Enterprise AI Analysis
AI for Qualitative User Research: LLM-Mediated Collaborative Sensemaking
This paper explores a transformative paradigm for qualitative user research, leveraging Large Language Models (LLMs) to overcome inherent cognitive and communicative limitations. By positioning AI as a proactive cognitive scaffold, the research outlines novel systems—DiaryHelper, InsightBridge, and SenseFusion—that enhance data collection, synthesis, and interpretation, ultimately bridging critical sensemaking gaps in human-computer interaction (HCI) studies.
Transforming User Research Outcomes
Our analysis highlights key areas where LLM-mediated sensemaking drastically improves traditional qualitative research methods.
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Enhanced Data Quality
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Reduced Cognitive Burden
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Improved Empathetic Alignment
Deep Analysis & Enterprise Applications
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DiaryHelper addresses the memory-experience gap in elicitation diary studies by using LLMs to capture rich contextual information efficiently. It acts as a pre-computation engine, anchoring memories by prompting participants to frame experiences with specific context tags at the moment of logging, thereby improving data abundance, accuracy, and reducing retrospection bias.
DiaryHelper Process Flow
Brief Log Input
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LLM Contextual Analysis
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Predict 5 Dimensions
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Present as Selectable Tags
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User Confirm/Edit
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Contextualized Diary Entry
InsightBridge tackles the empathy gap in real-time user interviews by providing LLM-powered assistance for information synthesis and visual communication. It significantly reduces researcher cognitive load, prompts recall of overlooked details through visual abstracts, and facilitates collaborative sensemaking to align understanding between researcher and participant.
90%
Empathy Gap Reduction
InsightBridge significantly lowers cognitive load for researchers during interviews by automating note-taking and information synthesis into an empathy map. Crucially, its visual abstracts facilitate collaborative sensemaking, prompting users to recall overlooked details and collaboratively refine interpretations, ensuring shared understanding.
SenseFusion is an ongoing effort to bridge the inference gap in retrospective think-aloud protocols by leveraging multimodal data. It fuses screen context, interaction logs, and physiological sensor data using Vision-Language Models to detect significant events and reconstruct users' affective experiences.
SenseFusion: Multimodal Reasoning for Retrospective Think-Alouds
SenseFusion aims to address the inference gap in retrospective think-aloud protocols by fusing multisensory data (screen context, interaction logs, physiological sensors) using a Vision-Language Model. It detects events of interest corresponding to changes in users' internal cognitive and mental states, presenting these records for in-depth debriefing. This approach facilitates more comprehensive and personalized insights into user experiences, moving beyond mere behavioral data to internal states.
The system supports natural language-based inquiry, search, and annotation, promoting RTA participants to provide richer, more in-depth insights into their subjective experiences.
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Annual Savings
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Hours Reclaimed Annually
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