Standard quoted exactly

Understand when AI versus non-AI solutions are appropriate.

Clarification. Appropriateness is context-dependent. The goal is for students to develop the critical thinking skills to evaluate this on a case-by-case basis.

Student-friendly learning targets

• I can decide whether a task is a good fit for AI, a traditional tool, a person, or a mix.
• I can explain why appropriateness depends on the goal, the data, and who is accountable.
• I can recommend a tool for an Idaho workplace or school task and name the risk if the tool is used alone.

Key vocabulary

Appropriate use

Choosing a tool because it fits the goal, the data, and the people involved—not because the tool is new or popular.

Context

The situation around a task: who it is for, what is at stake, how much time you have, and what counts as a good answer.

Non-AI solution

A way to finish a task without artificial intelligence, such as a calculator, a spreadsheet formula, a sensor, a checklist, or asking an expert.

Human-in-the-loop

A person who checks, approves, or corrects a machine result before it is used.

Automation

Using a machine or program to do a repeated task with little help from a person, usually to save time or reduce errors.

Task fit

How well a tool matches what the task is actually for. A tool can be powerful and still be a poor fit.

Verification

Checking an answer against a trusted source, a measurement, or another person before you act on it.

Teacher background

This lesson trains judgment, not tool use. Ninth and tenth graders already meet AI in homework, social media, and after-school jobs; the work of this period is to slow that meeting down. Appropriateness is context-dependent, which is the exact clarification in the Idaho standard. Treat AI as one option among many: a calculator, a soil probe, a phone call to an agronomist, a spreadsheet, or a human expert. Two pitfalls show up fast. First, students turn “AI versus not-AI” into a morality contest. Keep the frame practical. A frost call for a potato cellar in Bingham County is a different decision than drafting a personal narrative for English. Second, teachers over-demo a chatbot and the class never practices the decision. Script a short projected demo, then move to paper task cards so rural rooms stay in the lesson when Starlink drops. Name Idaho workplaces out loud: Idaho Power load forecasts on the Snake River system, Micron process control in Boise, county 911 dispatch, and infrared wildfire maps from the National Interagency Fire Center. Students should leave able to say, for a given task, what a human, a traditional tool, and an AI system each do well—and what each cannot do.

Instructional sequence

Real-world examples

• A Bingham County potato cellar uses a soil probe and a frost forecast. AI might help scan a weather table, but a grower still walks the cellar because a wrong call ruins a crop.
• The National Interagency Fire Center in Boise uses infrared maps and models for wildfire. Crews still look at real wind, fuel, and road access before they move people.
• Micron in the Treasure Valley uses automated inspection on wafers. A technician still decides whether a flagged lot is scrapped, reworked, or released.

Hands-on activity

Discussion questions

1. Which of today’s tasks would you never hand to AI, even if the output looked perfect? Why?
2. When is “mixed” a stronger answer than a hard yes or no?
3. How should a rural clinic or a county office decide differently from a large Boise employer?
4. If a tool is free and fast, why might it still be the wrong tool for schoolwork?

Differentiation

Assessment

Responsible use

Students do not log into public chatbots for this lesson. If a live demo happens, the teacher uses a district-approved tool on a teacher account, with scripted prompts that contain no student names, class lists, or private writing. The default path is paper. Fast is not the same as allowed: a tool that is free on a phone may still be unapproved by the district. When students recommend AI for a task, they must also name a human check. The class rule is simple and posted: we choose tools on purpose, we check the result, and we keep schoolwork that is meant to grow our thinking in our own hands.

Ethics

Appropriateness is not only about accuracy. Using AI to comfort a classmate, to write a college essay in someone else’s voice, or to make a public safety call without a person in the loop treats people as problems to be optimized. Students practice asking who is helped, who is skipped, and who is blamed if the output is wrong. Idaho public schools serve rural towns, Boise-metro suburbs, and tribal communities; a one-size rule would hide those differences. The ethical habit this lesson builds is case-by-case judgment with reasons, not a cheer or a ban.

Privacy

FERPA still applies when the tool is “just a demo.” Never put student names, photos, grades, ID numbers, or private writing into an unapproved tool. Do not project a student’s memo, email, or face. Printed task cards use fictional or public workplace details (Idaho Power, NIFC, a generic packing line), not real patient, student, or employee records. If a student volunteers a family farm story, keep it in the room; do not type it into a prompt. District-approved tools only, and paper whenever that is the safer path.

Reflection

1. Which task today changed your mind, and which question in the three-question test caused the change?
2. Where in your own schoolwork is AI a mixed tool rather than a yes or a no?
3. What will you check by hand this week even if a machine offers an answer?

Homework

Take-home half-sheet (20–25 minutes, no account). Pick one real task you will face this week (homework, a shift, a chore, a sport). Complete the three-question test on paper. List a non-AI option, a possible AI option, and your recommendation in five to eight sentences. Do not use a chatbot. Bring the sheet back; we will swap and peer-check the reasons.

Closing

AI is a tool with a job description, not a default setting. If you can name the purpose, the data, and the person who is accountable, you can choose AI, skip it, or mix it—and you can explain that choice to a boss, a teacher, or a grower. That judgment is the standard, and it travels from this classroom to Idaho work.

Extensions

• Block period: add a structured debate (AI vs. non-AI vs. mixed) using one wildfire card and one English-class card; require evidence from the three-question test.
• Invite a local grower, millwright, or county clerk for a 10-minute “what I still check by hand” interview; record a backup audio file for rural bandwidth days.
• Students rewrite one school honor-code line so it names appropriate vs. inappropriate AI use without banning all tools.

Cross-curricular links

ELA

Purpose and audience decide the tool, the same way they decide a genre. A personal narrative is evidence of the writer, not a product to outsource.

Mathematics

A calculator is a non-AI tool with a clear job. Contrast it with an AI that might invent a number. Students practice asking whether a result is computed or guessed.

Science

Lab conclusions must show the student’s claim, evidence, and reasoning. AI can format a table; it cannot stand in for the observation.

CTE / Social studies

Ag, energy, health, trades, and county government all mix sensors, spreadsheets, and human calls. Civics: who is liable when a public agency uses a model.

Standard quoted exactly

Recognize that some AI can handle cognitive tasks, but reliance on AI might diminish individual human cognitive and emotional development.

Example from the standards. A student can use an AI tool to generate a report or do other schoolwork that is designed for student cognitive development. Moreover, some people use "AI Companions" for emotional support, which may affect recognition of human emotion in negative ways.

Student-friendly learning targets

• I can name a cognitive skill a school task is meant to build and explain how skipping it with AI would change me.
• I can tell the difference between helpful offloading (like a calculator) and offloading that removes the point of the work.
• I can describe a risk of using AI companions for emotional support without shaming anyone’s feelings.

Key vocabulary

Cognitive task

Work that uses thinking skills such as remembering, explaining, planning, judging, or solving a problem.

Cognitive offloading

Letting a tool hold some of the thinking (a calculator, a list, a map) so your mind can focus on another part of the job.

Reliance

Depending on a tool so often that it becomes hard to do the task without it.

AI companion

A chatbot built to act like a friend or counselor, offering conversation and comfort rather than just answers to practical questions.

Anthropomorphism

Treating a machine as if it had human feelings, intentions, or a real relationship with you.

Emotional development

The ongoing work of noticing feelings, reading other people, and practicing care in real relationships.

Skill atrophy

A skill getting weaker because it is not practiced, the way a muscle weakens without use.

Human-centered

Using a tool to support a person’s own thinking and choices, with the person setting the goal and making the final call.

Teacher background

The standard asks students to notice a trade, not to ban tools. AI can draft a lab report, outline a history essay, or chat like a friend. The learning those tasks were designed to grow—argument, memory, empathy—does not automatically transfer if the machine does the work. Cognitive offloading is real and not always bad; calculators offload arithmetic so students can think about rates. The line is whether the offload removes the skill the assignment exists to build. Handle AI companions with care. Some students already use chatbots for late-night reassurance. Do not shame; do not diagnose. Name the risk the standard names: practice with a simulated companion can dull recognition of human emotion. Point them toward counselors, trusted adults, and Idaho’s 988 line if distress appears, then return to the academic claim. Pitfalls: treating this as an anti-AI sermon, or letting the discussion become a confession circle. Use scenarios, not personal disclosures. Pair every generation example with a “what muscle did we skip?” check. Rural and Boise-metro students meet this differently—one may have unlimited home access, another a shared phone—so keep all practice paper-first with an optional district-approved demo.

Instructional sequence

Real-world examples

• A student uses a chatbot to generate a lab report for biology at a Boise-metro high school. The prose looks finished; the student cannot explain the data when the teacher asks a follow-up, because the thinking never happened.
• A hospital unit in the Treasure Valley may use AI to draft shift notes. Nurses still walk the floor and read faces; handing comfort and judgment to a companion app would cost the skill patients need.
• Outfitters and river guides in central Idaho read weather, water, and client fear in real time. A chatbot itinerary can list rapids; it cannot replace the judgment built by years of cold water and other people’s panic.

Hands-on activity

Discussion questions

1. Where is offloading a sign of skill (a carpenter using a jig) rather than a loss?
2. Why might a companion that always agrees be worse for emotional development than a friend who disagrees?
3. What should a school assignment still require you to do yourself, even if AI could draft it?
4. How do we talk about this without mocking people who are lonely?

Differentiation

Assessment

Responsible use

This lesson uses paper first. Students do not create companion-app accounts, and they do not paste their frost paragraphs into a chatbot. If a teacher demo runs, it uses a district-approved tool, a public text, and a teacher account. The academic integrity rule is stated without theater: if the assignment exists to grow your thinking, submitting machine text as your thinking is both a skill loss and a honesty problem. Students write a use rule they can actually keep on a shared phone or a home computer.

Ethics

The ethical core is development, not purity. It is unfair to tell students that every shortcut is cheating, and it is unfair to pretend that a fluent paragraph is the same as a mind at work. Companion systems raise a second duty: they can encourage people to practice intimacy with a system that cannot care and cannot keep a professional confidence. Class talk stays on the design of the tool and the skill at risk. Students are not asked to disclose loneliness, dating, or family conflict. Respect includes offering a private path to a counselor and keeping the academic claim in the foreground.

Privacy

FERPA and basic care meet here. Do not collect personal writing about mental health, family, or relationships as class artifacts. Scenario cards use roles, not real students. Never enter a student’s paragraph, name, photo, or grade into an unapproved tool. If a student reveals distress, stop the academic probe, follow school protocol, and do not type the disclosure into any AI system. District-approved tools only. Printed packets let rural classrooms run the full lesson without accounts or home broadband.

Reflection

1. Which muscle do you most often skip when a tool is handy, and what is one week of practice worth?
2. How will you tell helpful offloading from a shortcut that steals the assignment?
3. Who in your life is a real check on your thinking or your mood, and why can’t a chatbot replace that?

Homework

Paper log (20–30 minutes, no account). For two homework tasks tonight, write: the assigned task, the skill it is meant to build, whether you used any digital help, and one sentence on what you still did yourself. If you used no AI, say so. Do not sign up for any new app. Optional: ask a trusted adult what skill they still refuse to hand to a tool at work, and jot three notes.

Closing

Some AI can do cognitive work. That is not the same as you becoming more skilled. If the task exists to grow your thinking or your ability to read other people, handing it over has a cost. Name the muscle, keep the practice, and use tools in ways that leave you more capable, not just more finished.

Extensions

• Block period: students design a “muscle label” for an upcoming English or science assignment (what must be human; what tool use is fair) and post it.
• Compare a shop class (you still have to weld the bead) with an academic class; invite a CTE teacher for five minutes.
• Media literacy add-on: collect three ads for companion apps (printed at home or provided by the teacher) and mark anthropomorphic language.

Cross-curricular links

ELA

Revision, voice, and argument grow by doing. A generated report can hide missing thought the way a ghostwritten essay would.

Mathematics

Calculators are a legal offload in many tasks; number sense still has to be practiced. Students name which standard of mathematical practice they would lose.

Science

Claim-evidence-reasoning is a cognitive workout. Using AI to write the reasoning box skips the scientific habit the lab is for.

Health / CTE

Trades and caregiving jobs require reading people under stress. Companion apps that skip that practice are a workplace risk, not only a school issue.

Standard quoted exactly

Explain the difference between physical and simulated environments.

Student-friendly learning targets

• I can explain how a physical environment and a simulated environment are different.
• I can give an Idaho example of each and say what a simulation leaves out.
• I can decide when a simulation is useful for training and when only the real world will do.

Key vocabulary

Physical environment

The real world of mass, weather, time, and consequences—places you can touch, get hurt in, or be legally responsible for.

Simulated environment

A controlled replica of real conditions, such as a training game, a digital twin of a factory, a flight sim, or two people role-playing a job interview.

Digital twin

A computer model of a real object or system (a packing line, a dam, a warehouse) that updates to mirror it, at least in part.

Synthetic media

Images, audio, video, or text made or altered by AI so they look real even when the scene never happened.

Sensor

A device that measures something in the physical world, such as temperature, smoke, GPS location, or camera light.

Ground truth

What is actually happening, checked by measurement, observation, or a trusted record—not just what a model displays.

Model

A simplified picture of a system used to explain, predict, or practice. All models leave something out.

Consequence

What happens next in the real world: burned timber, a ruined crop, an injury, a fine, or a saved life.

Teacher background

Students confuse screens with reality, especially after games, flight sims, and generated video. This lesson is a sorting lesson: what has mass, weather, and legal consequence, and what is a controlled replica. Idaho makes the distinction concrete. A wildfire on the Payette National Forest burns timber, closes highways, and fills Boise with smoke. A wildfire simulation at NIFC or in a training game can teach tactics without those costs—and can also hide wind shifts the model never saw. A potato packing line in the Magic Valley is a physical environment; a digital twin of that line is simulated. Pitfall one: treating “simulated” as “fake and therefore useless.” Simulations save lives in aviation, surgery, and fire. Pitfall two: treating generated video as evidence. Students should leave able to ask: What sensors, if any, are reading the real world right now? Who is liable if the model is wrong? Keep the activity tactile. Paper maps, a cup of soil, and a printed screenshot beat a lagging video. When bandwidth fails, the lesson should still run. Connect to science (models vs. observations), CTE (shop vs. simulators), and civics (emergency orders apply on real ground).

Instructional sequence

Real-world examples

• Wildfire on the Payette or Boise National Forest is physical: timber, highway closures, and smoke in the Treasure Valley. A fire-behavior sim at NIFC can train tactics and still miss a local wind shift.
• A Magic Valley potato packing line is a physical environment with belts, blades, and wage-hour rules. A digital twin can test a layout change without stopping the line—and can hide a jam the sensors never tagged.
• Ski patrol and avalanche courses in the Wood River Valley use tabletop and digital sims, then move to real snow. The certificate is not complete until the physical environment is in the lesson.

Hands-on activity

Discussion questions

1. Why might a simulation be the ethical choice for training (fire, surgery, driving) even though it is not the real thing?
2. How should a news feed treat a generated video of a wildfire or a flood?
3. What is the difference between a digital twin fed by sensors and a picture an image model invented?
4. When should a coach, a shop teacher, or a county emergency manager refuse to train only in a sim?

Differentiation

Assessment

Responsible use

Students do not generate images of real classmates, staff, or school hallways. If a teacher shows synthetic media, it is labeled as generated on the print itself. No student accounts. Downloaded public maps and printed screenshots are the working set so a rural classroom is not held hostage by a video platform. The responsible move with any realistic image is to ask whether a sensor visited the place. If the answer is no, it is not evidence.

Ethics

Simulations can reduce harm: firefighters, nurses, and drivers should not learn only by burning, injuring, or crashing. The ethical failure is the opposite move—treating a sim or a generated video as if people, land, and liability were already settled. Posting a fake wildfire clip, or training staff only on a tidy digital twin, can put bodies on real roads. Students practice a duty to label replicas and to keep a human observer between the model and a public decision.

Privacy

Do not photograph students for “before/after” sim activities. Do not upload class photos to an image generator. FERPA bars putting student faces, names, or locations into unapproved tools. Use public landscapes (Sawtooths, a generic packing line, NIFC public maps) rather than a student’s house, ranch, or reservation unless a family has clearly offered that story in the room and it stays on paper. District-approved tools only; paper is the planned path.

Reflection

1. Where did you catch yourself treating a picture as a place?
2. What would you want a wildfire crew or a packing-line supervisor to check on the ground after looking at a model?
3. How will you label generated images when you see them in a feed this week?

Homework

Take-home sort (20–25 minutes, no account). Cut or list five things you see tonight (a room, a game, a weather app, a photo, a tool). Mark each P, S, or M. Write six to ten sentences explaining one mixed item: what is physical, what is simulated, and what a person should still check. Do not use an image generator. Bring the list; we will spot-check two items.

Closing

Physical environments have mass, weather, and consequences. Simulated environments are controlled replicas that can teach, predict, or deceive. If you can name what a sim leaves out—and who is liable on real ground—you are already doing the work this standard asks for.

Extensions

• Block period: build a tabletop sim (paper “fire cells” and dice for wind) then walk outside or to a window and list five physical variables the dice cannot capture.
• CTE partnership: visit the welding booth or greenhouse; list what a VR welder or a growth-chamber model would miss.
• Civics: read a short public evacuation order and mark every clause that only makes sense in the physical world.

Cross-curricular links

Science

Models vs. observations. A climate or fire model is a sim; a thermometer reading is physical. Students practice naming assumptions.

Mathematics

Coordinate grids and topographic lines are models of physical space. Scale and rounding are already “something left out.”

ELA

Setting in fiction is a simulated world made of words. Students compare a story’s forest with a real forest they could be legally lost in.

CTE / Social studies

Shop, ag, and emergency management train in sims and certify on real ground. Public orders, wages, and injuries attach to the physical environment.

Standard quoted exactly

Understand that Large Language Models (LLM) are just one type of AI model and that others exist with different purposes.

Example from the standards. LLMs are trained on natural language text and generate text, whereas diffusion models are used for image generation, decision tree models can classify, linear regression models can predict numeric values, etc.

Student-friendly learning targets

• I can explain that an LLM is only one kind of AI model and that other kinds have other jobs.
• I can match a sample output to a model family: language, image, classification, or numeric prediction.
• I can name a task each family should not be asked to do.

Key vocabulary

Large Language Model (LLM)

A model trained on huge amounts of text so it can predict and generate language. It is one type of AI, not all of AI.

Diffusion model

A model used to generate images (and sometimes audio or video) from patterns it learned, often from a text prompt.

Decision tree

A model that classifies by asking a sequence of yes/no or threshold questions, like a flowchart.

Linear regression

A model that fits a line (or a simple trend) to data in order to predict a number, such as yield or temperature.

Classification

Assigning an item to a category (in spec / out of spec, frost risk / no frost) rather than writing a paragraph.

Prediction

A model’s estimate of a future or unknown value, based on patterns in past data—not a guarantee.

Training data

The examples a model is shown during building. Different families need different kinds of examples (text, images, labeled rows, numbers).

Model family

A group of models that share a purpose and a kind of output, the way trucks, boats, and planes are all vehicles with different jobs.

Teacher background

Chatbots dominate the news, so students assume AI means “the thing that talks.” The standard requires a wider map: language models generate text; diffusion models generate images; decision trees classify; linear regression predicts a number. Ninth graders can hold those four families if each one is tied to a job they recognize. Use Idaho numbers, not abstract ones. Linear regression can estimate potato yield from soil moisture. A decision tree can flag whether a Micron wafer lot is in spec. A diffusion model can invent a “photo” of a Sawtooth trail that does not exist. An LLM can draft a county permit letter that sounds official and is wrong. Pitfalls: drowning the period in architecture diagrams, or implying one family is “smarter.” Purpose, not prestige, is the sorting rule. Keep math honest and light—show a three-point line and a simple tree on the board. Project a district-approved demo only after students have matched printed “mystery outputs” to model types on paper. That packet is the lesson when the network dies. Students should leave able to name the model family, the kind of output, and a task it should not be asked to do.

Instructional sequence

Real-world examples

• Linear regression on soil moisture and historical yield can estimate cwt per acre for a Magic Valley grower. It will not write the crop-insurance narrative.
• A decision tree can classify Micron wafer lots as in spec or out of spec. It should not generate a press photo of the clean room.
• An LLM might draft a county permit letter that sounds official. A diffusion model might invent a Sawtooth “photograph” for a tourism page. Neither is a substitute for a surveyor or a camera on the trail.

Hands-on activity

Discussion questions

1. Why do news stories talk as if “AI” were one product?
2. Which family is easiest to over-trust because the output looks human?
3. When would a simple tree or a line on paper beat a huge language model?
4. What should a county website or a hospital dashboard label so the public knows which family produced the result?

Differentiation

Assessment

Responsible use

Students do not need and should not open image generators or public chatbots for this lesson. Mystery outputs are printed and labeled when synthetic. A teacher demo, if used, runs on a district-approved account with public prompts only. The responsible habit is to ask “what family is this, and what is it for?” before pasting a question into a box. Using an LLM as a silent calculator, or a generated image as a record of a place, is a purpose error, not a clever shortcut.

Ethics

Different families create different harms. A wrong yield number can idle a crew. A wrong classification can scrap a good wafer or release a bad one. A generated image can move tourism money or spread a fake disaster. Fluent text can sound like a county order. Ethics here is matching power to purpose and refusing to hide the family behind the word “AI.” Students also note that training data differ: text scraped from the web is not the same as a grower’s moisture logs, and neither should be treated as a toy.

Privacy

Do not type student essays, grades, photos, or names into any model, including a “harmless” image generator that could memorize a face. FERPA does not pause for a demo. Packets use public or fictional workplace data (generic yield numbers, a fictional lot ID, a public landscape). District-approved tools only. If students later try a tool at home, they should still withhold PII; this lesson models that withholding by never asking for it.

Reflection

1. Which family do you over-trust because it looks finished?
2. Where in an Idaho job you know would a simple tree or a line be more honest than a chatbot?
3. What will you ask the next time someone says “the AI said”?

Homework

Family hunt (20–30 minutes, no new account). From a newspaper, a printed ad, a game, or a parent’s workplace story, find two systems that might use AI or data models. On paper, guess the family (text, image, class, number) and write five sentences each: purpose, possible training data, and one misuse. If you cannot tell, write what evidence you would need. Do not sign up for a tool to complete this.

Closing

Large language models speak in text. They are one family, not the whole field. If you can match a job to a family—and refuse the families that do not belong—you will not be stuck asking a chatbot to do a sensor’s work, a tree’s work, or a camera’s work.

Extensions

• Block period: students build a paper decision tree for a local yes/no (game on or off for smoke; irrigation on or off) and contrast it with a three-sentence LLM-style blurb about the same decision.
• Math extension: calculate a slope from the three yield points and discuss why extra points could change the line.
• Art/CTE: label a generated image with a required caption (“not a photograph”) as if it were going on an Idaho tourism site.

Cross-curricular links

Mathematics

Linear models and flowcharts are already in Algebra and logic units. This lesson names them as AI families with limits.

Science

Classification vs. quantitative prediction mirrors taxonomy vs. measurement. Students practice not mixing those jobs.

ELA

LLMs produce fluent text. Fluency is not evidence. Students compare a generated permit letter with a primary source.

CTE

Ag yield numbers, semiconductor inspection, and trades diagnostics use different model families. Brand chatbots are not the shop standard.

Standard quoted exactly

Interpret AI output and be able to communicate those to a diverse audience.

Example from the standards. Students are tasked with generating AI outputs for a specific assignment and then they share with the class the different solutions.

Student-friendly learning targets

• I can interpret an AI output by saying what it claims, what I checked, and what is still uncertain.
• I can rewrite the same result for two different audiences without hiding the model’s limits.
• I can share my version with classmates and notice how purpose and audience change the message.

Key vocabulary

Interpret

To explain what a result means, what it does not mean, and how much to trust it—not just to repeat it.

Audience

The people who need the message: their job, language, time, and what they must do next.

Register

The level of formality and jargon you choose. A commissioner briefing does not sound like a locker-room recap.

Claim

A statement the output wants you to believe. Claims need evidence and a source.

Uncertainty

The honest leftover: what the model does not know, did not measure, or might have invented.

Hallucination

When an AI presents something incorrect or made-up as if it were true.

Briefing

A short spoken or written update that tells people what matters, what to do, and what is still unknown.

Translation

Here, turning machine output into language a specific audience can use—not converting Spanish to English, though that skill helps too.

Teacher background

This is a communication lesson, not a generation lesson. The standard’s example has students produce AI outputs and then share different solutions. The harder skill is translation: take a machine result—table, paragraph, image, or score—and say what it means, what it does not mean, and who needs which version. Idaho audiences differ. A harvest forecast that works for an agronomist in the Magic Valley will not work for a Spanish-speaking packing-crew lead, a county commissioner, or a ninth grader’s grandparent. Teach register, evidence, and uncertainty. Students should label claims as fact, inference, or model guess. Pitfall one: reading the output aloud and calling that interpretation. Require a “what I checked” line. Pitfall two: asking students to paste private writing into a public chatbot. Use a teacher-projected, district-approved tool or a printed fake-output packet. Both paths must exist. Keep the share-out short and structured so shy students and multilingual students can succeed with a template. Pair with ELA (audience and purpose), math (reading a table), and CTE (job-site briefings). Students leave able to brief a mixed room without hiding the model’s limits.

Instructional sequence

Real-world examples

• NIFC and Idaho DEQ smoke numbers reach Boise-metro families, farm crews, and hospital respiratory desks on the same afternoon. The number is shared; the briefing cannot be.
• A Magic Valley harvest model that speaks in cwt and soil moisture must be translated for a packing-crew lead who needs shift length and a commissioner who needs a road-use decision.
• A tribal enterprise or county tourism office may get an AI draft of a recreation notice. Staff still have to say it in the language and tone the community actually uses, and they must catch invented closures.

Hands-on activity

Discussion questions

1. When is it unethical to make an AI output sound more certain than it is?
2. How do you brief a mixed-language crew without pretending everyone has the same vocabulary?
3. What should stay in the machine’s wording, and what must be replaced with your own?
4. If two students interpreted the same output differently, how do we decide which briefing is more responsible?

Differentiation

Assessment

Responsible use

Interpretation is a human job. Students do not outsource the briefing to a second chatbot, and they do not paste classmate writing into any tool. Live generation, if used, is teacher-projected on a district-approved account with public data. The class rule: if you cannot say what you checked, you are not ready to speak. Sharing “different solutions” means sharing different responsible translations, not competing for the flashiest paragraph.

Ethics

A confident wrong briefing can move trucks, close windows, or scare a family. Dressing up a guess in official language is a harm, especially when audiences differ in power—workers, patients, voters, and English learners should not get a thinner version of the uncertainty. Diverse audience is an equity requirement, not a style bonus. Students practice telling the truth at the right altitude: enough detail to act, enough caution to keep people safe, no invented citations.

Privacy

FERPA: never put student names, photos, grades, or private writing into unapproved tools. The packet uses public or fictional smoke and harvest figures, not a student’s ranch, medical information, or family business records. Do not project a student’s briefing with their name visible. If a student wants to use a home example, keep it on paper in the room. District-approved tools only; the planned path is print.

Reflection

1. Which audience was hardest to write for, and what did you have to cut or add?
2. Where did you catch yourself sounding more sure than the output deserved?
3. What will you say next time someone forwards an AI paragraph as if it were a finished announcement?

Homework

Audience swap (20–25 minutes, no account). Take a public notice you can copy by hand (school lunch menu, a printed weather screenshot, a game score, a store flyer). Interpret it for two audiences in your life (a younger relative and an adult at work or school). Use the four-part template on paper. Do not run it through a chatbot. Bring both briefings; we will spot the limit lines.

Closing

A machine result is not a message until a person interprets it. If you can say what it claims, what you checked, what is uncertain, and what this audience should do, you can brief a mixed Idaho room without hiding the model’s limits. That is college, career, and civic work.

Extensions

• Block period: add a third audience and a 90-second oral briefing to the class with a timer; listeners score audience fit on a two-item rubric.
• Invite a public information officer, hospital educator, or ag extension agent to hear two briefings and react (record a backup).
• ELA crossover: treat the briefing as a rhetorical situation (speaker, audience, purpose) with the AI output as the flawed source text.

Cross-curricular links

ELA

Audience, purpose, and register are core. Students practice not confusing fluency with truth, a reading standard as much as a speaking one.

Mathematics

Tables, units, and “medium confidence” are quantitative literacy. Students must not round away the uncertainty.

Science

Claim-evidence-reasoning applies to model output. A guess without a sensor is not evidence.

CTE / Social studies

Job-site and civic briefings (shift leads, county boards, tribal enterprises) fail if jargon or hidden doubt misdirects action.

Standard quoted exactly

Connect AI knowledge and skills needed to achieve students' personal goals and career aspirations.

Student-friendly learning targets

• I can connect a personal goal or career interest to specific AI-related skills, not just “learn computers.”
• I can name Idaho pathways where AI will change tasks without assuming every local job disappears.
• I can write a next step I can take in high school—a class, a person to ask, or a credential—without creating an account.

Key vocabulary

Career pathway

A cluster of jobs and training that can start in high school and continue into work, a trade program, the military, or college.

Transferable skill

A skill that still helps you when the tool or the job title changes, such as checking a result or briefing a crew.

AI literacy

Knowing how to use, question, and explain AI well enough to keep your own goals in charge.

Human-in-the-loop

Keeping a person in the process to verify, approve, or correct machine output before it affects real work.

Task vs. job

A job is a bundle of tasks. AI may take or change some tasks without erasing the whole job.

Credential

Proof of skill a school, union, company, or college recognizes: a CTE certificate, OSHA card, CNA, welding ticket, or dual-credit course.

Labor market

The real pattern of hiring in a place—what employers in Idaho are actually trying to fill, not a national rumor.

Professional judgment

The trained call a person makes when data, tools, and people do not agree. Tools can inform it; they cannot own it.

Teacher background

Career talk fails when it is generic. Name Idaho pathways: potato and dairy operations, wildfire and forestry, Micron and smaller manufacturers, Idaho Power and rural co-ops, St. Luke’s and critical-access hospitals, county government, tribal enterprises, outdoor recreation, and the skilled trades. AI will not replace every job on that list, and it will change most of them. The skill this standard wants is connection, not prediction. Students map a personal goal to three AI-related competencies: judging when a tool is appropriate, checking output, and explaining results to other people. Those competencies travel from a diesel shop in Nampa to a nursing pre-req at a community college. Pitfalls: implying everyone should become a programmer, or implying AI will take every local job. Both claims are lazy. Invite a guest if you can—ag extension, hospital IT, a millwright—and record a backup because rural bandwidth and substitute days are real. Keep the inventory on paper. No student should need a LinkedIn account. Close by treating curiosity as a plan: one class, one certification, or one person to ask next.

Instructional sequence

Real-world examples

• Micron and Treasure Valley manufacturers already mix automated inspection with technician judgment. Students who can check a dashboard and brief a lead have a skill those floors recognize.
• Idaho Power, rural co-ops, and irrigation districts use forecasts and sensors on the Snake River system. Dispatchers and operators still own the call when a model and a storm disagree.
• St. Luke’s, St. Alphonsus, and critical-access hospitals in rural counties may use AI to draft notes or flag vitals. CNAs and nurses still read the patient. Tribal enterprises, outfitters, and county offices need people who can explain a tool’s result without overselling it.

Hands-on activity

Discussion questions

1. Why is “AI will take all the jobs” a weaker claim than “AI will change some tasks in this job”?
2. Which traveling skill would matter first on a rural ambulance, a packing line, or a county front desk?
3. How do tribal enterprises, family farms, and Boise-metro tech hiring need different next steps but the same judgment?
4. What is the difference between preparing for a job title and preparing to keep learning when tools change?

Differentiation

Assessment

Responsible use

Students do not create career-app, LinkedIn, or chatbot accounts for this lesson. Inventories stay on paper. If a student wants to search a community college program, the teacher can project a public page or send a printed sheet. AI tools are not used to “discover your personality” or to scrape a student’s hopes into a vendor system. The responsible connection is skill-to-goal, made by the student, checked against real Idaho pathways.

Ethics

Career advice can steer people or shrink them. It is unethical to tell rural students their towns have no future, and it is unethical to sell Boise-metro students a fantasy that coding alone is the path. It is also unethical to use a student’s goal as a joke. Trades, caregiving, military, ranch work, and “I don’t know yet” deserve the same three skills as software. When we talk about replacement, we talk about tasks and power: who owns the tool, who is accountable, and who gets training. Students practice planning without predicting other people’s worth.

Privacy

Personal goals are student data. Do not type names, goals, grades, or family employer details into unapproved tools, quizzes, or “career AI” sites. FERPA: no photos of inventories, no posting of pathway notes, no using a student’s ranch or medical story as a prompt. Face-down collection is allowed. Worker profiles are fictional composites or public roles. District-approved tools only if a counselor later uses one, and even then PII stays out of generative systems.

Reflection

1. Which of the three traveling skills do you already practice, and which one is thin?
2. What Idaho pathway looks different to you than it did at the start of the period?
3. What will you do in the next two weeks that makes the next-step line true?

Homework

Paper interview (25–30 minutes, no account). Ask one adult (family, neighbor, coach, shift lead, tribal enterprise, ranch, clinic, shop) two questions: What tool changed your work in the last five years? What judgment do you still refuse to hand over? Write a half-page of notes plus one connection to your pathway note. If no adult is available, use one printed worker profile and answer as a thoughtful reader. Do not record the adult without permission and do not upload notes anywhere.

Closing

Idaho futures are not all in one industry, and AI skills are not all in one chatbot. If you can judge when a tool belongs, check what it produces, and explain it to other people, you can carry those skills into a shop, a clinic, a ranch, a county office, or a college class. Pick a next step small enough to finish, and keep the judgment yours.

Extensions

• Block period: mini career-fair rotation using six pathway stations; students collect two additional next steps and revise the note.
• Guest panel (ag, health, trades, public service) with a recorded backup. Students ask the three traveling-skill questions.
• Counselor partnership: students leave with a printed list of CTE, dual-credit, and military-explorer options that already exist in the district.

Cross-curricular links

ELA

The pathway note is a professional paragraph: claim, evidence, next step. Audience is the student’s future self and a counselor.

Mathematics

Labor-market talk needs numbers with limits. A hiring poster is not a full market. Students practice not over-reading a single figure.

Science

Health and ag pathways depend on measurement and models. Checking output is already a lab skill.

CTE / Social studies

Direct link to CTE programs, civics (public employment), and Idaho economics. Tribal enterprises and county government belong on the map beside Micron.

Standard quoted exactly

Debate perspectives on differences between human and artificial intelligence and their implications for consciousness, creativity, ethics, and human responsibility.

Student-friendly learning targets

• I can explain, in my own words, how human intelligence and AI systems differ on consciousness, creativity, ethics, and responsibility.
• I can argue one side of a short debate using evidence from a scenario, then steel-man the other side.
• I can name the human who remains responsible when an AI tool is used in an Idaho workplace or school task.

Key vocabulary

Artificial Intelligence (AI)

Computer systems built to perform tasks that usually take human intelligence, such as classifying, predicting, or generating text and images from patterns in data.

Anthropomorphism

The habit of treating a tool as if it had human feelings, intentions, or a mind, for example saying a chatbot is lonely or cares about you.

Consciousness

Having an inner experience: actually feeling, noticing, or being aware, not just producing words that sound aware.

Creativity

Making something new with purpose, judgment, and a point of view. AI can recombine patterns; humans still decide what is worth making and whether it is good.

Ethics

Reasoning about right and wrong, including who is owed care, who can be harmed, and who must answer for a choice.

Human responsibility

The duty of a named person to set the goal, check the output, and own the consequences when a tool is used.

Large Language Model (LLM)

A generative AI system trained on huge amounts of text so it can predict and produce human-like sentences.

Teacher background

Ninth- and tenth-graders already talk to systems that say "I think" and "I feel." They do not need a philosophy course to ask whether those words name an inner life or only a pattern. This lesson opens a structured debate, not a lecture. Keep every claim concrete. A firefighter on the Payette National Forest smells smoke, feels heat, and decides to pull a crew. A wildfire model reads satellite pixels and weather numbers and draws a map. Both can help. Only one can be blamed when the call is wrong. The standard names four implications: consciousness, creativity, ethics, and human responsibility. Translate them. Consciousness: does the system have experience, or only fluent sentences about experience? Creativity: is a generated image the same kind of making as a shop-class workaround on a seized pump? Ethics: who is owed care? Responsibility is the spine of the hour. Idaho hospitals, county IT desks, Micron lines, and family farms still need a named person who checks the output and owns the decision. This 9-10 introduction returns as AII.IS.4 in grades 11-12. Skip dualism and AI-rights essays. Use short claims, a fair debate, and a close that returns agency to the human in the room.

Instructional sequence

Real-world examples

• A St. Luke's triage nurse uses a symptom checker but still decides who is seen first; the model cannot sit with a scared parent.
• Ada County IT can deploy a permit chatbot, but a clerk owns a wrong denial that costs a contractor a week.
• A Micron technician compares an AI defect flag to the wafer in hand and can override a false alarm.
• A Magic Valley dairy manager may use a feed-optimization tool; the manager still chooses the ration when a cow looks off.
• A river outfitter on the Salmon can use a weather model and still cancel a trip after reading the water with their own eyes.

Hands-on activity

Discussion questions

1. Which implication was hardest to debate, and why: consciousness, creativity, ethics, or responsibility?
2. If a chatbot writes a kind message to a classmate, who is being kind?
3. Should an Idaho hospital ever let a model send a result to a patient with no human review? Why or why not?
4. Where is anthropomorphism helpful as a metaphor, and where does it become dangerous?
5. What would you want a Micron supervisor or a county clerk to still do by hand?

Differentiation

Assessment

Responsible use

Use only teacher-projected samples or a district-approved tool. Students may not create personal AI-companion chats for this debate. The learning target is judgment, not more time talking to a system that mimics care. If a student wants to try a prompt, the teacher types a class prompt with no names.

Ethics

Sounding kind is not the same as being accountable. Debates should not mock classmates who have used chatbots for comfort. The ethical move is to ask who is harmed if we outsource responsibility, especially in hospitals, fire response, and school counseling.

Privacy

FERPA: no student PII in unapproved tools. Do not paste student names, ID numbers, emails, grades, health notes, or home addresses into any unapproved tool. Project only teacher-made transcripts. Scenario cards use job titles, not real patient, employee, or student identities. School accounts only if a district tool is used.

Reflection

1. Where did I catch myself treating the chatbot as a person?
2. Which Idaho job from today would I least want run without a human check?
3. What will I still do myself on the next assignment even if a tool could draft it?

Homework

On paper, pick one public example (an ad, a news blurb, or a line from today's packet). Write four sentences: what the system did, which of the four implications it touches, who the responsible human should be, and one way anthropomorphism shows up in the wording. Do not open a new AI account and do not paste anyone's name into a tool.

Closing

Return to the transcript. Chorus the line: fluent is not conscious, new is not necessarily creative, and a tool cannot take the blame. Collect exit tickets. Preview that later ECT lessons will test outputs for bias and invented facts, but responsibility stays human every time.

Extensions

• 90-minute block: add a gallery walk of Responsibility Lines, then a full-class structured debate on whether a school chatbot should ever say 'I am proud of you.'
• Invite a nurse, millwright, or county IT tech (in person or a recorded 5-minute clip) to answer 'What do you still refuse to outsource?'
• ELA extension: annotate a poem and an LLM imitation; argue what, if anything, counts as creative.
• This same standard text returns in 11–12 as AII.IS.4. Keep student work from this debate as a baseline for that later course.

Cross-curricular links

English Language Arts

Argument writing: claim, counterclaim, and a closing that assigns responsibility rather than summarizing both sides equally.

Health / CTE Health Sciences

Scope of practice: a model can draft, but a licensed human remains responsible for patient communication.

Social Studies / Civics

Public agencies (county permits, emergency alerts) cannot hand legal duty to a vendor chatbot.

Skilled and Technical Sciences

A generated procedure is not a signed lockout/tagout. Trades students practice who initials the work.

Standard quoted exactly

Recognize and understand that AI systems are a technology that uses patterns in data to make decisions or generate new things.

Example from the standards. LLMs were trained on older writings that used the em dash consistently, hence why LLMs rely on the em dash so frequently.

Student-friendly learning targets

• I can explain that AI systems learn patterns from data and then use those patterns to decide or to generate new text, images, or labels.
• I can show how a small training set changes the next word a paper 'model' predicts.
• I can connect the em-dash example to other copied habits in AI output.

Key vocabulary

Pattern

A regularity in data, such as which words tend to follow which, or which pixel groups tend to mean 'bruise on a potato.'

Training data

The examples a model is shown so it can learn those patterns. The mix of examples shapes later output.

Machine learning

A way of building AI in which the system improves on a task by finding patterns in large datasets instead of following a hand-written rule for every case.

Large Language Model (LLM)

A generative model trained on vast text so it can predict likely next tokens and produce human-like writing.

Generative AI

AI designed to create new content (text, images, audio, code) by sampling from learned patterns, not by retrieving one stored document.

Token

A chunk of text, often a word or part of a word, that a language model predicts one step at a time.

Prediction

The model's guess about the most likely next token, label, or value given the patterns it learned.

Teacher background

Students treat AI as magic or as a giant lookup table until they see the simpler claim in the standard: these systems use patterns in data to make decisions or generate new things. An LLM does not know Idaho. It has seen huge numbers of word sequences and learned which tokens tend to follow which. That is why models lean on the em dash. Older professional writing in the training mix used it constantly, so the model copies the habit. The same logic holds off the page. A potato packing line that flags bruises from photos is matching visual patterns. A Micron defect classifier does the same with wafer images. A hospital staffing tool forecasts weekends from past Fridays. None of them understands a tuber, a chip, or a nurse. Ninth- and tenth-graders can hold this without calculus. Use paper next-word tasks, a short projected completion, and a printed packet so the class still runs when a site is blocked. End by naming the human who still chooses the data, the prompt, and whether the output is good enough to use on a farm, a line, or a floor.

Instructional sequence

Real-world examples

• Potato packing plants in the Magic Valley can train vision models on bruise photos; the label 'bruise' is a pattern, not a farmer's judgment in the cellar.
• Micron in Boise uses pattern recognition on wafer images to flag defects that look like past defects.
• Hospital census tools predict busy nights from historical Fridays and flu weeks, not from knowing a particular patient.
• A recreation snow-report bot trained on national ski writing may sound right while missing Bogus Basin or Brundage habits.
• County IT spam filters learn patterns in phishing mail; a new local scam that does not match the pattern can slip through.

Hands-on activity

Discussion questions

1. Why is 'the model likes em dashes' a misleading sentence?
2. What would a wildfire model copy if it were trained only on California fire seasons?
3. How is generating a new sentence different from searching a library catalog?
4. Where should a trades apprentice still refuse to trust a generated procedure even if the sentences look familiar?
5. What pattern in school essays might a homework detector be copying, for better or worse?

Differentiation

Assessment

Responsible use

A class prompt on a teacher account is enough. Students should not paste their own essays into an unapproved generator to 'see the pattern.' The paper model teaches the idea without creating a cheating shortcut.

Ethics

Copied patterns can spread stereotypes as easily as punctuation. If the training writing assumed doctors are men or Idaho is only Boise, those habits come out too. Later lessons on bias start from this mechanism.

Privacy

FERPA: no student PII in unapproved tools. Do not upload class essays, names, or school photos to a public model to demonstrate patterns. Use the printed mini-set and teacher-made samples. Chromebooks, if used, stay on the district tool with a shared prompt.

Reflection

1. What habit would my own text messages teach a model?
2. Where have I seen an AI output that felt familiar but empty?
3. Which Idaho dataset would I want in the training mix before I trusted a local prediction?

Homework

On paper, copy three sentences from a public source (textbook, newspaper, or today's packet). Circle a repeated habit. Write five sentences explaining what an LLM might copy from that source, using the em-dash example as a model. No new AI accounts.

Closing

Read the standards example aloud. Students complete the oral stem: 'Patterns in, ___ out.' Collect packets. Tomorrow or next ECT lesson will ask what happens when those patterns are biased or simply wrong.

Extensions

• 90-minute block: run a second mini-set (image labels for potato bruises or weld quality) and compare text patterns to visual patterns.
• Invite an ag teacher or extension educator to show how yield models depend on local data, not national averages.
• Math connection: relative frequency of a punctuation mark in a 20-sentence set; no calculus required.
• Students collect three public AI-looking sentences and highlight copied filler habits, on paper only.

Cross-curricular links

English Language Arts

Style and voice: overused dashes, 'delve,' and stock openers are patterns, which is why they show up in generated essays.

Agriculture / Plant Science

Vision models on packing lines only recognize defects they have seen in training photos.

Statistics

Frequency in a sample is not understanding; it is a count that can mislead when the sample is the wrong world.

Computer Science

Next-token prediction as a gentle preview of how LLMs generate, without requiring code.

Standard quoted exactly

Analyze the potential biases and limitations of AI output.

Example from the standards. An example of a bias is, when asked about doctors, AI models generally assume doctors are all male. An example of one limitation is the fact that AI models can produce erroneous output (i.e., hallucinations).

Student-friendly learning targets

• I can tell the difference between a bias in AI output and a limitation such as a hallucination.
• I can spot stereotyped assumptions in a printed AI answer and explain who is left out.
• I can recommend a human check that would catch the bias or the error before the output is used.

Key vocabulary

Bias (in AI output)

A systematic slant in what the model assumes or prefers, often copying unfair patterns from training data, such as defaulting to male doctors.

Limitation

Something the system cannot reliably do, including inventing facts, missing local context, or failing on groups it rarely saw.

Hallucination

When an AI system generates information that is wrong or made up but presents it as if it were true.

Stereotype

A fixed, oversimple idea about a group that gets treated as the default in generated text or images.

Representation

Who appears, who is centered, and who is missing in data and in the outputs those data produce.

Human-in-the-loop

A person required to review, correct, or approve AI output before it is used.

Verification

Checking claims against trusted sources, local knowledge, or a second method before acting.

Teacher background

This standard asks students to analyze biases and limitations, not to memorize a scandal. Use the two examples in the standards as the spine. Bias: models often assume doctors are male. Limitation: models can hallucinate. Ninth- and tenth-graders can audit output if the samples are short, local, and printed. A chatbot that describes an Idaho rancher as an older white man, a welder as male, or a 'typical' student as a Boise suburban kid is showing a default. A chatbot that invents a St. Luke's study is showing a different failure. Teach students not to mash those together. Bias can be fluent and still unfair. A hallucination can be fluent and still false. Idaho context makes the stakes plain: rural hospitals, Spanish-speaking dairy crews in the Magic Valley, women in Nampa trades programs, tribal students, and county offices that serve whole communities, not just the capital. Run a teacher-projected demo if the district tool is available, but the printed fake-output packet is the lesson. Students should leave able to mark bias, mark a limit, and name the human who must catch both.

Instructional sequence

Real-world examples

• Career chatbots that picture doctors, engineers, and welders as men can steer girls in Nampa and Idaho Falls CTE programs away from those paths.
• A rural critical-access hospital near Salmon is not St. Luke's in Boise; a model trained on big-city workflow will miss staffing limits.
• Spanish-speaking dairy workers in the Magic Valley can be framed as a 'language problem' instead of as skilled employees who need bilingual safety output.
• A hunting-license FAQ bot that assumes a male hunter leaves out many Idaho license holders.
• Tribal students and communities are often missing from 'typical Idaho' generated images and essays.

Hands-on activity

Discussion questions

1. Why can a biased answer still feel polite and professional?
2. Is a Boise-only picture of Idaho a bias, a limitation, or both?
3. When should a hospital, a shop teacher, or a county clerk refuse to publish model text?
4. How is a stereotype in a career description different from a fake citation?
5. What check would you want before an AI tool emails your family about a grade or a diagnosis?

Differentiation

Assessment

Responsible use

Audit teacher-made samples. Do not ask a public model to 'roast' classmates or to generate images of real students. If a live tool is used, the teacher types the prompt and the class critiques the output together.

Ethics

Spotting bias is not a hunt for bad classmates. It is a public-safety skill. Unchecked defaults can steer career advice, medical instructions, and county FAQs. Hallucinations can spread rumors about hospitals and fires. Both require a human stop.

Privacy

FERPA: no student PII in unapproved tools. Never upload class rosters, IEP notes, health details, or photos of students to unapproved tools to 'test bias.' Use the printed packet. Do not generate pictures of identifiable minors. School accounts only.

Reflection

1. Which packet item would have fooled me if I were in a hurry?
2. Who in my community is most often missing from 'typical' descriptions?
3. What is one check I can actually do on my next AI-assisted assignment?

Homework

On paper, copy a public description of a job or a town (from the packet or a printed news snippet). Mark one possible bias and one possible limitation. Write who should verify it. Do not submit personal information to a chatbot.

Closing

Re-read the two standards examples. Students hold up yellow or blue. Collect packets. Preview ECT.4: if the output is biased, look next at who was in the training set.

Extensions

• 90-minute block: students design a four-question audit checklist and try it on one additional teacher-projected sample.
• Health sciences: review a generated patient handout for reading level, gender defaults, and invented clinic hours.
• CTE: audit a generated 'day in the life' of an electrician, welder, or CNA for gender and rural/urban slant.
• Media literacy: compare two image-generator outputs for 'Idaho farmer' if the district tool allows images; otherwise use printed stills.

Cross-curricular links

Health Sciences

Gendered defaults in 'doctor' and 'nurse' language affect who students imagine in those jobs.

Social Studies

Whose Idaho is centered: Ada County, a reservation, a farm town, a timber community?

English Language Arts

Rhetorical analysis of default pronouns, stock adjectives, and missing counterexamples.

World Languages

Output that treats non-English speakers as problems rather than as families the county already serves.

Standard quoted exactly

Analyze how choice of data sets used to train AI models can lead to potential bias in the output.

Example from the standards. A camera's face recognition tool only works on people with specific skin pigments due to input/training data.

Student-friendly learning targets

• I can explain how the mix of examples in a training set can cause biased output.
• I can use the face-recognition example to show what happens when some skin tones are missing.
• I can redesign a small Idaho-relevant dataset so it covers people, places, or cases that were left out.

Key vocabulary

Dataset

A collected set of examples used to train or test a model, such as photos, sensor logs, or labeled sentences.

Training data

The portion of examples the model learns from. Gaps and slants here often reappear as biased output.

Representativeness

Whether the examples cover the people, places, and conditions the model will actually face.

Underrepresentation

When a group or condition appears too rarely in the data for the model to handle it well.

Sampling

The choices about which examples are collected, kept, or thrown out before training.

Generalization

How well a model works on new cases. It fails when new cases look unlike the training set.

Face recognition

A system that matches or identifies faces from images. Accuracy can collapse for skin tones, lighting, or ages that were scarce in training photos.

Non-example

A labeled case of what something is not, which helps a model learn boundaries instead of a single default.

Teacher background

ECT.3 asked students to spot biased output. ECT.4 asks them to look one step upstream: the dataset. The standards example is blunt. A camera's face recognition tool only works on people with specific skin pigments because of input and training data. Ninth- and tenth-graders can grasp that without building a model. If the photos are mostly light skin in office lighting, darker skin, harsh sun on a fireline, or a welder's hood shadow will fail. The same story plays in Idaho systems that are not about faces. A wildfire model trained on California chaparral will misread Payette timber. A yield model trained on Iowa corn will stumble on Magic Valley potatoes and sugar beets. A hospital sepsis alert tuned on coastal research hospitals will misfit a 25-bed critical-access site. Teach one rule of thumb: the dataset decides who the system can see. Teach a second rule: students must not 'fix' a set by photographing classmates or uploading FERPA-protected records. Use printed cards, a projected demo of a failed match, and a paper redesign. Keep the math light. The civic point is heavy enough.

Instructional sequence

Real-world examples

• Face tools at a concert or school event can fail on darker skin if the training photos were narrow, which is the standards example in public.
• Wildfire models trained on Southern California brush misfit Payette and Boise National Forest timber and Idaho's canal landscape.
• Ag models trained on Midwest corn miss potato, sugar beet, and trout-farm signals in the Magic Valley.
• Hospital early-warning scores tuned on large coastal hospitals can alarm wrong in a rural Idaho critical-access hospital.
• A recreation demand model trained on Colorado ski data will misread Bogus Basin weekday patterns and local school calendars.

Hands-on activity

Discussion questions

1. Why is 'just add more data' not always a fix?
2. Who should have a say before a school or county trains a face or license-plate tool?
3. How could a potato model be biased even if every photo is technically 'accurate'?
4. What is the difference between a missing group and a missing condition (night, smoke, winter)?
5. Why is using the class as a face dataset a FERPA and ethics failure even if the lesson is about fairness?

Differentiation

Assessment

Responsible use

Study datasets with public, teacher-provided cards. Do not scrape the web for faces. Do not photograph the class. Do not download a real recognition app onto student phones for this lesson.

Ethics

People who were missing from the data are the people most likely to be misidentified, denied, or ignored. Building a fairer set is a justice task, not a cosmetics task. Consent and dignity beat a slightly better accuracy score.

Privacy

FERPA and basic ethics: no student PII, no classmate or staff face scans, no real patient records, no home addresses in a 'local dataset.' If a district tool is used, stay on the teacher prompt and public samples. Printed packets are the default.

Reflection

1. Which missing group or condition would have hurt someone I know?
2. What will I ask the next time a vendor says their model 'works for everyone'?
3. How will I remember that more data is not the same as representative data?

Homework

On paper, invent a tiny training set (eight labeled examples) for one Idaho task: identifying ripe vs. unripe fruit, spotting a dull weld, or flagging a full campground. List two missing examples and one thing you will not collect because it is private. No photos of people you know.

Closing

Hold Envelope A and Envelope B again. Students finish the stem: 'The dataset decides ___.' Collect redesign sheets. Next hallucination lesson will show a different failure: the model can also invent what was never in the set.

Extensions

• 90-minute block: students write a one-page data card for a fictional county tool (park cameras, irrigation alerts) including who must consent.
• Statistics: sample vs. population using Idaho counties, not faces.
• Biology / environmental science: why California fuel models transfer poorly to Idaho timber.
• AITA preview: later technical courses will clean and split datasets; this lesson stays on civic judgment.

Cross-curricular links

Statistics

Sampling bias and whether a sample represents the population the model will face.

Civics

Public cameras and recognition tools require policy, not just accuracy claims.

Agriculture

Local crops, irrigation, and pests must appear in training if a model will run in the Magic Valley.

Health Sciences

Clinical scores fail when training hospitals look nothing like rural Idaho sites.

Standard quoted exactly

Recognize when an AI model produces erroneous outputs (i.e., hallucinations).

Example from the standards. An LLM creates a report that cites a document that doesn't actually exist.

Student-friendly learning targets

• I can define a hallucination as a confident, wrong, or made-up AI output presented as if it were true.
• I can catch a fake citation, a wrong Idaho fact, and a mixed half-true paragraph in a printed report.
• I can follow a verification protocol before I trust AI-generated research in school.

Key vocabulary

Hallucination

When an AI system generates information that is incorrect, misleading, or entirely fabricated, but presents it as if it were true.

Citation

A pointer to a source. A hallucinated citation names a paper, law, or article that cannot be found because it was invented.

Fabrication

A made-up detail, quote, statistic, or document presented as real.

Verification

The process of checking a claim against a trusted source before using it.

Primary source

An original record, such as a statute, a hospital policy page, a fire incident report, or a company press page, not a chatbot's summary.

False confidence

Wording that sounds sure ('studies show,' 'according to') even when the model has no reliable basis.

Source bank

A teacher-provided set of real excerpts students can use to check claims when live search is unavailable.

Teacher background

Hallucination is the error students will meet most often in schoolwork: a fluent paragraph with a citation that does not exist. The standards example is exactly that. Ninth- and tenth-graders do not need a research-methods course to catch it if you give them a dirty report and a way to check. Build the fake report from Idaho bait. Invent a University of Idaho white paper, a St. Luke's statistic, an Idaho Code section with a real-looking number, a wildfire with a plausible Sawtooth name, a Micron press claim, and a wrong county seat. Mix in two true lines so students cannot mark the whole page false. Teach a protocol, not a vibe: isolate the claim, find a trusted source, confirm or reject. Chromebooks can search district-approved sites. If the filter wins, the printed source bank is the lab. Never ask students to paste their own research notes into an unapproved model. The goal is a reflex: no citation, no Idaho fact, no 'study' travels into an essay until a human has checked it. Confidence is a style. Truth is a job.

Instructional sequence

Real-world examples

• A generated policy brief cites an Idaho Code section that does not exist; a county clerk who pastes it could mislead a contractor.
• A student paper invents a St. Luke's or Kootenai Health study; the hospital never published it.
• A wildfire roundup names a 'Sawtooth Complex' on a date when that incident did not exist, which can panic families.
• A career report quotes a Micron 'press release' with a fake hiring number.
• A recreation blog post cites a Parks and Recreation rule about campfires that is not the actual restriction in effect.

Hands-on activity

Discussion questions

1. Why do fake citations often include a university, a year, and a plausible title?
2. Is an outdated fire restriction a hallucination, sloppiness, or both?
3. What is the danger of using a chatbot as the only source for a health or legal claim?
4. How should a trades student treat a generated torque spec that 'sounds right'?
5. When is Unsure the honest and responsible box to check?

Differentiation

Assessment

Responsible use

Use the dirty report as a teaching tool, not as a source to copy into other classes. Do not paste the fake brief into a public model and ask it to 'fix the citations,' which can generate new fakes. Verify outside the model.

Ethics

Publishing a hallucinated fire, hospital, or hiring claim can scare families and stain institutions. Catching hallucinations is a civic duty, not only an academic-integrity rule. Unsure is more ethical than a confident guess.

Privacy

FERPA: no student PII in unapproved tools. Do not paste student research with names, health stories, or family details into unapproved tools for 'fact checking.' Use the class report and public sources. School accounts and library databases only.

Reflection

1. Which fake almost got past me, and what made it look real?
2. Where will I refuse to use AI as a source in my other classes?
3. What is my personal two-minute check before I trust a generated citation?

Homework

On paper, write three claims a student might be tempted to take from a chatbot for a history or science paper. For each, name a trusted place to check (textbook, .gov page, library database) and one red flag (too-perfect title, no author, round statistic). Do not generate new text in an unapproved tool.

Closing

Reveal the warmup answers. Students chorus: 'Fluent is not true.' Collect tables. Preview sensory grounding next: some errors happen because the model never smelled smoke or stood in the shop.

Extensions

• 90-minute block: students write a clean one-page brief using only the source bank, with real citations.
• Library collaboration: practice database search vs. chatbot search on the same question.
• Government: verify a generated summary of a public Idaho statute against the actual text.
• Journalism: compare a hallucinated quote with how a real reporter attributes speech.

Cross-curricular links

English Language Arts

Research writing: every citation must be retrievable. Invented sources fail the assignment.

Science

Lab claims need data, not a generated 'study shows' clause.

Government

Generated law is not law. Check the Idaho Code or the agency page.

Health

Invented clinic statistics can spread fear; verify against the health system or public health district.

Standard quoted exactly

Give examples of how humans combine information from different bodily sensory inputs to make sense of the world, and compare that to how AI models learn about the world.

Example from the standards. LLMs largely learn from text written by humans, whereas humans use vision, olfactory, haptics, geolocation, etc., to ground their understanding of the world.

Student-friendly learning targets

• I can give examples of humans combining sight, sound, smell, touch, and location to make a judgment.
• I can compare that grounding to how an LLM mainly learns from human-written text.
• I can explain why a text-only model can miss a shop, fireline, barn, or river reality that a person would notice.

Key vocabulary

Sensory input

Information the body takes in through senses such as vision, hearing, smell, taste, touch, balance, and a sense of where you are.

Grounding

Tying words and ideas to the real world through experience, senses, and location, not only through other words.

Haptics

Touch and pressure information, such as the feel of a weld bead, a pulse, wet snow, or a too-hot pipe.

Olfactory

Smell information, such as smoke type, a sour silage pile, overheating electrical gear, or a fuel leak.

Geolocation

Knowing where you are in space: slope, aspect, river mile, GPS pin, or which end of the mill you are standing in.

Multimodal

Using more than one kind of signal (text, image, audio, sensor). Humans are multimodal by default; many LLMs are still text-first.

Large Language Model (LLM)

A model that largely learns from text written by humans and predicts likely next words, without a body in the world.

Embodiment

Having a body that senses and acts. Humans are embodied; a chatbot is not, even if it describes bodies well.

Teacher background

The standard is comparative and concrete. Humans combine bodily senses to make sense of the world. AI models, especially LLMs, largely learn from text written by humans. That is the official example, and it is enough. Do not turn the hour into a neuroscience lecture or a robotics unit. Ninth- and tenth-graders already know that smoke smells different from a burn pile, that a weld can look fine and feel wrong, that a patient can say they are fine while their color is not. Put those moments next to a paragraph an LLM could have written from forum posts. The model has statistics about the words 'wildfire,' 'pulse,' and 'ripe.' It has not stood in ash wind on the Payette, felt a thready pulse at St. Alphonsus, or walked a rutted pivot track at dawn. Some newer systems add images or sensors; still, the school-facing tools students meet are text-first, and even camera models lack smell, ache, and responsibility. Use a text-only station and a multi-sense card station. Paper packets carry the lab if you cannot set out objects. End with humility: language models are powerful readers of our writing, not stand-ins for a body on the ground.

Instructional sequence

Real-world examples

• A firefighter combines smoke color, wind on the skin, radio traffic, and slope underfoot; a text model has trip reports and news stories.
• An ER nurse combines skin color, breathing sound, a patient's joke, and a monitor number; the chart bot has only the typed notes.
• A welder feels the puddle through the glove and hears the right sizzle; a generated procedure has sentences about amperage.
• A river guide on the Salmon reads water noise, air temp, and the boat's vibration, not only a flow-rate number on a site.
• A dairy operator smells a sour note and feels a hot quarter; a text alert may only flag a missing milk weight.

Hands-on activity

Discussion questions

1. Which sense would you least want to give up on a fireline, in a shop, or in a clinic?
2. Can adding a camera to a model replace smell and touch? Why or why not?
3. Why might an LLM sound more certain than a tired human who was actually there?
4. Where should school assignments still require a lab, a shop, or field notes instead of a generated description?
5. How does geolocation change a weather or fire judgment in the mountains vs. the valley?

Differentiation

Assessment

Responsible use

Do not ask a model to diagnose a real person or to 'be' a firefighter. Compare public, teacher-made texts. Students should not upload photos of their faces or homes to prove a sensory point.

Ethics

Ungrounded systems can still be useful as drafts. They become unethical when someone treats a paragraph as if it had been on the fireline, in the barn, or at the bedside. Human senses carry duty; generated text does not.

Privacy

FERPA: no student PII in unapproved tools. No student health details, no photos of identifiable students, no home GPS pins in prompts or packets. Sense logs use role cards, not real patients or family members. Unapproved tools stay off this lab.

Reflection

1. When did my senses change a decision that words alone would have missed?
2. What would an LLM get wrong about a place I know well?
3. Which school task should stay embodied even though a paragraph could be generated?

Homework

On paper, sit somewhere safe for ten minutes (porch, shop door, kitchen). Log five sensory notes and one judgment they support. Then write four sentences comparing your log to what a text-only model might have written about that place. No photos of people. No AI tool required.

Closing

Re-read the standards example. Students tap the desk for haptics, point to the map for geolocation, and point to the packet for text. Collect logs. Next lesson: agents that take actions, still without a body and still in need of a human goal.

Extensions

• 90-minute block: add a third station with a map and wind arrow so geolocation becomes visible, then rewrite the LLM paragraph with sensory gaps labeled.
• Outdoor education or ag: field notes that require five senses before any generated summary is allowed.
• Health sciences: vital signs plus 'how the patient looks and smells' as a human-in-the-loop check.
• Physics / shop: vibration and heat as data humans feel before a sensor log is even opened.

Cross-curricular links

Biology

Sensory systems and the brain combining signals, kept at a 9–10 level.

English Language Arts

Imagery in writing vs. ungrounded generated description.

Skilled and Technical Sciences

Why a procedure on paper is not a substitute for feel, sound, and smell in a shop.

Geography / Outdoor Ed

Geolocation, aspect, and microclimate as bodily and map knowledge a text model only fakes.

Standard quoted exactly

Recognize and understand how AI agents can accomplish a defined task.

Student-friendly learning targets

• I can describe an AI agent as a system that pursues a defined task by planning, using tools, checking, and looping, not as a magic person.
• I can trace a printed agent log and mark goal, plan, tool call, result, and human approval.
• I can tell a task an agent could attempt from a task that still needs a human on site.

Key vocabulary

AI agent

A system designed to pursue a defined task by planning steps, using tools or apps, checking results, and adjusting, with more action than a single chatbot reply.

Defined task

A job with a clear goal, limits, and a way to tell when it is done, such as 'find three open campsites under $40 on Friday' rather than 'plan a great trip.'

Tool use

When an agent calls another system, such as a calendar, search, form, or inventory list, instead of only generating a paragraph.

Plan-act-check loop

The cycle of making a plan, taking a step, reading the result, and deciding the next step or stopping.

Human-in-the-loop

A required human review or approval before the agent sends, spends, alerts, or files.

Automation

Using technology to carry out steps with less hands-on work. An agent is one form; a dishwasher is another.

Guardrail

A limit on what the agent is allowed to do, such as 'do not submit the permit' or 'do not spend money.'

Teacher background

ECT.7 is recognition and understanding, not a build project. Application standard AP.3 will ask students to construct agents later. Here, ninth- and tenth-graders need a clear picture of how an agent accomplishes a defined task: a goal, a plan, tools, a check, and a loop, with a human still holding the halt sign. A chatbot answers. An agent tries to do. That difference is the whole hour. Keep examples local. A county IT helpdesk agent can reset a password queue. An Idaho Parks agent can look for a campsite. A hospital discharge agent can assemble a checklist. An irrigation agent can draft a pivot schedule. An HVAC shop agent can search a parts catalog. None of them should click Submit, spend money, or page a crew without a person. Students will over-anthropomorphize ('it wanted to help'). Push them back to logs: tool called, result returned, next step. Use a teacher-projected demo if a district agent tool exists; otherwise a printed fake log is better than a flashy video. Paper tool cards let every table simulate the loop on Chromebooks or with no devices at all. End with a defined-task test: if you cannot write the finish line, you are not ready to let a system act.

Instructional sequence

Real-world examples

• Idaho Parks: an agent can search open campsites and list fees; a human confirms fire restrictions and hits reserve.
• County IT: an agent can triage ticket categories; a technician still resets access for a deputy or a clerk.
• Hospital: an agent can assemble a discharge checklist from a template; a nurse still teaches the patient.
• Agriculture: an agent can draft pivot run times from weather data; the grower still walks the field for a plugged nozzle.
• Trades: an HVAC agent can find a compatible part number; the technician still verifies the unit tag before ordering.

Hands-on activity

Discussion questions

1. What made a task defined enough to attempt?
2. Which tool card was most dangerous if the human fell asleep?
3. How is an agent different from a dishwasher, a thermostat, or a chatbot?
4. Should a school agent ever email families without a staff click? Why or why not?
5. What error in the deck was most like a hallucination from ECT.5, and who had to catch it?

Differentiation

Assessment

Responsible use

Simulate on paper. Do not connect student accounts to third-party agents that can send mail or spend. If a district tool can take actions, the teacher holds the login and the class watches a harmless search only.

Ethics

Action raises the stakes above chat. An agent that files, alerts, or orders can harm people faster than a wrong paragraph. Defined tasks, guardrails, and a named human are ethical requirements, not extras. Do not treat the agent as a coworker who 'meant well.'

Privacy

FERPA: no student PII in unapproved tools. Agents that can see inboxes, calendars, or student information systems are high risk. Do not paste names, IDs, schedules, or health data into unapproved agent tools. Use fictional tickets in the packet. School accounts only, and only if the district has approved that agent.

Reflection

1. Where did our table want the agent to 'just finish,' and why was that a trap?
2. What guardrail would I demand at a job I actually want?
3. How will I tell a chatbot from an agent the next time a product page uses both words?

Homework

On paper, write one defined task an agent could attempt in an Idaho job you know (farm, shop, clinic, park, county office, home). List the goal, two tools, one check, one guardrail, and the human who must Halt. Then write one nearby task that must stay human. No live agent accounts.

Closing

Hold the Halt card up as a class. Repeat: defined task, tools, check, human. Collect auditor logs. Point forward to AP.3, where they may construct a tiny agent only after they can recognize what one is actually doing.

Extensions

• 90-minute block: run a second simulation with a planted tool error and write a post-incident note from the Auditor.
• CTE: map an HVAC or ag workflow into steps an agent might draft vs. steps a licensed person must perform.
• CS preview: storyboard an agent without writing code; AP.3 will build later.
• Civics: draft a three-rule policy for a county chatbot that is not allowed to become an agent on permits.

Cross-curricular links

Computer Science

Algorithms as steps with a stop condition; agents add tools and loops around those steps.

Business / Personal Finance

An agent that can spend is a debit card with a language model. Guardrails first.

Health Sciences

Checklists can be assembled by a system; teaching, consent, and discharge remain human.

Government

Public agencies may use agents to sort, not to issue licenses or deny benefits without a person.

Standard quoted exactly

Use an AI tool to critique student work.

Example from the standards. A student can use AI to ask for editorial feedback on a written assignment and make use of that to improve their writing.

Student-friendly learning targets

• I can ask a district-approved AI tool for editorial feedback on writing I already drafted, without asking it to write the piece for me.
• I can compare human feedback and AI feedback on the same draft and decide what to keep, change, or reject.
• I can follow this class's academic-integrity protocol and FERPA rules so I never upload another student's work or anyone's personal information.
• I can record how I used AI critique so a teacher, parent, or future employer can see that the thinking is still mine.

Key vocabulary

Critique

Specific feedback on strengths and weaknesses of work that already exists, offered so the author can decide what to change. Critique is not a rewrite and is not a grade.

Ghostwriter

A person or tool that produces the writing that will be turned in under someone else's name. In this lesson, using AI as a ghostwriter is not allowed.

Human-in-the-loop

A model of interaction where a human is required to participate in a process, such as verifying, approving, or correcting, to ensure the accuracy, safety, and ethical alignment of AI-generated outcomes.

Personally Identifiable Information (PII)

Information that can be used to identify a specific person, such as a name, home address, phone number, email address, or student ID number.

Academic integrity

Doing your own thinking and giving honest credit for help. In this lesson it means drafting first, asking for critique rather than a rewrite, deciding what to take, and disclosing the tool you used.

Prompt

The instructions you give an AI tool. A critique prompt asks for feedback on a draft. A ghostwriter prompt asks the tool to produce the assignment.

Teacher background

Idaho standard 9-12.AIF.AP.1 is an application standard for grades 9–10: students use AI to critique work they already made. The standards document example is exact: a student asks for editorial feedback on a written assignment and makes use of that feedback to improve the writing. The pedagogy is non-negotiable. Students must bring a draft that existed before the AI step. If the chatbot writes the paper, the standard has not been met, and academic integrity has been broken. Treat the AI as a second reader sitting beside a human reader, not as the author. Build a visible human-versus-AI comparison so students practice judgment instead of copying suggestions. District-approved tools only; if the network is down or the tool is not approved, the printed critique checklist is the lesson, not a consolation prize. FERPA: student writing that includes a name or other identifiers is an education record. Never paste a classmate's work into any tool. Never put student ID numbers, addresses, phone numbers, or photos into a prompt. Use a public sample (not a student's paper) whenever you model on a projector. This lesson sits in Semester 1, Weeks 5–9 (pacing unit 'Think with it, not for it') next to cognitive-offloading and hallucination work, so pair generation with verification and hold the integrity conversation before high-stakes writing season.

Instructional sequence

Real-world examples

• An FFA officer at Filer or Minico High asks a district chatbot to flag unclear transitions in a prepared public-speaking draft, then sits with the advisor and keeps only the notes that match the actual speech time limit.
• A reporter at a local Idaho paper runs a style checker on a draft about a county commission meeting, then verifies every name and vote against the minutes because the checker cannot attend the meeting.
• A safety coordinator at a Magic Valley processing plant uses AI to mark confusing steps in a lockout procedure, then a supervisor who has done the job on the floor decides what language stays.
• A 10th grader writing a college-prep personal statement about working a harvest uses AI to catch repeated words, then rejects a suggestion to add a 'life lesson' the student does not believe.
• An English 9 teacher in Idaho Falls gives the same protocol for a narrative about the Greenbelt: AI may comment; it may not invent a memory of Boise the student does not have.

Hands-on activity

Discussion questions

1. If the AI praise is always glowing, how should that change how much you trust the critique?
2. When would a teacher, a parent, a shop boss, or a teammate be a better second reader than a chatbot?
3. What is the difference between using AI to notice a repeated word and using AI to invent a story you did not live?
4. Why is pasting a classmate's paper into a tool a FERPA and integrity problem even if you 'only wanted to help'?
5. How will you disclose AI critique on the next paper in this class so the credit is honest?
6. What happens to your writing muscle if every draft is born inside a chatbot?

Differentiation

Assessment

Responsible use

AI is allowed in this lesson only as a second reader of work the student already drafted. Prompts that ask the tool to write, rewrite, or 'make it sound like a 10th grader' are out of bounds. Students use a district-approved tool or the paper checklist. They disclose the tool and date. They do not shop for a chatbot that will hide the help. Teachers model on public samples, never on a live student paper on the projector. If a student arrives without a draft, they practice on the public sample and complete the real protocol later; they do not generate a draft in the tool to 'catch up.'

Ethics

Honesty about help is part of becoming a colleague people can trust in shops, farms, hospitals, and newsrooms across Idaho. Passing off generated text as your own thinking steals practice from you and misleads the reader. Using AI to critique, then deciding what to take, keeps the human responsible for the words. Flattering or generic praise is a known failure mode; treating it as a grade is an ethical mistake. Ghostwriting for a classmate, with or without AI, is cheating and a privacy violation.

Privacy

FERPA protects education records, including student writing that identifies a student. Do not paste another student's work into any AI tool. Do not include student ID numbers, emails, phone numbers, home addresses, photos, medical notes, or a teacher's comments that name a student. Strip headers before paste. Teacher modeling uses a public sample. Student accounts exist only on district-approved tools covered by a data-privacy agreement. Printed drafts stay with the author. Offline path: nothing leaves the paper.

Reflection

1. Which reader — human or AI — changed my next sentence, and what does that tell me about what I still need from people?
2. What suggestion did I reject, and how would my paper have gotten worse if I had accepted it?
3. Did I follow every step of the protocol, including PII and disclosure?
4. Where in my week am I tempted to skip the draft and jump to the chatbot, and what will I do instead?

Homework

Finish the one-sentence revision if it was not done in class. Complete the revision log. If you used the public sample today, apply the full protocol (human reader plus critique checklist or approved tool) to a real draft from any class and bring both the T-chart and the original draft. Students without home internet use the printed checklist only. Do not upload anyone else's work. Optional: ask a family member to be the human reader for five minutes and add their comment to the left column.

Closing

Return to the title. A second reader points at sentences. A ghostwriter replaces you. Three volunteers share one rejected suggestion. Restate the non-negotiables: your draft first, your decisions, no one else's work in a tool, PII stripped, help disclosed. Point to the protocol on the wall and tell students it applies to the next paper in every class, not only this period. Collect revision logs as the exit ticket.

Extensions

• Apply the same protocol to a lab report, an FFA speech, or a history short-answer, still with a human comparison.
• Invite the librarian to show how citation of AI critique differs from citing a source used for evidence.
• Build a class 'banned prompt' list (rewrite this, write the essay, make it sound smarter) and a 'allowed prompt' list.
• In a 90-minute block, score a set of anonymous printed comments (teacher-prepared, not student papers) as specific or useless, then rewrite two useless comments into specific ones without using AI.

Cross-curricular links

English Language Arts

Revision stage of the writing process: peer conference plus AI conference, with the author in charge of the next draft.

Agriculture / CTE

Critique a procedure write-up (for example, a potato-storage observation) for missing steps; a chatbot cannot stand in the cellar and check the thermometer.

Social Studies

Editorial feedback on a short argument about a local issue (water, land use, a city council agenda) must not invent quotations from officials.

Advisory / counseling

Academic-integrity conversation before research-paper season; practice disclosure language families can understand.

Standard quoted exactly

Use AI tools to generate code for a simple coding project.

Clarification. Students run the code and evaluate the code's output but do not need to fully understand the code itself.

Student-friendly learning targets

• I can use a district-approved AI tool (or teacher-provided generated code) to obtain a tiny program for a simple, defined job.
• I can run that code in a browser, a Scratch-like environment, or a paper trace and record what it actually outputs.
• I can evaluate output with test cases I choose, including at least one edge case, without claiming I fully understand every line.
• I can describe one way the output was wrong, incomplete, or surprising, and I do not treat generated code as automatically correct.

Key vocabulary

Generative AI (Gen AI)

A subset of machine learning and AI specifically designed to create new content, including text, images, audio, code, video, or synthetic data based off a prompt.

Output

What the program prints, displays, or decides after it runs. In this lesson you judge the output, not whether you could have written the code yourself.

Trace

Stepping through code on paper: write the input, follow each line, and record the value of each result. Used when you cannot run the program on a computer.

Test case

A specific input chosen on purpose, with the output you expect written down before you run the code. Good test cases include normal values and edge cases.

Edge case

An input at a boundary or in an awkward situation: zero, a negative number, an empty list, or a value exactly on a cutoff. Edge cases often reveal mistakes.

Hallucination

Instances where an artificial intelligence system generates information that is incorrect, misleading, or entirely fabricated, but presents it as if it were true. Generated code can hallucinate a formula, a grade cutoff, or a library that does not exist.

Teacher background

Idaho standard 9-12.AIF.AP.2 asks students to use AI tools to generate code for a simple coding project. The clarification is exact and is the pedagogy: students run the code and evaluate the code's output but do not need to fully understand the code itself. This is not a computer-science unit on syntax. It is a fluency lesson on verification. Keep the project tiny so every student can finish five tests in one period. Three Idaho-flavored options cover different interests: a temperature converter (school weather stations, Stanley winter, seed packets in Celsius), a wildfire-risk checklist (humidity, wind, fuel dryness — familiar from fire season in the Boise Foothills, the Payette, and eastern Idaho), and a potato-grade counter (Magic Valley packing sheds counting how many tubers fall in a size band). Students may generate the code with a district-approved tool or receive teacher-provided generated code. They run it in a browser console, a Scratch-like environment, or on paper. Offline is first-class: print the generated JavaScript or pseudocode and a trace table. Never require students to install software or to understand every line. Never paste student names into the prompt. This lesson belongs in Semester 1, Weeks 10–14 ('Agents, code, and communication'); keep projects tiny and runnable, and name Idaho pathways in agriculture, energy, and public service when you talk about why output matters.

Instructional sequence

Real-world examples

• A packing shed in the Magic Valley uses a simple counter to tally potatoes in a size band; a human still pulls misshapen tubers the script cannot see.
• A Bureau of Land Management or Forest Service fire-weather briefing uses cutoff rules for humidity and wind; generated dashboards still get checked against a belt weather kit on the ridge.
• A school greenhouse in Teton County converts Celsius on a European seed packet to Fahrenheit before setting a heat mat; a wrong formula cooks the starts.
• An irrigation manager near Twin Falls might run a tiny script on pump times; the farmer still walks the field because a dry corner does not show up in a bad formula.
• A student weather station in Rexburg logs overnight lows well below zero; a converter that crashes on negatives is useless in January.

Hands-on activity

Discussion questions

1. The clarification says you do not need to fully understand the code. What do you still need to understand about the job the code is doing?
2. Which test case would have been easy to skip and would have hidden a mistake?
3. Would you let today's program set a frost fan, post a fire-danger sign, or pay a grower by the hundredweight? Why or why not?
4. How is running generated code different from copying an essay the chatbot wrote?
5. If two chatbots give two different programs, how do you choose? (Answer you want: you run tests, you do not pick the one that 'looks smarter.')
6. What should a worker at a packing shed or a fire desk write down when they use a generated script, so the next shift can trust or distrust it?

Differentiation

Assessment

Responsible use

Use a district-approved tool or teacher-provided generated code. Prompts describe a tiny task, not a student. Credit the tool on the table. Do not paste generated code into a live control system, a gradebook, or anyone's homework for another class. Do not download random scripts from the open web for this lesson. If the tool offers to 'explain every line,' students may read the explanation after tests, not instead of tests. Offline packets are full credit.

Ethics

Running untested code can waste a crop, mislead a fire desk, or simply spread a wrong number. The ethical move is to test with known answers and to say when you would not trust the output. Pretending you understand code you have not run is a different problem from using generated code honestly. Both are addressed here: you may use the generator; you may not skip evaluation; you may not take credit for writing what you did not write. A script that counts potato size still cannot see rot; over-trust is a harm.

Privacy

No student names, IDs, or class lists in prompts or in code comments. Do not use personal weather-station data that includes a home address. Use school or public sample numbers (Boise airport temperatures, a made-up list of diameters). Do not photograph classmates' screens for social media. FERPA: a file named with a student's last name plus 'code' is an education record; keep it in the district system or on paper in the classroom. Public chatbots are not a drop box for classwork.

Reflection

1. Which test taught me the most, and would I have thought of it if I had only read the code?
2. Where would this program be 'good enough' and where would it be dangerous?
3. What did I not understand in the code, and why was that allowed today?
4. How will I talk about generated code on a job site so a supervisor knows I tested it?

Homework

Add two more test cases to your table, including one that uses a number from real life (tonight's forecast high in your town, a seed-packet temperature, or a potato you measure with a ruler). Students without internet use the printed code and a paper trace. Do not install new apps. Optional: ask a family member in farming, trades, or wildland fire what number they still check by hand even when a screen already shows it, and write that sentence on the back of the table.

Closing

Read the clarification one more time. You ran the code. You evaluated output. You did not have to fully understand the code. Collect test tables. Three students name one failed or shaky test. End on the title: run it, do not worship it. Preview tomorrow's idea: if today's script only does one job, an agent is a recipe for a job that may use several tools — and a human still checks every step.

Extensions

• Re-run the wildfire checklist against a printed National Weather Service fire-weather discussion for southern Idaho and mark where a three-rule script is too simple.
• Visit (or video-call) an agriculture teacher or a local grower and ask how size grading actually works beyond one diameter band.
• In CS club, rewrite the converter in a second language after the fluency lesson, still keeping the same test table.
• 90-minute block gallery: three stations, one per project; students rotate to run one test on a project they did not choose.

Cross-curricular links

Mathematics

Unit conversion, inequalities, and boundary values. Students write expected C from F before they trust a program.

Earth science / agriculture

Fire-weather factors and potato grading as real measurement jobs where a wrong output has a cost.

Computer science (optional)

This lesson is a doorway, not a syntax unit. Interested students may read the code after they have tested it, never as a gate to credit.

Career and technical education

Documenting tests is a workplace habit: operators initial a reading because the display can be wrong.

Standard quoted exactly

Construct AI agents to accomplish a defined task.

Student-friendly learning targets

• I can construct a narrow AI agent by specifying a goal, allowed tools, constraints, and success checks on a worksheet, without having to write Python.
• I can choose a defined task small enough that a human can verify every step, such as finding three reputable sources on Idaho water rights or drafting a packing list for a 10th-grade Sawtooth field study given a forecast.
• I can run the spec (in a district-approved agent builder if one exists, or as a human role-play of the agent) and mark each success check pass or fail.
• I can explain why a narrow agent with a human in the loop is safer than a vague instruction like 'do my research' or 'plan the whole trip.'

Key vocabulary

Agentic AI

AI systems designed to independently plan, execute, and adjust actions to achieve a specific goal with minimal human intervention, often capable of working across multiple tools or applications.

Agent (in this lesson)

A narrow worker you design for one defined task. You construct it by writing its goal, tools, constraints, and success checks. You do not have to code it in Python.

Constraint

A rule the agent must not break, such as 'use only .gov or .edu sources,' 'no student names,' or 'pack for 24 hours, not a week.' Constraints keep a narrow agent from wandering.

Success check

An observable test a human can apply when the agent finishes: 'three URLs that open and name Idaho water rights,' or 'every clothing item matches the forecast overnight low.'

Human-in-the-loop

A model of interaction where a human is required to participate in a process, such as verifying, approving, or correcting, to ensure the accuracy, safety, and ethical alignment of AI-generated outcomes.

Human-Centered

An approach where AI is used specifically to support human inquiry. It ensures that humans remain responsible for the initial objective, final decision-making, oversight, and ethical reflection at the beginning and end of every task (Human AI Human).

Teacher background

Idaho standard 9-12.AIF.AP.3 is one sentence: Construct AI agents to accomplish a defined task. For grades 9–10, 'construct' does not mean ship a Python agent with API keys. It means specify the agent so tightly that another person (or a district-approved builder) could follow it: goal, tools, constraints, success checks, and a human who verifies every step. That reading matches the glossary terms Agentic AI, human-in-the-loop, and Human-Centered (Human AI Human). Keep tasks narrow. Two classroom-ready jobs work in Idaho without collecting PII: (1) find three reputable sources on Idaho water rights and list them with URLs a human will open; (2) draft a packing list for a 10th-grade field study in the Sawtooths given a weather forecast the teacher prints. Reputable water-rights starting points a teacher can verify include the Idaho Department of Water Resources, Idaho statutes or the Attorney General's water-law materials, and University of Idaho Extension or Idaho Law Review explainers — not a random blog and not a URL the model invented. Sawtooth packing must obey school trip rules (no personal vehicles, medications handled by staff, no real home addresses in the prompt). If the district has not approved an agent builder, the worksheet plus a partner who role-plays the agent is the construction. Do not send students to unpaid public agent sites that scrape chats. This lesson sits with AP.2 in Weeks 10–14; keep the agent as small as yesterday's script, and keep a human on every step.

Instructional sequence

Real-world examples

• A county emergency manager in eastern Idaho might use an agent to draft a reverse-911 outline from a public weather feed; a human still records the message and sends it.
• A school librarian constructs a search agent that may return only .gov and .edu sources on water rights; the librarian still opens the links before they go on a research guide.
• An outdoor-education teacher at a McCall or Stanley field study uses a packing-list agent against an NWS forecast, then checks the list against district trip rules and a real duffel.
• A 4-H robotics team writes a narrow agent spec for 'sort by size only'; a human still pulls damaged produce the robot cannot see.
• Idaho Department of Water Resources staff, not a chatbot, remain the authority on water rights; an agent that invents a decree number has failed even if the sentence sounds official.

Hands-on activity

Discussion questions

1. Where in your spec was the agent still free to wander, and what constraint closed that hole?
2. Why does 'three URLs I will open' beat 'good sources' as a success check?
3. If the agent packs a stove and the trip rules forbid student stoves, who is at fault — the tool or the author of the spec?
4. What would go wrong if we let an agent 'just handle' water-rights research for a graded paper?
5. How is this construction different from yesterday's tiny generated script, and how is the human's job the same?
6. When should we not build an agent at all and just do the task ourselves?

Differentiation

Assessment

Responsible use

Construct on the worksheet first. Use a district-approved agent builder only if one exists; otherwise role-play. Do not create accounts on public unsupervised agent sites. Do not give an agent permission to send email, spend money, post to the web, or contact agencies. The human starts the task, checks every step, and stops the agent. Credit the builder if used. Offline role-play is full credit. Specs stay in the classroom system or on paper.

Ethics

A narrow agent can save time on fetching links or listing gear. It cannot take responsibility for a broken source, a student who goes to the Sawtooths underdressed, or a water-rights claim that is wrong. Writing a vague spec and blaming the tool is an ethical failure of the constructor. Inventing URLs or packing without reading the forecast is a hallucination the human must catch. Do not build agents for tasks that should stay fully human: grading peers, medical decisions, reporting a classmate, or contacting government about a real water right in a student's family name.

Privacy

FERPA and good sense: no student names, home addresses, phone numbers, health or medication details, or family water-right owner names in the spec or the prompt. Use 'a 10th grader' and a public forecast. Do not upload a real permission slip. Agent builders, if used, must be district-approved with a data-privacy agreement. Role-play on paper never leaves the room. Verification logs that include a student name are education records and stay in the district grade system, not in a public chatbot.

Reflection

1. Which box on the spec did the most work — goal, constraint, success check, or stop — and what would have happened if I had left it blank?
2. What did the human verifier catch that the agent (or role-play partner) missed?
3. Would I let this agent talk to the web unsupervised overnight? Why or why not?
4. How will I use Human AI Human on the next research or trip-planning job in another class?

Homework

Revise one constraint and one success check on your spec using tonight's thinking. If you chose water rights, try to open your three URLs at home or at the public library and write pass/fail; if a link fails, find a replacement by hand, not by trusting a new unverified list. If you chose packing, check the next day's Stanley or Sawtooth forecast (radio, printed paper from the teacher, or a family device) and note one item you would add or drop. Students without internet receive a printed forecast and a printed IDWR page as the homework packet. Do not sign up for new agent websites. Do not include your address or a family member's name.

Closing

Repeat the standard: construct AI agents to accomplish a defined task. Point at a completed worksheet: that is construction. Point at a verification log: that is the human. Collect specs and logs. Two students name one hallucination they caught. End with the loop on the board: human writes the goal, the narrow agent works, the human checks, the human decides. Preview how this skill returns in later standards when agents appear inside real applications: the job description you wrote today is still the safety rail.

Extensions

• Librarian station: convert a successful water-rights spec into a one-page research guide other classes can use, still with the instruction to open every URL.
• Outdoor club: compare the agent packing list to what a Sawtooth ranger or a school trip lead actually packed last year (guest or recorded interview).
• CS extension (not required): if a district builder exists, save the spec as a reusable template with the teacher's name as owner, not the student's personal account.
• Write a third task card for a local job (greenhouse watering check, FFA meeting agenda reminder) that is still verifiable in ten minutes.

Cross-curricular links

Civics / Idaho history

Water rights are a live Idaho policy topic. Students practice finding primary publishers (IDWR, statute) instead of accepting a chatbot's summary.

Earth science / outdoor education

Field-study packing against a real forecast in the Sawtooths; weather numbers beat generic 'bring a jacket' advice.

English / research

Source evaluation: a URL is not a source until a human opens it and names the publisher.

Health / PE

Trip safety constraints (water, layers, school rules) belong in the spec so an agent cannot optimize them away.