Collaborative Research: CyberTraining: Implementation: Medium: ACAI-Train: Scalable Instructor Training for Infusing AI and Advanced CI Concepts Into Early Core Computing Courses

Artificial intelligence (AI), large-scale data, connected devices, cloud and high-performance computing systems, that together form the nation's cyberinfrastructure, are central to national competitiveness, yet most computing students encounter them only in advanced elective courses. This project infuses aspects of AI, Big Data, and Parallel and Distributed Computing concepts and practices into three foundational computing courses. The first two form the usual introductory programming sequence, and third is the computer systems course that is often taken shortly after them. Thus, all computing majors, not only those pursuing upper-level elective courses, will develop critical skills for understanding and contributing to the modern computational ecosystem. The project addresses a persistent barrier to such curriculum modernization: many instructors need focused preparation, classroom-tested examples, and adaptable teaching materials before they can confidently introduce these topics in early courses. To overcome this bottleneck, the project develops courses and materials to train about 200 current and future instructors through three intensive in-person summer workshops, an additional six hybrid tutorials at major conferences, and complementary online workshops. Summer trainees adapt and implement the course exemplars at their own institutions and contribute evaluation data, classroom-tested refinement and local adaptation, enabling broader adoption. With the potential to impact about 250,000 students over 5-10 years, the project serves NSF's mission by strengthening computing education, expanding access to AI and advanced cyberinfrastructure skills, and building the nation's long-term technological and research workforce capacity. The project advances knowledge in computing education by producing rigorously classroom-tested exemplars infused with Artificial Intelligence (AI), Big Data (BD), and Parallel & Distributed Computing (PDC) for Computer Science 1 (CS1), Computer Science 2 (CS2), and Computer Systems courses. Implementation across 60 diverse institutions will generate evidence-based models that can be widely adopted, thereby transforming early computing education at scale. The project investigates how AI-enabled learning tools and pedagogy can modernize core curricula by enabling students to construct, explore, and reason about modern computing systems earlier and in more depth than was previously possible. Evaluation data from trainees' implementations - including student learning, retention, and institutional adoptability - will contribute to generalizable knowledge on the design and scaling of Cyberinfrastructure-centric curriculum innovations. The project incorporates aspects of AI, BD, and PDC concepts and practices into the foundational computing courses ensuring that all computing majors, not only those pursuing upper-level electives, develop critical Cyberinfrastructure-ready skills. The project's three core course exemplars will be nationally adoptable, with trainees providing local adaptations to build a community-driven ecosystem of shared materials. Two key innovations in this project are: (i) harnessing AI both for pedagogy and for enabling course modernization: AI tools will make it possible for introductory students to develop, experiment with, and understand software, data, and other artifacts that were previously too complex to explore meaningfully at scale; and (ii) explicit integration of how BD and PDC power AI, giving computing students insight into the Cyberinfrastructure ecosystems underlying AI-driven discovery and innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2612359 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Charles Weems | Institution: University of Massachusetts Amherst, AMHERST, MA | Award Amount: $88,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2612359 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2612359.html