# Towards AI-Generated Essay Classification Using Numerical Text Representation
## 6 Conclusions and Future Work  
Based on the conducted analyses, it can be observed that the selected numerical text representation techniques combined with various classifiers generally exhibit very high accuracy. The primary criterion for evaluating their effectiveness was the analysis of accuracy. Methods such as Bag of Words, TF-IDF weights (both with default parameter values and those adjusted to the dataset's characteristics), and embeddings created using the fastText model trained on the analyzed training set (for Continuous Bag of Words and Skip-gram) showed accuracies exceeding 96.50% with all classifiers. Among these, the highest accuracy was observed for TF-IDF weights combined with logistic regression, reaching 99.82%. Regarding sentence embedding methods like MiniLM and distilBERT, accuracies ranged from 93.78% to 96.63%, indicating the potential need for further optimization of classifiers or data processing techniques before applying these methods. The lowest performance was noted for embeddings created using the pre-trained fastText model. In the analysis, one instance of accuracy below 90% was observed: 89.88% for logistic regression using these embeddings. The Matthews correlation coefficient (MCC) value of 79.83% also indicated generally lower performance compared to other methods. This might be due to the model's lower adaptation to the specificity of the analyzed dataset. It should also be noted that the XGBoost classifier achieved the highest minimum accuracy among the classifiers at 96.24%. This highlights its adaptability and effectiveness when combined with various numerical representation techniques. Analyzing the specificity values, all the results are equal to or greater than 91.90%. Excluding embeddings from the pre-trained fastText model, all others are equal to or greater than 95.45%, while all basic numerical representations (Bag of Words, TF-IDF weights, and parameterized TF-IDF weights) exceed 99%. This indicates that the models rarely classify negative cases (i.e., student-written essays) as positive (generated by large language models). This is crucial in applications where it is important not to falsely label positive content as AI-generated. A similar situation is observed for precision values. Here, too, for basic numerical representation methods, all results exceed 99%. Excluding the pre-trained fastText model (where the lowest value was 91.56%), precision is generally equal to or greater than 95.29%. This indicates the high effectiveness of the models at correctly classifying positive cases according to their actual class. Each text representation method has its inherent strengths and weaknesses. For example, TF-IDF is effective at capturing word frequency and importance within a corpus but lacks the ability to consider word context. BERT embeddings, on the other hand, provide contextualized word representations, which can better capture the meaning of a word in relation to its sentence. However, BERT embeddings are computationally expensive and may not significantly outperform simpler methods like TF-IDF or fastText in smaller datasets. BERT is also trained on large, general corpora, which may not perfectly align with the style and language patterns found in student essays or AI-generated texts. Furthermore, the domain-specific nature of educational texts may require fine-tuning or the use of specialized embeddings, which was not performed in this study. This trade-off between computational complexity and performance is a key limitation, particularly when applying these methods to real-world scenarios with limited resources. Another important limitation is the interpretability of these methods. While advanced models like BERT capture more complex relationships within the text, they lack transparency compared to traditional methods like TF-IDF. This lack of interpretability can be a disadvantage, especially in educational or academic contexts where understanding the reasoning behind model decisions is important.