The Ability of ChatGPT in Paraphrasing Texts and Reducing Plagiarism: A Descriptive Analysis.

Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out. Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot. Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated. Results: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference -0.51, 95% CI -0.54 to -0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference -0.06, 95% CI -0.08 to -0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001). Conclusions: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.

Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT.

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. CONCLUSION: 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.

Comparative analyses of the information content of letters, characters, and inter-word spaces across writing systems.

One difference among writing systems is how orthographic cues are used to demarcate words; although most alphabetic scripts use inter-word spaces, some Asian scripts do not explicitly mark word boundaries (e.g., Chinese). It is unclear whether these differences are arbitrary or whether they are designed to maximize reading efficiency. Here, we show that spaces inserted between words in non-demarcated scripts provide less information about word boundaries than spaces in demarcated scripts. Furthermore, despite the fact that less information is contained by inter-word spaces than characters/letters of the same size, the information content of inter-word spaces in demarcated scripts is closer to that of characters/letters compared to the information content of inter-word spaces that are inserted in non-demarcated scripts. These results suggest that the conventions used to demarcate word boundaries are sufficient to support efficient reading. Our findings provide new insights into the universals and variation across writing systems and shed light on the mental processes that support skilled reading.

Writing about a stressful experience can impair visual working memory.

Acute stress has been well-established to impair working memory. However, less is known about how writing about an unresolved stressor may influence working memory or working memory processes. We addressed these issues in the present study (N = 282) by randomly assigning participants to write about an unresolved stressful experience (stressful writing condition or the events of the previous day). We then both measured performance on a change detection task and used computational modeling to estimate the processes underlying performance: attention, capacity, and guessing bias. We found that, relative to the control condition, writing about a stressful experience impaired change detection task performance and significantly impaired task attention. These results show that the effects of writing about an unresolved stressor may mimic the effects of acute stress on working memory, rather than conforming to expectations from mood-as-information theory.

Reducing racial bias in scientific communication: Journal policies and their influence on reporting racial demographics.

Research titles with White samples, compared to research titles with samples of color, have been less likely to include the racial identity of the sample. This unequal writing practice has serious ramifications for both the history and future of psychological science, as it solidifies in the permanent scientific record the false notion that research with White samples is more generalizable and valuable than research with samples of color. In the present research, we experimentally tested the extent to which PhD students (63% White students, 27% students of color) engaged in this unequal writing practice, as well as the extent to which this practice might be disrupted by journal policies. In Study 1, PhD students who read about research conducted with a White sample, compared to those who read about the exact same research conducted with a Black sample, were significantly less likely to mention the sample's racial identity when generating research titles, keywords, and summaries. In Study 2, PhD students instructed to mention the racial identity of their samples, and PhD students instructed to not mention the identity of their samples (though to a lesser extent), were less likely to write about the White versus Black samples unequally. Across both studies, we found that PhD students were overall supportive of a policy to make the racial demographics of samples more transparent, believing that it would help to reduce racial biases in the field. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Perceptions and detection of AI use in manuscript preparation for academic journals.

The rapid advances in Generative AI tools have produced both excitement and worry about how AI will impact academic writing. However, little is known about what norms are emerging around AI use in manuscript preparation or how these norms might be enforced. We address both gaps in the literature by conducting a survey of 271 academics about whether it is necessary to report ChatGPT use in manuscript preparation and by running GPT-modified abstracts from 2,716 published papers through a leading AI detection software to see if these detectors can detect different AI uses in manuscript preparation. We find that most academics do not think that using ChatGPT to fix grammar needs to be reported, but detection software did not always draw this distinction, as abstracts for which GPT was used to fix grammar were often flagged as having a high chance of being written by AI. We also find disagreements among academics on whether more substantial use of ChatGPT to rewrite text needs to be reported, and these differences were related to perceptions of ethics, academic role, and English language background. Finally, we found little difference in their perceptions about reporting ChatGPT and research assistant help, but significant differences in reporting perceptions between these sources of assistance and paid proofreading and other AI assistant tools (Grammarly and Word). Our results suggest that there might be challenges in getting authors to report AI use in manuscript preparation because (i) there is not uniform agreement about what uses of AI should be reported and (ii) journals might have trouble enforcing nuanced reporting requirements using AI detection tools.

Generative AI enhances individual creativity but reduces the collective diversity of novel content.

Creativity is core to being human. Generative artificial intelligence (AI)-including powerful large language models (LLMs)-holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.

The gender agency gap in fiction writing (1850 to 2010).

Works of fiction play a crucial role in the production of cultural stereotypes. Concerning gender, a widely held presumption is that many such works ascribe agency to men and passivity to women. However, large-scale diachronic analyses of this notion have been lacking. This paper provides an assessment of agency attributions in 87,531 fiction works written between 1850 and 2010. It introduces a syntax-based approach for extracting networks of character interactions. Agency is then formalized as a dyadic property: Does a character primarily serve as an agent acting upon the other character or as recipient acted upon by the other character? Findings indicate that female characters are more likely to be passive in cross-gender relationships than their male counterparts. This difference, the gender agency gap, has declined since the 19th century but persists into the 21st. Male authors are especially likely to attribute less agency to female characters. Moreover, certain kinds of actions, especially physical and villainous ones, have more pronounced gender disparities.

Writing Groups for Healthcare Professionals in Academic Medicine.

Within the Department of Medicine (DOM) in a large tertiary academic health care facility in midwestern United States, we have developed an educational offering that incorporates an academic writing program (AWP) blending the approaches of a writing accountability work group, a writing workshop, and didactic writing courses. The purpose of this AWP was to assist healthcare professionals (HCP) with their manuscript writing skills to enhance academic productivity. We report our evolving journey and experiences with this AWP. To date, it has been offered 3 times to 25 HCP over the course of 3 years. Among those responding to a post program follow up survey (N = 11), 8 (73%) indicated that they completed the project that they were working on during the AWP and went on to publish the manuscript (N = 5) or were in the process of submission (N = 2). Some indicated they has also gone on to present posters (N = 2) or were in the process of presenting posters (N = 2) or had received grants (N = 1) or were awaiting grant notice (N = 1). A number of attendees have continued to use and share the tools presented during the AWP. Based on input from attendees and increased requests for this AWP, this educational program has been deemed a success and expansion of this program is currently underway.

Multiliteracies meet new methods: The case for digital writing in English education case study in G-7 countries.

Recently, globe has tried to transform populace activities to digital platform, wherefrom all stakeholders can attain their basic information. However, education sector cannot be excluded from this debate. Since, the pandemic mostly most economies have utilized digital transformation in different aspects of life, but digital education becomes more prominent. Therefore, this empirical research focuses on digital writing specifically to English education in G-7 economies between the time frame spanning from January 2000 to April 2022. This study considers urbanization, income, renewable energy, information & communication technology, renewable energy, English education, and pandemic as the key environmental determinants. To address the challenges posed by panel data, this study utilized an advanced set of estimators such as AMG stands for Augmented Mean Group. The estimate of urbanization and income per capita is positively significant, confirming that urbanization and economic development cannot protect the green economy by minimizing environmental pollution. Likewise, the estimated coefficient of English Education and internet use is negative and significant, implying that English Education and ICT can protect the green economy by reducing emissions. Conversely, renewable energy consumption (REC) is an element that can increase economic activity and therefore decline the environmental damages to secure a green economy. Likewise, the growth in cases of Covid-19 also reduces the usage of resources like land, water, and forests and subsequently decreases carbon emissions, promoting the green economy. The results also indicate that interaction term of English Education and ICT have adverse impact on Carbon emission (CO2). The outcomes suggest that internet usage (ICT) in English Education (EE) can deal efficiently with environmental issues for the green economy; therefore, EE and ICT should be part of green policies across the international level.