Every time I turn around, I read an article on cognitive offloading, or how Gen AI is wiping out swaths of entry-level and other jobs. I oppose blanket bans on Gen AI in K-16, but I do think students need some time to work their way through the SOLO Taxonomy levels (a way of describing the structure of observed learning outcomes). You might say, for Surface Learning (new info, ideas), Deep Learning (making connections between ideas at a conceptual level), Gen AI is anathema to learning. For Transfer Learning phase of learning, it can be a powerful essential tool for rapid prototyping and more.
It’s a nuance that’s lost on both teachers and students. The reason why? Gen AI has already “arrived” in schools and it’s a free for all for students who seize the opportunity to skip the productive struggle.
What About Workplace Struggle?
In the workplace, however, skipping the productive struggle is something you do at your peril UNLESS you have ample experience struggling already AND have a developed sense of what the end result a Gen AI produces is supposed to look like. That judgment is critical to success, and if you have it, great. If not, and you use Gen AI to skip developing it, you’re worthless.
It wasn’t until I was in my late 40s that I realized that maybe I should have gone and learned how to dig a trench and run fiber before I got to the vaulted position of technology operations director. By then, it was too late. I was supervising people who had to dig the trench, lay the fiber, and I had no choice but to count on my ability to manage people well to get it done. There are several of these little life and/or work experiences you need to pick up along the way. If you don’t, you may very well be useless. But no one can learn everything. That’s where relationships and negotiation are critical.
Employer demand for AI skills in entry-level jobs doubled from last fall to this spring, with 16.5% of the roles calling for AI skills, according to the National Association of Colleges and Employers. The sought-after skills include knowing the right AI tools for the task, giving good prompts that produce high-quality results and being able to assess AI outputs. Source: Andrew Keshner, At school, it’s cheating. At work, it’s essential. New grads are caught in a double standard around AI., Marketwatch
A Mixed Blessing
When I read articles like this, I’m taken back to my own thoughts about how you need to learn. When I’m learning something new, I break out paper and pen, then start outlining and organizing. I find ways to review the information to make my brain work. That’s true for work projects as well as personal learning. But when it’s time to get work done, I lean on Gen AI. I spend time up front organizing research and resources, writing prompts, trying them out, seeing if I’m getting the desired result. I create Projects that are closed and then add resources, custom instructions, skills or specific instructions about how to do certain tasks, etc. Then I turn the AI loose on it.
Doing that means using Claude Code or ChatGPT Codex these days because every complex project really is a COMPLEX project that I can get Code/Codex to use agents running in parallel to finish fast.
Connect
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