Catching Up: Meta's Billion-Dollar Payout, AI Search Bias, and Fixing Student Voice

In this episode of Catching Up, Nate McClennen and Mason Pashia get into what happens after a $2.5 billion settlement lands, and whether "fund real life" is actually a better answer than another wave of mental health apps. They also talk through a study showing AI models recommend completely different schools depending on which one you ask, and why that's a bigger deal than it sounds. From there, the conversation turns to a real tension in AI and education right now: efficiency versus efficacy. Nate makes the case that the real opportunity isn't faster lesson planning, it's building AI tools around what learning science already tells us works, things like spaced practice, real-time feedback, and targeted instruction. He also breaks down a simple way to think about how school systems actually learn, separating implementation, innovation, and continuous improvement, and why mixing them up is often where districts get stuck. Mason brings a deep dive on moving past tokenistic student surveys toward real intergenerational co-design, with some genuinely striking examples, a student-drafted AI policy bill that passed 82 to 16, and the Kentucky Student Voice Team's constitutional lawsuit...