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If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring the absolutely crucial topic of Sun School AI policy through an insightful article from Teacher Magazine titled School Leadership Developing an AI Policy, written by Kara Baxter, the Deputy Principal for for Learning and Teaching at Strathcona Girls Grammar School. What really struck me right off the bat is that Strathcona's policy is already on its second iteration. That's a powerful signal right there, isn't it? It tells us that this isn't a static document, it's a living thing, constantly evolving. Now, Cara Baxter opens by acknowledging that when generative AI first burst onto the scene in schools, a lot of the talk understandably revolved around cheating, disruption, and just general uncertainty. And those concerns, of course, are still important. But what she argues, and I completely agree, is that focusing solely on those risks overshadows a much bigger question. How do we genuinely help young people learn to use AI thoughtfully, ethically and effectively? It's about teaching students not to outsmart machines, but to outthink them. First, let's look at Strathcona's core philosophy. Because this is where the policy truly takes root. Their approach is guided by a very simple yet profoundly important principle. AI should enhance learning, not replace it. This aligns so perfectly with one of my own core pillars for AI in education that AI is about enhancement, not replacement. It's about augmenting human capability, never substituting for the unique spark of the educator or the intrinsic drive of the learner. Strathcona's philosophy, as Kara Baxter explains, is deeply grounded in the belief that human relationships, teacher expertise, critical thinking, and deep learning are always at the heart of education. AI, in this view, is a powerful tool, but it's fundamentally just that, a tool. The article highlights that this perspective isn't just internal to Strathconna, it resonates with global research and policy. UNESCO, for example, has been advocating for a human centered approach to generative AI, emphasizing that its educational use must protect human agency and genuinely benefit learners and teachers. This isn't about adopting technology for its own sake, but about ensuring ethical, safe and meaningful implementation. It's a classic example of starting with why not how? So how did Strathcona go about developing this policy? What I found really fascinating is that the process itself was seen as a way of building shared language, consistency and confidence right across the school. Think about that for a moment. It wasn't just about creating a rulebook. It was a collaborative act of sense making. As staff and students started engaging with generative AI in various ways, the need for clear expectations became obvious. We're talking about ethical use, academic integrity, privacy, safeguarding, cognitive engagement, and respecting teacher professional judgment. This policy then became a framework for decision making, a backbone for professional learning, and a way to ensure that all innovations stayed coherent rather than becoming fragmented or simply driven by the latest shiny tool. The policy, now in its second iteration, emerged from ongoing conversations between school leaders, teachers and their ICT team. Always viewed through a risk and compliance lens, it's a blend of cutting edge research and the practical realities of classroom experience. They looked at emerging national and international guidance, explored both the opportunities and the risks, and considered how these technologies intersect with teaching, learning and assessment. This iterative approach is crucial for any IOD AI policy development and crucially, the process remained grounded in the practicalities of school life. Teachers needed clear guidance on what responsible use looked like day to day, students needed support to navigate this rapidly changing digital landscape, and families needed confidence that innovation was balanced with appropriate safeguards for learners across all age groups. That consideration for all stakeholders, students, teachers and parents is absolutely key to success. One of the foundational tenets of their policy, which really echoes my outsource the do and not the thinkum pillar, is that staff are encouraged to use AI for professional tasks, drafting, communications, summarizing information generating ideas. But, and this is the critical distinction, it's explicitly not a substitute for core teaching responsibilities. The policy states that AI should enhance teaching and learning while safeguarding student engagement, cognitive development and ethical standards. It also makes it abundantly clear that AI use must not replace students thinking, writing or problem solving. This distinction, as Kara Baxter points out, is absolutely critical. We want AI to help us hold the complexity so we have capacity for creativity, but the intellectual heavy lifting the judgment, the wonder that stays firmly with the human now. While these principles apply school wide, the article explains that the way AI literacy is developed adapts to age and stage. For younger learners, the the focus is on curiosity, questioning information and understanding that digital technologies are created by people and reflect human decisions. As students move into the middle and senior years, conversations deepen, exploring bias, misinformation, privacy, intellectual property and responsible use. By year 12, the expectation isn't just appropriate use, but critical evaluation of AI outputs, understanding its limitations and making informed decisions about when it truly enhances learning and when independent thinking is non negotiable. This isn't about rote memorization of tool features, it's about developing collaborative reasoning ability, understanding AI limitations, and managing AI conversations with precision what I call AI literacy. This approach helps to build a comprehensive AI competency framework for teachers and students alike. What's more, AI literacy isn't siloed in a single lesson or subject. While students might get their initial grounding in digital technology classes, it's understood as a capability that needs to be developed right across the curriculum. Students encounter AI in research projects, inquiry learning assessment tasks, and interdisciplinary experiences that challenge them to evaluate information, challenge assumptions, and justify their thinking. And this, I think, is where the real magic happens. Increasingly, learning experiences are shifting focus from simply generating answers to asking good questions, exercising judgment, and applying knowledge in meaningful contexts. In this sense, AI literacy becomes an inseparable part of broader digital information and critical thinking literacies that young people will need long after specific technologies have evolved. Think about it. In a research project, a student might use AI to brainstorm inquiry questions or or get feedback on a draft, but they are still expected to evaluate the quality of the output and identify inaccuracies, challenge assumptions, and ultimately make their own decisions. The intellectual work, that productive struggle remains theirs. The real value is not in what the machine produces but in how the student responds. This approach is increasingly backed by research. The OECD Digital Education Outlook 2026, for instance, found that while generative AI can improve task performance, it doesn't automatically lead to stronger learning. The report makes a very strong case that successful outcomes depend on clear pedagogical design, and it warns that students can become overly reliant if they outsource too much cognitive effort. Its key conclusion, how AI is used, matters far more than whether it is used at all. That insight, the article tells us, resonates strongly with Strathcona's own experience. This is fundamental to ensure an effective AI in education policy. And just as important as how students use AI is the role of teachers. The have been voices out there predicting that AI tutors might eventually replace teachers, haven't there? But experience, as this article powerfully argues, tells us otherwise. Teacher expertise has never been more important. Effective AI implementation absolutely requires educators who can design meaningful learning experiences, exercise professional judgment, model ethical decision making, and help students navigate complexity. As education researchers Ethan Mollick and Lilac Mollock observed in 2023, the role of teachers is often overlooked, and AI tutors do not replace the complex role of a teacher in front of a class. This truly reinforces the human in the loop principle that underpins all successful AI integration. The clearest lesson from Strathcona's experience, Kara Baxter explains, is that the success of AI implementation depends less on the technology itself and much more on the people using it. While public discussion often fixates on tools and platforms, meaningful change in schools truly comes through teacher expertise. When teachers understand both the possibilities and the limitations of AI, they are better equipped to design learning experiences that harness its strengths and preserve the cognitive challenge necessary for deep learning. This is why staff professional learning has been central to their approach. Building confidence, capability, and shared understanding across the teaching profession will ultimately have a far greater impact than any individual technology. We often label teachers as resistant to change, but more often they just need time and space. And given that they become the best drivers of innovation, the future of education won't be defined by if schools adopt AI. As this article makes clear, that question has largely been answered. The more pressing challenge the real question for school leaders thinking about their shimsh AI policy for schools is ensuring AI strengthens human learning rather than diminishing it. Schools have this incredible opportunity to move beyond fear and hype towards informed, evidence based practice. If we can help young people become critical thinkers, ethical decision makers, and confident users of emerging technologies, we won't just be preparing them for a future shaped by AI, but for a future in which they can actually help shape AI itself. If you're listening to this and you're finding value in these conversations, please please consider following or subscribing to the podcast. It really helps us continue to bring you these insights into AI in education in that future. The goal is not for students to think less because AI exists, it is to help them think better. That's all for today. Thanks for listening.