SAIL kicked off its summer programming with an interactive workshop for local students. Rather than a lecture, the session was built around discussion and note-taking — students sketched out what they already believed about AI, then tested those ideas against how the technology actually behaves.
We started by asking the room what artificial intelligence is. The answers ranged from robots to ChatGPT to a general sense of something watching. All of those are reasonable starting points, and none of them are quite it, so we worked from there toward something more useful: systems that recognize patterns, make predictions, and generate content — tasks that used to require a person.
Our volunteers guided the group through real examples: where AI is genuinely useful, where it falls short, and how to spot the difference. Hands shot up throughout as students connected the concepts to the apps and assistants already part of their daily lives — the recommendation feeds deciding what they watch, the autocomplete finishing their sentences, the homework help that sometimes invents a source.
A good stretch of the session went to prompting. Students saw how much the phrasing of a request changes what comes back, and how a vague prompt produces a vague answer that can still sound authoritative. Asking a model to explain photosynthesis to a five-year-old produces something genuinely different from asking it to explain photosynthesis — and noticing that difference is a skill.
We closed on the part that matters most: what to do when you cannot tell whether an answer is right. Check it against a source you trust. Notice when a tool is confident about something it has no way to know. Treat the output as a draft rather than an answer.
The afternoon wrapped with a group photo and a lot of new questions — exactly the goal. AI literacy isn't about memorizing answers; it's about learning to ask better questions of the tools around us.
