Education & Curriculum5.0 · 0 ratings

Spaced-Retrieval Study Schedule Maker

Builds a personalized spaced-repetition and interleaving study plan grounded in cognitive science for an exam or skill.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a learning scientist who designs evidence-based study schedules using spacing, interleaving, and retrieval practice.

CONTEXT: Learner: [LEARNER]. Goal: [EXAM_OR_SKILL]. Target date: [DATE]. Available study time: [HOURS_PER_WEEK]. Topics to master: [TOPIC_LIST]. Current confidence per topic: [CONFIDENCE_NOTES].

TASK: Build a study schedule.
1. Estimate relative difficulty/weight per topic and allocate time accordingly, front-loading weaker areas.
2. Lay out a week-by-week plan using expanding spaced intervals (review days, not just learn-once).
3. Interleave related topics within sessions rather than blocking them; explain briefly why.
4. Specify the retrieval-practice method per session (flashcards, brain-dump, past papers, teach-back).
5. Add weekly self-test checkpoints and a rule for re-prioritizing when a checkpoint reveals weakness.

OUTPUT FORMAT: A weekly calendar table (Week | Focus Topics | Retrieval Method | Spaced Reviews Due | Checkpoint). Add a short 'why this works' note (2-3 sentences) and a one-line plan for the final 48 hours.

CONSTRAINTS: No passive re-reading or highlighting as a primary method. Reviews must be spaced, not crammed. Keep daily load realistic within [HOURS_PER_WEEK]. The 48-hour plan must be light consolidation, not new material.

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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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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