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As senior fellow at UPCEA, the online and professional education association, and professor emeritus from the University of Illinois Springfield, I am fortunate to devote recent efforts to track, analyze and project the impact of technologies in higher education. Beginning with my first instructor appointment in 1972 and continuing throughout the ensuing 52 years, I have closely followed, administered, researched, published and taught about the application of technologies in enhancing learning and teaching in higher education.
It has been an exciting ride from analog technologies such as film and audio tape of the early years to digital transformations that brought the web and online learning to students and faculty worldwide supported by a host of platforms and supportive technologies. Now, in a convergence of generative AI technologies bringing the agency, autonomy and even robotic embodiment, we face a moment that promises to dwarf that half century of advancements and higher education changes in the space of the coming months and grow through the years ahead.
To effectively envision the near future of generative AI in higher ed, we need to at least briefly consider the context of the broader economy, the general fiscal condition of higher education, the state of development/deployment of new and emerging AI technologies, and the emerging demand for graduates, upskilled and reskilled workers with certificates from colleges and universities.
In general, we see evidence in the broader business and industrial world that there is an enthusiasm for AI as a way to attain efficiency and effectiveness, even in roles traditionally held by humans. The shift is already taking place in the tech field. In this column, we have previously documented instances in which corporate leaders of IBM, Cisco, Microsoft and TurboTax have justified massive layoffs to launch AI initiatives and AI has taken over duties that were previously held by people. In an article, “Generative AI Update for 2024,” in the European Business Review earlier this year, my colleague Katherine Kerpan of the University of Illinois Chicago and I documented the beginnings of this movement, including strategies for ethically supporting workers with out-of-date, less efficient skills and approaches to their work. Suffice it to say that the competing forces of efficiency and innovation are driving the adoption of these technologies beyond the academy.
Meanwhile, a significant number of institutions of higher education are suffering from lower revenues and operating margins. Last month, Forbes released its “Forbes College Financial Grades” list, noting, “About 55% of schools, or more than 480, earned a C or worse, compared to only 20% in fiscal 2021. One hundred and eighty-two schools earned a D, the lowest possible grade, up from 20 in fiscal 2021.” Earlier this year, John Marcus wrote in the Hechinger Report that “Colleges are now closing at a pace of one a week.” Marcus documents that in far too many of the cases, surprised students are left in the lurch with a difficult road forward to completing degrees and certificates. Accrued student debt remains staggering, currently at one and three-quarters of a trillion dollars! A looming student demographic or enrollment cliff is scheduled to reach higher education in the next year. As a result of these factors, there is awareness and some anxiety in our field that we must become more efficient and effective in order to meet prospective student expectations and the intensity of competition that grows in our field as the number of institutions shrinks.
Research and development across the wide field of artificial intelligence is taking place at thousands of institutions and start-ups around the world. The recent release of OpenAI o1 is just the most recent, as I write this, of a long litany of incremental developments across platforms from some of the largest tech companies in the world to unleash the potential of AI in a wide variety of forms and ways. Taking just this one new development, we see the advent of level-two reasoning. In a report accompanying the release, OpenAI writes, “OpenAI o1 ranks in the 89th percentile on competitive programming questions (Codeforces), places among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME), and exceeds human PhD-level accuracy on a benchmark of physics, biology, and chemistry problems (GPQA).”
The report goes on to say, “We also evaluated o1 on GPQA diamond, a difficult intelligence benchmark which tests for expertise in chemistry, physics and biology. In order to compare models to humans, we recruited experts with PhDs to answer GPQA-diamond questions. We found that o1 surpassed the performance of those human experts, becoming the first model to do so on this benchmark. These results do not imply that o1 is more capable than a PhD in all respects—only that the model is more proficient in solving some problems that a PhD would be expected to solve.”
While the reasoning of o1 soars, we are witnessing the rise of autonomous artificial intelligent agents that are no longer simple chat bots. Instead, the agents that will be flooding the market this fall and beyond are able to accomplish complex, multistep, changing tasks. As Amazon Web Services explains it,
“An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals. Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals. For example, consider a contact center AI agent that wants to resolves [sic] customer queries. The agent will automatically ask the customer different questions, look up information in internal documents, and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human.”
Far more complex tasks also can be accomplished. We have seen multiple experiments using such agents in Minecraft as described in Toms Guide. Multiple societies have been formed and fascinating communities have been built by intelligent agents that have been given purposes and goals by humans. They organize and even in some cases implement democracies.
That leads us to a glimpse into higher education in the coming year. Given this background, join me in envisioning how we might begin using these technologies. I see us replacing midlevel administrators with intelligent agents that can efficiently and effectively make decisions that are thoroughly documented and adaptive to changing goals and outcomes. Such areas as admissions, financial aid, the division of financial affairs, facilities scheduling, human resources and many more are offices where some staff may first become artificial staff.
Startling as it may seem to some, I can see these advanced models, such as those with Ph.D. reasoning, filling adjunct faculty posts while overseen by human professors. The long-running OpenAI-funded Khanmigo project demonstrates that key teaching, tutoring and personalization skills can be delivered by generative AI.
On some enterprising campuses, I can see robotic embodied intelligent agents by the end of 2025. I envision autonomous intelligent robots working shoulder to shoulder with students, faculty and administrators in the library, the dining halls, health services, international student services, physical plant, campus grounds and many other units.
I hope you will follow the hyperlink citations to learn more about the topics in this column. Then, perhaps, you will begin forming your own vision of how and when these technologies will roll out in your university. This vision will help you to inform your university’s future and your own personal career plans.
