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

• Published in: ACM transactions on computer-human interaction Vol. 31; no. 1; pp. 1 - 38
• Main Authors: Gao, Jie, Choo, Kenny Tsu Wei, Cao, Junming, Lee, Roy Ka-Wei, Perrault, Simon
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 29.11.2023
• Subjects: Collaborative and social computing systems and tools; Human-centered computing; AI-assisted qualitative analysis; Qualitative coding; coding quality; collaboration; AI-assisted human-to-human collaboration
• ISSN: 1073-0516; 1557-7325
• DOI: 10.1145/3617362

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.