Systematic review on academic integrity and detecting AI-based plagiarism

Author

Tarangeyi, Kudzanai M.

Title

Systematic review on academic integrity and detecting AI-based plagiarism

Abstract

Ever since we entered the digital communication era, the ease of information sharing through the internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to superficial, brute-force string matching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection. This thesis provides novel perspectives on plagiarism detection and plagiarism direction identification tasks. The hypothesis is that original texts and rewritten texts exhibit significant but measurable differences, and that these differences can be captured through statistical and linguistic indicators. The conclusions of this study offer ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.

Date

June 2024

Publisher

BUSE

Keywords

Plagiarism detection,

Intellectual property theft.

Brute-force string matching techniques,

Supervisor

Dr. T. Chikerema,