Artificial intelligence (AI) education and literacy are gaining momentum over the past few years; AI systems are permeating our daily lives and mediate our social, cultural, and political interactions. The implications of AI extend beyond the technical aspects and involve ethical, cultural, and social issues such as misinformation and bias. Understanding how an AI system works and critical thinking skills have, therefore, become ever more crucial for children and young people in order to be able to identify the benefits and challenges of AI. The role of the educators is, at this point, critical. This chapter is situated in the context of AI education and literacy and aims to propose a framework for teacher training on AI and ML education. The design of the teacher training courses and initial findings are described. Through an exploratory approach, insights on the attitudes, the requirements, and the recommendations of the teachers emerged.

Game-Based Learning (GBL) has gained significant attention in recent years as an effective educational approach to enhance student engagement and learning outcomes in higher education. This conference paper aims to emphasise the significance of GBL in higher education and presents a comprehensive review of a case study involving the application of GBL in a linguistics course. The study explores the impact of games on student learning, motivation, and engagement with the course content.By integrating gaming principles and mechanics into the curriculum, students are encouraged to actively participate, explore concepts, and apply their knowledge in a dynamic and stimulating environment. The study examines data gathered from student surveys and structured interviews to evaluate the effectiveness of GBL in fostering student engagement and knowledge acquisition. Furthermore, this paper provides a valuable set of recommendations and strategies for educators and administrators interested in implementing GBL in higher education settings. It explores the potential benefits of GBL, including increased motivation, improved critical thinking skills, and enhanced problem-solving abilities. Additionally, it highlights the importance of aligning game design with specific learning objectives and ensuring the integration of assessment mechanisms to gauge student progress effectively. Moreover, this paper discusses potential pitfalls and challenges that may arise during the implementation of GBL in higher education. It addresses issues such as technology requirements, accessibility considerations, and the need for appropriate training and support for instructors. By acknowledging these challenges and offering potential solutions, this research aims to guide educators and administrators in overcoming obstacles and optimising the implementation of GBL in higher education courses.

MOOCs: Exploiting Networks for the Education of the Masses or Just a Trend?: 10.4018/978-1-4666-4904-0.ch018: MOOCs have become a new trend in education, taking the world by storm in 2012. Is this just a fad or is it because of their nature in opening education to the

Swimming performance analysis increasingly depends on multimodal sensing systems that capture physiological and biomechanical signals in real-world aquatic environments. While progress has been made in sensor fidelity and automated analysis, the interpretability of these systems remains limited, constraining their uptake in coaching practice. This paper presents early findings from the SWIM-360 project, which investigates how explainable artificial intelligence (XAI) can support transparent and actionable insights for swimming performance. We report preliminary results from EO SwimBETTER and TrainRed sensors, together with proof-of-concept outputs from video-based pose estimation. In parallel, we introduce mock-up visualisations and interaction concepts designed to elicit coach feedback on requirements for explainability. A qualitative questionnaire with eight professional swimming coaches was conducted to elicit requirements for explainable feedback. Their responses informed the design of a multimodal, coach-centred explainability framework. Rather than providing a fully integrated model, the paper proposes a methodological framework that combines multimodal sensing with explainability-driven design principles. Our findings highlight both the feasibility and the challenges of translating sensor data into interpretable knowledge for athletes and coaches. By embedding explainability at the earliest design stage, this study proposes an explainability-driven design framework linking multimodal sensing and user requirements. These early findings highlight how XAI principles can guide the creation of trustworthy, coach-centred decision-support tools in aquatic sports.