Prof. Benjamin W.Wah

The Chinese University of Hong Kong, China

Brief Introduction: Benjamin W. Wah is a Research Professor at the Chinese University of Hong Kong, and Franklin W. Woeltge Professor Emeritus of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. Previously, he served as the Provost and Wei Lun Professor of Computer Science and Engineering of the Chinese University of Hong Kong, as well as the Franklin W. Woeltge Endowed Professor of Electrical and Computer Engineering and Professor of the Coordinated Science Laboratory of the University of Illinois, Urbana-Champaign, USA. Wah received his Ph.D. degree in computer science from the University of California, Berkeley, CA, in 1979. He has received many awards for his research and service contributions, including the IEEE-CS W. Wallace-McDowell Award (2006), the IEEE-CS Richard E. Merwin Award (2007), the IEEE-CS Tsutomu Kanai Award (2009), the Distinguished Alumni Award in Computer Science of the University of California, Berkeley (2011), and the Bronze Bauhinia Star of the Hong Kong Self Administrative Region (2021). Wah's research interests are nonlinear search and optimization, multimedia technologies, and artificial intelligence. Wah co-founded the IEEE Transactions on Knowledge and Data Engineering in 1988 and served as its Editor-in-Chief between 1993 and 1996. He is the Co-Editor-in-Chief of Computers and Education: Artificial Intelligence and the Honorary Editor-in-Chief of Knowledge and Information Systems. In addition, Wah served the IEEE Computer Society in various capacities, including Vice President for Publications (1998 and 1999) and President (2001). He is a Fellow of the AAAS, ACM, and IEEE.

Prof. Philippe Fournier-Viger

Shenzhen University, China

Brief Introduction: Philippe Fournier-Viger (Ph.D) is distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving an important talent title from the National Science Foundation of China. He has published more than 400 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 15,000 citations (H-Index 63 - Google Scholar). He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate editor-in-chief of the Applied Intelligence journal and has been keynote speaker for over 30 international conferences and co-edited four books for Springer. He appears in the top 2% of researchers for scientific influence in the Stanford list, and is a Elsevier «Highly Cited Chinese Researcher» (2022). 

Prof. Ramlee Bin Mustapha

Universiti Pendidikan Sultan Idris (UPSI), Malaysia

Brief Introduction: Dr. Ramlee Mustapha is a Professor of Technical and Vocational Education at the Faculty of Technical and Vocational Education, Universiti Pendidikan Sultan Idris (UPSI) [the Sultan Idris Education University].  In 2010, he was appointed as the Dean for Post-Graduate Studies at UPSI. A year later, he is appointed as the Dean of the Faculty of Technical and Vocational Education at UPSI. In 2017, he was appointed as the 5th President of the Asian Academic Society for Vocational Education and Training (AASVET). 

In 2022, he was as appointed as the Head of TVET and Industry Cluster in National Professor Council. He is an expert in vocational education and training and human resource development especially in the Pacific Rim and East Asian region.  In 2023, Prof Dr. Ramlee Mustapha was appointed as a Member of Malaysia National TVET Council chaired by the Deputy Prime Minister.

He has written over 150 articles and 10 books in Technical-Vocational Education and Human Resource Development.  Dr. Ramlee Mustapha holds a Bachelor degree in Chemical Engineering (BSChE) from University of Alabama, USA. His first Masters degree in Educational Administration (M.Ed) from Eastern New Mexico University, USA and his second Masters degree in  Industrial Technology (M.Sc) from Purdue University, USA.  He earned a doctoral degree in Technical and Vocational Education (Ph.D) also from Purdue University, USA.  

Speech Title:The Future of TVET Education: Introducing TVET-AI Model for Fun Learning and Engaging

Abstract:Technical and vocational education and training (TVET) plays a vital role in preparing individuals for the workforce and empowering them with the relevant skills they need to succeed in a rapidly changing world. Artificial intelligence (AI) offers a powerful set of tools and technologies that can help make TVET education more fun, engaging, and effective.  Artificial intelligence (AI) is rapidly transforming the world as we know it, and its impact on education is no exception. This presentation will provide an overview of the future of TVET learning in the age of AI, the transformative potential of AI in TVET training, as well as practical insights into how they can harness AI technology to improve teaching and learning outcomes for TVET teachers and students. From personalized learning experiences to assessment and feedback  ̶  AI is revolutionizing the way teaching, learning and assessment are designed. I will also discuss about AI-powered in TVET learning platforms, adaptive assessments, and mixed-reality simulations. Specifically, the guiding questions include: (a) How can AI be used to personalize TVET learning experiences and adapt to each student's unique needs?, (b) What are the ethical implications of using AI in TVET, and how can we ensure fairness and equity? (c) How can AI empower TVET educators to become more effective and efficient in their roles?, and (d) What are the future trends in AI-driven TVET, and how can we prepare for them? Finally, the challenges and opportunities that AI presents for TVET educators, policymakers, and students will be encapsulated.