Shaping the Future of Learning with AI

Pat Yongpradit, Chief Academic Officer, Code.org

Pat Yongpradit empowers people to shape the world they want, not be consumed by the one they fear. As leader of TeachAI, a global initiative with Code.org, ETS, ISTE, Khan Academy and WEF, he partners with governments and organizations worldwide to advance AI in education responsibly. A former classroom teacher, Yongpradit has driven systemic change by expanding computer science education globally.

Through this interview, Yongpradit highlights the importance of clear guidance and leadership in AI integration and equipping students through AI literacy and responsible curriculum adaptation. He emphasizes preparing learners with technical fluency and uniquely human skills to thrive in an AI-driven future.

Equitable and Accessible Education: Guidance and Leadership in AI Integration

Looking across education systems today, the pattern becomes clear. AI integration happens whether there’s structure around it or not. The difference lies in the outcomes.

What strikes me most is how inconsistent classroom implementation has become without proper guidance. Teachers are experimenting, students are engaging, but the experiences vary wildly from productive learning opportunities to potentially harmful encounters with the technology. Some discover meaningful ways to enhance critical thinking. Others stumble into dependency patterns that undermine learning fundamentals.

The missing piece is systematic leadership at every level. State departments, district offices and individual schools need designated point people who understand technology and pedagogy. Without this coordination, even well-intentioned efforts fragment into isolated experiments that don’t scale or sustain.

From what our teams have observed working with districts nationwide, the most successful implementations start with someone taking ownership, overseeing and providing genuine leadership around training, standards and ongoing support. This person becomes the bridge between classroom realities and administrative vision.

The opportunity cost of delayed action keeps growing. Every semester without structured guidance means more students encounter AI education haphazardly. Meanwhile, districts with clear leadership structures build competitive advantages in student outcomes and teacher confidence.

The foundation requires both elements to work together. Guidance without leadership remains theoretical and leadership without clear guidance becomes reactive firefighting. Combined effectively, they create conditions for AI education to serve learning rather than simply following trends.

Adapting Curriculum for Emerging Technologies: Equipping Students for the AI Era

The timing taught us something valuable about preparation versus reaction. Building AI curricula years before ChatGPT arrived meant having foundations when the moment demanded. That head start matters more than anticipated.

Even with early momentum, refinement never stops. Each semester reveals new aspects of how students engage with these concepts and technology evolves faster than traditional curriculum cycles allow.

What resonates most is moving students beyond consumption to creation. Using AI tools represents just the entry point. Real learning happens when students grapple with responsible development and build solutions that matter to their communities.

The Presidential AI Challenge validates this direction. Student teams creating AI-driven community solutions reflect the same philosophy driving our AI Foundations course, which reached tens of thousands this year. Purpose-driven projects generate deeper engagement than abstract exercises.

The internal challenge proves equally demanding. Organizations claiming to lead AI education must demonstrate that commitment internally. Upskilling teams across curriculum design, engineering, policy and strategy isn’t optional. It’s foundational credibility

The broader lesson extends beyond any single initiative. Educational institutions serious about preparing students for an AIdriven future need both early investment and continuous adaptation. The alternative becomes reactive scrambling, while others set the standards students expect and deserve.

New Programs and Efforts: Turning Guidance into Lasting Policy

The policy vacuum revealed itself immediately. When partners launched the first TeachAI resources, almost no states had formal school AI guidance. That timing wasn’t planned, but it created unexpected leverage.

What surprised everyone was the hunger for structure. The AI Guidance for Schools Toolkit filled a gap that grew faster than anticipated. Less than two years later, 31 states developed AI guidance, most directly shaped by those early resources.

The ripple effect exceeded expectations. Framework recommendations from April 2024 found their way into the April 2025 White House AI Executive Order. Seeing policy language migrate from collaborative toolkit to national mandate demonstrated how grassroots educational resources can influence federal priorities.

The partnership model proved essential. Working alongside ETS, ISTE, Khan Academy and the World Economic Forum created credibility that no organization could achieve alone. Today, 44 U.S. state agencies and 30 international ministries participate, with over 100 advisory organizations contributing.

The broader lesson resonates across sectors. When new technologies create policy gaps, the organizations that move first with practical resources often shape long-term frameworks. Educational institutions willing to collaborate early gain disproportionate influence over how entire systems adapt.

The stakes justify the investment. These policies will guide student and educator engagement with AI for years ahead. Getting the foundation right matters more than getting credit for individual contributions.

Trends Shaping Education: Elevating Human Skills Through Innovation

The assessment shift becomes visible first. Traditional assignments submitted solely for teacher evaluation give way to oral presentations and projects engaging real audiences. The change reflects a deeper recognition that the format loses meaning when AI can produce written work that is indistinguishable from student effort.

What gets assessed matters more than how. The focus naturally gravitates toward uniquely human capabilities as tasks AI handles easily lose their evaluative power. This isn’t just pedagogical evolution but a survival instinct for educational relevance.

The curriculum integration follows predictably. Academic standards will embed AI literacy within five to ten years, not as separate courses but woven throughout core subjects. Computer science leads this transition, though every discipline eventually grapples with AI’s implications for its field.

The teacher experience transforms most comprehensively. Robust tools supporting classroom management, lesson planning, student evaluation and parent communication represent the beginning. The profession itself evolves as the administrative burden decreases and instructional creativity expands.

The timeline feels both urgent and manageable. Five to ten years provides enough runway for thoughtful implementation while acknowledging that change accelerates, whether institutions prepare or not. Organizations positioning themselves ahead of this curve will shape educational norms rather than scramble to meet them.

The fundamental question isn’t whether AI reshapes learning but how thoughtfully educational leaders guide that transformation.

Preparing Students for a Tech Future: AI Literacy First

The biology analogy clarifies the imperative. Not everyone becomes a doctor, yet studying anatomy and biological systems provides foundational literacy for navigating health decisions throughout life. AI literacy serves the same essential function for the technology reshaping every sector.

The knowledge spectrum starts with practical and deepens systematically. Safe and effective AI use provides the entry point. Understanding how to shape AI behavior through prompt engineering builds capability. Knowing how AI systems are built creates genuine appreciation for world-changing technology, even among students who never pursue engineering careers

The workforce implications compound quickly. Basic skills that sufficed for previous generations no longer provide a competitive advantage. Technical literacy becomes table stakes rather than a differentiator.

What distinguishes the most capable professionals is their combination of AI fluency with distinctly human capabilities. Communication skills, relationship building, emotional intelligence and public speaking represent irreplaceable advantages. These competencies gain value precisely because AI handles routine tasks efficiently.

The skill combination defines adaptability. Students mastering technical systems and human connection position themselves for careers that have yet to be invented. Those focusing exclusively on either domain risk obsolescence as technology and workplace expectations evolve.

The education challenge becomes clear. Developing AI literacy and human-centered skills requires intentional curriculum design that connects AI’s social and ethical impacts to its technical and conceptual underpinnings.