Prof. Jiyou Jia, Beijing University, China

Brief: Dr. Jiyou Jia is a full professor and the Head of the Department of Educational Technology, Graduate School of Education, Peking University, China and is also the founding director of International Research Center for Education and Information at Peking University. He serves concurrently as the Vice Principal of Science at Xinxin School affiliated to Peking University High School. He is the responsible professor for the national-level online and offline blended first-class undergraduate course "Education and Artificial Intelligence". He was invited to work as a guest professor by School of Education, Technical University of Munich, Germany, a Distinguished Professor at Institute for Research in Open and Innovative Education, the Open University of Hong Kong China, and a visiting professor by the Education University of Hong Kong China. His research interests include educational technology and artificial intelligence in education, especially in TELL (Technology Enhanced Language Learning), math education with ICT, and decision making support system. He has been responsible for a dozen of national projects and international cooperation projects. His has won a number of national and international prizes, and published more than 150 articles in internationally or nationally peer-reviewed journals and conferences.

Title: The Development and Application of a Math Intelligent Assessment and Tutoring System (MIATS V2.0) in Math Education Based on Big-data Mining

Abstract: This speech introduces an intelligent mathematical assessment and tutoring system MIATS V2.0, which can not only provide adaptive assessment for students based on big data mining, but also guide students individually to solve difficult questions step by step. Four quasi-experiments in schools were conducted to evaluate the effect of this system on students’ performance. The analysis of the collected data demonstrates that the system can provide personalized assessment and tutoring and enhance the students’ learning performance, and is an effective approach to realize the value-added evaluation, the integration of testing and learning, and the promotion of learning through testing. This study provides a valuable reference for utilizing intelligent assessment and tutoring systems to help implement the macro policies such as student assessment reform, reducing the students’ burden and increasing the educational efficiency.

Prof. Zi-Ke Zhang, Zhejiang University, China

Brief: Zi-Ke Zhang is a full Professor at Zhejiang University. He serves as Deputy Director of the Center for Digital Communication Studies at Zhejiang University, Deputy Head of the Textbook Development Group for Zhejiang Universitys Artificial Intelligence General Education Courses, and a member of the Zhejiang University Artificial Intelligence Ethics Committee. His primary research interest includes intelligence-driven complex social systems. He has led projects funded by the NSFC and NSSFC. His research has been published in authoritative domestic and international journals, including PNAS, Physics Reports, etc. He was awarded the Second Prize for Natural Science by the China Computer Federation.

Title: Toward the Predictability of Complex Networks

Abstract: In this talk, we establish network predictability theory by mapping link prediction onto a spin glass model, where network partitions correspond to spin configurations, and predictability equals the system’s average energy. Using the cavity method from statistical physics, we prove that global predictability decomposes into individual link contributions, enabling an efficient local sampling algorithm that reduces the computational complexity of evaluating individual link contributions from being dependent on the entire network to only its local neighborhood, scaling with the average degree. We derive exact results for canonical network models: ER networks exhibit universal predictability of 0.5 regardless of algorithm choice, establishing the random baseline, while structured networks show predictability controlled by their prior parameters. We introduce the predictability index, which quantifies the maximum achievable performance without information loss and accurately predicts algorithm performance under random division. Analysis of real networks validates our framework, revealing how degree heterogeneity and structural patterns govern predictability. This physics-based approach provides both theoretical insights into link prediction limits and practical tools for assessing network reconstruction potential, with implications for applications from biological network inference to social network analysis.

Prof. Juanjuan Chen, Zhejiang University, China

Brief: Dr. Juanjuan Chen is an associate professor in the College of Education, Zhejiang University (ZJU 100 young professor). She also served as the deputy director of Information Technology Center of Zhejiang University. Moreover, she is a visiting scholar at The University of Hong Kong.

Her research interests focus on technology-enhanced learning, cognitive development of higher order thinking and its assessment. In particular, Dr. Chen has done cutting-edge work on designing technology-enhanced learning environments (e.g., multi-scale modeling) and supporting strategies to enhance students’multi-level complex reasoning, systems thinking, and creative thinking. In addition, she has conducted a series of studies on the role of empathy in creative thinking. The work on creative empathy reveals how empathy affects motivation, problem understanding, and creative thinking. These studies have pushed the boundaries of these areas, contributing to the understanding of complex reasoning and creative thinking.

Dr. Chen’s research has been supported by one competitive research grant from the National Natural Science Foundation of China, one from the Social Sciences and Humanities Research Grants from the Ministry of Education of the People’s Republic of China, and one from the Key Project of Zhejiang Federation of Humanities & Social Sciences. She has published about 30 journal articles, with 23 articles in SSCI/SCI indexed journals, 21 in Q1 and 2 in Q2 journals, and 8 articles in top 10 journals in the disciplines of education. She has 1 ESI 1% highly cited article (published inReview of Educational Research) and received The University of Hong Kong’s Research Output Prize for this work.

Title: Multi-level modeling for students' systems thinking

Abstract: Understanding or addressing complex problems requires group members to jointly analyze and evaluate information across multiple levels of a system, a process known as collaborative systems thinking (CST). However, the neural mechanisms underlying CST remain obscure. Here, we developed a genetic-algorithm-based multi-level simulation framework to experimentally manipulate the levels of systems thinking elicited by presenting dyads with full-level (macro–meso–micro), dual-level (macro–meso), or single-level (macro) representations of a simulated foraging-evolution process. Analysis of dyadic utterances revealed a progression from surface-level reasoning to partial integration and higher-order CST across groups. Full-level representations elicited superior CST performance than reduced-level representations. Critically, enhanced CST performance was accompanied by inter-brain synchrony (IBS) within dyads in the left dorsolateral prefrontal cortex. Furthermore, dyads exposed to full-level representations showed dynamic IBS involving executive-control and mentalizing regions. These findings demonstrate that access to richer, multi-level data for systems representation is associated with enhanced CST and more flexible patterns of inter-brain coordination during collaborative inquiry.

Prof. Bin Jiang, The Hong Kong University of Science and Technology (Guangzhou), China

Brief: Dr. Bin Jiang is Professor at The Hong Kong University of Science and Technology (Guangzhou) [HKUST(GZ)], with dual appointments in Urban Governance and Design, and Computational Media and Arts. He is the founder and director of LivableCityLAB and serves as Master of Wholeness College (formerly Residential College 1). He also chairs the International Cartographic Association Commission on Digital Transformation and sits on editorial boards including Computational Urban Science. His research bridges urban informatics, complexity science, and architectural and urban design, advancing living structure theory as a foundation for more sustainable and human-centered cities. He has pioneered quantitative metrics such as the L-score (Living Structure Score) and B-score (Beauty Score), operationalized through the Beautimeter, to scientifically evaluate structural beauty and aliveness in architecture and urban space. These innovations highlight the value of crossdisciplinary approaches that connect spatial analysis, digital technology, and aesthetic theory. Dr. Jiang has held academic roles across Europe and Asia and advises organizations such as Beijing City Lab and the International Society of Biourbanism. His work continues to integrate theory, technology, and design practice to advance the creation of adaptive, livable cities.

Title: Living Structure + AI: Rethinking Whole-Person Education in the Age of Artificial Intelligence

Abstract: Artificial intelligence is rapidly transforming education, but much of the current discussion remains focused on efficiency, content generation, personalized learning, and automation. This keynote argues that the more fundamental question is not simply how AI can assist learning, but how it can contribute to whole-person education. Drawing on Christopher Alexander's theory of living structure and recent educational experiments with Living Structure + AI, the presentation proposes a new framework in which learning is understood as the cultivation of wholeness: the integration of analytical reasoning, perception, emotion, creativity, embodied experience, and the capacity to make meaningful judgments. Within this framework, AI is not treated primarily as an autonomous generator of answers, but as a cognitive amplifier and design partner that can help learners perceive structure, compare alternatives, reflect on quality, and engage more deeply with real-world environments. The keynote will illustrate how Living Structure + AI can support liberal arts education, experiential learning, design education, and human-centered learning environments. It will also discuss the broader implications for educational practice in the age of artificial intelligence: shifting from knowledge transmission toward the development of judgment, creativity, connectedness, and human wholeness. The central proposition is that the future of education should not be about making learners more machine-like, but about using AI to help human beings become more whole.

Prof. Yuxia Du, Guangzhou University, China

Brief: Prof. Yuxia Du has been working in teaching and research at the School of Education, Guangzhou University since June 2007. Additionally, the author served as a visiting scholar at The Ohio State University, USA, from 2014 to 2015. 

The research mainly focuses on teacher education informatization, artificial intelligence in education, thinking instruction, digital textbooks, smart education, and STEAM education. She has presided over more than 10 provincial and above key research projects, including the General Project of National Social Science Fund of China, Humanities and Social Sciences Project of the Ministry of Education, and key provincial laboratory research projects. Prof. Du has published over 50 academic papers and authored 5 academic monographs, focusing on GenAI-enabled teaching research and digital intelligent education research methods.