Abstract: Artificial intelligence has had a positive impact on education. Today we have accurate models of constructs many didn’t think we could model, dashboards and interventions and (some) evidence they work, and scaled solutions that are being used to change student outcomes. As a field, we have solved some challenging problems. So, what’s next? In this talk, I’ll discuss a few hard problems that could block AI in education from reaching its full potential; some of the big goals I think we can strive to achieve; some of the grand challenges we will need to — and I think can — solve; and perhaps most importantly — how we’ll know if we’ve gotten there.

Short Bio: Ryan Baker is Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 160 researchers in 6 countries. Predictive analytics models he helped develop have been used to benefit over a million students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining Society, is currently serving as Editor of the journal Computer-Based Learning in Context, is Associate Editor of two journals, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 300 colleagues.