Working notes from a practicing ACNP and graduate-program faculty member. Clinical practice, NP education, and the parts of AI showing up in both.
Slide rules, mainframes, encyclopedias, search engines. Every technology transition had holdouts who were right about the tradeoffs and wrong about the timeline. AI is not different.
Read the piece →The hype cycle leaves out the part where you throw away most of what the AI produces. Getting educational content right is iterative, expertise-dependent, and worth it. But it's not push-button.
The holdouts in every technology transition weren't wrong about the costs. They were wrong about the timeline. AI is not going to wait while faculty decide whether the tradeoffs are acceptable.
I built a system to feed my AI the right background automatically, then measured whether it actually helped. The system got better. I didn't. I built a safety net for exactly that problem and never once reached for it.
Graduate NP content needs the exact current recommendation, not the version you remember from the last time you looked. Verifying you have the right guideline is the first step that gets cut when you're busy, and nobody catches it until a student does.
A year of building products with AI as a primary work tool taught me three frustrations nobody warns you about, and produced results I couldn't have gotten any other way. The deal is real on both sides.
Twice this week I finished someone else's work in minutes with AI and handed it back. Twice, they didn't take it, and the reason has nothing to do with the tool.
Two years ago I stopped waiting for someone else to build AI tools for my nursing students and built my own. The debate about AI in higher education is asking the wrong question, and it's costing us the one that actually matters.
Practicing APPs are working from the version of a guideline they learned in training. Continuing education tracks hours, not currency, and there's no mechanism forcing an update.
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