Every so often, the world finds itself at a moment in history that is so transformative that it requires a rethinking of nearly every aspect of how society functions.
With the emergence of generative artificial intelligence, it is now fair to ask whether, as New York Times columnist Thomas Friedman described AI, we are again at a Promethean moment where, like civilization at the threshold of using fire, the world will soon never be the same.
I believe we are — do you?
It is a question I routinely ask higher ed stakeholders — university leaders, clinicians, researchers, teachers, staff and students. About one-third say yes, one-third say no, and one-third usually say something like, “I don’t have a clue and have never touched ChatGPT.”
Students are the most nonchalant, with a typical response along the lines of: “It’s here, I’m using it, the capabilities will continue to improve rapidly, and I wish my professors knew more about it.” For my third of folks who believe it is a Promethean moment, the ever-burning question is: What to do?
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As the head of a school of data science, I took this question to my school’s advisory board — a group of industry leaders who can offer advice at a time when everybody can use good advice. We made two decisions. First, don’t panic. Second, embrace a bias toward action and learning.
To many, the quickly rising sense of urgency will ring familiar. Already, AI can surpass humans at many tasks. Meanwhile it’s rubbish at others and is already posing thorny issues related to ethics, bias and fairness. There are so many open questions. But one thing we already know is its presence will only grow. It’s here to stay. In fact, one recent survey from BestColleges found that more than half of college students are already using AI in their coursework.
The questions then become: How soon, to what effect and how different will higher ed be as a result of AI?
For many of my peers, these are very hard questions. The capabilities and subsequent impact of generative AI caught us, like pretty much all of higher ed, off guard. Yet for those few in the know, the appearance of transformers — the “T” in ChatGPT — was a sign of what was coming. How to read the “T” leaves in the future? One answer starts with a vision of what that future might be like.
A history major is studying the life and times of Thomas Jefferson, the third president of the United States and founder of the University of Virginia. Today that involves online reading and human interpretation and possibly physical work in accessing special collections that have yet to be digitized.
In a few years it will be possible to call up, via a virtual reality headset, a timeline of Jefferson’s life and a social network of whom, when and where he interacted — a timeline automatically generated by natural language processing.
Zooming into the laying of the UVa cornerstone on Oct. 6, 1817, a simulated conversation between two former presidents, Jefferson and James Madison, and sitting President James Monroe can take place using generative AI. Looking around the small gathering using virtual reality, the student sees a 360-degree view of what UVa’s Academical Village looked like prior to any building occurring.
This is a simulated and possibly error-prone form of history brought to life at a price and technology point that is not here today but will be in five years. Who will not want to study history this way? What will they be missing? As data is generated from those interactions, which parts belong to the students?
Research universities will see fundamental changes in what and how research is done. The complete research life cycle from idea generation to grant funding to data generation to analysis and publishing will be driven by new tools and processes. Processing times and decision making will become acute strategic advantages — and possibly come to resemble a competitive sport.
Two years ago, AI helped researchers at DeepMind solve a crucial outstanding problem in molecular biology — the ability to accurately predict a protein structure. It’s a breakthrough that will have profound impacts on health care, food production, energy usage and much more — and that was just one application. You can make similar predictions for all of higher ed — education, research throughout its life cycle, how university administrative functions will change with virtual assistants and more.
So back to that burning question: What to do?
We can start by accepting this new reality. Next, we can take our most visionary people — in your institution and in mine — and set them to work on the daunting but invigorating task of forecasting our shared future with AI. This is important work because forecasts aren’t just about predictions; they also help us make sense of the present.
The question of AI in higher education leads to challenging conversations. Let’s be sure we are having them.
Philip Bourne is the dean of the University of Virginia’s School of Data Science and a professor of biomedical engineering. His previous work includes overseeing big data management for the National Institutes of Health. Contact Bourne at peb6a@ virginia.edu.
We can start by accepting this new reality. Next, we can take our most visionary people — in your institution and in mine — and set them to work on the daunting but invigorating task of forecasting our shared future with AI. This is important work because forecasts aren’t just about predictions; they also help us make sense of the present.
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