AI in schools / AI literacy for staff
What every teacher should know about AI
Five short modules: how AI actually works, where it helps and hurts learning, exercises to try yourself, what it’s doing to the jobs your students will have, and how to use it without losing people’s trust. No math or tech background needed.
Module 1
How AI works
Every AI system, from a spam filter to ChatGPT, is built from one simple idea: find the line that fits the data.
Try it: the simplest machine learning models find trendlines
Seven short steps. Click “Next” to go through them.
Draw a line through the dots
Drag across the plot from left to right, where you think the line belongs.
When you drew your line, you were doing in your head what a computer does with arithmetic: picking the numbers that make the line miss the dots by as little as possible. For a straight line that’s two numbers. For a curve, you add pieces: each one is a line that only switches on past a certain point.
That’s a neural network. Each “neuron” multiplies, adds, and bends. Stack enough of them and the network can bend to fit almost any shape. “Training” just means nudging all those numbers, over and over, until the misses get small.
From curves to letters
Now swap the dots for text. Instead of “given x, where is y?”, the question becomes “given the letters so far, which letter comes next?” The network gives every letter a score, and the highest score wins.
This model learned from one short paragraph about teaching. It looks at the last few letters and picks the letter that most often came next in what it read. Try “the teach” or “a good q”. It can only echo what it read, and it has no idea what any word means.
Learning without an answer key
The dots in the curve lab came with answers: every x had its y, and the computer learned to match them. That’s called supervised learning, and it needs someone to supply the answers. Chatbots learn mostly without one. Hide the next word of any sentence, and the sentence itself is the answer key. Every sentence ever written becomes practice, with no one grading.
To get good at guessing the next word, the model has to pick up what words mean. Nobody tells it that “teacher” and “student” are related; it notices they show up in the same kinds of sentences. Inside the model, each word becomes a list of numbers, like a location on a map, and words used the same way end up near each other.
From autocomplete to assistant
A model that has only read the internet is a very well-read autocomplete. Ask it a question and it might continue with more questions, because that’s what the text it read often did. Two more stages of training turn it into an assistant.
Why it makes things up: every stage rewards answers that look right. When the model doesn’t know something, the most likely-looking answer is still a confident one. That’s why it can invent a quote, a source, or a date, and why checking its work matters.
Why early AI models made things up
Early chatbots were built to produce the next likely word, not to look anything up. Ask one for sources and it wrote text shaped like a citation: a plausible author, a plausible title, a plausible year. Nothing in training checked whether the paper existed. In one 2023 test, most of the research references Bard gave were made up or badly wrong, and so were more than a quarter of GPT-4’s (Chelli et al., 2024).
Labs have since changed what they reward. Assistants now search the web and quote the pages they found, and training increasingly penalizes confident wrong answers more than an honest “I don’t know.” On one independent test of questions models often can’t answer, OpenAI’s GPT-6 Astra guessed wrong instead of admitting it didn’t know 51% of the time, down from 92% for the model before it (Artificial Analysis, 2026).
Better, not solved. Models still make things up, especially about niche topics or without search turned on. Check any source before you rely on it.
Now make it enormous
Chatbots work the same way: predict the next piece of text, add it, repeat. The difference is size. Trained on a huge share of everything ever written, with billions of numbers to tune, the network has to model grammar, facts, and lines of reasoning to make good guesses. That’s where the abilities come from: not from rules anyone wrote, but from these same building blocks at a scale that’s hard to picture.
The curve you just fit
The letter predictor above
GPT-2 (2019)
GPT-3 (2020)
Today’s largest models
Each step on this chart is many times the last: the bars use a log scale, or the first two would be invisible.
Module 2
How AI can help or harm learning
Both are happening in your classroom already. The goal is more of the first and less of the second.
Harm: cheating, on purpose and by accident
The obvious harm is a student asking a chatbot to write the essay. The less obvious one is that AI now shows up uninvited, so a student can lean on it without meaning to.
Search engines answer before the student clicks a source.
Docs, email and slides offer to write the next paragraph.
Writing helpers now rewrite whole sentences, not just spelling.
How to tell what a student did
The most reliable tools aren’t software. For writing, ask students to work in one Google Doc: its version history (File → Version history) shows how the work grew. In-person writing and exams settle it entirely.
Writing done in class, or an in-person exam
Google Doc version history (for writing)
Asking the student to talk through their work
An AI detector’s score
Treat AI detectors as one piece of evidence, not a conclusion. In a Stanford study, seven popular detectors flagged essays by non-native English speakers as AI-written 61% of the time on average, though the students wrote every word (Liang et al., 2023). And 51% of students say they worry about being falsely accused of using AI (RAND, 2025).
Why students reach for it
Cheating is rarely about the tool. A student facing three big assignments at midnight is the one most likely to hand one off, and every handed-off assignment is practice they didn’t get. Fewer, smaller, more meaningful assignments, with some of the work started in class, take the pressure off.
Help: what AI is good for
A next step for students who are ahead
A student who finished early can get a harder problem, a counterargument to answer, or an extension project, without waiting for you.
Feedback on what went wrong
Instead of just a score, a student can see which step of the problem broke and try again, coached rather than handed the answer.
Time back for you
With clear instructions, it can speed up grading against a rubric and busywork like forms and emails. In one trial, teachers using ChatGPT cut lesson-planning time by 31% with no drop in quality (EEF, 2024).
The catch: nobody knows yet which education software will turn out to be good. Learning is a very hard problem to solve in general, and many students are getting caught in the crossfire of imperfect edtech pilots.
Our advice: use the AI companies’ own tools directly, Claude and ChatGPT, and see for yourself what they can and can’t do. Stay open: they may be much better in a few months.
Module 3
Experiment with Claude or ChatGPT
Twenty minutes with these five exercises will teach you more than any slideshow.
First, set up your account
Both companies offer free plans for US K-12 teachers: Claude for Teachers and ChatGPT for Teachers. On any account, turn off training on your chats, and never paste in student names or personal details. If your district has an approved tool, use that for anything involving student work.
Claude
- Open Settings, then Privacy.
- Turn off “Help improve Claude.”
- For anything sensitive, start an incognito chat (the ghost icon). It isn’t saved to your history or used for training.
ChatGPT
- Open Settings, then Data controls.
- Turn off “Improve the model for everyone.”
- For anything sensitive, use a Temporary Chat. It isn’t saved to your history.
Five exercises
Plan a lesson
Make me a 45-minute lesson plan for 10th grade biology on cellular respiration. Include a five-minute hook, one hands-on activity, and an exit ticket with three questions.
Swap in your own subject. Then push back: “make the activity cheaper” or “my students already know glycolysis.”
Grade against your rubric
Here is my rubric and three student essays. Score each essay on every row of the rubric, quote the sentence that justifies each score, and tell me which score you are least sure about.
Take names out first. Compare its scores with yours: where does it agree, and where would you overrule it?
Let it fill out a form
Open my attendance form and mark these students from my notes: present — Ana, Ben, Chris; absent — Dana; late — Eli. Stop before you submit so I can check.
Needs a browser agent: Claude in Chrome, or ChatGPT’s agent mode. Watch it work, and check before anything is submitted.
Explain something with a picture
Build an interactive visualization that shows how a vaccine trains the immune system, for 9th graders. Let students click through each step.
Claude makes these as Artifacts; ChatGPT uses Canvas. Try a topic your students always struggle with.
Find where it breaks
Ask it a hard question from your own subject, one you know the answer to cold.
Look for confident mistakes. Ask it again tomorrow, and again in a few months: what it can’t do changes fast.
Module 4
How AI is changing work
Your students are preparing for jobs AI is reshaping right now. Here’s what it is and isn’t doing.
What AI does at work today
Most of it is busywork: summarizing, data entry, first drafts. The bigger shift is that teams with no engineers, like a legal team that used to copy answers from one form to another by hand, can now build their own software. Complex expert work is next, and still experimental.
- 1
Busywork
Summaries, data entry, first drafts
Working now
- 2
Software for everyone
Non-engineers build their own tools
Working now
- 3
Complex expert tasks
Drafting a patent, a legal brief
Early
- 4
Judgment and taste
Knowing what’s worth doing
Not yet
- 5
The physical world
Hands, robots, real objects
Hardest
The hard parts are still ahead. One is judgment: the intuition and taste to know what’s worth doing, which people still supply. Intelligence is almost solved, judgment is not.
The other is the physical world. Robots can plan what to do; the hard part is control. A human hand has 27 bones and makes constant tiny corrections to catch a falling ball. Copying that turns out to be extremely hard.
What’s an AI agent?
Most of the newest workplace AI is agents. A chatbot answers once. An agent works toward a goal on its own: it searches, reads, clicks and writes, one step at a time, checking each result before the next. Step through one below.
You give the agent a goal, not step-by-step instructions.
- You
When is the next school board meeting in Maple Grove, and is AI policy on the agenda?
The working definition many builders now use: “An LLM agent runs tools in a loop to achieve a goal” (Simon Willison).
Will AI take jobs or create them?
In the 1860s, economist William Stanley Jevons noticed that more efficient steam engines made Britain burn more coal, not less: when something gets cheaper, people use more of it (Jevons paradox). Automation often works the same way.
The Waymo effect. Self-driving cars were supposed to remove drivers. In the short run they created new human work: cleaning cars, charging them, helping remotely when one gets stuck. Waymo even pays DoorDash drivers to close doors that riders leave open, one offer was $11.25 (Bloomberg, 2026). Jobs that look easy to automate often aren’t yet.
But some jobs are shrinking. Call centers were the obvious job for AI to replace, and for years they kept growing. Now, finally, hiring has slowed: new hires per 100 workers fell from 25.1 to 17.2 (Revelio Labs).
Who wins right now
Models are trained on examples, so they handle common work well and the rarest, best work poorly. Work in the top 1% is out of reach for them, and the people who do it are becoming more valuable: they can now manage dozens of AI agents doing the routine parts.
The open question
If AI eventually handles all knowledge work, and then physical work, what is left for people? Nobody knows whether that day will come. But even with judgment solved, governance isn’t: people still have to take part in a democracy, build agreement, and make the big decisions together. Teaching students to do that well may be the most valuable thing a school can do.
Module 5
AI and trust
Work made with AI often reads as low-effort or inauthentic. As AI shows up in more of what we make, that reaction only gets more likely.
Why people push back
People value the same work less once they learn AI made it. In one study, people rated identical paintings as less likeable, less beautiful, less profound and worth less when they were labeled AI-made. In another set of 13 experiments, people who said they had used AI were trusted less than people who said nothing, and being found out was worse still.
How to keep people’s trust
Use human art for posters
Even when AI is faster. Ask students to make the poster for the club fair or the spring concert: they get a real audience, and you get something people stop to look at.
Don’t hand down one right way to use AI
The technology changes every few months. Share your opinions about how you use it, but don’t lock people into rules or views that are hard to understand, or that will be out of date by spring.
Say when you used it
If AI helped, say so up front. Disclosing costs a little trust; getting caught hiding it costs much more (Schilke & Reimann, 2025).
Run it with your staff
Use this at a staff meeting
The five modules fit in one PD session: about 15 minutes each, with the exercises done together. Want us to run it, or adapt it for your school? Email us.