Detecting and Comparing AI-Generated and Plagiarized Content in Academic Documents Using Python-Based Tools

This study introduces an intelligent, AI-powered system designed to detect and compare AI-generated content and academic plagiarism within student submissions [1,2].As AI tools become increasingly prevalent in content creation and plagiarism remains a persistent challenge, ensuring the originality and integrity of academic work has become critical [1,3].Traditional manual checking methods are often time-consuming, inconsistent, and prone to subjective bias, limiting transparency and effectiveness in academic evaluation [4].To address these challenges, the proposed system leverages Python-based libraries such as PyPDF2 and python-docx for robust text extraction from PDF, DOCX, and TXT formats [5], combined with Jupyter Notebook widgets for a highly interactive and user-friendly interface [6].The system analyzes documents by highlighting AI-generated sentences in blue and plagiarized sentences in pink, providing an immediate and intuitive visual differentiation [5,6].In addition to visual cues, the system quantitatively calculates the proportion of AI-generated, plagiarized, and original content within each submission, enabling educators to make informed, data-driven assessments of academic integrity [6,7].The tool is designed to complement existing academic integrity platforms, such as Turnitin for plagiarism detection [7] and GPTZero for AI content identification [2], thereby enhancing their effectiveness while offering a unified framework for monitoring both AI-generated and plagiarized content.By automating these processes, the system significantly reduces manual evaluation time, minimizes human bias, and provides consistent, objective, and transparent results [3,6].Overall, this AI approach provides a scalable solution for institutions, ensuring fairness, originality, and high-quality student-focused outcomes [1][2][3][4][5][6][7].