Cited by 76Artificial Intelligence in EducationAcademic integrity and plagiarismDiscourse Analysis in Language Studies

A scholarly dialogue: writing scholarship, authorship, academic integrity and the challenges of AI

Open access
Wise, Beck, Emerson, Lisa, Van Luyn, Ariella, Dyson, Bronwen, Bjork, Collin, Thomas, Susan E.·2024·Higher Education Research & Development

Abstract

Concerns about the role of technology and the quality of student writing in higher education are not new. Historically, writing scholars have been at the forefront of initiatives that scrutinise and integrate new technologies in higher education. This article contends that writing scholars are again uniquely equipped to assist students, teachers in all disciplines, and institutions of higher education in navigating the challenges created by the public availability of generative artificial intelligence (AI). Adopting a dialogical approach, the article brings together six scholars from diverse disciplinary backgrounds within the broad umbrella of writing studies. Through the lens of writing scholarship, this chorus of critical voices illuminates the important questions posed by, and possible responses to, AI in higher education. Although AI complicates key issues in higher education such as academic integrity, assessment, and authorship, writing scholarship provides an essential framework for educators to respond to these challenges. Collectively, these scholars map the history of writing scholars’ responses to technological change in higher education and suggest how writing scholars can contribute to the debates and discussions concerning the impact of AI on higher education.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/07294360.2023.2280195

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Universities have reported increased instances of ghost authorship, algorithmic plagiarism, and erosion of researcher accountability.

    “Universities have reported increased instances of ghost authorship, algorithmic plagiarism, and erosion of researcher accountability [68, 91], which highlighted a gap between technological adoption and ethical regulation.”

    From Developing the HAKI model as a conceptual framework for AI-assisted academic research (2026) · cites this paper as [91]

    The discussion of roles and responsibilities (p. 10) identifies accountability and a potential ‘responsibility gap’ when AI is used in writing. The paper discusses authorship and plagiarism concerns (pp. 7–8), but does not report increased university instances of ghost authorship or algorithmic plagiarism, or a measured erosion of researcher accountability. (LLM, conf=0.83)

    Experimental — accuracy not yet measured
  • There is a gap between technological adoption and ethical regulation.

    “Universities have reported increased instances of ghost authorship, algorithmic plagiarism, and erosion of researcher accountability [68, 91], which highlighted a gap between technological adoption and ethical regulation.”

    From Developing the HAKI model as a conceptual framework for AI-assisted academic research (2026) · cites this paper as [91]

    In the discussion of local responses (p. 4), the paper describes a rushed generative-AI policy, overwhelmed academic-integrity staff, and strategies changing as staff learn about the technology. This supports a gap between technological change and institutional responses. It does not report increased university-wide instances of ghost authorship or algorithmic plagiarism, as the citing sentence also suggests. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Emerson, L., & Clerehan, R. (2009). Writing program administration outside the North American context. In D. Strickland & J. Gunner (Eds.), The writing program interrupted (pp. 166 -174). Boynton Cook.

Cited by

Sorted by citations

Authors
Wise, B., Emerson, L., Van Luyn, A., Dyson, B., Bjork, C., & Thomas, S. E.
Affiliations
  • The University of Queensland (AU)
  • Massey University (NZ)
  • School of Humanities, Arts and Social Sciences, University of New England, Armidale, Australia
  • The University of Sydney (AU)
Corresponding author
Wise, Beck
Venue
Higher Education Research & Development
Published
2024-03-25
Volume
43
Issue
3
Pages
578-590
ISSN
  • 0729-4360 (ISSN-L)
  • 1469-8366
OpenAlex
W4393138976
Cited by
76
References
42
Citations by year
2026: 34 · 2025: 33 · 2024: 9
FWCI
28.45
Type
journal article
Language
en
Open access
Locations
  • Higher Education Research & Development (publishedVersion, cc-by)
  • RUNE (Research UNE) (submittedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Arts and Humanities › Literature and Literary Theory › Discourse Analysis in Language Studies
Keywords
generative AI, academic integrity, authorship, student writing, assessment, higher education, writing studies, higher education technology

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