让文献综述从繁琐操作转向深度思考，提升研究效率与可信度。
From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews
- 设计集成多数据库的探索式搜索系统，支持透明迭代查询。
- 用户对比实验表明，研究者可从管理负担转向战略探索。
- 适合需要高效、可验证文献综述的研究人员使用。
系统性文献综述（SLRs）是科学进步的基础，但当前工具生态碎片化导致认知负荷过高，抑制了学术研究的迭代探索特性。为探究这一问题，我们对20位资深研究人员开展了探索性设计研究，识别出三大核心痛点：1）在多个数据库间反复调整查询带来的高认知负担；2）现代文献数量庞大且更新迅速；3）自动化与研究者自主权之间的张力。基于此，我们开发了ARC系统，通过多数据库整合、透明的迭代搜索和可验证的AI辅助筛选来应对上述挑战。一项包含8名研究人员的对照用户研究显示，集成环境有助于研究者从处理行政事务转向战略探索。通过外部表征支持战略性探索及透明的AI推理，系统增强了判断的可验证性，旨在从知识生成到长期维护全过程增强专家贡献。
原文摘要 · Abstract (English)
Systematic Literature Reviews (SLRs) are fundamental to scientific progress, yet the process is hindered by a fragmented tool ecosystem that imposes a high cognitive load. This friction suppresses the iterative, exploratory nature of scholarly work. To investigate these challenges, we conducted an exploratory design study with 20 experienced researchers. This study identified key friction points: 1) the high cognitive load of managing iterative query refinement across multiple databases, 2) the overwhelming scale and pace of publication of modern literature, and 3) the tension between automation and scholarly agency. Informed by these findings, we developed ARC, a design probe that operationalizes solutions for multi-database integration, transparent iterative search, and verifiable AI-assisted screening. A comparative user study with 8 researchers suggests that an integrated environment facilitates a transition in scholarly work, moving researchers from managing administrative overhead to engaging in strategic exploration. By utilizing external representations to scaffold strategic exploration and transparent AI reasoning, our system supports verifiable judgment, aiming to augment expert contributions from initial creation through long-term maintenance of knowledge synthesis.
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。