Invited Speakers 邀请报告人

Prof. Heng Luo, Central China Normal University, China

Heng Luo received his Ph.D. degree in Instructional Design, Development, and Evaluation from Syracuse University in 2015, and worked as a Research Associate at John A. Dutton e-Education Institute in the Pennsylvania State University for two years. He is currently a Professor and Associate Dean in the Faculty of Artificial Intelligence in Education, Central China Normal University. His research work focuses on the effective integration of learning sciences, instructional design, and AI technologies to promote quality and equity of education in diverse contexts. Dr. Luo has been initiating socially responsive research programs that address the urgent needs and pressing issues in nowadays educational paradigm, such as inclusive e-learning, open education initiatives, self-regulated learning, STEM education, and safety education, leading to over 50 publications in SCI/SSCI/CSSCI journals. His academic contribution has been recognized with scholarly awards such as TICL Outstanding International Research Collaboration Award (AERA' 2022), Excellent Research Paper Award in International Conference on Blended Learning (2019 and 2020), and D&D Outstanding Practice Award (AECT' 2019).

Title: Empowering Safety and Health Education for K-12 Students with Immersive Technology

Abstract: This invited talk introduces VR‑powered instructional innovations and empirical evidence for K‑12 safety education. Conventional safety teaching through lectures and videos fails to deliver authentic risk experiences, limiting students’ practical awareness of road traffic hazards, drowning risks and school bullying. High‑fidelity virtual environments recreate these dangerous real‑life scenarios for safe experiential learning and interactive situational practice. Research findings demonstrate that VR‑based interventions boost learners’ risk recognition, self‑rescue skills and social empathy. This talk shares actionable, evidence‑based digital approaches to advance school safety education.

Prof. Hang Hu, Southwest University, China

Hu Hang, Professor/Doctoral Supervisor (Postdoctoral Cooperative Supervisor), Director of the Institute of Science Education of Southwest University, Director of the Virtual Teaching and Research Office of Southwest University Group [Science Education] of the Ministry of Education "Collaborative Quality" of Education, Convener of the National (Science) Education Alliance, TOP1% highly cited scholar of China Knowledge Network.
Expert of the Examination Center of the Ministry of Education, Vice Chairman of the Special Committee on Information Technology Education for Primary and Secondary Schools of the Chinese Society of Education, Vice Chairman of the Experimental Teaching Equipment Branch of the China Education Equipment Industry Association, Director of the Special Committee on Science and Technology Education of Chongqing Science Research Association, Chongqing Basic Education Quality Monitoring Expert, Chongqing Social Science Popularization Expert, Planner of the "Intelligent Education" column of Teacher Education Journal magazine.
He has presided over more than 20 national and provincial ministerial and school-level projects, published more than 80 papers in journals such as SCI, SSCI and CSSCI, and published 14 Chinese and English books (including translations and textbooks). The research result "Empirical Research on Technology to Promotes Deep Learning of Mathematics in Primary Schools" has won the excellent result of national education empirical research. Award, editorial committee and external review experts of many high-level journals. The experimental school of "Deep Learning and Intelligent Education" covers kindergartens, primary and secondary schools, vocational education and higher education. The project involves the reform of the evaluation mechanism of middle school and college entrance examination, early discovery and cultivation of top innovative talents, etc.

Title: Cultivating Mind-Humanity through Human-AI Collaboration

Abstract: The integration of artificial intelligence technologies into education has brought efficiency gains while raising profound questions about holistic human development. When educational practice becomes overly reliant on the optimization logic ofpure technological rationality, personalized learning may devolve into adaptive conditioning, immediate effectiveness may obscure sustained developmental value, and technological interaction may weaken interpersonal connections. Learners face the potential risk of regressing from generative subjects to merely adaptive subjects. Addressing this challenge requires a paradigm shift from tool empowerment to cognitive engagement. Drawing on theoretical perspectives from distributed cognition and philosophy of technology, the deep mechanism of human-AI collaboration lies in the dialectical unity of efficiency logic and generative logic, as well as the collaborative construction of subjectivity and intersubjectivity. On this foundation, "Mind- Humanity" emerges as a new form of educational subject for the intelligent age. In essence, it achieves harmonious symbiosis between computational intelligence and summoning thought. In terms ofpathway, it replaces "training" with cultivation and nurturing, following the dialectical generative logic of "awakening-transformation-integration" to cultivate subjectivity through spiral ascension. Ultimately, it aims to generate "artistic conception" within a harmonious symbiosis between humans and AI. The theoretical construction of Mind-Humanity reframes the fundamental proposition of educational digitalization from "how technology changes education" to "how education fulfills humanity through technology".

Prof. Zhi Liu, Central China Normal University, China

Zhi Liu is a Professor, PhD Supervisor, and Deputy Head of the Department of Data Science within the Faculty of Artificial Intelligence in Education at Central China Normal University (CCNU), who has also maintained a long-term appointment as a Guest Researcher at the Humboldt University of Berlin since 2017 and is currently a Visiting Scholar at the German Research Center for Artificial Intelligence (DFKI) for the 2025-2026 term. Specializing in text mining, educational data mining, and intelligent tutoring systems, Professor Liu has published over 60 SCI/SSCI-indexed papers in top-tier journals such as Knowledge-Based Systems, Computers & Education, and IEEE Transactions on Learning Technologies, including seven ESI Highly Cited Papers (Top 1%), and his significant scholarly impact is further underscored by his recognition as a Top 1% Highly Cited Scholar by CNKI for two consecutive years (2024-2025). As a leading researcher, he serves as the Principal Investigator for the National Key R&D Program of China (2030 Major Projects) and several projects under the National Natural Science Foundation of China, while also holding various key academic leadership roles, including Deputy Editor-in-Chief of the national-level journal Teacher Education Forum, Chair of the Organizing Committee for ICET, Guest Associate Editor for Frontiers in Artificial Intelligence, and Editorial Board Member for Discover Education. His distinguished contributions have earned him numerous prestigious accolades, most notably the First Prize of the Hubei Social Science Outstanding Achievement Award (2025), the First Prize of the Science and Technology Progress Award of Hubei Province (2024), and the First Prize of the Hubei Province Teaching Achievement Award (2022).

Prof. Guoshuai Lan, Henan University, China

Guoshuai Lan, Ph.D. in Educational Technology, Postdoctoral Fellow in Education, serves as Professor, Doctoral Supervisor, Postdoctoral Co-supervisor, and Director of the Department of Educational Technology in the Faculty of Education at Henan University. He is a Distinguished Teaching Master of Henan University, an Innovative Talent of Philosophy and Social Sciences for Universities in Henan Province, and Deputy Director of the Technical (Information Technology, General Technology) Teaching Guidance Committee of the Henan Provincial Basic Education Teaching Guidance Professional Committee. He has been consecutively selected as a "Top 1% Highly Cited Chinese Scholar (Education) in CNKI for 2024 and 2025." He has been appointed as a Youth Editorial Board Member and Review Expert for several core SSCI and CSSCI journals. His research interests focus on the application of intelligent technology in education, covering areas such as smart education and future education, educational digitalization and intelligent education, educational artificial intelligence and smart learning environments, and technology-enhanced construction of teachers’ digital capabilities and professional development.

He has published over 110 academic papers in Chinese and English as sole author or first author in core CSSCI and SSCI journals, including Educational Research, Journal of Higher Education, Research in Audiovisual Education, Open Education Research, and Education and Information Technologies. Many of these papers have been reprinted in full by Renmin University’s Photocopied Newspapers and Periodicals, the Academic Abstracts of Higher Education Institutions, and China Social Science Network. He has led more than 10 research projects, including the National Social Science Fund of China—Education General Program, the Ministry of Education’s General Program of Humanities and Social Sciences Research, the China Postdoctoral Science Foundation General Grant, the Henan Provincial Major Project of Philosophy and Social Sciences Research for Higher Education Institutions, the Henan Provincial Key Scientific Research Project for Higher Education Institutions, the Henan Provincial General Project of Philosophy and Social Sciences Planning, the Henan Provincial Teacher Education Curriculum Reform Research Project, and the Henan Provincial Soft Science Research Program. He has authored 14 academic monographs and co-authored 8 others, all published by prestigious national presses such as Science Press and China Social Sciences Press.

His research achievements have been awarded one Second Prize of the National Teaching Achievement Award for Higher Education; one First Prize of the Teaching Achievement Award for Teacher Education in Henan Province; two Special Prizes and three First Prizes of the Outstanding Achievement Award for Educational Science Research in Henan Province; three First Prizes and three Second Prizes of the Outstanding Achievement Award for Philosophy and Humanities and Social Sciences Research of Henan Higher Education Institutions; two First Prizes and one Second Prize of the Science and Technology Achievement Award of the Henan Provincial Department of Education; and five First Prizes of the Outstanding Achievement Award for Educational Informatization in Henan Province.

Title: Effective Models and Promotion Strategies for Large-Scale Personalized Education Enabled by Human-AI Collaboration

Abstract: This lecture is grounded in the contemporary context of deep integration between generative artificial intelligence and education. Centering on the core proposition of "how human-AI collaboration enables large-scale personalized education," it systematically constructs a comprehensive research framework that integrates six dimensions: theoretical foundation, framework construction, practical models, capacity cultivation, governance safeguards, and forward-looking prospects. The lecture aims to respond to the ontological question of educational transformation in the intelligent era: in today’s world, where algorithms increasingly intervene in cognitive processes, how can technological empowerment be genuinely translated into the inclusive reality of "personalized education" without exacerbating new forms of educational inequity?
First, it devotes efforts to building a theoretical foundation, revealing the paradoxical evolution of human cognition in the intelligent era, elucidating the theoretical core of human-AI collaborative education, and tracing its intellectual origins and theoretical underpinnings for enabling large-scale personalized education. Second, it turns to framework construction, originally proposing a ternary collaborative model of "teacher–educational agent–student" and a four-dimensional theoretical model of "data-driven, intelligent adaptation, dynamic intervention, and co-evolution," while systematically deconstructing the technical architecture and enabling logic of educational agents. Third, it delves into practical fields across different educational stages, extracting paradigm reconstructions and progressive pathways for human-AI collaborative empowerment of large-scale personalized education, respectively targeting vocational education, higher education, and basic education, each with its distinct ecological characteristics. Fourth, it focuses on the coordinate of subject capacities, constructing a cultivation system covering students’ human-AI collaboration ability, principals’ digital leadership, and teachers’ generative AI literacy. Finally, it consolidates governance safeguards, proposing a multi-level governance framework for addressing ethical risks and systematic readiness strategies for education. In addition, it offers forward-looking prospects, envisioning a future landscape where education ecosystems are reshaped by moving from single-agent "substitution" to multi-agent "emergence".

Prof. Shaoying Gong, Central China Normal University, China

Professor and Doctoral Supervisor at the School of Psychology, Central China Normal University. Serves as a council member of the Educational Psychology Professional Committee and the Cyber Psychology Professional Committee of the Chinese Psychological Society, a member of the Adolescent Mental Health Professional Committee of the Chinese Association for Mental Health, and a standing council member of the Hubei Psychological Society. Her primary research areas include online learning and instructional psychology, self-regulated learning, emotion and learning, and game-based learning. She has led over ten research projects funded by the National Natural Science Foundation of China and various provincial and ministerial agencies. She has published more than 100 papers in prominent academic journals both domestically and internationally, as well as one textbook and two translated books.

Title: From Willingness to Wise Use: How Metacognition Enables High-Quality Learning with Generative AI?

Abstract: Generative AI is becoming increasingly integrated into learning, yet willingness to use AI does not necessarily translate into reflective and cautious forms of high-quality AI use. Helping learners move from being willing to use AI to wise using AI has become an important issue in AI-supported education. Across two studies, we examined the role of metacognition in shaping AI use and whether metacognitive pretraining could promote higher-quality learning. Study 1 surveyed 2,238 university students and combined structural equation modeling, latent profile analysis, and machine learning. Results showed that AI use intention did not directly predict reflective or cautious AI use; instead, its associations with both forms of high-quality AI use were mediated by metacognition. Learners characterized by relatively high use intention, high metacognition, and moderate AI trust showed higher levels of reflective AI use. Machine learning further identified metacognition as a core predictor of reflective AI use. Building on these findings, Study 2 examined whether metacognitive pretraining could help learners engage more effectively in a Socratic tutoring environment designed to promote active thinking and reflection. Sixty-four university students were randomly assigned to pretraining or control conditions. Results revealed that pretraining increased enjoyment, intrinsic motivation, metacognitive accuracy, and learning gains, while reducing boredom without increasing extraneous cognitive load. Process analysis further revealed more timely help-seeking behaviors, more diverse review and integration behaviors, deeper reflection, and higher error-correction rates. Together, these findings highlight metacognition as a key pathway from willingness to use AI toward high-quality AI-supported learning.

Prof. Fang XU, Nantong University, China

Professor of Educational Technology, College of Educational Science, Nantong University, Master Supervisor, Ph.D., is engaged in the research of digitalisation in education. He has published more than 60 academic papers in domestic and international journals, including one SSCI source journal and 15 CSSCI source journals as the first author, of which two were reprinted in the Renmin University of China Newspaper and Periodical Reprints. He has published 6 academic monographs in Science Press, People's Publishing House, China Social Science Publishing House and Jilin University Press. He has presided over more than 20 projects, including the General Project of the National Social Science Foundation, the Key Project of the National Education Examination Scientific Research Planning Project, the Online Education Fund of the Ministry of Education, the Social Science Foundation of Jiangsu Province, the Social Science Foundation of Henan Province, the Key Research and Development and Promotion Programme of Henan Province (Soft Science Project), the Key Scientific Research Project of Henan Province Colleges and Universities, the Social Science Foundation of Gansu Province, the Key Project of the Chinese Society of Higher Education for Education Informatisation, and the National Scientific Research Project of Foreign Languages, and so on. He has won more than ten awards, including the Third Prize of Philosophy and Social Science Achievements of Jiangsu Universities, the First Prize of Excellent Scientific Research Achievement Award of Education Science Planning of Henan Province, the Second Prize of Excellent Scientific Research Achievements of Gansu Universities, the Second Prize of Philosophy and Social Science of Nantong City, and other various awards. He was awarded the 2020 Young Backbone Teachers of Universities in Henan Province. He is now an expert in appraising the achievements of the National Social Science Foundation.

Title: The effect of digitization on education: empirical analysis based on PISA 2022

Abstract: Currently, countries all over the world are deeply promoting the digital transformation of education. There is a lack of empirical research on the relationship between education digitization and education effectiveness. This study conducts a series of empirical analyses using the student, teacher, and school questionnaires from PISA 2022, aiming to explain the mechanisms through which educational digitalization affects various educational outcomes. These include effects on student academic performance, student life satisfaction, teacher life satisfaction, and student self-efficacy, and so on. The analysis of its impact on student academic performance serves as an illustrative example. The study first constructs a theoretical model of the impact of education digitization on education effectiveness based on relevant theories. The original data set of the 2022 PISA mainly consists of mathematics, reading, and science assessment scores obtained through machine tests or paper assessments and the results of the generic student questionnaire covering multiple measurement dimensions such as ICT familiarity. This study conducted an empirical analysis based on 53,908 data selected from 2022 PISA data. The results show that digitization does not have a significant direct positive effect on educational effectiveness but indirectly affects educational effectiveness by influencing teachers’ teaching behavior and students’ learning behavior. It is found that teachers’ teaching behavior and students’ learning behavior respectively play an intermediary role in the influence of digitization on educational effectiveness, but at the same time, teachers’ teaching behavior and students’ learning behavior also play a chain intermediary role in the influence of digitization on educational effectiveness. This study also shows that grade and gender are important moderating variables of the influence of digitization on educational effectiveness. Based on the research results above, relevant measures can be taken to promote the development of digital transformation of education.

Prof. Ma Jinzhong, Yanbian University, China

Ma Jinzhong is a professor in the Department of Educational Technology at the Yanbian University，Master's supervisor, and has been a visiting scholar at Michigan State University in the United States. His research focuses on applications of artificial intelligence in education. He has published more than 10 papers in core educational journals, including Procedia - Social and Behavioral Sciences, and has edited two textbooks.

Title: Human-Machine Collaboration and Teachers’ Roles in Classroom Teaching Supported by Intelligent Agents

Abstract: Generative artificial intelligence has had a significant impact on the teaching of teacher education. This report aims to illustrate the application of generative artificial intelligence large models and intelligent agents in teaching through the teaching practice conducted among our school's teacher candidates. It also explains the new changes in the role of teachers and provides a reference teaching practice path for the cultivation of new teacher competencies.Finally, the report looks forward to a redefined ecosystem of future teachers, classrooms, schools, and learning centers, where the teacher’s role shifts from knowledge transmitter to learning architect and value guide. The overarching thesis is that pedagogical reform must fundamentally reconstruct the logic of education, not merely update teaching materials, to harness AI as a partner in cultivating critical thinking, practical skills, and ethical judgment for the coming era.

Assoc. Prof. Xiang Feng, East China Normal University, China

Dr. Xiang Feng is an Associate Professor at East China Normal University, affiliated with the Department of Educational Information Technology and the Shanghai Digital Educational Equipment Engineering Technology Research Center. With two decades of extensive experience in information system architecture design, development, and implementation, Dr. Feng is an expert deeply committed to the field of educational informatization.

He earned his PhD in Geography Information Systems and a Master's degree in Computer Science & Engineering. From 2008 to 2010, he served as a postdoctoral researcher at the joint Shanghai Jiao Tong University and Alcatel-Lucent Shanghai Bell Postdoctoral Workstation. Since 2010, at ECNU, his research interests primarily span artificial intelligence in education, learning analytics, K-12 AI and programming education, and smart campus construction.

Dr. Feng is a committee member of the National Information Technology Standardization Technical Committee (Educational Technology Subcommittee) and Deputy Secretary of the IT Education Professional Committee for Primary and Secondary Schools of The Chinese Society of Education. He has led and contributed to numerous national and provincial-level projects,authored two monographs, published over 30 peer-reviewed papers, secured over 10 software copyrights, and holds 2 authorized invention patents from 5 applications.

Title: Making Learning Visible — Learning Analytics and Digital Learning Environments in AI and Computational Thinking Education

Abstract: At the intersection of the rise of artificial intelligence and fundamental curriculum reform, computational thinking — a core competency of the 21st century — is entering primary and secondary classrooms at scale, most often through block-based programming and AI learning platforms. Yet a deep problem has surfaced: when we shift our gaze from "what students ultimately produced" to "how they arrived there step by step," mainstream digital learning environments rarely offer an answer. Most block-based programming and AI platforms excel at sparking interest and lowering the entry barrier, yet few natively provide fine-grained evidence of the learning process — where students get stuck, how they try and err, and what thinking paths they traverse. As a result, "Can teachers make precise, evidence-based instructional decisions?" and "Can researchers reveal the formation mechanisms of computational thinking and AI literacy?" remain tantalizing but largely unanswerable in today's classrooms.

This talk responds to this challenge from the perspective of the research and design of digital learning environments. We argue that a truly intelligent classroom should not merely let learning happen; it should make learning visible — rendering students' hidden thought processes explicit through fine-grained, traceable, and reproducible learning-behavior data, thereby supporting data-driven instructional decisions, precise interventions, and scientific research into how computational thinking and AI literacy form. Toward this claim, the talk will: (1) map the landscape of AI education's turn toward data-driven practice and the persistent gap between technology, teaching, and research; (2) introduce a self-developed digital learning environment purpose-built for school settings — one in which the capture, analysis, and teacher-facing support of learning-behavior data are first-class citizens rather than afterthoughts; and (3) demonstrate, through real classroom cases, how the "data → insight → intervention" loop closes in authentic teaching, while frankly discussing its boundaries, ethics, and conditions for scalability.

Core message: When digital learning environments adopt "making learning visible" as their first design principle, they do not only transform teaching — they also reshape our understanding of how learning itself unfolds in the age of AI.

Assoc. Prof. Guolong Quan, Jiangnan University, China

Dr. Quan is an Associate Professor at the School of Education, Jiangnan University, holding a PhD from East China Normal University. With over 15 years of interdisciplinary research experience spanning learning science and technology, educational informatization, and AI-integrated pedagogy, his expertise centers on e-learning systems, knowledge visualization design, and technology-enhanced educational innovation.
Dr. Quan has spearheaded multiple national and institutional research initiatives, focusing on knowledge representation in STEM education, experiential learning empowered by intelligent technologies, and digital literacy frameworks for smart education. His collaborative projects extensively explore the intersection of pedagogical theory, data-driven instructional design, and adaptive learning analytics.
Currently, his primary research investigates the application architecture of adaptive learning systems under evolving curricular and technological paradigms. This includes systematic analysis of system functionalities aligned with next-generation curriculum standards, evidence-based design of AI-assisted teaching models, and empirical evaluations to optimize intelligent educational practices. His work contributes to advancing scalable, personalized learning and teaching solutions in digitally transformed educational ecosystems.

Title: AI Literacy of Primary and Secondary School Teachers: Contextualized Knowledge and Action

Abstract: Policies and trends in K-12 education require teachers to change their teaching methods and professional practices, and naturally, teachers' AI literacy has become an important condition for continuing to drive the digital transformation of education. Schools and teachers need to know what it is and how to do it in a clear and suitable way. They need to be familiar with the balance and the combination between the technical operations and its educational applications, so that they can ultimately achieve the expected practical performance and possess the desired AI literacy.

Based on research and practical surveys on AI literacy among K-12 teachers, this paper analyzes related literature separately, summarizes elements, models the relationships, and maps contexts, etc. The systematic 'scenario-based knowing-doing' can serve as a 'core strategy' for developing AI literacy in K-12 teachers, helping both teachers and schools to integrate AI literacy into their daily routines.

The study suggests that: (1) the logical connection between the theoretical nature of AI literacy and the scenario-based expressions of K-12 teachers' AI literacy should be explicitly presented to enhance communication effectiveness; (2) explaining and developing scenario-based values, ontologies, practices, and quality perspectives are key conditions for teachers to embody, internalize, and activate AI literacy in practice; (3) starting from daily scenario-based understanding, imitation, and action is the fundamental condition for deepening AI applications and embodying AI literacy.

Assoc. Prof. Taotao Long, Central China Normal University, China

Taotao Long is an associate  professor in the Department of Science Education at the Faculty of Artificial Intelligence in Education in Central Normal University. She has got the Ph.D in educational technology at the University of Tennessee, USA. Her research interests include professional development for science teachers, integrating technology in the classroom, and teaching of thinking. She has worked as the principal investigator of a variety of projects, including the NSFC (National Science Foundation in China) project. In the past five years, she has published more than 10 papers on the SSCI indexed journals as the first or corresponding author.

Title: Promoting pre-service science teachers' professional development via AI with the theoretical framework of Knowledge Integration

Abstract: Addressing critical challenges in science teacher education—including insufficient staffing, limited professional competence, and weak support systems—this study proposes a four-stage developmental pathway for future science teachers empowered by digital intelligence, grounded in Knowledge Integration theory. The pathway progresses through single-point breakthrough, spark-to-prairie expansion, collaborative co-creation, and AI-driven empowerment. By embedding generative AI into collaborative instructional design, intelligent tutoring, and multimodal teaching analytics, the approach facilitates a role shift from knowledge transmitters to thinking facilitators and learning experience designers. Empirical results demonstrate significant improvements in pre-service teachers’ inquiry-based design capabilities and reflective practices, offering a scalable model for science teacher preparation.