Protect privacy and reduce risk

Map four things: what enters the system, what the provider may retain, who can access output, and what action follows. For this topic the operative rule is: never submit names, grades, disability details, behavior records, private writing, or any identifiable student information to an unapproved system. “No secrets” is too vague; name prohibited fields and approved substitutes.

Classify the work by consequence. Low-risk ideation with synthetic data may need ordinary review. Internal drafts based on approved material need access and retention controls. Public claims, student decisions, deployments, impersonation, sensitive targeting, or automated external actions require a stricter gate and sometimes should not use the tool at all.

The scenario is design a differentiated fraction mini-lesson while keeping assessment judgment with the teacher. Ask:

• Do we have permission to process every input and license every asset?
• Could the output mislead someone about authorship, evidence, identity, or reality?
• Can untrusted text or media alter tool instructions?
• Is there a reversible draft stage before publication, sending, grading, or deployment?
• Can a person contest, correct, remove, or revoke the result?

Learning target: compare fractions with unlike denominators. Create a three-minute explanation using a pizza model, one worked example, four practice items from easy to challenging, and an answer key naming likely misconceptions. Use fictional learners only. Keep the target fixed and do not make grading or placement decisions.

The prompt can state boundaries, but prompts are not access control, consent records, or legal clearance. Configure minimum permissions, retention, sharing, and deletion in the surrounding system. Keep an incident route for accidental exposure and a kill switch for repeated workflows.

For the expected result—An age-appropriate explanation, a correctly solved example, practice that measures the same target, and a misconception note about comparing denominators directly—review privacy, security, bias, rights, and deception separately. Use provenance notes and disclosures where audiences could mistake synthetic media or generated claims for direct evidence. Preserve human ownership of consequential decisions.

Likely failures include wrong answer keys; difficulty changed by changing the target; accidental answer reveal in hints; biased examples; automated grading or discipline recommendations. Adapt the route to learning, not the destination. AI drafts examples and supports; the teacher remains responsible for accuracy, context, and consequential decisions. When local law, organizational policy, a contract, or platform rule is stricter than this lesson, the stricter rule wins.

Try one hostile or ambiguous input without using real sensitive information. Observe whether the model invents, follows embedded instructions, exceeds the schema, or proposes an irreversible action. A safe run should fail closed: return “unknown,” route to review, or stop.