# Towards AI-Generated Essay Classification Using Numerical Text Representation
###### Abstract  
The detection of essays written by AI compared to those authored by students is increasingly becoming a significant issue in educational settings. This research examines various numerical text representation techniques to improve the classification of these essays. Utilizing a diverse dataset, we undertook several preprocessing steps, including data cleaning, tokenization, and lemmatization. Our system analyzes different text representation methods such as Bag of Words, TF-IDF, and fastText embeddings in conjunction with multiple classifiers. Our experiments showed that TF-IDF weights paired with logistic regression reached the highest accuracy of 99.82%. Methods like Bag of Words, TF-IDF, and fastText embeddings achieved accuracies exceeding 96.50% across all tested classifiers. Sentence embeddings, including MiniLM and distilBERT, yielded accuracies from 93.78% to 96.63%, indicating room for further refinement. Conversely, pre-trained fastText embeddings showed reduced performance, with a lowest accuracy of 89.88% in logistic regression. Remarkably, the XGBoost classifier delivered the highest minimum accuracy of 96.24%. Specificity and precision were above 99% for most methods, showcasing high capability in differentiating between student-created and AI-generated texts. This study underscores the vital role of choosing dataset-specific text representations to boost classification accuracy.  
natural language processing; numerical text representations; text classification; large language models +
Footnote †: journal: _Sci._  
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prevent the unauthorized use of generated texts [1]. A significant threat to academic integrity is unauthorized content generation (UCG). According to [PERSON] et al. (2023), \"UCG is the production of academic work, in whole or in part, for academic credit, progression or award, regardless of whether or not a payment or other favour is involved, using unapproved or undeclared human or technological assistance\" (p. 2) [3]. This practice undermines the foundational values of honesty and fairness in academia. Many existing market solutions, such as OpenAI Text Classifier and ZeroGPT, are designed to handle a broad range of texts on diverse topics. However, an approach that focuses on the development of similar tools tailored to specific types of content may prove to be more effective.  
This paper explores various numerical text representation methods to enhance the classification process for detecting AI-generated essays. Using a comprehensive dataset and implementing a variety of pre-processing steps, we aim to evaluate the effectiveness of different text representation techniques. These include traditional methods such as Bag of Words (BoW) and TF-IDF, as well as more advanced embeddings such as fastText, MiniLM, and distilBERT. Specifically, for advanced embedding techniques like fastText, both pre-trained models and models trained on our own dataset are utilized. The results highlight the importance of selecting appropriate text representation techniques tailored to the dataset's characteristics and aim to bridge the gap by focusing on dataset-specific text representations and their impact on classification accuracy.  
This paper is organized as follows: Section 1 introduces the topic of this paper. Section 2 presents an overview of the work related to the problem of AI-generated text detection. In Section 3, we describe the theoretical background of natural language processing, and in Sections 3.1 and 3.2, we present numerical representations of text and large language models. In Section 4, we present our methodology for conducting research on AI-generated text detection. In Section 5, we present an analysis of our research on real-world datasets, and in Section 5.3, we discuss the experimental results. Finally, in Section 6, we conclude with general remarks on this work and possible directions for future research.