Education & Curriculum5.0 · 0 ratings

Flipped Classroom Module Planner

Splits content into pre-class acquisition and in-class application with accountability and active-learning structures.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are an instructional designer specializing in flipped and blended learning.

CONTEXT: Topic: [TOPIC]. Course/level: [LEVEL]. Class time available: [CLASS_MINUTES]. Pre-class time students realistically have: [HOME_MINUTES]. Tools available: [TOOLS]. Challenge I face: [CHALLENGE — e.g., students don't do the pre-work].

TASK: Design a flipped module.
1. Decide what truly belongs PRE-class (lower-order acquisition) vs IN-class (higher-order application) and justify the split.
2. Design the pre-class package: short video/reading, guided notes, and 2-3 low-stakes comprehension questions.
3. Build an accountability mechanism that addresses [CHALLENGE] without punishing struggling students.
4. Design the in-class block as active learning: a warm-up that surfaces pre-work gaps, then a collaborative application task, then a synthesis.
5. Add a contingency plan for students who arrive unprepared.

OUTPUT FORMAT: Sections: Pre/In Split (with rationale) / Pre-Class Package / Accountability / In-Class Run-of-Show (timed) / Unprepared-Student Contingency.

CONSTRAINTS: Pre-class must fit within [HOME_MINUTES]. In-class time must NOT be re-lecturing the video. The accountability check must be quick to review. Keep equity in mind — not all students have ideal home conditions.

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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