🕵️Mission #001 - The AI Investigator

Mission Level: AI Investigator

Goal: Activate your Agent Status. Level up from Civilian (User) to Investigator (Observer).
This mission introduces AI through observation and testing – exploring where AI appears, what it can do and where it fails.

Output: Create an 8-slide Investigation Report based on the challenges below.

Before You Start

You don’t need to know anything about AI before starting this mission.

Be curious, take notes and don’t worry about being “right”. Asking good questions matters more than having perfect answers.

Note: This mission is part of a larger learning series.

Tasks

📋 Task 1: The Secret Agent (History Part 1)

The Mission: Meet Alan Turing

In the 1940s, a scientist named Alan Turing built a machine called the Bombe to crack secret codes during World War II.

While working on machines like this, he started wondering something bigger:

👉 Could a machine ever think like a human?

To explore this question, Turing came up with the Turing Test.

Slide 1:

[ ] Explain the Turing Test in your own words.
[ ] Answer this question – If you chatted with a robot for 10 minutes without seeing it, do you think you could spot the fake human? Why or why not?
[ ] Does sounding smart always mean something is actually smart? Give one reason or example from the Turing Test.

📋 Task 2: The Time Machine (History Part 2)

The Mission: See how AI levelled up over time

AI didn’t suddenly appear. It improved step by step.

You’re going to travel through three moments when AI took a big leap forward.

1997 — Deep Blue A computer played chess against the world champion, Garry Kasparov.
2002 — The Chore Bot The first Roomba vacuum was released and started cleaning homes.
2022 — The Big Bang ChatGPT became available for anyone to try.

Each moment changed how people thought about machines.

Slide 2:

[ ] Create a 3-step timeline: 1997, 2002, 2022
[ ] Write one surprising or mind-blowing fact for each year

📋 Task 3: The Brain of the Machine (How AI Learns)

The Mission: Compare how humans learn vs how AI learns

Humans can learn from one experience.

AI needs lots of examples to spot patterns.

AI doesn’t understand — it learns by finding patterns in data. You’re going to look for AI you already use every day.

References: Watch “How AI Learns” by Code.org or play “Quick, Draw!” by Google

Slide 3:

[ ] Explain AI in your own words
[ ] List 3 everyday examples of AI you use (e.g. Netflix, autocorrect, YouTube)

📋 Task 4: The Silly Test (What AI can’t do)

The Mission: Test whether AI is truly “smart”

Some people think AI is intelligent like humans.

This test helps you see the difference.

You’ll ask AI:

One question it should know
One question it should fail at

Slide 4:

[ ] Write down the AI’s answer to a fact question e.g. “What is the tallest mountain on Mars?”
[ ] Write down the AI’s answer to a silly or impossible question e.g. ****”How do I teach a goldfish to ride a bicycle?”
[ ] Decide: Is the AI actually thinking or just finding information? Is the AI “smart” or just a fast librarian?

📋 Task 5: The Training Lab (Machine Learning)

The Mission: Train an AI and see where it gets confused

AI learns by being shown examples – sometimes thousands of them.

You’ll train an AI to recognize poses and then try to trick it.

The Experiment: Go to Google Teachable Machine. Select Image Project.

Example: Train your AI on two objects:

Class 1: Show one object

Class 2: Show another object

The Twist: Try to show both the objects together. Does the AI get confused?

Slide 5:

[ ] Add a screenshot of your trained AI
[ ] Describe what happened when you tried to confuse it
[ ] Explain why you think the AI made a mistake

📋 Task 6: The Hallucination Audit (Spotting AI mistakes)

The Mission: Find an AI hallucination

Sometimes AI confidently makes things up.

This is called a hallucination.

You’ll create an AI image and look closely for mistakes.

The Experiment:

Use Canva Magic Media or Adobe Firefly. Prompt: “A family of robots eating a picnic of giant spaghetti in a park.”

The Audit: Zoom in on the fingers, the spaghetti and the grass.

Slide 6:

[ ] Show the image you created
[ ] Circle or point out the “AI fail”
[ ] Explain why the mistake happened

📋 Task 7: The Investigator’s Toolbox (Trying AI tools)

The Mission: Explore what different AI tools are good at

Different AI tools do different jobs:

Research – Perplexity AI
Music – Suno AI
Video – InVideo AI

You’ll test and judge them like an investigator.

Slide 7:

[ ] Give each tool a ⭐ rating out of 5
[ ] Write one way each tool could be used at school

📋 Task 8: The No-Go Zone (Safety & Judgment)

The Mission: Learn the rules for using AI safely

AI can be helpful – but only if used responsibly.

You’ll find rules that protect your privacy and safety.

Slide 8:

[ ] List 3 rules kids should follow when using AI
[ ] Write where you found the rules (source)

Checklist For Completion

Use this checklist to make sure your Investigation Report tells the full story of what you observed and learned.

[ ] Slide 1: Turing Test explaination + Prediction
[ ] Slide 2: AI timeline (1997, 2002, 2022)
[ ] Slide 3: Your AI definition + everyday examples
[ ] Slide 4: “Silly Test” results and conclusions
[ ] Slide 5: Teachable Machine training screenshot
[ ] Slide 6: AI-generated image with a circled mistake
[ ] Slide 7: Three AI tool reviews with ratings
[ ] Slide 8: Three AI safety rules

Sharing your investigation

This mission works best when the Investigation Report is shared – with a parent, teacher or friend. Explaining what you learned helps you notice gaps in your own thinking and strengthens your understanding.

If you’d like to share what you discovered with us, ask a parent or teacher to submit your final 8-slide Investigation Report using the link below.

Submissions help improve future missions and understand how learners are thinking about AI. There are no right or wrong answers.

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If this mission raised a question you’re still thinking about, ask it here:

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This mission is part of the Learning to Think About AI series. The content is designed as a guided experience.