File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Students’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct

TitleStudents’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct
Authors
KeywordsAcademic dishonesty
AI-literacy
ChatGPT
Human machine co-partner
Integrity
Plagiarism
Issue Date11-Nov-2024
PublisherSpringer
Citation
Education and Information Technologies, 2024 How to Cite?
Abstract

This novel study explores AI-giarism, an emergent form of academic dishonesty involving AI and plagiarism, within the higher education context. The objective of this study is to investigate students’ perception of adopting generative AI for research and study purposes, and their understanding of traditional plagiarism and their perception of AI-plagiarism. A survey, undertaken by 393 undergraduate and postgraduate students from a variety of disciplines, investigated their perceptions of diverse AI-giarism scenarios. The findings portray a complex landscape of understanding with clear disapproval for direct AI content generation and ambivalent attitudes towards subtler uses of AI. The study introduces a novel instrument to explore conceptualisation of AI-giarism, offering a significant tool for educators and policy-makers. This scale facilitates understanding and discussions around AI-related academic misconduct, contributing to pedagogical design and assessment in an era of AI integration. Moreover, it challenges traditional definitions of academic misconduct, emphasising the need to adapt in response to evolving AI technology. The study provides pivotal insights for academics and policy-makers concerning the integration of AI technology in education.


Persistent Identifierhttp://hdl.handle.net/10722/351856
ISSN
2023 Impact Factor: 4.8
2023 SCImago Journal Rankings: 1.301

 

DC FieldValueLanguage
dc.contributor.authorChan, Cecilia Ka Yuk-
dc.date.accessioned2024-12-03T00:35:20Z-
dc.date.available2024-12-03T00:35:20Z-
dc.date.issued2024-11-11-
dc.identifier.citationEducation and Information Technologies, 2024-
dc.identifier.issn1360-2357-
dc.identifier.urihttp://hdl.handle.net/10722/351856-
dc.description.abstract<p>This novel study explores AI-giarism, an emergent form of academic dishonesty involving AI and plagiarism, within the higher education context. The objective of this study is to investigate students’ perception of adopting generative AI for research and study purposes, and their understanding of traditional plagiarism and their perception of AI-plagiarism. A survey, undertaken by 393 undergraduate and postgraduate students from a variety of disciplines, investigated their perceptions of diverse AI-giarism scenarios. The findings portray a complex landscape of understanding with clear disapproval for direct AI content generation and ambivalent attitudes towards subtler uses of AI. The study introduces a novel instrument to explore conceptualisation of AI-giarism, offering a significant tool for educators and policy-makers. This scale facilitates understanding and discussions around AI-related academic misconduct, contributing to pedagogical design and assessment in an era of AI integration. Moreover, it challenges traditional definitions of academic misconduct, emphasising the need to adapt in response to evolving AI technology. The study provides pivotal insights for academics and policy-makers concerning the integration of AI technology in education.</p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofEducation and Information Technologies-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAcademic dishonesty-
dc.subjectAI-literacy-
dc.subjectChatGPT-
dc.subjectHuman machine co-partner-
dc.subjectIntegrity-
dc.subjectPlagiarism-
dc.titleStudents’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct-
dc.typeArticle-
dc.identifier.doi10.1007/s10639-024-13151-7-
dc.identifier.scopuseid_2-s2.0-85208915197-
dc.identifier.eissn1573-7608-
dc.identifier.issnl1360-2357-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats