Keynote Speakers
Prof.
Elliot Soloway, University of Michigan, USA
Arthur F. Thurnau
Professor, College of Engineering
Co-Director, Center for Digital Curricula, University of
Michigan, USA
Elliot Soloway is an
Arthur F. Thurnau Professor, in the
Department of Computer Science and
Engineering, College of Engineering, at
the University of Michigan, Ann Arbor,
MI. In 2001, the UMich undergraduates
selected him to receive the “Golden
Apple Award” as the Outstanding Teacher
of the Year at the University of
Michigan. In 2004 and in 2011, students
in the College of Engineering HKN Honor
Society selected Dr. Soloway to receive
the “Distinguished Teacher of the Year
Award.”
40 years ago as an artificial
intelligence researcher, Dr. Elliot
Soloway was drawn to the challenge of
how to make computers learn. But, after
the birth of his first child, he had an
epiphany: Making children smarter would
be a much better use of his time than
making computers smarter. This inspired
him to stop doing AI research altogether
and start working on educational
technology. But as a college professor
with little experience in education
technology, he needed someone who really
understood K-12.
Co-speaker
Prof.
Cathleen Norris, University of North Texas, USA
Associate Dean of Research, College of Information
Co-Director, Center for Digital Curricula, University of
Michigan, USA
Cathleen Norris is a
Regents Professor in the Department of
Learning Technologies, College of
Information, at the University of North
Texas, Denton, TX. From 1995-2001,
Norris was President of the National
Educational Computing Association
(NCEA), and led its merger with ISTE,
the International Society for Technology
in Education, creating the largest,
international organization for
technology-minded educators in the
world. Norris was Co-President of ISTE
from 2001-2004. Norris’ 14 years in K-12
classrooms – receiving a Golden Apple
Award from Dallas ISD along the way –
has shaped her university R&D agenda:
developing resources to support K-12
teachers as they move into 21st century
classrooms.
Norris has also given presentations on
educational technology all over the
world for the past 20+ years and is the
co-founder of GoKnow, Inc., a
Dallas-based company that supports K-12
in using mobile learning devices.
Title: The AI-First, Beta Generation Children Will Enter School Soon: Are Schools Woefully Unprepared for the 3rd Educational Revolution?
Abstract：Today’s schools are already challenged to support the digital/screen-first Alpha Generation (2010-2024) children! While Chromebooks and laptops finally proliferated during and after the COVID lockdowns, many teachers find using digital curricula and digital pedagogy a challenge and voice sentiments such as “I have 3 years to retirement, and you expect me to learn something new?” Further, for the most part, current “digital curricula” are simply text-heavy, paper-and-pencil curricula put on a computer, e.g., digitized worksheets and textbooks, etc. But dramatic change is possible! In our presentation, we will report on graphically-oriented, colorful, deeply-digital, interactive, collaborative curricula and an associated learning platform that teachers and their Alpha students are using today with success. But this is just the tip of the iceberg! Looking to the near-term future, how are we going to change the way teachers are prepared, change curricular offerings, and prepare schools for the Gen Betas (2025-2039) in the “AI Age” – the 3rd educational revolution?
Prof.
Irina Lyublinskaya, Columbia University, USA
Irina Lyublinskaya received her Ph.D. in Theoretical and Mathematical Physics from Leningrad State University in 1991. She has taught at the university as well as the high school level for over 30 years. Among her research interests are topics of integrating technology into mathematics and science education, pre-service and in-service professional development of STEM teachers, curriculum development, and international STEM education. She is a recipient of various awards for teaching excellence, including Radioshack/Tandy Prize for Teaching Excellence in Mathematics, Science, and Computer Science, NSTA Distinguished Science Teaching Award and citation, Education’s Unsung Heroes Award for innovation in the classroom, and NSTA Vernier Technology Award. In 2011, she was inducted to the NYS Mathematics Educators Hall of Fame. In 2019 she was elected as a full member of the Russian Academy of Natural Sciences. She is a recipient of several outstanding paper awards from AACE and SITE, an author/co-author of 16 books, 14 book chapters, and has published substantially in academic journals.
Title: Integrating AI literacy into STEM teaching and learning: From theory to practice
Abstract: In the
rapidly evolving landscape of education,
Artificial Intelligence (AI) is
reshaping the way we teach, learn, and
prepare the next generation for the
challenges of the 21st century. Growing
influence of AI in schools requires
teachers to have essential knowledge and
skills to use, design, and adapt AI
literacy into their curriculum. In the
past, students learned about AI in
school from their computer science
teachers. Thus, the majority of early AI
resources and curricula were developed
for teachers who had prior training and
experience in programming. As AI-powered
experiences and applications become an
increasingly important part of everyday
personal and professional life, being AI
literate becomes an important component
of K-12 digital literacy and digital
citizenship.
This presentation introduces a
pedagogical framework for integrating AI
literacy into STEM teaching and
learning. It moves beyond traditional,
programming-centric approaches to
provide educators with practical
strategies for implementation across
grade bands and subject areas, fostering
interdisciplinary connections. The
framework is grounded in three key
theoretical foundations: the Five Big
Ideas in AI, which define core AI
concepts; the human-centered design
approach, which provides a structured
methodology for teaching with AI; and
the Science of Learning and Development
(SoLD) framework, which ensures
inclusivity and accessibility. This
framework also provides a foundation for
discussing the ethical and societal
implications of AI.
Through concrete examples, this keynote
bridges the gap between theory and
practice and demonstrates how to design
lessons that integrate core AI concepts
into existing STEM curriculum to empower
educators to cultivate AI literacy,
preparing students to become informed,
responsible, and innovative contributors
in the age of AI.
Prof.
Qun Jin, Waseda University, Japan
Foreign Fellow, The Engineering Academy of Japan (EAJ) Fellow
The Asia-Pacific Artificial Intelligence Association (AAIA)
Qun Jin is a professor in the Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He has been extensively engaged in research works in the fields of computer science, information systems, and human informatics, with a focus on understanding and supporting humans through convergent research. His recent research interests cover behavior and cognitive informatics, health informatics, artificial intelligence and machine learning, big data, personal analytics and individual modeling, trustworthy platforms for data federation, sharing, and utilization, cyber-physical-social systems, and applications in healthcare and learning support and for the realization of a carbon-neutral society. He authored or co-authored several monographs and more than 430 refereed papers published in academic journals and international conference proceedings. He is a foreign fellow of the Engineering Academy of Japan (EAJ) and a fellow of the Asia-Pacific Artificial Intelligence Association (AAIA).
Title: Technology-Enhanced Learning in the Age of Artificial Intelligence
Abstract: Over the past few decades, we have witnessed the rapid development of ICT and artificial intelligence (AI) technologies. Many aspects of learning activities can be supported and significantly enhanced by these emerging technologies, including, most recently, large language models (LLMs), generative AI, and AI agents. In this talk, after giving a brief overview of the paradigm shift in technology-enhanced learning, I will present our recent work in this field. This includes goal-driven and task-oriented learning process recommendations, collaborative learning support based on user networks and behavior analysis, social learning support and analytics, interactive online course recommendations, and on-demand learning support with individualized comments generated by ChatGPT. Finally, I will share our vision for the future of technology-enhanced learning leveraged by Metaverse, LLMs, and AI agents.
Prof.
Xi Chen,
Nanjing University,
China
Chair of Marketing and Electronic Business Department
Xi Chen, Ph.D., Full Professor, Ph.D. Supervisor, Chair of Marketing and Electronic Business Department, Nanjing University, Visiting Scholar of Michael G. Foster School of Business, University of Washington. He authors more than 110 refereed journal/international conference papers and book chapters, more than 90 papers among them have been indexed by SCI/SSCI and EI, including lots of papers in UTD list and JCR I section. He has published 3 monographs and owned several national authorized patents. He is the project chief and sub-project leader of more than 30 Foundations, including the key project of the National Natural Science Foundation, the key project of the National Social Sciences Foundation, National Science Foundation, and National Ministry of Education Science Foundation, and so on. He is the innovation team leader of Nanjing University. He got the Youth Award of Management in China. Best Paper Awards of the Chinese Academy of System Simulation and China Information Economics Society, Outstanding Achievement Award of Philosophy and Social Sciences of Jiangsu Province and so on. He has been elected to the Ministry of Education for New Century Excellent Talents Scheme since 2011, and 5 high-level talent plans in Jiangsu Province. He is the chair and the commissioner of some international/national academic associations, the chair of some technical committees, the chair of the organization committee of some refereed conferences, and the associate editor and editorial member of some refereed international journals and conferences. His research interests include business intelligence, services engineering and management, digital economy and complex systems.
Title: Knowledge Graph Construction Research in TCM
Abstract: This study presents the construction of a knowledge graph for acupuncture treatment of depression, integrating traditional Chinese medicine with advanced artificial intelligence technologies. Despite acupuncture's proven efficacy in alleviating depressive symptoms, its knowledge remains fragmented and lacks standardized semantic representation. To address this gap, we developed a domain-specific ontology using the Seven-Step Method, defining 11 core concepts. Leveraging large language models like ChatGPT, we extracted 12,148 triples from 1,263 screened academic papers, standardized entities using ICD-11 and TCM guidelines, and stored the KG in Neo4j for interactive querying and visualization. Our results demonstrate the KG's utility in mapping acupoint-symptom relationships, supporting clinical decision-making, and facilitating research. Challenges included data heterogeneity and multilingual extraction, while innovations centered on AI-assisted KG construction and the fusion of TCM with modern data science. The combination of traditional Chinese medicine frameworks with modern AI and data science techniques represents an innovative fusion of old and new knowledge paradigms. The innovation can be widely applied in e-learning and training.
More speakers will be added soon...