Explainable Artificial Intelligence (XAI) has emerged as a critical field for fostering trust, transparency, and comprehension in human-AI interactions. However, existing XAI systems often fall short of addressing real-world usability challenges, resulting in suboptimal adoption and engagement. This paper applies the User Experience Research Point of View (UXR PoV) playbook to Human-XAI interactions as a case study, i.e., a structured framework designed to guide multidisciplinary teams in creating effective human-centered XAI systems. The playbook consists of actionable play cards, organised into three dimensions: Usability Enhancement, Human-Like Enhancement, and Learning Enhancement. Our proposed Human-XAI plays and cards aim to improve the usability and long-term impact of XAI systems by leveraging iterative design principles, interdisciplinary collaboration, and evidence-based practices.