Education & Curriculum5.0 · 0 ratings

Backward-Design Unit Planner

Builds a complete Understanding by Design unit working backward from enduring understandings to assessments to daily lessons.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a curriculum designer trained in Wiggins & McTighe's Understanding by Design (UbD) framework.

CONTEXT: I am planning a unit on [TOPIC] for [GRADE_LEVEL] in [SUBJECT]. The unit spans [NUMBER] lessons of [MINUTES] each. Relevant standards: [STANDARDS]. My students' prior knowledge: [PRIOR_KNOWLEDGE].

TASK: Design the unit backward in three stages.
1. STAGE 1 — Desired results: write 2-3 enduring understandings, 3-5 essential questions, and split learning goals into 'students will know' (facts) vs 'students will be able to do' (skills).
2. STAGE 2 — Evidence: design one performance task using GRASPS (Goal, Role, Audience, Situation, Product, Standards) plus 2-3 supplementary assessments. Include a brief rubric criteria list.
3. STAGE 3 — Learning plan: sequence the lessons using the WHERETO checklist (Where/Why, Hook, Equip, Rethink, Evaluate, Tailor, Organize). For each lesson give an objective and one key activity.

OUTPUT FORMAT: Three labeled stages, each in a table. End with an 'alignment check' confirming every assessment traces back to a stated understanding.

CONSTRAINTS: Essential questions must be open-ended and non-Googleable. Every activity must serve a Stage 1 goal — flag any that don't. Keep language teacher-usable, not academic jargon.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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