Ideator Explorer: Enhancing AI-Assisted Ideation through Interactive Visualization

Abstract

Current AI-assisted ideation systems, often based on linear chat interfaces, struggle to help users effectively manage the complexity of creative exploration, hindering both divergent thinking across multiple paths and the convergent synthesis of ideas. This thesis introduces and evaluates Ideator Explorer, a human-AI ideation system built upon an interactive graph visualization interface designed to overcome these limitations. The core of the system is its spatial, tree-like representation of branching idea sequences. Formative user studies indicate that this visualization approach is preferred over chat interfaces for its organizational benefits and its effectiveness in helping users track parallel lines of thought during exploration. The spatial layout inherently supports both the exploration of diverse idea branches (divergence) and the identification of potential connections (convergence). This research focuses on the design and evaluation of this interactive graph interface, examining how its specific visualization and interaction techniques impact the user’s ability to navigate, organize, and develop ideas within complex ideation processes. The primary contribution is a novel, visually driven interface paradigm for human-AI collaboration that enhances the management and exploration of the creative solution space.