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: I4, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Dr Yanjie SONG, Associate Professor, Department of Mathematics and Information Technology, The Education University of Hong Kong

– Mr Kaiyi WU, Department of Mathematics and Information Technology, The Education University of Hong Kong

Abstract

Integrating artificial intelligence (AI) into educational settings is crucial for developing innovative teaching methods that enhance student learning. This study investigates the development and application of Learningverse, a 2D/3D metaverse platform that integrates digital humans with advanced Large Language Model Operations (LLMOps) to create AI teaching agents. Leveraging the capabilities of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), we designed intelligent digital human teachers. These LLMOps system-constructed multi-agents, including Communication Encoder, Body Movement Coding Encoder, Eye Gaze Coding Encoder, and Expression Coding Encoder, provide personalized and strategic scaffolding to students, offering real-time feedback and support to improve their learning outcomes. Additionally, the platform utilizes GPT-SoVITS trained TTS to clone real teachers’ voices, enhancing the realism of digital human teachers. The platform can customize digital teachers and build scenarios based on different subject courses, adapting them to various thematic curricula. A pilot study evaluated the effectiveness of these digital teachers in enhancing student engagement and performance in Learningverse. Preliminary findings reveal a significant improvement in students’ interactions, motivation, and overall learning achievements. This research highlights the potential of LLMOps-integrated digital human teachers in transforming teaching practices and enriching educational experiences in the metaverse.

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: 1600-1700
Poster Presentation Time: 1500-1600; 1700-1800

Oral Presentation Venue: Camomile Room, Lower Level II
Poster Presentation Venue: D2, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Ms Hong Ching CHAN, Senior Curriculum Development Officer, Centre for Advancement of Chinese Language Education and Research, Faculty of Education, The University of Hong Kong

– Ms Hongyun DENG, Research Manager, Centre for Advancement of Chinese Language Education and Research, Faculty of Education, The University of Hong Kong

Abstract

Empowering ethnic-minority students’ learning of writing is essential to students’ development in school and beyond. The advent of generative artificial intelligence (GenAI) has accelerated the transformation of education systems, providing new possibilities for addressing their challenges. Guided by the writing process theory, social constructivism, and self-regulated learning, this study proposes the WISE framework, comprising four key components: Write, Investigate, Synthesize, and Evaluate. It also reports on qualitative research, incorporating participant observation and content analysis, on WISE’s implementation among 10 ethnic-minority students in three primary schools in Hong Kong. The results suggest that this framework can systematically support students’ writing abilities in terms of 1) enriching vocabulary and language usage, 2) improving quality and depth of writing content, 3) enhancing writing structures and creativity, and 4) strengthening critical thinking.

Theme: 2. Thematic Exploration

Sub-theme: Diversity and Inclusion 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

Poster Presentation Time: 1225-1400; 1500-1600

Venue: I3, Tai Po-Shek-O Room, Lower Level I

Presenter(s)

– Ms Winnie WONG, Educational Development Manager, Centre for Learning, Teaching and Technology, The Education University of Hong Kong

– Mr Vincent CHAN, Educational Development Assistant, Centre for Learning, Teaching and Technology, The Education University of Hong Kong

Abstract

Background and Objectives: This project aims to leverage the immersive capabilities of the metaverse to enhance data security and privacy awareness among students and staff at the Education University of Hong Kong. Develop a series of interactive and engaging educational materials to enhance the understanding of data security principles and data privacy policies. Design and implement a dynamic and user-friendly online platform (Metaverse Space) to host the educational materials, facilitating easy access and learning for target audiences. Methods and Findings: Within this metaverse-based platform, users are presented with practical scenarios that simulate real-world situations involving security-critical concepts. The educational virtual environment is strategically gamified to motivate users through rewarding challenges and progressive levels, bridging the gap between theory and practice. The dynamic simulation exercises allow participants to directly experience the impact of security failures and rehearse protective actions in a risk-free, controlled setting, nurturing applied skills alongside conceptual understanding. The survey results, based on responses from (n=20), indicate that the gamified metaverse prototype is both engaging and effective in teaching data security and privacy concepts. The interactive scenarios and simulations were particularly praised for their usefulness in understanding real-world data security issues. The navigation of the metaverse environment was generally considered easy. Overall, the positive responses suggest that the gamified metaverse is a valuable tool for learning data security, demonstrating its effectiveness in an educational context. Discussion and Perspectives: Leveraging Metaverses data security learning platform offers an innovative and immersive approach to addressing the limitations of traditional training methods, empowering users with comprehensive knowledge and applied skills to mitigate evolving cyber threats.

Theme: 1. Showcase Project Achievements

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