Multilingual AI-Generated Text Detection in Arabic, English, and Turkish Using a Hybrid Transformer-Graph Convolutional Network

Abstract

Detecting AI-generated text has become a critical task as artificial intelligence systems are increasingly used in content creation. Current detection methods often suffer from limited accuracy and weak multilingual performance. This problem is especially challenging in Turkish, Arabic, and English due to their distinct linguistic structures, including agglutinative morphology in Turkish, root-based morphology in Arabic, and semantic ambiguity in English. To address these challenges, this study proposes a hybrid architecture that combines a Transformer-based DistilBERT model with a Graph Convolutional Network (GCN). While DistilBERT captures rich contextual and semantic information, GCN enhances detection by modeling structural relationships within text data. The proposed model is evaluated against other well-known approaches. Experimental results show that the hybrid DistilBERTGCN framework achieves high detection accuracy, reaching 99% for English and 98% for Turkish and Arabic. In addition, this study introduces new multilingual datasets, contributing to the advancement of the literature research.