GE Teaching Award
Lecturer
Department of Computer Science
Hong Kong Baptist University
Office: RRS 711
Office number: (852) 3411 7703
Email: cpchan@hkbu.edu.hk
Qualification
- Ph.D. in Computer Science, City University of Hong Kong
- B.Sc. in Computer Science, City University of Hong Kong
Research Interests
- Computer Graphics
- Computer Vision
- Machine Learning
- Character Animation
- Health Care Technology
Dr. Chan received his B.Sc. and Ph.D. degrees from the City University of Hong Kong, all in Computer Science. In Jan 2020, he joined Department of Computer Science at the Hong Kong Baptist University (HKBU) and now serves as a lecturer. Before joining HKBU, he received the St. Francis Teaching Excellence Award at Caritas Institute of Higher Education.
This team is recognised for their collaborative work on the GE course, “GTCU/GTSU/GTSC2805 Critical AI Literacies: Embracing AI for Social Good”.
The Panel is impressed by the alignment between the course’s pedagogies and assessments. The course is guided by a clear pedagogical framework—understand, apply, and create—which provides a robust structure to equip students with the integrated literacies essential to the AI era and drives them toward an ethical use of AI. This thoughtful design ensures that every teaching and learning activity and assessment directly supports the core goal: fostering not just technical skills, but critical judgement and social responsibility.
A key factor in this recommendation is the team’s innovative, transdisciplinary course structure. The members’ distinct areas of expertise in computer science, visual arts, communication, and business provide a comprehensive and synergistic foundation for the course. This blend of perspectives prepares students for real-world problem-solving by challenging them to analyse AI from multiple critical angles. Demonstrating remarkable foresight, the team has also developed a manual to ensure the course’s quality and to facilitate its expansion with new teaching members in the future.
The Panel also notes the team’s deep commitment to active learning, particularly their “create → critique → refine” methodology. This methodology is brought to life through a hands-on format centered on discussion and critique, made possible by a flipped classroom model. Furthermore, to address the challenge of AI-generated assignments, the team has incorporated live debates that require students to think on their feet, ensuring genuine understanding and engagement.
In recognition of their exceptional course design, innovative pedagogy, and impactful transdisciplinary collaboration, the Panel recommends the team for the GE Teaching Award (Team Teaching) to honour their excellent performance.