Generative Artificial Intelligence is revolutionizing the field of education, offering innovative tools to support students in the learning process and teachers in their activities. This paper presents an AI-powered software developed to support the teaching of systematic innovation courses, following TRIZ methodology, through the integration of Large Language Models (LLMs), Retrieval Augmented Generation (RAG) techniques and the access to patent source. The article describes the methodology implemented and the results of an experimental phase conducted on a large sample of students. The analysis shows how the use of AI in technical Problem-Solving enhances a more structured approach but at the same time is more effective in stimulating creativity and lateral thinking, reducing psychological inertia and boosting Technology Transfer. The findings highlight significant improvements over traditional didactics methods, both in learning effectiveness and instructional support.

In this paper we describe the evolution of the FPGA-based prototype deployed in the MANGO project, from a hardware prototyping platform of HPC architectures to a computing platform targeting HPC and AI applications. Our main goal is to reinvest on the MANGO cluster by providing a duality in its use for both large-scale hardware prototyping and highperformance computation. From our experience we can reach several interesting conclusions about the complexities and hurdles that lay below FPGA technologies, and therefore, shedding some light onto the real complexities that difficult the adoption of FPGAs on either large-scale pure HPC systems or on hybrid systems (HPC + BigData/Ai).

As the field of service robotics has been rapidly growing, it is expected for such robots to be endowed with the appropriate capabilities to interact with humans in a socially acceptable way. This is particularly relevant in the case of customer relationships where a positive and affective interaction has an impact on the users' experience. In this paper, we address the question of whether a specific behavioral style of a barman-robot, acted through para-verbal and non-verbal behaviors, can affect users' engagement and the creation of positive emotions. To that end, we endowed a barman-robot taking drink orders from human customers, with an empathic behavioral style. This aims at triggering to alignment process by mimicking the conversation partner's behavior. This behavioral style is compared to an entertaining style, aiming at creating a positive relationship with the users, and a neutral style for control. Results suggest that when participants experienced more positive emotions, the robot was perceived as safer, so suggesting that interactions that stimulate positive and open relations with the robot may have a positive impact on the affective dimension of engagement. Indeed, when the empathic robot modulates its behavior according to the user's one, this interaction seems to be more effective than when interacting with a neutral robot in improving engagement and positive emotions in public-service contexts.