Oral Presentation Time: 1400-1500

Venue: Fanling Room, Lower Level I

Presenter(s)

– Dr Richard Wing Cheung LUI, Senior Lecturer, Department of Computing, The Hong Kong Polytechnic University

Abstract

This presentation introduces the design and implementation of GPTutor, a Generative AI (GenAI) powered Intelligent Tutoring System (ITS) developed at the Hong Kong Polytechnic University (PolyU). GPTutor aims to enhance student learning experiences through personalised tutoring and interactive exploration. It helps students gain a deeper understanding of the course materials provided by their instructors. During the first phase of our implementation, we developed features for instructors to upload and manage their course content and to create learning scenarios based on the learning content. The system includes a conversational interface for students to ask questions and explore course content to deepen their understanding. As the answers are generated based on the instructor-uploaded content, GPTutor provides more factual responses, reduces hallucinations, and aligns better with the instructors’ intended learning outcomes (ILO). We will also share findings from our pilot study, which involved approximately 200 undergraduate and postgraduate students at PolyU. Finally, we will discuss our future plans for further development and enhancement of the platform.

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 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)

– Dr Theresa KWONG, Director, Centre for Holistic Teaching and Learning, Hong Kong Baptist University

Abstract

HKBU leads two UGC-funded projects to enhance digital citizenship education and promote digital ethics. The first project, “Enhancing Learning and Teaching of Digital Citizenship through Scenario-based AR Learning Trails”, pioneers innovative approaches, developing scenario-based AR learning trails to engage students as active partners in digital citizenship. With six university partners, the project team created a revised Digital Citizenship Framework and over 85 scenarios, implemented in the “AR-Trails” app. Initial findings from 2,045 students on a learning trail reveal a strong digital citizenship foundation. These insights led to the second initiative, the “Digital Ethics and Responsibilities (DEAR) Campaign”, which empowers stakeholders to make informed, responsible decisions in the digital world. The DEAR project leverages the Digital Citizenship project, creating a DEAR Hub, organizing digital ethics events, designing a micro-credential program, and curating educational content. Since January 2024, DEAR has made significant progress, including securing research ethics approval, developing an introductory micro-credential, and recording podcasts. The DEAR Hub is being established as a comprehensive digital ethics resource. These interlinked projects provide educators with professional development opportunities and resources. The presentation offers an overview of their progression and advancements, providing insights into digital citizenship education and promoting ethical, responsible digital practices.

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

Oral Presentation Time: 1400-1500

Venue: Camomile Room, Lower Level II

Presenter(s)

– Professor Michael BOTELHO, Clinical Professor, Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong

– Ms Trinity JIAO, HKU SaP CoP Project Coordinator, Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong

Abstract

The “Students as Partners (SaP)” support framework fosters active interdisciplinary collaboration. Key initiatives undertaken by the Students as Partners Community of Practice (SaP CoP) involve organizing seminar-sharing sessions, hosting internal CoP meetings, and conducting formal consultation sessions for those initiating SaP projects. These efforts offer valuable learning opportunities and continuous support for both educators and students. At HKU, the SaP CoP has developed a Scope, Process, and Levels framework for SaP activities. In partnership with TALIC, the SaP CoP has established a dedicated webpage and featured it in the HKU bulletin. These initiatives have been well-received by students and staff alike, providing meaningful experiences and practical guidelines that significantly contribute to their learning and engagement journey.

Theme: 1. Showcase Project Achievements

Sub-theme: Teaching Development and Language Enhancement