# One-Class Learning for AI-Generated Essay Detection
## 1 Introduction
Previous studies have used Part-of-Speech (POS) Tagging [18], an NLP task that involves assigning grammatical labels to each word in a given text based on the part of speech to which it belongs. Thus, words that are associated with emotions, such as adjectives and adverbs, present a higher frequency in human-generated text. In general, linguistic feature extraction is a viable approach to quantitatively represent relevant properties of the language, which supports machine learning models in text classification tasks. A common limitation of both neural-network-based and feature-based methods stands in their supervised nature, which relies on data availability for both classes to perform model training. Initial studies towards AI-generated essay detection also followed supervised approaches that require both human and AI-generated essays in training data [27]. However, this requirement becomes arduous due to the scarce availability of data for the positive class (phenomenon or anomaly class to detect) and due to the potentially different forms that the phenomenon could take, which are unknown at training time. In such a setting, one-class learning and anomaly detection approaches appear more ideal than fully supervised models. The adoption of one-class models is particularly important for the AI-generated essay detection task since AI-generated essays are not readily available to train and update the models by the end users of detection tools, i.e., instructors. Therefore, a one-class learning approach would offer the possibility to train models using exclusively human-written essays. The authors in [28] compare six unsupervised anomaly detection methods of varying complexity to determine a relationship between model complexity and effectiveness in detecting anomaly types. An anomaly detection approach for sequential data is proposed in [29], where tabular data are transformed into an image representation by means of a specialized filter, allowing the model to detect multiple types of outliers simultaneously. The work in [30] proposes a digital-twin-driven GAN-based method for an oil and gas station anomaly detection method. A spatially aware approach is proposed in [31] to deal with the detection of anomalies in geo-distributed smart grid data observed in smart grids. The authors in [32] propose a method to automatically highlight changing trends and anomalous behavior in music streaming data, which also delivers two levels of outlier explanations: the deviation from the model prediction and the explanation of the model prediction. The work in [33] leverages one-class models to perform anomaly detection with active learning in a continual learning setting, focusing on the cybersecurity domain. One-class ensembles with diverse models have shown to be effective in detecting rare biological sequences [34], where available data are characterized by almost only negative examples. Despite the wide range of applications covered by one-class learning studies, approaches addressing textual content are scarce and none so far has been devoted to essay data. On the other hand, supervised neural-network-based and feature-based approaches have been more pervasively applied to text data, dealing with issues pertaining to social media, such as fake news, hate speech, and misinformation. As a result, the effectiveness of different models in the context of education and academia is still unexplored. The present paper fills this gap, inspired by the ongoing discussion on the impact of ChatGPT in academic contexts. Specifically, this work investigates the impact of linguistic features on the predictive capabilities of one-class learning models on AI-generated essay detection. The contribution of this paper is threefold. First, it explores the adoption of linguistic features and one-class learning models for AI-generated essay detection. Second, it designs two datasets leveraging two real-world essay corpora in two languages, English and Spanish.