A Starlight reflection on credibility, professional trust and evidence-led teacher development.
One of the most valuable things for any education startup is not publicity. It is being challenged to explain clearly why the work matters.
Over recent weeks, we have been grateful for the support, guidance and visibility offered by Ross McGill and TeacherToolkit as we continue to develop Starlight, our AI-powered coaching platform for teachers.
Ross has long been one of the most influential voices in UK education, not simply because he comments on what is happening in schools, but because he understands the realities of teaching, the pressures teachers face, and the need for solutions that are genuinely practical, trustworthy and usable.
We are grateful to Ross for his recent TeacherToolkit blog post exploring AI coaching for teachers, and for helping to open up an important conversation about how AI can support professional reflection, reduce workload and strengthen the role of human coaching in schools.
That matters, because AI in education is moving quickly, and when something moves quickly the temptation is to lead with novelty. New tools, new features, new claims. In schools, novelty is not enough. Trust matters more.
Teachers have seen enough initiatives come and go. They have lived through enough accountability pressures, performance cycles and workload demands to be rightly cautious about anything that promises transformation. If AI coaching is going to have a meaningful place in education, it cannot arrive as another thing being done to teachers. It has to be something that works for them.
That is the central idea behind Starlight.
Starlight listens to a whole lesson, not a short clip or a single observed moment, and uses that audio and its transcript to offer private, personalised coaching feedback to the teacher. Working from the whole lesson is the point. A five-minute snippet or a generic AI prompt can only ever see a fragment. A full lesson reveals the patterns that actually shape learning: how questioning develops over time, where retrieval is used or missed, how explanations land, which pupils are drawn in and which are not.
The aim is not to replace human coaching. It is not to score teachers. It is not to create another surveillance mechanism. The aim is to make high-quality reflection more accessible, more frequent and more useful.
We explored this further in our recent post, Does AI Coaching Work? Why the Better Question Is: Where Does It Work Best?, where we argued that the value of AI coaching depends on context, purpose and the safeguards around its use.
That distinction is crucial. The best coaching is not about judgement. It is about noticing. It helps a teacher see something in their practice they might not have seen before: a pattern in questioning, a missed opportunity for retrieval, a moment where a pupil's answer could have been explored further, a strength worth repeating, a small and specific next step that might make the next lesson stronger.
Human coaches do this brilliantly, but they cannot be everywhere, all the time, for every teacher. That is the problem Starlight is trying to solve. We are not trying to deepen the surveillance culture in schools. We are trying to widen access to meaningful professional reflection.
This is why the support from TeacherToolkit matters to us, and not only because it helps a young company reach more schools. It places the conversation in the right professional space. AI coaching for teachers should not be treated only as a technology question. It is a teacher development question, a workload question, a trust question and a leadership question.
Ross’s recent TeacherToolkit article on AI coaching for teachers helps bring that wider conversation into view.
For us, this is part of building Starlight responsibly. We want school leaders to understand what the platform can do, and what it should not be used for. We want teachers to feel the feedback belongs to them. We want AI to support professional growth rather than flatten it into a number, and to make reflection easier rather than make teaching feel more exposed.
That is also why our language matters. We do not think Starlight is best described as “AI lesson feedback”. That phrase is too narrow, and it can sound mechanical and transactional. What we are really building is evidence-led teacher development at scale: taking the evidence already present in the lesson, the words spoken, the questions asked, the explanations given, the responses invited, and using it to help teachers reflect with more precision. It keeps the process private, professional and purposeful.
That thinking is also consistent with the Education Endowment Foundation’s guidance on effective professional development, which emphasises that professional learning should be carefully designed, rooted in evidence and focused on changes in classroom practice.
That is the kind of AI use schools need. Not hype, not surveillance, not replacement, but support, insight and a better feedback loop.
The early response to Starlight has shown us that teachers are open to AI when the purpose is clear and the safeguards are real, and especially when the feedback is useful, respectful and immediately connected to classroom practice. That is the path we want to keep following. Working with people like Ross helps us sharpen that message and keep the work grounded in the real world of schools.
For an education startup, visibility matters, but credibility is everything. In education, trust is not bought. It is earned by serving the real needs of teachers and school leaders over time. Ross McGill and TeacherToolkit have built a strong reputation for practical, teacher-first support, and that is why their interest in Starlight means something to us. We want Starlight to be judged in the same way: by whether it is useful, trustworthy and genuinely supportive of better teacher development.
If you would like to see what a Starlight report looks like for a lesson in your own school, you can book a demo at https://starlightmentor.com/demo-request.
Spark Insight with Starlight, and build trust in teacher development through meaningful reflection.
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The Insight Engine is written by Adam Sturdee, co-founder of Starlight, the UK’s first AI-powered coaching platform, and a senior leader with responsibility for teaching, learning and coaching. This blog is part of a wider mission to support educators through meaningful reflection, not performance metrics. It documents the journey of building Starlight from the ground up, and explores how AI, when shaped with care, can reduce workload, surface insight, and help teachers think more deeply about their practice. Rooted in the belief that growth should be private, professional, and purposeful, The Insight Engine offers ideas and stories that put insight, not judgment, at the centre of development.
