The field of AI is moving towards a more culturally aware and adaptive approach, with a focus on developing systems that can effectively interact with diverse populations and recognize the needs of different cultural contexts. This shift is driven by the recognition that current benchmarks and evaluation frameworks are limited in their ability to capture the effectiveness of systems built for different cultural and healthcare contexts. Researchers are working to develop more nuanced and culturally sensitive evaluation frameworks, as well as AI-powered tools that can facilitate global collaboration and provide more accurate and culturally appropriate responses. Noteworthy papers in this regard include: Beyond the Rubric, which highlights the limitations of current benchmarks in capturing the effectiveness of systems built for different cultural and healthcare contexts. Empa, which presents an AI-powered virtual mentor for developing global collaboration skills in HPC education. FanarGuard, which introduces a culturally-aware moderation filter for Arabic language models. TALES, which evaluates cultural misrepresentations in LLM-generated stories for diverse Indian cultural identities.