AI in academia: An overview of selected tools and their areas of application

F. J. Pinzolits, Robert

doi:10.53880/2744-2373.2023.4.37

Cited by 16 publications

(13 citation statements)

References 61 publications

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“…Among these are academic dishonesty and plagiarism (Chaka, 2023a(Chaka, , 2023cCotton et al, 2023;Ifelebuegu, 2023;Ifelebuegu et al, 2023;Kasneci et al, 2023;Kleebayoon & Wiwanitkit, 2023;Kooli, 2023;Perkins et al, 2023;Rudolph et al, 2023;Sullivan et al, 2023) and bias and unfairness (Dwivedi et al, 2023;Kasneci et al, 2023;Nah et al, 2023;Ray, 2023). To this effect, some review studies have been conducted on the use of the new AI chatbots in education in general (see Baidoo-Anu & Ansah, 2023;Dergaa et al, 2023;Ifelebuegu et al, 2023;Perera & Lankathilaka, 2023;Pinzolits, 2023;Sullivan et al, 2023;Thurzo et al, 2023;Yang et al, 2023). For example, Baidoo-Anu and Ansah's (2023) study investigated, among other things, the potential benefits of ChatGPT in education as reported in peer-reviewed journal articles and/or in preprints published between November 2022 and March 2023.…”

Section: Introductionmentioning

confidence: 99%

Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts: A literature and integrative hybrid review

2024

JALT

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The purpose of this study was to review 17 articles published between January 2023 and November 2023 that dealt with the performance of AI detectors in differentiating between AI-generated and humanwritten texts. Employing a slightly modified version of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol and an aggregated set of quality evaluation criteria adapted from A MeaSurement Tool to Assess systematic Reviews (AMSTAR) tool, the study was conducted from 1 October 2023 to 30 November 2023 and guided by six research questions. The study conducted its searches on eleven online databases, two Internet search engines, and one academic social networking site. The geolocation and authorship of the 17 reviewed articles were spread across twelve countries in both the Global North and the Global South. ChatGPT (in its two versions, GPT-3.5 and GPT-4) was the sole AI text generator used or was one of the AI text generators in instances where more than one AI text generator had been used. Crossplag was the top-performing AI detection tool, followed by Copyleaks. Duplichecker and Writer were the worst-performing AI detection tools in instances in which they had been used. One of the major aspects flagged by the main findings of the 17 reviewed articles is the inconsistency of the detection efficacy of all the tested AI detectors and all the tested anti-plagiarism detection tools. Both sets of detection tools were found to lack detection reliability. As a result, this study recommends utilising both contemporary AI detectors and traditional anti-plagiarism detection tools, together with human reviewers/raters, in an ongoing search for differentiating between AI-generated and humanwritten texts.

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Section: Introductionmentioning

confidence: 99%

Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts: A literature and integrative hybrid review

2024

JALT

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“…AI tools can parse bibliographies and integrate them with academic databases to compile and organize more focused studies and articles relevant to research topics [46]. In considering the different approaches and arguments of experts in the field, AI algorithms can analyze content, tone, and perspectives, providing an overview of diverse scholarly viewpoints as does Scite.ai [47]. This analysis aids researchers in understanding how their research question or methodology fits within the broader academic discourse.…”

Section: The Inevitability Of Ai In Academic Research and The Peer-re...mentioning

confidence: 99%

Rethinking Plagiarism in the Era of Generative AI

Hutson

2024

J. Intell. Commun.

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The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator perceptions. It then examines the repercussions of AI-enabled content proliferation, referencing the limitation of three books self-published per day in September 2023 by Amazon due to a suspected influx of AI-generated material. The discourse extends to the potential of AI in accelerating research akin to the contributions of digital humanities and computational linguistics, highlighting its accessibility to the general public. The article further delves into the implications of AI on pedagogical approaches to research and writing, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities. Finally, it proposes revisions to writing curricula, adapting to the transformative influence of AI in academic contexts.

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“…These AI tools have the potential to be employed in scientific writing and research, providing better support and insights for PhD students. Today, there are a number of AI tools for scientific writing and research, such as ChatGPT [5,22], ConnectedPapers [23], ResearchRabbit [24], Scholarcy, Elicit, Trinka, and Scite [25] (see in Fig. 1).…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the Impact of OpenAI’s ChatGPT on PhD Student Achievement

Boubker

2024

IJIET

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Nowadays, Artificial Intelligence (AI) tools have revolutionized scientists’ way of conducting research. Accordingly, the current study explores the role of OpenAI’s ChatGPT on PhD students’ achievement. For this purpose, a questionnaire was conducted among a sample of Moroccan PhD students in various scientific fields. The results achieved from structural equation modelling confirmed that perceived ease of use and perceived usefulness influence the degree of ChatGPT use and PhD students’ satisfaction, thereby enhancing individual net benefits. These findings offer a number of useful insights for academic leaders and doctoral supervisors regarding how they might take steps to update practices in using artificial intelligence tools.

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AI in academia: An overview of selected tools and their areas of application

Cited by 16 publications

References 61 publications

Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts: A literature and integrative hybrid review

Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts: A literature and integrative hybrid review

Rethinking Plagiarism in the Era of Generative AI

Examining the Impact of OpenAI’s ChatGPT on PhD Student Achievement

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