AI May Ruin the University as We Know It

A social-media post for a Google product known as NotebookLM outlines the following instructions to college students for “how to do school.” First, close your laptops, use your phone to record lectures, and write down only the important bits. Next, upload the recording and scans of any handwritten notes to Google. Finally, process the material through an executive summary generated by NotebookLM. An added perk, or shortcut, as the case may be: At the end of the week, generate a summary of the summaries in the form of a synthetic podcast narrated by a pair of conversational agents. No more extracting concepts from long-form arguments, no more psychic struggle with complex ideas: just autosummary on demand, made possible by a vast undifferentiated pool of content that every successive use of the service helps to grow.

Such is the ed-tech vision of higher education now. What the example of NotebookLM’s promotional campaign demonstrates is the emergence of a new model or template for education, if not for learning itself: a productivity schema ready to be laid across the full spectrum of the postindustrial knowledge economy. It is not difficult to see that in the next phase one can eliminate the lectures and discussions and simply start with the summaries (and eventually the summaries of the summaries), streamed on demand.

This vision of the future of education is either dystopian or utopian, depending on one’s sympathies for the idea that the classroom must necessarily once again be disrupted to better serve students, and depending too on the stake one has in the industries that are going to profit from the enterprise. In addition to the obvious red flags — shouldn’t the plan encouraging students to record their professors and classmates be run by the legal department? — such breezy promotions of the next big thing in the AI-powered ed-tech domain point to a significant shift in the higher-education landscape, one that is different in both degree and kind from the previous hype cycles that brought us iClickers, MOOCs, courseware, and Second Life. All of these tools were discrete, online, on separate platforms. But today, AI feature sets are integrated in all of the core educational enterprise systems — Google, Canvas, Zoom, and Office to name a few — and the tools are not only ready to hand, but always on, perhaps even requiring admin privileges to disable. Not surprising, then, is the deluge of higher-education summits, white papers, ad hoc committees and task forces, along with the many new research centers, curricular initiatives, and cluster hires — all suggesting that institutions are rushing to demonstrate that they too are embracing the new way to “do school.”

Soon the question likely won’t just be one of individual students recording their classes, surreptitiously or otherwise. Consider all the third-party software licensing for course management and evaluation, as well as the new entrepreneurial ventures that entice budget-strapped schools with the promise that they no longer need to worry about building and supporting a technological infrastructure. From one of the many start-up companies that offer note-taking, transcription, and digitization services, an exemplary corporate pitch: Because you lack both equipment and staff, we will remotely operate the cameras in your lecture hall so that you can meet the updated legal requirements for remote access. In exchange, we are going to store those lectures on our servers and use them to train our in-house language models — access to which we will then be able to license to you in the next funding cycle, when you are ready to add our premium transcription services to your subscription.

In essence, the university itself has become a service. The idea of the University as a Service extends the model of Software as a Service to education. Software as a Service refers to the practice of businesses licensing software and paying to renew the license rather than owning and maintaining the software for themselves. For the University as a Service, traditional academic institutions provide the lecturers, content, and degrees (for now). In return, the technological infrastructure, instructional delivery, and support services are all outsourced to third-party vendors and digital platforms.

Licensing and subscription agreements favor short-term budget planning; so too do they enable an administrative vision of universities as customizable, scalable, cost effective, and available on demand. Too often the decision-making about the IT systems that will shape the research and instructional environments is largely or even exclusively in the hands of CIOs, IT staff members, and instructional development, with academic affairs relegated to the position of managing the implementation of commercial ed-tech applications that promise continuous pedagogic improvement, which is now to be accelerated by new AI features, all of them generating revenue through the scraping of data. As with other industries like health care, the “service” that the university now provides is the concentration of human capital and engagement for the magnitude of data collection necessary to the continued growth and financial viability of AI systems.

Academic leaders in what positions of authority remain — holding on to governance offices, seated on the relevant committees, presumably able to transmit messages to the right sets of ears — may try to forestall these developments. But it is unlikely to be that simple. The firewalls are coming down, and the doors of the institution are open, not only to lifelong learners and citizen scientists but to start-up hucksters and the flash mobs mobilized in the culture war that is also a cold civil war. If it is true that the future belongs to crowds, it seems clear by now it will be less a valorous multitude than the malign cults of personality that accrete around billionaires, politicians, billionaire-politicians, celebrities, and other influencers, all targeted and manipulated by the outrage merchants who command the largest and most lethal followings on social media.

Recall here one of the promises of NotebookLM: It can ingest content directly from YouTube. To the extent LLMs continue to be trained on the torrents of platforms like 4Chan, Reddit, and X, as well as YouTube, the positions and agendas expressed therein will be imported directly into future machine-learning models. Those models will then be iterated, localized, and branded — complete with mascot imagery — before being sold off to individual institutions as boutique products vertically integrated with campus services, from marketing and communications to health and public safety. What this looks like for the several large public colleges and universities currently piloting or plotting exactly such services may initially appear relatively benign; but we wonder what it will look like at the growing list of places already overtly subject to ideological capture: The systems and campuses that find themselves deep in the red, whether politically, financially, or, as is often the case, both.

What to do with the remains of the day? Some will no doubt wish to declare crisis bankruptcy. We do not begrudge them — crisis is its own industry, and we are not crisis managers. None of what we diagnose is a function of claims about the technology or its capabilities as such, but rather the way in which the technology is symptomatic of latent logics and transformations that have long been underway. Put another way: If AI didn’t exist, it would be necessary to invent it.

This essay is adapted from the article “AI and the University as a Service” in PMLA. Reprinted with permission.