Abstract

Statistical education is rapidly evolving in response to global challenges and technological advancements. Emerging topics are reshaping both what and how we teach, with profound implications in terms of equity, relevance, and impact. A growing focus on gender in research and innovation encourages statistical educators to examine gender bias in data collection, analysis, and interpretation in order to promote more inclusive and reflective research practice. Parallel to this, there is a pressing need to integrate the 17 Sustainable Development Goals (SDGs) into curricula and research frameworks. Embedding the SDGs into statistical education not only contextualizes data within real-world challenges, such as poverty, climate change, and inequality, but also empowers students to engage with data with a keen sense of ethics and purpose. The rise of generative AI, machine learning, data-based algorithmic systems and data science presents both significant and complex challenges. Students not only must learn how to use these tools, but also how to critically evaluate them, underscoring the urgent need for updated curricula that emphasize critical thinking, data ethics, and a clear understanding of the limitations of such analyses. As educational programs become increasingly internationalized, inclusion must be aligned not only with the social model of disability but also with cultural diversity, ensuring that data literacy is accessible to all and that diverse perspectives are both respected and reflected in data narratives. This requires acknowledging different cultural approaches to knowledge and promoting multilingual learning environments within data practices. These topics, along with many other challenges, signal a shift toward a more responsible, inclusive, and forward-thinking approach in our discussion of statistical education.