Artificial intelligence and additive manufacturing as a coupled design system: Rethinking inference, manufacturability, and design education

Artificial intelligence (AI) is becoming deeply integrated into additive manufacturing (AM) workflows, reshaping how designers approach geometry, materials, and process constraints. AI holds significant potential by accelerating design exploration, revealing complex patterns in AM behavior, and supporting earlier assessment of manufacturability. At the same time, it introduces new risks related to model transparency, data quality, physical validity, and the potential for overreliance by students and practitioners. This perspective examines these issues through four guiding questions that address the role of AI in AM-enabled design, the gaps that limit or enable AI contribution, the implications for engineering education, and the responsibilities of the research community in ensuring trustworthy and secure AI–AM integration. The main contributions of this perspective include: (i) Highlighting AI and AM as a coupled inference–fabrication system rather than independent tools; (ii) identifying zones of strong interdependence where inference and manufacturability interact; and (iii) articulating implications for design reasoning, education, and responsible research practice.