Stephen Downes

I had a couple of hours this morning so I created a collaborative slide editor. Oh sure, it's still very primitive, but I have bigger plans for it. So I'm right with Miguel Guhlin here: "In the same way, blogs democratized publishing for every person, vibe-coding leverages Gen AI to create just-in-time solutions to problems, cutting out the intermediary enterprise-level company and their armies of professional coders. Is this a good thing? Yes, yes, it is." He provides examples of his own, but my own experience convinces me. I was looking at various products when I wondered, "well, what if I just code my own?" A bit of work with ChatGPT and there it is. I've replaced an estimated six weeks or work with a morning over coffee.

I haven't tried this - I don't have the AI tokens to spare - but the concept intrigues me (I tried the live demo but the credits disappeared quickly). "OpenMAIC turns a topic or a PDF into a full interactive classroom — AI teachers, classmates, a whiteboard, and quizzes — running on your own machine. This guide installs it with Docker, wires up a model, and generates your first lesson." This is a real challenge, I think, to what traditional companies and institutions are offering. Limitations are honestly stated at the end of the article, as well as a genuine success measure: "The useful test is not whether the classroom looks impressive — it does — but whether you learn faster from it than from the document alone." I would insert "faster and better" as the test. It's all open source on GitHub and you can run it on local AI models, it just won't be as good.

This is the sort of thing I like: "Centuries of copyright-free art, historical photography, illustration and archival footage. Find high-resolution files by describing what you're picturing, free to use forever. Every image and video is CC0 or public domain. Free to download and use, with no strings attached." How can they afford to do this? Decentralization: "Arkivism doesn't host the collections — the works belong with the museums and archives that keep them, listed with credit on the Sources page." There's also a search for video clips. Via Grant Potter.

This editorial (10 page PDF) introduces a special issue on AI-aided systemic reviews of research in online learning (also styled here as Open, Distance, and Digital Education (ODDE)). The authors respond to recent criticism of systemic reviews and survey some recent lapses, including "considerable variation in methodological approaches, reporting standards, and the transparency of review procedures." None of this is helped particularly by AI: "increased efficiency does not necessarily translate into improved methodological quality." So they propose standards for AI-assisted reviews that include stipulations that "methodological criteria must precede automation," transparency about the role of AI, systemic validation of AI decisions, and review process that are "sufficiently reproducible and auditable." There's nothing wrong with any of these, but they seem to me to set a pretty low bar. We're still in the era of (almost) any data counts, and (almost) any theory is a good one. 

I recognize that people will have a point of view on what it means when they share their online content, especially if they're institutions or commercial publishers. But I don't agree that "AI broke the open web's bargain" because there was never a 'bargain' to begin with. The idea that there is some sort of social contract describing a 'knowledge commons' is pure fiction. The authors here suggest that 'defensive enclosure' is the natural response to AI-based scraping, but that's only true if enclosure was always part of your 'business model' anyway. Most people don't work that way. They're not joining a 'knowledge commons', they're just talking to each other. The 'new internet bargain' being proposed will favour institutions and trample over people who just want to share their work without being drowned in legalese. Via Ben Werdmuller.

Just a day after I posted an article called Non-Cartesian Learning this article showing us just exactly where the Cartesian 'would' is coming undone appears in my inbox. It's a good read, accurate to my understanding of Descartes, and making a good argument. "LLMs are not programmed to carry out specific tasks, nor do they merely imitate; they learn, and like us, they do so by generalizing from individual examples and feedback to broader patterns... That doesn't mean these models work the same way our brains do (they do not), nor does it mean they are equivalent to people.28 But it does mean the dogma that the human mind and AI have nothing in common is increasingly tenuous." Quite right, and so is most of the rest of this article.