Recognition skills refer to the ability of a practitioner to rapidly size up a situation and know what actions to take. We describe approaches to training recognition skills through the lens of naturalistic decision-making. Specifically, we link the design of training to key theories and constructs, including the recognition-primed decision model, which describes expert decision-making; the data-frame model of sensemaking, which describes how people make sense of a situation and act; and macrocognition, which encompasses complex cognitive activities such as problem solving, coordination, and anticipation. This chapter also describes the components of recognition skills to be trained and defines scenario-based training.

This chapter revisits each of the design principles, summarizing and drawing connections between them. Many of the principles are based on empirical evidence from traditional learning environments; a discussion on the boundary conditions of the design principles explores the extrapolations of this evidence to training recognition skills in dynamic, high stakes environments. The chapter closes with a discussion of the contributions and challenges of augmented reality to training.

The Learn, Experience, Reflect framework is discussed as an overarching guide to training design. The Learn component focuses on the declarative information that learners need to fully learn from the Experience and Reflect portions of training. This often includes didactic presentation of information. The Experience component is generally scenario based and should be used to support learners in applying new knowledge and abstract concepts to realistic situations. The Reflect component employs strategies to encourage learners to reflect on what they have learned and how to apply their new knowledge in the future. Examples and links to theoretical models are provided for each component, along with discussions of how best to employ the capabilities of augmented reality to designing training elements for each component.