Picture a special educational needs coordinator (SENCO) on a Thursday evening with a stack of reports, three review meetings booked for the morning and a dozen support plans to update. Most of that pile is copying, summarising and rewording. Very little of it is time with children. That is where the most sensible work on artificial intelligence (AI) for special educational needs and disabilities (SEND) is happening, and these AI and SEND tools look a lot less dramatic than the sales pitches.

In short: three school trusts in England have built small, tightly focused AI tools for SEND work. One organises pupil records and caseload summaries, one helps staff draft targets for individual support plans (ISPs), and one suggests adaptive teaching strategies to teachers in the moment. Each insists the teacher stays in charge. A new governance guide from the Confederation of School Trusts (CST) says SEND decisions are exactly where that human control matters most. My view is that this narrow, teacher-owned approach is the right one, and schools should copy the discipline before they copy the tools.

What three trusts are building

Tes reported on 15 October how three multi-academy trusts are using AI to ease SEND pressures. The backdrop is a CST survey in which just 6 per cent of academy trusts said they were not using AI at all.

Records and caseloads at Bourne Education Trust

Bourne Education Trust, which runs 31 schools across Surrey, Hampshire and South London, is piloting an AI assistant for SEND teams. Chris Goodall, its director of data and digital innovation, says it “streamlines and organises a child’s SEND record, produces structured caseload summaries and helps to complete form fields from documents already held in school.” The aim, in his words, is “to cut the heavy administrative burden on SEND teams, meaning skilled staff spend more time with children rather than on paperwork.”

Goodall says the biggest challenge has been helping staff understand that the tool is not a finished product, because they are, in effect, beta-testing it. And the trust judges it on accuracy first, usefulness second. Its SENCOs report that it is releasing time for provision.

Support plan targets at Learning Academy Partnership

Learning Academy Partnership, with 22 academies in the South West, has a SENCO-led AI agent that helps staff set ISP targets. Emma Bone, the deputy trust lead, explains the problem it tackles: writing “precise, measurable and achievable targets can be difficult and time-consuming”, and approaches vary between teachers and schools. She describes the agent as acting “like a knowledgeable colleague”. The hardest part so far has been “being really tight with the prompts”. Running several agents across the trust costs around £30 a month in total.

Adaptive teaching ideas at Creative Education Trust

Creative Education Trust’s agent, AdaptEd, began with vice principal Louisa Burnett at The Hart School in Staffordshire. Ash Mudaliar, the trust’s director of information technology and edtech, says it addresses the “timing problem” in adaptive teaching, where teachers have seconds to decide how to respond to different learners while managing a whole class. It was built in Copilot Studio in around 15 to 20 hours. After formal feedback from 60 teachers, plus 29 responses at an inclusion conference, most said it gave them strategies they could use immediately or helped them adapt lessons. It is now used across all the trust’s schools.

Why AI and SEND tools need tight limits

Look at what these tools do not do. None of them decides whether a child has a need, what provision they get or whether a plan is working. Goodall put it most clearly:

“The teacher must own the process from start to finish and take full responsibility for the outcome, with the AI acting as a support to professional judgement rather than a replacement for it.”

That matches the CST’s new guide, Governing AI in school trusts, published this month. It separates everyday uses from high-impact ones, noting that drafting a communication “is materially different from using AI to influence recruitment, safeguarding, SEND provision, pupil intervention” and similar decisions. Those uses need meaningful human oversight, transparency and a route for challenge, because “accountability cannot be delegated to an algorithm.”

There is a practical reason too. A target that sounds measurable can still be wrong for the child. Suppose an AI agent suggests that a Year 5 pupil will “read 20 words per minute faster by Easter”. Only the adults who know that pupil can say whether reading speed is even the right goal, or whether the barrier is anxiety about reading aloud. A good tool gets you to a sensible first draft quicker. It cannot know the child.

The paperwork is about to grow

In the National School Trust Report 2026, run by the CST with Edurio, 75 per cent of trust leaders named inclusion and SEND provision as their main focus for this year. Some 37 per cent had struggled to fill SEND roles in the last academic year. One trust leader, describing how AI is used across their trust, listed “SENCO and colleague work efficiency and effectiveness”.

The Department for Education’s schools white paper, Every child achieving and thriving, proposes that all schools create digital ISPs for every child with identified SEND. The plans would record barriers to learning, day-to-day provision, reasonable adjustments and intended outcomes, be written with parents and be reviewed at least once a year. As Tes reported on 19 October, the consultation response has not yet been published, but schools would take responsibility for ISPs from 2029. That is a lot of writing, and trusts are sensibly testing tools before the duty arrives.

The same white paper offers a warning worth pinning above the staffroom kettle. It says “simply using general-purpose AI in education often has negative outcomes”, while tools built for an educational purpose show more promise. The three trust tools fit the second description. Pasting a pupil’s history into a free chatbot fits the first.

What schools can do this term

You do not need your own developer. You do need some honesty about what is already happening. The CST guide recommends a low-stakes, no-judgement survey to uncover “shadow AI”, meaning tools staff use without approval, and it cites a survey in which 36 per cent of professionals said they use AI tools their employer has not authorised. If a teaching assistant is already using a chatbot to reword support plans at home, you want to know, and you want them to feel safe telling you.

From there, a few steps make sense:

• Start with one named problem, such as target writing or summarising reports, rather than buying a general assistant.
• Keep identifiable pupil information out of any tool your school has not approved, and complete a data protection impact assessment before you put it into one that has.
• Write down who checks every output and who signs it off. In SEND work that should always be a person who knows the child.
• Judge the tool on accuracy before speed, as Bourne did, and ask SENCOs whether the time saved actually goes back to pupils.

The Department for Education’s generative AI product safety standards already expect filtering to adjust for users with SEND. Its new EdTech Testbed Programme, worth £24 million over four years, will test products, including generative AI tools, in more than 1,000 schools and colleges, with inclusion of learners with SEND among its priorities. The Education Endowment Foundation will run up to 15 randomised controlled trials as part of it. Until that evidence arrives, caution is the professional position.

For background, see our overview of the most common special educational needs and our guide to effective learning support in schools. Our piece on AI and teacher workload asks whether time saved really comes back to staff.

My take

I am more optimistic about this than about most AI in schools, because the problem is real and well defined. A tool that drafts a caseload summary for a human to check is a fair trade. A tool that quietly decides what support a child receives is not, however polished it looks. The trusts in this story seem to understand the difference. The test for the rest of us is whether we build the same habits before the ISP paperwork lands.

Is your school using AI for any part of SEND work yet, or have you decided to hold off? I would love to hear what you are trying and where you have drawn the line, so please share it in the comments.

Picture a Year 6 teacher on a Sunday evening. An artificial intelligence (AI) tool has drafted her parents’ evening letter in two minutes and turned a reading comprehension into three versions for different groups before the kettle has boiled. It is genuinely useful. She still closes the laptop at half past nine. That gap, between jobs that get quicker and weeks that stay just as long, is the most honest finding I have seen yet on AI and teacher workload. It comes from Bett’s latest national survey of United Kingdom (UK) teachers.

In short: Bett’s third annual YouGov survey of 1,033 UK teachers, carried out in June 2026, found that eight in ten now use AI for teaching-related tasks and weekly use has doubled in a year. Half of those users say AI has cut their workload, but only 35% say they actually work fewer hours. Two in five teachers have had no training from their school, and expectations for next year have cooled sharply.

What the Bett survey found

The report, AI in Education 2026: Working it out (full report), is the third in a series that Bett, the education technology show, has commissioned from YouGov. It is sponsored by Lenovo, which is worth keeping in mind, but the survey was run online by YouGov and weighted to represent UK teachers, with fieldwork from 16 to 29 June. Bett’s report page has the background.

Use has moved fast. In 2025, 26% of teachers used AI at least weekly. This year it is 52%, and 12% use it every day. Age matters more than role or phase: net use is 87% among the under-35s and 62% among the over-55s.

Among users, 76% build lesson resources such as plans and worksheets with it. Around four in ten draft letters and emails to parents (39%) or pupil reports (39%), 15% write references and 13% contribute to paperwork for special educational needs and disabilities (SEND). Only 8% let it near marking. For all the talk of automated assessment, the red pen is staying in teachers’ hands.

AI and teacher workload: quicker tasks, the same week

Here is the heart of it. Among teachers who use AI, 51% say it has reduced their workload, 40% say it has made no difference and 9% say it has added to it. Yet when asked about time, only 35% say they work fewer hours. More than half (55%) work the same hours as before, and 4% work more.

The savings themselves are modest. For regular users the most common answer was one hour a week (20%), with two hours close behind (19%). Rolled together, the report puts the typical saving at a little under two hours a week. That fits the controlled trials. The Education Endowment Foundation (EEF) ChatGPT lesson preparation trial found that Key Stage 3 science teachers using the tool with a guide spent 25.3 fewer minutes a week on planning, a 31% cut. The more recent Aila trial, which we covered in our piece on what the Aila trial found about AI lesson planning, put the saving at about 49 minutes.

Older teachers feel the benefit least. Just 26% of over-55s say they work fewer hours, against 41% of under-35s. Bett is also careful to say that its questions and answer options have changed across the three waves, so the jump from 34% reporting a lighter workload last year should be read with caution.

Duncan Verry, Bett’s portfolio director, puts the pattern neatly in his foreword: “The time comes back and something else fills it.” The report’s own conclusion is blunter. AI is “just not the workload cure it was once billed as, and teachers have worked that out for themselves.”

Where the saved time actually goes

I do not think this is a failure of the technology. It is a feature of schools. Time saved by one teacher, on one task, rarely comes back as a shorter day, because nobody has decided what it is for. The report shows the same gap at school level. When teachers described why their school had adopted AI, cutting teacher workload (52%) came far ahead of improving pupil outcomes (15%).

Imagine a head of science who saves forty minutes a week on a homework quiz bank. Within a month, that time has quietly gone on a new tracking spreadsheet and an extra intervention group. The space simply filled, as space in schools always does. If leaders want AI to change working hours, they have to protect the time on purpose, in the calendar, before it disappears.

A tool that saves you forty minutes is only a gift if someone decides, in advance, where those forty minutes go.

The professional cost nobody is timing

A second, smaller study published this month asks a harder question. Writing for the British Educational Research Association (BERA), Dan Walker of Rawlins Academy and Professor Ash Casey of Loughborough University report what 104 secondary teachers in England said about AI for lesson preparation, followed by seven interviews. It is a voluntary sample from a master’s study, so treat it as a snapshot.

The group split almost evenly: 48% said they used AI for lesson preparation and 52% said they did not. Around 40% worried about the professional cost, mainly that colleagues would come to depend on AI and lose competence. About 34% felt early career teachers (ECTs) were unlikely to have built the foundations needed to plan well, and 71% of teachers in their first five years used AI, compared with 26% of those with more than twenty years in the classroom.

The Bett report carries the same worry in an opinion piece by Alice Whitby, a teacher development lead. She describes how working through pupils’ responses builds a teacher’s expertise, and writes: “That cognitive friction is gold dust.” Her concern is that “we mistake removing cognitive effort for improving practice, when some of that effort is the very mechanism through which expertise develops.” It is the teacher’s version of the cognitive offloading problem we usually discuss for pupils.

I find that persuasive. A newly qualified teacher who never wrestles with sequencing a unit on fractions may save an hour now and pay for it for years. An experienced teacher who hands the parents’ letter to a chatbot loses nothing at all.

Training, policy and cooling expectations

Teachers are working most of this out alone. Two in five (41%) have had no AI training or help from their school, a small improvement on 46% last year. Where training exists, it is thin: 16% get it once a year and only 2% more than once a term. Teachers in independent schools are more likely to get support (61%) than those in state schools (41%).

Policy is patchy too. A third of teachers (33%) say their school has a policy on staff use of AI, 48% say it has none and 19% do not know. Even where one exists, only 18% say they understand it well.

Enthusiasm is levelling off. Fewer than half (47%) expect to use AI more next year, down from 68% in 2025, and those expecting a big increase have fallen from 15% to 3%. I read that as realism rather than rejection. Teachers have found the jobs AI helps with and stopped expecting it to rescue the week.

What I would do as a school leader

Decide where the time goes before you save it

If a department adopts AI for report comments or quiz banks, agree in the same meeting what the saved time is for. It might be one fewer after-school meeting each half term, or a protected planning hour in the timetable. Without that decision, the evidence suggests the hours will simply be absorbed.

Treat early career teachers differently

For ECTs, I would keep core planning deliberately effortful, with AI used for the genuinely tedious parts such as differentiating a worksheet or drafting retrieval questions. A mentor should then talk through what the teacher changed and why. Our list of practical ways teachers can use AI is a reasonable starting menu for those lower-stakes jobs.

Train on real tasks, and measure hours

Short, practical sessions on the jobs staff actually do, including when not to use AI, will beat a one-off launch. Then ask staff about hours worked as well as whether AI feels helpful. The Bett figures show those two answers can be very different.

AI is already part of most teachers’ weeks. The question for the next year is whether schools treat the time it saves as something to plan for, or let it vanish into the next initiative.

Has AI given you any time back, and if so, where has it gone? Tell me in the comments.

Ask any primary teacher where their evenings go and lesson planning will be near the top of the list. So when the Education Endowment Foundation (EEF) published a trial of AI lesson planning with Oak National Academy’s assistant, Aila, on 6 October 2026, I read it with real interest. The headline is encouraging. The detail underneath is more interesting, and more useful for anyone deciding whether AI belongs in their planning routine.

The short version: in a ten-week trial, KS2 teachers using Aila spent about 49 minutes less a week on planning, with no drop in quality.
But use of the tool fell away over the term, and the biggest gains went to newer teachers and those less sure of their subject.

What the trial actually tested

The study was a randomised controlled trial carried out by the National Foundation for Educational Research (NFER) and commissioned by the EEF. It ran for ten weeks in the autumn term of 2025 and recruited 464 Key Stage 2 teachers from 108 schools, with the final analysis drawing on data from 416 of them. One group was encouraged to use Aila. The other was asked not to use it, although both groups were free to use other generative AI tools such as ChatGPT or Microsoft Copilot. That last point matters, and I’ll come back to it.

Aila works from scratch. A teacher picks a year group, subject and topic, Aila suggests a learning objective based on the National Curriculum along with activities, and the teacher then refines the plan, either with Aila or offline. Time spent was tracked through weekly teacher diaries, with interviews and school visits alongside. If you want the full detail, the EEF project page has the full evaluation report (PDF).

The headline numbers on AI lesson planning

According to the NFER summary of the findings, teachers in the non-Aila group spent 3.3 hours a week preparing lessons and resources. The Aila group spent around 2.5 hours. That is a saving of 48.6 minutes a week on average, or 76% of the comparison group’s time. The EEF rounds this to a quarter less planning time.

Crucially, the quality of the lesson resources in the Aila group was no worse than in the comparison group. Quality is the first thing I would worry about with any shortcut, so that finding carries weight. The EEF gave the result a “high” security rating, which means a high degree of confidence in it.

Forty-nine minutes a week does not sound dramatic. Over a half term, though, it is most of a working day. For a teacher who plans on Sunday afternoons, it could be the difference between finishing before tea and finishing after it.

Who benefited, and who didn’t

This is the part I would want every school leader to read. The savings were not spread evenly. Teachers who were new to the profession, had lower confidence in their subject knowledge or lower pedagogical knowledge, or were more confident with technology were more likely to see their workload fall.

That makes complete sense to me. Picture an early career teacher in Year 5 who has been handed a science unit on forces and has never taught it. A tool that proposes a curriculum-linked objective and a sequence of activities gives them a scaffold to work from. Now picture a teacher with fifteen years in Year 3 who already has a folder of tried and tested Romans lessons. Starting from a blank page is the last thing they need. They want to tweak what already works.

The EEF press release says as much: some teachers found Aila accessible and useful for unfamiliar topics, while more experienced colleagues preferred to adapt existing plans. NFER also notes a perception that Aila’s outputs often needed adapting before they were ready for the classroom.

The drop-off problem

Here is the finding that I think deserves the most attention. Use of Aila fell over the ten weeks. On average it was used for only 7% of lessons, and 81% of the Aila group used it at least once. In a post-trial survey, fewer than one in five teachers felt the tool had meaningfully influenced their teaching and planning.

This echoes an earlier EEF trial, published in December 2024, which looked at teachers using ChatGPT for lesson planning. Those teachers spent 69% of the time of a comparison group planning without AI, and their use also declined across ten weeks.

So two well-designed trials now point the same way. AI can save planning time without harming quality, yet teachers do not keep using it. Professor Becky Francis, the EEF’s chief executive, put it plainly in the EEF press release, describing AI as far from a “silver bullet” for workload and noting that “just providing tools does not lead to sustained adoption.”

I don’t read the drop-off as teachers being stubborn. Planning is a habit built over years, tied to schemes of work, shared drives and conversations with the colleague next door. A new tool has to fit into that, not sit beside it. Remember too that the comparison group could use other AI tools, and NFER reports that many teachers preferred alternatives they found easier to use. Some of the savings question is really about which tool, not whether to use one at all.

What I would do with this as a school

My view is simple. Stop treating AI planning tools as a whole-staff initiative and start treating them as targeted support. The EEF’s own guidance on the project page lands in the same place. A new evaluation of an AI pilot in schools in Connecticut points the same way, with teachers valuing time with colleagues even more than formal training.

If I were a primary head, I would offer Aila or a similar tool first to early career teachers and to anyone teaching outside their specialism, such as a Year 6 teacher picking up computing for the first time. I would pair it with a mentor who checks the output, because Dr Ben Styles of NFER raises a fair concern: substituting for experience could help, or it could risk poorer lessons.

A tool that writes a confident lesson plan does not guarantee a confident lesson.

For experienced staff, I would not push. I would ask what part of their planning is genuinely tedious. Often it is differentiating a worksheet three ways or writing retrieval questions, not designing the lesson itself. That is where a quick AI task might earn its place. Our earlier list of practical ways teachers can use AI has more ideas along those lines, and our round-up of free digital tools for everyday teaching tasks covers some of the non-AI options too.

Whatever tool you choose, check how it handles data and whether your school has approved it. We looked at the wider questions in the ethical implications of AI in education, and they apply here as much as anywhere.

Where this leaves us

The Aila trial is good news, but it is modest good news. A free, curriculum-aligned tool saved teachers time without lowering quality, and that is a genuine result. It also showed that time saved in a trial does not automatically become a lasting change in how people work. A national survey of teachers points the same way, as our look at AI and teacher workload explains: half say AI has eased their workload, but only a third work fewer hours.

Oak’s chief executive, John Roberts, says new tools are coming soon, and the EEF is funding further research into how generative AI affects learning. I’ll be watching for the next step NFER mentions: whether a new teacher planning with AI actually teaches a better lesson. For now, the honest answer to “should I use AI to plan?” is that it depends on who you are and what you are planning. Pupils using AI raise a different question, which we look at in our piece on cognitive offloading and what AERO’s new advice means for teachers.

Frequently asked questions

Over to you. Have you tried Aila or another AI tool for planning, and did you keep using it after the first few weeks? Tell us in the comments what stuck and what didn’t.