strategy Archives - Learnlets

Transforming from knowledge to performance

16 September 2025 by Clark Leave a Comment

As I’ve mentioned, I’m working with a startup looking at extending training through small LIFTs. The problem is that most training is ‘event’ based, where learning is in a concentrated time. Which is fine for performing right after. However, much of what we train for are things that may or may not happen soon. What we want is to go from the knowledge after the event to actually performing in new ways after the event, possibly a long time. We need retention from the learning to the situation, and transfer to all appropriate (and no inappropriate) situations. Thus, we need to think differently. And, as I suggested, we’re looking at supporting people not just with formal learning, but beyond, to developing their ability over time. We really want to be transforming from knowledge to performance. So, what’s that look like?

As usual, when I’m supposed to be sleeping is one of the times I end up noodling things over. And, so it was some nights ago. I was thinking about (as I’m wont to do) the cognitive roles that we need. I talk about practice, and models, and examples, and more recently, generative activities. But that’s formal learning, and we have a good evidence base for that. But what about going forward? What sorts of activities make sense?

Here I’m going out of my comfort zone. Yes, I’ve been doing some reading about coaching, particularly domain-independent vs domain-specific coaching. Now, here I don’t necessarily know what the research says specifically, but I do see the convergence of a variety of different models. So, I can make inferences. And post them here to get corrected!

As you might expect, I made a diagram to help me understand. So, I reckon there’s an early, mid, and late stage of development of capability. Formal learning should really be about getting you ready to apply.

That is the early phase which includes reflection (really, a generative activity), which can be personal (ala scripts) or conceptual (schemas). Also, reactivation. That is, seeing different ways of looking at it (new models), more examples in context, and of course more practice. (Retrieval practice, of course, where you’re applying the knowledge.)

Then, in mid-phase, your learners are applying, but to real situations, not simulations. Their initial plan on how to apply the knowledge might be part of the end of the early stage, but then it’s time to apply. Which could (should?) lead to revisions of the plan, and on reflecting on any barriers. Those barriers could be internal (their own understanding or hangups), or external (lack of resources, situations, tools, etc). The former are grounds for discussion, the latter for action on the part of the org!

Then, at the late stage, learners should be looking at the impact. They can reflect on the impact on them, which could also be a mid-phase action, but ultimately you want to see if they’re having an impact overall. Then, of course, you could want to survey about the learning experience itself. While it’s all data, the org impact is useful data to evaluate what’s going on and how it’s going, and the survey can help you continue to improve either this or your next initiative.

Those’re my initial thoughts on transforming from knowledge to performance. There’s some overlap, no doubt, e.g. you could continue sending reapplications if there aren’t frequent opportunities in the real world. Likewise, your learners should be assessing impact in the need to revise a plan. Still, this seems to make sense in the first instance, at least to me. (Addressing the ‘when’, how much and what spacing, is what I’ll be talking about at DevLearn. ;) Now, it’s over to you. What have I got wrong, am missing, …?

Learning science on tap

11 September 2025 by Clark Leave a Comment

In the interest of the continuation of Quinnovation, Learnlets, and me, this is a solicitation post. If it’s not for you, kindly ignore. However, it may be for your boss; if so, please pass it on!

Do you run an L&D department, or make L&D decisions, and don’t have sufficient learning science background? You know, you get asked to make decisions that involve learning – responding to vendors, stakeholders asking “why”, etc – and you’re not sure how to respond. That’s not uncommon! While you know how to select technologies, design solutions, create strategies, etc in other areas, you don’t necessarily know how to do that with an enlightened view of how we think, work, and learn. L&D is unique because it deals with learning – skills, social, informal, and more. And your school experience is not a good guide. How do you cope? Learning science on tap!

Let me offer this solution, specifically Clark Quinn, Ph.D., on tap. There are reasons why: I’ve been recognized for my depth of knowledge and breadth of experience in translating learning science into practical terms. That includes writing books, keynoting, awards, and, of course, consulting. I’ve applied that background for literally decades in the design of solutions: games, mobile, strategy, processes, policies, and more. So, that’s available. For instance, you could send me something that needs a learning science perspective – an RFP, a memo, an organizational initiative, and I’ll break it down from a learning science perspective, and provide you with same. Or we can talk on a call. What’s more, as I’m wont to do, I’ll provide the underlying thinking. That is, you learn as you go, too! (Just how I roll.)

Of course, you don’t have to take my advice. You’ll have it, and can factor it into your thinking. And, I can adapt my thinking to specific constraints. I am known to come up with better ideas than had been proposed initially. But it’s up to you. I’ll give you my feedback, and you can do with it as you will. This service is for those that can’t come up with that advice on their own, and it’s an important perspective. What I’ll suggest as recommendations will be grounded in evidence-based approaches. I’ll research anything I need to know and don’t (no extra charge), so I learn too. But I have been involved in thinking at most levels and areas of an organization, in a multitude of roles.

I won’t be an employee (nor want to become one). And, I’m not generating new things (that’s a different engagement, we can talk about it), but I’ll review and opine, to your needs. So, I won’t write an RFP or a whitepaper for you; I won’t design a learning experience; nor will I read an article and summarize it for you. Those’d be different engagements. But I’ll review an RFP or whitepaper (incoming or outgoing) for the necessary learning science. I will review the rules and practices around such a design. If someone sends you an article and asks your opinion, I’ll give you the perspective on that. In particular, I’ll help evaluate any claims that you’re faced with, again either coming from inside or outside.

In short, I’m your learning science advisor. Anything you need. Of course you’ll also get any other thoughts my experience provides: how to deal with issues or people, possible solutions, and more. Comes with the territory.

I also know to respect confidentiality. Heck, my IP has been used to train LLMs, and that doesn’t sit well with me. I will also likely want to write up any learning I attain. I can anonymize it or profile you, your choice. Obviously, I won’t share anything proprietary. And my advice is yours, and you can choose to acknowledge me or keep my participation out of it; I really don’t care.

I’ve, over time, learned to be efficient. One of the benefits of knowing how our minds work is that I know what we’re not good at, and have developed practices to ensure that I don’t fall down on commitments. I have my own project management approach, which, coupled with my natural “just do it” inclination, means that you won’t be waiting weeks for a response. I’ll commit to 48 hours max on anything less than ebook length, and as folks who are using me in other ways (*cough* LDA and Elevator 9 *cough*) will tell you, I tend to do things in a matter of hours if it’s not too long.

So, what would such an engagement entail? I’d like to keep it simple and fair. I reckon there’s anywhere from 3 to 10 such things a month. Some will be short, some will be longer. Some months more, some less. My initial ask is $1K per month, and an initial $500 retainer (just to make sure payment systems work, and that’ll cover a call to set the context). If you want to sign up for a year, it’s $10K (9999.99 if necessary to stay under a cutoff ;). Either of us can terminate at any time; in the case of a year purchase, I’ll prorate. What I do for you is yours, what I know and learn is mine. I’ll prod you weekly to remind you to take advantage, and you don’t have to. (Heck, you can always think of it as supporting your friendly neighborhood research translator!)

This may not be you, but if it is, think through the tradeoffs. No overhead – taxes, benefits, etc – the cost is the cost. What you get is yours and your department’s. It’s an investment in learning, for that matter, because you will have the opportunity to improve your understanding as we go. My goal in this (and every) engagement is to remove the need for me in the loop, and learning about learning isn’t just for those developing learning, it’s a good practice for everyone. It’s even a competitive advantage.

Oh, one other thing. I reckon, what with my other commitments, I can only take on 10 such relationships. So, first come, first served. Learning science on tap. Your move! You can reach out here.

We now return you to your regularly scheduled day, already in progress.

Knowledge or ability?

9 September 2025 by Clark Leave a Comment

As in the last post, I’ve been judging the iSpring Course Contest (over, of course). And, having finished, one other thing I’ve noticed is a clear distinction between ‘knowing’ and ‘doing’. We’re seeing lots of interest in skills, yet the courses are, with one exception, really assuming that if you know about it, you’ll do it right. Which isn’t a safe assumption! Are you trying to develop knowledge or ability? I’ll suggest you want the latter. And, can do it!

So, in 9 of the 10 cases, the questions are essentially about knowing. Some of them better than others, e.g. some seem to follow Patti Shank’s advice about how to write better multiple choice questions. That is, for instance, reasonably balanced prose describing the alternatives, and only 3 options. Not all follow it, of course.

The problem is that knowing about something isn’t the same as knowing how to do it. So, for instance, knowing that you should calibrate after changing the reagent isn’t the same as remembering to do it. We’ve all probably experienced this ourselves. They pretty much all had quizzes, as required, but most were just testing if you recalled the elements of the course. Not good enough!

What the one course did that I laud was that the final quiz was basically you applying the knowledge in a situation. You weren’t asked what this situation was, but instead chose how to respond. They were linked, each continuing the story, so it was really a linear scenario. Which I realize can be just a series of mini-scenarios! Still, you dragged your response from a list of responses. They weren’t all that challenging to choose between, as the alternatives were pretty clearly wrong, but for good reasons, reflecting the common mistakes. This is the way!

I think some designers were aspiring to this, as they did put the learner into a situation. However, they then asked learners to classify the answer, rather than actually make a decision about action to take, e.g. a mini-scenario. There is an art to doing this well (hence my workshop in two days)! Putting people into a context to choose their actions like they’ll have to do in the real world is the important practice. Of course, mentored live performance is better. Or simulations (tuned to games, of course ;). Even branching scenarios. But mini-scenarios are easily doable within your existing practice.

The question of knowledge or ability is easily answered. In how many cases will the ability to recite knowledge versus make decisions be the defining success factor for your organization? I’ll suggest that making better decisions will be the differentiator your organization needs. The ability to write better mini-scenarios seems to me to be the best investment you can make to have your interventions actually achieve an impact. And if you’re not doing that, why bother?

Is ‘average’ good enough?

26 August 2025 by Clark Leave a Comment

As this is my place to ‘think out loud’, here’s yet another thought that occurred to me: is ‘average’ good enough? And, just what am I talking about? Well, LLMs are, by and large, trained on a vast corpora. Essentially, it’s averaging what is known. It’s creating summaries of what’s out there, based upon what’s out there. (Which, BTW, suggests that it’s going to get worse, as it processes its own summaries! ;) But, should we be looking to the ‘average’?

In certain instances, I think that’s right. If you’re below average in understanding, learning from the average is likely to lift you up. You can move from below average to, well, average. Can you go further? If you’re in well-defined spaces, like mathematics, or even programming, what LLMs know may well be better than average. Not as good as a real expert, but you can raise your game. Er, that is, if you really know how to learn.

Using these systems seems to become a mental crutch, if you don’t actually do the thinking. While above average people seem to be able to use the systems well, those below average don’t seem to learn. IF you used it to provide knowledge, and then put that knowledge into practice, and get feedback (so, for instance, experimenting), you could fine tune your performance (not as eloquently as having someone provide feedback, but perhaps sufficiently). However, this requires knowing how to learn, and the evidence here is also that we don’t do that well.

So, generative AI models give you average answers. Except, not always. They hallucinate (and always will, if this makes sense). For instance, they’ll happily support learning styles, because that’s a zombie idea that’s wrong but won’t die. They can even make stuff up, and don’t know and can’t admit to it. If you call them on it, they’ll go back and try again, and maybe get it right. Still, you really should have an ‘expert’ in the loop. Which may be you, of course.

Look, I get that they can facilitate speed. Though that would just seem to lead your employer to expect more from you. Would that be accompanied by more money? Ok, I’m getting a bit out of my lane here, but I’m not inclined. But is faster better?

Also, ‘average’ worries me. As I’ve written, Todd Rose wrote a book called The End of Average that is truly insightful. Indeed, one of those books that makes you see the world in a different way, and that’s high praise. The point being that average removes the quality. Averaging removes the nuances, the details, as does summarization. Ideally, you should be learning from the best, not the average, if learning is social (as Mark Britz likes to point out).

Sure, it can know the average of top thoughts, but what’s better is having those top thinkers. If they’re disagreeing, that’s better for dialog, but not summarization. In truth, I’d rather learn from a Wikipedia page put together by people than a Gen AI summary, because I don’t think we can trust GenAI summaries as much as socially constructed understanding. And it’s not the same thing.

So, I’ll suggest ‘average’ isn’t nearly good enough in most cases. We want people who know, and can do. I don’t mind if folks find GenAI useful, but I want them to use it as support, not as a solution. Hey, there’s a lot that can be done with regular AI in many instances, and Retrieval Augmented Generation (RAG) systems offer some promise of improvement for GenAI, but still not perfect outcomes. And, still, all the other problems (IP, business models, and…). So, where’ve I gone wrong?

Note, I should be putting references in here, but I’ve read a lot lately and not done a good job of saving the links. Mea culpa. Guess you’ll just have to trust me, or not.

Training Organization Fails

19 August 2025 by Clark Leave a Comment

I’ve worked with a lot of organizations that train others. I’ve consulted to them, spoken to them, and of course written and spoken for them. (And, of course, others!) And, I’ve seen that they have a reliable problem. Over the years, it occurs to me that these failures stem from a pattern that’s understandable, and also avoidable. So I want to talk about how a training organization fails. (And, realize, that most organizations should be learning organizations, so this is a bigger plea.)

The problem stems from the orgs’ offering. They offer training. Often, certification is linked. And folks need this, for continuing education needs. What folks are increasingly realizing is that much of the learning they’re offering is now findable on the web. For free. Which means that the companies not seeing the repeat business. Even if required, they’re not seeing loyalty. And I think there’s a simple reason why.

My explanation for this is that the orgs are focusing on training, not on performance solutions. People don’t want training for training’s sake, by and large. Sure, they need continuing education in some instances, so they’ll continue (until those requirements change, at least). Folks’ll take courses in the latest bizbuzz, in lieu of any other source, of course. (That’s currently Generative Artificial Intelligence, generically called AI; before that as an article aptly pointed out it was the metaverse, or crypto, or Web 3.0, …)

What would get people to do more than attend the necessary or trendy courses? The evidence is that folks persist when they find value. If you’re providing real value, they will come. So what does that take? I posit that a full solution would be comprised of three things: skill development, performance support, and community.

Part 1: Actual learning

The first problem, of course, could be their learning design. Too often, organizations are falling prey to the same problems that belabor other organizational learning; bad design. They offer information instead of practice. Sure, they get good reviews, but folks aren’t leaving capable of doing something new. That’s not true of all, of course (recently engaged with an organization with really good learning design), but event-based learning doesn’t work.

What should happen is that the orgs target specific competencies, have mental models, examples, and meaningful practice. I’ve talked a lot about good learning design, and have worked with others on the same (c.f. Serious eLearning Manifesto). Still, it seems to remain a surprise to many organizations.

Further, learning has to extend beyond the ‘event’ model. That is, we need to space out practice with feedback. That’s neglected, though there are solutions now, and soon to be available. (Elevator 9, cough cough. ;) Thus, what we’re talking about is real skill development. That’s something people would care about. While it’s nice to have folks say they like it, it’s better if you actually demonstrate impact.

Part 2: Performance support

Of course, equipping learners with skills isn’t a total solution to need. If you really want to support people succeeding, you need more than just the skills. Folks need tools, too. In fact, your skill development should be built to include the tools. Yet, too often when I ask, such orgs admit that this is an area they don’t address.

There are times when courses don’t make sense. There are cognitive limits to what we can do, and we’ve reliably built ways to support our flaws. This can range from things performed rarely (so courses can’t help), through information that’s too volatile or arbitrary, to things done so frequently that we may forget whether we’ve taken a step. There are many situations in pretty much any endeavor where tools make sense. And providing good ones to complement the training, and in fact using those tools as part of the training, is a great way to provide additional value.

You can even make these tools an additional revenue stream, separate from the courses, or of course as part of them. Still, folks want solutions, not just skill development. It’s not about what you do for them, but about who they become through you (see Kathy Sierra’s Badass!).

Part 3: Community

The final piece of the picture is connecting people with others. There are several reasons to do this. For one, folks can get answers that courses and tools are too coarse to address. For another, they can help one another. There’s a whole literature on communities of practice. Sure, there are societies in most areas of practice, but they’re frequently not fulfilling all these needs (and they’re targets of this strategic analysis too). These orgs can offer courses, conferences, and readings, but do they have tools for people? And are they finding ways for people to connect? It’s about learning together.

I’ve learned the hard way that it takes a certain set of skills to develop and maintain a community. Which doesn’t mean you shouldn’t do it. When it reaches critical mass (that is, becomes self-correcting), the benefits to the members are great. Moreover, the dialog can point to the next offerings; your market’s right there!

There’s more, of course. Each of these areas drills down into considerable depth. Still, it’s worth addressing systematically. If you’re an org offering learning as a business, you need to consider this. Similarly, if you’re an L&D unit in an org, this is a roadmap for you as well. If you’re a startup and want to become a learning organization, this is the core of your strategy, too. It’s the revolution L&D needs ;). Not doing this is a suite of training organization fails.

My claim, and I’m willing to be wrong, is that you have to get all of this right. In this era of self-help available online, what matters is creating a full solution. Anything else and you’ll be a commodity. And that, I suggest, is not where you want to be. Look, this is true for L&D as a whole, but it’s particularly important, I suggest, for training companies that want to not just survive, but thrive in this era of internet capabilities.

Beyond Design

12 August 2025 by Clark Leave a Comment

When you look at the full design process, I admit to a bias. Using Analysis-Design-Development-Implementation-Evaluation, ADDIE, (though I prefer more iterative models: SAM, LLAMA, …), I focus early. There are two reasons why, but I really should address them. So let’s talk beyond ‘design’ and why my bias might exist. (It pays to be a bit reflective, or defensive?, from time to time.)

I do believe that it’s important to get the first parts right. I’ve quipped before that if you get the design right, there are lots of ways to implement it. To do that, you need to get the analysis and design right. So I focus there. And, to be sure, there’s enough detail there to suit (or befuddle) most. Also, lots of ways we go wrong, so there’s suitable room for improvement. It’s easy, and useful, to focus there.

Another reason is that implementation, as implied in the quip, can vary. If you have the resources, need, and motivation, you can build simulation-driven experiences, maybe even VR. There are different ways to do this, depending. And those ways change over time. For instance, a reliable tool was Authorware, and then Flash, and now we can build pretty fancy experiences in most authoring tools. It’s a craft thing, not a design thing.

Implementation does matter. How you roll things out is an issue. As Jay Cross & Lance Dublin made clear in Implementing eLearning, you need to treat interventions as organizational change. That includes vision, and incentives, and communication, and support, and… And there’s a lot to be learned there. Julie Dirksen addresses much in her new book Talk to the Elephant about how things might go awry, and how you can avoid the perils.

Finally, there’s evaluation. Here, our colleague Will Thalheimer leads the way, with his Learning Transfer Evaluation Model (LTEM). His book, Performance Focused Learner Surveys comes closest to presenting the whole model. Too often, we basically do what’s been asked, and don’t ask more than smile sheets at best. When, to be professional, we should have metrics that we’re shooting to achieve, and then test and tune until we achieve them.

Of course, there’re also my predilections. I find analysis and design, particularly the latter, to be most intellectually interesting. Perhaps it’s my fascination with cognition, which looks at both the product and process of design. My particular interest is in doing two things: elegantly integrating cognitive and ‘emotional‘ elements, and doing so in the best ways possible that push the boundaries but not the constraints under which we endeavor. I want to change the system in the long term, but I recognize that’s not likely to happen without small changes first.

So, while I do look beyond design, that’s my more common focus. I think it’s the area where we’re liable to get the best traction. Ok, so I do say that measurement is probably our biggest lever for change, but we’ll achieve the biggest impact by making the smallest changes that improve our outcomes the most. Of course, we have to be measuring so that we know the impact!

Overall, we do need the whole picture. I do address it all, but with a bias. There are others who look at the whole process. The aforementioned Julie, for one. Her former boss and one of our great role-models, Michael Allen, for another. Jane Bozarth channels research that goes up and down the chain. And, of course, folks who look at parts. Mirjam Neelen & Paul Kirschner, Connie Malamed, Patti Shank, they all consider the whole, but tend to have areas of focus, with considerable overlap. Then we go beyond, to performance support and social, and look to people like Mark Britz, Marc Rosenberg, Jay Cross, Guy Wallace, Nigel Paine, Harold Jarche, Charles Jennings, and more.

All to the good, we benefit from different perspectives. It’s hard to get your mind around it all, but if you start small, with your area, it’s easy to begin to see connections, and work out a path. Get your design right, but go beyond design as well to get that right (or make sure it’s being done right to not undermine the design ;). So say I, what say you?

Continually learning

8 July 2025 by Clark Leave a Comment

I’ve been advising Elevator 9 on learning science. Now, while I advise companies via consulting, this is a different picture. For one, they’re keen to bake learning science into the core, which is rare and (in my mind) valuable. It’s also a learning opportunity for me. I’m watching all the things a startup has to deal with that I’ve avoided (I didn’t get the entrepreneurial gene). It’s also turning out to have a really interesting revelation, which is worth exploring. I like continually learning, and this is just such an opportunity.

To start, I’ve advised lots of companies over the years. This includes on learning design, product design, market strategy, and more. Of course, with me you always get more than anticipated (like it or not ;), because I’ve eclectic interests. I also collect models, and when they match, you’ll hear about it. (To be fair, most clients have welcomed my additional insights; it’s an extra bonus of working with me! :) It’s also fun, since I also educate folks as I go along (“working with you is like going to graduate school”). Rarely, however, have I been locked into the development. I come in, give good advice, and get out. Here, it’s not the same.

I’m always a sponge, learning as well as sharing. Here, however, I’ve had involvement for a longer time; from their first no-code version and now serious platform development (in User Acceptance Testing phase, which means we’re about to launch; exciting!). From CEO David Grad, through COO Page Chen, and then all the folks that have been added from tech, to sales and marketing, UI, and more, I’ve been usually at least peripherally involved and exposed. It’s fascinating, and I’m really learning the depths that each element takes, and of course it’s far more than my naive ideas had initially conceived.

There are two major elements to their solution. One is wrapping extended reactivation around training events. The second is taking the collected data and making it available as evidence of the learning trajectory. My role is essentially in the first; for one, there are lots of nuances going into the quantity and spacing of learning. While there’s good guidance, we’re making our best principled decisions, and then we’ll refine through testing. I’m also guiding about what those reactivation activities should be. We are extending learning, not quite to the continual, but certainly to the necessary proficiency.

This is where it’s getting interesting. I realized the other day that most of what learning science talks about is formal learning: practice before performance. Yet, here, we’re actually moving into applying the learning into the workplace, and having learners look at the impact they’re having. In many ways, this looks more like coaching. That is, we’re covering the full trajectory. Which means we have to base principles beyond just formal learning. This is serious fun! Our data collection, as a consequence, goes beyond just the cognitive outcomes, but also looks at how the experience is developing.

Sure, there are tradeoffs. The market demands that we incorporate artificial intelligence, and they’re not immune to the advantages. We’re also finding that, pragmatically, the implications can get complex really fast, and that we have to make some simplifying assumptions. Of course, they’re also needing to develop a minimally viable product first, after which they’ll see what direction extensions go. It’s not the ideal I would envision, but it’s also a solution that’s going to really meet what’s needed.

So, I’m continually learning, and enjoying the journey. We’ll see, of course, if we can penetrate awareness with the solution, which should be viable, and also handle the general difficulties that bedevil many startups. Still, it’s a great opportunity for me to be involved in, and similarly it’s one that can address real organizational needs.

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.

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!)

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.