I recently came across research on the worked example effect from Cognitive Load Theory, and it reinforced something I've been thinking about in the age of AI.

Too often, we ask AI for the answer, copy it, and move on. The task gets done, but the learning doesn't happen.

A better approach is to use AI the same way worked examples are used in education:
✅ Study a worked example step by step before attempting a similar problem on your own.
✅ Understand the reasoning behind each decision.
✅ Practice solving a similar problem yourself.
✅ Use AI as a teacher and guide, not just an answer machine.

The real value isn't in getting answers faster. It's in building the mental models that allow us to tackle new and unfamiliar problems independently.

A quote that resonated with me from Vasilios Syrakis (formerly at Atlassian):

"LLMs are learning on steroids because now you have access to a speaking encyclopedia that can break down problems for you."

I think that's exactly the point.

AI gives us unprecedented access to explanations, examples, and knowledge. But its greatest value isn't providing answers. It's helping us learn how to think.

If we become overly dependent on AI for thinking, we risk weakening the very skills that make us adaptable professionals:
• Critical thinking
• Problem solving
• Knowledge transfer
• Continuous learning

The future isn't AI vs. Humans.

It's Humans who know how to learn, amplified by AI.

As software engineers, QA professionals, and lifelong learners, we should treat AI as the world's most accessible worked-example generator and then do the work of understanding the "why" behind the answer.

🎥Great video on this topic:


Fight Brain-Rot with Worked Examples

This video does an excellent job explaining why studying worked examples often leads to deeper understanding than jumping straight into solving problems alone.

📚 Research also supports this approach: learners who study worked examples often achieve better understanding and transfer of knowledge than those who immediately jump into problem-solving.

Article: https://www.tandfonline.com/doi/full/10.1080/01443410.2023.2273762#abstract