Part I – The Academic Labor Question: 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 of the Internet by professors and students. Despite this, we all 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 with — 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.

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?

The outcome was unforeseeable to many, and that uncertain future was scary.

In the midst of that rapid transition, we struggled to see then what we struggle to see now: the unexpected benefits from a new technology.

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 quality of learning moved upward.

That pattern is worth holding onto as we confront the current shift to AI.

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Today, higher education stands at a similar moment. Generative AI (and increasingly 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.

I’ve seen how an empathic design approach and a metacognitive perspective can make academic work visible and provide a unifying language for in-class and out-of-class educators. The increased visibility into the mechanisms of academic labor empowers students to do more meaningful and rewarding work.

Higher education must quickly shed its general reluctance to AI and shape how it affects learning before external markets dictate its usage for us. The business community is already heading down this path, deconstructing workforce skills and tasks to determine how AI will affect how people work.

Recent research from Boston Consulting Group argues that AI will reshape far more jobs than it replaces. Rather than focusing on occupations, the researchers examined the underlying tasks that constitute work. Their conclusion was striking: AI’s primary impact will not be elimination but transformation. Many routine, structured, and repeatable tasks will be substituted, while human effort increasingly concentrates on judgment, interpretation, oversight, collaboration, and decision-making.

The same logic applies to academic work.

For academic leaders, the question is not whether AI will replace rigorous academic work. The question is which forms of student academic labor are likely to be substituted, and which forms can be amplified because AI exists.

The routine, repeatable tasks that BCG predicts will be substituted in the workforce are equivalent to low-cognition tasks in education. The tasks involving judgment, interpretation, collaboration, regulation and decision-making are equivalent to high-cognition tasks in academic work.

The categories below apply Boston Consulting Group’s workforce disruption framework to academic work. Rather than classifying jobs, the framework classifies forms of student academic labor according to how technological advances change their value and execution.

The Evolution of Academic Work

The history of educational technology reveals a consistent pattern. New technologies rarely eliminate learning. Instead, they substitute certain forms of academic labor while amplifying others. The Internet shifted students away from information retrieval and toward information analysis. Generative and Agentic AI appear poised to create a similar shift that will move students away from routine information processing and toward abstract thinking, judgment, interpretation, transfer, and meaning-making.

Academic Work Transformation	Pre-Internet	Post-Internet	Pre-Generative AI / Agentic AI	Post-Generative AI / Agentic AI
Substituted Academic Labor Tasks that become easier, faster, or largely automated because they are structured, repeatable, and governed by established procedures.	Searching card catalogs, locating books and journals, manually compiling bibliographies, physically accessing research collections	Search engines, online databases, digital libraries, citation management tools	Summarizing readings, creating flashcards, generating study guides, formatting citations, producing first drafts, organizing notes	AI-generated summaries, flashcards, study guides, citation formatting, first-pass essays, concept maps, practice questions, note organization
Amplified Academic Labor Forms of intellectual work that increase in value because technology frees time and cognitive resources for deeper thinking.	Analysis, interpretation, synthesis, evaluation, original thinking, scholarly discussion	Greater emphasis on evaluating sources, connecting ideas, interdisciplinary inquiry, collaborative problem solving	Faculty seek deeper learning but often struggle against students’ focus on task completion and information reproduction	Judgment, transfer, meaning-making, ethical reasoning, systems thinking, metacognitive awareness, intellectual self-authorship, critique, complex collaboration, and disciplinary decision-making

When comparing the second row to the first row of this chart, it’s clear why higher education has been historically siloed. Before the Internet, significant time and resources were devoted to locating and organizing resources. However, the Internet provided new infrastructure that allowed students to learn more deeply.

Perhaps the AI era will follow a similar trajectory. Since completing the task will consume less of students’ time and attention, academic work can be designed for deeper interactions, and leveraging AI, students may have the bandwidth to do it.

The important question for higher education is not whether students will use AI. The more important question is whether institutions will intentionally redesign academic work around the forms of thinking that technology amplifies. Every major technological shift in education has moved the center of gravity of learning upward. The Internet reduced the labor required to find information. AI reduces the labor required to organize and process information. What remains—and becomes increasingly valuable—is the work of deciding what information means.

The most important implication is that AI does not simply change what students produce. It changes the distribution of academic labor. As lower-order academic work becomes increasingly substituted, the value of higher-order academic work rises — along with students’ opportunities to engage in it. This mirrors what occurred during the Internet revolution. Students did not stop researching when search engines arrived. They simply spent less time locating information and more time interpreting, evaluating, and applying information.

This distinction matters because many current discussions about AI focus almost exclusively on academic products. Can students use AI to write papers? Can AI answer exam questions? Can assignments still measure learning?

These questions are understandable, but they risk overlooking a more important shift.

Academic products have always been proxies for academic work.

Faculty assign papers not because the paper itself is the goal. The paper is evidence of analysis, synthesis, interpretation, and judgment. Professors assign projects not because they need more projects in the world. Projects provide opportunities for students to engage in disciplinary thinking.

Similarly, mathematics professors do not assign problem sets because they need more answers; they assign them because students must learn math concepts, recognize patterns, select appropriate methods, and evaluate the reasonableness of solutions. Nursing faculty do not assign patient case studies because they need more care plans; they assign them because students must learn to interpret clinical evidence, prioritize competing concerns, and make sound judgments under conditions of uncertainty.

The danger is not that AI can produce academic products.

The danger is assuming that academic products and academic work are the same thing.

They are not.

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In Part 2: The Academic Labor Question – Redesigning Academic Work in the Age of AI, we’ll look at what AI actually does well, what it still struggles to do, and how institutions can redesign academic work so students spend more time in the kinds of thinking that matter most.

All thoughtful comments will receive the full PDF version of the articles.

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Candelon, F., Manyika, J., & colleagues. (2026). AI will reshape more jobs than it replaces. Boston Consulting Group. bcghendersoninstitute

Geddes, L. (2023). How to successfully transition students into college: From traps to triumph (1st ed.). Routledge.

Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business Review Press

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How AI Contributed to This Article

I chronicled how I used generative AI as an assistant to write this article.

Substituted academic labor

I normally spend far too much time editing and rephrasing as I write. I can fixate on a single word for several minutes, only to delete the entire sentence later. It is not a great use of time. AI stepped in to handle much of the editing and rephrasing that I usually get stuck on.

I estimate that researching and writing this article would previously have taken 40 or more hours. With AI helping, the process took about 12 hours spread over four days. Much of that time was spent on higher-value thinking because AI handled much of the routine clean-up, such as smoothing sentences, tightening wording, and fixing minor inconsistencies.

Amplified academic labor

I had already written extensively about the deeper levels of academic work. Because AI reduced the time I spent on wording, editing, and second-guessing, I had more focus available for the core intellectual work. That included the overall judgment, the conceptual framework, the historical comparisons, the interpretation of the BCG model, and the application to academic work.

For me, those are the most cognitively engaging and personally rewarding parts of writing. In past projects, especially when I wrote my book in the pre-AI era, I sometimes lost motivation because I would fixate on details and lose my mental flow. AI helped me maintain that flow this time, and I genuinely enjoyed the process more.

In practice, AI did for my writing process what I argue it can do for students. It reduced lower-level drafting and organizing, so I could spend more energy on abstraction, transferring concepts, judgment, synthesis, meaning-making, and application. It also gave me insight into personal things that may make writing more cumbersome than it needs to be.

Metacognitive benefit

Reflecting on my use of AI has helped me better understand the specific roadblocks I run into when I write. I have started to name and track the time‑wasting habits that eat into my writing time. AI has also suggested small adjustments I can make to avoid these patterns in the future. This combination of awareness and concrete fixes should strengthen my writing over time and help me write both better and faster.

I have some concern that I could lose certain skills over time as I increasingly rely on AI, but I also believe that I will gain greater capacity for deeper work. Just as important, it helped me notice the personal habits that make writing harder than it needs to be and prompted the kind of metacognitive reflection that will improve how I work going forward. (This last part is not automatic; it takes a lifelong learning disposition. But I think we can all benefit by using the technology in this way.)

Looking ahead

I did not use agentic AI for this piece. Still, I can already imagine a near future in which those tools help me bring a much larger catalog of existing material into publishable form more quickly.

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Ethical note

I am fully aware that AI relies on significant amounts of water and energy, and that many people are rightly concerned about its impact on future jobs and incomes. Those questions matter, but they are beyond my expertise and outside the scope of this piece. My focus here is much narrower: how education can use this technology to help students, faculty, and institutions thrive.