Shih‐Wei Tan

Papers

Total Citations

H-Index

About

Shih-Wei Tan is a leading researcher at the intersection of educational technology, artificial intelligence, and sustainable learning. His work focuses on integrating anticipatory computing, emotional big data, and robotic systems to transform pedagogical environments. Tan’s most cited paper, “ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation” (2020, 56 citations), introduces a novel framework that combines the ARCS motivation model with AI-driven robots to analyze and respond to learners’ emotional states in real time. This approach not only enhances engagement but also promotes long-term knowledge retention and sustainable learning outcomes. By leveraging big data analytics and predictive algorithms, Tan demonstrates how anticipatory computing can preemptively tailor instruction to individual needs, marking a significant leap from reactive to proactive teaching aids. His work has been instrumental in shaping how educators and technologists design intelligent tutoring systems that are both emotionally aware and data-driven. With growing recognition in the fields of human-computer interaction and learning analytics, Tan continues to push the boundaries of how AI can foster more adaptive, empathetic, and effective educational experiences.