Using AI to write scholarly publications
Open accessAbstract
Artificial intelligence (AI) natural language processing (NLP) systems, such as OpenAI's generative pre-trained transformer (GPT) model (https://openai. com) or Meta's Galactica (https://galactica.org/)may soon be widely used in many forms of writing, including scientific and scholarly publications (Heaven 2022). 1 While computer programs (such as Microsoft WORD and Grammarly) have incorporated automated text-editing features (such as checking for spelling and grammar) for many years, these programs are not designed to create content.However, new and emerging NLP systems are, which raises important issues for research ethics and research integrity.2 NLP is a way of enabling computers to interact with human language.A key step in NLP, known as tokenization, involves converting unstructured text into structured text suitable for computation.For example, the sentence "The cat sat on the mat" can be structured by tagging its parts: "the [article] cat [noun] sat [verb, past tense] on [preposition] the [article] mat [noun]."Once the parts of the text have been tagged, they can be processed by means of algorithms designed to produce appropriate responses to text (i.e., language generation).Rudimentary NLP-systems, such as the first generation of chatbots that assisted customers on websites, operated according to thousands of human-written rules for processing and generating text.Recent advances in computational speed and capacity and the development of machine-learning (ML) algorithms, such as neural networks, have led to tremendous breakthroughs in NLP (Mitchell 2020).Today's NLP systems use ML to produce and refine statistical models (with billions of parameters) for processing and generating natural language.NLP systems are trained on huge databases (45 terabytes or more) of text available on the internet or other sources.Initial training (or supervised learning) involves giving the system the text and then "rewarding" it for giving correct outputs, as determined by human trainers.3 Over time, NLP systems will reduce their percentage of erroneous outputs and will learn from the data (Mitchell 2020).While NLP systems continue to learn as they receive and process data beyond their initial training data, they do not "know" the meaning or truth-CONTACT
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Currently, several journals have attempted to develop guidelines.
“Currently, several journals have attempted to develop guidelines [28, 29].”
From Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review (2023) · cites this paper as [28]
AI tools tend to generate output that reproduces or amplifies biases inherent within the source data.
“A final issue is that AI tools will tend to generate output that reproduces or amplifies biases inherent within the source data [19].”
From Artificial intelligence-assisted academic writing: recommendations for ethical use (2025) · cites this paper as [19]
New models of ChatGPT cannot differentiate between real and factitious data that is received, integrated and used to generate new content.
“While new models of ChatGPT have ‘learned’ compared to prior releases, they cannot differentiate between real and factitious data that is received, integrated and used to generate new content [19].”
From Artificial intelligence-assisted academic writing: recommendations for ethical use (2025) · cites this paper as [19]
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“Ethical problems related to the misuse of statistics programs and digital image manipulation are well-known and have unfortunately been the subject of numerous research misconduct investigations (Gardenier and Resnik 2002; Rossner and Yamada 2004; Cromey 2013; Shamoo and Resnik 2022).”
Self-citationDigital images are data: and should be treated as such
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“Ethical problems related to the misuse of statistics programs and digital image manipulation are well-known and have unfortunately been the subject of numerous research misconduct investigations (Gardenier and Resnik 2002; Rossner and Yamada 2004; Cromey 2013; Shamoo and Resnik 2022).”
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“…(Resnik et al. 2016), and widely used contributor roles taxonomies (e.g., CRediT) make clear distinctions between writing the first draft and revising it (Hosseini et al. 2022), use of NLP systems should be acknowledged in the text (e.g., methods section) and mentioned in the references section.”
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“In general, AI writing tools are intended to analyze written materials and provide comments on many parts of the writing, such as grammar, vocabulary, syntax, content, and structure (Hosseini et al., 2023; Strobl et al., 2019; Thorp, 2023).”
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“Articles and editorials published in journals, including Nature (Nature, 2023), Accountability in Research (Hosseini et al., 2023), JAMA (Flanagin et al.”
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“Currently, several journals have attempted to develop guidelines [28, 29].”
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“We agree with the ICMJE’s position, which mirrors views we defended in print before the ICMJE released its guidance [101, 102].”
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“Additionally, there is growing apprehension regarding the impact these tools might have on the authenticity and credibility of academic work, resulting in ethical and societal dilemmas [34,35].”
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“Initially, the debate focused on big-picture issues, such as whether it is ethical to use AI tools in writing scientific articles and whether AI tools can be named as authors (Hosseini, Rasmussen, and Resnik 2023; Hosseini, Resnik, and Holmes 2023).”
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“Although myriad concerns are emerging around AI and research ethics (Hosseini et al., 2023), we believe that moral agency is a particularly intractable problem because it goes to the heart of our ability to ethically evaluate practices, while being shaped by generative AI ’ s complex relation to…”
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“…considered to lack transparency and explainability (Dwivedi et al. 2023), and reproduce or even amplify biases inherent in the information that was used to train them (Corless 2023; Hosseini et al. 2023), reproducing an “of the same old trivialities and stereo-types” (Teubner et al. 2023, p. 99).”
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“Recent discussions in the academic publishing community suggest that CRediT may need to evolve to explicitly include AI-assisted contributions (Hosseini, Rasmussen, and Resnik 2024).”
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“While the integration of LLMs presents exciting prospects in scientific writing, particularly within the medical literature, it brings forth several concerns (Hammad, 2023; Hosseini et al., 2023).”
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“While arguments can be made that generative AI possesses some aspects of authorial accountability, such as the capacity to provide an account or explanation of how an article was created, the aspect of accountability that generative AI genuinely lacks is moral responsibility (Hosseini et al. 2023b).”
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“It can also enhance reviewer selection by utilizing previous reviews and the expertise of reviewers (Hosseini et al., 2023).”
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“Hosseini, Rasmussen, and Resnik (2023) argue that the use of generative AI should be permitted and cited because its use is impossible to detect, and it is better to provide a pathway for it to be used and acknowledged as opposed to being driven underground.”
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“Despite the criticisms leveled by scholars, many studies indicate that there is a growing need for the acceptance of this technology (Hosseini et al., 2023; Hu, 2023; Yeo-The & Tang, 2021).”
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“A crucial stage in NLP, known as tokenization, is transforming unstructured information into organized text appropriate for computing (Hosseini et al., 2023).”
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“5, as well as their originality and the ethical and acceptable boundaries of using them in scientific writing (1, 12, 13).”
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“Moreover, students with access to these tools are also reported to take undue advantage, as most teachers and school administrators cannot detect AI-generated texts convincingly (Hosseini et al., 2023).”
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“Academic publishers are increasingly acknowledging the dynamic and complex roles that generative AI tools and human authors play in the writing process [33].”
Disclosing generative AI use for writing assistance should be voluntary
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“1 This position (that disclosing the use of GenAI for writing assistance should be voluntary) departs from views previously endorsed by authors of this work, where we held that disclosure should be mandatory (Hosseini et al., 2023a; Kaebnick et al., 2023).”
Self-citationA model of the interrelationship between research ethics and research integrity
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“While some have embraced their widespread availability, and urged for the necessity of regulating matters related to integrity, transparency, and authorship, others have chosen to completely ban their use (Hosseini et al., 2023).”
BackgroundToday's Academic Research: The Role of ChatGPT Writing
Chukwuere, J. E. (2024) · Journal of Information Systems and Informatics · doi:10.51519/journalisi.v6i1.639 · Cited by 26 · see citing works
“Hosseini, Rasmussen and Resnik [16] believe that the answers to queries are incorrect and irrelevant.”
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“OpenAI’s Chat Generative Pre-trained Transformer (ChatGPT), a 175-billion parameter, forms part of a nexus of natural language processing (NLP) models designed to enable computers to interact with human language (Hosseini et al., 2023; OpenAI, 2022).”
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“AI’s ability to write content has vastly accelerated scientific publications (Hosseini et al., 2023).”
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“18,50 This perspective is further discussed by Kaebnick et al., who argue that “ the requirement for accountability is grounded in an understanding of morally responsible publishing ” 18(p500) , a principle AI cannot embody.”
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“The researcher ’ s use of these tools should be documented in the manuscript ’ s Methods or Acknowledgments sections, according to the guideline [9] .”
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“Concerns about reduced linguistic and epistemic diversity in AI-assisted manuscripts are growing, along with calls for clearer disclosure policies and author accountability (Hosseini et al., 2023; Nature Machine Intelligence, 2022).”
Passive Contribution of ChatGPT to Scientific Papers
Rahimi, F., & Abadi, A. T. B. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03260-8 · Cited by 25 · see citing works
“This represents a confused stance about the complexity of the present dilemma regarding the validity of submitted papers written completely or partially by ChatGPT [23].”
BackgroundNavigating Generative AI: The Teacher Librarian's Role in Cultivating Ethical and Critical Practices
Oddone, K., Garrison, K., & Gagen-Spriggs, K. (2023) · Journal of the Australian Library and Information Association · doi:10.1080/24750158.2023.2289093 · Cited by 23 · see citing works
“Bias within content generated by GAI tools including ChatGPT emerges as a result of biases which may exist in the training data used in the platform ’ s development, as well as biases in the design of the algorithms that direct the tools ’ operation (Hosseini et al., 2023; Raicu, 2023).”
BackgroundMethodCan ChatGPT be trusted to provide reliable estimates?
Tsigaris, P., & Teixeira da Silva, J. A. (2024) · Accountability in Research · doi:10.1080/08989621.2023.2179919 · Cited by 22 · see citing works
Çelik, S. U. (2025) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2025.116657 · Cited by 28 · see citing works
Wainaina, P. K., & Sun, Y. (2025) · Discover Education · doi:10.1007/s44217-025-00820-z · Cited by 25 · see citing works
“On the staff side, GenAI can automate grading and planning [1, 79], help keep curricula current [5], and assist scholarly work such as grant applications, peer review, and manuscript improvement [4, 38, 46].”
InfluentialTang, G., & Eaton, S. E. (2024) · Journal of Scholarly Publishing · doi:10.3138/jsp-2023-0079 · Cited by 19 · see citing works
Understanding Researchers’ AI Readiness in a Higher Education Context: Q Methodology Research
Chaaban, Y., Qadhi, S., Chen, J., & Du, X. (2024) · Education Sciences · doi:10.3390/educsci14070709 · Cited by 19 · see citing works
Jagged competencies: Measuring the reliability of generative AI in academic research
Thomas, L. D. W., Romasanta, A. K. S., & Priego, L. P. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115804 · Cited by 20 · see citing works
TALAYHAN, Ö. G. (2023) · JOURNAL OF CURRENT DEBATES IN SOCIAL SCIENCES · doi:10.29228/cudes.71701 · Cited by 30 · see citing works
Kendall, G. (2024) · Journal of Academic Ethics · doi:10.1007/s10805-024-09581-0 · Cited by 19 · see citing works
“Hosseini et al. (2023) provides ways that AI (or Natural Language Processing (NLP) as it is referred to) can be used and presents a draft policy as to how the use of NLP-systems should be declared when submitting articles to Accountability in Research.”
Yasin, Y. M., & Al‐Hamad, A. (2023) · Research in Nursing & Health · doi:10.1002/nur.22326 · Cited by 18 · see citing works
Exploring the role of generative AI in academia: Opportunities and challenges
Panda, S., & Kaur, N. (2024) · IP Indian Journal of Library Science and Information Technology · doi:10.18231/j.ijlsit.2024.003 · Cited by 25 · see citing works
Unleashing the power of ChatGPT in finance research: opportunities and challenges
Feng, Z., Hu, G., Li, B., & Wang, J. (2025) · Financial Innovation · doi:10.1186/s40854-025-00770-3 · Cited by 37 · see citing works
“The chatbot can provide comprehensive feedback on the writing’s structure, tone, and style, ensuring the final product is polished, coherent, and well-organized (Hosseini et al. 2023).”
Exploring the ethical landscape of AI in academic writing
Ateriya, N., Sonwani, N. S., Thakur, K., Kumar, A., & Verma, S. (2025) · Egyptian Journal of Forensic Sciences · doi:10.1186/s41935-025-00453-1 · Cited by 22 · see citing works
“To maintain academic integrity, it is necessary to set clear ethical guidelines and ethically employ AI techniques (Hosseini, 2024).”
The Paradox of Ethical AI-Assisted Research
Mezzadri, D. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-025-09671-7 · Cited by 28 · see citing works
Leveraging Large Language Models in Radiology Research: A Comprehensive User Guide
Brown, J. D., Lenchik, L., Doja, B. F., Kaviani, P., Judd, D., Probyn, L. J., Lee, N., Goodman, E. M., Sedeh, A. E., Makary, M. S., et al. (2025) · Academic Radiology · doi:10.1016/j.acra.2024.11.053 · Cited by 16 · see citing works
Cheng, A., Nagesh, V., Eller, S., Grant, V., & Lin, Y. (2025) · Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare · doi:10.1097/sih.0000000000000877 · Cited by 16 · see citing works
Abernethy, N. J. (2024) · AI and Ethics · doi:10.1007/s43681-024-00575-7 · Cited by 17 · see citing works
“In the academic literature on the LLM coauthorship debate, the vast majority of people argue for journals to adopt blanket bans on LLM coauthorship [2–4], and many journals are beginning to do so.”
Yang, S., Chen, S.-K., Hailin, Z. H. U., Lin, J., & Wang, X. (2024) · System · doi:10.1016/j.system.2024.103494 · Cited by 16 · see citing works
Strategies for integrating ChatGPT and generative AI into clinical studies
Lee, J. (2024) · Blood Research · doi:10.1007/s44313-024-00045-3 · Cited by 16 · see citing works
ChatGPT in Academic Writing and Publishing: An Overview of Ethical Issues
Bakla, A. (2023) · Advances in Educational Technologies and Instructional Design · doi:10.4018/978-1-6684-9893-4.ch005 · Cited by 15 · see citing works
Impact of artificial intelligence text generators (AITGs) on libraries
Inamdar, S. (2023) · Library Hi Tech News · doi:10.1108/lhtn-03-2023-0048 · Cited by 16 · see citing works
Francisco, C. I., Pantaleon, C. E., Lantaya, G. M. A., Francisco, W. A. R., & Alieto, E. O. (2025) · Studies in big data · doi:10.1007/978-3-031-83911-5_4 · Cited by 16 · see citing works
An active aigiarism declaration for manuscript submission
Yeo-Teh, N. S. L., & Tang, B. L. (2023) · Accountability in Research · doi:10.1080/08989621.2023.2185776 · Cited by 15 · see citing works
Benefits and Challenges of Using AI for Peer Review: A Study on Researchers’ Perceptions
Giray, L. (2024) · The Serials librarian · doi:10.1080/0361526x.2024.2428377 · Cited by 13 · see citing works
The Evolution of Learning: Assessing the Transformative Impact of Generative AI on Higher Education
Krause, S., Panchal, B. H., & Ubhe, N. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-97-9255-9_24 · Cited by 35 · see citing works
Generative AI can effectively manipulate data
Kim, J. J. H., Srivatsa, A. V., Nahass, G. R., Rusanov, T., Hwang, S., Kim, S., Solomon, I., Lee, T. H., Kadkol, S., Ajilore, O., et al. (2025) · AI and Ethics · doi:10.1007/s43681-024-00546-y · Cited by 15 · see citing works
“Previous stud - ies also have revealed that tools like ChatGPT possess the capability to create deceptive research manuscripts and citations [ 10–12 ].”
Using Artificial Intelligence for Scholarly Writing
Oermann, M. H., Owens, J. K., Carter‐Templeton, H., Peterson, G. M., & Bailey, H. E. (2025) · AJN American Journal of Nursing · doi:10.1097/ajn.0000000000000179 · Cited by 14 · see citing works
“10,13,20,29,30 The author should state how the AI was used and”
Slow Writing with ChatGPT: Turning the Hype into a Right Way Forward
Sirisathitkul, C. (2023) · Postdigital Science and Education · doi:10.1007/s42438-023-00441-5 · Cited by 15 · see citing works
“Users of ChatGPT must therefore be vigilant about potential misinformation and ‘AI hallucination’ (Hosseini et al. 2023; Lakshman 2023).”
Li, D., & Zhang, Z. (2023) · PLoS ONE · doi:10.1371/journal.pone.0293034 · Cited by 15 · see citing works
Prajapati, J. B., Kumar, A., Singh, S. K., Prajapati, B. G., Thakar, Y., Tambe, P. R., & Ved, A. D. (2024) · SN Social Sciences · doi:10.1007/s43545-023-00818-0 · Cited by 17 · see citing works
Lin, Z. (2024) · Accountability in Research · doi:10.1080/08989621.2024.2405041 · Cited by 16 · see citing works
“Indeed, no system can prevent unethical behaviors altogether (Hosseini et al. 2023), and continued dialogue and the development of comprehensive strategies are needed to uphold the integrity of authorship in scientific research.”
Kaebnick, G. E., Magnus, D. C., Kao, A. C., Hosseini, M., Resnik, D. B., Dubljević, V., Rentmeester, C. A., Gordijn, B., & Cherry, M. J. (2023) · Ethics & Human Research · doi:10.1002/eahr.500182 · Cited by 14 · see citing works
Self-citationKaebnick, G. E., Magnus, D. C., Kao, A. C., Hosseini, M., Resnik, D. B., Dubljević, V., Rentmeester, C. A., Gordijn, B., & Cherry, M. J. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2292437 · Cited by 14 · see citing works
“…requirement of morally responsible publishing is that authors must be accountable for what they write, and generative AI tools lack accountability (Hosseini, Rasmussen, and Resnik 2023; Hosseini, Resnik, and Holmes 2023; International Committee of Medical Journal 2023; Liebrenz et al. 2023; Lund…”
Self-citationMessner, R., Smith, S., & Richards, C. (2025) · International Journal of Qualitative Methods · doi:10.1177/16094069251371481 · Cited by 35 · see citing works
“…literature on AI and how it transforms traditional human activities and occupations, research on the implications of AI for qualitative analysis and inductive/abductive theory development has also begun to surface (Christou, 2023b; Hosseini et al., 2023; Tabone & De Winter, 2023; Van Manen, 2023).”
Ten simple rules for optimal and careful use of generative AI in science
Helmy, M., Jin, L., Alhossary, A., Mansour, T. A., Pellegrina, D. V. D. S., & Selvarajoo, K. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013588 · Cited by 17 · see citing works
Academic journals cannot simply require authors to declare that they used ChatGPT
Tang, G. (2023) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-023-03374-x · Cited by 15 · see citing works
Using AI to Write Scholarly Articles in Nursing
Oermann, M. H. (2023) · Nurse Educator · doi:10.1097/nne.0000000000001577 · Cited by 13 · see citing works
- Authors
- Hosseini, M., Rasmussen, L. M., & Resnik, D. B.
- Affiliations
- Northwestern University (US)
- University of North Carolina at Charlotte (US)
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- Resnik, David B.
- Venue
- Accountability in Research
- Publisher
- Informa UK Limited
- Published
- 2023-01-25
- Volume
- 31
- Issue
- 7
- Pages
- 715-723
- ISSN
- 0898-9621 (ISSN-L)
- 1545-5815
- PMC
- PMC10366336
- OpenAlex
- W4318035272
- Cited by
- 286
- References
- 10
- Citations by year
- 2026: 56 · 2025: 80 · 2024: 85 · 2023: 65
- FWCI
- 71.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Accountability in Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Environmental Health Sciences
- National Center for Advancing Translational Sciences (UL1TR001422)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- AI-generated text, scholarly publishing, research ethics, research integrity, generative pre-trained transformer, large language models, tokenization, machine learning, chatbots, supervised learning, authorship attribution
- MeSH terms
- Scholarly Communication, Artificial Intelligence, Humans, Periodicals as Topic, Publications, Writing
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.
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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