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)

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

Poster Presentation Time: 1225-1400; 1500-1600

Venue: G3, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Professor Fraide A. Jr GANOTICE, Assistant Professor, Bau Institute of Medical and Health Sciences Education, Li Ka Shing Faculty of Medicine, The University of Hong Kong

Abstract

Disciplinary silos often perpetuate hierarchical relationships and competition, creating a significant barrier to teamwork and collaboration in healthcare. Students who are exclusively trained within the confines of their discipline and university miss out on opportunities to develop the interprofessional competencies necessary for managing complex medical conditions of patients. Therefore, cross-institutional interprofessional education is crucial to promote the sharing of expertise among professionals from different universities and to prepare students for clinical practice. This presentation will highlight the significant achievements of the project titled “Promoting cross-institutional collaboration through interprofessional education: Forging alliances in healthcare education.” Initiated by the University of Hong Kong (HKU) in partnership with the Hong Kong Polytechnic University (PolyU), this innovative project aims to break down educational silos and foster interprofessional competencies among healthcare students. The project’s core objective is to enhance patient-centered care by developing and implementing a model of interprofessional education that integrates various healthcare disciplines. Throughout the four phases of the project—Program Development, Pilot Testing, Actual Implementation, and Evaluation and Dissemination—numerous achievements have been documented. These include the successful integration of interprofessional education into the curricula, enhanced collaborative competencies among students. The project also pioneered the development of an evidence-based cross-institutional IPE Model, setting a benchmark for future educational endeavors in the healthcare sector. This presentation will delve into the methodologies employed, the collaborative initiatives between HKU and PolyU, and the positive impact on healthcare education highlighted by reduced medical errors and improved healthcare outcomes. By showcasing these achievements, the session aims to inspire continued progress in interprofessional education and collaboration across healthcare institutions globally.

Theme: 1: Showcase Project Achievements

Sub-theme: 1.1  Teaching Development and Language Enhancement Grant (TDLEG)