Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics

TL;DR

This paper introduces a multi-layer AI framework combining large language models and graph analytics to measure and analyze how interdisciplinary research teams converge on shared knowledge over time.

Contribution

It presents a novel, integrated approach using LLMs, graph visualization, and human validation to map research convergence and influence dynamics in interdisciplinary teams.

Findings

Increased viewpoint convergence observed in case study

Identified key influence patterns across domains

Demonstrated effectiveness of AI-driven analysis in research mapping

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.