Higher? Education?

A cat receives an honorary doctorate in “litter-ature”? (Image: publicity shot by Rob Franklin, Vermont State University)

Two alarms bells are ringing, not about this cat or the honour it received, but about the sector – higher education – that makes jokes like this about itself.

• One is dangerously boring: artificial intelligence.

• The other is boringly dangerous: the business model of universities.

Both are making the news, but what I’ve read so far doesn’t link them. The first, AI, is in the headlines all the time; evidence concerning higher education is building up, including in academic studies. On the second, bits of evidence are accumulating about business models: what degrees (products) we offer, with what resources and with what sources of revenue. For example, Peter Voser, the retired CEO of Shell who now chairs the board of ABB, a Swiss-based heavy industry firm, recently complained that universities are letting down the manufacturing sector. Graduates (in Britain, at least) are choosing careers in professional services and finance over those in industry, because that’s how universities work, he says. (We’ll come back to that in a bit.)

Governing universities in the face of these issues is an important – what – challenge, problem, issue? A disaster-in-the-making? Let’s make a start.

My credentials:

Over two decades, I’ve worked for four universities. I have more degrees than anyone needs – five, two of them PhDs – and a postgraduate certificate. They came from five universities in two countries, and I took an executive course at another. My studies involved domains of knowledge across humanities, social sciences, and practice-related subjects. And I’ve had two other rewarding careers, in journalism and management, which brought experience of work outside academia into the classroom. I think of myself as a strategist, someone who worries, professionally, about the future.

I’m worried. There is mounting evidence that students use AI to write essays. Some universities are experimenting with using artificial intelligence to mark those essays. It doesn’t take a doctorate, even of “litter-ature”, to realise that something’s amiss.

Both these actions – by universities and students – are motivated, initially, by the same concern: the need for economic efficiency.

Universities I know have asked lecturers this year to give up research hours for extra teaching. The number of teaching weeks in a year has eroded over the past decade. Exams are fewer, essays shorter, and “contact hours” – what universities market to would-be students – have been redefined to include the “office hours” spent waiting for students who don’t turn up, instead of time in the classroom and lectures. Heated discussions over lunches in the canteen have vanished, now that everyone works from home when they don’t have to come to what we used to call our “place of work”.

Electronic books are replacing library stacks. Many of those come in formats that are difficult to read on any electronic device, because books designed for an old technology assume people read them, not just use them to look things up. Reading is an effective way to learn, to be able to ask questions, but it’s inefficient when the goal is merely to find an answer. Libraries face an intractable buy-or-rent dilemma, too: a perpetually recurring capital outlay on a constantly expanding asset that repays its value over many years, if at all; or subscribing to ebooks, a current expense, cheaper in the current year, but likely to grow more expensive as times goes on.

These developments illustrate the mistake in thinking that the purpose of higher education is efficiency. Students need to develop their powers of reasoning, exercise mental capacities, and in so doing extending the capabilities for solving problems in the future. Degrees don’t evidence solutions to problems. They signify that a person has an ability to solve certain kinds of problems.

Efficiency is important, however. If the sums don’t add up, resources are misallocated, productivity is impeded, progress towards a better life stifled. And at universities – in the UK and elsewhere – the sums now come with a minus sign. Consider:

The UK Office for Students, which regulates the sector, recently published an alarming report on the state of system. Its summary, “Navigating financial challenges in higher education”, claims that universities have been resilient in recent challenges, but that the sand is shifting. “Approaches that worked well in more financially stable times may not be effective in the face of current pressures,” it concludes. The full report is much gloomier. Results for the sector deteriorated in the fiscal year 2022-23 and in the current year “40 per cent of providers [are] expecting to be in deficit and an increasing number showing low net cashflow”. It also cast doubt on the expectations of universities for improvement in a few years, expectations that weren’t all that optimistic in any case. The financial position is weakening and there’s little prospect of an improvement. In the trade you hear a lot of gossip about bankruptcies or shotgun marriages.

People I know in the sector – in Britain, at least – hope that a change in politics, in the looming elections, will bring about change favourable to the sector. Maybe. But the strategist, a worrier, sees mainly unmitigated risks:

• Higher education is only one sector of many (all?) clamouring for greater government spending, while everyone wants lower taxes. In higher education, one of loudest noises is the pressure to write off the debts of former students, as President Biden has started to do in the US, as well as lowering future tuition fees.

• The modest boom in the past few years was based on students from abroad. The students came because of generous visa conditions, now rescinded because of fears about the volume of migration. Might visas be relaxed again: Yes, but that only masks underlying problems.

• That boom also brought students with more modest language skills than their counterparts had a decade or two ago. It’s hard to tell sometimes whether the language deficit might also indicate a deficit in reasoning capabilities. What it does point to is the likelihood of increasing reliance on artificial intelligence to bridge the deficit(s).

• Meanwhile, at home, employers seek high passes on university degrees for a widening range of jobs. That adds pressure on students to get through the course and the temptation to use AI to boost their marks.

• These comments are based on trends in Britain, but higher education in other countries probably face similar conditions.

What to do? A thought experiment

AI will indeed change how much of the work that university graduates currently do will be automated away: think of accountancy, law, computer coding, financial investment, marketing. By contrast, demand for human labour will continue – and grow. Human interaction will continue to be required for research, policymaking, teaching, much of the creative sectors. Nowhere is it more likely to grow than in care sectors, both the professions, non-professional domain that call out for greater professionalisation, and in the management of the delivery of care. Those will require – already require – greater skills and stronger knowledge than we have now. More able-bodied people will shift careers later in life. Some will opt for teaching. Others will move off into less lucrative but more fulfilling occupations, ones which will draw upon knowledge they have developed in domains other than the ones they have dealt with in their careers. Whatever happens, we will all need to develop the capacity to ask questions and seek novel answers to them.

These conditions seem to point to a different system of higher education than what we have now. One way might be to organise the sectors along three dimensions, the types of wisdom that Aristotle taught in his academy: sophia and phronesis, theoretical and practical wisdom, and techne, or craft. These approaches require different business models, that is, the mixes of instruction, discussion and assessment (resources), as well as different sources of funding (revenue).

1. One sub-sector, the smallest one, we might call Humanism, heavy on sophia but not to the exclusion of phronesis. It might teach philosophy – the love of knowledge for its own sake – but also history, literature, languages, mathematics, with generous, high-level forays into psychology, physics, the history of science, political, social and economic theory, and possibly anthropology. Humanism might be offered by large universities – institutions offering a universe of knowledge – but it would be better coming from small places specialising in conversations. Degrees would be assessed for depth and integration of knowledge, by examining participation in debates, rather than just essays. Lecturers would be expected to read and discuss much, much more than they write. “Research” is optional. Communication is essential. AI will sit in the background, largely ignored by lecturers and students alike, hence the label “Humanism”. The model is the liberal arts colleges in America, a lousy business model, but an important element in the mix of higher education. It needs public and philanthropic support, just as primary and secondary education do. With goals so difficult to articulate, efficiency is impossible.

2. A second sub-sector, the largest, we might call Technology. It focuses on phronesis with heavy doses of techne but not without sophia. Engineering, medicine, the care professions, archaeology, natural sciences, working-life sector studies like tourism or journalism, business and management. It requires what we might loosely call “laboratories”, places to experiment, engage collaboratively in phronesis, making use of tools, including artificial intelligence, to anticipate and find solutions to future problems as well as today’s. Efficiency is desirable, so mass education – online and hybrid – will work for many aspects of it, but not all. Its laboratories are capital intensive; even elements like business school case studies involve upfront investment that pays off only over time, long after the costs has occurred. AI can help to collect and filter the data in the complex systems it examines, but the university won’t assess those outputs. Instead, “learning outcomes”, the term the sector uses as the object of assessment, isn’t what the AI produces. Instead, it’s the ability of the student to explain what they’ve done. Oral examinations become more important, essays and exams less. Collaboration with employing sectors can help provide the capital. Tuition fees can come from loans repaid by future earnings. Applied research is central, valuable and valued. It may require seed-capital but then it can pay for itself, often in the form of consultancy and royalties for commercialised inventions. Pure research might need state support, but with royalties for more speculative and distant inventions accruing to directly to states. Let’s also recall Peter Voser’s comments, above: If higher education in Nordic countries, Germany, Switzerland and elsewhere focuses more than Britain on paths to manufacturing careers, how do they create the laboratory-intensive, phronesis-oriented instruction and instructors, the resource side of the business model, or for scholarships on the revenue side? Are university leaders to blame, as Voser suggests, or has industry – companies and their trade associations – let universities down?

3. In terms of size, the third sub-sector sits in the middle. Let’s call it “Arts” while we look for a better name. Its focus is techne, a concentrated form of learning by doing, with plenty of phronesis and clear avenues for excursions into sophia: music, film-making, architecture, sports and their respective technologies, creative writing, the side of care that needs professionalising but is not yet a formal profession – social work, personal care. AI is a tool. Its use needs to be learned and assessed. Like Technology, much of this use is based in a laboratory-like setting. Like Humanism, the work is assessed through participation, though in the world of the “lab”, not the academic tutorial. Teaching is labour-intensive but also requires capital. Research is used more than generated. Many places on such courses could be funded as apprenticeships, perhaps a levy against the employing sectors.

These three approaches engage or evade artificial intelligence in different ways. Their business models differ in terms of type of human, physical and financial resources they require; they need differ sources of revenue needs to fund their ongoing operations (Magretta, 2002). They need to draw upon the different types of wisdom – sophia, phronesis, techne – in different measures, but they do not need to inhabit the same spaces.

The reason for dividing them into sub-sectors, and probably different organisations, is because the business models are different. In seeking to be fair to all, they devise inappropriate funding models. Right now, education management is muddled by chasing three different constellations of resources and revenue at once and making a mess of all. Management attention is the scarcest and perhaps most important resource (Ocasio, 1997).

Critique might also be needed of the governance of universities. Most are non-profit organisations; many are charities. They often have volunteers as directors/governors. Others are state-owned, with political forces playing into their decisions. Some questions: There is considerable evidence of poor decision-making in large groups. Are university boards small enough to engage in the constructive challenge that boards require? Do directors have the knowledge and skills required, individually or collectively, and do they apply that knowledge and those skills in their board work? Are volunteer, non-executive directors too easily captured by university senior managers, unable to see what strategic alternatives open to the organisations? I haven’t seen much research directly on these topics, though I know of cases in which university governors have intervened when the business has gone badly awry. Often leaving it quite late.

And this: With good reason, academics highlight the limitations of the arguments they make. I will too.

First, a challenge and rebuttal: This discussion focuses on the matter of undergraduate education and what the sector calls “conversion” Master’s degrees, a degree to change in career direction: e.g., a sociologist learning human resources management. It’s important, but not everything that universities do. Much could be lost. True. But across the sector, and with a handful of exceptions, undergrads form the core of the business model. It’s the part that is under threat in terms of revenue and resource base. But we should not ignore the importance of their research. However, only a few universities would have the resources for research if they didn’t have the base of revenue from undergrads to build their resource base.

Second, we might apply other lenses. This outline solution addresses only one scenario and then only two dimensions: AI and the business model. Any strategist will worry about how well a strategy would operate under other scenarios – other plausible futures, including climate change, large-scale migration, and a political climate shifting against the tenets of liberal education from both ends of the political spectrum.

Perhaps those are subjects for future posts here. Or for your comments.

Aristotle. (1893). Nicomachean Ethics (F. H. Peters, Trans. 5th ed.). London: Kegan, Paul, Trench, Trübner & Co. (See Book Six at https://oll-resources.s3.us-east-2.amazonaws.com/oll3/store/titles/903/0328_Bk.pdf.)

Magretta, J. (2002). Why Business Models Matter. Harvard Business Review, 80(5), 86-92.

Ocasio, W. (1997). Towards an attention-based theory of the firm. Strategic Management Journal, 18(S1), 187-206. doi:10.1002/(SICI)1097-0266(199707)18:1+<187::AID-SMJ936>3.0.CO;2-K