P29 – Perspectives on Teamwork and AI in Scientific Computing
Presenter
DescriptionThe development and use of high-quality software—a primary mechanism for sustained collaboration and progress in scientific computing—is undergoing profound change, driven by increasing complexity in scientific drivers and computing architectures and the rapid adoption of AI, placing demands on how teams collaborate and share expertise. The Next-Generation Ecosystems for Scientific Computing project addresses challenges in team-based scientific software through coordinated research and community engagement. A central component is understanding collaboration practices and patterns of AI use in scientific computing to inform the development and curation of resources that support cross-disciplinary teamwork.
This poster presents foundational empirical work for the project, focusing on the design, execution, and analysis of a community survey and follow-up interviews. We surveyed 79 scientists and software professionals to understand needs for enhancing teamwork and leveraging AI in service of accelerated scientific discovery. Results indicate that while teamwork is widely valued in scientific computing, awareness of collaboration best practices and access to training remain limited. Although most participants reported using AI, less than half indicated that their teams had considered AI policies. Findings underscore the need for intentional socio-technical co-design in scientific computing (see https://arxiv.org/abs/2510.03413) to support reproducible, reliable, and trustworthy science.
This poster presents foundational empirical work for the project, focusing on the design, execution, and analysis of a community survey and follow-up interviews. We surveyed 79 scientists and software professionals to understand needs for enhancing teamwork and leveraging AI in service of accelerated scientific discovery. Results indicate that while teamwork is widely valued in scientific computing, awareness of collaboration best practices and access to training remain limited. Although most participants reported using AI, less than half indicated that their teams had considered AI policies. Findings underscore the need for intentional socio-technical co-design in scientific computing (see https://arxiv.org/abs/2510.03413) to support reproducible, reliable, and trustworthy science.
TimeTuesday, June 3017:30 – 19:45 CEST
LocationBldg. 8 – Entrance Hall
SessionPoster Session and Reception
Event Type
ACM SRC Poster
PASC Poster
