Greg Weiner, a professor of political science, is the president of Assumption University in Worcester, Massachusetts.
Judging by the proliferation of programs of study in artificial intelligence, higher education is learning exactly the wrong lesson from that technology’s transformation of the workplace. Artificial intelligence makes purely technical skills less valuable and human judgment more essential. There has never been a more powerful case for the liberal arts, a kind of education that cultivates human discernment.
But making the case requires a fundamental shift in how those who provide, regulate and enroll in higher education think about its purpose. That shift is from literal education to liberal education.
At its best, liberal education is an integrated whole that pursues truth wherever it is found, emphasizing both a range of disciplines and the connections between them. It helps form human judgment by exposing students to complexity and nuance while encouraging curiosity. Literal education fragments that experience into individual units of utility — falsely assuming that what seems useful today will be useful tomorrow.
In higher education, this literalism plays out across a variety of fields. Students need to understand artificial intelligence, so we create majors or minors specifically in artificial intelligence. Literal education also encourages correspondingly narrow questions: Why take courses in literature unless a student intends to be a novelist? Why study psychology except to become a clinician or major in political science except to run for office?
But the best writers are not simply taught to write — they learn their craft by reading a range of great writing. The most successful innovators absorb and develop that ability through exposure to creativity as it is expressed in science, art, business and other fields.
The irony is that one power of artificial intelligence is its ability to overcome precisely this fragmentation of knowledge and to identify connections that might not otherwise be evident. A large language model that confined its learning on a topic only to literally and narrowly conceived disciplines would be neither large nor useful. But studying AI won’t teach students how to make those connections — that is what liberal education can do.
Literal education is an understandable response to a policy environment that almost exclusively emphasizes outcomes that are not only quantifiable but also immediately so. For several years, federal regulations have relentlessly emphasized return on investment. It is a worthy measure that addresses an important concern. But they have largely limited these metrics to the near term, such as income four years after graduation.
Especially when combined with professional preparation, the returns on liberal education are immense, but they take longer to accrue. As AI amplifies the value of human judgment, those returns are likely to grow. Literal education captures near-term returns, but only deceptively: Measuring only the immediate obscures the danger of obsolescence.
Literal instruction has its place. A good nurse must know anatomy, just as someone whose career requires the use of AI should learn how to use its technical tools. But none of these parts is as effective as the whole.
The workers who use AI best will deploy human judgment to analyze and evaluate information. To succeed in a workplace, not simply at a desk, they will be able to listen as well as to communicate. Their relentless curiosity will be grounded in intellectual humility, while their ethical behavior will require moral courage.
The worst way to cultivate these qualities is literally to teach them. There is no syllabus for Curiosity 101, no major in human discernment. In reading history or sharing residence halls, they learn by encountering nuanced situations and ideas.
To turn out inquisitive professionals who can evaluate and ask hard questions of what generative AI tells them, we should challenge students to evaluate and ask hard questions of what they study, whether it is a poem, a behavioral study or lab results. Socrates, after all, was the original prompt engineer. It is reasonable to bet that, by the time today’s college students are entering the primes of their careers, today’s AI tools will be obsolete while Platonic dialogues will still be taught.
We should not expect ethical behavior from professionals whose understanding of it derives simply from a course in business ethics. A literalist approach to education encourages colleges and universities to check that box without developing deeply rooted ethical dispositions that permeate how future citizens and professionals approach their lives.
For higher education and its regulators, there is an accompanying challenge. We are equipped to measure the impact of fragments, especially literal ones. A student who completes a course did or did not master its content; a graduate in this field earns an income of this much by this date. It takes both more time and more complex judgment to assess the impact of a whole education — that kind of education is both more economically and more academically valuable.
That does not mean liberal arts education is flawless. Colleges and universities must show a better capacity for self-regulation in the face of legitimate public concerns about political bias, affordability and professional relevance. And an institution whose graduates are unable to use artificial intelligence effectively and ethically has failed them.
But relevance and literalism are different things. Liberal education trusts students to absorb the habits of innovation, entrepreneurship and professionalism from the totality of an education. In that sense, realizing the full value of the liberal arts requires taking education less literally and students more seriously.
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