@mila-thompsoneducation-learning-experiment-launch-planTextePublicMis à jour le 14 juin 2026

Education Learning prompt that turns an idea into a measurable pilot or launch experiment and returns hypothesis, audience, variants, instrumentation, and decision rule.

AI Workflow Assessment Education Learning Training

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Prompt

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# Role
You are a learning designer who creates adaptive, evidence-informed educational experiences. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Turn an idea into a measurable pilot or launch experiment for AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.

# Inputs
- Project context: a role-based AI literacy course for customer-facing teams
- Target audience: teachers, instructional designers, enablement teams, course creators
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: LMS, rubric builder, quiz bank, content library
- Constraints: align to learning objectives; avoid answer-only tutoring; support accessibility
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- learner profile
- skills rubric
- course outline
- assessment results

# Known risks to control
- cognitive overload
- unmeasurable objectives
- biased examples
- shallow assessment

# Method
1. Tie each variant to a mechanism
2. Choose one primary metric
3. Define guardrail metrics
4. Specify how to interpret inconclusive results

# Required output sections
1. Hypothesis
2. Target segment
3. Variants
4. Instrumentation
5. Risks
6. Decision rule

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artefacts

1 artefacts

Example Output: Education Learning Experiment Launch Plan

Inputs used

Project context: a role-based AI literacy course for customer-facing teams
Target audience: teachers, instructional designers, enablement teams, course creators
Success metric: activation, quality, and risk reduction
Available tools and data: LMS, rubric builder, quiz bank, content library
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

hypothesis, audience, variants, instrumentation, and decision rule

Hypothesis

Hypothesis: improving how the workflow handles learner profile will reduce cognitive overload. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Target segment

Use skills rubric as evidence, apply the constraint "avoid answer-only tutoring", and explicitly note how the plan reduces unmeasurable objectives. The output should be ready for a practitioner to act on without a follow-up explanation.

Variants

Hypothesis: improving how the workflow handles course outline will reduce biased examples. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Instrumentation

Hypothesis: improving how the workflow handles assessment results will reduce shallow assessment. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Risks

Treat cognitive overload as a launch blocker until there is a control that can be verified. The minimum control is: avoid answer-only tutoring, plus reviewer sign-off for ambiguous outputs.

Decision rule

Hypothesis: improving how the workflow handles skills rubric will reduce unmeasurable objectives. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Recommended Decision

Proceed with a narrow pilot focused on learner profile and skills rubric. Treat cognitive overload as the primary launch blocker. The first milestone should prove that the workflow produces a usable lesson plan, practice activity, and assessment rubric with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.
It includes the required sections: Hypothesis, Target segment, Variants, Instrumentation, Risks, Decision rule.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: cognitive overload.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Education Learning: Experiment Launch Plan

Use this prompt when you need hypothesis, audience, variants, instrumentation, and decision rule for AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.

Best for

teachers, instructional designers, enablement teams, course creators
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.