Building Teacher Expertise in the Age of AI
There’s a lot of conversation about AI in education at the moment, so this paper shared by Tim Wilson felt particularly worth pausing on.
A couple of my thoughts on the paper, given my experiences in schools and my interest in EdTech and implementation.
‘The goal of great teaching has never been to make learning easy. It has been to make the right things the right amount of hard for each student in the class.’
This really resonates with me as it is all about the purpose of teaching - not the tools that we use to support learning to happen. It captures the day-to-day work of teaching – the ‘art’ of teaching. Not as something separate from research, but how evidence-based practices actually play out in various classroom contexts. Teachers are constantly making judgements about when learning is secure, when students are ready to transfer it, and what scaffolds are needed to help them engage and succeed. All of this sits alongside an understanding of who their students are and the dynamics of the group.
I think this also applies to how leaders work with staff. If we believe this for students, it makes sense that it applies to adults too. Each teacher is at a different point, and the role of leadership is to get that level of challenge right so they can keep engaged in their work, learning and improving.
Interestingly, none of this sits with the tools themselves, tech or otherwise. It sits with the professional engaging in the work.
‘The Dunning-Kruger effect compounds the problem—novice learners are least able to recognize their own gaps, especially when early AI-assisted success masks fragility.’
I’ve started to see and hear about this playing out in different professions. In areas like teaching, medicine and law, early career practitioners need time in the “sandpit” to build their expertise, even though it can feel slower or less efficient.
With AI, I believe this becomes even more visible. An AI generated lesson plan might look fine at first glance. Experienced teachers would tend to question and adapt it based on their understanding of what has impact and what doesn’t in their context. For those newer to the profession, that’s harder, simply because their discernment frame is still developing.
While some may see that as a flaw, I think that experienced educators understand that it’s part of learning. From a system perspective, it just means we need to continue to be deliberate about how we support teachers to build that judgement over time.
In that sense, AI can be really useful for experienced teachers as a cognitive support as identified in the paper, but for those earlier in their careers, it needs to be paired with opportunities to practise, get feedback and refine their thinking. The consideration here now for schools to understand AI and its implications.
Definitely food for thought about how we move into the next wave of teaching FOR learning.
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