Chang-Shing Lee
Papers
7
Total Citations
60
H-Index
5
About
Chang-Shing Lee is a pioneering researcher at the intersection of artificial intelligence, fuzzy systems, and educational robotics, with a sustained focus on developing intelligent agents that facilitate human-machine co-learning environments. His work centers on integrating computational intelligence, ontology construction, and Fuzzy Markup Language (FML) to create adaptive robotic systems capable of supporting diverse learners across mathematics, language, and computational thinking domains. Lee's most influential contribution, his 2019 work on robotic edutainment and humanized co-learning (25 citations), established a foundational framework for deploying intelligent agents in real-world educational settings. Building on this, his AI-FML robotic agent research introduced a sophisticated tripartite intelligence model — perception, computational, and cognition — enabling robots to construct student learning behavior ontologies and personalize instruction accordingly. His later transformer-based semantic robot work demonstrates a commitment to incorporating state-of-the-art natural language processing into co-learning ecosystems. Notably, Lee has collaborated extensively with the IEEE Computational Intelligence Society, extending his research outreach to high school students globally. Across his portfolio, his work reflects a coherent vision: making AI robots empathetic, adaptive educational partners rather than mere tools — a contribution increasingly relevant as AI reshapes modern pedagogy.
Research Focus
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Top Papers
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