Invited Speakers 邀请报告人

Prof. Jianwen SUN, Central China Normal University, China

 

Jianwen Sun is currently a Professor and Ph.D. Supervisor with the National Engineering Research Center of Educational Big Data and Faculty of Artificial Intelligence in Education, Central China Normal University. His education qualifications include Bachelor and PhD degrees in educational technology, both from the Central China Normal University. He is currently serving as the Deputy Secretary General of Research Association of Learning Sciences, CAHE (China Association of Higher Education), and also the Deputy Secretary General of Technical Committee on Intelligent Education, CAA (Chinese Association of Automation). His research interests include educational data mining, computational learning sciences, and intelligent tutoring systems. He has authored or coauthored more than 30 papers in refereed journals and conference proceedings including Nature Computational Science, ACM TOIS, IEEE TNNLS/TEVC/TII/TLT/TCE, AAAI, WWW, and ACM MM. He is a member of the Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), Chinese Association of Automation (CAA), and China Computer Federation (CCF).

 

Title: AI4LS: A New Research Paradigm for Learning Sciences

Abstract: The rapid development of the new generation of artificial intelligence technology has accelerated the transformation of scientific research paradigms, forming the fifth paradigm - AI4S (AI for Science). In response to the development trend of the intelligent era, it is necessary to accelerate the cross integration between learning sciences and artificial intelligence, develop a new research paradigm of AI4LS (AI for Learning Sciences), which can help break through the traditional academic boundaries of learning sciences and promote innovation in its theory, methods, and applications. Inspired by this concept, we propose a learning laws mining paradigm based on deep symbolic regression, which automatically discovers the symbolic laws governing skill acquisition from naturally occurring data. We have also established a learning technology innovation paradigm driven by both knowledge and data, forming a feedback loop where pattern discovery and model optimization mutually enhance each other. In addition, we have developed an intelligent teaching platform that integrates large and small models, and carried out personalized learning practices in multiple universities, supporting innovative explorations in the digital transformation and intelligent upgrading of education.

 

Assoc. Prof. Hang HU, Southwest University, China

 

Hu Hang, Doctor of Education, doctoral supervisor, Director of Teaching Excellence Center of Teacher Education College of Southwest University, Director of Digital Humanities and Venue Education Research Lab of Sino-Helian-Civilization Mutual Learning Center (postdoctoral supervisor), convener of National (Science and Technology) Subject Education Alliance, expert of Examination Center of Ministry of Education, Vice chairman of Experimental Teaching Branch of China Educational Equipment Industry Association, Deputy Director of the Academic Committee of the Primary and Secondary School Information Technology Education Special Committee of the Chinese Society of Education, Chongqing basic education quality monitoring expert, Chongqing social science popularization expert, a number of SCI, SSCI and CSSCI journals external review expert. In recent years, focusing on "deep learning, science and technology and intelligent education", it has published 4 monographs in Chinese and English and more than 60 academic papers. It has been deeply engaged in primary and secondary schools, kindergartens and vocational colleges all year long. Its research direction focuses on computing pedagogy, deep learning and educational application, science and technology education, and digital humanities of mutual learning among civilizations.

 

Title: From Human-machine Integration to Deeper Learning: Paradigm, Methodology and Value Implications

Abstract: Machine deep learning constantly breaks through its own functional boundaries in repeated collisions and interactions with humans, and continues to promote human deeper learning with human-machine integration. This research takes human deeper learning as the core, and based on human-machine consistency from the interdisciplinary perspective, demonstrates the human-machine integration of "learner-centered design" from four aspects of connotation, implementation, mechanism, and assessment to extract the deeper learning paradigm. Therefore, it focuses on the method of human-machine integration to deeper learning, and expounds its specific path with key words such as real situations, interdisciplinary, intelligentization, big idea, personalized cooperative learning, thinking and innovation, so as to build a new education ecology of human-machine integration and improve learners' real-problem-solving ability.
Keywords: deeper learning; interdisciplinary; real problem solving; man-machine integration; new ecology of education

 

 

Prof. Yu XIONG, Chongqing University of Posts and Telecommunications, China

 

Yu Xiong is currently a Professor and Ph.D. Supervisor with Chongqing University of Posts and Telecommunications (CQUPT), and the executive director of  Chongqing Municipal Research Center for Educational Big Data. He also serves as the Vice Chairman of Technical Committee on Intelligent Education of Chinese Association of Automation (CAA), the Secretary General of Chongqing Higher Education Steering Committee for Teaching Informatization and Teaching Innovation, and the Senior Member of China Computer Federation (CCF). His research interests include artificial intelligence and smart education, pattern recognition and machine learning, and educational data mining. He has taken more than 20 research projects of provincial and ministerial level, including the National Natural Science Foundation of China, Chongqing Special Key Project for Technology Innovation and Application Development, Chongqing Key Research Project for Higher Education Teaching Reform, etc. He has published more than 60 academic papers in SCI, EI, CSSCI journals and conference proceedings. Besides, he was awarded 3 the first prize of Provincial and Ministerial-Level Science and Technology Awards and 1 the first prize of Provincial and Ministerial-Level Teaching Achievement Award.

 

Title: AI+Data Boosting Generative Education Evaluation of Human-machine Collaboration

Abstract: With the support of the “business and data” dual-wheel-driven educational big data system, it is oriented to collect multi-source heterogeneous campus data at different granularities. This system not only conducts continuous data governance driven by business needs, but also implements scientific decision-making and actions for educational businesses driven by data applications, forming an "all-sample, all-process, all-dimensional" educational big data framework. Based on this, the human-machine collaborative hybrid-augmented intelligence technology is used to explore generative evaluation for students, teachers and majors. For student evaluation, we accurately create comprehensive learner profiles and use academic data to automatically generate descriptive evaluations, providing decision support for teachers to conduct personalized assessments. For teacher evaluation, the human-machine collaborative hybrid of knowledge graph and weight iterative optimization is constructed to enhance the intelligent teaching engagement evaluation model, realizing the intelligent generation of explainable teachers' teaching quality evaluation. For major evaluation, we propose a "1 theory + 1 system + 1 platform" paradigm. Under the support of the human-machine collaborative major monitoring theory, we established an index system for major monitoring and evaluation in universities, developed a major monitoring and evaluation information system, and carried out application demonstrations in universities in Chongqing. Ultimately, this leads to the formation of generative educational process evaluation, intelligent evaluation, and comprehensive evaluation, realizing deep value mining in education assessment.

 

Prof. Xuesong ZHAI, Zhejiang University, China

 

Xuesong Zhai is a senior researcher and Doctoral Supervisor in sector of Educational Technology, College of Education, Zhejiang University. Graduating from University of Science and Technology China (USTC) ,he obtained master degree in international relations and Ph.D in management fielding on higher education management. Since his graduation, he has pursuit of a postdoctoral researcher at the School of Educational Technology, Beijing Normal University and Department of Learning Technology at the University of North Texas in the United States.
Dr. Zhai obtained many distinguished awards and grants, such such the National Postdoctoral Fund, Anhui Provincial Excellent Young Talents Fund, Humanities and Social Science Fund of the Ministry of Education. He has participated in the Double Brain Program at Zhejiang University and the National Social Science Fund.
Dr. Zhai's research interests include but not limited to educational information systems, educational technology and equipment, intelligent learning environment construction, affection computing, etc. In recent years, he has published 17 SSCI and SCI indexed papers as the first or corresponding author, 3 EI indexed papers, and 13 CSSCI indexed papers as the first author. He obtained 7 software Patent as well. He is currently employed as the Area editor for the EAI Transaction on E-Learning. Guest Editor for IJERPH (SSCI), Current Bioinformatics (SCI), Sustainability (SSCI) and Frontiers in psychology(SSCI). Besides, he is contributing as a reviewer for many index journals, such as Interactive Learning Environments, Computer Assisted Language Learning, Education Technology Research & Development (SSCI), Educational Technology & Society (SSCI) .

 

Title: Integrating Generative AI and Reverse Engineering Pedagogy in Promoting AI-human Interaction: An empirical study from K-12 Programming Education
Abstract: The development of Generative Artificial Intelligence (GAI) has unlocked a portion of the learners' cognitive and transfer abilities. AI-human collaboration based on GAI will become an indispensable high-level skill in human learning and life. However, there is a lack of empirical research on exploring teaching models of human-AI interaction that are compatible with GAI, leading to an unclear path for learners to autonomously solve complex problems using GAI. This chapter proposed to introduce reverse engineering pedagogy with GAI to facilitate K-12 programming class. Incorporating Latent Dirichlet Allocation (LDA) for topic extraction, this study identified five distinct types of collaborative behaviors. Survey data from the participants indicate high levels of perceived contingency and collaborative perception, alongside a marked enthusiasm for continued learning within this paradigm. Based on these findings, the chapter proposes several strategies for enhancing human-computer collaboration, including the refinement of reverse engineering cognition to streamline the resolution of complex problems, the development of multi-agent systems to augment efficiency in scenarios involving multiple human and agent interactions, and the reconfiguration of labor dynamics to foster innovative forms of intelligent productivity.

 

Assoc. Prof. Vincent CS LEE, Monash University, Australia

IEEE Senior Member

 

Vincent CS Lee is currently an Associate Professor with the Faculty of IT, Monash University and a Senior Member of IEEE. His education qualifications include Bachelor and Master degrees in EEE, both from the National University of Singapore; MBA from Henley Management College in Oxford, England; BBus (Hons 1st class in Economics & Finance) and MBus (Accountancy), both from RMIT University in Melbourne; and PhD degree from University of Newcastle, NSW in Australia. He is an active researcher and educator (with Graduate Certificate in Higher Education Teaching from Monash University) with 30 years as academicians for four universities including Monash University and Swinburne University, both in Melbourne, joint Monash-South East University in Suzhou, Nanyang Technological University in Singapore. He was visiting Professors with School of Economics and Management, and School of Computing and Technology, Tsinghua University in Beijing. Lee’s research and higher education teaching (developed and delivered undergraduate and postgraduate courses) span multi-disciplinary domains across IT, Digital Health, Signal and Information Processing, Financial Engineering (FinTech), Educational Data Mining (with learner-centric education technology tools), Explainable AI, Deep ML, Computer Vision for dynamic objects tracking, and Multi-agent Autonomous Systems. Lee has published 200+ papers in IEEE/ACM SCImago ranked Q1 High Impact factors of Journals, and in CORE A/A* Peer-review International Conferences proceedings (AAAI, IJCAI, ICDM, ICWS, ICDE, PAKDD, CIKM, WWW, IEEE IC Signal Processing, IC-EDM). Lee also served as invited keynote speakers for a number of these IEEE and ACM Flagship conferences’ and General Chair and Co-chair of steering committees and technical programs.

 

Title:  Active Learning in Computer Networks Course: Challenges & Opportunities for Personalised Education

Abstract: Active learning is a form of teaching and learning in precision education, which is an approach to teaching and learning aiming to personalise education for each student. One of the major objectives of precision education via active learning is to improve prediction of educational outcome. This talk focuses on key challenges for active learning student’s education for cohort of computer networks enrolled in a higher education institution in Melbourne. I will base on the recent experience in conducting the “problem-solving” based assessment using progressive learning experience and learner performance evaluation assessment criteria.  I will articulate the issues when considering the application of artificial intelligence (AI), machine learning, and learning analytics to further improve and develop teaching quality and also learning performance. The scope of my talk covers Knowledge Tracing as a fundamental research issue in personalised education, aiming to monitor changes in students’ mastery of each knowledge point based on their online answer data.

 

Asst. Prof. Yizhou FAN, Peking University, China

 

Yizhou Fan is an Assistant Professor in the Graduate School of Education at Peking University and an Adjunct Research Fellow at the Centre for Learning Analytics at Monash University. He identifies as a learning analyst employing computational techniques to enhance the understanding of self-regulated learning and to develop next-generation learning environments for envisioning future education. In 2023, he received the Emerging Scholars Award and Early Career Research Grant from SoLAR. His recent research focuses on human-AI collaboration and the scaffolding of hybrid intelligence.

 

Title: Learning and Regulating with ChatGPT: What Experimental Study Tells Us
Abstract: The advances in artificial intelligence (AI) have profoundly transformed and will continue to influence the workforce by automating numerous tasks across various sectors. Consequently, it is vital for students and professionals to develop the capability to “learn and work with AI,” a focus that has increasingly become central in educational paradigms. As the practice and research of AI-assisted learning evolve, a significant advancement in learning analytics is the capacity to measure and understand how learning occurs with AI scaffolding. Nevertheless, empirical research in this area remains nascent, calling for further exploration.In this talk, Dr. Fan will present his recent study, which centers on understanding learners' interactions and regulation using ChatGPT. He and his colleagues conducted an experimental study involving 117 learners, who were randomly assigned to one of four groups, each provided with different forms of learning support (e.g., ChatGPT and human experts). His presentation will share insights into how these groups compare in terms of self-regulated learning processes, help-seeking behaviors, self-assessment skills, and overall learning performance. Additionally, Dr. Fan will discuss the promises and challenges of using generative AI in education that identified in his empirical study.

 

 

Assoc. Prof. Yang CHEN, Harbin Institute of Technology (Shenzhen), China

Yang Chen is currently an associate professor in the college of humanity and social sciences of Harbin Institute of Technology (Shenzhen), China. She received her bachelor’s degree in mass communication from Communication University of China, master’s degree in digital media from Harbin Institute of Technology, China, and doctoral degree in computer graphics technology with a concentration in human-computer interaction from Purdue University, USA. Her research interests include social media, user experience, environmental communication, and educational gamification. As principal investigator, she has undertaken funded research projects on gamified pro-environmental communication, gamification in second language acquisition, and big data and education resources, which were funded by national/provincial social science foundations. She has publications in international journals including International Journal of Human-Computer Interaction, sustainability, and International Journal of Language, Literature and Linguistics. She also published in international conferences such as ICBDE, ICESS, ICIET, WCEEE, and ELEARN. In addition, she serves as a reviewer for several prestigious international journals (such as Information, Communication & Society, Information Processing and Management, Social Media and Society, Behaviour & information Technology, and Interacting with Computers) and international conferences in the fields of social media, technology, and education.

 

Title: Understanding Chinese EFL Learners’ Acceptance of Gamified Vocabulary Learning Apps

Abstract: Implementing the idea of gamification in mobile-assisted language learning has recently been gaining increasing attention from academia and industry. I will introduce three studies about this topic. The first one is about investigating students’ perception, motivation to use, and acceptance of popular gamified English vocabulary learning apps. The second is a longitudinal study on students’ foreign language anxiety and cognitive load in gamified classes of higher education. The third is understanding Chinese EFL learners’ acceptance of gamified vocabulary learning Apps: An integration of self-determination theory and technology acceptance model.

 

 

Asst. Prof. Taotao LONG, Central China Normal University, China

 

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

 

Title: Promoting Pre-service Scinece Teachers' Design of Inquiry-based Instruction via Knowledge Integration (KI) based Collborative Learning Environment: a network analysis approach

Abstract: Inquiry-based instruction has played an important role in science education, and been recognized as a critical approach to improve students’ scientific learning effectiveness. However, current research revealed that it is a challenge for teacher education programs to improve pre-service science teachers’ inquiry-based instructional activity design competency. Due to the dynamic and complicated process of the instructional design competency improvement, there is a strong need for new methods that could trace this process. Considering the Knowledge Integration (KI) theory has been demonstrated to be able to help science teachers design their inquiry-based instructional activities in a large amount of existing research, in this study, a KI-based collaborative learning environment was designed to support 19 pre-service science teachers’ inquiry-based instructional activity design. Epistemic network analysis (ENA) was applied to trace the development process of their inquiry-based instructional activity design e behavior patterns. Data analysis revealed that the pre-service science teachers demonstrated gradually more active in “guiding students to design exploratory activities” and “guiding students to communicate and cooperate” in their instructional designs during the process of using the KI-based collaborative learning environment. Through identifying and comparing the design patterns of the high-performing and low-performing groups, the results showed that the low-performing groups demonstrated more active on “posing inquiry questions” and “guiding students to formulate scientific explanation,” while the high performing groups demonstrated more active in “guiding students to design exploratory activities” and “guiding students to communicate and cooperate.” Furthermore, the semi-structured interview results demonstrated that the KI-based collaborative learning environment not only provided the pre-service science teachers a convenient way on online collaboration, but also helped them form more normative and integ.

 

Senior Lecturer Dr. Qingqing XING

The Hong Kong University of Science and Technology (Guangzhou), China

 

Dr. Qingqing Xing is a Senior Lecturer at the University of Education Sciences, the Hong Kong University of Science and Technology (Guangzhou). She holds a PhD in Education from Peking University and has more than 23 years of teaching experience in science and technology-oriented universities. She is committed to promoting research ideas and interdisciplinary collaboration, including as a Project Manager in the Bureau of International Cooperation at the National Science Foundation of China and as the Associate Director of the International Office at the Beijing Institute of Technology. These experiences have given her insights into promoting research-oriented education internationally, especially for the world's first interdisciplinary university as HKUST(GZ).
As an education practitioner, Dr. Xing actively explores the pedagogy of Project-Based Learning. In addition to her efforts to teach Interdisciplinary Design Thinking and Effective Academic Communication, she collaborates with interdisciplinary research teams in computational media and arts, metaverse research, and health care. As part of this collaboration, it uses educational technologies and artificial intelligence generated content tools to help students present their research ideas in engaging ways to facilitate their “niche” exploration process, with a focus on developing Self-Organized Maker Education. Within just one year of its inception, HKUST(GZ) research students have actively contributed insights and examples of project-based learning in higher education.

 

Title: Investigating the Impact of Deliberate Metaphor in Introduction through Eye Tracking Analysis

Abstract: This study examines the relationship between writing introductions, visual summaries, and the deliberate use of metaphors in the context of English as a Foreign Language (EFL) learners, focusing on how these elements can improve the effectiveness of academic communication and scholarly dissemination. While previous research has extensively analyzed academic writing from various angles—such as organization, lexicon, cohesion, rhetorical features, and the role of metaphors—the combined effects of introductions, visual summaries, and the deliberate use of metaphors on cognitive processing have been studied only to a limited extent.

Using eye-tracking technology, the study aims to provide empirical evidence of the interactive effects of written introductions, visual summaries with deliberate metaphors on EFL learners. The research attempts to answer the most important questions: To what extent does the rhetorical structuring of slides, including deliberate metaphors, influence reading behavior in writing introductions? How does the combination of visual and textual information and metaphorical language influence readers' comprehension and learning outcomes? By answering these questions, the study aims to bridge the gap between metaphor use and cognitive processing in academic texts and scholarly communication, providing valuable insights for English for Academic Purposes (EAP) instruction and the broader field of scholarly communication.

 

 

Assoc. Prof. Anuchai Theeraroungchaisri, Chulalongkorn University, Thailand

 

Dr. Anuchai Theeraroungchaisri is an Associate Professor in the Department of Social and Administrative Pharmacy at the Faculty of Pharmaceutical Sciences, Chulalongkorn University. Additionally, he serves as the Deputy Director of Thailand Cyber University at the Office of Higher Education Commission, Ministry of Education. Moreover, he holds the position of Deputy Director at the College of Pharmacy Administration of Thailand.
He gots a bachelor's degree in Pharmaceutical Sciences and pursued further education at Chulalongkorn University, where he earned a master's degree in Computer Sciences and a Ph.D. in Educational and Communication Technology. With his role as the deputy director of the Thailand Cyber University Project, he has overseen several significant initiatives, such as Thai MOOC (Thailand Massive Open Online Courses), The Higher Education Credit Bank System, TCU-Globe (Interoperability among the learning object repository network, enabling search through a single query), e-Learning Professional Development (the pioneering fully online training certificate program).
In 2022, he was recognized as the "Most Valuable Person in Educational Technology 2022" by the Thai Association of Education and Communication Technology, as announced during the 35th Annual Conference of Thailand Educational and Communication Technology. Furthermore, in 2019 he received the "Outstanding Pharmacist in Pharmacy Education 2019" award from The Pharmacy Council of Thailand.
His research interests encompass a wide range of topics, including MOOC Policy, Academic credit bank and credit transfer, Learning Design, Online Pedagogy, e-Portfolio, Technology-Enhanced Learning, Learning analytics, and Health Informatics.

Title: Enhancing Pharmacy Education through AI-Assisted Role-Play: A Case Study Using ChatGPT in Community Pharmacy Course

Abstract: This presentation explores an innovative approach to pharmacy education using artificial intelligence, specifically ChatGPT, in a Community Pharmacy course at Chulalongkorn University. The study aimed to enhance student engagement and learning outcomes through AI-assisted role-play scenarios.
The research implemented ChatGPT in two primary roles: as a virtual pharmacy manager for student interactions and as an expert evaluator of student performance. This dual application allowed for realistic simulation of pharmacy situations and provided immediate, objective feedback on student questions and recommendations.
Key findings include increased student engagement, improved critical thinking skills, and enhanced ability to apply theoretical knowledge to practical scenarios. The AI's capacity to generate consistent, realistic scenarios and provide immediate feedback proved particularly valuable.
Challenges encountered included technical limitations in managing multiple student interactions simultaneously and occasional inconsistencies in AI-generated information. These were addressed through innovative solutions such as shared access and real-time error correction.
This presentation will discuss the methodology, outcomes, and lessons learned from this educational experiment. It will also explore the potential for wider application of AI in pharmacy education and other professional training contexts, considering both the benefits and limitations of this technology.
The findings of this study contribute to the growing body of knowledge on AI applications in higher education, particularly in professional fields requiring practical skills development.
The technique, results, and lessons discovered during this educational experiment will all be covered in this presentation. While taking into account both the advantages and disadvantages of this technology, it will also investigate the possibilities for a broader use of AI in pharmacy school and other professional training settings.
The results of this study add to the expanding corpus of research on artificial intelligence applications in higher education, especially in professions that need the development of practical skills.
 

 

 

Assoc. Prof. Fang XU, Nantong University, China

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

 

Title: Research on Educational Technology: Combination of Structural Equation and Qualitative comparative analysis of fuzzy sets

Abstract: At present, structural equation and qualitative comparative analysis of fuzzy sets are both methods used in social science research. But the combination of the two has not been paid attention to in research of educational technology. Both of them study the influencing factors, that is, the relationship between variables. Both of them have to go through theoretical model construction, empirical and quantitative research. At the same time, they are different, one is the relationship between two variables, and the other is the effect of variable combination. These two approaches can be used together to deepen existing research. There are also combinations, which are in the areas such as management, but not many in the fields of education. The combination of the two can confirm and complement each other. The combination of the two has applicability in practical problem solving in educational technology. Educational technology is a cross-discipline, itself involves a number of disciplines, such as computer science, pedagogy, management, economics, sociology, etc. The reality of the problem of educational technology often involves a number of factors. Qualitative comparative analysis of fuzzy sets is applicable in solving educational technology problems . At the same time, structural equation is applicable to solve the relationship between single variables. Education application of combinations of the two ways has its applicability, including the two complement each other, the results of qualitative analysis of fuzzy sets can confirm the results of structural equations and the qualitative analysis of fuzzy sets can also draw the conclusion that the structural formula can't be obtained. A case study on human-computer co-teaching is given. The combination of these two methods has a certain prospect for the future research on the development of educational technology.

 

Senior Research Fellow Dr. Feifei Han, Australian Catholic University, Australia

 

Feifei Han is a Senior Research Fellow of the STEM in Education Research Program at the Institute for Learning Sciences & Teacher Education, Australian Catholic University. She obtained her PhD from The University of Sydney. Her current research interests are in the areas of educational technology, learning analytics, STEM education, and learning and teaching in higher education. She has over 100 publications. Some of her publications appear in top-quality journals in educational technology (e.g., The Internet & Higher Education, Computers & Education, IEEE Transactions on Learning Technologies, and International Journal of Educational Technology in Higher Education) and higher education (e.g., Studies in Higher Education, Assessment & Evaluation in Higher Education, Higher Education Research & Development). She currently serves as an associate editor for The Australasian Journal of Educational Technology and Frontiers in Psychology (the Educational Psychology section).

 

Title: Generative Artificial Intelligence (GenAI) in Writing Research: A State-of-the-Art Review

Abstract: Writing is an essential life skill, while failure to learn to write is associated with poor physical and mental health, participation in crime, welfare dependency and reduced longevity (Cree et al., 2022). Despite its importance, students worldwide are struggling to develop writing skills appropriate to their expected grade level. The emergence of GenAI (e.g., ChatGPT and other similar AI based technologies) has generated significant interest and intense debate in different aspects of education, in particular, language and literacy education. It poses both opportunities and challenges for writing instructions across levels, from writing instructions in schools to professional and technical writing. This present will provide a state-of-the-art-review of the GenAI in writing research.