aspg-18-3603-1792025486 20261015005126 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 16 1 Mohammed Salah Ibrahim Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, 3100, Iraq Jabbar Abed Eleiwy Department of Applied Sciences, University of Technology-Iraq, 52 Alsena str., Baghdad, 10053, Iraq Hassan Mohamed Muhi-Aldeen Department of Computer Engineering, Aliraqia University, 22 Sabaabkar, Adamia, Baghdad, 10053, Iraq Yusra Al-Yasiri Department of Kindergarten and Special Education, Aliraqia University, 22Sabaabkar, Adamia, Baghdad, 10053, Iraq Ahmed Adil Nafea Department of Kindergarten and Special Education, Aliraqia University, 22Sabaabkar, Adamia, Baghdad, 10053, Iraq The fast growth of artificial intelligence technologies, especially language processing technology has obscured the lines in between human-generated text comparing to chatbot-generated message. Recognizing which generated such, a text is essential for applications like information generating and manipulated text in order to guarantee authenticity between communicated parties. This research applies to a set of machine learning models to identify text as either human-written or chatbot-generated. The methodology of this research starts with a dataset including text generated from different Large Language Models (LLMs) along with a text generated by a human. After that, Tf-Idf ranking vectorization was used to define word embedding has and represent the text numerically. Then, different Machine Learning (ML) models leveraged recognize whether a human or a chatbot generated a text. The ML models applied include Logistic Regression, Random Forest, Decision Tree, Gradient Boosting, Naïve Bayes, and XGBoost. For this study accuracy, precision, recall, F1-score were used to evaluate the system. The dataset first was split into 80% for training and 20% for testing. Out of all implemented models, the Random Forest model reported the best with accuracy of 88%. Logistic Regression reported a close accuracy of 85%. The Random Forest model showed an 8% improvement compared to previous studies that reported an accuracy of 80%. Confusion matrices revealed that the Random Forest model provided high precision and recall, minimizing classification misleading of human or chatbot text. The research focused on studying the ability of ML models in identifying human vs. chatbot-generated text. The results showed the RF model was the best among other models with 88% accuracy. This accuracy shows a possible usage of such models in real-world applications that requires the confidentiality of human writing. 2025 152 165 3603 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.160113 https://americaspg.com/journal/18/article/3603 https://americaspg.com/storage/41739938739.pdf