The rapid development of Large Language Models (LLMs) has transformed various fields, especially education, where their ability to generate human-like text enhances writing efficiency. However, these advancements present challenges in developing students’ critical thinking and writing skills. Therefore, important to distinguish between human-written and AI-generated text to maintain academic integrity. This study proposes a machine-learning approach that utilizes an ensemble of RoBERTa transformer models to classify AI-generated text in English essays. The proposed method combines three variants of the RoBERTa model with different training parameters to improve the classification model's performance. Evaluation results show good performance with a precision of 99.560%, a recall of 97.839%, and an F1-score of 98.692%. These results outperform the individual RoBERTa models and traditional machine learning models such as Naive Bayes, Support Vector Machine, and Random Forest. The findings highlight the effectiveness of using an ensemble method with RoBERTa transformer models for the classification of AI-generated text. This research contributes to the development of AI-generated text classification models and offers solutions to the challenges faced in education due to the growth of LLM.
Index Terms--large language model, AI-generated, text classification, ensemble model.