Analysis Education Review UK Middle East blog Inspections 25.06.2026
From Feasibility to Reality: AI Is Already Transforming School Inspections
Public debate around AI in education has largely centred on teaching and learning, and the question of the impact on students, teachers and school leaders when this technology is introduced into classrooms
Over the past 18 months, Etio has been at the leading edge of less visible, but potentially no less transformational, developments in one of the critical enablers of outstanding school performance; school inspections, quality reviews and accountability mechanisms.
The question here is: “What happens when AI is applied to how we define, measure and assure quality in education systems?”
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CatoAI represents a step-change in how education systems deliver inspection and quality assurance - not through automation of judgement, but by transforming the system that underpins it.
By embedding AI across the inspection lifecycle, CatoAI simultaneously drives efficiency, quality and system-level impact:
In short, CatoAI is not a tool layered onto existing processes. It is the foundation for a transformation of inspection itself - where human expertise is amplified, system capacity is expanded, and quality assurance becomes faster, fairer and more impactful. |
Our 2024 feasibility work explored, in practical terms, whether AI could meaningfully support inspection at all - testing its ability to process evidence, assist inspectors and improve outcomes across the review process. The conclusion was clear: AI has real potential to improve efficiency, quality and fairness in school inspection.
The implication for school inspection authorities, regulators and other quality assurance agencies (including school groups themselves): Given that AI works, how far are you willing to let it transform your approach to school quality reviews?
Inspection systems are under strain - and incremental change is no longer enough
School quality evaluation systems today are operating under conditions it was not designed for, (within finite, and often shrinking, resources).
- More schools.
- More complexity.
- Greater scrutiny.
- Higher expectations of consistency and transparency.
The many forms of quality reviews deployed by different countries/systems still play a vital role; providing assurance, informing policy and, at best, driving improvement across schools and other educational institutions.
But the way inspections/reviews are delivered has changed relatively little. Too much inspector time is still absorbed by activities that are necessary but not high value: assembling evidence, reconciling inconsistencies, writing reports under pressure, and revisiting issues that should have been addressed earlier in the process.
AI does not solve everything, but it changes what is possible.
What we now know - with much greater confidence
Our 2024 work did more than test capability – it revealed three specific real impact shits:
- Trust and reliability. Inspection has always depended on confidence in judgements. Yet variability between inspectors and opacity in how conclusions are reached have long been sources of tension. AI changes this dynamic by making the connection between evidence and judgement explicit, removing human bias. When every judgement can be traced, interrogated and explained, reliability stops being assumed and starts being demonstrated.
- Validity and inspector capability. One of the structural challenges in inspection is the limited pool of individuals who combine deep sector expertise with inspection experience. AI-supported “co-pilot” models begin to change that equation. They allow organisations to expand and diversify their inspection workforce, without sacrificing rigour, by supporting consistent application of frameworks and standards in real time.
- System insight. Traditionally, inspection reports describe what has happened in a single school at a single moment in time. AI fundamentally alters this by allowing systems to learn across inspections. Patterns, risks and opportunities become visible at a scale that was previously impractical. Inspection starts to move beyond measurement and toward insight, becoming a genuinely strategic asset for system improvement.
These were not theoretical conclusions. They were grounded in testing. What has changed since is that the same ideas are now starting to operate in live contexts.
This is no longer experimental
At Etio, we are now working with several government bodies, regulators and major school operator groups who are actively exploring how AI can be embedded into their inspection and quality assurance systems. These conversations are no longer speculative. They are grounded in real questions about system design, operational workflows and governance.
In one case, an organisation has already moved beyond exploration and committed to integrating CAI into an existing inspection programme.
This matters because it marks a transition from “could this work?” to “how do we make this work safely and at scale?” And once that shift happens, momentum tends to follow.
Where AI is actually changing the system
In our experience, by introducing AI into the inspection system one of the key benefits is removing the friction around judgement - freeing up time, improving visibility, and strengthening consistency.
The changes are operational, but the consequences are strategic. Evidence, which has traditionally been fragmented and synthesised under pressure, becomes structured and continuously visible. Instead of assembling a picture retrospectively, inspectors are working with a live, coherent evidence base.
Quality assurance, which has historically been a retrospective checkpoint, begins to move upstream. Issues are surfaced earlier, resolved earlier, and less time is spent correcting problems after the fact.
And perhaps most importantly, inspection stops being a series of isolated events. When evidence is structured consistently, it can be analysed across schools, over time, and at scale. Inspection begins to generate insight, not just reports. This is not a marginal improvement - it is a shift in how the system functions.
The real constraint is not technology - it is confidence
Inspection operates in a high-trust, high-stakes environment. Concerns around bias, transparency, governance and professional accountability are not barriers to be dismissed - they are central to the legitimacy of the system. But they do lead to a critical decision. Do you treat AI as a risk to be contained, or as a capability to be governed and applied deliberately?
The organisations moving forward are choosing the second path. That does not mean moving fast without caution. It means designing AI into the system in a way that is controlled, transparent and aligned to professional judgement. It means being explicit about where AI adds value, and where it should not be used.
Handled properly, the effect is not erosion of trust, but the opposite:
- Greater transparency.
- Greater consistency.
- Stronger, more defensible inspection outcomes
A clearer direction of travel - and a strategic choice
Taken together, these developments point toward a gradual but undeniable shift. Inspection will become:
- Less episodic
- Less manually intensive
- More structured
- More insight-driven
Inspectors will spend more time evaluating and less time assembling; Quality Assurance will become more targeted and scalable; and inspection data will become something systems actively use, not simply archive.
The key question is not whether this shift will happen. It is who will shape it.
Where to begin
For most organisations, the starting point is not transformation. It is clarity:
- Clarity about where current processes are constraining value.
- Clarity about where AI can credibly support change.
- Clarity about the governance and safeguards required to maintain trust.
From there, progress tends to follow. That is the stage many early adopters are in now - not experimenting in isolation, but beginning to reshape their systems in a controlled and deliberate way.
If you would like to explore what this could mean for your own inspection system, please request a discovery workshop with our Education Review team.
We will examine potential use cases for AI in school quality reviews in the context of your organisational objectives and ambitions, show you the system in action, and identify potential next steps to validate the transformation process and tool for your specific system.
