Case study
GenAI 101 for Professional Military Education
A practical introduction to generative AI for PME students and faculty, built around real coursework and planning tasks. I built it using AI itself, on purpose, as a way to test whether the idea held up.
Purpose
A practical introduction to generative AI for PME students and faculty, built around real coursework and planning tasks. I built it using AI itself, on purpose, as a way to test whether the idea held up.
How this started
In early 2024, I was teaching a lesson on problem definition, specifically how to write a clear, well-scoped problem statement. I gave the students time to draft their own statements by hand. While they worked, I ran the same exercise through AI on the side, just to see what it would do.
When the students finished briefing their handwritten statements, I mentioned that I had run the same exercise through AI and asked if they wanted to see the result. They did. What followed was an unplanned hour-long conversation about how AI works, whether it really thinks, and what it would mean for their work. It was one of the most engaged discussions of the semester.
That conversation made the need obvious. These were senior officers who would be leading organizations through AI adoption, and there was no structured way for them to build a working understanding of the technology. So I built one.
The curriculum
GenAI 101 is a modular lesson and lab series made for professional military education audiences, many of whom have little or no prior exposure to generative AI.
The course is about using AI on real work. It shows how AI can support research, writing, planning, and reflection without doing the thinking for you, and it gives students a way to judge what the AI produces instead of trusting it on faith.
The four labs are hands-on and tied to real academic work. They cover research and source evaluation, drafting and writing, designing planning prompts, and using AI as a partner for structured reflection.
The design experiment
I built the curriculum using AI from the first draft to the final version, and that was on purpose.
If the course was going to claim that AI could speed up curriculum work when used well, the course itself should be the proof. Building it that way showed me where AI genuinely speeds up content work, where it slips in errors that need an expert to catch, and what kind of workflow separates useful output from unreliable output. Those observations are now part of the curriculum.
Current state
GenAI 101 is active and reusable. It works as a foundation that feeds other PME AI efforts, including faculty workshops, the student prompt library, and the wider JFSC AI program.
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