Intent-Based Networking (IBN) enables operators to specify high-level outcomes while the system translates these intents into concrete policies and configurations. As IBN deployments grow in scale, heterogeneity and dynamicity, ensuring continuous alignment between network behavior and user objectives becomes both essential and increasingly difficult. This paper provides a technical survey of assurance and conflict detection techniques in IBN, with the goal of improving reliability, robustness, and policy compliance. We first position our survey with respect to existing work. We then review current assurance mechanisms, including the use of AI, machine learning, and real-time monitoring for validating intent fulfillment. We also examine conflict detection methods across the intent lifecycle, from capture to implementation. In addition, we outline relevant standardization efforts and open-source tools that support IBN adoption. Finally, we discuss key challenges, such as AI/ML integration, generalization, and scalability, and present a roadmap for future research aimed at strengthening robustness of IBN frameworks.

Intent-Based Networking (IBN) has simplified network management and orchestration at a high level, but configuring User Equipment (UE), like 5G modems, is still a complex and demanding task due to dynamic requirements and intricate device-specific settings, and scalability challenges, especially when dealing with distributed, edge-based devices. This paper explores the potential of using Intelligent Chatbots powered by Generative Artificial Intelligence and Large Language Models (LLMs) operating as co-pilots to automate and optimize modem configurations. We propose a scalable chatbot system that translates user intents into actionable configurations, enhancing security, performance, and adaptability. To this end, we introduce a middleware that bridges LLMs with 5G Modem interfaces, eliminating retraining needs while ensuring engaging, real-time interaction with users. Additionally, we analyze key challenges in integrating LLM-based chatbots with UE and discuss the benefits of query caching in optimizing response times. Our findings highlight the potential of Intelligent Chatbots in extending IBN principles to UE, enabling a more automated and user-friendly approach to network configuration.

We present TutorChat, an intelligent chatbot conceived to be able support search and synthesis of information during a learning task accomplishment, in particular for dyslexic students. TutorChat is based on ChatGPT; it is able to support question/answer inter-activity of learners, and to generate concept maps on the topics at hand, with the possibility, beside analysis, to have such maps extended with additional sub-maps starting from a selected concept. We let TutorChat be used by a sample of dyslexic learners, coming from different educational levels. Then we collected encouraging sample’s feedback, through a questionnaire, about appreciation of the system’s services, and perception of the usefulness coming from its use.

This paper introduces a novel approach to enhancing educational outcomes by integrating electroencephalography (EEG) and supervised machine learning. Our methodological framework leverages real-time EEG data analysis, focusing on alpha, beta, gamma, delta, and theta wave patterns to develop cognitive indices such as Focus, Engagement, Relaxation, Fatigue, Involvement, and Stress. These indices are pivotal for delineating the Flow state among learners, a mental state conducive to optimal learning. We detail the process of EEG data collection where students are equipped with a non-intrusive EEG headset that monitors their brainwave patterns in real time. This setup involves creating a baseline of each student's cognitive patterns during an initial calibration phase, which is refined over time to enhance system accuracy. Using this data, we employ feature extraction techniques to develop predictive models capable of assessing and predicting the learners' cognitive states. Our research advances the personalization of learning environments by providing real-time feedback to students about their mental states. This feedback allows students to adjust their engagement strategies dynamically, aiming to maintain or achieve the mental states that are most conducive to learning. Initial findings suggest that our approach can significantly improve educational practices by adapting to and fostering students' cognitive states. The implications of this study extend beyond simple academic performance enhancement, promoting a deeper integration of cognitive neuroscience within educational systems. By developing tools that adapt to students' cognitive needs, we aim to foster an educational environment that values and enhances individual learning capacities.

This paper presents a comparative analysis of novel LLM-based architectures designed specifically for system configuration purposes. Generative Artificial Intelligence (Gen AI) has rapidly evolved, offering transformative capabilities in content generation across various domains. Large Language Models (LLMs) stand at the forefront of this evolution, revolutionizing natural language understanding and enabling sophisticated conversational systems. Leveraging the potential of LLMs, our study introduces a novel system architecture centered around an intelligent chatbot tailored to assist learners in complex network configurations. By integrating Generative Pre-trained Transformer-based models with Retrieval Augmented Generation (RAG) and Function Calling features, our architecture aims to provide a co-pilot-like experience, guiding users through understanding requirements and generating configuration scripts. Through a comparative analysis of three LLM architectures, each tailored to handle system network configuration, we evaluate their effectiveness, strengths, and limitations. Our findings offer valuable insights into the potential applications of Generative AI in network operations and highlight avenues for future research and development.

Intent-based Networking (IBN) has emerged as an innovative approach to automate the provisioning of network services while simplifying the interaction between the users and the network, allowing users (e.g., administrators) to define high-level desired outcomes (i.e., intents), and translating expressed intents into automated network configurations. One of the main challenge in IBN is the correct acquisition of the user intents and subsequently the accurate translation into actionable configurations to enforce into the network. Despite some efforts in improving user-to-IBN system interaction, a gap still remains in ensuring satisfactory user experiences and contextually appropriate and coherent responses or translation results. To this purpose we consider using recent advancements in Generative AI, and in particular in Large Language Models, a promising approach to enhance IBN in the scope of intent acquisition and translation. Accordingly, this work investigates the integration of IBN systems with LLM-based Conversational Agents (i.e., intelligent chatbots), on the one hand to enhance the user experience while injecting intents and, on the other hand, to assure an accurate understanding of user intents and their translation into a coherent set of network configurations, which are generated automatically. The chatbot operation according to the proposed approach is illustrated in a DHCP configuration use case.

Intent-based networking has attracted interest in the academic research for enhancing network management operations with user-oriented features. One of the main challenge in this field is the acquisition of the user intents and subsequently the relative translation into policies for the automatic management of the network. Concerning this task, the primary technique employed is relying on Graphical User Interfaces (GUI)s. In addition, the use of Natural Language Processing techniques has been extensively adopted for improved user experience. Recently, some preliminary studies have shown that using Large Language Models (LLMs) for this purpose leads to achieve interesting results. However, based on a comprehensive analysis of the state of the art, it has emerged that the works utilizing the LLMs do not fully exploit all the capabilities these tools could potentially offer. For this reason, the doctoral work aims to address the following challenges: enhancing user experience through the utilization of intelligent chatbots, improving the correct understanding of user intents and ensuring the translation of user intentions into a coherent set of network configurations, which are generated automatically.

In recent years, also due to the covid-19 pandemic, the possibilities for distance learning have increased considerably, through web-based learning platforms, available on the Internet without space and time limits. As a result, the offer of courses and the number of enrolled students has grown exponentially. In order to be able to guarantee students a better learning support service, one of the proposals regards the intelligent Chatbots. These well known interactive applications are based mainly on machine or deep learning and in this paper we present Boulez, a system allowing the orchestration of a community of individual chatbots, each one with its algorithm and its private training dataset. We apply a technique called Federated Learning, where several individual chatbots, collaborate. In particular, here the approach is “centralized”, meaning that a main system orchestrates the collaboration of the federated systems. By addressing the communication inefficiencies and privacy issues of conventional federated learning, Boulez offers a more efficient and effective approach to chatbot interaction, ultimately leading to improved user experience. The paper presents the Boulez system, its operation principle, methods used, and potential benefits, along with a use case of its application.

Intent-based Networking (IBN) has emerged as an innovative approach to automate the provisioning of network services while abstracting the details of the underlying infrastructure and simplifying the interaction between the users and the network. In this paper, we present an intent-based framework that allows for the deployment of SDN-based and QoS-aware network slices. The main objective of the work is to describe the role of artificial intelligence techniques such as Natural Language Processing (NLP) and user profiling in helping non-expert users easily interact with the IBN system and express their desired operational goals. Such innovative solutions offer a customized support to the users to improve their Quality of Experience (QoE) while increasing the automation in the network configuration process.

In recent years there has been an exponential growth of distance learning, provided by both public and private institutions. As a matter of fact, the number of students enrolled in courses delivered through the Network, has dramatically grown, also due to the COVID-19 pandemic, which has forced millions of people not to move. Consequently, more and more courses delivered in a remote modality have been attended by a huge number of people, producing an increasing number of Massive Open Online Courses (MOOC)s. These kind of courses are imposing new challenges for teachers, especially for monitoring and assessing the community learning processes. On the one hand, the learning assessment cannot be carried out based solely on closed-ended tests, while, on the other hand, teachers cannot evaluate thousands of open-answer assignments: they should have at their disposition a set of tools helping them monitor the community learning progress. This paper investigates the possibility of using some of the Source Code Embedding techniques, to give teachers useful information about their learners' programming styles in Massive Open Online Courses. We propose a method to visualize each student's program, included the teacher's one, as a point in a 2-D space, using the doc2vec embeddings technique. Thanks to this representation, teachers can identify in the 2-D space groups of students having similar programming styles and reason on them to start a suitable didactic feedback. Moreover, teachers can reason on the relationship between each point compared to their own point as well, considered as the truth programming style. A first experimentation using Python as the programming language is performed with encouraging results.

Concept maps are graphic tools to organize, represent and share knowledge. In particular, a concept map can explicitly express the knowledge of a person or group, about a given domain of interest. Concept maps are used effectively to support learning of any topic, at any level: from Primary School to University, and to professional/vocational training, it can stimulate and unveil the occurrence of meaningful learning. In an educational context, having the possibility to compare Concept Maps coming from different students, also by means of an automated computation of map similarity, can reveal to be a great asset for a teacher. And this is so much more true when the number of students is very high, like in Massive Open Online Course. Here we propose a similarity measure based on two deep learning techniques that produce embeddings of the single structures that make up a concept map. We also report about a preliminary experiment, having encouraging results.

We discuss the problem of setting the best number of peers to which a given evaluation job should be assigned, in a Peer Assessment setting. The Peer Assessment is supposed to happen in a large scale class, such as in the case of Massive Open Online Courses. We use a dataset that simulate a large class (1000 students), based on Gaussian distributions of the Student Model features. Such features are related to the student's proficiency, and assessment capability. The number of peers assigned to the same evaluation job was controlled from 3 to 50 in 6 steps using 10-point scale. The abilities of participants were estimated using Item Response Theory. All parameters of IRT models, which is called as Generalized Partial Credit Model, such as "ability", "consistency", and "strictness", were estimated well using MCMC technique; their standard deviation errors gradually decrease with the number of peers. As a preliminary result of optimisation, an appropriate number of peers was 15 as comparing the stadardised errors across the conditions.

Massive Open Online Courses (MOOCs) are among the most popular online learning systems, with courses characterized by a very large number of attendees. Monitoring the students' learning process in a MOOC can be hard for the teacher, due to sheer numbers. Moreover, MOOC platforms produce large amounts of data related to the dynamics of the students community, which, if well used, can contribute to improving the educational offer. Here we present a web-based system, allowing the teacher to simulate a MOOC class of students, and experiment with it, by applying a pedagogic strategy based on Peer Assessment. The simulated students (peers) are supposed to produce answers to a question, and assessments of some other peers' answers, according to the models used to define the simulated class. Using our system the teacher can observe the dynamics of the simulated MOOC, based on a modified version of the K-NN algorithm, in line with the Teaching Analytics discipline. A first trial of the system produced promising results, showing its usefulness to support Teaching Analytics.

The users of the World Wide Web produce data continuously. This happens in varied areas such as trading on line, product ratings, support and use of services, and many more, comprising Distance Education. The ever increasing amount of such data can make analysis and extraction of meaningful information progressively harder, and sophisticated analysis techniques are to be used to extract added value from data. Many companies do collection and analysis of data with the purpose to develop their marketing strategies. In the field of education, and Distance Education in particular, data collected through online Learning Management Systems (LMSs) can provide a great resource, and a strong challenge, for the analysis of learning processes, the design of training paths, and the updating and personalization of learning environments. While, on the one hand, there is an increasing demand by educational institutions to measure, demonstrate, and improve the results achieved in distance learning, on the other hand the logic of traditional reporting included in LMS platforms does not satisfy that growing need. Learning Analytics is the answer to the need for optimization of learning through the techniques of analysis of data produced by learning processes, involving all stakeholders of the system. In this paper we show and discuss a brief state of the art of models of Learning Analytics presented in the literature.

Wikipedia is one of the most used repositories of free learning materials published by communities of experts: most of the students, and teachers do great use of it, despite the criticism about its reliability. Here we address the problem of helping teachers to build courses with Wikipedia pages to prepare on-the-fly courses. We use a web-based system called Wiki Course Builder, which allows teachers to query Wikipedia, by submitting a set of keywords, and having as a response four different ranked lists of retrieved Wikipedia pages, depending each list on a given retrieval metric implemented. In addition to the classic TF-IDF, Information Gain and Latent Semantic Indexing metrics, we propose a metric that highlights specific didactic aspects. This metric is based on the teacher's Teaching Styles namely, the Grasha teaching styles. Wikipedia pages are indexed with the Teaching Styles of the teachers that selected the given page; then, this value is compared with the Teaching Style of the teacher who is doing the search. The metric assigns higher rank to the Wikipedia pages that have a Teaching Style closer to the teachers'. We present an evaluation of the four retrieval metrics, showing that the teacher-styles based metric, Vs. the other three content-based metrics, allows for a better Wikipedia retrieval from a didactic point of view.

The organization of the information in the knowledge economy has become a priority business process. Better organization leads to faster retrieval of relevant information. The process of searching and sequencing didactic materials for course building is an articulated and time-consuming process that requires considerable effort by the user. The goal of this research is to implement a platform for supporting the course building from Wikipedia articles. The selected materials will be automatically sequenced on the base of prerequisite relation, in order to provide a knowledge graph, editable by the teacher, with prerequisite relationships, representing the optimum learning path for the students.

With its 5,006,202 articles, 49 millions of registered people and on average 800 new articles per day, Wikipedia provides a knowledge base for teachers and instructional designers to build didactic materials. As a matter of fact, teachers often consult this encyclopaedia to arrange, integrate or enrich their courses. Moreover, with the exponential growth of the Internet, didactic materials are freely available and usable by teachers, instructional designers and students from Learning Objects Repositories such as Mertlot or Ariadne and others. On the other hand, designing and delivering a new course is a crucial task for teachers, who have to face two main problems: building or retrieving and sequencing learning materials. Retrieving or building learning materials requires a great effort and is time-consuming, while sequencing requires an accurate didactic project. In this paper we present a sequencing engine of learning materials, embedded in the Wiki Course Builder system, a system capable of retrieving and sequencing Wikipedia web pages, taking into account both the teacher model based on the Grasha teaching styles and on a social didactic approach. The main goal is to support teachers building on-the-fly courses, i.e., building courses quickly, with a few clicks of the mouse. An important feature of the system is represented by its ability to allow teachers to interact with the recommended learning path through a graph-based interface where they can directly modify the proposed learning path, adding or deleting Wikipedia pages. A first questionnaire has been submitted to a sample of teachers with encouraging results.

Selecting and sequencing a set of Learning Objects (LOs) to build a course may turn out to be quite a challenging task. In this paper we focus on such an aspect, related to the verification and respect of the relationships of pedagogical dependence existing between two LOs added to a course (meaning that if a given LO has another one as "pre-requisite", then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). In our approach the sequencing of LOs in the course can still be managed by the instructor, basing on her/his taste and preferences, yet s/he can also be helped by a set of suggestions, related to the pre-requisite relationships existing among the LOs selected for the course. Such suggestions (such relationships, in effect) can be computed automatically and provide the instructor with significant help and guidance. We show a light-weight formalization of the LO, and how it can be "represented" by a set of WikiPedia Pages ("topics"); then we show how such set of topics, together with a set of relevant hypotheses we previously defined, can help establish the dependence relationship existing between two LOs. In this endeavor we exploit the classification in categories available for the WikiPedia topics, and obtain interesting results for our framework, in terms of precision and recall of the dependence relationships.

In this paper we present a comprehensive framework supporting the tasks of defining, retrieving, and importing Learning Objects (LOs) for personalized courses. It is partially implemented in a Moodle-based personalization system, where the instructional designer is guided through: 1) a theoretical specification of the needed LOs; 2) a retrieval function of actual LOs, by automatically querying standard-compliant repositories; 3) an analysis of such items, to import those selected by him, also adding metadata relevant to the personalization system, at hand. This work overcomes some well known shortcomings of the Moodle system in supporting retrieval of learning material in a personalization context.

Curriculum sequencing is one of the most appealing challenges in Web-based learning environments: the success of a course mainly depends on the system capability to automatically adapt the learning material to the student's educational needs. Here we address the problem of how to compare and to test different curriculum sequencing algorithms in order to reason about them in a self-contained and homogeneous environment. We propose LS-LAB, a framework especially designed for comparing and testing different curriculum sequencing algorithms. LS-LAB has been designed to run different algorithms, each of them provided with its own student model representation: a super student model is able to incrementally include all of them. In this framework, the learning node has to be compliant to the IEEE LOM specifications, while, through a suitable GUI, one can insert new algorithms or run already available ones. We are carrying out the implementation by using a 3-tier Java application technology, in order to make this environment available on the Internet. Finally we show an application example.

In this paper we present a business intelligence process to dynamically develop multidimensional OLAP schemas to support information retrieval in an ontology-based environment. The particular aspect of our work consists in the integration of information retrieval techniques, such as the semantic indexing of non-structured documents with some dynamic management techniques of unbalanced hierarchies stored in a data warehouse. We show how to develop an ETL process to automatically build OLAP dimensions, inheriting the hierarchic structure of ontologies, with the goal of using the semantically indexed data to carry out multidimensional OLAP analysis. We experimented our system in a company environment with encouraging results.

This paper presents a text categorization system, capable of analyzing HTML/text documents collected from the Web. The system is a component of a more extensive intelligent agent for adaptive information filtering on the Web. It is based on a hybrid case-based architecture, where two multilayer perceptrons are integrated into a case-based reasoner. An empirical evaluation of the system was performed by means of a confidence interval technique. The experimental results obtained are encouraging and support the choice of a hybrid case-based approach to text categorization.