David Joyner is the executive director of the Online Master of Science Computer Science at the Georgia Tech College of Computing and is one of the instructors in the new AI for Business Executive Education course. We talked with him to learn more about his background and his interest in AI and emerging tech.
When did you start becoming interested in AI and emerging tech?
I've always had a passion for education, but it was early in graduate school that I learned about the application of AI to education. From there, I started getting more into AI for personalized feedback and pacing. The main area I'm interested in is how AI can help scale up education.
What challenges in this industry are you most passionate about?
The biggest challenge I see is also an opportunity: Figuring out how we disentangle people using AI to do their work—whether that's in the workplace, the classroom, or elsewhere—from people using AI to help them work. This is a transition that has happened with calculators, word processors, search engines, and a lot of technologies before, but the pace of AI improvement is so much faster that it's harder to disentangle those as we go.
What is the biggest lesson that you've learned since you started teaching?
The biggest lesson I've learned is that a lot of people want to contribute to education. We have so many applicants to our teaching assistant positions because education seems to be almost a universal passion. The challenge is that many of those people aren't looking to be teachers full-time. If we can create ways they can participate in education without committing their lives and careers to it, we can radically expand access.
Tell us something about yourself we wouldn't know or guess.
I'm skeptical about the extent to which educational data is going to let us predict student success. I think there are a lot of other ways it can be useful in measuring things. We recently completed a study investigating how students use AI during exams when allowed. But success predictions are difficult because what often predicts success is something immeasurable about the student.
I've seen software engineers with 10+ years of experience at FAANG (Facebook, Amazon, Apple, Netflix, Google) companies fail out in the first year, and I've seen students with the bare minimum prior programming experience graduate with a 4.0. The difference is something underlying their transcript: How much work they're anticipating putting in.