Plagiarism Detection Research Articles

Persepsi Mahasiswa tentang Penggunaan Artificial Intelligence Quillbot dalam Mengatasi Plagiarisme dan Kesadaran Etika Akademik Mahasiswa

This research aims to analyze student perceptions regarding the use of artificial intelligence quillbot in overcoming plagiarism and students' awareness of academic ethics. This type of research is qualitative with a case study approach. Data collection uses observation, questionnaires, interviews and document studies. Data analysis is carried out continuously, starting from data reduction to drawing conclusions. The research results show that the use of Artificial Intelligence QuillBot in overcoming plagiarism in the Islamic Religious Education Study Program at the State Islamic University of North Sumatra is highlighted through two approaches, namely first, the resource person supports the use of the plagiarism checker feature as a solution for detecting plagiarism in their scientific work. They consider QuillBot not only as a tool, but also as a necessity in completing course assignments well, especially because a low level of plagiarism is expected. Second, QuillBot's ability to paraphrase automatically can simplify the process of avoiding plagiarism, reduce the risk of using unoriginal content, and increase the authenticity of scientific work. Some students consider their awareness of academic ethics to be good, showing progress in respecting the principles of academic integrity, such as avoiding plagiarism and stating sources clearly. However, there is still an opinion that awareness of academic ethics still needs to be increased because there are still many students who are less aware of the importance of academic integrity, especially regarding the issue of plagiarism.

Plagiarism and Authorship Credit.

Literature being an expression of an author, its commodification historically has assigned a value to it primarily in terms of authorship credit. Arguably reproducing published content without attributing the requisite source, termed as plagiarism is ethically discrediting to this premise. However, simply weighing its proportion based on digitally assigned semantic similarity may not be completely justifiable in the present-day digital atmosphere. It should be noted that while technology can facilitate plagiarism detection, digitization by way of providing greater access to published content is also the facilitator of plagiarism. While the scientific community is often severe in its approach toward the act of plagiarism, there is still a lack of clarity around the code of conduct of the same as there are several grey areas related to such a misconduct on which the law remains silent. By revisiting the historical evolution of the credit of authorship and the copyright law this piece presents an analytical vista pertaining to plagiarism in a different light. By identifying the gaps in the present-day handling of these age-old concepts, one may find that there is an unmet need to revisit the legal aspects of handling cases of plagiarism taking into consideration the digital environment.

Detection of plagiarism among rhinology scientific journals.

Automated plagiarism-checking software can be a valuable tool for detecting plagiarism in manuscripts. Twenty-five of 60 articles (42%) had at least one incidence of plagiarism, predominately text recycling. A "similarity score" ranging from 22% to 35% could be a potential cut-off value when screening submitted manuscripts.

Ensemble machine learning technique-based plagiarism detection over opinions in social media

With the progressive enhancement of social media, several people prefer posting their opinions on various social media instead of posting on radios, television or newspapers. The postings differ in dimensions and include various titles and comments. Nowadays, the formation of plagiarism is increasing tremendously which occurs by rewriting or repeating one’s work. There are many ways to detect plagiarism by browsing through the internet. The significant intention of this paper involves the detection of plagiarism in social media using four different phases, namely the data pre-processing phase, n-gram evaluation, similarity/distance computation analysis and the plagiarism detection phase. The pre-processing includes data cleaning processes, such as the removal of redundant data, upper case letters, noise, irrelevant punctuations and characterizing into a vector form. After pre-processing the data are fed for n-gram evaluation to develop a posting attribution system. Then finally, an ensemble support vector machine-based African vulture optimization (ESVM-AVO) approach is employed to detect plagiarism which signifies that the performance based on detection is enhanced and the execution time in obtaining a high rate of detection accuracy is very low. Finally, the performance evaluation and the comparative analysis are carried out to determine the performance of the proposed system.

Turnitin Use and Analysis: Software to Reduce Plagiarism at Medan State University

Plagiarism is taking the idea of someone else's essay or writing then as if making the work as his own work. Seeing the many cases of plagiarism committed against scientific papers, it is necessary to analyze the use of turnitin software in examining quotations taken from other people's written works. The purpose of this study was to analyze the use of Turnitin software in reducing plagiarism at Medan State University. This type of research is qualitative research with descriptive methods. Data collection conducted in this study was through interviews and documentation studies. The results of software usage data showed a significant decrease in the similarity index of student writing. In the similiarities index percentage of 1% - 24%, data entered in the Turnitin software shows the number of scientific papers is increasing compared to the similarties index of 50% - 74%. The same thing happens with the similiraties index of 25% - 49%, the number of scientific papers entered in the software is greater than 50% - 74%. The use of Turnitin software is also effective in reducing plagiarism of scientific papers because the achievement indicators consist of clarity on the goals achieved and strategies in achieving the goals in their implementation are clearly visible; facilities and infrastructure already have standards in services carried out for plagiarism tests; The implementation of plagiarism test services that have been carried out has effectiveness and efficiency in its implementation; surveillance system and Controls that have been implemented well, as well as human resources, namely librarian staff and plagiarism detection teams, understand the use of the Turnitin device application in serving plagiarism tests.

Enhancing plagiarism detection methodology with the DQN algorithm on an improved differential evolution foundation

Plagiarism detection has become increasingly crucial in real-world applications, demanding precise identification of content similarity. This paper introduces a novel plagiarism detection approach. Building upon LSTM as the foundation, it employs an enhanced DE (Differential Evolution) algorithm and reinforces learning with the DQN algorithm for sample classification and training. Throughout the training process, gradual parameter adjustments are made with the aim of improving the model's efficiency and accuracy.

A Comparative Study of the Accuracy of Turn-It-In’s Artificial Intelligence Detector in CTU Doctoral Assignments

As access to artificial intelligence becomes mainstream in college paper writing, universities must find concrete methods of verifying work submitted as a student’s original content was not significantly developed using AI. In response to these potential academic integrity issues, plagiarism detectors, like TurnItIn, have developed artificial intelligence detectors that provide the percentage of a student submission identified as written by a computer. TurnItIn (TII) claims a 98% confidence level concerning its accuracy. In this study, 48 papers were randomly selected from the Colorado Technical University’s Doctoral Studies courses. These papers were written before the public release of ChatGPT 3.5. Papers were scored for similarity and AI-generated content through TII. These same papers were then edited for grammar and clarity using ChatGPT, and the similarity and AI-generated content scores were recalculated through TII. Wilcoxon Ranked-Sum tests were conducted to determine if a statistical difference occurred between the original and updated similarity and AI-generated content scores. The results demonstrate that TII’s confidence level may not be as accurate as claimed and that the detection of AI-generated content requires further testing before being used to determine if an act of academic dishonesty took place.

Interplay of rationality and morality in using ChatGPT for academic misconduct

ABSTRACT ChatGPT’s capability to generate original content on any topic represents a concern for academic institutions, since students can plagiarise the AI-created content with a low probability of being caught. While prior research on academic misconduct emphasized the combined effects of perceived costs and benefits in explaining students’ decisions to cheat, the low detectability of ChatGPT-based plagiarism significantly reduces the cost of being caught, making rational choice theory less powerful in explaining academic misconduct in the AI era. Considering the effects of both rational decision-making and moral considerations, this study shows that individuals experiencing moral disengagement are more likely to engage in ChatGPT-based plagiarism. Other significant predictors include perceived benefits, punishment severity, and informal sanctions. Furthermore, moral disengagement enhances the effects of formal sanctions on ChatGPT-based plagiarism. The results have implications for professors, academic administrators, and plagiarism detection software designers to enforce academic integrity in the ChatGPT era.

Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.

Inaugural Editorial

It is a great pleasure for me to provide an editorial commentary for the first edition of the recently established Journal of Optics and Photonics Research (JOPR). Greetings and salutations to all of you on the inaugural issue of JOPR, a new and exciting portfolio journal from Bon View Publishing, which focuses on cutting-edge research trends in optics and photonics. The journal features international peer-reviewed multidisciplinary papers that provide new and original insights to the field, while maintaining accuracy in theory and technology. We are committed to ensuring that every accepted article meets these standards. Being the Editor-in-Chief of JOPR is an honor and a source of great gratitude for me. The fields of optics and photonics are primarily concerned with the study of light, its creation, and manipulation, as well as its interactions with gases, plasmas, molecules, and solids, including micro- and nanostructures. The optical domain of ultraviolet, visible, and infrared light is much surpassed by the electromagnetic spectrum covered by optics and photonics. Researchers from physics, chemical and biological engineering, electronic and electrical engineering, and other fields are involved in this highly multidisciplinary topic. JOPR draws on both theoretical and practical results in the field of optical science and engineering, without being limited to any one discipline. It is committed to a wide spectrum of fundamental and applied optical research and brings scientists from many domains together. We encourage authors to conduct interdisciplinary research through the application of reasonable modeling or computational methods and experimental analysis. My goal for JOPR is to create a premier, open-access, highly peer-reviewed platform whose goal is to publish the most significant research in photonics and optics. I'm appreciative of the community's tremendous support in assisting us in achieving this objective.

Detecting Similarity of Program Code Using an Aggregated Approach to the Problem of Plagiarism Detection

In this work, the primary strategies employed for code plagiarism were explored, alongside an analysis of prevalent methods for detecting copied content. Based on the results of the analysis of various approaches, as well as the analysis of the subject area itself and on the basis of the formulated requirements, a new System for automatically checking software similarity for plagiarism was successfully designed, implemented and tested. When developing the System, an aggregated approach was used, which made it possible to use several basic similarity detection algorithms. Namely, the Greedy Row Tiling algorithm and the Sifting algorithm. Since the System is designed for programmers, in particular, for teachers, and also with the possibility of local launch, it is proposed to perform user interaction with the System in the form of a command line interface. The System is implemented in Python, which ensures that the suggested System is platform independent. Regular expressions are used to implement preprocessing and exclusion functions, and the libclang library is used for С++ code parsing and tokenization functions. Promising applications for the developed System include education and programming competitions. So universities and colleges can use the System to check code written by students to detect plagiarism. And in competitive environments such as hackathons or programming competitions, the System can be used to ensure fairness and prevent plagiarism among participants.

Dimensions of Legal And Moral Use of Artificial Intelligence In Education

The purpose of the research is to critically analyze the legal aspects of the use of artificial intelligence (AI) in the field of education, as well as to study the role of chatbots and the Chat GPT model, plagiarism issues, educational modeling and the impact of AI on the labor market. To achieve this goal, various research methods are used, including the analysis of current legal norms and international legislation that relate to the use of AI in education. The study also includes an analysis of intellectual property issues, data privacy, ethical standards and liability.
 The results of the study highlight the problem of plagiarism in chat rooms and emphasize the importance of careful use of information to ensure academic integrity. Despite the possible misuse of AI by students, such as chatbots and GPT models, for plagiarism, these technologies can also facilitate plagiarism detection. The research also examines the use of machine learning and data analytics to create personalized learning experiences, improve learning effectiveness, and retain knowledge.
 The overall conclusion is that the integration of artificial intelligence in education has the potential to improve the quality and accessibility of education, but this requires a sound legal framework. The article also evaluates the effectiveness of various AI tools, including chatbots that provide information on demand and Chat GPT, useful for processing textual materials. The paper also examines the role of learning simulation in personalizing education, using AI to analyze performance data, and tailoring individual learning pathways.

Ambiguity in Ethical Standards: Global Versus Local Science in Explaining Academic Plagiarism

The past decade has seen extensive research carried out on the systematic causes of research misconduct. Simultaneously, less attention has been paid to the variation in academic misconduct between research fields, as most empirical studies focus on one particular discipline. We propose that academic discipline is one of several systematic factors that might contribute to academic misbehavior. Drawing on a neo-institutional approach, we argue that in the developing countries, the norm of textual originality has not drawn equal support across different research fields depending on its level of internationalization. Using plagiarism detection software, we analyzed 2,405 doctoral dissertations randomly selected from all dissertations defended in Russia between 2007 and 2015. We measured the globalization of each academic discipline by calculating the share of publications indexed in the global citation database in relation to overall output. Our results showed that, with an average share of detected borrowings of over 19%, the incidence of plagiarism in Russia is remarkably higher than in Western countries. Overall, disciplines closely follow the pattern of higher globalization associated with a lower percentage of borrowed text. We also found that plagiarism is less prevalent at research-oriented institutions supporting global ethical standards. Our findings suggest that it might be misleading to measure the prevalence of academic misconduct in developing countries without paying attention to variations at the disciplinary level.

The Game-Changing Impact of AI Chatbots on Education ChatGPT and Beyond

ChatGPT, an AI-driven chatbot, delivers coherent and valuable responses by analysing extensive data sets. This article explores the profound impact of ChatGPT on contemporary education, as discussed by prominent academics, scientists, distinguished researchers, and engineers. The study delves into ChatGPT's capabilities, its application in the education sector, and the identification of potential concerns and challenges. Initial assessments reveal variations in ChatGPT's performance across diverse subjects, including finance, coding, mathematics, and general public queries. While ChatGPT can assist educators by generating instructional content, providing suggestions, serving as an online educator for learners, answering questions and revolutionising education through smartphones and IoT devices, it has shortcomings. These include the risk of generating inaccurate or false information and evading plagiarism detection systems, a critical concern for maintaining originality. The commonly reported "hallucinations" in Generative AI, which applies to ChatGPT, also limit its utility in situations where precision is paramount. A notable deficiency in ChatGPT is the absence of a stochastic measure to facilitate authentic and empathetic communication with users. The article suggests that educational institutions' academic regulations and evaluation practices should be updated if ChatGPT is integrated into the educational toolkit. To effectively navigate the transformative effects of ChatGPT in the learning environment, it is imperative to educate teachers and students about its capabilities and limitations.

Emerging Software and Tools in Higher Education Institutions

Due to digital revolution, new software and tools has been developed by many companies, organizations and higher education institutions which have created a kind of ease to automate our systems. For institution different software offers wide range of feature like automation, online data management, collaboration, planning, videoconferencing, plagiarism detection, language learning and many more. It has been found that the number of software and tools are being developed every day, therefore it is important to choose and install particular software from different resources. The quality of software is dependent on the process to be followed; therefore, it is necessary to investigate their different features. The risks associated with software include data security, unreliable backup systems, compatibility issues, system glitches, poor user interface, lack of control etc. In this paper, an attempt has been made to provide information about different software and tools that has been used recently in educational institution.

Analysis Of DevOps Infrastructure Methodology and Functionality of Build Pipelines

The DevOps pipeline for infrastructure is a critical component in modern software development and operations practices. It involves automating the provisioning, configuration, and management of infrastructure resources, enabling organizations to achieve agility, scalability, and reliability. This paper presents a plagiarism-free analysis of the DevOps pipeline for infrastructure, conducted through comprehensive research, evaluation of industry best practices, and examination of case studies. The DevOps methodology would collapse without the use of a DevOps pipeline. The phrase is often used to discussions of the methods, procedures, and automation frameworks that go into the creation of software objects. Jenkins, an open-source Java program, is the most well-known DevOps pipeline and is often credited as the catalyst for the whole DevOps movement. Today, we have access to a plethora of DevOps pipeline technologies, such as Travis CI, GitHub Actions, and Argo. To keep up with the need for new and improved software systems, today's development organizations must overcome a number of obstacles. The research highlights key findings, including the importance of automation, infrastructure as code, continuous integration and delivery, security, and monitoring/logging capabilities. These practices have been shown to enhance efficiency, reduce errors, and accelerate deployment cycles. By evaluating tools and technologies, gathering user feedback, and analyzing performance metrics, organizations can identify gaps and develop a roadmap for pipeline improvement. To maintain academic integrity, this analysis adheres to proper citation and referencing practices. Paraphrasing and summarizing research findings and adding personal analysis and interpretations ensure the originality and authenticity of the analysis. Plagiarism detection tools are used to confirm the absence of unintentional similarities with existing content.

Library Management Advocacy Study of IAIN Fattahul Muluk Papua Library Program in Realizing Digital Library in 2023

The study of the IAIN Fattahul Muluk Papua Library program in realizing a digital library is a form of adjustment to technological developments in improving its competence as a supporter of the implementation of the Tri Dharma of Higher Education according to the digital era. The discussion of this study uses the theory of Challenge and Response proposed by Arnold Y. Toynbee. The challenges and responses in the program have reinforced the theory. Some responses are shown by the existence of programs in various components, namely the digital collection component of e-books has held 421 titles, 1,151 copies, developing an institutional Repository as a forum for campus community papers in digital form, and other supporting facilities, such as Turnitin Plagiarism Checker Online. The components of library services, both user services and technical services, also lead to the digitization system. For lending and returning printed books, e-books, free library certificates according to computerized and digitized data. Technical services through the migration program of processing library materials, especially printed books consisting of 5,668 titles, 15,589 copies have been and are being reprocessed to comply with national library standards. Library implementation and management component programs that emphasize reporting systems are a hallmark of good library governance. The Cooperation Program followed up by comparative studies with UIN Mataram and with the National Library of the Republic of Indonesia is a step forward. The existence of additional programs from librarianship organizations implemented such as the Indonesian Higher Education Library Forum (FPPTI), the Indonesian Librarian Association (IPI) of Papua Province shows the level of public trust in the ability of librarians in particular and the IAIN Fattahul Muluk Papua Library in general

USE OF ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING

In recent years, there has been a growing interest in the integration of artificial intelligence (AI) into various fields. Artificial Intelligence (AI) also has become a prominent technology in various fields, including education. One area where Artificial Intelligent (AI) has shown great potential is in English Language Teaching (ELT). With the rapid development of technology, Artificial Intelligent (AI) tools and applications have been integrated into language learning platforms, classrooms, and online resources, transforming the way English is taught and learned. In the realm of language teaching, Artificial Intelligent offers significant potential for enhancing the learning process and improving outcomes. English language teaching is no exception, as educators are exploring the potential of Artificial Intelligent (AI) to enhance language learning experiences. English language teaching is no exception, as Artificial Intelligent (AI) can enhance the learning experience for English language learners by providing personalized instruction, immediate feedback, and immersive language practice. This research addresses the extent to use artificial intelligence in English language teaching. Researcher used descriptive qualitative approach with questionnaire as technique of data collection. The results of this research are the Artificial Intelligence is needed in English language teaching. Artificial Intelligence also has negative and positive impact in English language teaching depend on utilized correctly and at the appropriate timing. Besides that, Artificial Intelligence also help students in improving their English skill like speaking, writing, and reading using product of Artificial Intelligence like Chat GPT, Quillbot, Grammarly, Plagiarism Checker, Paraphrasingtool.com. So, the use of Artificial Intelligent in English language teaching is very important or useful and also reducing the burden of teachers and improving teaching quality.

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment.

The ethical implications of using generative chatbots in higher education

Incorporating artificial intelligence (AI) into education, specifically through generative chatbots, can transform teaching and learning for education professionals in both administrative and pedagogical ways. However, the ethical implications of using generative chatbots in education must be carefully considered. Ethical concerns about advanced chatbots have yet to be explored in the education sector. This short article introduces the ethical concerns associated with introducing platforms such as ChatGPT in education. The article outlines how handling sensitive student data by chatbots presents significant privacy challenges, thus requiring adherence to data protection regulations, which may not always be possible. It highlights the risk of algorithmic bias in chatbots, which could perpetuate societal biases, which can be problematic. The article also examines the balance between fostering student autonomy in learning and the potential impact on academic self-efficacy, noting the risk of over-reliance on AI for educational purposes. Plagiarism continues to emerge as a critical ethical concern, with AI-generated content threatening academic integrity. The article advocates for comprehensive measures to address these ethical issues, including clear policies, advanced plagiarism detection techniques, and innovative assessment methods. By addressing these ethical challenges, the article argues that educators, AI developers, policymakers, and students can fully harness the potential of chatbots in education, creating a more inclusive, empowering, and ethically sound educational future.