Cited by 25Academic integrity and plagiarismArtificial Intelligence in EducationEthics and Social Impacts of AI

Leveraging ChatGPT for Rethinking Plagiarism, Digital Literacy, and the Ethics of Co-Authorship in Higher Education: A Position Paper and Comparative Critical Reflection of Composing Processes

Open access
McGuire, Abby·2023·Irish Journal of Technology Enhanced Learning

Abstract

Within higher education, technology has consistently influenced the writing process; however, no technology has composed and shaped the message in the same way as Large Language Model-based artificial intelligence tools. Despite the rapid adoption of generative AI tools in higher education contexts, ethical best practices for using LLMs for technology-enhanced learning experiences within higher education are still evolving. To further examine AI-based co-authorship, the ChatGPT 3.5-generated position paper featured in this article’s second section argues for redefinitions of plagiarism and co-authorship in higher education and presents implications for teaching students the necessary digital literacy skills for navigating responsible, ethical AI use. The final section of this paper presents a human-generated comparative reflection of various composing processes and technologies used to create this article and the significance of these composing processes on the craft of writing. This paper aims advance ongoing discussions about the changing nature of authorship in technology-enhanced education.

Integrity signals

Registry

Crossref · 10.22554/ijtel.v7i2.131

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Through measures such as ethical evaluation of AI applications used in learning, regulating access to generative models, establishing protocols for the use of AI in research, or promoting decision-making transparency in automated systems, institutions create a concrete framework that facilitates legal and ethical literacy.

    “Through measures such as ethical evaluation of AI applications used in learning, regulating access to generative models, establishing protocols for the use of AI in research, or promoting decision-making transparency in automated systems, institutions create a concrete framework that facilitates legal and ethical literacy [51-53].”

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 4 · Cited by 25

Authors
McGuire, A.
Affiliations
Central Michigan University (US)
Corresponding author
McGuire, Abby
Venue
Irish Journal of Technology Enhanced Learning
Published
2023-12-07
Volume
7
Issue
2
Pages
21-31
ISSN
2009-972X (ISSN-L)
OpenAlex
W4389439360
Cited by
25
References
4
Citations by year
2026: 10 · 2025: 7 · 2024: 8
FWCI
6.24
Type
journal article
Language
en
Open access
Locations
  • Irish Journal of Technology Enhanced Learning (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
ChatGPT, generative AI, plagiarism, co-authorship, digital literacy, AI ethics, authorship, writing process, technology-enhanced learning, higher education, large language models, responsible AI use

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review