提出人机协同新范式，让人类与智能体并行协作更高效。
"When to Hand Off, When to Work Together": Expanding Human-Agent Co-Creative Collaboration through Concurrent Interaction
- 设计可识别用户行为是反馈还是独立工作的新系统
- 31.8%的交互为并行模式，揭示协作切换规律
- 适合研究人机协同或交互设计的从业者
随着智能体进入共享工作空间且执行过程可见，人机协作正从顺序委托转向并行共创。本研究发现，过程可见性自然引发并行干预，但暴露了关键能力缺口：智能体缺乏区分用户反馈与独立并行工作的上下文感知能力。为此提出CLEO设计探针，能识别并发用户行为是反馈还是独立工作，并相应调整执行策略。研究2分析214轮交互，归纳出五类行为模式、十种编码，以及六种触发因素和四种促成因素，解释协作模式转换的时机与原因。并行交互占总交互的31.8%。本文提出决策模型、设计启示及标注数据集，强调并行交互是提升委托效率的关键。
原文摘要 · Abstract (English)
As agents move into shared workspaces and their execution becomes visible, human-agent collaboration faces a fundamental shift from sequential delegation to concurrent co-creation. This raises a new coordination problem: what interaction patterns emerge, and what agent capabilities are required to support them? Study 1 (N=10) revealed that process visibility naturally prompted concurrent intervention, but exposed a critical capability gap: agents lacked the collaborative context awareness needed to distinguish user feedback from independent parallel work. This motivated CLEO, a design probe that embodies this capability, interpreting concurrent user actions as feedback or independent work and adapting execution accordingly. Study 2 (N=10) analyzed 214 turn-level interactions, identifying a taxonomy of five action patterns and ten codes, along with six triggers and four enabling factors explaining when and why users shift between collaboration modes. Concurrent interaction appeared in 31.8% of turns. We present a decision model, design implications, and an annotated dataset, positioning concurrent interaction as what makes delegation work better.
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