In Supkis Cheek’s class, students are meant to learn the processes that real-world accounting professionals would follow, which might include AI. “The answer to me is not as important as the process by which the student got the answer,” she says, “and so I need them to use processes that are available to them in the real world so that they can use this in the university setting.” The experience is also meant to introduce students to the rigorous world of corporate finance, and help them learn when certain processes and techniques are acceptable. “The course is a safe place to fail so that when they get to the real world, they are more appropriately equipped,” she says. “Here, the worst thing that happens to you is to get a bad grade—versus the worst thing that happens if an auditor misses something may be a deficient audit report that results in significant fines, lawsuits, and erosion of public trust.”
Asking the right questions
For many educators, AI isn’t something to merely accommodate in their curricula: instead, they’re actively building their courses to teach their students how to use it effectively. After all, as Brokee’s Lushpenko puts it, “To get the most out of AI, you still need to know what questions to ask and how to apply the answers it gives you.”
“Other kinds of training need to be developed with AI tools specifically in mind,” says Risto Miikkulainen, AVP of evolutionary AI at Cognizant AI Labs and a professor of computer science at the University of Texas at Austin. “Such assignments may be larger than current ones, with overall design done by students, and detailed low-level implementation done by AI tools. They may include new assignments such as upgrading software, debugging, and repair, that are currently tedious but where AI tools can help significantly. They may also include designing software so that AI can be most effectively used in the future to upgrade and maintain it as well.”
