AI can help teachers make climate data easier to explore, build interactive scenarios, tailor practice, and find learning resources. It is an optional aid—not evidence that students will learn more by default—and it should be used only with educator oversight, reliable climate sources, age-appropriate design, privacy protections, and attention to access and environmental costs.
Why use AI in climate change education?
Climate change education helps learners understand climate issues, consider how attitudes and actions relate to them, and take informed action. UNESCO describes it as a component of education for sustainable development (ESD). Its broader “greening education” approach also includes teacher preparation, learning environments, education systems, and communities—not just classroom lessons. UNESCO’s ESD overview explains that wider scope.
There is a clear need for effective teaching, but AI alone is not the answer. A UNESCO survey and focus groups summarized in 2023, drawing on around 17,500 young people in 166 countries and research conducted in 2022, found that 70% of respondents could not explain climate change, knew only its general principles, or knew nothing about it. This is a survey result, not a census of all young people. UNESCO’s account of the study describes its findings and scope.
The teaching goal should come first; a tool is useful only if it helps learners reach it. UNESCO’s 2024 curriculum guidance calls for climate learning that is scientifically accurate, age-aware, action-oriented, holistic, justice-driven, and lifelong. Its Greening curriculum guidance sets out learning outcomes from age 5 through adult learning.
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What can AI help teachers and learners do?
The UNFCCC Technology Executive Committee describes possible AI applications for making climate information more accessible, engaging, and actionable. These are uses to evaluate, not proof that AI improves learning outcomes. Also distinguish AI-powered tools from conventional digital activities: a simulation or virtual-reality experience is not necessarily AI-driven. The committee’s technical paper discusses these applications and the limits of the evidence.
| Potential use | Classroom example | What the educator should check |
|---|---|---|
| Explore data and scenarios | Have learners compare climate data visualizations or examine how different assumptions shape a scenario. | Check the underlying data, dates, geographic scale, assumptions, and whether the display distinguishes observed change from projections or scenarios. |
| Interactive simulations | Use an AI-driven simulation or immersive activity to explore a climate process or decision. | Establish what the activity actually models and what it leaves out. Do not treat a vivid visualization as a prediction or conflate non-AI VR with AI. |
| Adaptive practice and feedback | Use a tutor to offer another explanation, adjust practice, or give learners feedback on their understanding. | Review explanations and feedback for accuracy, suitability for the learner’s age, and alignment with the lesson’s learning outcomes. |
| Find and organize learning materials | Ask a system to locate or recommend resources on a climate topic, then select appropriate materials. | Open and assess each resource, including its author, evidence, date, relevance, and perspective; a recommendation is not a fact-check. |
| Support lesson preparation and assessment | Draft discussion prompts, activities, or formative questions for teacher review. | Edit the draft for misconceptions, local context, accessibility, and age suitability before using it with learners. |
Personalization, rapid feedback, or an engaging interface may be convenient, but none guarantees that a learner understands climate science or will change behavior. Evidence about a particular tool and a particular learning outcome matters more than a general claim that a tool is “AI-powered.”
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How should a teacher use AI without outsourcing judgment?
- Set the learning outcome first. Decide what learners should understand or be able to do, and choose an age-appropriate activity. Use UNESCO’s curriculum guidance to connect a tool to climate-learning outcomes rather than letting the tool set the lesson’s aims.
- Limit the task. Use AI for a defined job—such as generating alternative explanations, organizing a data activity, or drafting questions—not as the authority on climate science.
- Verify factual claims. Check claims against authoritative climate sources. Confirm dates, units, location, and context; distinguish observations from projections, scenarios, and policy choices. Replace or correct plausible but unsupported output before sharing it.
- Review the teaching material. Check for misconceptions, sensationalized risk, missing perspectives, inappropriate difficulty, and inaccessible language or formats. Add transparent sources so learners can see where claims come from.
- Protect learners and their data. Follow applicable law and school or institutional policy. Do not enter identifiable or sensitive student information into a system unless its use is permitted and the privacy protections are suitable.
- Assess the learning, not the novelty. Use discussion, learner explanations, or other appropriate evidence to see whether the activity met its stated goal. Revise or discontinue it if it distracts from learning or introduces errors.
UNESCO’s guidance on generative AI emphasizes human-centered use, privacy, age appropriateness, and pedagogical design. It calls for age limits on independent conversations with generative AI, but educators must apply local rules and institutional requirements; global guidance is not a substitute for jurisdiction-specific policy. See UNESCO’s guidance for generative AI in education and research.
What are the main risks?
Confident errors and weak sourcing
Generative AI can produce plausible but incorrect language. Errors may be especially hard to spot when an explanation sounds fluent or a visualization looks authoritative. Treat generated text and recommended materials as drafts. Trace important claims to their original sources, and explain uncertainty rather than presenting a scenario as a forecast. The UNFCCC technical paper discusses these reliability concerns.
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Privacy and age suitability
Some AI services collect or process prompts and other user information. Before using one with students, find out what information it handles, how it is retained or used, and what controls apply. Avoid unnecessary personal data, check age restrictions, and follow local law and school policy. UNESCO’s generative-AI guidance provides global principles, not a replacement for those requirements.
Unequal access
Students and schools do not all have dependable devices, connectivity, language support, or computing resources. An AI-based activity can widen gaps if participation depends on access that some learners lack. Plan an equivalent low-tech or offline route, and check that materials work for learners with different accessibility needs.
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Misleading or harmful climate communication
Personalized messages and vivid visuals can draw attention, but they can also sensationalize local risks, obscure uncertainty, or leave out who is affected and why. Review content for scientific accuracy and justice, and give local claims the context and sourcing needed to interpret them responsibly.
Environmental demands
AI systems consume electricity, and some data-center operations use water. UNESCO’s Green Digital Transformation page, accessed 7 October 2026, displays an estimate of 4.2–6.6 billion cubic metres of AI-related water use per year by 2027 and an estimate of approximately 50 GWh of electricity to train one large language model. These are estimates displayed by UNESCO, not universal measurements for every model, query, or training run; resource use depends on systems and assumptions.
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UNESCO also reports potential reductions for particular approaches: shorter prompts can reduce energy use by over 50%; quantization by up to 44% without loss of accuracy in the described work; and suitable smaller, task-specific models by up to 90%. These are reported results for described approaches, not guaranteed savings for every tool or task. Where possible, choose an appropriately small system, avoid unnecessary repeated queries, and look for transparent reporting on energy, emissions, and water.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can schools evaluate an AI learning tool?
Evaluate the specific tool for the intended learners and outcome, rather than relying on a general claim about AI. Ask:
- Accuracy: Can educators check claims against trustworthy, traceable climate sources?
- Learning fit: Does the activity support a defined outcome and suit the learners’ age and context?
- Teacher control: Can a teacher review, correct, adapt, or reject the output?
- Privacy: Are data practices, age restrictions, and relevant institutional requirements acceptable?
- Accessibility and equity: Can learners use it across relevant languages, devices, connectivity conditions, and accessibility needs?
- Evidence: Is there evidence for the particular learning outcome—not merely evidence that the feature exists or attracts attention?
- Resource use: Is the tool’s environmental footprint transparent, and is AI necessary for this task?
These checks reflect the concerns raised in UNESCO’s climate and generative-AI guidance and the UNFCCC technical paper. They help distinguish a tool that serves the curriculum from one that adds complexity without a clear educational purpose.
What does wider adoption of climate education mean?
The Greening Education Partnership’s objective is for 90% of countries to include climate change in curricula by 2030. This is a stated target, not a reported achievement. It points to the scale of the curriculum task, but inclusion alone does not establish that teaching is accurate, effective, or available equitably. UNESCO’s guidance treats climate learning as part of a wider education effort, which is why an AI tool should complement curriculum planning and educator judgment rather than stand in for either.
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