Papers
1
Total Citations
56
H-Index
1
About
Yu-Lin Jeng is a leading researcher at the intersection of educational technology, artificial intelligence, and human-computer interaction. His work centers on leveraging anticipatory computing, emotional big data, and robotic systems to transform learning environments, with a particular focus on improving sustainable learning efficiency and student motivation. Jeng’s most cited paper, “ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation” (2020), has garnered 56 citations and exemplifies his innovative approach to integrating AI-driven learning analytics with the ARCS motivational model. This work demonstrates how emotional data from learners can be used to anticipate needs and personalize instruction, paving the way for more adaptive and empathetic educational robots. Beyond this flagship study, Jeng’s broader contributions include pioneering frameworks for using big data in education to provide real-time feedback, reduce dropout rates, and foster deeper engagement. His research has been instrumental in shaping how educators and technologists think about the role of emotion and prediction in learning. For students and researchers, Jeng’s work offers a compelling vision of how AI can humanize education, making it more responsive, inclusive, and effective.
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Top Papers
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