Pith. sign in

REVIEW 6 major objections 5 minor 52 references

Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing

T0 review · 6 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read AI-use declarations show ChatGPT dominates academic writing, with readability and grammar the leading purposes.

desk verdict Underlying dataset is new and worth having, but the paper's own numbers don't match across abstract and body, so it can't be used as posted. read the letter →

arxiv 2502.00632 v2 pith:LIPO5UTN submitted 2025-02-02 cs.CY

Ziyang Xu This is my paper · ORCID
classification cs.CY
keywords academicwritingChatGPTAIusagedeclarationsjournalpolicieslargelanguagemodelscontentanalysisFisher-Freeman-HaltonexacttestElsevierjournals
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the disclosure statements Elsevier journals require from authors provide a usable window onto how AI is actually used in academic writing. Analyzing 168 declarations collected from 8,859 articles and conference papers published in 2024 across 27 Scopus categories, the author reports that ChatGPT (including GPT-3.5 and GPT-4) dominates, accounting for 77% of declared tool mentions, and that the two most common declared purposes are improving readability (51%) and grammar checking (22%). The paper further claims an association between team composition and declared purpose: international teams lean more heavily on grammar assistance than single-country teams, with a Fisher-Freeman-Halton exact test yielding $p = 0.0012$. If these patterns hold, journal policies and AI-literacy programs can be targeted at the tasks authors actually use AI for, rather than treating all AI use as a single undifferentiated category.

What carries the argument

The machinery is Elsevier's standardized 'Declaration of Generative AI and AI-assisted technologies in the writing process' template, which asks authors to name their AI tool and state its intended purpose, supported by a three-part analytical workflow. First, content analysis uses a coding framework to classify tools and to sort purposes into nine categories such as readability, grammar, proofreading, translation, and content generation. Second, the Fisher-Freeman-Halton exact test, an extension of Fisher's exact test for contingency tables with small expected cell counts, tests whether native-speaker status or team composition is associated with purpose. Third, text mining—word frequencies, bigrams, and a bipartite tool-purpose network—cross-checks the coding and visualizes which tools are paired with which purposes. The load-bearing step is the coding of free-text declarations into the nine purpose categories, because every distribution and test result depends on that classification.

What would settle it

Take a random sample of the same 2024 Elsevier articles, run a validated AI-text classifier and have blinded human experts check for AI-assisted passages, then compare detected AI assistance against declared assistance; if undeclared AI use is common and correlates with team type, tool, or purpose, the reported distributions and p-values would shift.

Watch

Extended reading notes

Core claim

The central discovery, as the author states it, is that AI tool use in academic writing is both concentrated and purpose-driven: one tool family, ChatGPT, dominates, and the declared purposes skew toward lower-level language tasks rather than higher-level content generation. The quantitative evidence is a set of frequency distributions from coded declarations—77% of tool mentions are ChatGPT, 51% of purposes are readability, and 22% are grammar—plus two Fisher-Freeman-Halton exact tests on small contingency tables. The team-composition test is presented as highly significant ($p = 0.0012$), with international teams showing a higher share of grammar use (30.4% vs 21.3%) and no declared proofreading or analysis uses; the native-speaker test is reported in the body as significant at $p = 0.0483$, with non-native speakers using grammar checking more and translation tools exclusively. The author interprets these patterns as evidence that AI tools help level language barriers in scholarly communication and that policies should distinguish language polishing from deeper content generation.

Load-bearing premise

The load-bearing premise is that the AI-use declarations researchers submit to journals are complete and accurate enough that their patterns reflect real AI use rather than only what authors chose to disclose.

Editorial extensions

If this is right

  • Journal policies can stop treating AI use as one undifferentiated practice: since declared uses are mostly readability and grammar, tiered policies that permit language polishing while scrutinizing content generation would match observed behavior.
  • Because ChatGPT accounts for the large majority of declared usage, publisher guidance and detection efforts that focus on ChatGPT (across versions) would cover most of the current disclosure space.
  • International teams' higher reliance on grammar assistance suggests AI tools are serving as a language-equity mechanism; if that is true, restricting AI editing could disproportionately burden non-native-English-speaking researchers.
  • The significant team-composition association implies that usage patterns are not uniform across collaboration structures, so AI-literacy training and support should be tailored to team context rather than applied generically.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An editorial inference: the paper's aggregate percentages likely combine two selection effects—which journals require or encourage declarations and which authors choose to comply—so the 77% ChatGPT figure should be read as the share among declared users, not among all AI-assisted papers.
  • The paper's own limitation section notes that declarations may be incomplete or inaccurate; if under-reporting is more common among certain teams or purposes, the reported p-values describe declaration behavior rather than actual AI use.
  • A testable extension: run the same coding and tests on declarations from non-Elsevier publishers or on later years to see whether ChatGPT dominance and the readability/grammar focus are publisher-specific or a stable feature of AI-assisted academic writing.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 5 minor

Summary. The manuscript analyzes Elsevier journal declarations of generative-AI use in academic writing, combining content analysis, Fisher-Freeman-Halton exact tests, and text mining to describe which AI tools authors declare, for what purposes, and whether tool-purpose patterns differ by native-language status and team composition. The full-text version reports 168 declarations from 8,859 articles, finds ChatGPT dominant (77% of usage), readability and grammar as the top declared purposes, and significant associations for both native-speaker status (p = 0.0483) and team composition (p = 0.0012). The abstract reports different numbers, including 135 declarations, 73.3% ChatGPT usage, a non-significant native-speaker result (p = 0.2359), and a team-composition p-value of 0.0008.

Significance. If the results were internally consistent, the study would provide a useful descriptive snapshot of declared AI use in a large publisher's journals and could inform editorial policy discussions. The research question is timely, and the use of disclosure statements as a data source is a plausible approach, even though it captures declarations rather than actual use. The paper's value is currently undermined because its central descriptive and inferential claims are not stable across the abstract and the full text, and the lack of a public data release prevents independent adjudication.

major comments (6)
  1. [Abstract vs. §4.1, §5.1, §5.2] The sample size is inconsistent: the abstract reports 135 AI declarations from 8,633 articles, while Section 4.1 reports 168 declarations and the body text reports 8,859 articles (8,633 Elsevier plus 226 conference papers). All percentages and statistical tests depend on this sample, so the central quantitative claims are not defined as posted.
  2. [Abstract vs. §5.2 and Table 6, Panel A] The native-speaker-status hypothesis (H1) yields contradictory results: the abstract reports no significant association (p = 0.2359), while Section 5.2 and Table 6 report a significant association (p = 0.0483). Since H1 is one of the paper's two main hypotheses, the conclusion about language background is internally inconsistent and cannot be accepted as stated.
  3. [Abstract vs. §5.2 and Table 6, Panel B] The team-composition hypothesis (H2) also differs between abstract (p = 0.0008) and full text (p = 0.0012). Although both values indicate significance at the 0.01 level, the discrepancy shows that the abstract and the full text are based on different computations or datasets, which undermines confidence in the reported exact test results.
  4. [Table 1] The paper states that data were collected from 27 Scopus major categories, but Table 1 lists only 26 rows. Additionally, 'Ultrasonics Sonochemistry' appears twice (under Chemical Engineering and under Physics and Astronomy), and the journal listed for Nursing ('Journal of Functional Foods') is not a nursing journal. These errors cast doubt on the accuracy of the journal-selection and data-collection description in Section 4.1.
  5. [Abstract vs. §5.1] The distribution of declared purposes is inconsistent: the abstract reports readability at 57.8% and grammar checking at 19.3%, whereas Section 5.1 reports 51% and 22% for the same categories. Since these percentages are key descriptive results, the manuscript does not provide a single stable account of its own main findings.
  6. [Data availability statement] The data availability section states only that datasets are available 'on reasonable request' and does not provide code or a data repository. Given the internal contradictions between the abstract and the full text, the absence of a public, verifiable dataset makes it impossible for readers to determine which analysis generated the reported results.
minor comments (5)
  1. [§5.3, Table 7] The interpretation of the word-frequency differences would benefit from a clear statement that the reported 'Difference' values are raw per-1,000-word differences without a statistical test, because the text implies a meaningful contrast without providing uncertainty measures.
  2. [§5.1, Figure 2 caption] The sentence 'Figure 2 shows that 117 authors use ChatGPT... accounting for 77% of total usage' would be clearer if it stated the denominator (all tool mentions, which includes multiple tools per author) and how the percentage was calculated.
  3. [§6, Discussion] The discussion cites 'the significant influence of team composition (p = 0.0012)' and 'language background (p = 0.0483)' using the full-text values; the abstract uses different values, and this inconsistency should be resolved before the paper can be considered publishable.
  4. [Throughout] There are numerous typographical and stylistic errors, including the misspelling 'World-cloud Statement' in Figure 4, the inconsistent phrase 'bibliometric analysis.' in a reference, and the reference to 'W AME' with irregular spacing.
  5. [§7, Conclusion] The sentence 'Future research... focusing on evolution and current landscape' is incomplete and needs to be rephrased to clearly state the planned future work.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all reported distributions and test statistics follow from manual coding of external declaration texts, not from the hypotheses or from fitted parameters.

full rationale

The derivation chain is: collect published Elsevier AI declarations (Section 4.1), code tool types and purposes (Section 4.2.1), tabulate author background and team composition (Tables 3-5), run Fisher-Freeman-Halton exact tests on the 2x6 contingency tables (Section 5.2, Table 6), and interpret the resulting p-values. Each step transforms external, independently observable texts; no quantity is defined in terms of the outcome it is said to predict. The coding categories are conventional content-analysis labels applied to the declarations, not fitted to make the conclusions true. The only self-citation (Tate et al., 2023, which includes co-author Xu) appears in Section 6.1 to support background claims about calls for APA guidelines; it is not used to justify the empirical distributions or the p-values, so it is non-load-bearing. The abstract/full-text inconsistencies in sample size and p-values (135 vs 168 declarations; p=0.0008 vs p=0.0012 for team, etc.) and the Section 7 caveat about incomplete declarations are validity/reliability concerns, not circularity: they do not make any result equal to an input by construction. Consequently there is no circular step to report.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The study is empirical and does not fit a predictive model, so no free parameters or invented entities. It relies on several domain assumptions about declaration validity, sampling representativeness, coding reliability, and author-background classification.

assumptions (5)
  • domain assumption Elsevier AI-use declarations are accurate, complete, and representative of actual AI tool usage by authors.
    All content and statistical results are built on these declarations, but the paper itself notes authors may not always disclose fully (Section 7).
  • domain assumption The sampled journals (one Elsevier open-access journal per Scopus category, chosen by CiteScore, 2024) represent academic writing across disciplines.
    Section 4.1 defines the sampling frame; the paper generalizes to academic writing without evidence that this frame is representative.
  • domain assumption Manual coding of tool types and purposes is reliable and the categories are mutually exclusive.
    Section 4.2.1 describes a coding framework but no inter-coder reliability statistics are reported.
  • domain assumption First author's native-speaker status and team internationality can be determined from author metadata.
    Section 4.2.1 says binary coding was applied to first author's language background and team composition, but the operational definition is not validated.
  • standard math Fisher-Freeman-Halton exact test is an appropriate model for these contingency tables.
    Section 4.2.2 justifies the test because more than 20% of cells have expected frequencies below 5; this is standard practice.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing." pith.science (2026). https://pith.science/paper/LIPO5UTN

@misc{pith2026250200632,
  author       = {Pith},
  title        = {Pith review of: Patterns and Purposes: A Cross-Journal Analysis of AI Tool Usage in Academic Writing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LIPO5UTN}},
  note         = {Machine review of arXiv:2502.00632}
}
read the original abstract

This study investigates the use of AI tools in academic writing through an analysis of AI usage declarations in journals. Using a mixed-methods approach combining content analysis, statistical analysis, and text mining, this study analyzed 135 AI declarations from 8633 articles across 27 categories. Results show that ChatGPT dominates academic writing assistance (73.3 percent usage). The primary purposes of AI integration are concentrated on lower-level cognitive tasks, specifically improving readability (57.8 percent) and grammar checking (19.3 percent). Statistical analysis indicates a highly significant association between team composition and AI-use purposes (p = 0.0008), highlighting international teams' reliance on grammar assistance, while no significant association was found regarding authors' native-speaker status (p = 0.2359). These findings provide insights for journal policy development and for understanding the evolving role of AI in academic writing.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

52 extracted references · 45 canonical work pages

  1. [1]

    write newline

    " write newline " cite write " FUNCTION editor.postfix editor num.names #1 > "( )" "( )" if FUNCTION editor.trans.postfix editor num.names #1 > "( )" "( )" if FUNCTION trans.postfix translator num.names #1 > "( )" "( )" if FUNCTION authors.editors.reflist.apa5 'field := 'dot := field num.names 'numnames := numnames 'format.num.names := format.num.names na...

  2. [2]

    \ Abdel-Momen, S.M

    abd2023artificial APACrefauthors Abd-Elsalam, K.A. \ Abdel-Momen, S.M. APACrefauthors \ 2023 . Artificial intelligence's Development and Challenges in Scientific Writing Artificial intelligence's development and challenges in scientific writing . Egyptian Journal of Agricultural Research 101 3 714--717, APACrefDOI doi:10.21608/ejar.2023.220363.1414 APACrefDOI

    work page arXiv 2023

  3. [3]

    , Belavy, D.L

    Anderson2023 APACrefauthors Anderson, N. , Belavy, D.L. , Perle, S.M. , Hendricks, S. , Hespanhol, L. , Verhagen, E. Memon, A.R. APACrefauthors \ 2023 . AI Did Not Write This Manuscript, or Did It? Can We Trick the AI Text Detector into Generated Texts? The Potential Future of ChatGPT and AI in Sports & Exercise Medicine Manuscript Generation AI did not w...

    work page 2023

  4. [4]

    , Costa-Jussà, M.R

    Basta2019 APACrefauthors Basta, C. , Costa-Jussà, M.R. Casas, N. APACrefauthors \ 2019 . Evaluating the Underlying Gender Bias in Contextualized Word Embeddings Evaluating the underlying gender bias in contextualized word embeddings . Proceedings of the Workshop on Gender Bias in Natural Language Processing Proceedings of the workshop on gender bias in na...

    work page 2019

  5. [5]

    \ Wald, A.E

    Bjorvatn2019ComplexityAA APACrefauthors Bjorvatn, T. \ Wald, A.E. APACrefauthors \ 2019 . Complexity as a Driver of Media Choice: A Comparative Study of Domestic and International Teams Complexity as a driver of media choice: A comparative study of domestic and international teams . International Journal of Business Communication , APACrefDOI doi:10.1177/...

    work page doi:10.1177/2329488419874367 2019

  6. [6]

    \ Paglia, L

    caprioglio2023fake APACrefauthors Caprioglio, A. \ Paglia, L. APACrefauthors \ 2023 . Fake Academic Writing: Ethics During Chatbot Era Fake academic writing: Ethics during chatbot era . European Journal of Paediatric Dentistry 24 2 88--89, APACrefDOI doi:10.23804/ejpd.2023.24.02.01 APACrefDOI

    work page doi:10.23804/ejpd.2023.24.02.01 2023

  7. [7]

    , Huang, S T

    chen2012flow APACrefauthors Chen, M H. , Huang, S T. , Hsieh, H T. , Kao, T H. Chang, J.S. APACrefauthors \ 2012 . FLOW: A first-language-oriented writing assistant system Flow: A first-language-oriented writing assistant system . Proceedings of the ACL 2012 system demonstrations Proceedings of the acl 2012 system demonstrations \ ( \ 157--162). APACrefUR...

    work page 2012

  8. [8]

    , Hickman , K.E

    choi2021chatgpt APACrefauthors Choi , J.H. , Hickman , K.E. , Monahan , A.B. Schwarcz , D. APACrefauthors \ 2021 . ChatGPT goes to law school ChatGPT goes to law school . Journal of Legal Education 71 387, APACrefURL https://collimateur.uqam.ca/wp-content/uploads/sites/11/2023/01/SSRN-id4335905.pdf APACrefURL

    work page 2021

Show all 52 references
  1. [9]

    Authorship and AI Tools: COPE Position Statement

    COPE2023 APACrefauthors Committee on Publication Ethics APACrefauthors \ 2023 . Authorship and AI Tools: COPE Position Statement. Authorship and AI tools: COPE position statement. Updated 13 Feb 2023. Available at: https://publicationethics.org/cope-position-statements/ai-author

    2023

  2. [10]

    APACrefauthors \ 2023

    conroy2023scientists APACrefauthors Conroy, G. APACrefauthors \ 2023 . Scientists Used ChatGPT to Generate an Entire Paper from Scratch—But Is It Any Good? Scientists used ChatGPT to generate an entire paper from scratch—but is it any good? Nature 619 443--444, APACrefURL http...

    2023

  3. [11]

    , Cowling, M

    crawford2023artificial APACrefauthors Crawford, J. , Cowling, M. , Ashton-Hay, S. , Kelder, J A. Middleton, R. APACrefauthors \ 2023 . Artificial intelligence and authorship editor policy: ChatGPT, Bard Bing AI , and beyond Artificial intelligence and authorship editor policy:...

    2023 doi

  4. [12]

    , Chamari, K

    dergaa2023from APACrefauthors Dergaa, I. , Chamari, K. , Zmijewski, P. Ben Saad, H. APACrefauthors \ 2023 . From Human Writing to Artificial Intelligence Generated Text: Examining the Prospects and Potential Threats of ChatGPT in Academic Writing From human writing to Artifici...

    2023

  5. [13]

    \ Marshall, D

    dubose2023ai APACrefauthors DuBose, J. \ Marshall, D. APACrefauthors \ 2023 . AI in Academic Writing: Tool or Invader AI in academic writing: Tool or invader . Public Services Quarterly 19 125--130, APACrefDOI doi:10.1080/15228959.2023.2185338 APACrefDOI

    2023

  6. [14]

    So What if ChatGPT Wrote It?

    DWIVEDI2023102642 APACrefauthors Dwivedi, Y.K. , Kshetri, N. , Hughes, L. , Slade, E.L. , Jeyaraj, A. , Kar, A.K. Wright, R. APACrefauthors \ 2023 . Opinion Paper: “ So What if ChatGPT Wrote It?” Multidisciplinary Perspectives on Opportunities, Challenges and Implications of G...

    2023

  7. [15]

    Authors” and Implications for the Integrity of Scientific Publication and Medical Knowledge Nonhuman “authors

    Flanagin2023 APACrefauthors Flanagin, A. , Bibbins-Domingo, K. , Berkwits, M. Christiansen, S.L. APACrefauthors \ 2023 . Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and Medical Knowledge Nonhuman “authors” and implications for the integrity ...

    2023

  8. [16]

    APACrefauthors \ 2019

    Flowerdew2019TheLD APACrefauthors Flowerdew, J. APACrefauthors \ 2019 . The linguistic disadvantage of scholars who write in English as an additional language: Myth or reality The linguistic disadvantage of scholars who write in English as an additional language: Myth or reali...

    2019

  9. [17]

    , Djouvas, C

    Founta2018 APACrefauthors Founta, A M. , Djouvas, C. , Chatzakou, D. , Leontiadis, I. , Blackburn, J. , Stringhini, G. Kourtellis, N. APACrefauthors \ 2018 . Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior. Large scale crowdsourcing and characterizat...

    2018 arXiv

  10. [18]

    APACrefauthors \ 2023

    fyfe2023how APACrefauthors Fyfe, P. APACrefauthors \ 2023 . How to Cheat on Your Final Paper: Assigning AI for Student Writing How to cheat on your final paper: Assigning AI for student writing . AI & Society 38 4 1395--1405, APACrefDOI doi:10.1007/s00146-022-01397-z APACrefDOI

    2023 doi

  11. [19]

    , Eppler, M.B

    ganjavi2024publishers APACrefauthors Ganjavi, C. , Eppler, M.B. , Pekcan, A. , Biedermann, B. , Abreu, A. , Collins, G.S. Cacciamani, G.E. APACrefauthors \ 2024 . Publishers’ and journals’ instructions to authors on use of generative artificial intelligence in academic and sci...

    2024

  12. [20]

    \ Costa, M.U.P.d

    giglio2023use APACrefauthors Giglio, A.D. \ Costa, M.U.P.d. APACrefauthors \ 2023 . The use of Artificial Intelligence to improve the scientific writing of non-native English speakers The use of artificial intelligence to improve the scientific writing of non-native english sp...

    2023

  13. [21]

    , Guha, A

    GREWAL2021229 APACrefauthors Grewal, D. , Guha, A. , Satornino, C.B. Schweiger, E.B. APACrefauthors \ 2021 . Artificial Intelligence: The light and the darkness Artificial intelligence: The light and the darkness . Journal of Business Research 136 229-236, APACrefDOI doi:10.10...

    2021 doi

  14. [22]

    APACrefauthors \ 2023

    hsu2023can APACrefauthors Hsu, H P. APACrefauthors \ 2023 . Can Generative Artificial Intelligence Write an Academic Journal Article? Opportunities , Challenges, and Implications Can generative artificial intelligence write an academic journal article? Opportunities , challeng...

    2023 doi

  15. [23]

    APACrefauthors \ 2024

    Hu06122024 APACrefauthors Hu, G. APACrefauthors \ 2024 . Challenges for enforcing editorial policies on AI -generated papers Challenges for enforcing editorial policies on AI -generated papers . Accountability in Research 31 7 978--980, APACrefDOI doi:10.1080/08989621.2023.218...

    2024

  16. [24]

    , Zhang , X

    huang2024evaluating APACrefauthors Huang , L Y. , Zhang , X. , Wang , Q. , Chen , Z S. Liu , Y. APACrefauthors \ 2024 . Evaluating Media Knowledge Capabilities of Intelligent Search Dialogue Systems: A Case Study of ChatGPT and New Bing Evaluating media knowledge capabilities ...

    2024 doi

  17. [25]

    , Prabhakaran, V

    Hutchinson2020 APACrefauthors Hutchinson, B. , Prabhakaran, V. , Denton, E. , Webster, K. , Zhong, Y. Denuyl, S. APACrefauthors \ 2020 . Social Biases in NLP Models as Barriers for Persons with Disabilities Social biases in NLP models as barriers for persons with disabilities ...

    2020

  18. [26]

    APACrefauthors \ 2021

    Hutson2021 APACrefauthors Hutson, M. APACrefauthors \ 2021 . Robo-Writers: The Rise and Risks of Language-Generating AI Robo-writers: The rise and risks of language-generating AI . Nature 591 7848 22--25, APACrefDOI doi:10.1038/d41586-021-00530-0 APACrefDOI

    2021 doi

  19. [27]

    fire of Prometheus

    hwang2023chatgpt APACrefauthors Hwang , S.I. , Lim , J.S. , Lee , R.W. , Matsui , Y. , Iguchi , T. , Hiraki , T. Ahn , H. APACrefauthors \ 2023 . Is ChatGPT a "fire of Prometheus " for non-native English -speaking researchers in academic writing? Is ChatGPT a "fire of Promethe...

    2023

  20. [28]

    Defining the Role of Authors and Contributors

    ICMJE2023 APACrefauthors ICMJE APACrefauthors \ 2023 . Defining the Role of Authors and Contributors. Defining the role of authors and contributors. APACrefURL https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors...

    2023

  21. [29]

    , Lee, N

    Ji2023 APACrefauthors Ji, Z. , Lee, N. , Frieske, R. , Yu, T. , Su, D. , Xu, Y. Fung, P. APACrefauthors \ 2023 . Survey of Hallucination in Natural Language Generation Survey of hallucination in natural language generation . ACM Computing Survey 55 12 1--38, APACrefDOI doi:10....

    2023 doi

  22. [30]

    , Seßler, K

    kasneci2023chat APACrefauthors Kasneci, E. , Seßler, K. , Küchemann, S. , Bannert, M. , Dementieva, D. , Fischer, F. Kasneci, G. APACrefauthors \ 2023 . ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education ChatGPT for good? on opportunities ...

    2023

  23. [31]

    , Gamon, M

    leacock2009user APACrefauthors Leacock, C. , Gamon, M. Brockett, C. APACrefauthors \ 2009 . User input and interactions on Microsoft Research ESL Assistant User input and interactions on Microsoft research ESL assistant . Proceedings of the Fourth Workshop on Innovative Use of...

    2009

  24. [32]

    \ Biocca, F

    lim2012esl APACrefauthors Lim, J. \ Biocca, F. APACrefauthors \ 2012 . ESL learning through writing pattern: Development of a web-based planning and writing supporting system for ESL learner ESL learning through writing pattern: Development of a web-based planning and writing ...

    2012

  25. [33]

    , Cruz Rivera, S

    Liu2020 APACrefauthors Liu, X. , Cruz Rivera, S. , Moher, D. , Calvert, M.J. , Denniston, A.K. , SPIRIT-AI Group, C.W. APACrefauthors \ 2020 . Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence : The CONSORT-AI Extension Reporti...

    2020 doi

  26. [34]

    \ Wang, T

    lund2023chatting APACrefauthors Lund, B. \ Wang, T. APACrefauthors \ 2023 . Chatting About ChatGPT : How May AI and GPT Impact Academia and Libraries? Chatting about ChatGPT : How may AI and GPT impact academia and libraries? Library Hi Tech News 40 3 26--29, APACrefDOI doi:10...

    2023 doi

  27. [35]

    \ Studholme, R

    mrabet2023chatgpt APACrefauthors Mrabet, J. \ Studholme, R. APACrefauthors \ 2023 . ChatGPT : A Friend or a Foe? ChatGPT : A friend or a foe? 2023 International Conference on Computational Intelligence and Knowledge Economy ( ICCIKE ) 2023 international conference on computati...

    2023

  28. [36]

    , Černý, M

    Mjovsk2023ArtificialIC APACrefauthors Májovský, M. , Černý, M. , Kasal, M. , Komarc, M. Netuka, D. APACrefauthors \ 2023 . Artificial Intelligence Can Generate Fraudulent but Authentic-Looking Scientific Medical Articles: Pandora's Box Has Been Opened Artificial intelligence c...

    2023 doi

  29. [37]

    \ Zhang, W

    noy2023experimental APACrefauthors Noy, S. \ Zhang, W. APACrefauthors \ 2023 . Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence Experimental evidence on the productivity effects of generative Artificial Intelligence . Science 381 187--192...

    2023 doi

  30. [38]

    \ Rotman, J.D

    doi:10.1177/14413582231167882 APACrefauthors Polonsky, M.J. \ Rotman, J.D. APACrefauthors \ 2023 . Should artificial intelligent Agents be Your Co-author? Arguments in Favour, Informed by ChatGPT Should artificial intelligent agents be your co-author? arguments in favour, info...

    2023 doi

  31. [39]

    APACrefauthors \ 2020

    rahayu2020interaction APACrefauthors Rahayu, D. APACrefauthors \ 2020 . Interaction in collaborative writing between international and domestic students in an Indonesian university Interaction in collaborative writing between international and domestic students in an Indonesia...

    2020 doi

  32. [40]

    , Terano, H.J

    rahman2023chatgpt APACrefauthors Rahman, M.M. , Terano, H.J. , Rahman, M.N. \ . APACrefauthors \ 2023 . ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples ChatGPT and academic research: A review and recommendations based on practical exampl...

    2023

  33. [41]

    , Liu, X

    CruzRivera2020 APACrefauthors Rivera, S.C. , Liu, X. , Chan, A W. , Denniston, A.K. , Calvert, M.J. , Ashrafian, H. others APACrefauthors \ 2020 . Guidelines for Clinical Trial Protocols for Interventions Involving Artificial Intelligence: The SPIRIT-AI Extension Guidelines fo...

    2020 doi

  34. [42]

    APACrefauthors \ 2024

    selim2024transformative APACrefauthors Selim, A.S.M. APACrefauthors \ 2024 . The transformative impact of AI-powered tools on academic writing: Perspectives of EFL university students The transformative impact of AI-powered tools on academic writing: Perspectives of EFL univer...

    2024 doi

  35. [43]

    APACrefauthors \ 2008

    Shachaf2008CulturalDA APACrefauthors Shachaf, P. APACrefauthors \ 2008 . Cultural diversity and information and communication technology impacts on global virtual teams: An exploratory study Cultural diversity and information and communication technology impacts on global virt...

    2008

  36. [44]

    APACrefauthors \ 2015

    Singh2015InternationalGS APACrefauthors Singh, M.K.M. APACrefauthors \ 2015 . International Graduate Students' Academic Writing Practices in Malaysia : Challenges and Solutions. International graduate students' academic writing practices in Malaysia : Challenges and solutions....

    2015

  37. [45]

    \ Song , Y

    song2023enhancing APACrefauthors Song , C. \ Song , Y. APACrefauthors \ 2023 . Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI -assisted language learning for EFL students Enhancing academic writing skills and motivation: assessing the...

    2023

  38. [46]

    Craft Skills

    storey2023ai APACrefauthors Storey, V.A. APACrefauthors \ 2023 . AI Technology and Academic Writing: Knowing and Mastering the “Craft Skills” AI technology and academic writing: Knowing and mastering the “craft skills” . International Journal of Adult Education and Technology ...

    2023 doi

  39. [47]

    , Doroudi, S

    tate2023educational APACrefauthors Tate, T. , Doroudi, S. , Ritchie, D. , Xu, Y. Warschauer, M. APACrefauthors \ 2023 1 . Educational Research and AI -Generated Writing: Confronting the Coming Tsunami. Educational research and AI -generated writing: Confronting the coming tsunami

    2023

  40. [48]

    , Bhosale, U

    thomas2023impact APACrefauthors Thomas, R. , Bhosale, U. , Shukla, K. Kapadia, A. APACrefauthors \ 2023 . Impact and Perceived Value of the Revolutionary Advent of artificial intelligence in Research and Publishing among Researchers: A Survey-Based Descriptive Study Impact and...

    2023 doi

  41. [49]

    APACrefauthors \ 2023

    thorp2023chatgpt APACrefauthors Thorp, H.H. APACrefauthors \ 2023 . ChatGPT is Fun, but Not an Author ChatGPT is fun, but not an author . Science 379 6627 313, APACrefDOI doi:10.1126/science.adg7879 APACrefDOI

    2023 doi

  42. [50]

    Chatbots, Generative AI , and Scholarly Manuscripts

    WAME2023 APACrefauthors WAME APACrefauthors \ 2023 . Chatbots, Generative AI , and Scholarly Manuscripts. WAME Recommendations on Chatbots and Generative Artificial Intelligence in Relation to Scholarly Publications. Chatbots, generative AI , and scholarly manuscripts. WAME re...

    2023

  43. [51]

    APACrefauthors \ 2020

    wohlert2020communication APACrefauthors W \"o hlert, R. APACrefauthors \ 2020 . Communication in international collaborative research teams. A review of the state of the art and open research questions Communication in international collaborative research teams. A review of th...

    2020 doi

  44. [52]

    \ Andrew , N.R

    zenni2023artificial APACrefauthors Zenni , R.D. \ Andrew , N.R. APACrefauthors \ 2023 . Artificial Intelligence text generators for overcoming language barriers in ecological research communication Artificial intelligence text generators for overcoming language barriers in eco...

    2023 doi

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.