构建可配置平台，研究人与大模型代理协作的互动机制
Through the Lens of Human-Human Collaboration: A Configurable Research Platform for Exploring Human-Agent Collaboration
- 设计模块化平台，支持经典人机协作实验的快速复现
- 通过16人实验验证资源协商与信息共享的有效性
- 适合人机交互、社会计算领域研究者使用
智能系统传统上被设计为工具而非合作者，常缺乏协作所需的关键特征。大语言模型（LLM）代理的兴起，使自然对话与多种社会认知行为成为可能，为人类-LLM协作带来新机遇。然而，在人机协作中，人机交互（HCI）与计算机支持协同工作（CSCW）中建立的协作原则是否依然有效、如何演变或失效，仍不明确。为此，我们提出一个开放且可配置的研究平台，供HCI研究者系统探究相关问题。平台采用模块化设计，支持经典CSCW实验的无缝迁移，并可操控基于理论的交互变量。通过三个案例研究验证其有效性：（1）16名参与者参与的两个“Shape Factory”实验，研究资源协商；（2）16名参与者参与的“Hidden Profile”实验，研究信息聚合；（3）5名HCI研究者参与的参与式认知走查，优化实验设置与分析界面流程。
原文摘要 · Abstract (English)
Intelligent systems have traditionally been designed as tools rather than collaborators, often lacking critical characteristics that collaboration partnerships require. Recent advances in large language model (LLM) agents open new opportunities for human-LLM-agent collaboration by enabling natural communication and various social and cognitive behaviors. Yet it remains unclear whether principles of computer-mediated collaboration established in HCI and CSCW persist, change, or fail when humans collaborate with LLM agents. To support systematic investigations of these questions, we introduce an open and configurable research platform for HCI researchers. The platform's modular design allows seamless adaptation of classic CSCW experiments and manipulation of theory-grounded interaction controls. We demonstrate the platform's research efficacy and usability through three case studies: (1) two Shape Factory experiments for resource negotiation with 16 participants, (2) one Hidden Profile experiment for information pooling with 16 participants, and (3) a participatory cognitive walkthrough with five HCI researchers to refine workflows of researcher interface for experiment setup and analysis.
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