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Clark Quinn’s Learnings about Learning

Archives for August 2026

My (final) Top 10 Learning Tools

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I’ve been supporting Jane Hart’s Top 10 Learning Tools since I first heard about them. It appears I missed a few years here and there, but it’s interesting to see that it’s at least as far back as 2008! I see many tools that have passed on, or I’ve passed on. Let’s see where we’re at this year. And it’s the final, because this is Jane’s last go. Fare well, dear lady.

I’ve been categorizing them, for my own thinking. In ways that flux. I can’t see how that’s going to change…but note that I’m deliberately trying to move away from the mainstream tools. I’m finding that the big companies are increasingly aversive. Of course, that may be just a “get off my lawn” moment!

Writing

So, one of the things I continue to do is write. I use lots of tools, actually, but let’s talk about two of the most important.

Notes has become a place I store thoughts. I’ll create a note for something I’m thinking of while out for my walk, or at my desk. If I have a thought while I’m out and about, I may well lose it if I don’t capture it. So I do.

Word, however, is still my industrial-strength option, e.g. for books, because of outlining. I tried LibreOffice, and Pages, and Docs (trying to avoid Google), and … but nothing handles outlining with the elegance of Word. Sigh. So much for my effort to step away from Microsoft (though I’ve fortunately been able to turf the Teams app!).

Graphics

For some reason, I haven’t been creating as many graphics as I used to. Still, I occasionally try, and that’s still important to me.

OmniGraffle continues to be my diagramming tool. I continue to try to find another that’s less dear (Freeform is the latest), but nothing else seems to work as well. Mapping concepts to elements and relationships to arrows, in short re-representing, is key.

Keynote is a presentation tool, but it’s about visuals as well. I structure my talks this way, including diagrams and photos (royalty/attribution free from Unsplash, Pixabay, or Pexels), and some prose. But thinking how to talk about something forces me to think about it anew, and that’s more learning.

Social

As part of the Seek-Sense-Share cycle of Harold Jarche’s Personal Knowledge Mastery (PKM), being social is part of both the seek and share. Thus, social networks are ways I learn. For instance, today I just found out about the passing of Dolly Parton, which floored me; such a great person!

My probably favorite tool is Mastodon. I bemoaned the loss of Twitter, but it’s now a memory. Mastodon isn’t quite as lively as my next item, but it’s more intimate, and the underlying tech approach means it’s highly unlikely to be taken over by someone with an agenda (any, good or ill!). Fingers crossed.

Closer to what Twitter was is BlueSky. It’s bigger, so more different thoughts, though it still has the potential to be used for ill.

LinkedIn is, of course, the business network. That’s of course, what I try to use it for. Posts like this appear there, and there’s increasingly interaction there rather than here, but it’s not all of ‘me’, just ‘biz me’.

Slack is a tool I use to connect with folks on particular topics. So, for instance, both of my ‘sweat equity’ initiatives, Learning Development Accelerator (LDA) and Elevator 9, both use Slack channels for communication.

Project Management

While project management might not be considered learning, it does mean that it’s a way to manage my commitments, which frequently involve things that lead to learning.

Notion has been introduced for project management in the LDA, and it’s been a pretty good way to document and manage our tasks. We use Slack (above) for communication, but Notion has become a place to put and receive ideas, and give/get feedback. Both are part of the PKM idea.

Reminders has become how I manage my commitments to Quinnovation, LDA, Elevator 9, family, etc. If it doesn’t get in there, we never had the conversation!  I’ve tried for a long time to manage same, and sometimes it’s a combination of Calender with Reminders, but it’s made it easier to meet my commitments.

There’s more. I’d put in Vivaldi as my browser, through which I surf the web, and DuckDuckGo‘s NoAI option as how I search. Wikipedia is undoubtedly my first check as a result of a search. That’d be three more, and they might be more frequently used than most anything else, but I wanted to focus on the apps and not the activity. I guess (he says, rationalizing post-hoc).

So there’s my last Top 10 Learning Tools for Jane. Maybe someone else will pick it up, or maybe I’ll do it again if something changes drastically, but it’s been interesting to think about mine, and to see the results she finds. As usual, it’s typically not formal learning, a lesson she likes to promote, and I’m not alone in thinking is interesting an important. So, what are yours?

Generalists and Specialists

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It’s always been a battle between generalists and specialists. Despite folks like Don Norman making the case why we need both, we’re seeing this play out again. A post by Tom McDowell triggered this recognition, so let me talk a wee bit about what I see going on.

Tom’s post was about how you shouldn’t automate a process until you understand it. The issue is that until you map it, you can’t say you truly comprehend it. Further, if you don’t comprehend it, you can’t decide what the real tradeoffs are in automating it. This triggered a realization in me.

So, I’ve argued in the past that we shouldn’t digitize processes until we’ve looked at them. If you automate before reviewing them, you merely solidify your existing paths. Which, if like the Humanities & Social Sciences building at UCSD, you’ll find that people cut paths cross the grass, not stay on the concrete. That’s because the design wasn’t considering human behavior. (I think this example was in one of Don’s books; I can’t find which, but I lived it.) Digitization instantiates your processes, so make sure your processes are good first!

Which brings me to the current situation. Large Language Models (LLM)  are in use more broadly than their real affordances. The underlying technology is more general, but these have been optimized for language, that is to make good sounding language. And they’ve been trained (illegally) on a vast corporate of (English) language. Consequently, they make very convincing statements about the world, whether correct or not. They make it easy to do more tasks than just language, so people do that. There are consequences.

For one, we’re losing the development path. Doing low-level work established the foundations for high-level work. Er, if you’re human. If you don’t get that indoctrination (not hazing), you don’t become an expert. Outsourcing that work means you don’t have a way for your new workers to develop the necessary expertise, which will walk out the door. And you can’t just hire experts. Knowing what you can outsource, then, becomes critical.

Another, of course, is that outsourcing the wrong things can cause trouble. We have seen bad processes reinstantiated in generated content, instead of actual learning experiences. I’m sure you’ve had the dubious pleasure of interacting with a chatbot instead of customer service (there can be good ones, but it takes a fair bit of work to make it so, contrary to the claims). Then, even if you have oversight and responsibility, you can fatigue if there are expectations to oversee something that’s mostly right. You learn to trust it, despite a conscious awareness that’s wrong.

The problem that struck me is that it’s likely the case that folks are over-trusting these generalist systems that seem intelligent. (They’re really just probabilistic engines, but to some extent so are we.) They do have lots of knowledge, but it’s context free, and trying to get them to understand context is what is known as prompt engineering (a term that’s disappearing). Still, it seems like a quick and cheap fix to get these generalist engines to do specialist tasks. I’ve been arguing that rather it makes sense to make specialist agents. The recognition came that the reason is because we really don’t have AGI (artificial general intelligence) yet (and we’re a long way from doing that).

So, yes, I have some bias. I’ve been and am pro-AI, but I don’t like the way the big companies have built their systems, they’re overhyped, and the costs are yet to be seen. Take all this with the proverbial boulder of salt. Still, I reckon there are better alternatives, at least in the long run. That is, small agents you build and train, so that you know what they can, and can’t, do. Little systems are cost-effective and more secure, too. It’s being smart about sensibly distributing roles for generalists and specialists across technology.  That’s where my thinking (and recommendations) are going. What are your thoughts?

 

AI Slop and Brain Rot

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I’ve been complaining about AI slop, probably not publicly, but clearly. And, I’ve been noticing the complaints about brain rot from using AI. Let’s be clear, this is an instance of people saying “AI” and meaning generative artificial intelligence, a subset. Still, both are being seen, and not surprisingly, they’re linked. At least, let me make the case that AI slop and brain rot are related.

AI slop, to be clear, is when folks are using AI to generate content. It can be passive, as that awful image that had me as one of the ‘wealthy influential L&D leaders’. Wealthy? As if (as in, in my dreams). Sorry, my bad habit of ‘standing up‘ for what I believe gets in the way, way too much! AI slop can also be interactive, such as the chatbots (textual or auditory) that are replacing people. In either case, it’s using an average of stolen intelligence to create responses, and that’s only minimally worthwhile even if, as Markus Bernhardt reminds us, we provide oversight. Which doesn’t happen, because it’s too cheap to just have AI create worthless content.

There is also the phenomena of brain rot, where people who use AI, unless using it well (which is rare) get dumber. If you outsource the mental challenge, you aren’t doing the work that maintains and builds mental capacity. We’re increasingly seeing evidence that folks who outsource to AI end up diminishing their capability. Unless they’re using it as an idea partner and a form of feedback – generating their own ideas and using the AI output as a check – they’re setting themselves up for failure. It’s like with your muscles, use ’em or lose ’em. Same with your brain.

Similarly, companies that let AI handle low-level tasks are breaking up the pathway to expertise. Sure, it’s a short-term win, but…what happens when the expertise retires, and you’ve no one who’s been developing to replace? Maybe AI will get good enough to handle that. But there’s also evidence that the technology is reaching the law of diminishing returns. And, what with the move to the market, the costs are likely to go up. Finally, the tech is optimal for language tasks; using it to do other things, such as numeric tasks, is a quick trip to inaccuracies. There are better approaches.

At core, my point is that the source of the problem is the same: the technology isn’t being used in ways consonant with it’s strengths. (Let alone in ways consonant with society as a whole!) We’re not using it to augment us in ways that are beneficial, instead we’re using it in ways that produce short-term savings at the expense of long-term benefits. And that, to me, is a path to a worse future, and I think we can do better.

Overall, the rush to ‘AI’ is pretty short-sighted. Like most things, we see an overexcitement, and then we face the reckoning. The problem is that this rush has been substantially bigger than any ever before, and the consequences of the resulting rationalization could be onerous. Maybe it’s time, as we’ve seen before, that we slow down and look at how to use this new tech appropriately. I like a meme I just heard: instead of move fast and break things, move slow and fix things. Is it time?

Knowing Cognition

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Regardless of what tech stack you’re using, it’s increasingly clear that tech is changing the environment in which we work. In particular our cognitive environment. The generative artificial intelligence wave has made clear that the ways we use technology are likely to change. There are lots of issues, so how do we handle it? As a complement to my post about technologies, I think that a necessary component of an AI approach will increasingly be an understanding of human learning, that is: knowing cognition.

Using ‘smart’ technologies is changing how we think, work, and learn. Ed Hutchins helped us understand distributed cognition, but we’re also seeing that it has effects if used wrongly. For instance, folks seem to be less connected because the technology substitutes. There are also impacts on what we learn when we use technology as a substitute for thinking, apparently an all-too-common phenomena. That means we need to know when, and how to use technology to supplement our thinking, not replace it.

And, yes, we definitely change how we do tasks when we can. With the advent of the GPS, we changed how we navigate. We no longer needed to bring, and be able to interpret, a mapbook. Though it helps to still be able to use one. Particularly when the GPS sends you awry. Similarly with digital address books; I don’t know my kids phone numbers, because they’re in my system!

Moreover, we prefer to outsource different things. Some folks still want to code, while I’ve left that. Others have handed off some tech responsibilities; my wife now depends on me to use the television! Which is all fine, if we’re making conscious decisions. When we become dependent on someone or something without recourse, however, we can face problems.

Which brings us to the question of how we make the decisions. I’ll suggest that if we don’t know what matters in our personal choices of cognition, we won’t make good decisions. That goes, by the way, for whoever is making decisions (we hear of execs who have unrealistic expectations of what the technology will do). I’ve long argued that what will be the sustainable knowledge base, going forward, is what technology can do (and what it can’t), and how people think. And work. And learn. Plus, some design and business basics, probably. That’s relevant here.

To make good decisions about what you can outsource, or not, depends on what its impact will be on your thinking. Does it support you making good decisions, and improving them? Or are you outsourcing them, and therefore diminishing your ability to make them? It may be as simple as that, but I believe it’s more. I’m probably biased, but even with that caveat, I’m inclined to think that everyone needs to know the basics of cognition. (Hence why I made my take freely available.)

I continue to think that we have to do the thinking first. It doesn’t have to be elegant, a sketch or a few bullets will do, but you have to do the important thinking first. Then, you can use tech to also address that thought, and use the tech as a way to get feedback. However, you’re the one analyzing results, seeing what it comes up with, improving your thoughts if it’s come up with something you like, and rejecting anything that doesn’t resonate. It’s a support, not a replacement.

I wanted my Learning Science book to not say ‘“or Instructional Designers” in the title, though the publisher realized that’s their market, because we’re all instructional designers. (And, to be fair, the later chapters err on the side of learning, as opposed to interface design or public speaking. Still…) Whether for ourselves, our kids, friends, there are always people we’re helping learn. We may not have the formal role, but we do a better job when we know how we think.

So, in addition to suggesting a focus on small hybrid multi-agent systems for AI, I also think we need to also personally know cognition, and learning in particular, so we know how and when to augment our thinking. And, when not to. That’s why I’m involved with the Learning Development Accelerator, by the way. And am working on a ‘learning to learn’ curriculum. Those are my thoughts, what are yours?

Small hybrid multi-agent systems

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So, I’ve been pretty harshly critical of the current generative artificial intelligence (Gen AI) situation. Again, I have no problem with the tech itself, but the big providers I have several problems with. Not to rehash those objections, but I’ve been investigating alternatives. And, I think I’ve now converged on a solution: small hybrid multi-agent systems. I owe thanks to several informants, including Elizabeth Dalton, Markus Bernhardt, and Scott Parker.

First, despite my other objections, what I see is that the companies are pushing these changes as ‘inevitable’ as well as desirable. And, sadly, way too many orgs have jumped on board. Which, while understandable, isn’t desirable. What we’re now seeing are the results. Companies are hiring folks back that were fired. We’re also seeing that we’ve hollowed out the pathways for new hires to acquire the necessary skills. Which is part of the larger picture of negatively impacting learning.

Yet, I’ve been a proponent of AI in the past. The technologies on tap have real benefits, whether they’re AI or something that we now understand so we can’t call it AI anymore. So I need to converge on an integration. And, that utility for specific needs is the key to my reconciliation.

In interpersonal situations, I remain a strong belief in ‘partnering’. That is, recognizing that others have complementary knowledge to your own, and different perspectives. So, why have one supposedly all-seeing all-knowing solution, instead of smaller more targeted ones?

Ok, yes, there’re issues of cost. Right now, it’s seemingly cheap to get an AI to do your rote tasks. However, two things are relevant here. For one, the costs are going up. Companies are going public, and that means that they’ll have to start generating revenues to repay the venture capital investments. And we’re seeing that companies are realizing that their AI expenses are growing, to the extent in some cases where they’re more than the people they’re replacing. We’re also seeing that they haven’t yet faced all the incumbent costs: IP, environment, labor. That’ll change the game too.

The flip side is that smaller language models are more cost-effective. There are open source models that you can obtain,  install locally (or on private servers), train on specific documents, and use for specific purposes. Yes, there’s the development costs, but they’re more secure, faster, and less costly. As well as addressing the other concerns.

Moreover, you can have more than one. You can have specialized agents to handle X, Y, and Z. If it’s a language task, you can use a language model. If it’s another type of task, you can use another model. You can even mix in symbolic with sub symbolic, and programs versus AIs. If diversity is good for people, it also appears to be useful for systems. A mix of custom agents with an agreed upon interchange protocol is, it appears, more cost-effective, private, maintainable, and more.

Look, if you don’t mind the costs, and the questionable ownership of data, and security, and dumbing down your workforce, and … go with the big ones. They certainly will benefit. But if you want to be secure and sustainable, small hybrid multi-agent systems makes more sense to me, at least in many instances. Sure, language models trained on all the language out there is useful. Er, for general purpose language tasks. So make that one agent in your solution, if necessary (tho’ aren’t your language tasks typically more constrained?). Which, I’ll suggest, are a small subset of all the things you need to get done by technology. As I learned from the Aussies (originally a Brit saying), “horses for courses”, in this case meaning use the right tool for the job. Am I missing something?