Intelligent Detection and Feedback System for Plagiarism and AI – Generated Text

Over the past few years, exponential increases in digital content and development of generative Artificial Intelligence (AI) have introduced new issues in guaranteeing originality and authenticity of written materials. Conventional plagiarism scanners can identify only direct text copying or paraphrased equivalent but cannot identify AI-generated content, which typically imitates human writing. In an effort to bridge this gap, this project suggests a Plagiarism and AI Generated Content Detection and Feedback System that will search textual data for both traditional plagiarism and artificially created results. The system combines Natural Language Processing (NLP) methods, semantic similarity indicators, and machine learning classifiers to separate human and artificially generated text. Besides detection, the system also gives users rich feedback, such as highlighting plagiarized text, possible AI-generated regions, and suggesting improvement in writing style and originality. Not just an academic integrity increase, as well as content genuineness, it is ethical writing practice as well because it leads users to originality. The solution can be used across all education systems, research fields, and industries based on content to provide stability and originality in the age of AI-driven content generation. Keywords: Plagia