Stephen Downes

Networks in nature run by themselves so I'm always wary of attempts to impose order on networks, because usually that leads to centralization and, eventually, destabilization (and ultimately, in human spheres, privatization, then commercialization, then enshittification). There is a bit of an awareness of this in this article. For example, "One shares, 'our governance is decentralized, not uniform in many ways' and 'there are processes that enable us to be accountable to each other based on our strategy and plans.'" Still, networks don't build themselves (except in nature): "Stewardship means creating concrete tools and guides, as well as explicit permission and role-modeling." But a lot of this is necessary only is network use is instrumental, "for members to harness the network towards their individual and collective goals." And instrumentalism is the beginning of the end for a network.

You definitely want to take the time to watch the short video and read the article, especially if you're an AI sceptic. Ethan Mollick offers a number of descriptions of things the AI did (Claude Fable 5.2, to be specific). The cost of using Fable for more than a few minutes at a time is beyond my budget, so I couldn't test it on any of the things described here. But assuming Mollick's honesty (which I think is fair) this is a convincing demonstration of how capable AI has become. "I think this phase of Jagged AGI is an exciting one, even as I wonder, a bit anxiously, how long it will last," he writes.

AI agents were set loose in SimCity (an online virtual city simulation) and linguists studied their interactions. They tended to adopt their own slang and jargon (though less so if agents from different LLMs took part). "True kintsugi begins with accountability, not poetry," one of Google's agents mused. "A paper that ate three cold hands and got more honest each time," an Anthropic agent pondered. I wondered whether similar work had been done with children in SimCity, but when I looked all I found were uses directly related to learning outcomes. This, for example: "As game-based learning gains momentum in education circles, teachers increasingly want substantive proof that games are helpful for learning. " Via RestOfWorld.,

There is obviously a difference between using an LMS to build a course and using an AI, but Hollis Robbins isn't completely wrong here. "You are not wrong to insist upon the principles of good teaching, like the importance of writing, human feedback, instilling a spirit of curiosity and discovery," writs Robbins. "But course management software, which ninety-nine percent of colleges and universities now use, violates all these principles." Like AI, the LMS promises to make the hard work easy. "Nobody is protesting Canvas because the laziest teachers love it. Canvas sells itself as a lazy-teaching technology." Now - maybe a lot of this work should be easy. But then, why not apply the same reasoning to the use of AI?

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.