Your students are already using AI. This is a free, hands-on way to teach them how it actually works — and how to use it honestly — with nothing to install, no accounts, and no data leaving the room.
Free · No login · No accounts · No live AI · Nothing collected (COPPA/FERPA-friendly) · Standards-aligned
📄 Print the one-page Teacher Starter Kit →
Two age tracks, same honest voice — the hands-on kids rooms (ages 8–12, projectable in Presentation Mode) and the teen track (13+ — the feed, deepfakes, AI companions, and homework, honestly), which is already a station-by-station walk built to project. Both are jump-to-any-room, no login, nothing collected.
The kids lab (LearnAI4Kids) is content-complete, standards-aligned, and safe to use with students right now. The "we welcome educator feedback" notes on the site are about continuous improvement, not a safety hold. The practical details:
Nothing to install or sign into. Works in any browser — Chromebooks, tablets, phones, the projector. Just open the link.
No accounts, no logins, no tracking. The only thing stored is which rooms a student finished, kept locally on their own device. Nothing they type leaves the browser. COPPA/FERPA-friendly by design.
There's no chatbot and no AI running on the site. Every AI response a student sees is a real example, captured ahead of time and played back by the page. The activities are plain in-browser lessons (just JavaScript) that teach how AI works — so nothing a student types is ever sent to an AI.
Each exhibit ~3–5 min; each room ~15–25 min; the full lab ~2 hours — or one room at a time as a warm-up.
Assign rooms on devices and circulate — or, to walk a room on the board, use Presentation Mode: it steps through one concept at a time, big and high-contrast for the back of the room, with the narration ready to play. Advance with a clicker or arrow keys; the activities still run live on the board. (Or tap 🖥 Present on any room.)
The kids rooms are built for ages 8–12. Older students have their own teen track (13+) — the same fundamentals, pitched for high-schoolers. Ready for more depth? The adult journey.
Most teachers are. Do the 2-minute primer or the adult journey first — it makes leading the discussion easy.
Every exhibit cites its AI4K12 Big Idea on the page. Here's the room-by-room map you can paste into a lesson plan or hand to an administrator. Big Ideas: 1 Perception · 2 Representation & Reasoning · 3 Learning · 4 Natural Interaction · 5 Societal Impact.
| Room | Teaches | Big Ideas | A discussion question |
|---|---|---|---|
| 1 · What is AI? | AI predicts; it doesn't "know" | 3, 5 | What changed when you picked a less-likely word? When is a fast guess good enough? |
| 2 · The whole AI family | AI is many tools, not one chatbot | 1, 5 | Which kind of AI surprised you? Where do you meet AI without noticing? |
| 3 · When AI is wrong | Confidently wrong; why it happens | 2, 3, 4 | How could you tell it was wrong? What would you check before trusting it? |
| 4 · Is AI biased? | Bias comes from lopsided data | 3, 5 | What does "bias" mean? Where did it come from, and how would you make the examples more even? |
| 5 · Using AI well | Prompting; tutor vs. answer-machine | 4, 5 | How did changing your question change the answer? When should AI be a tutor? |
| 6 · Real worries | Deepfakes, privacy, cheating, "AI friends" | 1, 4, 5 | How can you spot a fake photo? Why isn't an AI "friend" a real one? |
| 7 · Your turn | Consolidation; the verify habit | 5 | Which super-question will you use? What will you try with AI this week? |
For older students (13+), the teen track teaches the same AI fundamentals in a voice built for teenagers — the same honesty, tuned to high-school topics (the feed, deepfakes, AI companions, privacy). The 10 teaching stations (plus an intro and a wrap-up) map to AI4K12 the same way the rooms do. Earning the Scouting America AI merit badge? The requirements line up with these stations.
| Station | Teaches | Big Ideas | A discussion question |
|---|---|---|---|
| 01 · What it actually is | AI predicts the next word | 3 | If it's "just guessing the next word," why does it sound so smart? |
| 02 · Under the hood | Tokens (not letters) + how it's trained | 2, 3 | Why can't it count the R's in "strawberry"? What did the rating step teach it to value? |
| 03 · Confidently wrong | Hallucination; confidence ≠ correct | 3 | How would you check a confident-sounding claim before you trust it? |
| 04 · The feed | Recommender AI optimizes for watch-time | 5 | What is your feed optimizing for — and how could you tell? |
| 05 · Is it biased? | Bias from a lopsided training pile | 3, 5 | Where does AI bias come from, and who has to fix it? |
| 06 · Use it well | Prompting — who / what / how | 4 | How did adding context change the answer? When should AI be a tutor, not an answer-machine? |
| 07 · Homework, honestly | Learning vs. offloading; policy first | 5 | Where's the line between using AI to prep and cheating future-you? |
| 08 · Real or fake | Deepfakes (photo/voice/video) + your likeness | 1, 5 | How can your own face or voice be misused — and what's the move if it happens? |
| 09 · The friend that isn't | Sycophancy + the Eliza effect | 4, 5 | Why does an AI "friend" agree with you — and why is that risky? |
| 10 · Free vs paid & privacy | Data, training-on-you, redaction | 5 | What should never go into a chatbot, and why does "free" change the answer? |
AI4K12 is the framework we map to exhibit-by-exhibit (table above). The approach — concept-first, honest about limits, safety- and citizenship-minded — is also designed to be consistent with the other widely-used K-12 AI guidance an administrator may want to see. Every link goes to the source.
| Framework | Who's behind it | How LearnAI4Kids relates |
|---|---|---|
| AI4K12 Five Big Ideas in AI | AAAI + CSTA | Our formal spine — every exhibit cites its Big Idea (table above). |
| TeachAI AI Guidance for Schools | Code.org, ISTE, Khan Academy, ETS | Matches its "teach about AI, use it responsibly, keep a human in the loop" stance — our AI-detector guidance follows it. |
| Common Sense Education AI literacy + family toolkit | Common Sense Media | Shares its digital-citizenship and family-conversation approach; our For Parents page plays the same role. |
| UNESCO AI competency frameworks (2024) | UNESCO | Reflects its human-centred competencies for students — understand, use critically, and question AI. |
| Digital Promise AI Literacy framework (2024) | Digital Promise | Built on the same three modes we practice: understand, evaluate, use. |
| ISTE Standards + AI guidance | ISTE / ASCD | Supports the same student-as-critical-user goals in the ISTE Standards for Students. |
We map formally to AI4K12; the others are the recognized landscape this resource is built to be consistent with — listed so you can check it against whatever your school or district already uses.
A "green / ask-first / not-OK" sort for real homework situations. Teaches the line between using AI and cheating better than a blanket rule — and starts the class conversation for you.
A printable card: "Did you make this up? · What if I'm wrong? · How could I check?" Laminate it and put it on the wall — it's a verify habit students can use on any AI, forever.
The one norm that handles most AI-use questions. Pair it with the copy-paste tutor prompt (on the parents page) that turns AI from answer-machine into patient tutor.
Don't rely on AI-writing detectors. They are not reliable enough to accuse a student. They falsely flag human writing — disproportionately for multilingual students and neurodivergent writers — and they miss plenty of real AI use. Several universities and districts have turned them off for exactly this reason.
What works better than detection:
Nothing here asks you to take our word for it. If you want to check the approach against the evidence, hand a skeptical administrator the citations, or brush up on a term before you teach it — it's all in one place, and all free.
Each factual claim on the site links to its primary source — public-opinion data, energy & water figures, deepfake-fraud reports, the cognition/learning-science studies, and the "how AI actually works" papers. Primary research and government reports only, no marketing listicles.
The honest read on the learning science — London cab drivers, the Google Effect, Lee et al. (Microsoft/CMU), MIT Media Lab — and why it depends on how AI gets used. The evidence you'd want to justify the "did you try first?" norm to an administrator or a parent.
Every term the lab uses — tokens, hallucination, training, bias, sycophancy, the Eliza Effect — defined in one or two sentences, with where it shows up. Good for your own prep or to project as a word wall.
LearnAI4Kids was built by one parent — a dad of four and a non-profit IT director — who uses AI every day and wanted honest, agenda-free AI information for his own kids, but couldn't find it. No ads, no sign-up, nothing collected, nothing for sale. The full story is on About this site.