Sustaining Human Agency, Attending to Its Cost: An Investigation into Generative AI Design for Non-Native Speakers' Language Use

Abstract

AI systems and tools today can generate human-like expressions on behalf of people. It raises the crucial question about how to sustain human agency in AI-mediated communication. We investigated this question in the context of machine translation (MT) assisted conversations. Our participants included 45 dyads. Each dyad consisted of one new immigrant in the United States, who leveraged MT for English information seeking as a non-native speaker, and one local native speaker, who acted as the information provider. Non-native speakers could influence the English production of their message in one of three ways: labeling the quality of MT outputs, regular post-editing without additional hints, or augmented post-editing with LLM-generated hints. Our data revealed a greater exercise of non-native speakers' agency under the two post-editing conditions. This benefit, however, came at a significant cost to the dyadic-level communication performance. We derived insights for MT and other generative AI design from our findings.