Most usage tracking tells you whether a product was used. Teachers and schools want to know whether the usage was meaningful. We track learning events across four dimensions: who is using the tool, what they're doing, what results it produces, and where it sits in the learning journey.

For products where AI is part of the learning interaction, we standardise and benchmark those signals too — what the AI is actually doing, and whether it's supporting retrieval, scaffolding explanation, or just generating responses.

As more products connect, the benchmarking becomes genuinely meaningful — not just "here's how your students are doing" but "here's how that compares to schools like yours, using products in the same category." We're working toward an open standard for learning events that any product, school or system can use.

Most edtech evidence is difficult to sustain at scale. Independent studies are expensive and hard to translate across contexts. Provider case studies rarely build into a continuously evolving evidence picture.

We accumulate evidence over time. Agentic workflows manage collection and analysis automatically, drawing on teacher feedback, school leader views, pupil voice and usage patterns. A provider that has been on EdTech Impact for three years has been building evidence since day one.

The output isn't a static report that ages. It's a compounding evidence picture — tracking how a product is perceived, where implementation is working, and whether it's delivering value for money.

Most schools don't have a clear picture of their own edtech ecosystem. What tools do they have? How are they being used? Where do they overlap? Where are the gaps? The evidence is scattered, the picture is incomplete, and renewal decisions get made on instinct rather than insight.

EdTech Impact Manager continuously audits a school's entire edtech ecosystem — turning portfolio complexity into the self-knowledge schools need to make decisions that reflect their reality. It's not a procurement tool or IT dashboard. It's a system governance layer — giving schools the oversight and intelligence to manage their digital environment with confidence.

For providers, it offers something genuinely new — a personalised workspace where each customer can monitor real-world performance, benchmark against alternatives, and see exactly how the product sits within their organisation's broader portfolio.

The next frontier for edtech evidence isn't a report or a dashboard. It's infrastructure — a standardised, machine-readable layer that any AI system, platform, or tool can connect to and query.

We're building an MCP layer that makes our evidence data accessible as a live, structured resource. Schools using AI assistants can query their edtech portfolio directly. Platforms building on top of our data can surface evidence at the moment of decision. Policymakers can interrogate the evidence base without commissioning a study.

This is what it means to democratise edtech evidence at scale — not just making it available to humans through an interface, but making it accessible to the systems that are increasingly shaping how schools make decisions.

Data tells you whether a product was used. It doesn't tell you whether it was any good. Behind every usage metric is a pedagogical question — does this tool actually support the way children learn? That question requires human judgement, and the best people to make that judgement are teachers.

We believe teachers should be the primary evaluators of edtech quality. Which is why we're training educators to become certified pedagogical evaluators using the EAF assessment framework — the global quality standard for learning solutions developed by Education Alliance Finland. Trained evaluators assess learning design, curriculum alignment, and cognitive challenge against a rigorous, research-informed methodology that goes far beyond star ratings.

Evidence without pedagogy is just data. We intend to keep the two together.

The most valuable evidence isn't produced by a single school or a single provider. It comes from many schools, teaching similar students, using the same tools, in different contexts — and comparing what they find.

We're building a network of schools willing to share anonymised data and co-design the research itself — identifying the learning events that matter to them, defining what good looks like in their context, and deciding what questions the evidence should answer. Schools that participate help determine what gets measured, and why, for themselves and for every school that comes after them.

The evidence edtech needs has always been a collective problem — and we're building the community to solve it, starting with the EdTech Impact Summit.