Yi-Jung Chen
Papers
1
Total Citations
4
H-Index
1
About
Yi-Jung Chen is a forward-thinking researcher at the intersection of artificial intelligence, technical education, and fuzzy decision systems. Their work centers on developing innovative pedagogical models that integrate deep learning into thematic teaching modules, with a particular focus on enhancing professional human resource quality for the AI-driven industrial landscape. Chen’s most cited study, "The Establishment and Evaluation Model of the Thematic Deep-Learning Teaching Module" (2025, 4 citations), introduces a novel evaluation framework employing double-triangular fuzzy numbers and gray relational analysis to assess and optimize deep-learning curricula. This contribution not only bridges the gap between emerging AI technologies and practical education but also provides educators with a robust, data-driven tool for curriculum design. By addressing the critical need for deeper technical learning and integration of existing technologies, Chen’s work has immediate relevance for vocational training and higher education reform. Their research is particularly notable for its methodological rigor, combining fuzzy logic with educational assessment to create actionable insights. As AI continues to reshape industries, Chen’s pioneering efforts in technical education evaluation position them as a key contributor to preparing a future-ready workforce.
Research Focus
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Top Papers
- 1