Leon Furze dismantles recent headlines claiming AI can “save teachers six weeks a year.” He shows how time‑saved tallies in the Walton Foundation/Gallup survey and Microsoft’s Copilot pilot gloss over what tasks are being sped up—and why they exist in the first place. Measuring efficiency, he argues, ignores the deeper causes of burnout: relentless compliance work, eroded autonomy and a culture that treats teaching as piece‑work.

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Leon Furze sets aside the chatbot hype to show how the underlying components of AI—image recognition, speech-to-text, text-to-speech and transformer language models—already power a growing suite of assistive technologies.

He argues that genuine progress depends on lived-experience design, open standards and a focus on specific user needs, not generic “GPT in everything” solutions. By mapping near-future advances—offline multimodal models, speech-to-sign avatars, adaptive reading platforms and low-cost robotics—Furze invites educators and developers to steer AI toward accessibility rather than spectacle.

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Responding to newspaper calls for tighter controls on generative-AI in senior-school “take-home” tasks, Leon Furze argues that the real culprit is the assessment format itself, not ChatGPT. The article shows that home-based essays and projects have long privileged students with money, tutors or stable study spaces, while disadvantaging those with caring duties, disruptive households or limited technology access—long before large-language models entered classrooms.

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SynopsisThis post expands Leon Furze’s earlier “expertise problem” argument by introducing a three-dimensional model of expertise for working productively with generative AI. Drawing on Punya Mishra’s domain × technology matrix and adding insights from Dreyfus & Dreyfus, Lave & Wenger and James Paul Gee, Furze distinguishes domain expertise, technological expertise, and a newly foregrounded situated […]

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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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Leon Furze answers a reader’s simple question—“Why do you have a blog?”—by invoking nostalgia for the free-form GeoCities era to argue that writers need a “little plot of land” online that they truly own. Adopting the POSSE model, he shows how a single post can be syndicated to LinkedIn, Bluesky, Mastodon, Substack and Medium while the blog remains the canonical source. Furze warns that AI-driven search and “AI slop” content farms are eroding discovery and trust, yet insists that personal blogs still offer control over platform whims, spam filters and intrusive AI assistants. In a GenAI-saturated web, keeping a blog is both an act of digital self-reliance and a teaching tool for open, authentic communication.

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