Quinnovating

Where’s quality?

1 July 2025 by Clark 4 Comments

I get it, when you’ve a hammer, the whole world looks like a nail. Moreover, there’s money on the table, and it’d be a shame not to grab onto it. Still, there’s also integrity. And, frankly, I fear that we’re going down the wrong path. So I’ll rail again, by asking “where’s quality?”

So, a colleague recently provided a link to a report by a well-known analyst. In the report, they call for an AI revolution for L&D. And, yes, I do believe L&D needs a revolution, I wrote a whole book about it. However, I fear that the direction under advisement is focusing on the wrong thing. So here’s what the initial post summarized about the article:

* Despite significant investment, many companies are utilizing outdated learning models that do not deliver substantial business impact.

* Learning needs to be dynamic, personalized, and focused on enablement.

* Chief Learning Officers (CLOs) should re-establish themselves as leaders within the enterprise, focusing not just on learning but on employee enablement.

* Artificial intelligence (AI) offers the potential to speed up content creation, lower costs, and improve operational efficiency, which allows Learning and Development (L&D) to adopt a wider and more strategic role.

Do you see anything wrong with this? I actually agree with the first point, and probably the third. However, I think we can make a strong case that the second is not the primary issue. And very clearly the fourth point identifies what’s wrong in the second, at least before the last phrase.

So, first, when we invoke learning, we should be very careful to do it right. There are claims that up to 90% of our investment in training is going to waste. However, it’s not because our learning designs aren’t ‘dynamic, personalized, and focused on enablement’, it’s because our learning isn’t designed according to what research says works. Now, our learning needs change as our abilities improve. We start knowing what we need and why. There’re also times when performance support can be more effective than courses. Courses can still be valid, if they’re done well.

That’s the point I continue to make: I maintain that we’ll save more money and have more impact if we focus on good learning design before we invest in fancy technology. That includes AI. We want meaningful practice (which I suggest is still a role for designers, as AI doesn’t understand context), not information dump. Knowledge <> ability to perform. What we need is practice of doing. At least for novices. But beyond that, only effective self-learners will be truly able to leverage information on their own to learn. Even social learning gets better when we understand learning.

So, learning needs to be evidence-informed, first. Then, and only then, can it be dynamic, personalized, etc. Even knowing when and how to use AI as performance support counts (a more valid role, tho’ there needs to be scrutiny of the advice somehow, as AIs can give bad advice). Sure, CLO’s do need to be leaders in the enterprise, but that comes from understanding cognition and learning, and then using those to better enable innovation as well as optimizing performance. Enablement’s fine as a premise, but it’s got to come from understanding. For instance, you can’t get employees contributing just because you put in AI, you need to create a learning culture. (Putting AI into a Miranda organization isn’t going to magically fix the problem.)

Let me be clear: my argument is not Gen AI bad vs Gen AI good. No, it’s learning science involved versus not. I am fine if we start using AI, Gen or otherwise,, but after we’ve made sure we’re doing the right things first. Let me pose a hypothetical: for $30K, would you rather have 3 courses versus 10? What if those 3 courses were designed to actually have an impact, versus 10 that are pretty and full of information, but won’t move a single meaningful needle the organization? Sure, I’ve made up the numbers, but the reality is that we’re talking about achieving real outcomes versus making folks feel good; I’ll suggest “it’s pretty and people like it” is no substitute for improving the outcome.

This makes the last line above more problematic: we don’t need to speed up content creation. Content dump <> learning. Lowering costs and improving efficiency is all good, but after you’ve ensured adequate effectiveness. And no one seems to be talking about that. That’s why I’m asking “where’s quality?” It’s not being discussed, because AI is the next shiny object: “there’s plenty of money to be made”. Anyone else sensing a bubble? And that’s without even considering IP ethics, environmental impact, security, and VC funding. The business model is still up in the air. Hence, my question. Your thoughts?

As an aside, there’s a quote in the paper that illustrates their lack of deep understanding: “As our attention spans shorten”. Ahem. While there’s a credible argument made by Gloria Marks, I still suggest it’s not a change in our cognitive architecture, but instead availability and familiarity. We can still disappear for hours into a novel, movie, or game. It’s a fallacious basis for an argument.

Truth in advertising: I was tempted to title this “WTAH”, but…I decided that might be too incendiary ;). Hence, “Where’s quality?” Still, you can imagine my mood while reading and then writing this.

Writing for learning

24 June 2025 by Clark Leave a Comment

We write for lots of reasons. It’s all about communication, but with different purposes, there should be different writing. Just for books, the language in a thriller should be different than for thoughtful stories. Writing for ads is different than writing for science. And, writing for learning is different than writing for other purposes. What am I talking about?

What research tells us, as Ruth Clark lets us know, is that we learn better from conversational language. Formal language, such as in an encyclopedia, or a textbook, doesn’t work for elearning or how an instructor talks to an audience. You want to be informal, personal, and more. Yet too often our prose is tedious.

Dialog, in particular, should be authentic to the speaker. I quail when I see characters spouting language straight out of an instructional manual or, worse, a marketing spiel. Good character development goes beyond stereotypes and develops some personality. This should come through in their language. Writing dialog, then, isn’t what most designers have been trained in. Which means that designers shouldn’t write dialog, or at least get external support whether training, even just peer review.

Writing for learning needs to be clear, of course. It also needs to be accurate. And yet, it shouldn’t be onerous to read. If there are barriers to comprehension, you’re putting in unnecessary barriers to your learning outcome. Really, you’re managing cognitive load. Obtuse language impedes processing, and learning is processing-intensive enough!

I’ve talked before about the importance of emotion in learning, for motivation, keeping anxiety under control, building confidence, and more. Writing is one of the most compact forms of media for communicating, and so we want our language to address these issues as well. Conversational language helps reduce anxiety by being familiar, and shows relatedness, part of the Self-Determination Theory of motivation. When folks believe we care about them, they’re more inclined to succumb to our ministrations.

Writing for learning is one of the elements necessary for the appropriate use of media. We should use the right media for the message (with a caveat about the value of novelty), and then we should apply the right media correctly. That is, ensuring we apply the appropriate expertise. We can make changes, such as my common example of Ken Burn’s compelling use of still images in his video documentary of the Civil War, but even then there are accommodations. In short, writing for learning has some particular constraints, and we as designers should be aware of them.

There’s more, of course. What you write in an introduction is different than what’s presented about a model, than the narrative for an example, for the instructions versus the description of the context for retrieval practice, etc. Knowing what the role is, and the appropriate writing, becomes habit with experience, but like all learning, models and feedback help accelerate the path there. You need to know not just what to write, but how and when. Those are my thoughts, what are yours?

In praise of reminders

17 June 2025 by Clark Leave a Comment

I have a statement that I actively recite to people: If I promise to do something, and it doesn’t get into a device, we never had the conversation. I’m not trying to be coy or problematic, there are sound reasons for this. It’s part of distributed cognition, and augmenting ourselves. It’s also part of a bigger picture, but here I am in praise of reminders.

Schedule by clock is relatively new from a historical perspective. We used to use the sun, and that was enough. As we engaged in more abstract and group activities, we needed better coordination. We invented clocks and time as a way to accomplish this. For instance, train schedules.

It’s an artifact of our creation, thus biologically secondary. We have to teach kids to tell time! Yet, we’re now beholden to it (even if we muck about with it, e.g. changing time twice a year, in conflict with research on the best outcomes for us). We created an external system to help us work better. However, it’s not well-aligned with our cognitive architecture, as we don’t naturally have instincts to recognize time.

We work better with external reminders. So, we have bells ringing to signal it’s time to go to another course, or to attend worship. Similar to, but different than other auditory signals (that don’t depend on our spatial attention) such as horns, buzzers, sirens, and the like. They can draw our attention to something that we should attend to. Which is a good thing!

I, for one, became a big fan of the Palm Pilot (I could only justify a III when I left academia, for complicated reasons). Having a personal device that I could add and edit things like reminders on a date/time calendar fundamentally altered my effectiveness. Before, I could miss things if I disappeared into a creative streak on a presentation, paper, diagram, etc. With this, I could be interrupted and be alerted that I had an appointment for something: call, meeting, etc. I automatically attach alerts to all my calendar entries.

Granted, I pushed myself to see just how effective I could make myself. Thus, I actively cultivated my address book, notes, and reminders as well as my calendar (and still do). But this is one area that’s really continued to support my ability to meet commitments. Something I immodestly pride myself for delivering on. I hate to have to apologize for missing a commitment! (I’ll add multiple reminders to critical things!) Which doesn’t mean you shouldn’t, actively avoid all the unnecessary events people would like to add to your calendar, but that’s just self-preservation!

Again, reminders are just one aspect of augmenting ourselves. There are many tools we can use – creating representations, externalizing knowledge, … – but this on in particular as been a big key to improving my ability to deliver. So I am in praise of reminders, as one of the tools we can, and should, use. What helps you?

(And now I’ll tick the box on my weekly reminder to write a blog post!)

Expert in the loop

10 June 2025 by Clark Leave a Comment

A couple of recent occurrences have prodded me to think. (Dangerous, I know!). In this case, generative AI continues to generate ;) hype and concern in close to equal measure. Which means it dominates conversations, including one I had recently with Markus Bernhardt. Then, there was a post by Simon Terry that said something related that doesn’t completely align. So, some thoughts arguing to have an expert in the loop.

First, as a neighbor as well as an AI strategist of renown, I’m grateful Markus and I can regularly converse. (And usually about AI!) His depth and practical experience in guiding organizations complements my long-standing fascination with AI. One item in particular was of note. We were discussing how you need a person to vet what comes out of Generative AI. And it became clear that it can’t just be anybody. It takes someone with expertise in the area to be able to determine if what’s said is true.

That would suggest that the AI is redundant. However, there are limitations to our cognition. As I’ve recounted numerous times, technology does well what we don’t, and vice-versa. So, we use tools. One of the things we do is unconsciously forget aspects of solutions that we could benefit from. Hence, for instance, checklists. In this case, Generative AI can be a thinking partner in that it can spin up a lot of ideas. (Ignoring, for the moment, issues like intellectual property and environmental costs, of course.) They may not be all good, or even accurate, but…they may be things we hadn’t recalled or even thought of. Which would be a nice complement to our thinking. It requires our expertise, but it’s a plausible role.

Now, Simon was talking about how ‘human in the loop’ perpetuates a view of humans as cogs in a machine. And I get it. I, too, worry about having people riding herd on AI. That is, for instance, AI doing the creative work, and humans taking responsibility. That’s broken. But, having AI as a thinking partner, with a human generating ideas with AI, and taking responsibility for the accuracy as well as the creativity, doesn’t seem to be problematic. (And I may be wrong, these are preliminary thoughts!)

Still, I think that just a ‘human in the loop’ could be wrong. Having an expert in the loop, as Markus suggested, may be a more appropriate situation. He pointed out a couple of ways Generative AIs can introduce errors, and it’s a known problem. We have to have a person in the loop, but who? As I recounted recently, are we just training the AI? Still, I can see a case being made that this is the right way to use AI. Not as an agent (acting on its own, *shudder*), but as a partner. Thoughts?

What does ‘evidence-informed’ mean?

3 June 2025 by Clark Leave a Comment

We colloquially tout the Learning Development Accelerator as a society for ‘evidence-based’ practice. Or, more accurately, as ‘evidence-informed’, as Mirjam Neelen & Paul Kirschner advise us in their tome. But, what does ‘evidence-informed’ mean, in practice? Does everything you do have to align with what research tells us? What’s the practical interpretation? So, I have an admission to make.

To start, if you go to the LDA site (I just did), it says: “Explores and encourages research-aligned practices”. That is a noble goal, to be sure. Let’s be clear, however: research doesn’t cover all our particular situations. In fact, it’s unlikely to cover any of our specific situations. Much of the research we use is done on psychology undergraduates, and frequently for education purposes, e.g. K12 or higher ed. Which means it’s indicative of our general cognitive processing, but not our specific situations.

There is research on organizational learning, to be sure. It’s not always pristine laboratory conditions, as it may well be meeting real-world needs. Of course, we do see some A/B-type studies. Still, while legitimate, they’re not likely to be our particular situation. That is, our particular audience, our specific learning objectives, our timeline, our urgency, etc.

So what does one do? We must abstract the underlying principles, and reinstantiate for our circumstances. There are good overall principles, such as the benefit of generative activities and spaced retrieval practice. The nature of these, of course, such as choosing the right activities (Thiagi & Matt have a whole book on this!), and the right parameters for retrieval (we’re asking for that at Elevator9), means that we have to customize. Which means we have to test and tune. We can’t expect to get it right the first time. (Though, we’ll get better over time.)

There will be times, when we’re doing something that’s far enough away that we’re kind of making it up as we go along. (An area I love, as it requires considering all the models I’ve mentally collected over the years.) Then, we may find good examples to use as guidance. Someone’s tried something, and it worked for them. If you look at the LDA Research Checklist, for instance, you’ll see that replicated research is desirable. Well, that’s ideal. We live in the real world, however. BTW, this is a good reason to share what you learn (you may have to anonymize it, for sure): so others benefit.

So, and this is where I make an admission, there will be times where we don’t have adequate guardrails. There are times when we have only some examples, or basically we’re wading into new areas. Then, we are free, with a caveat: we can’t do what’s been shown to be wrong. For instance, learning styles. Or attention-span of a goldfish. Or any of the other myths. My take, and I require this for LDA Press as well, is that we ask for the evidence-base, but we require that submissions not violate what’s known.

So, evidence-based, research-aligned, etc, at least means avoiding what has been shown not to work. It starts from using the best evidence-available to guide design, and then testing (which research also tells us to do!). Why? Because we get better outcomes. We do know that not following research is unlikely to have an impact. Learning design is, at core, a probabilistic game. Increasing the likelihood of a real impact should be what we’re about. Doing so on the basis of research is a faster and more reliable path to having an impact. Ultimately, the answer to the question “what does ‘evidence-informed’ mean?” is better outcomes. Who doesn’t want that?

What sorts of activities?

27 May 2025 by Clark Leave a Comment

When we do learning, we must be active. That is, it’s not enough to receive information. (Unless we’re actively practicing and attending presentations are reflection.) We must do! Then the question becomes one of doing ‘what’? I’m seeing too many of the wrong sorts of things in play, so it’s worth asking: what sorts of activities should we be doing?

Cognitively, we need to perceive information to get it into working memory. From there, to get into long-term memory – and be useful – we need to elaborate and practice retrieval. Elaboration is the process whereby we strengthen connections between the new material and the familiar. This increases the likelihood of activation in context. Then, we need to practice retrieving the knowledge for use. This strengthens our ability to retrieve and apply as we need.

One thing to note is that research shows that we don’t need to retrieve the fact-based knowledge before practicing retrieval to actually use. Our goal for organizational learning is to use information to make meaningful decisions. Better fact-recall isn’t likely to be what will help your organization thrive. Instead, what matters is acquiring the new skills that will define the ability to adapt.

For elaboration, what we increasingly hear is about ‘generative‘ learning activities. These are when you’re taking new information, and processing it more deeply. It can involve rephrasing, visualizing, and of course connecting it to your prior experience. These activities help strengthen the information into long-term memory.

An associated task it to practice using the information. That is, putting learners into situations where they need to use the new information to make decisions that they couldn’t before. The ideal situation, of course, is mentored live practice, but…there are limitations. Individual mentoring isn’t always cost-effective. Also, live practice may have consequences for wrong answers. In many cases, we use simulations. These can be programmed, or branching scenarios. Even mini-scenarios (e.g. better written multiple-choice questions) are a good option.

What we don’t need are fact-check questions. As above, there’s no real benefit. They may make us feel good, but they aren’t inclined to make us better at using the information. There are lots of bad practices around this. We can just use knowledge questions, thinking we’re helping learning (and not). Worse, I’ve seen many cases where they’re asking for arbitrary bits of information that aren’t highlighted. Also, too often we’re presenting way too much information than people can remember at one time (or at all).

So, if we’re to design effective learning, what sorts of activities is an important question. We don’t need fact-checks. We do benefit from processing, and retrieval. That’s worth practicing and performing. Review your work and look at what you’re having learners do. If it’s not elaboration and retrieval, you’re wasting learners’ time and your efforts. Why do that?

Software engineer vs programmer

20 May 2025 by Clark Leave a Comment

If you go online, you’ll find many articles that talk about the difference in roles between software engineers and programmers. In short, the former have formal training and background. And, at least in this day and age, oversee coding from a more holistic perspective. Programmers, on the other hand, do just that, make code. Now, I served in a school of computer science for a wonderful period of my life. Granted, my role was teaching interface design (and researching ed tech). Still, I had exposure to both sides. My distinction between software engineer vs programmer, however, is much more visceral.

Early in my consulting career, I was asked to partner with a company to develop learning. The topic was project management for non-project managers. They chose me because of my game design experience as well as learning science background, The company that contracted me was largely focused on visual design. For instance, the owner also was teaching classes on that. Moreover, their most recent project was a book on the fauna of a fictitious world in the Star Wars universe (with illustrations). He also had a team of folks back in India. Our solution was a linear scenario, quite visual, set in outer space both because of experience of their team and the audience of engineers.

After the success of the project, the client came back and asked for a game to accompany the learning experience. Hey, no problem, it’s not like we’ve already addressed the learning objectives or anything! Still, I like games! This was going to be fun. So I dug in, cobbling together a game design. We used the same characters from the previous experience, but now focused on making project management decisions and dealing with different personality types (the subtext was, don’t be a difficult person to work with).

The core mechanic was:

choose the next project
assess any problem
find the responsible person,
ask (appropriately) for the fix

Of course, the various rates of problems, stage of development and therefore person, stage and scope of the project, were all going to need tuning. In addition, we wanted the first n problems to deal with good people, to master the details, before beginning to deal with more difficult personality types.

So, from my development docs, they hired a flash programmer to build the game. And…when we tried to iterate, we got more bugs instead of improvement. This happened twice. I realized the coders were hard-wiring the parameters throughout the code, which meant that if you wanted to tune a value, they had to search throughout the code to change all the values. Now, for those who know, this is incredibly bad programming. It wasn’t untoward to develop a small Flash animation, but it didn’t scale to a full game program.

We had a discussion, and they finally procured someone who actually understood the use of constants, someone with more than just a programming background. Suddenly, tweaks were returning with short-turnaround, and we could tune the experience! Thus, we were able to create a game that actually was fun. We didn’t really get to know whether it was effective, because they hadn’t set any metrics for impact, but they were happy and touted the game in several venues. We took that as a positive outcome ;).

The take-home lesson, of course, is if you need tuning (and, for anything of sufficient size and user-facing, you will), you need someone who understands proper code structures. I’ll always ask for someone who understands software engineering, not just a programmer. There’s a reason that a) they’re known as ‘cowboy coders’, and b) there’s software process! That’s my personal definition of a software engineer vs programmer, and I realize it’s out of date in this era of increasingly complex software. Still, the value of structure and process isn’t restricted to software, and is ever more important, eh?

The illusion of (my) competency

13 May 2025 by Clark 2 Comments

I have a cobbled together tech environment. My monitor and printer are years old, and my laptop is relatively new. To make them fit together, particularly with the limited ports of a laptop, I’ve a hub. And, now a few weeks ago, things went wonky. In weird ways. And I still don’t know it all, but there are some lessons here, not least the illusion of (my) competency. We all have travails, but this one has some lessons.

So, the first symptom was my microphone suddenly not working. I’d gotten a reasonable one, but it died (and apparently wasn’t good enough anyway), so my co-director in the LDA, Matt Richter, got me a nice one. It was this fancy, heavy one, that stopped working. It’s USB, so has LEDs, which are blue normally but green when in use. Except what happened was that when I wanted to use it, it started not working and blinking between blue and green. Hold that thought.

Then, the camera in my monitor stopped working. I have one in my laptop that’s better/newer, but it’s a bit away (I prefer to work on the big monitor, of course). I use the laptop camera when I want to look impressive and show my book case beside me ;). But I prefer the monitor camera for work meetings and the like. Yet, I couldn’t. Now hold that thought, too!

The icing on the proverbial cake came when my backup drive stopped backing up. It would start, and say it was finding probs, but then would time out with an error. I tried to run diagnostics, which said it was corrupt. I also got SMART results saying it was fine. All very confused.

Finally, I decided I had to contact Apple (fortunately Matt’s also insisted I keep up my service plan). After a lot of shenanigans that I won’t bore you with, the question came: is my hub powered? As it was, the answer’s no. If memory serves (dodgy proposition), I got it for free when Amazon was experimenting with providing things to people who wrote reviews. Which would be a weird issue, as I’d been using it for several years this way.

Still, went and ordered a powered hub. Then ordered another, realizing I wanted the faster version. BTW, I had to pick up both, because the other was fulfilled before I got the second, and the solution seemed to be that I picked up both and returned the first. Which took an extra 5 minutes, apparently to defend against fraud. Why we can’t have nice things…(Lesson: allow people to cancel orders via chat or online, don’t make them come in, take and immediately return).

While I was dealing with the hub, I decided to call the maker of the microphone (remember?). I’d called them before and left a message, and also emailed, to no reply. (Lesson: return your customer queries.) I was leaving a voicemail when I got a call from them, so I switched. It turns out that the blinking means the mic’s muted. There’s a knob on the back that’s ‘gain’, BUT it turns out it’s also a button, and if you push it, it mutes the mic. Now, I’d gone to the site and the mic instructions, and it doesn’t talk about that at all! I mentioned it to the person on the phone, and they said that they’d tried to get it changed, to no avail. (Lesson: put all the information about operation in the <expletive deleted> documents!) Problem fixed.

Then, when I installed the powered hub, the video camera in the monitor started working again. It’s not clear why it suddenly stopped, but…it was fixed. The lesson here, and for me (tho’ feel free to take it to heart), is probably never to trust an unpowered hub to be sufficient. However, and in my defense, it had been working for months if not years. (And, if you could’ve told me that, I don’t want to hear it.)

Finally, we get back to the drive. The powered hub didn’t fix it. I spent an unreasonable amount of time trying to run diagnostics, both Apple’s and the manufacturers. Finally, I delisted it from the backup software, erased it, then reintroduced it. And, voila’, it’s working! Not sure what the problem was. (Lesson: provide more feedback to the user on what’s going on.)

As a weird aside, I asked for a support call, but when it came it shut off my wifi calling. I live in a slight depression and have a bad signal, so I use wifi. But by turning it off, they ensured that the call would get dropped. I can’t imagine why they would do such a thing, but I’ve had real trouble getting calls from them, and this time just happened to see that the wifi calling had stopped at the time of the call. I had to use chat. Very puzzling and unresolved.

All told, this took way too much time, and while I learned one lesson, there was too much the result of bad design, not incompetence. As Don Norman said in The Design of Everyday Things, if it’s difficult to use, blame the designer, not the user. We know how to do better, we just don’t do it frequently enough. (I also recommend Kathy Sierra’s Badass as a guiding light.) I’m willing to assume responsibility for my culpability, and admit to the illusion of (my) competency. But I also recognize that I’m not stupid, and better design would’ve limited my frustrations and time wasting.

Locus of intelligence

6 May 2025 by Clark 1 Comment

I’m not a curmudgeon, or even anti-AI (artificial intelligence). To the contrary! Yet, I find myself in a bit of a rebellion in this ‘generative‘ AI era. And I’m wondering why. The hype, of course, bugs me. But it occurs to me that a core problem may reside in where we put the locus of intelligence. Let me try to make it clear.

In the early days of the computer (even before my time!), the commands were to load memory into registers, conduct boolean operations on them, and to display the results. The commands to do so were at the machine level. We went a level above with a translation of that machine instructions into somewhat more comprehensible terms, assembly language. As we went along, we went more and more to putting the onus on the machine. This was because we had more processor cycles, better software etc. We’re largely to the point where we can stipulate what we want, and the machine will code it!

There are limits. When Apple released the Newton, they tried to put the onus on the machine to read human writing. In short, it didn’t work. Palm’s Pilots succeeded because Jeff Hawkins went for Graffiti as the language, which shared the responsibility between person and processor. Nowadays we can do speech and text recognition, but there are still limitations. Yes, we have made advances in technology, but some of it’s done by distributing to non-local machines, and there are still instances where it fails.

I think of this when I think of prompt engineering. We’ve trained LLMs with vast quantities of information. But, to get it out, you have to ask in the right way! Which seems like a case of having us adapt to the system instead of vice versa. You have to give them heaps more context than a person would need, and they still can hallucinate.

I’m reminded of a fictional exchange I recently read (of course I can’t find it now), where the AI user is being advised to know the domain before asking the AI. When the user queries why they would need the AI if they know the domain, they’re told they’re training the AI!

As people investigate AI usage, one of the results is that your initial intelligence indicates how much use you’ll get out of this version of AI. If you’re already a critical thinker, it’s a good augment. If you’re not, it doesn’t help (and may hinder).

Sure, I have problems with the business models (much not being accounted for: environmental cost, IP licensing, security, VC boosting). But I’m more worried about people depending too much on these systems without truly understanding what the limitations are. The responsible folks I know advocating for AI always suggest having a person in the loop. Which is problematic if you’re giving such systems agency; it’ll be too late if they do something wrong!

I think experimenting is fine. I think it’s also still too early to place a bet on a long-term relationship with any provider. I’m seeing more and more AI tools, e.g. content recommenders, simulation avatars, and the like. Like with the LMS, when anyone who could program a database would build one, I’m seeing everyone wanting to get in on the goldrush. I fear that many will end up losing their shirts. Which is, I suppose, the way of the world.

I continue to be a big fan of augmenting ourselves with technology. I still think we need to consider AI a tool, not a partner. It’s nowhere near being our intellectual equal. It may know more, but it still has limitations overall. I want to develop, and celebrate our intelligence. I laud our partnership with technologies that augment what we do well with what we don’t. It’s why mobile became so big, why AI has already been beneficial, and why generative AI will find its place. It’s just that we can’t allow the hype to blind us to the real locus of intelligence: us.

Before ‘skilling’?

29 April 2025 by Clark Leave a Comment

I’m not immune to the current trends, I just tend to want to cut through the hype. Sometimes, there’s some ‘there’ there (to paraphrase Gertrude Stein). And, of course, most of the time it’s old wine in new bottles. Right now, the trend is ‘skilling’. Which, to me, raises the question: what, then, were you doing before ‘skilling’?

As context: I’d (mostly) gone the academic route. That is, doctorate, post-doc, academic position. For family reasons, I wanted to come back to the US, and took up a corporate position (a whole bunch of stories there, but not now…). I was excited, actually. Here, from science, we knew heaps! Yet we weren’t applying it in schools and higher ed, for pragmatic reasons like history and budget. However, industry would be different. They had real needs, and real money to address them. To quote my eldest, this was “going to be great”.

So, you might imagine my dismay as I came to realize that organizations weren’t spending money on learning. Somehow, learning was a cost. L&D took orders for courses, and expected that for a few thousand dollars, you could take PPTs and PDFs and make a course. That’s what the authoring systems portrayed, and workshops focused on. Still, at that time I wasn’t ready to look to see the bigger picture.

Regardless, I started lobbying for more learning science in what we do. I’ve railed against tools that don’t have different feedback for every wrong answer, pushed design tweaks to incorporate learning science into our processes, and written and spoken and worked to develop the necessary understanding. I co-authored the Serious eLearning Manifesto. I’ve supported and now co-direct the Learning Development Accelerator. I’ve written books!

Thus, hearing the recent call for ‘upskilling’ (with a variety of names and areas of focus) seems to me to be a bit silly. Of course we need to think about organizational directions and capabilities. We should be looking at directions and needs, and making decisions like ‘build or buy’, and when the answer’s ‘build’, we should do it right. We should look at performance consulting up front to make the right determinations, and then see if we’ve created an environment (social and technological) that means folks can get what the need. That could be from courses, job aids, learning resources, coaching/mentoring, the network internal or external, what have you.

Which begs my main question, what were you doing before hand? I know that ‘competency’ assessments were the thing many years ago, but those exercises were costly, time-consuming, and from what I understand, were out of date by the time they were done. Idiosyncratic approaches would be a tactical approach, but not strategic. Maybe strategy is something we’re late to? To be fair, I see more books about it emerging, from folks like Nigel Paine and forthcoming from Lori Niles-Hoffman, and of course I wrote about it about a decade ago.

We’ve got to get more systemic, where we haven’t been. We need to liaise with the relevant entities in our org, like talent, and knowledge management, and org development, etc. We need to work with IT to create an ecosystem, not just rely on one platform to meet all needs (metaphor: the Swiss Army knife is great on the go, but at home I have a richer suite of separate tools). I think it’s time we recognize what was wrong before skilling, and take what’s right and expand it. Let’s be a learning organization, master that, and take it forward. That’s what I think, what think you?