The generation and identification of paraphrases are essential challenges in natural language processing (NLP), involving considerable consequences for education, plagiarism detection, and content analysis. Despite significant advancements in sentence-level paraphrasing, paragraph-level paraphrasing is still inadequately investigated, especially across various fields. This research is the first to examine the effectiveness of SALAC algorithms and Transformer-based models in generating coherent and semantically accurate paraphrases across various domains, utilising the ALECS dataset, which includes text samples from five different educational fields, such as Economics and Anthropology. The methodology uses SALAC algorithms for sentence reordering, according to coherence scores obtained from the ALBERT model's sentence Order Prediction (SOP). Real-world human evaluation is performed on the paraphrased paragraphs, measuring semantic similarity and coherence via a Likert scale of five pre-defined points. Evaluators involve academic students and researchers, ensuring the reliability of outcomes. Research findings show that SALAC algorithms successfully maintain semantic integrity and coherence across several domains, highlighting their generalisability. Additionally, the study examines how domain-specific readability impacts annotation reliability and paraphrasing performance. This research contributes to the development of robust, domain-independent paraphrasing techniques at the paragraph-level for educational applications and plagiarism detection, thereby advancing NLP solutions across multiple domains.
The problem of updating the logic of ITS operation when introducing innovative functions and adapting to the requirements of a particular educational institution (localization) is considered. Arguments in favor of multi-stage decision-making based on the model of P.K. Anokhin are given. The architectural solution of such ITS is proposed and the stages of generalization of processed data from the digital educational footprint are described. The possibilities of using the mechanism of recommender systems and fuzzy logic for reconfiguring the logic of decision-making are discussed. By the example of using the mechanism of competency development assessment and course individualization, the process of changing the logic to fit the new functionality of the system is shown. The results of implementation in the ITS are demonstrated for an experimental ITS used in the educational process of the master's degree program at the Siberian Federal University.
Question-answer generation (QAG) focuses on creating answers to a set of questions based on a pre-specified context. Some applications for QAG are included in the field of information retrieval and data augmentation for QA models. However, one of the most popular uses of automatic answer retrieval presently is notably in the field of education. This study focuses on harnessing advanced artificial intelligence (AI), in particular, large language models (LLMs) for creating a Question-Answering system by generating answers for a set of predefined questions in healthcare. This sub-system will feed into and be integrated into a larger framework for a project with a goal of building a bilingual (English and Spanish) intelligent tutoring system (ITS) geared towards educating survivorship skills to low-literacy Hispanic breast cancer survivors. Several Hugging Face’s Models and evaluation metrics were used for generating and evaluating answers based on previously-generated health-related questions. Synthetic data generation and Code-switching functionality was also incorporated to determine how well the models perform when accounting for both English and Spanish. GPT-generated English answers achieved higher accuracy than Spanish answers (96.38
The evaluation of neural network models, particularly for classification tasks, is a critical phase in the development pipeline, ensuring models perform reliably across diverse datasets and real-world scenarios. A robust evaluation framework relies on multiple performance assessment techniques, each addressing different aspects of model accuracy, generalization, and robustness. This paper presents a study on the development and evaluation of a deep learning-based classification system for UML diagrams, focusing on measuring accuracy through various techniques. The research leverages deep learning techniques to classify UML diagrams into predefined categories, such as class, sequence, state, other UML and non-UML diagrams. The system was evaluated using cross-validation with a k-folding approach, to confirm the model does have a reliable performance estimation and minimizing overfitting. Automatic evaluation and self-training methods were explored to enhance the model’s robustness and adaptability to new datasets. The experiments demonstrated an average accuracy of 82.8
This paper explores a neuromorphic approach to semantic knowledge representation using spiking neural networks (SNNs) for relational inference within knowledge graphs (KGs). While traditional KG models often rely on dense embeddings that lack interpretability, SNNs offer a biologically inspired alternative by encoding relationships through discrete, event-driven spikes. We evaluate three architectures-LIF-Base, RecurrentSNN, and LiquidSNN-on a dataset of 54 computer science-related KG triplets. Performance is assessed using relationship classification accuracy, temporal and spatial stability, and spike variability. Results show that relationship types produce distinct spike activation patterns, with LiquidSNN achieving the highest accuracy and spatial coherence. These findings support the potential of SNNs for structured, interpretable KG reasoning, with future applications in adaptive learning systems and explainable AI.
