Stephen Downes

Tom Woodward links to three interesting graphing resources in one post. This first item, a tool for selecting the sort of graphic you want to use, is a number of chart type selections classified according to the number of variables you're looking at. Their poster is probably the best value of the three. If you prefer a more open-ended selection, there's this complete guide to graphs and charts. This page also links to "on-demand courses show you how to go beyond the basics of PowerPoint and Excel to create bespoke, custom charts" costing about $100 per. And how do you make the charts? You could use SciChart, a 'high-performance' Javascript chart and graph library. But the pricing is insane, starting at $116 per developer per month. I'm pretty sure ChatGPT will teach you about the types of charts (actually, I just made one for you while writing this post) and Claude Code will be able to write you a free version of SciChart. 

I like this analogy. "Some of you may remember the Apple ads that emphasized the computer as a 'bicycle for the mind.' GenAI is not like a bicycle for the mind. Instead, it's more like an automobile." Or, says Mark Guzdial, "As Paul Kirschner recently wrote, GenAI is not cognitive offloading. It's outsourcing. We don't think about how to do the tasks that we ask GenAI to do. As the recent Anthropic study showed, you don't learn about the libraries that your code uses when GenAI is generating the code for you (press release, full ArXiv paper)." Maybe. But it depends on how you use AI - there is a 'bicycle method' (to coin a phrase) when using AI, which is what (I think) I do - making sure I understand what's happening each step of the way. As Guzdial says, "Generative AI is a marshmallow test. We will have to figure out that we need to exercise our minds, even if GenAI could do it easier, faster, and in some cases, better." See also: To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making.

This is pretty cool: it's a collaborative markdown editor with a couple of interesting features: "all docs auto-delete 99 hours after creation. This is for quick sharing + collab"; and "Roundtripping: Download then import by drag and drop on the homepage: all suggestions and comments are preserved." Built over the weekend using Claude Code. And it reminds me or a remark I heard on TWIT: coding with AI is the best video game out there right now. "You know it's very addictive using Claude Code over the weekend. Drop in and write another para as a prompt, hang out with the family, drop in and write a bit more, go do the laundry... scratch that old-school Civ itch, 'just one more turn.' Coding as entertainment."

I've made a similar argument in my own writings on ethics: "diversity in general is intrinsically valuable, and there's no good reason to treat moral diversity as an exception." People will have as different understanding than you or I on what's right and good, and overall (within reason) that's a good thing. Now the reasoning offered here is based on aesthetic premises: "a world where everyone liked, or loved, the same things would be a desperate, desolate world." Or as Eric Schwitzgebel summarizes, "An empty void has little or no value; a rich plurality of forms of existence has immense value, no further justification required." My own reasoning is more pragmatic: a world where we all valued the same things would be static and unchanging, and therefore, could never learn or adapt. 

It's like we're asking the same question over and over again. Maybe they can be reframed? Marc Watkins begins with the ethical perspective, but looks at whether institutions should buy AI tools from three different perspectives: would students even use the tools (or would they distrust them); would students use their own AI to bypass institutional guardrails; and why would institutions use a tool that would eliminate the positions they are preparing students for? "Institutions like Gonzaga University," writes Watkins, "are making AI part of their core curriculum by putting it in conversation with their institutional values." Specifically, "Because a commitment to inquiry and discernment serves as the foundation of our core curriculum, our students will engage with AI in ways that are both practical and critical." That makes sense, but there's also the risk that this is just wishful thinking.

Dean Shareski pushes back against "the so-called thought leaders out there who seem to have a clear handle on how to best consider AI for learning and schools." You see them a lot on LinkedIn and, of course, on their own web pages, offering "frameworks and approaches neatly packaged, intended to support leaders, educators and students in their professional and instructional use of AI." The reality isn't that straightforward. Take the simple question of using AI to help design slides for a presentation. PowerPoint will incessantly offer suggestions. Sometimes they're useful, but sometimes the personal touch is what's needed. There's no general rule. Me, I prefer to design by hand, but that's mostly because I enjoy designing. Though I like to think there's an intuitive aspect, where my design reinforces my message in a way that an AI-generated design would not. It's hard to say. Image: one of mine, that I'm pretty sure an AI would never use to illustrate this post.