# A Deep Learning Approach to Classify AI-Generated and Human-Written Texts
## 6 Conclusions  
This study aimed to develop and evaluate a robust deep learning model capable of accurately distinguishing between human-written and AI-generated text in Turkish, a relatively underexplored linguistic context. To achieve this, we implemented LSTM networks and systematically examined the effects of various hyperparameters, including patch size, embedding size, number of epochs, dropout rate, and test-train data ratios. Our experimental results revealed that model configurations significantly influence detection performance. Specifically, models trained for fewer epochs (20) outperformed longer training runs (50 epochs), as extended training led to increased validation loss and overfitting. Similarly, models with a higher embedding size (300) and lower regularization (0.0001) demonstrated improved learning stability and generalization. Additionally, a 30% test data ratio yielded better overall performance than 60%, highlighting the impact of data distribution on model accuracy.  
While increasing model complexity can enhance training accuracy, our findings emphasize the trade-off between accuracy and overfitting risk. The use of dropout and early stopping techniques successfully mitigated overfitting in optimized configurations, as indicated by stable validation losses and minimal training-validation divergence. Nonetheless, simpler models exhibited faster training times but struggled with classification precision, leading to increased false positives when detecting AI-generated text. The value of this research lies in its focus on Turkish-language AI-generated text detection--a niche area with limited prior studies--and in demonstrating that carefully tuned LSTM models can achieve high accuracy (above 97%) in this task. Moreover, this study contributes practical insights into the trade-offs between model complexity, training duration, and generalization performance. Future research should extend these findings by leveraging larger and more diverse Turkish-language datasets to improve robustness and linguistic coverage. Also, exploring hybrid deep learning models (e.g., BiLSTM-CNN, transformer-based architectures) could further enhance detection accuracy. Furthermore, the integration of semantic and contextual linguistic features to better handle rephrased or adversarial AI-generated texts could be explored. By addressing these avenues, future studies cannot only refine detection techniques but also contribute to broader efforts in combating the misuse of generative AI in education, journalism, and digital communication.  
Conceptualization, A.K.; methodology, M.Y. and A.E.T.; validation, A.E.T. and M.Y.; analysis, Y.I.A. and A.K.; writing--original draft preparation, A.K. and Y.I.A.; writing--review and editing, Y.I.A. and A.E.T. All authors have read and agreed to the published version of the manuscript.  
This research was not funded by any organization.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.