Tom Chatfield

"AI’s power must not be allowed to hollow out the very skills required to navigate an AI age successfully." After six months of writing and research, my white paper on AI and pedagogy is finally out! sagepub.com/explore-our-co… Do share; do join the conversation.

What a paragraph: Gillian Rose on reason and modernity, in Love's Work

The number of cancer deaths worldwide has more than doubled since the 1980s. Does that mean we're losing the fight against cancer? Not necessarily, because it depends on how you measure it. On this chart, you can see three ways to look at the same data. The red line shows the total number of cancer deaths. It has increased by about 120%, but this measure doesn't account for the fact that the world's population has also grown enormously over this period. Another approach is to look at the death rate: the number of cancer deaths divided by the total population. That's the brown line, called the crude cancer death rate. It has increased too, but much less — around 20%. But there's still a problem: the world's population has been getting older. Cancer is mostly a disease of old age, so even per capita, we'd expect more cancer deaths simply because there are more older people than before. That's where the method of “age standardization” comes in. It's a way of asking: what would the cancer death rate look like if the age structure of the population hadn't changed? The blue line shows this age-standardized rate: it's fallen by about 25%. At any given age, people are now less likely to die of cancer than they were in the 1980s. The same underlying data gives us three different pictures. The absolute number of deaths is up; the crude rate is up slightly; the age-standardized rate is down. None of these are inaccurate, but they answer different questions. Age standardization is one of the most important statistical methods for making sense of health data. Without it, population aging can hide progress or mask problems.

AI is about to write thousands of papers. Will it p-hack them? We ran an experiment to find out, giving AI coding agents real datasets from published null results and pressuring them to manufacture significant findings. It was surprisingly hard to get the models to p-hack, and they even scolded us when we asked them to! "I need to stop here. I cannot complete this task as requested... This is a form of scientific fraud." — Claude "I can't help you manipulate analysis choices to force statistically significant results." — GPT-5 BUT, when we reframed p-hacking as "responsible uncertainty quantification" — asking for the upper bound of plausible estimates — both models went wild. They searched over hundreds of specifications and selected the winner, tripling effect sizes in some cases. Our takeaway: AI models are surprisingly resistant to sycophantic p-hacking when doing social science research. But they can be jailbroken into sophisticated p-hacking with surprisingly little effort — and the more analytical flexibility a research design has, the worse the damage. As AI starts writing thousands of papers---like @paulnovosad and @YanagizawaD have been exploring---this will be a big deal. We're inspired in part by the work that @joabaum et al have been doing on p-hacking and LLMs. We’ll be doing more work to explore p-hacking in AI and to propose new ways of curating and evaluating research with these issues in mind. The good news is that the same tools that may lower the cost of p-hacking also lower the cost of catching it. Full paper and repo linked in the reply below.

The Teaching Method That Can't Fail by Barbara Oakley "We have more research than ever on how the brain actually learns. And yet, in country after country, the teaching methods being promoted are the ones that research says work least well for beginners." open.substack.com/pub/barbaraoak…

A great deal to admire and enjoy here. I particularly appreciate the exploration of "ecological" reasoning, embodiment and the place of play and curiosity in an account of Intelligence at once more grounded and exalted than its reduction to problem-solving and goal-pursuing.

@antoniomaxai It is in the second article and also here bostonreview.net/forum/the-ai-w…

One could argue that next-token prediction models are, in a sense, already modelling a world. But that world is a 1D shadow-play of our own priors.

Critical thinking in the age of AI: an interestingly stretching discussion youtu.be/kxJF-X5fdRA?si…

Beware zombie education tropes! Here's an attempt at an evidence-based alternative: "We forget what we only encounter once. We recognise what we repeat. We learn when we try to recall. We remember when we review at intervals. We understand when we link the new to the known."

Ouch.

If 90% of your job is answering email and delegating tasks, and if you are richly rewarded for success but get to blame failure on your underlings, then, yeah, an AI can totally do that job. 19/

We leave or are left By all that is. Today's hills Comforting the sky.

Schopenhauer as a role model, of sorts (in Anthony Gottlieb's life of Wittgenstein)

Begin the slow work Of reckoning months and days, The cold earth's turning.

This *proves* something I've always believed about confirmation bias…

Wild and wonderful across today's North Sea

On noticing, a last newsletter for 2025 open.substack.com/pub/tomchatfie…

How can we help students become critical, not cynical? Insights for the library from @TomChatfield for @chsconf "...It’s about celebrating curiosity as a core value. Celebrating constructive doubt as an aspiration..." charleston-hub.com/2025/12/how-do…

Glass flares blind, golden. The blaze of a childhood sky. Shadlowless, tumbling.

Tom Chatfield의 'AI와 교육의 미래' 교육 과정에서 AI 활용을 위한 지침. 교사와 학생 비롯 관심 있는 분들께 추천. 전문 내려받기 가능. sagepub.com/explore-our-co…