AI may help people learn faster, practice more often, and gain exposure to situations they might not otherwise encounter. But what happens when expertise develops faster than experience? This post explores a question I've been wrestling with lately and introduces the concept of experience debt.

As organizations adopt AI, much of the conversation focuses on productivity and efficiency. A less discussed question is how people develop expertise when AI is handling more routine work. This post explores how judgment traditionally develops, why that pathway may be changing, and what new opportunities might emerge for capability development.

AI can process information, identify patterns, and generate suggestions. But someone still needs to decide what matters, what should happen next, and what success looks like. Drawing from a real AI-assisted workflow project, this post explores why human judgment remains a critical part of effective AI adoption.

Many organizations are investing in AI training, yet adoption remains inconsistent. Based on my experience working with AI-assisted workflows, I'm starting to think the problem may not be training at all. This post explores why understanding the work itself might be more important than teaching people how to use the tool.

We'll define the Overjustification Effect, illustrate its significance, and provide insights into how it impacts the effective design.

Motivation is a powerful force that drives our actions and behaviors. It's essential for instructional designers to recognize the two primary types of motivation: intrinsic and extrinsic. But what if we could tap into the potential for motivation to evolve, shifting from one form to another? In this post, we'll explore the concept of motivational shifts and how instructional designers can leverage them to create more effective and engaging learning experiences. The Dynamics o