AI Scavenger Hunt

Learning Objectives

"AI Scavenger Hunt" is the third lesson in World 1 (What Is AI?). By this point students have already met the idea that AI is a computer system that learns from data rather than just following fixed rules. This lesson doesn't add new theory — it asks students to go find that idea in the real objects around them. By the end of the lesson, a student should be able to:

• Point to specific devices in a home (a smart speaker, a smart TV, a smartwatch, a phone camera) and say why each one counts as AI.
• Point to ordinary devices (a toaster, a couch, a lamp, a spoon) and explain why they are not AI, even though they're just as electronic or just as common.
• Apply one consistent test to a new example they haven't seen before — "does this thing learn from data and change its behavior, or does it always do the exact same thing?" — instead of guessing from surface clues like "it's expensive" or "it's electronic" or "it has a screen."
• (Hacker and Architect bands) Name the general technique behind a few of these examples — speech recognition, recommendation systems, motion classification, computer vision — well enough to describe in one sentence what each one is actually doing with data.
• Recognize that AI is often invisible: it is sitting inside devices that don't look "smart" at all, which is part of why this is framed as a hunt rather than a lecture.

This lesson is deliberately lighter on new vocabulary than the two before it. Lesson 1.1.1 ("AI Is Everywhere") and 1.1.2 ("Smart vs. Intelligent") introduce the core distinction between rule-following software and data-driven AI; this lesson is the practice round, where students apply that distinction to twelve concrete objects instead of hearing it described in the abstract. If a student can only walk away with one thing, it should be the test in objective three — everything else in the lesson is that test applied over and over.

Teacher Background

You do not need a computer science background to teach this lesson well — the app does the heavy lifting. Your job is to know enough to answer the "wait, why is that one AI?" questions that come up, because that curiosity is the whole point of the activity.

The lesson walks students through three rooms — kitchen, living room, bedroom — each with four objects, two that use AI and two that don't. The pairing is deliberate: a toaster sits next to a smart speaker, a couch sits next to a smart TV, a desk lamp sits next to a smartwatch. Students aren't just memorizing "speaker = AI." They're comparing two similar-looking objects and figuring out what actually separates them.

That room-by-room structure isn't just a theme — it's doing real pedagogical work. Anchoring each example to a physical location a student already knows (their own kitchen, their own bedroom) gives the abstract test something concrete to hook onto, which tends to make it stick better than a list of examples with no spatial context. It also sets up the extension activities on each age page: once a student has practiced sorting AI from not-AI inside the app's three rooms, sending them to do the same thing in their actual house is a small, natural next step rather than a big leap.

Here's what's actually happening behind each type of "smart" device in the lesson, in plain terms:

• Smart speakers (Alexa, Google Home) use speech recognition to turn your voice into text, and then a language-understanding system to figure out what you meant ("play music" vs. "set a timer") and reply.
• Smart TVs suggest shows using a recommendation system — one common approach looks at what people with similar taste to yours have watched, another looks at what a given show is actually about (genre, actors, themes) and matches it to what you've liked before. Real systems usually blend both.
• Smartwatches and fitness trackers have a motion sensor (an accelerometer) constantly recording tiny movements. A model trained on lots of labeled examples of "this pattern of movement = walking," "this pattern = running," etc. is used to guess what you're currently doing.
• Phone cameras use computer vision to find faces, judge lighting, and decide what to focus on and how to process the image — all before you've even pressed the shutter.
• Game consoles can use AI for opponent behavior — enemies that react to what you do, adjust difficulty, or path-find around a map — though plenty of game logic is just fixed rules too.

The lesson intentionally includes some AI examples that surprise people — a fitness tracker, a game console, a phone camera's autofocus — precisely because most people's mental picture of "AI" is a chatbot or a robot. Naming the boring, everyday cases is what makes the "does it learn from data" test stick, instead of a vaguer sense that AI means anything futuristic.

It's also worth knowing which objects in the lesson are meant to be genuinely tricky, so you can slow down there on purpose rather than rushing past them. A game console showing up as AI surprises almost every student — it doesn't look "smart" the way a talking speaker does, and its intelligence is about reacting to the player rather than understanding language. A phone camera is the opposite kind of surprise: students use it constantly without ever thinking of it as AI, because the face-detection and auto-focus work invisibly, in the background, with no obvious "AI moment" the way a voice assistant's reply feels like one. Both are useful precisely because they break the assumption that AI always announces itself.

Materials & Prep

Nothing beyond what AIQ always needs: one device per student (or per pair) with a browser and an internet connection for the first load — the lesson itself runs entirely on-screen, walking through the three rooms in order. No printouts, slides, or extra apps are required.

Optional, if you want to extend the lesson (see the wrap-up on each age page): paper and pencils for a "real home" list, index cards for a classroom sorting game (Explorer), or a way for students to report back the next day on what they found at home (Builder, Hacker, Architect). None of these are required to complete the core lesson — they exist to stretch a naturally short activity (the in-app portion runs roughly 5–15 minutes, depending on age band) into a fuller class period.

If you have a spare five minutes before class, it can help to glance around your own classroom or staff room for a couple of home-grown examples — a smartboard with handwriting recognition, an attendance system that flags patterns, a translation app on your phone. You don't need these for the lesson itself, but having one or two ready gives you a quick answer if a student asks "is there anything like this at school?"

Common Misconceptions

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