Artificial Intelligence in Research Funding: A Bibliometric Analysis of Grant Discovery, Grant Matching, Proposal Development, and Funding Ecosystems
DOI:
https://doi.org/10.37497/rev.artif.intell.educ.v7ii.1388Keywords:
Artificial Intelligence, Grant Discovery, Research Funding, Grant Matching, Proposal Development, Proposal Evaluation, Bibliometric Analysis, Funding EcosystemAbstract
Background: AI is increasingly reshaping how research funding is discovered, pursued, and awarded, yet the fragmented literature on this emerging domain lacks a systematic synthesis of its intellectual structure and an integrative model connecting its diverse applications. This study addresses that gap by examining research trends and developing a conceptual framework for AI in research funding.
Objectives: The objectives of the study is to identify and characterize research trends relating to AI-assisted research funding and to develop a conceptual framework synthesizing these applications into an integrated model.
Method: Bibliographic data were extracted from the Scopus database, resulting in a final dataset of 3,267 journal articles, which were subsequently analyzed using Biblioshiny (Bibliometrix package in R) and VOSviewer.
Findings: The results reveal a rapidly expanding field, with a 40.22% annual growth rate and publications rising from 22 in 2017 to 826 in 2025, concentrated among a core set of journals and prolific authors, and dominated by research-intensive economies led by the United States, China, and the United Kingdom. Thematic analysis shows a shift from foundational AI and data-centric themes toward funding- and method-centric applications, reflecting the field's transition from technical novelty toward operational implementation. Building on these findings, a five-stage conceptual framework, grant discovery, grant matching, proposal development, proposal evaluation, and funding ecosystem, is proposed to guide the responsible integration of AI across the research funding lifecycle.
Conclusion: AI is reshaping the research funding lifecycle by creating opportunities for more efficient, accessible, and data-driven funding processes. The proposed five-stage framework provides a foundation for understanding and guiding the responsible integration of AI in research funding while emphasizing human oversight, transparency, fairness, and accountability.
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