The AlphaGo Moment — And What It Means for Teacher Coaching

In March 2016, millions watched as AlphaGo, an AI developed by DeepMind, faced off against Lee Sedol, one of the greatest Go players of all time.

It was game two. Midway through the match, AlphaGo played a move so unexpected that commentators thought it was a mistake. Move 37. A stone placed not where convention, training, or human instinct suggested — but where a deep, unseen strategy was unfolding. Lee Sedol left the room. Spectators murmured. In the hours that followed, it became clear:

AlphaGo had “seen” dozens of moves into the future. The move wasn’t random — it was a calculated risk, part of a far-reaching plan that redefined how humans thought about the game.

This was a watershed moment in AI history. Not because AlphaGo won, but because it demonstrated that AI could think in ways that were not just faster or more accurate than humans — but genuinely different. It wasn’t imitating human mastery; it was building its own.

From Go Boards to Classrooms

At Starlight, we think a lot about this moment. Not because we’re teaching AI to play games, but because we’re building something that, like AlphaGo, can “see ahead” — not in stones and grid lines, but in the arc of a teacher’s professional growth.

Today, Starlight analyses the audio from a single lesson and returns insights almost instantly.

But what happens when Starlight starts to build memory? When it can connect this week’s lesson to last term’s, to last year’s — spotting subtle changes, recurring strengths, and emerging opportunities? That’s when we move from feedback in the moment to feedback with foresight.

Professional Development as a Long Game

Great teaching isn’t a single winning move. It’s the accumulation of hundreds of small adjustments — experimenting with questioning, refining explanations, managing transitions, scaffolding differently. Some changes pay off immediately. Others only reveal their impact over months or even years.

Starlight’s future is about tracking these patterns across time. If AlphaGo could play a move today because it knew what would happen twenty turns later, Starlight aims to guide teachers towards choices now that will improve learning outcomes well into the future.

That means:

• Connecting the dots between different moments in a teacher’s practice.

• Spotting trends invisible in the day-to-day rush.

• Building a narrative of growth that teachers and leaders can reflect on, not just a snapshot.

Why This Matters

In Go, Move 37 shifted the entire match. In teaching, the “moves” are quieter — an open-ended prompt here, a pacing change there. But over time, they shape the game entirely. With a memory of professional development, Starlight won’t just be an AI that reacts to what it hears. It will be an AI that learns with you, adapting its feedback to your evolving style, context, and goals.

The AlphaGo moment showed the world that AI could surprise us — not by replacing human skill, but by extending it in new directions. In education, that same leap could mean AI that doesn’t just help teachers in the moment, but helps them play — and win — the long game.

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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 Assistant Headteacher at St Augustine’s Catholic College. 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.