Abstract

With the recent advances in AI, more AI-powered interactive systems have been designed to support people in various everyday tasks. The tension arises, however, when we apply state-of-the-art AI technology to a real-world task without careful consideration of users and their context. I argue that human-AI interaction should be considered a first-class object in designing AI-powered systems. In this talk, I’ll present a few novel interactive systems that use AI techniques to support everyday tasks such as learning, data analytics, and group discussion. I’ll discuss how human-AI interaction is considered in these systems, what tensions we encountered and how we addressed them, and how to design better intelligent and adaptive systems for real-world use.

Bio

Juho Kim is an Associate Professor in the School of Computing at KAIST, and directs KIXLAB (the KAIST Interaction Lab). His research in human-computer interaction focuses on building interactive systems that support interaction at scale. Research interests include crowdsourcing and human computation, online education and learning at scale, civic engagement and collective action, and interactive data analytics and mining. He earned his Ph.D. from MIT in 2015, M.S. from Stanford University in 2010, and B.S. from Seoul National University in 2008. In 2015-2016, he was a Visiting Assistant Professor and a Brown Fellow at Stanford University. He is a recipient of KAIST’s Songam Distinguished Research Award, Grand Prize in Creative Teaching, and Excellence in Teaching Award, as well as 12 paper awards from ACM CHI, ACM CSCW, ACM Learning at Scale, ACM IUI, ACM DIS, and AAAI HCOMP.

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