Étiquette : Artificial intelligence

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Artificial Intelligence in Scientific Publishing: Citation Impact and Thematic Trends

Author : Mohammadamin Erfanmanesh

Introduction: The rise of artificial intelligence (AI) has a significant impact on scientific publishing and librarianship. This study explores the citation impact and thematic patterns of AI-related research in scientific publishing through four objectives: (1) to analyze bibliometric characteristics; (2) to evaluate citation performance using normalized metrics; (3) to identify major thematic trends; and (4) to review AI-related publications that specifically address librarianship and examine their implications for library practices.

Methods: A bibliometric analysis was conducted on 295 AI-related publications indexed in the Web of Science Core Collection. Citation performance was assessed using two field-normalized metrics from Clarivate’s InCites platform: the Category Normalized Citation Impact (CNCI) and the Journal Normalized Citation Impact (JNCI). To determine whether the median CNCI and JNCI values differed significantly from the category and journal benchmarks, a one-sample Wilcoxon signed-rank test was performed. Sub-themes within AI-related research were identified through thematic analysis. Additionally, to highlight important themes and professional implications, publications that specifically mention libraries, librarians, or librarianship were reviewed.

Results: The median CNCI was 2.7, and the median JNCI was 2.2, both significantly above the benchmark of 1.0 (P < .001). This indicates that AI-related research in scientific publishing receives significantly more citations than comparable publications within the same subject areas and journals. Seven sub-themes were identified, all showing above-average citation impact. Among these, themes focused on frameworks, policies, and ethical guidelines for AI integration achieved the highest normalized citation scores. Additionally, four publications focusing specifically on librarianship explored how AI intersects with open access and scholarly communication, ethics and governance, professional training, the development of machine-readable collections, and the integration of AI technologies into library services.

Discussion: These findings suggest that AI-related research has rapidly emerged as a highly influential area within scientific publishing. The variety of themes demonstrates the broad impact of AI technologies on scientific publishing.

Conclusion: Research on AI in scientific publishing is a rapidly growing and dynamic area, notable for its high citation impact, broad thematic scope, and growing influence on libraries and librarianship.

URL : Artificial Intelligence in Scientific Publishing: Citation Impact and Thematic Trends

DOI : https://doi.org/10.31274/jlsc.20363

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Are AI-Empowered Early Career Researchers Proving To Be the Harbingers of Change?

Authors : David Nicholas, Jorge Revez, Abdullah Abrizah, John Akeroyd, Marzena Swigon, Blanca Rodríguez-Bravo, David Clark, Eti Herman, Jie Xu, Tatyana Polezhaeva

The Harbingers longitudinal study of early career researchers and their pathfinding scholarly communication attitudes and practices is a decade old and much of its findings have been published in this journal.

We are now moving on to the new generation of researchers—Gen-Z and it is fitting that we draw things to a close by summing up whether our largely millennial researchers have proved to be the harbingers of change.

We focus on the data from the last two rounds of interviewing, conducted in the last 2 years, at a time when AI was (increasingly) propelling change. These two rounds covered over 150 ECRs from all subject fields and from China, Malaysia, Poland, Portugal, Russia, Spain, UK and US.

It was found that ECRs are, indeed, at the forefront of the technological adaptation in research, actively exploring the potential of AI and new communication channels. However, their ability to be truly transformative was constrained by a traditional, quantitative evaluation/reputational system.

This could change with Gen-Z because there were ECRs in their twenties included in our previous studies and they displayed higher practical integration of AI tools and appeared to be more strategically adopting AI for efficiency and career advancement.

URL : Learned Publishing – 2026 – Nicholas – Are AI‐Empowered Early Career Researchers Proving To Be the Harbingers of Change

DOI : https://doi.org/10.1002/leap.2099

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If You Really Want to Know How AI Is Changing Peer Review Talk to Early Career Researchers

Authors : David Nicholas, Blanca Rodríguez-Bravo, David Clark, Abdullah Abrizah, Jorge Revez, Marzena Swigon, John Akeroyd, Jie Xu, Tatyana Polezhaeva, Eti Herman

The Harbingers study of early career researchers (ECRs), their work life and scholarly communications, began by studying generational—Millennial—change (H1 c.2016), then pandemic change (H2 c2020) and is now investigating yet another: artificial intelligence (H3 2024–).

We report here on a scoping pilot study, which looks at the impact of AI on peer review from over 150 international ECRs from all disciplines. The data were collected by open-ended, in-depth interviews, but being a convenience sample the findings need to be treated with caution.

We find that ECRs are displaying a complex, shifting attitude towards the integration of AI into peer review, characterised by cautious optimism on the one hand and scepticism on the other. ECRs are already reporting specific instances of AI increasingly influencing reviewing practices: both potentially beneficial applications and actual misuse.

AI is seen as a new systemic threat, potentially worsening the alleged exploitative nature of publishing if adopted without ethical guidelines.

URL : If You Really Want to Know How AI Is Changing Peer Review Talk to Early Career Researchers

DOI : https://doi.org/10.1002/leap.2082

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EN

(De)generative AI and research integrity

Author : Jurij Selan

The phenomenon of artificial intelligence (AI) is inherently paradoxical. On one hand, it is generative. This generative quality has benefited contemporary research by enabling researchers to generate ideas and enhance research opportunities while saving time and costs.

On the other hand, the generative nature of AI appears inevitably to lead to antagonism, resulting in entropy through model collapse and becoming degenerative. In this article, we explore the extent to which the implicitly degenerative nature of AI could be regarded as the main long-term threat to research integrity (RI), as many other problems associated with the impact of AI on RI may be seen as its effects.

In the first part, we provide an overview of the impact of AI on RI, including AI ethics, the use of AI in education (AIED), AI as a “grey area” or questionable research practice (QRP), the implementation of principles for AI use in codes of conduct, and the attitudes of academic publishers and universities towards AI. In the second part, we examine how the collapse of generative AI into degenerative AI poses a critical threat to RI in the future.

We emphasise that the only way to prevent the harmful effects of the degenerative nature of AI on RI is to retain the original human-generated datasets as the basis for AI systems and continually add new human-generated datasets.

One of the key principles regarding the impact of AI on RI is therefore the responsibility to ensure that AI remains grounded in human-created reality. This, however, leads us to the sociotechnical perspective on degenerative AI, which we address in the third part, where we evaluate the broader social and moral impact of degenerative AI.

We stress a fundamental shift in human trust requirements towards society and make a plea for more inclusive anticipatory risk management of AI with respect to RI.

DOI : https://doi.org/10.1057/s41599-026-08248-y
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Trouver sa voix en anglais académique : apport et limites d’un assistant d’écriture basé sur l’IA

Autrices : Jennifer Lucas, Irina Otmanine

Cet article présente un retour d’expérience sur l’intégration d’un assistant conversationnel fondé sur l’IA générative – le GPT Writing Coach – dans un cours d’écriture académique et professionnelle en anglais. Inscrite dans une démarche de Scholarship of Teaching and Learning et s’appuyant sur la théorie de l’auto-efficacité de Bandura, l’étude interroge la manière dont l’IA peut soutenir l’engagement, la créativité et le développement d’une voix personnelle en langue étrangère.

La méthodologie mixte retenue combine l’usage du questionnaire SAWSES au début et à la fin du semestre et l’analyse qualitative de verbatims recueillis via une plateforme d’autoévaluation. Les premiers résultats suggèrent des évolutions intéressantes concernant la perception de l’écriture académique et le rôle attribué au feedback généré par l’IA, tout en révélant plusieurs questions pédagogiques et éthiques. Ces éléments invitent à une réflexion approfondie sur la place de l’IA dans l’apprentissage des langues.

URL : http://journals.openedition.org/dms/12818

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EN

AI In Academic Publishing for Non-Native English Speakers: The Good, the Bot, and the Ugly

Authors : Talip Gönülal, Ramazan Güçlü, Salih Güçlü

This exploratory study investigated the impact of artificial intelligence (AI) tools on academic publishing for non-native English-speaking researchers. Through a mixed-methods convergent parallel design, it examined how these scholars utilize AI tools, their perceived benefits, and concerns regarding AI’s influence on academic publishing.

Data were collected from 105 non-native English-speaking academics coming from 25 language backgrounds. Participants primarily employed AI tools for grammar improvement, writing style enhancement, and translation, while maintaining control over higher-level intellectual tasks such as organizing manuscripts.

Three key dimensions of the perceived impact of AI were identified in this study: the good, reducing linguistic inequalities by improving paper quality and decreasing language-related challenges; the bad, involving inaccurate or misleading AI suggestions, over-reliance on AI tools, and diminished engagement with manuscripts; and the ugly, characterized by failure to disclose AI use, lack of clear guidelines for responsible AI integration in research, homogenization of academic writing, and the emergence of new forms of inequality.

The study concluded with several recommendations for individual researchers, academic institutions, and publishers and journals to promote the ethical and effective use of AI in academic publishing.

URL : AI In Academic Publishing for Non-Native English Speakers: The Good, the Bot, and the Ugly

DOI : https://doi.org/10.1002/leap.2070

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EN

Do Early Career Researchers Consider AI as an Opportunity or a Threat? A Pathfinding Study

Authors : David Nicholas, David Clark,  Abdullah Abrizah, John Akeroyd, Eti Herman, Jorge Revez, Blanca Rodríguez-Bravo, Marzena Swigon, Tatyana Polezhaeva, Anne Gere

The article presents the latest (2025) iteration of the Harbingers longitudinal project on early career researchers (ECRs), artificial intelligence (AI) and scholarly communications. In conversation with a purposive and diverse sample of more than 60 ECRs in six countries and numerous subjects, we present an evaluation of a pressing issue: what impact will AI have on their work and career?

An important issue is that widespread media speculation suggests that it is entry-level positions that will be hit hardest by AI. While ECRs were asked 50 plus questions during interviews, none were directly asked about changes to job security and employment prospects, yet much of relevance was volunteered in answering related AI questions.

Adding a new methodological dimension to the Harbingers project, we employed AI (NotebookLM) for an initial qualitative analysis of the interview data, with findings reviewed and corrected by the national interviewers. We conclude that AI is a double-edged sword which has huge potential as well as posing significant challenges.

The AI-assisted analysis proved effective at identifying broad themes, though human oversight was essential to capture nuance, differences between cohorts, and unusual cases. Finally, given that we were working with a select and relatively small sample to inform a larger study, the data should be seen as illuminating and filling a research lacuna, rather than a definitive result in a fast-changing field.

URL : Do Early Career Researchers Consider AI as an Opportunity or a Threat? A Pathfinding Study

DOI : https://doi.org/10.1002/leap.2068