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Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting, arXiv, 2025/01/03

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This article offers an alternative to the common human-in-the-loop (HIL) perspective of AI: "AI-in-the-loop (AI2L) systems: the human is in control of the system, while the AI is there to support the human.characteristic." That is certainly my own experience working with AI. As the title suggests, the terms reveal two different perspectives on computing generally: automation versus collaboration. And these, in turn, are two different approaches to ed-tech and e-learning. Most work in the field seems dedicated toward automation. But because engagement in key to learning, I think collaboration - with the learner making the calls - seems to me to be the better approach. "Moving from HIL to AI2L is likely to help build AI systems where AI truly enhances human expertise, resulting in smarter, more resilient solutions that thrive on collaboration, not automation." Via Ross Dawson.

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Desirable difficulties and cognitive load: Three authors solve a non-existent problem - kirschner-ED
kirschner-ED, 2025/01/03

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Always generous when describing others, Paul Kirschner discusses "another worthless article presenting and then solving a non-existent problem." The article in question is Wesley Pyke, Johan Lunau and Amir-Homayoun Javadi's Does difficulty moderate learning? It rankles Kirschner for two reasons: one is that "increasing difficulty (thus relative effort) during retrieval-based learning can help achieve superior long-term retention," which seems contrary to Cognitive Load Theory (CLT); and the second is that it references perceptual Load Theory (PLT), which offers an alternative explanation to that provided by CLT. Kirschner complains, "there's no conflict between the two and that they fit together like a hand in a glove," once again demonstrating CLT's extraordinary flexibility in the face of conflicting data.

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The Two Meanings of Reasoning and the Future Path of AI
Mike Caulfield, 2025/01/03

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The most important claim in this article is right at the top, but the discussioning of reasoning as a whole is insightful and there's a Yakutsk of an example near the end. The top: "Reasoning can be an activity — something that you do either well or poorly in a given instance. Reasoning is also an artifact and more specifically a path: 'What's your reasoning here' asked in the sense of 'how did you get to this decision?'" We do one in our head and the other on paper. The end: "I have no idea how it actually made the call that this building is in Yakutsk. I know the reasons it tells me."

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Mark All as Read
Audrey Watters, Second Breakfast, 2025/01/03

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Audreay Watters announces, "I'm back to writing about ed-tech (and no surprise, AI)," and weighs in with this contribution mostly on the debate over whether people still read books. Concerned that "we've fully embraced a series of beliefs and technologies that are profoundly anti-reading, and as such, we're spiraling farther down into a deeply anti-intellectual, anti-education period of history," Watters argues, "AI in education demonstrates that we do not care."

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We publish six to eight or so short posts every weekday linking to the best, most interesting and most important pieces of content in the field. Read more about what we cover. We also list papers and articles by Stephen Downes and his presentations from around the world.

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