The DOI matches its Crossref record; 57 of the 58 references it cites were found in a registry or an index, and 1 are not checked yet; 3 of the 29 sources its claims cite were checked for support.
Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis
Open accessAbstract
Background As Artificial Intelligence (AI) technologies such as Generative AI (GenAI) have become more common in academic settings, it is necessary to examine how these tools interact with issues of authorship, academic integrity, and research methodologies. The current landscape lacks cohesive policies and guidelines for regulating AI’s role in academic research which has prompted discussions among publishers, authors, and institutions. Methods This study employs inductive thematic analysis to explore publisher policies regarding AI-assisted authorship and academic work. Our methods involved a two-fold analysis using both AI-assisted and traditional unassisted techniques to examine the available policies from leading academic publishers and other publishing or academic entities. The framework was designed to offer multiple perspectives, harnessing the strengths of AI for pattern recognition while leveraging human expertise for nuanced interpretation. The results of these two analyses are combined to form the final themes. Results Our findings indicate six overall themes, three of which were independently identified in both the AI-assisted and unassisted, manual analysis using common software tools. A broad consensus appears among publishers that human authorship remains paramount and that the use of GenAI tools is permissible but must be disclosed. However, GenAI tools are increasingly acknowledged for their supportive roles, including text generation and data analysis. The study also discusses the inherent limitations and biases of AI-assisted analysis, necessitating rigorous scrutiny by authors, reviewers, and editors. Conclusions There is a growing recognition of AI’s role as a valuable auxiliary tool in academic research, but one that comes with caveats pertaining to integrity, accountability, and interpretive limitations. This study used a novel analysis supported by GenAI tools to identify themes emerging in the policy landscape, underscoring the need for an informed, flexible approach to policy formulation that can adapt to the rapidly evolving landscape of AI technologies.
Verified 2026-11-13 · 1 reference not checked yet
Overview
CoChat Summary
TL;DR
This study analyzes 28 unique academic publisher AI policies using manual thematic analysis and ChatGPT-assisted analysis, finding broad consensus that human authorship remains paramount and GenAI use is allowed but must be disclosed. It also demonstrates a dual-method approach, with three themes identified independently by both analyses and AI-generated quotes requiring verification due to hallucinations.
Key findings
- From 107 entities reviewed, 36 had AI policies; after removing 8 replicas, 28 unique policies were analyzed. Six final themes emerged: Human-Exclusive Authorship, Author Accountability, Disclosure and Transparency, Research Integrity, Fluid Policy Landscape, and Constraints and Exclusions; three were independently identified in both AI-assisted and manual analyses.
- Publishers broadly agree AI tools cannot be listed as authors because they cannot assume ethical or legal responsibility, and authors remain accountable for all content, including AI-assisted or generated portions.
- GenAI use is generally permitted for support such as text generation and data analysis, but disclosure is a near-universal requirement, though the degree and format of disclosure vary; some policies impose constraints or outright prohibitions.
- No significant differences in approach were noted across publisher categories, and the AI-assisted analysis produced inaccurate, misattributed, condensed, or fabricated policy quotes that required manual verification.
Methods
- Inductive thematic analysis following Braun & Clarke’s six-phase approach; manual analysis by Researcher 2 using QDA Miner, and AI-assisted analysis by Researcher 1 using ChatGPT Plus/GPT-4 default and Advanced Data Analysis modes on July 27 and September 11, 2023, with results combined into final themes.
- A master list of 107 entities was compiled from publisher rankings/lists and web searches, excluding suspected predatory publishers; manual website searches found 36 entities with AI policies, 8 replicas, leaving 28 unique policies.
- Coding was at the semantic level; final themes were developed by comparing and grouping manual and AI-assisted themes, with manual editing and verification of quotes.
Limitations
- Stated: the analysis was confined to a limited set of publisher policies available at the time; it focused on English-language and flagship/major publishing houses, excluding non-academic publishing and professional/governmental guidelines; it used a single AI tool, though four analyses over two time periods; GenAI may have missed nuanced interpretations while human analysis was limited by time and cognitive load; and the study did not empirically test the effectiveness or reliability of AI tools.
- Stated: the data were straightforward and simple compared with interview data, so further studies are needed, and findings should be considered a snapshot of an evolving landscape.
- Evident from the text: the AI-assisted analysis hallucinated, misattributed, condensed, or fabricated policy quotes, and only 28 policies were analyzed, so coverage may be incomplete and rapidly outdated.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
3 Valid · 2 Not valid · 21 Not checked · 152 Own results and interpretations
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Despite the widespread use of digital and AI technologies, only a small percentage of higher education institutions (HEIs) have developed formal policies surrounding their use since the launch of ChatGPT ( Perkins & Roe, 2023; Xiao et al., 2023).”
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Introduction
What the sources say
- Not matchedcites Perkins & Roe, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Xiao et al., 2023 sourceExperimental — accuracy not yet measured
Xiao et al. (2023) do report that only a small share of institutions had issued such policies — the Introduction (p. 1) states 'less than a third (26.53%) of the top 500 universities have responded with a ChatGPT [policy]', and Section 2 gives 132 of the top 500 (26.35%), with the Abstract and Discussion repeating 'less than one-third'. However, that figure is bounded to the 500 highest-ranked universities in the 2022 QS World University Rankings, a selective, reputation- and English-speaking-country-skewed sample; the paper nowhere estimates a rate across higher education institutions generally, so the citing sentence's extension to 'higher education institutions (HEIs)' at large is not supported by this source. The source's policies are also specifically ChatGPT/generative-AI policies identified by searching each university's official website, rather than formal GenAI policy in general. (LLM, conf=0.78)
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.
- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“Even advanced AI detection software tools struggle, with a detection rate of only 54.8% in examples of work submitted to the Turnitin platform ( Perkins et al., 2023), and inaccuracies in a range of detection tools have been identified and discussed by Weber-Wulff et al. (2023).”
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Literature review > AI’s role and ethical implications in academic research
What the sources say
- Supportedcites Weber-Wulff et al., 2023 source
The Discussion (p. 25) states outright that 'Detection tools for AI-generated text do fail, they are neither accurate nor reliable (all scored below 80% of accuracy and only 5 over 70%)', with both false positives and false negatives across the 14 tools tested (tool list and selection described p. 9). The results sections quantify these inaccuracies per tool (Table 14 / p. 21 false-negative rates; pp. 20-21 false accusations), and the Conclusion (p. 27) summarises the 'serious limitations' of the tools, so the attribution of identified and discussed inaccuracies in a range of detection tools to this paper is supported. (LLM, conf=0.92)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, these guidelines can only go so far, given that accurately identifying text produced by tools such as ChatGPT remains a major challenge ( Hu, 2023; Weber-Wulff et al., 2023; Perkins, Roe et al., 2023; Anderson et al., 2023).”
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Literature review > AI’s role and ethical implications in academic research
What the sources say
- Not matchedcites Hu, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Weber-Wulff et al., 2023 source
The Related work section (p. 8) states outright that 'Detection of the AI-generated text remains challenging for existing detection tools', noting accuracy around 50% for AI-generated text versus above 80% for human-written text. The Discussion (p. 25) reinforces this with the paper's own findings: detection tools 'do fail, they are neither accurate nor reliable (all scored below 80% of accuracy and only 5 over 70%)', with a main bias towards classifying output as human-written. The hedged/broad framing of the citing sentence is therefore fully carried by this source. (LLM, conf=0.92)
- Not matchedcites Perkins, Roe et al., 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.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Ambiguity in this sense does not cover whether it is unacceptable or acceptable to use a GenAI tool, as it is implied and explicitly stated in several policies that their use is allowed in the preparation of manuscripts for publication.”
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Results > Results section one: Manually generated thematic analysis
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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although it may seem that publisher policy documents would be formulaic and straightforward, with little room for deeper analysis, three key themes were identified from the data.”
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Results > Results section one: Manually generated thematic analysis
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.
- Rests on an assumption not shown here.
49 more claims
Create a free account to see all 178 claims and to verify the 13 references still open.
- Probably not a substantial claim. 100
- It may state two separate claims as one. 57
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Results > Results section two: GenAI supported thematic analysis
“| Policy Evolution | _ | Impact of Evolving AI | Evolving Policies | Adaptability of Policies | Indicates evolving policies due to rapid AI advancements. |”
§ Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis > Results > Results section two: GenAI supported thematic analysis
“| Ethical Considerations | Mentions or refers to ethical guidelines and frameworks like COPE, focusing on the need for AI tools to meet ethical standards. | Ethical Considerations | Addresses ethical considerations such as plagiarism and research integrity. |”
Cited works that were retracted
Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
A note, not an issue: for example, a cited work that was retracted only after this paper was published, which its authors could not have known.
In the paper
“Y Kumar, 2023, Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda., J. Ambient. Intell. Humaniz. Comput., 14, 8459-8486, 10.1007/s12652-021-03612-z”
Cited work 10.1007/s12652-021-03612-z · retracted 2026-03-09 · notice 10.1007/s12652-026-05063-w · this paper published 2024-01-16
Citations
Cites
1 retracted
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040
RetractedUsing thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
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
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
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
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
Risko, E. F., & Gilbert, S. J. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.07.002 · Cited by 1,502 · cited 1×
“On the one hand, these tools may accelerate the research process by supporting in cognitive offloading (Perkins et al. , 2023; Dawson (2022) as cited in Sparrow, 2022); the process of reducing the cognitive demands of a task (Risko & Gilbert, 2016).”
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · doi:10.1101/2022.12.23.521610 · Cited by 1,099
Research works printed but not linked: 4
- 2023, February 20, What is SSRN’s policy on the use of generative AI and AI-assisted technologies in papers posted at SSRN? - SSRN Support Center.
- J Henley, 2023, June 20, German tabloid Bild cuts 200 jobs and says some roles will be replaced by AI., The Guardian.
- L Nowell, 2017, Thematic analysis: Striving to meet the trustworthiness criteria., Int. J. Qual. Methods., 16, 10.1177/16094069177338 · DOI
- 2023, Rankings of academic publishers., Wikipedia.
Cited by
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/q3azde36 · Cited by 291
Self-citationSimple techniques to bypass GenAI text detectors: implications for inclusive education
Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00487-w · Cited by 117
“…in enhancing academic practices for both students and teachers alike, and most publishing houses now permit the transparent use of such tools (Perkins & Roe, 2024a), there are circumstances in which the detection of GenAI texts remains necessary, such as in assessment practices which do not…”
BackgroundSelf-citationGenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education
Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024) · arXiv · doi:10.48550/arxiv.2403.19148 · Cited by 116
Self-citationBjelobaba, S., Waddington, L., Perkins, M., Foltýnek, T., Bhattacharyya, S., & Weber-Wulff, D. (2025) · International Journal for Educational Integrity · doi:10.1007/s40979-025-00191-w · Cited by 110
Self-citationLuo, X., Tham, Y. C., Giuffrè, M., Ranisch, R., Daher, M., Lam, K., Eriksen, A. V., Hsu, C.-W., Ozaki, A., Moraes, F. Y. D., et al. (2025) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2025-113825 · Cited by 97
Agency in the age of generative AI: a critical review of educational implications
Roe, J., & Perkins, M. (2026) · Academia AI and Applications · doi:10.20935/acadai8422 · Cited by 85
Self-citationCan ChatGPT evaluate research quality?
Thelwall, M. (2024) · Journal of Data and Information Science · doi:10.2478/jdis-2024-0013 · Cited by 68
Sinha, R., Solola, I., Nguyen, H., Swanson, H., & Lawrence, L. (2024) · Symposium on Learning, Design and Technology (LDT) · doi:10.1145/3663433.3663456 · Cited by 57
Evaluating literature reviews conducted by humans versus ChatGPT: comparative study
Mostafapour, M., Fortier, J. H., Pacheco, K., Murray, H., & Garber, G. (2024) · JMIR AI · doi:10.2196/56537 · Cited by 55
Ahn, S. (2024) · The Korean Journal of Physiology & Pharmacology · doi:10.4196/kjpp.2024.28.5.393 · Cited by 51
Embracing AI in English Composition: Insights and Innovations in Hybrid Pedagogical Practices
Hutson, J., Plate, D., & Berry, K. (2024) · International Journal of Changes in Education · doi:10.47852/bonviewijce42022290 · Cited by 46
Systematic analysis of generative AI tools integration in academic research and peer review
Salman, H., Ahmad, M. A., Ibrahim, R. B., & Mahmood, J. (2025) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/15832 · Cited by 39
The EAP‐AIAS: Adapting the AI Assessment Scale for English for Academic Purposes
Roe, J., Perkins, M., & Tregubova, Y. (2026) · TESOL Journal · doi:10.1002/tesj.70122 · Cited by 38
Self-citationGenerative AI as Cultural Artifact: Applying Anthropological Methods to AI Literacy
Roe, J. (2025) · Postdigital Science and Education · doi:10.1007/s42438-025-00547-y · Cited by 31
Self-citationAI Tools in Academia: Evaluating NotebookLM as a Tool for Conducting Literature Reviews
Shor, R., Greene, E. A., Sumberg, L. L., & Weingrad, A. B. (2025) · Psychiatry · doi:10.1080/00332747.2025.2541531 · Cited by 27
Kangwa, D., Msafiri, M. M., & Fute, A. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70109 · Cited by 22
Dellafiore, F., Saba, A., Collaro, C., & Artioli, G. (2025) · Qualitative Health Research · doi:10.1177/10497323251389800 · Cited by 22
Ahangama, N. (2026) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-026-00582-0 · Cited by 21
Anakok, I., Katz, A., Chew, K. J., & Matusovich, H. (2025) · International Journal of Qualitative Methods · doi:10.1177/16094069251338898 · Cited by 21
Applying the AI assessment scale in writing and translation for EFL
Roe, J., Perkins, M., & Furze, L. (2025) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0034 · Cited by 21
Self-citationNabavi, A., Safari, F., Shmoury, A. H., Tabet, S., Perdomo-Luna, C., & Celi, L. A. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106418 · Cited by 21
Rughiniş, C., Vulpe, S., Țurcanu, D., & Rughinis, R. V. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1608276 · Cited by 20
Hui, L., & Wu, X. (2025) · AI and Ethics · doi:10.1007/s43681-025-00827-0 · Cited by 19
Roe, J., Perkins, M., Somoray, K., Miller, D., & Furze, L. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00568-4 · Cited by 19
Self-citationAns, M., Maggio, L. A., Algodi, H. S., Costello, J. A., Driessen, E. W., Oswald, K., & Lingard, L. (2026) · Perspectives on Medical Education · doi:10.5334/pme.2431 · Cited by 19
Nyaaba, M., Min, S., Apam, M. A., Acheampong, K. O., Dwamena, E., & Zhaı, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5174910 · Cited by 19
Lingard, L., Driessen, E., & Oswald, K. (2025) · Perspectives on Medical Education · doi:10.5334/pme.2326 · Cited by 16
Educational Utility of Clinical Vignettes Generated in Japanese by ChatGPT-4: Mixed Methods Study
Takahashi, H., Shikino, K., Kondo, T., Komori, A., Yamada, Y., Saita, M., & Naito, T. (2024) · JMIR Medical Education · doi:10.2196/59133 · Cited by 16
Artificial intelligence in writing and research: ethical implications and best practices
AlSamhori, A. F., & Alnaimat, F. (2024) · Central Asian Journal of Medical Hypotheses and Ethics · doi:10.47316/cajmhe.2024.5.4.02 · Cited by 15
Bobier, C. A., Rodger, D., & Hurst, D. J. (2025) · BMC Medical Ethics · doi:10.1186/s12910-025-01239-9 · Cited by 15
How to Use Generative AI in Educational Research
Roe, J. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009675338 · Cited by 14
Self-citationAnalyzing AI use policy in LIS: association with journal metrics and publisher volume
Kim, E. (2024) · Scientometrics · doi:10.1007/s11192-024-05189-8 · Cited by 14
Murch, H. E., Worley, M., & Volk, F. A. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09634-y · Cited by 14
Pesante, B. D., Mauffrey, C. P., & Parry, J. A. (2024) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-24-00318 · Cited by 13
The crucial role and challenges of medical journal editors in the modern era
Vaishya, R., Vaish, A., Scarlat, M. M., & Jain, V. K. (2025) · Journal of Clinical Orthopaedics and Trauma · doi:10.1016/j.jcot.2025.102918 · Cited by 13
Technology Readiness for Generative AI Among Academic Researchers
Salman, H., Ahmad, M. A., Ibrahim, R. B., & Mahmood, J. (2024) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict64318.2024.10824523 · Cited by 12
Roe, J., O'Sullivan, D., Arumynathan, P., & Kappen, B. (2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.2.11 · Cited by 10
Self-citationRughinis, R. V., Vulpe, S., & Țurcanu, D. (2025) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs66924.2025.00091 · Cited by 10
Artificial intelligence in academic practices and policy discourses across ‘Big 5’ publishers
Lendvai, G. F., & Petra, A. (2026) · Research Evaluation · doi:10.1093/reseval/rvag004 · Cited by 10
Kempny, C., Frings, J., Rust, P., Meister, S., & Fehring, L. (2026) · BMC Medical Research Methodology · doi:10.1186/s12874-026-02913-1 · Cited by 9
Ranking ‘by Relevance’ in Academic Literature Searches: Prevalence, Definitions, and Implications
Jordan, K., & Tsai, S. P. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00530-z · Cited by 9
Girlovan, A., Horobeț, A. L., Boţoroga, C.-A., Belaşcu, L., & Lucian, P. (2024) · Studies in Business and Economics · doi:10.2478/sbe-2024-0047 · Cited by 8
Buitrago, J., Campos, E. M., & Romero, C. L. V. (2025) · Investigación Bibliotecológica Archivonomía Bibliotecología e Información · doi:10.22201/iibi.24488321xe.2025.104.59032 · Cited by 8
Blatch-Jones, A., Church, H., & Crane, K. (2025) · F1000Research · doi:10.12688/f1000research.160142.1 · Cited by 8
Fontenot, J. (2025) · Nurse author & editor · doi:10.1111/nae2.70002 · Cited by 8
Editorial policies for use and acknowledgment of artificial intelligence in dental journals
de Queiroz, A. B. L., Sartori, L. R. M., Lima, G. D. S., & Moraes, R. R. (2025) · Journal of Dentistry · doi:10.1016/j.jdent.2025.105923 · Cited by 8
Monforte, J. (2025) · Qualitative Health Research · doi:10.1177/10497323251365198 · Cited by 7
Charbonneau, D. H., & Zhang, M. (2026) · Accountability in Research · doi:10.1080/08989621.2026.2632083 · Cited by 7
Weaver, K. D. (2026) · Information Services & Use · doi:10.1177/18758789261435716 · Cited by 7
Generative AI in Academic Writing and the Library Services
Giray, L., Canlas, C. M., Labajo, K. D., & Matencio, F. A. (2025) · Internet Reference Services Quarterly · doi:10.1080/10875301.2025.2500027 · Cited by 7
Leveraging Artificial Intelligence (AI) in Competitive Intelligence (CI) Research
Hair, J. F., & Sabol, M. A. (2024) · Revista Inteligência Competitiva · doi:10.24883/eaglesustainable.v15i.469 · Cited by 6
Hsu, H.-Y., Hakouz, A., & Fotouhi, G. (2025) · AI and Ethics · doi:10.1007/s43681-025-00794-6 · Cited by 5
Rughiniş, C., Matei, Ș., & Corcaci, A. (2025) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs66924.2025.00094 · Cited by 5
AI hallucinations in academic writing: implications for research integrity
Giray, L., Islam, M. E., & Glo, J. A. (2026) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-026-05494-4 · Cited by 5
From Skepticism to Acceptance: On the Dynamics of Elderly Engagement with Mixed Reality
Sehrt, J., Mbamara, E., Rafati, M., & Schwind, V. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3670666 · Cited by 5
Lilly, J. M., Almanzar, D., Pace, S. R., & Bryant, D. D. (2024) · Health Promotion Practice · doi:10.1177/15248399241300570 · Cited by 5
Mallard, K., Stadnick, N. A., Bouchard, I., Du, Z., Brookman-Frazee, L. I., Aarons, G. A., Treichler, E. B. H., Gholami, M., & Rabin, B. A. (2025) · Frontiers in Health Services · doi:10.3389/frhs.2025.1535693 · Cited by 5
Anthony, A., & Sharma, S. (2025) · Journal of Educational Technology Systems · doi:10.1177/00472395251356520 · Cited by 4
Madsen, D. Ø., & Silva, E. S. (2026) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2026.103267 · Cited by 4
Identifying Strategies Teachers Apply to Establish Positive Relationships with Students
Ødegård, M., Pesonen, H. V., & Solberg, S. (2025) · Human Arenas · doi:10.1007/s42087-025-00514-w · Cited by 4
The Artificial Intelligence Dilemma in Academic Writing: Balancing Efficiency and Integrity
Mondal, H., Mondal, S., & Jana, S. (2025) · Indian Journal of Cardiovascular Disease in Women · doi:10.25259/ijcdw_86_2024 · Cited by 4
Challenges and Opportunities of Using Generative AI for Research: Opening the Discussion
Goretti, L., Küsters, A., Morgana, S., & Sharma, A. (2024) · The International Spectator · doi:10.1080/03932729.2024.2435176 · Cited by 4
AI-Generated Text in Orthopaedic Articles: A Cross-Sectional Analysis
Sweeney, P., Yuro, M. R., & Berberian, W. S. (2025) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.25.00971 · Cited by 3
Mazlan, C. A. N., Othman, M. A., Noor, A. R. M., Jamnongsarn, S., & Hidayatullah, R. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10630-1 · Cited by 3
When AI outputs become documents: Documentation activity in human– AI dialogue
Donner, S. (2026) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.70090 · Cited by 3
Alchokr, R., Starzew, E., Saake, G., Leich, T., & Krüger, J. (2024) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3677389.3702508 · Cited by 3
AI in Scholarly Publishing: A Study on LIS Journals' Guidelines and Policies
Gao, W., Liu, G., Huang, M. B., & Yao, H. (2025) · International Journal of Librarianship · doi:10.23974/ijol.2025.vol10.2.419 · Cited by 3
Ethical Challenges in the Era of Generative AI: Insights from a Practice-Informed Rapid Review
Hiep, N. T. (2025) · International Journal of Research and Innovation in Social Science · doi:10.47772/ijriss.2025.910000666 · Cited by 3
Gow, S., & Illingworth, S. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-06-2025-0151 · Cited by 3
Balahadia, F. F. (2025) · AIP conference proceedings · doi:10.1063/5.0262061 · Cited by 2
Newspaper framing of attempts to ban LGTBQ books in the U.S., U.K., and Ireland
Marshall, L. H., & Bien-Aimé, S. (2025) · Media Culture & Society · doi:10.1177/01634437251331901 · Cited by 2
The ethics of using artificial intelligence in writing medical research papers
Yu, S., & Hwang, H. (2025) · Kosin Medical Journal · doi:10.7180/kmj.25.141 · Cited by 2
Peterson, C., Anderson, C., Berdine, G. G., & NUGENT, K. M. (2026) · Journal of Electronic Resources in Medical Libraries · doi:10.1080/15424065.2026.2626575 · Cited by 2
Al-Jarf, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-22032-5_10 · Cited by 2
Algorithmic scholarship and academic evaluation: governance misalignment in the age of generative AI
Baciu, C., Mohan, G., & Wilson, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1862980 · Cited by 2
Singh, J. P., & Kaur, B. (2025) · Advances in library and information science (ALIS) book series · doi:10.4018/979-8-3693-5807-8.ch009 · Cited by 2
A taxonomy of standardized terms for generative AI use in the composition classroom
D’Addario, M. (2025) · Discover Education · doi:10.1007/s44217-025-00655-8 · Cited by 2
I Used AI in the Drafting of this Editorial. How Should I Reference AI’s Contribution?
Paul E. Terry, P. D. E. I. C. (2025) · American Journal of Health Promotion · doi:10.1177/08901171251324731 · Cited by 2
On Artificial Critical Reflection
Thorne, S. E. (2025) · Nursing Inquiry · doi:10.1111/nin.70046 · Cited by 2
Response to “The application of ChatGPT in the peer-reviewing process”
Madsen, D. Ø., & Sohail, S. S. (2025) · Oral Oncology Reports · doi:10.1016/j.oor.2024.100709 · Cited by 1
Song, S., Kim, H., Simpson, L., & Rivas, J. (2025) · Marketing Education Review · doi:10.1080/10528008.2025.2545805 · Cited by 1
Evaluating the Reliability of Generative AI in Distinguishing Machine from Human Text
Yuhefizar, Y., Watrianthos, R., & Marzuki, D. (2025) · Data & Metadata · doi:10.56294/dm20251181 · Cited by 1
Illingworth, S., & Gow, S. (2025) · Bridging human and machine: future education with intelligence · doi:10.1007/978-981-95-0950-8_4 · Cited by 1
Abubakar, P. S., & Adan, M. Y. (2026) · Frontiers in Education · doi:10.3389/feduc.2025.1711539 · Cited by 1
Ok, F., Sukur, I. H., Gül, M., & van Renterghem, K. (2026) · International Journal of Impotence Research · doi:10.1038/s41443-026-01295-8 · Cited by 1
Kelly, N., Neuner, J., & Sabharwal, S. (2026) · JBJS Open Access · doi:10.2106/jbjs.oa.26.00221 · Cited by 1
Navigating academic integrity in the Era of generative AI: a systematic literature review
Hendal, B. A., & Chowdhury, G. G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01431-6 · Cited by 1
Artificial Intelligence (AI) in Competitive Intelligence (CI) Research
Hair, J. F., & Sabol, M. A. (2025) · SDGs Studies Review. · doi:10.37497/sdgs.v6istudies.43 · Cited by 1
Editorial: Research ethics and integrity in the artificial intelligence era
Chigwada, J. P., & Ngulube, P. (2026) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2026.1931866 · Cited by 1
Политика журнала в отношении искусственного интеллекта: на какие вопросы нужно ответить?Preprint
Зельдина, М. М. (2024) · doi:10.24108/preprints-3113045
Madsen, D. Ø., & Sohail, S. S. (2025) · Oral Oncology Reports · doi:10.1016/j.oor.2024.100712
Solórzano, R. S., & Cerquedo, D. E. B. (2025) · Biblioteca Universitaria · doi:10.22201/dgbsdi.0187750xp.2024.2.1554
AI-Powered Research: Transforming You into a Futuristic Researcher
van As, J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1414-3.ch003
Mbuva, J. M. (2025) · Journal of Higher Education Theory and Practice · doi:10.33423/jhetp.v25i3.7794
Contact-Tracing App Acceptance Decisions are Multifaceted and Entangled
van Schaik, P., Renaud, K., Campbell, M., & Pigera, S. (2025) · Digital Government Research and Practice · doi:10.1145/3765625
Balancing AI and Pedagogy: The Future of Technology in Education
Manoj Kumar, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2185-1.ch012
Kayusi, F., Juma, L., Omwenga, M. K., Chavula, P., Mijwil, M. M., Hiran, K. K., Kayus, B. A., & Tiwari, M. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3063-1.ch005
Stress Less, Research More: Mitigating Technostress Among Doctoral Researchers
Giri, S., & B, H. O.-K. A. (2025) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3757980.3757986
Andonotopo, W., Pramana, C., Bachnas, M. A., Dewantiningrum, J., Pramono, M. B. A., & Stanojević, M. (2025) · Research Square · doi:10.21203/rs.3.rs-8252644/v1
Assessing chatgpt’s potential as an autonomous grant writing tool: a pilot study
Chhabra, B. R., Brinkman, S. S., Tichansky, S. J., Madan, A. K., Narvaez, J., DuCoin, C. G., & Tichansky, D. S. (2025) · Innovative Surgical Trends · doi:10.1007/s44414-025-00007-x
Metadata
- Authors
- Perkins, M., & Roe, J.
- Affiliations
- British University Vietnam (VN)
- James Cook University Singapore (SG)
- Corresponding author
- Perkins, Mike
- ORCID
- Perkins, Mike
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2024-01-16
- Volume
- 12
- Pages
- 1398
- ISSN
- 2046-1402 (ISSN-L)
- PMC
- PMC10844801
- OpenAlex
- W4387881071
- Semantic Scholar
- a0968924129aee94ca2070004e657df3d34a41dd
- Cited by
- 123
- References
- 53
- Citations by year
- 2026: 44 · 2025: 59 · 2024: 20
- FWCI
- 35.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- creativecommons.org/licenses/by/4.0/ (tdm)
- Locations
- F1000Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ResearchOnline - JCU (James Cook University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Durham Research Online (Durham University) (submittedVersion)
- Topics
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Library and Information Sciences › Publishing and Scholarly Communication
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, scholarly publishing, publisher policies, AI-assisted writing, authorship, academic integrity, AI disclosure, AI-assisted research, ChatGPT, algorithmic bias, research accountability
- MeSH terms
- Data Analysis, Artificial Intelligence, Authorship, Humans, Organizations, Software
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.
The claim verdicts this report counts are experimental: their accuracy is not yet measured.
A language model drew the claims from the paper on 2026-11-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (qwen3.8-omni-flash).
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
