Artificial intelligence: Threat or asset to academic integrity? A bibliometric analysis

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dc.contributor.authorRodrigues, Margarida
dc.contributor.authorSilva, Rui
dc.contributor.authorBorges, Ana Pinto
dc.contributor.authorFranco, Mário
dc.contributor.authorOliveira, Cidália
dc.date.accessioned2024-02-02T18:30:45Z
dc.date.available2024-02-02T18:30:45Z
dc.date.issued2024-01-29
dc.description.abstractPurpose This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community. Design/methodology/approach This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity. Findings With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity. Practical implications This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies. Originality/value This study proposes a new, innovative framework summarising the balance between AI and academic integrity.
dc.identifier.citationRodrigues, M., Silva, R., Borges, A. P., Franco, M., & Oliveira, C. (2024). Artificial intelligence: Threat or asset to academic integrity? A bibliometric analysis. Kybernetes, (Published online: 29 january 2024), 1-32. https://doi.org/10.1108/K-09-2023-1666. Repositório Institucional UPT. https://hdl.handle.net/11328/5365
dc.identifier.doihttps://doi.org/10.1108/K-09-2023-1666
dc.identifier.issn0368-492X
dc.identifier.urihttps://hdl.handle.net/11328/5365
dc.language.isoeng
dc.publisherEmerald
dc.relationFCT – Portuguese Foundation for Science and Technology within the project UIDB/04007/2020, UIDB/05105/2020, UIDB/04630/2020, UI/BD/151029/2021, UIDB/04011/2020 (https://doi.org/10.54499/UIDB/04011/2020), UIDB/04630/2022 and by CEECINST/00127/2018/CP1501/CT0010.
dc.relation.hasversionhttps://www.emerald.com/insight/content/doi/10.1108/K-09-2023-1666/full/html
dc.rightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectAcademic integrity
dc.subjectPlagiarism
dc.subjectDishonesty
dc.subjectStudents
dc.titleArtificial intelligence: Threat or asset to academic integrity? A bibliometric analysis
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage32
oaire.citation.issuePublished online: 29 january 2024
oaire.citation.startPage1
oaire.citation.titleKybernetes

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