Freund, R. (2026): Open-Source AI for Open User Innovation: Designing a Personal Fabrication Framework, in Anisic, Z., Forza, C. (Eds): Proceedings of the 12th International Conference on Customization and Personalization, September 17-18, 2026, Balatonfüred, Hungary, pp. 76-85 · 17. Sep. 2026
Abstract: While the role of generative AI in producer-centric open innovation has attracted growing scholarly attention, its potential within Open User Innovation (OUI) — where individuals innovate for personal use rather than commercial sale — remains largely unexplored. This paper argues that Open-Source AI is structurally better aligned with user-driven innovation than proprietary models, because both share the principles of democratization, transparency, and contextual adaptability. Drawing on a literature-based conceptual approach, we examine the theoretical distinction between Chesbrough’s Open Innovation and von Hippel’s Open User Innovation paradigm, with particular attention to the user’s system of use — a framework that integrates product and behavioral innovations in context-specific ways. We then extend Baudisch and Müller’s (2017) six-challenge personal fabrication framework by incorporating Open User Innovation principles, Open-Source Software, Open-Source AI models, and Open-Source AI agents. A proof-of-concept illustration, using Mistral AI Vibe to support a FabLab-based fabrication task, demonstrates the framework’s practical applicability. The paper concludes by identifying three priority areas for future empirical research: validation through platform-based case studies, responsible integration of autonomous AI agents in user innovation, and cultural adaptation of the framework to regional contexts.
