Empowering Students in Emerging Technology: A Framework for Developing Hands-on Competency in Generative AI with Ethical Considerations

Chun Kit Chui; LEI YANG; Ben Kao

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: Technological and Engineering Literacy/Philosophy of Engineering Division (TELPhE) Technical Session 1
Tagged Division: Technological and Engineering Literacy/Philosophy of Engineering Division (TELPhE)
Page Count: 17
DOI: 10.18260/1-2--47250
Permanent URL: https://peer.asee.org/47250
Download Count: 91

Paper Authors

biography

Chun Kit Chui University of Hong Kong

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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). Innovation Wing aims to unleash students' creativity by entrusting them to spearhead ambitious innovation and technology projects that will shape the future. The iconic facility is located 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.

He also holds the position of Senior Lecturer in the Department of Computer Science at HKU. His research interests include database and data mining, as well as pedagogical research in computing education. Dr. Chui has received several education awards, including 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 2012-13 academic year. Additionally, he has been honored 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).

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LEI YANG The University of Hong Kong orcid.org/0000-0002-3284-4019

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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.

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Ben Kao University of Hong Kong

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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.

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Abstract

This practice paper introduces a framework to enhance the practical skills of undergraduate engineering students in generative AI technologies. Our goal is to transform students from users of generative AI software into professional creators of new AI technologies. We begin by defining guidelines, emphasizing ethical, responsible, and lawful practices. Then we define the core practical competencies and design learning activities. The framework involves collaboration among undergraduate students, postgraduate tutors, instructors, technicians, legal experts, academic partners, and industry professionals. The framework adopts a three-stage progression approach. At the adoption stage, students become familiar with generative AI software, such as composing textual prompts for image generation with the stable diffusion model. This helps students stay updated on the latest tools and developments in generative AI applications. In the development stage, the focus is on technical training for application programming interfaces (APIs), including language completion, text-to-speech conversion, and semantic search. This covers hands-on learning of using open-source large language models such as Llama2, and commercial cloud services such as Microsoft Azure OpenAI and Google GCP Vertex AI, etc. Techniques like Retrieval-Augmented Generation (RAG) and model fine-tuning are part of this training, equipping students with the necessary skills to enter the final application stage. In this stage, students participate in designing and developing generative AI-based solutions to address real-world problems. Our partnerships extend to the law and social science faculties, where we build customized chatbot solutions. The framework was implemented and evaluated at the Tam Wing Fan Innovation Wing (a.k.a. HKU Inno Wing) [1], a facility within the Faculty of Engineering at the University of Hong Kong dedicated to improving students' practical abilities. Students demonstrate increased awareness of ethical, responsible, and lawful practices in generative AI technologies under the careful guidance of instructors. We conducted an analysis of the written reflections from students in the 2023/24 cohort regarding their understanding of the strengths and weaknesses of generative AI technologies. Furthermore, we assessed how students' awareness of generative AI ethics, responsibility, and legal considerations evolved throughout their reflections. By identifying common blind spots, we gained valuable insights to continually enhance guidance for students at various stages of their learning progress.

Citation
Format

Chui, C. K., & YANG, L., & Kao, B. (2024, June), Empowering Students in Emerging Technology: A Framework for Developing Hands-on Competency in Generative AI with Ethical Considerations Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47250

TY - CPAPER
AB - This practice paper introduces a framework to enhance the practical skills of undergraduate engineering students in generative AI technologies. Our goal is to transform students from users of generative AI software into professional creators of new AI technologies. We begin by defining guidelines, emphasizing ethical, responsible, and lawful practices. Then we define the core practical competencies and design learning activities. The framework involves collaboration among undergraduate students, postgraduate tutors, instructors, technicians, legal experts, academic partners, and industry professionals.
The framework adopts a three-stage progression approach. At the adoption stage, students become familiar with generative AI software, such as composing textual prompts for image generation with the stable diffusion model. This helps students stay updated on the latest tools and developments in generative AI applications. In the development stage, the focus is on technical training for application programming interfaces (APIs), including language completion, text-to-speech conversion, and semantic search. This covers hands-on learning of using open-source large language models such as Llama2, and commercial cloud services such as Microsoft Azure OpenAI and Google GCP Vertex AI, etc. Techniques like Retrieval-Augmented Generation (RAG) and model fine-tuning are part of this training, equipping students with the necessary skills to enter the final application stage. In this stage, students participate in designing and developing generative AI-based solutions to address real-world problems. Our partnerships extend to the law and social science faculties, where we build customized chatbot solutions.
The framework was implemented and evaluated at the Tam Wing Fan Innovation Wing (a.k.a. HKU Inno Wing) [1], a facility within the Faculty of Engineering at the University of Hong Kong dedicated to improving students&#39; practical abilities. Students demonstrate increased awareness of ethical, responsible, and lawful practices in generative AI technologies under the careful guidance of instructors. We conducted an analysis of the written reflections from students in the 2023/24 cohort regarding their understanding of the strengths and weaknesses of generative AI technologies. Furthermore, we assessed how students&#39; awareness of generative AI ethics, responsibility, and legal considerations evolved throughout their reflections. By identifying common blind spots, we gained valuable insights to continually enhance guidance for students at various stages of their learning progress.

AU - Chun Kit Chui
AU - LEI YANG
AU - Ben Kao
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Empowering Students in Emerging Technology: A Framework for Developing Hands-on Competency in Generative AI with Ethical Considerations
UR - https://peer.asee.org/47250
DO - 10.18260/1-2--47250
ER -