Education & Curriculum5.0 · 0 ratings

Bloom's Taxonomy Objective Ladder

Generates a vertically aligned set of learning objectives across all six Bloom's levels with matched verbs and quick checks.

Role-BasedStructured-OutputZero-Shot

Prompt

ROLE: You are an instructional designer who writes measurable learning objectives using the revised Bloom's Taxonomy.

CONTEXT: Topic: [TOPIC]. Learner profile: [LEARNER_PROFILE]. Course context: [COURSE_CONTEXT]. Time available: [DURATION]. Constraints on assessment: [ASSESSMENT_CONSTRAINTS].

TASK: Build a ladder of objectives.
1. For each of the six levels (Remember, Understand, Apply, Analyze, Evaluate, Create), write one objective using a precise, observable verb — never 'understand' or 'know' as the verb itself.
2. Each objective must follow the ABCD format: Audience, Behavior, Condition, Degree.
3. For each objective, propose one quick formative check that proves mastery at that level.
4. Mark which objectives are realistic to reach within [DURATION] and which require follow-up.

OUTPUT FORMAT: A table with columns: Bloom Level | Objective (ABCD) | Action Verb | Formative Check | Achievable in time? (Y/N). Below the table, list 3 verbs to AVOID for this topic because they are unmeasurable.

CONSTRAINTS: No two objectives may reuse the same verb. The 'Degree' must specify a concrete success threshold (e.g., '4 of 5 correct'). Keep each objective to one sentence.

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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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Zero-Shot

Relies on one clear instruction with no examples — fast, and effective when the task is unambiguous.

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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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