# A Deep Learning Approach to Classify AI-Generated and Human-Written Texts  
[PERSON]  
1 Department of Computer Engineering, Faculty of Engineering and Architecture, Kirsehir Ahi Evran University, 40100 Kirsehir, Turkey, [EMAIL_ADDRESS] (A.K. ); [EMAIL_ADDRESS] (M.Y.) [PERSON]  
2 College of Engineering and Technology, American University of the Middle East, Egalia 54200, Kuwait 2* 2  
The misuse of AI-generated content can lead to the spread of misinformation, manipulation of public opinion, academic dishonesty, and the erosion of trust in digital communication [4]. For instance, AI-generated texts can be used to create fake news articles, impersonate individuals online, or produce fraudulent academic work, which can have severe consequences for society at large [5]. These risks necessitate the development of effective tools and methodologies to identify and mitigate such misuse. From a social perspective, empowering institutions, educators, journalists, and the public with reliable AI detection tools fosters transparency, safeguards intellectual integrity, and promotes the responsible use of AI technologies [6]. Especially in low-resource language settings like Turkish, ensuring that communities are not disproportionately vulnerable to AI misuse is both an ethical and practical imperative [7]. Thus, this study contributes not only to technological development but also to the broader goal of maintaining ethical standards and social trust in the age of generative AI. One subset of machine learning is deep learning [3]. It focuses on algorithms known as artificial neural networks, which are modeled after the composition and operations of the human brain [8]. As deep learning continues to evolve, LSTM networks remain a cornerstone in the toolkit of researchers and practitioners, driving innovation in diverse fields, from language processing to predictive analytics [9]. LSTM networks have demonstrated their versatility across a wide range of applications. In NLP, LSTM is employed for text classification, sentiment analysis, and machine translation [4]. In finance, LSTM is used for market trend prediction and price stocking. Their ability to process sequential data makes them suitable for speech recognition, handwriting recognition, and bioinformatics. Using LSTM networks to detect AI-generated text offers unique advantages since it can capture temporal dependencies, understand context, and handle non-linear patterns in sequential data. LSTMs can distinguish between fake and original texts by identifying unique patterns, such as unnatural transitions or repetitive structures [9]. Their adaptability to diverse text types and integration with pre-trained models enhances performance, making them particularly effective for natural language processing tasks. By leveraging these capabilities, LSTMs provide robust and reliable tools for highly precise text classification [2]. Consequently, this study aims to address the following research question: can LSTM-based models accurately detect AI-generated Turkish text, and what are their limitations and strengths in this context? This study mitigates the above constraints by creating advanced detection systems explicitly tailored for Turkish AI-generated text. Despite substantial advances in detecting AI-generated content, especially in English, the field still faces ongoing challenges, particularly regarding language diversity and generalization [10; 11]. Many existing models focus exclusively on high-resource languages, leaving underrepresented languages like Turkish largely unaddressed. This lack of focus creates a pressing gap in the development of effective and equitable AI detection tools.