invited speakers / 特邀报告专家

 

British Educational Research Association Workshop: Advancing AI and Digital Education

Speech Title: Generative AI and Educational Advancement – UK Higher Education Perspectives

Abstract: Generative AI (GAI) is driving significant advancements in education, reshaping teaching, learning, and assessment in Higher Education (HE). These advancements offer global insights into how GAI tools can be effectively integrated into diverse teaching and learning (T&L) contexts and their impact on the efficacy of learning. Local perspectives about GAI in T&L provide valuable frameworks and innovations. Moreover, international perspectives promote collaboration, drive innovation, and enhance the overall effectiveness of GAI adoption in education. This interactive panel, convened by Special Interest Group (SIG) convenors from the British Educational Research Association (BERA), brings together representatives from UK Higher Education Institutions (HEIs) to showcase how GAI is being integrated into academic practices. Each panel member will present insights from their institutional context, addressing key themes such as pedagogical innovation, academic integrity, student engagement, and faculty development. The discussion will critically examine institutional policies, ethical dilemmas, and the implications of GAI on curriculum design and assessment strategies. The session is designed to be highly interactive, incorporating audience engagement throughout. A live Q&A will be embedded within the discussion, encouraging participants to reflect on how educators can navigate the complexities of GAI adoption while maximizing its benefits.

Onsite Speaker (Digital Education group convenor for the British Educational Research Association (BERA)

Dr. Nashwa Ismail
Fellow of Advance HE and Digital Education group convenor for the British Educational Research Association (BERA)
University of Liverpool, UK

Biography: Dr. Nashwa holds an MSc and PhD from the University of Southampton. She is a Fellow of Advance HE and Digital Education group convenor for the British Educational Research Association (BERA). Currently, she is a lecturer in Digital Education and Innovation at the University of Liverpool, UK. Her expertise lies in Technology-Enhanced Learning (TEL), with a particular focus on Artificial Intelligence (AI) and Games-Based Learning (GBL). Dr. Nashwa has extensive international experience, including work in the Global South, to equip academics with the skills and knowledge required to integrate technology including AI into teaching and research effectively.

 

Online speakers (Digital Education group convenors for the British Educational Research Association (BERA)

 

Dr. Koula Charitonos
Open University, UK

Biography: Dr Koula Charitonos is a Senior Lecturer at the Institute of Educational Technology in the Open University. Her research is integrative and spans three areas: 1. Professional learning for complex professional knowledge work; 2. Pedagogies and educational practices for connected learning across formal and informal settings and 3. Participatory approaches to research with educational technology.

 

Dr. Felix Kwihangana
Kings College London, UK

Biography: Dr Felix is a Senior Lecturer in Transnational Education at Kings College London. His research and teaching focus on the educational use of digital technologies, especially teacher education and digital technologies in under-resourced contexts. He is also co-leading the Digital Inequalities strand of the Digital Technology, Communication & Education research group (DTCE RSG) at Manchester.

 

Jennifer Crowdy
PhD Convenor for the British Educational Research Association (BERA)’s Digital Education Special Interest Group, Peter Gosden Fellow for the History of Education Society
University of Winchester, UK

Biography: Mrs Jennifer Crowdy is a final year PhD student and Senior Digital Ambassador at the Faculty of Education and the Arts, University of Winchester. Her research is on ‘rethinking the concept of creativity in technology education through ‘philosophies of the encounter’’. Alongside her studies, from 2022-2024 Jennifer created and co-led the Winchester Digital Academy Pilot Programme, where students from all backgrounds and modes of study learnt and harnessed the latest trends and development in digital education and AI. She is also a former successful content creator, who created live educational and edutainment content on the broadcasting platform Twitch. Jennifer is the PhD Convenor for the British Educational Research Association (BERA)’s Digital Education Special Interest Group, and is currently the Peter Gosden Fellow for the History of Education Society. She also currently works as a Personal Assistant for the University of Winchester’s Dean of Health & Wellbeing. Jennifer has worked in state education since 2011, with a wealth of experience teaching all phases of educational life with a technology specialism.

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Prof. Xiangjie Kong
Vice Dean in the College of Computer Science & Technology, Distinguished Member of CCF
Zhejiang University of Technology, China

Speech Title: Knowledge and Data Driven Computational Social Science: From Academic Networks to Urban Networks

Abstract: The rapid development of technologies such as online social networks, intelligent monitoring, automatic data collection, intelligent sensing, and high-performance computing in recent years has contributed to the explosive growth of big data. The accessibility of various types of human-related data has significantly influenced the research topics and methods that researchers focus on. These data allow for traditional social issues to be studied from new perspectives and enable the discovery of more social phenomena. Simultaneously, the availability of data has led to the emergence of new research topics or methods. As a result, exploring research topics in the field of computational social science, which centers on knowledge and data, has garnered increasing attention. Traditional methods for retrieving empirical data to analyze issues in the social sciences often rely on manual processes, such as human resource surveys, which are not only resource-intensive but also prone to significant inaccuracies due to human error or inherent limitations. Data-driven computational social science uses mathematical theories and data processing and analysis techniques from computer science to address these social issues. This approach has attracted widespread attention and recognition from research institutions and scholars in disciplines such as computer science, network science, data science, management science, social science, behavioral science, and physics. This report will introduce some relevant research efforts in computational social science based on academic big data and urban big data, taking the fields of academic collaboration and smart cities as examples.

Biography: Xiangjie Kong is currently a Full Professor and Vice Dean in the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), China. Previously, he was an Associate Professor in School of Software, Dalian University of Technology (DUT), China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) (http://cssclab.cn/). He is/was on the Editorial Boards of 6 International journals. He has served as the General Chair or Program Chair of more than 10 conferences. Dr. Kong has authored/co-authored over 200 scientific papers in international journals and conferences including IEEE TKDE, IJCAI, ACL, IEEE TMC, ACM CSUR, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, ACM TSON, ACM TSAS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 20 papers are ESI-Highly Cited Papers (Top 1%). His research has been reported by Nature Index and other medias. He has been invited as Reviewers for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 30 filed patents. He has an h-index of 53 and i10-index of 131, and a total of more than 9700 citations to his work according to Google Scholar. He is named in the 2019 - 2024 world's top 2% of Scientists List published by Stanford University. He is named in the 2022-2024 Best Computer Science Scientists List published by Research.com. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, IEEE CSCWD 2024 Best Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at more thant 10 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide. His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.

 

Prof. Zhiquan Liu
Jinan University, China

Speech Title: Security Trust and Privacy in Vehicular Networks

Abstract: Vehicular networks, as an important application of Internet of things in the automotive industry, and as the core component of intelligent transportation system, can realize all-round network connection and efficient information interaction between vehicles and other nearby vehicles, road infrastructures, pedestrians, and network, etc., so as to provide various information services, improve driving safety and efficiency, and promote energy saving and emission reduction. Vehicular networks are regarded as a global innovation hotspot and an important commanding point of economic development, with huge industrial development potential and application market space. However, due to the large, open, highly dynamic, delay sensitive, and other characteristics, the security, trust, and privacy in vehicular networks face huge challenges. Thus, this talk will focus on highlighting the recent advances, challenges, and approaches for the security, trust, and privacy in vehicular networks.

Biography: Zhiquan Liu is a full professor with the College of Cyber Security, Jinan University. In recent years, Prof. Liu has published more than 100 SCI/EI-indexed papers on authoritative journals and conferences (including more than 50 papers on CCF-A/JCR-1/TOP journals, 4 best papers on international conferences, 2 most popular papers on international journals, 1 ESI hot paper, and 4 ESI highly cited paper), and has applied for/been authorized more than 100 invention patents and PCT patents. Besides, Prof. Liu has served as the Chair, Co-Chair, Program Committee Chair, Publication Chair, Publicity Chair, Finance Chair, Workshop Chair, or Program Committee Member for more than 20 international conferences. Meanwhile, Prof. Liu has served as the editor-in-chief of Advances in Transportation and Logistics, and has served as the area editor, associate editor, or academic editor for more than 10 SCI-indexed journals. His homepage is https://www.zqliu.com.

 

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

Speech Title: Learned Indices for Spatial Data

Abstract: An index is a structure or organization on data for effectively managing data item in terms of time and space, such as hash tables, binary search trees, and B-trees. As the properties and types of data change over time, new appropriate indices for efficient management on data become more and more important and necessary. On the other hand, as the techniques of machine learning or deep learning advance, many applications using machine learning for a better performance have been explored. Recall the idea and objective of an index. The index now can be seen as a model in machine learning, which can locate the data item effectively by prediction. With this observation, a learned index, a model that considers the patterns and distributions of data, has been proposed to facilitate search processing. Some learned indices have been provided for one-dimensional data, including Range Index and Recursive Model Index (RMI). For multi-dimensional (or spatial) data, it is always a challenging work to have effective index structures. Some well-known spatial indices, like kd-trees, quad-trees, and R-trees, with their variants for improvement on the efficiency have been studied till now. It thus is interesting and worthy to study the learned indices on spatial (multi-dimensional) data for a better performance. In this talk, the learned indices will be introduced starting with the ones for one-dimensional data. We then focus on the learned indices for spatial data and present our learned indices based on index tree structures. With the learned indices as models, evaluation on preprocessing, training, prediction, error, as well as query processing for point, range and kNN queries will be addressed as well.

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 paper awards in many conferences, including ICUFN 2015, ICS 2016, MC 2017, WOCC 2018, MC 2019, WOCC 2021, TCSE 2022, and TANET 2023. His current research interests include data science, big data management, uncertain data management, spatial data processing, data streams, ad-hoc and sensor networks, location-based services.

 

Prof. Kasturi Vasudevan
Indian Institute of Technology Kanpur, India

Speech Title: Turbo Coded OFDM-OQAM using Hilbert transform

Abstract: In this talk, we focus on the use of Hilbert transform in orthogonal frequency division multiplexing with offset quadrature amplitude modulation (OFDM-OQAM). A Nyquist pulse and its Hilbert transform is used as the transmit filter, resulting in single sideband modulation, that has roots in analog telecommunications. The transmitted signal has half the bandwidth of the regular QAM, enabling packing of twice the number of subcarriers (users) in a given bandwidth, compared regular QAM. The proposed method also uses T-spaced OQAM, where T is the symbol duration. At the receiver, a matched filter can be used with no intersymbol interference (ISI). In contrast, the approaches in the literature related to OFDM-OQAM use non-Nyquist pulses as well as T/2-spaced OQAM, resulting in ISI at the receiver. Turbo code is used to attain error-rates of 10^{-4} at signal-to-noise ratio (SNR) close to 0 dB.

Biography: Prof. Kasturi Vasudevan completed his Bachelor of Technology (Honours) from the department of Electronics and Electrical Communication Engineering, IIT Kharagpur, India, in the year 1991, and his MS and PhD from the department of Electrical Engineering, IIT Madras, in the years 1996 and 2000 respectively. During 1991–1992, he was employed with the Indian Telephone Industries Ltd, Bangalore, India, as Assistant Executive Engineer. He was a Post Doctoral Fellow at the Mobile Communications Lab, EPFL, Switzerland, between Dec 1999 and Dec 2000, and an engineer at Texas Instruments, Bangalore, between Jan 2001 and June 2001. Since July 2001, he has been a faculty at the Electrical department at IIT Kanpur, where he is now a full Professor. His interests lie in the area of telecommunications and signal processing. He has authored three books, namely, Digital Communications and Signal Processing, CRC Press, Analog Communications: Problems & Solutions, Springer and Basic Electronics: Problems & Solutions, Ane Books. He has published many articles in journals and conferences. He is a Senior Member of the IEEE and the Editor-in-Chief of Semiconductor Science and Information Devices, Bilingual Publishing, Singapore. He was nominated for the Marquis Who's Who Lifetime Achievement Award in 2019. He is a Reviewer for many journals and conferences.

 

Prof. Zhi Liu
Central China Normal University, China

Speech Title: Profiling students’ learning engagement to identify learning achievement: An automated configurational approach

Abstract: In the Massive Online Open Course (MOOC) forum, learning engagement encompasses three fundamental dimensions—cognitive, emotional, and behavioral engagement—that intricately interact to jointly influence students’ learning achievements. However, the interplay between multiple engagement dimensions and their correlations with learning achievement remain understudied, particularly across different academic disciplines. This study adopts an automated configurational approach that integrates bidirectional encoder representation from transformers (BERT) and fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of learning engagement, their connections with learning achievement, and variations across disciplines. Our analysis reveals a nuanced profile of learners' learning engagement, indicating the high-achieving individuals demonstrated more frequent posting and commenting behaviors and the high-level cognitive engagement than low-achieving individuals. Second, our analysis revealed multiple configurations where the coexistence or absence of factors at different levels of the cognitive, behavioral, and emotional dimensions significantly impacted learning achievement. Learners who conducted posting and replying behaviors, expressed positive emotions, and engaged in deep cognitive engagement tended to achieve superior learning outcomes. Third, there were significant differences in behavioral and emotional engagement among learners across different academic disciplines. Specifically, pure discipline learners were more inclined to engage in posting behaviors than the applied discipline learners. Across academic disciplines, positive emotions correlated strongly with higher achievement. These findings deepen our understanding of the multifaceted characteristics of learning engagement in MOOCs and highlight the importance of disciplinary distinctions, providing a foundation for educators and designers to optimize learners’ MOOC effects and tailor learning experiences in diverse disciplinary contexts.

Biography: Zhi Liu is a senior fellow researcher and PhD supervisor at the National Engineering Research Center of Education Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University. He also holds a position as a guest researcher at the Computer Science Institute, Humboldt University of Berlin. With deep expertise in educational data mining, learning analytics, and intelligent tutoring systems, Liu has published over 50 SCI/SSCI indexed papers in top journals, including Knowledge-Based Systems, Computers & Education, Internet and Higher Education, and IEEE Transactions on Learning Technologies. In addition, he serves as a key member of the national expert database for graduate education evaluation, a peer review expert for the National Natural Science Foundation of China, and the principal investigator of National Natural Science Foundation and the National Key R&D Program of China (2030 Major Projects). Liu is actively involved in international academic communities, serving in various leadership roles including as the chair of the organizing committee for the ICET. He is a guest associate editor for the international journal Frontiers in Artificial Intelligence and sits on the editorial boards of Discover Education and Frontiers in Psychology, and holds the Lifetime Member status of the Chinese Association of Automation. His contributions have been widely recognized, earning him numerous awards including the First Prize of the Science and Technology Progress Award of Hubei Province in 2024, First Prize of the Teaching Achievement Award of Higher Education Institutions in Hubei Province in 2022, and the honor of being a Top 1% Highly Cited Scholar in China National Knowledge Infrastructure (CNKI) for 2024.

 

Prof. Jiuhong Yu
Ningbo University of Finance and Economics, China

Speech Title: Key technology research and industry demonstration application of AIGC technology intelligence services driven by knowledge data hybrid

Abstract: Professor Yu will focus on three issues in his speech:
Data: A hybrid knowledge industry data system adapted to large models, forming an intelligent analysis expression based on multi-dimensional full spectrum technology and industry reports.
Algorithm: A large model RAG intermediate layer method driven by mixed knowledge.
Platform: A new generation AIGC technology intelligence service demonstration platform, including intelligence mining models, knowledge retrieval and analysis, to support industrial application demonstrations of technology intelligence services.

Biography: Prof. Jiuhong Yu, the Deputy Director of the Academic Committee at Ningbo University of Finance and Economics, specializes in the fields of artificial intelligence and finance. He has led two national projects and five provincial and ministerial-level research projects, won five second-class provincial and ministerial-level science and technology progress awards, published more than 30 SCI/ CSSCI/EI-indexed papers, and has long served as a judge and mentor of the Ministry of Science and Technology's Innovation and Entrepreneurship Competitions.

 

Assoc. Prof. Khairul Azhar Bin Hj Mat Daud
Co-founder of Research Ideation Canvas (RIC@), Editor-in-Chief of International Journal of Creative Future and Heritage (TENIAT)
Universiti Malaysia Kelantan, Malaysia

Speech Title: ENHANCING ACADEMIC WRITING QUALITY THROUGH AI AND THE RIC FRAMEWORK WITH A QUANTITATIVE PATH MODEL USING PLS-SEM

Abstract: The rapid advancement of Artificial Intelligence (AI) has significantly influenced the way academic writing is conceptualised and executed, particularly in higher education. However, many researchers and postgraduate students still struggle with structuring their ideas and translating them into coherent, high-quality academic manuscripts. To address this challenge, the integration of AI applications with the Research Ideation Canvas (RIC) offers a new framework to support innovation and personalisation in academic writing. Previous studies have highlighted the potential of AI in enhancing writing productivity, language accuracy, and citation management. Separately, visual frameworks like RIC have been recognised for their ability to scaffold research thinking and improve clarity in early research stages. Yet, the lack of integration between cognitive structuring tools and intelligent writing aids remains a gap in current educational practice. This study aims to explore how the combined use of AI tools and the RIC framework can empower academic writing through a personalised and structured approach. Specifically, it investigates the extent to which this integration enhances idea generation, abstract formulation, and manuscript development among postgraduate students. Data were collected using a PLS-SEM methodology through a survey, which involved distributing a set of questionnaires to participants who attended a workshop on the integration of AI and RIC for preparing quality research papers. The data were analysed using PLS-SEM to identify patterns of engagement, challenges, and perceived benefits. Findings indicate that participants experienced increased clarity in research focus, improved confidence in writing, and greater engagement in the academic writing process. AI provided immediate feedback and linguistic enhancement, while RIC enabled systematic ideation and coherence. The synergy between both tools contributed to the production of more structured, relevant, and personalised research outputs. This study suggests that educators and institutions adopt an integrated AI-RIC model to support academic writing, particularly at the postgraduate level. The conclusion underscores the need for continuous training and ethical guidance to ensure that AI serves as a complement to, rather than a replacement for, critical academic thinking.

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). He is also the co-founder of the Research Ideation Canvas (RIC@) and serves as the Editor-in-Chief of the International Journal of Creative Future and Heritage (TENIAT). With a Ph.D. in Educational Technology from Universiti Sains Malaysia (USM), Dr. Khairul Azhar has made significant strides in bridging the gap between education and technology, fostering innovation in teaching and learning. Dr. Khairul Azhar holds a Bachelor of Science in Mechanical Engineering (Manufacturing) from Universiti Teknologi Malaysia (UTM) and a Master’s in Education. His deep understanding of educational technology was further developed through his doctoral research at USM, where he focused on the integration of technology to improve educational practices. As the head of the Creative Technology Research Group (RG CREATE), Dr. Khairul Azhar has been working toward transforming the group into a Centre of Excellence. His research is centered on the application of augmented reality (AR) to enhance technical education, specifically in engineering drawing and improving workplace safety compliance in the manufacturing sector. His work has earned recognition through numerous accolades, including the Gold Award at the 2016 UMK Research & Innovation Exhibition, as well as several Silver Awards at renowned events such as ITEX and PECIPTA. Additionally, he was awarded a scholarship for postgraduate training in Innovation and Entrepreneurship at Trinity College Dublin in 2014. Dr. Khairul Azhar is a dedicated mentor who inspires students and researchers alike. His innovative contributions continue to shape the field of educational technology, making a lasting impact on both academia and industry.

 

Assoc. Prof. A.Y.M. Atiquil Islam
Founder and Lead Editor of the book series Assessment of Educational Technology (AET) with Routledge Taylor & Francis Group
East China Normal University, China

Speech Title: Preprints in the ChatGPT Era: A Threat to the Credibility and Quality of AI Research?

Abstract: The rapid development of generative AI tools, such as ChatGPT, has sparked significant scholarly discussions on their potential use in various interdisciplinary fields. This talk will explore the surge in AI-related preprints since the introduction of ChatGPT, examining their implications for research quality and credibility. Drawing from a scoping review of AI-related preprints across multiple platforms (including Web of Science, ArXiv, MedRxiv, and others), this presentation will discuss the characteristics of these preprints, focusing on their accuracy, reliability, and the concerns raised by AI experts. The findings highlight the need for robust evaluation processes to ensure the integrity of AI-related research and promote open science objectives. Additionally, expert opinions emphasize the importance of maintaining ethical standards, author accountability, and clear content guidelines from publishers. The talk will conclude with a call for future research into the impact of AI-related preprints on decision-making in educational research and practice.

Biography: Assoc. Prof. A.Y.M. Atiquil Islam serves as the Director of the International Graduate Program in Educational Technology at East China Normal University. He is also a Guest Professor at the School of Teacher Education, Jiangsu University, and an Honorary Chair Professor at Shanghai Jian Qiao University. Dr. Islam earned a multidisciplinary PhD by integrating two faculties—Education and Computer Science & Information Technology—at the University of Malaya. With nearly 21 years of experience across academia, industry, and business, he has made significant contributions to his field. Notably, he developed and validated three influential models: the Technology Adoption and Gratification (TAG) Model, the Technology Satisfaction Model, and the Online Database Adoption and Satisfaction (ODAS) Model. An accomplished author, Dr. Islam has published nearly 100 papers in leading international journals and conferences. In the last two years, he authored two books published by the Taylor & Francis Group: The Technology Adoption and Gratification (TAG) Model and Its Application and Applying the Rasch Model and Structural Equation Modeling to Higher Education. In addition to his academic roles, Dr. Islam is an Editorial Board Member of the British Journal of Educational Technology, Executive Editor of the International Journal of Smart Technology and Learning, and Editor of Cogent Education. Dr. Islam is particularly enthusiastic about leveraging his expertise in areas such as Artificial Intelligence in education, STEM education, and the Metaverse to inspire students, enhance teaching and learning experiences, and drive cutting-edge research initiatives.

 

Assoc. Prof. Jian Liao
Southwest University, China

Speech Title: Enhancing Automatic Evaluation of Short Answer Using Large Language Models through In-Context Learning, Chain-of-Thought Prompting, and Stringent Coefficients

Abstract: This paper aims to optimize large language models for scoring short-answer questions in datasets. The study analyzed 4,500 short-answer responses sourced from an e-learning platform at a university, encompassing five academic disciplines, and assessed nine leading large language models. During the evaluation process, it was observed that the base models exhibited hallucination phenomena, which compromised score consistency. To address these issues, the researchers employed in-context learning and chain-of-thought prompting techniques as optimization strategies. The results indicated that these methods significantly improved model performance. Notably, while the chain-of-thought approach introduced greater stringency into the scoring process, discrepancies between machine and human grading still existed. The study proposes integrating stringent coefficients into the calculation process to address these remaining inconsistencies. The findings suggest that this integration can further enhance the reliability and consistency of model-generated scores.

Biography: Liao Jian is an Associate Professor at the School of Educational Technology, Southwest University. He serves as a master's thesis supervisor and holds a Ph.D. in Learning, Design, and Technology from Pennsylvania State University, as well as a Master's degree in Educational Technology from Beijing Normal University under the supervision of Professor Ronghuai Huang. His research interests include artificial intelligence supported teaching and learning, intelligent analysis of educational videos, and robot-assisted education. He has published over twenty journal articles, including three first-author papers in Chinese Academy of Sciences (CAS) 1st Quartile and more than ten CSSCI core journal articles, along with over thirty international conference papers. He leads a National Natural Science Foundation general project and participates in multiple national and provincial-level projects under the Ministry of Science and Technology's National Key R&D Program subtopics. Additionally, he serves as a reviewer for the international academic journals such as Computers & Education.

 

Assoc. Prof. Dr. Cong Wang
Member of IEEE, Member of IEEE Systems, Man, and Cybernetics Society
Northwestern Polytechnical University, China

Biography: CONG WANG is currently an associate professor in the School of Mathematics and Statistics, Northwestern Polytechnical Universit (NWPU), China. He received his Ph.D. in Mechatronic Engineering in 2021 from Xidian University, China. He is a member of IEEE and IEEE Systems, Man, and Cybernetics Society. His current research interests include vicinagearth security, artificial intelligence, high-dimensional image analysis, as well as wavelet analysis. He has a textbook, a monograph, nine patents and over 40 papers (20+ in IEEE TRANSACTIONS) and hosts over 10 research projects. He is funded by the China National Postdoctoral Program for Innovative Talents and the Excellent Chinese and Foreign Youth Exchange Program of the China Association for Science and Technology. He serves as an Editorial Board Member of 10+ international journals like IEEE TFS and a Program Committee Chair, Track Chair, Publication Chair, Program Committee Member, and Technical Committee Member of 30+ international conferences. He also serves a Frequent Reviewer of 60+ international journals, including a number of the IEEE TRANSACTIONS and many international conferences.

 

Assoc. Prof. Di Sun
Dalian University of Technology, China

Speech Title: Research and Applications of Generative Artificial Intelligence in Education

Abstract: The emergence of generative artificial intelligence has had a significant impact on education. Using a PRISMA-based approach, we have summarized the performance characteristics of generative AI literature in the field of education. We analyzed the current research and applications of generative artificial intelligence in education from four dimensions: benchmarking, technology development and optimization, subject applications of large model functions, and practical teaching scenarios. Furthermore, we summarized the challenges and reflections it faces in terms of technology, safety, and ethics. Based on this analysis, we proposed recommendations for empowering the future development of education through generative artificial intelligence, including the development of vertical models and diversified technological tools, the improvement of supporting environments and human-machine collaboration mechanisms, the innovation of educational teaching models, the establishment of large models specifically designed for education with Chinese characteristics, and the enhancement of international cooperation. These initiatives will help lay the foundation for the comprehensive development of generative artificial intelligence in the field of education and facilitate a smooth transition towards digital transformation in education.

Biography: Dr. Di Sun is an Associate Professor at the Graduate School of Education, Dalian University of Technology, specializing in learning analytics, artificial intelligence in education, and educational measurement and evaluation. After earning her B.S. and M.S. degrees from Beijing Normal University, she received her PhD in Instructional Design, Development, and Evaluation from Syracuse University. She has published nearly 30 articles in SCI, SSCI, and CSSCI journals and at international conferences, authored an academic book funded by Taylor & Francis, and led and contributed to multiple research projects both domestically and internationally. She also serves as a reviewer for SCI/SSCI journals, such as Computers & Education and Interactive Learning Environments. Dr. Sun was invited by Professor Ryan Baker of the University of Pennsylvania to visit the Penn Center for Learning Analytics as a Research Scholar in 2023. After returning to China, her current research focuses on using generative artificial intelligence to enhance learner development and address educational challenges through advanced quantitative research methods.

 

Assoc. Prof. Tze Wei Liew
Multimedia University, Malaysia

Speech Title: Socio-Emotive Cues for GenAI in Learning

Abstract: In this talk, I synthesize empirical research conducted by my Special Interest Group (SIG), which I lead, along with findings from other studies, to examine the role of socio-emotive cues in artificial learning agents and their potential to enhance learning outcomes. Grounded in theories such as the Computers Are Social Actors (CASA) paradigm, the Cognitive-Affective Theory of Learning with Media (CATLM), the Cognitive-Affective-Social Theory of Learning in Digital Environments (CASTLE), Emotional Contagion Theory, and the Emotions as Social Information (EASI) model, this discussion explores how Generative AI (GenAI) agents—including AI-generated embodied characters, synthetic speech systems, and GenAI-powered facial animations and gesture modeling—can be strategically designed to prime and elicit emotional responses in learners, fostering greater engagement, motivation, and deep learning. While much of the existing research focuses on positive emotional cues such as enthusiasm and encouragement, this talk also examines the pedagogical role of negative emotional cues in GenAI agents—including disappointment, frustration, and confusion—in shaping learning behaviors, cognitive effort, and self-regulation.

Biography: Tze Wei Liew is an Associate Professor of Information Science at the Faculty of Business and Deputy Director of the Centre for Interaction and Experience Design at Multimedia University (MMU), Malaysia. His research interests and scholarly contributions focus on human-media and human-agent interaction, with an emphasis on educational technology, instructional design, media studies, and cyberpsychology. A member of the Association for Computing Machinery (ACM), he also serves on the editorial boards of Elsevier's Learning and Instruction, Wiley's Human Behavior and Emerging Technologies, and Frontiers in Computer Science. He has actively collaborated on research presentations, invited lectures, and scholarly activities at international conferences and academic venues across Australia, China, Cambodia, Denmark, Hong Kong (China), India, Indonesia, Japan, Portugal, Singapore, Spain, Sweden, Taiwan (China), Thailand, and Vietnam, while also serving as a Technical Program Committee (TPC) member and program chair for ACM and IEEE conferences in information sciences.

 

Dr. Alexandre St-Vincent Villeneuve
McGill-UQAM Université du Québec à Montréal, Canada

Speech Title: GenAI in special education: Exploring leadership paradigms and practical insights

Abstract: The speech examines the transformative potential of Generative Artificial Intelligence (GenAI) in special education, positioning leadership as a foundational element for innovative management frameworks. By integrating insights from educational methodologies, organizational leadership, and GenAI tools, it investigates a spectrum of leadership paradigms to address the multifaceted challenges of special education. Among these, caring leadership is presented as an emerging construct, informed by the most recent advancements in leadership scholarship, underscoring empathy and strategic foresight as critical enablers of collaboration and educator empowerment. The discussion is anchored in a practical case study, showcasing measurable outcomes one year post-implementation and illustrating the tangible impact of these approaches.

Biography: Dr. Alexandre St-Vincent Villeneuve is an academic researcher and multi-entrepreneur, recognized for his transformative work at the intersection of neuropsychology and artificial intelligence. His research drives innovation in special education, where advanced tools support neurodiverse learners and in cancer detection, where AI enhances diagnostic precision. He also explores algorithmic video surveillance to improve security in schools and other facilities. As a leader with extensive experience across entrepreneurship, education, healthcare and management, he excels at translating complex research into practical solutions with real-world impact. His ventures combine technological innovation with a commitment to improving outcomes for vulnerable populations. He has shared his advancements at international conferences in Asia, Europe and the United States, contributing to global efforts in leveraging AI for societal progress.

 

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

Speech Title: Cognitive Performance Music™: Creating the World’s First Sound-Based Intervention for Boosting the Entrepreneurial Mindset

Abstract: This paper presents Cognitive Performance Music™ (CPM) as the world’s first sound-based intervention specifically engineered to enhance the entrepreneurial mindset. Positioned at the intersection of cognitive neuroscience, AI, music, and entrepreneurial education, CPM constitutes a novel genre of functional music grounded in scientific principles. This empirical study investigates the effects of CPM on student motivation and emotional engagement. The intervention was implemented during the 2024 Immersive Technopreneurship Summer School at XJTLU, engaging 39 students from five nationalities through strategically embedded CPM tracks before, during, and after class sessions. Methodologically, the study adopts a qualitative approach using semi-structured interviews and reflective logs to capture participants’ subjective experiences and mindset shifts. Preliminary findings indicate that CPM fosters heightened motivation and focus, offering a promising, scalable tool for experiential entrepreneurship education. This research inaugurates a new field of inquiry—sound-based cognitive interventions for learning—and advocates for deeper exploration across academic and applied domains.

Biography: Dr. Vik Perez is Associate Professor of Practice at the Entrepreneurship and Enterprise Hub at Xi’an Jiaotong-Liverpool University (XJTLU). He is the creator of Cognitive Performance Music™ (CPM)—the first sound-based intervention designed to support the development of entrepreneurial mindset and cognitive focus. His work integrates neuroscience, motivational psychology, and music to enhance student engagement and learning outcomes. As the inventor of the WNYLE Method—the first brain-driven approach to entrepreneurial learning—Dr. Perez has played a key role in shaping innovative pathways within entrepreneurship education. His classroom interventions and Cognitive Performance Music™ innovations have engaged individuals in over 25 countries and 49 cities—including students, educators, and global listeners. He continues to explore how emotionally intelligent, sound-based learning environments can help students unlock their creative and cognitive potential.