Las Vegas, Nevada
April 18, 2024
April 18, 2024
April 20, 2024
13
10.18260/1-2--46032
https://peer.asee.org/46032
167
Han Kyul Kim is a Ph.D. student in the Department of Industrial and Systems Engineering at the University of Southern California. He earned his M.S. in Industrial Engineering from Seoul National University and holds B.S. degrees in Industrial & Systems Engineering and Business and Technology Management from KAIST. Previously, he worked as a data scientist and software engineer at Deloitte Consulting, Samsung Electronics, and Seoul National University Hospital. His research interests include machine learning, natural language processing, and learning analytics.
Aleyeh Roknaldin is earning her Ph.D. in industrial and systems engineering from the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California (USC). Aleyeh earned an M.S. in Engineering Management in 2022 from USC and her B.S. in Chemical Engineering from the University of California, Davis in 2020. Her research interests include learning analytics to investigate how students learn from one another in computer-supported collaborative learning environments and how students interact with generative artificial intelligence in learning contexts.
received his Bachelor’s in Industrial
Engineering and a minor in Mathematics from the
Pennsylvania State University in 2021. Currently, he
is pursuing his Master’s in Industrial and System
Engineering at the University of Southern California.
His research interests include machine learning, data
analysis, and mathematical optimizations.
Stephen Lu is the David Packard Chair in Manufacturing Engineering at University of Southern California. His current professional interests include design thinking, collaborative engineering, technological innovation, and education reform. He has over 330
The surging popularity of generative AI, especially ChatGPT, has evoked both enthusiasm and caution within the education community. To effectively harness the full potential of ChatGPT in educational contexts, it is crucial to thoroughly analyze its impact and suitability for different educational purposes. The current paper aims to contribute to this understanding by investigating the impact of ChatGPT on student interactions in a computer-supported collaborative learning (CSCL) environment when deployed as a conversation agent. Furthermore, we present a concise overview of our iBot system, which seamlessly incorporates ChatGPT’s capabilities into CSCL, providing technical guidelines for potential future adoptions in similar educational contexts. Through extensive statistical analysis, we numerically examine how including ChatGPT affects the dynamics of student interactions. Our findings reveal a significant shift in the patterns of student interactions, with a notable increase in active learning interactions occurring predominantly between individual students and ChatGPT, even in the presence of other students. While recognizing that the observed changes may not be universal in every educational context, our quantitative analysis emphasizes the need to consider the potential impact of integrating ChatGPT into collaborative learning scenarios. By shedding light on the effects of ChatGPT in CSCL, this study offers valuable insights for educators and researchers to better understand its role as a conversation agent in collaborative learning settings. It encourages thoughtful consideration of the appropriate use case of ChatGPT and emphasizes the importance of further exploring its potential to enhance collaborative learning experiences.
Kim, H. K., & Roknaldin, A., & Nayak, S. P., & Zhang, X., & Twyman, M., & Hwang, A. H., & Lu, S. (2024, April), Empowering computer-supported collaborative learning with ChatGPT: investigating effects on student interactions Paper presented at 2024 ASEE PSW Conference, Las Vegas, Nevada. 10.18260/1-2--46032
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