Papers
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Total Citations
4
H-Index
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About
Yaxuan Song is a rising scholar in the learning sciences, whose work explores how failure—particularly when observed in others—can be a powerful catalyst for classroom learning. Grounded in productive failure (PF) theory, Song’s research challenges traditional assumptions about problem-solving and instruction. In their most-cited paper, "Observing a robot peer’s failures facilitates students’ classroom learning" (2025, 4 citations), Song demonstrates that students can acquire knowledge more effectively not by struggling through problems themselves, but by watching a robot peer fail and recover. This innovative approach reframes failure as a low-stress, high-impact learning tool, potentially reducing the emotional and cognitive burdens that often accompany direct problem-solving. Though early in their career, Song’s work has already garnered attention for bridging human-robot interaction with educational psychology. Their findings offer practical implications for designing AI-assisted classrooms where robotic peers serve as models of productive struggle. As Song continues to develop this line of research, they are poised to contribute meaningfully to how educators and technologists think about failure, feedback, and the design of supportive learning environments.
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