@mila-thompsoneducation-learning-risk-governance-reviewTextPublicUpdated Jun 14, 2026

Education Learning prompt that reviews a workflow for operational, privacy, and safety risk and returns risk register, severity ranking, controls, and verification checklist.

AI Workflow Assessment Education Learning Training

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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
Review a workflow for operational, privacy, and safety risk 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. Trace data and permissions
2. Rank risk by likelihood and impact
3. Map every high risk to a concrete control
4. Identify who must approve residual risk

# Required output sections
1. System boundary
2. Data sensitivity
3. Risk register
4. Controls
5. Residual risk
6. Verification checklist

# 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.

Artifacts

1 artifacts

Example Output: Education Learning Risk Governance Review

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

risk register, severity ranking, controls, and verification checklist

System boundary

Use learner profile as evidence, apply the constraint "align to learning objectives", and explicitly note how the plan reduces cognitive overload. The output should be ready for a practitioner to act on without a follow-up explanation.

Data sensitivity

Treat unmeasurable objectives 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.

Risk register

Treat biased examples as a launch blocker until there is a control that can be verified. The minimum control is: support accessibility, plus reviewer sign-off for ambiguous outputs.

Controls

Treat shallow assessment as a launch blocker until there is a control that can be verified. The minimum control is: align to learning objectives, plus reviewer sign-off for ambiguous outputs.

Residual risk

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.

Verification checklist

Use skills rubric as evidence, apply the constraint "support accessibility", 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.

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: System boundary, Data sensitivity, Risk register, Controls, Residual risk, Verification checklist.
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: Risk Governance Review

Use this prompt when you need risk register, severity ranking, controls, and verification checklist 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.