CoChat
Sign Up
Cited by 219Academic integrity and plagiarismArtificial Intelligence in EducationEthics and Social Impacts of AI

GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a Time of Generative AI

Open access
Bozkurt, Aras·2024·Open Praxis
DOI 10.55982/openpraxis.16.1.654
Open this paper in CoChat — free accountOpen PDF

Abstract

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the ‘organic versus synthetic’ paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent ‘publish or perish’ culture in academia, highlighting the need for systemic reevaluation due to generative AI’s emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI’s diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change.

AI snapshot

Written from the paper’s full text.

TL;DR

This position paper examines generative AI’s impact on academic writing, authorship, ownership, and integrity, arguing that AI should not be credited as a co-author and that human authors remain responsible. It proposes the aiTARAS framework for transparent, multilayered disclosure of generative AI use.

Key findings

  • Argues generative AI should not be credited as a co-author because authorship requires specific criteria; Table 1 cites ICMJE’s four criteria: substantial contributions, drafting/reviewing, final approval, and accountability. It notes some have credited AI as co-author, while others treat it as a ghost contributor.
  • Highlights legal and ownership grey areas: under the 1976 US Copyright Law requiring human authorship, AI-generated works are ineligible for registration and lack copyright protection, potentially entering the public domain immediately (citing Schroeder, 2023); only humans can bear legal responsibility.
  • Identifies academic integrity risks: generative AI can enhance efficiency but also enable misconduct and plagiarism; academic integrity is defined by six fundamental values—honesty, trust, fairness, respect, responsibility, and courage. It states there is no effective working solution to unattributed sourcing from unpublished online materials and that banning generative AI is not viable.
  • Proposes the aiTARAS framework for disclosure, with categories such as direct contribution, general assistance, specific sections, idea development, editing/reviewing, translation/localization, data analysis, data visualization, and code/algorithms, plus a final human approval statement; primary responsibility rests with the human author.

Methods

  • Design: critical/position paper offering a conceptual examination of generative AI in academic writing and publishing, including cocreation, authorship, ownership, ethics, integrity, and reporting.
  • Data: no datasets were generated or analysed; the Data availability statement says data sharing is not applicable. The analysis draws on cited literature and policy documents, including ICMJE, COPE, and CSE.
  • Main analysis: argumentative synthesis and proposal of the aiTARAS framework with sample disclosure statements; no empirical testing or systematic sampling is described.

Limitations

  • Stated: analyses of many papers focus predominantly on ChatGPT; relying on a single example for generalization can mislead, and a wider array of generative AI tools/examples is needed. Also stated: detection/similarity tools are not 100% accurate and often produce false positives, so new mechanisms are needed to identify AI use and violations.
  • Stated: discussions often assume English is the native language of all researchers; researchers using generative AI for translation, proofreading, or editing face dilemmas about originality versus AI-produced content.
  • Evident from the text: no empirical data, sample, or systematic review is presented; the aiTARAS framework is proposed but not tested or evaluated.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.55982/openpraxis.16.1.654

Cites

54 of 55 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

3 of 23 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    There is a growing consensus on the necessity to verify human contribution, originality, and authenticity in academic outputs.

    “Therefore, there is a growing consensus on the necessity to verify human contribution, originality, and authenticity in academic outputs (Lee, 2023;Perera Molligoda Arachchige & Stomeo, 2023).”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites Lee, 2023 source

      The Conclusion (page not provided) says the authenticity of AI-generated text must be verified, and the Introduction reports journal policies requiring disclosure of AI use and accountability for authorship. The copyright-law section discusses human authorship, but the paper does not establish a growing consensus that human contribution and originality must also be verified across academic outputs. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites Perera Molligoda Arachchige & Stomeo, 2023 source

      In the “Dear Editor” section (page unavailable), the authors call for acknowledging ChatGPT use, preserving human expertise, and upholding academic integrity and transparency. That supports concern about human contribution, but the paper does not establish a growing consensus or specifically call for verification of originality and authenticity in academic outputs. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    Generative AI use can technically be detected.

    “Though generative AI use can technically be detected (Herbold et al., 2023), the focus is on encouraging authors to be transparent about the extent of its use (Tang, 2023b).”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Herbold et al., 2023 source

      The Discussion (page unavailable) identifies measurable linguistic differences between ChatGPT and human-written essays, and Related work reports that some human raters could identify AI-generated answers. These passages support the possibility of distinguishing some AI-generated text, but the paper does not establish a technical method for detecting generative-AI use in authors’ writing generally. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
  • Premisecitation

    Currently, there appears to be no effective working solution to the issue of generative AI sourcing ideas from unpublished online materials without proper attribution.

    “Currently, there appears to be no effective working solution to this issue (Dien, 2023).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Dien, 2023 source
  • Premisecitation

    Generative AI learns and produces responses based on pre-existing work.

    “However, the fact that generative AI learns and produces responses based on pre-existing work introduces serious concerns about the ownership of such content, creating a grey area (Bakla, 2023;Eke, 2023).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Bakla, 2023 source
    • cites Eke, 2023 source
  • Premisecitation

    One of the primary concerns with generative AI is its ability and the degree to source ideas from unpublished online materials without proper attribution.

    “One of the primary concerns with generative AI is its ability and the degree to source ideas from unpublished online materials without proper attribution (Dien, 2023;Weissman, 2023).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Dien, 2023 source
    • cites Weissman, 2023

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. 8
  • May combine two claims. 4

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Generative AI, when expertly trained, is a potent tool capable of learning, unlearning, and relearning, making it a continually adapting entity.

    “Generative AI, when expertly trained, exemplifies a potent tool capable of learning, unlearning, and relearning which makes it a continually adapting to the evolving entity (Bozkurt, 2023b).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bozkurt, 2023b

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    Generative AI has the potential to enhance effectiveness and efficiency in academic endeavors, but also presents opportunities for misuse in the form of academic misconduct and plagiarism.

    “While generative AI has the potential to enhance effectiveness and efficiency in academic endeavors, it also presents opportunities for misuse in the form of academic misconduct and plagiarism (Bin-Nashwan et al., 2023;Currie, 2023).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Bin-Nashwan et al., 2023 source
    • cites Currie, 2023 source

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    Generative AI is a disruptive technology, importantly because of its ability to utilise language, the most sophisticated technology invented by humans.

    “Generative AI is considered a disruptive technology in many ways, but more importantly because of its ability to utilise the most sophisticated technology ever invented by humans, namely language (Bozkurt, 2023b;Harari, 2023).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Bozkurt, 2023b

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Harari, 2023

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationcitation

    Banning generative AI technologies is not considered a viable solution because of the near-impossibility of avoiding their use in education, owing to their ease of use and usefulness.

    “Given the near-impossibility of avoiding the use of generative AI tools in education, owing to their ease of use and usefulness (Davis, 1989), banning these technologies is not considered as a viable solution (Eke, 2023;Kirwan, 2023).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Davis, 1989 source
    • cites Eke, 2023 source
    • cites Kirwan, 2023 source

  • May combine two claims.

    Interpretationcitation

    The fact that generative AI learns and produces responses based on pre-existing work introduces serious concerns about the ownership of such content, creating a grey area.

    “However, the fact that generative AI learns and produces responses based on pre-existing work introduces serious concerns about the ownership of such content, creating a grey area (Bakla, 2023;Eke, 2023).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Bakla, 2023 source
    • cites Eke, 2023 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    Generative AI, being non-human, suggests a potential for ownership-free educational content.

    “This unique characteristic of generative AI, being non-human, suggests a potential for ownership-free educational content (Bozkurt, 2023c).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bozkurt, 2023c

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Academic writing is fundamentally a creative process aimed at producing original and authentic work.

    “However, considering that academic writing is fundamentally a creative process aimed at producing original and authentic work (Bozkurt, 2023d), this creates a critical juncture.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bozkurt, 2023d

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    With the ability of generative AI to produce sophisticated and seemingly authentic work, traditional forms of assessment, such as written essays or reports, may no longer be sufficient to gauge a student's true understanding or original thinking.

    “With the ability of generative AI to produce sophisticated and seemingly authentic work, traditional forms of assessment, such as written essays or reports, may no longer be sufficient to gauge a student's true understanding or original thinking.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Many academic outputs, such as essays, are used for assessment and evaluation.

  • Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing). May combine two claims.

    Premisecitation

    Only humans can bear legal responsibility for their creations, so they should be the sole entities recognized as authors.

    “Another perspective posits that, as only humans can bear legal responsibility for their creations, they should be the sole entities recognized as authors (Nature, 2023;Stokel-Walker, 2023).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Nature, 2023

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Stokel-Walker, 2023 source

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    In some cases, merely reporting the use of generative AI is inadequate.

    “In some cases, merely reporting the use of generative AI is inadequate.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Relying on a single example for generalization can be misleading and potentially misguide us.

    “Relying on a single example for generalization can be misleading and potentially misguide us.”
    Quote in the paperNot a substantial claim
11 of 11

  • The paper relies on this without support.

    Generative AI systems are designed to comprehend and replicate human language.

    “These generative AI systems, developed to analyze complex patterns and structures in human language, are primarily designed to comprehend and replicate it.”

  • The paper relies on this without support.

    The analyses of many papers examined predominantly focus on one generative AI tool, ChatGPT.

    “It is also crucial to acknowledge that the analyses of many papers examined predominantly focus on one generative AI tool, ChatGPT.”

  • The authors state this as an assumption.

    The frequent assumption in discussions on academic ethics that English is the native language of all researchers.

    “An additional point of consideration is the frequent assumption in discussions on academic ethics that English is the native language of all researchers.”

  • The paper relies on this without support.

    Researchers proficient in various native languages often utilize generative AI for translation, proofreading, or editing.

    “It is important to remember that researchers proficient in various native languages often utilize generative AI for translation, proofreading, or editing.”

  • The paper relies on this without support.

    Many academic outputs, such as essays, are used for assessment and evaluation.

    “Lastly, many academic outputs, such as essays, are used for assessment and evaluation.”
5 of 5

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?

  • Among university teachers, academic integrity and cheating are major concerns.

    “Among university teachers, academic integrity and cheating are major concerns (Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Overono & Ditta, 2025;Perkins et al., 2024;Xia et al., 2024;Yusuf et al., 2024).”

    From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Bozkurt, 2024

    The Academic Integrity section says generative AI raises concerns about academic integrity and creates opportunities for misconduct and plagiarism. It does not identify university teachers as the group holding those concerns, so that population-specific part of the claim is unsupported. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Academic integrity was a focus, especially for teachers.

    “As in other empirical contexts (e.g., Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Yusuf et al., 2024), academic integrity was a focus, especially for teachers.”

    From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Bozkurt, 2024

    The Academic Integrity section discusses risks to academic integrity from generative AI, and the Final Remarks connect integrity to student assessment. Our copy does not present teachers as a particular focus or describe an empirical study, so it supports the integrity focus but not those qualifications. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Research participants' concerns included and extended beyond academic integrity, the topic that dominates much academic research on genAI in higher education.

    “Research participants' concerns included and extended beyond academic integrity, the topic that dominates much academic research on genAI in higher education (Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Overono & Ditta, 2025;Perkins et al., 2024;Xia et al., 2024;Yusuf et al., 2024).”

    From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Bozkurt, 2024

    The Academic Integrity section treats integrity as a significant concern in academic uses of generative AI, and the Abstract discusses it alongside authorship, ownership, and other ethical issues. The paper does not establish that academic integrity dominates research on generative AI in higher education. The citing authors’ description of their own participants’ concerns is not a finding attributed to Bozkurt. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Authorship in the age of generative AI cannot be settled by detecting tool use alone, but depends on who can take accountability for the content.

    “This pattern is consistent with broader arguments that authorship in the age of generative AI cannot be settled by detecting tool use alone, but depends on who can take accountability for the content (Bozkurt, 2024).”

    From From production to verification: generative AI, doctoral formation, and the leadership of digital education (2026) · cites this paper as Bozkurt, 2024

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. CSE. (2022). Recommendations for promoting integrity in scientific journal publications. Council of Science Editors. www.councilscienceeditors.org/recommendations-for-promoting-integrityin-Scientific-journal-publications
  2. ICMJE. (2022). Recommendations. International Committee of Medical Journal Editors. www.icmje.org/icmje-recommendations.pdf
  3. ICMJE. (2024). Defining the Role of Authors and Contributors. International Committee of Medical Journal Editors. https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html

Cited by

Sorted by citations

Related

Authors
Bozkurt, A.
Affiliations
Anadolu University (TR)
Corresponding author
Bozkurt, Aras
ORCID
Bozkurt, Aras
Venue
Open Praxis
Publisher
International Council for Open and Distance Education
Published
2024
Volume
16
Issue
1
Pages
1-10
ISSN
  • 1369-9997 (ISSN-L)
  • 2304-070X
DOI
10.55982/openpraxis.16.1.654
OpenAlex
W4391488437
Semantic Scholar
39d26fd00ba7b6bd7a642a10a0287234157cb3e7
Cited by
215
References
43
Citations by year
2026: 106 · 2025: 89 · 2024: 20
FWCI
89.51
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Open Praxis (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Anadolu University (SBA-2023-1852)
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
generative AI, academic integrity, authorship, intellectual property, copyright, AI-generated content, publish or perish, academic writing, AI detection, academic ethics, human-AI collaboration

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-29.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-29.

Cited sources are checked by gpt-6-sol.

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