Need for Anti Plagiarism Device integrated with Artificial Intelligence
Keywords:
AI, Plagiarism Detection, Natural Language Processing (NLP), BERT, Semantic Analysis, Deep Learning, Text Similarity, Education TechnologyAbstract
The rapid expansion of online content and academic publications has raised significant concerns about maintaining
academic integrity. With the increasing use of online sources, plagiarism in educational settings has become an alarming
challenge. Traditional plagiarism detection tools like Turnitin and Copyscape often focus on simple string matching, which is limited
when it comes to detecting paraphrased content or semantic similarities. This paper presents an AI-based plagiarism detection
system that integrates advanced Natural Language Processing (NLP) techniques such as Term Frequency-Inverse Document
Frequency (TF-IDF), cosine similarity, and deep learning-based semantic analysis using the Bidirectional Encoder Representations
from Transformers (BERT) model. The proposed system is designed to enhance the accuracy and efficiency of plagiarism
detection, offering superior capabilities compared to conventional methods. We benchmark the system against widely used plagiarism detection tools such as Turnitin and Copyscape, evaluating performance using metrics like accuracy, recall, precision, false
positive rate, and processing time. The results indicate that the AI powered system significantly outperforms traditional tools,
providing a promising solution for academic institutions striving to maintain integrity.
References
[1]. G. Lieberman, “The Use and Detection of AI-Based Tools in Higher Education,” Grand
Canyon University, 2023. https://files.eric.ed.gov/fulltext/EJ1441999.pdf.
[2]. K. Ibrahim, “Using AI-Based Detectors to Control AI-Assisted Plagiarism in ESL Writing:
‘The Terminator Versus the Machines’,” Language Testing in Asia, 2023.
https://languagetestingasia.springeropen.com/track/pdf/10.1186/s40468-023-00260-2.
[3]. V. Lannoy, “AI-Based Plagiarism Detectors: Plagiarism Fighters or Makers?,” Medical
Writing,
2023.
https://journal.emwa.org/artificial-intelligence-and-machine
learning/aibased-plagiarism-detectors-plagiarism-fighters-or-makers/article/15182/8-ai
basedplagiarism-detectors.pdf.
[4]. D. Suresh Babu, “Artificial Intelligence and Plagiarism: Challenges, Detection, and
Prevention,” International Journal of Engineering Research and Development, vol. 20, no. 10,
pp. 169–170, 2023 https://www.ijerd.com/paper/vol20-issue10/2010169170.pdf.
[5]. B. Zhou, “The Use of Artificial Intelligence in Academic Dishonesty: Ethical Considerations
and Prevention Strategies,” Information Systems and Computing Academic Professionals,
2023. https://iscap.us/proceedings/2023/pdf/5957.pdf.
[6]. M. A. Quidwai, C. Li, and P. Dube, “Beyond Black Box AI-Generated Plagiarism Detection:
From Sentence to Document Level,” arXiv, 2023 https://arxiv.org/pdf/2306.08122.pdf.
[7]. S. Pudasaini, L. Miralles-Pechuán, D. Lillis, and M. Llorens Salvador, “Survey on Plagiarism
Detection in Large Language Models: The Impact of ChatGPT and Gemini on Academic
Integrity,” arXiv, 2024. https://arxiv.org/pdf/2407.13105.pdf.
of
[8]. X. Peng, Y. Zhou, B. He, L. Sun, and Y. Sun, “Hiding the Ghostwriters: An Adversarial
Evaluation
AI-Generated
Student
Essay
Detection,”
arXiv,
2024.
https://arxiv.org/pdf/2402.00412.pdf.
[9]. M. Khalil and E. Er, “Will ChatGPT Get You Caught? Rethinking of Plagiarism Detection,”
arXiv, 2023. https://arxiv.org/pdf/2302.04335.pdf.
[10]. J. Xian, J. Yuan, P. Zheng, and D. Chen, "BERT-Enhanced Retrieval Tool for Homework
Plagiarism Detection System," arXiv, Apr. 2024. https://arxiv.org/pdf/2404.01582.pdf
