Abstract
This study highlights the critical role of Large Language Model (LLM) in simplifying technical content and integrating visual data for accessible communication. It compares GPT-4 and Llama-3.2-90b-Vision-Preview, focusing on readability, semantic similarity, and multimodal interpretation using robust metrics like Flesch Reading Ease, Gunning Fog Index, and CLIP Score. GPT-4 retains key information and achieves high semantic and textual integration scores, making it more suitable for complex technical scenarios. Furthermore, LLaMA prioritizes readability and simplicity, outperforming in generating accessible captions. Both models show optimal performance with a temperature setting of 0.5, balancing simplicity and meaning preservation. The research underscores LLM potential to democratize technical knowledge across disciplines but notes precision and multimodal integration limitations. Future directions include fine-tuning for domain-specific applications and expanding input modalities to enhance accessibility and efficiency in real-world technical tasks.

Abstract
As industrial product development becomes increasingly complex and knowledge-intensive, the integration of Artificial Intelligence (AI) agents into design workflows offers great potential to improve efficiency and decision making. However, the opacity of current AI reasoning processes remains a major obstacle for adoption in engineering domains. This position paper explores the need for Explainable AI (XAI) within agentic design systems, proposing a conceptual architecture where agents, powered by Large Language Models (LLMs), not only perform domain-specific tasks, but also generate human-readable justifications for their decisions. Unlike black-box systems, these agents are designed to promote transparency, trust, and traceability, all of which are critical in high-stakes industrial contexts. Building upon the foundation of the Agentic Approach to Product Design, we outline how roles such as requirement analysis, material selection, and specification interpretation can be reimagined with explainability at their core. This work advocates for a shift towards interpretable, auditable AI assistants, capable of supporting collaborative engineering processes. An illustrative scenario is used to exemplify the practical value and challenges of agents supported by XAI. Future research directions are highlighted, including evaluation metrics for explainability and potential integrations into existing agent orchestration platforms such as CrewAI. As a conceptual position paper, this work aims to stimulate the development of explainable multi-agent design systems and guide future empirical validation in industrial contexts.

Abstract
Software ecosystems have emerged as a paradigm to structure software products, communities and business models, in a form inspired by the natural ecosystems. Mobility solutions are also evolving from individual vehicles to soft mobility services based on electric vehicles. This paper aims to address the creation of a software platform to support an ecosystem of mobility solutions-the Intelligent Mobility Ecosystem, based on connected electric vehicles. It follows the paradigm of software ecosystems, in which a technological platform provides the functionalities needed to create solutions within the ecosystem. The work being carried out is part of the A-Mover project, which aims to develop a connected electric motorcycle and electronic services to support driving and use of the vehicle in individual and business contexts. The aim is to develop a set of functionalities around the vehicle to create specific mobility solutions. The concept of a software ecosystem is reviewed below and the proposed architecture for the software platform that will support the ecosystem is described.

Abstract
The video game industry has grown to become one of the largest in the market, surpassing even the film industry over a decade ago (Statista in Video game industry revenue worldwide 2000-2020). However, the development of games designed with visually impaired players in mind is still almost non-existent when compared to the sheer number of games released yearly. NonVisual Pong is our approach to addressing this challenge, providing blind players with a way to engage in competitive fun through gaming. We took the original Pong game from 1972 and fully adapted it to be played using only a controller-no visual display required. Following the development process, we tested our implementation with experts, discovering that, overall, our game was easy to pick up, required no overly complex setup, and successfully delivered the intended experience. Players enjoyed a balanced challenge and immersion, facilitated by audio cues and the controller's vibrations.

Abstract
As technology continues to evolve, organizations face growing and complex challenges and opportunities that affect their ability to govern, manage and harness data as a key source of competitive advantage. Equally, data are considered a powerful and unique source of success for organizations, which in turn, can impact their decision-making capabilities and play a critical role in their success. Hence, this article aims to provide a detailed identification, analysis and discussion over the current data governance context and its existing frameworks, highlighting their commonalities, differences and gaps, including ones related to data governance relationship with Generative Artificial Intelligence (GenAI). This article conducts an extensive methodological and in-depth analysis over a set of sixteen data governance frameworks based on different key data governance attributes, denoting that although there are numerous frameworks, they hold weaknesses, limitations and challenges which prevent them from being capable of incorporating and governing the use and management of AI, particularly the demands originating from GenAI. Our findings provide and propose a new and enhanced data governance framework which integrates the best features and ideas from the existing ones and initiatives derived from the advancements and particularities of AI and GenAI models, systems, and overall usage.

Abstract
Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), PPS2 defined the presentation of a new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and efficient urban mobility. green. In this context, the need to develop interfaces that meet the expectations of end users, promoting user experience and security are crucial. Therefore, following a User-Centered Design (DCU) methodology, a co-design perspective and UX data collection methods, this article presents the steps and preliminary results of the preparation, face-to-face session and subsequent analysis of results of a preliminary moment of acquiring knowledge on how to optimize motorcycle user interfaces. Specifically: script planning, requirements and analysis of user feedback collected through audiovisual recording, in a focus group, are described.