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Protect student privacy by collecting only the climate data the project needs, removing identifying details before any AI tool receives it, and using a school-approved service reviewed for the students’ ages and the project’s purpose. Student observations can become identifiable when combined with names, account details, faces, precise locations, or schedules. A teacher should oversee tool use and review AI outputs before they inform conclusions or are shared.
1. Decide what the project needs to know
Start with the learning question, then list the evidence needed to answer it. A project studying shade, temperature, rainfall, or tree cover around a school may need measurements and broad locations—not students’ names, personal accounts, exact home addresses, household energy bills, or personal schedules. This is an application of data-minimization principles, not a climate-project checklist prescribed by a regulator.
Ask whether AI is necessary for the task. If students can organize readings in a spreadsheet or discuss patterns without sending data to an AI service, keeping the data within the school’s existing tools may reduce exposure.
2. Prepare data before using AI
Remove or generalize identifying details before pasting text or uploading files. Prefer aggregate measurements, fictional examples, or broad location descriptions when those will serve the learning goal.
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- Remove names, student IDs, contact details, faces, and other direct identifiers.
- Reduce location precision; use a schoolyard zone or neighborhood rather than an exact address or precise coordinates.
- Check captions, filenames, document properties, and image metadata for identifiers.
- Do not upload raw student records or sensitive observations to an unreviewed public AI service.
These steps apply established school-data privacy guidance to climate-project materials; they are not a guarantee that data is anonymous. Information that appears harmless on its own can still identify a student when combined with other details.
3. Have the school vet the specific AI service
Ask the district privacy or technology lead to review the actual service and account configuration—not just the product’s name. UNESCO’s 2023 guidance for generative AI in education and research calls for institutional validation, privacy protection, age-appropriate use, and human-centred design. UNICEF’s August 2024 school data-protection guidance also treats educational technology as a governance and cybersecurity issue.
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- Is the service approved by the school or district for this purpose and for the students’ ages?
- What prompts, files, and account information does it collect?
- How long are inputs retained, and may they be used to improve models or for other purposes?
- Who can access the information, including subcontractors?
- What security, deletion, and incident-response processes apply?
- Do accessibility and educational-suitability needs fit the planned activity?
- Does a school agreement cover this use and the information involved?
The reviewed guidance does not establish current terms or settings for any particular vendor. The school must check the provider’s applicable terms and configuration before use.
4. Set student rules and keep an adult in the loop
Before the activity, explain why the class is using AI, what information must not be entered, and how students can ask for help. Do not require children to create independent accounts if school guidance does not allow it. UNICEF’s December 2025 version 3.0 of its Guidance on AI and children includes children’s data privacy among its child-centred requirements and identifies AI’s environmental impacts as a child-rights concern.
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A teacher should check AI-generated summaries, classifications, or suggestions before they shape the class’s conclusions or are published. Treat an AI response as material to review, not as verified evidence about local climate conditions.
5. Plan access, sharing, and deletion
Decide before collecting data where project files and AI outputs will be stored, who may access them, whether work will be shown outside the class, and when files should be deleted. Share aggregate findings when they answer the project question; avoid publishing identifiable student work or observations unnecessarily. UNESCO IITE’s 2020 handbook on privacy in online learning offers classroom routines for before, during, and after learning, although it predates the current generative-AI context.
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If an outside provider will handle identifiable education records, the school should review the legal basis for sharing and the written terms with the provider before sending the information.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Check the rules that apply where the school is located
There is no single consent rule that can be applied to every student climate project or AI prompt. In the United States, the Department of Education’s FERPA guidance describes a general rule against disclosing personally identifiable information from education records without written consent, alongside defined exceptions. Whether an exception applies depends on the circumstances and parties; some service-provider arrangements have conditions and may require written agreements. FERPA does not mean every AI use automatically requires parental consent, nor does it give every vendor permission to receive student data.
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UNICEF’s school data-protection publication is framed around GDPR and EU accession contexts, though UNICEF says it may be useful elsewhere. Other jurisdictions can have different rules on children’s ages, consent, data protection, and school procurement. Ask the district privacy officer or the relevant local authority to confirm the applicable requirements.
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