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Artificial Intelligence Basics

Eighth graders learn what artificial intelligence really is, how it learns from data, where it shows up in daily life, and what its limits are, and check what they know.

Grade 8Artificial Intelligence Basics50 minutes1 class periodUse → Modify → CreateExplicit teaching4 StandardsCSTA
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Lesson at a Glance

Everything you need before the bell rings

Learning Objectives

Students will be able to…

  • ✓Define AI.
  • ✓Explain training data.
  • ✓Spot AI in daily life.
  • ✓Know its limits.
Essential Question

AI is everywhere, but it is a tool, not magic. What is artificial intelligence really, how does it learn, and what can it not do?

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Lesson Phases
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Vocabulary Terms
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Standards Aligned
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Interactive Task
Explore · Interactive

AI Check

Project this and answer each question about what AI is and how it works — read the explanation after each one.

🤖 Check what you know about artificial intelligenceTry it
Answer each question about what AI is and how it works.
The Lesson · Use → Modify → Create (a coding progression with Unplugged practice)

50 minutes, five moves

Tap any phase to open the teacher moves and student actions.

1

Hook — Smart or Not?

5 min

Your phone suggests words, recommends videos, and recognizes faces. Is that really “intelligence,” and how does it work?

👩‍🏫 Teacher Moves

  • Ask where they meet AI.
  • List examples.
  • Set the goal.

🎒 Student Actions

  • Think.
  • Share.
  • Get ready.
2

Use — AI Check

12 min

Students answer questions about AI.

👩‍🏫 Teacher Moves

  • Send students to the AI Check.
  • Answer each question.
  • Read the why.

🎒 Student Actions

  • Answer.
  • Check.
  • Read it.
3

Modify — Spot the AI

13 min

Students find AI in real tools.

👩‍🏫 Teacher Moves

  • Pick a tool you use.
  • Say where AI is in it.
  • Explain what it does.

🎒 Student Actions

  • Pick.
  • Say.
  • Explain.
4

Create — AI Explainer

15 min

Students explain AI simply.

👩‍🏫 Teacher Moves

  • Define AI in one sentence.
  • Give two real examples.
  • Note one limit.

🎒 Student Actions

  • Define.
  • Give.
  • Note.
5

Reflect — Tool, Not Magic

5 min

Students reflect on what AI can and cannot do.

👩‍🏫 Teacher Moves

  • How does AI learn?
  • What is one limit of AI?
  • Complete the exit ticket.

🎒 Student Actions

  • Say it.
  • Say it.
  • Complete the exit ticket.
Standards Alignment

Built to the standards you report on

Aligned to the CSTA K-12 Computer Science Standards and ISTE Standards for Students.

CSTA
2-AP-10

Describe how AI uses data to make decisions.

CSTA
2-IC-20

Discuss where AI is used and its tradeoffs.

CSTA
2-IC-21

Explain impacts and limits of AI systems.

ISTE
5

Understand how data-driven systems make choices.

Differentiation

One lesson, every learner

Multilingual Learners

ELL / EMERGING READERS
  • AI-example picture cards.
  • Sentence frame: “AI is ___ .”
  • Answer one question at a time.

Support & Access

IEP / 504
  • Start with everyday examples.
  • Discuss one scenario together.
  • Review one idea at a time.

Stretch & Extend

GIFTED / EARLY FINISHERS
  • Explore how bias enters data.
  • Research a specific AI tool.
  • Debate a benefit vs a risk of AI.
Materials

What to gather

  • 📽️Projector / board
  • 🖼️AI-example cards
  • 💻AI Check
  • 🧠Data-pattern demo
  • ✏️Pencils
  • 🎫Exit-ticket slips
Vocabulary

Key terms — hover for a quick definition

artificial intelligencesoftware that learns patterns from data to make decisionsmachine learningAI that improves by learning from datatraining datathe examples an AI learns patterns fromalgorithma step-by-step set of instructionspatterna regularity an AI finds in databiasan unfair slant in data or resultspredictionan AI's best guess based on patternsrecommendation systemAI that suggests what you might like
Evaluate

Exit Ticket

Preview the three formative checks. Tap “Sample answer” to see what mastery looks like — hide them before you print for students.

QUESTION 1
What is artificial intelligence?
Software that learns patterns from data to make predictions or decisions; it is not a human-like mind.
QUESTION 2
How does most AI learn?
By training on large amounts of example data and finding patterns in it (machine learning).
QUESTION 3
Name one limit of AI.
It can be wrong or biased if its training data is flawed, and it does not truly understand like a person.

Create: an AI explainer.

Have students define AI in one clear sentence, give two real examples from their own life, and note one limit, so they can explain it to someone else. A printable AI-basics worksheet is in the Computer Science library.

Study · Flashcards

Study the key terms

Tap a card to flip it, then rate whether you knew it. Built from this lesson’s vocabulary.

🃏 Artificial Intelligence BasicsFlip
Card 1
Term
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Meaning
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Practice · Quiz

Check your understanding

A quick self-check with instant feedback, drawn from this lesson’s key terms.

📝 Artificial Intelligence BasicsQuiz
Score: 0
1 / 6
Question 1
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Practice · Worksheet

Printable worksheet

A print-and-go review sheet with a built-in answer key. Tap “Show answer key” to reveal answers, or print the clean version for students.

🖨️ Artificial Intelligence BasicsPrint
Name: ________________________
Date: ____________

Part A · Write the word that matches each meaning

Word bank: algorithm, artificial intelligence, bias, machine learning, pattern, prediction, recommendation system, training data
  1. a step-by-step set of instructions
  2. a regularity an AI finds in data
  3. an AI's best guess based on patterns
  4. AI that suggests what you might like
  5. the examples an AI learns patterns from
  6. an unfair slant in data or results
  7. software that learns patterns from data to make decisions
  8. AI that improves by learning from data

Part B · Show what you learned

  1. What is artificial intelligence?
  2. How does most AI learn?
  3. Name one limit of AI.
Answer key — Part A: 1) algorithm · 2) pattern · 3) prediction · 4) recommendation system · 5) training data · 6) bias · 7) artificial intelligence · 8) machine learning
Part B: 1) Software that learns patterns from data to make predictions or decisions; it is not a human-like mind. 2) By training on large amounts of example data and finding patterns in it (machine learning). 3) It can be wrong or biased if its training data is flawed, and it does not truly understand like a person.