提出两个评估人机协作质量的量表，可量化合作感知与团队感。
Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales
- 基于协同活动与演化合作理论设计量表
- 三组实验验证量表能区分不同合作质量
- 适合研究人机协作体验与系统评估
随着人机协作日益普遍，亟需可靠的主观评估工具。本文提出两个理论驱动的量表：基于协同活动理论的感知合作度量表（PCS），衡量单次交互中合作能力与实践；基于演化合作理论的团队感知量表（TPS），反映相互贡献与支持带来的团队感。两量表均适配人类间协作以实现跨主体比较。在三项研究（总计409人）中，涵盖合作扑克游戏、大模型交互及决策支持系统，量表的维度结构、信度与效度分析表明其能有效区分不同合作质量，且构念效度符合预期。该工具为广泛人机协作场景下的实证研究与系统评估提供支持。
原文摘要 · Abstract (English)
As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounded scales: the Perceived Cooperativity Scale (PCS), grounded in joint activity theory, and the Teaming Perception Scale (TPS), grounded in evolutionary cooperation theory. The PCS captures an agent's perceived cooperative capability and practice within a single interaction sequence; the TPS captures the emergent sense of teaming arising from mutual contribution and support. Both scales were adapted for human-human cooperation to enable cross-agent comparisons. Across three studies (N = 409) encompassing a cooperative card game, LLM interaction, and a decision-support system, analyses of dimensionality, reliability, and validity indicated that both scales successfully differentiated between cooperation partners of varying cooperative quality and showed construct validity in line with expectations. The scales provide a basis for empirical investigation and system evaluation across a wide range of human-AI cooperation contexts.
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