I’ve been rethinking the capstone course on AI that I last taught two years ago for undergraduate students in our information studies minor. In spring 2024, the class met on Tuesdays and Thursdays for 75-minute class session. This year, it’s being offered on Fridays with 3-hour class sessions. I’m having to pour the wine into larger bottles, thereby rethinking the entire flow of in-class activities and homework assignments. I’ve had to take a step back to get a high-level snapshot of the topics we discussed in 2024 to see how they can be grouped into a once-a-week cadence of classes.
Here’s where I am with what I did in 2024 and intend to mostly cover again (albeit in an updated fashion) for 2026:
- Course intro
- syllabus review
- document sharing in MS Office
- AI intro
- history of AI
- kinds of AI
- how AI systems are built
- glossary assignment
- gen AI
- Educational aspects
- values of students, faculty, staff, administrators, etc.
- predictive analytics in higher ed ICTs
- pros / cons of AI use in higher ed
- kinds of use by students
- why we write and how we learn to write
- AI policies
- Information quality aspects
- training data quality
- algorithmic bias
- misinformation, disinformation, malinformation
- AI transparency
- info ecosystem
- AI slop
- Economics and labor aspects
- job loss
- outsourcing of AI development labor
- costs of developing AI models
- costs to communities near data centers
- Legal aspects
- copyright and training data
- copyright status of inputs and outputs
- privacy
- libel
- Environmental aspects
- energy use and climate change
- water
- mineral extraction
In thinking about the sociotechnical analysis that we’ll be doing in the course to understand AI, I’m still working out how I’ll bring to bear the model of “multi-level perspectives on transition” that F. W. Geels lays out in “The Dynamics of Transitions in Socio-Technical Systems: A Multi-Level Analysis of the Transition Pathway from Horse-Drawn Carriages to Automobiles (1860-1930).” (Technology Analysis & Strategic Management, vol. 17, no. 4, Dec. 2005, pp. 445–76. https://doi.org/10.1080/09537320500357319).