Hybrid NLP and Machine Learning Framework for Detecting Human and AI-Written Texts

With the rise of powerful AI language models like GPT-4 and LLaMA, distinguishing between AI-generated and human-written text has become increasingly challenging. This project presents a detection system that utilizes Natural Language Processing (NLP) and Machine Learning (ML) to identify AI-generated content. It integrates deep BERT embeddings with carefully crafted linguistic features such as perplexity, sentence structure, sentiment, and word usage. These features train two classifiers -XGBoost and Support Vector Machine (SVM)—which are combined into an ensemble model for enhanced accuracy. Trained on a balanced dataset of AI and human-written texts, the ensemble model achieved up to 93% accuracy, while XGBoost and SVM individually attained 84% and 81%, respectively. The system also includes a user-friendly interface for real-time text analysis and generates an HTML report detailing predictions and confidence scores. This solution provides an effective tool for educators, researchers, and institutions to detect AI-generated text and promote the ethical use of AI technologies.