Data mining of scientific research on artificial intelligence in teaching and administration in higher education institutions: a bibliometrics analysis and recommendation for future research

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dc.contributor.authorUllrich, André
dc.contributor.authorVladova, Gergana
dc.contributor.authorEigelshoven, Felix
dc.contributor.authorRenz, André
dc.date.accessioned2024-01-26T16:17:31Z
dc.date.available2024-01-26T16:17:31Z
dc.date.issued2022
dc.description.abstractTeaching and learning as well as administrative processes are still experiencing intensive changes with the rise of artificial intelligence (AI) technologies and its diverse application opportunities in the context of higher education. Therewith, the scientific interest in the topic in general, but also specific focal points rose as well. However, there is no structured overview on AI in teaching and administration processes in higher education institutions that allows to identify major research topics and trends, and concretizing peculiarities and develops recommendations for further action. To overcome this gap, this study seeks to systematize the current scientific discourse on AI in teaching and administration in higher education institutions. This study identified an (1) imbalance in research on AI in educational and administrative contexts, (2) an imbalance in disciplines and lack of interdisciplinary research, (3) inequalities in cross-national research activities, as well as (4) neglected research topics and paths. In this way, a comparative analysis between AI usage in administration and teaching and learning processes, a systematization of the state of research, an identification of research gaps as well as further research path on AI in higher education institutions are contributed to research.en
dc.identifier.citationUllrich, A., Vladova, G., Eigelshoven, F., & Renz, A. (2022). Data mining of scientific research on artificial intelligence in teaching and administration in higher education institutions: a bibliometrics analysis and recommendation for future research. Discover Artificial Intelligence, 2(16), o.S. https://doi.org/10.1007/s44163-022-00031-7
dc.identifier.issn2731-0809
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/480
dc.language.isoeng
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleData mining of scientific research on artificial intelligence in teaching and administration in higher education institutions: a bibliometrics analysis and recommendation for future research
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.doi10.1007/s44163-022-00031-7
dcterms.bibliographicCitation.issue16
dcterms.bibliographicCitation.journaltitleDiscover Artificial Intelligence
dcterms.bibliographicCitation.pageendo.S.
dcterms.bibliographicCitation.pagestarto.S.
dcterms.bibliographicCitation.urlhttps://link.springer.com/10.1007/s44163-022-00031-7
dcterms.bibliographicCitation.volume2
local.researchgroupBildung und Weiterbildung in der digitalen Gesellschaft
local.researchtopicMensch – Arbeit – Wissen
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