AIQ
AIQ
Part of the AI in Transportation lesson guide. Teaching a different grade? 🌈 Explorer (5–7) · 💻 Hacker (11–14) · ⚡ Architect (15–18)
Open with a quick show of hands: "Who has ever used a car with a self-parking feature, or seen a self-driving car on the news, in a movie, or on YouTube?" Most Builder-age students will have seen something — reference whatever comes up before moving into the lesson's own framing.
"Self-driving cars, smart traffic lights, delivery drones — AI is transforming transportation. But how does a car 'see' the road?"
That's the app's opening line for this age band — read it with genuine curiosity, like you're posing a real puzzle, not a rhetorical question. Give students ten seconds to guess before continuing.
"Here's the short answer: it doesn't have eyes, so it builds its own picture of the road out of sensors — cameras, and something called lidar, which uses lasers to measure exactly how far away everything is. Then its computer brain has to make a decision — brake, turn, speed up — and it does that thousands of times every second, way faster than you or I could ever react."
Ask: "Why do you think a self-driving car needs MORE than one kind of sensor, instead of just a camera?" Take a few guesses — you're planting the seed for the sensor-combination idea the Main Activity develops properly.
Add one more thought before moving on: "A regular car has one 'sensor' too — the driver's own eyes. And human eyes are honestly incredible: color, depth, motion, all processed instantly by a brain that also understands context, like knowing a ball rolling into the street probably means a kid is about to follow it. A self-driving car doesn't have that instinct built in — it only has whatever its sensors and its training have taught it to expect. Keep that gap in mind as we go, because it explains both why self-driving cars can be very good at some things and why they can still get surprised by things a human driver would see coming."
This age band's in-app lesson has three scenes — Self-Driving, Traffic, and Future Travel — each with four items to sort as AI or not-AI, plus a short fact about each. Walk through all three scenes as a guided discussion, building toward the deeper "why" behind each fact, then let students explore the app version themselves.
Introduce the four items one at a time, asking "AI or not AI?" before revealing the fact.
Ask: "So the car has camera-eyes AND laser-eyes. Why do you think having both is safer than having just one?" Guide toward: each sensor has weaknesses — cameras struggle in fog or glare, lidar can struggle in heavy rain — and using both means one can cover for the other's blind spot.
A good way to make this concrete: ask students to imagine driving with sunglasses that are also foggy — that's roughly what a camera-only car "experiences" in bad glare or weather. Now imagine also having a very accurate tape measure that can instantly tell you how far away everything is, even with the foggy sunglasses on, just not what color anything is. That's roughly what lidar adds. Neither one alone is a complete picture, which is exactly why combining several kinds of sensors — something engineers call sensor fusion — makes the whole system more reliable than any single sensor could be by itself.
Ask: "A stop sign and a smart traffic light are both metal poles at an intersection. What's the actual difference in what they're doing?" — this is the same static-vs-sensing contrast as Compass vs. Car Cameras, and naming that pattern out loud helps it transfer to Scene 3.
If your students commute along a major road with heavy traffic — EDSA and other Metro Manila corridors are a familiar example for many Filipino students — mention that route-planning apps use exactly this kind of AI to warn commuters about slowdowns before they hit them, by combining the live location and speed data of many other phones running the same app. It's a good moment to point out that this only works because so many other people are also using the app at the same time — a single phone's data alone wouldn't be enough to know that traffic ahead has suddenly slowed down.
Now hand out devices and let students go through the app's version of all three scenes themselves. Ask a few students afterward to name one item from each scene and explain, in their own words, why it's AI or not.
As a wrap-up to the activity, have students try to sort all twelve items from the three scenes into two piles just by asking one question about each: "does this thing take in information about what's happening right now, and then act differently depending on what it noticed?" Everything they marked as AI should pass that test — camera-eyes, lidar, decision-making, smart lights, route apps, traffic drones, delivery robots, smart trains, and air taxis. Everything marked not-AI should fail it — a compass, a stop sign, and a bike all behave the same regardless of what's happening around them. Naming that single underlying test, rather than just memorizing which of the twelve items happens to be AI, is what lets students correctly guess about something new they haven't seen in the lesson at all.
Give students a minute to think before calling on anyone — several of these questions reward a moment of quiet reasoning over an instant guess.
Close with: "Today you saw that AI shows up in transportation in more places than just self-driving cars — traffic lights, delivery robots, even trains and flying taxis. The pattern is always the same: something senses what's happening around it, and then decides what to do based on that, over and over, really fast. A compass, a stop sign, and a bicycle don't do that — they're the same no matter what's going on around them."
Extension activity: Have students design their own "AI vehicle" on paper for a specific job (getting kids to school safely, delivering food in the rain, moving cargo across the Philippines). For each design, they must list: what sensors does it need, and why? What decision does it have to make constantly? Have a few students present their design and have the class try to poke holes in it — "what happens if it's foggy?" is a great question to encourage, since it echoes the real sensor-fusion reasoning from Scene 1. Push groups that finish early one step further: ask them to name one thing their vehicle should NOT be trusted to decide on its own, and who should make that decision instead (a human driver, a remote operator, a safety rule built in ahead of time). That question previews, in a very approachable way, one of the real debates engineers and regulators are having right now about autonomous vehicles.