The field of explainable machine learning is moving towards more human-centered approaches, focusing on the development of systems that provide comprehensible explanations of decisions and predictions. Recent research has introduced new methodologies, such as Narrative Learning, which defines models entirely in natural language and refines their classification criteria using explanatory prompts. This shift towards more interpretable models is driven by the need for trust-preserving intelligent user interfaces, where users can understand and assess the impact of policy updates. Furthermore, the application of explainable AI techniques to analyze human learning and expertise has shown promise in illuminating how humans develop efficient strategies in complex tasks. Noteworthy papers include: On the Design and Evaluation of Human-centered Explainable AI Systems, which provides a comprehensive review of user studies evaluating XAI systems and proposes objectives for human-centered design. Reversing the Lens: Using Explainable AI to Understand Human Expertise, which applies computational tools from XAI to analyze human learning and expertise in complex tasks.