invited speakers / 特邀报告专家

 

Prof. Henghua Su, Xi’an Jiaotong-Liverpool University, China

Biography: Henghua Su is the Head of the Modern Languages Centre and a Senior Associate Professor at Xi’an Jiaotong-Liverpool University. She is a consultant for the American Council on Teaching Foreign Languages (ACTFL). Dr. Su received her Ph.D. in Chinese Linguistics from University of Wisconsin-Madison. She has published widely in Linguistics, Second Language Acquisition and International Chinese Language Education. She has been teaching and training Chinese instructors for more than twenty years. Previously she taught and coordinated the Chinese language programme at Indiana University Bloomington and the University of Wisconsin-Madison summer study abroad program at Nankai University and Tianjin Normal University. She also taught at Utah State University and worked as a certified translator and interpreter. Many of her postgraduate students have become faculty members in top universities in North America.

 

Prof. Richard Lamb, University of Georgia, USA

Speech Title: Designing Adaptive Learning Ecosystems: Generative AI for Personalized Education and Cognitive Augmentation.

Abstract: Neuroadaptive artificial intelligence (AI) can tailor instruction using real‑time psychophysiological measurement. We evaluated a neuroadaptive AI ecosystem coupled with fNIRS to personalize literacy instruction for children aged 8–12 with reading disabilities (n = 62). The system operationalized fNIRS‑driven design principles: (1) Neural efficiency targeting for down‑titration of task load when left Inferior Frontal Gyrus (IFG) Oxygenated Hemoglobin (HbO) during decoding exceeds a calibrated threshold, and up‑titrate when subthreshold; (2) Executive load banding maintain Dorsolateral Prefrontal Cortex (DLPFC) HbO within a moderate “productive” band via micro‑pacing and item difficulty; (3) Circuit‑specific recruitment reinforce orthographic phonological mapping when Temporoparietal Junction (TPJ) HbO rises within target bands; (4) Temporal smoothing & windows adapt using 5–8 s hemodynamic windows with motion‑robust preprocessing; (5) Adaptive rest‑to‑task transitions trigger brief recovery when sustained HbO suggests overload; (6) Personalized calibration session‑wise re‑estimation of thresholds. Across six weeks, IFG HbO during phonological decoding decreased (M_pre = 0.84 µM, M_post = 0.51 µM), t(61) = 4.72, p < .001; DLPFC activation declined (M_pre = 0.77 µM, M_post = 0.49 µM), t(61) = 4.11, p < .001; TPJ activation during sight‑word recognition increased (M_pre = 0.42 µM, M_post = 0.63 µM), t(61) = –3.28, p = .002. Behavioral gains included decoding (+14.2%), fluency (+18.7 WPM), and error reduction (–22.4%), all p < .001. Moment‑to‑moment congruence between AI difficulty and fNIRS‑measured load predicted literacy gains, β = .37, p < .001. Findings support fNIRS‑informed neuroadaptive AI to enhance cognitive efficiency and reading outcomes.

Biography: Richard L. Lamb, Ph.D., LCMHC, is a Professor of Translational Education in the Health Professions at the University of Georgia and Director of the Neurocognition Science Laboratory, an internationally recognized research hub. His interdisciplinary work integrates artificial intelligence, machine learning, and psychophysiological measurement to advance personalized learning and mental health interventions. Dr. Lamb’s research focuses on identifying the cognitive markers of learning, integrating and designing neuroadaptive systems, and intelligent environments for educational purposes and clinical practice. He has authored numerous publications and books, including Digital Therapeutics: Integrating Artificial Intelligence, Extended Reality, and Wearable Sensors in Mental Health Care (IGI Global, 2026), and serves on editorial boards for leading journals such as PLOS ONE and the Journal of Counseling and Development. With multimillion-dollar funding from agencies including the National Science Foundation and the Department of Defense, his projects span AI-enabled healthcare education, neuroadaptive training for defense applications, and XR-based therapies for trauma and anxiety. His work has earned international recognition, including the ScholarGPS Top Scholar Award and the Association for Child and Adolescent Mental Health Digital Impact Award.

 

Prof. Kelum Gamage, University of Glasgow, UK

Speech Title: Enhancing Student Engagement and Experience in Remote Learning Environments

Abstract: This paper investigates student engagement and learning experiences within remote learning environments. As student engagement is closely linked to academic achievement, retention, and overall satisfaction, understanding how learners interact with digital and blended delivery modes has become increasingly important. The study examines students’ perceptions of teaching practices, learning activities, and digital platforms that shape their engagement in online and hybrid contexts. The findings indicate that while flexibility and accessibility are key strengths of these learning environments, challenges remain in fostering meaningful interaction, sustained motivation, and a sense of belonging. The paper identifies specific areas where engagement can be strengthened, including instructional design, communication practices, assessment strategies, and the use of interactive technologies.

Biography: Prof. Kelum Gamage (BSc, PhD, PgCAP, CEng, PFHEA, FIET, FRSA, SMIEEE) is a Full Professor in the James Watt School of Engineering at the University of Glasgow and a winner of the University of Glasgow Teaching Excellence Individual Award (2020/21). He is the Learning & Teaching Enhancement lead of the College of Science and Engineering and the Co-Director of the Centre for Educational Development and Innovation. Prof. Gamage is the lead editor of "The Wiley Handbook of Sustainability in Higher Education Learning and Teaching" (ISBN: 978-1-119-85283-4) and also the Editor-in-Chief for the STEM Education Section of the Education Sciences Journal (Publisher: MDPI, Switzerland, ISSN 2227-7102). He is a Principal Fellow of the Higher Education Academy (PFHEA), a Chartered Engineer (CEng) of the Engineering Council (UK), a Fellow of the Institution of Engineering and Technology (FIET), a Fellow of the Royal Society of Arts (FRSA) and a Senior Member of the Institute of Electrical and Electronics Engineers (SMIEEE).

 

Prof. Rustam Shadiev, Zhejiang University, China

Speech Title: Advancing Global AI Literacy and Intercultural Competence through AI-Enhanced and Immersive Learning

Abstract: This speech examines how AI-enhanced and immersive learning environments support the development of global AI literacy and intercultural competence in higher education. Drawing on empirical studies from international projects involving students from multiple countries, it illustrates how learners first acquired foundational AI concepts and explored a range of applications across educational and real-world contexts. Students then applied this knowledge by using diverse AI tools to complete authentic tasks, thereby linking theoretical understanding with practical application. After creating AI-supported content, learners shared their work across cultural boundaries, engaged with peers’ outputs, exchanged questions and responses, and participated in cross-cultural discussion and reflection. These collaborative and reflective processes deepened learners’ understanding of AI concepts and their culturally situated applications, shaping the course into a genuinely global AI literacy learning experience.
The learning designs further integrated virtual reality (VR) technologies to create authentic and immersive environments that supported cross-cultural collaboration and communication among learners from diverse cultural backgrounds. Throughout the learning process, AI-driven tools guided learning activities, supported content creation, monitored and evaluated learner-generated outputs, and provided timely feedback to encourage reflection and improvement. The findings suggest that such AI-supported and immersive experiences not only strengthen learners’ conceptual understanding of AI but also enhance awareness of culturally situated AI practices and foster intercultural competence through meaningful interaction and collaboration. The speech concludes by offering practical implications for educators and researchers seeking to design AI-enhanced learning environments that advance global AI literacy and intercultural understanding.

Biography: Dr. Rustam Shadiev is a Professor at the College of Education, Zhejiang University, China. He received his Ph.D. in Network Learning Technology from  Taiwan Central University, China. Dr. Shadiev is a Fellow of the British Computer Society (BCS), a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), and a member of the Association for Computing Machinery (ACM). In 2019, he was honored with the title of Distinguished Professor of Jiangsu Province, China. His research focuses on advanced educational technologies and their applications in language learning, intercultural communication, AI-assisted education, and immersive learning environments such as Virtual Reality based on 360-degree video technologies. Dr. Shadiev has published extensively in top-tier SSCI journals, including Computers & Education, British Journal of Educational Technology, and Journal of Computer Assisted Learning. He has been recognized among the Most Cited Chinese Researchers in Education (2020–2024) and listed in the Stanford/Elsevier Top 2% Scientist Ranking (2023-2025). He serves on editorial boards of several international journals and frequently delivers keynote speeches at major international conferences. His recent projects explore AI-powered multimodal learning, cross-cultural competence development, and human–AI collaboration in education.

 

Prof. Xiwen Zhang, Beijing Language and Culture University, China

Speech Title: Intelligently Recognizing Digital Ink Chinese Text by Junior International Students

Abstract: Chinese characters have complex structures. Their writing plays an import role in learning Chinese. Junior international students can use digital pen to record their handwriting as digital ink. Various information can be extracted from the digital ink text, such as text line, Chinese characters, stroke errors, shape normalization.
Digital ink is a new media compared with digital image and digital video. It is captured from handwriting and freehand drawing using digital pen. Point samples are captured by digital pens, containing positions, time stamp, and pressures. A stroke is a list of sampling points from pen down and movement to pen up. A list of strokes consists of a digital ink. Digital ink Chinese text are stroke sets, have neither text line, nor Chinese characters.
Digital ink Chinese texts written by junior international students contain many information including errors and unnormal issues. It is difficult to recognize them. We proposed some intelligent methods to extract information, such as adaptive segmentation based on statistics analysis, classification using machine learning and deep learning, stroke matching using Genetic Algorithm, evaluating the normalization for entire characters and their components using knowledge bases. With developing new intelligent methods and collecting more data, more valued information can be extracted.

Biography: XiWen Zhang is currently a full professor of Digital Media Department, School of Information Science, Beijing Language and Culture University.
Prof. Zhang worked as an associated professor from 2002 to 2007 at the Human-computer interaction Laboratory, Institute of Software, Chinese Academy of Sciences. From 2005 to 2006 he was a Post doctor advised by Prof. Michael R. Lyu in the Department of Computer Science and Engineering, the Chinese University of Hong Kong. From 2000 to 2002 he was a Post doctor advised by Prof. ShiJie Cai in the Computer Science and Technology department, Nanjing University.
Prof. Zhang's research interests include pattern recognition, computer vision, and human-computer interaction, as well as their applications in digital image, video, and ink. Prof. Zhang has published over 60 refereed journal and conference papers. His SCI papers are published in Pattern Recognition, IEEE Transactions on Systems Man and Cybernetics B, Computer-Aided Design. He has published more than twenty EI papers.
Prof. Zhang received his B.E. in Chemical equipment and machinery from Fushun Petroleum Institute (became Liaoning Shihua University since 2002) in 1995, and his Ph.D. advised by Prof. ZongYing Ou in Mechanical manufacturing and automation from Dalian University of Technology in 2000.

 

Prof. Yong Cao, University of Alaska Anchorage, USA

Biography: Yong Cao received his PhD in Marketing from the University of Iowa. He is a Professor of Marketing at the University of Alaska Anchorage. His research interests include E-commerce, International Business, Pricing Strategies, Customer Relationship Management and the Applications of Artificial Intelligence in Marketing. He published his studies in the Journal of Marketing, the Journal of Retailing and the Journal of Service Research.

 

Prof. Yanqing Duan, University of Bedfordshire, UK

Speech Title: Trustworthy AI and Decision-Making: What Higher Education Can Learn from AI Adoption in Business.

Abstract: Artificial intelligence (AI) is increasingly embedded in managerial decision-making in business, where predictive AI (PAI) supports structured, data-driven decisions and generative AI (GAI) enables creativity, sensemaking, and analytical augmentation. In contrast, while AI adoption in higher education has focused primarily on teaching, learning, assessment and student support, the use of AI by higher education institution (HEI) managers for enhancing managerial decision-making remains comparatively underexplored. This talk draws on a large-scale empirical study of AI adoption in business to examine how trustworthy AI shapes the use and impact of predictive and generative AI in managerial decision-making contexts. The study conceptualises trustworthy AI through ethics, transparency, and explainability dimensions. Although grounded in the business sector, the findings and their implications offer important insights for higher education. The talk will discuss implications and future research directions for understanding and designing AI-supported decision-making in higher education.

Biography: Yanqing (Yan) Duan (BSc, MSc, PhD, SFHEA) a Full Professor of Information Systems and Director of the Business and Management Research Institute (BMRI) at the University of Bedfordshire Business School. Her research focuses on emerging digital technologies and their impact on organisational performance, decision-making, innovation, and sustainable agri-food supply chains. She is a regular expert evaluator for major funding bodies, including EU Horizon 2020 and Horizon Europe, and a frequent keynote and invited speaker on digital transformation and AI for decision-making. Professor Duan has led numerous international and UK research projects and has published over 280 peer-reviewed articles in leading journals. She has published over 280 peer-reviewed articles, including papers in European Journal of Operational Research, European Journal Information Systems, European Journal of Marketing, IEEE transaction on Engineering Management, Information & Management, The Information Society, Journal of Business Research, Industrial Marketing Management, Technovation, Journal of Environmental Management, Information Technology and People.

 

Prof. Anand Nayyar, Duy Tan University, Vietnam

Speech Title: Artificial Intelligence in Teaching and Learning: From Classroom Innovation to Future-Ready Education

Abstract: Artificial Intelligence is rapidly transforming the educational landscape, reshaping how teachers teach, how students learn, and how institutions make decisions. This keynote explores the growing role of AI in teaching and learning, highlighting its potential to personalize instruction, enhance student engagement, support assessment, generate feedback, and improve learning outcomes. Drawing on current examples such as ChatGPT, adaptive learning platforms, analytics-driven systems, and immersive tools, the talk examines both the promise and the challenges of AI integration in education.
The keynote also addresses critical concerns surrounding AI use, including academic integrity, overdependence, misinformation, privacy, bias, and the need for human oversight. Rather than viewing AI as a replacement for educators, this session positions it as a powerful assistant that can augment teaching, foster productive struggle, and enable more meaningful, student-centered learning experiences. Special attention will be given to assessment redesign, AI literacy, ethical practice, and the importance of preparing students and teachers for a future in which human and machine collaboration becomes increasingly common.
Ultimately, this keynote invites educators, leaders, and institutions to move beyond fear or passive acceptance and toward thoughtful, responsible, and future-ready adoption of AI in education.

Biography: Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks, Swarm Intelligence and Network Simulation. He is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. A Certified Professional with 280+ Professional certifications from CISCO, Microsoft, CompTIA, Amazon, Alibaba Cloud, Oracle, Google, Salesforce, Tableau, FinOps, Beingcert, EXIN, GAQM, Cyberoam and many more. Published more than 200+ Research Papers in various High-Quality ISI-SCI/SCIE/SSCI Impact Factor- Q1, Q2, Q3, Q4 Journals cum Scopus/ESCI indexed Journals, 80+ Papers in International Conferences indexed with Springer, IEEE and ACM Digital Library, 60+ Book Chapters in various SCOPUS/WEB OF SCIENCE Indexed Books with Springer, CRC Press, Wiley, IET, Elsevier with Citations: (Google Scholar): 21600+, H-Index: 74 and I-Index: 300; (Scopus): 12000+; H-index: 58. Member of more than 60+ Associations as Senior and Life Member like: IEEE (Senior Member) and ACM (Senior Member). He has authored/co-authored cum Edited 70+ Books of Computer Science. Associated with more than 600+ International Conferences as Programme Committee/Chair/Advisory Board/Review Board member. He has completed 1 Grassroot and 1 ASEAN Project. He has 18 Australian Patents, 16 German Patents, 4 Japanese Patents, 44 Indian Design cum Utility Patents, 13 UK Patents, 1 USA Patent, 3 Indian Copyrights and 2 Canadian Copyrights to his credit in the area of Wireless Communications, Artificial Intelligence, Cloud Computing, IoT, Healthcare, Drones, Robotics and Image Processing. Awarded 55 Awards for Teaching and Research—Young Scientist, Best Scientist, Best Senior Scientist, Asia Top 50 Academicians and Researchers, Young Researcher Award, Outstanding Researcher Award, Excellence in Teaching, Best Senior Scientist Award, DTU Best Professor and Researcher Award- 2019, 2020-2021, 2022, 2022-2023, 2023-2024, Distinguished Scientist Award by National University of Singapore, Obada Prize 2023, Lifetime Achievement Award 2023, 2024; Asian Admirable Achievers 2024; Distinguished Academic Leader 2024, Lifetime Achievement Award 2024 and many more.
He is listed in Top 2% Scientists as per Stanford University (2020, 2021, 2022, 2023, 2024, 2025), Ad Index (Rank No:1 Duy Tan University, Rank No:2 Computer Science in Viet Nam) and Listed on Research.com (Top Scientist of Computer Science in Viet Nam- National Ranking: 2; D-Index: 56; World Ranking: 3694).
He is acting as Associate Editor for Computer Communications (Elsevier), International Journal of Sensor Networks (IJSNET) (Inderscience), Tech Science Press- IASC, Cogent Engineering, Human Centric Computing and Information Sciences (HCIS), IET-Quantum Communications, IET Networks, IEEE Transactions on Artificial Intelligence (IEEE TAI), Indonesian Journal of Electrical Engineering and Computer Science, IJFC, IJISP, IJDST, IJCINI, IJGC, IJSIR, IJBDCN, IJNR, IJSI, IJIES. He is acting as Managing Editor of IGI-Global Journal, USA titled “International Journal of Knowledge and Systems Science (IJKSS)”. He has reviewed more than 5500+ Articles for diverse Web of Science and Scopus Indexed Journals. He is currently researching in the area of Wireless Sensor Networks, Internet of Things, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Drones, Blockchain, Cyber Security, Healthcare Informatics, Big Data and Wireless Communications.

 

Prof. Chuan-Ming Liu, Taipei University of Technology, Taiwan, China

Biography: Dr. Chuan-Ming Liu is a professor in the Department of Computer Science and Information Engineering (CSIE), National Taipei University of Technology (Taipei Tech), TAIWAN, where he was the Department Chair from 2013-2017. He received his Ph.D. in Computer Science from Purdue University in 2002 and joined the CSIE Department in Taipei Tech in the spring of 2003. In 2010 and 2011, he has held visiting appointments with Auburn University, Auburn, AL, USA, and the Beijing Institute of Technology, Beijing, China. He has services in many journals, conferences and societies as well as published more than 150 papers in many prestigious journals and international conferences. Dr. Liu was also the co-recipients of the best/excellent paper awards in many conferences, including ICUFN 2015, ICS 2016, MC 2017, WOCC 2018, MC 2019, WOCC 2021, TCSE 2022, TANET 2023, and MC 2024. His current research interests include data science, big data management, uncertain data management, spatial data processing, AIoT, data streams, ad-hoc and sensor networks, location-based services, algorithm design and analysis.

 

Prof. Jinju Duan, Southwest University, China

Speech Title: Connectivist knowledge production and learning success in distributed social networks

Abstract: Connectivist knowledge production has changed how knowledge is shared, generated, and co-created in networks, and the mechanisms for co-creating knowledge deserve further attention. In this study, we use the connectivism framework as a guiding concept and investigate how students generate knowledge and their effects on learning success in distributed social environments. First, we extend existing research by explaining how connectivist knowledge production behaviors and processes affect learning success. Second, our results confirm the posited direct and indirect effects, thus evidencing knowledge sharing, aggregation, integration, and creation as components of the connectivist knowledge production in distributed social network environments and drivers for learning success. Third, our results underscore the importance of knowledge aggregation and integration in distributed social network environments and connectivist knowledge production on learning success and shed light on the new features of knowledge production from the perspective of connectivist learning.

Biography: Dr. Jinju Duan is a professor and doctoral supervisor in the Department of Education, Southwest University, she is also a visiting professor Doctoral Advisor at Phranakhon Si Ayutthaya Rajabhat University of Thailand.
Currently, she engages in the design of learning environments based on innovative technologies and research on large-scale online open education and AI-enhanced learning. Dr. Duan's research project aimed to create a new, flexible, and connected
learning environment and social knowledge space, promoting connectivist learning and knowledge creation. Her research has been applied in her curriculum design and teaching practices for several years.
In recent years, Dr. Duan has presided over two National Natural Science Foundation projects and multiple provincial- and ministerial-level projects, including the China Postdoctoral Science Foundation Special Support Project and the Ministry of Education Humanities and Social Sciences Project, and has published papers in International Journal of Educational Technology in Higher Education、British Journal of Educational Technology and Interactive Learning Environments. Her research: A new approach to observing different learners' interactive behavior and learning performance in cMOOCs was shared by the British Educational Research Association (BERA) blog.
She currently also serves as a reviewer for several international journals.

 

Prof. Hongliang Ma, Shaanxi Normal University, China

Speech Title: Exploring the Integration of Computational Thinking into Scientific Inquiry: Effects on Knowledge, Higher-Order Thinking, STEM Attitudes, and Collaborative Behaviors

Abstract: This study employed a pretest–posttest quasi-experimental design to examine the effects of integrating computational thinking (CT) into scientific inquiry (SI) in ninth-grade chemistry, comparing CT-SI, SI, and lecture-based instruction on student learning outcomes. Results showed that: (1) both CT-SI and SI instruction significantly improved students’ science knowledge, with CT-SI instruction yielding a significant increase over the lecture-based approach; (2) both CT-SI and SI instruction significantly improved students’ higher-order thinking tendency, with both yielding a significant increase over the lecture-based approach; (3) CT-SI instruction significantly improved students’ STEM attitudes, yielding a significant increase than both the SI and lecture-based approaches; (4) The high-order thinking tendency group engaged more frequently in cognitive behaviors and demonstrated more comprehensive behavioral transitions. These findings provided evidence and practical insights into the design and implementation of CT-SI activities in the science education.

Biography: Hongliang Ma, Ph.D., is a Professor of Educational Technology, Associate Dean of the Faculty of Education, and Director of the Research Center for AI and STEM Education at Shaanxi Normal University. With over 20 years of experience, he has led multiple nationally funded research projects and remains actively engaged in research on STEM education. He has authored more than 60 academic SSCI /CSSCI-indexed papers. His recent work focuses on computer science education and its integration within STEM, primarily in K–12 contexts.

 

Prof. Noor Maizura Mohamad Noor, University of Terengganu, Malaysia

Speech Title: Artificial Intelligence–Driven Decision Support Systems for Strategic Decision-Making in Education

Abstract: The growing complexity of educational systems requires more advanced approaches to support effective and evidence-based decision-making. Traditional decision-making processes in education are often constrained by fragmented data, subjective judgment, and limited analytical capabilities, which may reduce the accuracy and timeliness of strategic decisions. In response to these challenges, the integration of Artificial Intelligence (AI) with Decision Support Systems (DSS) has emerged as a transformative approach for enhancing institutional decision-making processes. AI techniques such as machine learning, predictive analytics, and intelligent data processing can enhance the analytical capabilities of conventional DSS by enabling the extraction of meaningful patterns from large and complex educational datasets. These intelligent systems facilitate improved forecasting, scenario analysis, and risk assessment, allowing educational leaders and policymakers to make more informed strategic decisions related to resource allocation, academic performance monitoring, curriculum planning, and institutional governance. Conceptually, AI-driven DSS can significantly improve decision quality, operational efficiency, and strategic responsiveness in education. However, the successful implementation of such systems requires careful consideration of challenges including data governance, technological infrastructure, ethical considerations, and human competency development. It concludes that the convergence of AI and DSS represents a significant step toward the development of intelligent educational ecosystems capable of supporting sustainable and data-driven institutional strategies.

Biography: Noor Maizura Mohamad Noor is a Professor of Computer Science at Universiti Malaysia Terengganu (UMT). She earned her PhD in Computer Science from The University of Manchester, United Kingdom, and holds master’s, bachelor’s, and diploma qualifications in Computer Science from Universiti Putra Malaysia (UPM). Her research focuses on intelligent decision support systems and information systems aimed at enhancing organizational decision-making. She has an extensive publication record, has secured competitive research funding, supervised numerous postgraduate students, and actively contributes to the international academic community as a keynote speaker, reviewer, and visiting professor. She is a senior member of the International Association of Computer Science and Information Technology (IACSIT) and a member of IEEE and the Association for Computing Machinery (ACM).

 

Assoc. Prof. Victor Perez, Xi'an Jiaotong-Liverpool University, China

Speech Title: From AI Systems to Brain-Aligned Pedagogies: Redefining the Future of Entrepreneurship Education

Abstract: Despite rapid advances in artificial intelligence, many educational applications remain focused on optimising systems, tools, and content delivery rather than addressing how learners actually think, attend, decide, and perform under conditions of uncertainty. As a result, the transformational potential of AI in education risks being constrained by pedagogical models that are misaligned with the cognitive realities of learning.
This talk proposes a shift from AI-enhanced systems toward brain-aligned pedagogical paradigms, arguing that meaningful educational transformation requires integrating insights from neuroscience into the design of AI-supported learning environments. Drawing on work in brain-driven entrepreneurship education, the presentation reframes learning not as information acquisition but as the cultivation of cognitive capacities such as sustained attention, cognitive flexibility, emotional regulation, and judgment under uncertainty.
The session introduces a conceptual framework for understanding how AI technologies can support—rather than substitute—these core human capacities, particularly in innovation-driven and entrepreneurial contexts. By moving the discussion beyond tools and platforms, the talk highlights how aligning AI with the brain’s mechanisms of learning opens new pathways for designing education that enhances human performance, adaptability, and decision-making in complex, unpredictable environments.
The presentation concludes by outlining future directions for AI-enhanced education that prioritise cognitive alignment, interdisciplinary collaboration, and paradigm-level innovation, positioning neuroscience as a critical missing link in the next evolution of educational design.

Biography: Dr. Victor (Vik) Perez is an Associate Professor and an internationally recognised voice in brain-driven entrepreneurship education, an emerging field that integrates insights from neuroscience into how entrepreneurship, innovation, and performance are taught and developed.
His work challenges traditional approaches to entrepreneurship education that emphasise tools, models, and processes, proposing instead a paradigm shift toward cognitive alignment—focusing on how attention, judgment, emotional regulation, resilience, and decision-making under uncertainty shape entrepreneurial behaviour and learning outcomes. By reframing entrepreneurship as a cognitively demanding human activity rather than a purely managerial skillset, his work positions neuroscience as a critical missing link in the future evolution of education.
Dr. Perez is the founder of the Global Brain-Driven Entrepreneurship Network (BRANET), an international initiative connecting scholars and practitioners exploring the intersection of cognition, learning, and entrepreneurial performance. He is also the creator of the WNYLE Method, a neuroscience-aligned framework designed to enhance entrepreneurial learning and human performance by sequencing attention, emotion, reflection, and action in ways that mirror how the brain learns under uncertainty.
His work spans education, cognitive science, and emerging technologies, with applications in entrepreneurship education, AI-enhanced learning environments, and performance-oriented pedagogical design. He has led and advised international initiatives across Europe and Asia, and has piloted brain-aligned learning interventions with international student cohorts in innovation-focused educational settings.
Dr. Perez’s ideas have been disseminated through international academic forums, invited talks, and public scholarship, including The Conversation, where his work contributes to global debates on whether—and how—entrepreneurship can be taught in ways that align with how the human brain actually learns and performs. Increasingly, his work informs interdisciplinary conversations on the future of education, AI-enhanced pedagogy, and human performance in complex, unpredictable environments.

 

Assoc. Prof. Thomas Selig, Xi'an Jiaotong-Liverpool University, China

Biography: Dr. Thomas Selig is an Associate Professor in the Department of Computing, School of Advanced Technology, at Xi'an Jiaotong-Liverpool University (XJTLU). He completed his PhD in Computer Science from the University of Bordeaux, France, in 2014, and followed this with postdoctoral research positions at the University of Strathclyde and Iceland University, before starting work at XJTLU in 2019. Thomas is keenly invested in enhancing students’ learning experience through the use of technology, in particular through developing and deploying AI-assisted tools in Higher Education courses and programmes. He is a Fellow of the UK Advance Higher Education Academy, and currently serves as School Learning and Teaching Committee Chair, and as Programme Director of XJTLU's BEng (Hons) Computer Science and Technology programme.

 

Assoc. Prof. Sri Nurhayati, IKIP Siliwangi, Indonesia

Speech Title: Extended Reality for Agricultural Supply Chain Education: A Systematic Review of Effectiveness, Implementation Challenges, and Future Directions

Abstract: The increasing complexity of agricultural and agri-food supply chains has intensified demand for innovative educational approaches that support systems-oriented learning, practical skill development, and experiential understanding. The integration of immersive technologies such as VR, AR, and MR under the Extended Reality (XR) framework offers new pedagogical opportunities to overcome limitations in conventional education systems. However, empirical evidence on their effectiveness, implementation barriers, and future research trajectories within agricultural supply chain education remains fragmented. This study aims to systematically review and synthesize peer-reviewed literature on the application of XR technologies for education and training in agricultural and agri-food supply chains, with a focus on educational effectiveness, implementation challenges, and emerging research directions. The study adopts a Systematic Literature Review (SLR) methodology guided by the PRISMA framework. Data were collected exclusively from the Scopus database using structured and refined Boolean search strings. A multi-stage screening process based on relevance, publication period (2019–2026), language, and accessibility yielded 30 peer-reviewed articles for inclusion in the qualitative synthesis. Data analysis was conducted through thematic coding and comparative synthesis to identify recurrent patterns, dominant themes, and cross-study consistencies. The results reveal six key themes: the effectiveness of XR-based learning for knowledge and skill acquisition; enhanced learner engagement and experiential learning; applications across different stages of the agricultural supply chain; technological and infrastructural constraints; human and institutional readiness challenges; and emerging research directions. Overall, XR-based educational interventions demonstrate consistent positive impacts on cognitive, procedural, and experiential learning outcomes, particularly for complex supply chain processes.

Biography: Dr. Sri Nurhayati received her Ph.D in Community Education from the Indonesia University of Education, Indonesia. Dr. Sri Nurhayati is currently an Associate Professor in Community Education at IKIP Siliwangi, Indonesia. Dr. Sri Nurhayati is a researcher and lecturer at IKIP Siliwangi, Indonesia. She holds a Web of Science ResearcherID (MGT-1919-2025) and an ORCID (0000-0002-2273-9143). Her research spans digital literacy, artificial intelligence in education, teacher resilience, and technology-enhanced language learning. Over the past five years, she has published more than twenty peer-reviewed papers in international journals, including International Journal of Learning, Teaching and Educational Research, Educational Process: International Journal, and Edelweiss Applied Science and Technology.
Dr. Nurhayati’s scholarly contributions include works on AI-driven learning, educators’ adaptability in digital education, and pedagogical innovation in the era of Society 5.0. She is also an active peer reviewer for journals such as Cogent Education, NJAS: Impact in Agricultural and Life Sciences, and International Journal of Information and Education Technology. Her current academic focus involves integrating cognitive and digital literacy to foster inclusive and sustainable education in Southeast Asia.

 

Assoc. Prof. Mustafa Ozguven, Xi'an Jiaotong-Liverpool University, China

Speech Title: AI-Augmented Entrepreneurship Education: Practical Approaches to Experiential and Personalized Learning

Abstract: Artificial intelligence is rapidly transforming how entrepreneurship is practiced, taught, and learned. While AI tools are widely discussed in higher education, many educators are still exploring how these technologies can be meaningfully integrated into entrepreneurship classrooms without losing the experiential and human-centered nature of entrepreneurial learning. This talk presents practical approaches for incorporating AI into entrepreneurship education to enhance student engagement, opportunity recognition, and venture development.
Drawing on international teaching experience and industry-engaged entrepreneurship programs, the presentation illustrates how AI tools can support key stages of the entrepreneurial learning process, including idea generation, market exploration, data-informed decision making, and prototype development. Practical examples demonstrate how educators can integrate AI into project-based courses, collaborative international learning environments, and industry-linked assignments to create more adaptive and skill-oriented learning experiences.
Artificial intelligence should not replace entrepreneurial thinking. Instead, should expand it, enabling entrepreneurs to explore opportunities more deeply, experiment more rapidly, and make better-informed decisions in an increasingly complex world. By embedding AI within experiential learning environments, entrepreneurship educators can better prepare students to navigate evolving innovation ecosystems and develop the practical capabilities required to build ventures in an AI-driven economy. The challenge for educators is no longer whether students will use AI, but how we design learning environments where AI strengthens entrepreneurial thinking rather than replaces it.

Biography: Dr. Mustafa Ozguven is an Associate Professor at Xi’an Jiaotong-Liverpool University (XJTLU), where he is affiliated with the Entrepreneurship and Enterprise Hub. Prior to his academic appointment, he held senior executive leadership roles in multinational organizations across the finance, technology, and international development sectors, including Director-level, Chief Marketing Officer, and Chief Executive Officer positions, as well as advisory engagements with international institutions and corporations.

Dr. Ozguven is an Honorary Research Fellow in the School for Data Science and Computational Thinking at Stellenbosch University, Cape Town, South Africa. His research lies at the intersection of entrepreneurship, international business, and emerging technologies, with particular emphasis on artificial intelligence, autonomous vehicle technologies, and corporate social responsibility. A core strand of his scholarly work focuses on the internationalization of entrepreneurship education, examining how digitally enabled, cross-border pedagogies foster entrepreneurial competencies, global mindsets, and responsible innovation. He is committed to evidence-informed, technology-enhanced, and industry-engaged learning, and regularly contributes to international academic conferences and professional development initiatives.

 

Assoc. Prof. Rui Zhang, Tongji University, China

Speech Title: Personalized University Physics learning Resources by Context-Engineered Generative AI Workflow: Efficacy, Reliability, and Mechanisms for Preference

Abstract: This study examines the effectiveness of a context-engineering-based generate–evaluate–integrate workflow employing large language models to produce personalized preview learning materials. Compared to alternative strategies, the context-engineering approach yielded materials of significantly higher quality in accuracy, vividness, coherence, and pedagogical robustness. High inter-rater reliability across students, domain experts, and large language model evaluators supported the use of automated rubric-based assessment as a reliable alternative to human evaluation. Casual analysis identified distinct motivational pathways grounded in self-determination theory: extrinsic motivation directly predicted preference for advanced materials, whereas intrinsic motivation exerted indirect effects through emotional and cognitive engagement, with no significant mediation observed in the extrinsic pathway. These research results demonstrate the potential of context-engineered generative AI to accommodate learner diversity and enhance engagement in preview contexts. Practical implications for adaptive educational resource design are discussed alongside suggestions for future longitudinal and cross-disciplinary investigations in this paper.

Biography: Zhang Rui is Associate Professor at the College of Physical Science and Engineering, Tongji University. He also serves as the discipline leader for Tongji’s Educational Artificial Intelligence program and for the Physics Education , and is a member of the Teaching Reform & Research Sub-committee of the National University Physics Teaching Steering Committee. He has published more than 70 research papers on physics education. His research interests focus on educational artificial intelligence and physics-education research.

 

Assoc. Prof. Jiawen Han, Xi’an Jiaotong-Liverpool University, China

Speech Title: From Ideation to Execution: How GenAI Reshapes Students’ Creativity and Originality in Interdisciplinary Design Education

Abstract: Although generative artificial intelligence (GenAI) enhances creative possibilities in design education, it also raises concerns with originality and creative agency. When students regularly produce refined visual outputs or generic design ideas with AI assistance, their understanding of and contributions to authentic creative design processes remain unclear. Learners’ perspectives should be used as a critical lens to better understand both the potential and challenges of AI in design education, for only then can teachers modify curricula and studio education to suit the new paradigm. Furthermore, the boundaries between design fields are blurring, and interdisciplinary investigation is needed to understand the emerging commonalities and differences across disciplines regarding AI’s integration. Therefore, in our study, we examined how students across different design programs incorporate GenAI in four phases of their studio practice—information gathering, conceptualising, optimising, and implementing—and how those interactions influence their understanding of creativity and originality. According to our findings, although AI supports early information gathering and conceptualisation in students’ design projects with discipline-specific variations, human creative agency and tutoring remain critical for later stages of refinement, with shifting concerns of plagiarism throughout. We argue that design education should not merely focus on the human originality of final products but encourage AI collaboration and document human–AI interactions as valid learning evidence within the interdisciplinary design field while acknowledging disciplinary differences in professional expectations.

Biography: Dr. Jiawen Han is an Associate Professor in the Department of Architecture at the Design School, Xi’an Jiaotong-Liverpool University, Suzhou. She earned her PhD in architecture from the University of New South Wales in Sydney and authored the book China’s Architecture in a Globalizing World: Between Socialism and the Market. She has published monographs, edited volumes, and journal articles that situate architecture within a transnational cultural context and a transdisciplinary framework. Her recently co-authored book, Chinese Cities as Pedagogy: Interdisciplinary Teaching Practice, introduces innovative teaching practices and methods with a critical focus on understanding and interpreting Chinese cities as a rich context. She has actively engaged in humanistic research through the integration of AI and digital tools, foregrounding hybrid approaches to scholarship and pedagogy.

 

Assoc. Prof. Hai Min DAI, Shanghai Jiao Tong University, China

Speech Title: Human-Generative AI Collaboration in Academic Inquiry: A Case Study of Graduate Students

Abstract: Researchers are increasingly using Generative Artificial Intelligence (GenAI) in their work, yet little is known about how researchers engage with these tools and how individual cognitive characteristics shape human–GenAI collaboration. This issue is particularly salient for graduate students who are transitioning into independent scholars. This study investigates how graduate students with different levels of critical thinking disposition (CTD) engage with GenAI across stages of research inquiry. Using a process-oriented mixed-method design, the study followed a cohort of first-year graduate students in Education as they incorporated GenAI into their research activities over a five-month period. The analysis focuses on how students perceive GenAI, how they interact with it during research tasks, and how these interactions evolve over time. Findings suggest that students tend to position GenAI as a supportive yet subordinate partner in academic inquiry. More importantly, CTD appears to shape the quality and character of this human–GenAI collaboration rather than the frequency of tool use. These findings contribute to emerging discussions on human–GenAI collaboration and highlight the importance of pedagogical scaffolding for novice researchers working with GenAI.

Biography: Dr. Rita Hai Min Dai is Associate Professor of Education at the Research Centre for Future Education, Shanghai Jiao Tong University. Her research examines technology adoption, user perceptions of technology use, and human-AI interaction in education. She has published in leading journals including Computers & Education, Computers in Human Behavior, Interactive Learning Environments, Journal of Computer Assisted Learning, British Journal of Educational Technology, and Higher Education. With a Scopus FWCI of 4.2, her work is cited over four times the global average. She has led or participated in 13 funded projects across China, the UK, Australia, and Macao SAR. Dr. Dai is Associate Editor for the Australasian Journal of Educational Technology and the Journal of University Teaching and Learning Practice, Guest Editor for the special issue on generative AI in the British Journal of Educational Technology, and Academic Advisor for Education University of Hongkong’s CRAC project on AI competency in higher education.

 

Assoc. Prof. Ts. Dr. Khairul Azhar Bin Hj Mat Daud, Universiti Malaysia Kelantan, Malaysia

Speech Title: Creative Multimedia and Artificial Intelligence for Future Digital Learning Environments

Abstract: The rapid advancement of digital technologies has significantly transformed teaching and learning practices in higher education. Creative multimedia and artificial intelligence (AI) are increasingly playing an important role in enhancing digital learning environments by enabling more interactive, adaptive, and personalized learning experiences. This presentation explores how integrating creative multimedia elements such as interactive media, digital storytelling, animation, and immersive content being combined with AI-driven technologies can support innovative pedagogical approaches and improve student engagement. The talk will also discuss the potential of intelligent learning systems, learning analytics, and AI-assisted content creation in shaping the future of digital education. By examining current developments and practical applications, this presentation highlights how educators and institutions can leverage creative multimedia and AI technologies to design more effective, engaging, and future-ready digital learning environments.

Biography: Assoc. Prof. Ts. Dr. Khairul Azhar Bin Hj Mat Daud is the Deputy Dean (Academic) at the Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan (UMK), and Vice Chair of the Association of Creative Technology Innovator Malaysia (ACTIM). He is the Editor-in-Chief of both the International Journal of Creative Future and Heritage (TENIAT) and Jurnal TENIAT UMK, and co-founder of the Research Ideation Canvas (RIC@). With a Ph.D. in Educational Technology from Universiti Sains Malaysia (USM), and degrees in Mechanical Engineering and Education, Dr. Khairul Azhar bridges education and technology through research and innovation. As head of the Creative Technology Research Group (RG CREATE), he leads efforts to establish it as a Centre of Excellence, focusing on augmented reality (AR) applications in technical education and workplace safety. His work has earned multiple awards, including Gold and Silver recognitions at UMK, ITEX, and PECIPTA, and a scholarship at Trinity College Dublin for Innovation and Entrepreneurship. A visionary academic and mentor, he champions collaborative advancement across academia, industry, and community.

 

Assoc. Prof. Jian Liao, Southwest University, China

Speech Title: Evaluating Classroom Teaching with School-Based Criteria Using Multimodal Large Language Models

Abstract: Traditional manual methods for teaching evaluation are time-consuming, making it difficult for teachers to obtain timely and sufficient feedback from instructional experts. Meanwhile, conventional automated evaluation tools typically apply generic, one-size-fits-all rubrics that fail to reflect individual school contexts, specific curricular goals, and localized instructional priorities. To address this lack of relevance and specificity, this study proposes an evaluation framework that uses school‑based criteria to assess classroom teaching. Employing Multimodal Large Language Models (MLLMs), we automatically evaluated 29 classroom sessions against the tailored evaluation standards of a participating primary school. We collected participant feedback regarding the usefulness of the MLLM-generated evaluations, their acceptance of the technology, and their perceptions of both the scores and the school-based criteria. Results indicated high levels of perceived usefulness and technological acceptance. Interview data revealed that teachers generally viewed the evaluation scores positively and endorsed the objectivity and practical relevance of the school-specific criteria. Suggestions for future improvement included developing more personalized evaluations, aligning the criteria with updated curriculum standards, and incorporating other potential enhancements.

Biography: Jian Liao, an associate professor in the College of Educational Technology, Faculty of Education, Southwest University. He is a master's advisor, holds a Ph.D. in Learning, Design, and Technology from Pennsylvania State University, and a master's degree in Educational Technology from Beijing Normal University, where he studied under Professor Ronghuai Huang. His research focuses on the application of artificial intelligence in education, intelligent analysis of educational audio and video materials, and robot-assisted education. To date, he has published over 20 journal papers, including three as the first author in JCR Q1 journals of the Chinese Academy of Sciences, more than 10 papers in CSSCI core journals, and over 30 international conference papers. He has led two general projects of the National Natural Science Foundation of China and participated in several national and ministerial-level projects, including a sub-project of the National Key R&D Program of the Ministry of Science and Technology. He also serves as a reviewer for international journals such as Computers & Education.

 

Assoc. Prof. Xi Lin, East Carolina University, USA

Speech Title: Longitudinal Insights into Emotional Engagement in Student-AI Dialogues

Abstract: This study explored how the use of Artificial Intelligence (AI), specifically ChatGPT, impacted students’ emotional engagement during learning interactions. Data were collected from two graduate-level courses in the field of education. Students’ responses from 650 discussion board posts, 219 AI prompts, and 1,710 coded reflections were analyzed. Results show that students expressed a higher level of negative emotions when interacting directly with ChatGPT compared to directly interacting with peers. The finding suggests that learners perceive ChatGPT as a permissible outlet for negative emotional disclosure than peers. Additionally, longitudinal analysis reveals an increase in expressions of concern without a decline in participation, indicating a maturing human-AI relationship. Furthermore, emotional openness is found to significantly predict ongoing engagement, with students who were emotionally expressive demonstrating high persistence across modules. These findings highlight the importance of designing emotionally responsive AI systems to support learning.

Biography: Dr. Xi Lin holds a B.M. degree in Public Utilities Management from North China Electric Power University (Beijing), an M.A. in Communications, and a Ph.D. in Adult Education from Auburn University, USA. She is currently an Associate Professor in the Department of Interdisciplinary Professions at East Carolina University. Dr. Lin is dedicated to advancing the use of artificial intelligence (AI) in education. Her research focuses on 1) implementing AI in teaching and learning to enhance student learning, 2) examining students’ emotional engagement in human–AI interaction, and 3) exploring ethical issues related to AI in education and beyond. She has published extensively in peer-reviewed journals and has edited multiple books and journal special issues, contributing valuable insights into best practices for online instruction and the use of multimedia in education.

 

Assoc. Researcher Xiaojie Lin, Zhejiang University, China

Speech Title: Empowering Energy Engineering Education with Generative Artificial Intelligence: Teaching Innovation and Practice Based on Large Language Models

Abstract: The rapid development of generative artificial intelligence (GAI) and large language models (LLMs) is transforming both industry and higher education. In engineering disciplines, particularly in energy systems and intelligent energy management, the integration of AI technologies into teaching has become increasingly important. This work presents a teaching innovation framework that incorporates generative AI tools into undergraduate and graduate courses in energy engineering. Based on long-term research and engineering practice in intelligent energy systems, we design a teaching approach that integrates domain knowledge, AI methodologies, and real-world engineering cases. A domain-specific large language model assistant for integrated energy systems is developed using approaches such as retrieval-augmented generation (RAG), task-oriented fine-tuning, and customized agent capabilities. The tool supports students throughout the learning process, including knowledge exploration, code generation, modeling assistance, and problem analysis. The framework has been applied in multiple courses such as Automatic Control Theory and Artificial Intelligence for Smart Energy Systems. Through AI-assisted learning, students are able to visualize complex system models, interactively explore control and optimization problems, and bridge the gap between theoretical knowledge and engineering practice. Teaching results show that generative AI significantly enhances students’ engagement, interdisciplinary thinking, and ability to solve complex engineering problems. This study provides practical insights into integrating generative AI into engineering education and offers a scalable approach for cultivating interdisciplinary AI+engineering talent in the era of intelligent energy systems.

Biography: Dr. Xiaojie Lin is an Associate Researcher and Ph.D. Supervisor selected for the Postdoctoral International Exchange Program by the Ministry of Human Resources and Social Security of China.
As a Principal Investigator (PI), Dr. Lin leads a National Key R&D Program International Cooperation Project focusing on Smart Energy and Energy+AI, as well as one General Program project and one Youth Program project under the National Natural Science Foundation of China (NSFC). He also serves as a task leader for a National Key Basic Research Project. In the field of education, he presided over Zhejiang University’s inaugural "AI for Education" Key Empirical Teaching Reform Project, which achieved an "Excellent" rating upon conclusion. He currently leads the Zhejiang Province Higher Education "14th Five-Year" Graduate Teaching Reform Project and a Key Project for the Zhejiang Graduate Education Society.
By integrating thermodynamic mechanisms with artificial intelligence algorithms, Dr. Lin’s research proposes dynamic modeling methods for multi-energy flow systems, technologies for source-load uncertainty quantification and energy-quality matching design, and flexible scheduling strategies featuring multi-time scale coordination. His research findings have been published in top international journals and conferences, including npj Thermal Science and Engineering, Applied Energy, Energy, Engineering Applications of Artificial Intelligence, and AAAI-2026. He led the team that won the 2023 Zhejiang Province Science and Technology Progress Award.

 

Assoc. Prof. Priti Srinivas Sajja, Sardar Patel University, India

Biography: Dr. Priti Srinivas Sajja (b. 1970) is a Professor and Head of the PG Department of Computer Science at Sardar Patel University. She earned her M.S. in 1993 and Ph.D. in 2000, both in Computer Science from the same university. She specializes in Artificial Intelligence, Machine Learning, and Systems Analysis & Design, and has authored several international books with a total of 243 publications. She served as Principal Investigator on a major UGC-funded research project and on an Indo-Russian DST (Department of Science & Technology) project. She has mentored numerous PhD scholars, too. She has rendered her expertise as a resource person for AICTE and NAAC, contributing to quality and accreditation processes in India. She also held a key state-government role as Director of the Gujarat University Granth Nirman Board, a committee formed by the Governor on the recommendation of the Chief Minister of the state in India. She is a recipient of multiple awards — notably the Sardar Patel Research Award (five time) and a Lifetime Achievement Award from the National Foundation for Entrepreneurship Development.

 

Assoc. Prof. Tarasova Ksenia, National Research University Higher School of Economics, Russia

Biography: Prof. Ksenia Tarasova received her PhD in Education specializing in quality of education and its evaluation in 2009. Before becoming the Director of the Centre for Psychometrics and Measurement in Education at HSE University, she contributed to large-scale national and international assessment projects, including the READ program (Russia Education Aid for Development) and several World Bank initiatives. She has also served as an independent consultant for the Inter-American Development Bank and the American Center for Education and Research.

Prof. Tarasova specializes in evidence-centered assessment design (ECD) and the development of virtual performance-based assessments, including digital, interactive, and scenario-based assessment tools. Her research focuses on log-process data analytics and the application of artificial intelligence in educational measurement, including automated task generation, AI-supported scoring of open-ended responses, training large language models (LLMs), and measuring complex constructs using AI-driven methodologies. She was a key developer of Russia’s first patented ECD-based interactive assessment instrument within the ICL assessment project. Prof. Tarasova currently leads a Russian Science Foundation (RSF) grant focused on developing an AI literacy assessment framework and methodology. Her research is further supported by international collaborations in AI and psychometrics with leading research institutions in China and worldwide.

 

Assoc. Prof. Martin Lukas, Czech University of Life Sciences Prague, Czech Republic

Biography: Martin Lukáš is an Associate Professor at the Department of Information Technologies, Czech University of Life Sciences Prague. His research focuses on digital transformation, artificial intelligence, enterprise architecture, eGovernment, information systems, IT governance. He has authored one monograph, three academic textbooks, and numerous peer-reviewed articles on ICT management and digital maturity. Dr. Lukáš actively contributes to higher education by teaching courses in information systems, software engineering, and ICT management, and by developing learning materials for ICT curricula. He has supervised numerous bachelor’s and master’s theses in ICT and digital transformation and serves as a strategic consultant for digital initiatives. In addition to his academic work, Dr. Lukáš brings extensive professional experience in program and project management, service management, and enterprise architecture, supported by certifications such as PRINCE2 Practitioner, MSP, ITIL, and ArchiMate. He has successfully led cross-cultural and geographically distributed teams, facilitated stakeholder meetings to align strategic objectives with operational improvements, and applied frameworks such as TOGAF and ArchiMate for AS-IS vs. TO-BE analysis. His ability to integrate practical expertise with academic research strengthens his contributions to both the scientific community and industry practice. Dr. Lukáš serves on editorial review boards, acts as a blind reviewer for conference proceedings, and participates in scientific committees of international conferences.
ORCID: https://orcid.org/0000-0003-0356-5480
ResearchGate: https://www.researchgate.net/profile/Martin-Lukas-5

 

Assoc. Prof. Leslie A. Cordie, Auburn University, USA

Speech Title: Global Perspectives and Innovative Practices: The Future of AI in Higher Education, Training, and Adult Learning

Abstract: Artificial Intelligence, particularly generative AI, is no longer a distant prospect but a transformative force reshaping the landscape of adult education, professional training, and higher education worldwide. This presentation explores the critical findings from the upcoming volume, Navigating the AI Frontier in Adult Education, which synthesizes a large-scale global study involving 1,947 educators across 36 countries and 41 researchers.
The rise of generative AI is reshaping education and work, with adult educators are at the forefront of preparing learners for an AI-driven world. While AI adoption is accelerating, the data reveals a complex reality: significant disparities exist in institutional readiness, digital access, and infrastructure. Educators are increasingly leveraging AI to personalize learning and streamline administration, yet they remain deeply concerned about ethical implications, data privacy, and the potential for a widened digital divide.
Participants will hear about trends, insights and global lessons designed to help policymakers and practitioners build inclusive, ethical, and future-ready learning systems. As we navigate this new frontier, the focus remains on fostering competence and leadership to ensure AI serves as a tool for meaningful innovation.

Biography: Dr. Leslie Cordie is an Associate Professor in the Adult Education programs within the Educational Foundations, Leadership and Technology at Auburn University. Her interdisciplinary and collaborative research involves a variety of areas including faculty and professional learning, curriculum and instructional design, and learning technologies. Her passion is connecting with learners across the world.
Dr. Cordie earned her doctorate in Adult Education and Technical Communication from Colorado State University, which included an emphasis on Distance Learning, adult learning theory, and instructional design and curriculum development. She also has an MBA from the University of Texas at Austin and has experience working with the airlines and the military in quality and performance improvement. She began her career as a community health nurse.
Dr. Cordie was a Fulbright Core Scholar (2020-21) where she conducted research and taught in the West Indies. She is currently the lead US collaborator for Research Network #3 for the ASEM Lifelong Learning Hub (see asemlllhub.org), in conjunction with ACE at University College Cork.

 

Assoc. Prof. Liqiao Nong, Guangxi Polytechnic of Construction, China

Speech Title: A case study on based on VR-based Inquiry Learning: Microcomputer Assembly Example

Abstract: The new curriculum standard issued by the Ministry of Education clearly advocates heuristic and inquiry-based teaching. Cultivating students' moral qualities and key abilities of independent inquiry and independent thinking are emphasized. Based on embodied cognition theory and with the advantages of immersion and interaction for VR technology, this paper constructs a theoretical model of VR Inquiry-based learning with the characteristics of situational awareness, intelligent control, open resources and omnidirectional interaction. VR Inquiry-based learning theoretical model focuses on learners' multi-sensory perception embodied experience and multi-modal interaction in VR Inquiry-based learning space. Students can achieve knowledge, ability and emotion teaching goals in VR Inquiry-based learning environment. The realization path of VR Inquiry-based learning theory model is to adopt intelligent learning mode, rational use of technology, reconstruct new teacher-student relationship and build digital resource platform.

Biography: Dr. Liqiao Nong received her PhD in Curriculum and Teaching Methodology from Southwest University of China. Dr. Nong is currently an associate professor in school of humanities and education at Guangxi Polytechnic of Construction. Her research interests include intelligent teaching, AI applied in education, educational technology integrated into language teaching. Dr. Nong currently serves as technical member for International Conference on Education and Information Technology, ICEIT and Global Chinese Conference on Computers in Education Application, GCCCE and the editorial board member for Journal of Applied Research in Higher Education and US-China Foreign Language. Dr. Nong is Educational Technology Engineer and senior member of Institute of Educational Technology.

 

Assoc. Prof. Jining Han, Southwest University, China

Speech Title: GenAI-Supported Teacher Feedback on Students' Writing: Teachers' Perspectives

Abstract: This study explored an innovative approach to providing written feedback, namely, ChatGPT-supported teacher feedback, in the Chinese tertiary EFL context. Two research questions guided this study: (1) What are EFL instructors’ perceptions of ChatGPT-supported teacher feedback? (2) How do EFL instructors revise ChatGPT output while providing ChatGPT-supported teacher feedback? Specific training was conducted with four course instructors and two classes of students, and the instructors provided ChatGPT-supported teacher feedback on two writing tasks across seven weeks. Perception data were collected from four instructors via individual semi-structured interviews, supplemented with records of ChatGPT feedback, teachers’ responses to ChatGPT output, and students’ uptake of ChatGPT-supported teacher feedback. Qualitative content analysis was conducted, and the constant comparative method was utilized. The findings revealed the instructors’ perceived benefits of ChatGPT-supported teacher feedback and the perceived constraints of ChatGPT output. In implementing ChatGPT-supported teacher feedback, the instructors added new comments, extended the feedback, modified inappropriate suggestions, condensed repetitive comments, and deleted excessive praise. This study provides novel pedagogical insights into the integration of ChatGPT’s capabilities with teachers’ agency when providing written feedback.

Biography: Dr. Jining Han is an associate professor in the Faculty of Education at Southwest University. He earned his Ph.D. in Second Language Acquisition and Educational Technology from the University of South Florida and holds a Master’s degree in Pedagogy from Arizona State University. He also received a postdoctoral fellowship position at the Georgia Institute of Technology. He conducts research in applying AI-supported learning, virtual reality in education, and smart learning environment.

 

Assoc. Prof. Jiyao Xun, Xi’an Jiaotong-Liverpool University, China

Speech Title: The Dual Acceleration: A Theoretical and Practical Framework for Entrepreneurial Education in the Era of Exponential AI

Abstract: Generative AI demands a fundamental reconceptualization of entrepreneurship education. This paper introduces the Entrepreneurial Capability Density Growth Theory, integrating the Densing Law of LLMs and the Great Compression Effect. It posits AI as a dual catalyst, exponentially increasing the density of entrepreneurial capabilities acquired per unit time while compressing traditional development pathways. This framework is operationalized through AI-Infused, Density-Oriented Project-Based Learning, with capability growth measured quantitatively via the Entrepreneurial Capability Density Index (ECDI). This provides educators and policymakers a blueprint for cultivating founders capable of leveraging exponential AI.

Biography: Dr Jiyao Xun works in Entrepreneurship Education at the XJTLU Entrepreneurship College (XEC), an environment characterised by the synergy of industry practice and scholarly research. He holds a PhD in Business & Management from the University of Nottingham. His professional background includes experience in UK retailing and a senior role with a top-five UK MBA programme. His core research explores retail technology (e.g., the metaverse), digital marketing, syntegrative education, and AI education. His doctoral supervision includes completed projects on metaverse retailing and business intelligence adoption, and he co-supervises a PhD project investigating work-based learning. As an ILEAD trainer, he disseminates principles of syntegrative education and innovative learning design to wider educator communities.

 

Assoc. Prof. Ying Tang, Southwest University, China

Speech Title: (Inter)Disciplinary Identity of Educational Technology

Abstract: Educational technology is a highly interdisciplinary field, yet its core nature remains under continuous discussion. This study examines the field’s 20-year evolution from the perspective of knowledge flow, based on large-scale longitudinal data of publicatiosn and their citations. Using descriptive analysis, knowledge flow metrics, and AI-assisted topic modeling, we identify strong interdisciplinary connections and a clear shift from basic pedagogical frameworks to intelligent and interactive learning systems. This work provides a clearer understanding of educational technology’s disciplinary nature and transformative role in education.

Biography: Dr. Ying Tang is an Associate Professor of Educational Technology in the Faculty of Education at Southwest University, China, and Associate Editor of Future in Educational Research. She was previously a postdoctoral researcher at Indiana University Bloomington. She holds a PhD from The University of Hong Kong, an MEd from Vanderbilt University, and a BA from University of International Relations.
She has published over 40 peer-reviewed papers in leading venues such as Computers & Education, Educational Research Review, British Journal of Educational Technology, and International Journal of Educational Technology in Higher Education. Her research examines how to improve learning in computer-mediated environments through educational technologies and pedagogy, and more recently the ethical use of AI and big data in education to support fair, responsible learning outcomes.

 

 

Asst Prof. Crisianee Berry, East Carolina University, USA

Biography: Dr. Berry specializes in the development of instructional materials and multimedia designed to support diverse learner needs. Leveraging her Ed.D. in Curriculum and Instruction from the University of North Carolina at Chapel Hill and her M.A. in Biomedical Communication from the University of Texas Southwestern Medical Center, she integrates learning theory with emerging technologies to create evidence-based teaching strategies, effectively bridging the gap between technical communication and pedagogical practice. An active presenter, Dr. Berry has presented at international and national conferences including the American Educational Research Association (AERA), the Society for Information Technology and Teacher Education (SITE), and the Association for Educational Communications and Technology (AECT). Her recent projects include the exploration of the impact of Generative AI on both industry and academia and the investigation of communication and collaboration skills needed by instructional designers. Additionally, she regularly facilitates workshops focused on professional development, innovative pedagogy and the implementation of AI-driven strategies in the classroom.

 

 

Asst. Prof. Midya Yousefi, Wenzhou-Kean University, China

Biography: Dr. Midya Yousefi is an Assistant Professor in the Department of Educational Leadership at Wenzhou-Kean University (WKU), where she teaches and supervises doctoral and master’s students in educational leadership. With over nine years of international experience, she has led academic programs, developed leadership curricula, and advised universities and schools on global education strategy and institutional development. She is also an active consultant supporting internationalization, digital innovation, and faculty leadership in China and beyond. She is a member of the Global Ambassador Management Committee of the Academy of Management (AOM) and serves as a judge for international education awards. Her research focuses on digital leadership, cross-border higher education, and academic engagement in emerging contexts. She is widely recognized for her contributions to global academic collaboration and is frequently invited as a speaker and research chair at international education forums.

 

Asst. Prof. Fergie Yu Wang, Shanghai Jiao Tong University, China

Biography: Dr. Wang holds a Ph.D. in Education from the Faculty of Education at Beijing Normal University and completed a joint doctoral program at Nanyang Technological University in Singapore.
Her research interests primarily include cognitive neuroscience, STEM education, collaborative learning, and the application of artificial intelligence in education. Her specializes in utilizing neuroimaging and eye-tracking technologies to investigate the learning and cognitive development processes of individuals and groups in various contexts. She has dedicated his work to researching themes such as peer interaction, learning engagement, and project-based learning within STEM educational settings.

 

Asst. Prof. Xiaofei Zhou, Shanghai Jiao Tong University, China

Speech Title: Contextualize AI Literacy for K-12 Teachers and Students

Biography: Xiaofei Zhou is a tenure-track assistant professor at Shanghai Jiao Tong University. She has a Bachelor's degree in Industrial Engineering from Tsinghua University, a Master's degree in Educational Technology and Applied Learning Sciences from Carnegie Mellon University, and a PhD degree in Computer Science from the University of Rochester. Her research interests lie in AI-empowered interdisciplinary learning and AI literacy for K-12. She publishes in research journals and conferences, such as the International Journal of Human-Computer Studies (IJHCS), the International Journal of Artificial Intelligence in Education (IJAIED), the ACM CHI Conference on Human Factors in Computing Systems (CHI), the International Conference on Artificial Intelligence in Education (AIED), ACM Interaction Design and Children (IDC), etc. Please check out her full publications on Google Scholar.

 

Asst. Prof. Jinyang (Sam) Song, Xi'an Jiaotong-Liverpool University, China

Biography: Jinyang (Sam) Song is a Fellow of the Higher Education Academy (UK). He holds a degree from University College London (UCL) and has extensive experience in English for Academic Purposes (EAP) and English-medium higher education across both the UK and China. He currently teaches EAP at Xi’an Jiaotong-Liverpool University.

His teaching and research interests centre on the pedagogical integration of artificial intelligence in language education, including AI literacy development, AI-supported critical thinking, and the use of AI agents to support collaborative learning in English-medium higher education. He also has a growing research interest in learner wellbeing and pedagogical practices, particularly in technology-enhanced and syntegrative education contexts.

 

Asst. Prof. Qin Wang, Shaanxi Normal University, China

Speech Title: Explainable Machine Learning for Understanding Student Behavioral Engagement

Abstract: This talk explores how machine learning and explainable AI can unlock insights from student process data. Using PISA 2022 creative thinking task data from Canada, Germany, and Hong Kong, our study first identifies distinct behavioral patterns of task engagement and disengagement. We then employ the XGBoost model to predict these states. SHAP analysis reveals that cognitive and academic factors are the strongest predictors, moving us beyond correlation to understanding key drivers. By translating raw behavioral logs into interpretable student profiles and transparent decision rules, this work moves beyond the “black box” to offer actionable, evidence-based insights for fostering engagement in creativity education.

Biography: Qin Wang is an assistant professor in the Faculty of Education, Shaanxi Normal University. She has a bachelor’s and master’s degree from Xi’an Jiaotong University, and a PhD degree from the University of Saskatchewan in Canada. She has led a project funded by the MOE Liberal Arts and Social Sciences Foundation and has published numerous SSCI-indexed articles. Her research interests include educational data mining, learning analytics, computational thinking, and AI applications in STEM education.

 

Dr. Luan Trong Nguyen, FPT University, Vietnam

Speech Title: AI-Driven Entrepreneurship Education for Sustainable Development in the Orange Economy: Students’ Orange Entrepreneurship Intention

Abstract: In the era of digital transformation and sustainable development, the integration of artificial intelligence (AI) into entrepreneurship education has become a strategic direction in higher education. However, few studies have examined how AI-based learning interacts with sustainability knowledge and cultural innovation in shaping students’ Orange Entrepreneurship
Intention (OEI) within the context of the Orange Economy. This study investigates the effects of AI-Driven Education, Entrepreneurship Education, Orange Economy Knowledge, and Sustainable Development Knowledge on OEI. Using a quantitative approach, data were collected from 403 students at FPT University and analyzed through SPSS and AMOS using Cronbach’s Alpha, Exploratory Factor Analysis (EFA), and Structural Equation Modeling (SEM). The findings reveal that AI-Driven Education, Orange Economy Knowledge, and Sustainable Development Knowledge positively influence OEI, with sustainability knowledge exerting the strongest effect. In contrast, traditional entrepreneurship education shows no significant impact. This research contributes an integrated theoretical model linking AI, sustainable development, and cultural creativity, while offering practical implications for SDG-oriented entrepreneurship education within the Orange Economy.

Biography: Luan Trong Nguyen (Ph.D.), Head of Department of Entrepreneurship, FPT University, Vietnam, IEEE member and IEEE Education Society (IEEE Membership ID is: 10129128). He received his Ph.D from Kanazawa University of Technology, Japan in 2013. He is a collaborative professor in Kanazawa Unversity, Japan. Dr. Luan is served as chair or co-chair, publicity chair,Technical committee chair/member, and reviewer in many prestigious conferences and journals. He has been involved in multiple research projects in social sciences such as ISTEM, entrepreneurship, Gen Z’s behaviors, environmental science, material sciences, economics, management with over 65 academic work, which has published by the most reputable publishing houses such as Elsevier, Taylor & Francis, Springer. For community, He served as is also a committee member for Startup Network Mekong Delta, Vietnam and He is a special consultant of SEA (strategic environmental assessment ), ESG and Manager/coordinator for many international projects and having experience from many countries (Japan, Netherlands, Malta, South Korea, Sweden, Taiwan (China), Malaysia, Thailand, Germany, France… He is an international speaker on sustainable development and environmental issues, ESG, entrepreneurship, climate change, SDG 4, and iSTEM education.
ORCID link: https://orcid.org/0000-0002-3489-1628
Scopus: https://www.scopus.com/authid/detail.uri?authorId=54942364900