The application of generative AI in education has shown significant potential to enhance learning outcomes by providing personalized, adaptive feedback to students. In this work, we present a novel Retrieval-Augmented Generation (RAG) system designed to improve the explanations and feedback provided to students during self-assessment activities. The system we developed is grounded in the course's reference material, ensuring that the feedback remains accurate, consistent, and contextually relevant to the student's curriculum. The system retrieves information directly from the textbook, reducing ambiguity and interpretation errors, and generates responses tailored to the specific needs of each student. The feedback is not only designed to correct misconceptions but also to reinforce key concepts, making the system a valuable tool for self-guided learning. In this study, we also explore the importance of prompt engineering in creating effective AI-generated feedback. We detail the iterative process used to optimize the prompts and the strategies employed to ensure high-quality, interpretable responses. The findings from this work suggest that generative AI, when integrated with subject-specific textbooks and careful prompt engineering, can significantly enhance the educational experience by providing dynamic, and contextually accurate feedback. This approach opens new possibilities for AI-driven education tools, contributing to more personalized and effective learning experiences.

Assigning a numerical value to a temporal expression (TE), known as temporal expression normalization, is a crucial process for tasks like timeline creation and temporal reasoning. Rule-based and classical deep-learning normalization systems lack versatility because they are limited to specific domains and languages, while current Large Language Models (LLMs) solutions are relatively unexplored. To overcome the current limitations in adaptability, we suggest utilizing five of the latest generative Large Language Models (LLMs) - Mistral 7B, Gemma 7B, Gemma 2B, Phi-2, and Llama-3 8B. We have explored various performance enhancement strategies, including using different prompts, contexts, and training techniques like Neftune. Our proposed models demonstrate the ability to adapt to diverse domains (news and biomedical) and multiple languages (Spanish, English, Italian, French, Portuguese, Catalan, and Basque) simultaneously. These models can handle expressions in various domains and languages, making them more versatile and useful for a wide range of applications. As a result, our approach offers significant performance improvements when compared to existing LLM-based and rule-based solutions for TE normalization and a promising solution for the challenges of temporal normalization.

Personalization of self-assessment educational tools plays a key role in enhancing the learning process. By allowing students to tailor the content and pace of their learning according to their individual needs, these tools encourage a more learner-centered approach. Our proposal consists in defining different personalization mechanisms for a self-assessment tool. This tool allows students to navigate the hierarchy of concepts of the subject and suggests them exercises covering the different concepts. After the student selects one of the possible answers, the tool informs if the student is right or wrong, The study has been carried out in a Computer Science course devoted to the teaching of algorithms and advanced data structures. However, the proposed tool and mechanisms can be easily exported to other subjects and topics. The tool has been implemented as a Telegram bot, which facilitates access to the tool from different platforms. The personalization mechanisms introduced in the tool on the one hand, allow to present the questions on each topic in increasing order of difficulty. This is achieved by analyzing the results of the exams in which the questions were originally proposed. In addition, the tool records the user's history, avoiding repeating questions already answered correctly and providing data on the user's performance. According to a questionnaire answered by the students, the tool, and in particular the personalization mechanisms, has been very useful for them in the preparation of the subject.

Algorithms and data structures are one of the most difficult parts of Computer Science for students to learn. It is therefore essential to develop approaches and tools that facilitate the acquisition of this crucial part of a computer scientist’s training. Based on the hypothesis that making the access to information and self-assessment easier improves the learning of the concepts, in this work we propose a graphical tool that allows the student to navigate through the topics of the subject, and to reach collections of self-assessment exercises on each of the topic. For this purpose, the information of the subject has been organized in a navigable hierarchy of topics that allows students to access exercises in a simple and visual way, on the one hand, but also to easily learn the relationships between the different concepts of the subject. These exercises allow students to self-evaluate and solve their doubts, as well as to find analogies between different topics in the hierarchy. This process is complemented by a web page recommender for the topics of the hierarchy. Results according to a questionnaire about the tool answered by the students indicate that it is considered of high utility and interest.

Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph. The main novelty of this algorithm is its ability to identify semantic relationships between words whose presence is statistically significant. Our method constructs a co‐occurrence graph in which words appearing in the same document are linked, provided their presence in the collection is statistically significant with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet. We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure over the two labeled sets of keyphrases used in the evaluation of SemEval‐2010.

Abstract In this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most suitable translations from the dictionary. Different algorithms that combine both the dictionary and the co-occurrence graph are then used for performing this selection of the final translations: techniques based on sub-graphs (communities) containing clusters of words with related meanings, based on distances between nodes representing words, and based on the relative importance of each node in the whole graph. The initial output of the system is enhanced with translation probabilities, provided by a statistical bilingual dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 2010, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed and compared to those obtained by the systems participating in the competitions. Our system offers the best results in comparison with other unsupervised systems in most of the experiments, and even overcomes supervised systems in some cases.

Web spam is a serious problem for search engines because the quality of their results can be severely degraded by the presence of this kind of page. In this paper, we present an efficient spam detection system based on a classifier that combines new link-based features with language-model (LM)-based ones. These features are not only related to quantitative data extracted from the Web pages, but also to qualitative properties, mainly of the page links. We consider, for instance, the ability of a search engine to find, using information provided by the page for a given link, the page that the link actually points at. This can be regarded as indicative of the link reliability. We also check the coherence between a page and another one pointed at by any of its links. Two pages linked by a hyperlink should be semantically related, by at least a weak contextual relation. Thus, we apply an LM approach to different sources of information from a Web page that belongs to the context of a link, in order to provide high-quality indicators of Web spam. We have specifically applied the Kullback-Leibler divergence on different combinations of these sources of information in order to characterize the relationship between two linked pages. The result is a system that significantly improves the detection of Web spam using fewer features, on two large and public datasets SUchasWEBSPAM-UK2006 and WEBSPAM-UK2007.

In this paper we generalize the Continuous Adversarial Queuing Theory (CAQT) model [5] by considering the possibility that the router clocks in the network are not synchronized. Clearly, this new extension to the model only affects those scheduling policies that use some form of timing. First, if all clocks run at the same speed, maintaining constant differences, we show that all universally stable policies in CAQT that use the injection time and the remaining path to schedule packets remain universally stable. These policies include, for instance, Shortest in System (SIS) and Longest in System (LIS). Then, if clock differences can vary over time, but difference is bounded, we show the universal stability of SIS and a family of policies related to LIS. The bounds we obtain in this case depend on the maximum difference between clocks. We then present a new policy that we call Longest in Queues (LIQ), which gives priority to the packet that has been waiting the longest in edge queues. This policy is universally stable and, if clocks maintain constant differences, the bounds do not depend on them. To finish, we provide with simulation results that compare the behavior of some of these protocols in a network with stochastic injection of packets.