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Part 1: What AI Is — worksheets and answer key

10 worksheets, then the answer key. One lesson per A4 page.

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Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 01

How does AI talk?

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Probability slider

Try it — the next-word picking machine

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1What does AI do in a single step?

    1. ① It makes the whole sentence in one go
    2. ② It picks the next piece and adds it on
    3. ③ It finds a similar sentence on the internet
    4. ④ It pulls out one of the answers it saved earlier

  2. Q2In the experiment above, what happens if you turn “Boldness” down to 0?

    1. ① You get a different sentence every time
    2. ② You get the same sentence no matter how many times you press
    3. ③ The AI gets more accurate
    4. ④ It gets faster

  3. Q3“AI searches for the answer somewhere and then tells you.” True or false?

    1. ① True
    2. ② False

The takeaway in my own words

“AI is a machine that picks the next piece over and over, really fast.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 02

What's a token, the 'piece' AI talks in?

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Split it up

Try it — the sentence-splitting machine

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1What is a “token”?

    1. ① Always exactly one letter
    2. ② Always exactly one word
    3. ③ A piece AI cuts text into to make computing easier (its size varies)
    4. ④ The whole word list people set up in advance

  2. Q2What happens for the AI when the number of tokens goes up?

    1. ① It gets faster
    2. ② There's more to compute, and it takes longer
    3. ③ No effect at all
    4. ④ It always gets smarter

  3. Q3Different AIs can split the same sentence into tokens differently. True or false?

    1. ① True
    2. ② False

The takeaway in my own words

“To AI, a sentence isn't letters or words — it's a line of pieces called tokens.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 03

Why does AI answer differently every time?

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Probability slider

Try it — turn the temperature knob

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1What happens if you turn the temperature down very close to 0?

    1. ① The ranking of the candidates flips
    2. ② Almost always only the #1 candidate gets drawn
    3. ③ There are more candidates
    4. ④ The AI gets smarter

  2. Q2When you raise the temperature, what grows along with it?

    1. ① Answers get more varied, but so does the risk of going off the rails
    2. ② Computing gets faster
    3. ③ Accuracy always goes up
    4. ④ The number of tokens goes down

  3. Q3Set the temperature to 0, and the same question always gets exactly the same answer. True or false?

    1. ① True
    2. ② False

The takeaway in my own words

“Temperature isn't a knob that makes AI smarter — it's a knob for how bold it gets.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 04

What does it mean for AI to 'learn' something?

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Put it in order

Try it — put the training cycle in order

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1What exactly does it mean when AI 'learns'?

    1. ① It comes to understand meaning, like a person
    2. ② It nudges numbers (weights) a little at a time to get closer to the right answer
    3. ③ It saves the whole internet
    4. ④ It memorizes a fixed table of rules

  2. Q2Training means making big fixes so it gets the answer exactly right in one go.

    1. ① True
    2. ② False

  3. Q3What happens if training runs just once?

    1. ① It gets smart right away
    2. ② Almost nothing changes
    3. ③ It actually gets worse
    4. ④ Training is finished

The takeaway in my own words

“Training isn't a 'lightbulb moment' — it's 'fixing a little,' over and over.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 05

Just how big is this number? — Getting a feel for parameters

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Number sense

Try it — the number-sense slider

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1The more parameters, the smarter the AI — always.

    1. ① True
    2. ② False

  2. Q2What exactly is a parameter?

    1. ① The number of sentences the model memorized
    2. ② How many weight numbers keep getting adjusted during training
    3. ③ The number of servers
    4. ④ The number of people using it at the same time

  3. Q3As far as we know, the estimated number of synapses in a human brain is bigger than the parameter count of any AI model released so far.

    1. ① True
    2. ② False

The takeaway in my own words

“A bigger number doesn't always mean smarter.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 06

Why does AI calmly make up facts? — Hallucination

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Probability slider

Try it — probability slider

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1What's the root cause of hallucination?

    1. ① The AI means to lie
    2. ② The AI is built to pick words that sound likely, by probability
    3. ③ Server errors
    4. ④ The user asked the question wrong

  2. Q2Hallucination happens more often when you ask about things that are missing or rare in the training data.

    1. ① True
    2. ② False

  3. Q3If AI answers in a confident tone, what it says is always true.

    1. ① True
    2. ② False

The takeaway in my own words

“Saying it confidently doesn't make it true.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 07

Why does AI forget what you just said? — Memory limits

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Context window tracker

Try it — context window tracker

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1Why does AI ‘forget’ the start of a chat?

    1. ① Its feelings got hurt
    2. ② It can only read so much at once (its context)
    3. ③ It ignores you on purpose
    4. ④ It's testing you

  2. Q2Even in a really long chat, AI always remembers exactly what you said first.

    1. ① True
    2. ② False

  3. Q3If you repeat important info partway through a chat, AI uses it better.

    1. ① True
    2. ② False

The takeaway in my own words

“If it matters, say it again.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 08

Why does changing one question change the whole answer? — Prompts

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Before & after

Try it — sharpen the prompt

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1What's the key to a good prompt?

    1. ① A secret spell that tricks the AI
    2. ② Saying clearly what result you want
    3. ③ Keeping it as short as possible
    4. ④ Using lots of big words

  2. Q2Change the prompt, and the odds for the AI's next word change too.

    1. ① True
    2. ② False

  3. Q3No matter how specific your prompt is, the result barely changes.

    1. ① True
    2. ② False

The takeaway in my own words

“Don't change the AI — change the question.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 09

Is a bigger model always better? — Big models, small models

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Pick one

Try it — which one gets this job?

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1When a model is ‘big,’ what does it mostly have a lot of?

    1. ① Letters shown on the screen
    2. ② Parameters (numbers tuned during training)
    3. ③ People using it
    4. ④ Time it takes to answer

  2. Q2What does a small model clearly do better than a big one?

    1. ① Solves hard new problems better
    2. ② Knows more facts
    3. ③ It's fast, cheap and runs offline on your device
    4. ④ Never says anything wrong

  3. Q3There's a way to pour what a big model learned into a small model.

    1. ① True
    2. ② False

The takeaway in my own words

“Big isn't what's good — the right size for the job is.”

Rewrite this line in your own words.

Next TokenFive-Minute AI Lessons · WorksheetPart 1: What AI Is
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DAY 10

Could this AI fit on my laptop? — Open and closed models

1 · Think first

Before the lesson, write down what you think the answer is.

2 · Experiment · Split it up

Try it — split ‘open’ into four pieces

What did you change in the experiment, and what happened?

3 · Quiz

Circle the right answer.

  1. Q1When a model is ‘open,’ what can you mainly get?

    1. ① The password to the company's servers
    2. ② The numbers tuned in training — the weights
    3. ③ The AI's chat history with people
    4. ④ A share of the company's money

  2. Q2What can you do when you use a closed model?

    1. ① Download the model file onto your laptop
    2. ② Look at the data it was trained on
    3. ③ Ask and get answers through an app or API
    4. ④ Change the numbers inside the model yourself

  3. Q3If a model's weights are released, its training data is usually released too.

    1. ① True
    2. ② False

The takeaway in my own words

“Open doesn't mean free — it means you can get the core in your own hands.”

Rewrite this line in your own words.

Next TokenFor the teacherPart 1: What AI Is

Answer key · Part 1: What AI Is

DAY 01 · How does AI talk?

  1. Q1 · Answer ② It picks the next piece and adds it on AI does just one thing: pick the next piece. A long sentence is what you get from doing that hundreds of times — it was never made all at once.
  2. Q2 · Answer ② You get the same sentence no matter how many times you press At 0 it keeps picking only the #1 candidate, so the answer is always the same. But it gets boring, and if #1 is wrong there's no way out. Its real name is temperature.
  3. Q3 · Answer ② False False. By default, AI doesn't search — it works out the next piece right there and builds the answer. That's why it sometimes makes up facts that don't exist, very confidently. This is called hallucination. (When today's chatbots search the web, that's a separate feature added on top.)

Today in one line · AI is a machine that picks the next piece over and over, really fast.

DAY 02 · What's a token, the 'piece' AI talks in?

  1. Q1 · Answer ③ A piece AI cuts text into to make computing easier (its size varies) A token is a piece cut to make AI's computing easier. Sometimes it's one letter, sometimes a whole word — there's no fixed size.
  2. Q2 · Answer ② There's more to compute, and it takes longer More tokens means more to compute, and more time (and cost). Say the same thing more briefly and the AI handles it faster.
  3. Q3 · Answer ① True True. Some AIs use a tokenizer that cuts by character, others one that cuts by common chunks. So the same sentence can come out as a different number of tokens in different AIs.

Today in one line · To AI, a sentence isn't letters or words — it's a line of pieces called tokens.

DAY 03 · Why does AI answer differently every time?

  1. Q1 · Answer ② Almost always only the #1 candidate gets drawn Temperature doesn't change the ranking. It only changes how far ahead #1 is. Near 0, #1's chance gets close to 100%, so you always get the same answer.
  2. Q2 · Answer ① Answers get more varied, but so does the risk of going off the rails Turn it up and lower candidates get drawn often, so things get more varied — but the risk of awkward or wrong answers creeping in grows too. That's why people turn the temperature down for jobs that must be exact, like code or math.
  3. Q3 · Answer ② False False. A 2026 study ran 4,705 runs and found that even at temperature 0, the bigger the model, the less often it gave the same answer — models with more than 120 billion parameters were reproducible less than 20% of the time. That's because the computer does its math in a slightly different order each time.

Today in one line · Temperature isn't a knob that makes AI smarter — it's a knob for how bold it gets.

DAY 04 · What does it mean for AI to 'learn' something?

  1. Q1 · Answer ② It nudges numbers (weights) a little at a time to get closer to the right answer Training isn't understanding — it's nudging the weights a little at a time by however much it was off, again and again.
  2. Q2 · Answer ② False It only fixes a little at a time. That way it doesn't wreck what it learned from other sentences.
  3. Q3 · Answer ② Almost nothing changes One fix is tiny. That's why it takes hundreds of billions of repeats before you notice anything.

Today in one line · Training isn't a 'lightbulb moment' — it's 'fixing a little,' over and over.

DAY 05 · Just how big is this number? — Getting a feel for parameters

  1. Q1 · Answer ② False Size is just the container — the amount and quality of the data have to be good too for its skill to grow.
  2. Q2 · Answer ② How many weight numbers keep getting adjusted during training Parameters are the total number of numbers (weights) that get adjusted bit by bit during training.
  3. Q3 · Answer ① True The parameter counts of publicly known models are still short of the estimate for human brain synapses (about 100 trillion).

Today in one line · A bigger number doesn't always mean smarter.

DAY 06 · Why does AI calmly make up facts? — Hallucination

  1. Q1 · Answer ② The AI is built to pick words that sound likely, by probability AI is built to pick the most likely next words, not to look up facts — so even with no right answer, it produces something that sounds believable.
  2. Q2 · Answer ① True The rarer the fact, the blurrier the probabilities get — and it fills the gap with believable-sounding words.
  3. Q3 · Answer ② False A confident tone and whether something is true are two different things. Always check important info separately.

Today in one line · Saying it confidently doesn't make it true.

DAY 07 · Why does AI forget what you just said? — Memory limits

  1. Q1 · Answer ② It can only read so much at once (its context) AI isn't a memory bank. It rereads the whole chat every time, so once the chat is longer than it can read, the start gets pushed out.
  2. Q2 · Answer ② False In a long chat, the early part can get pushed 'out of the window' and stop counting.
  3. Q3 · Answer ① True Anything you repeat is back 'inside the window', so the AI can use it more.

Today in one line · If it matters, say it again.

DAY 08 · Why does changing one question change the whole answer? — Prompts

  1. Q1 · Answer ② Saying clearly what result you want A prompt is the starting point for the text the AI continues. The more clearly you say what you want, the more the odds lean your way.
  2. Q2 · Answer ① True When the input text changes, the words most likely to come next change completely.
  3. Q3 · Answer ② False The more you spell out who it's for, the format, the length and the tone, the more useful the result gets.

Today in one line · Don't change the AI — change the question.

DAY 09 · Is a bigger model always better? — Big models, small models

  1. Q1 · Answer ② Parameters (numbers tuned during training) Size means the number of parameters — the number you got a feel for on DAY 05. The more there are, the more it can hold.
  2. Q2 · Answer ③ It's fast, cheap and runs offline on your device A small model doesn't win by being smarter — it wins by being close by. Speed, cost, and the fact that it keeps working when the connection drops.
  3. Q3 · Answer ① True It's called distillation. Teach a small model using a big model's answers as its textbook, and the small model does far better than its size suggests.

Today in one line · Big isn't what's good — the right size for the job is.

DAY 10 · Could this AI fit on my laptop? — Open and closed models

  1. Q1 · Answer ② The numbers tuned in training — the weights The weights. That pile of numbers you got a feel for on DAY 05 — a trained model is really just a file full of those numbers.
  2. Q2 · Answer ③ Ask and get answers through an app or API You can ask questions at the window and get answers — and that's it. The core never leaves the server, so when the internet drops, you can't do anything.
  3. Q3 · Answer ② False Most models open the weights but not the training data. So the word ‘open’ alone doesn't tell you how much is open.

Today in one line · Open doesn't mean free — it means you can get the core in your own hands.