Weishan Chang

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About

Weishan Chang is a leading researcher at the intersection of artificial intelligence, fuzzy logic, and human-robot interaction, with a particular focus on educational technology. Their work centers on developing intelligent robotic agents that enhance cooperative learning environments, bridging the gap between computational intelligence and human cognition. Chang’s most influential contribution is the development of a Fuzzy Markup Language (FML)-based reinforcement learning agent integrated with fuzzy ontology for human-robot cooperative edutainment, which has garnered 16 citations for its novel approach to combining education and entertainment. This work demonstrates how robots can adaptively respond to human behavior in learning contexts. Additionally, their research on AI-FML robotic agents for student learning behavior ontology construction (6 citations) provides a framework for modeling and understanding student engagement in English speaking and listening domains. This ontology-based approach integrates perception, computation, and cognition intelligence, enabling robots to better interpret and respond to learner needs. Chang’s innovative fusion of fuzzy systems with robotic agents represents a significant advancement in creating more intuitive, responsive educational technologies that can transform how students interact with AI in learning environments.