How to Know When an AI Model Doesn't Work?

Artificial intelligence has a lot of potential for higher education. It can automate onerous repetitive tasks for teachers, help researchers leapfrog mountains of data crunching and make higher education more accessible and personalised for students. But AI also presents risks, including biases that can become embedded into algorithms and a lack of transparency around data use.

Though we may be a long way from understanding exactly how higher education can harness AI and machine learning’s great potential in a safe way, this episode's guests say that continuing to test and explore it is the only way to make progress.

Join THE Campus editor Sara Custer and senior content curator Miranda Prynne as they speak with Ashok Goel, a professor of computer science and human-centered computing at the Georgia Institute of Technology and the developer of the first automated teaching assistant, as well as John Wu an assistant astronomer at the Space Telescope Science Institute and an associate research scientist at Johns Hopkins University.

Find resources from your peers exploring the benefits and costs of AI in higher education on THE Campus.