This research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any training platform. Influenced by research surrounding Social Cognitive Theory and Cognitive Load Theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. The resulting data supports the application of using an EPA embedded in GIFT’s TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment. This inference is based on evidence showing reliable differences across conditions on the metrics of performance and self-reported mental demand and feedback usefulness items. This research provides source modality tradeoffs linked to tactics for relaying training relevant explicit information to a user based on real-time performance in a game.