From State Changes to Creative Decisions: Documenting and Interpreting Traces Across Creative Domains

TL;DR

This paper introduces three innovative methods for capturing and interpreting creative activity traces across different domains, addressing limitations of existing approaches in preserving intent and structure.

Contribution

It presents a node-based interface, a vocabulary of visual cues, and a programming model to better document and understand creative decision-making processes.

Findings

Enhanced preservation of creative intent and structure

Improved interpretation of creative activity traces

Applicability across multiple creative domains

Abstract

Analyzing creative activity traces requires capturing activity at appropriate granularity and interpreting it in ways that reflect the structure of creative practice. However, existing approaches record state changes without preserving the intent or relationships that define higher-level creative moves. This decoupling manifests differently across domains: GenAI tools lose non-linear exploration structure, visualization authoring obscures representational intent, and programmatic environments flatten interaction boundaries. We present three complementary approaches: a node-based interface for stateful GenAI artifact management, a vocabulary of visual cues as higher-level creative moves in visualization authoring, and a programming model that embeds semantic histories directly into interaction state.