“I think that a life properly lived is just learn, learn, learn all the time.”
— Charlie Munger
I’m just back from the ATD Core4 conference in Orlando, still buzzing from a couple of fun sessions. On Tuesday, Bianca and I teamed up for L&D’s Marketing Makeover: Engaging Learners in the Attention Economy. The next day, I went solo with Ignite Your Training with Tools You Probably Don’t Know About. If you’re curious, you can poke around the slides and resources I shared—and if you’ve got the right ATD membership, both sessions were recorded and you can dip into those as well. What's buzzing in your world?
Thanks for reading!
Format experiment alert: I’ve trimmed things down this week. Love it? Hate it? Want the longer reads back? Hit reply and let me know.
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Cognitive Load + Learner Differences: Why One Size Fails
This article revisits cognitive load theory through the lens of individual differences—i.e. learners aren’t blank slates, and what “load” means changes from person to person.
Key points:
Cognitive load theory basics: working memory is limited; instructional design must manage intrinsic and extraneous load.
The twist: individual factors (prior knowledge, domain expertise, strategies) moderate how much load a person can handle.
Implication: an “optimal” instructional design for one learner might overwhelm another.
The authors argue for more adaptive design, tailored support, scaffolding, and flexibility—not rigid templates.
👉 Takeaway: Great training anticipates variation and adjusts load dynamically—don’t force everyone into the same mold.
Metacognition Is the New Frontier in AI-Powered Learning
In “The Metacognition Revolution”, Google + Atlantic explore how AI isn’t just automating content—it’s turning learning inside out by helping learners think about their own thinking.
At the University of Kentucky, students use AI simulations (e.g., roleplaying a patient) and then reflect on their strategies—what worked, what didn’t, and why.
Some classes go further: students build simple chatbots with no-code tools. The act of instructing “the AI” forces them to deconstruct their own thinking.
Traditional assessment (essays, tests) is being challenged. The model shifts toward workflow, revision history, decision paths—i.e., the process, not just the deliverable.
👉 Takeaway: With AI, the highest payoff in learning may be helping people become aware and agile thinkers, not just information digesters.
The Birth of the H-Corp: What’s a “Human-Centered Corp” and why it matters
This piece asks: if organizations call themselves “ethical,” “responsible,” or “human-centered”—what does that actually mean? And how do you build it, not just brand it?
They argue that “H-Corp” is more than a label—it’s a shift in how you design systems so humans thrive alongside AI (not get steamrolled).
It challenges surface-level ethics and calls for real structure: governance, feedback loops, metrics of flourishing over efficiency.
The real litmus test: when AI breaks (it will), does your system lean human first or tech first?
👉 Takeaway: If your org hasn’t codified how people win, your “human-centered” talk is just marketing gloss.
Come along for a journey through imaginative, innovative, and inspirational use cases for AI centered around compliance training.
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✔️ Personalize knowledge coaching ✔️ Build dynamic scenarios in real-time ✔️ Foster deeper learning comprehension ✔️ And transform compliance training from a tedious obligation into an engaging and enjoyable experience
This is the conversation that caught my ear this week. Check out previous episodes in the Friday Finds podcast playlist.
AI, People and the Creative Spark
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