If you’ve ever wondered whether AI has 'broken' school, this conversation with Stanford associate professor Victor Lee cuts through the noise and gets to the heart of the issue.

We start by mapping out three lenses every learner needs:

1. The user who applies tools well.
2. The developer who grasps core concepts like models and training data.
3. The critic who sees bias, persuasion, and societal impact.

That trifecta becomes a practical compass for teachers, parents, and leaders trying to decide what matters by the time students graduate.

Victor also shares fresh findings from his widely-cited study on AI and academic integrity:

• Cheating didn’t spike after ChatGPT arrived. The baseline was already high.
• When work is irrelevant or purely procedural, students seek shortcuts.
• When tasks demand interpretation, personal voice, and real evidence, AI becomes a helper rather than a loophole.

We also explore how to rethink writing beyond the five-paragraph essay, turning “writing is thinking” into prompts that reward judgment over regurgitation. Think role-play, multimedia analysis, and context-rich arguments that students can own and AI outputs can’t fake.

Lastly, Victor examined computer science in a world of copilots. Coding isn’t going away, but the value shifts from syntax to decomposition, abstraction, testing, and reasoning about systems. The best AI-assisted developers have strong fundamentals and the same is true in every field — domain knowledge multiplies AI. That’s the essence of AI readiness: deep subject-grounding plus AI fluency and skepticism.

If you care about smarter classroom assessment, meaningful tasks, and preparing students for an AI-shaped world, this episode offers a grounded and hopeful roadmap.

aiEDU: The AI Education Project

• aiEDU.org
• linkedin.com/company/aiedu/