MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration

TL;DR

MindTrellis is an interactive visual system enabling users and AI to collaboratively construct and reorganize dynamic knowledge graphs, enhancing information synthesis and understanding.

Contribution

It introduces a novel collaborative system for knowledge graph creation combining user input and AI assistance, improving knowledge organization and reducing cognitive load.

Findings

Participants created more comprehensive slide decks with MindTrellis.

MindTrellis outperformed retrieval-only baselines in structural quality.

Users reported lower cognitive load using MindTrellis.

Abstract

Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This process is inherently iterative: users explore content, identify relationships between concepts, and continuously reorganize their mental models. However, current approaches offer limited support. LLM-based systems let users query information but not shape how knowledge is organized; manual tools like mind maps support structure creation but lack intelligent assistance. This leaves an open opportunity: supporting collaborative construction where users and AI jointly develop an evolving knowledge representation. We present MindTrellis, an interactive visual system where users and AI collaboratively build a dynamic knowledge graph. Users can query the graph to retrieve document-grounded information, and contribute by introducing new concepts, modifying relationships, and reorganizing the hierarchy to reflect their developing understanding. In a user study where 12 participants created slide decks, MindTrellis outperformed retrieval-only baselines in knowledge organization and cognitive load, as measured by expert ratings of content coverage and structural quality.