The DOI matches its Crossref record; 54 of the 55 references it cites were found in a registry or an index; 3 of the 23 sources its claims cite were checked for support.
GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics and Integrity in a Time of Generative AI
Open accessAbstract
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.
Overview
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
Cites
Statistics
Verification
Claims the paper makes
6 No full text of the sources · 4 Sources not found · 3 Own interpretation · 2 Partly supported · 2 Not checked yet · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Partly supportedcites Lee, 2023 sourceExperimental — accuracy not yet measured
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)
- Partly supportedcites Perera Molligoda Arachchige & Stomeo, 2023 sourceExperimental — accuracy not yet measured
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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Partly supportedcites Herbold et al., 2023 sourceExperimental — accuracy not yet measured
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)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
In the paper
“Currently, there appears to be no effective working solution to this issue (Dien, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not checked — no full text of the sourcecites Dien, 2023 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not checked — no full text of the sourcecites Dien, 2023 source
- Not matchedcites Weissman, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Another notable problem is the unedited use of AI-generated outputs as if they were the original work of the researcher (Bhatia & Kulkarni, 2023).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not checked — no full text of the sourcecites Bhatia & Kulkarni, 2023 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 8
- It may state two separate claims as one. 4
In the paper
“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).”
§ Integrity in a Time of Generative AI > Abstract
What the sources say
- Not matchedcites Bozkurt, 2023b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Integrity in a Time of Generative AI > CAN GENERATIVE AI BE CREDITED AS A CO-AUTHOR?
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“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).”
§ Integrity in a Time of Generative AI > Abstract
What the sources say
- Not matchedcites Bozkurt, 2023b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Harari, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This unique characteristic of generative AI, being non-human, suggests a potential for ownership-free educational content (Bozkurt, 2023c).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
- Not matchedcites Bozkurt, 2023c
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“However, considering that academic writing is fundamentally a creative process aimed at producing original and authentic work (Bozkurt, 2023d), this creates a critical juncture.”
§ Integrity in a Time of Generative AI > "A lack of transparency results in distrust and a deep sense of insecurity." -Dalai Lama
What the sources say
- Not matchedcites Bozkurt, 2023d
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Integrity in a Time of Generative AI > FINAL REMARKS
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: Many academic outputs, such as essays, are used for assessment and evaluation.
None of the sources cited for this claim have been checked yet.
In the paper
“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).”
§ Integrity in a Time of Generative AI > OWNERSHIP AND AUTHORSHIP: GEN AI IS THE NEW KID ON THE BLOCK WHO OWNS WHAT?
What the sources say
- Not matchedcites Nature, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Stokel-Walker, 2023 source
plaindirect- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In some cases, merely reporting the use of generative AI is inadequate.”
§ Integrity in a Time of Generative AI > "Ancora Imparo" Michelangelo
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Relying on a single example for generalization can be misleading and potentially misguide us.”
§ Integrity in a Time of Generative AI > FINAL REMARKS
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
ChatGPT is fun, but not an author
Thorp, H. H. (2023) · Science · doi:10.1126/science.adg7879 · Cited by 1,437
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919
ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
Tools such as ChatGPT threaten transparent science; here are our ground rules for their use
(2023) · Nature · doi:10.1038/d41586-023-00191-1 · Cited by 605
Research works printed but not linked: 3
- CSE. (2022). Recommendations for promoting integrity in scientific journal publications. Council of Science Editors. www.councilscienceeditors.org/recommendations-for-promoting-integrityin-Scientific-journal-publications
- ICMJE. (2022). Recommendations. International Committee of Medical Journal Editors. www.icmje.org/icmje-recommendations.pdf
- 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
Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S. L., Dron, J., Stracke, C. M., Singh, L., Crompton, H., et al. (2024) · Open Praxis · doi:10.55982/openpraxis.16.4.777 · Cited by 320
Self-citationBalalle, H., & Pannilage, S. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101299 · Cited by 137
Bozkurt, A. (2024) · Open Praxis · doi:10.55982/openpraxis.16.3.739 · Cited by 105
Self-citationMaphoto, K. B., Sevnarayan, K., Mohale, N. E., Suliman, Z., Ntsopi, T. J., & Mokoena, D. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.649 · Cited by 129
Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI
Bozkurt, A. (2024) · Open Praxis · doi:10.55982/openpraxis.16.2.661 · Cited by 87
Self-citation
Metadata
- Authors
- Bozkurt, A.
- Affiliations
- Anadolu University (TR)
- Corresponding author
- Bozkurt, Aras
- ORCID
- Bozkurt, Aras
- Venue
- Open Praxis
- Published
- 2024
- Volume
- 16
- Issue
- 1
- Pages
- 1-10
- ISSN
- 1369-9997 (ISSN-L)
- 2304-070X
- 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
- diamond
- doi.org
- 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 › Education › Academic integrity and plagiarism
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › 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.
A language model drew the claims from the paper on 2026-10-29, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-29 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (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 ›
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