인터랙션 연구실

My research is driven by a fundamental belief: technology should amplify human capabilities rather than replace human judgment. I design interactive systems that leverage the complementary strengths of humans and AI, creating experiences where both can contribute their unique strengths. My PhD thesis introduced "learnersourcing"—the idea that learners themselves can generate valuable educational content through their collective activity. This work established my interest in systems that scale through user participation rather than just automation. Building on learnersourcing, I expanded my research to "Interaction at Scale"—designing systems that support interaction at massive scale, powered by crowdsourcing, data-driven techniques, and AI. This era bridged my early work on learnersourcing with my current focus on interaction-centric AI, exploring how to leverage collective human activity and data to create more powerful interactive systems. I've developed a broader vision of "Interaction-Centric AI"—designing AI systems where the interaction itself is the primary design consideration, not just the AI model. This means creating interfaces that enable users to understand, control, and collaborate with AI effectively, building on the foundation of scalable interaction systems. Impact: Moving beyond "chat with AI" to designing purposeful, controllable, and creative human-AI partnerships. Transforming how people learn with intelligent, adaptive, and scalable educational technologies. Impact: Democratizing education through AI that adapts to individual needs and scales expert knowledge. Building rigorous methods to understand what AI can and cannot do. Impact: Ensuring AI systems work reliably across diverse contexts and users. Published 100+ papers in top-tier venues (CHI,