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Lesson 1 of 12 · What AI is

What counts as AI?

Students sort everyday machines into ones that follow fixed rules and ones that learned from examples, and build a working idea of what AI is.

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Lesson 1 of 12 · What AI is · Teacher's plan

What counts as AI?

Students sort everyday machines into ones that follow fixed rules and ones that learned from examples, and build a working idea of what AI is.

Students will be able to

  • tell apart a machine that follows fixed rules from one that learned from examples
  • explain in their own words that AI learns patterns from data to make guesses
  • give two examples of AI they meet in daily life
  • explain why some machines are hard to sort

Prepare

  • One set of 10 sorting cards per group of 4 to 6 (write them on scrap paper or print them)
  • Two headings on the board: "Follows fixed rules" and "Learned from examples"
  • One worksheet per student, or one per pair in a large class
  • Tape or magnets to stick cards on the board

Words to know

Rule:
An instruction a person wrote in advance: if this happens, do that.
Data:
Facts, examples or measurements collected so a person or a machine can use them.
Pattern:
Something that repeats or tends to go together, so you can use it to guess.
AI (artificial intelligence):
Computer systems that learn patterns from lots of data and use them to make guesses.

Main activity · Unplugged: paper and pens

The rules or examples card sort

Groups sort cards such as a calculator, phone face unlock, autocorrect, an alarm clock, a map app choosing a route, a spam filter, a timed traffic light and video suggestions into two piles. Arguing about the hard cards shows that AI means learning from examples, and that many apps mix rules and AI.

ManuAI pages for this lesson

The 45-minute plan · 45 minutes

Warm-up7 min

Is a calculator smart?

  • Ask: "Is a calculator smart? Is your phone smart?" Take a few quick answers.
  • Write the word AI on the board. Ask students to name anything they think uses AI.
  • Collect answers without judging. Tell them that by the end they will have a test to check each one.
Activity18 min

Card sort in groups

  • Hand out the cards. Explain the two piles: someone wrote every rule in advance, or the machine learned from many examples.
  • Give one hint question: "Could a person write down every rule for this?" If no, it probably learned.
  • Groups sort for 8 minutes and put any card they cannot agree on in a third pile, "Not sure".
  • Each group sticks one card on the board and gives a reason. Others agree or challenge.
  • Go through the answers: calculator, alarm clock and timed traffic light follow rules. Face unlock, spam filter and video suggestions learned from examples.
  • Discuss the mixed cards: a map app uses rules to find roads and learned guesses to predict traffic. Modern autocorrect learns from how people type.
Practice15 min

Worksheet: test it yourself

  • Students work on the worksheet alone or in pairs.
  • Walk around and ask "What data could it have learned from?" for each AI answer.
  • Ask two or three students to read out their own definition of AI.
Wrap-up5 min

One sentence definition

  • Write together on the board: AI learns patterns from data to make guesses.
  • Point out the word "guesses": guesses can be wrong.
  • Hand out the exit ticket.

Teacher tips

  • Common misconception: "anything electronic or fast is AI". Keep asking the rules question. A calculator is fast and accurate, and it learned nothing.
  • Large classes: give each group only 6 cards and do the hard cards (map app, autocorrect) together on the board. Stronger groups can invent two cards of their own for another group to sort.
  • There is no shame in the "Not sure" pile. Real experts disagree about some cards, and explaining a reason matters more than the pile.

Exit ticket answers

  1. Which of these most likely learned from examples? C. An email spam filter
  2. Why do we say AI makes guesses? A good answer: It uses patterns it found in past data. A pattern does not fit every new case, so its answers can be wrong.
  3. Face unlock on a phone is AI because: A. it learned what faces look like from many example photos
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Worksheet · Lesson 1 of 12 · What AI is

Rules or examples?

NameClassDate

Work alone or with a partner. For each machine, decide how it works and say why.

  1. 1Sort these machines. Tick one column and write a short reason.

    MachineFixed rulesLearned from examplesReason
    Calculator
    Phone face unlock
    Email spam filter
    Traffic light on a timer
    Video app suggesting the next video

  2. 2Which question helps you most to decide if something uses AI?

    • Does it use electricity?
    • Could a person write down every rule for it in advance?
    • Is it expensive?
    • Does it have a screen?

  3. 3A map app is a mix of rules and AI. Explain which part follows rules and which part learned from data.

  4. 4Finish the sentence in your own words: AI is...

  5. 5Draw one machine at home or at school that you think uses AI. Label what data it might have learned from.

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Cut along the dotted lines: two tickets per page.

Exit ticket · Lesson 1ManuAI

What counts as AI?

NameClassDate

  1. 1Which of these most likely learned from examples?

    • AAn alarm clock
    • BA calculator
    • CAn email spam filter
    • DA traffic light on a timer

  2. 2Why do we say AI makes guesses?

  3. 3Face unlock on a phone is AI because:

    • Ait learned what faces look like from many example photos
    • Bsomeone wrote a rule for every face in the world
    • Cit is on a phone
    • Dit is very fast