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.