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.
Free training videos
AI fundamentals for library workers and patrons.
Two video series, 11 lessons, free on YouTube. Plain-language introductions to how AI works and what to watch out for — built for library workers, suitable to share with patrons.
Series 1 — How AI Works (for non-techies)
A gentle overview of modern AI: what it is, what it isn't, and how it's used. Three lessons, roughly an hour total.
How AI Learns: Turning Data Into a Large Language Model (LLM)
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.
How AI Is Used Today: Everyday, Workplace, and Library Applications
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.
Series 2 — Risks and Limitations of AI
An eight-part series on the challenges posed by AI technologies — from “hallucinations” to bias to environmental impact.
AI's Black Box Problem: Why Transparency and Trust Matter
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.
AI Accuracy & Context: Why AIs “Make Stuff Up” (and how to stop 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.
Privacy and Security Basics for Using AI in Libraries
Practical guidance for library workers on protecting patron and organizational data when using AI tools.
Copyright & AI: What Library Workers Need to Know
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.
AI Divide: Cost, Access, and Who Gets Left Behind
A close look at how pricing and infrastructure can limit who benefits from AI, and what communities and libraries can do to help.
Bias and Fairness in AI: What It Is, Why It Matters, and What Libraries Can Do
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.
Job Displacement by AI: Risks, Real Examples, and What Libraries Can Do
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.
AI's Environmental Footprint: How Data Centers Use Energy and Water
A realistic look at where AI lives, why it consumes large amounts of electricity and water, and what that means for communities and policymakers.