Prof. Xiangen Hu began his academic journey in applied mathematics, earning his Bachelor's and Master's degrees from Huazhong University of Science and Technology in 1982 and 1985, respectively. He then moved to the United States to further his education, obtaining a Master's in social sciences in 1991 and a Ph.D. in cognitive psychology in 1993. Before his current position as a chair professor in learning sciences and technologies at PolyU, Prof. Hu held several positions. He was a professor in the Departments of Psychology, Electrical and Computer Engineering, and Computer Science at The University of Memphis (UofM) for 30 years, where he also worked as a senior researcher at the Institute for Intelligent Systems (IIS). His leadership roles included serving as a professor and Dean of the School of Psychology at Central China Normal University (CCNU), leading the Advanced Distributed Learning (ADL) Partnership Laboratory at UofM, and working as a senior researcher at the Key Laboratory of Adolescent Cyberpsychology and Behavior, backed by the Chinese Ministry of Education. Prof. Hu's research focuses on four key areas: developing mathematical models to decode human cognitive behavior, specializing in research design and statistical analysis particularly for categorical data using general processing tree models, delving into artificial intelligence for knowledge representation, creating computerized tutoring systems, and enhancing distributed learning technologies. His work has attracted significant funding from prestigious bodies like the US National Science Foundation, the US Institute of Education Sciences, the Advanced Distributed Learning initiative of the US Department of Defense, the US Army Medical Research Acquisition Activity, the US Army Research Laboratories, and the US Office of Naval Research. As the lead principal investigator, Prof. Hu has managed projects with over $10 million in funding, and as a co-principal investigator, he has been involved in projects amassing more than $30 million in grants.  

Title: CbITS & LLM: Teaching an Old Dog New Tricks

Abstract: We will explore how Conversation-based Intelligent Tutoring Systems (CbITS) are being enhanced through the integration of Large Language Models (LLMs). Both CbITS and LLMs draw their power from natural language—the “universal interface” for human communication—making them highly impactful in learning environments. Whether it’s through a traditional tutor or advanced AI, conversation remains the foundation of effective learning.
CbITS have been successful in delivering personalized, conversation-driven tutoring for years. However, as educational needs evolve, even the most reliable systems benefit from an upgrade. Enter LLMs. With their advanced natural language processing abilities, LLMs are perfectly positioned to supercharge CbITS, making them more adaptive, responsive, and engaging. We’ll examine how the first and most natural application of LLMs in education is to enhance CbITS, expanding their ability to deliver deeper, more personalized learning experiences.
In this talk, we will explore real-world examples of how LLMs are revitalizing CbITS and improving learning outcomes. We’ll also discuss the broader implications of this integration, particularly in bridging educational gaps between mainstream and marginalized contexts.
Finally, we’ll introduce the Socratic Playground for Learning (SPL)—a practical, “lowest-hanging fruit” example that demonstrates how naturally LLMs can enhance CbITS, showing that even the “oldest” systems can learn new tricks.
 

Prof. Minhong(Maggie) WANG

The University of Hong Kong, China

Director of the Laboratory for Knowledge Management & E-Learning in the Faculty of Education

Dr. Minhong (Maggie) Wang is Professor and Director of the Laboratory for Knowledge Management & E-Learning in the Faculty of Education, The University of Hong Kong (HKU, ranked World Number One for Education and Educational Research by U.S. News & World Report in the 2022-2023 Best Global Universities subject rankings). She is also Eastern Scholar Chair Professor at East China Normal University and Visiting Research Professor at the Advanced Innovation Center for Future Education of Beijing Normal University. She is the Editor-in-Chief of Knowledge Management & E-Learning (indexed in Scopus & ESCI). Her research focus is on learning technologies for cognitive development, creative thinking and complex problem solving, knowledge management and visualization, and artificial intelligence applications. She has published more than 200 items including one monograph and 117 journal articles (73 in SSCI/SCI indexed journals; 48 in Q1 and 18 in Q2 journals) among others. She is recognized as ESI Top 1% Scholar in (a) Social Sciences, General, and (b) Economics & Business.

More details can be found at http://web.edu.hku.hk/staff/academic/magwang.

Title: Rethinking How People Learn for Effective Learning Design and Analysis

Abstract: How people learn has long been discussed, revealed by many learning theories, explored in extensive practices, and analyzed in numerous studies. This talk will present a high-level view of human learning from four fundamental perspectives, that is, learning by interaction with content (C), learning by interaction with other people (O), learning by interaction with self (S), and learning by interaction with tasks or practices (T), so-called COST model. Based on this model, this talk will summarize how technology supports human learning, how to design effective learning to address learners’ needs and challenges, and how to make meaningful analysis of human learning with the support of technology.

Prof. Qi LIU

University of Science and Technology of China, China

H-index 60

Qi Liu is currently a Professor in the State Key Laboratory of Cognitive Intelligence at University of Science and Technology of China (USTC), and he serves as the Vice Dean of the School of Artifical Intelligence and Data Science, USTC. His general area of research is data mining and knowledge discovery, with a focus on mining massive user-generated data for intelligent applications, e.g., intelligent education. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, ACM TOIS, ACM SIGKDD, NeurIPS, ICML, IJCAI, AAAI. These papers have been cited for more than 14,000 times, and his H-index is 61. Some of the proposed techniques have been applied in the real-world products for large-scale adaptive learning, serving for more than 55 million learners from both basic education and lifelong education. Dr. Liu is the recipient of the ACM SIGKDD2018 Best Student Paper Award (Research Track), IEEE ICDM2011 Best Research Paper Award, CIKM2023 Best Paper Runner-Up Award, and he was invited to deliver the Early-Career Spotlight Talk at IJCAI 2021. He is also one of the recipients of the Green Orange Award of Alibaba DAMO Academy. As the Principal Investigator, he has undertaken several projects such as the National Science Fund for Excellent Young Scholars. He is an Associate Editor of IEEE Transactions on Learning Technologies, Neurocomputing, and IEEE Transactions on Big Data.

Title: Cognitive Diagnosis for Intelligent Education: A Machine Learning Perspective

Abstract: Cognitive diagnosis is a type of assessment for automatically measuring individuals’ proficiency profiles from their observed behaviors, e.g. quantifying the mastery level of students on specific knowledge concepts/skills. As one of the fundamental research tasks in intelligent education, a number of Cognitive Diagnosis Models (CDMs), rooted in psychometric theories, have been developed in the past decades. This talk aims to provide the recent development of cognitive diagnosis from a novel machine learning perspective, where both the routine behaviors of students and the detailed information of learning resources can be well-exploited. Furthermore, the applications of cognitive diagnosis in adaptive learning and adaptive testing will be discussed, especially the way of its integration with large language models. Also, two public libraries, EduData and EduCDM ( https://github.com/bigdata-ustc/EduCDM ), are given for offering valuable resources for the research community of cognitive diagnosis.