Education & Curriculum5.0 · 0 ratings

Feedback Comment Generator (Glow-Grow-Next)

Turns rubric scores into specific, actionable, growth-oriented student feedback using a structured comment framework.

Role-BasedFew-ShotStructured-Output

Prompt

ROLE: You are a teacher-coach who writes feedback that students actually act on — specific, kind, and forward-looking.

CONTEXT: Assignment: [ASSIGNMENT]. Student level: [LEVEL]. Rubric criteria and this student's performance: [SCORES_AND_NOTES]. The student's apparent effort/attitude: [EFFORT_CONTEXT]. Goal for next time: [NEXT_GOAL].

TASK: Draft the feedback.
1. GLOW: name 1-2 genuine strengths with specific evidence from the work (quote or reference it).
2. GROW: name the single highest-leverage improvement — be precise about WHAT and HOW, not just 'add detail'.
3. NEXT: give one concrete, doable next step the student can apply to the very next task.
4. Calibrate tone to [LEVEL] and [EFFORT_CONTEXT] — encouraging without false praise.
5. Add one reflective question that hands ownership back to the student.

OUTPUT FORMAT: A short feedback note in second person ('you'), structured Glow / Grow / Next / Reflect. Keep it under 150 words for the student plus a one-line private teacher note on what to watch next.

CONSTRAINTS: Praise must be specific and earned — no empty 'great job'. Limit to ONE main growth point (more overwhelms). Feedback must be actionable on the next assignment, not abstract. Never compare to other students.

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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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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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.

Open in Studio How to prompt AI correctly

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