This study explores the extent to which Grammarly can be a reliable assessment tool for academic English writing. Ten articles published in high-status scholarly Q.1 journals and written by specialist English native speakers were used to evaluate the accuracy of Grammarly\'s flagged issues. The results showed that Grammarly tends to over-flag many issues resulting in many false positives; besides, it does not take into consideration optional usage in English. The study concluded that although Grammarly can identify many ambiguous instances of language use that writers would do well to review and consider for revision, it does not seem to be a reliable tool for assessing academic written English.

This study investigates persuasive strategies used in the writings of Iranian university students in the field of teaching English as foreign language (TEFL). The study utilized the 7 principles of persuasive strategies presented by Cialdini (The psychology of persuasion, Quill William Morrow, New York 1984; Pre-suasion: A revolutionary way to influence and persuade, Simon & Schuster, New York 2016), which include \'reciprocity\', \'commitment and consistency\', \'social proof\', \'liking\', \'authority\', \'scarcity\', and \'unity\'. The results indicate that strategies such as \'liking\', \'unity\', and \'authority\' were used more frequently than other persuasive strategies. On the other hand, \'scarcity\' was the least used strategy by the participants. Significant gender differences were also observed in the data. These findings have important pedagogical implications and suggest the need to incorporate persuasive strategies into instructional materials and teaching practices to enhance the persuasive writing skills of university students. Furthermore, gender differences highlight the importance of considering individual differences when teaching persuasive writing. Finally, the study discusses the pedagogical implications of these findings in the context of learning and teaching.

BACKGROUND: Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students.
METHODS: A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students\' experience in using ChatGPT.
RESULTS: After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research.
CONCLUSIONS: The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.

In recent decades, members of the general public have become increasingly reliant on findings of scientific studies for decision-making. However, scientific writing usually features a heavy use of technical language, which may pose challenges for people outside of the scientific community. To alleviate this issue, plain language summaries were introduced to provide a brief summary of scientific papers in clear and accessible language. Despite increasing attention paid to the research of plain language summaries, little is known about whether these summaries are readable for the intended audiences. Based on a large corpus sampled from six biomedical and life sciences journals, the present study examined the readability and jargon use of plain language summaries and scientific abstracts on a technical level. It was found that (1) plain language summaries were more readable than scientific abstracts, (2) the reading grade levels of plain language summaries were moderately correlated with that of scientific abstracts, (3) researchers used less jargon in plain language summaries than in scientific abstracts, and (4) the readability of and the jargon use in both plain language summaries and scientific abstracts exceeded the recommended threshold for the general public. The findings were discussed with possible explanations. Implications for academic writing and scientific communication were offered.

Although previous preconceived notions discourage authors from asserting their presence in research articles (RAs), recent studies have substantiated that the use of self-mention markers offer a means to establish authorial identity and recognition in a given discipline. Few studies, however, explored specific sections of research articles to uncover how self-mentions function within each section\'s conventions. Exploring the use of self-mention markers, the present study aimed at comparing the method sections written by native English writers and L-1 Persian writers in the field of psychology. The corpus contained 120 RAs, with each sub-corpora including 60 RAs. The RAs were then examined structurally and functionally. The data were analyzed both quantitatively, using frequency counts and chi-square analyses, and qualitatively through content analysis. The findings indicated a significant difference between English and Persian authors concerning the frequency of self-mentions and the dimension of rhetorical functions; however, the differences in the dimensions of grammatical forms and hedging and boosting were found insignificant. Native English authors were inclined to make more use of self-mentions in their research articles. The findings of the current study can assist EAP and ESP novice researchers in taking cognizance of the conventions of authorial identity in each genre.

The world of publication can seem intimidating and closed to the newcomer. How then does one even begin to get a foot in the door? In this paper, the authors draw from the literature and their recent lived experience as editorial interns to consider this challenge under the theme of access, and how it overlaps with the various components of academic publication. The main three components of the publication \'machine\' are discussed in this article, authoring, reviewing, and editing. These are preceded by the first, and arguably foundational, interaction with academic journal publishing-reading. Without reading articles across different journals, and even in different disciplines, understanding the breadth of scholarship and its purpose is impossible. The subsequent components of authoring, reviewing, and editing, which are all enhanced by ongoing familiarity with current literature through further reading, are considered in further detail in the remainder of this article, with practical advice provided as to how to gain access and experience in each of these areas, for example, writing non-research article manuscripts, engaging in collaborative peer review, and applying for editorial opportunities (with perseverance) when the opportunity presents itself. Medical education publication can seem daunting and closed to entry-level academics. This article is written to dispel this view, and challenges the notion that the world of publication is reserved for experts only. On the contrary, newcomers to the field are essential for academic publications to retain relevance, dynamism, and innovation particularly in the face of the changing landscape of medical education.

This study scrutinizes free AI tools tailored for supporting literature review and analysis in academic research, emphasizing their response to direct inquiries. Through a targeted keyword search, we cataloged relevant AI tools and evaluated their output variation and source validity. Our results reveal a spectrum of response qualities, with some tools integrating non-academic sources and others depending on outdated information. Notably, most tools showed a lack of transparency in source selection. Our study highlights two key limitations: the exclusion of commercial AI tools and the focus solely on tools that accept direct research queries. This raises questions about the potential capabilities of paid tools and the efficacy of combining various AI tools for enhanced research outcomes. Future research should explore the integration of diverse AI tools, assess the impact of commercial tools, and investigate the algorithms behind response variability. This study contributes to a better understanding of AI\'s role in academic research, emphasizing the importance of careful selection and critical evaluation of these tools in academic endeavors.

OBJECTIVE: To evaluate the ability of ChatGPT-4 to generate a biomedical review article on fertility preservation.
METHODS: ChatGPT-4 was prompted to create an outline for a review on fertility preservation in men and prepubertal boys. The outline provided by ChatGPT-4 was subsequently used to prompt ChatGPT-4 to write the different parts of the review and provide five references for each section. The different parts of the article and the references provided were combined to create a single scientific review that was evaluated by the authors, who are experts in fertility preservation. The experts assessed the article and the references for accuracy and checked for plagiarism using online tools. In addition, both experts independently scored the relevance, depth, and currentness of the ChatGPT-4\'s article using a scoring matrix ranging from 0 to 5 where higher scores indicate higher quality.
RESULTS: ChatGPT-4 successfully generated a relevant scientific article with references. Among 27 statements needing citations, four were inaccurate. Of 25 references, 36% were accurate, 48% had correct titles but other errors, and 16% were completely fabricated. Plagiarism was minimal (mean = 3%). Experts rated the article\'s relevance highly (5/5) but gave lower scores for depth (2-3/5) and currentness (3/5).
CONCLUSIONS: ChatGPT-4 can produce a scientific review on fertility preservation with minimal plagiarism. While precise in content, it showed factual and contextual inaccuracies and inconsistent reference reliability. These issues limit ChatGPT-4 as a sole tool for scientific writing but suggest its potential as an aid in the writing process.

ChatGPT, which can automatically generate written responses to queries using internet sources, soon went viral after its release at the end of 2022. The performance of ChatGPT on medical exams shows results near the passing threshold, making it comparable to third-year medical students. It can also write academic abstracts or reviews at an acceptable level. However, it is not clear how ChatGPT deals with harmful content, misinformation or plagiarism; therefore, authors using ChatGPT professionally for academic writing should be cautious. ChatGPT also has the potential to facilitate the interaction between healthcare providers and patients in various ways. However, sophisticated tasks such as understanding the human anatomy are still a limitation of ChatGPT. ChatGPT can simplify radiological reports, but the possibility of incorrect statements and missing medical information remain. Although ChatGPT has the potential to change medical practice, education and research, further improvements of this application are needed for regular use in medicine.

BACKGROUND: Large language models (LLMs) have gained prominence since the release of ChatGPT in late 2022.
OBJECTIVE: The aim of this study was to assess the accuracy of citations and references generated by ChatGPT (GPT-3.5) in two distinct academic domains: the natural sciences and humanities.
METHODS: Two researchers independently prompted ChatGPT to write an introduction section for a manuscript and include citations; they then evaluated the accuracy of the citations and Digital Object Identifiers (DOIs). Results were compared between the two disciplines.
RESULTS: Ten topics were included, including 5 in the natural sciences and 5 in the humanities. A total of 102 citations were generated, with 55 in the natural sciences and 47 in the humanities. Among these, 40 citations (72.7%) in the natural sciences and 36 citations (76.6%) in the humanities were confirmed to exist (P=.42). There were significant disparities found in DOI presence in the natural sciences (39/55, 70.9%) and the humanities (18/47, 38.3%), along with significant differences in accuracy between the two disciplines (18/55, 32.7% vs 4/47, 8.5%). DOI hallucination was more prevalent in the humanities (42/55, 89.4%). The Levenshtein distance was significantly higher in the humanities than in the natural sciences, reflecting the lower DOI accuracy.
CONCLUSIONS: ChatGPT\'s performance in generating citations and references varies across disciplines. Differences in DOI standards and disciplinary nuances contribute to performance variations. Researchers should consider the strengths and limitations of artificial intelligence writing tools with respect to citation accuracy. The use of domain-specific models may enhance accuracy.