Montreal, Quebec, Canada
June 22, 2025
June 22, 2025
August 15, 2025
Design in Engineering Education Division (DEED) - Poster Session
Design in Engineering Education Division (DEED)
14
https://peer.asee.org/55941
Dr. Lei Yang is a lecturer of Innovation Academy of the Tam Wing Fan Innovation Wing under the Faculty of Engineering, The University of Hong Kong. Before that, he worked as a Research Officer at Centre of Transformative Garment Production from 2021 to 2023 and as a postdoctoral fellow at Department of Computer Science, The University of Hong Kong from 2018 to 2021. Dr. Yang received his Bachelor's degree and Ph.D. degree from Dalian University of Technology in 2012 and 2018, respectively. Dr. Yang's research interest includes Computer-Aided Design, Computer-Aided Engineering, and Geometry Modeling and Multimedia.
Dr. Kevin Tien-Hsuan Wu is a Lecturer at the Tam Wing Fan Innovation Wing within the Faculty of Engineering at the University of Hong Kong. Prior to this role, he served as a Post-Doctoral Fellow at the Law and Technology Centre at HKU. Dr. Wu specializes in developing Natural Language Processing applications and guiding student project development initiatives.
Dr. Chun Kit Chui serves as the Director of the Tam Wing Fan Innovation Wing in the Faculty of Engineering at the University of Hong Kong (HKU). The Innovation Wing aims to unleash students' creativity by entrusting them to spearhead ambitious innovation and technology projects that will shape the future. This iconic facility is situated at the heart of the campus, offering 2400m2 of space with state-of-the-art resources and a supportive environment to enhance hands-on and experiential learning for undergraduate students.
In addition to his role as Director, Dr. Chui holds the position of Assistant Dean (Teaching and Learning) in the Faculty of Engineering at HKU, responsible for driving curriculum reform and active learning activities. His research interests include database and data mining, as well as pedagogical research in engineering education.
Dr. Chui has been the recipient of several prestigious awards, including the University Distinguished Teaching Award (Individual Award) at the University of Hong Kong for the 2024-25 academic year, the University Outstanding Teaching Award (Individual Award) at the University of Hong Kong for the 2015-16 academic year, and the Faculty Outstanding Teaching Award (Individual Award) in the Faculty of Engineering for the 2023-24 and 2012-13 academic years. Additionally, he has been honoured with the Teaching Excellence Award in the Department of Computer Science for the academic years 2011-12, 2012-13, 2013-14, 2014-15, and 2015-16. Furthermore, he was a shortlisted candidate for the UGC Teaching Award (Early Career Faculty Member).
Ryan Chan Chun Kit is a Mechanical Engineer with a Bachelor's degree in Mechanical Engineering from the University of Hong Kong. During his experience working in Tam Wing Fan Innovation Wing, Ryan has demoonstrated a diverse skillset and rich teaching experience in the field of engineering.
Throughout his academic journey, Ryan has excelled in various engineering projects and research endeavors. He has been actively involved in designing workshops on cutting-edge technologies such as embedded systems, ROS and IoT. Additionally, Ryan has led a undergraduate student research team on robotics, developing various types of smart robots.
Moreover, Ryan has been contributing to the community of Robotics through volunteer means. He has served as an Adviser for BREED and Nestspace at HKU, where he offers consultation and guidance on various projects. Ryan's technical proficiency includes 3D Printing, robotics, 3D modeling and mechanical machining.
With a passion for creating a better learning environment for fellow engineers, Ryan is dedicated to enhancing his skills and contributing to the advancement of mechanical engineering.
The rise of Large Language Models and other artificial intelligence (AI) technologies has sparked significant interest among students and industrial employers. Consequently, there is a growing need for academic makerspaces to incorporate AI elements—such as AI-powered chatbots and robotics. These AI-related practical experiences are expected to complement the theoretical knowledge acquired in the classroom for computer science (CS) students, while also providing foundational exposure for students from other engineering disciplines. However, many makerspaces, even within universities, face substantial challenges in adapting to this rapidly evolving landscape. To address this challenge, this paper presents an experiential learning framework implemented in a university’s student innovation center and makerspace from June 2023 to December 2024. This framework is designed to accommodate students from various fields, effectively integrating AI elements into their extracurricular activities in the makerspace. Specifically, we adopt a project-based learning approach that invites students with either technical backgrounds or professional training related to the problems being tackled. For example, we assembled teams of CS students and social work students to develop a chatbot for interactive coaching of social workers. Recognizing that AI applications extend beyond chatbots, we encourage exploration of diverse topics (e.g., AI and robotics), seamlessly integrating AI elements into the traditional focus areas of makerspaces. For students with limited experience, a series of hands-on workshops were carefully designed, starting from foundational concepts in training a neural network to more practical experience of building their own chatbots. These series of workshops are expected to progressively build up their skills for involving in or initiating AI-related innovations. We have also made the teaching materials of the workshops publicly available to our makerspace community. In addition to the educational content, computing facilities are a significant concern for many makerspaces, as AI-related projects often require substantial computational resources. To address this, we devised a cost-effective strategy for establishing the necessary facilities to support these activities. While high-performance computing workstations may be essential for some real-world projects, cloud services can be leveraged to facilitate hands-on workshops, providing scalable resources without the need for significant investment. To assess the effectiveness of our proposed framework, we have collected and analyzed post-workshop surveys. Additionally, we invited students working on projects to reflect on their learning experiences, providing qualitative insights to our designed framework. We position our makerspace within the classification system proposed by Wilczynski (2017) to facilitate comparisons with other university makerspaces in terms of resources. Surveying feedback were reported, which demonstrates the preliminary effectiveness of the proposed framework and highlight both the successes and the challenges. We hope this initial discussion on integrating AI into makerspaces will be inspiring to other institutions to respond to the shifting demands of the AI era.
YANG, L., & Wu, T., & Chui, C. K., & Chan, C. K. (2025, June), BOARD #124: Equipping Academic Makerspaces with Artificial Intelligence Elements Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55941
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