Étiquette : Artificial intelligence

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(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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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

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EN

Assessing open access scholarly journals for integration into artificial intelligence research assistants

Authors : Sanja Gidakovic, Heather Moulaison-Sandy, Jenny Bossalle

Introduction

Freely available standalone AI research assistants such as Elicit and Consensus are used by academics to find relevant literature. These systems rely extensively on freely available sources, including open access journal content. No baseline for understanding the level of quality of such journals used in these assistants has been carried out.

Method

A sample of 807 English-language journals from the Directory of Open Access Journals that became open access before 2021 was investigated for quality metrics using SCImago rankings and other defining characteristics and analysed in conjunction with the Directory data.

Analysis

Scimago journal ranking quartile scores were recorded for each of the journals. Descriptive statistics were produced using Excel, and visualizations using Tableau Public.

Results

Of our sample, over half were ranked in Scopus, and many were in quartile 1. Many university or small association journals were unranked.

Conclusions

AI research assistants may miss some high-quality open access content due to reliance on metrics. Commercial enterprises play a large role in sources used to produce content, effectively gatekeeping the process and potentially shaping the creation of new knowledge.

URL : Assessing open access scholarly journals for integration into artificial intelligence research assistants

DOI : https://doi.org/10.47989/ir31263095

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When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge

Authors : Andrés F. Castro Torres, Joan Giner-Miguelez, Mercè Crosas

The extent to which Artificial Intelligence (AI) can trigger generalized paradigm shifts in science is unclear. Although some of these technologies have revolutionized data collection and analysis in specific scientific fields such as Chemistry, their overall impact depends on the scope of adoption and the ways scholars use them.

In this study, we document substantial differences in the timing and extent of AI adoption across countries and scientific domains from 1960 to 2015. After 2015, we find generalized exponential growth in AI adoption, with the number of AI-supported works multiplying by at least four across all domains. The transformative nature of this rapid growth is less apparent and points to multiple challenges should adoption trends persist.

According to our analyses, AI-supported research is confined to very few topics with strong ties to Computer Science and conventional statistical frameworks, suggesting limited transformational potential in epistemological terms. AI-supported works are also associated with an unwarranted citation premium and exhibit substantially higher retraction rates than non-AI-supported works across most fields.

Geographically, AI adoption displays pronounced heterogeneity at the country level, along with an acceleration in the relevance of middle-income countries in Asia, from China and beyond.

Thus, the transformative capacity of AI in science remains largely untapped, and its rapid adoption underlines challenges in research openness, transparency, reproducibility, and ethics from a global perspective. We discuss how best research practices could boost the benefits of AI adoption and highlight fields and geographies where these trends warrant closer scrutiny.

DOI : https://doi.org/10.48550/arXiv.2605.06033

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AI And the Editors’ Ghost: Who Is the Writer Now?

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

This an exploration of the use of AI in research and writing. It builds upon the ‘Harbingers’ project, an international and longitudinal study of early career researchers (ECRs) and scholarly communication.

In the fourth phase of the project, we returned to the theme of AI, in particular AI as ‘ghostwriter’. Our sources are transcripts of conversational, open-form interviews with over 60 ECRs from Britain, Malaysia, Poland, Portugal, Spain, Russia, and other countries.

For an initial analysis of the transcripts, we used Google NotebookLM. An overarching and thematic summary of the data was produced in minutes, that would otherwise have occupied our research team for weeks. The unprompted text, immediately plausible and coherent, was regarded by all national interviewers as impressive.

Here, using a relatively small, convenience sample, we compare the AI generated summaries both against our original data and those first impressions. We reflect upon our own experience of using AI and that of our interviewees.

This paper is about how we used AI as an experiment, our reaction to it, how that chimes, resonates, echoes the experiences of the ECRs. It is a calibration for our future data analysis.

URL : Learned Publishing – 2026 – Clark – AI And the Editors Ghost Who Is the Writer Now

DOI : https://doi.org/10.1002/leap.2051