XIONG Ke, School of Computer Science and Technology, Beijing Jiaotong University, China (kxiong@bjtu.edu.cn)
TAO Dan, School of Electronic and Information Engineering, Beijing Jiaotong University, China (dtao@bjtu.edu.cn)
ZHOU Wei, School of Computer Science and Technology, Beijing Jiaotong University, China (wzhou@bjtu.edu.cn)
In order to respond to the demand for versatile and innovative talents in computer science, we focus on the deep integration of AI and digital technology in the entire process of talent cultivation, summarizing the practical achievements in innovation of talent cultivation methods, application of core technologies, and development of high-quality resources, building a high-quality computer science education system. In the invited session, we call for excellent academic papers and practical research results worldwide, focusing on the key areas of talent cultivation (including computer science and technology, software engineering, artificial intelligence, network engineering, data science, and big data technology, etc.), which are empowered by AI and digitalization. The session covers three core directions: method innovation, technology application, and resource development.
Topics include but are not limited to:
● Reconstruction of the computer science talent cultivation system, exploration and practice of the "AI+X" composite talent cultivation path;
● The innovation of "student-centered" teaching mode, including the design and implementation of personalized learning, inquiry-based learning, project-based learning, blended learning, and other modes;
● Innovative applications of large language models, generative AI, and multimodal in computer-related course teaching (such as code generation, case design, tutoring and answering questions, paper polishing, etc.);
● The application practice and effect analysis of tools such as intelligent teaching assistants, virtual teachers, and intelligent evaluation systems in the entire teaching process (lesson preparation, teaching, homework correction, assessment and evaluation);
● Development of core computer course (group) resources empowered by AI, including three-dimensional textbooks, online courses, virtual simulation resources, case libraries, question banks, etc.;
● The digital upgrading and sharing path of first-class courses and high-quality teaching resources under the "101 Plan" background, as well as the collaborative construction mechanism of cross-school and cross-regional teaching resources;
● The construction of computer-related faculty teams, including methods and paths for enhancing teachers' digital literacy and training in AI teaching capabilities;
● Innovation in industry-education integration and school-enterprise collaborative education mechanisms, such as the construction and practice of industry colleges, AI joint laboratories, and internship and training bases;
● Development and localization practice of computer talent training resources oriented towards regional characteristics and industry needs (such as smart manufacturing, intelligent transportation, medical health, etc.).
Prof. XIONG Ke is associate dean of Computer Science and Technology, Beijing Jiaotong University.
His main research interests focus on the intersection of artificial intelligence, digital education, network intelligence and mobile computing, low-altitude navigation safety assurance, intelligent perception and embodied AI. He was selected as a Highly Cited Chinese Researcher by Elsevier in 2023 and 2024, and listed among the World's Top 2% Scientists by Stanford University in 2022, 2023, and 2024.
See more: https://faculty.bjtu.edu.cn/8584
Prof. TAO Dan is associate dean of School of Electronic and Information Engineering, Beijing Jiaotong University.She received her Ph.D. degree from Beijing University of Posts and Telecommunications.
Her main research interests focus on the intersection of artificial intelligence, digital education, communication engineering, signal processing and electronic systems, low-altitude intelligent communication and networking technology. She was awarded the Beijing "Pioneer in Teaching and Educating" (2023), Baosteel Excellent Teacher Award (2025) etc.
See more: https://faculty.bjtu.edu.cn/8036/
Prof. ZHOU Wei is senior researcher from School of Computer Science and Technology, Beijing Jiaotong University. She received the Ph.D. degree from Nagoya University (Japan).
With main research interests in Data Science, AI, System Engineer, Education Technology, Information Services, she has published many papers of international conference and journals, and served on some editorial boards. She received the Third Batch of National First-Class Undergraduate Courses (2025), and the "Outstanding Teacher Award" from the National Association of Computer-Based Education in Higher Education Institutions (2025), etc.
See more: http://faculty.bjtu.edu.cn/8405/
WENG Yu, School of Information Engineering, Minzu University of China, China. (wengyu@muc.edu.cn)
KEBAITULI Gulibanumu, School of Foreign Studies, , Minzu University of China, China. (banumu@muc.edu.cn)
XU Guixian, School of Information Engineering, Minzu University of China, China. (guixian_xu@muc.edu.cn)
Marginalized communities, such as remote areas, low-income regions and ethnic minority-inhabited areas, are frequently plagued by educational inequities including inadequate high-quality educational resources, shortage of professional teachers, and uneven educational development. With the rapid advancement of artificial intelligence (AI) technology, it has become a critical opportunity to bridge the educational gap and promote educational equity by leveraging digital means. AI not only innovates the forms of educational service delivery but also provides new solutions for optimizing resource allocation and realizing personalized learning in resource-constrained contexts.
This special session aims to bring together researchers, educators, policymakers, and industry practitioners worldwide to explore the construction path, application strategy and implementation effect of AI-enhanced digital safeguard systems for education in marginalized communities. The core focus is to discuss how to use AI technology to build a multi-dimensional digital support system that covers equitable resource distribution, teacher capacity empowerment, and personalized learning support, thereby breaking through the bottlenecks of traditional education in marginalized communities. This session is expected to provide theoretical support and practical guidance for promoting educational equity in marginalized areas, and contribute to the realization of the United Nations Sustainable Development Goal 4 (Quality Education for All).
Topics include but are not limited to:
● AI-driven equitable distribution and intelligent allocation of educational resources in marginalized communities;
● Development of lightweight AI educational tools adapted to resource-constrained environments;
● AI-enabled personalized learning support for students in marginalized communities;
● AI-assisted teacher professional development systems for marginalized areas;
● Ethical norms and data privacy protection in AI-enhanced educational practices in marginalized communities;
● Localized adaptation of AI educational technologies;
● Integrating AI Ethics and Value-Oriented Learning in Computer Science Curricula
Prof. WENG is Dean of the School of Information Engineering, Minzu University of China. He received his Ph.D. degree in Computer Science and Technology from University of Science and Technology Beijing. His main research interests focus on the intersection of artificial intelligence, digital education, and educational equity, with a particular emphasis on digital education development.
He has presided over multiple national and provincial-level scientific research projects, published numerous high-quality academic papers in international conferences and journals in the field of educational technology and AI application, and served as a reviewer for several authoritative international journals and conferences. He has rich experience in promoting the integration of digital technology and education in marginalized communities, providing important technical and theoretical support for improving educational equity in these areas.
KEBAITULI Gulibanumu, PhD, an Associate Professor, Master’s Supervisor, and the Vice-Dean of the School of Foreign Studies at Minzu University of China. Concurrently serves as the Vice-Dean of the Institute of Area Studies and the Vice-Dean of the Institute of Central Asian Studies at the same university.
The director of a National First-Class Undergraduate Course and has been honored as a “Model Young Teacher” by the State Ethnic Affairs Commission. Dr. KEBAITULI is also a member of the Expert Committee for Reviewing Major Translation Projects, jointly established by the China International Communications Group (CICG) and the Translators Association of China (TAC).
Her primary research interests include Uzbek linguistics, translation studies, and the society and culture of Central Asia. She has authored 2 academic monographs, published 10 academic translations, and reviewed and finalized the official Uzbek translations of 4 major national works. Additionally, she has published 2 textbooks and translated 14 textbooks and reference tools for teaching Chinese as a foreign language. Dr. KEBAITULI has published over 20 academic papers in domestic and international journals.
Prof. XU Guixian is a Professor and Doctoral Supervisor at Minzu University of China. She holds a Ph.D. in Computer Software and Theory from Beijing Institute of Technology and was a visiting scholar at Georgetown University and the Mayo Clinic in the United States.
Her main research interests lie at the intersection of Artificial Intelligence, Data Mining, and Natural Language Processing, with a specific focus on Web Text Mining and Social Network Public Opinion Analysis. A significant portion of her research is dedicated to the intelligent information processing, applying big data and deep learning techniques to areas like sentiment analysis, and knowledge management. Prof. XU's work exemplifies the application of cutting-edge computational technologies to address culturally significant challenges, providing important technical support for information processing and public opinion governance.
Hiroki TAKADA, University of Fukui, Japan. (takada@u-fukui.ac.jp)
In today’s digital era, where images and videos are constantly entering our homes through various platforms, it has become increasingly important to examine the safety of visual exposure from an academic perspective. This session introduces recent developments in biosignal analysis and biomedical imaging, and explores their applications in this emerging field. Mathematical modeling, including artificial intelligence (AI), has been recognized as a fundamental technique for biosignal processing and analysis. In the context of 5G and beyond-5G technologies and networks, biosignals and their diverse applications have attracted growing attention. Furthermore, the application of AI—given its remarkable advancements in recent years—will also be discussed in relation to this domain.
Topics include but are not limited to:
● Machine Learning/AI
● Biomedical Imaging
● Computer–Human Interact
● Control and Communication
● Deep Learning
● Mechatronics and Robotics
● Visualization of Big Data
● Techniques, Models, and Algorithms
Prof. Hiroki TAKADA, is a tenured Professor in the Department of Human and Artificial Intelligent Systems, the Graduate School of Engineering, University of Fukui, Japan. He is also the Co-Director of the Nonlinear Science Lab. His research is centered on the nonlinear analysis of time sequences. In his research, mathematical models have been obtained from the data sequences in Economics, Meteorology, and Erectrophysiology based on the stochastic process theory. He also received the Organization Contribution Award from the International Conference of Computer Science and Education (ICCSE) in 2020. Prof. Takada also serves as an editor in Environmental Health and Preventive Medicine and an editor-in-chief of Forma. He is a member of IEEE, Physical Society of Japan, and other organizations.
LUO Juan, Hunan University, China (juanluo@hnu.edu.cn)
ZHAO Huan, Hunan University, China (hzhao@hnu.edu.cn)
CAI Yuhui, Hunan University, China (rj_cyh@hnu.edu.cn)
The widespread application of artificial intelligence (AI) technology is accelerating the transformation of the educational ecosystem, driving innovation in curriculum systems, interdisciplinary integration, and the modernization of teaching and management models. To address this trend, this session focuses on core issues such as the design of AI general education systems, multidisciplinary collaborative innovation, and AI-empowered teaching, aiming to explore new educational paradigms tailored to the intelligent era.
Topics include but are not limited to:
● Design and Exploration of AI General Education Curriculum Systems: Development of AI literacy frameworks and curriculum reforms for students across diverse academic disciplines;
● Interdisciplinary Integration and Research in AI: Curriculum reforms for cultivating cross-disciplinary talent through AI applications in fields such as humanities, social sciences, and engineering;
● Practical Teaching Research in AI: Innovations in teaching models, laboratory platform design, case study development, student competency training, and evaluation of educational outcomes in AI practice;
● AI-Empowered Teaching: Research on AI-driven personalized learning, intelligent tutoring systems, automated assessment, and educational big data analytics. This session seeks to establish a collaborative forum for AI education research, promote the translation of theoretical advancements into practical teaching, and provide systematic solutions for educators worldwide.
Juan Luo, Ph.D., is a professor, doctoral supervisor, and associate dean at the College of Computer Science and Electronic Engineering. She earned her bachelor's degree from the National University of Defense Technology and her master's degree and Ph.D. from Wuhan University. Previously, she worked at Fiber home Networks, a company affiliated with the Wuhan Academy of Posts and Technology, and was a visiting scholar at the University of California, Irvine. She has been recognized as a New Century Outstanding Talent by the Ministry of Education and was granted the Hunan Province Outstanding Youth Fund. Her current research focuses on IoT, cloud computing, and artificial intelligence.
See more: http://csee.hnu.edu.cn/people/luojuan
ZHAO Huan, Ph.D., Professor, doctor supervisor, associate dean, College of Computer science and Electronic Engineering. She is visiting scholar at the University of California, San Diego, the member of the Computer Basic Teaching Steering Committee of the Ministry of Education, and the member of the Steering Committee of the Education and Training of Industrial and Information Talents. She won the second prize of National Teaching Achievement Award, the Outstanding prize of BAOGANG distinction teacher and Huo Yingdong Education Foundation Education and Teaching Award.
Her research interests include embedded computer systems and speech information processing.
See more: http://csee.hnu.edu.cn/people/zhaohuan
CAI Yuhui is an associate professor at the College of Computer Science and Electronic Engineering in Hunan University whose research interests include computer networks, image processing, and artificial intelligence. He won the second prize of the Chinese University Science and Technology Award. And he was employed by the Ministry of Education of PRC in 2023 as a member of the course construction team for Computer Science Undergraduate Education and Teaching pilot reform program.
See more: http://csee.hnu.edu.cn/people/caiyuhui
WU Yonghui, Fudan University, China. (yhwu@fudan.edu.cn)
Masroor Hussain, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan.(hussain@giki.edu.pk)
Sheng-Lung PENG, Taipei University of Business.
Nowadays, on the one side, programming technology has become the implementation for the society; and on the other side, all professions and works reliant on tool-based skills, including most programmers' work, are being displaced by AI technologies. Cultivating students' computational and mathematical thinking by solving programming contest problems can be as a breakthrough point for the reform of computer education in the AI era.
Topics for cultivating students' computational and mathematical thinking are included but not limited to:
● Constructions of teaching materials;
● Curricula system for programming;
● Cross-institutional and cross-regional programming training system;
● Innovations and effects;
● Smart Learning Environment and Supporting Technology
Dr. Yonghui Wu, associate professor at Fudan University, visiting scholar at Stony Brook University, and, the chair of the ICPC Asia Training Committee. He won three medals in ACM ICPC World Finals for Fudan University.
His book series “Collegiate Programming Contests and Education” has been published in simplified and traditional Chinese and English: the former by respective publishers of mainland China and Taiwan, and the latter, the first book’s translation, by CRC Press. Since 2013, he has been giving lectures not only in China, but also in other countries.
Dr. Masroor Hussain is a distinguished academic leader serving as Professor and Head of the Department of Computer Engineering & Data Science (CE & DS) at the Ghulam Ishaq Khan Institute of Engineering Sciences and Technology. He is recognized for his contributions to artificial intelligence, high-performance computing, and advanced computing, while promoting outcome-based education and research excellence.
He is also the CEO of Kawai Enterprise (Private) Limited, where he leads AI-driven, data engineering, and digital transformation initiatives that strengthen industry-academia collaboration. As Regional Contest Director for the ICPC Asia Topi Regional Contest, he mentors and organizes competitive programming platforms enabling students to compete internationally.
His recent work focuses on agentic AI systems, autonomous AI agents, and scalable intelligent infrastructures, reflecting a strong blend of academic leadership, innovation, and commitment to student talent development.
Sheng-Lung PENG is a Professor at the Department of Creative Technologies and Product Design, and the Dean of the School of Innovative Design and Management,Taipei University of Business. He received the PhD degree from Computer Science Department of Tsing Hua University. He is an honorary Professor at Beijing Information Science and Technology University and a visiting Professor at Ningxia Institute of Science and Technology in China. He is also an adjunct Professor at National Dong Hwa University and Kazi Nazrul University in India. He is an Honorary Adjunct Professor in School of Management of Sir Padampat Singhania University and in School of Computer Science and School of Business of ITM (SLS) Baroda University, India. He serves as the president of the Association of Taiwan Computer Programming Contest and the Association of Algorithms and Computation Theory. He is a co-director of the ICPC Asia Pacific, and a director of the Institute of Information and Computing Machinery and the Taiwan Association of Cloud Computing. He is also a supervisor for the Chinese Information Literacy Association. Dr. Peng has edited several special issues at journals, such as Journal of Internet Technology, IEEE Internet of Things Magazine, Computers and Electrical Engineering, Journal of Information Science and Engineering, and so on. His research interests are algorithm design in the fields of artificial intelligence, bioinformatics, combinatorics, data mining, and networking.
XIONG Yu, Chongqing University of Posts and Telecommunications (CQUPT), China.(xiongyu@cqupt.edu.cn)
With breakthroughs in AI-generated content technology and enhanced multimodal interaction capabilities, educational AI agents are evolving from single-function tools into multifaceted educational partners equipped with perception, decision-making, interaction, and evolutionary capabilities. This transformation provides key technological support for building a new ecosystem of human-machine collaborative education. How to scientifically construct agents tailored for different educational scenarios, how to achieve effective collaboration between agents and learners, and how to evaluate the application effects of agents in educational practice have become cutting-edge issues in the field of intelligent education. This invited session aims to provide a platform for researchers, developers, and educational practitioners in the field of educational agents to engage in in-depth dialogue, exploring design paradigms, core technologies, application scenarios, and evaluation systems for educational agents.
Topics of interest include, but are not limited to:
● Design of Subject-Specific Educational Agents for Disciplinary Instruction
● Explainability Mechanisms and Transparent Decision-Making in Educational Agents
● Automatic Generation of Multimodal Educational Content Driven by Educational Agents
● Personalized Learning Path Recommendation and Adaptive Agents
● Learning Process Analysis and Diagnosis in Human-Machine Collaborative Scenarios
● Design and Application of Evaluation Agents
● Enhanced Intelligent Tutoring Systems and Virtual Teachers
● Trustworthy Mechanisms, Ethical Standards, and Privacy Protection in Educational Agents
● Multi-Agent Collaborative Systems in Classroom Teaching Applications
● Empirical Research and Effectiveness Evaluation of Educational Agents
XIONG Yu is now the professor and doctoral supervisor at Chongqing University of Posts and Telecommunications (CQUPT), and the director of Research Center for Artificial Intelligence and Smart Education at CQUPT. He is also currently Executive Director of Chongqing Research Center for Educational Big Data and Director of Chongqing Key Laboratory of Hybrid Enhanced Intelligent Education in Universities. He also serves as the Vice Chairman of Technical Committee on Smart Education of the Chinese Association of Automation, the Member of the Intelligent Education Committee, Chinese Association of Educational Technology, Secretary-General of the Steering Committee for Education Digitalization and Teaching Method Innovation in Chongqing Higher Education Institutions. He has published more than 100 academic papers, and won 3 first prizes of provincial and ministerial-level scientific and technological achievements, as well as 2 first prizes of teaching achievements. His research interests include artificial intelligence and smart education, pattern recognition and machine learning, and educational data mining.