The Challenges With Artificial Intelligence in Scientific Writing.

The connection between the field of computer and artificial intelligence has emerged as a potential new tool for enhancing students’ writing performance at the University of Tabuk. The goal of this research was to investigate the use of artificial intelligence to augment scientific and technical writing among computer science students at the University of Tabuk. Thirty-five students aged nineteen to twenty years old participated in this study. This research employed quantitative research design. A survey questionnaire with 11 questions was designed to assess the university students’ perceptions of artificial intelligence use in learning. The study illustrated that students with a positive attitude toward artificial intelligence performed better, improved their language skills, and enhanced their learning. The study also established that artificial intelligence use among students increases academic writing. This research contributes significantly to understanding of how artificial intelligence impacts the scientific and technical writing proficiency of computer science students. Although artificial intelligence shows great potential, it should be seen as an additional tool that enhances rather than replaces the crucial function of human educators. In results indicate that, chatbot was demonstrated to significantly improve the writing skills of computer science students at the University of Tabuk through the use of artificial intelligence, including chatbots.

Peer-reviewed publications using local data are critical for understanding disease burdens, generating evidence, and shaping policies tailored to community needs. Although low- and middle-income countries (LMICs) account for 80% of cardiovascular disease (CVD) deaths, they contribute only 2.8% of CVD publications. To address this gap, the US Centers for Disease Control and Prevention (CDC) and partners launched the Emerging Authors Program for Global Cardiovascular Research (EAP), supporting early- to mid-career LMIC practitioners. EAP coordinated mentorship, provided virtual writing tools, training, technical support, and financial assistance for open access. Between 2019 and 2023, three waves engaged 33 authors from 11 countries, resulting in 31 published manuscripts in six journals, with support from 23 global mentors. The success was driven by committed mentors, motivated authors, local collaboration, accessible resources, and strong communication. Strengthening LMIC authors’ writing and publication skills is essential for advancing rigorous research and global health equity in scientific publishing.

This study aims to analyze the influence of emotion regulation and self-efficacy on academic anxiety, with motivation as a moderating variable, among students majoring in education in Ponorogo Regency. This study uses a correlational, ex post facto, quantitative approach. A total of 370 respondents who were writing scientific papers were involved. This study employed a validated and reliable questionnaire. Path analysis reveals that emotion regulation has a strong and significant impact on reducing academic anxiety, both directly and through its influence on motivation, whereas self-efficacy has a relatively weak influence. These results confirm that emotional management skills, such as cognitive reappraisal and expression control, are key factors in reducing psychological stress in students during the scientific writing process. Theoretically, these findings support the notion that emotion regulation is a crucial determinant of academic success. Yet, it also practically provides implications for higher education to design interventions based on emotion regulation and motivation enhancement to reduce students' academic anxiety. The results of the study can serve as the basis for a predictive relationship study of the variables.

Research writing is by rehearsal. Hence, this paper aimed at discussing the importance of research writing for undergraduate students. This papers employed library research where the data were gathered from journal, articles, books and so forth. The finding asserted that research writing is important to extend students’ knowledge, experiences, and research partners. The advices are bestowed for students, lecturers, and curriculum makers. For students, it is required for their study fulfilments and extend knowledge. For lecturers, it is to trait and enhance students’ knowledge toward research writing and to gain more experiences within. For curriculum makers, it is needed to integrate scientific writing as obligation lesson for undergraduate students to advance their proficiency. Additionally, it is needed more deeper research and discussion for future research on how research writing affect students proficiency levels based on their study fields.

Abstract Introduction The emergence of large language models (LLMs) such as ChatGPT has begun to influence academic writing across biomedical disciplines. Prior studies have identified measurable linguistic shifts following ChatGPT’s release, including increased usage of stylistic enhancer terms often associated with LLM-generated text. However, the extent to which generative AI tools have impacted scientific writing within sexual medicine and fertility literature remains unstudied. Objective To evaluate the early linguistic influence of ChatGPT on sexual medicine and fertility journal abstracts by quantifying deviations in ChatGPT-associated vocabulary through counterfactual trend analysis. Methods Abstracts published between 2010 and 2024 were retrieved from 15 major sexual medicine and fertility journals via PubMed (n = 34,471). After exclusion of empty entries, 29,670 abstracts were included for analysis. A predefined list of 118 ChatGPT-associated marker terms was applied. Annual frequencies from 2010–2020 were used to model expected term usage had ChatGPT not been introduced (counterfactual projection). Observed frequencies for 2023–2024 were compared to projections using excess frequency gap (absolute difference) and excess ratio (relative increase). Results Counterfactual modeling using 2010–2020 data established baseline expectations for ChatGPT-associated vocabulary. In 2024, among 118 tracked ChatGPT-associated terms, 39 terms exhibited ≥2-fold excess ratios, and 7 terms exceeded 4-fold increases relative to projection. The leading terms by excess ratio were unparalleled (9.98-fold), intricate (6.77), intersection (4.69), elevate (4.44), underscore (4.15), delve (4.08), and valuable (4.07). The top terms by absolute excess frequency included comprehensive (+5.24%), additionally (+4.18%), valuable (+3.15%), crucial (+3.11%), and explore (+2.29%). While absolute frequencies for some of these stylistic terms remain modest, several (e.g. comprehensive, valuable, crucial) have reached usage rates exceeding 4–10% of abstracts in 2024, reflecting their growing integration into the narrative style of recent publications. In parallel, the prevalence of abstracts containing ≥1 ChatGPT-associated term rose from 5–8% during 2010–2019 to 9.5% in 2023 and 9.7% in 2024. By contrast, common control phrases demonstrated only minor changes over time, supporting the specificity of the observed linguistic shifts. Conclusions This counterfactual analysis provides early evidence that generative AI tools are introducing recognizable stylistic shifts into sexual medicine and fertility academic writing. Although absolute prevalence remains modest relative to broader biomedical literature, the direction and timing of change parallel global trends seen in AI-assisted scientific writing. These results underscore the need for ongoing monitoring, ethical guidelines, and transparent disclosure regarding the use of LLMs in academic publishing. Disclosure No

Undergraduate students in the field of Science, Technology, Engineering, Mathematics (STEM) are regularly encouraged and instructed to develop and write an original research paper in their field of expertise. Some undergraduate students, however, may lack the essential skills and strategies to write a research paper/manuscript of a quality suitable for possible publication in a peer-reviewed journal. A sixteen-week writing course was developed for STEM undergraduate students to improve their presentation and writing skills toward increasing their chances of publishing a research article in a peer-reviewed journal. This paper is a report describing a scientific undergraduate writing course (established across a variety of disciplinary STEM fields) and its evaluation based on scoring rubrics and the instructor’s oral feedback advice to the students. It was designed and implemented using teaching strategies aligned with pedagogical and learning theories addressing three domains: developmental planning, pedagogical planning, and implementation planning. Pedagogical strategies utilized the Visual, Aural, Read/Write, Kinesthetic (VARK) model and multimodal skills. Implementation of the course content includes using an academic student writing center, oral presentations of the student’s research project for the midterm and final course evaluations, and ongoing constructive feedback from the instructor. Undergraduate students showed an increase in their understanding of developing and presenting their proposed research in their respective fields. These strategies have demonstrated a positive outcome in reaching the course goals for STEM undergraduate students during this initial phase of the course development and can be designed and adapted across other academic disciplines. We urge others to implement a similar format for undergraduate STEM courses.

Clinicians have a greater impact in academia when they demonstrate mastery in both clinical and research domains. However, planning a research project and writing a manuscript are among the most intimidating and difficult activities for the clinical faculty. This manuscript aimed to provide a thoughtful and informative description of the fundamental elements of research and essential writing guidelines of a scientific paper. This document was created using personal experiences and literature guides related to research and scientific writing. The article provided stepwise guidelines and pearls to help the clinical faculty, residents, and medical students improve their research and writing skills. This manuscript outlines the essential steps of research and scientific writing. It provides an informative introduction to the fundamental elements of scientific writing guidelines. This initiative is expected to enhance the faculty's research and writing abilities. At the same time, the information should benefit the residents and medical students with their research projects.

This Inside Story discusses the the role of generative artifical intelligence in writing papers.

Principles of Scientific Writing and Biomedical Publication: A JAMA Editors Guide for Authors.

Navigating the ethical context: Artificial Intelligence in Scientific Writing

Artificial intelligence is transforming the landscape of clinical research and scientific writing, offering innovative solutions to address inefficiencies in trial design, patient recruitment, and manuscript development. This review explores applications of artificial intelligence in patient matching, endpoint design and predictive trial outcomes, and real-time patient monitoring. It also discusses its role in assisting with literature review, generating content, and refining language in scientific writing, especially for nonnative English speakers. Challenges such as data standardization, explainability, and ethical concerns are highlighted alongside emerging regulatory frameworks to ensure transparent and responsible artificial intelligence integration. By examining its current capabilities and future potential, this article underscores the transformative role of artificial intelligence in enhancing efficiency, reducing costs, and advancing innovation in medical research.

Large language models (LLMs) are artificial intelligence (AI) tools that can generate human expert-like content and be used to accelerate the synthesis of scientific literature, but they can spread misinformation by producing misleading content. This study sought to characterize distinguishing linguistic features in differentiating AI-generated from human-authored scientific text and evaluate the performance of AI detection tools for this task. We conducted a computational synthesis of 34 essays on cerebrovascular topics (12 generated by large language models [Generative Pre-trained Transformer 4, Generative Pre-trained Transformer 3.5, Llama-2, and Bard] and 22 by human scientists). Each essay was rated as AI-generated or human-authored by up to 38 members of the Stroke editorial board. We compared the collective performance of experts versus GPTZero, a widely used online AI detection tool. We extracted and compared linguistic features spanning syntax (word count, complexity, and so on), semantics (polarity), readability (Flesch scores), grade level (Flesch-Kincaid), and language perplexity (or predictability) to characterize linguistic differences between AI-generated versus human-written content. Over 50% of the stroke experts who reviewed the study essays correctly identified 10 (83.3%) of AI-generated essays as AI, whereas they misclassified 7 (31.8%) of human-written essays as AI. GPTZero accurately classified 12 (100%) of AI-generated and 21 (95.5%) of human-written essays. However, the tool relied on only a few key sentences for classification. Compared with human essays, AI-generated content had lower word count and complexity, exhibited significantly lower perplexity (median, 15.0 versus 7.2; P<0.001), lower readability scores (Flesch median, 42.1 versus 26.4; P<0.001), and higher grade level (Flesch-Kincaid median, 13.1 versus 14.8; P=0.006). Large language models generate scientific content with measurable differences versus human-written text but represent features that are not consistently identifiable even by human experts and require complex AI detection tools. Given the challenges that experts face in distinguishing AI from human content, technology-assisted tools are essential wherever human provenance is essential to safeguard the integrity of scientific communication.

Research paper "The Role of Artificial Intelligence in Scientific Writing: A Higher Education Review" studies Artificial Intelligence (AI) transformation in scientific writing across educational institutions at the higher education level. Academic institutions now handle AI-based tools that provide opportunities but also create difficulties for their environments. The study conducts a thorough review of research articles to determine how AI technology supports writing quality enhancement and speed alongside analyzing its ethical and teaching dimensions. Qualitative research of 40 peer-reviewed articles uses elite databases Scopus, Web of Science, IEEE Xplore and Google Scholar. AI writing assistants Grammarly and ChatGPT demonstrate exceptional effectiveness by advancing language abilities and writing velocity and precision especially for users with foreign language backgrounds. The study presents ethical problems about plagiarism and academic honesty as well as a danger of excessive AI-controlled writing practices. The research demonstrates why higher education institutions need to build substantial policies which will effectively integrate AI benefits with ethical values. Students and their teachers should gain necessary technology literacy which helps them use AI tools properly to maintain their unique creative abilities and analytical thinking skills. The study finds that artificial intelligence tools deliver substantial benefits for scientific writing though universities need to address ethical issues properly to maintain academic standards during their appropriate utilization.

Scientific writing is essential for sharing research findings, yet it poses significant challenges due to the need for precision, clarity, and adherence to strict standards. Generative AI (GenAI) tools, such as ChatGPT, have emerged as potential aid in this process, offering capabilities in linguistic editing, translation, and summarization. However, the integration of GenAI in academic writing raises ethical concerns regarding plagiarism, data fabrication, authorship and the preservation of the researcher’s original voice. This study empirically investigates researchers' ethical considerations regarding the use of generative AI in scientific writing. Through a survey of 102 researchers and research students in Israeli academia, the study explores the perceived ethical boundaries and conditions for appropriate GenAI use. Findings indicate that while GenAI can be beneficial for technical tasks, its role in content creation remains contentious. Transparency, critical evaluation of GenAI outputs, and maintaining originality are highlighted as essential conditions for ethical AI integration.

Background: The rapid advancement of artificial intelligence (AI) tools, especially in natural language processing, is transforming scientific writing by improving efficiency, consistency and accessibility, especially for non-native English speakers and early-career researchers. This study aimed to evaluate the effectiveness of Compilatio, a widely used plagiarism detection software, in identifying AI-generated scientific content. Materials and Methods: Four commonly used and freely available AI tools [ChatGPT, Gemini, Perplexity, and synthesis of topic outlines through retrieval and multi-perspective question asking (STORM)] were prompted to generate introductory texts on the burden of diabetes. Each output was copied into a Word document, uploaded and analyzed by Compilatio, which provided integrity score, similarity index, and likelihood of AI-generated content. Results: Integrity scores varied substantially, ranging from 32% (STORM) to 100% (Gemini), while similarity indices remained consistently low (0-6%), indicating minimal direct text overlap with existing sources. The likelihood of AI authorship also varied, with STORM yielding the lowest detection rate (27%) while Gemini yielded the highest (100%). Conclusion: These findings highlight the distinct textual characteristics produced by different AI models and demonstrate the overall effectiveness of Compilatio in identifying AI-generated content from three out of four tools. However, the limited performance observed with STORM-generated text underscores the need for more sophisticated and adaptable detection systems to uphold academic integrity in the evolving landscape of AI-supported scientific writing.

Artificial intelligence (AI) is increasingly capable of academic writing, with large language models such as ChatGPT showing potential to assist or even generate scientific manuscripts. However, concerns remain regarding the quality, reliability, and interpretive capabilities of AI-generated content. The authors' study aimed to compare the quality of a human-written versus an AI-generated scientific manuscript to evaluate the strengths and limitations of AI in the context of academic publishing. Two manuscripts were developed using identical titles, abstracts, and tables of a simulated analysis: one authored by a physician with multiple publications, and the other generated by ChatGPT-4o. Three independent and blinded reviewers-two human and one AI-assessed each manuscript across five domains: clarity and readability, coherence and flow, technical accuracy, depth, and conciseness and precision. Each category was scored on a 10-point scale, and qualitative feedback was collected to highlight specific strengths and weaknesses. Additionally, all reviewers were asked to deduce authorship of the manuscripts. The AI-generated manuscript scored higher in clarity and readability (mean 9.0 vs 7.2), but lower in technical accuracy (mean 6.3 vs 9.3) and depth (mean 5.5 vs 7.5). However, reviewers noted that the AI version lacked depth, critical analysis, and contextual interpretation. All reviewers accurately identified the authorship of each manuscript and tended to rate the version more favorably when it aligned with their own origin (human or AI); i.e., human reviewers assigned higher scores to the human-written manuscript, while the AI reviewer scored the AI-generated manuscript higher. Although AI models can improve some aspects of scientific writing, particularly clarity and readability, they fall short in critical reasoning and contextual understanding. This reinforces the importance of human authorship and oversight in maintaining the critical analysis and scientific accuracy essential for academic publishing. AI may be used as a complementary tool to support, rather than replace, human-led scientific writing.

Effective scientific writing is a keystone to success in higher education. To excel and advance, aspiring academicians need to master the art and science of scientific writing. The following brief report describes the types of manuscripts accepted by research publications. More specifically, it highlights the parts of a research report, their inclusions, and guidelines on how to write them effectively and comprehensively. Suggestions on the writing process are also provided. Mastering scientific writing has a steep learning curve that requires perseverance, practice, collegiality, and hours of revising and editing.

Purpose – Scientific writing is one primary way scientists disseminate their discoveries to the broader scientific community. International journals’ extensive scope and stringent peer review procedures have earned them the gold standard for academic publications. However, writing in reputable international journals and publishing them is no easy task. This prompted various online courses to assist researchers in overcoming writing obstacles and raising the caliber of their work to the point where it is acceptable for publication in esteemed publications. The study had two goals: (i) to investigate the correlations between perceived self-efficacy, enjoyment, motivation for courses, online course design, time management, Technology Acceptance Model (TAM), and satisfaction with E-courses, and (ii) to examine whether the motivation for courses and perceived self-efficacy moderate time management. Methodology – This study employed a quantitative approach and randomly selected 184 trainees from Indonesia who took online courses for scientific writing. The questionnaire to gather the quantitative data used a five-point Likert scale format. Descriptive statistics were obtained using IBM’s SPSS 25 statistical software. The statistical program Smart-PLS 3.0 was used to handle the data analysis, and it used structural equation modeling to assess the measurement model and test the hypotheses. Findings – The measuring methodology supported the study constructs' convergent and discriminant validity. Three factors directly impacted Indonesian trainees' perceptions of the ease of use of e-courses: perceived self-efficacy, enjoyment, and online course design. Furthermore, there was a negative and significant moderating effect on motivation and the relationship between time management and perceived enjoyment. However, the relationship between online course design and time management was positively moderated by perceived self-efficacy. It was found that 69.9% of the variance in online course satisfaction could be explained by exogenous factors, such as perceived self-efficacy, enjoyment, and online course design, influencing perceived ease of use. Significance – E-courses in scientific writing assist in improving writing and getting published in reputable journals. Apart from enhancing the academic quality of one’s writing, they helped in networking and gaining a reputation abroad. Besides, it encourages Indonesian researchers and the Indonesian Ministry of Education, Culture, Research, and Technology to develop a more evidence-based national development framework that serves as a basis for more effective policy development and decision-making. The impact of perceived self-efficacy on perceived usefulness was found to be negligible. On the other hand, perceived usefulness and simplicity were positively and directly impacted by both perceived enjoyment and online course design. The TAM was not significantly affected by course motivation, although time management was. The satisfaction of trainees was also positively and indirectly impacted by the following four external factors: perceived self-efficacy, perceived enjoyment, course motivation, and online course design.

The high level of academic procrastination in the Scientific Writing course needs serious attention because, if left unchecked, it can result in failure to achieve academic achievement. The main focus of this study is to analyze the impact of Project-Based Learning Model on academic procrastination and examine the impact of Self-Regulated Learning as a mediating variable among students in Scientific Writing courses. This research uses an experimental research design. The research population was all students from the Faculty of Teacher Training and Education, Universitas PGRI Madiun, Indonesia, who were taking the Scientific Writing courses. A sample of 186 students was obtained by using the cluster random sampling technique. The instruments used were the Self-Regulated Learning scale and the academic procrastination scale. Data analysis includes descriptive data analysis, prerequisite tests, and inferential data analysis using a two-way ANOVA. The following research findings support the following findings: (1) students in the Scientific Paper course who received a project-based learning model showed significantly higher levels of academic procrastination than students who received a conventional learning model; (2) students in the Scientific Paper course who had high levels of self-regulated learning and those who had low levels of self-regulated learning showed significantly higher levels of academic procrastination; and (3) there was an interaction effect between the project-based learning model and self-regulated learning on academic procrastination of students in the Scientific Paper course. It is thought that the growth of SRL will improve academic performance and boost students' accountability for their education. To track the evolution of SRL and the calibre of students' scientific work over time, further longitudinal study can be done. Another possible line of inquiry is the creation of a hybrid learning paradigm that specifically combines SRL training and PBL.