Pondering the fall AI semester with the Chronicle of Higher Ed

Last week the Chronicle of Higher Education published a very thoughtful article on how AI might play out in academia for the rest of 2024. I’m not praising it because the author quotes me, although she does, especially for the piece’s last third. I think it’s excellent because Beth McMurtie refuses to settle on simple conclusions and instead meditates on many open possibilities.

“What Will Determine AI’s Impact on College Teaching?” begins with a good summary of where things stand. Textbots keep hallucinating, companies keep developing them, AI detectors don’t work anywhere near well enough to use, the public is increasingly worries, and large language models appear to be here for a while.

As part of that summary the article finds academic responses to be all over the place. McMurtie quotes Gary Marcus arguing that AI quality is falling, especially compared to hype, and will plummet in use. In contrast “Annette Vee, director of composition and an associate professor at the University of Pittsburgh [sees] ‘[AI as] everywhere in professional writing.’” Vee also wants us to radically redesign writing instruction, while Michael McCreary (“an educational developer at Goucher College who has written about the evolution and future of AI”) describes a very different response: “a lot of faculty members have spent a lot of time practicing prompt engineering with ChatGPT to create rubrics and assignments.”

Overall, “The vast majority of colleges had no formal policy on the use of AI tools in the spring, according to a survey by Tyton Partners… Colleges seem to be deferring to faculty members to set their own classroom policies on appropriate use” - I’m seeing this across the country, personally.

Next, McMurtie steps back to raise the possibilities of outside forces shaping academic AI use, and this is where I came in. She cites one of my recent posts here, then we discuss various legal and regulatory possibilities which could cramp AI or shut some of it down. Then Beth pins me down to my own teaching practice at Georgetown, and I readily admit that if any of those things come to pass I’ll have to change up some of my class designs.

Midjourney imagines “teaching with generative AI in higher education class”

In this expanded context, how might higher ed change as AI develops? The article offers several possibilities:

1. Increasing in-class assessments and practices. (Although McMurtie is skeptical on this)

2. Cutting back on one kind of course: “In higher ed, say educational developers, courses largely focused around rote skills and content memorization could become redundant. Think of large introductory classes, for example, with multiple-choice exams testing the ability to remember formulas and facts. Or poorly designed writing courses in which generic prose is considered passable.”

3. Institutions automating some aspects of courses, or entire classes.

4. Instructors redesigning classes to be more inspiring to students, offering more meaningful work. (I’m skeptical of this for many reasons. First, faculty report already being exhausted, anxious, and underresourced; how will they respond to this additional, unfunded mandate? Second, many faculty tell me that they already offer authentic assessment and meaningful class work; how will campuses encourage and support them to rethink what they think they already do decently or well?) One way it could work is if departments or programs came up with clear strategies for making class work more engaging, then ordered the biggest instructional population to make it so: adjuncts.)

5. Rethinking curriculum and assessment; “The advent of user-friendly AI that mimics human writing has led to serious soul-searching in the teaching community. If you can outsource something to a chatbot, does that mean it’s not worth learning? More fundamentally, what is it that you want students to know, and how can you tell if they have mastered it?

   Professors have long struggled to design assessments to produce evidence that students are learning, with or without ChatGPT on the scene. As Betsy Barre, executive director of the Center for the Advancement of Teaching at Wake Forest University, put it earlier this year, ‘We can’t see inside your brain.’”

I’m not sure about how many of these will actually appear in the real world over the next three-four months. We’re already in the academic year, so changing up class offerings or introducing new automation is challenging. Rethinking class fundamentals - assessment, curriculum - is similarly difficult in mid-stream, especially for the adjunct population, along with instructors of all sorts at institutions suffering from resource problems.

There’s a lot more that we can say in response to the article, of course. It focuses on the United States, which is the Chronicle’s beat, and I’d like to hear from academics across the rest of the world. I’d like to see who else is interested in the climate change aspects. I wonder what business and economics faculty think about generative AI’s financials in the short and medium term, and then how they think they might influence the technology’s classroom use.

And that’s what good writing can do, stir up follow-on thoughts and more writing.

Coming up next: trying out an AI in higher education scanner.