揭示人机协作脆弱性根源，提出三类工作模式框架
The Collaboration Gap in Human-AI Work
- 区分三种人机协作模式：单次协助、不对称修复与有根基协作
- 发现协作失败源于表面合作超越实际交互基础能力
- 适合关注人机协同设计、AI工具落地的开发者与研究者
大型语言模型（LLMs）被广泛视为编程、设计、写作和分析中的合作者，但实际使用中常因误解、假设缺失和反复修复而体验不佳。本研究基于对16位设计师、开发人员及应用型AI从业者的一致性扎根理论访谈，结合人机协作文献，提出一个概念框架，解释为何协作仍脆弱。研究表明，稳定协作不仅依赖模型能力，更取决于交互的根基条件。识别出三种典型的人机工作结构：一次性协助、不对称修复的弱协作，以及有根基的协作。论文指出，当合作表象超越交互根基能力时，协作便破裂，并提供讨论交互结构、修复机制与根基性的框架。
原文摘要 · Abstract (English)
LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them often falls short of this promise. In many settings, users must diagnose misunderstandings, reconstruct missing assumptions, and repeatedly repair misaligned responses. This poster introduces a conceptual framework for understanding why such collaboration remains fragile. Drawing on a constructivist grounded theory analysis of 16 interviews with designers, developers, and applied AI practitioners working on LLM-enabled systems, and informed by literature on human-AI collaboration, we argue that stable collaboration depends not only on model capability but on the interaction's grounding conditions. We distinguish three recurrent structures of human-AI work: one-shot assistance, weak collaboration with asymmetric repair, and grounded collaboration. We propose that collaboration breaks down when the appearance of partnership outpaces the grounding capacity of the interaction and contribute a framework for discussing grounding, repair, and interaction structure in LLM-enabled work.
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