Plagiarism Detection Research Articles

Correlation Among Plagiarsm, Grammatical, Artificial Intelgency, ChatGPT in Research Paper of Journal Open University of Asean and Indonesia

From the four variables, namely plagiarism, grammar, Artificial Intelligence (AI) consisting of ChatGPT and the journal publishing countries, namely Open University ASEAN and Indonesia, six hypotheses can be developed. The samples used were 40 research papers from two journals. However, the only one that has a correlation is a negative correlation between plagiarism and Artificial Intelligence (AI) detection. As plagiarism increases, Artificial Intelligence (AI) detection decreases. Conversely, when plagiarism decreases, Artificial Intelligence (AI) detection increases. This is a new problem that researchers in scientific writing must be faced.

Survey on AI-Generated Plagiarism Detection: The Impact of Large Language Models on Academic Integrity

Survey on AI-Generated Plagiarism Detection: The Impact of Large Language Models on Academic Integrity

ChartLine: Automatic Detection and Tracing of Curves in Scientific Line Charts Using Spatial-Sequence Feature Pyramid Network.

Line charts are prevalent in scientific documents and commercial data visualization, serving as essential tools for conveying data trends. Automatic detection and tracing of line paths in these charts is crucial for downstream tasks such as data extraction, chart quality assessment, plagiarism detection, and visual question answering. However, line graphs present unique challenges due to their complex backgrounds and diverse curve styles, including solid, dashed, and dotted lines. Existing curve detection algorithms struggle to address these challenges effectively. In this paper, we propose ChartLine, a novel network designed for detecting and tracing curves in line graphs. Our approach integrates a Spatial-Sequence Attention Feature Pyramid Network (SSA-FPN) in both the encoder and decoder to capture rich hierarchical representations of curve structures and boundary features. The model incorporates a Spatial-Sequence Fusion (SSF) module and a Channel Multi-Head Attention (CMA) module to enhance intra-class consistency and inter-class distinction. We evaluate ChartLine on four line chart datasets and compare its performance against state-of-the-art curve detection, edge detection, and semantic segmentation methods. Extensive experiments demonstrate that our method significantly outperforms existing algorithms, achieving an F-measure of 94% on a synthetic dataset.

Impact of the EduIntegrity Suite on Academic Integrity: A Qualitative Study

The EduIntegrity Suite, a collection of tools designed to promote academic integrity, has garnered significant interest within educational institutions. This study explores the Suite's perceived effectiveness and its influence on various stakeholders through a mixed-methods approach. Semi-structured focus group interviews were conducted with students, educators, and administrators at institutions utilizing the Suite. Student discussions explored their perceptions of the Suite's impact on their learning environment and potential concerns. Educators discussed how the Suite influenced their teaching practices and addressed suspected misconduct. Administrator focus groups provided insights into the decision-making process for implementing the Suite, its effectiveness, and its broader cultural influence. An online component complemented the focus groups, involving a comprehensive review of documents and websites related to the Suite. Company websites and product information for the Suite's individual tools were examined. Academic resources discussing the Suite's functionalities and potential applications were reviewed. News articles and blog posts offering insights from educators, administrators, and students regarding their experiences were explored. The findings highlight the Suite's multifaceted functionalities, including plagiarism detection, grammar and citation checking, originality reports, and remote proctoring services. While students acknowledged the Suite's potential deterrent effect, concerns about workarounds and a shift in focus from learning to detecting cheaters were also voiced. Administrators, however, emphasized the Suite's potential as a tool for promoting a culture of academic integrity, not just detecting plagiarism. Transparency and clear communication emerged as crucial for fostering trust and student acceptance. The EduIntegrity Suite appears to offer a promising approach to academic integrity when implemented strategically and combined with effective pedagogical practices. However, the success hinges on a multi-pronged approach that addresses student concerns, utilizes the Suite's functionalities effectively, and fosters a culture of ethical learning within the institution. To achieve this, institutions can prioritize clear communication about the Suite's purpose and limitations, support and develop staff with the Suites functionality and ethical consideration. Moreover, encourage educational initiatives that promote critical thinking, responsible research practices, and proper citation skills. Collaboration between educators and administrators can leverage the Suite's capabilities to create a more supportive and ethically sound learning environment.

Open Source Plagiarism Checking Software: An Assessment of Awareness and Utility Among Social Science Research Scholars of Lalit Narayan Mithila University, Darbhanga, Bihar: A Study

This study investigates the awareness and usage of open-source plagiarism checking software among research scholars in the Social Sciences discipline at Lalit Narayan Mithila University (LNMU), Darbhanga, Bihar. Given the increasing importance of academic integrity, this study aims to identify the level of knowledge, usage patterns, and perceived benefits of these tools among researchers. A structured questionnaire was administered to 60 participants, revealing a moderate level of awareness but limited usage. The findings suggest the need for enhanced training and institutional support to promote the effective use of open source plagiarism detection software. Although there is a notable intention to use plagiarism detection resources, various barriers remain that must be addressed to optimize their utilization and reinforce academic integrity in research practices.

Application of the KARP RABIN Algorithm for Plagiarism Detection System in Thesis Proposal Submission in the Department of Informatics Engineering STMIK Kaputama

Plagiarism is a serious threat, especially to academic honesty, so a detection system that can analyze various types of documents is needed. This research develops a plagiarism detection system using Optical Character Recognition (OCR) to convert image text into digital text. Rabin – Karp algorithm with rolling hash and Dice Coefficient Similarity is applied to measure similarities between documents. Testing is carried out on .doc, .txt, .jpg files. As a result, the system can detect plagiarism well in clear text and image documents, but accuracy can decrease in low-quality images. In conclusion, the similarity of content, sentence structure, and format affects the degree of similarity, while OCR techniques work effectively even though they are limited to low-quality images.

Plagiarism detection of anime character portraits

With the expansion of animation industry, there has been a recurring issue of animation plagiarism. Currently, the technology for copyright protection and plagiarism detection in animation is limited. This paper proposes a framework of plagiarism detection for animation character portraits. A Seq2Seq-based model is proposed for extracting the morphological features of animation portraits. By studying portrait morphological fitting simulation, a significant progress is made in learning facial characteristic of animation portraits. With the fitting mechanism, the proposed model can effectively learn the representative essential features of animation portraits. We also propose several loss functions to enhance recognizability and feature mapping ability between original and pirated animation portraits. By comparing the similarity between the pirated and original versions, plagiarism detection is carried out. Experimental results demonstrate that the proposed model has a good fitting effect and effectively detect the authenticity and piracy of the animation portraits.

Artificial Intelligence In Education: Ethics & Responsible Implementation

This study investigates the ethical use of Artificial Intelligence in academia, with an emphasis on AI integration in education and its possible impact on learning experiences. It investigates factors to consider when implementing AI in education, such as costs, privacy concerns, job displacement, biases, and safety concerns. In addition, the paper examines techniques for effectively integrating AI in education, such as institutional regulations, plagiarism detection technologies, teaching students and staff, fostering critical thinking, peer review, and organizing ethical AI usage workshops. The study presents findings from several studies and underlines the importance of constant monitoring and evaluation of AI systems in educational settings to ensure responsible and productive use.

Application of Machine Learning in Education: Recent Trends Challenges and Future Perspective

In recent times, Machine learning (ML) is one of the most valuable fields of artificial intelligence (AI) that is transforming education. The application of ML in education provides a promising benefit both to the scientists and researchers and this is the focus of this study. This paper reviews recent trends and advancements of ML in education focusing on areas such as personalisation of learning, predictive analytics, plagiarism detection, intelligent tutoring systems, gamification of learning and recommendation systems. After conducting the literature review we found out the current benefits and challenges of ML in education. The paper also provides insight into the applications and provide the recommendations to address the challenges of ML in the field of education.

Are AI detection and plagiarism similarity scores worthwhile in the age of ChatGPT and other Generative AI?

Recent advancements in chatbots have provided students and academics with a new mode of how knowledge can be sourced and composed. Within a very short space of time, students and academics have flocked to use ChatGPT and other Generative Artificial Intelligence (GAI) platforms owing to their capable responses. Additionally, apart from the generative chatbots (such as ChatGPT and Gemini), AI writing tools for paraphrasing, summarising, and co-writing have also become capable and increasingly prevalent to such a degree that the public is spoilt for choice. Having conducted tests on popular chatbots and AI writing tools, it became clear that while programs like Turnitin are developing new algorithms to detect plagiarism and AI-generated content, the initial findings from this study suggest that this may be an increasingly difficult task. These tests were published on YouTube, and within a few weeks, the evidence garnered tens of thousands of views as students and educators seemed uncertain about the strengths, weaknesses, and legalities of these AI tools. What is clear is that we have passed the tipping point, and AI assistance is no longer just a grammar fixer. The implications of this are concerning and far-reaching, as plagiarism is already a significant problem in universities. This position paper reports on tests conducted using Turnitin software and AI writing tools such as ChatGPT and QuillBot. These real-world tests support the paper’s position that it is becoming increasingly difficult to determine what constitutes original work in a world of GAI. The aim of this article is to provide evidence that educators who rely on similarity checking and AI detectors in their current form may inadvertently be supporting plagiarism rather than reducing it. A new method of academic plagiarism detection is proposed, utilizing large language models to generate and track ideas, thereby serving as an idea database. The proposed method focuses on the "understanding" of the work rather than on text similarity.

Are They Literate on ChatGPT? University Language Students’ Awareness, Benefits and Challenges in Higher Education Learning

This study explored university language students’ usage, awareness, readiness, and concerns, as well as the perceived benefits and challenges of utilizing ChatGPT in higher education learning. An explanatory sequential mixed-method design was used in this study, combining an online survey and semi-structured interview. The respondents consisted of 355 undergraduate and postgraduate students from various disciplines of five universities in Indonesia. The findings reveal that a majority of students had been using ChatGPT for their learning purposes, especially for writing assignments. The findings also highlight that those university language students had a high level of awareness, readiness, and concerns of using ChatGPT for their academic activities. Additionally, students acknowledged the potential benefits for writing support, personalized learning, increased productivity, brainstorming for generating ideas, and additional resources. However, challenges regarding inaccuracy, lack of critical thinking, plagiarism and AI detection, and technical problems. These findings provide a nuanced understanding of how university language students utilize and perceive ChatGPT for their learning purposes.

The Application of Artificial Intelligence in Education – The Current State and Trends

The aim of this paper is to discuss the application of contemporary AI tools and technologies in education. The paper will mainly focus on a close examination of relevant academic literature as well as the current situation and methods of AI implementation in different sectors, finally, in education. Particularly, it was crucial that there were identified the major directions of applying AI in learning with an emphasis on learners and educators being the basic aspects of AI use within modern educational approaches. Secondly, some benefits and drawbacks concerning AI implementations in education supported by experiences from industry leaders and specific satisfaction measures among existing users, are analyzed. The most significant part covers integrating findings that show facts about artificial intelligence being used in education for 2024 like top online learning statistics for 2024, top AI statistics for 2024, and best AI plagiarism checkers for teachers in 2024 respectively. This includes evaluating teacher or instructor preparation skills to employ artificial intelligence (AI) tools and technology as well as ways students can learn how to use them without misuse towards quality education improvement.

Knowledge and practices of plagiarism among journal editors of Nepal

BackgroundThis study was conducted to assess the knowledge and ongoing practices of plagiarism among the journal editors of Nepal.MethodsThis web-based questionnaire analytical cross-sectional was conducted among journal editors working across various journals in Nepal. All journal editors from NepJOL-indexed journals in Nepal who provided e-consent were included in the study using a convenience sampling technique.A final set of questionnaires was prepared using Google Forms, including six knowledge questions, three practice questions (with subsets) for authors, and four (with subsets) for editors. These were distributed to journal editors in Nepal via email, Facebook Messenger, Viber, and WhatsApp. Reminders were sent weekly, up to three times.Data analysis was done in R software. Frequencies and percentages were calculated for the demographic variables, correct responses regarding knowledge, and practices related to plagiarism. Independent t-test and one-way ANOVA were used to compare mean knowledge with demographic variables. For all tests, statistical significance was set at p < 0.05.ResultsA total of 147 participants completed the survey.The mean age of the participants was found to be 43.61 ± 8.91 years. Nearly all participants were aware of plagiarism, and most had heard of both Turnitin and iThenticate. Slightly more than three-fourths correctly identified that citation and referencing can avoid plagiarism. The overall mean knowledge score was 5.32 ± 0.99, with no significant differences across demographic variables.As authors, 4% admitted to copying sections of others' work without acknowledgment and reusing their own published work without proper citations. Just over one-fifth did not use plagiarism detection software when writing research articles. Fewer than half reported that their journals used authentic plagiarism detection software.Four-fifths of them suspected plagiarism in the manuscripts assigned through their journal. Three out of every five participants reported the plagiarism used in the manuscript to the respective authors. Nearly all participants believe every journal must have plagiarism-detection software.ConclusionsAlthough journal editors' knowledge and practices regarding plagiarism appear to be high, they are still not satisfactory. It is strongly recommended to use authentic plagiarism detection software by the journals and editors should be adequately trained and update their knowledge about it.

Digital Competence among Araling Panlipunan Teachers in the Division of El Salvador City

Araling Panlipunan teachers' digital competence theorizes that transformative teaching practices and ongoing professional growth as the driving force behind enables educators to flourish in the digital era. This study aimed to determine the level of teachers' digital competence in teaching and professional development. Specifically, it accomplished three primary objectives on characterizing the respondents; examining teachers' digital competence in terms of teaching and professional development; and determining correlations between the respondents' profiles and their digital competence. The study involved one hundred sixty-four (164) Araling Panlipunan teachers, using a universal sampling method. A correlational-descriptive research design was employed, utilizing a patterned and modified questionnaire. Statistical tools such as means, frequency, standard deviation, and Pearson-r were employed to analyze the collected data, allowing for a comprehensive examination of the teachers' digital competence in teaching and professional development. Findings showed that majority of the respondents are handling Grade 1-3 with units in Master’s degree and have a teaching experience of 10 years or more. They have personal ownership of digital tools with daily internet and duration of ICT usage of 4-8 hours. The respondents have a high level of digital competence in teaching and professional development. There is a significant relationship between daily internet and duration of ICT usage and the respondents’ digital competence. It is important for teachers to be good at using digital tools. Teachers who actively engage in collaborative learning experiences using digital platforms and tools are more likely to enhance their digital competence and improve their teaching practices. It is recommended that teachers who have accrued credits in Master’s degree continue their pursuit of completing the program. Additionally, it is imperative for teachers to employ plagiarism detection software to thoroughly examine students’ work for any signs of plagiarism.

Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity

Document similarity is a fundamental task in natural language processing and information retrieval, with applications ranging from plagiarism detection to recommendation systems. In this study, we leverage the term frequency-inverse document frequency (TF-IDF) to represent documents in a high-dimensional vector space, capturing their unique content while mitigating the influence of common terms. Subsequently, we employ the cosine similarity metric to measure the similarity between pairs of documents, which assesses the angle between their respective TF-IDF vectors. To evaluate the effectiveness of our approach, we conducted experiments on the Document Similarity Triplets Dataset, a benchmark dataset specifically designed for assessing document similarity techniques. Our experimental results demonstrate a significant performance with an accuracy score of 93.6% using bigram-only representation. However, we observed instances where false predictions occurred due to paired documents having similar terms but differing semantics, revealing a weakness in the TF-IDF approach. To address this limitation, future research could focus on augmenting document representations with semantic features. Incorporating semantic information, such as word embeddings or contextual embeddings, could enhance the model's ability to capture nuanced semantic relationships between documents, thereby improving accuracy in scenarios where term overlap does not adequately signify similarity.

Impact of Generative AI in Academic Integrity and Learning Outcomes: A Case Study in the Upper East Region

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

The Influence of Internet Use on University Students’ Moral Behavior

The purpose of the study was to investigate the influence of the internet use on university students` moral behavior in South West Region Cameroon. The researcher used mixed research method design involving both qualitative and quantitative methods. The sample size used was 100 students from HTTTC Kumba all departments from levels 200 and 300. Four lecturers were also interviewed. From the findings, majority of the respondents indicated that some of their private information has been disclosed without their permission; they have been involved in plagiarism, betting, cyber bullying, illegal downloading and many other vices. These issues can be handled through guidance and counselling sessions for students. Also, each department should use plagiarism detection software and each lecturer is expected to be trained in using the software in order to combat plagiarism.

Plagiarism Detection using iThenticate Software: An Experience

The main purpose of this paper to discuss the practical guidelines for detecting plagiarism using iThenticate Software is commendable. Plagiarism detection is crucial in maintaining academic and research integrity, and providing a step-by-step procedure using iThenticate adds practical value to the academic community.

ChatGPT in academia: exploring university students’ risks, misuses, and challenges in Jordan

ABSTRACT ChatGPT, a user-friendly and accessible AI tool, offers a revolutionary approach to academic learning. In spite of its benefits, the implementation of ChatGPT into university assignments presents possible risks for students. While extensive global research has studied these risks from students’ perspectives, a notable gap exists in comprehending academics’ standpoints, specifically, in Jordan. This study addresses this gap by conducting semi-structured interviews with 25 academics from various professional backgrounds in both public and private Jordanian universities. Thematic analysis revealed four key risks associated with ChatGPT integration: plagiarism and compromised originality; overdependency on technology; diminished critical thinking skills; and reduced overall assignment quality. The study suggests risk mitigation strategies, including using plagiarism detection software, implementing disciplinary measures upon discovering students resorting to ChatGPT for assignments, raising awareness about ChatGPT’s advantages and risks, and establishing clear guidelines for usage within Jordanian academic institutions. Theoretical contributions encompass filling a literature gap by recognising risks from academics’ perspectives in Jordan and providing deeper insights into their impact on student learning. Practically, the findings emphasise the need of applying guidelines to prevent ChatGPT misuse, thus, enhancing the learning and teaching environment. Recognising study limitations, for instance, context specificity and methodology, underlines the necessity for future research to explore diverse educational contexts and employ mixed methodologies for a more comprehensive understanding of ChatGPT’s impacts on education in Jordan.

Human Versus Machine Intelligence: Assessing Natural Language Generation Models Through Complex Systems Theory.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.