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.