Privacy Should Be Part of the Conversation About AI in Schools

When a student types a question into an AI tool, the information shared may be more than the words needed to get an answer. A prompt could include a name, a personal concern, a draft containing identifying details, or a description of a learning difficulty. Even without intending to disclose private information, a student may reveal something about their interests, circumstances, or schoolwork. That possibility makes privacy part of the educational question, not a separate technical concern. Schools should consider not only whether an AI tool produces useful responses, but also what information students are asked to provide, who is responsible for protecting it, and whether the tool is suitable for the students using it.

The Children’s Online Privacy Protection Act, or COPPA, illustrates why age matters. The Federal Trade Commission explains that COPPA gives parents control over information websites can collect from children and that its rule provides additional protections. The FTC also notes that the law can apply to websites aimed at general audiences, not only sites designed for children. This matters in a school setting because a service’s broad intended audience does not, by itself, settle whether children’s privacy is relevant. Age changes the responsibilities surrounding information: younger users may need protections and adult oversight that cannot simply be assumed from a tool’s general availability. [1]

The FTC’s description also helps distinguish a legal baseline from a complete school policy. COPPA concerns covered websites and the collection of children’s information; it does not answer every question about how a school should use AI with students of different ages. A classroom tool might be used for a teacher’s demonstration, for a student’s independent research, or to help provide feedback on writing. Those settings involve different users, purposes, and amounts of information. The practical implication is not that schools should treat every AI interaction as prohibited, but that they should identify who is using a service and why before deciding what safeguards, guidance, or adult involvement are appropriate.

Context matters because the same piece of information can carry different risks depending on its purpose and audience. A general question about a science concept is different from a prompt that includes a student’s name and a personal account of a family or health concern. The second prompt connects learning to identifiable, sensitive circumstances, so a school should have stronger reasons for allowing that information to be entered and clearer rules for handling it. Students also need practical instruction: they can learn to remove names and other identifying details, ask whether a prompt needs personal information, and follow school rules about which tools are approved. Privacy becomes more meaningful when students can apply it to ordinary choices.

A useful approach to those choices is risk management rather than either automatic acceptance or blanket rejection. The National Institute of Standards and Technology describes its AI Risk Management Framework as a voluntary resource intended to help manage risks to individuals, organizations, and society. It is meant to help incorporate trustworthiness considerations into the design, development, use, and evaluation of AI systems. For schools, the framework’s value is its process-oriented perspective: consideration of risk should continue across a tool’s use, rather than stop once a school has chosen it. That supports asking what a tool is used for, what information the task requires, and whether its use remains appropriate. [2]

There is a real limitation to relying on such a framework: NIST says the AI RMF is voluntary. A framework can organize questions, but its existence alone does not require a school or a company to follow particular practices. Nor does a general risk-management approach settle how a specific service handles a particular student’s information. This is a serious reason not to treat guidance as proof of safety. However, it does not make structured review pointless. Schools can use a framework to ask consistent questions and then make concrete local decisions, such as setting limits on student use, providing age-appropriate instructions, and requiring staff to review whether the tool fits the task.

The competing concern is that strict privacy rules could make useful educational tools harder to use. AI may help students explore ideas, get explanations, or revise work, and an approach that blocks every tool could sacrifice those benefits. But usefulness and privacy are not opposites. A school can distinguish between a low-risk activity that needs no personal details and an activity that would require a student to disclose sensitive information. It can also teach students to use generic examples instead of identifiable personal stories and make clear when a teacher’s approval is needed. These measures preserve room for learning while reducing unnecessary disclosure.

Ultimately, AI privacy in schools is not only a question of what a technology can do; it is a question of what students should be asked to share, given their age and the purpose of the activity. COPPA shows that children’s information receives particular attention and that the audience a website claims to serve does not end the inquiry. NIST’s voluntary framework offers a way to think systematically about risk, while its voluntary status reminds schools that guidance must be translated into decisions. Schools should explain approved uses, teach students to minimize identifying details, and review tools in context. That balance makes privacy part of responsible learning rather than an obstacle to it.

How the reasoning works

The essay defines the privacy problem through concrete examples of what a prompt may reveal, then uses COPPA to explain why a child’s age and a website’s audience matter. It does not treat a law about covered websites as a complete policy for every classroom use. Instead, it distinguishes uses by purpose and sensitivity, applies NIST’s risk-management approach, and acknowledges that the framework is voluntary. The counterargument about losing educational benefits is answered with a narrower proposal: preserve appropriate uses while limiting unnecessary disclosure.