Adaptive training avoids the "one size fits all" model that typically exists. Scenarios can be created that fulfill a specific trainee's needs (using a trainee profile of past performance). They can also be adapted during the training exercise as performance warrants. In addition, after action review (AAR) provides the reflection necessary to improvement, and feeds future scenarios!

Game-based learning focuses on the use of games and gamification and its uses to improve learning.  The lab explores this area both in advancing game-based learning itself and also in applying game-based learning to training contexts.  The former looks at applying game-based learning to new areas and also at laying a scientific foundation to using games for learning and training; the latter uses a game-based setting in already known training contexts (such as a replacement for the typical virtual environment used in military training).

Multi-modal simulation refers to the triggering of all of a human's senses within a virtual, augmented or mixed environment.  A typical video game include visual and auditory cues.  The IRL also looks at haptic (sense of touch) and olfactory (sense of smell) cues.  Across all the senses, the IRL investigates driving the cues in new ways as well as their effects on training. 

This can range from a fully-immersive virtual reality simulator down to desktop video games and even hands-on haptic-based trainers.

The notion of competitive learning is still quite young as a potential research area. However, it basically uses the notion of competition to drive performance. Somewhat against the "everybody gets a trophy" philosophy, competitive learning actually uses that competitive feeling to advance training.

The lab is currently exploring this topic in typical competitive environments, but hopes to take it into atypical environments, ultimately.

The Institute for Simulation and Training contains a 6,600-core High Performance Computing cluster that is used for multiple research topics across the university.  The IRL is investigating interactive uses of such large computing platforms.  As compared to the typical computational use of such clusters (where jobs get queued up and then run for hours or even days), the IRL is interested in interacting with applications driven by these clusters.  For example, a highly physics-based dynamic environment could be simulated by the nodes as a user interacts with the world.

As a part of the Institute for Simulation and Training, the IRL performs multi-disciplinary research across computer science, engineering, psychology and digital media.  While the lab pursues many of its own research ideas, it also collaborates with others and mutually supports interdisciplinary goals.  Technologies taken from work in the above areas are applied to new domains (for example, the lab has explored after action review for both cognitive rehabilitation and student-teacher training).