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- Introduction to generative artificial intelligence tools for academic article writing
- Joon Seo Lim
- Received January 20, 2026 Accepted January 27, 2026 Published online February 2, 2026
- DOI: https://doi.org/10.6087/kcse.393 [Epub ahead of print]
- 47 View
- 4 Download
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Abstract
PDF - Since the advent of ChatGPT, researchers have rapidly adopted generative artificial intelligence (AI) for academic work, with monthly use reported by 69.4% of natural science researchers and 51.2% of medical researchers. This educational article surveys AI tools for literature search and trend analysis, study-oriented article organization, and manuscript drafting and editing, while emphasizing that these tools complement—not replace—critical reading and standard database searches. For discovery and mapping, Research Rabbit and Connected Papers visualize related papers through citation links or content similarity, while Consensus summarizes the direction and strength of evidence addressing a focused research question. Elicit and SciSpace can extract methods and conclusions into structured tabular summaries to support scoping and gap identification, and STORM can generate knowledge maps for topic exploration; Liner offers research agents to support hypothesis generation and literature review. To extend reference-management workflows, the article proposes downloading relevant PDFs, uploading them to a large language model, extracting predefined fields (e.g., design, participants, interventions, outcomes, key statistics, limitations, and DOI) into a CSV file, and importing the output into a Notion database for tagging and tracking reading status. For writing support, SciSpace and Liner provide outline generation, citation assistance, and peer review style checks, whereas Paperpal, Wordvice.ai, and DeepL focus on grammar, paraphrasing, and translation, and Scite contextualizes citations by identifying whether they are supporting or contrasting. Key cautions include manual verification of AI outputs, awareness of English-language bias, avoidance of reliance on a single tool, and protection of manuscript confidentiality; authors must disclose AI use and remain accountable for accuracy. When used judiciously, these tools can streamline screening, summarization, and revision without eroding scholarly judgment.
Original Articles
- Prevalence of generative artificial intelligence guidance statements in the urology literature: a descriptive study
- Mandy Hsu, Max S. Yudovich, Jay D. Raman
- Sci Ed. 2025;12(2):138-142. Published online August 5, 2025
- DOI: https://doi.org/10.6087/kcse.375
- 1,242 View
- 43 Download
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Abstract
PDF
- Purpose
The adoption of generative artificial intelligence (AI) in medical literature has increased exponentially over the past 2 years. Many journals have introduced AI guidance statements for authors during the manuscript submission process. This study characterizes the extent and types of AI guidance statements among urology journals.
Methods
A total of 112 urology journals indexed on PubMed were identified. Each journal’s website was searched for the presence of an AI guidance statement. Specific aspects of AI guidance assessed included manuscript content generation, manuscript writing, and manuscript editing. Additional variables such as journal data, region, subspecialty, society affiliations, and impact factor were also collected.
Results
Of the total 112 urology journals, 61 (54.5%) had an AI guidance statement. Most journals with statements (n=58, 95.1%) permitted the use of AI for manuscript editing. A slightly smaller majority (n=53, 86.9%) explicitly allowed AI-assisted manuscript writing. No journals definitively prohibited AI use for manuscript editing. Twenty-three journals (37.7%) permitted AI-generated manuscript content, while 11 (18.0%) explicitly did not, and 27 (44.3%) were unclear regarding their stance. Among journals with any AI usage, 60 (98.4%) required a disclosure statement on AI use. Only one journal (1.6%) did not provide any guidance.
Conclusion
More than half of urology journals offer author guidance on the use of AI in manuscript submission. However, these instructions are not standardized across journals. As AI continues to permeate medical literature, the development of consensus policies is advisable.
- Global research trends and thematic developments in artificial intelligence applications in medical education: a bibliometric study
- Wang Bo, Dhakir Abbas Ali, Oyyappan Duraipandi
- Sci Ed. 2025;12(2):143-151. Published online August 4, 2025
- DOI: https://doi.org/10.6087/kcse.373
- 1,319 View
- 74 Download
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Abstract
PDF
- Purpose
Artificial intelligence (AI) is rapidly transforming medical education through innovative methods in instruction, assessment, and simulation. This study systematically analyzes global research trends and thematic developments in AI applications within medical education.
Methods
A total of 732 English-language articles were identified in the Scopus database prior to April 10, 2025, using the keywords “medical education” and “artificial intelligence” within titles, abstracts, or keywords. Bibliometric analysis was conducted using VOSviewer to investigate publication trends, keyword co-occurrence, and citation coupling, complemented by cluster-based content analysis. Additional analyses included publication characteristics, regional distribution, author collaboration, and the evolution of core topics.
Results
Publication output increased markedly after 2018, reaching a peak in 2024. The United States, China, and the United Kingdom were leaders in research volume, while smaller nations such as Ireland and Singapore exhibited high citation impact. Author analysis demonstrated robust collaboration networks and a growing trend of interdisciplinary engagement. Keyword clustering revealed four primary themes: AI-driven simulation and training, intelligent assessment systems, personalized learning environments, and ethical and pedagogical considerations. The average year of keyword publication (2023–2024) underscores the recent acceleration of the field, particularly in generative AI and large language models.
Conclusion
The integration of AI in medical education is accelerating, characterized by thematic diversification and broader global participation. This study provides a comprehensive overview of the field’s intellectual landscape and highlights critical areas for future advancement, including curriculum reform, faculty development, and responsible AI integration to optimize educational outcomes and learner preparedness.
- Ethical guidelines for the use of generative artificial intelligence and artificial intelligence-assisted tools in scholarly publishing: a thematic analysis
- Adéle da Veiga
- Sci Ed. 2025;12(1):28-34. Published online February 5, 2025
- DOI: https://doi.org/10.6087/kcse.352
- 12,351 View
- 985 Download
- 2 Web of Science
- 3 Crossref
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Abstract
PDF
Supplementary Material - Purpose
This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.
Methods
A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.
Results
The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.
Conclusion
The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools. -
Citations
Citations to this article as recorded by
- “An Assistant in Your Pocket”: How Generative AI Shapes the Publishing Practices of Russian Postgraduate Students
E. A. Koval, S. G. Ushkin, O. N. Ageeva, N. V. Zhadunova
Vysshee Obrazovanie v Rossii = Higher Education in Russia.2026; 34(12): 107. CrossRef - Uso ético y eficiente de la inteligencia artificial en trabajos académicos: Veritas e interacción crítica escalonada
Lluís Codina
BiD: textos universitaris de biblioteconomia i documentació.2026;[Epub] CrossRef - Biomedical research publication in the age of artificial intelligence: Current prospects for balancing integrity and innovation
Vivek Kumar Bains, Ujjal K Bhawal
Journal of Healthcare Research and Education.2025; 1: 3. CrossRef
- “An Assistant in Your Pocket”: How Generative AI Shapes the Publishing Practices of Russian Postgraduate Students
Review
- Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review
- Sang-Jun Kim
- Sci Ed. 2024;11(2):96-106. Published online August 20, 2024
- DOI: https://doi.org/10.6087/kcse.343
- 32,903 View
- 1,395 Download
- 21 Web of Science
- 21 Crossref
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Abstract
PDF
- While generative artificial intelligence (AI) technology has become increasingly competitive since OpenAI introduced ChatGPT, its widespread use poses significant ethical challenges in research. Excessive reliance on tools like ChatGPT may intensify ethical concerns in scholarly articles. Therefore, this article aims to provide a comprehensive narrative review of the ethical issues associated with using AI in academic writing and to inform researchers of current trends. Our methodology involved a detailed examination of literature on ChatGPT and related research trends. We conducted searches in major databases to identify additional relevant articles and cited literature, from which we collected and analyzed papers. We identified major issues from the literature, categorized into problems faced by authors using nonacademic AI platforms in writing and challenges related to the detection and acceptance of AI-generated content by reviewers and editors. We explored eight specific ethical problems highlighted by authors and reviewers and conducted a thorough review of five key topics in research ethics. Given that nonacademic AI platforms like ChatGPT often do not disclose their training data sources, there is a substantial risk of unattributed content and plagiarism. Therefore, researchers must verify the accuracy and authenticity of AI-generated content before incorporating it into their article, ensuring adherence to principles of research integrity and ethics, including avoidance of fabrication, falsification, and plagiarism.
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Citations
Citations to this article as recorded by- Understanding haze data contestations in Singapore: between accuracy and affect
Nurul Amillin Hussain
Environmental Sociology.2026; 12(1): 126. CrossRef - Generative AI in academia: Efficiency versus scholarship
Daniela Schnitzler
The Journal of Physiology.2026; 604(1): 31. CrossRef - A Cross‐Disciplinary Analysis of AI Policies in Academic Peer Review
Zhongshi Wang, Mengyue Gong
Learned Publishing.2026;[Epub] CrossRef - AI detecting AI in academic writing: Why most AI detector findings are false
Panagiotis Tsigaris, Jaime A. Teixeira da Silva
Next Research.2026; : 101396. CrossRef - Human-AI collaboration in vocational writing: Building a framework for adaptive English learning
Şükran Türkmen Çiçek, Dilek Tüfekci Can
Journal of Educational Technology and Online Learning.2026; 9(1): 45. CrossRef - Generative artificial intelligence tools in journal article preparation: A preliminary catalog of ethical considerations, opportunities, and pitfalls
Robin R. White
JDS Communications.2025; 6(3): 452. CrossRef - Ethics For Responsible Data Research: Integrating Cybersecurity Perspectives In Digital Era
Sheetal Temara
SSRN Electronic Journal.2025;[Epub] CrossRef - Ethical guidelines for the use of generative artificial intelligence and artificial intelligence-assisted tools in scholarly publishing: a thematic analysis
Adéle da Veiga
Science Editing.2025; 12(1): 28. CrossRef - Artificial intelligence-assisted academic writing: recommendations for ethical use
Adam Cheng, Aaron Calhoun, Gabriel Reedy
Advances in Simulation.2025;[Epub] CrossRef - Exploring AI Hallucinations of ChatGPT
Adam Cheng, Vikhashni Nagesh, Susan Eller, Vincent Grant, Yiqun Lin
Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare.2025; 20(6): 413. CrossRef - Research trends and comparisons of major generative artificial intelligence platforms for systematic literature reviews
Sang-Jun Kim
Science Editing.2025; 12(2): 200. CrossRef - Are Teachers Assessing Work Written by Students or by AI? A Rapid Literature Review of Research on Detecting Content Generated by Generative AI
Jining Han, Yuying Yang, Geping Liu
European Journal of Education.2025;[Epub] CrossRef - Applications of artificial intelligence in healthcare simulation: a model of thinking
Adam Cheng, Carolyn McGregor
Advances in Simulation.2025;[Epub] CrossRef - ¿Cómo está transformando la inteligencia artificial la comunicación científica? Desafíos, oportunidades y el papel de los actores involucrados: una revisión de alcance
Jairo Buitrago-Ciro, Estela Morales Campos, César Leonardo Villamizar Romero
Investigación Bibliotecológica: archivonomía, bibliotecología e información.2025; 39(104): 111. CrossRef - ChatGPT: how to use it and the pitfalls/cautions in academia
Jeong-Moo Lee
Annals of Pediatric Endocrinology & Metabolism.2025; 30(5): 229. CrossRef - Initial Validation of the IMPACT Model: Technological Appropriation of ChatGPT by University Faculty
Luz-M. Pereira-González, Andrea Basantes-Andrade, Miguel Naranjo-Toro, Mailevy Guia-Pereira
Education Sciences.2025; 15(11): 1520. CrossRef - The Epistemic Downside of Using LLM-Based Generative AI in Academic Writing
Bor Luen Tang
Publications.2025; 13(4): 63. CrossRef - Ethical Considerations for the Use of Artificial Intelligence in Linguistics Journal Publishing: Combining Hybrid Thematic Analysis and Critical Discourse Analysis
Xuan Wang, Xinyi Zhang
Publications.2025; 13(4): 61. CrossRef - Ethical Implications of Using Artificial Intelligence in Intellectual Property Creation: Authorship, Ownership and Responsibility Issues
K. Afuwape
Journal of Digital Technologies and Law.2025; 3(4): 677. CrossRef - How is ChatGPT acknowledged in academic publications?
Kayvan Kousha
Scientometrics.2024; 129(12): 7959. CrossRef - Appliances of Generative AI-Powered Language Tools in Academic Writing: A Scoping Review
Lilia Raitskaya, Elena Tikhonova
Journal of Language and Education.2024; 10(4): 5. CrossRef
- Understanding haze data contestations in Singapore: between accuracy and affect
Original Article
- Publications on COVID-19 and artificial intelligence: trends and lessons
- Yeong Jae Kim, Yang Liu, Youngeun Kim, Ho Won Jang
- Sci Ed. 2024;11(2):142-148. Published online August 20, 2024
- DOI: https://doi.org/10.6087/kcse.338
- 3,774 View
- 83 Download
- 1 Web of Science
- 1 Crossref
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Abstract
PDF
- Purpose
This study investigates shifts in scientific research focus, particularly the decline in COVID-19-related research and the rapid growth of artificial intelligence (AI) publications.
Methods
We analyzed publication data from the Web of Science, comparing yearly publication counts for COVID-19 and AI research. The study also tracked changes in the impact factors of leading journals like Science and Nature, alongside those of top AI journals over the past decade. Additionally, we reviewed the top 10 most cited articles in 2021 from Science and Nature and the most influential AI publications from the past five years according to Google Scholar. The impact trends of the top 100 AI journals in computer science were also explored.
Results
The analysis reveals a noticeable decline in COVID-19 related publications as the pandemic urgency diminishes, contrasted with the continued rapid growth of AI research. Impact factors for prestigious journals have shifted, with AI journals increasingly dominating the academic landscape. The review of top-cited articles further emphasizes these trends.
Conclusion
Our findings indicate a significant shift in research priorities, with AI emerging as a dominant field poised to address future challenges, reflecting the evolving focus of the scientific community. -
Citations
Citations to this article as recorded by- Where did all the AI experts come from? They used to be virologists…
Yana Suchikova, Natalia Tsybuliak
AI & SOCIETY.2025; 40(7): 5579. CrossRef
- Where did all the AI experts come from? They used to be virologists…
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
- 16,162 View
- 523 Download
- 13 Web of Science
- 12 Crossref
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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.
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Citations
Citations to this article as recorded by- The Future of Publication Ethics in University Research Systems: What Scenarios Exist for Publication Ethics?
Sara Dakhesh, Shahnaz Khademizadeh, Abdolhossein Farajpahlou, Hamid Farhadirad
Public Integrity.2026; : 1. CrossRef - Uso ético y eficiente de la inteligencia artificial en trabajos académicos: Veritas e interacción crítica escalonada
Lluís Codina
BiD: textos universitaris de biblioteconomia i documentació.2026;[Epub] CrossRef - Exploring the Impact of Artificial Intelligence on Research Ethics - A Systematic Review
Gabriel Andrade-Hidalgo, Pedro Mio-Cango, Orlando Iparraguirre-Villanueva
Journal of Academic Ethics.2025; 23(3): 1053. CrossRef - Meeting report on the 8th Asian Science Editors’ Conference and Workshop 2024
Eun Jung Park
Science Editing.2025; 12(1): 66. CrossRef - Research and publication ethics with generative artificial intelligence-assisted tools
Cheol-Heui Yun
Science Editing.2025; 12(1): 1. CrossRef - The assisted Technology dilemma: a reflection on AI chatbots use and risks while reshaping the peer review process in scientific research
Helmi Ben Saad, Ismail Dergaa, Hatem Ghouili, Halil İbrahim Ceylan, Karim Chamari, Wissem Dhahbi
AI & SOCIETY.2025; 40(7): 5649. CrossRef - Plagiarism in the system of academic integrity in medical research (part 2)
M.V. Krasnoselskyi, N.O. Artamonova, О.М. Sukhina, T.V. Rublova, Yu.V. Pavlichenko
Український радіологічний та онкологічний журнал.2025; 33(1): 113. CrossRef - Prompt engineering for generative artificial intelligence chatbots in health research: A practical guide for traditional, complementary, and integrative medicine researchers
Jeremy Y. Ng
Integrative Medicine Research.2025; 14(4): 101222. CrossRef - Adapt or Lag Behind: Why Researchers in Traditional, Complementary, and Integrative Medicine Must Master Prompt Engineering in the Era of Artificial Intelligence
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Perspectives on Integrative Medicine.2025; 4(3): 127. CrossRef - Regaining Scientific Authority in a Post-Truth Landscape
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Saba Mansoor Qadhi, Ahmed Alduais, Youmen Chaaban, Majeda Khraisheh
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Zafer Kocak
Journal of Korean Medical Science.2024;[Epub] CrossRef
- The Future of Publication Ethics in University Research Systems: What Scenarios Exist for Publication Ethics?
- 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
- 14,633 View
- 389 Download
- 14 Web of Science
- 14 Crossref
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Abstract
PDFSupplementary 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.
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Citations
Citations to this article as recorded by- Evaluating AI Excellence: A Comparative Analysis of Generative Models in Library and Information Science
Raiyan Bin Reza, Md. Rifat Mahmud, S.M. Zabed Ahmed
Science & Technology Libraries.2025; 44(2): 136. CrossRef - Does ChatGPT affect users’ continuous knowledge contributions in online Q&A communities?
Guo Li, Mark Xuefang Zhu
Aslib Journal of Information Management.2025;[Epub] CrossRef - Prediction of the Use of Generative Artificial Intelligence Through ChatGPT Among Costa Rican University Students: A PLS Model Based on UTAUT2
Julio Cabero-Almenara, Antonio Palacios-Rodríguez, Hazel de los Ángeles Rojas Guzmán, Victoria Fernández-Scagliusi
Applied Sciences.2025; 15(6): 3363. CrossRef - Artificial intelligence-assisted academic writing: recommendations for ethical use
Adam Cheng, Aaron Calhoun, Gabriel Reedy
Advances in Simulation.2025;[Epub] CrossRef - How appropriately can generative artificial intelligence platforms, including GPT-4, Gemini, Bing, and Wrtn, answer questions about colon cancer in the Korean language?
Sun Huh
Annals of Coloproctology.2025; 41(3): 190. CrossRef - Research trends and comparisons of major generative artificial intelligence platforms for systematic literature reviews
Sang-Jun Kim
Science Editing.2025; 12(2): 200. CrossRef - “It Becomes Very Intelligent”: ChatGPT as an Academic Reading Tool for Postgraduates
Afza Diyana Abdullah, Xiaoting Qiu, Huan Li, Muhammad Kamarul Kabilan
Reading Research Quarterly.2025;[Epub] CrossRef - Applications of artificial intelligence in healthcare simulation: a model of thinking
Adam Cheng, Carolyn McGregor
Advances in Simulation.2025;[Epub] CrossRef - A Structural Model of Distance Education Teachers’ Digital Competencies for Artificial Intelligence
Julio Cabero-Almenara, Antonio Palacios-Rodríguez, Maria Isabel Loaiza-Aguirre, Dhamar Rafaela Pugla-Quirola
Education Sciences.2025; 15(10): 1271. CrossRef - Initial Validation of the IMPACT Model: Technological Appropriation of ChatGPT by University Faculty
Luz-M. Pereira-González, Andrea Basantes-Andrade, Miguel Naranjo-Toro, Mailevy Guia-Pereira
Education Sciences.2025; 15(11): 1520. CrossRef - The emergence of generative artificial intelligence platforms in 2023, journal metrics, appreciation to reviewers and volunteers, and obituary
Sun Huh
Journal of Educational Evaluation for Health Professions.2024; 21: 9. CrossRef - Explosive increase and decrease in articles, citations, impact factor, and immediacy index during the COVID-19 pandemic: a bibliometric study
Sang-Jun Kim
Science Editing.2024; 11(2): 107. CrossRef - Research ethics and issues regarding the use of ChatGPT-like artificial intelligence platforms by authors and reviewers: a narrative review
Sang-Jun Kim
Science Editing.2024; 11(2): 96. CrossRef - Appliances of Generative AI-Powered Language Tools in Academic Writing: A Scoping Review
Lilia Raitskaya, Elena Tikhonova
Journal of Language and Education.2024; 10(4): 5. CrossRef
- Evaluating AI Excellence: A Comparative Analysis of Generative Models in Library and Information Science
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
- 11,380 View
- 569 Download
- 12 Web of Science
- 16 Crossref
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Abstract
PDFSupplementary 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- Do AI-related papers in top management science journals contribute greater academic influence and online attention?
Lu Guo, Yezhu Wang, Ayuan Zhang
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Hardiyanti Pratiwi, Suherman, Hasruddin, Muhammad Ridha
European Journal of Education.2025;[Epub] CrossRef - Examining predictors of generative-AI acceptance and usage in academic research: a sequential mixed-methods approach
Sushma Verma, Neerja Kashive, Ashish Gupta
Benchmarking: An International Journal.2025;[Epub] CrossRef - Harnessing the Potential of AI Tools for Student Thesis Research and Writing: An Appreciative Inquiry
Dan Namanya, Mennen Pearl C. Talibong
Pan-African Journal of Education and Social Sciences.2025; 6(1): 30. CrossRef - Perceptions of artificial intelligence in academic teaching and research: a qualitative study from AI experts and professors’ perspectives
Ana Daniela Peres Rebelo Verboom, Leonor Pais, Fred R. H. Zijlstra, Frederick L. Oswald, Nuno Rebelo dos Santos
International Journal of Educational Technology in Higher Education.2025;[Epub] CrossRef - Generative AI and academic scientists in US universities: Perception, experience, and adoption intentions
Wenceslao Arroyo-Machado, Jinghuan Ma, Tipeng Chen, Timothy P. Johnson, Shaika Islam, Lesley Michalegko, Eric Welch, Kingsley Okoye
PLOS One.2025; 20(8): e0330416. CrossRef - ¿Cómo está transformando la inteligencia artificial la comunicación científica? Desafíos, oportunidades y el papel de los actores involucrados: una revisión de alcance
Jairo Buitrago-Ciro, Estela Morales Campos, César Leonardo Villamizar Romero
Investigación Bibliotecológica: archivonomía, bibliotecología e información.2025; 39(104): 111. CrossRef - Beyond English Hegemony
ZIYANG XU
Proceedings of the ALISE Annual Conference.2025;[Epub] CrossRef - Artificial intelligence in higher education and research: A sample of tourism academic opinions
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Abdulrahman M. Al-Zahrani
Innovations in Education and Teaching International.2024; 61(5): 1029. CrossRef - Evaluating the Influence of Artificial Intelligence on Scholarly Research: A Study Focused on Academics
Tosin Ekundayo, Zafarullah Khan, Sabiha Nuzhat, Tze Wei Liew
Human Behavior and Emerging Technologies.2024;[Epub] CrossRef - Publish or perish in the era of artificial intelligence: which way for the Kenyan research community?
Stephen Oloo Ajwang, Anselimo Peters Ikoha
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Experimental and Applied Medical Science.2024; 5(2): 49. CrossRef - Evaluating the significance of artificial intelligence (AI) in academic platforms by using PIPRECIA-S method
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Ekonomika.2024; 70(3): 11. CrossRef - Scholarly Discourse on GenAI’s Impact on Academic Publishing
Yogesh K. Dwivedi, Tegwen Malik, Laurie Hughes, Mousa Ahmed Albashrawi
Journal of Computer Information Systems.2024; : 1. CrossRef
- Do AI-related papers in top management science journals contribute greater academic influence and online attention?
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
- 21,915 View
- 988 Download
- 9 Web of Science
- 11 Crossref
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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.
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Citations
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- 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
- 12,298 View
- 521 Download
- 10 Web of Science
- 18 Crossref
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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.
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Citations
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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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Abstract
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- 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
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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
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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
- 31,750 View
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- 28 Web of Science
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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.
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- 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
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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.
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