Most schools and organizations have just run the year's first AI training session, and in most of them the same people are using AI this week as were using it last week. The session was probably fine. Three surveys published this year explain why it did not move anyone.
Bett's YouGov survey of 1,033 UK teachers, The State of AI in Education 2026 (fieldwork June 2026, published August 27, 2026), found that 80% now use AI for work and weekly use has doubled in a year, while 41% have had no AI training from their school and the share expecting to use AI more next year has fallen from 68% to 47%. Gallup, surveying 2,069 US public school teachers for the Walton Family Foundation between February 9 and March 2, 2026, found that only 18% receive formal guidance on how to apply AI tools to their work and 34% receive no guidance at all. And the Royal Society's Teacher Tapp survey of more than 9,000 teachers in England, submitted to the Education Committee in May 2026, found only 14% confident across the full range of AI literacy.
Use is going up. Guidance is not, and confidence is not following either. The standard response, another training day, is the smallest lever a leader has.
How do you get staff to actually use AI?
You get staff using AI by creating three conditions that a training session cannot create on its own: permission, so people know what they may use it for; practice, so they have used it on their own work repeatedly, alongside someone who has done it before; and protection, so it is safe to be seen using it and safe to say when it went wrong. Training is the third thing you do, not the first, and it only works once the other two exist.
The usual sequence fails in a predictable way. The organization runs the session, waits a term, and concludes that its people are resistant. Mostly they are not resistant. They are unsure what they are allowed to do, they have never used the tool on anything that mattered to them, and they have never seen anyone senior admit to using it. Fix those three things and the session you already ran starts working in retrospect.
So the first move is not to book anything. It is to find out which of the three conditions is missing, which takes about ten minutes and three questions.
The Confidence Test: three questions to ask your most reluctant member of staff
The Confidence Test is three questions I ask a member of staff, not their leader, to find out whether AI confidence exists in an organization: Do you know what you are allowed to use it for? Have you used it on your own work this week? If it got something wrong, would you tell someone? Confidence is when all three answers are yes from the people least likely to give them.
Copy these three questions, put them to five people one at a time, and write down what they say:
- Do you know what you are allowed to use it for? A no means permission is missing.
- Have you used it on your own work this week? A no means practice is missing.
- If it got something wrong, would you tell someone? A no means protection is missing.
Ask the five people least likely to volunteer for anything about AI. A yes from your enthusiasts tells you nothing you did not already know.
I ask staff rather than leaders for a reason. Leaders describe the organization they intended to build. Staff describe the one they work in. The Royal Society survey found headteachers among the least confident groups of all, according to EdTech Innovation Hub's coverage of the results on September 2, 2026, which is one more reason the view from the top is not the measure. For a good many leaders the honest position sits further back still, because their staff already know more about AI than they do.
When one answer is no, you have found the condition that is missing, and that is the thing to fix before anyone books another trainer. This is my suggested way of reading an organization, not a validated instrument. It is also deliberately narrow: confidence is one of the things an organization can be judged on rather than the only one, and a team that fails the Confidence Test usually finds the thinness runs wider than its staff. The seven dimensions of AI readiness is the longer diagnostic, and permission, practice and protection sit inside it alongside the governance and ownership questions this article leaves alone.
Why doesn't AI training make staff use it?
Training on its own does not move people because the evidence says confidence follows structured support combined with clear whole-school guidance, and neither of those is a session. The Royal Society's written evidence puts it in one sentence: teachers in schools with structured professional development and whole-school guidance are "roughly three times more likely to be confident across all dimensions of AI literacy than those receiving none." Only 18% had received structured professional development, 15% had clear whole-school guidance, and 23% had received no support of any kind. The lever is the combination. Guidance without practice produces people who know the rules and never use the tool. Practice without guidance produces people who use the tool and hope nobody asks.
Two more findings sharpen the picture. David Marshall of Auburn University and Tim Pressley of Christopher Newport University surveyed more than 400 US teachers and reported in Education Week on April 24, 2026 that "AI didn't directly reduce teachers' workload"; what mattered was confidence, and their advice to leaders was "repeated, practice-based support", clear rules and protected time. And training is rising while its depth is not: the EdWeek Research Center's surveys, reported on May 18, 2026, show the share of US teachers with no AI training falling from 60% in October 2024 to 42% by early 2026, while only 9% describe their training as ongoing. Training is up, ongoing training is rare, and confidence is flat. Whatever is missing, it is not a shortage of sessions.
Using AI is not the same as being confident with it
AI confidence is the state in which a member of staff can use AI on real work without asking anyone whether they are allowed to, and would tell someone if it got something wrong; usage figures tell you nothing about whether that state exists. The Bett survey makes the gap visible on a single page: 80% of teachers use AI for work, 48% say their school has no written AI policy, and only 18% understand the policy their school does have. A person using AI in an organization with no rules is not confident. They are unsupervised. From the outside the two look identical.
Confidence is not enthusiasm either. Enthusiasm is what your early adopters have, and they will have it whatever you do. Confidence is what your skeptics have when they can use the tool on their own work without asking permission and without hiding it. Do not count the enthusiasts. Ask the skeptics.
The Royal Society data draws one more line worth keeping. A third of teachers were confident in the practical use of the tools but not in the rest of AI literacy: understanding what the tools are doing and judging when to trust them. Fluency is a skill, and you can teach it in an afternoon. Confidence is a condition, created or withheld by the organization around the person. That distinction is the whole argument of this article.
Permission: people need to know what they may do, not only what they may not
The first condition of confidence is a permission that a member of staff can state in one sentence without looking it up. Most AI policies fail this test because they were written to prevent harm rather than to direct use, so they list prohibitions and leave the permissions implied. Staff are not confused about what is banned. They are confused about what is allowed, and careful people, given no answer, assume the answer is nothing.
The second question in the Readiness Test is "What may I do with it?", and the Bett finding that only 18% of teachers understand their school's policy well tells you how rarely that question has an answer people can carry in their heads. Weak permission reads: "Staff may use AI tools appropriately and in line with data protection requirements." Strong permission reads: "You may use the two approved tools to draft resources, letters and first versions of reports. You may not paste in a pupil's name, a medical detail or anything from a safeguarding record. If you are unsure, ask your head of department, who has been asked to say yes wherever they can."
A permission that specific rarely arrives on its own. It is the visible surface of a decision the leadership team made earlier about what AI is for in this organization, which is the work of an AI strategy people actually use. Where no such decision exists, the person drafting the policy has nothing to be specific about, and vagueness is the only honest thing left to write.
Permission is also the condition with the clearest evidence behind it. Gallup found that 69% of teachers who receive formal guidance on using AI to make materials for students say it encourages them to use AI, against 51% of those whose guidance is only informal. A corridor conversation is not a permission. Written, specific and repeated is what changes what people do. And where no permission exists, people do not stop; they carry on quietly, which is how you end up with staff using tools nobody approved.
Practice: confidence comes from using it on your own work, more than once
The second condition is repeated use on the person's real work, alongside someone who has done it before. This is where the training day fails, and it fails structurally rather than through anyone's fault. A training day teaches the tool on somebody else's example, in a room with no marking in it, and then sends people back to a week with no time in it. RAND's study of US districts (April 8, 2025) found that nearly all district AI training was voluntary. Voluntary training reaches the enthusiasts. The skeptics were not in the room.
What builds confidence is smaller and more frequent. A hypothetical example: a head of science gives the last twenty minutes of a department meeting to one task, drafting feedback comments for the mock exam everyone is marking that week, using the approved tool, and does it alongside them on her own class set. Two weeks later they do it again with a different task. Nobody has attended a course. Everybody has used the tool on their own work, twice, next to someone who could answer the question they were embarrassed to ask. That is the part of structured support most organizations leave out.
Protection: it has to be safe to be seen using it and safe to be wrong
The third condition is that nobody is punished for trying and nobody is punished for reporting a mistake. The fourth question in the Sponsorship Test is "Have we made it safe to be seen using it?", and for most organizations the honest answer is that they have never said. Silence is read as risk. Where senior leaders have not been seen using AI, a teacher who uses it to draft reports is taking a small professional gamble every time, and confidence does not grow in people who feel they are gambling.
The mistake question matters even more. AI will produce something wrong in your organization this term. The only question is whether you hear about it. The people who would tell you are exactly the people you need, because they are the ones checking, and the first reported error should be called good news in public. An organization that treats the first AI mistake as a disciplinary matter has just guaranteed it will never hear about the second.
What do you do about staff who won't use AI?
Treat refusal as information about the organization until you have ruled out the three conditions, and only then as a professional expectation. Almost every population of reluctant staff is three groups wearing the same expression, and the three need opposite responses.
The first group does not know they are allowed. From the outside this looks exactly like refusal, and it is caution. These people are the fastest to move and they move on a sentence: a written, specific permission from someone senior, repeated twice. The Gallup finding above is the whole intervention.
The second group tried it once, got something mediocre, and stopped. They are not waiting for a policy, they are waiting for evidence, and they will not go looking for it again on their own time. What moves them is a second attempt on a task where the tool is genuinely good and the stakes are low, sitting next to someone who has done it before. Enthusiasm from a colleague is not enough here. Neither is a champion demonstrating something impressive at the front, which is the ceiling most AI champions programs run into.
The third group has a reasoned objection, and this is the one leaders most often mishandle. A teacher who says AI feedback is worse than their own, for this subject and this class, is frequently right, and treating that judgment as a training need is how you lose the people whose judgment you most need. Ask them to name the task where it fails and the task where it would not. A skeptic who can tell you exactly where the tool breaks is more use to your organization than an enthusiast who cannot.
What does not work is mandating use and counting logins. You will get logins. Measured adoption of that kind tells you who complied, which is the same mistake as measuring attendance at the training, and it hands your skeptics a grievance to organize around. The question is not how to make everyone use AI. It is how to be sure that nobody is holding back for a reason you could have removed — and the fear of being replaced is the reason staff are least likely to say out loud.
Why the time saved decides whether confidence lasts
Confidence collapses when the time AI saves is absorbed by the organization without anyone deciding what it was for. This is the finding in the Bett survey that leaders should sit with longest. Among teachers using AI, 51% said it had eased their workload, but only 35% were working fewer hours and 55% said their hours were unchanged. Bett's Duncan Verry put it plainly: teachers "tell us it saves them time on particular jobs, but most say they are not working fewer hours", because something else fills it. The drop from 68% to 47% in those expecting to use AI more next year is what cooling enthusiasm looks like in a survey, and I read it as the sound of a promise not being kept.
The promise was less work. Nobody decided what the saved time was for, so the organization took it back. I wrote in Why AI Pilots So Often Go Nowhere that "a time saving that no one redirects is not an outcome. It is a rounding error." The same rule applies to confidence. Say publicly what the hour it saves is for: earlier finishes, fewer written reports, more time with the pupils who need it, anything, as long as it is named and then protected. A saving that vanishes teaches people the tool changed nothing. A saving they can see teaches them it was worth learning.
What I Have Learned From Working With Organizations
I have sat on both sides of the training day, as the assistant headteacher responsible for planning it and now as the person invited in to deliver it, and the lesson from both sides is the same. The session is never the thing that changes behavior. What changes behavior is what the leadership team does in the fortnight afterwards. When I write about this for Forbes, the replies are rarely from people asking how to run a better session. They are from leaders who ran a good one and cannot understand why nothing moved.
Across the organizations I work with, the pattern is consistent enough that I can usually predict it before I arrive. The enthusiasts have been using AI for a year. The middle tried it once, got something mediocre, and stopped. The skeptics have decided it is a fad and are waiting to be proved right. A training day makes the enthusiasts happy, gives the middle a second mediocre experience, and confirms the skeptics' suspicion. The moment the room changes is not when the trainer shows something impressive. It is when someone senior says what they used it for last week and what it got wrong. Permission and protection arrive in the same sentence, and the skeptics start listening. The mistakes I see most often all follow from the same misreading: training the enthusiasts, teaching the tool instead of the task, a policy that only says no, and measuring attendance, which tells you who was in the room and nothing about who changed.
What good looks like, and what to do in the next three weeks
An organization with AI confidence is one where the least enthusiastic member of staff can pass the Confidence Test without preparing for it: a permission short enough to remember, practice built into meetings that already exist, senior leaders who have been heard describing what AI got wrong for them, and a mistake that was reported, thanked and fixed within the last month. If nothing has gone wrong yet, either nobody is using it or nobody is telling you.
Here is the sequence, and none of it requires a trainer. If you are new in post, it sits inside a longer one: the first 90 days of leading AI change puts these three weeks in the order the rest of the job allows.
- This week. Run the Confidence Test with the five people least likely to volunteer for anything about AI. Write down which of the three conditions failed most often.
- This week. Write the one-sentence permission, name the approved tools and the two or three things staff may do with them, name what may never be pasted in, and have the most senior person available say it out loud rather than email it.
- Weeks two and three. Put twenty minutes of real work into the next two team meetings, on a task everyone in the room already has to do, with team leaders doing it alongside their people rather than presenting it.
- Before the end of the month. Have a senior leader describe, in public, something AI got wrong for them and what they did about it. Thank the first person who reports an error, in front of others.
- Then decide what the time saved is for, say it out loud, and defend it when something tries to fill it.
Run that, and the training you do next lands on an organization ready for it. Skip it, and the training will do what training done first always does: please the people who were already convinced, and leave everyone else where they were. The same order holds when you move from one enthusiastic team to the whole workforce, which is the harder half of adoption.
If your leadership team is working out what comes after the training day, this is the kind of work I do with school and trust leaders through AI coaching for school leaders, built around these conditions rather than around a single session. If you would rather read than book anything, I write a newsletter for school and trust leaders that covers this ground as it comes up.
Sources and further reading
- The State of AI in Education 2026 (Working It Out), Bett with YouGov, survey of 1,033 UK teachers, fieldwork June 2026, published August 27, 2026. uk.bettshow.com; coverage in Tes and Schools Week, August 27, 2026.
- Ash, A. M. and Senseman, K., Most Teachers Receive No Formal Guidance on AI Use, Gallup, May 26, 2026, reporting the Walton Family Foundation-Gallup survey of 2,069 US public school teachers, fieldwork February 9 to March 2, 2026, margin of error plus or minus 2.5 percentage points. news.gallup.com
- Written evidence from the Royal Society to the House of Commons Education Committee (AIE0215), including the Teacher Tapp survey of more than 9,000 teachers in England, fieldwork March 2026, submitted May 2026. committees.parliament.uk; project page at royalsociety.org; coverage in EdTech Innovation Hub, September 2, 2026.
- Marshall, D. T. and Pressley, T., We Studied How AI Shapes Teachers' Well-Being. Here's What We Found, Education Week (opinion), April 24, 2026. edweek.org
- Heubeck, E., More Schools Are Providing AI Training for Teachers. Is It Any Good?, Education Week, May 18, 2026, reporting EdWeek Research Center survey data. edweek.org
- Diliberti, M. K., Lake, R. J. and Weiner, S. R., More Districts Are Training Teachers on Artificial Intelligence: Findings from the American School District Panel, RAND, April 8, 2025. rand.org
Dan Fitzpatrick is the founder of The AI Educator, a Forbes contributor, a former assistant headteacher and Director of Digital Strategy, and works with schools and organizations on AI strategy, leadership and capability. About Dan.
