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Good high-school AI literacy covers three strands: how AI works, how to use it well, and how it shapes society. Two widely used frameworks help structure this. The AI4K12 initiative organizes content into five big ideas: perception, representation and reasoning, learning, natural interaction, and societal impact. UNESCO published an AI competency framework for students in 2024 that emphasizes a human-centred mindset, ethics, AI techniques and applications, and system design. Most schools cannot add a stand-alone course, so integration works better. Math and statistics classes are natural homes for data, probability and model evaluation. English and language classes handle prompting, evaluating AI-written text, source verification and authorship. Social studies covers bias, labor, surveillance and regulation. Science classes can treat models as tools with assumptions and error, like any instrument. Computer science can go deeper into training and building simple models. A sample six-unit sequence: (1) What counts as AI, with everyday examples such as recommendations and spam filters; (2) How machines learn from data, using a hands-on classifier; (3) How language models generate text by predicting tokens; (4) Evaluating outputs, including hallucinated facts and citations; (5) Bias, fairness and who is affected; (6) Privacy, policy and careers, ending in a capstone where students audit an AI tool they actually use. Common mistakes include teaching only tool tips, which date quickly, or only ethics without mechanics, which leaves students unable to reason about why errors happen. Another misconception is that AI detectors can reliably catch AI-written work; they produce false positives, so policies should rely on process evidence such as drafts and conversations rather than detector scores alone. Free materials include MIT RAISE's Day of AI and Code.org's AI lessons.