Session 4 — What AI Can (and Can't) Do

Duration: 75 min · Format: live online · Ages: 12–15

Session goal: by the end, students can name what AI is great at and where it struggles, explain bias as garbage in, garbage out, and make responsible choices as a builder.

Before class — prep (5 min)

• Have the two diagrams below ready to share on screen (what AI is great at vs. struggles with and how biased data makes an unfair model).
• Have the four "Judge the AI" scenarios (in the Activity) ready to drop into the chat one at a time.
• Optional: open Teachable Machine in a tab if you want to demo bias live by training on unbalanced examples.

Agenda

0:00 · Hook (5 min)

Ask the class and take a few answers (chat or unmute) — would you trust an AI to:

• Recommend a song?
• Drive a school bus?
• Decide who gets a scholarship?

Some feel fine, some feel risky. Let them vote or react, then tell them: today they'll learn why the feeling changes — and how to tell the difference for real.

0:05 · Teach — Superpowers and blind spots (14 min)

Explain: AI is amazing at some things and surprisingly weak at others.

Share this diagram and contrast the two panels:

• Great at: speed, spotting patterns, remembering huge amounts, working 24/7 without tiring.
• Struggles with: common sense, real feelings, being fair on its own, and brand-new situations it never saw in its data.

Ask: "Can anyone name a task where AI's speed is a huge win — and one where its lack of common sense would be dangerous?" (Take 2–3 answers.)

⚠ Watch for the key point: AI has no understanding and no feelings — it does math on patterns. That's why it can be brilliant and make silly mistakes a child never would. Correct any language that treats the AI as if it "knows" or "cares."

0:19 · Teach — Bias: garbage in, garbage out (14 min)

Explain: a model only knows what it's shown. If the data is unfair, the model becomes unfair too.

Share this diagram and trace how unbalanced data leads to a biased result:

• Train a "cat detector" on only ginger cats → it may fail on black cats.
• Train a hiring AI on old, biased decisions → it repeats the same unfairness.

This is called bias, and fixing it starts with good, balanced data.

Ask: "If you trained a face-unlock app using only photos of the team who built it, who might it fail for later?" (Answer: anyone who doesn't look like that small group — the data wasn't representative.)

⚠ Watch for: students often assume bias means someone was being deliberately mean. Reframe it — bias usually creeps in through careless or unbalanced data, not bad intentions. That's why care with data matters.

0:33 · Activity — Judge the AI (22 min)

Have students judge each situation: Good use, Risky, or Needs a human — and say why. Drop them into the chat one at a time and take a vote plus a reason for each.

1. Suggesting the next video to watch.
2. Grading a student's final exam all by itself.
3. Spotting tumours in X-rays to help (not replace) a doctor.
4. Deciding who gets a bank loan.

Circulate the discussion toward the pattern: the higher the stakes and the more it affects a person's life, the more a human needs to stay in charge.

Then pose the bias case: "A face-unlock app works great for the team who built it, but fails for many other people." Ask: "What went wrong with their data?" (Answer: it wasn't balanced or representative — they trained on people who looked like themselves.)

0:55 · Check for understanding (12 min)

Ask these aloud or drop them in the chat. Answer key (for you):

1. Name one thing AI is great at and one it struggles with. → Great at: speed / patterns / memory. Struggles with: common sense / feelings / fairness / new situations.
2. What is bias in AI? → When a model is unfair because it learned from unfair or unbalanced data — garbage in, garbage out.
3. What's the best first step to reduce bias? → Use good, balanced, representative data — and have humans check important decisions.

1:07 · Wrap-up + homework (8 min)

• Point out that this session completes the unit: students learned to code in Python, wrangle data, build a predictive model, and use AI responsibly — genuine data-science skill.
• Ask one student to finish: "A responsible AI builder always…"
• Homework — Spot the AI: find one AI you use (a recommendation feed, a voice assistant, autocorrect). Write 3 lines — one thing it does well, one thing it gets wrong, and one way its data might be biased.

Teaching notes

• Correct this misconception: "AI is objective, so it must be fair." A model is only as fair as its data — it can copy and amplify human unfairness without anyone intending it.
• Correct this misconception too: "bias means someone was malicious." It usually comes from unbalanced or careless data, not bad intent.
• Fast finishers (extension): real scientists document their work honestly, limits included. Have them write a short project report on the predictor they built last session, one line per section:
• Question — what were you trying to predict?
• Data — where it came from, how big, any gaps.
• Method — what model and features you used.
• Results — accuracy / error, with numbers.
• Limitations — where could it be wrong or biased? Who might it treat unfairly?

Stress that admitting limitations isn't weakness — it's what makes work trustworthy, and this is exactly the report format used for competitions later in the course. - Low-tech fallback: run the whole session as a discussion — no devices needed. If you have a screen, demo bias live in Teachable Machine by training an image model on lots of one kind of example and very few of another, then watch it fail.

Vocabulary

Resources

• Elements of AI — a famous free course on what AI really is.
• Google — Teachable Machine — retrain a model with unbalanced data to see bias happen.
• MIT — AI ethics for youth — activities on responsible AI.

Next session

Block 2 — Research & Engineering: students become researchers and makers — how science works, reading and writing it, and building electronics with Arduino.