.

Why This Matters More Than Another AI-in-Schools Headline

At Auxesis, we’ve spent a long time naming this exact problem: the illusion of mastery. It’s what happens when a student looks like they’ve learned something, the essay reads fine, the answer is correct, but the actual thinking never happened. Good output, no understanding underneath it.

Usually we’re the ones making that case, citing a study or a researcher. This time, 98 teenagers made it themselves, from the inside, without anyone telling them to. They didn’t call it the illusion of mastery. They called it AI doing their thinking for them. Same problem, plainer words.

That cuts against the two lazy stories adults tend to tell about kids and AI: either kids will use it responsibly with no guardrails needed, or kids can’t be trusted with it at all. The teens at the Kennedy Institute didn’t take either position. They drew a real line, AI for editing and brainstorming, yes, AI for the writing itself, no, the same kind of line Auxesis draws with the CPA framework (Concrete, Pictorial, Abstract) in math: AI can support you at any stage, but it can’t do the stage for you. Skip the struggle, and the mastery you end up with is fake, no matter how correct the final answer looks.

One of the provisions the teens landed on: no AI for writing. Editing, brainstorming, and studying with AI would be allowed starting in eighth grade — but the writing itself has to stay theirs.

What This Means for You

If you’re a homeschool parent, this is permission to trust your instinct that letting AI just explain it and letting AI help you think it through are not the same thing, a group of teenagers just spent a weekend proving they know that too.

If you’re an educator, this is a template. The Students First Act draws a line by task, not by blanket ban: editing and brainstorming are fine, the core cognitive work isn’t. That’s a more useful starting point for your own classroom AI policy than most of what’s floated around this year, and it’s the same logic behind Auxesis’s Facilitator Certification, which is built around exactly this judgment call: knowing when AI belongs in the room and when it doesn’t.

There’s still no national policy. But for one weekend, in a replica Senate chamber in Boston, 98 teenagers showed they’d already figured out what the adults are still arguing about.

Source: NPR, July 30, 2026 (npr.org/2026/07/30/nx-s1-5853571/students-set-ai-policy)

Team the 98 teens’ rule, AI for editing and brainstorming, never for the writing itself, or team stricter: no AI at all until high school? Curious which side your house or classroom lands on.

Want a framework for making this call yourself? The Parent Track and Educator Track both walk through exactly where AI belongs, and where it doesn’t.

Two weeks ago, a new AI tutoring company called Bloomy launched out of Y Combinator with a number attached: students in an early pilot grew 1.8 times faster than expected on the NWEA MAP assessment, a widely used test of academic growth. The founder has been careful — he’s called it an observational pilot, not a randomized study, and the sample was about 150 middle schoolers at one Massachusetts charter school.

That caveat matters, and other outlets have already covered it well: no control group, no randomization, one school. If you want the stats critique, it’s out there.

We want to talk about something the coverage has missed — because it’s sitting in Bloomy’s own product design, not in a footnote.

The part of Bloomy that looks a lot like us

Bloomy’s platform moves students through three stages for every skill: Base Camp (worked examples), Climb (guided practice with an AI tutor), and Summit — an unaided, ten-question test with the tutor deliberately absent. Students don’t advance until they clear roughly 90% on Summit.

If that structure sounds familiar, it should. It’s the same logic behind our Mastery Gates: Explain, Apply, Sustain. A student doesn’t move forward because they got through the material. They move forward because they can perform without support, cold, when nobody’s coaching them through it.

Here’s the question that matters: why build that gate at all?

Because a growth number doesn’t tell you what you think it tells you

NWEA MAP growth is a well-respected, widely used measure — but it measures performance on a norm-referenced test. It tells you a student answered more items correctly, or harder items correctly, than before. It does not, by itself, tell you why — whether a student built durable understanding, or got faster at pattern-matching the kind of problems that show up on that kind of test

That gap between “the score went up” and “the understanding is real” is what we call the illusion of mastery: the condition where a student looks like they’ve learned something, but the underlying thinking never actually happened. Good grades without reasoning. A rising RIT score without a student who can explain what they did.

The interesting thing is that Bloomy’s own architecture seems to already know this. If a rising test score were sufficient proof of learning, you wouldn’t need a Summit gate. You’d just assign more problems and let the score climb. The fact that Bloomy built an unaided, high-bar checkpoint before letting a student move on suggests someone on that team understands the difference between “the number went up” and “the student can actually do it alone” — even while the marketing leans on the number.

What this means for you

If you’re evaluating any AI tutoring tool — Bloomy or otherwise — for your family or your classroom, the growth-percentage headline is the least useful thing to ask about. The better questions:

• Does the tool require a student to perform without help before it lets them move forward?
• Can you see why a student got something wrong — a specific misconception — not just that they got it wrong?
• Does “mastery” mean “answered correctly,” or does it mean “can explain the reasoning, apply it somewhere new, and still have it three weeks later”?

That’s the standard we hold our own Mastery Gates to, and it’s the standard worth holding any tool to — including ours.

If you run a homeschool co-op, a microschool, or lead professional development for other educators, here’s a piece of news worth five minutes of your attention.

The National Science Foundation just awarded $11 million to the Computer Science Teachers Association to run something called AI Professional Development Weeks — a multistate effort to train K-12 teachers in AI and computer science fundamentals. Over the next two years, it will run in Indiana, South Carolina, Minnesota, New Jersey, Iowa, and Illinois, plus at least three more states still being added. The program will directly train roughly 2,500 to 3,000 teachers, with a goal of reaching 500,000 to 600,000 students through them.

That’s a real, federally-funded bet that teachers — not just students — need structured AI training. It’s also, if you lead a co-op or a microschool, a signal worth reading closely, even though almost none of the coverage so far has said a word about you.

What the coverage is missing

Every article on this program so far has been written for a district administrator or a policy reporter. It covers funding numbers, participating states, and rollout timelines. What it doesn’t cover – because it isn’t built to – is what any of this actually looks like once a teacher walks back into a classroom with a week of AI training under their belt.

A week of AI PD can teach a teacher what tools exist and how to use them. It can’t teach them how to use those tools without quietly letting AI do the students’ thinking for them – the difference between augmenting a lesson and replacing the learning inside it.

Why this matters for co-ops and microschools specifically

You likely can’t register for this one. The one piece of eligibility detail available – Illinois’s stipend criteria – limits funded participation to “public and public charter school teachers,” and nothing in CSTA’s public materials suggests a path in for homeschool co-ops or independent microschools. But the demand it’s responding to is one independent educators feel just as much, with no federal grant answering it.

Here’s the concrete version: imagine a co-op teacher who hears about AI PD Weeks, wants the same caliber of training, but isn’t in one of the funded states or isn’t part of a public district at all. She doesn’t need a district-scale program. She needs the same underlying question answered: how do I use AI in my teaching in a way that builds my students’ thinking instead of shortcutting it? That’s a pedagogy question, not a tools question – and it’s the one federal AI-in-education funding, so far, hasn’t been built to answer.

Where Auxesis fits

This is the exact gap the Educator Track exists to close – not tool training, but the facilitation layer underneath it. Moving from “explainer” to “facilitator,” learning to ask instead of tell, and using COMPASS as the pedagogy layer that sits on top of whatever AI tools a teacher already has access to. A teacher doesn’t need to wait for a state grant to start building that skill.

The takeaway

$11 million in federal funding for teacher AI training is a genuine signal: this is now considered infrastructure, not an extra. If you lead a co-op, a microschool, or PD for other educators, the honest takeaway isn’t “we should get a grant like that.” It’s that the underlying need – teachers who can use AI well, not just teachers who’ve seen a demo – is universal, funded or not. The Educator Track was built to meet that need directly, no state eligibility required.

Half of U.S. school districts now say they’ve trained their teachers on AI. A year earlier, it was about a quarter. That’s a real, fast shift — backed by a nationally representative RAND survey of the American School District Panel, not a press release.

Here’s the part worth sitting with: when RAND’s researchers asked district leaders what “training” actually meant in practice, the honest answer, over and over, was we made it up as we went. Eleven of the fourteen district leaders RAND interviewed built their AI training programs themselves, from scratch, because the outside options weren’t good enough. One leader put it plainly: “There are people that are claiming to have the best practices and are making money hand over fist… if they claim to be telling you best practices, they don’t have them yet. They don’t exist yet.”

That’s not a knock on those district leaders — they’re doing real work under real time pressure. It’s a diagnosis of the moment. AI arrived in classrooms faster than anyone built the professional development to match it, so schools are running the experiment live, with actual students, mostly measuring success by whether teachers stopped being afraid of the tool.

That’s the real gap this piece is about. Most of the training districts describe is tool training: how ChatGPT works, how to write a prompt, how to use an AI lesson-planning assistant. That’s a reasonable first step — teachers who are anxious about a tool can’t use it well, and RAND found addressing that fear was nearly every district’s starting point. But tool literacy and facilitation are two different skills, and only one of them protects the thinking in the room.

A teacher who knows how to prompt ChatGPT can still end up, without meaning to, running a classroom where the AI does the reasoning and the student does the copying. Nothing about “how to use the tool” teaches a teacher to notice that moment, or to redirect it. That’s a facilitation skill — the ability to read whether a student is genuinely stuck or just handing off the thinking, and to ask the next question instead of supplying the next answer. It’s the same shift Auxesis’s Educator Track builds toward: moving from Explainer, who fills every silence with an answer, to Facilitator — from Practitioner to Coach.

Picture two versions of the same fifth-grade classroom, six months into AI rollout (illustrative, not a real case). In the first, teachers got a solid half-day on an AI planning tool and were told to “play around with it.” Word-problem homework improves fast — suspiciously fast. Kids paste the problem into a chatbot, copy the steps, turn it in. The teacher, trained on the tool but not on what to watch for, sees clean homework and reasonably assumes the class is ahead of schedule. In the second, teachers got the same tool training, plus one more habit: asking “walk me back through how you got there” before accepting an answer as done. Same tool. Very different classroom, six months in.

That second habit is what Auxesis calls a Facilitation Brief — a short, structured read before a session on where a student actually stands, so class time goes to facilitating instead of re-diagnosing from scratch. It’s one piece of COMPASS, the operating layer Auxesis builds around AI in the classroom: AI helps before the session and after it, in the briefing and the five-minute note; the actual teaching stays human, on purpose.

There’s also an equity story here, and it’s worth naming plainly. RAND found low-poverty districts have consistently trained teachers on AI faster than higher-poverty ones — 43% versus 6% in fall 2023, 67% versus 39% by fall 2024 — and district leaders’ own projections show that gap holding into the 2025–2026 school year, with almost all low-poverty districts trained and only around six in ten high-poverty districts there yet. Whatever training model turns out to work will reach wealthier schools first. That’s one more reason the training that does exist should be built around facilitation, not just tool onboarding — a district that gets one real shot at AI professional development shouldn’t spend it on prompt-writing alone.

None of this argues against training teachers on AI faster. It argues for training them on the right thing. The tool is the easy part to teach. Reading a classroom, protecting productive struggle, and knowing when to step back instead of stepping in — that’s the harder, more durable skill, and it’s the one most current AI-in-schools training is skipping past.

If you’re building or choosing AI professional development for your school or district, the question worth asking isn’t “does this cover the tool.” It’s “does this teach my teachers to notice when a student stopped thinking.” The Educator Track’s facilitation modules exist to answer exactly that — not as a replacement for tool training, but as the layer most programs are currently missing.

Source: Melissa Kay Diliberti, Robin J. Lake, and Steven R. Weiner, “More Districts Are Training Teachers on Artificial Intelligence: Findings from the American School District Panel,” RAND Corporation, 2025.

A follow-up to The 17% Tax — same research family, different question.

We wrote recently about the OECD’s finding that AI-assisted math practice can look great and still leave nothing behind — up to 17% worse performance once the AI is taken away. That post ended with one diagnostic question: “walk me through how you got this.”

There’s a second piece of evidence worth its own post, because it answers a question the math study doesn’t: how fast does the gap open, and does it show up outside of math?

The one-hour test

A study cited in the same OECD Digital Education Outlook 2026 had students across several US universities write a short essay — one group alone, one with a search engine, one with a general-purpose chatbot doing much of the drafting. One hour later, researchers asked each student to quote a sentence from what they’d just “written.” Among the unaided and search-engine students, 89% could. Among the chatbot group, only 12% could.

Worth being precise here, in the same spirit as the caveat we ran last time: the specific study behind this appears to be MIT Media Lab’s “Your Brain on ChatGPT” research (Kosmyna et al.) — a small trial (54 participants), still a preprint, not yet peer-reviewed. Different write-ups of it report slightly different numbers (some cite 90%/17% instead of 89%/12%), which is normal for early-stage research moving through secondary coverage, but it means this shouldn’t be treated as a settled, precise figure — just a strong, repeatable signal in the same direction as the math result: fast AI produces work that looks finished but was never really held by the student who “wrote” it.

One hour. Not a semester, not a unit test — sixty minutes was enough for four out of five students to lose their grip on their own sentences.

Why one question isn’t enough for this one

The “walk me through how you got this” check works well for a worked math problem because there’s a step-by-step path to retrace. Writing doesn’t hand you that same rope. A finished essay doesn’t show its work the way a solved equation does — which means the single-question check from the math post genuinely won’t catch this failure mode. You need something with more structure.

That’s what Auxesis’s Metacognition framework is for — three questions, asked in sequence, that work regardless of subject:

• Monitor — “Before you turn this in: what’s the one sentence in here that’s most you? Point to it.” If your child can’t find one, that’s the tell — not a bad grade, just a flag that something outside their own head produced the words.
• Regulate — “What would you do differently if you had to write this again without any help?” This isn’t a punishment question. It’s the moment that turns a flagged gap into an actual second pass — the regulation step is what separates “I noticed this wasn’t mine” from “I fixed it.”
• Evaluate — Circle back a day or two later, unannounced, with a version of the one-hour test: “Explain the argument you made in that essay.” If they can’t, you’ve learned something real about the assignment — and caught it while it’s still cheap to address, not at the next test.

Why this can’t be a one-time fix

The math study and the essay study point at the same underlying mechanism from two directions: performance during the task tells you almost nothing about what’s going to stick. The only way to know is to check after — which is exactly what Monitor/Regulate/Evaluate is built to do, and exactly what a finished worksheet or a polished essay can’t tell you on its own.

This is also why it’s a framework and not a one-off question: the math post’s single check catches one failure mode; a repeatable three-step loop catches it across subjects, because “did my child actually think this through” isn’t a math-specific problem — it’s the same one whether the tool wrote an equation or a topic sentence.

If you want the full walkthrough of how we teach Socratic questioning at home — the same instinct that powers Monitor/Regulate/Evaluate — that’s covered in the Parent Track.