PureGenius - AI Powered Learning Platform
Dr. Krockpock,
2026/07/31
I can only imagine how much work it took to set up this brilliant parody of educational technology (as I type the support chatbot is having a meltdown). It has everything - ridiculous reserach papers, a haunted book, genius-level learning that starts at birth, 24-hour degree programs. Enjoy. Via Doug Levin. For more, see the Kickstarter.
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Holding Democracy Together
Gabriela Rogowska, Diego Dylan Domenici,
Innovade,
2026/07/31
The focus of this newsletter issue is a document (15 page PDF) on necessary conditions for digital democracy. It's divided into three sets of conditions: support, design, and external foundations (such as digital literacy). The emphasis is on trust and legitimacy, so support addresses things like procurement, accountability and identity. Design addresses hybrid participation, regulating AI, and multiple models of participation. All these make sense and would apply equally well to digital education technology. But missing from the account are questions of privilege and insider access. All the digital democracy provisions in the world won't make up for the lobbyist who meets with or emails the government official on a regular basis. Privileged access and influence undermines democracy faster than anything else, and this applies equally to education systems.
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Einstein’s Miracle Year
Ray Monk,
London Review of Books,
2026/07/31
Ray Monk was the author of one of my favourite books, a biography of Ludwig Wittgenstein (The Duty of Genius). In this article he reviews Diana Kormos Buchwald and Michael D. Gordin's biography of Albert Einstein. That provides a platform on which Monk can relate the two seminal figures of twentieth century thought: "for Einstein, the attempt to grasp the world consists in the search not for the right words but for the right picture, a picture that would order other picture... Einstein echoes Wittgenstein, who emphasised in his later work that his aim was to provide 'the understanding that consists in seeing connections'... to burrow beneath conscious thoughts, typically expressed in words, to the images that lie behind them and shape them, images of which we are often unaware." Despite Einstein, even to this day people are still unwilling - or unable - to abandon "fundamental intuitions about space, time, mass, motion and gravity." And yet - we've always known these are inventions, created in language, not found in nature.
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Understanding
Stephen Grimm,
Stanford Encyclopedia of Philosophy,
2026/07/31
We often read that an AI, unlike a human, does not 'understand' what it is saying. But what is it even to 'understand'? There's no real consensus on this, and the best way to talk about it is to look at the varieties of understanding, for example as outlined in the book edited by the same author - here's the intro. In academia there's a sense that understanding amounts to being able to explain by means of a theory - this is known as 'objectual' understanding, but even here there are different ways of explaining - for example, do we limit our theory to causal explanation, or do we also take into account non-causal but environmental conditions for the possibility of the event (the kairetic criterion)? And then, there is what might be called first-order understanding, which does not involve theory or explanations, but is rather a more fundamental 'grasping' of the meaning and significance of something, as in 'I understand that murder is wrong'. Anyhow, this post was prompted by my reading this current paper on "non-factive objectual understanding' (NFOU)... the sort of understanding we have when we grasp the range of possible accounts of a phenomenon."
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AI Did Not Introduce Cognitive Offloading. Education Had Already Set the Stage.
Tim Moon,
Silicon and Soul,
2026/07/31
The 'horns' here are the two predictions for AI: doom or utopia. The future is (of course) somewhere in the middle, and this is where we should direct our learning strategies in the age of AI. There is a current danger of 'cognitive offloading', argues Tim Moon, but this is an unsurprising result of a system that was already biased in favour of output over process. "The role was waiting: produce the paper, complete the worksheet, answer the question, produce the artifact." Learning, though, he writes, is produced by friction, by the resistance one feels when struggling to recall, to create, to make distinctions, or to decide. That should inform how we direct AI. Remove the pointless friction, like formatting references correctly, and focus on the productive friction, where people are genuinely learning. But learning what? This is where the article leaves me wanting. Instead of looking more deeply into the problem, it begins to cycle the same points - like an AI would. Disappointing.
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What Every Adult Should Know About Parasocial Media and Teens
Colin Angevine,
Connected Learning Alliance,
2026/07/31
In this interview danah boyd argues that the nature of social media has changed. "In 2006, most people who logged into MySpace and Facebook and Twitter regularly posted content because they were co-constructing sociable spaces to enjoy the companionship of others. In 2026, the content that is posted on the major social media sites is published by a very small subset of the platform's users." She calls this new evolution 'parasocial media', explaining "parasociality is the idea that you can be paying attention to someone who is typically a celebrity, someone who has built an audience and a following, but they don't know that you exist." How does this apply to learning? "The 'connected' was never just about digital networks, it was about social networks. The point was never the power of the media, it was the power of connection."
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Have your say on advancing AI transparency in Canada
2026/07/31
This is a discussion paper intended to inform public feedback about what should be done (if anything) to ensure AI transparency. It is organized into five major themes; each theme is structured as an introduction, case sudy, market responses, regulatory environment, discussion, and finally, a set of framing questions. It's well done and well presented. The five areas are:
- AI-Generated Content: Detecting and identifying AI-generated content;
- AI Interaction: Empowering individuals to know when they are interacting with an AI system;
- Information about AI Systems: Improving the availability of consistent and understandable information about AI systems, including their development, capabilities, and limitations;
- AI Incidents: Enabling the tracking of serious incidents related to AI systems; and
- AI Agents: Advancing ways to better track the activity and interactions of AI agents.
Via Scott Leslie.
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