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Intro to AI & Machine Learning

Tenth graders get an introduction to artificial intelligence and machine learning — how AI systems learn patterns from data to make predictions, where they show up in daily life, and the ethical questions they raise.

Grade 10AI & Society50 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 and ML.
  • ✓Explain training data.
  • ✓Describe predictions.
  • ✓Discuss AI ethics.
Essential Question

AI recommends your videos, filters your spam, and answers your questions. But how does a machine “learn” to do these things — and what should we watch out for?

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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

The AI & Machine Learning Explorer

Project this and tap each idea. Have students explore what AI is, how ML learns from data, and the ethics involved.

🤖 Explore AI & machine learning — tap eachTry it

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 — AI Everywhere

5 min

AI recommends videos and filters spam — but how does it “learn”?

👩‍🏫 Teacher Moves

  • List AI they use daily.
  • Ask how it works.
  • Set the goal.

🎒 Student Actions

  • List them.
  • Wonder.
  • Predict.
2

Unplugged — Unplug It

11 min

Students practice.

👩‍🏫 Teacher Moves

  • Sort examples the “ML” way.
  • See how patterns emerge from data.
  • Discuss bias.

🎒 Student Actions

  • Sort them.
  • See patterns.
  • Discuss.
3

Use — Explore AI & ML

14 min

Students use.

👩‍🏫 Teacher Moves

  • Send students to the AI & ML Explorer.
  • Note training data.
  • Note predictions.

🎒 Student Actions

  • Tap each.
  • Note data.
  • Note ethics.
4

Modify — Question It

14 min

Students modify.

👩‍🏫 Teacher Moves

  • Find a case of possible AI bias.
  • Explain the ethical issue.
  • Suggest a fix.

🎒 Student Actions

  • Find it.
  • Explain.
  • Suggest.
5

Create — Create

6 min

Students conclude.

👩‍🏫 Teacher Moves

  • Explain how ML learns.
  • Raise an ethics question.
  • Complete the exit ticket.

🎒 Student Actions

  • Explain it.
  • Raise one.
  • 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
3A-DA-12

Create computational models that learn from data.

CSTA
3A-IC-24

Evaluate the impacts of computing, including AI.

CSTA
3A-AP-13

Explain how AI systems make decisions.

ISTE
5

Computational Thinker.

Differentiation

One lesson, every learner

Multilingual Learners

ELL / EMERGING READERS
  • AI-concept picture cards.
  • Sentence frame: “AI learns from ___.”
  • Use everyday AI examples.

Support & Access

IEP / 504
  • Start with “AI you use.”
  • Use clear examples.
  • Focus on one idea.

Stretch & Extend

GIFTED / EARLY FINISHERS
  • Explore bias in a dataset.
  • Explain neural networks simply.
  • Debate an AI ethics case.
Materials

What to gather

  • 📽️Projector / board
  • 🗂️Example datasets
  • 💻The AI & ML Explorer
  • 📰AI news articles
  • ✏️Pens
  • 🎫Exit-ticket slips
Vocabulary

Key terms — hover for a quick definition

artificial intelligencetech that does tasks needing human-like thinkingmachine learningAI that learns patterns from datatraining dataexamples a model learns frommodela trained system that makes predictionspredictiona model’s best guess on new databiasunfair patterns learned from dataalgorithmthe method a model usesethicsideas about right and wrong use
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 machine learning?
A type of AI that learns patterns from data instead of following exact rules for every case.
QUESTION 2
Why is training data so important?
Models learn from it — more and better data usually means better performance (and biased data means biased results).
QUESTION 3
Name one ethical question AI raises.
Is it fair or biased? How is your data used? Who is responsible for its decisions?

Create: question the AI.

Have students pick one AI they use and describe what data it likely learns from and one ethical question it raises. A printable AI sheet 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.

🃏 Intro to AI & Machine LearningFlip
Card 1
Term
Tap to flip →
Meaning
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Practice · Quiz

Check your understanding

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

📝 Intro to AI & Machine LearningQuiz
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.

🖨️ Intro to AI & Machine LearningPrint
Name: ________________________
Date: ____________

Part A · Write the word that matches each meaning

Word bank: algorithm, artificial intelligence, bias, ethics, machine learning, model, prediction, training data
  1. AI that learns patterns from data
  2. a trained system that makes predictions
  3. a model’s best guess on new data
  4. unfair patterns learned from data
  5. examples a model learns from
  6. ideas about right and wrong use
  7. tech that does tasks needing human-like thinking
  8. the method a model uses

Part B · Show what you learned

  1. What is machine learning?
  2. Why is training data so important?
  3. Name one ethical question AI raises.
Answer key — Part A: 1) machine learning · 2) model · 3) prediction · 4) bias · 5) training data · 6) ethics · 7) artificial intelligence · 8) algorithm
Part B: 1) A type of AI that learns patterns from data instead of following exact rules for every case. 2) Models learn from it — more and better data usually means better performance (and biased data means biased results). 3) Is it fair or biased? How is your data used? Who is responsible for its decisions?