Prof. LOOI Chee Kit, National Institute of Education, Nanyang Technological University, Singapore
Biography: Chee-Kit LOOI is Professor of Education at the Learning Sciences and Assessment Academic Group of the National Institute of Education (NIE), Nanyang Technological University (NTU), and Co-Director at the Centre for Research and Development in Learning (CRADLE), NTU. He started his research career studying AI and Education and designing intelligent tutoring systems, and later moved on to be a learning scientist. He is an Associate Editor of the International Journal on AI & Education. Chee-Kit has given keynote talks at conferences in the World Conference on AI & Education, and the Intelligent Tutoring Systems Conference series. He is a Fellow of the International Society on the Learning Sciences as well as a Fellow of the Asia-Pacific Society on Computers in Education.
Prof. Looi’s research interests include Computer-Supported Collaborative Learning, Mobile Learning, Computational Thinking, and Scaling of Educational Innovations.
Abstract: This talk will start with a review of the history of AI & Education research. I reflect on the past work, and identify its key contributions and impacts. One objective of the early AI and Education work is the use of AI as a tool for understanding human cognition. Since the early 1990s, the field of the learning sciences has largely moved away from the use of computational models to understand human learning. However, in recent years, AI with advances in machine learning has made a big rebound. AI and Education has received renewed attention and seems to be rejuvenated and enjoy a renaissance. Indeed, we see major convergences again, with many issues related to AI being pursued in the learning sciences and in distance education and learning. I will discuss the road ahead for the renaissance, proposing what new research directions may emerge.
Assoc. Prof. Rui Zhang, Tongji University, China
Biography: Zhang Rui, Associate Professor, School of physical science and engineering, Tongji University, head of educational technology discipline Tongji University. Member of Engineering Physics sub committee, medical physics sub committee and teaching reform and research sub committee of University Physics Teaching directing Committee of the Ministry of education. Research interests: physics education, educational technology. At present, the research work mainly includes: the application of artificial intelligence technology in blended teaching, academic warning and collaborative learning. He has completed more than 20 relevant research papers and translated one textbook.
Abstract: Educational Data mining is used to evaluate the effectiveness of college physics blended learning from 2020 to 2022, Tongji university. A modified Cognitive Tracing model(BKT-IRT) is used to analyze the average cognitive level in different blended learning model. The results show that student in the broadcast blended learning model in spring term 2020 has a lower cognitive level in average compared with the tranditioal blended learning model. Singular value decomposition (SVD) is used to decompose and reduce the dimension of the group collaborative learning results in blended learning. The decomposition results show that the student attributes influencing group discussion results are knowledge mastery and learning perception style. These research results may help the instructors to understand the blended learning better and improving the learning performance.
Assoc. Prof. Eric C.K. Cheng, The Education University of Hong Kong, China
Biography: Dr. Eric Cheng is a specialist in educational management, knowledge management and Lesson Study. He is currently associate professor of the Department of Curriculum and Instruction of the Education University of Hong Kong. He is now serving as a school manager of Pentecostal Yu Leung Fat Primary School, C.C.C. Tam Lee Lai Fun Memorial Secondary School and E.L.C.H.K. Lutheran Secondary School. He is an Associate Editor of the International Journal of Educational Administration and Policy Studies (IJEAPS), a visiting scholar of Nagoya University, Aichi University of Education and Budapest Metropolitan University, an external examiner of The Open University of Hong Kong on the Master of Education programme and doctoral thesis examiner of Nottingham University on Doctor of Education program.
Eric earned his Doctor of Education in education management from the University of Leicester. His publication covers the areas of school management, Learning Study and knowledge management. He is the author of an academic book entitled Knowledge Management for School Education published in 2015 by Springer.
Eric has been successful in launching more than 10 research and development projects with external and competitive funds in the capacity of Principal Investigator (PI). He is a PI of a Research Grants Council GRF funded project. He was a PI of University Grants Committee funded Communities of Practices project, Quality Education Fund project, and Standing Committee on Language Education and Research project.
He received the Knowledge Transfer Project Award from HKIEd in 2014-15, Scholarship of Teaching Award in 2013-14 and Knowledge Transfer publication Awards in 2012-13 form Faculty of Human Development of HKIEd.
Abstract: Artificial intelligence (AI) incorporation in school education is increasingly explored with varied publicity. Despite many school leaders, policymakers, and frontline teachers understanding the need to embrace AI in education (AIED), there remain significant challenges in its incorporation process. This presentation will report an explorative study to identify barriers to incorporating AIED in school education. Three critical directions of AIED were recognised: Learning from AI, Learning about AI, and Learning with AI. A mixed-method was conducted to explore the barriers to incorporating AIED in Hong Kong schools pursuing different directions. Qualitative data were gathered via ten semi-structured interviews with key stakeholders from two schools in stage 1. Ertmer’s (1999) typology was applied to segregate the barriers. A questionnaire was designed to collect data from 200 principals and teachers in Hong Kong. Structural equation modelling was applied to examine the barriers affecting the applications of AI in school education. The findings showed both first-order and second-order barriers existed and predicted the AI applications. These findings provide essential information to the government, higher education institutes and schools for formulating strategies for AIED.
More info. will come soon...