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?

Today I read that Anthropic has released Claude for Education (thanks, David ;). And, it triggered some thinking. So, I thought I’d share. I haven’t fully worked out my thoughts, so this is preliminary. Still, here’re some triggered reflections on Intelligent Tutoring via models.

So, as I’ve mentioned, I’ve been an AI groupie. Which includes tracking the AI and education field, since that’s the natural intersection of my interests. Way back when, Stellan Ohlsson abstracted the core elements of an intelligent tutoring system (ITS), which include a student (learner) model, a domain (expert on the content) model, and an instruction (tutoring) model. So, a student with a problem takes an action, and then we see what an expert in the domain would do. From that basis, the pedagogy determines what to do next.  They’ve been built, work in research, and even been successfully employed in the real world (see Carnegie Learning).

Now, I’ve largely been pessimistic about the generative AI field, for several reasons. These include that it’s:

• evolutionary, not revolutionary (more and more powerful processors using slight advances on algorithms yields a quantum bump)
• predicated on theft and damage (IP and environmental issues)
• likely will lead to ill use (laying off folks to reduce costs for shareholder returns)
• based upon biz models boosted by VC funds and as yet still volatile (e.g. don’t pick your long term partners yet)

Yet, I’ve been upbeat for AI overall, so it’s mostly the hype and the unresolved issues that are bugging me. So, seeing the features touted for this new system made me think of a potential way in which we might get the desired output. Which is how I (and we) should evolve.

As background, several decades back I was leading a team developing an adaptive learning system. The problem with ITS is that the content model is hard to build; they had to capture how experts reasoned in the field, and then model it through symbolic rules. In this instance I had the team focus on the tutoring model instead, and used a content model based upon learning objects with the relationships between them capturing the knowledge.  Thus, you had to be careful in the content development. (This was an approach we got running. A commercial company subsequently brought it to market successfully a decade after our project. Of course, our project was burned to the ground by greed and ego.)

So, what I realized is that, with the right constraints, you could perhaps do an intelligent tutoring system. So, first, the learner model might be primed by a pre-test, but is built by learner actions. The content model could come from training on textbooks. You could do either a symbolic processing of the prose (a task AI can do), or a machine learning (e.g. LLM) version by training. Then, the tutoring model could be symbolic, capturing the best of our rules, or trained on a (procured, not stolen) database of interventions (something Kaplan was doing, for instance). (In our system, we wrote rules, but had parameters that could be tuned by machine learning over time to get better.)

My thought was that, in short, we can start having cross-domain tutoring. We can have a good learning model, and use the auto-categorization of content. Now, this does beg the problem of knowledge versus skills, which I still worry about. (And, continue to look at.) Still, it appears that the particular solution is looking at this opportunity. I’ll be keen to see how it goes; maybe we can have learning support. If we blend this and a coaching engine…maybe the dream I articulated a long time ago might come to fruition.

I’ve written in praise of the cognitive and learning sciences. I, however, need to take a step back. It’s becoming increasingly clear to me, sadly, that there are attacks on science itself.  Yet, I have a strong belief that it matters. So let me briefly address the question of why science.

As background, I have been steeped in science. It was one of my favorite topics in school, and in college. My PhD is in the underpinnings of how we think. Though it’s been a long while since I was an active scientific researcher, I still apply what’s known. Moreover, I continue to track developments, so I can continue to do so. 

As a result, I’ve been a fan of the work of scientists in the cognitive and learning fields. I’ve not only had training in the methods, but I also continue to explore more broadly the methods and the applications. I also love the translators who take that research written in the original academese and turn it into practical advice. Heck, I’m co-director of a society about evidence-based practices. 

There has been some ‘confusion’ about the scientific process. “How can you trust it if it admits it’s been wrong?” Er, that’s what it’s about, continually creating explanations about the world. When we know more, we may need to change our explanations. We went from the sun circling the earth to the other way around, and we no longer (should) think the world is flat. If you don’t believe in the findings, how (and why) are you reading this? Technologies developed from scientific endeavor. 

To be fair, science has been used for ill as well as good. That’s about people’s ethics, not the outcomes. We have to be mindful of how we apply what we learn. That’s up to our values and morals, which science actually has a lot to say as well. For instance, I’ve made the case that research tells us we do better when we’re inclusive. That’s science telling us what values lead to the best outcomes. When we work with what we know about how we think, work, and learn, we improve the outcomes. 

The evidence says that science is better than any alternative. When we apply evidence-based practices, we get the best results. That’s a win. When we turn our backs on it, we lose. Lives can be negatively impacted or lost. That’s not a win. And for our orgs, ignoring science in marketing, operations, sales, etc doesn’t make sense. So, too, for learning and ‘human resources’ in general. And, that’s true for society and government as well. So let’s make sure we’re making decisions in ways that align with science. It may seem more expedient in the short-term to do otherwise, but the long-term results argue for us doing the right thing. When there’re conflicts between beliefs and the evidence, things go better when we adapt beliefs and go with the evidence. “Why science” is because it works better.