(Dis)placed Contributions: Uncovering Hidden Hurdles to Collaborative Writing Involving Non-Native Speakers, Native Speakers, and AI-Powered Editing Tools

Content creation today often takes place via collaborative writing. A longstanding interest of CSCW research lies in understanding and promoting the coordination between co-writers. However, little attention has been paid to individuals who write in their non-native language and to co-writer groups involving them. We present a mixed-method study that fills the above gap. Our participants included 32 co-writer groups, each consisting of one native speaker (NS) of English and one non-native speaker (NNS) with limited proficiency. They performed collaborative writing adopting two different workflows: half of the groups began with NNSs taking the first editing turn and half had NNSs act after NSs. Our data revealed a 'late-mover disadvantage' exclusively experienced by NNSs: an NNS's ideational contributions to the joint document were suppressed when their editing turn was placed after an NS's turn, as opposed to ahead of it. Surprisingly, editing help provided by AI-powered tools did not exempt NNSs from being disadvantaged. Instead, it triggered NSs' overestimation of NNSs' English proficiency and agency displayed in the writing, introducing unintended tensions into the collaboration. These findings shed light on the fair assessment and effective promotion of a co-writer's contributions in language diverse settings. In particular, they underscore the necessity of disentangling contributions made to the ideational, expressional, and lexical aspects of the joint writing.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2024-11-07
DOI
https://doi.org/10.1145/3686942
Citations
5
Primary Topic
Speech and dialogue systems
Type
article
Field-Weighted Citation Impact
1.74
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article

(Dis)placed Contributions: Uncovering Hidden Hurdles to Collaborative Writing Involving Non-Native Speakers, Native Speakers, and AI-Powered Editing Tools

Naomi Yamashita, Ge Gao, Yuexi Chen, Yimin Xiao et al.
5 citations
Proceedings of the ACM on Human-Computer Interaction
Speech and dialogue systems
1.74
article

(Dis)placed Contributions: Uncovering Hidden Hurdles to Collaborative Writing Involving Non-Native Speakers, Native Speakers, and AI-Powered Editing Tools

Naomi Yamashita, Ge Gao, Yuexi Chen, Yimin Xiao, Zhicheng Liu, Yuewen Chen
article en
5 citations

Abstract

Content creation today often takes place via collaborative writing. A longstanding interest of CSCW research lies in understanding and promoting the coordination between co-writers. However, little attention has been paid to individuals who write in their non-native language and to co-writer groups involving them. We present a mixed-method study that fills the above gap. Our participants included 32 co-writer groups, each consisting of one native speaker (NS) of English and one non-native speaker (NNS) with limited proficiency. They performed collaborative writing adopting two different workflows: half of the groups began with NNSs taking the first editing turn and half had NNSs act after NSs. Our data revealed a 'late-mover disadvantage' exclusively experienced by NNSs: an NNS's ideational contributions to the joint document were suppressed when their editing turn was placed after an NS's turn, as opposed to ahead of it. Surprisingly, editing help provided by AI-powered tools did not exempt NNSs from being disadvantaged. Instead, it triggered NSs' overestimation of NNSs' English proficiency and agency displayed in the writing, introducing unintended tensions into the collaboration. These findings shed light on the fair assessment and effective promotion of a co-writer's contributions in language diverse settings. In particular, they underscore the necessity of disentangling contributions made to the ideational, expressional, and lexical aspects of the joint writing.

Proceedings of the ACM on Human-Computer InteractionVol. 8(CSCW2)
Kyoto University (JP), University of Maryland, College Park (US)
Quality Education
Openalex Percentile: Top 13%
Speech and dialogue systems
1.74
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