Three Ways AI Is Already Reshaping How I Think About Learning Design

(expressed opinions are my own)

As someone who has spent the better part of two decades trying to understand why we learn-not just how we learn, I often find myself searching for that one moment in a learning journey that connects deeply with the learner. That one flicker of insight. The tiny, spark that precedes the “aha.” The understanding that follows. Designing for that moment — that intellectual and emotional connection with content —especially when working with technical mathematical models, economic complexity and systems design, has been the heart of my work.

In recent months, I’ve encountered something new.

AI isn’t just another tool in the kit. It’s reshaping the entire canvas.

And while none of us really knows what the next year; or even the next week may hold, I’m already finding that my role as a knowledge architect and learning designer is shifting in profound ways.

Here are three ways I’ve been using AI that are changing the game for me:

1. Using AI to make specific learning moments stick emotionally.

Micro-learning isn’t new. There’s plenty of research and lived experience supporting its effectiveness. But what makes it sticky? What makes someone recall three core messages from a training—not just three months later, but even three years later?

To me, the answer lies not just in brevity or clarity, but in creating moments of emotional resonance. Specific learning, when paired with socially and emotionally engaging experiences, becomes memorable.

Recently, I participated in a foresight and future-planning workshop. We were asked to envision an AI-powered future and capture it as a newspaper headline. Not just the words but the illustration, drawn with pens and crayons. That one tactile act—framing a future in ink—has stayed with me far longer than any slide deck or discussion point from that day. I still remember the other teams’ headlines, but more vividly, I remember the images they drew. In some cases, I remember only the images.

That could just mean I’m a visual learner. But when I asked my colleagues what stood out for them, their answers echoed mine. It was the moment of translating thought to paper. A brief return to the emotional act of drawing, as we did in our childhood.

This is where AI enters: not to replace the act, but to support it. Through generative nudges, AI can help create such emotionally resonant prompts or analogy builders, visual storytelling cues, even moment-based simulations. It becomes a guide for helping learners frame complex technical content into their own narrative.

2. Bridging the tangible and intangible through artifact-making

There is something powerful about expressing thought through creation. The artifact—whether it’s a sketched diagram, a Lego model, or a messy wireframe—is less important than the thinking that led you there. It’s in the strategy. The sequencing. The problem-solving. This is especially valuable when learners work with abstract or theoretical content. By creating a tangible form of expression—even a quickly generated visual or a role-played script—they externalize their thinking.

AI can help amplify this. It can become the medium that brings internal processes into visible form. Even more powerfully, this opens doors for learners who may not traditionally express themselves through visuals or physical modeling.

Now, with AI tools, they can co-create a digital artifact—an image, a layout, a metaphor—by describing their thought process and watching it come to life. The artifact becomes a reflection of thinking, not just output.

3. Using AI as a practice buddy and adaptive design partner

One of my favorite uses of AI is having it play different roles in the learning design process. Sometimes, I pretend it’s a learner. Sometimes, I treat it as me—past or future. Sometimes, it’s my audience: a visual thinker, a policy maker, a textbook learner, or a field-level technical expert.

I take a piece of content, for example - a chapter from a report or a section from a toolkit—and ask AI what it understood. What stuck. What was confusing. And then I tweak the way I present it. I try again. I compare.

The beauty of this approach is that I can generate multiple versions of the same learning artifact—each designed for a different learner type.

A policy maker may need a quick visualization and three decision-relevant takeaways. A technical ministry staffer may want the underlying methodology. An NGO partner may look for real-world application. What AI enables is the ability to scaffold and scale that process quickly and personally.

To me, the future of learning isn’t about replacing the spark of human curiosity or the magic of peer-driven insight. It lies in facilitating and amplifying it. Whether in person, hybrid, or virtual, learning will increasingly rely on our ability to co-create—using AI, human peers, personal research, and reflection.

We’re rapidly building learning ecosystems that are as flexible as they are rigorous, and as personal as they are technical.

And that’s a future I’m excited to be part of.

#AIinEducation #GenerativeAI #EdTech #ArtificialIntelligence #LearningWithAI #LearningDesign #InstructionalDesign #AdultLearning #EducationInnovation #KnowledgeSharing