In this episode from the archives, we’re joined by Vera Cubero, the Emerging Technologies Consultant for the North Carolina Department of Public Instruction (NCDPI) and a co-author of one of the nation's first K-12 AI guidelines. Vera shares her frontline experience transitioning from a classroom teacher piloting 1-to-1 Chromebooks to leading a statewide AI initiative. This conversation is a crucial exploration of how education must fundamentally change its approach—moving beyond simple tech "substitution" to truly "redefine" learning, assessment, and the role of the teacher to prepare all students for an AI-driven future.

• Beyond the Digital Worksheet: Vera warns that AI in education risks repeating the failures of 1-to-1 Chromebook adoption, where "substitution" (digital worksheets) won out over true learning "redefinition."

• The AI-Enabled Project: The future of learning isn't just using AI; it's pairing AI with Project-Based Learning (PBL). AI becomes a powerful tool for students to solve complex, real-world problems, moving assessment away from simple essays.

• Durable Skills Over Rote Answers: Vera argues that AI makes rote memorization obsolete. The new curriculum must focus on building "durable skills" like critical thinking, collaboration, and creativity—skills the future workforce demands.

• The Guide on the Side: AI doesn't replace teachers; it changes their role. The focus must shift from the "sage on the stage" (delivering content) to the "guide on the side" (coaching, fostering human connection, and guiding student inquiry).

• AI as the Great Equalizer: Vera's biggest concern is equity. Public schools must act as the "great equalizer," ensuring all students—especially from marginalized communities—gain AI fluency, or the economic divide will widen dramatically.

In this episode, we’re joined by Kris Younger, a longtime technologist and the Director of Education at Zip Code Wilmington, a nonprofit coding bootcamp. Zip Code is on the absolute frontier of technology, helping adults from diverse backgrounds, who often earn between $30,000 and $35,000 per year, rapidly transition into tech careers with salaries in the mid-eighties, all in just 12 intense weeks.

Kris shares his unique perspective on how the role of the software developer is fundamentally changing, shifting from a "coder" to a "programmer" who is more like a business analyst and a director. This conversation is an urgent exploration of how to make education nimble enough to prepare students for the future of work, not the past.

• The Age of Steam Programming: Kris likens the arrival of generative AI to the shift from sailing ships to steam engines, the fundamental skills needed to build software have changed forever.

• From Coding to Management: Traditional computer science knowledge of search routines and algorithms is being taken over by LLMs. The crucial human skills are now critical thinking, communication, and management of the AI tools.

• Projects are the New Exam: In a world where LLMs can generate code, the only effective way to assess knowledge is through project-based work that demands group collaboration and real-world delivery (like building a Slack clone in a week).

• Weaponize AI in Response: Instead of trying to ban AI, educators must change the assignment. AI is now a power tool; the education challenge is to teach people how to think critically enough to manage that tool effectively.

• The On-Ramp Problem: Kris's biggest concern is that businesses, confused about the future, will cut off entry-level hiring, denying themselves the adaptable, open-minded new talent who haven't yet learned "what's impossible."

In this episode, we’re visiting Duke University to meet Vihaan Nama, an AI engineer, researcher, and teaching assistant helping shape how AI is taught and built for the real world. From roles at PS&S and JPMorgan to graduate courses on explainable AI and product management, Vihaan brings a rare combination of technical depth and educator insight.

If you’ve ever wondered how to make AI education more human, or how to turn student learning data into actionable insight, personalized support, or even a study partner, Vihaan offers both clarity and concrete examples.

We talk about everything from his early experiments in sentiment analysis to why open-source models matter for student privacy, how retrieval-augmented generation (RAG) is quietly transforming knowledge work, and what schools can do right now to prepare for custom AI tools of their own.

• Your Notes, Your Assistant: Vihaan envisions a future where students can chat with their own lecture notes, using LLMs to review, revise, and apply information in their own language and context.

• From Archive to Advantage: Companies (and schools!) are sitting on decades of underused data. With the right AI systems, that information becomes actionable knowledge.

• Trust Through Transparency: Grounding AI outputs in clear, credible sources is key to building trust, especially in high-stakes environments like education and public services.

• Small Models, Big Wins: As open-source LLMs become lighter and faster, even modestly funded schools can host private AI tools, no cloud dependency required.

• Responsible AI = Responsive Leadership: From sustainability audits to ethical guardrails, Vihaan emphasizes that building AI responsibly starts with knowing what your organization values most.