Tricia Friedman shares a real-life story about a flooded basement, an insurance claim, and what it revealed about AI literacy. After a major storm in Ottawa caused basement flooding, Tricia found herself facing the kind of problem many people recognize: too many damaged items, too many documents, too many decisions, and a process that can quickly become overwhelming. But this time, compared with a similar flood two years earlier, the experience felt different. Using AI tools, Tricia organized photos of damaged belongings, created an insurance-ready inventory, reviewed policy details, searched for replacement costs, identified relevant local supports, and built a project plan for the weeks ahead. She also used AI to think through communication with the insurance company, plan around disruption at home, and even create a small family recipe resource in the middle of the chaos. The point is not that AI made the flood easy. It did not. The point is that AI changed Tricia's capacity to respond. For educators, that distinction matters. Too often, school conversations about AI focus on shortcuts, cheating, prompt-writing, or which tool to use. This episode asks us to widen the frame. AI literacy is also about helping students become stronger problem solvers. That means knowing how to break down a complex situation, gather evidence, ask better questions, compare options, verify claims, communicate clearly, and decide what to do next. This kind of AI literacy belongs across the curriculum. A flood insurance claim can become a math problem, a media literacy problem, a civics problem, a consumer rights problem, a writing problem, a design problem, and a wellbeing problem. That is the real invitation here: bring AI bigger problems, not just cleaner prompts. In this episode: Tricia explains how AI helped her organize a flood-related insurance claim. She reflects on the difference between using AI for a task and using AI to manage a complex problem. The episode explores why AI literacy should include verification, judgment, documentation, and communication. Tricia makes the case that schools need to help students practice using AI in realistic, interdisciplinary situations. She also raises an equity question: if AI can help people navigate systems like insurance, consumer rights, city services, and legal language, then every student deserves to understand how to use it responsibly. For educators, this episode asks: What kinds of real problems are we inviting students to bring to AI? Are students learning how to check AI outputs against evidence, policy, data, and lived context? How might AI help students see connections across math, language, civics, research, and design? What does it mean to teach AI literacy as a form of agency, not just technical skill? Big idea: AI literacy is not mainly about knowing how to use a chatbot. It is about knowing how to think with a tool when the problem is complicated, consequential, and real.
