Design of an Integrated Collaborative Environment for Projects with Plagiarism Checker DOI

P. Ezhumalai,

V. Ceronmani Sharmila, Daniela Selvi

et al.

Published: Oct. 15, 2024

Language: Английский

A comprehensive strategy for identifying plagiarism in academic submissions DOI Creative Commons

Deblina Mazumder Setu,

Tania Islam, Md. Erfan

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Abstract Plagiarism is a major problem in education, especially higher education environments. To address this problem, comprehensive detection method proposed, utilizing cutting-edge models like Bidirectional Encoder Representations from Transformers (BERT) and cosine similarity for detecting direct copy semantic changes. The methodology involves evaluating the BERT model first, then performing pairwise comparisons to determine how similar student’s work reference text. If changes are higher, content classified as paraphrased; otherwise, it considered cases where threshold greater than 30%. When less or equal 30%, moves online plagiarism detection. This includes web scraping, generating search queries, employing Term Frequency-Inverse Document Frequency (TF-IDF) vectorization with assess similarities between students’ content. As result, study detects distinguishes plagiarizing authentic purpose provide an effective maintain academic integrity enhance originality student work. In testing on MIT datasets, demonstrated average recall of 80%, precision 68%, accuracy 71%, f1-score 74%. With 0.2, reached 65%, accompanied by 56% recall, 60%, 60%. results demonstrate efficiency strategy enhancing while reducing false positives.

Language: Английский

Citations

0

Let ChatGPT Write Your Master’s Thesis: An Exploratory Case-Study (Preprint) DOI Creative Commons
Pål Joranger, Sara Rivenes Lafontan, Asgeir Brevik

et al.

Published: Feb. 28, 2025

BACKGROUND Large language models (LLMs) can aid students in mastering a new topic fast but for the educational institutions responsible assessing and grading academic level of it be difficult to discern whether text originates from student’s own cognition or if is synthesized by an LLM. Universities have traditionally relied on submitted written thesis as proof higher-level learning, which grant grades diplomas. Ubiquitous availability LLMs challenges this practice. OBJECTIVE In study we assumed role hypothetical health science master’s student actors looking leverage full power LLM completing scientific research paper manuscripts that could graduation. METHODS exploratory case-study used ChatGPT generate two papers conceivable submissions graduation program. One simulated qualitative another quantitative project. RESULTS Using stepwise approach prompted 1) synthesize credible datasets, 2) manuscripts, less than day, that—in our judgment—would been able pass at program authors are currently affiliated with. CONCLUSIONS Our demonstration highlights ease with data, conduct analyses, produce To uphold integrity standards, recommend programs prioritize oral examinations school exams. This shift crucial ensure fair rigorous assessment higher-order learning abilities level.

Language: Английский

Citations

0

Comparative analysis of text-based plagiarism detection techniques DOI Creative Commons
Muhammad Sajid, Muhammad Sanaullah, Muhammad Fuzail

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(4), P. e0319551 - e0319551

Published: April 8, 2025

In text analysis, identifying plagiarism is a crucial area of study that looks for copied information in document and determines whether or not the same author writes portions text. With emergence publicly available tools content generation based on large language models, problem inherent has grown importance across various industries. Students are increasingly committing as result availability use computers classroom generally extensive accessibility electronic found internet. As result, there rising need reliable precise detection techniques to deal with this changing environment. This paper compares several into how well different systems can distinguish between created by humans Artificial Intelligence (AI). article systematically evaluates 189 research papers published 2019 2024 provide an overview computational approaches (PD). We suggest new technically focused structure efforts prevent identify plagiarism, types detecting organize way contributions presented. demonstrated field rife ongoing research. Significant progress been made throughout time we reviewed terms automatically highly obscured hence difficult recognize. The exploration nontextual contents, machine learning, improved semantic analysis key sources these advancements. Based our concluded combination analytical methodologies textual features most promising subject future further improve plagiarism.

Language: Английский

Citations

0

Enhancing anti-plagiarism literacy practices among undergraduates with AI DOI
Yin Zhang, Yonghui Liu, Xinghua Wang

et al.

Interactive Learning Environments, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: April 21, 2025

Language: Английский

Citations

0

ChatGPT and Me: A Dual Factor Examination DOI
Smriti Mathur,

Christina Sanchita Shah,

Sushant Kumar Vishnoi

et al.

Journal of Computer Information Systems, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: May 6, 2025

Language: Английский

Citations

0

Evaluating a Large Language Model's Ability to Synthesize a Health Science Master's Thesis: A Case Study (Preprint) DOI Creative Commons
Pål Joranger, Sara Rivenes Lafontan, Asgeir Brevik

et al.

JMIR Formative Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

Citations

0

ChatGPT's application in management education: In-depth critical analysis of opportunities, threats, and strategies DOI
Mohammad Tariq Hasan, Ha Jin Hwang, Monowar Mahmood

et al.

Multidisciplinary Reviews, Journal Year: 2025, Volume and Issue: 8(10), P. 2025338 - 2025338

Published: April 21, 2025

ChatGPT is a language model developed by OpenAI that both powerful and comprehensive. This study looks into the pros cons of from perspectives students teachers in field business mathematics education. The effectiveness increasing students' mathematical skills also examined here. In conclusion, this research assesses how effective app for teaching mathematics. provides an in-depth review short quantitative findings survey, highlighting improving These prospects educators include personalized feedback, enhanced accessibility, interactive dialogs, lesson planning, assessment, novel pedagogical approaches to intricate ideas. Also, provide insights academic administrators ensure student work unique. enables academics instructors understand evaluate assignments projects. Finally, it will increase literacy policymakers. revealed interaction between learner behavior education sector. solid ground use management To author's knowledge, one unique studies related effect

Language: Английский

Citations

0

Design of an Integrated Collaborative Environment for Projects with Plagiarism Checker DOI

P. Ezhumalai,

V. Ceronmani Sharmila, Daniela Selvi

et al.

Published: Oct. 15, 2024

Language: Английский

Citations

0