PICTURE THIS · TEACHER GUIDE
Picture This: Image Classification + Data Bias
A complete five-day, teacher-led sequence with whole-group decks and ready-to-print student materials.
Topic: Image classification, training data, model failure, and responsible AI
Objective: Students will move from sorting images by hand to training an image classifier and coding with its predictions.
Demonstration of learning: Students submit a working .sb3 project and explain one model failure and one revision that improved it.
The week at a glance
| Day | Learning move | Student evidence |
|---|---|---|
| 1 · Meet the AI | Train thumbs-up/thumbs-down, then deliberately try to break the classifier. | Two trained classes + a named failure condition. |
| 2 · Data makes the model | Independent packet: collect, train, test; then connect missing variety to data bias. | Annotated three-page packet and five responses. |
| 3 · Be the AI | Sort cat/dog images by hand, write rules, recreate the classifier, then test it with images that expose its gaps. | Rules, test results, and exit explanation. |
| 4 · Train + connect | Design two or three classes, train with variety, partner-test, export, and connect to RAISE Playground. | Planning sheet, model link, and working response. |
| 5 · Remix + critique | Change the sprite/actions/classes, retest for failures, revise, share, and submit. | .sb3 upload + short reflection. |
Teacher files by day
The slide decks are the whole-group teaching route. Canvas pages are the student directions and absence route.
Day 1: Download Day 1 deck · Make a Google copy
Day 2: Packet PDF Editable packet · Make a Google copy
Day 3: Download Day 3 deck · Make a Google copy Cat/dog datasets Bias test photos Exit ticket
Day 4: Download Day 4 deck · Make a Google copy Planning sheet PDF Editable planning sheet · Make a Google copy
Prep before the week
- Test webcam permission on a student Chromebook in Teachable Machine.
- Open the RAISE Playground once on a student device; the first load can be slow.
- Print Day 2 one packet per student.
- For Day 3, print pages 1 through 7 of the cat/dog dataset packet per pair, page 1 of the bias-test packet per table, and one exit ticket per student. Add scissors and glue or tape. Keep the final credits pages with the digital files.
- For Day 4, print one planning sheet per pair. Students upload a photo, scan, or digital copy of the sheet and paste the model URL in the Canvas submission comment so they can reopen it on Day 5.
- The final turn-in is an .sb3 file. Reserve the last five minutes for File → Save to your computer, then Canvas upload.
Pacing + leave-up screens
- Day 1: teach slides 1-9, then leave the build/help screen up while students train and test.
- Day 2: project the Canvas page or packet directions; this day is intentionally independent/sub-friendly.
- Day 3: move quickly through the opening sort so the final 14 minutes remain for the Teachable Machine bias test.
- Day 4: leave the training checklist up during work time; students should get one response working in Playground before the bell.
- Day 5: leave the remix menu and success criteria up; stop with enough time for a quick share and file submission.
What to listen for
- A strong explanation names the missing pattern in the training data, not just 'the AI was wrong.'
- Class names in Teachable Machine and Playground must match exactly.
- More photos are not automatically better. Students need useful variety: people, distance, angle, lighting, and background.
- The model makes a prediction; the student-written code decides what happens next. Keep those two ideas separate.
RAISE Playground was created by the MIT RAISE Initiative and the Personal Robots Group at the MIT Media Lab.