# Teacher Education — steal this assignment
_EL3vate 2026 · Day 8 · Part 13 of 15 · build 3dfcaff_

## Try it Tuesday (90 minutes)
90 minutes, one real standard. Minutes 0–15: name the grade level, the standard, and the specific learners. Minutes 15–50: run the differentiation prompt to generate four versions of one activity — below level, at level, above level, and an emerging multilingual learner — then evaluate each against the actual standard, rejecting what is subtly wrong for the grade. Minutes 50–90: with a partner, check the AI's most confident-looking choices — vocabulary, any translation, cognitive demand — and mark what a teacher must verify before using it. The deliverable is the evaluation, not the generated material; it grades professional judgment about AI output.

## The assignment
**Make it (3D print · laser cut).** A classroom manipulative designed for a specific partner teacher’s specific lesson. The constraint that makes it real: it has to survive thirty second-graders and cost almost nothing to reproduce.

**Build it (AI chat · no code).** Differentiated versions of one lesson artifact, generated fast, then evaluated. Students see how quickly AI produces plausible-looking material and how much of it is subtly wrong for the grade level, which is the professional judgment being taught.

## 4-week plan
- **Wk 1.** Secure the partner classroom and the specific lesson. Students observe before designing, and cardboard-prototype the manipulative the same week so the fabricated part can be the second version, not the first.
- **Wk 2.** Submit the refined manipulative fabrication file at the start of the week. Generate differentiated variants with AI, then evaluate against the actual standard and the actual learners. Most get rejected.
- **Wk 3.** While the manipulative is fabricated, students finalize the lesson and rehearse teaching it.
- **Wk 4.** Teach the lesson in the partner classroom. Debrief with the partner teacher, revise, hand it over.

## What this replaces
- **Replaces:** The lesson-plan submission graded on format and completeness.
- **What is lost:** The from-scratch lesson-writing practice.
- **What is gained:** Students practice the judgment that now matters most — spotting where fast, plausible AI material is wrong for these learners and this standard — instead of being graded on a template the AI can fill in seconds.

## Where AI is bad at this
A model produces differentiated lesson material that looks classroom-ready and is subtly wrong for the grade — misjudging cognitive load, mislabeling a standard, and generating confident 'translations' or cultural references it cannot vouch for. It changes the thinking when asked only to change the scaffolding, and it never flags that a human must check the pieces most likely to mislead a specific learner.

## Rubric
| Criterion | Weight | What it assesses |
|---|---|---|
| Standard alignment | 25% | Each version is checked against the actual grade-level standard, and any misalignment the AI introduced is caught. |
| Constant cognitive demand | 25% | Differentiation changes scaffolding, not the thinking; the student verifies the AI did not lower the demand. |
| Learner-specific scrutiny | 30% | Flags what must be verified for real learners — translations, cultural references, reading load — rather than trusting the output. |
| Rejection judgment | 20% | Rejects the versions that are wrong for the grade and explains why, rather than accepting plausible material. |

## Starter prompt
> I am teaching this lesson to [grade level] students: [describe the lesson and standard]. Produce four versions of the main student activity, differentiated for: significantly below grade level, at grade level, above grade level, and an emerging multilingual learner. Keep the cognitive demand the same across all four; change the scaffolding, not the thinking. Then flag anything in your own versions that a teacher should check before using it.

## Budget & logistics
- **Instructor prep:** 1.5 hours
- **Class time:** 90 minutes
- **Per-student cost:** $0 for the Tuesday version; roughly $4–10 per student in filament or cut stock **[unverified]** for a durable manipulative in the four-week build.
- **Fabrication file due:** First day of Week 2 — cardboard-prototype the manipulative during week-1 observation, then submit the refined fabrication file at the start of week 2 (still the second version, iterated earlier). PACE quotes 7–10 business days (up to 14 calendar days), so it clears before week-4 classroom teaching.
- **Calendar dependency:** Material generation and evaluation need no lead time. The manipulative is for the specific lesson secured in week 1; students cardboard-prototype it that week and submit the refined file at the start of week 2, so the up-to-14-calendar-day turnaround clears before week-4 teaching.

## Three sizes
- **One session:** The 90-minute generate-and-evaluate session producing an evaluation of the AI material; no fabrication.
- **4 weeks:** The seeded four-week plan: secure a partner classroom, generate and vet differentiated variants, prototype then fabricate the manipulative, then teach and revise with the partner teacher.
- **One semester:** A partner-classroom project — observation, AI-differentiated materials vetted against standards, a prototyped-then-fabricated manipulative, taught in the real classroom and revised with the partner teacher.

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_PACE · Shidler College of Business · University of Hawaiʻi at Mānoa · pace.shidler.hawaii.edu/maker_
_All fifteen assignments, the demos and every handout: https://el3vate.vercel.app_