# One-Class Learning for AI-Generated Essay Detection
## 4 Discussion
Based on the assumption that machine-generated text presents shorter and less complex sentences, less use of adjectives and adverbs, and overall shorter sentence length [18; 24], POS tagging was expected to have higher classifying power. However, the results indicated that it was the fourth-best in English and the least competitive in Spanish. While sentence length is a differentiating feature between AI- and human-generated essays, it is notcompetitive enough when compared to semantic features. Overall, the results showed that linguistic features enabled one-class models to effectively classify human vs. AI-generated essays, an unexplored venue in detection studies. **RQ3:** This research question addresses the differences in detection accuracy in English and Spanish essays. The results highlighted that Readability is the setting that could be considered optimal for both languages. This may be the case because of the features used in this setting. Overall, lower model performance in the Spanish dataset is observed. This phenomenon could be due to two main reasons. First, human essays were produced by L2 learners of English and Spanish, which could exhibit language transfer, a process by which learners use their knowledge of other languages and apply it to another [51]. Thus, linguistic features that highly depend on meaning--Emotional Semantics, Readability, and POS--may have been influenced by learners' inaccuracy and/or misspelling of words. Second, Spanish is a morphologically synthetic language, relying on inflection to express syntactic meaning, whereas English is analytic in nature, using words and word order to convey syntactic meaning [52]. More inaccurate use of morphological inflections was present in Spanish human-generated essays compared to the English dataset. Additionally, the use of the Oxford comma is found in certain dialects of English. Evidence of its use in Spanish has not been reported in the literature. Oxford commas in L2 Spanish essays were found since the essays were written by L1 (First Language) American English students. However, this single feature alone could not help the model discern between both text modalities. What is more, Spanish essays generated by ChatGPT exhibited the use of the Oxford comma, depicting a strong influence of the English language in ChatGPT outcomes. Thus, considering that [PERSON] has been mostly trained on English texts ([[https://seeo.ai/blog/how-many-languages-does-chatgpt-support](https://seeo.ai/blog/how-many-languages-does-chatgpt-support)]([https://seeo.ai/blog/how-many-languages-does-chatgpt-support](https://seeo.ai/blog/how-many-languages-does-chatgpt-support)) (accessed on 1 July 2023)), it is likely that the class overlap between essays written by L2 learners of Spanish with English as their L1 and AI-generated essays is smaller than that observed for the English dataset, resulting in a more difficult detection task for one-class learning models with the Spanish dataset. Average sentence length is longer in Spanish than in English [53]. Considering the aforementioned influences, the complexity of the detection task using simple features in the one-class learning setting was increased. Identifying effective features and models for the Spanish language is a crucial aspect to be further investigated in future research.