Designing by Degree: Levels of Automation and Team Design Patterns as a Shared Language

Rapid advances in Information Technology (IT) and Artificial Intelligence (AI) have resulted in increasingly complex socio-technical systems, placing new demands on human–automation collaboration. These demands are particularly acute in VUCA (Volatile, Uncertain, Complex, Ambiguous) environments, where failures can have immediate and severe consequences. Effective human-automation teams must therefore adapt task and control allocation dynamically, while maintaining safety, accountability, and operator understanding. Adaptive automation (AA) has been widely studied as a means to support such flexibility, often described using Levels of Automation (LoA) frameworks. However, many existing LoA frameworks do not align well with decision-cycle models commonly used to reason about responsibility distribution in VUCA contexts. This misalignment complicates the design and reuse of AA-solutions for real-world applications. Team Design Patterns (TDPs) offer a promising approach by capturing reusable solutions to recurring challenges in human–automation teamwork. Yet, the systematic construction of coherent TDP sets remains difficult due to a lack of structured design support. To address this, we present an integrated human-automation teaming framework that facilitates TDP development and supports cross-disciplinary dialogue between designers, engineers, command staff, and policy-makers. The framework extends NASA’s eight-level LoA framework by assigning descriptive level names, explicit human-loop status (in/on/out-of-the-loop), and visual responsibility mapping inspired by the European Defence Agency’s methods-of-control framework. Based on a naval Uncrewed Surface Vessel use case, we developed and evaluated three TDPs with domain experts: uniform task delegation, uniform goal delegation, and a mixed-level pattern combining different LoA across decision-cycle stages. Expert evaluation confirmed that mixed-level TDPs best reflect operational, regulatory, and technological realities. Overall, the framework provides a structured basis for designing flexible and context-appropriate adaptive automation in VUCA environments. © 2026 The Authors.