Software engineering is a profession of continuous change. New languages, frameworks, tools, practices, and forms of collaboration constantly emerge, while existing knowledge can quickly become outdated. As a result, software engineers do not simply apply what they once learned – they must keep learning throughout their careers. This learning rarely happens in formal training settings alone. Instead, it happens in everyday work: when developers join a new team, adopt a new technology, interpret unfamiliar code, learn from peers, consult documentation, participate in online communities, or increasingly interact with AI-based assistants. However, despite its centrality to professional practice, lifelong learning in software engineering remains underexplored as a unifying research perspective. Existing research communities each address parts of the problem – empirical software engineering studies expertise, education research studies novices, human-computer interaction studies tool use – but none treats long-term professional learning as a first-class concern.

This Dagstuhl Seminar invites participants to jointly examine software engineers as lifelong learners. The goal is to bring together researchers and practitioners from empirical software engineering, human factors, software visualization, computing education, program comprehension, and related areas to discuss how learning unfolds across careers, teams, and tool ecosystems. We are particularly interested in the tension between short-term productivity and long-term capability development: How can tools help developers solve the task at hand while also deepening their understanding, strengthening their judgment, and supporting sustainable growth over time?

The topic is especially timely because generative AI is reshaping software development. AI assistants can reduce barriers, accelerate tasks, and provide just-in-time support, but they also raise important questions. What do developers learn when AI provides answers immediately? How does reliance on AI affect knowledge retention, professional self-efficacy, or decision-making, and how can we design development environments that keep humans actively engaged in reflection and understanding? These questions matter, but they are part of a larger picture. Onboarding, knowledge sharing in teams, skill decay, and the design of learning-oriented tools all predate the current wave of AI and remain pressing in their own right. No single discipline can answer them; they require a broad conversation that connects technical, cognitive, educational, and organizational perspectives.

The seminar will provide a space to exchange methods, concepts, and visions. We aim to discuss learning processes in software practice, knowledge and skill decay, onboarding and adaptation, knowledge flow within teams, empirical and observational methods for studying learning, and the design of learning-oriented tools and interfaces. We also aim to identify shared terminology, open research questions, and promising directions for collaboration across communities that do not often meet in one room.

Lifelong learning looks different for an industry developer adopting a new framework than for a researcher studying how that adoption happens. It varies further across career stages, roles, and institutional settings. By bringing this conversation to Dagstuhl, we seek to connect these perspectives around an important but still fragmented topic. Participants can expect to help shape a shared research agenda, define open questions, and lay the groundwork for collaborations that carry the conversation beyond the seminar week.

Creative Commons BY 4.0

Sebastian Baltes, Fabian Beck, Paige Rodeghero, and Bonita Sharif