ScholarMate: A Mixed-Initiative Tool for Qualitative Knowledge Work and Information Sensemaking
This addresses the problem of complex knowledge synthesis for qualitative researchers and knowledge workers, offering an incremental improvement in human-AI collaboration tools.
The paper tackles the challenge of integrating AI into human-centric qualitative analysis workflows by introducing ScholarMate, an interactive system that unifies AI assistance with human oversight, and initial pilot studies indicate users value its mixed-initiative approach for maintaining interpretability and trust.
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.