ChatGraPhT: A Visual Conversation Interface for Multi-Path Reflection with Agentic LLM Support

TL;DR

ChatGraPhT introduces a visual, non-linear dialogue interface with agentic LLM support to enhance reflection in complex knowledge work, enabling users to revisit, branch, and merge ideas for deeper engagement.

Contribution

It presents a novel node-link visual interface for reflective dialogue with agentic LLMs and offers transferable design insights for balancing structure and AI support.

Findings

Visual conversation structure increased user reflection.

Branching and merging ideas deepened engagement.

AI guidance supported both moment-to-moment and higher-level reflection.

Abstract

Large Language Models (LLMs) are increasingly used in complex knowledge work, yet linear transcript interfaces limit support for reflection. Schon's Reflective Practice distinguishes between reflection-in-action (during a task) and reflection-on-action (after a task), both benefiting from non-linear, revisitable representations of dialogue. ChatGraPhT is an interactive tool that shows dialogue as a visual map, allowing users to branch and merge ideas, edit past messages, and receive guidance that prompts deeper reflection. It supports non-linear, multi-path dialogue, while two agentic LLM assistants provide moment-to-moment and higher-level guidance. Our inquiry suggests that keeping the conversation structure visible, allowing branching and merging, and suggesting patterns or ways to combine ideas deepened user reflective engagement. Contributions are: (1) the design of a node-link, agentic LLM interface for reflective dialogue, and (2) transferable design knowledge on balancing structure and AI support to sustain reflection in complex, open-ended tasks.