Stephen Downes

This article blends a bunch of things together. Let me tease them out. The first is the main point, that "AI is an amplifier, not a driver." We've been seeing this a lot; the human who needs the work done is the driver. "Judgment still requires context; context still requires experience." This isn't strickly true (context can come from anywhere) but human experience is the part of context AI cannot provide on its own. "Design experiences that build relationships as well as skills." Right. But do we need this? "Facilitate the conversations that support shared interpretation." This is based on the idea that "the center of the framework is human meaning-making." In other words, "meaning still emerges through human conversation and reflection." But (in my view) the task here isn't 'making meaning'. Anyhow, these are all blended together in what eventually becomes a word salad. 

This is the first part of a paid article but there's more than enough here to justify reading the free segment. I'm noting it here mostly for my own purposes. I found it after seeing an algorithm-recommended LinkedIn post that referenced some of the approaches but did not link to actual code; it turns out that the content was lifted word for word from this article without attribution. It's a list of methods people can use to optimize their Claude usage so their requests don't consume so much computer time and token use. It looks like good stuff. I use the cheapest possible Claude plan so this will help a lot. Maybe.

This article offers a much-needed corrective to the application of cognitive load theory in education, based on a paper on the same subject by the author and colleagues. "The core concern is not that CLT is totally wrong or irrelevant, but that it is radically incomplete, and that treating it as a general theory of education distorts both pedagogy and teacher understanding." In particular, "Children are not fragile information processors waiting to be protected from difficulty. They are unusually exploratory, socially attuned, and cognitively plastic... We propose treating learning as a dynamic interaction between cognitive, emotional, social, and self-regulatory processes, unfolding across development." That's actually about as far from cognitive load theory as you could get, but it's accurate.

I'm not sure where this will fit into the definition of learning technology exactly, but there has to be some overlap, and it's better to be thinking of these issues before the fact rather than while we're in the middle of it. "Human brain organoids (HBOs) are three-dimensional structures derived from human stem cells that model aspects of brain development." They're not conscious, sentient, or capable of experience the way we define it, but the ethical issues are still numerous, from grounds of privacy (regarding stem cell donors, for example), commercialization, and application (such as transplanting of human brain cells into animals). This paper is a good overview of the ethical issues that may arise, with due regard for public misperception, cultural variation, and future developments. Image: PubMedCentral.

This effort goes about the way you would expect it to: "(the) entire class plan was to bring down carts of books related to the class topic, have the students pick something they were interested in, and then read for about an hour." So they read, they fidgeted, and in the end, everyone marvelled at how great an hour of reading was. And sure, I get it. But what struck me is that when I was in school I used to get into trouble for reading in the classroom. The books were apparently a distraction from the much more important (and oh so boring) stuff happening at the front of the room. For the rest of my life, I've always had something to read with me (more usually digital these days) under the desk. It has always been one of the differences between me and the people who just did what they were told.

Good article that makes the following case: "If AI is so impactful, why isn't it showing up in the productivity stats? The Solow paradox answer is that firms haven't reorganized yet. The computer took nearly a decade to show up in productivity numbers because the organizational work - flattening hierarchies, redrawing workflows, retraining workers, rebuilding integration machinery around the new technology - took nearly a decade to do." I would argue that this is also why we are not seeing 'learning gains' (whatever those are) as a result of AI intervention. The necessary reorganization and rethinking of methods and pedagogy hasn't happened yet.