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).

Much of what I have read on AI or want to read I learn about from specific people and blogs that I’ve learned to trust. I thought I’d give them a shout out here.

The Library Society of the World, a loose cohort of libraryland folks, has been together online since 2007. I’ve learned so much from them over the years; they have been absolutely essential to me since day one. You can join us in the LSW at Mokum.

As an inveterate reader of blogs, I’d like to share the ones that have consistently fed me ideas and reading recommendations on the subject of AI:

• 404 Media
• Aaron Tay’s Musings about Librarianship
• Ars Technica
• The End(s) of Argument (Mike Caulfield trying out AI tools for evaluating claims in online sources)
• Futurism
• Generating Fail
• inkdroid (Ed Summers; especially his Weekly Bookmarks posts)
• Librarian of Things (Mita Williams)
• Marcus on AI (Gary Marcus)
• Second Breakfast (Audrey Watters’s posts are so essential that I pay to subscribe to them)

Since I agreed in September to teach the course again, I’ve been playing catch up with all the books that have come out since I last taught and trying to go back to ones I didn’t know about when I first taught the class. Here are the ones I’ve read this fall:

• Bender, Emily M., and Alex Hanna. The AI Con : How to Fight Big Tech’s Hype and Create the Future We Want. First edition, Harper, an imprint of HarperCollinsPublishers, 2025.
• Franklin, Ursula M. The Real World of Technology. Revised edition, Anansi, 1999.. First learned about this book a long time ago from Ed Summers but didn’t read it until this year. While she doesn’t specifically cover AI, this work has deepened my understanding of the social aspects of technological change.
• McQuillan, Dan. Resisting AI : An Anti-Fascist Approach to Artificial Intelligence. Bristol University Press, 2022.
• Narayanan, Arvind, and Sayash Kapoor. AI Snake Oil : What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Princeton University Press, 2024.
• Susskind, Richard. How to Think About AI : A Guide for the Perplexed. Oxford University Press, 2025.
• Winner, Langdon. The Whale and the Reactor : A Search for Limits in an Age of High Technology. University of Chicago Press, 1986. Also not on the subject of AI but rather a useful book to deepen my understanding of socio-technical systems. Includes the often anthologized and cited chapter, “Do Artifacts Have Politics.”

I’m currently half way through this book and am appreciating the author’s expertise as a linguist for her analysis of the impact of generative AI:

• Baron, Naomi S. Who Wrote This? : How AI and the Lure of Efficiency Threaten Human Writing. Stanford University Press, 2023.

I’ll try to read a few more by the start of my class next January:

• Warner, John. More than Words : How to Think About Writing in the Age of AI. First edition, Basic Books, 2025. I’ve been reading his pieces on college writing instruction in Inside Higher Ed for years and think this would be a good book to follow Naomi Baron’s.
• Hao, Karen. Empire of AI Dreams and Nightmares in Sam Altman’s OpenAI. Penguin Press, 2025.

I’m also trying to read more about socio-technical analysis, particularly as it relates to ICTs, and have been focusing my efforts on journal articles and book chapters. I’ll see if I have enough for a roundup of sources in a future blog post.

Next spring, I’ll be teaching for the second time the capstone course for our library’s information studies minor. Listed in our course catalog as “Advanced Topics in Information Studies,” each instructor can focus that course on the topic of their choosing. I’ve decided I’d like to take another crack at teaching a socio-technical analysis of artificial intelligence. As you can see from my course website from spring 2024, my focus is not at all on how to use AI but rather on AI came to be and how various forces and organizations in society have been shaping its development.

It has been close to two years since I last taught this course. Although I have continued to pay close attention to developments in AI since then, in the past few months I have been making a concerted effort to shore up the foundations of what I know about the technology and about socio-technical analysis. I’ve also been talking everyone’s ear off if they happen to ask about my course, as I’m full of ideas and opinions lately that are still gelling and in need of being spoken so that I can figure out what I will do with them in my class. To save my friends, family, colleagues at work, and random strangers from consistently getting trapped in my current conversational enthusiasms (and may be looking for an exit from said conversations), I’m going to begin working things out here on my blog.

The inspiration to return to a practice of thinking by writing came from one of the books I’ve been reading in preparation for spring 2026 course: Who Wrote This? How AI and the Lure of Efficiency Threaten Human Writing by Naomi S. Baron. In a chapter that categorizes and details the reasons why humans feel compelled to write, she finds a rich history of writers who are “looking inward” and need to “bring into focus what they’re thinking,” such as:

• Flannery O’Connor “I write because I don’t know what I think until I read what I say.”
• George Bernard Shaw “I do not know what I think until I write it.”
• William Faulkner “I never know what I think about something until I read what I’ve written on it.”
• Joan Didion “I don’t know what I think until I write it down.”

I haven’t finished Baron’s book yet, but I’m sure that she’ll return to these quotes again when she examines what will be lost if the mass of humanity embraces text extruding machines (AKA generative AI) as the primary method of composing written communication. This worry is one that I’m guessing is also at the core of John Warner’s book, More Than Words: How to Think About Writing in the Age of AI, which I hope I can find time to read before my course begins.