# Hybrid ANN-Based and Text Similarity Method for Automatic Short-Answer Grading in Polish
## 2 Related Work
It showed how to develop an approach to automatically score responses using an existing language model (distilBERT) that successfully identifies the amount of internal and external content in each sentence. In [31], the authors reviewed the automatic assessment of text-based responses in higher education. Their review focused on the period between 2017 and 2023 to capture work conducted in the years when NLP advances, such as BERT and Large Language Models (LLM), became available. Also, ref. [32] reviewed the current understanding of online assessment, identifying major online assessment approaches and the different functions online technologies serve. While tests have been the dominant approach, in which technologies substituted existing assessments, online assignments and skills assessments involved more innovative assessment practices. In [33], a Hierarchical Rater Model based on Signal Detection Theory (HRM-SDT) showed an optimal performance by considering it a viable solution for Automated Essay Scoring (AES) score integration regarding automated content scoring on multiple scoring items leveraging feature-based models using four different machine learning algorithms, Random Forests (RF), Gradient Boosting (GB), Linear Discriminant Analysis (LDA), and Explainable Boosting Machines (EBM), and also the Transformer-Based Models GBERT, T5, and MBART. Traditionally, grading requires educators to design a comprehensive rubric and meticulously review each student's answers [34]. A promising application in higher education involves leveraging LLMs to enhance the grading process for students. Instead, using Artificial Intelligence (AI) has been around for a while but was oftentimes hampered by the effort and cumbersomeness of training and validation. Even for short answers, AI systems (including LLMs like BERT) needed to be specifically trained for each class of problems [35], with very few exceptions. Recently, the idea was focused on developing a technique to validate LLMs' output in the context of program synthesis to generate predicates for testing. In [36], an approach is to use back-translation models that generate buggy programs from explanations to validate the generated content and show the use of human-in-the-loop for validating low-confidence outputs. Additionally, ref. [37] used trained models on only essays of the highest quartile of students in terms of performance, proving that these models are unsuitable for students from the other quartiles. Furthermore, the research highlighted that the fairness of AES systems is compromised if such models are used on students or tasks for which they have not been trained. A clustering model can help teachers analyse students' assignments and is also an effective method to support essay grading. For instance, to automatically cluster programming assignments (C programming), \(k\)-means was applied in the study by [38] that proposed a clustering method to cluster sentences to support grading the essays written in Finnish by bachelor students. A step toward Generative Artificial Intelligence (GAI), such as ChatGPT 3.5 could be found in the study by [39] that showed several jobs of text summarisation, machine translation, problem-solving, sentiment analysis, question-answering, and creative writing that can be easily adjusted without training by engaging in context-aware, human-like conversations and producing clear, educational, and pertinent responses. In [40], a new method was proposed using item response theory that scores different AES models and considers the differences in the characteristics of scoring behaviour among models. The study by [41] presented a novel architecture for the problem of language-independent sentiment analysis of text classification. It was focused on English and German languages. The proposed model outperforms the baseline by a significant margin with respect to F1 score on English and mixed-language datasets. Some researchers consider text-based sentiment analysis an essential field in NLP to be worked on [42; 43].