Robotic coaches have been recently investigated to promote mental well-being in various contexts such as workplaces and homes. With the widespread use of Large Language Models (LLMs), HRI researchers are called to consider language appropriateness when using such generated language for robotic mental well-being coaches in the real world. Therefore, this paper presents the first work that investigated the language appropriateness of robot mental well-being coach in the workplace. To this end, we conducted an empirical study that involved 17 employees who interacted over 4 weeks with a robotic mental well-being coach equipped with LLM-based capabilities. After the study, we individually interviewed them and we conducted a focus group of 1.5 hours with 11 of them. The focus group consisted of: i) an ice-breaking activity, ii) evaluation of robotic coach language appropriateness in various scenarios, and iii) listing shoulds and shouldn'ts for designing appropriate robotic coach language for mental well-being. From our qualitative evaluation, we found that a language-appropriate robotic coach should (1) ask deep questions which explore feelings of the coachees, rather than superficial questions, (2) express and show emotional and empathic understanding of the context, and (3) not make any assumptions without clarifying with follow-up questions to avoid bias and stereotyping. These results can inform the design of language-appropriate robotic coach to promote mental well-being in real-world contexts.

翻译：近期，机器人教练在促进工作场所、家庭等场景的心理健康方面受到关注。随着大型语言模型（LLM）的普及，人机交互研究者需审视此类生成语言用于现实世界心理健康机器人教练时的语言适宜性。为此，本文首次探讨了工作场所中机器人心理健康教练的语言适宜性。我们开展了一项实证研究，17名员工与基于LLM能力的机器人心理健康教练进行了为期四周的互动。研究后，我们对参与者进行单独访谈，并邀请其中11人参加了一场1.5小时的焦点小组讨论。该讨论包含：(i) 破冰活动，(ii) 评估不同场景下机器人教练语言的适宜性，(iii) 列出设计心理健康适宜机器人教练语言时应当与不应当遵循的准则。通过定性评估发现，语言适宜的机器人教练应(1) 提出深入探索来访者感受的问题，而非表面化提问；(2) 表达并展现对情境的情感共鸣与理解；(3) 避免未用追问澄清即做出假设，以减少偏见与刻板印象。这些结果可为设计促进现实场景心理健康的语言适宜型机器人教练提供指导。