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Enregistrement W4401459648 · doi:10.4103/picr.picr_67_24

Artificial intelligence in academic writing: Insights from journal publishers’ guidelines

2024· article· en· W4401459648 sur OpenAlexaboutno aff
Himel Mondal, Shaikat Mondal, Joshil Kumar Behera

Notice bibliographique

RevuePerspectives in Clinical Research · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueArtificial Intelligence in Healthcare and Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAuditPublicationComputer scienceThe InternetLibrary sciencePsychologyWorld Wide WebPolitical scienceManagementLaw

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION Generative artificial intelligence (AI) technologies have the potential to be incorporated into scientific research and scholarly writing. Large language models (LLMs), such as ChatGPT, Gemini, and Copilot, have created a ripple in the scientific writing process, as these LLM-based freely accessible chatbots are capable of generating content at very high speeds that humans may never achieve.[1] However, a question remains in authors’ mind – is it ethical to use AI in writing process? To find answer, we analyzed the available guidelines of journal publishers regarding the use of AI in manuscript preparation. METHODS This was a cross-sectional audit of public domain data available freely on the journal or publisher’s websites (cutoff April 15, 2024). Two authors individually made their list of 20 prominent (from internationally reputed journals and gained knowledge based on previous literature and Internet search) publishers.[2] A consensus was reached to make a final list of 20 publishers and their websites were searched for guidelines regarding the use of AI in the writing process. The list of the publishers can be accessed from https://doi.org/10.6084/m9.figshare.25975279.v1. Themes were identified from the text in QDA Miner Lite v3.0.5 (Provalis Research, Montreal, Canada). RESULTS From publishers’ guidelines on the use of AI in manuscripts, we have identified a total of six themes described below. Responsibility Authors are expected to use AI tools responsibly, with human oversight, to ensure the accuracy, validity, and integrity of the content. Elsevier mentioned that authors “should carefully review and edit the result” before using it in the manuscript. Authorship AI tools including generative AI like LLMs cannot fulfill the criteria for authorship according to the guidelines set by the International Committee of Medical Journal Editors criteria of authorship. Scientific Scholar suggests not adding chatbots as authors as it does not fulfill the ICMJE criteria and along with that, it does not have “affiliation independent of their developers.” Declaration Authors are required to disclose the use of AI tools in their manuscripts, including details such as the name, version, and purpose of the AI tool used. Springer suggests that the use of LLM should be “properly documented in the methods section.” Wiley suggests adding details in “the methods section (or via a disclosure or within the acknowledgments section, as applicable).” Productivity Copywriting and copyediting are two interlaced parts of a manuscript. AI can help in both. Taylor and Francis admits that gradually AI is being assimilated into academic writing and its proper use has “the potential to augment research outputs and thus foster progress through knowledge.” Limitation There are several limitations to using AI in academic writing. SAGE pointed out that AI chatbots including “LLMs can ‘hallucinate,’ i.e. generate false content” and they “can generate content that is linguistically but not scientifically plausible.” Future prospect The role of AI in research and scholarly publishing is evolving, suggesting that AI will become increasingly integrated into the publishing process. MDPI predicts that “in a few years, AI will become the norm, like how the Internet or Google are now.” DISCUSSION Committee on publication ethics suggests transparent declaration of AI with details of the tool and agrees that “the use of AI tools such as ChatGPT or LLMs in research publications is expanding rapidly.”[3] The World Association of Medical Editors has suggested that authors can use AI for a variety of tasks like “(1) simple word-processing tasks, (2) the generation of ideas and text, and (3) substantive research.”[4] From publishers’ guidelines, it is evident that there is no prohibition against the acceptance of AI-generated content in general. However, as AI is not an author according to ICMJE criteria, authors should check accuracy and plagiarism and edit the content before using it in the manuscript. Authors bear the responsibility for the content they publish and should ensure transparent declaration or acknowledgement of the help taken from the AI. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,050
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,890
Score d'incertitude au seuil0,995

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0110,050
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,007
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,824
Tête enseignante GPT0,703
Écart entre enseignants0,120 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2024
Routes d'admission1
Résumé présentoui

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