Over the last few years, neuroscience has become increasingly influential in the world of learning and development. Concepts around attention, memory, cognitive load and emotion now appear regularly in conversations about learning design — and with good reason.

One of the first neuroscience-related concepts that really stuck with me was the role of the amygdala in how we respond to challenge and uncertainty. Michael W. Allen references this in his book Designing Successful E-Learning, explaining how the brain can trigger a kind of cognitive “flight mode” when we encounter something stressful, difficult or emotionally uncomfortable.

Most of us have experienced this in learning situations.

You reach a moment in a course, workshop or e-learning module where something suddenly feels difficult, awkward or mentally draining — and you quietly disengage. Your attention drifts. You stop processing properly.

This matters because learning design doesn’t happen in a vacuum. It happens within the constraints of how human brains actually work. And when you start exploring books like John Medina’s Brain Rules, that reality becomes even clearer. Medina highlights several principles that feel especially relevant to anyone working in L&D:

• The brain doesn’t pay attention to boring things

• Attention is limited and selective

• Stress interferes with thinking and memory

• Multitasking reduces effectiveness

• Emotion strongly influences what we notice and remember

The practical implication is obvious enough: if learning design ignores how people actually process information, outcomes become unpredictable. Which is why many good learning design practices already compensate for these realities. We:

• chunk information into manageable sections

• repeat important ideas

• simplify complex tasks

• use prompts, scaffolds and job aids

• reduce unnecessary cognitive overload

All sensible and valuable approaches. But I think there’s an interesting tension hidden inside all this. Because taken too far, the “work with the brain” perspective can unintentionally become limiting. It risks framing learners primarily in terms of their constraints and weaknesses.

Anyone who has worked in L&D for a while will probably recognise the following situation. Two people attend the same programme. They receive the same content, the same practice and the same support.

Yet what happens afterwards is often very different. One person experiments, adapts, recovers from mistakes and gradually improves in the workplace. The other hesitates, delays, loses confidence or reverts to old habits under pressure.

We often explain this through motivation, personality or mindset — and all of those factors matter. But I suspect there may also be something slightly deeper involved: what we might call learning capability.

This is about the ability to:

• act despite uncertainty

• recover when things don’t go smoothly

• monitor whether something is working

• adapt rather than abandon

And this raises an interesting possibility for L&D. What if understanding how the brain works isn’t just about designing around human limitations? What if part of our role is also helping people become better at managing those limitations themselves?

That doesn’t mean ignoring cognitive science or pretending attention and memory don’t matter. Quite the opposite. It means building capability on top of that understanding.

Because perhaps the long-term goal isn’t simply to design learning that compensates for fragile attention, unreliable memory and uncertainty. Perhaps it’s also to help learners become more resilient, adaptive and self-aware when facing those realities in the workplace.

And that, I think, may be a slightly bigger and more interesting challenge for modern L&D.

A fuller exploration of these ideas — including the tension between cognitive constraints and learner capability — can be found in the original Learning Re-Framed article on Substack.

Most of us in L&D have had plenty of practice in using words to communicate clearly.

We know how to structure content. How to explain ideas. How to sequence information logically. How to write learning objectives, facilitator notes, e-learning copy and assessment questions.

But visual communication? That’s different.

Despite working in a profession that relies heavily on visuals — slides, graphics, scenarios, video, interfaces, layouts and e-learning interactions — very few of us have ever been taught how visual communication actually works.

And yet we instinctively know when something visual feels right.

We recognise strong visual storytelling immediately in films, television, photography, comics and advertising. We can usually tell when a slide deck looks polished or amateurish. We notice when an e-learning course feels visually polished and engaging versus visually flat.

The strange thing is this: most of us consume visual language fluently every day… while feeling far less confident about creating it ourselves.

Why This Matters for L&D

If you’ve ever:

• struggled to make slides feel visually engaging

• created visuals for scenario-based e-learning that felt a bit unnatural, or 

• found yourself thinking, “I know the look I want, but don't seem able to achieve it”

then you’ve already experienced this gap.

And historically, there’s been a practical problem sitting behind it. Even if someone in L&D developed stronger visual instincts, actually bringing ideas to life often required access to:

• graphic designers

• illustrators

• photographers, or

• video specialists.

Most small L&D teams simply didn’t have those resources available.

Why AI Changes the Equation

That resource barrier is suddenly much lower than it used to be. AI image generation tools now allow L&D professionals to create visuals that would previously have required specialist design support.

But there’s an important catch. The quality of the output depends heavily on how clearly you can describe what you want visually. And this is where many people hit frustration.

A prompt might feel perfectly logical in everyday language but still produce disappointing results because AI image tools “think” more like photographers, directors and visual storytellers than instructional designers.

They respond much more effectively when prompts include things like: framing, composition, camera angle, lighting, perspective and visual focus.

In other words, the more fluent you become in visual language, the better your AI-generated visuals are likely to become.

Learning the Vocabulary of Visual Communication

The good news is that this visual “grammar” is surprisingly learnable. You don’t need to become a professional film-maker or illustrator. Even a small amount of familiarity with visual storytelling principles can significantly improve your ability to:

• direct AI tools more effectively

• create stronger learning visuals

• design more authentic scenarios, and

• communicate ideas more clearly.

Two resources I’ve personally found useful are: Making Comics by Scott McCloud and The 5 C’s of Cinematography by Joseph V. Mascelli.

Neither was written specifically for L&D, yet both contain extremely practical ideas about visual storytelling, framing, composition and sequencing that transfer remarkably well into learning design and AI prompting.

And importantly, they help develop something many of us were never formally taught: the ability to think visually — not just verbally.

A Final Thought

For years, many of us in L&D have quietly accepted that visual design was something best left to specialists. AI is beginning to change that.

But making the most of these tools will require more than simply learning how to write prompts. It may also require us to become more fluent in the language of visual communication itself. And that feels like a genuinely useful new skill for modern L&D professionals to develop.

A fuller exploration of these ideas — including the connections between AI prompting, cinematography and visual storytelling — can be found in the original Learning Re-Framed article on Substack.

In my previous post, I explored why learning styles fail to stand up to scrutiny when examined through the lens of evidence. Despite decades of popularity, there is no reliable research showing that matching instruction to a learner’s preferred style leads to better learning outcomes.

But that only addresses part of the problem

A more interesting question is this: if learning styles are so poorly supported by evidence, why do they still feel so intuitively right to so many people in L&D?

Learning styles emerged as a genuine attempt to fix a real and persistent problem — but in doing so, they quietly pulled L&D’s attention in the wrong direction.

Why learning styles gained traction

Learning styles rose to prominence at a time when L&D was beginning to confront an uncomfortable reality: one-size-fits-all training wasn’t working.

Courses were generic. Learners were disengaged. Completion rates looked fine, but application and impact were questionable at best. Learning styles offered a compelling response:

• People are different
• Those differences matter
• Learning shouldn’t look the same for everyone

At face value, that instinct was (and still is) entirely reasonable. The problem wasn’t the intent. It was how the idea evolved.

From design challenge to classification exercise

Learning styles transformed a complex design problem into a categorisation problem. Instead of asking, “What does someone need to be able to do?”, we started asking, “What type of learner is this?”.

That subtle shift matters. It moves the focus away from performance and toward cognitive processing preferences. In doing so, it anchors L&D firmly in the knowledge-acquisition phase of learning — not in the messy, pressured reality where learning needs to be applied.

Modality is not style

This is where a lot of confusion crept in. Research by Richard Mayer on multimedia learning — later synthesised and extended by Ruth Clark — demonstrated something important: how information is presented does matter.

But Mayer and Clark were never talking about learner preferences. They were talking about matching instructional design decisions to the:

• nature of the content
• type of task, and
• limits of working memory

For example:

• Visual-spatial tasks benefit from diagrams
• Procedural tasks benefit from step-by-step visuals with minimal text
• Language-heavy tasks are often better supported by text than audio

This is modality as a design principle — not a learner trait.

Unfortunately, modality was often simplified and misinterpreted as learning style. For example, dual channels - the idea that we process words and visuals through different mental pathways - became “visual vs auditory learners”. A design insight turned into a personal label.

That kind of leap was never supported by the research.

The self-diagnosis problem

There is another, deeper issue with learning styles — one that Ruth Clark addresses particularly clearly in her book Building Expertise.

Learning styles rely heavily on learners diagnosing their own needs and preferences. The problem with that? Research consistently shows that learners are very poor at self-diagnosis.

Across multiple studies, learners:

• overestimate how well they have learned
• confuse fluency with understanding
• prefer easier, more comfortable instruction even when it produces worse outcomes
• struggle to identify which instructional approaches actually help them perform better

In other words, people are not reliable judges of how they learn best. So, a model that encourages learners to self-diagnose and request a specific, instructional “prescription” rests on a very shaky foundation.

Why none of this matters when application is required

The massively important design insights of Mayer, Clark and others tell us lots about knowledge processing and recall but very little about what happens next.

When someone is back at work, under time pressure, trying to complete a task they:

• don’t choose their preferred style
• don’t reflect on presentation format
• do what the task demands

In performance mode, preference disappears. What matters instead is:

• Clarity
• Relevance
• Confidence
• access to the right support at the right moment

Learning styles have little or nothing useful to say about any of that.

 A more productive shift

If learning styles were an early attempt to personalise learning, the good news is that we now have far better, evidence-based ways of doing this.

Instead of asking, “What type of learner is this?” we get much further by asking:

• What does good performance actually look like?
• Where do people struggle when they try to apply this?
• What decisions, judgements, or actions really matter?
• What support would help most at that moment?

That shift moves L&D away from categorising learners and toward designing for performance.

Reframing the legacy of learning styles

Learning styles weren’t foolish. They were a signal. A signal that L&D wanted to move away from content dumping and toward something more thoughtful, more learner-centred and more effective.

But focusing too heavily on cognitive preferences distracted us from the harder — and more important — question, “How do we help people apply their learning better when it actually matters?” 

Few ideas in Learning & Development have been as popular (or as persistent) as learning styles. Most of us are familiar with the basic idea: people have different styles of learning (‘visual’ or ‘auditory’ are couple of examples). Identify someone’s style, match the training to it, and ‘hey presto’ learning will be more effective.

You can understand the attraction. It’s an idea that seems deeply intuitive. That feels totally learner-centred. That is, in fact, irresistible to many learners and L&D professionals.

Which goes a long way to explaining why, for years, learning styles have been taught, assessed, and embedded into L&D practice. But there’s a big problem with this idea. Because when you look closely at the evidence, the idea simply doesn’t hold up.

Let’s be precise about what’s being challenged

The specific claim that research has examined and failed to support, is what psychologists call the meshing hypothesis. The idea that learners learn better when instruction is matched to their preferred learning style (e.g., visual learners taught visually, auditory learners taught verbally).

Now it’s important to note, that this is different from saying:

• people don’t have preferences (they do),
• or that variety is bad (it isn’t),
• or that accessibility doesn’t matter (it absolutely does).

But back to that central question: does matching instruction to a learner’s declared style improve learning outcomes?

Fans of the learning styles idea are going to be disappointed. Because after more than two decades of research, the answer is still: no compelling evidence.

What the research actually says

One of the most widely cited reviews was published by Harold Pashler and colleagues in 2008. They set out very clearly what evidence would be required to support learning styles — and then examined the available studies.

Their conclusion was blunt: there was no credible evidence showing that matching instruction to learning styles improves learning.

Other reviews have reached the same conclusion. A very major UK review by Coffield et al. examined dozens of learning style models and found serious issues with reliability, validity, and practical usefulness.

As a result, learning styles are now commonly described in the research literature as a “neuromyth” — a belief about the brain or learning that sounds scientific but isn’t supported by robust evidence.

So much so that for years there has even been an open “Learning Styles Challenge”, offering a cash prize to anyone who can demonstrate real-world benefits from designing instruction around learning styles. To date, the prize remains unclaimed.

Why the idea refuses to die

If the evidence is so weak, why does the idea persist? Partly because learning styles play into something everyone is a fan of: personalisation. They give learners language to describe themselves and give designers a sense that they are tailoring learning rather than mass-producing it.

They’re also easy to explain, easy to assess, and easy to sell. But ease and intuitiveness are not the same thing as effectiveness.

What actually works better

Ironically, many people who believe in learning styles are already doing things that do work.

Research consistently shows that learning is more effective when:

• the mode of instruction matches the nature of the content (for example, diagrams for spatial information or audio for pronunciation),
• learners get practice, not just content exposure,
• retrieval and feedback are built into practice
• complexity is managed carefully,
• learners are supported in applying learning in context.

In other words, the question isn’t, “What type of learner are you?” It’s, “What kind of learning does this task require?” That shift — from labelling learners to analysing tasks — is what helps L&D become more effective.

Preferences still matter, BUt...

None of this means learner preferences should be ignored. Preferences matter for all sorts of reasons like:

• motivation
• engagement,
• inclusion and accessibility.

But preference is not the same as learning effectiveness. Giving learners choice in how they engage with content can be valuable. Designing everything around a fixed learning style is not.

A more helpful way to think about it?

If we really want to personalise learning, there are better levers to pull than learning styles:

• design for the task, not the learning style label
• design for application and performance, not just exposure to or consumption of content
• design for context, not abstraction

Learning styles promised personalisation. The evidence suggests we can do better — by focusing less on categories and labels and more on what people actually need to do more effectively in the workplace.

I’ve written a lot over the last few months about the significance of shifting from a training-focused paradigm to a performance-support focused one. It’s a big shift. Understandably, plenty of people feel like this is me saying, ‘do away with your training events.’

Why training still matters — but needs to evolve

Definitely not the case. But it does raise a big question that people often ask:

"What would training look like if it were designed with performance support in mind from the start?"

Let’s dig into answering that, to see how you might refine what you do during a training event, when designing for a more performance-support focused paradigm.

It’s easy to see performance support as a nice ‘add-on’. Something to highlight to the learners towards the end of the event, as a rather perfunctory ‘transition to the workplace’ segment. I confess, I’ve run my fair share of such segments over the years.

Unfortunately, this approach will pretty much guarantee that your performance support solution gets little use or has low perceived value.

From event thinking to performance thinking

So, if you are providing performance support tools or materials, then they need to be introduced and used as an integral part of the training – not as an afterthought.

The question remains: 'How to do that?'

In our impact and instructional design programme, we introduce attendees to a very simple, very flexible but nevertheless very robust design flow that works very well during in-person and live online events.

It’s a three part design flow. It starts with presenting to learners, moves to providing structured practice, and finished with much less structured extended practice. The idea being that as you go through this sequence, you are helping the learners’ capability and self-direction to increase

From a learning design point of view, this is about providing multiple points of practice that is highly interactivity. Learners gradually become more autonomous and increasingly confident in their ability to apply what they are learning.

Ideally, by the time you reach the extended practice phase of a session, the trainer will be able to fully step back, observe the learners and only provide feedback (where needed) during a group de-brief after the activity.

The good news is that from a performance support perspective, this is a very effective design flow, too. The difference comes in how you think about and design your practice activities.

What a performance-support-focused training session looks like

So, from a learning design perspective, a good flow of practice activities will help the learners to process and embed what they have learnt; the only ‘external’ assistance comes from the trainer’s input during the structured practice stage and any feedback or de-brief after the extended practice stage.

From a performance support perspective, the structured and extended practice activity stages of the flow are the perfect moments to enhance that trainer input and support by introducing and phasing in use of the performance support tools or materials.

This familiarises learners with the tools and how to use them; and positions them as the natural post-training ‘go-to’ which will help to get the job done.

Bringing performance support into the classroom

For example, imagine a training event that is focused on helping learners to plan and prepare annual performance reviews with their team members.

The course design analysis phase highlighted the benefit of providing a simple performance support tool to guide learners through the planning and preparation.

Having learners use the planning tool as part of any practice activity makes the practice much more authentic; and, ensures the learners see the tool as part and parcel of how to apply what they are learning.

Training as the gateway, not the goal

All this highlights clearly that selecting and designing performance support tools or materials must be an integral part of your existing impact and instructional design process. Not an afterthought. The training is vital but only the first stage of a journey to the really important goal: improved workplace outcomes.

Achieving this level of performance support integration requires L&D to get close to the business. To have a good understanding of what desired workplace performance looks like. To be clear about how knowledge and skills covered during a training event are applied in the learners’ workflow, once they are back on the job.

If that sounds a bit challenging, it shouldn't. In reality, performance-support-focused training isn’t a different discipline — it’s simply the next logical step in doing what great L&D has always aimed to do: help people do their jobs more effectively.

Context, they say, is everything. Very true in many areas of life, especially true of instructional design. And just as significant when it comes to performance support. Without carefully considering context (and its close relative integration) you are likely to come unstuck.

I was reminded of this just recently when I re-discovered a wonderful example from Allison Rossett. Her example was written in the 90s, meaning the original tech references are out of date now. Below, you’ll find my (minor) re-working of the example to include current tech references.

But before recounting the example, I want to add a bit of context of my own.

Why context matters 

Ever since the arrival of cognitive learning theories, we’ve recognised the importance of context in learning. Viewed through a purely instructional design lens, understanding how learners actually apply their learning in the workplace, will avoid teaching knowledge and skills in the abstract. A clear understanding of the specific ways in which knowledge and skills are being applied, helps you craft a tailored learning experience that closely reflects the workplace reality of the learners.

When thinking about supporting learners after a learning event, understanding that aspect of context is equally important. After all, the content of the support materials will need to be equally well contextualised and tailored.

But you will also want to focus on actual physical surroundings. Literally, where are they when they need that support. At a desk? At the top of an electricity pylon? In a noisy, crowded space?

That’s a much narrower and much more specific aspect of context. But it’s vital because it helps determine what a usable performance support tool needs to look like. 

A job-aid printed on a piece of paper won’t be very effective at the top of a pylon. Audio support won’t be much help in a noisy, crowded workspace. Granted, these are obvious, simplified examples. But you get the idea.

Consider integration for EVEN greater effectiveness

Within that aspect of context, you also need to consider integration. What do I mean by that? Essentially, it’s about how embedded the support is within the task. Making sure that learners can get to the support they need as quickly and easily as possible, while still taking into account the type of performance support tool that will make most sense in that context.

In some cases, you only need to think about these questions for a minute or two. The answer will stare you in the face. Other times, it’s a bit more nuanced and you might need to work through a few options and possibilities before you reach the best result. Other times, you may end up having to compromise a little bit.

And this is why I like Rossett’s example, so much. It takes an example that everyone understands and readily walks us through many of the considerations connected to context and integration. So, here’s my slightly updated version of her example…

Context and integration example

Imagine that you have to drive to an event on the other side of the town where you live. Your brother works on that side of town and knows it well; but you rarely have cause to go there and only a very limited experience of driving around there.

How might you go about finding your way there?

Look at the city map you keep in your home office. 
Not very integrated and only tailored in the sense that it’s a map of the place you need to drive in.

Go to Google Maps and have it map out the route. Print out the results to have with you in the car.
There is significant tailoring here. You get the exact route that you need. The support is integrated. But dangerously so. Glancing at the print out of the directions while driving is not recommended!

Ask your brother for directions
Tailored. And depending on your memory and the accuracy of his directions, this could work quite well. But not hugely integrated.

Pull out a city map that you keep in your glove box. Look at this before your journey and keep consulting it as you drive.
As with the map in the home office, not very tailored. As with the printed directions, integrated but dangerously so.

Use your sat nav
Lots of tailoring (and adapting). Deeply integrated into the task.

Of course, in this example most people will see immediately the benefits of the sat nav over all other options. This is one of those ‘staring you in the face’ decisions. 

However, just to be a little bit contrarian, I continue to be a sat nav refusenik. I could share multiple examples where I have sat as a passenger and watched people make the dumbest of navigation decisions because they have switched off their navigation brain and rely slavishly on a sat nav master. Another post for another day, I think!

But in conclusion, whatever decisions you make regarding context and integration, the important point to remember is that the more embedded, intuitive and tailored you can make that support solution, the more your learners are likely to see value in it and engage with over and over.

You can read a more personal view of my journey to create an AI-driven performance support tool in my weekly PerformaGo Diary.

Looking into the L&D world from the outside, it’s tempting to think that more is better. More slides, more content, more detail. After all, if learners have all the information, they’ll surely be more successful, won’t they?

But anyone who’s worked in L&D knows that is rarely true. It's information or content overwhelm that can discourage learners from implement learning successfully; too much information rather than not enough of it.

Typically, they don’t need more information, but they could surely benefit from more relevant and better-structured information. And that’s where a simple but immensely powerful design principle comes in. The principle of chunking with relevance.

My Early Introduction: Information Mapping

I was first introduced to the principle of chunking with relevance through a methodology called Information Mapping. It’s a structured way of presenting information that relies heavily on breaking content down into smaller, clearly defined units.

With good reason. There’s plenty of evidence to suggest that we all (not just learners) process and understand information much more easily when it’s organised into meaningful, focused “chunks.”

Even outside of the framework of a formal methodology like Information Mapping, chunking with relevance is something all of us in L&D should be aiming to apply.

Fundamentally, chunking with relevance helps shift us from a mindset of, ‘what do I need to tell them’, to a more learner-focused approach that considers two things: ‘what do they need to know’ and ‘what will be manageable for them to process’.

 Why Chunking Works: The Cognitive View

The roots of chunking with relevance lie in cognitive psychology. We don’t have unlimited working memory. So, when information is presented in large, unstructured blocks, our brains struggle to process it.

Chunking with relevance reduces that cognitive load by grouping information into smaller, meaningful units. This makes it easier for learners to:

• Understand new material as it’s introduced.
• See the relationships between pieces of content.
• Retain and recall knowledge more successfully.

Chunking with relevance doesn’t guarantee perfect recall, but it does make initial comprehension much, much easier.

A Principle Shared with AI

Interestingly, chunking isn’t just good for us. It’s also vital for AI systems like GPTs.

For example, when you create a knowledge base for a custom GPT, dumping in a 100-page PDF won’t produce great results.

The GPT will work better when the information is broken down into smaller chunks. Each chunk gives the AI clearer context, helping it generate more accurate and relevant responses.

In other words: both humans and machines process information better when it’s structured thoughtfully. It’s a reminder that chunking is a universal design principle, not just a quirk of instructional theory.

 Why This Matters for L&D Now

Learners often face information overload at every turn — in their jobs, in their inboxes, and even in the learning materials we create. If we want our learning interventions to be effective, we can’t add to that overload.

Applying chunking with relevance is one of the simplest ways to reduce the cognitive burden. It helps learners see the signal in the noise. It makes content usable rather than intimidating. And crucially, it demonstrates the value that L&D brings: not by producing more information, but by structuring it in a way that is meaningful and relevant.

And if you’re curious to find out a bit more about the importance of chunking for AI, take a look at this week’s diary post 

Blended learning has been around for a while now. Plenty of organisations claim to use it. Some actually do. Not so many learners claim to like it. But there are some who actually do.

However, in this post we'll focus on the reasons a blended approach might not be popular because this can offer some clues about to how to change or refine the approach.

The objections take a variety of angles:

For some, it's just a fad. A new name for something we've always done. Long before computer technology there were alternatives or complements to classroom teaching. People listened to cassettes, worked through self-study packs, went to seminars or had one-to-one coaching. What's suddenly so new?

Others worry that it's just about choice. It's not about really providing a coherent mix of learning. They point to the duplication of content that happens in many organisations. Just the same old stuff being churned out in a variety of flavours.

What about the work involved? Another common and very valid objection. Aiming for a coherent blend of learning provided through a variety of delivery mediums and instructional techniques is hard. It will take some careful thought and planning. Why bother some might ask, if only a handful of learners fully engage with all the elements.

It's just a marketing ploy. A ploy dreamt up by e-learning vendors and/or management.  A ploy to get more e-learning in through the back door allowing them to slash the classroom training budget. 

It gives e-learning a bad name. The people who develop blended learning would much rather be using classroom training throughout. They deliberately put all the boring bits of the blend into e-learning and save the fun bits for the classroom training.

It's frequently not necessary. Short training programmes or knowledge that can be covered in a day or two simply doesn't require the complexity of a blended approach. To provide it in these circumstances is just overkill.

As you see, the reasons people don't like blended learning are many and varied. And all those reasons, to a greater or lesser extent have validity.

However, overall, they miss a key point, which is this. Certain contexts or certain workplace performance needs or certain types of content are, generally speaking, better suited to certain types of delivery medium.

This isn't always immediately obvious - especially to non-learning and development people. And this can lead to problematic mismatches which cause headaches for learners and instructional designers alike. For example, you spend lots of time and budget creating a piece of e-learning covering some simple product updates, which could easily be covered in a short presentation style webinar. 

In this example, learners will suffer because they are being asked to complete some learning that is less than optimal for their needs. Instructional designers will be frustrated because they have spent precious time and resource that could have been better allocated elsewhere.

There are several factors that we need to consider and that should feed into any decision-making about a delivery medium: how much the learning is used; the complexity of the topic or the skill being learnt; and, how much and how often content changes or needs updating.

Systematically weighing all of these factors up doesn't absolutely guarantee the best decision about which delivery medium is best to use but it significantly increases the chances of getting that decision right.

If you want to know more about how to systematically evaluate the best delivery medium for a given piece of e-learning, take a look at these modules from our impact and instructional design programme.

Many of us in L&D still hesitate about AI. The reasons vary enormously. For example:

• “It’s not good enough yet.”
• “It’s going to steal my job.”
• “I’m not technical enough.”

Last week’s post explored the flaw in that last reason. We saw that most of us in L&D already hold a hidden set of technical skills — more than most of us give ourselves credit for.

This week, I want to dig into that idea a bit more, with a personal story and a reminder of another skill that many of us underestimate.

You Don’t Need to Know the Detail to Benefit

I’ve never been particularly strong in or had much of an interest in maths. Yet here I am, building a software tool. Even more surprising, as part of deepening my understanding of how AI works, I became quite fascinated by the elegant mathematics that sits behind GPTs.

Luckily, I don’t need to do the maths to be in awe of it — or to benefit from what it enables. I can use GPTs without knowing the detail of semantic maps, tokens, or probability models. I simply need to know how to make the best use of what that ‘behind the scenes’ math can produce.

And I think that highlights an interesting and important parallel for us in L&D. We don’t have to understand every last underlying detail of the subject matter we turn into courses for our learners.

Instead, we need to filter out irrelevant complexity and translate what remains into a learning experience that is relevant, authentic, and usable. This is the hidden L&D skill that I'd like to focus on here.

The Hidden Skill: Turning Complexity Into Clarity

Plenty of SMEs know their subject in depth. A few are even instinctively good instructional designers. But a key understanding that separates a good instructional designer from many an SME is perspective. We design for the learner, not for the content. We can take a step back in order to

• see it from the learner’s point of view
• strip away the content clutter
• sequence ideas clearly and logically, and
• build authentic practice out of all that.

This, I believe, is one of our greatest professional assets.

From Learning Design to Performance Support

Not everyone reading this will have designed performance support materials or content before. If you haven’t, the good news is that the skills you use to design an effective course are very similar to the ones you’ll need to design useful performance support in the workplace.

And here’s the even better news: performance support is the perfect way to combine those existing skills with the power of AI.

Just as we filter complexity into clarity for learners during the course design process, we can use AI to help us produce, usable workplace performance support scaffolding.

Put those two forces together — L&D’s eye for learner relevance and AI’s knack for simplification — and you have a winning formula for extending learning into the flow of work.

Harnessing AI without Being a Techie

You don’t need to be a techie to make AI work in L&D. What you need is a recognition of the skills you have already — and a willingness to apply them in new contexts.

The maths that underpins GPTs may remain out of reach for me, but the elegance of what it makes possible is not. The same is true for L&D. Our job isn’t to master the inner workings of AI, but to harness its power in ways that help people learn, perform, and succeed at work.

If you’d like to see the personal story that sparked this reflection, take a look at this week’s post from my PerformaGo diary: In Awe of the Math I’ll Never Do 

As a customer, it’s a generally a pleasant experience going to a restaurant.

We order the food we want. It arrives as specified and we get to enjoy it. And if anything is not quite as we’d like it, we can always have a word with the staff.

But what about if you are the chef and his team. Always busy. Always harassed. Always churning out the same predictable product. Frequently dealing with picky customers who don’t like this or that about what you produce.

In theory, the chef should be very much in control of his or her universe. In practice it may not be like that.

Does that chef’s kitchen sound like your L&D function? If not, I’m delighted to hear it. No need to read on.

If yes, then you are currently facing a very unattractive future. Because the orders are only going to increase in volume and the ‘customers’ are only going to get pickier and more demanding.

If your ‘customers’ are telling you what they want and how they want it, fundamentally they don’t value you. In their heads, you don't know anything much about the best approach to learning for their particular need. So they formulate their own plans and just present them to you for implementation.

There could be various reasons for finding yourself in a situation like this.

Perhaps you've inherited it from a predecessor. Perhaps this is all you've ever known and you just assume this is the way it's done. Perhaps you'd love to do things differently but can't see a way to turn things around.

Whatever the causes, this is a terrible situation to find yourself in as part of a learning and development function. And it doesn't have to be this way. The ship can be turned around.

If you are serious about turning your L&D department into the trusted and respected part of your organisation it deserves to be, take a look at our on-demand webinar on this topic: How to amplify learning: the journey from order-taker to trusted expert.