(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
The rapid proliferation of large language models (LLMs) has transformed the landscape of academic writing and scientific communication, yet it has also raised serious concerns about the authenticity and integrity of scholarly publications. This study proposes an integrative Machine Learning (ML) and Natural Language Processing (NLP) framework designed to detect and classify AI-generated texts within academic research articles. The framework combines linguistic, semantic, and stylometric features extracted from large, open-source datasets collected from Kaggle and other academic repositories. The analytical pipeline—implemented and validated using Google Colab—integrates both supervised and deep transformer-based models such as Support Vector Machines (SVM), Random Forest, and fine-tuned BERT-derived architectures. Experimental results demonstrate that the ensemble classifier achieves an overall F1-score of 0.94 and exhibits strong generalization when applied to unseen AI-generated texts. The proposed model not only outperforms existing AI-text detectors in cross-domain evaluations but also remains robust against paraphrased and hybrid human-AI compositions. The findings contribute to the development of transparent and reliable tools for preserving academic integrity in the era of generative artificial intelligence.