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.

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.

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.