KnowledgeTrail: Generative Timeline for Exploration and Sensemaking of Historical Events and Knowledge Formation

2025-10-13

AI Summary

Existing timeline tools predominantly employ static visualizations, limiting users’ capacity for active exploration and knowledge construction. To address this, we propose “Generative Timelines,” a novel interactive paradigm that integrates generative AI, interactive visualization, and citation provenance tracking to enable dynamic timeline generation, adaptive expansion/contraction, and verifiable source attribution. Our core contribution lies in reimagining timelines as responsive exploration interfaces—capable of answering user queries and collaboratively supporting the co-construction of historical and conceptual evolution narratives. A user study demonstrates that our system significantly enhances curiosity-driven exploration, serendipitous discovery, and deep relational tracing of complex events. Furthermore, it empirically confirms that source credibility is a critical determinant of user trust formation.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The landscape of interactive systems is shifting toward dynamic, generative experiences that empower users to explore and construct knowledge in real time. Yet, timelines -- a fundamental tool for representing historical and conceptual development -- remain largely static, limiting user agency and curiosity. We introduce the concept of a generative timeline: an AI-powered timeline that adapts to users' evolving questions by expanding or contracting in response to input. We instantiate this concept through KnowledgeTrail, a system that enables users to co-construct timelines of historical events and knowledge formation processes. Two user studies showed that KnowledgeTrail fosters curiosity-driven exploration, serendipitous discovery, and the ability to trace complex relationships between ideas and events, while citation features supported verification yet revealed fragile trust shaped by perceptions of source credibility. We contribute a vision for generative timelines as a new class of exploratory interface, along with design insights for balancing serendipity and credibility.

Problem

• Creating dynamic AI-powered timelines for historical exploration
• Enabling user-driven expansion and contraction of event sequences
• Balancing serendipitous discovery with source verification mechanisms

Innovation

• Generative timeline adapts to user questions dynamically
• AI-powered system co-constructs historical event timelines
• Balances serendipitous discovery with source credibility features