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- Oral Presentation ×
- The Chinese University of Hong Kong ×
- The Education University of Hong Kong ×
- The University of Hong Kong ×
- Lingnan University ×
- 1. Showcase Project Achievements ×
- 1.2 Fund for Innovative Technology-in-Education (FITE) ×
- 1.4 Other UGC grants, Quality Education Fund (QEF), and Quality Enhancement Support Scheme (QESS) ×
- 2.2 Diversity and Inclusion Education ×
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2 posts found
Oral Presentation Time: 1400-1500
Venue: Peony Room, Lower Level II
Presenter(s)
– Ms Carrie Ka Yee CHENG, Head of Academic Programmes (Full-time), School of Continuing and Professional Studies, The Chinese University of Hong Kong
Abstract
Encompassing 20 Virtual Reality (VR) simulations across 15 academic disciplines for pre-internship training, InternVision – a dual platform of a VR head-mounted display system and a mobile application (app), is developed and launched for enhancing the work-readiness of all full-time students at School of Continuing and Professional Studies, The Chinese University of Hong Kong (CUSCS). InternVision envisages to be an ingenious and resourceful platform with interactive contents of real-life workplace scenarios for elevating job competency, enhancing professional related concepts, reactions and decision making at internships, and thus formulating an insightful immersive experience for empowering learners to a multitude of careers. This oral presentation endeavours to share ideas and success factors on how InternVision is designed, constituted and operated in line with our expected high effectiveness and promising satisfaction. Via seamlessly integrating technologies at the VR system and appending with its user-friendly mobile app, InternVision brings forth greater flexibility, confidence and motivation for students to gear up for their internships and maneuver their learning more freely beyond time and location constraints. By visualising the simulations, a deeper understanding of their roles regarding their own profession can be yielded for further self-reflection, and hence achieving the intended learning outcomes. InternVision can be framed as a comprehensive and impactful solution for enhancing internship preparation and consolidating student success for a world of future profession.
Theme: 1. Showcase Project Achievements
Sub-theme: Quality Education Fund (QEF), and Quality Enhancement Support Scheme (QESS)
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