Prof. Antoine Bossard,
Kanagawa University, Japan

Biography: Antoine Bossard is a Professor of the Graduate School of Science, Kanagawa University in Japan. He received the BS and MS degrees from Université de Caen Basse-Normandie, France in 2005 and 2007, respectively, and the Ph.D. degree from Tokyo University of Agriculture and Technology, Japan in 2011. Amongst others, he is in charge of the computer architecture and functional programming lectures for undergraduate students, and of a graph theory lecture for master students. He also is responsible for the functional and logic programming lecture at Tokyo University of Agriculture and Technology.
Regarding research activities, Antoine mostly focuses on the following two subjects: interconnection networks (network topologies, routing problems, fault tolerance) and information representation and processing of Chinese characters (e.g. fingerprinting). He is the author of multiple papers in these fields, papers published in international journals and conference proceedings. He has also written several books, for instance for his students of computer architecture and functional programming, and on Chinese characters, with notably a commented translation of the first part of the Dictionarium anamitico-latinum of Jean-Louis Taberd.

Prof. Hamid Mcheick,
University of Quebec at Chiocoutimi, Canada

Speech Title: Design Contextaware Healthcare Framework

Abstract: Nowaday, ubiquitous/IoT healthcare model is reshaping the research in the medical domain due to its potential to concurrently overcome the challenges encountered in the traditional healthcare systems. Prediction of exacerbation of Chronic Obstructive Pulmonary Disease (COPD) is considered incurable disease and the fourth difficult problem in the medical field. Many issues face researchers in the medical domain, such as modelling and representation of patient’ context (risk factors), uncertainty, accuracy of decision, and preventing exacerbations. These issues have been handled in may research projects. However, healthcare systems for COPD need to identify and represent the complexity of medical facors and to design prediction model to increase the accuracy. Traditional treatment plan and non-fully automatic applications are still used and have many issues, such as performance (accuracy) and Interpretability. The goal of this research is to design reliable mechanisms to improve life quality of COPD patients and to protect them against risk factors, as well as help the physiciens by providing recommendations. In this talk, I will present contextaware healthcare architectural framework, including context modelling, context representation and rule-based recommendations.

Biography: Professor Hamid Mcheick is a full professor in Computer Science department at the University of Québec at Chicoutimi, Canada. He has more than 25 years of experience in both academic and industrial area. He has done his PhD in Software Engineering and Distributed System in the University of Montreal, Canada. He is working on : designing and adaptation of smart software applications; designing healthcare frameworks for medical domain; Design smart Cloud-IoT model; and designing smart Internet of Things and edge frameworks for smart city. He has supervised many post-doctorate, PhD, master and bachelor students. He has nine book chapters, more than 60 research papers in international journals and more than 150 research papers in international/national conference and workshop proceedings in his credit. Dr. Mcheick has given many keynote speeches and tutorials in his research area. Dr. Mcheick has gotten many grants from governments, industrials and academics. He is a chief in editor, chair, co-chair, reviewer, member in many organizations (such as IEEE, ACM, Springer, Elsevier, Inderscience) around the world.

Senior Lecturer Sokratis Karkalas,
University of Derby, UK

Speech Title: From Black Boxes to Pedagogical Insight: Designing Authorable Learning Analytics for Diverse Digital Learning Tools

Abstract: Learning analytics dashboards are often treated as black boxes, reflecting the dominance of linear, behaviourist, and cognitivist instructional models in formal education. These approaches privilege simple, binary data derived from structured activities, such as SCORM-based tasks, which lend themselves to institutional reporting but provide little insight into complex learning processes. In contrast, emerging ecosystems of constructionist tools generate heterogeneous data across multiple modalities, offering opportunities for richer insights but also presenting significant challenges for interpretation. Generic dashboards that treat all data uniformly are no longer sufficient in this context.
This research introduces a framework for authorable, skill-based learning analytics designed to operate across diverse tools and data modalities and bridge the gap between complex, multi-modal learning data and meaningful pedagogical insight. The study explored how tool affordances relate to the development of 21st-century skills, generating a nuanced understanding of which learner interactions are most relevant. Building on these insights, the research developed methods for translating raw tool data into interpretable metrics, carefully balancing the need for educator-friendly authoring with the inherent complexity of diverse data streams. A prototype system was co-designed with learning design experts, drawing on example-tracing approaches from intelligent tutoring systems: teachers interact with learning activities in “learner mode,” allowing the system to capture their actions and directly map them to pedagogical constructs. This approach empowers non-technical users to define meaningful metrics, determine the appropriate granularity of analysis, and align analytics outputs with their instructional objectives, providing a flexible, actionable bridge between learner activity and skill-oriented assessment.
These findings underscore the importance of moving beyond one-size-fits-all dashboards toward adaptable, educator-driven analytics. By enabling teachers to author meaningful metrics and interpret multi-modal data, this approach not only supports more informed instructional decisions but also lays the groundwork for future innovations in learning analytics that can respond to the evolving demands of 21st-century education.

Biography: Dr. Sokratis Karkalas has been working at the intersection of industry and education since 1991. He holds degrees in economics, business administration, computer science, and pedagogy. Currently, he is a Senior Lecturer in Software Engineering at the University of Derby, where he heads the Education and AI Research Group. He also serves as a Visiting Research Fellow at the UCL Knowledge Lab, University of London.
Dr. Karkalas earned his PhD in Computer Science from the University of London, where he was awarded the Best PhD Project Award by INSTICC (Institute for Systems and Technologies of Information, Control and Communication) in 2015. He is an accredited TOGAF Enterprise Architect, a member of the Association of Enterprise Architects (UK), an Associate Fellow of the Higher Education Academy (UK), and a member of the British Computer Society – The Chartered Institute for IT.
Prior to his academic career, Dr. Karkalas held the position of Group Chief Information Officer (CIO) for a multinational industrial group and worked as a senior / lead software engineer and project architect at major consulting firms. In these roles, he led the design and development of prototypes for R&D departments. He has contributed to numerous research projects - academic and industrial - funded by the EU, local governments (ESRC/EPSRC), and the private sector.
With over 25 years of academic experience, including 17 years at leading UK universities, Dr. Karkalas' research focuses on computer-supported education, particularly the application of artificial intelligence to improve learning. He applies machine learning techniques to provide personalized support to both students and educators. Dr. Karkalas also has extensive experience designing and implementing information systems for educational and industrial applications, as well as working on technologies that enable the semantic enhancement, integration, and interoperability of diverse components within learning platforms.

Assoc. Prof. Herminiño Lagunzad,
National University, Philippines

Biography: Herminiño C. Lagunzad is an academic and researcher serving as Program Chair of the Information Technology program at National University – Fairview, Philippines, and a full-time IT faculty member. He is pursuing a Doctorate in Information Technology at Pamantasan ng Lungsod ng Maynila, deepening his expertise in advanced computing, data science, and emerging technologies. At NU, he teaches programming, networking, data security, and research, and provides academic leadership to strengthen curriculum quality and scholarly output, backed by 12 years of teaching experience.

Lagunzad is an active scholar with multiple IEEE-indexed publications, working across IoT, AI, data security, healthcare technologies, and predictive analytics. His projects include modeling student dropout and mental health awareness with Naive Bayes, developing an IoT-based smart shopping system, applying ID3 for early diabetes prediction, creating an AR tool for learning car parts, and designing Arduino-powered wearable gloves to monitor hand rehabilitation. His work has been presented in Australia, China, Japan, and Thailand, earning the PRAI 2023 Excellent Paper Presentation award.

He contributes to the international academic community as a technical committee member for several conferences, an Invited Speaker at IPAI 2025, a Session Chair at PRAI 2025, and he also chaired sessions at ICCAE 2024 and ICINT 2025, advancing discourse in computer science and emerging technologies. His affiliations include IEEE, the Philippine Society of Information Technology Educators (PSITE), the Council of Deans and Program Heads for Information Technology Education, and the Integrated Society of Information Technology Enthusiasts. He is also a Microsoft Innovative Educator Expert (2024–2026), underscoring his commitment to technology-enhanced teaching and learning.