Sparking Scientific Creativity via LLM-Driven Interdisciplinary Inspiration
2026-03-12 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors developed Idea-Catalyst, a tool that helps researchers think creatively across different fields instead of sticking to just one area. It starts with a general research goal and breaks it down into core questions, then looks for similar problems and solutions in other disciplines. By combining ideas from multiple fields, the tool helps people and AI generate more novel and insightful research directions. Their tests showed that this approach leads to more creative and relevant ideas without losing focus on the original problem.
interdisciplinary researchscientific discoverycreative reasoninglarge language modelsbrainstormingmetacognitionresearch goalsconceptual problemknowledge synthesisnovelty
Authors
Priyanka Kargupta, Shuhaib Mehri, Dilek Hakkani-Tur, Jiawei Han
Abstract
Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain's opportunities and unresolved challenges, and (c) strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea-Catalyst decomposes an abstract goal (e.g., improving human-AI collaboration) into core target-domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain-agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea-Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem.