Building an AI Use Policy for Your School or Training Program

A usable AI policy does not start from a blank page or a generic template. It starts from a simple structural idea that UNESCO’s own guidance uses: not all AI use in a school carries the same level of risk, so a policy should not treat it as if it does. Administrative AI, teacher-support AI, and student-facing AI each need different rules, different oversight, and different levels of caution, and conflating them is the most common reason school AI policies end up either too vague to use or too restrictive to survive contact with an actual classroom.

Start with UNESCO’s three-tier structure

UNESCO’s Guidance for Generative AI in Education and Research groups AI applications into three confidence tiers, and building a policy around this structure, rather than one flat set of rules, is the single most useful starting decision a school can make.

Tier	Examples	UNESCO’s confidence level
Administrator-facing	Scheduling, predictive analytics, language access tools	Highest current confidence
Teacher-supporting	Lesson planning assistance, formative assessment help, grading time-savings	Medium-high confidence
Student-facing generative AI	Students directly using chatbots or generative tools for coursework	Lowest confidence; UNESCO explicitly flags this as too new for stable, settled policy

Practically, this means your policy should be least restrictive and most settled for administrative tools, moderately structured for teacher use, and most cautious and most subject to revision for student-facing tools. A single blanket rule across all three (either “AI is banned” or “AI is allowed”) ignores a distinction UNESCO itself considers foundational.

The four principles underneath the tiers

Beneath the three tiers, the OECD’s AI Principles supply the operating rules that should govern any AI use in any tier: human-in-the-loop on consequential decisions, transparency to the people the AI affects (students, parents, staff), explainability of what the AI actually did, and clear accountability for whoever deployed it. These four principles are what should actually appear as concrete policy language, tier by tier, rather than staying abstract.

For example, applied to a teacher-supporting grading tool: human-in-the-loop means the teacher, not the tool, assigns the final grade. Transparency means students and parents know AI assisted the process. Explainability means the teacher can say what the tool flagged and why. Accountability means the school, not the vendor, owns the outcome.

Why this is becoming a legal requirement, not just good practice

This is no longer purely a best-practice recommendation in every jurisdiction. North Carolina’s 2026 state law, for instance, requires the state Department of Public Instruction to adopt a model AI policy by the end of 2026, and requires every local school district, charter school, and affiliated school system to adopt their own policy based on it by mid-2027. The EU AI Act goes further for systems used in assessment and admissions specifically, classifying them as high-risk and requiring formal risk management, data governance, human oversight, and conformity assessment before deployment, with phased enforcement running through 2026-2027. Even where no law yet mandates a policy, this direction is clearly where the regulatory environment is heading, which makes building one proactively, on a sound structure, more useful than waiting to be required to.

What a usable policy document actually contains

Real school AI policies that are actually in use, rather than aspirational documents, tend to share a consistent structure: a stated purpose, a defined scope (which staff, students, and visitors it covers), the legal framework it operates under, clear definitions of what counts as AI use, defined roles and responsibilities for oversight, a section on appropriate use, a section on misuse and its consequences, an implementation plan, and a scheduled review mechanism, typically annual, given how quickly the underlying tools and regulations are changing.

That review cadence matters as much as the initial content. A policy written once and left untouched will be describing tools and rules that no longer match reality within a year, given the pace covered in our explainer on AI agents and elsewhere on this site.

Before approving any specific tool under the policy

A policy sets the rules; it does not evaluate individual tools against them. For that step, use a structured process rather than a vendor’s own claims, our framework for evaluating an AI tool covers exactly the data-handling and oversight questions the OECD principles above require you to be able to answer before approving a tool for classroom use. A sound policy also depends on a literacy component; see AI literacy: what it actually means for the skills a policy alone cannot teach. For a live 2026 case of what happens when assessment rules do not match AI use, see the Brown University AI cheating case.

Key takeaways

• Structure your policy around UNESCO’s three confidence tiers, administrator, teacher-support, and student-facing, rather than one flat rule for all AI use.
• Apply the OECD’s four principles, human-in-the-loop, transparency, explainability, accountability, as concrete language within each tier, not as abstract values.
• This is increasingly a legal requirement, not just best practice; North Carolina and the EU AI Act are early examples of a broader regulatory direction.
• Build in an annual review cycle; a static policy will fall out of date faster than the tools it governs.

Sources: UNESCO, Guidance for Generative AI in Education and Research (2023, updated 2024); OECD AI Principles (2019, updated 2024); EU AI Act (2024); North Carolina General Statutes §115C-102.13 (2026).