KnowledgeTrail: Understanding Patterns of Exploratory Sensemaking and Serendipitous Discovery in Generative Timelines

Abstract

Generative AI is reshaping how interactive systems support the discovery and understanding of knowledge, such as how events and ideas developed over time. Yet timelines, one of the most fundamental tools for enabling this kind of temporal sensemaking, remain largely static, limiting user agency and curiosity-driven exploration. Moreover, little is known about how people may explore and make sense of information if the structure of a timeline itself can evolve through interaction. To investigate this question, we designed KnowledgeTrail, a generative timeline that grows and adapts to users' questions, expanding and contracting with their evolving curiosity. Through an exploratory study, we examined how people engage with KnowledgeTrail and identified several patterns of curiosity-driven exploration, serendipitous discovery, and relational sensemaking that differ from conversational AI interfaces. Based on these analyses, our work contributes design implications for future generative systems that support dynamic, user-driven tracing of ideas, events, and their relationships.