Archives des mots-clés : Generative Artificial Intelligence

Global insights: ChatGPT’s influence on academic and research writing, creativity, and plagiarism policies

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Authors : Muhammad Abid Malik, Amjad Islam Amjad, Sarfraz Aslam, Abdulnaser Fakhrou

Introduction: The current study explored the influence of Chat Generative Pre-Trained Transformer (ChatGPT) on the concepts, parameters, policies, and practices of creativity and plagiarism in academic and research writing.

Methods: Data were collected from 10 researchers from 10 different countries (Australia, China, the UK, Brazil, Pakistan, Bangladesh, Iran, Nigeria, Trinidad and Tobago, and Turkiye) using semi-structured interviews. NVivo was employed for data analysis.

Results: Based on the responses, five themes about the influence of ChatGPT on academic and research writing were generated, i.e., opportunity, human assistance, thought-provoking, time-saving, and negative attitude. Although the researchers were mostly positive about it, some feared it would degrade their writing skills and lead to plagiarism. Many of them believed that ChatGPT would redefine the concepts, parameters, and practices of creativity and plagiarism.

Discussion: Creativity may no longer be restricted to the ability to write, but also to use ChatGPT or other large language models (LLMs) to write creatively. Some suggested that machine-generated text might be accepted as the new norm; however, using it without proper acknowledgment would be considered plagiarism. The researchers recommended allowing ChatGPT for academic and research writing; however, they strongly advised it to be regulated with limited use and proper acknowledgment.

URL : Global insights: ChatGPT’s influence on academic and research writing, creativity, and plagiarism policies

DOI : https://doi.org/10.3389/frma.2024.1486832

The impact of generative AI on the scholarly communications of early career researchers: An international, multi-disciplinary study

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Authors : David Nicholas, Marzena Swigon, David Clark, Abdullah Abrizah, Jorge Revez, Eti Herman, Blanca Rodríguez Bravo, Jie Xu, Anthony Watkinson

The Harbingers study of early career researchers (ECRs), their work life and scholarly communications, began by studying generational—Millennial—change (c.2016), then moved to pandemic change (c.2020) and is now investigating another potential agent of change: artificial intelligence (2024–). We report here on a substantial scoping pilot study that looks at the impact of AI on the scholarly communications of international ECRs and, extends this to the arts and humanities.

It aims to fill the knowledge gap concerning ECRs whose millennial mindset may render them especially open to change and, as the research workhorses they are, very much in the frontline. The data was collected via in-depth interviews in China, Malaysia, Poland, Portugal, Spain and (selectively) the United Kingdom/United States. The data show ECRs to be thinking, probing and, in some cases, experimenting with AI.

There was a general acceptance that AI will be responsible for the growth of low-quality scientific papers, which could lead to a decline in the quality of research. Scholarly integrity and ethics were a big concern with issues of authenticity, plagiarism, copyright and poor citation practices raised. The most widespread belief was AI would prove to be a transformative force and would exacerbate existing scholarly disparities and inequalities.

URL : The impact of generative AI on the scholarly communications of early career researchers: An international, multi-disciplinary study

DOI : https://doi.org/10.1002/leap.1628

How to make sense of generative AI as a science communication researcher? A conceptual framework in the context of critical engagement with scientific information

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Authors : Inbal Klein-Avraham, Esther Greussing, Monika Taddicken, Shakked Dabran-Zivan, Evelyn Jonas, Ayelet Baram-Tsabari

A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments.

Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a holistic yet practical assessment of various AI-based information technologies, providing both a reflection aid and a conceptual baseline for scholarly references.

URL : How to make sense of generative AI as a science communication researcher? A conceptual framework in the context of critical engagement with scientific information

DOI : https://doi.org/10.22323/2.23060205

Open Science at the Generative AI Turn: An Exploratory Analysis of Challenges and Opportunities

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Authors : Mohammad Hosseini, Serge P.J.M. Horbach, Kristi L. Holmes, Tony Ross-Hellauer

Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools/platforms for collaborative research and sharing results. Due to this direct relationship, characteristics of employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) models are increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, data curation/analysis.

GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values such as fairness, transparency and integrity, and harm various social actors. In this paper, we explore possible positive and negative impacts of GenAI on OS.

We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by further broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems.

However, due to GenAI limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research, while also having potential implications for the political economy of research and its infrastructure. Hence, sufficient checks, validation and critical assessments are essential when incorporating GenAI into research workflows.

URL : Open Science at the Generative AI Turn: An Exploratory Analysis of Challenges and Opportunities

DOI : https://doi.org/10.31235/osf.io/zns7g