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.

Learner engagement is the foundation for effective training. This chapter describes two design principles for creating engaging augmented reality-based recognition skills training. The Immersion Principle describes ways in which training designers can create a sense of learner presence in the training through cognitive and physical engagement. The Hot Seat Principle describes a strategy to increase engagement by making the learner feel a sense of responsibility for training outcomes. This is particularly useful for team and small group training. The discussions of both principles include examples, theoretical links, and implications for people designing augmented reality 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.

Building scenarios for training recognition skills in complex domains requires the addition of hard-to-detect cues and unexpected events. This chapter describes the Periphery Principle, which emphasizes the importance of including critical cues in nonobvious ways so trainees learn how to seek them, and the Perturbation Principle, which encourages training designers to incorporate unexpected events into training scenarios so trainees learn to adapt to novel situations. The chapter presents methods for identifying peripheral cues and important perturbations for a particular domain or task, and gives examples of critical cue inventories and complexity tables that can be useful tools for training designers.

Augmented reality offers the opportunity to increase the fidelity of training. This chapter describes three principles related to fidelity that augmented reality can effectively support in ways that are difficult for other training modalities. The first, the Sensory Fidelity Principle, describes how realistic cues are needed for perceptual skill development. Training designers often need to make decisions about which cues require high levels of fidelity; domain familiarization activities can help guide these decisions. According to the Scaling Fidelity Principle, virtual props should be represented close to their real-world size. This allows trainees to practice important physical skills, such as body positioning. The Assessment-Action Pairing Principle describes how being able to seamlessly assess a situation and act yields better transfer of training to on-the-job performance than part-task training approaches that separate assessment from acting.

Mental models are the internal representations that guide interactions with the world. Mental models are experience based and they inform an individual’s understanding of what is going on, how things work, and how a situation is likely to evolve. This chapter provides two principles for supporting the development of robust mental models in trainees. The Mental Model Articulation Principle emphasizes building training experiences that encourage learners to verbalize aspects of their mental models to identify flaws and gaps. The Many Variations Principle highlights the value of providing learners with a range of experiences with the intent of expanding their mental models to support performance in diverse conditions.

Augmented reality technology enables the creation of training that more closely resembles real-world environments without the cost and complexity of organizing large- scale training exercises in high-stakes domains that require recognition skills (e.g., military operations, emergency medicine). Augmented reality can be used to project virtual objects such as patients, medical equipment, colleagues, and terrain features onto any surface, transforming any space into a simulation center. Augmented reality can also be integrated into an existing simulation center. For example, a virtual patient can be mapped onto a physical manikin so learners can practice assessments skills on the highly tailorable virtual patient, and practice interventions on the physical manikin using the tools they use in their everyday work. This chapter sets the stage by describing how the author drew from their own experiences, reviewed scientific literature, and consulted with skilled instructors to articulate eleven design principles for creating augmented reality training.

This chapter describes two principles for supporting trainees. The Scaffolding Principle describes how support should be adapted to accommodate a trainee’s current skill to keep them in a “high challenge/high support” learning mode. The Reflection Principle describes ways in which training should encourage active reflection in trainees so they can learn how to constantly reflect on their own performance and apply new insights to future situations. The chapter provides examples of intelligent tutoring systems that employ adaptive scaffolding techniques, along with other types of learning applications. The chapter also discusses strategies for encouraging learners to reflect on their training experiences.