Marguerite Matherne
I really want to personalize my students’ learning experience, to meet them where they are in terms of struggles and interests. There was no way to use the strategy I had in mind with a large class, so I decided to see if AI could help. It worked, and now I am able to close the loop on their understanding and meet my students where they are.
Marguerite Matherne
Assistant Teaching Professor, Mechanical and Industrial Engineering, College of Engineering
Using Generative AI to analyze student pre-work helped me make some interesting discoveries. For example, I learned that what some students thought was most confusing, others thought was most interesting. Those were the areas I made sure to address in class. The “Just-in-Time” approach acted as a catalyst, helping me think creatively about how an instructor should use class time. As a result, students’ perception of the value of class pre-work increased, and they asked deeper questions in class, which generated a lot of interest in why engineers should care about challenging concepts.
I would characterize my process of using AI in support of my teaching practice as trial and error, and that is my advice to other educators. How can I use this? If the first thing I try doesn’t work, I experiment with something else. That also includes checking—not assuming—that Gen AI will be accurate. I found it was good at spotting patterns of interest and confusion, but not good at evaluating the accuracy of student work. What is it good for, and what are its current limitations? Don’t be afraid of AI. It’s not coming for your job because you have expertise that is important. It’s a great analysis tool, but remember that it’s not really thinking. You are still in charge.