AIQ
AIQ
Part of the AI Career Paths lesson guide. Teaching a different grade? 🌈 Explorer (5–7) · 💻 Hacker (11–14) · ⚡ Architect (15–18)
Read the app's hook line to the class as a genuine question you want answered, not a rhetorical one:
"AI isn't just for programmers. Artists, doctors, teachers, and athletes all use AI. What AI career path matches YOUR interests?"
Before opening the app, run a quick show-of-hands warm-up: "Raise your hand if you think you need to be really good at coding to have a job that uses AI." Expect most hands up — that's the exact assumption today's lesson is going to challenge. Then ask, "What's something you're really interested in right now — a hobby, a subject, a sport, a kind of art?" Take four or five answers and write them on the board. Tell the class: "By the end of today, we're going to come back to this list and find an AI career that could connect to each one."
Add one more layer to the warm-up if you have a minute: ask, "Do you think the job you want when you grow up even exists yet?" This is a genuinely interesting question at this age — many students will have heard an adult say some future job "hasn't been invented yet," and today's lesson gives that idea some real substance, since several of the twelve careers you're about to cover (AI safety researcher, AI policy writer) barely existed a decade ago. You can add one more concrete example to make this land: "app developer" as a common job barely existed before smartphones became widespread — an entire career category appeared because of one piece of new technology, and something similar could easily be true of several jobs in today's lesson.
Open the lesson and go through all three scenes as a class, pausing after each one to connect it back to the interests list on the board.
Scene 1 — Technical Paths 💻: These four are the "build the AI itself" jobs. An ML (machine learning) engineer builds and trains AI models — the app's own figure is an average salary of $150K+, which requires Python, math, and curiosity. Explain "training" concretely: "It's a lot like teaching, actually — you show the model thousands or millions of examples of something, and correct it when it gets things wrong, until it starts getting things right on its own." A data scientist digs through huge piles of data to find patterns and tell the story behind them — average salary $130K+, requiring statistics, programming, and storytelling. It's worth pointing out why "storytelling" is on that list at all: finding a pattern in a spreadsheet is useless if you can't explain clearly what it means and why it matters to someone who has to make a decision based on it. AI safety researchers make sure AI doesn't go wrong — the app calls this "a GROWING field" because companies are actively hunting for people to do exactly this, the same way a car company hires safety engineers whose whole job is crash-testing before a car ever reaches a customer. A robotics engineer builds physical robots that use AI to move, sense, and manipulate objects — hardware and software combined in one job, which is why it tends to require both a strong engineering background and AI skills at once. Flag the salary numbers honestly here: "These are real U.S. job postings — they're not a promise of what you'll earn, and pay looks different in different countries, but they show these are real, well-paying jobs, not made-up ones."
Scene 2 — Creative + Social 🎨: Slow down here, because this is the scene that breaks the "AI = coding" assumption from the warm-up. AI + design means designing how AI products look and feel to use, or creating AI-assisted art — a bad design can make even the smartest AI feel confusing or untrustworthy, so this is a genuinely important, genuinely creative job. AI policy means writing the actual rules and laws for how AI can be used — governments everywhere need people who understand both the technology and its effect on society, since a law written by someone who doesn't understand how AI actually works tends not to work well in practice. AI journalism means investigating and explaining AI to regular people — one of the fastest-growing beats in journalism today, because most readers aren't technical and need someone trustworthy translating what's actually happening. AI education means teaching people about AI, from kindergarten all the way to university — "which is literally the job of whoever made this app you're using right now, and also my job today, teaching you."
Scene 3 — Every Field + AI 🌍: This is where you connect back to the board list from the warm-up. Doctors who understand AI will lead the next wave of healthcare improvements — not by letting AI make decisions alone, but by using it to catch things a busy human eye might miss. Climate scientists use AI to fight environmental challenges and are hiring fast, since AI is genuinely good at finding patterns in the huge, messy datasets that come from weather sensors and satellites. Music producers and composers increasingly need AI skills as the industry changes, from AI-assisted mixing tools to entirely AI-generated backing tracks that a human musician then shapes and edits. Sports teams hire AI analysts for training, injury prediction, and strategy — professional teams now track enormous amounts of data on every player's movement, and someone has to turn that into useful advice for a coach. Go down the interests list on the board and, for each one, ask the class to guess which AI career from today might connect to it — sports fans will land on "AI + sports" quickly, and it's a fun challenge to find a connection for a trickier interest like "gaming" or "cooking" (gaming connects to AI-assisted design and testing; cooking connects surprisingly well to data science, since recipe and flavor-pairing tools use exactly that kind of pattern-finding).
If your device/time allows, run the app's built-in sorting activity next: students quickly sort job-related tasks as "AI" or "not AI." A couple of the sorts are deliberately tricky — journalism writing and teaching kids both sort as "not AI," even though both jobs increasingly use AI tools, because the core task itself (writing a story, connecting with a student) is still fundamentally human work. That distinction is worth pausing on out loud if a student gets it "wrong."
Before moving to discussion, do a quick "12 jobs, 3 buckets" recap on the board: draw three columns headed Technical, Creative + Social, and Every Field, and have students call out the twelve job names from memory to fill them in. This doesn't need to be perfect — the point is testing whether the three-scene structure of the lesson actually stuck, and it gives you an easy, low-stakes way to see who needs a quick review before the quiz.
These work well either as a whole-class conversation or as a quick pair-share before opening it up — at this age, giving students thirty seconds to talk to a neighbor first tends to get more, and more thoughtful, hands raised than jumping straight to "who wants to share."
Close with: "AI careers aren't one narrow path — they're twelve different doors, and probably a hundred more we didn't even mention today. The question isn't 'am I a computer person or not,' it's 'what do I already love, and how could AI make me better at it?'" Point back at the board one final time and remind students that everything on that list is a legitimate starting point for an AI-connected future.
Extension activity — Career Interview: Have each student interview a parent, relative, or family friend about their job, asking two questions: "Do you use any AI tools in your work?" and "Can you imagine an AI tool that would help you do your job better?" Students report back the next class with a one-minute summary. This turns the 5–15 minute core lesson into a full take-home assignment, and it grounds the lesson's "AI is everywhere" claim in a real job a student actually knows something about, rather than an abstract example from the app.
If a family interview isn't practical for your class, an in-class alternative works just as well: have students pair up and each pick a different one of the twelve careers from today, then spend five minutes together sketching a poster with the job's name, one tool or skill it needs, and one drawing representing the work. Post the finished posters around the room as a gallery, and let students do a quick walk-around to see all twelve. Either version ends the lesson with something concrete the class produced, rather than just a memory of the quiz.