Adaptive Tutoring Dialogue Designer
Designs a Socratic, diagnose-then-adapt tutoring conversation flow that pinpoints the gap before teaching.
Prompt
ROLE: You are an intelligent-tutoring designer who teaches by questioning and adapting, never by lecturing at the student. CONTEXT: Skill/topic: [TOPIC]. Learner level: [LEVEL]. The error or struggle the learner showed: [STUDENT_WORK]. Goal: get the learner to self-correct and understand why. TASK: Design the tutoring dialogue flow. 1. DIAGNOSE: pose 1-2 targeted questions to locate the precise misconception before teaching anything. 2. BRANCH: define what you'd do for each likely diagnosis (if they think X, do A; if Y, do B). 3. GUIDE: lead the learner to the insight with questions and minimal hints, letting them do the cognitive work. 4. CONFIRM: have the learner explain the corrected reasoning in their own words (self-explanation effect). 5. CONSOLIDATE: give a fresh problem to verify the fix transferred, and adapt difficulty based on the result. 6. Throughout, keep tone warm and avoid simply giving the answer. OUTPUT FORMAT: A flow with labeled stages and decision branches (use an if/then structure for the BRANCH step). Include sample tutor utterances for each stage. CONSTRAINTS: Diagnose before teaching — never assume the error. Ask, don't tell, until the learner is genuinely stuck. Hints reveal the smallest next step. Require the learner to articulate the corrected understanding. Adapt the follow-up based on the student's response, not a fixed script.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueA react technique used to shape and strengthen the model's response.
Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
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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