CSCW studies have increasingly explored AI's role in enhancing communication efficiency and productivity in collaborative tasks. AI tools such as chatbots, smart replies, and language models aim to optimize conversation management and improve team performance. Early AI assistants, such as Gmail smart reply, were limited by predefined knowledge bases and decision trees. However, the advent of large language models (LLMs) such as ChatGPT has revolutionized AI assistants, employing advanced deep learning architecture to generate context-aware, coherent, and personalized responses. Consequently, ChatGPT-based AI assistants provide a more natural and efficient user experience across various tasks and domains. In this paper, we formalize the concept of AI Collaborative Tools (ACT) as AI technologies in human collaborative work and discuss how the emergence of ChatGPT has transformed the AI landscape and increased focus on ACT for improving team performance. Meanwhile, we present an LLM-based Smart Reply (LSR) system utilizing the ChatGPT API to generate personalized responses in daily collaborative scenarios, considering context, tone, and communication style. Our two-step process involves generating a preliminary response type (e.g., Agree, Disagree) to provide a generalized direction for message generation, thus reducing response drafting time. We conducted an experiment in which participants completed simulated work tasks, involving Google Calendar manipulation and a double-back N-back test, while interacting with researchers posing as teammates requesting scheduling changes. Our findings indicate that the AI teammate increases perceived performance and reduces mental demand, as measured by the NASA TLX, and improves performance in the N-back task. We also provide qualitative feedback on participants' experiences working with the AI teammate.

翻译：CSCW研究日益探索人工智能在协作任务中提升沟通效率和生产力中的作用。聊天机器人、智能回复和语言模型等AI工具旨在优化对话管理并改善团队绩效。早期AI助手（如Gmail智能回复）受限于预定义的知识库和决策树。然而，ChatGPT等大语言模型的问世彻底革新了AI助手，其采用先进的深度学习架构生成情境感知、连贯且个性化的回复。因此，基于ChatGPT的AI助手能够在各类任务和领域中提供更自然、高效的用户体验。本文正式提出了人工智能协作工具的概念，将其定义为人类协作工作中的AI技术，并探讨了ChatGPT的出现如何改变AI格局，使得提升团队绩效的ACT工具得到更多关注。同时，我们提出了一种基于LLM的智能回复系统，该系统利用ChatGPT API，在日常协作场景中考虑上下文、语气和沟通风格，生成个性化回复。我们的两阶段流程包括生成初步回复类型（如“同意”“不同意”），为信息生成提供大致方向，从而减少回复起草时间。我们开展了一项实验，参与者需完成模拟工作任务（包括操作谷歌日历和双任务N-back测试），同时与扮演提出日程调整请求队友的研究人员互动。结果显示，根据NASA TLX量表测量，AI队友提升了感知绩效并降低了脑力负荷，同时改善了N-back任务中的表现。我们还提供了参与者与AI队友协作体验的定性反馈。