Artificial intelligence is rapidly transforming engineering practice, changing not only the tools engineers use but also the knowledge, skills, and professional judgment expected of future graduates. While AI offers unprecedented opportunities to enhance learning, it also challenges long-held assumptions about how engineering fundamentals should be taught, assessed, and applied.
This special session aims to bring together educators, researchers, and industry practitioners to explore how engineering education should evolve in the age of AI. Rather than focusing solely on AI-enabled educational technologies, the session emphasizes preserving strong engineering fundamentals while leveraging AI to enrich learning, design, experimentation, and problem solving. Contributions addressing curriculum innovation, laboratory experiences, authentic assessment, ethics, professional skills, and the integration of AI into engineering programs are especially encouraged.
The session will provide a forum for exchanging evidence-based practices, emerging research, and forward-looking perspectives that help prepare future engineers to work effectively alongside increasingly capable AI systems.
Session Organizer
Prof. Hamid Timorabadi, University of Toronto, Canada
Building Engineering Fundamentals in an AI World
Submission Methods
Submit your Full Paper (no less than 8 pages with one column) or your paper abstract-without publication (200-400 words) via Online Submission System, then choose Special Session 1 (Engineering Education in the Age of AI: Building Engineering Fundamentals in an AI World)
The topics of interest include, but are not limited to:
Curriculum Design for AI-Enabled Engineering Programs
Authentic Assessment and Academic Integrity in the Age of Generative AI
Project-Based, Experiential, and Laboratory Learning with AI
Human–AI Collaboration in Engineering Design and Problem Solving
Developing Engineering Judgment, Critical Thinking, and Creativity
Ethics, Responsibility, and Trustworthy AI in Engineering Education
Digital Twins, Virtual Laboratories, and Simulation-Based Learning
Preparing Future Engineers for AI-Driven Industry
Case Studies, Best Practices, and Lessons Learned in AI-Enhanced Engineering Education
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Introduction of Session Organizer
Prof. Hamid Timorabadi
Hamid Timorabadi is a Professor (Teaching Stream) in the Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto, Canada. He received his B.A.Sc., M.A.Sc., and Ph.D. in Electrical Engineering from the University of Toronto and previously worked in industry as a project and design engineer. His teaching and scholarly activities focus on embedded systems, artificial intelligence, autonomous cyber-physical systems, engineering design, and engineering education. His recent work explores AI-enhanced engineering education, experiential learning, curriculum innovation, and the integration of emerging technologies into engineering curricula. He is particularly interested in how engineering education should evolve in the age of AI while preserving strong engineering fundamentals, professional judgment, and hands-on learning. He has received the Gordon R. Slemon Award, the Faculty's highest teaching honour, on two occasions.
