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2011_12_I/ITSEC - Understanding the Impact of Intelligent Tutoring Agents on Real-Time Training Simulations

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05/02/2013

Abstract: Over the past two decades, the use of agent-based technology within simulated training environments has
increased. Intelligent Tutoring Systems (ITS) technology may include reactive or proactive simulation agents that
monitor and support computer-based training without human tutors. Reactive agents are able to provide hints and
feedback on trainee performance within static scenarios. Based on the trainee’s competency and their progress
toward training objectives, proactive ITS use computational methods in real-time to decide when to change content,
complexity and/or instructional methods within a training scenario (Niehaus & Riedl, 2009). This paper evaluates
the advantages and disadvantages of reactive and proactive agents in computer-based tutoring systems; and discusses
design considerations for the use of reactive and proactive agents in training simulations.

Historically, intelligent tutoring agents have been simple, passive observers within simulation environments. These
reactive agents monitor the trainee’s progress and provide hints or other feedback only when there is sufficient
variance from expected norms. Reactive agent actions are often based on simple heuristics or scripted behaviors.
This can be desirable if the goal of the training is repeatability. However, reactive agents often know little about the
trainee and the training context beyond performance data.

Proactive agents have a higher computational cost in that they need to sense and understand more about the trainee,
environment and training context, but are better able to predict trainee needs and adapt both feedback and scenario
content. Complex military scenarios (e.g. ill-defined domains like bilateral negotiations) provide the opportunity to
use more proactive agent techniques in assessing individual and team performance, and in adapting training
scenarios to maintain challenge and flow.

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