What Is AI? A Gentle Introduction to Modern AI Tools
A beginner-friendly overview explaining what AI does, how it learns from data, common examples (like ChatGPT and image generators), and what AI is not.
Three video series, 14 lessons, free on YouTube. Plain-language introductions to how AI works, what to watch out for, and how to put it to work — built for library workers, suitable to share with patrons.
A gentle overview of modern AI: what it is, what it isn't, and how it's used. Three lessons, roughly an hour total.
A beginner-friendly overview explaining what AI does, how it learns from data, common examples (like ChatGPT and image generators), and what AI is not.
A clear, non-technical overview of how modern AI (especially large language models) is trained from lots of data, how it represents knowledge, and the practical limits and ethical and legal issues to watch for.
A detailed overview of how people are using AI at home and work, what libraries can do with it, and the practical benefits, risks, and next steps for library workers.
An eight-part series on the challenges posed by AI technologies — from “hallucinations” to bias to environmental impact.
An accessible look at the “black box” in AI: what it means, why it's risky in real-world settings (healthcare, driving, criminal justice), and how explainable AI aims to fix it.
A plain-language look at AI “hallucinations”: why large language models can confidently give false answers, how knowledge cutoffs and missing context cause those errors, and simple ways to ground AIs so they're more accurate.
Practical guidance for library workers on protecting patron and organizational data when using AI tools.
A plain-language overview of how AI is trained on human-created work, why that raises copyright disputes, and what's currently settled (and unsettled) about who owns AI-generated content.
A close look at how pricing and infrastructure can limit who benefits from AI, and what communities and libraries can do to help.
An introductory review of how AI can reflect human bias (from image tools to hiring and criminal justice), real-world examples, and practical steps libraries can take to understand and respond.
A short primer on how AI-driven automation can replace jobs, real-world examples of when that went wrong, which roles are most at risk, and how libraries and community organizations can respond.
A realistic look at where AI lives, why it consumes large amounts of electricity and water, and what that means for communities and policymakers.
Three full-length sessions, recorded in 2026, that put AI to work in the library: drafting human-centered AI policies, weighing equity and community concerns, and building software with “vibe coding.”
A recorded workshop on AI policy for public libraries: the core components of AI literacy, adoption, and usage policies, a walk through a dozen real policy documents, and AI tools used live to draft policy language you can adapt for your own library.
How to examine AI through an equity lens — bias and discrimination, access and barriers, environmental impact, and workforce effects — with practical ways libraries can evaluate AI tools and respond to community concerns.
An introduction to “vibe coding” — directing an AI tool to build websites and small applications on your behalf — with live examples and practical guidance for library workers who aren't developers.