Da-Young Kim
Papers
1
Total Citations
4
H-Index
1
About
Da-Young Kim is a researcher at the intersection of human-robot interaction and informal science education, with a focus on how personalized robotic systems can enhance visitor experiences in museums. Her most-cited work, "User Perception on Personalized Explanation by Science Museum Docent Robot" (2022), investigates how a robot docent’s tailored explanations—based on visitors’ background knowledge—affect user engagement and satisfaction. Through survey-based experiments, Kim demonstrated that personalization significantly improves perceived relevance and enjoyment, offering a blueprint for designing more adaptive, socially aware museum guides. This foundational study has garnered early attention (4 citations) and is paving the way for broader applications in public-facing robotics. Kim’s contributions are particularly notable for bridging technical personalization algorithms with empirical user studies, making her work valuable for both roboticists and museum educators. Her research underscores the potential of robots not just as information providers, but as empathetic, context-aware companions in learning environments. As the field of social robotics grows, Kim’s insights into user perception and personalization are poised to influence the next generation of interactive museum technologies.
Research Focus
Key Achievements
Top Papers
- 1