The 6 AI Shifts for School Leaders

What AI literacy really means

AI literacy for leaders isn't about learning to code or understanding algorithms. It's actually about making considered changes in how you think about teaching, learning and professional practice.

And it will help you define what it means to lead with AI, not simply manage it.

These are the six essential shifts school leaders need to consider in terms of AI literacy.

Shift 1: From routine tasks to reflective practice

Teaching has long been shaped by the volume of tasks: lessons to plan, books to mark, reports to write.

Reflection often happened only when time allowed.

AI can help us shift that balance. If routine work is automated, teachers can focus more on activities with the highest professional value.

This includes thinking deeply about learning, evaluating impact, and refining their practice.

Without space for reflection, teachers may become passive users of systems rather than active designers of learning.

Reflection keeps creativity, expertise and purpose at the centre of education.

Example:

• Record a one-minute voice note after a lesson, reflecting on what went well and what to work on next


• Use an AI tool to transcribe, summarise, and then produce an actionable change for next time

Shift 2: From data consumption to data literacy

AI operates on data. Every system that makes predictions or recommendations is built on choices about what information is collected and how it's used.

Leaders now need the skills to understand those choices and to judge their implications for fairness, privacy and accuracy.

Data literacy gives leaders clearer insight into how systems work and why they sometimes fail.

It supports ethical oversight by helping identify bias and ensuring transparency.

It also improves strategic planning in areas like budgeting, procurement and curriculum development without reducing learning to numbers alone.

Most importantly, it builds accountability by enabling leaders to explain AI-driven decisions with clarity and confidence.

When data literacy becomes part of school culture, it helps ensure innovation serves education responsibly. Data becomes a tool for improvement, not a force of disruption.

Example:

• Staff work through a data scenario where an analytics dashboard flags a pupil as at risk


• They interrogate what data was collected, what was inferred, and where the model might be wrong


• In triads, they decide one proportionate classroom action, one safeguarding check, and what decisions must remain human — recording a two-line rationale


• This builds practical data literacy, ethical oversight and clear communication without reducing the pupil to a metric

Shift 3: From individual experimentation to collective learning

Many teachers are exploring AI independently. They try prompts, test ideas and adjust their planning or feedback.

This curiosity is valuable, but isolated efforts will not lead to lasting change.

Schools should treat AI as a shared learning opportunity. When professional development is collective rather than individual, innovation becomes more effective, safer and more sustainable.

Collaboration allows schools to share good practice, support each other and build consistent experiences for pupils.

It also strengthens ethical thinking. Teams can discuss how they manage issues like bias, privacy and transparency.

Teachers can contribute to shaping AI strategies, drawing on insights from different subjects and phases to create fairer and more creative approaches.

AI changes quickly. Trying to keep up alone is not sustainable.

Schools that learn together will be better equipped to adapt and to turn curiosity into meaningful progress for all learners.

Example:

• Run a monthly 15-minute AI learning huddle in each department using a simple protocol: one try, one tweak, one risk


• A colleague shows a real classroom use, states one improvement they'll make next time, and names a risk to watch


• Capture the points in a shared doc so practice becomes consistent and safer across the school

Shift 4: From policy compliance to ethical leadership

Policies and regulations are essential, but they only define the minimum standard.

AI introduces ethical challenges that rules alone cannot address. Bias, transparency and autonomy are complex issues that sit at the core of education.

Ethical leadership means guiding decisions with conscience.

It means ensuring that AI aligns with the school's deeper mission and values, such as equity, inclusion, curiosity and dignity.

This type of leadership grows through open dialogue. Discussing scenarios and dilemmas helps staff, pupils and families understand the technology and build shared responsibility.

It also prepares schools to respond when new risks or unexpected consequences arise.

Example:

• Use a “values to guardrails” pre-mortem exercise for any new AI use in your school


• Name the core value at stake, agree one acceptable use, set one clear red line, and choose one check to review impact in four weeks


• Then ask: what’s the worst that could happen?


• Update your guidance as it evolves and make judgements visible for the community

Shift 5: From tool adoption to system stewardship

AI should not be treated as a product to add to a checklist.

Schools will increasingly rely on systems of connected tools that influence both operations and outcomes.

Leadership in this area is about stewardship — curating an ecosystem of tools that work together, serve the school's purpose, and protect its community.

Leaders must ensure that as technology evolves, it remains accountable to core values and continues to safeguard privacy and data.

The focus shouldn't be on adopting more technology, but on making sure the right tools are used in the right ways.

Don’t implement AI for its own sake.

Think about where it adds value by allowing you to offer things that work but aren't possible within current workload or budget constraints — like tutoring — or where it reduces workload, like automated marking.

Example:

• Create a central register of AI tools that lists each tool’s purpose, owner, data in and out, and risk status


• Run a half-termly stewardship review to remove duplication, check privacy, and test how tools work together on priority workflows


• This keeps adoption purposeful and accountable to values

Shift 6: From defensiveness to confident professionalism

AI has caused anxiety among some teachers. It's raised concerns about professional identity and the future of the role.

The goal of leadership is to move staff from uncertainty to confidence.

Teaching is a deeply human profession. Empathy, interpretation and creativity cannot be automated.

AI should be seen as a partner that enhances these qualities, not as a threat to them.

When teachers feel confident in their expertise, they use AI more thoughtfully and creatively.

They model digital wisdom for pupils and maintain high ethical standards.

This confidence also positions teachers as active participants in change, shaping how AI supports education.

As technology continues to develop, the professional judgement of educators remains the foundation for meaningful progress.

Example:

• In a staff meeting, a senior leader does a live demonstration — one example where AI is used cautiously, and one where it is used to replace labour


• They talk through the decision-making process, inputs, questions and outputs

Closing reflections

At the end of this module, here are three things to do:

• Post a one-page staff explainer titled “AI in our school: what it is, how it works, and why it matters”


• Reflect on three decisions in your school that must stay strictly human, with a one-line reason for each


• Ask each department or team lead for a mapped list of any current or planned AI use

In our next module, we'll explore AI literacy through the lens of leadership, vision and culture. We'll consider core decisions, modelling confidence, and approaching boundaries. See you there.