By David Robinson
Technology and education have long had an uneasy relationship. New advances have often prompted bold predictions of major disruptions to where, when, and even how we teach and learn.
In 1885, the founder and future president of the University of Chicago, William Rainey Harper, suggested that innovations in transportation and postal delivery signalled that “the day is coming when the work done by correspondence will be greater in amount than that in the classrooms of our academies and colleges.”
Harper didn’t get it completely wrong. Universities and colleges did develop correspondence courses, but they didn’t eclipse in-class instruction.
Later, similar predictions were made about the phonograph, film, radio, television and the internet — who can forget the insufferable hype over MOOCs? More than a hundred years after Harper’s prediction, a Coopers and Lybrand report confidently asserted that online education was about to supersede traditional in-person instruction because online learning had two significant cost advantages: “The first is the need for bricks and mortar; traditional campuses are not necessary. The second is full-time faculty. Distributed [online] learning involves only a small number of professors but has the potential to reach a huge market of students.”
Like Harper’s correspondence courses, online learning has become an important component of post-secondary education. It was critical during the pandemic. But online teaching hasn’t replaced campuses, professors, or the desire of most students for in-person educational experiences.
Given the history, it might be tempting to dismiss artificial intelligence (AI) as simply the latest innovation prompting misguided predictions about the coming end of education as we know it. Is the potential impact of AI just another over-hyped phenomenon, or is there something fundamentally different about the technology this time? Given the track record of previous prognosticators, I’ll leave that question for braver souls to answer. What I am confident in saying is that like previous technologies, there are both promises and perils emerging from AI.
AI allows researchers to more efficiently process large datasets and vast amounts of information. It can help instructors develop dynamic problem-solving exercises and simulations to aid student learning. But the technology also raises questions about academic integrity, data privacy, intellectual property, academic decision-making, and the environment.
AI-driven systems can inadvertently reproduce social biases and even manufacture non-existent or erroneous content — more euphemistically referred to as “hallucinations.” They can also be deployed to deliberately produce “deep fakes” and other misinformation.
Without proper guard rails, personal information and content produced by academics can be harvested and repackaged as AI-generated content. Human decision-making, including in areas of tenure and promotion, could be aided by or even displaced by AI. Could academic staff in fact be supplanted by the algorithms?
Then there is the question of the environmental impact. Many of our institutions have committed to reducing greenhouse gas emissions to combat global warming. However, AI requires enormous amounts of water and power to process even simple queries.
Because AI implicates fundamental questions about academic integrity, the environment, and the work we do, academic staff must be involved in deciding how the technology is best deployed within our colleges and universities. As the adage goes, if we’re not at the table we’re likely to be on the menu.