Case study 02 · Learning prototypes

Smart Systems Learning Lab

A hands-on lab where students work out the difference between a sensor, a rule and a smart decision.

Prototype under reviewSuggested grades 5–8

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The learning problem

A traffic light simulation can look perfect even when the student can’t explain how it works. Learners need to understand the input, decision, output loop, predict what changes, fix broken rules, and tell the difference between fixed automation and an AI prediction.

Design solution

The lab shows a road junction, sensors to choose from and a rule panel students can edit. Learners connect the parts, write simple IF/ELSE pseudocode, run the simulation, watch what happens and fix it. A separate AI mode introduces predictions based on patterns, and asks a big question: should a prediction ever overrule a safety rule?

Desired learner flow

Choose a scenario: quiet crossing, busy junction or emergency vehicle priority.
Add a sensor (for example: vehicle count or pedestrian request) and map its input to the system.
Write or complete IF/ELSE pseudocode to control traffic lights.
Run and observe: inspect the simulation and use a simple event log.
Debug edge cases: stalled sensor, conflicting signals, unusually high traffic.
Explore AI-assisted suggestion as a separate layer and decide what humans must still control.
Explain: What changed? Why? What would you test next?

Sample learning prompts

What evidence tells you the sensor is working?
What happens if two conditions are true at the same time?
Which rule prevents unsafe signals?
What prediction might help the system, and when should it be ignored?
Can you explain your final logic without running the simulation?