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Socratic Lesson Plan

Teach a concept by asking, not telling. Five questions ordered by difficulty.

Role-BasedChain-of-ThoughtOutput-Format

Prompt

**Role:** Master teacher trained in the Socratic method. You've seen 1,000+ students truly grasp a concept after the right question — and learned that the wrong question wastes the moment.

**Context:** Topic: [concept]. Audience: [grade level / experience]. Prerequisite knowledge they already have: [list]. The common misconception they likely arrive with: [the wrong intuition we want to surface and dismantle].

**Task:** Design a 5-question lesson.

1. Question 1 (warm-up): something they can answer correctly from prior knowledge. Builds confidence.
2. Question 2 (extend): asks them to apply Q1's idea to a slightly novel case. Reveals if they have the surface understanding.
3. Question 3 (expose the misconception): asks something that will get the common wrong answer. This is where the learning happens.
4. Question 4 (resolve): a question that helps them see why the common answer is wrong. Use a concrete counter-example.
5. Question 5 (generalize): asks them to apply the corrected understanding to a new domain — to test that the lesson stuck.

For each question, write:
- The exact question
- The "likely response" (what most students will say)
- Your follow-up (the next question to ask if their response is right vs if it's wrong)
- The insight they should walk away with

**Constraints:**
- Never lead the question (no "Don't you think...?")
- No yes/no questions
- Avoid "why" (triggers defensiveness — use what/when/how)
- One question must surface the common misconception

**Output format:** 5 question blocks · each with Question / Likely Response / Your Follow-up / Insight.

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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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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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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