For the past few months, I've been visiting schools and hearing from students about why and how they're using (or refusing) generative artificial intelligence. In this article, I'm talking about two of the more problematic perspectives that have emerged from those conversations: it's good enough, and it's better than me.

This is an updated post in the series exploring AI ethics, building on the original 2023 discussion of privacy concerns. As generative AI has become embedded in our daily digital lives from chatbots to smart glasses the privacy implications have grown more complex and immediate. This post explores how GenAI has transformed privacy risks and provides updated resources for teaching these issues across subject areas.

In Five Principles for Rethinking Assessment with Gen AI, Leon Furze argues that assessment reform should focus on good pedagogical practice rather than policing AI use. While the AI Assessment Scale has become widely adopted, Furze emphasizes that the principles behind it—validity, authenticity, transparency—were important long before ChatGPT.

In About, With, Through, Without, Against: Five Ways to Learn AI, Leon Furze pushes back against simplistic binaries about whether AI helps or harms learning. He argues that young people are adaptable and capable, and proposes five overlapping approaches to thinking about AI in education. Learning about AI covers AI literacy—how to use and understand the technology—but Furze warns against making every teacher responsible for teaching AI skills. Learning with AI includes tutor chatbots and using AI for feedback, though he remains skeptical about personalized learning claims. Learning through AI treats the technology as a conduit or medium, like semantic search engines.

When Canva first launched its code tools in Canva AI, I was pretty cynical. In fact, I was definitely too harsh, with my first post based on a few basic experiments. I wasn't impressed because I couldn't see how it was any different to other offerings such as ChatGPT's Canvas or Claude's Artifacts - it was quite a bit less sophisticated than those products.

This is an updated post in the Teaching AI Ethics series, originally published in 2023. Given the explosive developments in AI and copyright over the past two years - including major court cases, government decisions, and the first billion-dollar settlements - it felt essential to revisit this intermediate-level ethical concern. For the previous updated post on Truth, click here.

In Open Source AI is Going Mainstream, Leon Furze explores the growing significance of open source artificial intelligence beyond the GPT-5 hype cycle. He begins by clarifying what "open source AI" actually means, noting that the term has become contentious—many so-called "open source" models from companies like Meta are more accurately described as "open weights," since their underlying architecture, training processes, and datasets remain proprietary. True open source projects expose their entire codebase to public examination and adaptation.

I'm excited to launch a new course offering AI professional development for educators. The Practical AI Process covers GenAI from foundation to advanced levels. Learn more in the article.

This post is a lament. I thought we were done with the 2023-2024 bad habit of every person attending an online meeting with their AI notetaking assistant in tow, but here we are, November 2025, and I have just attended not one but three meetings which gradually filled up with Otters, Fireflies, and other assorted disembodied stenographers.

He explores how learning management systems already constrain pedagogy by forcing educators to systematize learning into predetermined formats, and questions what happens when OpenAI's chatbot takes control of content production across these platforms. Furze is particularly concerned about OpenAI's study mode—which he found pedagogically flawed despite claims of expert input—and sees it as part of a larger strategy to position the company as the solution to problems it helped create.

This post outlines five practical steps for using GenAI platforms. Applying expertise, selecting the right model, adding context, using the internet, and iterating and refining are useful skills for working with these technologies.

What is AI is "normal" technology? If we don't have to fear being "left behind", and if AI will change education not in years, but in decades, how might we rethink our approaches, policies, and pedagogies?

When the AI bubble bursts - and it will burst - CEOs will be dethroned. Companies will lose billions. The economy - particularly in the US - will take a catastrophic hit. Data centres will suddenly need to downsize their operations and close down entirely. And in education, we'll need to take yet another long hard look at ourselves, and ask, what's next?

In Stuck Thinking and End-of-School Exams, Leon Furze examines the assumptions that anchor our current model of exam-based assessment—and argues that many of them are “stuck thinking.” He questions why we let the high-stakes end exam dictate upstream assessments, curriculum priorities, and even our trust in students. He suggests we treat exams differently: as one piece of evidence rather than the organising point. He also links this critique to the challenges and opportunities posed by generative AI in assessment. The piece invites educators to reconsider what exams should do (and when), rather than automatically preserving the status quo.

In our new commentary for the Journal of Applied Learning & Teaching, we explore everything we have learned after two years of working on the AI Assessment Scale AIAS

Technology is great: it's the tech industry that's the problem. This post explores why we need to reject the Big Tech myth of inevitability.

The AI Assessment Scale (v2) has officially been published in the open access Journal of University Teaching & Learning Practice (JUTLP). Read more here!

In our new commentary for the Journal of Applied Learning & Teaching, we explore everything we have learned after two years of working on the AI Assessment Scale AIAS

The concept of "digital plastic" in generative AI highlights its characteristics: cheap, malleable, ubiquitous, and persistent. This framework promotes critical AI literacy through classroom strategies, encouraging purposeful use, awareness of implications, and fostering critical judgement in synthetic media creation.

I recently asked a group of around 3000 educators from my mailing list what they'd like to learn about GenAI. I'm incredibly lucky to have feet in a couple of camps as both an early career researcher and a consultant/author. It means I have access to the training and skills for working with lots of information, and a large audience to draw on when I'm looking for ideas.

PD for GenAI isn't a "one and done" event - it needs to fit with teachers' existing disciplinary expertise and their deep, contextual understanding of what it means to teach.