# One-Class Learning for AI-Generated Essay Detection
## 4 Discussion  
In this section, the experimental results obtained for the AI-generated detection task are discussed. **RQ1:** This question pertains to the accurate detection of human vs. AI-generated essays using one-class learning models. The results showed that AutoEncoder achieved the best results on the English dataset, followed by IsolationForest. Analyzing this result from the perspective of recent research works involving one-class learning methods, a similar pattern was observed in [33], where AutoEncoder significantly outperformed other approaches in terms of both model accuracy and robustness to contamination in training data. The same behavior was observed in [31], where AutoEncoder outperformed both IsolationForest and OneClassSVM in the context of geo-distributed sensor network data, and in [29; 30; 32], where deep learning approaches based on neural networks such as AutoEncoder yielded remarkable detection performances. This result shows that such approaches are particularly effective when the ability to catch the non-linearities across features is beneficial for the detection task. However, it is noteworthy that this phenomenon was not systematically observed in the literature. For instance, the study in [28] compared deep learning and classical machine learning methods and identified that classical methods outperformed deep learning approaches on the considered time series domains. The work in [34] also showcases the effectiveness of one-class ensembles of classical machine learning approaches in complex domains. This result, which seems to contradict what is observed in the present paper for the English dataset, is actually in line with the results for the Spanish dataset. On that dataset, OneClassSVM, which is regarded as a classical machine learning approach for one-class learning, yielded the best performance, outperforming other approaches including AutoEncoder. Therefore, different one-class learning methods present varying robustness and performance in the presence of different data characteristics. Overall, the general results of the experiments are significant. Their practical implication is that it is possible to train models for the detection task that yield accurate predictions without exploiting positively labelled training data (AI-generated essays), which are usually unavailable to end users, i.e., instructors. Thus, the classification of human vs. AI-generated essays using one-class learning methods without leveraging positively labeled data during the model training process is an achievable undertaking. **RQ2:** Regarding the importance of linguistic features for accurate essay detection, the results showed that Readability was on average the best-performing setting for the English and Spanish datasets. This finding contradicts the results in other previously published studies on fake news [21; 49], which deemed Readability as the features with the lowest detection capabilities. Such an anomaly can be explained by the number and complexity of the features used. The authors in [21; 49] used seven and four features, respectively. The current study employs thirteen features, including some that are specific to academic texts and the Spanish language. Furthermore, Readability possessed the highest F1-Score performance across all methods in Spanish and the second-highest in English. Readability refers to the complexity of the vocabulary of a text; given that essays display elaborate and specific lexicon, as opposed to that of fake news, it is not surprising for these linguistic features to rank high. In the English data, Repetitiveness obtained the highest F1-Score. This setting was the third-highest in Spanish. As noted in [50], AI essays are repetitive, not only in the use of vocabulary but also in the expression of ideas. Essays generated by ChatGPT exhibited great repetition of ideas and phrases in the current study, particularly those generated in English.