Cross-lingual effects of AI-generated content on human work.

ABSTRACT: Artificial intelligence (AI) technologies, especially large language models (LLMs), have permeated human work around the globe, but how effective is workers' usage and application of AI-generated content across language settings? This research examines the quality of (1) AI-generated business recommendations in English, Arabic, and Chinese and (2) business emails written by human participants in those languages. In Study 1, trained evaluators rated the quality of AI-generated content. Study 2 is a randomized experiment in which 480 human participants (160 in each language setting) wrote emails to address business issues, with or without the help of AI-generated content. Trained evaluators then rated the quality of those emails. This research finds that the AI-generated responses were of lower quality in Arabic and Chinese than in English. Importantly, human participants' usage of AI-generated contentin email-writing was associated with less actionable and less creative work output in Arabic and Chinese than in English. Non-English participants were particularly disadvantaged when dealing with more technical work tasks (e.g., tasks related to scientific discovery or product design). These findings underscore the need for more language-inclusive LLMs to support workers worldwide, as current technologies may inadvertently widen the productivity gap between English and non-English speakers, particularly in more technical domains.

SUBMITTER: Koo WW 

PROVIDER: S-EPMC12373962 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature