National Institute of Technology
Japan
Bio Hisayuki Sasaoka received his Ph.D. in Engineering from the Graduate School of Engineering, Hokkai-Gakuen University in 2000. From April to September 2000 he was a postgraduate at the Graduate School of Engineering, Hokkaido University. He then served in the Department of Electronics and Computer Engineering at the National Institute of Technology (Asahikawa KOSEN), Japan, starting from October 2000 until March 2025. Since April 2025 he has been a Professor (Thailand KOSEN) at the National Institute of Technology Headquarters (Japan) and a visiting professor at King Mongkut’s University of Technology Thonburi (Thailand). His research interests include e-Learning, Artificial Intelligence, Natural Language Processing, Machine Learning, and Machine Translation.
Navy Federal Credit Union
USA
IEEE Senior Member
Bio Ravi Kiran Pagidi is an AI and Data Systems Researcher and Senior AI Data Engineer with 11+ years of experience in scalable data architectures, intelligent systems, and cloud-native big data platforms. His work spans AI, machine learning, generative AI, agentic AI, and production-ready data systems across both research and enterprise environments. A Senior Member of IEEE, he contributes to the global research community through publications, peer review, and invited Technical Program Committee roles at international conferences. His interests focus on artificial intelligence, generative AI, big data engineering, data analytics, and data-driven intelligent systems.
Title From Big Data to Intelligent Learning: Designing Production-Ready Generative AI Systems for Education
Abstract Generative AI is rapidly transforming digital education by enabling personalised learning, intelligent tutoring, automated content support, and real-time academic assistance. However, moving from experimental prototypes to production-ready educational AI systems requires far more than model selection. It demands robust big data foundations, scalable architectures, reliable pipelines, governance controls, and continuous monitoring to ensure quality, trust, and operational sustainability. This presentation explores how big data engineering principles can be used to design production-ready generative AI systems for education that are scalable, secure, and aligned with real-world institutional needs. Drawing on reference architectures, case study evidence, and operational lessons from deployments, it provides a practical blueprint for institutions and EdTech platforms ready to move from pilot to production.
Gheorghe Asachi Technical University of Iași
Romania
Bio Prof. Adrian A. Adăscăliței, PhD, is a retired Professor of Electrical Engineering and Computer Science at the “Gheorghe Asachi” Technical University of Iași, Romania. His academic and research activities focus on engineering education, computer-assisted instruction, educational technology, e-learning, blended learning, and the integration of Artificial Intelligence into teaching and learning. He has been actively involved in the development and implementation of technology-enhanced learning environments, including virtual laboratories and online educational platforms. He is currently an Associate Editor of Computer Applications in Engineering Education (Wiley) and an Honorary Member of the Technical Sciences Academy of Romania. His recent work explores Artificial Intelligence as a new stage in the evolution of computer-assisted engineering education, with particular emphasis on pedagogical transformation, critical thinking, and the responsible use of AI in preparing future.
Title AI-Supported Engineering Education: From Traditional Teaching to Cognitive Partnership
Abstract Artificial Intelligence is increasingly transforming engineering education by extending the possibilities of teaching, learning, assessment, experimentation, and educational decision support. Rather than representing a rupture with previous educational technologies, AI can be understood as a new stage in the evolution of computer-assisted engineering education, building upon simulations, multimedia learning environments, virtual laboratories, web-based learning, e-learning, and blended learning.
This presentation examines how the integration of AI contributes to the evolution of the traditional pedagogical model while preserving the fundamental roles of the teacher and the learner. Particular attention is given to AI-supported instructional design, personalised learning, engineering applications and simulations, formative feedback, and assessment. A methodological example from electrical engineering illustrates how AI can be incorporated into a complete teaching and learning scenario rather than used merely as an information-generation tool.
The presentation also discusses the changing distribution of cognitive effort in AI-supported learning and emphasizes the growing importance of higher-order thinking, verification, validation, and professional judgment. It argues that AI should function as a cognitive partner rather than a substitute for the teacher or student. In this evolving educational model, critical thinking and cognitive, professional, ethical, and technological resilience become essential competences for future engineers.