Interviews with UK Multi-academy Trust Leaders

Executive Summary

The following field notes are drawn from interviews with UK MAT leaders. The semi-structured interviews focused on evidencing edtech and data fragmentation across teaching, learning, and governance. The goal of the research was to better understand the lived experience of what schools need (and don't want) from edtech. 

Increasingly, it is said that school need more evidence. However, none of the respondents could point to a measurable outcome from an edtech deployment. One reason for this is that evidence is scattered across systems that do not speak to each another. Further, the fragmentation of data causes more administration, blindspots, and missed interventions for leadership and educators to help learners. More broadly, this can be conceptualised as a fragmentation problem with many surface-level symptoms for teachers, learners, and boards. For example, the time-intensive task of synthesising external communications about policy changes was an area identified where technology can liberate busy leaders who prefer to prioritise more impactful work.

Fragmentation looks different at each scale

• National scale, dozens of academies: a coordination problem. Several improvement priorities are held at once, and attention to one lets another slip.
• Regional scale, heads holding their own budgets: an oversight gap. Purchases are made school by school and never reviewed centrally.
• System scale: an interoperability ceiling. Schools hold vast quantities of data in products that cannot exchange it, which caps what any AI layer on top can do.

Information arrives faster than anyone can act on it

One trust pays an agency for weekly policy summaries. A director then filters those again into a short digest for principals. Long updates go unread, so brevity is the condition of the update working at all.

Each update demands a different response. A statutory change needs a policy and a recording system that week. A landscape shift needs a review of current practice. Primary, secondary and specialist provision rank the same update differently, and the sorting happens inside one person's head.

Feedback runs at the speed of the term

Interventions are agreed in termly pupil progress meetings and reviewed the term after. Trust oversight sits on top of that: one improvement lead collecting data from heads and passing it upward in generic form.

Asked how quickly they would know a school was drifting, one chief executive was blunt. You never quite know what is going on across six sites when it reaches you through one person, once a term.

Scepticism about AI is an asset

Leaders were consistently sceptical of AI claims: products marketed as intelligent that are not, selective vendor evidence, peer recommendations that may themselves be vendor-influenced, and evaluations too thin to rely on.

One trust moved a maths intervention from one-to-one human tutoring to an AI equivalent. It cost more and produced worse outcomes, and was discontinued.

Where confidence scores, editable outputs and explicit flagging of data gaps were shown, every conversation turned positive. Human-in-the-loop is the entry requirement in this market.

What follows

The sharpest gap is evaluation. Leaders wanted something that could say which of the tools they already own earn their place, and at what cost.

Two data streams would make that possible. Spoken observation captured as it happens, and context that accumulates across a year instead of resetting each term.

Method and anonymisation

Structured discovery calls with executive and national-director level leaders in English multi-academy trusts, collectively responsible for around 50,000 students. Recorded with consent, coded from transcripts, and reported here in anonymised paraphrase. No trust, school or individual is identified.

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