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Cited by 128Academic integrity and plagiarismArtificial Intelligence in EducationMisinformation and Its Impacts

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

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Chan, Cecilia Ka Yuk·2025·Education and Information Technologies
DOI 10.1007/s10639-024-13151-7
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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.

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  • The use of GenAI to complete assignments or exams without explicit permission is often treated as unauthorized assistance or plagiarism.

    “The use of GenAI to complete or exams without is often treated as unauthorized assistance.”

    From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Chan, 2024

    Section 2.2 (page unavailable) defines AI-giarism as using AI-generated content without appropriate acknowledgement and says submitting work that is not one’s own is academic misconduct, though it may not be plagiarism in the strict sense. The paper supports the concern about AI completing assignments, but the cited passages do not establish a rule about explicit permission or address exams. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The rapid growth of GenAI raises important concerns about academic integrity, overreliance, and the reliability of AI-generated content, particularly when students use such tools without sufficient judgment or verification.

    “However, its rapid growth also raises important concerns about academic integrity, overreliance, and the reliability of AI-generated content, particularly when students use such tools without sufficient judgment or verification [3,25,26].”

    From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [3]

  • Students may distinguish between clearly unacceptable uses, such as submitting AI-generated work, and more nuanced uses, such as using AI to expand or refine their own ideas.

    “Related discussions further suggest that students may distinguish between clearly unacceptable uses, such as submitting AI-generated work, and more nuanced uses, such as using AI to expand or refine their own ideas [3].”

    From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [3]

  • AI detectors are more likely to flag the work of non-native English speakers.

    “The observation that AI detectors are more likely to flag the work of non- native English speakers is a”

    From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Chan, 2024

    The Introduction discusses difficulty detecting AI-generated work, and Section 2.2 discusses challenges in identifying it, but neither addresses whether detectors flag non-native English speakers’ writing more often. Searches for non-native English speakers and false-positive detection found no passage in our copy supporting that comparison; page numbers were not available. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Chan, C. K. Y.
Affiliations
University of Hong Kong (HK)
Corresponding author
Chan, Cecilia Ka Yuk
ORCID
Chan, Cecilia Ka Yuk
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2024-11-11
Volume
30
Issue
6
Pages
8087-8108
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
DOI
10.1007/s10639-024-13151-7
OpenAlex
W4404247421
Cited by
128
References
17
Citations by year
2026: 78 · 2025: 50
FWCI
53.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Education and Information Technologies (publishedVersion, cc-by)
  • The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
Funders
  • University Research Committee, University of Hong Kong (CRF Seed Funding)
  • University Grants Committee (FITE)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords
academic misconduct, generative AI, plagiarism, student perceptions, academic integrity, artificial intelligence in higher education, scale development, AI content generation, undergraduate and graduate students

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