Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics
Abstract
Understanding how interdisciplinary research teams converge on shared knowledge is a persistent challenge. This paper presents a novel, multi-layer, AI-driven analytical framework for mapping research convergence in interdisciplinary teams. The framework integrates large language models (LLMs), graph-based visualization and analytics, and human-in-the-loop evaluation to examine how research viewpoints are shared, influenced, and integrated over time. LLMs are used to extract structured viewpoints aligned with the \emph{Needs-Approach-Benefits-Competition (NABC)} framework and to infer potential viewpoint flows across presenters, forming a common semantic foundation for three complementary analyses: (1) similarity-based qualitative analysis to identify two key types of viewpoints, popular and unique, for building convergence, (2) quantitative cross-domain influence analysis using network centrality measures, and (3) temporal viewpoint flow analysis to capture convergence dynamics. To address uncertainty in LLM-based inference, the framework incorporates expert validation through structured surveys and cross-layer consistency checks. A case study on water insecurity in underserved communities as part of the Arizona Water Innovation Initiatives demonstrates increasing viewpoint convergence and domain-specific influence patterns, illustrating the value of the proposed AI-enabled approach for research convergence analysis.
Cite
@article{arxiv.2603.20204,
title = {Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics},
author = {Wenwen Li and Yuanyuan Tian and Sizhe Wang and Amber Wutich and Paul Westerhoff and Sarah Porter and Anais Roque and Jobayer Hossain and Patrick Thomson and Rhett Larson and Michael Hanemann},
journal= {arXiv preprint arXiv:2603.20204},
year = {2026}
}