Prof. Shan Wang

University of Saskatchewan, Canada

Shan Wang is a Professor at the Edwards School of Business at the University of Saskatchewan, Canada. Prior to joining the Edwards School of Business, she has worked as an Associate Professor for Renmin University of China. She received her Ph.D in Management Information Systems from McMaster University and Master Degree in Economics from Queen’s University, Canada. Her research interests include electronic commerce, social commerce, online communities, social network analysis, IT business value and IT in education. Her work has been published in several peer reviewed journals, such as Information and Management, Decision Support Systems and Journal of Business Research.

Title: From human to GAI bias: Assessing AI-generated review summaries on online review platforms

Abstract: Online review platforms are increasingly harnessing generative artificial intelligence (GAI) to enhance user information experience, for example, by generating concise summaries of extensive customer reviews. This research investigates the sentiment biases in GAI-generated review summaries, which may skew toward either positive or negative sentiments relative to the original customer reviews. Theoretically, building upon literature on online review and GAI biases, this research introduces a conceptual framework elucidating the process from customer review bias to biases in GAI review summaries. Moreover, we identify patterns and quantify degrees of biases in GAI review summaries. The empirical analysis is performed using a dataset comprising GAI review summaries of 847 hotels and over 110,000 corresponding customer reviews from the travel platform Ctrip. The findings reveal a predominant positive sentiment bias in GAI review summaries on Ctrip, with the degree of bias varying across features of GAI review summary format, customer reviews, and hotels. This research advances scholarly understanding of GAI and information biases on online review platforms and offers crucial insights for industry stakeholders on crafting responsible GAI systems.

Assoc. Prof. Yasushi Akiyama

Saint Mary's University, Canada

Dr. Yasushi Akiyama is an Associate Professor in the Department of Mathematics and Computing Science at Saint Mary’s University, Canada, with a PhD in Computer Science from Dalhousie University, Canada. His research focuses on Human-Computer Interaction, multimedia technologies, and educational technologies, aiming to improve digital tools for complex multimedia tasks. He has extensive experience in academia and industry, supervising numerous research projects in image analysis, video segmentation, and UI/UX design of educational technologies. Dr. Akiyama has received multiple grants, collaborated widely across disciplines, and actively participates in curriculum development and student mentorship at Saint Mary’s University.

Title: Interaction Design in Educational Technologies
Abstract: This talk will provide a brief history of eLearning and educational technologies and illustrate common interaction design issues in current educational technologies. These issues often arise due to historical distance learning concepts and traditional pedagogical styles in higher education. Despite technological advancements, online learning platforms often fail to fully leverage these innovations to create more engaging and effective learning experiences. The presentation will conclude with discussions of opportunities to reimagine educational technologies for the future.

Prof. M. Fahim Ferdous Khan (Senior Member of IEEE)

Toyo University, Japan

Dr. Fahim Khan is a Professor at the Department of Information Networking for Innovation and Design (INIAD) in Toyo University, Tokyo, Japan. Prior to joining Toyo University, he served as a faculty member at the University of Tokyo, from where he also obtained his MS and PhD in Applied Computer Science. His current research focus includes developing security measures for IoT and smart spaces; designing distributed systems using machine learning, generative AI, and blockchain; and leveraging EdTech and learning sciences for CS, STEM and SDGs education. His research publications have won multiple best paper awards at IEEE conferences. He actively serves as a committee member at numerous IEEE and ACM conferences. A Senior Member of IEEE, Khan is a recipient of IEEE Japan Medal. He is also a globally selected member of ACM Future of Computing Academy (ACM-FCA), an initiative that brings together next-generation leaders in computing to carry the computing community into the future.

Title: GenAI in Education: Panacea or Pandora's Box?
Abstract: Generative AI (GenAI) is rapidly transforming numerous sectors, and education stands poised to be significantly impacted. In this talk, we explore the transformative potential of GenAI in revolutionizing the education industry. GenAI has the unprecedented ability to personalize learning experiences, democratize access to quality education, and support educators in creating engaging and interactive content. Its promise as a catalyst for innovation and inclusiveness positions GenAI as a potential panacea for many of the challenges faced by modern education systems.
However, alongside these optimistic prospects, we must also address the accompanying Pandora's box: risks and ethical considerations that come with this powerful technology. Potential dangers, such as data privacy concerns, biases in AI-generated content, and the erosion of critical thinking skills, will be examined. In particular, the latter half of the talk will showcase an example from our ongoing project, demonstrating how multimodal GenAI can be harnessed for inquiry-based and self-paced programming education in a cost-effective manner without compromising the essence of learning.
The goal is not to simply embrace or reject GenAI in education, but to explore how we can leverage its transformative power responsibly, ensuring that it becomes a tool for empowerment and equitable access to quality education for all. This talk aims to spark a constructive dialogue about the future of learning with GenAI, exploring how we can unlock its full potential while carefully managing the inherent risks.
 

Prof. Atilla Wohllebe,

University of Applied Sciences Wedel, Germany

Atilla Wohllebe is a Professor for E-Commerce and Digital Business Models at the University of Applied Sciences Wedel, Germany, and responsible for the E-Commerce Bachelor and Master programs. He has published several books, book chapters and academic papers and has presented his research at various conferences. Prior to his academic career, Atilla Wohllebe worked in leading German e-commerce and retail companies. He is a member of the Association for Computing Machinery (ACM) and of the Special Interest Group on Computer-Human Interaction (SIGCHI) within the ACM, the German Society for Online Research (DGOF) and the International Association of Online Engineering (IAOE). Atilla Wohllebe completed his PhD at MATE Hungarian University of Agriculture and Life Sciences on consumer attitudes towards mobile apps in retail.

Title: From technology-oriented to user-oriented product development
Abstract: Artificial intelligence, augmented reality, chatbots and much more - numerous technologies seem to open up completely new possibilities for e-education and e-business. But do users actually “want” these technologies? This presentation combines practical experience with scientific knowledge. To this end, the talk briefly introduces the Kano model for evaluating user requirements and provides selected findings from research work in the field of mobile apps. The keynote is a call for researchers and practitioners in particular not to forget the main element despite all the enthusiasm for technology: The user.

Prof. Lee Li,

York University, Canada

Lee Li is a Professor in Marketing at the School of Administrative Studies of York University, Canada. His research is centered around internationalization processes, with a focus on high-tech firms and partnership strategies. His work has appeared in the Journal of International Business Studies, Strategic Management Journal, and Entrepreneurship Theory and Practice, among others.

Title: Enhancing E-Marketing Effectiveness with AI Technologies
Abstract: AI technologies have revolutionized marketing by enabling data-driven decision-making, personalization, and automation. Machine learning algorithms analyze vast amounts of consumer data to predict behavior, optimize ad targeting, and improve customer segmentation. AI-powered chatbots and recommendation systems enhance customer engagement by providing real-time, personalized interactions. Additionally, AI-driven content generation and sentiment analysis help brands craft more relevant messaging and respond effectively to consumer needs. By automating repetitive tasks and improving predictive analytics, AI significantly increases marketing efficiency, enhances customer experience, and drives higher conversion rates.

Prof. Nurbiha Shukor,

Univerisiti Teknologi Malaysia, Malaysia

Nurbiha is an educational technologist, an Associate Professor from School of Education, Universiti Teknologi Malaysia for the past 14 years. She received her doctoral degree in Educational Technology and her research interest is on educational data mining, online learning and learning analytics. She is currently serving as the Deputy Director of Center for Advancement in Digital and Flexible Learning at Universiti Teknologi Malaysia and entrusted to be the Chairman of Malaysia Head of Public University e-Learning Council (MEIPTA) since 2020 until now. She served as the Secretary General for Young Scientist Network (YSN), Academy of Sciences Malaysia from 2023-2025. She has conducted various trainings and workshops locally and internationally related to designing online learning, MOOCs and Micro Credential. She headed research grants both local and international research grants related to learning analytics and online learning. Her recent project is with UNESCO Mahatma Gandhi Institute of Education for Peace (MGIEP) to develop online course on Media Literacy.

Title: Learning Analytics in Higher Education
Abstract: Learning analytics represents a transformative approach in higher education, leveraging data collection and analysis to enhance teaching practices and student outcomes. By systematically gathering information on student engagement, performance patterns, and learning behaviors, institutions can develop evidence-based interventions and personalized educational experiences. This emerging field combines educational theory with computational methods to identify at-risk students, optimize resource allocation, and inform curriculum design. Despite promising applications, the implementation of learning analytics faces challenges including privacy concerns, data interpretation complexities, and the need for institutional capacity building. As higher education continues to evolve in response to technological advancements and changing student demographics, learning analytics offers powerful tools for improving retention rates, supporting diverse learning needs, and enabling data-driven decision-making. Ultimately, the strategic integration of analytics within educational contexts has the potential to transform traditional approaches to teaching and learning while promoting more equitable and effective educational practices.

Prof. Joseline M. Santos,

Bulacan State University, Philippines

Dr. Santos holds a Doctorate in Education major in Educational Leadership and Management. Currently serving as a full-time Professor IV at Bulacan State University College of Education, Dr. Santos also serves as an adjunct faculty member in the Graduate School. In addition to her teaching roles, she holds the positions of Director of the Research Management Office and concurrently serves as the Graduate School Head for Publications. Dr. Santos's research contributions are widely recognized, with her work being published in prestigious international journals indexed in Web of Science, Scopus, and other reputable publications. She actively engages with the academic community, participating in the scientific committees of prominent international conferences Furthermore, Dr. Santos serves on the Editorial Board of Frontiers Psychology and Education Journals, both indexed in SCOPUS and Web of Science and the Editor-in-Chief of the GEAPHEI Research Journal in the Philippines.

Title: Artificial Intelligence Utilization Scale (AIUS) in Research Writing
Abstract: The rapid evolution of Artificial Intelligence (AI) has reshaped research and education; it is a prompt in every institution to establish ethical guidelines for AI use in research writing. This policy addresses the responsible integration of AI to enhance research quality while maintaining academic integrity. 
The Artificial Intelligence Utilization Scale (AIUS) categorizes AI involvement in research writing into five levels: No AI, AI-Assisted Idea Generation, AI-Assisted Editing, AI Task Completion, and Full AI Collaboration. These levels range from no AI use to comprehensive AI-supported research, with distinct requirements to ensure ethical and transparent practices. The framework emphasizes critical human oversight, accountability, and originality in research outputs.
The guidelines outline tasks appropriate for AI, such as brainstorming, editing, and formatting, while discouraging sole reliance on AI for substantive content creation. Faculty are encouraged to educate students on ethical AI use, monitor its integration, and promote originality. Similarly, administrators play a crucial role in facilitating AI training programs to prepare the academic community for technological advancements.
The policy includes mechanisms to detect and address unethical AI use, leveraging tools like Turnitin for AI detection. By implementing these measures, BulSU aims to foster a culture of innovation, academic excellence, and ethical AI adoption, ensuring that AI serves as a supportive tool rather than a replacement for human intellectual contributions.