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.

翻译：从大规模文档集合中综合知识是定性研究与知识工作中至关重要却日益复杂的环节。尽管人工智能提供了自动化潜力，但将其有效整合到以人为中心的意义建构工作流中仍具挑战性。我们提出了ScholarMate，一个通过将AI辅助与人工监督相统一来增强定性分析的交互式系统。该系统使研究者能够在非线性画布上动态排列并与文本片段交互，利用AI进行主题建议、多层级摘要及基于证据的主题命名，同时通过溯源至原始文档确保透明度。初步试点研究表明，用户重视这种混合主动式方法，认为AI建议与直接操作之间的平衡对于保持可解释性与信任至关重要。我们进一步通过分析24篇论文的案例研究展示了该系统的能力。通过平衡自动化与人工控制，ScholarMate提升了效率并支持可解释性，为知识工作中常见的高要求意义建构任务提供了一种高效的人机协作方法。