Exploring the Stratified Nature of Artificial Intelligence Research Funding in United States Educational Systems

A Bibliometric and Network Analysis

Bibliographic Data

ID22046351
AuthorsZ W Taylor (0000-0002-6085-2729, University of Southern Mississippi, corresponding author), Kayla Stan (0000-0002-2159-3090, University of Southern Mississippi)
Year2024
Volume14
Issue11
Pages1248
Publication date2024-11-14
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci14111248
OpenAlexW4404357323
LanguageEN
Citations received1
References cited11

Little is known about the funding organizations and mechanisms behind artificial intelligence (AI) research conducted in United States (U.S.) educational systems (K12 and higher education). This study therefore performs a bibliometric and network analysis of AI research conducted in U.S. educational systems to explore which types of organizations fund peer-reviewed scholarship, which organizations receive this funding, and how these organizations form funded research networks. The results suggest evidence of institutional stratification, with non-U.S. government organizations (such as in China and Europe) funding many AI studies within U.S. educational systems. Moreover, the data suggest stratified funding networks have marginalized Minority-Serving Institutions, consolidating the influence of AI research conducted in U.S. educational systems among few, elite, and predominately White institutions. The implications for research and policy advocacy are also addressed

Data science · Educational research · Knowledge management · Mathematics education · Network analysis · Political science · Regional science · Social network analysis · Social science · Sociology · Big Data and Business Intelligence · Computer Science · Engineering · Online Learning and Analytics · Psychology · Scientific Computing and Data Management

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