📫 Subscribe | 🗣️ Hire us to speak | 🤝 Sponsor this newsletter | 🎓 Events

Welcome to the Friday edition of our newsletter. We spend Fridays going deeper into tools and trends related to generative AI (and Tuesdays sharing news updates). This week, we’re sharing a lesson plan for teaching AI literacy to high school students.

There’s a lot of discussion about what students need to be taught about AI at what level of school (or if they even need to be taught at all). Should they learn to write prompts? Use it for research? Build apps? Those are useful skills, but we believe everyone needs a basic level of AI literacy, even if they’re never taught to become proficient with AI tools.

At Innovation Profs, we distinguish between AI proficiency and AI literacy. Proficiency is knowing how to put AI to work: write better prompts, build useful workflows and choose tools that help you get things done. That’s what we teach in many of our workshops (there’s still time to sign up for Monday’s Hands-On With AI workshop). Literacy is understanding what AI can do, how it can fail and when its output needs a closer look.

One essential lesson: AI can sound confident and still be wrong. It can invent a statistic, make up a quotation or cite a source that doesn’t exist. These errors are often called hallucinations. A polished answer is not proof, and neither is a convincing-looking citation.

This matters even if you never write a prompt. You may encounter AI-generated information in a social post, a workplace document or something someone shares with you. For students, evaluating that information is part of being an informed reader, researcher and citizen in 2026.

That’s why we created “Think Before You Trust,” a 30-minute lesson plan for high school students as part of Drake University’s upcoming Bucksbaum Lectureship in Business. It helps teachers guide students through spotting flawed answers, checking sources and deciding how much verification a situation requires. Brainstorming names for a school club calls for different scrutiny than using a statistic in a presentation or following a health recommendation.

Even if you’ll never teach this lesson, its central habit applies to you: Before trusting an important AI-generated claim, look for evidence beyond the answer itself.

This includes teacher instructions, a student worksheet and a presentation.

Whether you use the lesson or not, here’s a framework for evaluating AI content (and non-AI content shared on social media as well): pause, pick apart, check, compare and decide. Start by pausing before you accept or share an answer. Then pick apart what it says. Which statements are facts you can verify? Which are opinions, predictions or recommendations? Exact numbers, dates, quotations and sweeping claims deserve particular attention.

Watch for warning signs: a statistic with no clear source, a citation you can’t find, an answer that presents only one perspective or language that sounds more certain than the evidence allows. These are reasons to investigate, not automatic proof that something is wrong. And an answer with no obvious warning signs can still contain errors.

Check the important claims by opening the original sources. Does the source exist? Does it actually support the statement? Is it current enough for the topic? Then compare with an independent, reliable source. Asking the same chatbot “Are you sure?” is not a substitute for checking evidence outside its answer.

Finally, decide what to do based on the consequences of being wrong. A brainstorming suggestion may need only your judgment. A claim in a public post or work presentation needs verification. A recommendation affecting someone’s health, safety, money or legal rights calls for stronger evidence and qualified guidance. The more an error could matter, the more carefully you should check.

If you are high school teacher, we hope you will use this lesson plan with your students. If you know a high school teacher, we hope you will send it there way. We would love to hear if you use this in a classroom.