提出双维度框架，量化人机协作中认知参与质量
From Consumption to Collaboration: Measuring Interaction Patterns to Augment Human Cognition in Open-Ended Tasks
- 按探索/利用与建设性/破坏性划分交互模式
- 揭示主动协作可提升认知能力，被动消费易导致认知退化
- 适合研究人机协同或设计增强型AI系统的学者
生成式AI特别是大语言模型（LLMs）正深刻改变知识工作中的认知过程，引发关于其对人类推理与问题解决能力影响的深层思考。随着这些AI系统日益融入工作流，它们既提供了前所未有的思维增强机会，也存在因被动消费生成答案而导致认知能力退化的风险。这一矛盾在开放式任务中尤为突出，有效解决方案需深度情境化和领域知识整合。不同于有明确评估标准的结构化任务，此类开放任务中的人机交互质量难以衡量，因缺乏真实答案且解法具有迭代性。为此，本文提出一个双维度分析框架：认知活动模式（探索 vs. 利用）与认知参与模式（建设性 vs. 破坏性）。该框架可系统测量LLM是作为思维工具还是替代人类认知，推动理论理解与实践指导，助力开发既能保护又能增强人类认知能力的AI系统。
原文摘要 · Abstract (English)
The rise of Generative AI, and Large Language Models (LLMs) in particular, is fundamentally changing cognitive processes in knowledge work, raising critical questions about their impact on human reasoning and problem-solving capabilities. As these AI systems become increasingly integrated into workflows, they offer unprecedented opportunities for augmenting human thinking while simultaneously risking cognitive erosion through passive consumption of generated answers. This tension is particularly pronounced in open-ended tasks, where effective solutions require deep contextualization and integration of domain knowledge. Unlike structured tasks with established metrics, measuring the quality of human-LLM interaction in such open-ended tasks poses significant challenges due to the absence of ground truth and the iterative nature of solution development. To address this, we present a framework that analyzes interaction patterns along two dimensions: cognitive activity mode (exploration vs. exploitation) and cognitive engagement mode (constructive vs. detrimental). This framework provides systematic measurements to evaluate when LLMs are effective tools for thought rather than substitutes for human cognition, advancing theoretical understanding and practical guidance for developing AI systems that protect and augment human cognitive capabilities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。