An ML Engineer and Professor on AI, Bias, and What's Really Happening in the Classroom

In this episode we explore what AI looks like from the inside: not as a shiny classroom tool, but as something real teams have to build, test, constrain, and question before it ever reaches teachers or students.We talk with Jon Landrigan, about what he is seeing from today’s college students as they learn, work, and think alongside AI.Topics covered:How college students are using AI todayHow AI tools move from prototype to production in educationWhy a tool that works once may not work safely or fairly at scaleThe risks of “vibe coding” for schools and district leadersPrompt injection, edge cases, and other safeguards behind AI productsAI feedback, writing assessment, and the limits of automated gradingAutomation bias and why teachers may over-trust AI recommendationsPersonalization, learner profiles, and the danger of biased metadataCognitive offloading, student pressure, and what schools still need students to internalizeResources for educators who want to better understand AI systems