ResearchCube: Multi-Dimensional Trade-off Exploration for Research Ideation

TL;DR

ResearchCube introduces a multi-dimensional, interactive system for research ideation, enabling users to explore and refine ideas through bipolar evaluation spectra and spatial manipulation.

Contribution

It presents a novel 3D evaluation space with bipolar dimensions and spatial interactions, supporting multi-dimensional reasoning in research ideation.

Findings

Bipolar dimensions serve as cognitive scaffolds for evaluative thinking.

Spatial representation enhances sense of agency compared to chatbot tools.

Users desire fluid transitions and control over dimensionality.

Abstract

Research ideation requires navigating trade-offs across multiple evaluative dimensions, yet most AI-assisted ideation tools leave this multi-dimensional reasoning unsupported, or reducing evaluation to unipolar scales where "more is better". We present ResearchCube, a system that reframes evaluation dimensions as bipolar trade-off spectra (e.g., theory-driven vs. data-driven) and renders research ideas as manipulable points in a user-constructed 3D evaluation space. Given a research intent, the system proposes candidate bipolar dimension pairs; users select up to three to define the axes of a personalized evaluation cube. Four spatial interactions -- AI-scaffolded dimension generation, 3D navigation with face snapping, drag-based idea steering, and drag-based synthesis -- enable researchers to explore and refine ideas through direct manipulation rather than text prompts. A qualitative study with 11 researchers revealed that (1) bipolar dimensions served as cognitive scaffolds that externalized evaluative thinking and offloaded working memory, (2) the spatial representation provided a sense of agency absent in chatbot-based AI tools, (3) participants desired fluid transitions across dimensionality levels -- from single-dimension focus to more than three dimensions, and (4) a productive tension emerged between AI-suggested starting dimensions and users' evolving desire for control. We distill these findings into design implications for multi-dimensional research ideation tools, including progressive dimensional control, fluid dimensionality, and transparent synthesis with provenance.