Plagiarism Checking Research Articles

OpenAI ChatGPT vs Google Gemini: A study of AI chatbots’ writing quality evaluation and plagiarism checking

This study explores the writing quality of two AI chatbots, OpenAI ChatGPT and Google Gemini. The research assesses the quality of the generated texts based on five essay models using the T.E.R.A. software, focusing on ease of understanding, readability, and reading levels using the Flesch-Kincaid formula. Thirty essays were generated, 15 from each chatbot, and evaluated for plagiarism using two free detection tools—SmallSEOTools and Check-Plagiarism—as well as one paid tool, Turnitin. The findings revealed that both ChatGPT and Gemini performed well in terms of word concreteness but demonstrated weaknesses in narrativity. ChatGPT showed stronger performance in referential and deep cohesion, while Gemini excelled in narrativity, syntactic simplicity and word concreteness. However, a significant concern was the degree of plagiarism detected in texts from both AI tools, with ChatGPT's essays exhibiting a higher likelihood of plagiarism compared to Gemini’s. These findings highlight the potential limitations and risks associated with using AI-generated writing.

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.

Cross-Lingual Short-Text Semantic Similarity for Kannada–English Language Pair

Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language processing (NLP). The computation of semantic similarity is essential for a variety of tasks such as evaluating machine translation systems, quality checking human translation, information retrieval, plagiarism checks, etc. In this paper, we propose a method for measuring the semantic similarity of Kannada–English sentence pairs that uses embedding space alignment, lexical decomposition, word order, and a convolutional neural network. The proposed method achieves a maximum correlation of 83% with human annotations. Experiments on semantic matching and retrieval tasks resulted in promising results in terms of precision and recall.

Online Integrated Platform for Project Management

The Integrated Platform for University Projects (IPUP) aims to streamline student project management across academic disciplines. Utilizing web technologies and project management principles, IPUP provides a centralized hub for collaboration, communication, and progress tracking. Additionally, IPUP utilizes a plagiarism checking API as a method to ensure academic integrity. Project documentation submitted on the platform is analysed using the SmallSEO Tools Plagiarism checking API. This API employs algorithms defined by SmallSEO Tools to detect any plagiarized content within the documentation. The API returns a response indicating the percentage of content identified as copied. A predetermined threshold is set for this percentage; if the value exceeds this threshold, the submission is rejected by the website. IPUP serves as a driving force for academic innovation, enabling cross-disciplinary learning and excellence in project-based education, while also integrating communication tools and prioritizing data security.

Prevalence of plagiarism in hijacked journals: A text similarity analysis

ABSTRACT Background The study examines the prevalence of plagiarism in hijacked journals, a category of problematic journals that have proliferated over the past decade. Methods A quasi-random sample of 936 papers published in 58 hijacked journals that provided free access to their archive as of June 2021 was selected for the analysis. The study utilizes Urkund (Ouriginal) software and manual verification to investigate plagiarism and finds a significant prevalence of plagiarism in hijacked journals. Results Out of the analyzed sample papers, 618 (66%) were found to contain instances of plagiarism, and 28% of papers from the sample (n = 259) displayed text similarities of 25% or more. The analysis reveals that a majority of authors originate from developing and ex-Soviet countries, with limited affiliation ties to developed countries and scarce international cooperation in papers submitted to hijacked journals. The absence of rigorous publication requirements, peer review processes, and plagiarism checks in hijacked journals creates an environment where authors can publish texts with a significant amount of plagiarism. Conclusions These findings suggest a tendency for fraudulent journals to attract authors who do not uphold scientific integrity principles. The legitimization of papers from hijacked journals in bibliographic databases, along with their citation, poses significant challenges to scientific integrity.

Pemanfaatan Artificial Intelligence (AI) Pada Penyusunan Aksi Nyata Paltform Merdeka Mengajar di SDN 02 Medalkrisna

This training program is designed to enhance the competency of elementary school teachers in developing practical actions on the Merdeka Mengajar Platform (PMM) by utilizing Artificial Intelligence (AI) technology. Through the use of Turnitin for plagiarism checks and Gamma for document preparation, the program aims to ensure that the work produced is original and well-structured. The training involves teachers in intensive sessions that include the introduction and use of Turnitin to detect plagiarism, as well as Gamma to assist in preparing practical action documents that meet Kemendikbud's validation standards. The results of the training show a significant improvement in teachers' ability to produce creative, innovative, and compliant practical actions according to the assessment criteria. Teachers participating in this program can produce more professional and plagiarism-free documents, thanks to the constructive feedback from AI. Furthermore, the resulting practical action documents have a clear and attractive structure, facilitating the validation process. This program also encourages the formation of a learning community among teachers, where they share best practices and support continuous improvement. Thus, this training program not only enhances teachers' digital competence but also contributes to the overall improvement of education quality. The use of AI in education becomes an important step in preparing teachers to face challenges in the digital era.

Enhancing Academic Integrity for Bangladesh's Educational Landscape

Integrating Artificial Intelligence (AI) tools in Bangladesh's academic landscape has ignited concerns about potentially eroding students' creative writing and critical thinking abilities. While AI offers efficient and personalized learning, there is a looming risk of students using it as a shortcut to success. Educators and policymakers must emphasize the cultivation of writing skills and critical thinking while guiding students to recognize the limitations of AI. While plagiarism checking is crucial for academic integrity, it often falls short in acknowledging students' originality. Natural Language Processing (NLP) emerges as a promising solution, augmenting plagiarism detection by comprehending context, nuances, and novel expressions. This paper proposed a model based on NLP that can incorporate a robust token identification mechanism into AI tools used by the students. A unique identification token is generated at the time of system login and stored in a publicly accessible resource, acting as a distinct marker for user activity. These identification tokens support a plagiarism detection system that continually reviews and compares activity records. It also enables a thorough examination of user involvement across various academic supplementary tools, such as academic writing, presentation creation, image generation, etc.

University Students’ Plagiarism Behaviours in Writing Instruction: A Systematic Review

The literature indicates that students' understanding of plagiarism and academic writing skills impact their plagiarism behaviour. However, there has yet to be a review that explores the methods, supporting tools, and content of writing instruction that can effectively reduce plagiarism behaviour at the university level. Therefore, this systematic review aims to present academic writing instruction that can prevent plagiarism behaviour. The review data was derived from four different databases, EBSCO, Wiley, Proquest, and JSTOR, published from 2010 to 2022. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) standards were used to ensure that the review was systematically carried out. Based on the 22 articles reviewed, tutorial methods and workshops were most used to provide knowledge on plagiarism, followed by writing skills content, and guided writing practice. Writing skills content comprised word choice, grammar, citation, paraphrasing, and referencing concerning plagiarism prevention. This study also found that Rubrics and Turnitin applications were widely used as supporting tools for plagiarism checking and as feedback for the students. Implementing writing instruction coupled with plagiarism education and feedback can change students' plagiarism behaviour and improve their confidence in academic writing.

Plagiarism Detection

We are in Information age and there is lots of information available over internet. Most of the information over internet is produced as duplicate by the different authors. In order to maintain credentials of the original work of the author and improve the quality of research, plagiarism checking software may be useful. With help of plagiarism checking software, we can avoid the duplicacy in research and academic writings. This paper gives an overview of various effective plagiarism detection methods that have been used for plagiarism detection to identify that how much work is original and how much work is illegal and copied from others’ original works

Code Plagiarism Checking Function and Its Application for Code Writing Problem in Java Programming Learning Assistant System

A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code writing problem (CWP), which asks students to create a source code that can pass the given test code. The correctness of this answer code is validated by running them on JUnit. In previous works, a Python-based answer code validation program was implemented to assist teachers. It automatically verifies the source codes from all the students for one test code, and reports the number of passed test cases by each code in the CSV file. While this program plays a crucial role in checking the correctness of code behaviors, it cannot detect code plagiarism that can often happen in programming courses. In this paper, we implement a code plagiarism checking function in the answer code validation program, and present its application results to a Java programming course at Okayama University, Japan. This function first removes the whitespace characters and the comments using the regular expressions. Next, it calculates the Levenshtein distance and similarity score for each pair of source codes from different students in the class. If the score is larger than a given threshold, they are regarded as plagiarism. Finally, it outputs the scores as a CSV file with the student IDs. For evaluations, we applied the proposed function to a total of 877 source codes for 45 CWP assignments submitted from 9 to 39 students and analyzed the results. It was found that (1) CWP assignments asking for shorter source codes generate higher scores than those for longer codes due to the use of test codes, (2) proper thresholds are different by assignments, and (3) some students often copied source codes from certain students.

Editorial

Editorial

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

Sử dụng công cụ AI phục vụ nghiên cứu và xuất bản học thuật

With the development of AI technology, researchers now have many tools to support the research and academic publishing process, helping to improve work efficiency. This article introduces some typical AI tools that support information retrieval, literature review, drafting, idea development, target journal selection, and plagiarism checking. In addition, the article also discusses some potential challenges of using AI as a basis for proposing recommendations for scientists, publishing houses and management teams in the use of AI technology in research and publishing activities

Full-privacy secured search engine empowered by efficient genome-mapping algorithms.

Since the 90s, keyword-based search engines have been the only option for people to locate relevant web content through a simple query comprising one to a few keywords. These free or paid services operate by storing users' search queries and preferences for personal profiling and targeted ads delivery, while user-uploaded articles for plagiarism detection can further be stored as part of service providers' expanding databases for profit. In short, it has never been an option for users to search the web without revealing their queries, some of which can be sensitive, to search engine providers. Here we demonstrate that an internet search, provided with the entire article as a query, can be correctly carried out without revealing users' query content by an irreversible encoding scheme and an efficient FM-index search routine that is generally used in the next generation sequencing (NGS) of human genomes. In our solution, Sapiens Aperio Veritas Engine (S.A.V.E.), every word in the query is encoded into one of 12 "amino acids", constituting a pseudo-biological sequence (PBS) at users' local machines. The PBS-mediated plagiarism detection is carried out by users' submission of locally encoded PBSs through our cloud service to locate identical duplicates in the collected web contents, currently including all the English and Chinese Wikipedia pages and Open Access journal articles, as of April 2021, which had been encoded in the same way as the query. It is found that PBSs with a length longer than 12, comprising a combination of more than 12 "amino acids", can return correct results with a false positive rate <0.8%. S.A.V.E. runs at a similar genome-mapping speed as Bowtie and is >5 orders faster than BLAST. Functioning in both regular and in-private search modes, S.A.V.E. provides a new option for efficient internet search and plagiarism detection in a compressed search space where users' confidential contents can never be revealed. We hope the reported algorithm and implementation could introduce a new paradigm for future privacy-aware search engines. S.A.V.E. is currently running at https://dyn.life.nthu.edu.tw/SAVE/.

The significance of computer technologies in detecting plagiarism in scientific works

The article analyses problematic issues regarding the importance of using computer technologies in detecting plagiarism in scientific works. It was determined that the problem of plagiarism in science is not only a matter of legislation, but a manifestation of disdain for scientific ethics and moral standards by the scientists themselves. The issue of academic plagiarism must be solved comprehensively and with joint efforts, by applying effective mechanisms to prevent plagiarism.&#x0D; There is no single, complete, comprehensive and generally accepted definition of the category of plagiarism. The essence of plagiarism is the assignment of authorship to someone else’s original (literary, artistic, scientific) work or its part.&#x0D; The development of information technologies creates conditions for detecting illegal borrowing. Special computer programmes and online services check works for originality much faster. The essence and nature of computer technologies, their importance in detecting plagiarism in scientific works are studied. Positive and negative points in the application of computer anti-plagiarism technologies were identified, and an appropriate assessment was given to them.&#x0D; It is substantiated that plagiarism has a certain personification and specificity in various spheres and branches of human activity. The anti-plagiarism system almost does not take into account official parts of scientific works (cover letter, introduction, references to normative legal acts, conclusions, list of used sources) and evaluates them as plagiarism. The algorithm of the anti-plagiarism service must take into account constant stylistic turns and peculiarities of certain branches of science and technology. We believe that the results of new interpretations of scientific work should not be completely new. It is noted that checking with online services does not always reveal translations of texts or borrowings from editions not published online.&#x0D; Accordingly, the results of information processing with the help of specialized anti-plagiarism programmes and services cannot independently be circumstances that allow making reasonable conclusions about the presence or absence of plagiarism in a scientific work. Anti-plagiarism services and programs really help in detecting plagiarism as an additional tool for searching for possible matches of the text, followed by their analysis and processing by a specialist.&#x0D; But the situation with plagiarism still requires the use of plagiarism checking services (even with all their shortcomings). However, no matter how attractive and easy-to-use anti-plagiarism services are, it is necessary to do some work to improve information culture and respect for the copyrights of others.

ONLINE AND E-LEARNING BEST PRACTICES, NEEDS AND HABITS FOR THE INTERNATIONAL AGRIMBA NETWORK

From the literature part of this research, it shows that, some of the most popular Learning Management Systems (LMS), such as Moodle, Canvas and Blackboard, are used by many universities and colleges worldwide and their popularity is steadily increasing as more institutions adopt online learning. The usage statistics of LMSs by universities can be influenced by a number of factors, such as the size of the university, the specific requirements of the institution, the availability of alternative solutions, and the preferences of faculty and students. In addition, the popularity of LMSs among universities may change over time as new systems enter the market or as existing systems improve and evolve. Based on the number of customers, Moodle's three biggest competitors in the learning management systems category are Google Classroom with 11.70%, LinkedIn Learning with 8.87% and TalentLMS with 5.16% market share. The most frequently used functionalities of the e-learning system are: study content creation, course management and content library, and the least frequently used are integration with other systems, multilanguage utility, plagiarism checking, accessibility to people with disabilities and personalized learning. Similarly, the most popular functionalities are course management, study content creation and assessment and testing. Respondents least liked the functions of integration with other systems, webinars, accessibility for inclusion, and video hosting and streaming. Lectures or slides are most often uploaded to platforms, followed by written materials and links, then videos, pictures and tables. Judging by the answers received, the majority of respondents are either completely satisfied (34%) or moderately satisfied (42%) with the e-learning systems they are using now.

Knowledge, attitude, opinion, perspective, and agreement of Palestinian medical students on strategies/recommendations to curb plagiarism: A multicenter cross-sectional study

ABSTRACT Plagiarism is a common issue in written academic assignments and graduation theses. This multicenter study was conducted to assess the knowledge, attitude, opinion, perspective, and agreement of Palestinian medical students on strategies/recommendations to curb plagiarism. The study was conducted in a cross-sectional design using a questionnaire in all universities with medical education programs. The questionnaire contained 12 knowledge items, 8 attitude items, 6 opinion/perspective items, and 8 strategies/recommendations to curb plagiarism. Of the 550 invited medical students, 474 completed the study tool. Knowledge, attitude, opinion, perspective, and agreement on strategies/recommendations scores correlated positively. Higher knowledge, attitude, opinion, perspective, and agreement on strategies/recommendations scores were significantly associated with higher academic/training year, grade point average, satisfaction with academic achievement, academic writing skills, informational skills, using citation managers, receiving courses/workshops/lecturers on plagiarism, using plagiarism checking tools, and participation in a scientific paper/graduation thesis writing. Gaps in knowledge about plagiarism were identified among Palestinian medical students. Educators/trainers and other decision-makers in medical schools and higher academic institutions might use the strategies on which the students agreed to curb plagiarism.

The Landscape of Digital Technology to Enhance the Digital Researcher

The objectives of this research were to synthesize the competencies of the digital researcher, carry out an empirical investigation of the digital researcher landscape, and evaluate the results of a synthesis of digital researcher competency. To conduct the research, the researchers carried out a review of the literature related to researcher competency, digital competency, digital researcher competency and digital technology for researchers. Then, a focus group discussed the conclusion of the digital technology landscape used to enhance the digital researcher. The results showed that digital researchers&amp;rsquo; competency had six features: 1) Personalize and Security Competency, 2) Literature Review and Reference Management Competency, 3) Communication and Collaboration Management Competency, 4) Analyzing and Reporting Competency, 5) Proofreading and Plagiarism Checking Competency, and 6) Publication Competency.

9Mobile news SMS entrepreneurship and citizen journalism potentials in Nigeria

There is increased mobile telecommunications penetration across Nigeria. One of the realities connected to such penetration is the news-text services offered by the country’s 9Mobile mobile telecommunications company. This content analysis and covert non-participant observation research examined news entrepreneurial and citizen journalism potentials of the service. Findings show that news SMS service is encouraging, but issues such as delayed delivery and incomplete replications concurrently hamper entrepreneurial and citizen journalism benefits. The degree of diversity of 9Mobile news SMS contents is also poor. Nevertheless, there was significant use of prominent news stories – as derived from the hardcopies of the sourcing national newspapers. The research notes the need for news SMS copyright and plagiarism checks alongside other professional standards. Overall, it is pertinent to reiterate that irrespective of shortcomings, convergent news deliveries of 9Mobile SMS-MoreNews retain significant potentials for entrepreneurship and citizen journalism.

Software System of Checking for Plagiarism of Ukrainian Texts

The purpose of this work is to describe the methodology of building a software system (application) for plagiarism checking of scientific publications in the Ukrainian language using two machine learning models, Word2Vec and BERT. We consider the detection of external plagiarism in Ukrainian texts.Plagiarism is usually defined as the passing off someone else’s ideas as your own. As the Internet becomes more and more accessible every day, a huge amount of data becomes available to people. Nowadays, it is quite easy to find a suitable study and plagiarize it instead of developing one’s own from scratch.Plagiarism undermines the efforts of the researcher whose work has been plagiarized and gives the plagiarist the opportunity to over-praise himself; such a person can be detrimental when appointed to an important position.Many fields of life are susceptible to plagiarism, including research and education. Plagiarism can also take many forms: from straight up copy-paste to paraphrasing and sentence restructuring. This makes plagiarism a rather complex problem, where methods, such as longest common subsequence or n-grams, based on finding shared words between documents, might not work. Therefore, we might consider applying deep learning to the problem of plagiarism detection.In this article we discussed the concept of plagiarism and listed its types. Two machine learning models have been proposed for plagiarism detection: Word2Vec and BERT. We also provided an overview of both models and described how they could be used in the problem of plagiarism detection.A web application for plagiarism detection in the Ukrainian language has been developed. This application features React, a JavaScript framework, on the frontend and Python on the backend. To store application data, MongoDB is used.This application allows a user to input a text that will be compared with the texts from the application database using cosine similarity or Euclidean distance as metrics. Comparison is performed using word embeddings, calculated by pre-trained BERT or Word2Vec model. A user can choose the model and similarity metrics using the application’s UI.The application can be further improved to not only output similarity metric but also highlight the similar sentences in the texts.