Artificial Intelligence (AI) is increasingly being integrated into a wide array of Extended Reality (XR) applications, including sophisticated navigation systems, immersive training simulations for educational use-cases and data analysis in medicine, e.g. for MRI scans. Consequently, ensuring the transparency and interpretability of these AI-driven applications has become a major challenge. This paper examines the growing importance of Explainable AI (XAI) in Extended Reality environments and identifies key challenges in developing effective explanation systems. We analyze how these AI-powered XR applications particularly benefit from transparent explanations that build trust, enhance user understanding and improve overall adoption. After summarizing the general challenges in the field of XAI, we investigate how these challenges manifest in the specific context of XR. By synthesizing current research and identifying critical open questions, this work aims to guide future XAI development towards more transparent, trustworthy systems that prioritize human needs across XR applications and beyond.