Yuliana Flores
Papers
1
Total Citations
4
H-Index
1
About
Yuliana Flores is a researcher in human-robot interaction and educational technology, with a focus on designing social robots that enhance learning through iterative, user-centered approaches. Her most-cited work, "Using iterative design to create efficacy-building social experiences with a teachable robot" (2018), demonstrates her commitment to developing robots that not only teach but also build learners' confidence and self-efficacy. By employing iterative design methods, Flores ensures that these robotic systems are responsive to user feedback, making them more effective in real-world educational settings. Though her citation count is modest, her contributions are foundational in the niche of teachable robots, where she explores how social interaction with robots can foster deeper engagement and learning outcomes. Flores’s work stands out for its emphasis on user experience and the psychological impact of human-robot collaboration, offering practical insights for designing empathetic, adaptive educational tools. Her research is particularly valuable for students and researchers interested in the intersection of robotics, education, and social psychology, as it highlights the importance of iterative design in creating meaningful, efficacy-building experiences.
Research Focus
Key Achievements
Top Papers
- 1