AI How AI can give school leaders their summer back
TIM HANDLEY, former teacher, data lead and primary headteacher and now a certified Google AI practitioner and developer of Edu Intelligence’s sector-leading 22 specialist agent education- specific AI, tells us more.
The summer holiday is supposed to be the great reward of school life, with six weeks to ‘recover’ before a new year begins. The reality, of course, often looks vastly different. Around nine in 10 teachers admit to checking work emails over the break, and more than half of headteachers have received at least one that raised their anxiety.
The National Education Union’s most recent State of Education survey, of more than 14,000 teachers, found that 62% feel stressed for the majority of their working time. Well over half regularly work evenings and weekends, and more than a third frequently cancel plans with family and friends to stay on top of their workload. A 2026 wellbeing report found 71% now describe their workload as unmanageable, with just 3% working within their contracted hours.
Much of the hidden work for leaders is analysis. Summer is when the year’s data finally sits still long enough to be made sense of. The self-evaluation is updated. The improvement plan is written. Results are examined in detail in August, cohorts compared, priorities set for September. It is genuinely important work, and it routinely consumes the very weeks meant for rest.
This is where AI, used well, can change things. Much of that analytical work is exactly what AI now does in minutes rather than days. General-purpose tools can already help here when used securely. Something like Google’s NotebookLM, used inside a Google for Education account, for instance, can summarise reports, interrogate a set of documents and draft a first analysis. The limitation is that these tools
rely on you doing the groundwork first: manually pulling, exporting and uploading the data, and doing so safely.
The more powerful approach is education-specific AI that connects securely to your data directly, so the analysis is always current and the manual assembly disappears altogether. Instead of exporting spreadsheets, a leader can simply ask a question in plain language and receive an evidence-backed answer drawn from across their systems and documents. It extends well beyond analysis. Strategic planning is stronger when it is built on connected evidence, seen whole. AI can read a school’s improvement plan alongside its data and show where intention and reality have drifted apart, surfacing the priorities that genuinely deserve attention next year. It can draft a first version of a governor report, a subject review or a departmental summary, leaving the leader to do what only a leader can: apply judgement, context and professional wisdom. The aim, of course, is not to simply hand strategy to a machine, but to remove the drudgery beneath it, so that the plan a leader takes into September is already grounded in the previous year’s evidence, and the school is genuinely inspection- and board-ready – without the analysis having claimed the summer to get there.
Two key considerations, of course, matter throughout. The AI must be education-specific, because generic tools do not understand cohorts, attendance metrics, SEND or progress measures well enough to be trusted with them. Second, the tools you use must be secure and ethical by design, keeping sensitive pupil data properly protected and never fed into public systems.
Get those right, and the prize is real. Not just sharper analysis and stronger plans, but leaders who have actually had a summer.
Only 2% of schools have AI strategies, research finds
New research from Accenture, in partnership with Teach First, reveals that AI is widespread in classrooms across England, but most schools lack the strategy, training and guidance needed to manage it effectively. Based on a survey of nearly 200 school leaders and 30
interviews, the research
shows that while pupils and teachers are already using AI tools, only 12% of schools report having an AI policy in place, and just 2% say they have a fully developed AI strategy.
While leaders broadly agree that AI has strong potential to improve teaching and learning, their understanding remains skewed towards risk rather than opportunity. Concerns around plagiarism, safeguarding, bias and teacher deskilling dominate, with 63% citing lack of staff confidence and 51% pointing to data privacy concerns as barriers to adoption, far outweighing cost (16%). Despite this caution, the research finds that AI is already delivering tangible benefits. Teachers are using it to support lesson planning, generate quizzes and draft exam questions, saving hours of time. However, only 20% of schools currently provide AI-focused training, and three in 10 leaders estimate fewer than 20% of teachers feel confident using the technology.
Leadership engagement emerges as a critical factor in determining progress. While most school leaders believe AI-use is inevitable, only
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16% say they use it daily, and a significant minority do not use it at all. Schools where leaders actively use and model AI see more consistent adoption, while those with disengaged or sceptical leadership experience slower, patchier uptake.
The research also highlights a regional divide, with 29% of school leaders in London reporting daily AI-use, compared to just 12% in the rest of England. Without coordinated action, AI adoption could exacerbate existing inequalities between schools and regions.
To support more effective and confident adoption, Accenture and Teach First’s report identifies five priorities for schools:
• Engage directly with AI as a leader: schools making the most progress are typically those where leaders visibly use AI themselves, modelling thoughtful and responsible adoption in their own day-to- day work.
• Define purpose and boundaries before scaling AI-use: schools that make effective use of AI tend to be clear about both what they want AI to achieve and where its use should remain limited, helping staff navigate uncertainty with greater confidence.
• Start where the value is clearest, but build towards something bigger: successful adoption usually begins with practical, low-risk applications that save time and build trust, creating the foundations for more ambitious use over time.
• Create permission to experiment within clear guardrails: schools that move beyond reactive adoption tend to create cultures where staff feel safe to trial and refine AI use within clearly defined professional boundaries.
• Build capability through shared learning, not just formal training: progress depends on creating opportunities for staff to share practice and build confidence collectively.
July/August 2026
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