Étiquette : Artificial intelligence

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Why the Current Model oAcademic Publishing Is Ethically Flawed—and What We Can Do to Change It

Author : Emilia Kaczmarek

This article offers a reasoned call for urgent reform of the academic journal publishing system. It focuses on the ethical flaws of the current for-profit model. This model enables the transfer of public funds into the profit margins of private companies that add no meaningful value to research and even limit access to knowledge.

The article describes how feedback loops in metrics used in the evaluation of scientific publishing exacerbate structural inequalities and make it difficult to break out of the system. Moreover, the opportunity for easy profit attracts dishonest actors and fuels the rise of predatory journals, which in turn corrodes public trust in science.

Without systemic reforms, the current system could also undermine artificial intelligence–driven research outcomes by enabling models to be trained on a growing number of substandard scientific publications. The article concludes with ten specific proposals for action, aimed at stimulating further discussion within and beyond academia.

DOI : https://doi.org/10.3138/jsp-2025-0047

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Ethical and practical implications of AI in academic library research

Author : Nuno Sousa

This article offers a critical and integrative review of how artificial intelligence (AI) is being incorporated into academic library systems, particularly in the context of scientific research production. Based on 29 studies, the review explores ethical practices, institutional boundaries, and epistemological challenges surrounding AI adoption.

Findings reveal that AI is reshaping scholarly workflows, such as metadata creation, information retrieval, and literature review, while also introducing unresolved ethical concerns, including data privacy, algorithmic bias, academic integrity, and diminished human agency.

The study identifies a misalignment between the rapid pace of AI implementation and the capacity of academic institutions to regulate its use responsibly. Librarians are situated at the intersection of innovation and ethical mediation, often without formal training or institutional support.

The review concludes that AI should not be viewed merely as a functional tool but as a socio-technical agent requiring ethical governance, critical AI literacy, and structural accountability across academic ecosystems.

URL : Ethical and practical implications of AI in academic library research

DOI : https://doi.org/10.1177/03400352251391753

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Shifting norms in scholarly publications: trends in readability, objectivity, authorship, and AI use

Authors : Padraig Cunningham, Padhraic Smyth, Barry Smyth

Academic and scientific publishing practices have changed significantly in recent years. This paper presents an analysis of 17 million research papers published since 2000 to explore changes in authorship and content practices. It shows a clear trend towards more authors, more references and longer abstracts.

While increased authorship has been reported elsewhere, the present analysis shows that it is pervasive across many major fields of study. We also identify a decline in author productivity which suggests that `gift’ authorship (the inclusion of authors who have not contributed significantly to a work) may be a significant factor. We further report on a tendency for authors to use more hyperbole, perhaps exaggerating their contributions to compete for the limited attention of reviewers, and often at the expense of readability.

This has been especially acute since 2023, as AI has been increasingly used across many fields of study, but particularly in fields such as Computer Science, Engineering and Business. In summary, many of these changes are causes of significant concern. Increased authorship counts and gift authorship have the potential to distort impact metrics such as field-weighted citation impact andh-index, while increased AI usage may compromise readability and objectivity.

DOI : https://doi.org/10.48550/arXiv.2510.21725

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AI and Open Science: Implications and Library Practice

Author : Nicole Helregel

With the increasing proliferation of artificial intelligence (AI) in higher education and science, technology, engineering, and mathematics research, what are the implications for open science?

As the open science movement advocates for increased transparency and openness in the research process, where do AI and machine learning fit in? And where does that leave library and information science professionals in roles related to open science?

This article explores several approaches and considerations for how AI impacts open science, including whether AI has sufficient openness and transparency to align with the goals of open science, whether AI can be used to further open science goals, and the effects of AI use on researcher and public attitudes and actions.

The article provides recommendations for library practice, including knowledge-building, connections and advocacy, consultations and liaison work, licensing, and science communication and engagement.

URL : AI and Open Science: Implications and Library Practice

DOI : https://dx.doi.org/10.1353/lib.2025.a961191

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Assessing the Societal Impact of Academic Research With Artificial Intelligence (AI): A Scoping Review of Business School Scholarship as a ‘Force for Good’

Authors : David Steingard, Kathleen Rodenburg

This study addresses critical questions about how current evaluative frameworks for academic research can effectively translate scholarly findings into practical applications and policies to tackle societal ‘grand challenges’.

This scoping review analysis was conducted using bibliometric methods and AI tools. Articles were drawn from a wide range of disciplines, with particular emphasis on the business and management fields, focusing on the burgeoning scholarship area of ‘business as a force for good’.

The novel integration of generative AI research approaches underscores the transformative potential of AI-human collaboration in academic research. Metadata from 4051 articles were examined in the scoping review, with only 370 articles (9.1%) explicitly identified as relevant to societal impact.

This finding reveals a substantial and concerning gap in research addressing the urgent social and environmental issues of our time. To address this gap, the study identifies six meta-themes related to enhancing the societal impact of research: business applications; faculty publication pressure; societal impact focus; sustainable development; university and scholarly rankings; and reference to responsible research frameworks.

Key findings highlight critical misalignments between research outputs and the United Nations Sustainable Development Goals (SDGs) and a lack of practical business applications of research insights.

The results emphasise the urgent need for academic institutions to expand evaluation criteria beyond traditional metrics to prioritise real-world impacts. Recommendations include developing holistic evaluation frameworks and incentivising research that addresses pressing societal challenges—shifting academia from a ‘scholar-to-scholar’ to a ‘scholar-to-society’ paradigm.

The implications of this shift are applied to business-related scholarship and its potential to inspire meaningful societal impact through business practice.

URL : Assessing the Societal Impact of Academic Research With Artificial Intelligence (AI): A Scoping Review of Business School Scholarship as a ‘Force for Good’

DOI : https://doi.org/10.1002/leap.2010

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Evaluating the predictive capacity of ChatGPT for academic peer review outcomes across multiple platforms

Authors : Mike Thelwall, Abdallah Yaghi

Academic peer review is at the heart of scientific quality control, yet the process is slow and time-consuming. Technology that can predict peer review outcomes may help with this, for example by fast-tracking desk rejection decisions. While previous studies have demonstrated that Large Language Models (LLMs) can predict peer review outcomes to some extent, this paper introduces two new contexts and employs a more robust method—averaging multiple ChatGPT scores.

Averaging 30 ChatGPT predictions, based on reviewer guidelines and using only the submitted titles and abstracts failed to predict peer review outcomes for F1000Research (Spearman’s rho = 0.00). However, it produced mostly weak positive correlations with the quality dimensions of SciPost Physics (rho = 0.25 for validity, rho = 0.25 for originality, rho = 0.20 for significance, and rho = 0.08 for clarity) and a moderate positive correlation for papers from the International Conference on Learning Representations (ICLR) (rho = 0.38). Including article full texts increased the correlation for ICLR (rho = 0.46) and slightly improved it for F1000Research (rho = 0.09), with variable effects on the four quality dimension correlations for SciPost LaTeX files.

The use of simple chain-of-thought system prompts slightly increased the correlation for F1000Research (rho = 0.10), marginally reduced it for ICLR (rho = 0.37), and further decreased it for SciPost Physics (rho = 0.16 for validity, rho = 0.18 for originality, rho = 0.18 for significance, and rho = 0.05 for clarity). Overall, the results suggest that in some contexts, ChatGPT can produce weak pre-publication quality predictions.

However, their effectiveness and the optimal strategies for employing them vary considerably between platforms, journals, and conferences. Finally, the most suitable inputs for ChatGPT appear to differ depending on the platform.

URL : Evaluating the predictive capacity of ChatGPT for academic peer review outcomes across multiple platforms

DOI : https://doi.org/10.1007/s11192-025-05287-1

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Use of artificial intelligence innovations in public academic libraries

Authors : Amogelang Isaac Molaudzi, Patrick Ngulube

Public academic libraries are among the many organisations concerned about using artificial intelligence (AI) technologies. The study adopted a mixed methods research (MMR) approach using a concurrent research design to examine the use of AI innovations in public academic libraries. Thematic and descriptive statistical data analysis was used to analyse the data gathered from questionnaires, interviews and document content analysis. The findings revealed that public academic libraries in South Africa did not have clear strategies for adopting AI innovations.

Consequently, AI was not widely used. Library management systems can support AI, but some must be upgraded. Librarians had excellent computer literacy, although many had not received AI training to broaden their expertise and awareness of this innovation. Results suggested that public academic libraries should create comprehensive AI adoption strategies responsive to AI trends.

This study highlights the need for strategies that ensure AI technologies are utilized ethically, equitably, and with accountability. It also contributes to the literature on the use of AI in academic libraries. The results of this study may encourage public academic librarians to begin planning the incorporation of AI technology into their strategies.

URL : Use of artificial intelligence innovations in public academic libraries

DOI : https://doi.org/10.1177/0340035224130178