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?
School quality evaluation systems today are operating under conditions it was not designed for, (within finite, and often shrinking, resources).
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.
Our 2024 work did more than test capability – it revealed three specific real impact shits:
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.
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.
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.
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:
Taken together, these developments point toward a gradual but undeniable shift. Inspection will become:
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.
For most organisations, the starting point is not transformation. It is clarity:
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.