Abstract
Explainable Artificial Intelligence (XAI) aims to enhance the interpretability of Artificial Intelligence (AI) systems for humans. The goal is to ensure that algorithmic decisions and underlying data are understandable to non-technical stakeholders. Advanced Machine Learning (ML) models, such as deep neural networks, enable AI systems to process vast data and extract intricate patterns, akin to the human brain, but this complicates XAI development. Complex ML models require substantial data for training, exacerbating the challenge. Consequently, this paper proposes a novel approach to improve XAI for complex ML models, particularly those with large data needs. Using K-Means clustering, the paper proposes to identify relevant data instances to create similarity clusters. This filtering process focuses XAI on essential information, even with complex models, reducing the data set to find patterns and explanations, so that, using the same approach, only the best explanations are filtered efficiently. The paper proposes to implement and test this model with a case study on ML for PV generation forecasting in buildings. Results show that the proposed approach is able to generate explanations that are very similar to those generated when using the entire available data, in only a portion of the execution time, leveraging from the identification of a small number of representative data points. This approach, therefore, enhances the efficiency of XAI by achieving promising results with a smaller dataset. It also facilitates the development of more understandable and fastly provided solutions, which is essential for real-world XAI users such as electric mobility users that need PV forecasting explanations as support for their vehicles charging management.

Abstract
Professional training presents a significant challenge for organizations, particularly in captivating and engaging employees in these learning initiatives. With the ever-evolving landscape of workplace education, various learning modes have emerged within organizations, and e-learning stands out as a prominent choice. This increasingly cost-effective and adaptable solution has revolutionized training by facilitating numerous learning activities, including the seamless integration of educational games driven by cutting-edge technologies. However, incorporating serious games into educational and professional settings introduces its own set of challenges, particularly in quantifying their tangible impact on learning and assessing their adaptability across diverse contexts. Organizations require a consistent framework to guide best practices in implementing e-learning combined with serious games in professional training. The primary objective of this research is to bridge this gap. Rooted in the methodology of Design Science Research, it aims to provide a comprehensive framework for creating and assessing adaptive serious games that achieve desired learning and engagement outcomes. The overarching goal is to enhance the teaching-learning process in professional training, ultimately elevating student engagement and boosting learning outcomes to new heights. The proposal is grounded in a review of literature, expert insights, and user experiences with Serious Games in professional training, considering learning outcomes and forms of adaptation as essential characteristics for developing or evaluating Serious Games. The result is a framework designed to guide learners toward improved learning outcomes and increased engagement. The proposal underwent evaluation through triangulation, involving focus groups and expert interviews. Additionally, it was utilized in the development and assessment of a Serious Game, offering new insights and application suggestions. This experiment provided an evaluation of the framework based on real courses. In summary, this investigation contributes to the development of evidence-based approaches for the effective use of Serious Games in professional training.

Abstract
The potential of digital games, when transformed into Serious Games (SGs), Games for Learning (GLs), or game-based learning (GBL), is truly inspiring. These forms of games hold immense potential as effective learning tools as they have a unique ability to provide challenges that align with learning objectives and adapt to the learner's level. This adaptability empowers educators to create a flexible and customizable learning experience, crucial in acquiring knowledge, experience, and professional skills. However, the lack of a standardised design methodology for challenges that promote skill acquisition often hampers the effectiveness of games-based training. The four-step Triadic Certification Method directly responds to this challenge, although implementing it may require significant resources and expertise and adapting it to different training contexts may be challenging. This method, built on a triadic of components: competencies, mechanics, and training levels, offers a new approach for game designers to create games with embedded in-game assessment towards the certification of competencies. The model combines the competencies defined for each training plan with the challenges designed for the game on a matrix that aligns needs and levels, ensuring a comprehensive and practical learning experience. The practicality of the model is evident in its ability to balance the various components of a certification process. To validate this method, a case study was developed in the context of learning how to drive, supported by a game coupled with a realistic driving simulator. The real time collection of game and training data and its processing, based on predefined settings, learning metrics (performance) and game elements (mechanics and parameterisations), defined by both experts and game designers, makes it possible to visualise the progression of learning and to give visual and auditory feedback to the student on their behaviour. The results demonstrate that it is possible use the data generated by the player and his/her interaction with the game to certify the competencies acquired.

Abstract
As sports analytics evolve to include a broad spectrum of data from diverse sources, the challenge of integrating heterogeneous data becomes pronounced. Current methods struggle with flexibility and rapid adaptation to new data formats, risking data integrity and accuracy. This paper introduces PlayField, a framework designed to robustly handle diverse sports data through adaptable configuration and an automated API. PlayField ensures precise data integration and supports manual interventions for data integrity, making it essential for accurate and comprehensive sports analysis. A case study with ZeroZero demonstrates the framework's capability to improve data integration efficiency significantly, showcasing its potential for advanced analytics in sports. © 2024 IEEE.

Abstract
Mutation testing has evolved beyond academic research, is deployed in industrial and open-source settings, and is increasingly part of universities' software engineering curricula. While many mutation testing tools exist, each with different strengths and weaknesses, integrating them into educational activities and exercises remains challenging due to the tools' complexity and the need to integrate them into a development environment. Additionally, it may be desirable to use different tools so that students can explore differences, e.g.. in the types or numbers of generated mutants. Asking students to install and learn multiple tools would only compound technical complexity and likely result in unwanted differences in how and what students learn. This paper presents FRAFOL, a framework for learning mutation testing. FRAME provides a common environment for using different mutation testing tools in an educational setting.

Abstract
[No abstract available]