00:00  – 09:23

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Pablo and Matt start by unpacking why so many AI initiatives in schools never translate into real classroom change, and why the issue is almost never the tool itself.

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Key points from the conversation:

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• Utah’s progress was not driven by AI alone, but by decades of prior investment in infrastructure, connectivity, and system readiness.
• Adoption accelerates when systems remove friction upfront, through centralized procurement, pre-vetted tools, and shared data privacy frameworks.
• Without these conditions, schools spend disproportionate time on access, approvals, and compliance, rather than classroom application.
• Teachers engage more meaningfully when given time to experiment and understand the technology, not just instructions on how to use it.
• Early signs of success came from practical outputs, including hundreds of teacher-developed lesson plans and increased collaboration across districts.

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“It’s not something that happened overnight. We had the conditions set for literally decades beforehand to be able to move into this. So when AI came in, there were already people asking, ‘how do we do this?’ and working together very naturally.” - Matt Winters

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09:23 – 16:48

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Pablo and Matt then turn to what it actually takes for educators to move beyond initial skepticism and start using AI in ways that meaningfully change their practice.

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Key points from the conversation:

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• A program initially designed for ~1,500 teachers expanded to 6,000–7,000+ educators, reflecting strong demand across the system.
• Teachers were primarily motivated by a need for understanding and clarity, not just access to tools.
• Adoption tends to follow a progression:
  • Fear (concerns about plagiarism, replacement, uncertainty)
  • Productivity (efficiency gains in daily tasks)
  • Creativity (rethinking teaching and learning approaches)
• Most professional development efforts stop at productivity, failing to reach the creative and pedagogical transformation stage.
• Effective training focuses on transferable concepts (e.g. types of AI, underlying systems), rather than specific platforms that may quickly change.
• Gaps in adoption are often due to inconsistent leadership alignment and communication, not a lack of willingness among educators.

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“Most teachers, as we’ve learned, they just really wanted the knowledge on this. They wanted to understand where they’re at, and then take that back to their classroom and figure out what it looks like in practice.” - Matt Winters

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16:48 – 43:45

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The conversation then shifts to the policy layer, where Pablo and Matt explore how oversimplified thinking about AI leads to the wrong decisions at a system level.

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Key points from the conversation:

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• A common misconception is treating generative AI as equivalent to all AI, leading to overly broad or ineffective policy decisions.
• Calls to “ban AI” overlook the reality that many essential systems, such as translation tools, accessibility features, and grammar support, are already AI-driven.
• The core challenge is not the presence of AI, but the lack of nuanced understanding among policymakers, educators, and stakeholders.
• Teachers are increasingly aware of data privacy risks, prompting stronger demand for clear agreements and safeguards.
• Districts are beginning to respond by consolidating tools and standardizing usage, reducing fragmented or “shadow” adoption.
• Effective governance requires clear frameworks, shared definitions, and system-wide alignment, rather than reactive restrictions.

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“I’ve talked to policymakers who said, ‘we need to ban all AI in schools.’ And I keep reminding them — if you ban all AI, you’re banning language translation, grammar help, all these different things. You can’t just say ban AI. You have to really think through what that means.” - Matt Winters

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43:45 – 46:50

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From there, Pablo and Matt move beyond adoption and into how AI is starting to reshape how teaching and learning actually work in practice.

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Key points from the conversation:

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• AI is not simply accelerating existing practices, but reshaping how learning is structured and experienced.
• The role of the student is shifting toward actively guiding and teaching AI systems, particularly in areas like writing and problem-solving.
• Effective use of AI requires strong thinking, structure, and clarity, reinforcing rather than replacing core academic skills.
• Technology has already lowered barriers to creation (e.g. video production), and AI extends this further by expanding what is possible in the classroom.
• The real opportunity lies in rethinking pedagogy, rather than layering AI onto traditional models.

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“There’s this idea that students are getting to the end of the journey too quickly because of AI. And I went — maybe they’re not getting to the end of the journey. Maybe we’re getting to a space that is completely brand new teaching and learning.” - Matt Winters

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46:50 – end

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Pablo and Matt close by looking ahead at what’s coming next, and the bigger questions systems will need to answer as AI becomes more embedded in education.

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Key points from the conversation:

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• The emergence of agentic AI systems introduces new questions about autonomy, control, and decision-making.
• A central challenge is understanding the trade-offs: what humans give up versus what they gain as AI takes on more responsibility.
• Global approaches to AI are diverging significantly, with different priorities around regulation, innovation, data sovereignty, and access.
• Education systems across regions face different realities, from advanced infrastructure to fundamental access challenges.
• AI adoption is increasingly a global systems issue, requiring coordination beyond individual institutions or countries.
• The long-term opportunity is to build more responsive, personalized learning environments, if these systems are designed intentionally.

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“When we get deeper into things like agentic AI, we have to ask — what does that take away, and what are we giving up? And what do we get in return? That’s something we’re going to have to reckon with as a civilization.” - Matt Winters

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AI in education is not simply a question of technology. It is a question of systems, leadership, and how we support educators to translate potential into practice. As this conversation makes clear, meaningful adoption depends less on the tools themselves and more on the conditions around them: clear governance, confident educators, and a deliberate focus on equity.

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For EDT&Partners, this sits at the core of our work. We partner with education systems to navigate complexity, make informed decisions, and build approaches that are both practical and sustainable.

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The opportunity ahead is not just to adopt AI, but to shape how it is used. To move beyond experimentation and towards models that genuinely improve teaching and learning.

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We invite you to continue the conversation, reflect on what this means in your context, and consider what intentional, responsible adoption could look like in practice.

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