The Real Solution to the EdTech Backlash is Digital Leadership
Eric Sheninger,
A Principal's Reflections,
2026/08/10
I don't really agree with the argument in this post. The proposal is that better leadership will counter the tech backlash in schools, where this amounts to: first, better pedagogy focused on high-level cognitive work; second, transparent multi-channel communications with parents and students; and third, high-touch leadership "to stay present in classrooms, mentor teachers, and support students." While I understand that 'leadership' is no longer viewed as 'telling people what to do', this approach feels pushy to me. It treats people as though they don't really understand technology and that the leader's touch will solve this problem. And that's where I don't agree. If I were a teacher I wouldn't want the principal pushing pedagogy on me, short-cutting interactions with parents, and hovering in the classroom. I'd want to room and the space to explore technology on my own terms, with my colleagues, and making my own decisions. Students, too, would probably prefer this approach.
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Making universities a part time world
Tom Worthington,
Higher Education Whisperer,
2026/08/07
Tom Worthington points to Kelly Linden's report Is the problem us? Helping part-time students succeed in a full-time world (90 page PDF) which asserts (probably correctly) that "part-time students on average have lower rates of success and retention, and are more likely to belong to an equity group than full-time students." While Linden makes a number of progressive recommendations making exceptions for part-time students, Worthington argues this misses the point. "Programs should be designed for this mode of learning. This is not difficult or expensive to do, and ultimately simpler than designing for full time face to face students, then have to make special arrangements when it turns out most students are not." It's hard to imagine universities moving away from the full-time residential (or commuter) student model, but if they want to be more central and responsive to the needs of the community, they should be thinking this way.
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'Very hard on your mental health': Canadian workers speak out over software that tracks screen time
Kevin Maimann,
CBC,
2026/08/07
I think that the main lesson to be learned here is that when companies and organizations say that surveillance won't be used for something, don't believe them. "Her managers currently use ControliQ... Introduced about two years ago, the tool is increasingly used to 'shame and belittle' workers by displaying and comparing their 'productivity' metrics at meetings, the employee said. At a certain point, the power in the work-employee relationship needs to be rebalanced, I think. "I don't know if the company actually understands how almost degrading and humiliating it is to know that you have to have a babysitter when you're in your 50s, and you've been doing your job for 30 years, and you've been doing it well."
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Responsible AI in Academic Research
Michael J. Zyphur,
Instats,
2026/08/07
By 'responsible' the report (64 page PDF) means "AI use that is ethical, valid, reproducible, and transparent." The phrase is widely defined in principle, but "still lacks an operational definition". That is what this report is intended to address. It offers a "five-dimension competency framework" where each dimension is a precondition for the next. For example, "An institution that has not made the human-in-the-loop call (Dimension 1) cannot define responsible use in practice (Dimension 2)." The dimensions are scored from absent through nascent and established to leading. It's worth noting: "Responsible AI use is a research-integrity question, not an academic-integrity question." Also, "AI literacy at the PhD level is not a tooling-skills problem; it is a judgment problem."
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A Visual Guide to Quantization
Maarten Grootendorst,
Exploring Language Models,
2026/08/07
When I say 'AI is just math' it really sounds like I'm just waving my hands. This article adds all the detail you could want to that statement, and for extra measure is lavishly illustrated, making the math jump off the page. It takes you all the way from simple quantification of input data through to ternary values and BitLinear layers. I know AI has its sceptics, but at a certain level scepticism dissolves into questioning whether math actually works (which it does, but as always, whether any math applies to the world or our perceptions of it is always an empirical question). This and two others today via Data Science Weekly 663.
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Retire the Abstractions
Hazy Research,
2026/08/07
This article discusses abstractions for machine learning specifically, but the argument generalizes. Most of computer science consists of abstractions. At the base level is the machine language that is the direct interface with the hardware. But because this is so complex, more abstract languages are built on top of it (most notably the computer language 'C'). But even this is pretty complex, so above C even more abstractions are built: languages like Python or Perl, which are based on C compilers. But with each abstraction, there is a trade-off: clarity versus efficiency. But if AI, not humans, are writing the code, we don't need the abstractions. "What do we keep? The intent, the invariants, the tests, and the hard-earned domain knowledge that currently lives inside ThunderKittens's abstractions and keeps them correct on hardware."
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How Do I Build a Model?
Chris Brown,
2026/08/07
This guide won't actually get you from knowing nothing to creating actual models, but it will point you to the right sort of reading you need to do to gt you all the way there. The 'models' it talks about aren't AI models (not exactly, at least) but rather flow diagrams, causal or statistical models. The models might be diagnostic, or they might be used to address design challenges. It's definitely a good overview of the topic, written in a non-technical and accessible manner.
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How stronger meta-skills will prepare teams for an AI age of continual learning
Phanish Puranam,
World Economic Forum,
2026/08/06
According to this article, " instead of only asking whether employees have the right skills for the tasks on hand, leaders should ponder how fast - and how well - they can acquire the skills they will need next." These skills - here called 'meta-skills' (but also known as 'soft skills' or 'durable skills') break down into two major categories: critical thinking skills, and social skills (I'm sure there's an interesting story to be found detailing how critical thinking and social skills lead to better learning). According to the article, meta-skills can be assessed via three major indicators: "people with strong meta-skills acquire new skills faster"; they "show up when people apply principles learned in one context to structurally similar but unfamiliar problems": and "meta-skills are visible in how people work." And people can learn them. "Higher order thinking, metacognitive regulation, analogical reasoning and social coordination all improve with structured practice, feedback and reasonably demanding environments."
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ASR In Literacy - What Is It Good For?
Owen Henkel, et al.,
The Learning Agency,
2026/08/06
This article raises some interesting questions about early language learning (ELL) and AI. Specifically, it makes the point that while AI is already quite good for some things (such as speech to word transcription) it needs specialized training for others, such as low-stakes oral reading fluency, and absolutely flounders at others, such as phoneme-level assessment. The model needs "a separate step that compares the recording with the expected sounds." And "Improvements on this narrower task also may not translate into better general transcription." So what does this tell us about phonics as a thing that is learned? Maybe (and I'm speculating here) we learn how words are pronounced first, and parts of words later. The pronunciation of 'cat' and transcription to the letters c-a-t happen as a single step, and are not composed of separate steps (pronunciation of 'k', pronunciation of 'at', transcription to correct letters). Something to think about.
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Designing the AI-Integrated Classroom: A Triadic Learning Ecosystem
Matthew Brophy,
EDUCAUSE Review,
2026/08/06
This article describes a Triadic Learning Ecosystem (TLE) that "offers an architecture of that near future, connecting teachers, students, and AI-enabled tools in a collaborative model of learning." The three elements are students (as peer groups), the teacher (as conductor), and pedagogical AI assistant (PAIA) (as scaled support). "Teachers are elevated from content-communicator to classroom conductor. They encourage, challenge, intervene, motivate, and lead." Interesting word choice, 'elevated'. "The students are not passive recipients of information but active builders of it. In small peer groups, they produce, debate, and explain to one another." There's a five phase model that describes class sequencing: co-design, student exploration and creation, PAIA simulation, real-time feedback loop, reflection and synthesis. There's also a (very simple) technology mapping to the framework. There's some good thinking here, but I think it's far too rigid a structure.
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The Professional Toolkit
Tim Klapdor,
Heart Soul Machine,
2026/08/06
A professional can, as Tim Klapdor says, pick up their tools, find a new chair, and keep going. "The skill is his. The relationship with his clients is his. The craft he's spent years developing is entirely portable." Why isn't that the same with university professors? Klapdor explains this by saying each university is a unique machine, but I don't think that's it. True, "the academic, at their best, owns a body of work and the reputation that comes with it." But they don't own their own client list (and don't have the marketing reach to generate one), and they don't own the credential that they, uniquely, can confer.
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Design The Meeting
Corey Ford,
Point C,
2026/08/06
There's so much talk about in-person for the sake of in-person, and then our actual in-person interactions (whether online or in-person) are so badly managed. This article seeks to correct that by focusing on designed meetings, where the purpose, roles, flows and outcomes are determined (so far as possible) to maximize and respect people's time. I appreciate that. Now this article is focused on leadership in a corporate mode, but of course if we elide the business elements (i.e., expenses, value, leverage, etc.) we get a good model for in-person interactions in general, which can inform what we're doing when we collaborate and learn together. Via Ben Werdmuller.
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Critical Perspectives on AI, Digitalisation, and Human-Centred Learning
David C. L. Lim, Mohd Tajudin Md Ninggal,
Springer,
2026/08/05
This open access book (116 page PDF) is a collection of essays that asks of digital education "what kinds of futures are being imagined and enacted for education, whose interests these futures serve, what assumptions underpin the current drive towards digitalisation, and what remains unexamined or quietly displaced in its wake?" The focus is less on the stated (Utopian) intentions and more on what is actually being put into practice. "A central concern running through the volume is the place of the human in digitally mediated education," write the editors. But this is a moving target; "the chapters attend to the ways in which human agency, judgement, and relationships are being reshaped under conditions of digitalisation." The volume considers tensions such as those between scale and care, efficiency and responsibility, and access and meaningful participation. A deeper current is what Melinda dela Peña Bandalaria calls "the erosion of shared frameworks through which universities justify their actions, define value, and make their commitments intelligible." Some characterize this as "chasing technology", some focus on emerging social values, and some characterize it as a recharacterization of our own humanity.
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Choosing What’s Next: National Priorities for Open Education
Nicole Allen,
Open Education Association,
2026/08/05
The Open Education Association is a relatively new organization (b. 2025) that evolved out of the U.S.-based Open Education Conference. It is now soliciting contributions to a national (ie., U.S.) consultation on policy. "The process will focus on identifying challenges facing higher education today where openness is part of the solution but is not yet a large enough part of the conversation." They seem to be looking for examples that support "connecting our national priorities to those of leaders and policymakers." An odd wording. Anyhow, there's a webinar next week and consultations will continue through the fall. The site sadly has no RSS but there is a newsletter. If you want to join the Association, you'll have to pay.
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A profile is not a relationship. These standards must support human flourishing. Here's how
Kelly Ilich,
AARE,
2026/08/04
This article addresses an ongoing review of the the Australian Professional Standards for Teachers but at the same time uses the exercise to differentiate between what teachers and AI can do. Obviously everything suffers from overreach here, but the exercise is still worth a look. For example, how do we depict Standard 1, "know students and how they learn"? Kelly Ilich responds, "Being modelled by a system is different from being known by a trusted adult," which is true, but how often does the adult simply fall back on a common stereotype? And how limited are modern AI models, really? Similarly, for Standard 2, "know the content and how to teach it." it makes sense to "emphasise the teacher as a designer, interpreter and responsible steward of knowledge," but what happens when doing these properly requires skills well beyond the ken of the average instructor? Yes, with Ilich, I think the test should be to ask "what must teachers now be and do so that children flourish in a world shaped by it?" but my focus is on society, not only teachers.
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Preprints Without Curation Are Increasingly Cited by Journals
Chiaki Miura, Ichiro Sakata,
arXiv,
2026/08/05
Peter Suber cites this article (11 page PDF) saying "Preprints are increasingly treated as legitimate, autonomous objects of citation independent of their later publication status... I'm fine with this, and so are the authors of this study." So am I. As Suber writes, "I'm from the humanities, where it's common to cite unpublished letters and manuscripts, countless fiction and nonfiction genres never subject to peer review, and even an occasional 'personal communication to the author'. Cite your best sources, or your actual sources, and let readers judge their relevance and credibility." Indeed, we might think of citation of a preprint (the way I'm doing here) as a form of unofficial, distributed and decentralized peer review.
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How To Give Everyday People A Say In AI Governance
Hélène Landemore,
NOEMA,
2026/08/05
I think this article taps into a genuine problem identified in two important essays - AI 2027 and Europe 2031 - but falls far short of a solution. The problem is this: "A small number of people make decisions for everyone else, without their input. Neither essay asks what ordinary people actually want from this technology or proposes a way of finding out." Quite so. Indeed, I dare say most people wouldn't see much difference between a society ruled by AI and a society ruled by wealthy out-of-touch elites. But asking AI companies themselves to "help bring democratically written global and local constitutions on AI into being, because right now no government is moving fast enough, or has the will or standing, to do it alone." The reason is that AI is only one of many issues that would benefit from the people having a genuine say in the outcome. The real question here is: could AI help society become more democratic? But I don't really see people addressing this.
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Meso and macro-level impacts from implementing open badges and microcredentials - A scoping review
Abbigail Harris, Richard E. West, Tyler Westerberg,
Journal of Open, Distance, and Digital Education,
2026/08/04
This is a good paper (25 page PDF) focusing on the meso and macro effects of digital badges, drawn from a study of 83 studies. I liked the literature review in particular, which is a good overall framing of how digital badges support new learning opportunities and recognize mastery of skills. Readers might like the badge taxonomy provided (illustrated). It's worth comparing this set of factors with the findings described in the fifth section, as there's a pretty clear mapping there. Most work on digital badges is qualitative or mixed methods; the mapping I would think opens an avenue for more quantitative work.
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Not all collaboration benefits from competition: Collaboration modes in a computational thinking game
Ching-Huei Chen,Kun Huang,
British Journal of Educational Technology,
2026/08/05
We probably know this intuitively, but this limited study shows "competition undermined the collaborative turn-taking mode while enhancing cooperative and paired gaming modes, highlighting the need to carefully match collaboration structures with competitive elements in designing game-based learning." Competition does have its benefits - where would we be without sports? - but not everything needs to be defined through success in competition.
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Impact in Journalism
Jazmín Acuña, Flora Pereira,
Pulitzer Center,
2026/08/04
By impact "we mean qualitative changes journalism may contribute to, such as increased awareness, narrative reframing, accountability, community organizing, behavioral shifts and policy reform," writes Jazmín Acuña in this summary. Impact journalism is a process by which impact is planned for, taken into account while writing and publishing, and followed up on through audience engagement. The full report (28 page PDF) details (after lengthy introductory material; findings and trends begin on page 15) impact journalism as a trend that is under-institutionalized and needs to be developed, especially after publication. I can think of analogies in education, which we might call "impact education", or "impact pedagogy", which follows the same principles. Via News Alchemists.
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Return-to-office mandates are killing workplace trust
Gleb Tsipursky,
The Hill,
2026/08/05
I feel pretty strongly about this. After being more than productive during the Covid shutdown, office workers were forced to return to the office (at their own time and expense) in the name of... well, not productivity. Where I worked it was clear that productivity was not the issue. We were told stories about 'culture' and 'collaboration' - in an office where I would work a full day without seeing anyone else on my team (except, of course, online). "Leaders say they want culture, mentorship, innovation and accountability. But workers increasingly hear 'surveillance,' 'attrition' and a quiet test of 'loyalty.'" I think the same is true of online learning. Admittedly, this is a harder case to make, since a lot of what passed for online learning during the pandemic was just classroom instruction over Zoom (and therefore, equally unbearable). But I think that a lot of the motivation for in-person learning has to do with control, and not student growth, learning or development.
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Claude Is Free for Teachers. Here Are Fifteen Things I Use It For.
Stefan Bauschard,
2026/08/03
This is quite a good article, and while I know there are AI sceptics, especially when it comes to teaching, it would be well worth reading this, not so much as a list of things to do with AI, but for its focus on the really pragmatic and practical uses to which it can be put. In none of these is the teacher's judgment being replaced by AI; rather, what we see is an emphasis on saving time and improving quality in rote tasks. The author uses AI to update examples in a debating textbook, for example, instead of not doing this at all. Or for looking up 'Boltzmann brains' to learn something about which he knows nothing. Or writing camp reports where this would have been impossible previously. Turning "distribution into a five-minute job instead of an hour of clicking."
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Deskilling, AI and the questions we should be asking
Treca Bourne,
Chief Learning Officer,
2026/08/04
This is yet another article warning that the use of AI will lead to deskilling. We've discussed this before and I won't cover it again here. What I want to note is the set of sources used by the author: Fortune, the American Enterprise Institute, and the Atlantic. Now while I'm pretty open to a diverse range of sources I have to say that this should not be your evidence set for a conclusion on anything. Seriously, if you're going to write about AI and learning, get out of the narrow world of Amercian commercial media and read and reference much more widely. That is all.
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Copyright 2026 Stephen Downes Contact: stephen@downes.ca
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