There’s a problem with these debates, we’re focusing on the tools instead of the outcome.

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Most conversations start with, “How can we use AI in schools?” This may feel like progress, but it’s the wrong place to begin conversations and planning.

‍

Education has never struggled because it lacked technology. It struggles when systems lose sight of their purpose, improving learning and empowering teachers to help students succeed. When institutions start with technology, they risk investing in solutions that look impressive but deliver little educational value and also risk missing what we already know works for teaching and learning. Research consistently shows that teachers are one of the most important factors influencing student learning. Even more powerful is teacher efficacy, the belief that teachers can meaningfully influence student outcomes. When that belief is strong and supported, learning improves significantly. Yet this is not where most AI strategies are focused.

‍

So instead of asking, “How can we use AI?” institutions should be asking, “What improves learning outcomes, and how can technology help support that?” This is a subtle shift, but it changes how decisions are made. AI doesn’t change the mission of education, it adds a potentially powerful new capability. The priority should not be AI adoption itself, but ensuring that learning outcomes such as critical thinking, understanding, and the ability to apply knowledge remain central to institutional strategy.

‍

AI is not the strategy. Learning is the strategy.

‍

Every tool, AI or otherwise, should be evaluated against a clear standard:

‍

• Does it improve student understanding?
• Does it help teachers teach more effectively?
• Does it provide better insight into learning?

‍

If the answer is no, it’s not innovation. It’s a distraction. If the answer is maybe but we’re not sure, dig deeper and look for the evidence that it has the potential and where possible, implement small-scale pilots to establish local efficacy. The real opportunity is not replacing teaching, but enabling every teacher to operate at their best.

‍

For policymakers and education leaders, this reframing has clear implications.  First, it changes how decisions are made. Before adopting any new technology, institutions need to define what specific learning outcome they are trying to improve and how success will be measured.  

‍

Second, it changes what we prioritise. Education doesn’t need more tools, it needs better outcomes, which means focusing on what we know has impact in classrooms:

‍

• clear explanations
• formative assessment
• high-quality feedback
• strong teacher–student relationships

AI can support these practices, but it cannot replace them.

‍

Third, it requires discipline. There will always be a new tool, a new platform, a new model, but technology adoption without measurable improvements in learning simply adds complexity to already overloaded systems.

‍

Start with outcomes, not tools
Before adopting any new technology, define the specific learning outcomes you are trying to improve. AI should be evaluated against its ability to support those outcomes, not its capabilities alone.

‍

Define how impact will be measured
Move beyond usage metrics and activity. Establish clear ways to measure whether student understanding is improving, whether misconceptions are being addressed, and whether teaching effectiveness is strengthening.

‍

Align AI strategy with learning strategy
AI should not sit as a standalone initiative. It needs to be embedded within broader institutional priorities around teaching, learning, and student success.

‍

Invest in capability, not just technology
The effectiveness of any tool depends on how it is used. Institutions should prioritise building teacher capability and confidence alongside any technology investment.

‍

Avoid scaling without evidence
Pilot new approaches, evaluate their impact, and scale only where there is clear evidence of improvement. Without this discipline, technology risks adding complexity without delivering value.

‍

The future of education won’t be defined by the tools institutions adopt, it will be defined by the learning we enable. For leaders and policymakers, the challenge is not to move as fast as possible with AI, it’s to stay focused on what matters most — and ensure that every decision, including those about AI, serves that goal.

There’s a problem with these debates, we’re focusing on the tools instead of the outcome.

‍

Most conversations start with, “How can we use AI in schools?” This may feel like progress, but it’s the wrong place to begin conversations and planning.

‍

Education has never struggled because it lacked technology. It struggles when systems lose sight of their purpose, improving learning and empowering teachers to help students succeed. When institutions start with technology, they risk investing in solutions that look impressive but deliver little educational value and also risk missing what we already know works for teaching and learning. Research consistently shows that teachers are one of the most important factors influencing student learning. Even more powerful is teacher efficacy, the belief that teachers can meaningfully influence student outcomes. When that belief is strong and supported, learning improves significantly. Yet this is not where most AI strategies are focused.

‍

So instead of asking, “How can we use AI?” institutions should be asking, “What improves learning outcomes, and how can technology help support that?” This is a subtle shift, but it changes how decisions are made. AI doesn’t change the mission of education, it adds a potentially powerful new capability. The priority should not be AI adoption itself, but ensuring that learning outcomes such as critical thinking, understanding, and the ability to apply knowledge remain central to institutional strategy.

‍

AI is not the strategy. Learning is the strategy.

‍

Every tool, AI or otherwise, should be evaluated against a clear standard:

‍

• Does it improve student understanding?
• Does it help teachers teach more effectively?
• Does it provide better insight into learning?

‍

If the answer is no, it’s not innovation. It’s a distraction. If the answer is maybe but we’re not sure, dig deeper and look for the evidence that it has the potential and where possible, implement small-scale pilots to establish local efficacy. The real opportunity is not replacing teaching, but enabling every teacher to operate at their best.

‍

For policymakers and education leaders, this reframing has clear implications.  First, it changes how decisions are made. Before adopting any new technology, institutions need to define what specific learning outcome they are trying to improve and how success will be measured.  

‍

Second, it changes what we prioritise. Education doesn’t need more tools, it needs better outcomes, which means focusing on what we know has impact in classrooms:

‍

• clear explanations
• formative assessment
• high-quality feedback
• strong teacher–student relationships

AI can support these practices, but it cannot replace them.

‍

Third, it requires discipline. There will always be a new tool, a new platform, a new model, but technology adoption without measurable improvements in learning simply adds complexity to already overloaded systems.

‍

Start with outcomes, not tools
Before adopting any new technology, define the specific learning outcomes you are trying to improve. AI should be evaluated against its ability to support those outcomes, not its capabilities alone.

‍

Define how impact will be measured
Move beyond usage metrics and activity. Establish clear ways to measure whether student understanding is improving, whether misconceptions are being addressed, and whether teaching effectiveness is strengthening.

‍

Align AI strategy with learning strategy
AI should not sit as a standalone initiative. It needs to be embedded within broader institutional priorities around teaching, learning, and student success.

‍

Invest in capability, not just technology
The effectiveness of any tool depends on how it is used. Institutions should prioritise building teacher capability and confidence alongside any technology investment.

‍

Avoid scaling without evidence
Pilot new approaches, evaluate their impact, and scale only where there is clear evidence of improvement. Without this discipline, technology risks adding complexity without delivering value.

‍

The future of education won’t be defined by the tools institutions adopt, it will be defined by the learning we enable. For leaders and policymakers, the challenge is not to move as fast as possible with AI, it’s to stay focused on what matters most — and ensure that every decision, including those about AI, serves that goal.