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About

Sheng-Chi Yang is a researcher whose work sits at the intersection of artificial intelligence, educational technology, and human-machine interaction. His scholarship focuses on intelligent agent design, fuzzy logic systems, ontology-based frameworks, and transformer-based models, all applied to create richer co-learning environments between humans and machines. Yang's most notable contributions include developing transformer-based semantic robots that leverage computational intelligence mechanisms to support collaborative learning among students, teachers, and AI systems. His integration of Fuzzy Markup Language (FML) with domain ontology represents a particularly innovative approach, enabling intelligent agents to adapt to individual student learning profiles — especially in domains like mathematics. Beyond education, Yang has extended these methods into brain-computer interface (BCI) research, designing semantic agents that combine particle swarm optimization and fuzzy logic for complex game-learning applications such as the ancient board game Go, including the development of a dedicated cloud platform in collaboration with Facebook AI Research. With citations spanning educational AI and cognitive computing, Yang's research demonstrates a consistent commitment to bridging human cognition and machine intelligence. His work offers meaningful frameworks for adaptive learning systems, positioning him as a thoughtful contributor to the evolving field of human-AI collaboration.