We're spending too much time restating how important "productive struggle" is, and not enough time talking about why we do it. ** Why would I spend 2 hours toiling over something that will inevitably produce a less-than-polished product, when I could spend 10 minutes offloading that task and creating something that looks polished and (marketing insists!) sounds just like me? ** Increasingly, I think this is becoming this moment's defining question. I was thinking about it recently when discussing an The Atlantic article with Christopher Ostro's book club. The writer shares her experience of preparing for her daughter's birthday. In the midst of preparing (and putting a lot of time and effort into it), she finds out about an app that would do almost everything without too much effort. But she does things herself, because the very act of making an anti-ROI decision expresses value. Here's how the author (Miranda Rake) ends the article: "We didn’t have balloon arches, the cake came out a bit funky, and the Elsa experience was unbelievably awkward for the adults (though the preschoolers loved it). Still, I am so glad we all planned it—me, my daughter, and our community—slowly, imperfectly together." Going through a difficult process -- and engaging in struggle -- communicates value to her daughter. What does this mean for our classroom? As we teach and tout the power of "productive struggle," we need to think carefully about why students would go through it. What's on the other side? One of the problems with the grade-centered approach (and the transactional model of education, more generally) is that the motives for going through productive struggle may not be sustainable. What happens if/when the student leaves school, and doesn't have the impetus that we've build the system around? Anyway... Those are my thoughts for the day. | 36 comments on LinkedIn

My experience with AI-amplified coding is not primarily about doing something I already know how to do faster.

EDUCATION WEEK September 22, 2025 The Chatbot in the Classroom, the Forklift at the Gym By Alfie Kohn [This is a significantly expanded version of the published article, which was given a different title.]  …

How can educators critically engage with the affordances provided by Generative Artificial Intelligence (GenAI) while remaining committed to the core tenets of the multiliteracies project, such as ...

Puppetry as Dream Analysis for AI Anxiety This is a discussion between Camila Galaz, Emma Wiseman, and Eryk Salvaggio, collaborators behind an experimental workshop linking puppetry and generative AI that took place at RMIT in Melbourne this summer at the invitation of Joel Stern and the National Communications Museum. We met online to discuss what emerged from Camila's workshop: personal imaginations of AI made physically manifest into puppets.  Earlier this year, we spent five days in resid

Students don’t need to be exposed to generative AI in elementary school or middle school any more than a kid learning the fundamentals of racing driving karts needs to practice in an F1 car. Over the last few months, we’ve gotten hooked on Formula 1 in the Brake household. Because I can’t help myself, I’ve been thinking about the pedagogy of training F1 drivers and what it might say about teaching more generally. A lot of what I see about the need for “AI literacy” in K-12, feels to me like the equivalent of putting a kid in an F1 car. It’s not that the kids won’t ever be ready to handle the power, but it’s just foolish to think that it won’t take a lot of practice and hard work to build the fundamentals before they’re ready. The training pipeline for a future F1 driver has a very well-calibrated sequence from karting to Formula 4, 3, 2, and then 1. At each stage, the power of the machine is matched to the intended learning goals and the maturity of the driver. The karts are slow (comparatively), but they help a driver to build intuition and skill on finding a line, timing breaking points, and managing traction, and attending to the current status of the car. Here’s where I can hear the objection: “well, this is why we should expose students to AI with significant guardrails when they are young—to help them to build the necessary skills to thrive with AI.” But this fundamentally misunderstands what it takes to use AI well. Yes, we need to learn how AI works and how best to think about using it in our work. But this is something like learning where the pedals are on the F1 car, not learning the fundamentals of how to drive a race car. What we need to focus on in education is helping students to build the character and the fundamental skills that will enable them to thrive when they are exposed to the intelligence amplifier of generative AI. This process takes a lot of hard work and don’t look nearly as cool as driving the car during qualifying or on race day. It’s the workouts to stay in tip-top physical shape, the drills to train your reflexes, the hours in the simulator to hone all the little parts of your race craft. Students don’t need to be exposed to generative AI when they’re developing these skills any more than a kid learning how to kart needs to drive an F1 car. What they need is a space where the tools they are provided can help them to focus on the truly fundamental and foundational skills that will ultimately enable them to pursue their craft at the higher level. They need to learn how to think, write, read closely, communicate with others, accept criticism, persevere when the going gets tough, stoke their curiosity to dig into new areas of interest.

An old tale with modern revelance

As part of my PhD studies, I read and write a lot of stuff that doesn’t really fit into my research, but which I find interesting anyway. I’m categorising these “spare parts” on my blog, and if you’re interested in following them you’ll find them all here. I’ve written a fair bit about AI ethics, […]

In "Metaphors We Live By," Lakoff and Johnson emphasise that metaphors are fundamental to human thought and language, not just decorative language. In this post, I've examined my own use of metaphors to describe AI and analysed their implications, highlighting the power and limitations of these metaphors in shaping our understanding of AI and its impact.

Analyzing the metaphors we use to talk about ChatGPT can reveal how we’re thinking about the new tech

OpenAI’s chatbot offers paraphrases, whereas Google offers quotes. Which do we prefer?

There is a deep disorder in the discourse of generative artificial intelligence (AI). When AI seems to make things up or distort reality — adding extra fingers

We shouldn’t need any illusions to understand how generative tools might be useful. This obsession with anthropomorphization hinders our ability to understand what these systems can and cannot do, leaving us with a confused and muddled idea of their capabilities. An LLM’s ability to predict patterns is impressive and quite useful in many contexts, but that doesn't make it conscious.

The post reflects on metaphors used to discuss artificial intelligence, particularly introducing an origami analogy. It emphasises that AI outputs are intricately shaped by the training data like origami models are formed from a single sheet of paper. This analogy reveals insights into AI's limitations, creativity, and the nature of its outputs.

By Lew Ludwig Baking, Decorating, or Planning: What Role Are Your Students Playing? Are your students learning to bake the cake? Are they learning to decorate the cake? Or are they acting as wedding planners so they can outsource the cake so they can focus on the other stuff? This analogy,

Is AI like the abacus and map—tools that reinforce understanding even after they're taken away? Or is it more like the calculator and GPS—merely a substitute… | 18 comments on LinkedIn