Plagiarism Detection Research Articles

Writing with ChatGPT

Many instructors see the use of LLMs like ChatGPT on course assignments as a straightforward case of cheating, and try hard to prevent their students from doing so by including new warnings of consequences on their syllabi, turning to iffy plagiarism detectors, or scheduling exams to occur in-class. And the use of LLMs probably is cheating, given the sorts of assignments we are used to giving and the sorts of skills we take ourselves to be instilling in our students. But despite legitimate ethical and pedagogical concerns, the case that LLMs should never be used in academic contexts is quite difficult to see. Many primary and secondary schools are cutting back their writing instruction in an effort to teach to the test; at the same time, many high-end knowledge workers are already quietly expected to leverage their productivity with LLMs. To prepare students for an ever-changing world, we probably do have to teach them at least a little bit about writing with ChatGPT.

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review.

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.

AI in academic writing: Assessing current usage and future implications

Artificial intelligence (AI) integration in academic writing has gained significant attention due to its potential impact on authorship, the natural character of academic works, and ethical considerations. This Study aims to assess the faculty members' perceptions on their current usage of AI in academic writing and explore its future implications. The research involved an online survey administered to 68 faculty members responding to closed and open-ended questions. The study revealed faculty members' perceptions of AI integration in academic writing and its implications for authorship and the authenticity of academic work. Findings reveal widespread adoption of AI tools among faculty members, offering efficiency, productivity, and accuracy benefits in areas like grammar checks, reference management, writing assistance, and plagiarism detection. However, concerns arise over authorship preservation and maintaining academic work's unique character, emphasizing the need for clear guidelines. Ethical considerations and best practices are also highlighted to use AI while safeguarding academic integrity effectively. These insights extend to educators, policy makers, and researchers, offering a comprehensive view of AI's current role in academic writing and guiding ethical discussions and best practices. Ultimately, this research enhances teaching and learning practices in Indonesian higher education institutions through responsible AI integration.

Comparison of algorithms for estimating the distance between words for finding similar sentences

The article in question delves into the intricacies of a newly developed system designed for the identification of similar sentences. This system operates on the principle of measuring the distance between words, utilizing algorithms that are adept at recognizing inexact matches. Such algorithms are vital as they enable search engines to process queries with a deeper understanding of context. They take into account potential discrepancies or variations in word spellings, a feature that becomes critically important when users employ diverse expressions to convey identical ideas. These algorithms form the backbone of intelligent search engines, equipping them with the ability to grasp the core intent of a query. This ensures the delivery of pertinent results, even when the input is marred by spelling or typographical errors. The software crafted as a result of this development finds its application across a broad spectrum of fields including information retrieval, natural language processing, plagiarism detection, and genomics, to name a few. The methodologies and algorithms highlighted in the article have significant implications in domains that demand a high degree of precision in text interpretation and comparison. In the realm of information search, these algorithms are instrumental in enhancing result quality, offering users more accurate responses to their queries, regardless of the presence of errors in spelling or grammar. In natural language processing, the algorithms play a pivotal role in analyzing and interpreting human language. This capability is fundamental for the development of advanced chatbots, machine translation systems, and intelligent digital assistants. Their application in plagiarism detection is equally noteworthy. Here, the algorithms demonstrate an exceptional ability to ascertain the degree of similarity between texts, a function that holds immense value in academic and research settings. In the field of genomics, these methods are employed for the intricate task of mapping genetic sequences, a vital process in bioinformatics research. In conclusion, the software developed as per the article presents a versatile tool, finding relevance in various scientific and technological arenas. Its ability to conduct a thorough analysis and comprehend textual data is unparalleled, marking a significant advancement in these fields

The Use of Information Retrieval in Student Academic Document Plagiarism Detection System

The practice of plagiarism can threaten the credibility of academic documents such as final assignments, theses, theses, and dissertations. Universitas Muhammadiyah Ponorogo (UMPO) is one of the best Muhammadiyah Universities in Indonesia. All academic documents at UMPO are well stored. However, plagiarism is only found in the Turnitin database and in the title of academic documents. This research aims to pre-screen academic documents in the internal scope by creating a plagiarism application that is easy to use and cheaper in terms of cost than using Turnitin. The steps of making a plagiarism detection system in academic documents using information retrieval (IR) with waterfall development. The design process starts from system requirements analysis, design, implementation, and testing. This plagiarism detection system goes through several stages, namely tokenisation, stopword removal, stemming, and termweigthing. Tokenisation involves converting uppercase letters into lowercase letters and removing punctuation marks. Stopword removal removes noise, stemming converts words into basic forms, and termweigthing uses local and global weighting to calculate similarity with the Vector Space Model. Document similarity is calculated using cosine similarity, rejected documents are considered free of plagiarism. If there is no similarity, the document is accepted as a source document, while the rejected document is not stored in the database. Further research on the document plagiarism detection system that previously only used the txt file extension, in the future it can use the .doc or HTML extension so as to increase the effectiveness of the performance of academic document examination time.

Editorial

Editorial

Pelatihan Publikasi Artikel Ilmiah Melalui Open Journal System (OJS) dan Plagiarism Bagi Komunitas Literasi Guru SMK

Since the Minister of Administrative and Bureaucratic Reform Decree Number 26 of 1989, namely regarding changing teacher positions to functional positions, teachers have been required to develop their careers. This explains that teachers are required to disseminate the results of Classroom Action Research through seminars, publications, or other means so that the research results can be known to the general public. In general, the dissemination of scientific work is carried out online through the Open Journal System system. However, with the increase in information technology, scientific work produced is prone to plagiarism due to the ease of accessing information. Partners are teachers at SMKN 3 Tanjungpinang, Riau Islands Province. Partners have the problem of a lack of knowledge about how to submit articles via the Open Journal System (OJS) system and plagiarism as well as how to check plagiarism in a scientific work. The method used is to provide knowledge about the Open Journal System (OJS) and Anti-Plagiarism systems and training using software or online Anti-Plagiarism checking applications which can be used for free. The results obtained are that partners understand how to use the OJS system, Plagiarism Checker and Turnitin applications, from 25 (twenty five) participants with a 'Very Good' score of 12%, a 'Good' score of 60%, a 'Medium' score of 28% and 'Not Appearing' value is 0%. Other results in the form of teaching materials and articles with draft status to the Community Service Journal will be published in 2024. The conclusion obtained is the increased capacity of partners in using the OJS system and plagiarism check applications.

Design and Implementation Plagiarism Checker Application with DetectGPT using Scheduler Algorithm

The increasing use of Generative AI in the field of writing in the academic world creates problems in the field of plagiarism. this problem has prompted an urgent need for plagiarism detection tools. However, not all universities are able to implement a system that is able to detect sentences or paragraphs resulting from generative AI. Recent research seeks to overcome this obstacle using AI itself, specifically through the DetectGPT model. However, the implementation of this technology has limitations, such as requiring large computing resources and quite a long examination time. Using a GPU can speed up the process, but not all users can implement it. Solutions to minimize processing time include using more than one worker, however, scheduling is essential to maximize plagiarism detection efficiency. This research proposes the implementation of the above system with the help of the scheduler. The results obtained in this system prototype are an average waiting time for checking of 457,778 seconds to complete 10 tasks with the help of 3 workers running at the same time.

Exploring Attentive Siamese LSTM for Low-Resource Text Plagiarism Detection

Abstract Low-resource text plagiarism detection faces a significant challenge due to the limited availability of labeled data for training. This task requires the development of sophisticated algorithms capable of identifying similarities and differences in texts, particularly in the realm of semantic rewriting and translation-based plagiarism detection. In this paper, we present an enhanced attentive Siamese Long Short-Term Memory (LSTM) network designed for Tibetan-Chinese plagiarism detection. Our approach begins with the introduction of translation-based data augmentation, aimed at expanding the bilingual training dataset. Subsequently, we propose a pre-detection method leveraging abstract document vectors to enhance detection efficiency. Finally, we introduce an improved attentive Siamese LSTM network tailored for Tibetan-Chinese plagiarism detection. We conduct comprehensive experiments to showcase the effectiveness of our proposed plagiarism detection framework.

Legal and Ethical Dimensions of AI in Education: Navigating New Frontiers

Objective: This study critically examines the legal aspects of using artificial intelligence (AI) in education, exploring the roles of chatbots, Chat GPT, chat-based plagiarism issues, educational simulations, and AI's impact on the labor market.
 Methods: Employing a variety of research methods, the study analyzes current legal norms and international laws relevant to AI in education. This includes considerations of intellectual property, data privacy, ethical standards, and liability.
 Results: The paper highlights the challenge of chat-based plagiarism, emphasizing the need for careful information use to maintain academic integrity. While students might misuse AI tools like chatbots and GPT models for plagiarism, these technologies also aid in plagiarism detection. The study also explores the use of machine learning and data analysis in developing personalized learning experiences, enhancing learning effectiveness and knowledge retention.
 Conclusion: AI's integration into education promises to improve quality and accessibility, but necessitates a robust legal framework. The article evaluates the effectiveness of various AI tools, including chatbots, which provide on-demand information, and Chat GPT, useful for textual material handling. It also discusses the role of learning simulations in personalizing education, using AI to analyze performance data and tailor individual learning paths.

Findings from a survey looking at attitudes towards AI and its use in teaching, learning and research

Artificial Intelligence (AI) is having an advancing dramatic impact on Technology Enhanced Learning (TEL) in Higher Education. (Popenici & Kerr, 2017) observed an emergence of the use of AI in HE (Higher Education) and pinpointed challenges for institutions and students including issues of academic integrity, privacy and “the possibility of a dystopian future” (p. 11). Potential benefits of AI in HE includes creating learning communities through chatbots (Studente & Ellis, 2020), automated grading, individualized learning strategies and improved plagiarism detection (Owoc et al., 2019). It is unclear how often, and in what manner, students are engaging with AI during their learning and in creating submissions for assessments tasks and if this engagement is creating unrealistic outcomes. It is also unclear how educators are engaging with AI during their teaching and curriculum/assessment design and how this may be impacting the learning outcomes of their cohorts. This research study was conducted to investigate the perceived immediate and long-term implications of engaging with AI of both staff and students on learning and teaching within the University of Adelaide. The design of the research study is underpinned by a blended approach combining Situational Ethics and Planned Behavior Theory to understand the ethical considerations and behavioral activities and future intentions of staff and students regarding the use of AI. Situational Ethics provides a framework for examining the contextual nature of ethical decision-making regarding AI (Boddington, 2017; Memarian & Doleck, 2023). Planned Behavior Theory provides understanding of individuals' motivation and rationalization to engage with AI (Wang et al., 2022). By employing a mixed qualitative and quantitative design, collecting data via online surveys, the study's findings shed light on the ethical challenges and attitudes associated with AI implementation in higher education and provided insights into the factors that influence staff and students’ individual intentions to engage with AI technologies in Learning and Teaching. Participants from all faculties across a wide diversity of student cohorts and staff responded to the surveys. Initial findings reveal educators are suspecting a greater student use of AI than the data demonstrates. The most frequent use of AI by students is for checking grammar and this is more prominent in the international student cohort. Students trust their human educators more than AI for course content and feedback on assessments. Educators are comfortable using AI but feel also they need greater support and training. The majority of students (70%, n=126) are not concerned about the implications of using Generative AI in higher education, regarding issues related to privacy, bias, ethics, or discrimination. However, demonstrating an active concern in this field, the most common use of AI by university staff is to test its capabilities to complete assignments. These and other findings from the study can provide guidance to staff and students by describing current practices and making recommendations regarding assessment, curriculum design, and Learning and Teaching (L&T) activities.

A Study on Distinguishing ChatGPT-Generated and Human-Written Orthopaedic Abstracts by Reviewers: Decoding the Discrepancies.

ChatGPT (OpenAI Incorporated, Mission District, San Francisco, United States)is an artificial intelligence (AI)-based language model that generates human-resembling texts. This AI-generated literary work is comprehensibleand contextually relevant and it is really difficult to differentiate from human-written content. ChatGPT has risen in popularity lately and is widely utilized in scholarly manuscript drafting. The aim of this study is to identify if 1) human reviewers can differentiate between AI-generated and human-written abstracts and2) AI detectors are currently reliable in detecting AI-generated abstracts. Seven blinded reviewers were asked to read 21 abstracts and differentiate which were AI-generated and which were human-written. The first group consisted of three orthopaedic residents with limited research experience (OR). The second group included three orthopaedic professors with extensive research experience (OP). The seventh reviewer was a non-orthopaedic doctor and acted as a control in terms of expertise. All abstracts were scanned by a plagiarism detector program. The performance of detecting AI-generated abstracts of two different AI detectors was also analyzed. A structured interview was conducted at the end of the survey in order to evaluate the decision-making process utilized by each reviewer. The OR group managed to identify correctly 34.9% of the abstracts' authorship and the OP group 31.7%. The non-orthopaedic control identified correctly 76.2%. All AI-generated abstracts were 100% unique (0% plagiarism). The first AI detector managed to identify correctly only 9/21 (42.9%) of the abstracts' authors, whereas the second AI detector identified 14/21 (66.6%). Inability to correctly identify AI-generated context poses a significant scientific risk as "false" abstracts can end up in scientific conferences or publications.Neither expertise nor research background was shown to have any meaningful impact on the predictive outcome. Focus on statistical data presentation may help the differentiation process. Further research is warranted in order to highlight which elements could help reveal an AI-generated abstract.

Tackling the Challenges of Plagiarism in the Age of Information Overload by LIS Professionals in Nigerian Academic Institutions

The advancement in information and communication technologies (ICTs) has increased the generation and dissemination of information. This has created a remarkable shift from the previous promulgation of information explosion to the concept of information overload in this age, which seems to be characterised by the ease of intellectual theft in various forms. The sophistication of ICTs and the overwhelming availability of information have subjected many research outputs to the affliction of ‘copy-and-paste’ syndrome and copyright abuse; and there appears to be a dearth of literature on efforts being made by LIS professionals to tackle the challenges in Nigerian academic institutions. This study, therefore, sought to draw attention to how the challenges of plagiarism are tackled by LIS professionals in the age of information overload in Nigerian academic institutions. The study adopted the phenomenological research design of qualitative methodology, using semi-structured written interview that was developed by the researchers and sent electronically to the respondents. The data were presented textually with the insertion of verbatim quotations where necessary while the research findings were analysed using thematic analysis. Findings from the 45 respondents across the six geopolitical zones in Nigeria revealed a high level of plagiarism and a moderate level of engagement in curbing the menace by LIS professionals. The study recommends a multifaceted approach to curb plagiarism; comprising information literacy instructions in academic institutions, institutionalised adoption and access to plagiarism detection software and advocacy programmes that promote ethical writing and ICT skills for LIS professionals.

APLIKASI ALJABAR LINEAR UNTUK MENDETEKSI ADANYA PLAGIARISME SERTA DALAM METODOLOGI SEARCH ENGINE

The theory of algebra is highly beneficial and used in daily life. Identifying plagiarism is one of many activities that can be solved with the concept of linear algebra. Even today, plagiarism is still a crucial issue to discuss, preferably in academic scope, both in assignment papers and academic theses. Identifying plagiarism aims to provide a document as a vector. Even the basic concept of linear algebra can be applied in plagiarism detection applications. This paper proposed a modeling example of plagiarism checking on a document with a matrix representation and the calculation of angles among subspaces of each compared document. Finally, the results can be used as one of the considerations to determine the similarity index.Keywords: plagiarism, linear algebra, vector, matrix representation

Authorship identification methods in student plagiarism detection

In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, detect and analyze plagiarism approaches in various works are investigated. Both classical methods, which include text comparison and similarity search, and modern methods based on machine learning algorithms, as well as their combination and potential modifications, are considered. The advantages and limitations of each method are also discussed, and recommendations are given for choosing one or another approach according to the specific requirements of the research.Special attention is paid to such modern methods as metadata analysis and the application of neural networks. Stylistic analysis reveals authorial peculiarities such as word choice, preferred wording, and even punctuation. Lexical and syntactic models are used to identify repetitive phrases and structures that may indicate plagiarism. Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data.Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments.

A Review on Plagiarism Detection Methods in Digital Documents

Abstract: In the age of the Internet, it has become easy to copy someone else's work. Plagiarism refers to reusing someone else's work without acknowledging the source of the work or citing it. Because of the widespread availability of information and documents on the Internet and in online libraries, plagiarism has grown to be one of the major concerns for the educational and research sectors. It is actually a distinct offense against intellectual property. A variety of plagiarism detection software programs are currently available on the market or online for identifying certain sorts of plagiarism, but they still have some limitations. Typically, numerous documents are compared in order to find plagiarism, and a score is given depending on how similar they are to one another. In this paper, some of the plagiarism detection methods are reviewed and compared their performances.

The Use of Grammarly by Tertiary English Language Learners in Their Online Writing Classes

In writing English texts, grammatical errors are a problem that is always faced by writers. Not to mention other writing errors such as typos, punctuation, and capitalization can also be done by writers. Therefore, the Grammarly application was created to overcome this problem. This study aims to find out how Grammarly helps tertiary-level students in their online Writing Classes. The participants of this study were 78 students studying in an English Language Education study program. A questionnaire was used as the data collection instrument by adopting and adapting closed-ended questions from Hakiki's research (2021) and adding open-ended questions to find out the respondent's perceptions about the use of Grammarly in their online Writing activities. The findings of this study show the majority of respondents perceived Grammarly as a very helpful writing tool. Grammarly helps them by providing vocabulary and grammar suggestions to avoid language errors in their writing, providing mechanics corrections to help them revise their writing errors, and providing plagiarism checkers for premium users.

Exploring Plagiarism Awareness, Motivation, And Intentions Within The Ayurvedic Domain

As plagiarism is a serious offense, it is critical for researchers to examine their work for plagiarism. Consequently, this study aims to shed light on the issue of plagiarism within the Ayurveda domain, providing valuable insights into the awareness, motivations, and purposes behind plagiarism. This is an empirical study based on the primary data collected from the field of Ayurved domain using a survey approach. The collected data was analyzed using descriptive and inferential statistics for hypothesis testing to draw conclusions. Researchers collected data from 194 participants (38% male and 62% female) with an average age 31.19 years (± 0.80) and SD 11.11 years. They included 58.76% of students, 40.72% of teachers, and 0.52% of researchers having education qualifications as 37.63%, 40.72%, and 21.65% for undergraduate (UG), Post Graduate (PG), and Doctoral (Ph.D.) respectively. Formulated four research hypotheses, through their respective sub-hypotheses, are tested for independence using parametric χ2 test of independence at 95% confidence level (α = 0.05). Awareness about plagiarism depends upon educational qualification as well as professional-category of the personnel, It is found to be in increasing order with respect to educational qualification, more in teachers than students. The motivation behind the plagiarism check was also found to be dependent upon educational qualification and professional category. Mandatory-requirement factor increases with an academic qualification and is found to be higher in teachers than students. The researcher further observed that the selection of plagiarism detection software is independent of educational qualification and professional category. It found effectiveness as the primary factor for selection followed by price. By understanding these plagiarism aspects, it is possible to develop strategies to prevent plagiarism and promote ethical research practices in the field of Ayurveda, ultimately preserving the integrity of this traditional system of medicine.

Automatic plagiarism detection using fuzzy-logic

Plagiarism occurs when a researcher copies fellow researcher’s work verbatim without acknowledging the author. This work developed an automatic plagiarism detector using fuzzy logic. The system developed was tested with 4 different text documents and evaluated using portability, efficiency, functionality, ease of use and accuracy metrics. Results show that the developed plagiarism detector is very easy to use with high functionality and accuracy as well as moderate efficiency and portability based on user’s assessment. However, future work can increase the data size for model training and consider machine learning techniques to improve accuracy.

ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools?

This study aims to assess the capabilities of ChatGPT in comparison to widely utilized writing assistance tools such as Grammarly and Quillbot. The evaluation involves an examination of ChatGPT's performance across multiple dimensions, encompassing grammar and style correction, plagiarism detection, and overall enhancement of writing quality. Employing a series of tests and comparative analyses, this research investigates the potential suitability of ChatGPT as a viable alternative to Grammarly and Quillbot in supporting writers and enhancing the quality of written content. The outcomes and insights derived from this evaluation offer valuable guidance for individuals and professionals seeking effective writing assistance tools. This article comprehensively reviewed 10 distinct cases of Question(, covering aspects like grammar, parts of speech, proofreading, rephrasing, plagiarism detection, citation, audience engagement, writing style, tone (with a focus on optimism), and the creation of a short story complete with a title. This evaluation encompassed an assessment of various artificial intelligence tools, with specific attention given to ChatGPT. The results of this assessment demonstrated enhanced efficiency and effectiveness in achieving desired writing goals, with notable exceptions observed in addressing plagiarism and citation-related issues.