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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesSet AI boundaries by specifying who may use which approved tool for what task, what information may be entered, when AI use must be disclosed, and who must check or own the result. Use stricter controls when an output could affect learning, employment, rights, or access to an opportunity. A useful policy permits low-risk applications, routes uncertain ones for approval, and prohibits uses that cannot be made safe or accountable.
Start with the use case, not a blanket ban or blanket permission
“AI use” covers very different activities: a student asking for feedback on a draft, an employee using a tool to organize public information, and a system influencing a grade or hiring decision do not carry the same risks. A workable rule evaluates the particular task and its consequences rather than treating every tool or use as equivalent.
For each proposed use, ask:
- Purpose: What task will the system perform, and what benefit is expected?
- Input: What information will the user provide, and how sensitive is it?
- Consequence: What happens if the output is wrong, biased, incomplete, or misunderstood?
- Reversibility: Can someone correct the result or appeal a decision based on it?
- Human review: Who checks the output, and does that person have authority to reject it?
- Notice: Who needs to know that AI contributed, and how should that contribution be described?
As consequences rise and reversibility falls, stronger safeguards are appropriate. A drafting aid may need ordinary data protections and a user who verifies the work. A tool that could influence grades, discipline, hiring, pay, performance evaluation, or access to services calls for more careful assessment, meaningful human oversight, notice, documentation, and a route to raise or appeal concerns. This is a proportionate policy approach, not a universal legal threshold.
Define three use tiers users can understand
Give people clear outcomes instead of relying on a vague instruction to “use AI responsibly.” Apply the tiers to named tasks and approved tools, and explain who can grant permission.
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| Tier | Policy meaning | Example of how to define it |
|---|---|---|
| Permitted | The use is approved under stated conditions. | A student may use an approved tool to brainstorm when the assignment permits it, but must complete and check the submitted work themselves. |
| Permission required | The use needs approval before it begins because the purpose, data, or consequences need review. | An employee asks a manager or designated reviewer before using an AI tool to analyze internal material. |
| Prohibited | The use is not allowed under the policy, such as entering protected information into an unapproved tool or delegating a consequential decision without required human review. | State the prohibited data or activity directly, rather than expecting users to infer it from a general warning. |
Examples should be specific to the organization and its approved tools. The table describes a policy design pattern, not a finding that any particular tool is suitable for these uses.
Choose where the rules belong
An organization can integrate AI rules into existing policies or publish a standalone AI policy. UNESCO’s 2021 AI and education: guidance for policy-makers discusses independent, integrated, and thematic approaches to education policy responses; it does not prescribe one choice for every institution.
| Approach | Can work well when | Watch for |
|---|---|---|
| Integrated rules | Existing academic-integrity, acceptable-use, privacy, or employment policies have clear owners and are easy to update. | AI-specific permissions, disclosure expectations, and responsibilities may be hard to find if scattered across documents. |
| Standalone AI policy | Users need one visible place to find approved tools, use tiers, review duties, and escalation routes. | A separate policy can conflict with established rules or be overlooked unless policy owners keep it aligned and explain how it interacts with them. |
Whichever approach is chosen, name the owner or review group, identify the systems and people covered, and make clear which existing privacy, academic, or employment rules continue to apply.
Write the policy around practical responsibilities
A policy should answer the questions a user faces before, during, and after using an AI system. Include the following elements in the policy itself or in a clearly linked companion guide.
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- Scope and ownership: Identify covered tools, users, settings, and the person or group responsible for keeping the rules current.
- Use tiers: List permitted, permission-required, and prohibited uses, with task-specific examples and an approval route.
- Approved tools and data: Identify tools cleared for defined purposes. Tell users which personal, student, employment, confidential, or otherwise protected information must not be entered into unapproved systems.
- Human responsibility: Require users to check facts, quality, appropriateness, and possible bias. For consequential uses, name the human decision-maker and explain that the person—not the tool—owns the decision.
- Disclosure and attribution: Specify when users must disclose AI assistance, to whom, and in what form. Make classroom expectations assignment-specific; at work, explain when material AI assistance in work or decisions must be identified.
- Fairness, accessibility, and rights: Consider who may be affected, whether the process creates access barriers or uneven impacts, and how affected people can raise concerns.
- Training and incidents: Provide examples of safe and unsafe use, a way to ask questions, and a route for reporting an error, privacy concern, or other incident.
Tool approval should depend on the intended task and review of data handling, privacy and security, contractual terms, age suitability where relevant, and fit for purpose. Approval for one use should not silently become approval for every use of the same tool.
For schools, connect permissions to learning and assessment
Schools should define AI permissions in terms students and families can understand, then make expectations concrete for each assignment. UNESCO’s Guidance for generative AI in education and research, published in 2023 and updated on its page in January 2026, advocates a human-centred approach and identifies privacy, human agency, inclusion, age-appropriate use, teacher and researcher capacity, and coherent policy frameworks as considerations. It is international policy guidance, not a classroom regulation that applies identically in every country.
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Explain what students may do for each assignment
Say whether AI is permitted for activities such as brainstorming or feedback, if those uses support the learning goal. Also say what work students must do themselves, what use must be disclosed, and how the teacher will evaluate learning. A general school rule can set the boundaries; assignment instructions should resolve what is allowed for that task.
Protect student information and match tools to age and purpose
Teachers and administrators should use tools approved for student data and assess whether a tool is age-appropriate and suited to its educational purpose. Do not ask students to put personal or otherwise protected information into an unapproved tool. Explain the relevant rule in language students and families can follow.
Make the evidence standard explicit
Do not treat an AI-detection result by itself as proof of misconduct. The guidance cited here does not establish detector reliability or a universal disciplinary standard. Schools should set evidence and review procedures through their responsible academic and policy processes, and follow applicable local requirements.
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- Construction Site Book offers quick access to essential OSHA regulations, jobsite hazards, and practical safety tips. It also helps employees identify hazards and prevent injuries and illnesses.
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For workplaces, involve workers and protect employment rights
The U.S. Department of Labor’s October 16, 2024 workplace AI best-practices announcement recommends meaningful human oversight for significant employment decisions, transparency with workers, worker input, protection of labor and employment rights, training, and worker-data security. These recommendations are guidance, not a comprehensive statement of employment law or a private-employer mandate.
Explain where AI is used and what workers can do
Tell workers which approved systems are used for relevant tasks, what kinds of information may be entered, when AI materially contributes to work or a decision, and where to ask questions or report a concern. Involve affected workers in policy development and review; provide practical training and examples of safe and unsafe prompts.
Keep people accountable for significant decisions
If AI may influence a person’s job, pay, evaluation, discipline, or access to an opportunity, identify the responsible human reviewer and what that reviewer must examine before acting. Oversight should be meaningful: the reviewer needs enough information and authority to question or reject an output, rather than merely forwarding it as a decision.
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The EEOC’s September 20, 2024 compliance plan concerns the agency’s own governance of AI under a federal memorandum. Its attention to reliability, bias, fairness, accountability, transparency, security, and privacy can illustrate governance dimensions, but the plan is not a private-employer rule.
Use risk-management guidance without mistaking it for law
NIST describes its AI Risk Management Framework as voluntary. It helps organizations organize risk management across AI design, development, use, and evaluation; it does not determine an organization’s legal duties. NIST’s trustworthiness characteristics include validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and fairness with harmful bias managed. Its Generative AI Profile was released on July 26, 2024, and NIST says the framework is being revised, so consult the latest NIST materials when using it.
UNESCO’s education guidance and the U.S. Department of Labor’s workplace recommendations are also resources for policy design, not universal legal requirements. Legal obligations depend on jurisdiction, the data, the use case, and the institution. Ask the responsible legal or policy team to check applicable privacy, education, employment, accessibility, records, and collective-bargaining requirements.
Train users, collect feedback, and revise the boundaries
Publish the rules where students, families, and workers can find them, then teach them with task-specific examples. A user should be able to determine whether a use is allowed, what data is safe to enter, whether disclosure is required, who checks the output, and where to seek approval or report a problem.
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