Science Editing

Reviews

Influence of artificial intelligence and chatbots on research integrity and publication ethics: Payam Hosseinzadeh Kasani, Kee Hyun Cho, Jae-Won Jang, Cheol-Heui Yun; Sci Ed. 2024;11(1):12-25. Published online January 25, 2024; DOI: https://doi.org/10.6087/kcse.323

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Abstract PDF: Artificial intelligence (AI)-powered chatbots are rapidly supplanting human-derived scholarly work in the fast-paced digital age. This necessitates a re-evaluation of our traditional research and publication ethics, which is the focus of this article. We explore the ethical issues that arise when AI chatbots are employed in research and publication. We critically examine the attribution of academic work, strategies for preventing plagiarism, the trustworthiness of AI-generated content, and the integration of empathy into these systems. Current approaches to ethical education, in our opinion, fall short of appropriately addressing these problems. We propose comprehensive initiatives to tackle these emerging ethical concerns. This review also examines the limitations of current chatbot detectors, underscoring the necessity for more sophisticated technology to safeguard academic integrity. The incorporation of AI and chatbots into the research environment is set to transform the way we approach scholarly inquiries. However, our study emphasizes the importance of employing these tools ethically within research and academia. As we move forward, it is of the utmost importance to concentrate on creating robust, flexible strategies and establishing comprehensive regulations that effectively align these potential technological developments with stringent ethical standards. We believe that this is an essential measure to ensure that the advancement of AI chatbots significantly augments the value of scholarly research activities, including publications, rather than introducing potential ethical quandaries.

Trends in research on ChatGPT and adoption-related issues discussed in articles: a narrative review: Sang-Jun Kim; Sci Ed. 2024;11(1):3-11. Published online December 18, 2023; DOI: https://doi.org/10.6087/kcse.321

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Abstract PDF Supplementary Material: This review aims to provide guidance for those contemplating the use of ChatGPT, by sharing research trends and evaluation results discussed in various articles. For an objective and quantitative analysis, 1,105 articles published over a 7-month period, from December 2022 to June 2023, following the release of ChatGPT were collected. These articles were sourced from PubMed, Scopus, and Web of Science. Additionally, 140 research articles were selected, including archived preprints and Korean articles, to evaluate the performance of ChatGPT. The analysis of research trends revealed that related communities are rapidly and actively responding: the educational community is redefining its directions, the copyright and patent community is monitoring lawsuits related to artificial intelligence creations, the government is establishing laws to regulate and prevent potential harm, the journal publishing community is setting standards for whether artificial intelligence can be considered an author, and the medical community is publishing numerous articles exploring the potential of ChatGPT to support medical experts. A comparative analysis of research articles on ChatGPT’s performance suggests that it could serve as a valuable assistant in human intellectual activities and academic processes. However, its practical application requires careful consideration to overcome certain limitations. Both the general public and researchers should assess the adoption of ChatGPT based on accurate information, such as that provided in this review.

Original Article

Impact and perceived value of the revolutionary advent of artificial intelligence in research and publishing among researchers: a survey-based descriptive study: Riya Thomas, Uttkarsha Bhosale, Kriti Shukla, Anupama Kapadia; Sci Ed. 2023;10(1):27-34. Published online February 16, 2023; DOI: https://doi.org/10.6087/kcse.294

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336 Download
1 Web of Science
1 Crossref

Abstract PDF Supplementary Material

Purpose: This study was conducted to understand the perceptions and awareness of artificial intelligence (AI) in the academic publishing landscape.
Methods
We conducted a global survey entitled “Role and impact of AI on the future of academic publishing” to understand the impact of the AI wave in the scholarly publishing domain. This English-language survey was open to all researchers, authors, editors, publishers, and other stakeholders in the scholarly community. Conducted between August and October 2021, the survey received responses from around 212 universities across 54 countries.
Results
Out of 365 respondents, about 93% belonged to the age groups of 18–34 and 35–54 years. While 50% of the respondents selected plagiarism detection as the most widely known AI-based application, image recognition (42%), data analytics (40%), and language enhancement (39%) were some other known applications of AI. The respondents also expressed the opinion that the academic publishing landscape will significantly benefit from AI. However, the major challenges restraining the large-scale adoption of AI, as expressed by 93% of the respondents, were limited knowledge and expertise, as well as difficulties in integrating AI-based solutions into existing IT infrastructure.
Conclusion
The survey responses reflected the necessity of AI in research and publishing. This study suggests possible ways to support a smooth transition. This can be best achieved by educating and creating awareness to ease possible fears and hesitation, and to actualize the promising benefits of AI.

Citations

Citations to this article as recorded by

The impact of generative AI tools on researchers and research: Implications for academia in higher education
Abdulrahman M. Al-Zahrani
Innovations in Education and Teaching International.2023; : 1. CrossRef

Reviews

The current state of graphical abstracts and how to create good graphical abstracts: Jieun Lee, Jeong-Ju Yoo; Sci Ed. 2023;10(1):19-26. Published online February 16, 2023; DOI: https://doi.org/10.6087/kcse.293

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363 Download
3 Web of Science
2 Crossref

Abstract PDF

Graphical abstracts (GAs), also known as visual abstracts, are powerful tools for communicating complex information and ideas clearly and concisely. These visual representations aim to capture the essential findings and central message of a research study, allowing the audience to understand and remember its content quickly. This review article describes the current state of GAs, including their benefits, limitations, and future directions in the development of GAs. It also presents methods and tips for producing a GA. In Korea, more than 10 medical journals have introduced GAs from 2021 to 2022. The number of citations was higher in articles with GAs than in those without GAs in the top 10 gastroenterology journals. There are five types of GAs: conceptual diagrams, flowcharts, infographics, iconographic abstracts, and photograph-like illustrations. A limitation of the GA system is the absence of a universal standard for GAs. The key steps for creating a GA are as follows: (1) start by identifying the main message; (2) choose an appropriate visual style; (3) draw an easy-to-understand graphic; (4) use colors and other design elements; and (5) request feedback. Available tools that are useful for creating GAs include Microsoft PowerPoint, Mind the Graph, Biorender, and Canva. Another effective method is collaborating with experts. Artificial intelligence will soon be able to produce GAs more efficiently from raw data or manuscripts, which will help researchers draw GAs more easily. GAs have become a crucial art for researchers to master, and their use is expected to expand in the future.

Citations

Citations to this article as recorded by

Your message in pictures – Adding a graphical abstract to your paper
Péter Pongrácz, Irene Camerlink
Applied Animal Behaviour Science.2023; 263: 105946. CrossRef
Current status and demand for the advancement of Clinical Endoscopy: a survey-based descriptive study
Tae Hoon Lee, Jimin Han, Gwang Ha Kim, Hyejin Han
Science Editing.2023; 10(2): 135. CrossRef

Can an artificial intelligence chatbot be the author of a scholarly article?: Ju Yoen Lee; Sci Ed. 2023;10(1):7-12. Published online February 16, 2023; DOI: https://doi.org/10.6087/kcse.292

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430 Download
3 Web of Science
8 Crossref

Abstract PDF

At the end of 2022, the appearance of ChatGPT, an artificial intelligence (AI) chatbot with amazing writing ability, caused a great sensation in academia. The chatbot turned out to be very capable, but also capable of deception, and the news broke that several researchers had listed the chatbot (including its earlier version) as co-authors of their academic papers. In response, Nature and Science expressed their position that this chatbot cannot be listed as an author in the papers they publish. Since an AI chatbot is not a human being, in the current legal system, the text automatically generated by an AI chatbot cannot be a copyrighted work; thus, an AI chatbot cannot be an author of a copyrighted work. Current AI chatbots such as ChatGPT are much more advanced than search engines in that they produce original text, but they still remain at the level of a search engine in that they cannot take responsibility for their writing. For this reason, they also cannot be authors from the perspective of research ethics.

Citations

Citations to this article as recorded by

The ethics of ChatGPT – Exploring the ethical issues of an emerging technology
Bernd Carsten Stahl, Damian Eke
International Journal of Information Management.2024; 74: 102700. CrossRef
ChatGPT in healthcare: A taxonomy and systematic review
Jianning Li, Amin Dada, Behrus Puladi, Jens Kleesiek, Jan Egger
Computer Methods and Programs in Biomedicine.2024; 245: 108013. CrossRef
“Brave New World” or not?: A mixed-methods study of the relationship between second language writing learners’ perceptions of ChatGPT, behaviors of using ChatGPT, and writing proficiency
Li Dong
Current Psychology.2024;[Epub] CrossRef
Emergence of the metaverse and ChatGPT in journal publishing after the COVID-19 pandemic
Sun Huh
Science Editing.2023; 10(1): 1. CrossRef
ChatGPT: Systematic Review, Applications, and Agenda for Multidisciplinary Research
Harjit Singh, Avneet Singh
Journal of Chinese Economic and Business Studies.2023; 21(2): 193. CrossRef
ChatGPT: More Than a “Weapon of Mass Deception” Ethical Challenges and Responses from the Human-Centered Artificial Intelligence (HCAI) Perspective
Alejo José G. Sison, Marco Tulio Daza, Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán
International Journal of Human–Computer Interaction.2023; : 1. CrossRef
Universal skepticism of ChatGPT: a review of early literature on chat generative pre-trained transformer
Casey Watters, Michal K. Lemanski
Frontiers in Big Data.2023;[Epub] CrossRef
ChatGPT, yabancı dil öğrencisinin güvenilir yapay zekâ sohbet arkadaşı mıdır?
Şule ÇINAR YAĞCI, Tugba AYDIN YILDIZ
RumeliDE Dil ve Edebiyat Araştırmaları Dergisi.2023; (37): 1315. CrossRef

Original Article

Comparing the accuracy and effectiveness of Wordvice AI Proofreader to two automated editing tools and human editors: Kevin Heintz, Younghoon Roh, Jonghwan Lee; Sci Ed. 2022;9(1):37-45. Published online February 20, 2022; DOI: https://doi.org/10.6087/kcse.261

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326 Download
1 Crossref

Abstract PDF

Purpose: Wordvice AI Proofreader is a recently developed web-based artificial intelligence-driven text processor that provides real-time automated proofreading and editing of user-input text. It aims to compare its accuracy and effectiveness to expert proofreading by human editors and two other popular proofreading applications—automated writing analysis tools of Google Docs, and Microsoft Word. Because this tool was primarily designed for use by academic authors to proofread their manuscript drafts, the comparison of this tool’s efficacy to other tools was intended to establish the usefulness of this particular field for these authors.
Methods
We performed a comparative analysis of proofreading completed by the Wordvice AI Proofreader, by experienced human academic editors, and by two other popular proofreading applications. The number of errors accurately reported and the overall usefulness of the vocabulary suggestions was measured using a General Language Evaluation Understanding metric and open dataset comparisons.
Results
In the majority of texts analyzed, the Wordvice AI Proofreader achieved performance levels at or near that of the human editors, identifying similar errors and offering comparable suggestions in the majority of sample passages. The Wordvice AI Proofreader also had higher performance and greater consistency than that of the other two proofreading applications evaluated.
Conclusion
We found that the overall functionality of the Wordvice artificial intelligence proofreading tool is comparable to that of a human proofreader and equal or superior to that of two other programs with built-in automated writing evaluation proofreaders used by tens of millions of users: Google Docs and Microsoft Word.

Citations

Citations to this article as recorded by

Navigating the impact: a study of editors’ and proofreaders’ perceptions of AI tools in editing and proofreading
Islam Al Sawi, Ahmed Alaa
Discover Artificial Intelligence.2024;[Epub] CrossRef

Reviews

Types, limitations, and possible alternatives of peer review based on the literature and surgeons’ opinions via Twitter: a narrative review: Sameh Hany Emile, Hytham K. S. Hamid, Semra Demirli Atici, Doga Nur Kosker, Mario Virgilio Papa, Hossam Elfeki, Chee Yang Tan, Alaa El-Hussuna, Steven D. Wexner; Sci Ed. 2022;9(1):3-14. Published online February 20, 2022; DOI: https://doi.org/10.6087/kcse.257

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Abstract PDF: This review aimed to illustrate the types, limitations, and possible alternatives of peer review (PR) based on a literature review together with the opinions of a social media audience via Twitter. This study was conducted via the #OpenSourceResearch collaborative platform and combined a comprehensive literature search on the current PR system with the opinions of a social media audience of surgeons who are actively engaged in the current PR system. Six independent researchers conducted a literature search of electronic databases in addition to Google Scholar. Electronic polls were organized via Twitter to assess surgeons’ opinions on the current PR system and potential alternative approaches. PR can be classified into single-blind, double-blind, triple-blind, and open PR. Newer PR systems include interactive platforms, prepublication and postpublication commenting or review, transparent review, and collaborative review. The main limitations of the current PR system are its allegedly time-consuming nature and inconsistent, biased, and non-transparent results. Suggestions to improve the PR process include employing an interactive, double-blind PR system, using artificial intelligence to recruit reviewers, providing incentives for reviewers, and using PR templates. The above results offer several concepts for possible alternative approaches and modifications to this critically important process.

Artificial intelligence-assisted tools for redefining the communication landscape of the scholarly world: Habeeb Ibrahim Abdul Razack, Sam T. Mathew, Fathinul Fikri Ahmad Saad, Saleh A. Alqahtani; Sci Ed. 2021;8(2):134-144. Published online July 27, 2021; DOI: https://doi.org/10.6087/kcse.244

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625 Download
12 Web of Science
12 Crossref

Abstract PDF

The flood of research output and increasing demands for peer reviewers have necessitated the intervention of artificial intelligence (AI) in scholarly publishing. Although human input is seen as essential for writing publications, the contribution of AI slowly and steadily moves ahead. AI may redefine the role of science communication experts in the future and transform the scholarly publishing industry into a technology-driven one. It can prospectively improve the quality of publishable content and identify errors in published content. In this article, we review various AI and other associated tools currently in use or development for a range of publishing obligations and functions that have brought about or can soon leverage much-demanded advances in scholarly communications. Several AI-assisted tools, with diverse scope and scale, have emerged in the scholarly market. AI algorithms develop summaries of scientific publications and convert them into plain-language texts, press statements, and news stories. Retrieval of accurate and sufficient information is prominent in evidence-based science publications. Semantic tools may empower transparent and proficient data extraction tactics. From detecting simple plagiarism errors to predicting the projected citation impact of an unpublished article, AI’s role in scholarly publishing is expected to be multidimensional. AI, natural language processing, and machine learning in scholarly publishing have arrived for writers, editors, authors, and publishers. They should leverage these technologies to enable the fast and accurate dissemination of scientific information to contribute to the betterment of humankind.

Citations

Citations to this article as recorded by

Navigating the impact: a study of editors’ and proofreaders’ perceptions of AI tools in editing and proofreading
Islam Al Sawi, Ahmed Alaa
Discover Artificial Intelligence.2024;[Epub] CrossRef
Beyond Plagiarism: ChatGPT as the Vanguard of Technological Revolution in Research and Citation
Hanni B. Flaherty, Jackson Yurch
Research on Social Work Practice.2024;[Epub] CrossRef
Recent Issues in Medical Journal Publishing and Editing Policies: Adoption of Artificial Intelligence, Preprints, Open Peer Review, Model Text Recycling Policies, Best Practice in Scholarly Publishing 4th Version, and Country Names in Titles
Sun Huh
Neurointervention.2023; 18(1): 2. CrossRef
Artificial intelligence-assisted medical writing: With greater power comes greater responsibility
Rhythm Bains
Asian Journal of Oral Health and Allied Sciences.2023; 13: 2. CrossRef
Emergence of the metaverse and ChatGPT in journal publishing after the COVID-19 pandemic
Sun Huh
Science Editing.2023; 10(1): 1. CrossRef
Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend Analysis
Mehmet Yașar Bayraktar, Mehmet Kaya
IEEE Access.2023; 11: 45826. CrossRef
Utilization of artificial intelligence technology in an academic writing class: How do Indonesian students perceive?
Santi Pratiwi Tri Utami, Andayani Andayani, Retno Winarni, Sumarwati Sumarwati
Contemporary Educational Technology.2023; 15(4): ep450. CrossRef
The impact of generative AI tools on researchers and research: Implications for academia in higher education
Abdulrahman M. Al-Zahrani
Innovations in Education and Teaching International.2023; : 1. CrossRef
Slow Writing with ChatGPT: Turning the Hype into a Right Way Forward
Chitnarong Sirisathitkul
Postdigital Science and Education.2023;[Epub] CrossRef
Editorial policies of Journal of Educational Evaluation for Health Professions on the use of generative artificial intelligence in article writing and peer review
Sun Huh
Journal of Educational Evaluation for Health Professions.2023; 20: 40. CrossRef
Current Status of Neurointervention, the Official Journal of the Korean Society of Interventional Neuroradiology
Dae Chul Suh, Sun Huh
Neurointervention.2022; 17(2): 67. CrossRef
Profiles of Technology Use and Plagiarism in High School Education
Juan Carlos Torres-Diaz, Pablo Vicente Torres Carrión, Isidro Marín Gutierrez
SSRN Electronic Journal .2021;[Epub] CrossRef

Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review: Seong Ho Park, Young-Hak Kim, Jun Young Lee, Soyoung Yoo, Chong Jai Kim; Sci Ed. 2019;6(2):91-98. Published online June 19, 2019; DOI: https://doi.org/10.6087/kcse.164

14,744 View
406 Download
15 Web of Science
17 Crossref

Abstract PDF

This review article aims to highlight several areas in research studies on artificial intelligence (AI) in medicine that currently require additional transparency and explain why additional transparency is needed. Transparency regarding training data, test data and results, interpretation of study results, and the sharing of algorithms and data are major areas for guaranteeing ethical standards in AI research. For transparency in training data, clarifying the biases and errors in training data and the AI algorithms based on these training data prior to their implementation is critical. Furthermore, biases about institutions and socioeconomic groups should be considered. For transparency in test data and test results, authors should state if the test data were collected externally or internally and prospectively or retrospectively at first. It is necessary to distinguish whether datasets were convenience samples consisting of some positive and some negative cases or clinical cohorts. When datasets from multiple institutions were used, authors should report results from each individual institution. Full publication of the results of AI research is also important. For transparency in interpreting study results, authors should interpret the results explicitly and avoid over-interpretation. For transparency by sharing algorithms and data, sharing is required for replication and reproducibility of the research by other researchers. All of the above mentioned high standards regarding transparency of AI research in healthcare should be considered to facilitate the ethical conduct of AI research.

Citations

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Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare: State-of-the-Art and Future Prospects
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