International Research Journal of Education and Technology

This study explores the application of machine learning techniques for plagiarism detection in educational content, addressing the growing concern of academic dishonesty. By utilizing natural language processing (NLP) and various machine learning algorithms, the research aims to develop a robust system capable of identifying similarities and potential plagiarism in student submissions. The proposed approach involves feature extraction, model training, and evaluation using diverse datasets, ensuring adaptability to different writing styles and subject matters. The findings demonstrate that machine learning can significantly enhance the accuracy and efficiency of plagiarism detection, ultimately promoting academic integrity and improving educational outcomes.

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