Machine learning offers some powerful tools for enhancing education, but education also introduces some key challenges that have received less attention in typical machine learning settings. I will discuss some of work work on addressing these challenges, in order to create self-optimizing tutoring systems that learn to improve learning.
Emma Brunskill is an Assistant Professor of Computer Science and an Affiliated Assistant Professor of Machine Learning at Carnegie Mellon University. Prior to this, she completed her PhD at the Massachusetts Institute of Technology and was a NSF Mathematical Sciences Postdoctoral Fellow at UC Berkeley. She works on reinforcement learning, focusing on applications that involve artificial agents interacting with people, such as intelligent tutoring systems. She is a Rhodes Scholar, Microsoft Faculty Fellow and NSF CAREER award recipient, and her work has received best paper nominations in Education Data Mining (2012, 2013) and CHI (2014).