| Prof. Hai LiuCentral China Normal University, China Hai Liu is a Professor and PhD Supervisor at the Faculty of Artificial Intelligence in Education, Central China Normal University. He received his Ph.D. degree in Pattern Recognition and Intelligent Systems from Huazhong University of Science and Technology. His long-term research interests cover self-regulated learning, learning resource recommendation, knowledge graphs, computer vision, and machine learning. Dr. Liu was selected for the "Hong Kong Scholar" talent program and conducted a two-year research visit at the Robotics and Vision Laboratory, City University of Hong Kong. He has presided over 6 projects funded by the National Natural Science Foundation of China (NSFC) and participated in 8 others. He has published more than 120 academic papers in prestigious domestic and international journals and conferences, among which over 60 SCI/SSCI/CSSCI journal papers were published as the first or corresponding author, including 25 IEEE Transactions papers in CAS Zone 1. Eighteen of his papers have been selected as ESI Highly Cited Papers and 12 as ESI Hot Papers. He has filed more than 80 national invention patents, with over 40 granted. Dr. Liu was included in the World's Top 2% Scientists 2025 list released by Stanford University. Since 2022, he has served as a review expert for the Young Changjiang Scholars Program of the Ministry of Education. He has been awarded the First Prize of Hubei Science and Technology Progress Award (2020) and the First Prize of Science and Technology Progress Award (Scientific and Technological) in the Scientific Research Outstanding Achievement Award for Institutions of Higher Education (2019).。 Speech Title: Research on Key Technologies for Fine Estimation of Learners' Head Postures in Intelligent Learning Evaluation Abstract: In this talk, we present our research on key technologies for fine-grained head pose estimation of learners for intelligent learning evaluation. Accurate head pose estimation serves as a core upstream technique for perceiving learners’ attention status and evaluating classroom engagement in intelligent learning evaluation scenarios, which is of great research value and practical significance for improving the precision of teaching evaluation and the level of personalized instruction. However, complex challenges prevalent in real classroom scenarios, including facial occlusion, large-angle head deflection and uneven illumination, cause deformation and distortion of facial features, severely restricting the accuracy and robustness of pose estimation and becoming a key bottleneck for the deployment of intelligent learning evaluation. Over the past decade, our team has carried out systematic research on head pose estimation. Built upon the state-of-the-art Transformer architecture and integrated with technologies related to large model representation and educational scenario adaptation, we thoroughly characterize the morphological deformation laws of different facial regions during head deflection and construct a morphology-aware relational modeling framework, effectively overcoming core difficulties such as occlusion and large-angle pose estimation. Our methods achieve leading performance on three authoritative international benchmark datasets: BIWI, AFLW2000 and 300W-LP, and the series of research results have been published in more than ten papers in top international journals and conferences including IEEE TIP, CVPR, IEEE TII, IEEE TMM and IEEE TCSVT, providing reliable technical support for learner pose perception in intelligent learning evaluation scenarios. |
| Prof. Songhua DuGuangdong University of Technology, China Helen S. Du is the “100-talent program” distinguished professor at Guangdong University of Technology, China. She received her Ph.D from the City University of Hong Kong, department of Informatin Systems. Prof. Du is currently the Director of Internatinal Cooperation and Certification Center at School of Management, GDUT. Her recent research interests include gamification design and innovation, human-AI interaction, digital consumer behavior, and sustainable management. As the principal investigator, she has taken charge of three National-level research grants, and five Provincial-level research grants. She has published over 60 articles in leading journals and international conferences, such as International Journal of Information Management, Internet Research, Information Technology & People, Journal of the Association for Information Science and Technology, among others. She has served as the editorial board member of Online Information Review (SSCI indexed) and the AE of Journal of Information & Knowledge Management (EI indexed) for many years. She is also serving as the Deputy Director of Expert Committee of the Guangdong Live Streaming E-commerce Association, as well as the member of the China System Engineering Society Entrepreneurship and Innovation System Engineering Professional Committee, and the Guangdong Province Undergraduate E-commerce Teaching Steering Committee. Title：The Knowledge Framework of Augmented Reality in Education Abstract：This study aims to systematically review the current research status and future research orientation in the field of augmented reality (AR)-based education. Through the bibliometric and content analysis of 437 articles indexed in the Web of Science database (2010-2024), an overview of AR-based education research was synthesized and a dynamic feedback knowledge framework was constructed. Three primary thematic clusters were identified, namely, system design, regular education application and special education application. Differences in AR applications between general education and special education were also compared and discussed. The finding of our study suggests that future AR-based education research and educational practice should focus on leveraging learner data for adaptive teaching support, expanding explorations into informal educational scenarios, and optimizing mechanisms for cultivating higher-order thinking skills through AR technology. |
| Prof. Shaodong PengHunan Normal University PENG Shaodong is the head of the doctoral program in Educational Technology at Hunan Normal University, a professor (Level II), doctoral supervisor, and academic backbone of the "Shicheng Talent Program." He serves as a council member of the Information Technology Education Professional Committee of the Chinese Society of Education, a review expert for the Academic Degrees and Graduate Education Development Center of the Ministry of Education, and was honored as an Outstanding Teacher of the Sixth National Master of Education Program. Since July 1984, he has been continuously engaged in work, study, and research in the field of educational technology. His primary research interests include AI in education and blended collaborative learning. He has published 8 books and over 100 papers, and has led multiple research projects, including those funded by the National Education Science Planning Project and the Provincial Social Science Foundation. In March 2003, his research "New Discipline and Curriculum Development in Information. Technology Education" received the First Prize for Higher Education Teaching Achievements from Hunan Province. In January 2016, his project on "Design and Interaction Research in Blended Collaborative Learning" was awarded the Second Prize for Outstanding Social Science Achievements by Hunan Province. In September 2019, his research "Practical Exploration of Blended Collaborative Learning (BCL) Aligned with the 'High-Level, Innovative, and Challenging' Curriculum Philosophy" won the Second Prize for Higher Education Teaching Achievements from Hunan Province. |
