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
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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
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- 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
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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
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“…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
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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 ›
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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.
