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.

This chapter examines how context-aware artificial intelligence (AI) enhances virtual reality (VR) by increasing immersion, user engagement, and adaptive interactivity. Drawing on foundational theories of presence and immersion (e.g., Slater, 2009; Csikszentmihalyi, 1990), this investigation examines how AI systems interpret environmental, behavioural, and verbal cues to personalise and enrich immersive experiences. Reviewing recent innovations in generative AI, reinforcement learning, and intelligent agent design, the chapter analyses the capacity of these technologies to support dynamic content generation, adaptive gameplay, and naturalistic interaction. Complementing the literature review, a user-centred survey captures VR user perceptions and priorities regarding intelligent, adaptive systems. The chapter concludes with a discussion of ethical considerations, user modelling challenges, and future research directions, offering a timely contribution to ongoing debates in AI-driven entertainment design.