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

arXiv: https://arxiv.org/abs/2503.07970 · HTML: https://mlsanity.com/paper/2503.07970/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2025.

## Built on by (most cited first, 5 of 5)

- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation](https://mlsanity.com/paper/2505.24683/) (2025)
- [Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations](https://mlsanity.com/paper/2510.09994/) (2025)
- [Creating Disability Story Videos with Generative AI: Motivation, Expression, and Sharing](https://mlsanity.com/paper/2601.12617/) (2026)
- [Measuring User's Mental Models of Speech Translation in Human-AI Collaboration](https://mlsanity.com/paper/2606.24644/) (2026)

## Abstract (arXiv)

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.