Making Your Data Ready for Agentic AI
Pramod Sadalage, Prem Chandrasekaran,
martinfowler.com,
2026/08/28
I don't really deal in sales figures and pricing tables - that's the sort of grist for a corporate AI, not something based as I am in the humanities and (to a lesser degree) social sciences. So a lot of this article is orthogonal to what I would be looking for in a description of how to make my data AI-ready (assuming I have anything that would be considered 'data' in the first place). And yet, the model offered here by Pramod Sadalage and Prem Chandrasekaran is, with a little imagination, sufficiently generalizable to offer guidance to the rest of us. What we want, they argue, is a framework that supplies "the context, judgment, and skepticism to work around data that's incomplete or wrong." That seems like good advice even for a database filled with hot takes on learning technology. My readers are certainly grounded and sceptical, but I can't assume an AI will be.
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Teaching the Ocean to Swim
Hollis Robbins,
Anecdotal Value,
2026/08/28
This is probably the best actual argument against learning styles I've seen, though the author does not address this issue in any way. Instead, Hollis Robbins is focused on the 'Big Five' personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism. As Robbins explains, the taxonomy has its origin in Gordon Allport and Henry Odbert's 1936 Trait-names: A psycho-lexical study (176 page PDF) (interesting to me already because of my familiarity with Allport's The Individual and His Religion, which I studied in the 1980s). Through various processes of refinement these were reduced to the five, known as Norman's five. Long story short, Robbins considers various critiques, runs the Allport-Odbert list through Claude Fable, which concludes that "the compression didn't lose the relational, historical, addressed, and valuing vocabulary by accident at step forty; it discarded it at step one, as category error, and kept the words that predicate a lone individual in a vacuum." In other words, "character is built via a sequence of encounters in language, not an isolated trait item... calling correlations of self-descriptions a model of personality has always been a mistake."
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My Top Learning Tools 2026
Stephen Downes,
Half an Hour,
2026/08/28
As Mike Taylor writes, "Every year, Jane Hart asks people around the world to share the tools they use to learn. I always enjoy seeing the results." This is my list. I'm not sure how to communicate them to Jane Hart (her site is offline for a few days) but I'm always very much an outlier, so it probably doesn't matter. This will be her last year doing this, apparently. See also: Clark Quinn.
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How Youth and Educators Can Fight Enshittified Tech
Colin Angevine,
Connected Learning Alliance,
2026/08/28
Short interview with Cory Doctorow on how to respond to tech that serves the producer rather than the user - what he calls 'enshittified tech'. "We need to seize the means of computation," he argues. "If there's a technology that you get benefit out of, but that is exploitative, you should be able to adapt it and modify it. I find it baffling that the response instead so often seems to be: just stop using it." Of course, we can't simply "seize" Microsoft or Apple or OpenAI. That leaves (to my mind) two options: either we build alternatives ourselves, or we use legal means to open up the tech we already use. "Kids and educators can play a role in the kind of activism that we need. One great exercise would be for educators to support a kid who runs up against something that's offensive in their school's tech environment... then FOIA the school district's information about how much they've spent on this technology, what the procurement process was," etc. See also Tom Watson, A Tech Freedom startup stack? "Don't start with what is the alternative to Microsoft Teams. Start with we need a way for people to talk to each other."
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Prototype testing, scaling, and standardizing AI tutors in schools
Ben Williamson,
Code Acts in Education,
2026/08/28
This post summarizes a research article (17 page PDF) offering "an analysis of the preceding three years of policy developments in England leading to the AI tutoring tools and testbed policy programs." In England, "experimental futuring has been put into practice through (1) proof of concept prototyping and (2) pilot testing, with the anticipated outcome being (3) scaling of a standardized model of AI tutoring with expected impacts on schools nationwide." However, cautions Ben Williamson, "The proposal of a national AI tutoring standard in England continues to run against the idea that AI may be a contested technology, a source of sociotechnical controversy, or a social and public problem deserving of wider democratic deliberation." It's not clear that the future of AI will "straightforwardly" enough to support the linear model of progress envisioned by this approach. See also: AI Opportunities Action Plan, Generative AI in education.
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