AI, Academic Labor, and the Future of Academic Work
Some of you remember these ole’ days.
A student walks into the library carrying a notebook, a stack of index cards, and a list of sources scribbled on a sheet of paper. She flips through a card catalog drawer, writes down call numbers, wanders the stacks looking for books that may or may not be available, photocopies journal articles, and manually records citations for later use. She may even ask a librarian for help using the microfiche machine to view archived documents.
Hours pass before she begins the more valuable intellectual work her professor intended: understanding, interpreting, evaluating, and synthesizing ideas.
I’m a month shy of my fiftieth birthday, and I remember these days vividly. For many seasoned faculty, this scene isn’t nostalgia; it’s their orientation to academic work. It was how everyone worked.
Then the Internet arrived.
I was an undergraduate student in the mid-1990s, just as this new and poorly understood technology, the World Wide Web, began reshaping academic work. I lived through that rapid transition. I remember the shared reluctance and initial rejection felt by professors and students who tried to adapt to the burgeoning new world of the Internet.
Soon, tools we learned as first-year students were obsolete by senior year. Faculty were teaching — or more like experimenting — their students to use this new tool they did not fully understand or yet trust themselves. We didn’t simply adopt new technologies; we had to rethink what “doing the work” meant.
At the time, the conversation sounded familiar. There was justified concern that easier access to information would weaken learning. The past frictions were necessary for academic growth. Besides, what would students lose if these frictions disappeared?
What we struggled to see then, in the middle of the transition, is what we struggle to see now: what would be gained.
The Internet didn’t eliminate academic work. It redistributed it. It consolidated it. Ultimately, it enhanced it.
Once time-intensive and procedural tasks like locating sources, compiling bibliographies, and accessing materials were compressed into minutes. The result was not less rigor, but a relocation of rigor.
Students could spend less time finding information and more time deciding what it meant. Group work became less about logistics and more about problem-solving. Research became more equitable as students attending smaller colleges gained access to nearly unlimited resources, and their work became more dependent on higher-level cognitive skills, such as analysis, judgment and synthesis.
The center of gravity moved upward.
That pattern is worth holding onto as we confront the current shift to AI.
Today, higher education stands at a similar moment. Generative AI (and soon agentic AI) is redistributing academic labor. Some forms of the work are being substituted. Others are being amplified.
The challenge facing institutions is not determining whether AI belongs in higher education; it is understanding which forms of academic work are being, can be, and perhaps should be substituted, which are being, can be, and should be amplified, and how learning experiences should evolve in response. Leading in the age of AI requires Learning Centers and Teaching and Learning Centers to develop new levels of collaboration. Together, they must quickly deconstruct academic work both inside and outside of the classroom and articulate new paths for complementary learning environments.
Part I of this article will drop here on Monday, June 8, at 7 am EST.
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