Universities and other educational institutions have had a few years now to come to grips with generative AI and its implications. For universities, what I have seen is a lot of angst about student cheating and the impacts on learning, courses set up to take advantage of the demand for students with AI skills, but mostly a lot of tinkering around the edges and hoping for the best.
A new working paper by Jason Potts (Alfaisal University) outlines the fundamental challenge to the entire business model of universities. Potts notes that universities are a very complex multi-sided platform market, matching at least fifteen disparate groups including, but not limited to, students, employers, and government.
Potts also notes that what universities offer to students is not content at all, but a 'warrant' - a signal to employers of the student's quality (see my post here for more on that point). This overstates the case of course. Universities do offer students the opportunity to develop important knowledge and skills. Even Bryan Caplan, in his book The Case Against Education (which I reviewed here), acknowledges that education develops human capital, despite arguing that most of the financial return to education comes from signalling.
Now, the challenge is that generative AI makes content delivery a commodity, so Potts argues that universities should concentrate more on providing the warrant of quality, essentially becoming a certifying organisation that provides a signal of the quality of students to future employers. He notes that:
The strategic form this implies is a university that examines and certifies as its primary business, and that treats instruction as a service which may be produced internally, purchased, or left to the student. Almost no one working inside a university right now wants this. But it is the logical direction this new wave of technologies is pushing the university.
I think Potts is right that no one wants this outcome. But he is also right that the value that universities provide to students will be increasingly embedded within the quality of the signal. And it is there that generative AI presents another challenge. Effective signals need to be able to separate high-quality and low-quality students, and one of the ways that occurs is by education being more costly (more difficult or time-intensive, rather than necessarily more financially costly) for lower quality students. Generative AI lowers the cost of producing assessment work that is of high quality, meaning that low-quality students are more difficult to distinguish from high-quality students. The signal value of education is reduced, unless universities make changes to preserve the value of the signal. On that point, Potts argues to:
...move the assessment to a modality where the production cost remains type-dependent. At this stage, the best candidate is probably a return to the viva, common in earlier times as an observed, embodied, high-bandwidth, unscripted examination conducted as an oral defense, or as live problem-solving before an examiner... This is expensive per student and is complementary with cheap AI-delivered instruction.
I'm not convinced that oral examination is the only way to achieve this aim. Properly invigilated in-person written exams are similarly resistant to unauthorised AI use, and are considerably more efficient than oral examinations. However, they are not appropriate to all courses, and may best suit courses where basic foundational knowledge needs to be assessed (although noting that it is possible to assess higher-level skills in a written exam as well). Potts notes that, in terms of university revenue, this means:
...open entry, examination-only pathways to a degree, and pricing on the assessment rather than on tuition or contact hours.
So, in Potts' view, universities wouldn't necessarily do any teaching, only assessing. Or rather, they might do teaching, but it would be complementary to their main activity of assessing students and certifying their knowledge and skills.
There is a positive element to this for some university academic staff though. If you think through the incentives here, it becomes clear that the endpoint of this transition could easily be for universities to become diploma mills, offering certifications to paying students that bear no meaningful relation to students' actual learning, knowledge, or skills. Universities can only avoid that outcome if they can credibly demonstrate to employers that the credentialling process is not influenced by student demands. And so, academic staff retain some academic freedom:
Tenure exists so that an examiner can fail a candidate, reject a result, or refuse a certification without personal consequence. It is therefore the certifying body that must inherit the collegiate form (i.e. peer judgement, protected tenure, academic freedom) while the instructional arm can be run on ordinary managerial lines.
I'm not convinced that the market will discipline universities to the extent that Potts believes. Diploma mills already coexist alongside credible institutions, demonstrating that there is demand for credentials of questionable value, although some employers may already recognise those differences. The more serious problem is that employers may not recognise gradual changes in assessment standards and the quality of certification. Universities might slowly drift towards satisfying student demand for credentialling, rather than learning, while continuing to benefit from reputations built up over many years. By the time employers recognise that the qualifications no longer signal what they once did, their value may already be substantially less than it was.
Complementary to credentialling, Potts notes that the decline in junior positions at many employers opens an opportunity for universities. If employers are unwilling to invest in students to train them in what were previously junior roles, universities could step into that gap. In part, universities have already started moving in that direction - witness the rise of 'work-integrated learning', which is now a feature of many universities' offerings, and increasingly compulsory for students (including at Waikato).
Potts notes that the challenge here will be to find a financing mechanism. However, to me there is an obvious solution. Traditionally, junior roles have bridged the gap between university study and the point at which employers see graduates as sufficiently experienced and skilled to be fully productive. Employers have effectively funded that transition through on-the-job training. If employers are increasingly unwilling to employ graduates in these junior roles, universities could provide that bridge instead, through postgraduate qualifications involving work-integrated learning, either with an employer or in a 'simulated' work environment. Employers may not be willing to pay for this (after all, they are increasingly unwilling to employ juniors), but students may, especially if it smooths their pathway into employment. Again, this might not be something that we like, but it may be the reality in the future.
What about research? Potts has some interesting things to say about that as well, in particular noting that the minimum efficient scale for research is declining and fast approaching one. What would previously have required a large lab team or research group, can increasingly be undertaken by a sufficiently motivated researcher working with agentic AI. Of course, Potts is overstating the case here, because there remains a lot of research that relies on expensive infrastructure, specialised equipment, labour-intensive fieldwork, or collaboration among researchers. However, Potts' view suggests a fundamentally different relationship between research-intensive academic staff and universities. Specifically, Potts proposes that universities:
...unbundle affiliation from employment, making the university more like a club... Rather than the binary of tenured staff or nothing, universities can offer graded affiliation over institutional standing, grant-holding capacity, facility access, and the right to publish under institutional imprimatur, priced as a service and subject to quality review. This converts a fixed cost into a revenue line, retains scholars the institution could not otherwise afford to employ, and extends the warrant into the market for independent research.
This may work for high-profile researchers who can maintain an ongoing flow of external research grant money. However, most researchers are not in that privileged position. Also, Potts ignores that research funding is under pressure, and his proposal would make researchers even more beholden to grant funders than they are now. And it would create an even starker division among academic staff, between those who are highly-research active, bringing in substantial external research funding, and maintaining independence from the institution they are currently affiliated with, and those who are reliant on a role that simply certifies students. This problem is going to affect disciplines to different extents. For example, in a research environment like New Zealand at the moment, where funding changes have severely undercut research funding in humanities and the social sciences, this would be particularly problematic.
Potts also notes that the type of research that universities should specialise in should change:
AI has commoditized knowledge and particularly its search and discovery, including in domains that were previously high in expertise and difficulty such as literature review, statistical analysis and theorem proving. The implication is that the comparative advantage of universities has moved further afield, into the harder to conceptualize and reach corners of new ideas and possibility.
However, that proposal really relies on the willingness of research funders to fund frontier research. That might work in the US, but New Zealand's current research funding environment is increasingly emphasising economic benefits rather than fundamental research. That may make it more difficult to obtain funding for speculative frontier research, where economic benefits are uncertain.
Potts' working paper has a lot of other proposals for universities. However, I think the most important ideas are those I have outlined above. Potts is clear that not all universities are equally affected by generative AI. In particular, he notes that the elite universities are somewhat insulated from changes by their reputations. However, the vast majority of universities are not elite institutions. Potts' vision of universities is radically different from the status quo. We might buy into his vision, or not. But regardless, the economics of higher education are changing, and universities will need to adapt. Whether that requires moving away from the traditional model that combines teaching, credentialling, and research, remains to be seen.
Read more:
- Some notes on generative AI in higher education
- Some notes on generative AI and assessment (in higher education)
- More notes on generative AI and teaching (in higher education)
- Universities' (and teachers') cheap talk on generative AI and assessment
- Simas Kucinskas on AI, university education, and the 'mushy middle'
- The wicked problem of generative AI and assessment
- Matthew Kahn on generative AI and the economics major
- David Deming on generative AI and commitment to learning, and the impact of generative AI on signalling in education
