I’m standing in an airport bookstore, holding a ๐๐ช๐จ๐ฉ๐ญ๐ช๐จ๐ฉ๐ต๐ดย magazine, grinning like a kid.
I remember circling hidden objects and working my way through the puzzles in ๐๐ช๐จ๐ฉ๐ญ๐ช๐จ๐ฉ๐ต๐ด. It never felt like learning. It felt like play.
Our teachers gave us the magazine in school, and looking back, I think they understood something important about how children learn.
Children don’t expect every new activity to come with a correct sequence of steps. They explore until something clicks. And when it clicks, they feel a jolt of excitement, propelling more exploration.
The problem is that somewhere along the way, many adults stop learning this way. Instead, we start wanting instructions:
๐๐ฆ๐ญ๐ญ ๐ฎ๐ฆ ๐ฆ๐น๐ข๐ค๐ต๐ญ๐บ ๐ธ๐ฉ๐ฆ๐ณ๐ฆ ๐ต๐ฐ ๐ค๐ญ๐ช๐ค๐ฌ.
๐๐ฉ๐ฐ๐ธ ๐ฎ๐ฆ ๐ต๐ฉ๐ฆ ๐ณ๐ช๐จ๐ฉ๐ต ๐ฑ๐ณ๐ฐ๐ฎ๐ฑ๐ต.
๐๐ช๐ท๐ฆ ๐ฎ๐ฆ ๐ข ๐ฒ๐ถ๐ช๐ป ๐ด๐ฐ ๐ ๐ฌ๐ฏ๐ฐ๐ธ ๐ธ๐ฉ๐ฆ๐ฏ ๐’๐ท๐ฆ ๐ฎ๐ข๐ด๐ต๐ฆ๐ณ๐ฆ๐ฅ ๐ช๐ต.
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AI Learning That Lasts
I often hear from people who say, “I went to an AI training with my team last month, but I already forget everything.”
I would bet that the session was taught by an IT professional who knew all the features and tools, but didn’t know anything about teaching or how to make learning stick.
When I teach AI, I don’t want people to feel trapped inside a click-by-click pattern.
I want them to explore the tools for themselves and notice what happens.
A rigid set of instructions may help someone complete one exercise, but that kind of learning often doesn’t last.
I see this play out in my own AI training sessions. The real “aha” moments happen afterward, in the hands-on time, when learners are just messing around with a particular feature or use case that I had introduced
I recently trained a university prospect researcher on Copilot once. Every few days, she’d message me, amazed by something she’d stumbled into by just trying things.
The training got her curious and showed her what is possible. Her curiosity turned into play and experimentation. Then came the “Wow!” moments that built her AI confidence.
Show That Even Prompting Requires Imagination
When I teach AI beginners today, the first question I usually hear is: “How do I write a prompt?”
But writing a prompt isn’t actually where good prompting starts. It starts with picturing what you want to create, whether that’s a table, a story, or an image.
As a kid, I spent hours playing with the toy Lite-Brite. I never once worried whether I was doing it right. Lite-Brite came with hundreds of colored pegs you could stick into a board. When you turned off the lights, the board would light up.
The set came with a pattern sheet, but that was never the fun part. The fun part was picturing something in my mind and trying to build it out of colored pegs.
Then I’d turn off the lights and see what lit up. Did it match what I imagined? If not, I’d pull a few pegs and try again.
I never called this learning. It was just fun. And that’s exactly how prompting AI works.
The words in your prompt are the pegs. You choose them and arrange them until the output matches what’s in your mind’s eye.
And just like Lite-Brite without the pattern sheet, prompting isn’t one and done. If the output doesn’t match what you picture, you add more words and reshape it until it does.
Good prompting isn’t about knowing the formula. It’s about narrowing the field of every possible response down to the one you actually had in mind.
The Best AI Training for Beginners: Hands-On Fun
If you’ve got a high-powered team of adults who are new to AI, the best thing you can do is get them in their childhood mindset of exploration and play. Engage their imaginations about what’s possible, because with AI, if you can envision it, you can build it. Let them get hands-on with tools. Encourage them to try anything, make mistakes, see what’s possible.
It’s this heightened state of engagement, excitement and fun that’s exactly what will make the learning stick and ultimately deliver results to your organization.