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ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing
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Abstract This article discusses OpenAI's ChatGPT, a generative pre‐trained transformer, which uses natural language processing to fulfill text‐based user requests (i.e., a “chatbot”). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT‐3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing.
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“Many authors have expressed concern that students are submitting papers generated by ChatGPT and other AI tools as their own original work, thereby attaining the performance goal but bypassing the learning goal.”
From The Effectiveness of Software Designed to Detect AI-Generated Writing: A Comparison of 16 AI Text Detectors (2023) · cites this paper as Lund et al., 2023
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“This is due to concern centered on plagiarism because it is challenging to trace and verify the originality of AI-generated content [59, 60].”
From Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals (2025) · cites this paper as [59]
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“They are discussed not only as tools for writing, summarizing, or retrieving scientific texts, but also as systems that may help evaluate research quality, generate manuscript feedback, and support peer review workflows (Kousha & Thelwall, 2025;Lund et al., 2023;Thelwall, 2025a).”
From Beyond Human-Likeness: Mapping the Scientific Critique Profiles of LLMs and Human Reviewers (2026) · cites this paper as Lund et al., 2023
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“This uncritical dependence not only hinders the development of critical thinking skills [6], [7].”
From A Meta-Synthesis of Factual Accuracy and Citation Hallucination in LLM Academic Assistants (2026) · cites this paper as [7]
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In the citing paper
“Misattribution can lead to wrongful accusations or misinterpretation of an author's intent [Lund et al., 2023].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Lund et al., 2023
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“Finding answers to this question is urgent as LLMs are already starting to permeate the academic landscape (10–17).”
From How should the advancement of large language models affect the practice of science? (2025) · cites this paper as [14]
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“This forecast supports claims made by other scholars, who argue that LLMs will help automate mundane tasks and free up space for creative thinking (Lund et al. 2023;Dwivedi et al. 2023).”
From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Lund et al. 2023
In the citing paper
“This outcome resonates with the scholarly discourse which highlights how generative AI can be used to enhance texts, such as with brainstorming (Staiman 2023), crafting literature reviews (Lucey and Dowling 2023), and improving text clarity (Lund et al. 2023).”
From Friend or foe? Exploring the implications of large language models on the science system (2025) · cites this paper as Lund et al. 2023
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Alam, A., Sargam, S., Rani, J., Saini, P., Bhilare, A., & Balakrishnan, S. (2025) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v6.i5s.2025.6908
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Su, L. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-95-2518-8_22
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Eulenstein, M. L., & Schoormann, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-11043-5_35
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Zhang, H., & Nie, C. (2025) · International Conference on Implementing Generative AI into Telecommunication and Digital Innovation · doi:10.1145/3771792.3773856
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Matthies, D. J. C., Schlonsak, R., & Gabrecht, M. T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13312-0_3
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Metadata
- Authors
- Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z.
- Affiliations
- University of North Texas (US)
- Emporia State University (US)
- Zhejiang Tongji Vocational College of Science and Technology (CN)
- Babasaheb Bhimrao Ambedkar University (IN)
- Baker University (US)
- Corresponding author
- Lund, Brady D.
- Venue
- Journal of the Association for Information Science and Technology
- Publisher
- Wiley
- Published
- 2023-03-10
- Volume
- 74
- Issue
- 5
- Pages
- 570-581
- ISSN
- 2330-1635 (ISSN-L)
- 2330-1643
- OpenAlex
- W4323848232
- Cited by
- 919
- References
- 97
- Citations by year
- 2026: 171 · 2025: 293 · 2024: 282 · 2023: 173
- FWCI
- 230.20
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of the Association for Information Science and Technology (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Library and Information Sciences › Publishing and Scholarly Communication
- Keywords
- ChatGPT, large language models, generative AI, academic writing, scholarly publishing, research ethics, AI-generated text, GPT-3, natural language processing
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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