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AI Basics

Seventh graders learn how AI actually learns. A machine-learning model is trained on many examples, tested on new ones, and then makes predictions — getting better with feedback. They order the learning pipeline, then discuss where the data (and its bias) comes from.

Grade 7AI Basics50 minutes1 class periodCSTA / ISTEExplicit teaching4 StandardsCSTA
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Lesson at a Glance

Everything you need before the bell rings

Learning Objectives

Students will be able to…

  • ✓Explain machine learning.
  • ✓Order the training pipeline.
  • ✓See why data matters.
  • ✓Spot possible bias.
Essential Question

How does an AI learn to make predictions?

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Lesson Phases
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Vocabulary Terms
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Standards Aligned
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Interactive Task
Put in Order · Interactive

Order the Learning Pipeline

Project this and have students put the machine-learning steps in order.

🧠 Put the machine-learning steps in orderTry it
Tap the first step a model needs!
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 — How Does It Guess?

6 min

How does a photo app know a cat from a dog?

👩‍🏫 Teacher Moves

  • Show an AI prediction.
  • Ask how it learned.
  • Set the goal.

🎒 Student Actions

  • Look.
  • Guess.
  • Get ready.
2

Use — AI Basics

12 min

Students use the idea.

👩‍🏫 Teacher Moves

  • Send students to Order the Learning Pipeline.
  • Order the steps.
  • Note data → train → test → predict.

🎒 Student Actions

  • Order.
  • Say.
  • Note it.
3

Modify — Change the Data

13 min

Students modify.

👩‍🏫 Teacher Moves

  • Swap in different training data.
  • Predict how results change.
  • Spot a possible bias.

🎒 Student Actions

  • Swap.
  • Predict.
  • Spot.
4

Create — Apply It

13 min

Students create.

👩‍🏫 Teacher Moves

  • Pick a task AI could learn.
  • List the data it would need.
  • Name one risk or bias.

🎒 Student Actions

  • Pick.
  • List.
  • Name.
5

Reflect — Reflect

6 min

Students close.

👩‍🏫 Teacher Moves

  • Say what training means.
  • Say why data matters.
  • Complete the exit ticket.

🎒 Student Actions

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

Built to the standards you report on

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

CSTA
2-DA-09

Use and refine models to make predictions.

CSTA
2-IC-21

Discuss issues of bias in computing systems.

CSTA
2-AP-13

Decompose a process into steps.

ISTE
5 Computational Thinker

Build and evaluate data models.

Differentiation

One lesson, every learner

Multilingual Learners

ELL / EMERGING READERS
  • Use a cats-vs-dogs example.
  • Frame: “learn from examples.”
  • Order picture cards.

Support & Access

IEP / 504
  • Use 3 steps (data, train, predict).
  • One step at a time.
  • Use pictures.

Stretch & Extend

GIFTED / EARLY FINISHERS
  • Explain overfitting simply.
  • Design a fair dataset.
  • Debate an AI use case.
Materials

What to gather

  • 📽️Screen / board
  • 🧠Pipeline step cards
  • 💻Order the Learning Pipeline
  • 🗂️Data examples
  • 📓Notebooks
  • 🎫Exit-ticket slips
Vocabulary

Key terms — hover for a quick definition

artificial intelligencecomputers doing tasks that seem to need thinkingmachine learninglearning patterns from examplesdatathe examples a model learns fromtrainto teach a model from datamodelwhat the AI builds to make predictionspredictto make a best guess on new inputbiasunfairness that can come from the datafeedbackcorrections that help a model improve
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 does a machine-learning model learn from?
Lots of examples (data).
QUESTION 2
What are the main steps?
Collect data → train → test → predict → improve.
QUESTION 3
Why can AI be biased?
If its training data is unfair or incomplete.

Reflect: AI basics.

Have students name one task AI could learn and the data it would need. A printable AI 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.

🃏 CS AI Basics (Grade 7)Flip
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.

📝 CS AI Basics (Grade 7)Quiz
Score: 0
1 / 6
Question 1
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Nice work!

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.

🖨️ CS AI Basics (Grade 7)Print
Name: ________________________
Date: ____________

Part A · Write the word that matches each meaning

Word bank: artificial intelligence, bias, data, feedback, machine learning, model, predict, train
  1. to make a best guess on new input
  2. learning patterns from examples
  3. what the AI builds to make predictions
  4. unfairness that can come from the data
  5. corrections that help a model improve
  6. the examples a model learns from
  7. to teach a model from data
  8. computers doing tasks that seem to need thinking

Part B · Show what you learned

  1. What does a machine-learning model learn from?
  2. What are the main steps?
  3. Why can AI be biased?
Answer key — Part A: 1) predict · 2) machine learning · 3) model · 4) bias · 5) feedback · 6) data · 7) train · 8) artificial intelligence
Part B: 1) Lots of examples (data). 2) Collect data → train → test → predict → improve. 3) If its training data is unfair or incomplete.