Essay by Dennis M. Hogan: “Imagine a large online lecture class taught by a tenured professor with the assistance of AI tutors. The professor writes and delivers her lectures once; they are infinitely re-playable as long as the course content does not change. Meanwhile, AI agents can design assessments, deliver course material beyond lectures, hold students’ hands through the completion of the assignments, and even evaluate students’ work, eliminating the need for human grading. The professor can simply review the AI’s work and enter the grades, though even this is essentially a formality—there’s nothing stopping the AI agent from entering the grades with the registrar directly. What I have just described is a class that costs almost nothing in labor, especially after the professor recorded the lectures in a previous semester. A really ambitious university might assign a single grad student or teaching assistant to the class just to oversee the automated processes, but even this isn’t strictly necessary. The machinery runs on its own.
If you, like me, are attached to the old model of education, which presupposes that teachers, in delivering instruction, enter into personal relationships with students, you might find this scenario depressing. After all, if no one is really teaching, is anyone really learning? If, on the other hand, you are a college administrator desperately trying to align your instructional obligations to your budgetary limitations, you might find such a model enticing. As a student, you might find it alienating, but then again, if you are already used to Zoom school and distance learning, it might not feel all that different. And perhaps the course doesn’t matter to you all that much. You’re just trying to get a requirement or a prerequisite out of the way. This class is not going to change your life.
Much of the discourse around generative AI in education revolves, fairly or unfairly, around the ways that students can use the technology to do their work for them. AI is frequently connected to what commentators have called a “literacy crisis”—a longstanding decline in Americans’ levels of reading fluency affecting adults as well as children. While some early childhood educators and policy experts are optimistic about the potential for AI tools to improve childhood reading acquisition alongside traditional classroom instruction and reading with adults at home, many college instructors have been more skeptical, suggesting that students who rely on AI tools lack the motivation to absorb written texts and do not build the skills to understand them effectively. But if you listen to its boosters in academia and industry, AI is much more than a cognitive shortcut or a text-generating machine: It is a technology that is rapidly threatening to transform the entire economy, as companies make massive bets on its potential to increase productivity and even solve previously insoluble problems such as cancer and climate change. It could even amount to a new industrial revolution. So far, the promises of AI have been slow to materialize: Although the technology offers applications for tasks like coding, there remains an abiding sense of confusion about whether—and how—to integrate AI products into other lines of work. In fact, among the general public, AI skepticism abounds. This probably has something to do with the fact that AI boosters tell us, repeatedly, that AI is going to put many of us out of work…(More)”.
