Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

Hajkowicz, Stefan; Sanderson, Conrad; Karimi, Sarvnaz; Bratanova, Alexandra; Naughtin, Claire K.

doi:10.1016/j.techsoc.2023.102260

Cited by 37 publications

(2 citation statements)

References 63 publications

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“…The findings highlighted the leadership roles of China and the USA while showcasing a striking 78% surge in AI research in higher education over the past five years. Additionally, the comprehensive bibliometric analysis of Hajkowicz et al (2023), from 1960 to 2021, reported a notable rise in AI adoption rates across multiple disciplines. This finding indicated the growing trend of AI applications beyond specific domains, pointing at promising multidisciplinary applications, potentially shaping the later phase of AI integration within HE.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence in Higher Education: A Bibliometric Approach

Kavitha,

Joshith,

Rajeev

et al. 2024

EUROPEAN J ED RES

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<p>The world eagerly anticipates advancements in AI technologies, with substantial ongoing research on the potential AI applications in the domain of education. The study aims to analyse publications about the possibilities of artificial intelligence (AI) within higher education, emphasising their bibliometric properties. The data was collected from the Scopus database, uncovering 775 publications on the subject of study from 2000 to 2022, using various keywords. Upon analysis, it was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022. China and the United States emerged as the most influential countries regarding publications in this area. The findings revealed that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals. “S. Slade” and “P. Prinsloo” received the most citations, making them highly effective researchers. The co-authorship network primarily comprised the United States, Saudi Arabia, the United Kingdom, and China. The emerging themes included machine learning, convolutional neural networks, curriculum, and higher education systems are co-occurred with AI. The continuous expansion of potential AI technologies in higher education calls for increased global collaboration based on shared democratic principles, reaping mutual advantages.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence in Higher Education: A Bibliometric Approach

Kavitha,

Joshith,

Rajeev

et al. 2024

EUROPEAN J ED RES

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“…9 Additionally, the Lens database which is a free platform facilitating the discovery of both scholarly and patent literature, started in 1998 as Patent Lens offering access to patent literature. 10 Established through a partnership between the Queensland University of Technology and Cambia, 11 the database has evolved to encompass over 225 million scholarly works, and more than 127 million global patent records. This unique compilation of both scholarly works and patent data serves as a powerful resource for exploring the connections between research and innovation.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Bibliographic Data Sources Using PubMed, Scopus, Web of Science, and Lens

Tarazi

2024

HYMR

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Background: Bibliometric analysis is a quantitative tool to evaluate collaboration and the impact of publications within a given field. This study aims to elucidate the methodologies used in different databases for bibliometric analysis, offering a detailed comparison of their pros and cons. Methods: PubMed, Scopus, Web of Science (WOS), and Lens were used in this comparative study, with search examples of the “University of Jordan” in the period (2019-2023). 2739, 7777, 7518, and 4326 publications were retrieved from these databases, respectively. PubMed has the least number of documents due to its limited scope. Microsoft Excel 2019 and VOSviewer 1.6.20 were used to assess the data analysis. Results: Annual growth was observed across all databases, except for Lens database. The majority of top authors were found to be shared among different databases, with variations in the number of documents, and WOS had the least number of documents per author. The top countries were shared between Scopus and WOS, but there was a substantial difference in the number of citations between WOS and Scopus. In institutions analysis, most were ranked as institute with their department, except in WOS where the results were reported as generalizable to the institute level. Keyword analysis revealed a significant similarity between different databases. Journals distribution also had a great similarity across different databases and variable documents. Conclusion: Researchers should choose a bibliographic database based on their specific needs, considering factors like coverage and accessibility. This study provides a comparative analysis of various databases, including the Lens database.

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Building Trust in Scientific Results from the Perspective of a Scientist

Lewis

2024

Signals and Communication Technology

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Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

Cited by 37 publications

References 63 publications

Artificial Intelligence in Higher Education: A Bibliometric Approach

Artificial Intelligence in Higher Education: A Bibliometric Approach

Comparative Analysis of the Bibliographic Data Sources Using PubMed, Scopus, Web of Science, and Lens

Building Trust in Scientific Results from the Perspective of a Scientist

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