2026/05/28 by Michelle Barker, Daniel S. Katz, Kim Hartley +31 · 2 voices
paper · doi:10.5281/zenodo.20320884
openalex created_date 2026/05/28 · openalex publication_date 2026/05/28 · openalex updated_date 2026/07/01
The Research Software Engineering in the Age of Generative AI: Building a Community Vision workshop, held in March in Edinburgh, UK, brought together participants to explore how Generative AI may reshape the research software ecosystem, and to help inform a broader community vision for the future of the field. Before the workshop, attendees contributed to a draft vision statement that workshop organisers used to identify areas of agreement and difference. A version of this, published as Research Software in an Age of AI-Assisted Development: Reflections from Edinburgh, is intended to provide principles that will guide the community during this time of rapid change. In the workshop, the participants also discussed emerging practices, identified opportunities and risks, and proposed a range of high-impact pilot activities to support the safe, reproducible, and effective use of AI in research software and workflows. The areas focused on included: Suggesting policies and narratives for research-performing institutions Developing a framework to discover, document and address costs, benefits, and risks Understanding future incentives around publishing, preserving and crediting software Verifying and validating research software Defining pathways for the evolution of the research software engineers (RSE) role Identifying and developing necessary training Developing a playbook for RSE managers and open-source software project leaders Making GenAI accessible to all Collaborating together across people, community, and disciplines, not just with AI Across these areas, participants identified 46 different activities, ranging from writing sprints and community-of-practice activities that could begin soon, to longer-term research studies to investigate how verification practices, collaboration patterns, and training needs are changing as AI tools become embedded in research workflows. These projects shared a common focus on community-maintained practices, actionable guidance, and sustained coordination.