Asst. Prof. Sinan Chen
Kobe University, Japan
Bio:Sinan Chen received his M.S. degree in System Informatics and Ph.D. degree in Computational Science from Kobe University, Japan, in 2019 and 2021, respectively. He served as a JSPS Postdoctoral Research Fellow at Kobe University in 2022. He is currently an Assistant Professor at the Center for Mathematical and Data Sciences, Kobe University, Japan. His research interests include machine learning, smart home technologies, cloud computing, software engineering, and the application of intelligent digital technologies to human-centered systems. His recent research also explores the use of artificial intelligence and digital technologies in areas such as e-learning, smart healthcare, multimodal sensing, and interactive systems. He is particularly interested in developing practical data-driven approaches that connect computational technologies with real-world applications and user needs. He is a member of IEEE, ACM, Sigma Xi, the Chinese Association for Artificial Intelligence (CAAI), China Computer Federation (CCF), IEICE, and IEEJ.
Speech Title: AI-Enhanced Digital Humans for Personalized E-Learning: From Content Generation to Interactive Learning Experiences
Abstract: The rapid development of artificial intelligence and digital human technologies is creating new opportunities for more personalized, interactive, and scalable e-learning environments. This invited talk will present our recent work and perspectives on the use of AI-enhanced digital humans to support customized educational content and learner-centered online experiences. The presentation will first introduce the motivation for integrating digital humans into e-learning and discuss how artificial intelligence, machine learning, and cloud-based technologies can support the generation and delivery of adaptive learning content. It will then describe a framework for combining digital human interfaces with personalized content solutions to improve the accessibility, flexibility, and interactivity of online learning. Particular attention will be given to the potential of generative AI and intelligent interaction technologies to support content customization and more natural learner-system communication. Finally, the talk will discuss practical challenges, including personalization quality, system reliability, learner engagement, scalability, and responsible use of AI. Future directions toward intelligent, multimodal, and human-centered digital learning environments will also be discussed.