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Schools and colleges are using AI for lesson preparation, quizzes, administrative writing, tutoring, feedback and science practice; Peru has also reported an AI math tutor, teacher training and AI-supported classroom observation. These are 11 documented examples of use, not 11 equally evaluated products. Some are workflows reported across a group of providers, and the evidence ranges from interviews and program descriptions to trials and course-specific comparisons. None of the examples alone establishes that AI reliably improves learning across schools.
What counts as one of these 11 examples?
The examples below are counted by distinct documented use or program, not by vendor or school. The first six come from an Ofsted study of 21 schools, colleges and trusts in England that had been embedding generative AI for at least 12 months. Ofsted’s 2025 report describes a cohort and recurring workflows; it does not name six separate institutions or independently evaluate each workflow. The remaining five are programs or tools described in sources from Peru and the United States.
The evidence therefore is not directly comparable. A reported use, a program’s reach, teacher adoption, agreement with human observers, and a course-grade comparison measure different things. The labels below distinguish documented use from trial evidence and reported reach; they do not imply a shared standard of proof.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesWhat are the 11 documented examples?
| # | Example | Primary user and setting | Evidence described by the source |
|---|---|---|---|
| 1 | England early-adopter cohort | School and further-education leaders and staff | Interviews with 21 providers; exploratory, purposive sample |
| 2 | Lesson and resource preparation | Teachers in England | Provider-reported workflow |
| 3 | Quizzes and revision materials | Teachers and learners in England | Provider-reported workflow |
| 4 | Administrative drafting and summarizing | School and college staff in England | Provider-reported workflow and perceived time savings |
| 5 | Pupil-facing tutoring and chatbots | Pupils in some English providers | Use reported; less common than staff-facing applications |
| 6 | Feedback and assignment review | Teachers and learners in England | Examples reported; assessment use approached cautiously |
| 7 | Eligiendo mi Camino / Choosing My Path | Fifth-year secondary students in Lima, Peru | World Bank-reported reach of about 4,500 students in 85 public schools since March 2026 |
| 8 | AI training for teachers | Teachers in Lima, Peru | Randomized trial across 390 schools; 1,270 participating teachers |
| 9 | AI-supported classroom observation | Classroom coaching in Peru | World Bank account reports scoring results comparable to trained human observers |
| 10 | Inq-ITS virtual science labs | Students in grades 4–10 in the United States | IES summary describes studies including randomized trials and longer-term follow-up |
| 11 | Jill Watson at Georgia Tech and Wiregrass Georgia Technical College | Online computer science and an English course in the United States | University-reported deployment, controlled comparison and course-grade differences |
1. England’s early-adopter schools and colleges
Ofsted interviewed leaders from 21 schools, colleges and trusts that had been embedding generative AI for at least 12 months. The 2025 report describes what those providers said they were doing, rather than measuring a national adoption rate. Ofsted characterized the work as exploratory; the providers were purposively selected, and many other providers had not adopted AI.
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2. Preparing lessons and adapting resources
Teachers in the Ofsted interviews used AI to help draft lesson plans, adapt texts and create teaching resources. This is a reported workload-support use, not evidence that AI-generated materials improve attainment. Teachers remain responsible for checking accuracy, curriculum alignment, accessibility and suitability for their pupils.
3. Making quizzes and revision materials
Some providers described generating quizzes and revision materials from resources already used in teaching. That can help produce practice material, but the Ofsted report does not establish that AI-generated quizzes themselves raise achievement or are accurate without review.
4. Drafting and summarizing administrative documents
School and college staff reported using AI to draft or proofread letters, reports and policies and to summarize documents. Leaders described time savings as a perceived benefit. Human review matters because an incorrect summary or inappropriate wording can still affect families, staff or institutional decisions.
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5. Pupil-facing tutoring and chatbots
Some longer-standing adopters reported pupil-facing tools, including chatbot or tutoring functions. Ofsted found these uses less common than staff-facing applications and noted that teachers often modeled their use. The interviews document reported use; they do not independently measure whether the tools improved learning.
6. Feedback and assignment review
Ofsted recorded examples of AI-assisted essay feedback and learners using AI to review submitted assignments. Leaders were more cautious about assessment than about preparation tasks, citing accuracy concerns and the need for teacher oversight. These examples do not support treating AI as a replacement for qualified assessment or teacher judgment.
7. Eligiendo mi Camino / Choosing My Path in Peru
In a report published on 2 July 2026, the World Bank Group described the program as an AI math tutor and career coach for fifth-year secondary students in Lima. It reported that about 4,500 students in 85 public schools had been reached since March 2026. That is a reach figure, not a causal estimate of improved math performance or career outcomes.
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The World Bank account includes testimonials from a Lima teacher and a student. Such experiences can illustrate how participants view a program, but they are individual accounts rather than controlled outcome evidence.
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The World Bank Group also described practical AI training for teachers and a randomized trial across 390 Lima schools involving 1,270 participating teachers. It reported increased active AI use and improvements in teachers’ prompting and critical evaluation of outputs. Those findings concern teacher adoption and skills; they do not by themselves show gains in students’ learning.
9. AI-supported classroom observation in Peru
The World Bank account says AI-based classroom observations produced results comparable to trained human observers when scoring pedagogical and instructional quality. The stated purpose is to help scale coaching where expert support is scarce. This reported comparison is not evidence that AI can replace human observers in every context, nor that observation scores alone improve teaching.
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10. Inq-ITS virtual science labs
The Institute of Education Sciences describes Inq-ITS as browser-based inquiry labs for grades 4–10. The system assesses students’ actions and written work, provides real-time feedback and alerts teachers when learners struggle. IES’s program summary describes research that includes randomized trials, agreement between system scoring and human coders, and longer-term follow-up. Those results concern this particular tool and setting; the summary should not be read as proof that all AI tutoring or feedback systems work similarly.
11. Jill Watson in Georgia Tech and Wiregrass courses
Georgia Tech reported use of Jill Watson in its online computer science program and in an English course at Wiregrass Georgia Technical College. The university said the online master’s program deployment served more than 600 students and described a controlled comparison and course-grade differences. These are university-reported findings, and a comparison in particular courses cannot establish that AI assistants generally improve grades. Georgia Tech’s report is an institutional account of a system associated with the university.
What can these examples establish about learning?
They show that institutions have tried or used AI for identifiable educational tasks. They do not yield one defensible effect size for “AI in education”: the examples vary in age group, task, duration, oversight and evaluation method. Reach figures indicate how many people a program reports serving; usage or teacher-skill findings indicate adoption; observer agreement indicates scoring similarity; and course grades are tied to the courses and comparison described. None should be substituted for another.
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Ofsted’s 2025 account says providers often relied on user feedback and usage monitoring rather than measures isolating AI’s contribution to pupil outcomes, and it notes little reliable evidence of actual impact on education outcomes. A Stanford SCALE Initiative review published 11 March 2026 examined more than 800 K–12 AI papers and identified 20 high-quality causal studies. That is the review’s finding about its evidence base, not a census of every study or an effect estimate for the cases above.
What should schools check before adopting an AI tool?
The examples point to implementation conditions as important as the tool itself. Ofsted identified privacy and safeguarding risks associated with data collection, possible bias, overreliance that may weaken writing or higher-order skills, and concerns about whether automated markers can explain decisions or assess nuance, creativity and exceptional work. These are risks to manage, not inevitable outcomes of every deployment.
- Protect pupil and staff information. Establish what data the tool collects, where it goes, who can access it, and whether the proposed use meets the school’s privacy and safeguarding requirements.
- Keep a qualified person accountable. Review generated content and feedback for accuracy, fairness, age suitability and curriculum fit; do not delegate consequential assessment or safeguarding decisions to an unreviewed system.
- Train staff to evaluate outputs. The Peru teacher-training account highlights prompting and critical evaluation, not simply access to an AI tool.
- Test the task that matters. A system that drafts a resource, answers course questions or scores classroom activity needs evaluation against that specific job and student group.
- Measure outcomes beyond usage. Track errors, teacher workload and student outcomes with a comparison appropriate to the claim. Usage counts alone cannot show that learning improved.
Announced programs are not completed deployments
Two further initiatives illustrate what may come next but are not included in the 11 examples. Azerbaijan’s Ministry of Science and Education announced a collaboration to build an adaptive tutor within its Digital School network, beginning with a limited pilot under teacher oversight and with an intention to scale. The 29 May 2026 announcement describes a planned rollout, not demonstrated reach or learning outcomes.
The UK’s AI Tutoring Tools Pioneers Programme, announced by the Department for Science, Innovation and Technology and Department for Education on 16 April 2026, describes co-design and school testing. It says successful tools are intended for national availability from 2027. That is a future intention, not evidence of a current nationwide deployment.
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