CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis

要旨

While AI-assisted individual qualitative analysis has been substantially studied, AI-assisted collaborative qualitative analysis (CQA) – a process that involves multiple researchers working together to interpret data—remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we designed and implemented CoAIcoder, a tool leveraging AI to enhance human-to-human collaboration within CQA through four distinct collaboration methods. With a between-subject design, we evaluated CoAIcoder with 32 pairs of CQA-trained participants across common CQA phases under each collaboration method. Our findings suggest that while using a shared AI model as a mediator among coders could improve CQA efficiency and foster agreement more quickly in the early coding stage, it might affect the final code diversity. We also emphasize the need to consider the independence level when using AI to assist human-to-human collaboration in various CQA scenarios. Lastly, we suggest design implications for future AI-assisted CQA systems.

著者
Jie Gao
Singapore University of Technology and Design, Singapore, Singapore
Kenny Tsu Wei Choo
Singapore University of Technology and Design, Singapore, Singapore
Junming Cao
Fudan University, Shanghai, China
Roy Ka-Wei Lee
Singapore University of Technology and Design, Singapore, Singapore, Singapore
Simon Tangi. Perrault
Singapore University of Technology and Design, Singapore, Singapore
動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Research Methods and Tools A

323C
5 件の発表
2024-05-14 23:00:00
2024-05-15 00:20:00