让研究者与AI协作分析文献，边看边改更高效。
ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking
- 用可拖拽的画布动态管理文本片段，结合AI主题建议和摘要。
- 在24篇论文分析中提升效率，保持结果可追溯、可解释。
- 适合需要深度理解的科研人员或知识工作者使用。
从大量文档中整合知识是定性研究和知识工作的关键但日益复杂的任务。尽管人工智能具备自动化潜力，但如何将其有效融入以人为核心的认知过程仍具挑战。我们提出ScholarMate，一个交互式系统，通过融合AI辅助与人类监督来增强定性分析。用户可在非线性画布上动态排列和操作文本片段，利用AI进行主题建议、多层级摘要及基于证据的主题命名，同时通过溯源到原始文档保障透明性。初步试点研究显示，用户重视这种人机协同方式，认为AI建议与直接操作的平衡对保持可解释性和信任至关重要。一项案例研究分析了24篇论文，验证了系统在提升效率的同时支持可解释性。ScholarMate为复杂认知任务中的高效人机协作提供了可行方案。
原文摘要 · Abstract (English)
Synthesizing knowledge from large document collections is a critical yet increasingly complex aspect of qualitative research and knowledge work. While AI offers automation potential, effectively integrating it into human-centric sensemaking workflows remains challenging. We present ScholarMate, an interactive system designed to augment qualitative analysis by unifying AI assistance with human oversight. ScholarMate enables researchers to dynamically arrange and interact with text snippets on a non-linear canvas, leveraging AI for theme suggestions, multi-level summarization, and evidence-based theme naming, while ensuring transparency through traceability to source documents. Initial pilot studies indicated that users value this mixed-initiative approach, finding the balance between AI suggestions and direct manipulation crucial for maintaining interpretability and trust. We further demonstrate the system's capability through a case study analyzing 24 papers. By balancing automation with human control, ScholarMate enhances efficiency and supports interpretability, offering a valuable approach for productive human-AI collaboration in demanding sensemaking tasks common in knowledge work.
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