Stephen Downes

This article effectively draws a link between what it means to learn a discipline and the concept of epistemic justice. "In mastering a discipline, learners need to master the 'underlying game' associated with disciplinary epistemes reflecting ways of thinking and practicing within a particular discipline." Quite right. Consequently, "If these disciplinary epistemes are based on epistemic hegemonies from the global north, then they are potentially exclusionary by definition and will ensure that certain learners either never grasp the 'underlying game' or have significant difficulty in doing so." To oversimplify (only a bit), there are two approaches. One is to change the learner. That's colonization (of the person from the South by the values of the North). The other is to change the game. That's decolonization. The challenge, though, lies in how to decolonize a discipline without undermining its factual basis, utility or relevance. The discipline may, for example, devalue "knowledge that is derived from everyday experiences or common-sense ways of thinking," but it may do so for a very good reason. Image: Loring.

This is an interesting question. Companies collect information about you, and you can demand to see what they've collected and correct it if the information is wrong. But what about what a company believes about you? For example, the data might be 'Stephen missed a payment on May 18', while the corresponding belief might be 'Stephen is a bad credit risk'. My rights with respect to the first seem to be different than my rights with respect to the second. And to the extent that these beliefs are increasing generated by AI, we are developing a new class of information - 'generated information' - what we might not even be allowed to see, much less correct. Image: ChatGPT 4, "a line drawing of an AI with 'opinions'".

This report (28 page PDF) focuses on four major ways the authors believe AI will serve education: personalized learning content and experiences; refined assessment and decision-making processes; optimization of teacher roles through augmentation and automation of tasks; and teaching both with and about AI. I'm not seeing this as especially visionary, despite the urgency of messages like "education systems must adapt to prepare young people for tomorrow's technology-driven economies." The meat of the report, I think, can be found in the nine case studies that form the second half of the report; I think they could all have been developed well before the current flurry of developments in AI (and indeed, probably were).

GÉANT is a "collaboration of European National Research and Education Networks (NRENs)" that delivers "an information ecosystem of infrastructure and services to advance research, education, and innovation on a global scale." Today they announced their "decision to stop activities on the main GÉANT profiles on the X social media platform (Formerly Known As Twitter) as of 2 May 2024." Why? "We have seen Twitter go through radical transformations, changed ownership, morphing into X and into a completely different platform which increasingly amplifies hate speech, fake news, scams, extreme views, and illegal content. Verification badges that were once a symbol of trust have lost all meaning, essential features were dropped or limited to paying users, and costs seem to have been cut at the expense of security, privacy, and content moderation." If you are still using X/Twitter, you should ask, what are you supporting?

This is in response to a contribution to an OAS meeting distributed in my office this morning. It is of course my set of opinions only, and not reflective of any official policy or practice, though I would add that most of these have been undertaken to one degree or another by various levels of Canadian government organizations.

I want to say something like 'this paper describes a core tenet in connectivism' although of course we never conceived of it in anything like the richness and detail collective intelligence across 'scales and substrates' described here. This, in particular, is crucial: "collective intelligence is not only the province of groups of animals, and... an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales." The way networks work is tied up in the way evolution works, and these are tied up in how we describe learning and cognition generally. Or - how we should describe learning and cognition (as most people still labour under the mythology of folk-psychological information processing types of pictures such as 'executive function' and 'cognitive load' theories).