Abstract
<jats:p>Modern software is increasingly parallel, distributed, networked, event-driven, graphical, API-based, and data-intensive. However, introductory computing courses still commonly begin with a sequential-first model of computation which does not fully reflect the systems students encounter today. Students regularly use applications that depend on multicore processors, remote services, asynchronous events, large data streams, and libraries that hide complex computation. Yet many students do not learn these ideas formally until advanced electives, if at all. This gap limits their preparation for later study and work in software engineering, artificial intelligence, cybersecurity, high-performance computing, computational science and engineering, and other areas of computing and applications.</jats:p> <jats:p>This volume responds with a practical adoption guide for the introductory computing course sequence for instructors, course coordinators, departments, and academic leaders. Rather than only arguing that introductory courses should be modernized, it provides classroom-tested, modular course materials that can be adopted at different scales. The goal is not to turn introductory computing students into parallel programmers, but to help them develop an early conceptual model in which computation may involve multiple workers, remote data, performance tradeoffs, and coordination among interacting components, all the while ensuring that the basic CS1 learning outcomes are preserved.</jats:p>