The free availability of a variety of big data presents an invaluable opportunity for educators to scale assignments and projects and use real-life problems in their courses to better motivate students.

Computational thinking is now considered the fourth basic skill alongside reading, writing and arithmetic. This provides an opportunity for computer science programs to offer courses for non- majors, both as a service and a recruiting tool. Similarly, interdisciplinary options (CS + X) provide opportunities for computer science educators to collaborate and create programs that will also enhance the learning experience of computer science students. Resource availability is the primary constraint for availing both these worthwhile opportunities.

Computer Science education research has lately been gathering momentum. It is now a mainstream area of doctoral research. Professional conferences catering to it are increasing in number and ranking. This portends well for computer science education by providing a feedback loop for improvement that could not have come sooner. It signals the maturing of computer science education.

Generative AI, like other emerging technologies, has the potential to revolutionize computer science education. It will impact course content, pedagogy, and assessment techniques. Harnessing generative AI in service of the goals of formal education will be one of the most significant challenges for the community over the next few years.

Theoretical and mathematical underpinnings make computer science a science. They are essential for long-term career success whereas tools and technologies prepare students for immediate employability. Striking the right balance between these dual objectives will continue to be a challenge, given the increasing need for mathematics in computer science (e.g., in machine learning) and often inadequate mathematical preparation of students entering computer science programs.