A Novel Approach for Distinguishing Human and AI-Generated Texts
Ahmed A. Abdulhamed, Prabhat Ranjan, S. Xiong · ACM Transactions on Asian and Low-Resource Language Information Processing · 2026
This research introduces innovative methods for differentiating between human-generated and AI-generated text. We developed two new approaches: a hybrid model combining a genetic algorithm with a multilayer perceptron (GA-MLP) and a bidirectional encoder representation from transformers (BERT) approach. Both models show significant improvements over traditional techniques. The article includes a comparative analysis of various conventional methods, such as support vector machines, Naive Bayes, logistic regression, multilayer perceptron, and convolutional neural networks. The experimental results indicate that the GA-MLP approach improves the classification accuracy by approximately 18% compared to traditional models. Furthermore, the BERT approach improves classification accuracy by around 20% over the same conventional methods. This study demonstrates the robustness and effectiveness of the proposed strategies for text classification tasks, which offer superior performance in distinguishing text types compared to traditional techniques. The project can be accessed at https://github.com/zxzxzx77/Human-and-AI-Generated-Texts .