This first instalment in the Teaching AI Ethics 2025 series revisits the theme of bias in generative AI. It explains how data bias, model bias and human bias interact to produce skewed or discriminatory outputs in large-language and image-generation systems, illustrates those problems with up-to-date research and examples, critiques the limitations of current “guard-rail” fixes, and closes with practical ways teachers can embed critical discussions of AI bias across English, Mathematics, Civics, Visual Arts and other subjects.

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The opening article in Leon Furze’s new “How I Use the AI Assessment Scale” series demystifies what the AI Assessment Scale (AIAS) actually is—a conversation-starter and design lens for assessment—and, just as importantly, what it is not (an academic-integrity detector, a tech checklist, or a universal benchmark). Leon explains why the latest iteration drops colour-coding in favour of clearer descriptors, stresses the need for discipline-specific judgement alongside AI literacy, and offers a step-by-step preview of how he deploys the scale with teachers before any task is set. By clarifying scope, common misconceptions and practical workflow, Part 1 lays the groundwork for the hands-on demonstrations that will follow in the series.

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