Kyoungwha Chae

Korea Education and Research Information Service

Papers

Total Citations

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About

Kyoungwha Chae’s research lies at the intersection of human-robot interaction, affective computing, and educational technology, with a focus on understanding and enhancing engagement in child-robot interactions. Her most-cited work, “Building an automated engagement recognizer based on video analysis” (2014, 13 citations), introduces a data-driven framework for classifying children’s engagement during a robot-based math quiz game. By collecting video recordings from human-robot interaction experiments, annotating social signals and engagement states, and extracting relevant features, Chae’s methodology enables the automated recognition of engagement—a critical step toward adaptive, responsive educational robots. This contribution is foundational for developing systems that can dynamically adjust their behavior to maintain learner attention and motivation. Chae’s work exemplifies a rigorous, interdisciplinary approach, bridging computer vision, machine learning, and developmental psychology. Her research has practical implications for designing more effective and empathetic robotic tutors, particularly in STEM education. With a growing citation footprint, Chae is recognized for advancing the technical and conceptual tools needed to build socially aware robots that can genuinely connect with and support young learners.