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2023 Keynote Speakers

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2022 Keynote Speakers

 

Speaker I

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Prof. John Benedict du Boulay, Emeritus Professor, University of Sussex, UK

Speech Title: The Overlapping Ethical Imperatives of Human Teachers and their Artificially Intelligent Assistants

Biography: Benedict du Boulay is an Emeritus Professor of Artificial Intelligence in the School of Engineering and Informatics at the University of Sussex and Visiting Professor at University College London. Following a Bachelors degree in Physics at Imperial College London, he spent time both in industry and as a secondary school teacher before returning to university to complete his PhD in 1978 in the Department of Artificial Intelligence at the University of Edinburgh working on Logo. After a post-doc position at Edinburgh, a lectureship at the University of Aberdeen and a Sloan Fellowship at the University of California San Diego, he joined Sussex as a lecturer in 1983. He has been at Sussex since then, taking many roles of responsibility including Dean of Cognitive and Computing Sciences (COGS, 1994-1998) as well as Dean of Science and Technology (2002-2009). He has held two Erskine Fellowships at the University of Canterbury, New Zealand (2010, 2012) where he taught a course Artificial Intelligence in Education.

He has two main research areas. The first is the Psychology of Programming where his main work has been in the area of novices learning programming and the development of tools to assist that process. The second is the application of Artificial Intelligence in Education. Here he is particularly interested in issues around modelling and developing students' metacognition and motivation.

He was President (2015-2017) and is currently Treasurer and Secretary of the International Society for Artificial Intelligence in Education and an Associate Editor of its International Journal of Artificial Intelligence in Education. He is General Chair for the Society's forthcoming conference on Artificial Intelligence in Education (AIED2018) in London, was General Chair of the same conference in Wuhan in China in 2017, and was the local organiser of its conference AIED2009 in Brighton. He was Programme Chair for AIED1997 in Kobe, Japan. He has co-organised various workshops on related areas over the years. Recently these include the 1st and 2nd International Workshop on Affect, Meta-Affect, Data and Learning (AMADL 2015 in Madrid, and AMADL 2016 in Zagreb) and the workshop on "Les Contes du Mariage: Should AI stay married to Ed?", also in Madrid in 2015. He has successfully supervised 25 PhD students in the above areas and examined more than 40 PhDs.

He has edited/written 9 books and written some 190 papers in the areas indicated above. In particular, he has published 14 papers in the International Journal of Artificial Intelligence in Education. These include 3 invited commentary papers in the 2016 Anniversary Issue of the International Journal of Artificial Intelligence in Education celebrating highly cited papers over the last 25 years.

 

Abstract: The power of Artificial Intelligence (AI) in transforming future of workforce has prompted education authorities in many countries to include AI into K-12 teaching and learning. This inclusion necessarily involved designing a new formal curriculum that appropriately depicts the subject matter and psychologically foster students’ continuous motivation to learn about AI. This presentation shares three major findings of an inter faculty project - AI for future that is funded by the Hong Kong Jockey Club Charities Trust. In this project, we (i) suggest a co-design process, and curriculum framework, (ii) explore teachers’ conception of AI based on the grounded theory, (iii) evaluate the co-designed curriculum and identify factors affecting students’ learning (e.g., motivation, knowledge). Our pre- and post- experimental study revealed the design of the curriculum significantly fostered students’ motivation to learn AI, and enhanced their AI knowledge and confidence. Our interview revealed that most of the teachers’ lack of technological knowledge about Al; and the co-design process served as a crucial professional development activity and enhanced their technological pedagogical content knowledge. Our structural equation modelling analyses revealed that AI for social good, AI confidence and optimism are the influential factor affecting student behavioral intention to learn AI by examining a Theory of Planned Behavior based (TPB) model. Future studies of AI education could synthesize TPB with theories of learning to further explore how to design an effective AI learning and draw on an epistemic cognition framework to understand the process of teachers’ creation of AI education and AI in education.

Speaker II

Prof. Chai Ching Sing, Chinese University of Hong Kong, HK

Speech Title: Promoting AI Education in Hong Kong Secondary Schools

Biography: Ching Sing CHAI received his B.A. from the National Taiwan University; PGDE and MA from Nanyang Technological University; and his Ed D from the University of Leicester. He served as a secondary Chinese language teacher and head of department, and as an associate professor in Nanyang Technological University. He is currently a professor in the Chinese University of Hong Kong. He has published more than 100 SSCI papers. His research interests include technological pedagogical content knowledge (most published researcher in the SSCI database), language learning, STEM education, and teacher professional development.

 

Abstract: The power of Artificial Intelligence (AI) in transforming future of workforce has prompted education authorities in many countries to include AI into K-12 teaching and learning. This inclusion necessarily involved designing a new formal curriculum that appropriately depicts the subject matter and psychologically foster students’ continuous motivation to learn about AI. This presentation shares three major findings of an inter faculty project - AI for future that is funded by the Hong Kong Jockey Club Charities Trust. In this project, we (i) suggest a co-design process, and curriculum framework, (ii) explore teachers’ conception of AI based on the grounded theory, (iii) evaluate the co-designed curriculum and identify factors affecting students’ learning (e.g., motivation, knowledge). Our pre- and post- experimental study revealed the design of the curriculum significantly fostered students’ motivation to learn AI, and enhanced their AI knowledge and confidence. Our interview revealed that most of the teachers’ lack of technological knowledge about Al; and the co-design process served as a crucial professional development activity and enhanced their technological pedagogical content knowledge. Our structural equation modelling analyses revealed that AI for social good, AI confidence and optimism are the influential factor affecting student behavioral intention to learn AI by examining a Theory of Planned Behavior based (TPB) model. Future studies of AI education could synthesize TPB with theories of learning to further explore how to design an effective AI learning and draw on an epistemic cognition framework to understand the process of teachers’ creation of AI education and AI in education.