Episode

6 Feb 2025 · 42 min

0:00

In this Class Disrupted episode, hosts Michael Horn and Diane Tavenner interview AI policy and education expert John Bailey. Bailey explains why large language models (LLMs) represent a fundamentally new technology: they can instantly provide expert‑level output across many professions, effectively democratizing access to expertise rather than just information. He describes how teachers can use LLMs as on‑demand curriculum designers, lesson‑plan creators, and tutoring assistants, and how students could eventually receive personalized feedback, voice‑based encouragement, and project guidance. Bailey outlines a realistic rollout: back‑office and teacher‑productivity tools arriving now, with broader student‑facing applications emerging over the next two to five years, especially via platforms like Google Classroom. He warns of several risks: faster work does not equal better learning; over‑reliance may erode teachers’ judgment and students’ struggle‑based learning; AI‑generated “companions” could increase social isolation; and hallucinations in high‑stakes tutoring could cause harm. The conversation moves to policy, noting bipartisan federal roadmaps but emphasizing the flood of state bills (≈400) affecting AI use, data privacy, deep‑fakes, and model regulation. Bailey highlights pricing models that treat AI as a labor‑market service (e.g., $200/month for a high‑capability model) and how this could help schools with tight budgets. He also shares hands‑on examples—code interpreter, vision models, custom GPTs for parent communication, travel itinerary planning, and using AI to build reading lists for a Greece trip—illustrating both the promise and the need for thoughtful integration. The episode ends with personal recommendations and reflections on AI’s broader cultural impact.

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