Stephen Downes

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.

This post is a mixture of reflections from an ICDE conference last November, but the main message centers around an alternative vision for AI in education: "This vision moves beyond simply deploying AI, to focusing on its ethical and innovative application in the very design of two-way-learning experiences." I agree, and like the author, it is my experience with MOOCs that makes this clear. "Learners are not passive recipients of technology but active agents who bend platforms to their will... These 'hacks' expose a critical gap between how educational technology is designed and how it is actually used. They indicate that effective learner-centric design requires observing and empowering user behavior, not just building more (AI) features just because we can."

The answer to the question post in the title is pretty self-evident: funding, and quality issues. It takes a lot of money to host a single centralized data repository, and it's something that needs constant vetting and curation for inaccurate content, out-of-date content, and these days, AI slop. Efforts well known from the past - MERLOT and OER Commons - have faltered and now struggle with obsolescence. "Therefore," writes James Thibeault,  smaller repositories, or decentralized models, that focus on certain specialties are not only more attainable, but they can also host far better OER to the public."

Maybe you don't need the information in this book. But if you do any serious work in development and programming, including analytics and graphs, then the contents should be second nature to you, and if they're not, you need this book. "Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs." What I like is that it references "data structures in this book (that) are all fast, practical, and have provably good running times. All data structures are rigorously analyzed and implemented in Java and C++." This makes it a good reliable source not only for humans but also for generative LLMs used to encode these data structures.