Maurice Conti opened the Learning Guild’s Learning Solutions & Human Resources Technology conference (April ’24 Orlando). He proceeded to talk about the implications of artificial intelligence (AI) for Human Resources (HR).
He started with the history of AI, and proceeded to focus on Generative AI. His core message was that you needed three things: your toolset, your skillset, and your mindset. He evangelized for people be optimistic, not stressed.
Recently, the LDA had a debate about coaching, following on the podcast interview. The wise Emma Weber represented the pro argument, while the LDA’s own Matt Richter was con. (Note that these are false divides, we explore the topic for the sake of unpacking issues.) Superb moderating from Kat Koppett was a bonus! As the discussion went, it uncovered more on coaching, without yielding any finality (for reasons we’ll explore).
So, one of the problems emerged immediately, getting into definitions. Matt pushed a bit on the ‘like sport’ notion, where coaching has lots of specific knowledge, while Emma was more on the domain-independent side of coaching. What emerged was that different people have different definitions. Some folks (like me) put coaching further on the domain-dependent side, with mentoring being the more abstract. However, it’s clear others view coaching as the more advanced and deeper side.
This divide isn’t new, but it does provide some barriers, not least to research! As that issue came up, Kat pointed us to a study that began by saying “However, the coaching research suggests a large variety of processes and outcomes, lacking clarity on the primary psychological dimensions most impacted.” Their meta-analysis suggested that “executive coaching is a powerful instrument for organizations to support positive change and personal development.” Which is a good thing, for sure. Their definition does seem to err more on the general side, which is interesting. And, to my own understanding, an important lesson.
One issue that stuck with me was thinking through the range of development. After the formal learning experience, I think there’re times when folks need to be observed, and provided some feedback as they perform. It became clear that the domain-independent model wants the learner to recognize for themselves when they’re not doing well and need to ask for assistance. Yet, a crucial inflection point is making that transition, and I believe that folks aren’t there right away. Similarly, we may not have the resources to add in all the complexities to a particular model for this task initially. So, we expect coaches (read: supervisors and managers) to help develop understanding. Maybe that’s not coaching, by definition, but it’s a task.
I’ll agree at some point you can start guiding folks to their own improvements, but I suspect that only comes when some base level of understanding is reached. We should be clear about this type of interaction as well as the one advocated for coaching! Similarly, we need clarity on labelling! We didn’t end up coming to any finality on that, sadly.
An issue I hadn’t thought about, but became important in the discussion is the issue of appropriate coaching. Clearly, some approaches to coaching don’t work . Knowing when you can expect the coachee to be capable of domain-independent coaching would be one important criteria. Knowing how to ask questions appropriately is another. My concern here is that there are a fair few models about coaching, and with the terminological and empirical barriers, how do you determine the best methods? If we’re to be evidence-based, how can we be?
I can’t say we came to any conclusions, but I do feel we unpacked more of the issues, and did give ourselves some guidance as to what to do when, even if we don’t have agreed upon names for it all yet. Coaching is important, of both types. The data from that study shows coaching can help. We know also that extending the learning experience through feedback on performance helps. We just need to figure out how best to combine them so we know more about coaching. Those are my thoughts , at least, I look forward to yours.
On recent edition of the Learning Development Accelerator‘s Think Like A… series, I interviewed Kevin Wheeler. He represented, in our discussion, the role of talent in the organization. Now, I’ve been talking the organizational perspective for a while. Despite that, amongst the pearls of wisdom he dropped was one that really resonated. It had to do with the forces that are gathering, and his suggestion was that L&D should start being proactive.
He was actually talking about talent and L&D in conjunction. One of his points is that we’re two sides of the same coin. There’s a decision about ‘build vs buy’ when meeting the needs of the organization. In this case, L&D is the build while talent is the ‘buy’. His metaphor about a ‘supply chain’ for thinking about talent is apt; his point is to be looking to the sources of talent.
However, what struck me was his perspective that both haven’t been proactive enough. He sees talent & learning being too reactive to needs, instead of looking ahead and making plans. For instance, what skills are necessary to cope with the emergence of generative AI? What do you need? Do you have the foundations in the org or will you need new capabilities that are available? He envisions an executive role that encompasses both L&D and talent to be responsible for ensuring that the org is forward looking in skills and meeting them.
This aligns nicely with the current focus on ‘upskilling’, as everyone’s going nuts trying to figure out what skills, and how to develop or acquire them, at scale. Thinking ahead might not anticipate every revolution, but it’s clear that the foundational technology base has mutated, and that these new capabilities are likely to stick around. The revolution may be over (guesses on that?), but there’s certain to be evolution, likely rapid! How do you cope?
I think there’s strong evidence that L&D has been too reactive – order-taking – and that there are several ways we can be more strategic. That includes being proactive, as well as having a richer suite of solutions instead of courses über alles. It’s also about taking ownership of innovation by practicing it internally, as well. Listening to Kevin was a great opportunity to think about the bigger picture of what we do.
BTW, with the clear caveat that I’m a co-director, we really are trying to make what appears in the LDA be of value. There’re no vendors, it’s all evidence-based principles and practices for L&D. We invite you to check us out.
I’ve been talking about impact in a variety of ways, and have also posited that decisions are key. I really haven’t put them together, so perhaps it’s time ;). So here’re some thoughts on impactful decisions.
To start with, I’ve suggested that what will make a difference to orgs, going forward (particularly in this age of genAI), is the ability to make better decisions. That is, either ones we’re not making right now, or new ones we need to be able to make. When we’re moving away from us doing knowledge tasks (e.g. remembering arbitrary bits of information), our value is going to be in pattern-matching and meaning-making. When faced with a customer’s problems, we’ll need to match it to a solution. We need to look at a market, and discern new products and approaches. As new technologies emerge, we’ll have to discern the possibilities. What makes us special is the ability to apply frameworks or models to situations despite the varying contexts. That’s making decisions.
To do this, there are several steps. What are the situations and decisions that need to be made? We should automate rote decisions. So then we’ll be dealing with recognizing situations, determining models, using them to make predictions of consequences, and choose the right one. We need to figure out what those situations are, the barriers to success, and figuring out what can be in the world, and what needs to be in the head. Or, for that matter, what we can solve in another way!
We also need to determine how we’ll know when we’ve succeeded. That is, what’s the observable measure that says we’re doing it right. It frequently can be triggered by a gap in performance. It’s more than “our sales aren’t up to scratch”, but specifics: time to close? success rate? Similarly for errors, or customer service ratings, etc. It needs to be tangible and concrete. Or it can be a new performance we need. However, we need some way to know what the level is now and what it should be, so we can work to address it.
I note that it may feel ephemeral: “we need more innovation”, or “we need greater collaboration”, or… Still, these can be broken down. Are people feeling safe? Are they sharing progress? Is constructive feedback being shared? Are they collaborating? There are metrics we can see around these components, and they may not be exhaustive, but they’re indicative.
Then, we need to design to develop those capabilities. We should be designing the complements to our brain, and then developing our learning interventions. Doing it right is important! That means using models (see above) and examples (models in context), and then appropriate practice, with all the nuances: context, challenge, spacing, variation, feedback… So, first the analysis, then the design. Then…
The final component is evaluation. We first need to see if people are able to make these decisions appropriately, then whether they’re doing so, and whether that’s leading to the needed change. We need to be measuring to see if we’re getting things right after our intervention, it’s translating to the workplace, and leading to the necessary change.
When we put these together, in alignment, we get measurable improvement. That’s what we want, making impactful decisions. Don’t trust to chance, do it by design!
Conjunctions are interesting learning opportunities. When two things provide different facets, particularly on something you’ve been thinking about, it’s serendipitous. In this case, two widely different readings triggered some reflections asking whether perhaps we’ve a misplaced organizational focus.
So, I’ve been a bit concerned about the rabid interest in generative AI. Not that I think it’s inherently bad, despite its flaws. Instead, my concern is the uses it’s put to. If you think about the classic engineering proposition – cheap, fast, or good; pick 2 – you know you can apply AI to any of the areas. Always, however, it seems that the focus is on cheap and fast. Which concerns me. There’s substantial evidence that our L&D efforts aren’t having an impact. Thus, doing bad faster and cheaper is still bad!
Part of this, it seemed to me, to stem from a rabid focus on short-term returns. I read The Japan That Can Say No many moons ago, and became convinced that a purely financial focus isn’t in the long-term interests of organizations. Now, there’re reinforcement!
First, in Australian news was a report about how a famous economist was rethinking the role of economics. While I didn’t agree with all of it, one aspect that resonated was captured in these bits:
“…we have largely stopped thinking about ethics and about what constitutes human well-being. We are technocrats who focus on efficiency…We often equate well-being with money or consumption, missing much of what matters to people.”
The juxtaposition happened with this quote aggregated by Learnnovators and posted to LinkedIn:
” The early signals of what A.I. can do should compel us to think differently about ourselves as a species. …Those skills are ones we all possess and can improve, yet they have never been properly valued in our economy or prioritized in our education and training…”
– Aneesh Raman, VP, Workforce Expert at LinkedIn & Maria Flynn, President & CEO of Jobs for the Future (JFF)
The overlap, to me, has to do with the undervaluing of what humans bring to the economic table. Efficiency isn’t the only good. Pushing L&D to do ‘box ticking’ learning design faster and cheaper isn’t consonant with recognizing what gives our work meaning. Besides undervaluing what learning design could and should be, it’s disrespectful to the learners and the organization.
I think that what’s driving organizations should be how they contribute to society as a whole. The means to that end is creating an internal environment conducive to supporting people, individually and collectively, to contribute their best in ways that respect what we offer. There are things technology can do that, frankly, we as people shouldn’t. Similarly, there are things we can do that we shouldn’t abrogate. To paraphrase the meme, I don’t want people doing menial tasks leaving the creativity to machines.
A holistic synergy, each doing what they do best to augment the other, alone and together, is optimal. Our economics should support that as well, and to the extent our structures don’t, it may be time to rethink them. Otherwise, it’s a misplaced organizational focus. Thoughts?
Every once in a while, I wonder what I’m doing (ok, not so infrequently ;). And it’s easy to think it’s about applying what’s known about learning to the design of solutions. However, it’s more. It is about applying science results to designing improvements, but, it’s broader than learning, and not just individual. Here are some reflections on engineering solutions.
As I’ve probably regaled you with before, I was designing and programming educational computer games, and asking questions like “should we use spacebar and return, or number keys to navigate through menus?” (This was a long time ago.) I came across an article that argued for ‘cognitive engineering’, applying what we knew about how we think to the design of systems. Innately I understood that this also applied to the design of learning. I ended up studying with the author of the article, getting a grounding in what was, effectively, ‘applied cognitive science’.
Now, my focus on games has been on them as learning solutions, and that includes scenarios and simulation-driven experiences. But, when looking for solutions, I realize that learning isn’t always the answer. Many times, for instance, we are better off with ‘distributed‘ cognition. That is, putting the answer in the world instead of in our heads. This is broader than learning, and invokes cognitive science. Also, quite frankly, many problems are just based in bad interface designs! Thus, we can’t stop at learning. We truly are more about performance than learning.
In a sense, we’re engineers; applying learning and cognitive science to the design of solutions, (just as chemical engineering is about applying chemistry). Interestingly, the term learning engineering has another definition. This one talks about using the benefits of engineering approaches, such as data, and technology-at-scale, to design solutions. For instance, making adaptive systems requires integrating content management, artificial intelligence, learning design, and more.
Historically, our initial efforts in technology-facilitated learning did take teams. The technology wasn’t advanced enough, and it took learning designers, software engineers, interface designers and more to generate solutions like Plato, intelligent tutoring systems, and the like. I’ve argued that Web 1.0 took the integration of the tech, content design, and more, which usually was more than one person could handle. Now, we’ve created powerful tools that allow anyone to create content. Which may be a problem! The teams used to ensure quality. Hopefully, the shift back comes with a focus on process.
We can apply cognitive science to our own design processes. We’ve evolved many tools to support not making reliable mistakes: design processes, tools like checklists, etc. I’ll suggest that moving to tools that make it easy to produce content haven’t been scaffolded with support to do the right thing. (In fact, good design makes it hard to do bad things, but our authoring tools have been almost the opposite!) There’s some hope that the additional complexity will focus us back on quality instead of being a tool for quantity. I’m not completely optimistic in the short term, but eventually we may find that tools that let us focus on knowledge aren’t the answer.
I’m thinking we will start looking at how we can use tools to help us do good design. You know the old engineering mantra: good, fast, and cheap, pick 2. Well, I am always on about ‘good’. How do we make that an ongoing factor? Can we put in constraints so it’s hard to do bad design? Hmm… An interesting premise that I’ve just now resurrected for myself. (One more reason to blog!) What’re your thoughts?
At the event I attended a bit ago, one of the discussions was on Diversity, Equity, and Inclusion (DEI). I attended, to hear what was up. There were discussions of how to instigate DEI, but one thing I felt was missing, so of course I chimed in at the end. Actually, I learned something else as well, so that’s worth reciting to. So, why DEI?
There are, of course, lots of good reasons. For one, the privileges I’ve had haven’t been shared. Folks often come from less opportune backgrounds than others have had the advantage of. Moreover, such advantage hasn’t been accounted for before they get to work. Unfortunately, schools and social welfare haven’t adequately addressed this We have racism, and misogyny, and other forms of discrimination to deal with. ‘Us against them’ isn’t a healthy perspective. However, perhaps you wonder, why should organizations be a source of remedy?
My argument it pretty simple, really. Research says that we get better results when we have diversity in looking for solutions. There’s a pretty simple explanation why, too. What we’re doing, when looking for answers (research, design, trouble-shooting) is searching a potential solution space. It’s easy to not explore thoroughly. I’ve talked about brainstorming, for instance, as something we can do badly or well. That’s about process. But there’s more.
Garvin, Edmondson, & Gino wrote about learning organization dimensions, and one of the four aspects of a supportive learning environment was “appreciation of differences”. I want to emphasize it’s not ‘tolerating diversity’, it’s valuing it! In exploring that space of solutions, the more diversity in the group, the more likely we are to cover a big range. (There’re caveats, of course, particularly that all have to share a commitment to finding an answer.) Homogeneity is the enemy here!
Of course, this means equity in treatment, and inclusion. If you’re excluding people, you’re not taking advantage of diversity. If you’re not promoting equity, the injustices perpetuate. The only good way to get people to feel good about diversity if it is equitable and inclusive.
Interestingly, one of the hosts mentioned that there’s separate evidence of value. This was something I hadn’t heard. Apparently, having more diversity in the room makes people more diverse in their thinking. That is, even before getting people to generate ideas, people’s attitudes are more diverse because of the observed variety. I haven’t been able to confirm this, but I have no reason not to believe it, and it’s an interesting (and valuable) result.
Now, as said, there are lots of good reasons. But one that is very pragmatic is that you get better solutions when different viewpoints are incorporated. We should be looking at complementary and varied viewpoints. That involves bringing different people together that have something to offer, and just being different is one! Celebrate that!
So, that’s why DEI in my mind; done right, the outcome is better! Overall, we fare better when we work in the ways that align with how our brains operate. That’s alone and together. Let’s do the best for us and our organizations.
At an event this past week, I sat in on a discussion of coaching. Asking folks what coaching was, there were lots of responses about ‘establishing rapport’, ‘asking questions’, etc. I admit I was a wee bit curious amongst all this, thinking about specifics. Which prompted some reflections. My question is about whether there can be domain-independent coaching.
To start, I was thinking about how to develop people just after a learning ‘event’ or experience. They’ve been developed to a certain level, and then we’d like to continue their development. To do so, I thought feedback would be useful, and specifically tying the learning to any relevant task, and providing feedback to fine-tune their performance. Specifically, this requires knowing the domain they’re learning about, observing their performance (in some way), and identifying ways in which they went right, or wrong. That, in my mind, requires specific knowledge about how the mental models play out in context. This, for example, is what we see in sports coaching.
As context, I remember talking to a very smart individual who runs a business that does coaching as a service, at scale. To do this, they have to have folks who know coaching, but pragmatically can’t necessarily know the domain. I was curious how this could work, but empirically it does. Coupled with the responses of folks around the table, I had to reconcile my specifics with a more general approach. How can this work?
Of course, I started thinking about the trajectory of learners. They start as novices in any particular domain, then proceed to become practitioners, and can become experts. As they progress, they need less specifics. If you look at situated leadership as a model, you go from providing direction and support, to eventually removing the (domain-specific) direction, then the support, as they become capable. Thus, coaching can move to asking about how they’re feeling about it, and to apply their own knowledge to the situation. That is, you can start asking about the process and their thoughts rather than focusing on specifics.
Of course, to me, if you apply the domain-independent coaching at the wrong time, you can delay (or extinguish) their development. On the other hand, continuing with micromanaging performance can be similarly restricting. So, I reckon you can shift to domain-independent coaching, after you have developed a minimum viable level of capability. That’s my reconciliation; what’re your thoughts?
I’ve been on the stump for mental models since, well, I left for grad school. My work colleagues, as a going away present, gave me a copy of Gentner & Stevens’ Mental Models. My Ph.D. thesis also incorporated them. (In it, I looked at how people used such models, and then looked at the effects of training interventions. TL;DR: there were some benefits!) However, I suspect that the idea is a bit complex, and I tend to err on the side of the conceptual (mea culpa!). So, I thought it was time to be getting concrete about mental models.
For background, mental models are simplified explanations about how some portion of the world works. They’re causal conceptual models that connect the relationships and how effects propagate through systems. Done well, they give us a basis to explain and predict outcomes. For performance, they give us a basis to make decisions; we can run them and see what the various outcomes of choices of actions would be. Then we can choose the best one. And they guide decisions in many different circumstances.
For instance, for something procedural, they tell us why we doing things in a certain order. For instance, when I’m cooking breakfast, I want the onions nicely browned. I know not to put them in before the pan heats, because my mental model of heat transference and vegetable properties lets me know that throwing them into a hot pan will brown them. Instead, if I throw them into a cold pan and raise the heat, they’re likely to just get soft. (There’re nuances around this, of course.) And this can stretch to let me know when to throw in various things, and also why to cook the meat, take it out, and then throw in the veggies. If I forget a step, my mental models can fill in and help me regenerate the necessary step.
Similarly for other decisions. In trouble-shooting, for instance, my knowledge of what makes cars work lets me know that if it’s not running, it’s likely one of two sources. Given that petrol engines require an air/fuel mixture ignited by a spark, I can suspect that either the fuel or electrical system is at fault. Then I can take steps to test each. (Ok, at least I could, back in the days of caburetors and distributors. Electronic ignition and fuel injection have thrown off my game!) Knowing the causal properties let me break down the possible contributors. It’s not going to be the brakes!
And so on. In fact, models guide many types of decisions. Good models, based on empirical research, such as cognitive load theory, give us reasons to do things like precede practice with examples. (Bad models, of course, like learning styles, lead us to waste time and money chasing unobtainable benefits.)
Research on mental models tells us several things. For one, our brains are always building models. It also tells us that once we have a model, we’re reluctant to replace it (no “please sir, may I have another model”), and instead, patch it. Thus, if we want to support the optimal performance, we should provide good models, and build their relevance through examples that show them, and addressing them in feedback.
I hope that the two examples here make the use of models a bit more comprehensible. The goal, of course, is that you start paying more attention to them in your learning designs. They’re not necessarily obvious to elicit, but they are there. Do try to find them, represent them explicitly, and refer to them in examples and feedback. Oh, and do avoid the bad ones (you can look to the research translators for guidance). They’re part of what makes us best at performing optimally, and so should be part of your learning solutions. (In other words, build and use your model of mental models! :)
I apparently talk about alignment a lot. There’re good reasons, of course. First, I am referring to two different alignments. One is aligning organizations with the way our brains work. Otherwise, we won’t get the best out of people. The other is aligning our learning experience designs with what we know about how our brains work. Also critically important. In this case, prompted as always by conversations, I realized that I wanted to explore the nuances of aligning for the latter.
So, I’ve talked before about how we should make sure we have meaningful objectives, and then align practice to that. I’ve also become enlightened about how important examples are as well. But within both of those, there’s more.
It came up in reviewing a design, looking to refine the approach. In it, there were examples, but they weren’t being used quite systematically enough. Then, the practice also wasn’t quite reflecting what people did. In the course of the conversation, I realized that there were nuances that seemed to be missing.
When you’re focusing on performance, you should be looking at what people will need to be doing. Too often, folks can talk about what they want people to know. However, what matters is what people do. Thus, you really need to dig down into that.
Then, you need to be making sure your examples show people doing whatever it is they need to be able to do. Similarly, you need to be asking people to be doing that, as well. For good examples, they should have a narrative flow, and show the underlying thinking. Good practice should require contextualized decision making like they’ll have to actually perform. Not characterizing the situation, but making decisions based upon those situations. So, not saying “is this an X or a Y situation”, but instead “do you choose action A or B”?
Then, of course, there are the actual choices of situation. The first task should be elementary. It may require scaffolding, so the circumstances might be simple, or some of the task is performed, etc. Then, you systematically add complexity in the task, while also broadening the situations seen. You’re simultaneously supporting both the acquisition of skill, and the ability to transfer to appropriate situations.
Then, of course, you want to make the situations appropriately compelling. That may mean choosing the best stories, some exaggeration, and storytelling. For practice, of course, there’s also the feedback: performance-focused, model-based, and minimal.
Look, I’m not saying this is easy. If it was easy, we’d get AI to do it ;). Yet AI doesn’t, and really can’t, understand the nuances of aligning. We can, and do. Yes, it is somewhat rocket science, done properly. We’re talking about systematically creating change in arguably the most complex thing in the known universe, after all. However, we do have good principles and practices. We just need to make sure we know, and use them.
That’s what makes our field so fascinating and important, after all. The creativity involved is also why it’s fun. Then, we’re also achieving important goals, improving people. We owe it to our stakeholders to do it right. (We are the leaders of the future economy, after all!) That’s my take, what am I missing?