IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback

要旨

Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on broad idea generation, yet offer little support for iterative specification, refinement, and evaluation needed to further develop initial ideas. To bridge this gap, we introduce IdeaSynth, a research idea development system that uses LLMs to provide literature-grounded feedback for articulating research problems, solutions, evaluations, and contributions. IdeaSynth represents these idea facets as nodes on a canvas, and allow researchers to iteratively refine them by creating and exploring variations and combinations. Our lab study (𝑁 = 20) showed that participants, while using IdeaSynth, explored more alternative ideas and expanded initial ideas with more details compared to a strong LLM-based baseline. Our deployment study (𝑁 = 7) demonstrated that participants effectively used IdeaSynth for real-world research projects at various ideation stages from developing initial ideas to revising framings of mature manuscripts, highlighting the possibilities to adopt IdeaSynth in researcher’s workflows.

著者
Kevin Pu
University of Toronto, Toronto, Ontario, Canada
K. J. Kevin Feng
University of Washington, Seattle, Washington, United States
Tovi Grossman
University of Toronto, Toronto, Ontario, Canada
Tom Hope
Allen Institute , Seattle, Washington, United States
Bhavana Dalvi Mishra
Allen Institute for AI, Seattle, Washington, United States
Matt Latzke
Allen Institute for AI, Seattle, Washington, United States
Jonathan Bragg
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
Joseph Chee Chang
Allen Institute for AI, Seattle, Washington, United States
Pao Siangliulue
Allen Institute for AI, Seattle, Washington, United States
DOI

10.1145/3706598.3714057

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714057

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Co-ideation

G314+G315
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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