At a glance
Australian schools are not ready for AI. That is the consistent message from the educators we asked.
Teachers know AI matters. Their schools, as institutions, have not built the capability to use it well. Only 9.9% of schools have a formal AI strategy. Roughly six in ten educators rate their AI confidence as moderate or low. Workload reduction is the runaway priority for professional learning.
The gap is not awareness. The gap is structured support, strategic direction, and professional learning that connects AI to the work teachers actually do. This report sets out what we heard, why schools are stuck, and a six-step framework leaders can use to move from individual experiments to whole-school capability.
Teachers know AI matters. Their schools, as institutions, have not built the capability to use it well. Only 9.9% of schools have a formal AI strategy. Roughly six in ten educators rate their AI confidence as moderate or low. Workload reduction is the runaway priority for professional learning.
The gap is not awareness. The gap is structured support, strategic direction, and professional learning that connects AI to the work teachers actually do. This report sets out what we heard, why schools are stuck, and a six-step framework leaders can use to move from individual experiments to whole-school capability.
1. The state of play
The findings draw on the Educator Intelligence AI in Schools Survey, published late 2025, which reached classroom teachers, school leaders, PD coordinators, and support staff across five states and territories. NSW and SA were strongly represented, with respondents from QLD, ACT, VIC, and TAS. Government, independent, and Catholic systemic schools were all included.
This was not only a teacher survey. About half the respondents were classroom teachers. Just over a third were school leaders, including principals, deputies, head teachers, and assistant principals. The remainder were PD coordinators, digital learning leads, regional coaches, and business managers. The mix gives a view from the classroom and the leadership table.
Confidence is moderate and unevenly distributed
We asked educators to rate their confidence in using AI in their teaching or leadership practice on a scale of 1 to 5. The average was 3.3. Right in the middle, leaning slightly positive.
The distribution tells a different story. Over a fifth rated themselves at 1 or 2: essentially unconfident. Another 37.2% sat at 3, the "aware but not sure what to do next" zone. Only 14.9% rated themselves at 5.
In practical terms, around six in ten educators feel somewhere between uncertain and only moderately capable when it comes to AI in their professional practice. That is not a readiness signal. That is a capability gap.
Matthew Esterman, founder of The Next Word and the lead practitioner behind Educator Intelligence, frames it this way:
The strategy vacuum
The most revealing finding in the survey is about school-level AI strategy.
When asked whether their school has a strategy for upskilling staff in AI, 33.9% said "no current plans." Another 28.1% described "informal discussions or early planning." A further 12.4% were unsure whether their school had a plan at all.
Add those numbers up. Roughly three-quarters of educators are working in schools with no formal approach to AI capability. Only 9.9% reported a formal strategy in place.
This is not a technology problem. It is a leadership and governance problem. Schools without a strategy are not failing to upskill their staff. They are making a strategic choice, whether they realise it or not, to leave AI adoption to chance. Chance is not a strategy.
2. What educators want from AI professional learning
We asked educators to select up to five AI-related topics most relevant to their professional growth. The responses reveal clear priorities, and a sharp mismatch with what most schools are currently offering.
The workload signal
The number one priority, by a clear margin, is using AI to reduce teacher workload and admin (65.3%). Not surprising. Important all the same.
Teachers are exhausted. Workload is consistently identified in Australian education research as a top driver of teachers leaving the profession, according to the Australian Teacher Workforce Data published by AITSL. For many educators, AI is the first credible tool that might actually help.
Most teachers are working this out on their own. They discover AI through personal experimentation, word of mouth, or social media. There is no structured, school-led process behind it. The quality of their use varies wildly. Privacy and governance implications are going unaddressed.
The assessment reckoning
Three topics tied for second place at 56.2%. Together they describe a profession in transition: assessment design in the age of AI, differentiating instruction with AI, and understanding which AI platforms exist and how to use them.
The assessment question is the urgent one. Every school is wrestling with the same problem. If students can use AI to complete traditional assessments, what does a valid, meaningful assessment look like now? This is not a question individual teachers should be solving in isolation. It needs a school-wide response, ideally a system-wide one.
What is missing from the conversation
Worth noting what ranked lower. Data privacy and security came in at 20.7%. Foundations of AI sat at 21.5%. Both are essential. Both ranked near the bottom.
The pattern suggests most educators see governance and infrastructure as somebody else's problem. They sit outside the classroom teacher's sphere of influence, which is exactly why they need to be led from the top.
Academic integrity and policy development (24.8%) ranked lower than several practical classroom topics. Teachers want to know how to use AI, not how to police it. That is a healthy instinct. It also creates a risk. Schools that do not build governance frameworks alongside capability will spend the next two years reacting to incidents rather than preventing them.
3. Why schools are stuck
Individual educators are curious, capable, and increasingly willing to engage with AI. Schools, as institutions, are moving slowly. The survey points to three structural barriers.
Barrier 1. No strategy, no direction
Only 9.9% of schools have a formal AI strategy. The 28.1% with "informal discussions or early planning" are often just having conversations. No roadmaps. No milestones. No accountability.
Without a strategy, AI adoption becomes a patchwork of individual experiments. Some teachers embrace it. Others avoid it. The school, as an institution, holds no coherent position on what responsible AI use looks like.
This matters for governance. Boards and principals need to understand that the absence of an AI strategy is itself a risk. It exposes the school to inconsistent practice, potential privacy breaches, and reputational issues. All three are problems that basic strategic planning can mitigate.
Barrier 2. The leadership mindset problem
In the open responses, several educators pointed directly to leadership as the bottleneck. One wrote that schools need to change the "current leadership belief that using AI is cheating." Another flagged reluctance to invest, driven by concerns over data privacy and academic integrity. The concerns are legitimate. They are often used as reasons to delay rather than to act.
The pattern shows up across schools consistently. Leaders know AI is important. They are caught between competing pressures: day-to-day operations, the complexity of AI governance, an absence of clear regulatory guidance, and the understandable caution that comes with introducing powerful new technology into environments full of children.
The result is institutional paralysis. Not opposition. Paralysis. And the longer it lasts, the wider the capability gap becomes.
Students and staff are using AI outside school anyway. They use it for school-related work and for everything else. A school in AI paralysis is letting students cross unfamiliar terrain on their own, with no map and no guardrails.
4. From individual skills to institutional capability
The reframe is straightforward. AI readiness is not an individual professional learning problem. It is an institutional capability challenge.
The difference matters. Treating AI as a PD problem leads to one-off workshops, optional online modules, and the assumption that capability will spread organically. It will not. The survey explains why.
Four layers of whole-school AI capability
Most schools are only addressing the first layer. Whole-school AI capability has four.
Layer 1. Individual awareness: Teachers and leaders know AI exists and have some familiarity with the tools. This is where most schools are. A 3.3 out of 5 average confidence rating tells us awareness is moderate but shallow.
Layer 2. Practical application: Educators can use AI tools effectively in their daily work: lesson planning, differentiation, report writing, assessment design. The survey's top priorities cluster here. 65.3% want workload reduction. 56.2% want to know which platforms exist and how to use them.
Layer 3. Governance and policy: The school has clear policies on AI use, data privacy, academic integrity, and risk management. Only 9.9% of schools have reached this layer in any formal sense.
Layer 4. Strategic integration: AI is embedded in the school's strategic plan, budget allocation, hiring, and professional learning architecture. This is where AI moves from being a tool to being a capability multiplier. Almost no Australian schools are here yet.
The gap between Layer 1 and Layer 4 is where the real work needs to happen. It cannot happen through bottom-up experimentation alone. It needs leadership, investment, and a structured approach.
5. A six-step framework for school AI readiness
Drawing on the survey and what we see on the ground across Australian schools, here is what an effective school AI readiness approach looks like.
Step 1. Audit your current state
Before investing in AI professional learning, run an honest assessment of where your school currently stands. Use the four-layer framework above. Where are most of your staff? Where is your leadership? Where are your policies? The baseline informs every decision that follows.
If you want a structured way to run this, the free AI Readiness Self-Assessment for Schools walks leadership teams through the questions and produces a starting picture you can share with your board or executive team.
Step 2. Set a strategic direction
Develop a school-level AI position that addresses four things: which AI tools staff and students can use, how data privacy and academic integrity will be managed, what professional learning will be prioritised, and how the school will measure progress.
This does not need to be a 50-page document. A clear two-page position statement, endorsed by the board or principal, is enough to start.
Step 3. Invest in structured professional learning
Move past ad hoc workshops. The data shows educators want practical, sustained learning that connects AI to their actual work. Design a PD pathway that builds from awareness (Layer 1) through practical application (Layer 2) to governance understanding (Layer 3). Make it blended. Make it flexible. Make it evidence-informed.
Step 4. Build internal champions
The 27.3% who identified "leading AI adoption at school level" as a priority are your early adopters. The 25.6% interested in coaching and mentoring teachers are your potential multipliers. Identify them. Invest in them. Give them the mandate and time to support their colleagues. Whole-school capability is built through networks, not mandates.
Step 5. Create governance before you need it
Do not wait for an incident to write your AI policy. The schools that handle this transition best are the ones building governance proactively: acceptable use, data handling, procurement decisions, academic integrity. Build it before you are forced to react to a problem.
Step 6. Measure and iterate
Track the metrics that matter. Staff confidence (you now have a baseline of 3.3 out of 5). Time saved through AI adoption. Quality of AI-assisted work. Student outcomes. Report these to your board or leadership team quarterly. That creates accountability. It also builds the evidence base that 38.8% of respondents said would drive further investment.
6. What comes next
The window for action is now. AI is moving faster than school policies, faster than teacher preparation, and faster than most leadership teams' understanding of the implications. Schools that move first will be better prepared. They will also attract better staff, serve their students more effectively, and build the institutional resilience every school needs.
The data is both a warning and an opportunity. A warning, because the capability gap is real and widening. An opportunity, because the demand is there. 76% of respondents asked to be notified when new AI professional learning becomes available. 41.3% want whole-school solutions. The appetite exists. What has been missing is the supply.
That is what Educator Intelligence was built to address. Practical, evidence-informed professional learning for schools that want to move from awareness to capability. From individual experiments to institutional readiness. From reacting to leading.
Building AI Capability for Schools and Educators.
Social
An Initiative by
