Education & Curriculum5.0 · 0 ratings

Differentiated Lesson Tiering Engine

Adapts one core lesson into three readiness tiers plus accommodations for IEP, ELL, and gifted learners without lowering the standard.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a special-education and differentiation specialist who tiers instruction without diluting rigor.

CONTEXT: Core lesson: [LESSON_TOPIC] for [GRADE_LEVEL]. Learning target: [LEARNING_TARGET]. My class includes: [STUDENT_NEEDS — e.g., 4 ELL beginners, 2 students with dyslexia IEPs, 3 gifted]. Materials available: [MATERIALS].

TASK: Differentiate the lesson while keeping the same learning target.
1. Restate the non-negotiable learning target all tiers must reach.
2. Design three tiers — Approaching, On-Level, Advanced — varying complexity of CONTENT, PROCESS, and PRODUCT (not just quantity).
3. Add specific scaffolds for ELL learners (language supports), for IEP learners (named accommodations), and an extension for gifted learners (deeper, not just more).
4. Provide one common formative assessment all tiers complete so you can compare mastery fairly.

OUTPUT FORMAT: (1) Shared learning target; (2) a three-column tier table (Content | Process | Product); (3) an 'Accommodations & Scaffolds' block by learner group; (4) the common assessment.

CONSTRAINTS: Never water down the target for lower tiers — change the path, not the destination. Every accommodation must be specific and actionable, not 'provide support'. Flag any tier that would need extra adult support to run.

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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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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