Prof. Minjuan Wang
San Diego State University, USA
Generative AI in Teaching and Training, Chief Editor, IEEE-TLT
Dr. Minjuan Wang is Professor and
Program Head of Learning Design and
Technology (LDT) in the School of
Journalism and Media Studies at San
Diego State University and
Editor-in-Chief of the IEEE Transactions
on Learning Technologies (TLT).
Dr. Wang teaches Methods of Inquiry,
Designing and Developing Learning for
the Global Audience, and Mobile Learning
Design. Her research specialties are
multidisciplinary, focusing on learning
across the Metaverse, Cross-Reality (XR)
and Immersive Learning, AI in education,
and the sociocultural aspects of
learning design and the use of
technology.
She has been collaborating with scholars
worldwide on research and development
projects. She is a high-impact author,
an internationally recognized scholar
and has keynoted more than 30
international conferences. In addition
to serving as the EiC for IEEE-TLT, she
co-chairs the Education Society’s newly
established Technical Committee on
Immersive Learning (TC-ILE) and
co-organizes several IEEE’s flagship
conferences including TALE and
Intelligent Environments.
Title: Generative AI in Teaching and Training
Abstract:
Generative AI is a type of Artificial
Intelligence (AI) algorithm that is
capable of producing new outputs such as
text, audio and video, based on the
massive data they have been trained on.
One example of generative AI is ChatGPT,
which uses a deep learning-based natural
language processing model and
transformer- based architecture to
respond to users’ prompts by generating
meaningful text that is similar to
human- constructed text (IEEE-TLT
special issue).
Generative AI such as ChatGPT entered
the spotlight in 2023 and stirred
conversations around the world on its
usage and “threats” to teaching,
learning and training. In this
presentation, Dr. Wang will discuss the
impact and implications of Generative AI
on education, including effective usage
and ethical considerations. She will end
the presentation with a brief
introduction to IEEE-TLT journal and its
current special issue on Education in
the World of ChatGPT and other
Generative AI. One of the associate
editors (Dr. Junjie Gavin Wu) will join
the presentation to share insights on
publishing with high-impact and
internationally indexed journals.
Prof. Sean (Shuying) Li
Shenzhen University, China
Dr. Sean (Shuying) Li obtained his PhD degree in
Higher Education from the Faculty of
Education at the University of Alberta
in Canada. Dr. Li is an
internationally-recognized expert in the
area of tertiary education management,
instructional innovation, teacher
professional development and university
quality assurance. Previous to this
appointment, he was the Pro-Rector of
City University of Macau, responsible
for teaching and learning, quality
assurance and international education.
He was Education Developer at the Center
for Teaching and Learning, a centrally
administered unit under the direct
leadership of the Provost’s Office of
University of Alberta. Over the past
three decades, Dr. Li has worked in
various reputable universities as a
faculty member in teaching, research and
university administration including
University of Alberta, Chinese
University of Hong Kong, Hong Kong
Institute of Education, Nanjing
University of Information Science and
Technology. He was COO (Chief Operation
Officer) and Vice President Academic
Affairs of Hunan International Economics
University (with over 28000 students and
2000 staff), a flagship private
university in China under the leadership
of Laureate Education Group,
headquartered in Baltimore, Maryland of
the United States.
Dr. Li brings in his 30+ years’ rich
experience in cross-disciplinary,
cross-cultural teaching, research and
tertiary administration in Mainland
China, Hong Kong, Macau and Northern
America universities, his profound
knowledge on higher education
management, business operation, quality
assurance, and his outstanding
contribution to international education
and exchange. Dr. Li has published
widely in national and international
journals. His research grants as a
principal investigator and a
co-investigator in various countries and
regions have amounted to over 4 million
US dollars (Equivalent). He is a pioneer
in phenomenological thoughtful pedagogy
in Mainland China, Hong Kong S.A.R.
Prof. Matthew Ohland
Purdue University, USA
(IEEE Fellow)
Dr. Matthew Ohland is the Dale and Suzi Gallagher Professor and Associate Head of Engineering Education at Purdue University. He earned a Ph.D. in Civil Engineering from the University of Florida, M.S. degrees in Materials Engineering and Mechanical Engineering from Rensselaer Polytechnic Institute, and a B.S. in Engineering and a B.A. in Religion from Swarthmore College. He Co-Directs the National Effective Teaching Institute (NETI) with Susan Lord and Michael Prince. His research has been funded by over USD 20M, mostly from the United States National Science Foundation. Along with his collaborators, he has been recognized for his work on longitudinal studies of engineering students with the William Elgin Wickenden Award for the best paper published in the Journal of Engineering Education in 2008, 2011, and 2019. He has also been recognized for the best paper in IEEE Transactions on Education in 2011 and 2015, multiple conference Best Paper awards, and the Betty Vetter Award for Research from the Women in Engineering Proactive Network. The CATME Team Tools developed under Dr. Ohland’s leadership and related research have been used by over 1.9 million students of more than 23,000 faculty at more than 2500 institutions in 88 countries, and were recognized with the 2009 Premier Award for Excellence in Engineering Education Courseware and the Maryellen Weimer Scholarly Work on Teaching and Learning Award in 2013. Dr. Ohland received the Chester F. Carlson Award for Innovation in Engineering Education from the American Society for Engineering Education (ASEE) for his leadership of that project. He is a Fellow of ASEE, IEEE, and AAAS. He has received teaching awards at Clemson and Purdue. Dr. Ohland is an ABET Program Evaluator and has previously served as an Associate Editor of IEEE Transactions on Education. He was the 2002–2006 President of Tau Beta Pi.
Title: Using Machine Learning and
Natural Language Processing to Analyze
Peer Evaluation Ratings and Comments
Abstract:
Whereas peer evaluation is a
proven pedagogical approach, its
adoption is hindered by the difficulty
of reviewing both quantitative and
qualitative results on a large scale and
knowing how and when to intervene.
Machine learning and natural language
processing techniques have the potential
to identify patterns of dysfunction and
flag comments that require greater
scrutiny. The identification of patterns
using qualitative methods and the
application of those patterns to develop
human-in-the-loop approaches to
quantitative analytics will be
addressed.