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Schools and colleges are using AI to help with lesson planning, learning materials, administrative writing, differentiation, and some student learning activities. But the available reports do not verify a list of 21 separately named deployments: Ofsted interviewed 21 education leaders in England, while Estyn visited 21 schools in Wales. Those are research samples, not counts of deployments.
What the “21” refers to
The number appears in two official reports, but neither presents 21 distinct AI deployments. Ofsted’s 2025 work drew on 21 interviews with leaders from English schools, further education colleges, and multi-academy trusts that had used AI for at least 12 months. Estyn’s report describes visits to 21 Welsh schools in spring 2024. Both sources offer examples of practice, not a catalogue matching the original title’s count.
The reports are useful for understanding how AI is being tried in education, but their findings should be read within their limits: Ofsted focused on early adopters, and Estyn reported observations and staff accounts rather than results from a causal trial. Ofsted’s account of its interviews and Estyn’s report on its school visits describe the evidence behind those figures.
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Planning lessons and preparing materials
In Ofsted’s interviews, education leaders reported using AI to support lesson planning and create resources. Estyn also observed teachers using it for planning, resource creation, and differentiation. These are preparation tasks: the reports describe AI assisting staff, not replacing teachers’ professional responsibility for what is taught.
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Drafting routine communications and administration
Ofsted interviewees described using AI to draft communications to parents and handle administrative work. Estyn reported examples of AI-assisted drafting for letters and policies. These are writing-support uses; neither report establishes that AI-generated drafts can be sent or adopted without staff review.
Adapting support for learners
Estyn described uses in special schools and pupil referral units, including communication stories and bespoke literacy pathways. Teachers also reported using AI to differentiate learning materials. The report presents these as observed practices and examples, not as proof that AI itself caused better educational outcomes.
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Supporting student activity
Estyn observed pupils using AI in collaborative creative projects and for independent revision. The report also records concerns about overreliance, which makes the design of the activity important: an AI-supported task still needs a clear learning purpose and appropriate teacher oversight.
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Teachers interviewed by Estyn emphasized professional scrutiny when AI assists with assessment or feedback. The report does not support treating an AI-generated judgment as a substitute for an educator’s decision.
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What the documented examples show
The cases below differ in scope and evidence type. The interview and inspection findings are reported practices; Estonia’s reach figures are claims by OpenAI; and the Digital Promise awards fund research infrastructure rather than established classroom deployments.
| Example | Setting and activity | Scale and evidence |
|---|---|---|
| England, Ofsted report released 27 June 2025 | Schools, further education colleges, and multi-academy trusts; reported uses include planning, resource creation, parent communications, and administration. | 21 interviews with leaders at providers that had embedded and used AI for at least 12 months. This is an early-adopter interview study, not a national prevalence estimate. Ofsted source |
| Wales, Estyn report dated 9 October 2025 | Primary, secondary, special, and all-age schools; reported uses include planning, differentiation, report writing, learner activities, and administrative drafting. | Inspectors visited 21 schools in spring 2024. The report describes visits, conversations, staff survey evidence, and examples; it is not a national effectiveness trial. Estyn source |
| Estonia, OpenAI announcement dated 21 January 2026 | ChatGPT Edu was described by OpenAI as deployed across public universities and secondary schools nationwide. | OpenAI reported more than 30,000 students, educators, and researchers reached in the first year. The company also described a University of Tartu and Stanford research partnership involving 20,000 students over time. These are reported reach and study-scale figures, not evidence of learning gains. OpenAI announcement |
| United States, Digital Promise announcement dated 21 September 2026 | K–12 AI Infrastructure Program grants support work on formative assessment, shared datasets, and openly licensed outputs. | Digital Promise described the initiative as a multi-year $26 million program and announced eight organizations for six-to-twelve-month projects. These are research and infrastructure grants, not eight operational classroom deployments. Digital Promise announcement |
Do these deployments prove AI improves learning?
No. The reported activities show that educators and students are using AI in specific workflows, but use and reach are not the same as demonstrated learning benefit. Ofsted says reliable evidence on educational outcomes remains limited, with much of the research explorative, short-term, and confined to limited domains.
OpenAI’s announcement describes a longitudinal research partnership with the University of Tartu and Stanford involving 20,000 students. That is a planned evidence effort at the scale the company reports; the announcement’s participant figure is not an outcome. The sources cited here do not establish a general causal learning benefit from AI in schools.
Risks and safeguards schools report
School leaders and teachers described governance and implementation concerns alongside potential uses. Their reports point to practical questions schools need to address:
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- Data protection: Decide what learner or staff information may be entered into an AI system and how it will be handled.
- Safeguarding: Consider whether the tool or activity could expose pupils to unsuitable material or interactions, and define appropriate oversight.
- Bias: Review outputs for unfair or inaccurate assumptions, particularly when they affect learning materials, feedback, or decisions about pupils.
- Intellectual property: Consider rights and permissions when using AI-generated or AI-assisted materials.
- Staff capability: Provide training and a route for colleagues to get help. Ofsted found that nearly all providers it visited had an AI champion, commonly a teacher with technology expertise who supported other staff.
- Equitable access: Estyn noted concerns about unequal access to paid tools, as well as staff training gaps and the risk of overreliance.
- Human review: Keep educators responsible for checking resources, communications, assessment, and feedback where accuracy or pupil welfare matters.
These concerns are reported implementation issues, not evidence that every school using AI has experienced a failure. They are reasons to set clear rules and review practices before expanding use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge an AI deployment
A school considering a tool can separate three questions that are often conflated: whether it is being used, whether it saves staff time or changes a workflow, and whether it improves student learning. A credible evaluation should track the intended outcome rather than treat adoption or usage volume as success.
- Name the task: Specify whether the tool is intended for planning, drafting, differentiation, revision, or another defined activity.
- Identify the users and oversight: Record who uses the system, which staff member reviews its output, and what pupils are expected to do themselves.
- Set a relevant measure: For a workload aim, examine the workflow and staff experience; for a learning aim, define a measure of learning rather than counting logins or generated materials.
- Record the evidence type: Distinguish staff reports, observations, vendor-reported reach, and independently measured outcomes.
- Review safeguards and access: Check data protection, safeguarding, bias, intellectual property, staff training, and whether access is equitable.
This distinction matters because a deployment can be real and useful for a particular task without establishing that it improves attainment or learning more broadly.
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