Zhan Liang-tong
Papers
1
Total Citations
4
H-Index
1
About
Zhan Liang-tong is a pioneering researcher in educational technology and learning sciences, whose work centers on how failure and social observation shape classroom learning. Drawing on productive failure (PF) theory, Liang-tong has made significant contributions by demonstrating that observing a robot peer’s mistakes can be as powerful—if not more so—than experiencing failure firsthand. Their landmark 2025 study, “Observing a robot peer’s failures facilitates students’ classroom learning,” has already garnered 4 citations, signaling growing influence in the field. This work challenges traditional assumptions about the necessity of direct struggle, offering a scalable, emotionally supportive alternative for learners who may lack the prior knowledge or resilience to benefit from unguided problem-solving. By bridging human-robot interaction and educational psychology, Liang-tong has opened new pathways for designing intelligent tutoring systems that model productive failure. Their research not only advances theoretical understanding of learning from errors but also provides practical frameworks for integrating robots into classrooms as non-judgmental learning companions. Liang-tong’s work is essential reading for educators, instructional designers, and AI researchers seeking to harness failure as a catalyst for deeper learning.
Research Focus
Key Achievements
Top Papers
- 1