# A Deep Learning Approach to Classify AI-Generated and Human-Written Texts
## 2 Background and Related Literature
### Related Literature
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Study** & **Focus** & **Deep Learning Technique Used** & **Findings** \\ \hline \multirow{8}{*}{Using deep learning models} & [15] & Proposed a new model for text classification. & DRNN & Test accuracy of 88.52\% using a dataset of 900 samples. \\ \cline{2-4}  & \multirow{3}{*}{[14]} & Combined existing datasets, real-time tweets, and synthetic data generated by AI models. & CNN, ANN, RNN, LSTM, GRU, BiLSTM, and BiRNN & BiLSTM achieved the highest F1 score of 98.77\%. \\ \cline{2-4}  & & LSTM-based approach for classification. & LSTM & Achieved accuracy, precision, recall, and F1 scores above 98\%. \\ \cline{2-4}  & \multirow{3}{*}{[9]} & Introduced the TSA-LSTM-RNN. & TSA-LSTM-RNN & Achieved 93.17\% (accuracy for human dataset) and 93.83\% (ChatGPT 4o dataset). \\ \cline{2-4}  & & & & \\ \hline \multirow{8}{*}{Alternative approaches} & [18] & Classification using statistical methods. & SVM & Binary classification achieved 99.87\% accuracy; accuracy for rephrased texts was 87.58\% (classification) and 84.91\% (statistical). \\ \cline{2-4}  & \multirow{3}{*}{[10]} & \multirow{3}{*}{DeepFake text detection.} & \multirow{3}{*}{TCN and four modified TCN models} & Modified TCN models \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \multirow{8}{*}{This study} & \multirow{3}{*}{Differentiate and classify AI and human-generated text.} & \multirow{3}{*}{LSTM} & F1 score for both AI and human generated text was best achieved for model 1 (20 epochs and 128 patch size), achieving above 98\%. Other models achieved above 96\%. \\ \cline{1-1} \cline{3-4}  & & & & \\ \cline{1-1} \cline{3-4}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Findings from previous studies. STM configurations. These deep learning models offer high adaptability but often lack interpretability and require large datasets for optimal performance. Several studies have explored combining deep learning with traditional or optimization techniques. The authors in [18] applied SVM and linguistic analysis to reach an accuracy of 99.87% on binary classification tasks, outperforming many neural models. The authors in [10] employed temporal convolutional networks (TCNs), creating modified variants that reached F1 scores above 98%. These advanced methods highlight a trend toward building hybrid models that balance interpretability, performance, and robustness. A major gap in the literature is the linguistic diversity of research. The overwhelming majority of existing models have been developed and tested on English datasets (e.g., [9; 14]. Few studies have considered low-resource languages, such as Turkish. Moreover, many of the datasets used are either small scale or artificially constructed, limiting generalizability. The review by [1], covering 17 detection tools, emphasized this issue and recommended the integration of multiple evaluation methods, including human review, for enhanced reliability. Despite the growing body of research, two critical gaps persist: (1) the limited application of deep learning techniques to Turkish language detection, (2) the absence of high-quality, native Turkish datasets that include both human-written and AI-generated texts. To address these gaps, our research proposes an LSTM-based framework specifically trained and evaluated on Turkish texts. Our study introduces a novel, domain-specific Turkish dataset composed of original and ChatGPT-generated content. The model achieved a testing accuracy and F1 score exceeding 97%, outperforming previous LSTM approaches (e.g., [2; 9]) and closely matching top performers like [10; 14]. This positions our study as a pioneering contribution to multilingual AI detection research and offers practical implications for educational, journalistic, and governmental use cases in Turkish-speaking contexts.