2026 ASEE Annual Conference & Exposition

Developing AI Applications Through International Partnership: An Experiential Learning Approach in Engineering Education

Presented at CIT Technical Session 6: Experiential Learning and Capstone.

In recent years, interdisciplinary and cross-institutional collaboration in engineering education has become increasingly popular for students to apply technical knowledge in real-world contexts. This practice paper describes a joint initiative between [Our Institution] and [Partner Institution]. In 2025 summer, the two institutions partnered to design and deliver a collaborative student project focusing on developing AI-powered museum tour guide applications. The initiative brought together two students from [Our Institution] and two from [Partner Institution], along with faculty advisors from both universities, in a program that combined experiential learning, cultural exchange, and project-based teamwork.
The collaboration was structured into three phases: an initial project kick-off at [Our Institution], a remote continuation period at each student’s home institution, and a concluding sprint in [Partner Institution]. This structure allows students to benefit from immersive cross-cultural experiences, and also provides students with distributed teamwork experience. The "AI Museum Tour Guide" was selected because it aligns with both institutions' objectives by presenting AI, large language models (LLMs), and data science within a social context. By talking to the guide on the mobile phones, the system provides interactive answers to users’ questions, which ameliorates the deficiencies of traditional pre-recorded audio tour guides.
The project cultivates students’ technical and professional growth. Besides acquiring technical competencies in natural language processing and AI systems, the students were immersed in the intercultural environment when carrying out the project. Initial challenges, such as the different expectations for the project and the collaboration under significant time zone differences, provide the student with valuable learning opportunities. Students learned how to plan and work asynchronously, and also demonstrated effective communication skills through discussion about the project deliverables. These experiences enhanced their expertise and equipped them to succeed as professional engineers.
This paper will discuss three key dimensions of this initiative: (1) the design of the collaboration between [Our Institution] and [Partner Institution]; (2) the process of developing the AI Museum Guide project between the two institutions and the [Partner Museum]; and (3) educational outcomes of this project and student feedback. By sharing this experience, we hope to contribute a replicable model for cross-institutional AI-focused collaborations in engineering education.

Authors

Dr. Tien-Hsuan Wu University of Hong Kong [biography]

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 (NLP) applications and guiding student project development initiatives.
LEI YANG http://orcid.org/0000-0002-3284-4019 University of Hong Kong [biography]

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.
Prof. Chun Kit Chui University of Hong Kong [biography]

Prof. 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, Prof. 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.
Prof. 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).
Ben Kao University of Hong Kong [biography]

Ben Kao received the B.Sc. degree in computer science from the University of Hong Kong in 1989 and the Ph.D. degree in computer science from Princeton University in 1995. He is currently full professor in the Department of Computer Science at the University of Hong Kong, the Associate Head of the Innovation Academy, and the co-Director of Hong Kong University’s Law & Technology Centre. From 1992 to 1995, he was a research fellow at Stanford University. His research interests include database management systems, data mining, information retrieval systems, AI and NLP.
Raymond Ng

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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