Grade & subject
Written for Grade 3 (adaptable across grades 3–5). Cross-curricular: ELA discussion and vocabulary, plus computer science.
At a glance
Is this right for my class?
Time & prerequisites
One 60-minute session. No prior AI experience needed. A natural follow-on from the Intro to AI lesson.
Devices & internet
Partly. The story, sorting activity, and discussion run offline. ArtBot and the Kahoot review need a device.
Learning goal
What students will learn
Essential question: How do computers learn from examples, and how is that different from being given step-by-step rules?
By the end, students can
- Explain that machine learning means a computer improves by learning from examples and data.
- Describe how training data shapes what a model predicts.
- Train and test a simple model by playing ArtBot.
- Give everyday examples of machine learning.
- Discuss benefits and concerns of letting computers learn from data.
Big idea
Machines can learn. Instead of being told every rule, a machine-learning system finds patterns in examples - which is powerful, and also only as fair as the data it learns from.
Before you teach
Prepare & materials
Required (works offline)
- Printed copies of the Cece machine-learning story.
- Sorting-activity cards (cut from the pack).
- Notebook or paper for vocabulary and reflection.
- The printable Exit Ticket, one per student.
Prep time: about 15 minutes to print and cut.
Optional (needs devices)
- Devices running ArtBot for the training activity.
- A Kahoot quiz for the review (the Exit Ticket is a paper alternative).
- Projector for the whole-class discussion.
The 60-minute agenda
Teaching sequence
Seven steps that add up to one 60-minute class. Times include transitions.
- Introduction to machine learning (5 min) - share the learning goals and hook students.
- Short story with Cece (10 min) - introduces the vocabulary in context.
- ArtBot gameplay (15 min) - students train a model by example and watch its outputs change.
- Real-world applications & discussion (10 min) - where does machine learning show up?
- Sorting activity (10 min) - students sort examples and defend their choices.
- Kahoot quiz, or the paper Exit Ticket (5 min) - quick review.
- Closing discussion & reflection (5 min) - what did the computer actually learn?
Total: 60 minutes.
Want more? The teaching guide also includes a longer Challenge-Based Learning version (Engage, Investigate, Act) that spreads the same ideas across two to three sessions. Use it as a multi-day project.
Checking for understanding
Assessment
Along the way (formative)
- Vocabulary checks and group discussion.
- Student explanations during the sorting activity.
At the end
The included Exit Ticket checks that students can explain machine learning and give an example. A Kahoot quiz is an optional digital alternative.
Open the Exit Ticket ↗Adapt it
Differentiate & extend
Support
- Read the Cece story aloud.
- Provide sentence stems for the discussion.
- Pair students for the ArtBot activity.
Stretch
- Investigate a real machine-learning tool and what data it learns from.
- Run the Challenge-Based Learning extension.
The bundle
What's included
Download everything at once, or grab the pieces you need. All files are PDFs.
- Teaching guide - the full lesson plan, standards, and agenda. Open ↗
- Cece story - the machine-learning short story. Open ↗
- Exit ticket - the end-of-lesson check. Open ↗
- Resources - extra facilitator resources. Open ↗
Standards referenced
CCSS ELA-Literacy SL.3.1 (collaborative discussion) and L.3.5 (word relationships), plus NGSS, CSTA, and ISTE connections. These are referenced in the lesson; confirm the exact alignment for your curriculum before formal planning.
© 2026 Morgan State University. Free to download and use in your classroom. Version 2026. Questions or want to adapt and share it more widely? k12ceamls@morgan.edu.