A Comprehensive Scientometric Analysis of ChatGPT Research Global Trends and Future Prospects.
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| Title: | A Comprehensive Scientometric Analysis of ChatGPT Research Global Trends and Future Prospects. |
|---|---|
| Authors: | Monib, Wali Khan1 (AUTHOR) walikhan.szu@gmail.com, Qazi, Atika1 (AUTHOR), Jain, Jasmine2 (AUTHOR), Ponniah, Logendra2 (AUTHOR) |
| Source: | TechTrends: Linking Research & Practice to Improve Learning. Nov2025, Vol. 69 Issue 6, p1260-1278. 19p. |
| Subject Terms: | *Research personnel, *Data analysis, ChatGPT, Scientometrics, Electronic publications |
| Abstract: | ChatGPT, recognized for its potential human-like performance, has attracted considerable attention from researchers, leading to an extensive body of scientific literature. However, it remains underexplored in terms of comprehensive quantitative analysis, posing challenges for researchers aiming to discern the intricate development of specific topics. This paper uses scientometric analysis to assess trends and patterns in ChatGPT research, focusing on authorship, publications, sources, organizations, countries, key interest areas and future trajectories. Utilizing data extracted from the Scopus database and employing tools such as Biblioshiny, VOSviewer and Microsoft Excel, we analyzed 1478 documents from 799 sources. The findings uncovered an exceptional annual growth rate of 1289.24% in ChatGPT research, indicating an unprecedented sudden surge within a brief time. In addition, the analysis revealed the most influential authors, publications, sources organizations and countries, showing a diverse global engagement. The analysis further unveiled primary research topics and gaps within ChatGPT research. The findings suggest opportunities for increased international collaborations, extending research in underexplored fields and regions, along with emerging themes. In addition, these findings offer valuable insights into the trajectory of ChatGPT research and suggest promising directions for future scholarly inquiry in this field. [ABSTRACT FROM AUTHOR] |
| Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 189935023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Comprehensive Scientometric Analysis of ChatGPT Research Global Trends and Future Prospects. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Monib%2C+Wali+Khan%22">Monib, Wali Khan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> walikhan.szu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Qazi%2C+Atika%22">Qazi, Atika</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jain%2C+Jasmine%22">Jain, Jasmine</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ponniah%2C+Logendra%22">Ponniah, Logendra</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22TechTrends%3A+Linking+Research+%26+Practice+to+Improve+Learning%22">TechTrends: Linking Research & Practice to Improve Learning</searchLink>. Nov2025, Vol. 69 Issue 6, p1260-1278. 19p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22ChatGPT%22">ChatGPT</searchLink><br /><searchLink fieldCode="DE" term="%22Scientometrics%22">Scientometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+publications%22">Electronic publications</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: ChatGPT, recognized for its potential human-like performance, has attracted considerable attention from researchers, leading to an extensive body of scientific literature. However, it remains underexplored in terms of comprehensive quantitative analysis, posing challenges for researchers aiming to discern the intricate development of specific topics. This paper uses scientometric analysis to assess trends and patterns in ChatGPT research, focusing on authorship, publications, sources, organizations, countries, key interest areas and future trajectories. Utilizing data extracted from the Scopus database and employing tools such as Biblioshiny, VOSviewer and Microsoft Excel, we analyzed 1478 documents from 799 sources. The findings uncovered an exceptional annual growth rate of 1289.24% in ChatGPT research, indicating an unprecedented sudden surge within a brief time. In addition, the analysis revealed the most influential authors, publications, sources organizations and countries, showing a diverse global engagement. The analysis further unveiled primary research topics and gaps within ChatGPT research. The findings suggest opportunities for increased international collaborations, extending research in underexplored fields and regions, along with emerging themes. In addition, these findings offer valuable insights into the trajectory of ChatGPT research and suggest promising directions for future scholarly inquiry in this field. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of TechTrends: Linking Research & Practice to Improve Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11528-025-01110-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1260 Subjects: – SubjectFull: Research personnel Type: general – SubjectFull: Data analysis Type: general – SubjectFull: ChatGPT Type: general – SubjectFull: Scientometrics Type: general – SubjectFull: Electronic publications Type: general Titles: – TitleFull: A Comprehensive Scientometric Analysis of ChatGPT Research Global Trends and Future Prospects. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Monib, Wali Khan – PersonEntity: Name: NameFull: Qazi, Atika – PersonEntity: Name: NameFull: Jain, Jasmine – PersonEntity: Name: NameFull: Ponniah, Logendra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 87563894 Numbering: – Type: volume Value: 69 – Type: issue Value: 6 Titles: – TitleFull: TechTrends: Linking Research & Practice to Improve Learning Type: main |
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