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Schools do not have to choose between allowing every use of AI and banning it outright. Better guidance starts with the learning purpose, sets clear rules for each activity, checks privacy and access, and keeps educators responsible for human decisions. That approach also leaves room to revise policy as evidence develops: there is not yet a shared standard for judging AI’s impact on learning.
What does a general ban leave unresolved?
A blanket prohibition can make a rule sound simple, but it does not tell students or teachers what counts as an acceptable use in a particular class or assignment. It also does not resolve questions about student access, age restrictions, privacy, security, academic integrity, or whether a tool serves a learning goal.
TeachAI’s school guidance recommends carefully evaluating student access rather than defaulting to general bans. It also cautions that restricting school access while some students can use tools elsewhere may widen an existing digital divide. This is guidance, not a universal legal requirement; local rules and circumstances matter.
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The practical question is not simply “Is AI allowed?” It is: How does our guidance highlight the purposeful use of AI to achieve our shared education vision and goals? TeachAI presents that question as a prompt for schools to examine whether their rules support their own educational aims.
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How should a school set purpose-based AI guidance?
Start with the task and the people affected, not with a tool’s features or default settings. TeachAI says decisions should reflect local values and legal frameworks, and should not be driven by vendor defaults alone.
- Name the purpose. Specify whether the proposed use is intended to support learning, student or staff well-being, or an administrative need. If the purpose is unclear, pause before adopting the tool or allowing the use.
- Define what learning the activity is meant to show. Decide whether students are practicing a skill independently, using AI as a support, evaluating AI output, or completing another kind of task. The answer affects what assistance is appropriate.
- Set rules for the activity. State whether AI is permitted, limited, or prohibited for that assignment, and explain what students must disclose or acknowledge. Apply school-wide rules where necessary, but give teachers room to clarify expectations for different tasks.
- Review the conditions of use. Check relevant age restrictions, terms of use, privacy and security practices, consent requirements, and fit with the curriculum and learning goals. TeachAI recommends reviewing the tool’s current user agreement and getting local legal review where appropriate; its guidance is not jurisdiction-independent legal advice.
- Decide how people will stay accountable. Specify what educators will review, what students are responsible for, and which decisions must remain with a human. Do not treat a tool’s output as a substitute for educator judgment.
- Revisit the guidance. Make room to revise rules when tools, local requirements, or the school’s experience change. A policy should describe how it will be reviewed, not imply that one decision settles every future use.
What safeguards should schools review?
Use a consistent set of questions for any proposed classroom use. The answers will depend on the students, activity, tool, and local requirements.
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- Educational purpose: What goal does this use serve, and how will it fit the curriculum and the work students need to do?
- Age and consent: Are students old enough to use the tool under its current terms? Are consent requirements relevant to this use?
- Privacy and security: What information does the tool receive, and what protections apply? Set a clear school rule about what student information may be entered.
- Access and equity: Who can use the tool, under what conditions, and with what support? Consider whether school restrictions leave some students with less access than others.
- Accuracy, bias, and transparency: How will users check outputs, recognize uncertainty, and question possible bias? Make clear when an answer needs verification rather than acceptance.
- Academic integrity and attribution: What assistance is allowed, and how should students disclose or acknowledge it? Explain how expectations differ by assignment rather than relying on an undefined rule against “cheating.”
- Human decision-making: Which judgments belong to educators or students, and how will they be protected from being handed over to a tool?
These are policy considerations, not a substitute for checking applicable law or the tool’s current terms. TeachAI specifically advises schools to evaluate age restrictions, privacy, security, and consent in their local context.
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A school policy works only if students can tell what it means when they sit down to do the work. For each assignment, describe the permitted role of AI and the student’s responsibility for the final work. A simple three-part rule can help:
- Permitted: Identify specific uses students may make of AI, such as a use the teacher has explicitly approved for the task.
- Not permitted: Identify uses that would replace the learning the assignment is intended to assess.
- Disclosure: Say what students must report about any permitted use, in what form, and where to include it.
For example, an assignment might permit AI-assisted brainstorming but require students to write and support their own final analysis. Another task might prohibit AI assistance because the teacher needs to see each student’s unaided practice. These are examples to adapt, not universal rules. Students should not have to guess whether a particular use is acceptable.
TeachAI’s discussion prompts ask whether guidance sufficiently covers academic integrity, plagiarism, and proper attribution. Address those directly: define what your school or class means by unauthorized assistance, explain how attribution works for the activities students are doing, and distinguish a disclosed, permitted use from a prohibited one.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can schools discuss uncertainty and gray areas?
Rules are easier to apply when students and educators can talk through cases that do not fit neatly into “allowed” or “not allowed.” The Center for Digital Thriving at Harvard Graduate School of Education offers free classroom practices for grounding discussion in values, surfacing hidden use and pressures, mapping gray areas, reflecting on AI experiences, and building shared language.
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That conversation matters because uncertainty is not only about whether a response is correct. In its summary of the Futures Dialogue held on 22 October 2025, UNESCO reports concerns raised about opaque systems, possible cognitive dependency, and tools that may mirror a user’s expectations while giving inaccurate or misleading responses. These are concerns described in the dialogue, not quantified findings about how often those outcomes occur.
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The UNESCO summary also attributes this warning to Wayne Holmes: “Until we know, we’re effectively experimenting on children.” Treat it as a caution from a dialogue participant about the need to understand long-term consequences, not as a measured estimate of harm. The same dialogue describes AI literacy as more than operating a tool: it includes social and human dimensions such as empowerment and child and human rights, alongside technical proficiency.
How should schools judge whether their approach is working?
Schools can check whether guidance is understandable and usable without claiming that a local review proves AI improves learning. UNESCO’s account of the Futures Dialogue says there is no shared framework or consensus on what counts as valid independent evidence of AI’s impact in learning environments. That means broad claims of educational effectiveness should be treated as unsettled.
A local review can still ask practical questions: Can students explain what is permitted on a given task? Do teachers and students understand disclosure expectations? Are access and safeguards being handled as intended? Are educators retaining responsibility for decisions? Use feedback and documented experience to identify where guidance is unclear, then revise it. Keep these implementation checks distinct from evidence that AI caused a change in learning.
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The Center for Digital Thriving at Harvard Graduate School of Education says its report draws on a nationally representative survey of more than 1,000 U.S. public school teachers and principals, along with interviews with educators and 31 young people. Its page reports that, among teachers and principals who had experienced a dilemma related to AI, 74% of teachers and 69% of principals described something cheating-related. The accessed page does not display the report’s publication year, so these figures should not be treated as a dated trend.
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