A lot has changed in tech writing during the past two years when it comes to LLM tools (a.k.a. AI). That time frame coincides with my tenure teaching API documentation and watching how my students adapt to these tools has given me some insight into how our profession is evolving.
More to the point, it’s forced me to develop a systematic approach to AI integration for my next course.
The challenge: Teaching moving targets
When I started teaching API documentation in spring 2024, LLM tools felt like the “Apple II” stage of PC evolution. Interesting, but not quite ready for serious work. My students were “cautiously skeptical,” and treated AI as a curiosity rather than a necessity.
Some students used LLM tools to help create rough drafts while others wanted to avoid the AI tools to get a more hands-on experience.
That changed rapidly. By the third course, students weren’t asking whether to use AI, they were asking how to use it effectively. The industry had moved very quickly, and my students needed practical frameworks, not philosophical debates, to confront this new reality.
What I learned from watching students evolve
Rather than ban AI tools, I decided to lean into them and watch what happened. I asked students to describe if, and how, they used AI tools in their assignments. This gave them a record for their portfolio presentations, but it also created an informal longitudinal study of AI application in technical writing education. Here’s a summary of what I observed:
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