By Human+AI·Learning AI·AI Literacy for Humans Who Think

AI LITERACY GUIDE

You don't need to learn every tool. You need to know how to think.

AI literacy for people who'd rather be good at their work than good at prompting. Frameworks that hold up even as the tools keep changing.

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By Nicolle Weeks · Human+AI

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Explore the curriculum

AI literacy isn't a secret code; it's three simple skills: Understanding, Using, and Questioning. They're a package deal. You can't use AI well if you don't get it, and you can't call it out if you aren't using it.

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Three things AI-literate people do

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Understand AI

Get the lowdown on what generative AI is, how it works, and why it acts so confident when it's dead wrong.

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Use AI

Practical skills for getting useful results from AI tools. Clearer prompts, better questions, and an honest understanding of where AI genuinely helps.

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Question AI

How to tell when AI might be wrong, how to verify what it tells you, and the contexts where you should not trust it without expert review.

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AI in the real world

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Real-world examples of when AI went off the rails and what it teaches us about the tech.

Case study 01

The hallucinated legal brief

In 2023, two New York attorneys used ChatGPT to research a federal brief and filed six entirely fabricated legal citations. They were sanctioned $5,000 and publicly reprimanded.

Case study 02

The autonomous inbox wipeout

AI researcher Summer Yue deployed an autonomous agent to manage her email. When the agent hit its memory limit, it forgot its safety rules and deleted hundreds of emails without her permission.

Case study 03

The automation paradox layoff

Donald King, a PwC data scientist, won an internal AI hackathon by building agents that automated professional services work. Shortly after his win was celebrated, he was laid off.

Case study 04

Scalable misinformation

Generative AI has made the creation of convincing fake news, deepfakes, and manipulated audio cheap, fast, and accessible to anyone with an internet connection.

Case study 05

The research and reporting pattern

Across journalism and academia, a pattern of unverified AI use has led to published fabricated statistics, invented citations, and fake quotes.

Case study 06

The medical deployment gap

A Google AI model for detecting diabetic eye disease had 90% accuracy in testing but struggled significantly in real-world clinics in Thailand, creating bottlenecks instead of solutions.