I'm a former term adjunct and current staff member in a university computer science department. I'm presently helping new adjuncts prepare for teaching in September.
They've asked how to best incorporate AI tools in their courses and assignments. I've read guidance and policies from my and other institutions, but:
- they mostly assume that the assessments are written (e.g., essays); and
- they recommend focusing on the process, not the result (e.g., projects with milestone submissions).
Not every course is well-suited for projects, particularly introductory courses.
The example assignments I've found aren't from computer science and tend to:
- encourage using AI for a specific component (e.g., create a logo); or
- go through iterations of prompt-analyze-refine with an emphasis on challenging an LLM and/or writing in-depth reflections on the experience.
As good as these examples sound, I was hoping to find a few strategies from computer science education that would work in a variety of courses and across multiple assessments.
I genuinely want to give the new adjuncts solid advice regarding AI beyond "test more". Calculating final grades with miniscule weight placed on assignments is discouraging to students.
If you're willing to share what you did in your course, what worked and what didn't, I would be very happy to hear it. Thank you.
