2025 ASEE Annual Conference & Exposition
BOARD #124: Equipping Academic Makerspaces with Artificial Intelligence Elements
Presented at Design in Engineering Education Division (DEED) - Poster Session
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.
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Dr. Tien-Hsuan Wu University of Hong Kong [biography]
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Dr. Chun Kit Chui University of Hong Kong [biography]
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Chun Kit Chan University of Hong Kong [biography]
