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AI is already being used in education to adapt practice, answer questions, help teachers prepare lessons, support accessibility, and handle some administrative work. These applications can make learning and teaching more responsive, but their presence does not prove that they improve learning outcomes. Whether they help depends on the task, the quality of the tool, appropriate data practices, and people’s ability to check and act on its output.
How is AI changing education?
AI is being applied across instruction, student support, assessment, accessibility, and school operations. The examples below are practical uses, not a ranking or a claim that each one is equally mature. OECD cautions that the effectiveness of many AI-enabled tools—and whether they improve learning or reduce educator workload—is not always well established. OECD Digital Education Outlook 2023
10 ways AI is being used in education
1. Adaptive practice can respond to a learner’s progress
Adaptive learning tools can track student responses and help identify topics where a learner may need more practice or is ready to move ahead. Personalization is a capability, not a guaranteed outcome: it depends on sound instructional design, useful data, and teacher oversight. A system’s recommendation should inform instruction rather than dictate it.
2. AI can offer tutoring-style explanations
Generative AI can respond to a student’s question with a customized explanation or another example, potentially supporting independent study. Its answers may be inaccurate, irrelevant, or poorly matched to a learner’s age and needs. Students should be taught to question and verify responses, and educators should guide use where accuracy or understanding matters.
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3. Teachers can use it to draft lessons and curriculum materials
Generative AI can help produce a first draft of a lesson plan, activity, or curriculum material. A teacher still needs to check the facts, learning objectives, accessibility, cultural fit, and suitability for the class. Generated material is a starting point for professional judgment, not an automatically validated lesson.
4. Learning analytics can inform feedback and instruction
AI systems may monitor progress, while classroom analytics can give teachers information about classroom activity or a lesson. Such information may help educators decide what to revisit or how to adjust instruction. A dashboard does not establish why a learner is struggling, nor does collecting analytics by itself improve teaching.
5. Speech-to-text and captions can support accessibility
Speech recognition and automatic captions may help learners with hearing or visual impairments participate in learning activities. Their usefulness depends on accuracy, accessibility, and how well they work for the learner and setting. Schools should test these supports with the people who rely on them rather than assume that an available feature is an effective accommodation.
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6. Some tools may help identify learning needs earlier
Patterns in student work or progress data may help educators notice that a learner could need additional support. That is not a diagnosis. OECD describes follow-up as involving both technology and human intervention; qualified people must interpret signals, consider context, and decide what support is appropriate.
7. Early-warning systems can flag students who may need support
In education systems with advanced information infrastructure, AI-powered tools may flag students at risk of leaving school so staff can plan interventions. A flag is a prompt to offer support, not an objective verdict about a student. If it is treated as a label or used to restrict opportunity, it can stigmatize or penalize the very learner it was intended to help.
8. Simulations can provide a setting for vocational practice
Simulators and virtual or augmented reality can give vocational learners a way to practice skills in a safe environment modeled on workplace situations. They may complement practical instruction, but the cited guidance does not establish them as replacements for hands-on training.
9. Administrative tools may change teachers’ workload
Some AI tools may help with administrative tasks or return feedback, potentially leaving teachers more time for instructional design. They can also create new work: checking outputs, learning a system, correcting errors, or managing data. Schools should involve teachers in adoption and assess the effect on workload rather than assume that automation saves time.
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10. AI makes verification and assessment more important
When students can obtain fluent answers from generative AI, educators face new questions about how learning is demonstrated and assessed. OECD suggests using AI to prompt questions, navigate ambiguity, and distinguish fact from opinion. Teaching source evaluation and verification helps students use these systems critically; assessments should make expectations for AI use clear and preserve opportunities to demonstrate understanding.
What are the benefits and risks of AI in schools?
Potential benefits include more responsive practice, on-demand explanations, accessible learning supports, and information that may help educators plan support. None is automatic. A tool may be unsuitable for a particular subject, learner, or classroom, and use alone is not evidence of better grades or deeper learning.
UNESCO’s guidance calls for “a human-agent and age-appropriate approach to the ethical validation and pedagogical design processes.” It identifies concerns that include privacy and the need for age-appropriate use. OECD and Education International also discuss risks including reliability, bias, unequal access, security, and cultural representation. UNESCO, Guidance for generative AI in education and research and OECD and Education International, Opportunities, guidelines and guardrails for effective and equitable use of AI in education
These issues matter in ordinary classroom decisions and become especially consequential when tools handle children’s data, inform assessment, or influence access to support. Predictive systems can reflect or amplify bias; staff should examine what a flag means, what information produced it, and whether the response is fair. Human review is essential for feedback and any high-stakes decision about a student.
How widely are schools using AI, and what does the evidence say?
Adoption figures describe particular places and periods; they are not a current global measure. A 2025 ERIC record summarizing a RAND report says that 25% of surveyed U.S. teachers used AI tools for instructional planning or teaching during the 2023–24 school year, while nearly 60% of surveyed U.S. principals reported using AI tools for their work. These figures concern different roles and should not be read as evidence of impact. ERIC record: Uneven Adoption of Artificial Intelligence Tools among U.S. Teachers and Principals in the 2023–2024 School Year
In OECD’s 2023 Digital Education Outlook, around 20% of secondary education teachers across the OECD reported needing further training in digital education, while 60% had undergone digital-education training in the prior year. These are not AI-specific training figures. The same Outlook reported that, among 18 jurisdictions with comparative information, none had issued an education-specific regulation on generative AI and nine had published non-binding guidance. This is a 2023 snapshot, not a current count of global policy. OECD Digital Education Outlook 2023
For context on later policy documents, OECD’s Education Policy Outlook 2024 includes an inventory identified through research conducted between May and October 2024. That inventory has a different scope and collection period from the 2023 jurisdiction snapshot. OECD, Education Policy Outlook 2024
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a school evaluate an AI tool?
Start with the learning or operational problem, not the novelty of the technology. Before adopting a tool, educators and school leaders can ask:
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- Evidence: What outcome is claimed, and is there evidence for that outcome in a comparable setting?
- Age and accessibility: Is it suitable for the learners who will use it, including students who need accessibility supports?
- Privacy and security: What student or staff data does it collect, how is that data handled, and who can access it?
- Quality and bias: Can users check the basis for an answer or recommendation, and might its errors or assumptions disadvantage some learners?
- Teacher control and workload: Can educators review and adapt its output, and does using it reduce work or add checking and administration?
- Practical fit: Does it align with curriculum and assessment, and are cost, connectivity, and human support adequate?
- Results: How will the school assess learning, equity, and workload after adoption, and what would prompt it to change or stop using the tool?
UNESCO Director-General Audrey Azoulay said, “Generative AI can be a tremendous opportunity for human development, but it can also cause harm and prejudice.” The balance depends on how schools design and govern its use: AI can assist teachers and learners, but responsibility for instruction, relationships, and consequential decisions remains with people. UNESCO’s guidance for generative AI in education and research
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