AI

How to quickly use the new Claude Design:

Almost a year ago, I responded here on Thought Shrapnel to what I thought was a terrible paper which claimed to show, via brain scans, that using LLMs was bad for students' cognitive development. As Philippa Hardman notes in this article, the academic literature has begun caught up with what people actually using these tools already know: The theoretical picture sharpened in 2025–26. Favero et al. (2025) warned that cognitive offloading undermines learning outcomes unless the mental effort that’s freed up gets redirected towards other meaningful tasks.

Anna Mills asked me about friction and AI. I thought I might put down my thoughts here. What follows is not about students specifically but about us all -- I get that you can't learn physics by copy-pasting. But let's "start at the end" as they say: where do we want people to get to with cognitive offloading and AI? First, an important point: cognitive offloading is behind most human achievement. Once you see the primary way we "cognitively offload" is to have other people think for us, you see it's the reason humans control our world. It's what allows this complex system where everyone largely cedes the work of homebuilding, farming, manufacturing, doctoring, etc. to other people and applies their cognitive effort to the smaller set of things that are personal to them: where they want to live, what they want to eat, what they need to buy, and their specific job. Even there, most of the time we offload much of that. The reason we have this whole system is that we relentlessly conserve cognitive energy, and that has to be seen, first and foremost, as not a human flaw but as a secret to our success -- albeit one with downsides. The way we often make this work, however, is to develop little cheap heuristics that tell us *when* to apply more effort. They aren't always the best signals, but they are ones that is recognizable. The doctor comes in, and you're ready to accept her diagnosis. But then she refers to you by the wrong name, you correct her. She pulls out yet another patient record that is not yours and asks now about an ailment you do not have. You decide you're going to push back a lot more on her findings. I think one of the core problems of AI is normally our decision is as much about *who* we offload to as *when*. We often rightly surrender cognitive autonomy when another person has met our bar on the three things I talk about in my first book (WLFSFC): in a much better "position to know", shares our values, history of being "careful with the truth" (subject-dependent). We assert autonomy when those things are not in effect. With AI one of the problems is there is no who. We get the information smoothie. So what are the signs to selectively shock us out of cognitive conservation? In Verified we propose the one of the biggest signals is internal: it's precisely when you see something and get very excited that it PROVES you were right in a contested space that you should apply some effort. Another signal in search is "wrong neighborhood" -- the results do not seem like they come from the sources you'd expect; might be time to tweak that search. That's search of course. Maybe it transfers, maybe not. So what are the signals with AI that we need to apply more effort, both from the system and outside it? I think as we approach the question of offloading in general users that's a core question. | 12 comments on LinkedIn

29 likes, 1 comment. "How Gen Z feels about AI #Vergecast"

From exploring career paths to negotiating salary, these tested AI tools can help you research, prepare, and stand out in your job search.

40 likes, 1 comment. "I Turned My iPhone Into an AI Productivity Machine."

While nobody seems ready to jump on board with Melania Trump’s silly robot teacher idea, some folks just can’t help trying to tweak it a little.

Three goals for Australian education and training in the age of AI AI is fundamentally reshaping the capabilities our learners need, the work they will do, and the society they will build. Multiple national bodies have called for urgent action. The Castlereagh Statement is a vision for the coordination and collective courage to deliver transform […]

Australia may have just set the benchmark for AI in education. The Castlereagh Statement is one of the most serious and comprehensive frameworks I have come across. It brings together over eighty educators, researchers, school leaders, university vice-chancellors, industry figures, and students, following a national summit at the University of Sydney in 2025. It is worth reading wherever you are in the world. At its core is a ‘unified national vision’. Ambitious, but necessary if education and AI are to be meaningfully integrated across society. It also confronts an uncomfortable truth: ‘systems were designed for an era in which access to information and cognitive labour was scarce...education and training systems still largely prioritise information transmission and reward the reproduction of knowledge’. Six principles underpin the vision: 1. Redefine what it means to be educated. A commitment to cultivating ‘the enduring dispositions and capabilities that we will always value humans exhibiting’. This includes ‘compassion, curiosity, creativity, collaboration, and courage’ alongside metacognitive skills. At the same time, ‘deep domain expertise and practical experience are necessary’ and learners must also ‘understand the affordances and limitations of AI’. 2. Institutional and individual humility. Many inherited structures were designed for a different era and ‘would benefit from being transformed, consolidated, or retired, recognising that institutions exist primarily to serve learning and learners’. 3. Reconceptualise learning and assessment. ‘A commitment to reconceptualising the processes of learning and assessment to prioritise deep understanding, human connections and visible skill development over the mere production and evaluation of outputs.’ 4. Design an agile, capability-focused curriculum. ‘Integrating all education and training sectors and industry’ and ‘shift the focus from content transmission to development of valued capabilities and dispositions’. It also requires guidance on ‘when, how, and why to use AI at each developmental stage’. 5. Empower teachers and redefine teaching. No transformation is possible without addressing time, funding, and workload. AI must serve teachers, not sideline them. 6. Place technology in service of pedagogy and trust. ‘We commit to ensuring technology serves education, not the reverse, with developmentally appropriate applications of AI tools matched to learners' age, stage, and needs’. Alongside this, a three-horizon framework sets out a practical, operational approach to educational reform in the coming months and years. It is hard to think of many national systems taking this kind of joined-up approach. Ultimately, what is even more important here is that this starts to tackle the first principles question many are still avoiding: what is education really for in a world of AI? (Link in comments, and thanks to Vince Wall for highlighting this.) | 18 comments on LinkedIn

Never forget.

The complete breakdown across Chat, Cowork, and Code.

Learn to use AI as a thinking partner. This skills survive every AI update

🫠😮‍💨🤦‍♀️🥵

Part 2 of an interview with Bryan Alexander

h/t Tricia on LinkedIn

How to use Claude in Excel + 3 real use cases: data cleaning, dashboards, and a SaaS financial model — tested with prompts you can copy.

AFT made plenty of folks sad when they decided to jump on the AI bandwagon last year (kind of reminds me of the days they were resolutely on the wrong side of Common Core).

WAO's report for the BBC's Responsible Innovation Centre is now live!

Like it says on the tin, folks. we have a bash loop that looks for a notes syntax (==!). We leave claude notes on what we don't like about the wiki Sometimes...

Integration with Claude

Generate leads, track competitors, research markets, and build spreadsheets from any website.

The "Wild West" or the next great tool? Instructors from across the University of Minnesota campuses share their honest reflections on the rise of Artificial...

Not many facts are on the ground, but here are some starting points for sober thinking

“The language models we have now are probably the most significant thing to happen in security since we got the Internet.”

Teaching in ways that are beyond the reach of large language models.

Every expert you follow has already published what they know. For free. NotebookLM is how you actually absorb it instead of letting it pile up.

Pick one reply tomorrow that you would normally spend 10 minutes writing. Dictate it instead. See what the output looks like.

Thirsty for nonpartisan info on AI’s environmental impact? Watch us debunk misleading claims on both sides and demo a student project that quantifies your AI footprint. The recording is now live for “How Green Is Your Prompt,” organized last Thursday by FromThePage in conjunction with UMaine’s UMaine Digital Curation program. Ben Brumfield, Greg Nelson, Ethan Morin, and I demo tools that help you compare the environmental impact of your chatbot queries to other tasks. This includes new calculations of the footprints of non-AI workplace actions, like digitizing a photo or storing data in the cloud. I also challenge seven frequently cited claims that I believe either exaggerate or underestimate AI’s environmental impact, from the water required for an individual prompt to what’s really driving data center construction. (Hint: I don’t think it’s AI.) One of the most exciting moments of the webinar for me was lifting the veil on a prototype chat interface that reports your energy and water use as you chat with AI. Dubbed CollaborAITE, this system draws from data I’ve compiled for the What Uses More app to insert real-time feedback on the environmental impact of the prompts you type as well as ways you might offset them (measured in units like miles spent on public transport or number of hamburgers not eaten). CollaborAITE faculty lead Greg Nelson and student contributor Ethan Morin explain their motivation for this work in progress; I hope it will eventually be a tool that anyone can use to understand the impact of their AI use and make informed decisions about how to reduce it. Links in comments. #AIliteracy #AIethics #AIinEducation #Environment #Climate #Sustainability

Ever since Generative AI (GenAI) chatbots and content creation tools hit the scene back in 2022, their implications for education have been profound. On the ground, I’ve seen a lot of discussion from teachers and university professors about the nature of assessment and what they might do to either integrate this new technology into their […]