Working with SMEs: 6 Types Every Instructional Designer Should Know - Scissortail Creative Services, LLC
Kayleen Holt describes six types of SMEs with tips on how to work productively with each one.
When AI Gets Better, Do We Get Worse? – Usable Learning
AI is getting better at lots of tasks. But even though we know we should rigorously verify AI outputs, we're less likely to do so when AI is generally better. If the AI does well 4 times in a row, you're not going to check the 5th time very closely. Julie Dirksen explains the psychology and gives examples.
Why Good Evidence Sometimes Fails to Change Our Minds
This article is about teachers changing beliefs about instructional methods, but the points about psychology and learning apply broadly. Changing people's minds isn't just about providing good information, especially when those beliefs are tied to key values and identity.
We do not evaluate new evidence in isolation. We interpret it through the understandings we already possess, sometimes reshaping new ideas so that they fit our existing thinking.
If the new information fits comfortably with our existing understanding, learning can be relatively straightforward. But when it conflicts with the way we make sense of the world, we rarely abandon our existing understanding immediately. Instead, we usually reinterpret the new information in ways that preserve as much of our existing understanding as possible.
All in Frame
Free stock photos showing authentic images of people with disabilities. This library includes suggested alt text for each image. (Thanks to Kayleen Holt for sharing this.)
Enhancing workplace eLearning with branching scenarios: An action case study
A case study on branching scenarios for workplace learning, looking at motivation, engagement, and knowledge retention. The researchers asked how engaging with branching scenarios influenced motivation, what design features supported engagement and minimized cognitive load, and learner perceptions of decision-making support. This is a small study (n=10), with participants from a range of backgrounds and roles.
"Branching scenarios offer design features that can help reduce extraneous cognitive load through segmented content and scaffolded decision-making. Rather than overwhelming learners with dense information, these structured interactions promote focused engagement and facilitate problem-solving."
"The findings indicate that two key design features support sustained learner engagement and minimise cognitive overload. First, relevance was critical: scenarios that closely aligned with real workplace contexts enhanced motivation and perceived usefulness, whereas generic or abstract content reduced engagement (Theme 2). Second, cognitive load was shaped by how content was structured. Overly complex tasks overwhelmed learners, but well-sequenced, chunked scenarios helped them stay engaged and retain information effectively"
"Learners perceived branching scenarios as valuable tools for developing workplace decision-making and applying skills in realistic contexts"
"Participants consistently identified the branching scenarios as a distinctive and valuable feature of the training. This interactive format enabled them to explore different choices, including deliberately selecting “incorrect” options to better understand consequences and reinforce learning. This aligns with the concept of “safe failure” (Sennett & Vasquez, 2021), which allows learners to make mistakes in a controlled environment without real-world repercussions."
"Interviews revealed that participants felt less overwhelmed by branching scenarios compared to previous, text-heavy training modules. Instead of being overloaded with information, scenarios progressed step by step, providing only the necessary details at each decision point."
Training Magazine Network: Christy Tucker
All of my recorded webinars on Training Mag Network
My Turn with Claude — Design, Code, and Everything In Between – Visual-e-Learning
Jerson Campos published a great "show your work" post about his experiences rebuilding some old training using Claude, specifically comparing having Claude Design do the work for him versus using Claude AI and Claude Code with more explicit direction. Jerson was happier with the results when he did more steering of the AI. The first Claude Design version looked polished but lacked interaction and depth.
This, I think, is where it’s going to separate instructional designers who let AI build their projects for them from those with a wider breadth of knowledge who can actually direct AI to build effective training.
Test your prototype early. Once you have a sound design for a few slides, have the AI build it out so you can test it. Nothing is more frustrating than finishing an entire storyboard, only to find the course player isn’t what you imagined — and then having the AI go back and edit every slide or module. It’s a great way to introduce new bugs.
Be specific about what you want and what you can provide — videos, audio, narration, animations (if the AI won’t generate them for you). Then ask for an asset list at the end covering the deliverables you’ll be responsible for.
Now that the bar has been raised for the average instructional designer, it’s more important than ever to not be average.
Bot or Not? Text Edition (v2), Lancaster University
Digital Learning Nook | Digital Learning Nook
Pooja Jaisingh created a quick warm-up practice activity for L&D called "Learndle." Given a brief description of situation, type the model, tool, or approach to solve the problem. Each time you get it wrong, you get a new hint.
How to Write Scenario-Based eLearning That Doesn't Feel Fake | Embark IQ
Tips for making scenarios feel authentic, creating plausible distractors, and supporting learning with feedback.
Recover Your Unpaid Invoices
I've had several conversations with other freelancers and consultants recently about clients not paying their invoices. It's usually not enough money to justify hiring an attorney or sending them to collections, but it's also painful when you work for yourself. This is an AI-supported tool to generate and send a certified letter and follow up until you get paid. Will it work? I don't know. For $50, I'd probably try it myself if I had an unpaid bill. I might even pay the extra for an attorney to send the letter on their letterhead.
![The Anatomy of a Modern L&D Team [2026 Edition]](https://rdl.ink/render/https%3A%2F%2Fbeehiiv-images-production.s3.amazonaws.com%2Fuploads%2Fasset%2Ffile%2Fb6a3211d-07fc-439b-8b39-d7a5d9d2916b%2FModern_L_D_Team.png%3Ft%3D1782581924?width=350&height=&ar=&mode=&dpr=2)
The Anatomy of a Modern L&D Team [2026 Edition]
Ross Stevenson shares research, trends, and outlook for how roles in L&D have shifted and are continuing to change. What do those roles look like moving forward? What skills should L&D professionals focus on?
A New Era of Midjourney
This is all very early and not in production yet, but Midjourney is working on using their expertise working with image data into developing a body scan machine to provide medical imaging. I think skepticism is smart until it's actually in use and we see results, but I like the idea of using AI to generate images that help doctors and patients make more informed decisions.
The Vibe Coding Crisis: Why AI is Manufacturing Accessibility Debt
Jacob Wood shares examples of technical debt for accessibility caused by using the messy code that vibe coding generates. Yes, we can build activities and websites quickly, but how are we going to make it accessible? If we ignore accessibility and don't plan for it from the start, we're going to accumulate a lot of cleanup work later.
Why AI Needs Vygotsky: The Case for AI-Based Intentional Friction - Learning Guild
LLMs can give nearly instant answers with almost no effort--but effort is what helps us learn and grow. This article connects classic learning science of Vygotsky and the ZPD to how AI can cause harm through cognitive outsources and provides principles for designing for intentional friction.
The absence of “desirable difficulty” or friction in AI interactions is among the most serious concerns for learning and instructional design experts today. This concern is not due to tool speed, but rather because the lack of friction bypasses or eliminates essential stages of the learning process that strengthen human cognitive and neurological foundations.
When machines respond effortlessly, the natural learning pathway described in Vygotsky’s ZPD and scaffolding is disrupted. While this may seem “efficient” in the short term, over time it can undermine higher-order cognitive skills such as problem-solving, critical thinking, and creativity.
AI and Copyright: What You Can and Can't Do with AI-Generated Content (2026)
An overview of AI and copyright. Who owns it? When can you use it for commercial purposes? How can you protect your AI-assisted work?
New Scenario-Question Generator -- Version 5 » Work-Learning Research
Will Thalheimer released an updated version of his template for generating scenario questions. This includes notes about differentiating people who know how to do a task successfully and those who don't know. The template also includes an annotated example scenario.
Image AI prompts
A large collection of images generated in Nano Banana, ChatGPT, and Seedream plus their prompts. Seeing how others have prompted for images is helpful in figuring out what works and what doesn't.
After Automation | Every
If you've ever spent time cleaning up an AI-generated draft, you'll get the initial point here: AI creates more work for humans, not less. But this article digs deeper into how working with AI, especially AI agents, changes the nature of work. Employees at this author's company spend more time directing agents (deciding the goal and what "good" looks like) and on judging the results of AI. The easy work that AI can do gets commoditized, but the hard work of taste and judgement become more important.
There’s no tipping point coming where things flip and the jobs are gone. The new reality is the opposite—the more we automate, the more expert human work there is to do.
Here’s why: AI commoditizes the residue of human expertise—whatever can be made explicit enough to train on. That collapses the value of default model output and creates demand for what’s different. Demand for what’s different is demand for human experts, even as we approach artificial general intelligence (AGI).
Across both forms—coworker and embedded—the pattern is the same. Employee agents take over more of the stable, repeatable, well-framed layer of work. But there is a lot of work that still requires a human being in the loop. We’ve found over and over that for any kind of complex task, the best way to get great work is to have an AI and a human going back and forth in the same workspace.
In every example, the agent needs a human in order for the work to, well, work.
Someone has to point it at the right thing, decide whether the output is good, catch the places where it is wrong, and turn the result into a real-life decision or process.
The further away an agent gets from a human who is in charge of making sure it works well, the less well it works.
When work is abundant and looks alike everywhere, the work that doesn’t fit the pattern becomes the rare, valuable, and high-status thing(5).
This is why, in practice, AI does not eliminate expert human knowledge work. It dramatically increases the volume of work being done, and none of that work is differentiated or valuable unless a human being is involved.
Clinical Case Study: Diane
This is a short scenario built in 7Taps by IDLance to use as spaced repetition to reinforce prior training. One aspect I really liked in this was the interaction where you listen to two people's arguments for different courses of action. Then, you have to decide which is the better way forward. This technique could help make binary choices feel more realistic in the context of a scenario, especially if the justification for the worse answer is plausible for how people think.
Oboe
Ask this AI about a topic and get a lesson on it. This is for self study, and it seems potentially useful for basic topics where there's a lot of high-quality publicly available information to draw from. You can generate flash cards or study guides too. This might be something that's more useful for students in school, but I can see it for employees wanting to learn the basics of visual design, copywriting, etc.
AI Effectiveness Rating
Christopher Lind's simulation tool for rating effectiveness working with AI
21 Ways To Get Visual Ideas
Connie Malamed recently published a significant update to her article with resources for getting visual ideas and inspiration. This includes so many links to visual resources across the 21 categories.
AI Brain Fry, Workslop and the Ironies of Automation
This is a long article, but worth spending some time to digest. One of the ways that AI changes the nature of work is by increasing the amount of time we spend in cognitively challenging tasks like evaluation. But human brains need breaks and variety.
What remains after automation is not a simplified role but an arbitrary residue of the most demanding, most ambiguous, and least supported work in the entire system. The human is not replaced. In other words, the human is paradoxically left with the hardest parts, and given almost no preparation for them.
Surveying nearly fifteen hundred full-time workers across industries, roles, and seniority levels, the researchers found that intensive oversight of AI tools was the single most mentally taxing form of engagement their participants described. Workers required to monitor AI agents closely reported fourteen percent more mental effort, twelve percent more mental fatigue, and nineteen percent greater information overload than those whose AI engagement was less demanding.
The final irony of automation, she wrote, is that the most successful automated systems, those with the rarest need for human intervention, are precisely the systems that require the greatest investment in human skill.
Mayer’s Principles of Multimedia Learning - Educational Technology
This article summarizes Mayer's Principles of Multimedia Learning with a few tips for instructional design aligned with each principle.
7 Ways to Automate Repetitive Design Tasks with Affinity and Claude
I use Affinity as my primary tool for editing images. Affinity now can connect with Claude to automate repetitive tasks like renaming layers and prepping files. It looks like a great way to speed up some boring tasks so you have more time on the fun work. This is currently in beta and free, but will probably become a paid feature later. Still, if it saves time, it may be worth a paid upgrade.
How Much Water Does AI Use? An Expert Analysis of the Real Footprint.
The water use for AI data centers isn't as big of a problem as it's often made out to be. Energy use is a separate question, but genuinely--don't let the water use keep you up at night.
For 11 weeks, I tracked all of my AI use. One hundred sessions. I counted the tokens processed and applied publicly available numbers on per-token energy and water intensity from Epoch AI and operator-reported data from Microsoft and Google. Anyone can run this math.
In those 11 weeks, I built an iOS app from scratch and wrote policy briefs on extreme heat for nonprofits I work with. I produced documentary pitch decks and drafted a 15,000-word climate fiction piece about the Colorado River collapse. I used AI every single day, often for hours at a time.
Total lifecycle water footprint of all that work: about five gallons. That accounts for everything: the water used to cool the data centers, the water consumed at power plants to generate the electricity, and the water embedded in manufacturing the hardware.
When an Outside editor reached out to ask me to write this story, I was on a trip to Marble Canyon, Arizona, to train raft guide companies on what is happening with the river. I drove my diesel Sprinter van from Tucson to the site, which tallied 383 miles at 20 miles per gallon of gasoline. When I ran the numbers later, the lifecycle water footprint of my fuel was around 110 gallons. One drive to the work I do on the Colorado River used more than 20 times the water of everything I did with AI in 11 weeks. That comparison stopped me cold—and I study this for a living.
Style of language Formal Versus Conversational
This guide provides a summary of the personalization principle with a focus on the writing or speaking style. A polite, conversational style is more effective for learning in general (with some exceptions noted). I appreciate the examples in this guide so you can compare the difference between formal and conversational style.
Vois - Professional AI Voice Studio
While Vois doesn't have as many voices as some other platforms, it has several other advantages. It runs locally on your machine, so there's no risk of content being used to train AI. You can tag your script for multiple speakers, making it easier to manage dialogue. You can also buy just the credits you need rather than paying a monthly or annual fee, and you only use credits when you publish (not for each iteration and typo fix).
Seven Prompts No AI Image Generator Can Get Right
Really interesting research on the limits of AI image generation. Hands and text are both much better than a year ago, but multi line text (especially with numbers) fails because text isn't generated sequentially. AI images approximate rather than counting, and all models fail with prime numbers. Reflections are also approximate; there's no geometry behind them.