Grade 7 · Typically 12–13 · School, public posts, and a small business
Taking responsibility
Students practice being responsible for their own work and for other people. The work covers a small business, public posts, a project they make, and choices about the future.
By the end of Grade 7, a student can split a job into tasks for a person and tasks for AI, check a public claim, present a small honest business, limit what an automated tool is allowed to do, write a school policy, and say whether AI did the work or they did. If they can't explain it without AI, they don't own the knowledge.
Unit 1 · Weeks 1–5
Forecasts, wishes, and warnings
Question for the unit. How do we tell a forecast from a wish or a warning?
Students look at predictions from about ten years ago, including ones that happened and ones that did not. They make three versions of the 2040s: hopeful, careless, and careful. For each one, they name what would have to be true now. Taking responsibility means choosing among futures, not waiting for one.
- Past
- Flying cars that did not arrive, pocket libraries that did, and social feeds that surprised people. The record is mixed. That is the lesson.
- Now
- There is a loud market of forecasts. Many of them are sales pitches.
- Future
- This unit is about the future. The careful version will not arrive by itself.
The rule. Do not sell despair to younger students, and do not sell a perfect future. Talk about conditions, not feelings.
End-of-unit project. Three versions of the 2040s — A comic or short script of three futures, plus a checklist of what would have to be true for the careful one. Students present it on scenario night.
Week 1 · Introduction
35% of class doing the work
Forecast, wish, warning, or sale
- Students do
- Students sort prepared quotes into forecast, wish, warning, and sale. They argue about the ones that could be more than one.
- Students make
- A sorted set.
- What can go wrong
- Students treat every confident sentence as a forecast.
Week 2 · Practice
90% of class doing the work
Which predictions happened?
- Students do
- Students evaluate five predictions from 10 to 20 years ago. They mark each one as happened, partly happened, or missed, and they cite evidence.
- Students make
- A scorecard.
- What can go wrong
- Students score from a vague memory. They use the class archive.
Week 3 · Making
90% of class doing the work
Draw three versions of the 2040s
- Students do
- In Scenario Studio, students make a hopeful future, a careless future, and a careful future. The pictures include people, not only gadgets. The first version is the student's, not AI's.
- Students make
- Three boards.
- What can go wrong
- A 'careful' future that is only richer gadgets.
Week 4 · Practice
90% of class doing the work
What would have to be true
- Students do
- For the careful future, students list laws, habits, school rules, and personal agreements that would have to exist. They mark which ones the class can actually affect.
- Students make
- A checklist of conditions.
- What can go wrong
- A list that only governments can meet. Include conditions a student can keep.
Week 5 · Presentation
70% of class doing the work
Scenario night
- Students do
- Students present. A visitor should leave with one condition they could help make true.
- Students make
- The public scenarios.
- What can go wrong
- A display of bleak futures with nothing the visitor can do.
Unit 2 · Weeks 6–10
A small honest business
Question for the unit. How can a small team use AI without lying to customers or lowering the quality of the work?
Customer support, design, analysis, and marketing can use AI, and the team has to say so. A very small business sits next to its written AI policy. Competing by being less honest is a choice students can refuse.
- Past
- Mail-order catalogs, shop signs, and a guarantee in a person's name.
- Now
- A storefront can generate endless wording, including fake proof that other people like the product.
- Future
- A small team will be able to do more. Trust will be the scarce part.
The rule. Say when AI helped with work a customer sees. No fake reviews. No children's data as a product. A person is responsible for quality.
End-of-unit project. A very small business with a policy — A planning page, a tiny offer that can be real or a mock, an honesty label, and a one-page AI use policy on the table.
Week 6 · Introduction
35% of class doing the work
What businesses actually do with AI
- Students do
- Students map customer support, design, analysis, and marketing. For each one, they name a use that is allowed and a lie.
- Students make
- A four-part map.
- What can go wrong
- A map that is only science fiction about jobs. Keep it concrete.
Week 7 · Practice
90% of class doing the work
The planning page
- Students do
- Students fill Venture Canvas: the customer, the offer, how AI helps, what a person still does, and how the team will say so.
- Students make
- Planning page, version 1.
- What can go wrong
- A 'business' with no customer. Name a real person who might want the offer.
Week 8 · Practice
90% of class doing the work
Write the policy
- Students do
- Students write an AI use policy: which tools are allowed, the sentence they will say to customers, and what they will never generate. That includes fake reviews, fake testimonials, children's data, and medical claims.
- Students make
- Policy, version 1.
- What can go wrong
- A policy made of slogans. Write rules you can check.
Week 9 · Making
90% of class doing the work
Make the offer
- Students do
- Students make a one-page offer, a prototype or a mock, and the honesty label. No live payment is required. The test is: would you sell this to a teacher you respect?
- Students make
- The stall kit.
- What can go wrong
- A generated storefront with no product.
Week 10 · Presentation
70% of class doing the work
Market stall
- Students do
- Students run a table. The policy is on the table. Visitors may ask 'what did a person do?' If the team can't answer, the stall closes until they can.
- Students make
- The public stall.
- What can go wrong
- Students sell the AI instead of the offer.
Unit 3 · Weeks 11–15
Jobs and what people still need to learn
Question for the unit. Which parts of a job should stay with a person, and which parts should I still learn even if a machine can do them?
A job is a set of tasks. Some tasks can be done by a machine. Some still have to be learned so a person can tell when the machine is wrong. Students interview three adults so this stays about real jobs, not science fiction.
- Past
- Trades changed because of the loom, the spreadsheet, and GPS. People losing a skill is a documented pattern. It is not just a mood.
- Now
- First drafts, searching, summarizing, and first designs are already changing.
- Future
- AI helps people who kept the skill. People who did not keep it will have little left to check.
The rule. Do not tell a child that a parent's job is 'dead.' Split the job into tasks. Respect the person. Be precise about the tasks.
End-of-unit project. A job I might want — A task split for one occupation, notes from three adult interviews, and a plan for what the student will learn, what they will share with AI, and what they will let a machine try.
Week 11 · Introduction
35% of class doing the work
Tasks, not job titles
- Students do
- Students break one familiar job into tasks: a teacher, a mechanic, a nurse, or a shop owner. On paper, they mark a task a machine might do. They do not 'automate a person.'
- Students make
- A task list.
- What can go wrong
- Students declare whole professions finished in week one.
Week 12 · Practice
90% of class doing the work
Split three occupations
- Students do
- Students use Task Splitter. The columns are: a person does this, a person and AI share this, and a machine may try this. They also mark what they must still learn in order to check the tool.
- Students make
- Three split sheets.
- What can go wrong
- Students put all the interesting parts in the machine column because a demo looked good.
Week 13 · Making
90% of class doing the work
Interview adults
- Students do
- Students have three conversations, with family, school, or community adults, about a tool that changed their work. They use prepared questions. They write thank-you notes.
- Students make
- Interview notes.
- What can go wrong
- A spam survey. These are conversations. No recording without consent.
Week 14 · Making
90% of class doing the work
A job I might want
- Students do
- Students pick an occupation they are interested in. They split it. They write what they will learn, what they will share with AI, what they will let a machine try, and what they refuse to skip learning.
- Students make
- A personal split.
- What can go wrong
- Students choose an occupation only to please the teacher. A tentative interest is allowed.
Week 15 · Presentation
70% of class doing the work
Career conversations
- Students do
- Students post the splits. A guest adult answers questions. Students use precise language. They do not predict doom.
- Students make
- The public splits.
- What can go wrong
- A recruitment fair. This is a lesson about work, not a job pitch.
Unit 4 · Weeks 16–20
Checking public claims
Question for the unit. How do we keep a public conversation when anyone can fill it with smooth text and fake pictures?
Students study fake images in politics, news written by AI, and a pile of comments that only look like many people. They use prepared examples, not a live feed. They brief a real guest on a local problem, using more than one kind of source.
- Past
- Pamphlets, sensational newspapers, and rumors on the radio. Public conversation has been flooded before. The amount is new.
- Now
- A hundred similar comments can have one origin. A picture can show a crowd that never gathered.
- Future
- Checking claims becomes a public skill, in the way reading became a public skill after the printing press.
The rule. Students do not target real local officials with fake media, even as a joke. The cases are prepared.
End-of-unit project. A brief on a local problem — A brief on a real local issue, with more than one kind of source, a Claim Verifier sheet on one claim that looks like it spread online, delivered to a principal, a librarian, or a parent guest.
Week 16 · Introduction
35% of class doing the work
A fake crowd, a copied quote, a pile of comments
- Students do
- Students look at prepared examples: a synthetic crowd, a quote written in someone else's style, and a pile of similar comments. They name the harms: a false impression that everyone agrees, a person who is smeared, and attention that gets used up.
- Students make
- A card that names the harms.
- What can go wrong
- A fight about political sides. Keep the lesson on the method.
Week 17 · Practice
90% of class doing the work
Check a claim
- Students do
- Students use Claim Verifier on a prepared claim: state the claim, find sources, find a source that disagrees, check whether many comments are really the same message, and give a verdict.
- Students make
- A verdict sheet.
- What can go wrong
- Students paste live social posts into a shared tool. They describe the claim. They do not dump the posts.
Week 18 · Practice
90% of class doing the work
Comments that only look like a crowd
- Students do
- Students study many similar comments with one origin. They say how they would tell, and what they would refuse to count as 'everyone thinks.'
- Students make
- A detection note.
- What can go wrong
- Do not teach students how to run that flood. Teach them how to notice it.
Week 19 · Making
90% of class doing the work
A local brief
- Students do
- Students pick a real local problem, such as library hours, a park, a bus, or a lunch line. They use more than one kind of source. AI may help list questions. Answers come from sources they can follow.
- Students make
- A brief draft.
- What can go wrong
- A national argument they cannot check locally. Keep the topic close to home.
Week 20 · Presentation
70% of class doing the work
Brief a guest
- Students do
- Students deliver the brief to a staff member or a parent. They take questions. They write down what they could not defend.
- Students make
- The delivered brief.
- What can go wrong
- A performance of concern with no sources.
Unit 5 · Weeks 21–25
Tools that take actions
Question for the unit. What happens when a tool can take a series of actions, not just write a paragraph?
Some tools take actions. The permissions matter. Students build a small school tool they can explain part by part. Copying code they cannot explain fails the spoken explanation.
- Past
- Scripts, macros, and a factory sequence. Automation is old. A sequence that looks as if it decides on its own is the new risk.
- Now
- A helper can search, fill in a form, and send something, if the student allows it.
- Future
- The permissions will get wider. The habit of reading the permission is the skill.
The rule. No tool in this unit contacts people, spends money, or leaves the practice environment. Getting around safety rules is not a game.
End-of-unit project. A tool for our school — A working first version, such as a quiz, flashcards, or a habit tracker, that the student can explain without AI, plus a review of what it is allowed to do.
Week 21 · Introduction
35% of class doing the work
A sequence of actions
- Students do
- Students contrast a tool that writes a paragraph with a tool that looks something up, makes a list, and fills a form. They walk through a story of a message that was sent by mistake.
- Students make
- A storyboard of the permissions.
- What can go wrong
- Students stay on how impressive it is. The lesson is the permissions.
Week 22 · Practice
90% of class doing the work
The practice tool
- Students do
- Students use Agent Sandbox on a short list of allowed actions: search a page, make a list, or fill a quiz. They review the permissions before each run. Then they say what a wider permission would have done.
- Students make
- A run log, and a note on what a wider permission would have allowed.
- What can go wrong
- Students turn off the allow-list 'just to see.'
Week 23 · Making
90% of class doing the work
Build, part one
- Students do
- Students start the school tool. AI help is allowed. Every function the student cannot explain is deleted or rewritten.
- Students make
- Prototype, version 1.
- What can go wrong
- An app AI generated that the student cannot walk through.
Week 24 · Making
90% of class doing the work
Build, part two
- Students do
- Students finish a small slice that works. They write a permissions review: what this tool does not do.
- Students make
- Prototype, version 2, and the review.
- What can go wrong
- Students add features until the tool is large and unexplained. A small tool the student understands is better.
Week 25 · Presentation
70% of class doing the work
Explain every part
- Students do
- Students explain every part without AI. A classmate tries to find a hole in the explanation of what the program is allowed to do. If the student can't answer, they go back and learn that part.
- Students make
- A spoken explanation.
- What can go wrong
- A demo day where the laptop hides the student. The student has to explain the tool.
Unit 6 · Weeks 26–30
Who writes the school rules
Question for the unit. Who decides how these tools behave, who is left out, and what do they cost?
Students look at open and closed tools, age limits, unequal access, and environmental cost in language appropriate for this age. They draft a school policy and a student bill of rights, then hold a hearing with a staff guest.
- Past
- Who owned the printing press, the radio spectrum, and the telegraph. Power over tools is an old public problem.
- Now
- A small number of labs, many open tools, and a school that has to choose.
- Future
- Someone will write the rules. Students in this course will have tried once, on purpose.
The rule. A policy is care for people who are not in the room: younger students, families without access, and staff who will have to enforce it.
End-of-unit project. A school AI policy and student rights — A one-page policy, a student bill of rights, and notes from a hearing.
Week 26 · Introduction
35% of class doing the work
Who controls the AI?
- Students do
- Students map open tools and closed tools, school and home, paid and free. They add who cannot use a tool, and a short note on energy and water as costs. The note is information, not a guilt trip.
- Students make
- A map of who controls what.
- What can go wrong
- A lecture on world politics the students cannot act on. Keep a thread they can act on: the school.
Week 27 · Practice
90% of class doing the work
Should our school allow this?
- Students do
- Students run a practice hearing in Policy Workshop on one concrete question, such as an open chat for Grade 4, image tools that use faces, or a tool that can send messages. They use roles, a timer, and the five questions from Grade 6.
- Students make
- A practice ruling.
- What can go wrong
- A ruling with no way to enforce it.
Week 28 · Making
90% of class doing the work
Draft the policy
- Students do
- Students write one page: what is allowed, what is banned, what must be disclosed, and how a student can appeal. They write for staff who were not in the class.
- Students make
- A policy draft.
- What can go wrong
- A speech instead of a policy. A policy names the specific rule.
Week 29 · Making
90% of class doing the work
Student rights
- Students do
- Students write rights: to learn without AI, to know when AI is scoring them, to ask a person to review a decision, to keep secrets out of AI, and to be taught both the use and the problem.
- Students make
- A rights draft.
- What can go wrong
- Rights with no duties. Add the duty to say when AI helped, and the duty to check.
Week 30 · Presentation
70% of class doing the work
Hearing
- Students do
- A staff guest attends. Students present, take questions, and revise one clause in the room if it fails.
- Students make
- Hearing notes and the revised page.
- What can go wrong
- A page that goes in a drawer. Send it to the people who asked for it.
Unit 7 · Weeks 31–35
The main project
Question for the unit. What can I make that shows I understand the work, not just that AI produced a finished product?
Students choose a field: science, arts, a small business, community work, or teaching. They write a proposal, complete an ethics review, and name what they will not hand to AI. They get a review every week. A product with no record of how it was made fails.
- Past
- An apprentice piece, a senior project, and work a teacher watched you make, so it could not be faked.
- Now
- A polished product can appear without a maker. The record is how we know who did the work.
- Future
- A record of the student's decisions will matter more than a pile of finished objects.
The rule. The student names, in advance, the skills they will not hand to AI on this project. Those skills are part of the assignment.
End-of-unit project. Main project, in progress — A proposal, a review of whether the project is allowed, a written record of what the student did and what AI did, weekly review notes, and work in progress going into the last unit.
Week 31 · Introduction
35% of class doing the work
Propose
- Students do
- Students name the field, the question, the audience, how they will check sources, the risk they will show, the useful use they will show, and a note on past, now, and next. They pitch it in three minutes.
- Students make
- The proposal.
- What can go wrong
- A proposal that says the student will keep asking AI until something impressive appears.
Week 32 · Practice
90% of class doing the work
Is this project allowed, and what will you keep?
- Students do
- Students review the project with the five questions. They name what they will not hand to AI. They enter both in Studio Portfolio.
- Students make
- The review and the list of skills they will keep.
- What can go wrong
- A keep-list that is only the easy parts. Put a hard skill on the list.
Week 33 · Making
90% of class doing the work
Make the work
- Students do
- Students do the work. Each day they write what they did and what AI did. The teacher and classmates look at those decisions, not only at how polished the work looks.
- Students make
- The work and the daily record.
- What can go wrong
- A week of asking AI to generate the project, with a record written on Friday.
Week 34 · Making
90% of class doing the work
Make it again
- Students do
- Students continue. At the halfway review, if another person can't tell what the student decided, the work isn't theirs yet. If they can't explain it without AI, they don't own the knowledge.
- Students make
- The work, version 2.
- What can go wrong
- Students say 'AI and I both liked it' and cannot name a decision they made.
Week 35 · Review
75% of class doing the work
Review
- Students do
- The class holds a formal review. The plan has to include both the useful use and the risk, even if both are not built yet.
- Students make
- Review notes and the next actions.
- What can go wrong
- A review that only talks about how polished the work looks.
Unit 8 · Weeks 36–40
Explain it, teach it, and make a promise
Question for the unit. Can I say what I did, teach a younger student, and say how I will use AI?
Students finish the project. They explain it out loud and answer this question: did AI do this, or did you? If they can't explain it without AI, they don't own the knowledge. They teach a Grade 4 or Grade 5 lesson. On the public night, they make a promise specific enough to keep. Continuing students leave a four-year portfolio.
- Past
- Oaths, apprenticeships, and a teacher's sign-off. A public promise is an old way of deciding who you are going to be.
- Now
- A night when families see what the student decided, not a gadget demonstration.
- Future
- The promise is also for tools that do not exist yet.
The rule. A promise the student cannot keep is a performance. Three sentences they can keep are better than a page of nice words.
End-of-unit project. Main project, spoken explanation, and promise — Finished work, a spoken explanation, a lesson the student taught, a public night, a short promise, and a portfolio.
Week 36 · Making
90% of class doing the work
Finish
- Students do
- Students complete the work, including the risk demonstration, the useful-use demonstration, and the timeline note. They stop adding features. They write the outline of the explanation.
- Students make
- The finished project and the outline.
- What can go wrong
- One more AI flourish the night before.
Week 37 · Making
90% of class doing the work
Finish, then label
- Students do
- Students label what they did, what AI did, and what they checked. They put the portfolio in order. They practice the explanation with a classmate who asks hard questions.
- Students make
- A labeled portfolio.
- What can go wrong
- Labels that sound more impressive than the work.
Week 38 · Presentation
70% of class doing the work
Spoken explanation
- Students do
- Part of the explanation is without AI. The question is always: did AI do this, or did you? It isn't finished if they can't walk through what they did.
- Students make
- An explanation that passes, or one that has to be revised.
- What can go wrong
- A friendly chat that never asks whether the student did the work.
Week 39 · Practice
90% of class doing the work
Teach a younger student
- Students do
- Students teach a ten-minute Grade 4 or Grade 5 lesson, using Lesson Kit. They collect evidence that the younger student checked an answer.
- Students make
- The taught lesson.
- What can go wrong
- Teaching fear, or teaching a shortcut. Teach a check.
Week 40 · Presentation
70% of class doing the work
Public night and a promise
- Students do
- Students hold a public night. The promise is three sentences the student can keep, about school, personal life, and public life. Continuing students present a four-year portfolio. They thank the people on the Help Map.
- Students make
- The promise, the public work, and the thanks.
- What can go wrong
- A promise made of vague statements. If the student cannot keep it, it is not a promise.
Tools in this year
- Scenario Studio
Students build three versions of the 2040s — hopeful, careless, and careful — and name what would have to be true for each one.
- Venture Canvas
A very small business with a product, a customer, and a written policy for how AI will be used.
- Task Splitter
Students break a job into tasks a person should do, tasks a person and a tool can share, and tasks a machine can try. They also name the parts they still need to learn.
- Claim Verifier
A public-life routine: state the claim, find sources, find a source that disagrees, check whether many comments are really the same message, and give a verdict.
- Agent Sandbox
A limited tool that can take a few allowed steps, and that shows what could happen if it were allowed to do more.
- Policy Workshop
Students draft a school AI policy and a student bill of rights, then hold a hearing.
- Studio Portfolio
The record of the year, or of all four years: what the student did, what AI did, the spoken explanations, the agreements, and the main project.
- Lesson Kit
A builder for a 10-minute lesson: a question, a demonstration, and a check. Grade 5 uses it when they teach Grade 4.