Academic Seminar: Bridging AI Research & Engineering Education
From Intelligent Systems to Intelligent Learning: Bridging AI Research & Engineering Education
The IEEE Student Branch at The University of Melbourne is organising an academic seminar titled “From Intelligent Systems to Intelligent Learning: Bridging AI Research & Engineering Education.”
This event will feature Dr. Lili Chen, Associate Lecturer (Education Specialist) at The University of Melbourne, who will present her research and teaching work at the intersection of artificial intelligence, wireless communication systems, and engineering education.
The seminar will cover two main themes. The first part will introduce AI-driven resource allocation in wireless communication networks, with a focus on graph neural network (GNN)-based approaches for modelling interference relationships and enabling efficient, scalable decision-making. The second part will explore the use of GenAI-assisted, criterion-referenced grading frameworks in engineering education to improve feedback quality, consistency, and efficiency in large-cohort teaching contexts.
This event is intended for students, researchers, and early career engineers who are interested in emerging AI applications in both technical research and engineering learning environments. The seminar aims to promote academic exchange, technical awareness, and interdisciplinary discussion within the IEEE student community.
Refreshments will be provided after the seminar.
Date and Time
Location
Hosts
Registration
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- University of Melbourne, Parkville Campus
- Melbourne, Victoria
- Australia 3010
- Building: EEE Building
- Room Number: Newton Room, Level 2
- Starts 14 April 2026 02:00 PM UTC
- Ends 29 April 2026 02:00 PM UTC
- No Admission Charge
Speakers
Dr. Lili Chen
From Intelligent Systems to Intelligent Learning: Bridging AI Research & Engineering Education
Dr. Lili Chen will present an integrated seminar on artificial intelligence in both wireless communication research and engineering education. The first part of the talk will introduce AI-driven resource allocation in wireless communication networks, with a focus on graph neural network (GNN)-based approaches for modelling interference relationships and enabling efficient, scalable decision-making. The second part will explore recent work on GenAI-assisted, criterion-referenced grading frameworks designed to improve feedback quality, consistency, and efficiency in large engineering courses. This talk highlights how AI can support both technical innovation and improved learning experiences in engineering.
Address:Australia
Agenda
2:00 PM – 2:05 PM: Welcome and introduction
2:05 PM – 2:45 PM: Seminar presentation by Dr. Lili Chen
2:45 PM – 2:55 PM: Q&A session
2:55 PM – 3:00 PM: Closing remarks
3:00 PM onwards: Refreshments and informal networking
Hosted by the IEEE Student Branch, The University of Melbourne