Poster Presentation Time: 1225-1400; 1500-1600

Venue: I1, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Selena YAN, Senior E-learning Assistant, Li Ka Shing Faculty of Medicine, The University of Hong Kong

– Ms Leah LI, Senior E-learning Assistant, School of Clinical Medicine, The University of Hong Kong

Abstract

Generative Artificial Intelligence (GenAI) is a transformative technology that fosters creativity, problem-solving, and critical thinking skills in various industries, including healthcare and medical education. At HKU Medicine, we appreciate the importance of advancing GenAI literacy and propose to develop and deliver a new online course that covers the latest advances, pitfalls, ethical and professional aspects of GenAI. We aim to advance AI and digital competency for all students, professoriates and clinicians at HKU Medicine and the Hong Kong medical community. To evaluate the effectiveness of this online GenAI literacy course, an assessment study will be conducted. We will use a mixed methods approach to collect both quantitative and qualitative data through pre- and post- surveys, individual interviews and practical exercises. The survey will be adapted from the “Scale for the Assessment of Non-Experts’ AI Literacy” (SNAIL), while the interviews will provide additional context and insights into participants’ experiences and challenges with GenAI. Practical exercises will provide objective data for evaluation. Target participants will be recruited through convenience sampling, and data analysis will include both statistical and thematic analyses. The study has already obtained ethical approval. Results from this study will be used to identify improvement needs for the online course and inform the integration of GenAI in medical and health sciences education and clinical practices. Overall, the project will contribute to the advancement of GenAI literacy in medical education and healthcare professions, supporting the ethical and effective application of GenAI technology in these fields.

Theme: 1: Showcase Project Achievements

Sub-theme: 1.2  Fund for Innovative Technology-in-Education (FITE)

Poster Presentation Time: 1225-1400; 1500-1600

Venue: K2, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Sean MCMINN, Director, Center for Education Innovation, The Hong Kong University of Science and Technology

Abstract

This project aims to develop a Co-Instructrional Designer platform to assist instructors in creating course materials. By leveraging Generative AI, the platform will support faculty members in designing course outlines, content, learning activities, assignments, and rubrics aligned with learning outcomes. The platform will connect to a Center for Education Innovation (CEI)-managed knowledge base containing curated pedagogies and best practices. Instructors will interact with the platform through pre-defined prompts, receiving tailored guidance that they can evaluate and adapt to meet their specific course needs. Key features of the platform include front-end interfaces for instructors and system administrators, robust technical architecture for file storage, and conversation history management. Having completed the Proof-of-Concept phase, implementation will proceed with structuring the knowledge base, developing the frontend, integrating the system, and conducting testing, with the final rollout planned for Fall 2025/26. The project will benefit approximately 750 faculty members and teaching staff at HKUST. This tool has multiple applications: it can serve as a co-designer for faculty, support quality assurance, assist with faculty development, and aid in Teaching and Learning Innovation Pedagogy and Blended Learning Projects. Success will be measured by the quality of responses in testing scenarios, training participation and satisfaction, and overall platform usage.

Theme: 1. Showcase Project Achievements

Sub-theme: 1.2  Fund for Innovative Technology-in-Education (FITE)

Oral Presentation Time: 1400-1500

Venue: Fanling Room, Lower Level I

Presenter(s)

– Mr Jac LEUNG, Lecturer, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology

Abstract

This project explores the intersection of Generative AI, reflection and experiential learning, highlighting GenAI’s pivotal role in fostering deeper cognitive processes and the attainment of complex knowledge structures. In recognition of the multifaceted dimensions of reflection, we aim to examine GenAI’s role in promoting different focuses of reflection including technical reflection on efficiency of attaining goals; practical reflection on challenging assumptions and establishing identities; and critical reflection on reflecting within a broader consideration of socio-historical and political-cultural context. GenAI is widely praised for its ability to serve as agent to writing and agent to knowledge. This study explores GenAI’s potential as agent to reflect, offering a perspective transformation devoid of judgement and social bias. We adopt an action research approach to accommodate both the rapidly growing research area and state-of-the-art teaching innovations. To examine the roles of GenAI in various types of experiential learning contexts, a 3-year collaboration project consists of four local universities in Hong Kong was initiated in early 2024. Participating students are of diverse background in science, social science, engineering, business, and health profession (radiography). Reflective exercises are designed according to the course context and the type of experiences within entrepreneurship education, social innovation, and health professional training.

Theme: 1. Showcase Project Achievements

Sub-theme: Innovative Technology-in-Education

Oral Presentation Time: 1400-1500

Venue: Fanling Room, Lower Level I

Presenter(s)

– Mr Jungjin PARK, PhD student, Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology

– Professor Larry LI, Associate Head & Associate Professor, Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technolog

Abstract

Immersive technologies come in various forms and names, such as virtual reality (VR), augmented reality (AR), and recently spatial computing. While higher education has always been at the forefront of experimenting with such technologies in the classroom, the ubiquity of smartphones and tablets – capable of creating robust AR experiences – has made it possible for wider adoption in recent years. In this presentation, we highlight lessons learned from a pilot project that leveraged AR to enhance aerospace laboratory training at the Hong Kong University of Science and Technology, and how this effort is being expanded across multiple disciplines such as pulmonary physiotherapy and forensic pathology. In particular, we share our vision to combine AR and large language models (LLMs) to design truly immersive learning experiences that can be effectively deployed into classrooms. When combined, the two technologies mutually benefit and supplement their respective advantages and limitations, thereby overcoming many of the current challenges faced by educators when deploying either on their own.

Theme: 1. Showcase Project Achievements

Sub-theme:  Innovative Technology-in-Education

Oral Presentation Time: 1400-1500
Poster Presentation Time: 1225-1400; 1500-1600

Oral Presentation Venue: Fanling Room, Lower Level I
Poster Presentation Venue: I2, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Ting Leung Albert LEE, Lecturer, Department of Electrical and Electronic Engineering, The University of Hong Kong

– Dr Victor LEE, Lecturer, Department of Electrical and Electronic Engineering, The University of Hong Kong

– Dr Zhengyuan WEI, Research Associate, Department of Electrical and Electronic Engineering, The University of Hong Kong

– Mr Alex KIANG, The University of Hong Kong

Abstract

Retrieval-augmented generation (RAG) has been demonstrated to be highly effective in generative AI applications, resulting in substantial improvement in accuracy and reliability of large language model responses. The success of this approach is accomplished through seamless integration of AI capabilities and practical knowledge base, which fosters an interactive learning mechanism conducive to automatic question-answering augmented with references and refined prompts, leading to a more vibrant and connected learning environment. Communication portals enable effective inquiry and prompt responses while the course-specific chatbot helps reduce teachers’ workload and streamline classroom management. In this project, the RAG approach is applied to a discipline-core course named Integrated Design Project (IDP) in the second semester 2023-24. This project-based course consists of 78 EEE undergraduate students with diverse programming experience. The IDP-specific AI chatbot is developed using the Coze platform with a Discord server. To name a few, the main contents of practical knowledge base include the procedures for setting up a Raspberry Pi webcam, the installation process of Jetson Inference library on Jetson Nano, how to use YOLO model for object detection, how to install Jetson inference library on Jetson Nano, etc. The anonymous feedback survey conducted at the end of the course shows high utilization and satisfaction of the chatbot, confirming the effectiveness of this approach in facilitating students’ learning.

Theme: 1. Showcase Project Achievements

Sub-theme: Innovative Technology-in-Education