Artur Gunia

Papers

Total Citations

H-Index

About

Artur Gunia is a researcher at the intersection of human-robot interaction, child-robot interaction, and educational technology. His work focuses on how robots can enhance learning experiences, particularly through affective feedback and unexpected behavioral cues. In his most-cited study (13 citations), Gunia explores the role of affective feedback in robot-assisted learning, demonstrating that emotionally responsive robots can significantly improve student engagement and learning efficiency in programming education. Another notable contribution (8 citations) investigates children's reactions to robots' unexpected behaviors—such as social faux pas or errors—during interactive reading workshops at a museum of modern art, revealing critical design implications for child-robot interfaces. Gunia’s research also extends to safety and behavior change, as seen in his work on metaphor-based nudging technologies (2 citations). His studies, grounded in real-world settings like museums and classrooms, offer practical insights for designing more effective, empathetic, and engaging robotic systems for education and child development. With a growing citation impact, Gunia is shaping how we understand and design robots that learn with—and from—young users.