In traditional scenario-based serious games, players can choose a skill level and then proceed to game play whereby they are exposed to challenging, ambiguous situations that mimic real-world reality through intelligent agents, 3-D environments, and other computer-based stimuli. During game play, users are presented learning activities which involve complex decision-making and problem solving in order to advance to future time horizons and complete the scenario. However, some scenario-based serious games on the market today don't adapt to the player's skill level during game play. Moreover, most serious games don't offer opportunities for players to learn about their strengths and weaknesses, receive real-time in-game assessment feedback on their performance, and share diverse solutions and strategies during and after game play in order to update, adapt, and advance their understanding of the knowledge being presented to them.

In this paper, we will overview several types of adaptive learning environments and present a conceptual model for integrating an intelligent adaptive learning architecture to an existing government-owned, web-based simulation gaming platform. Additionally, we will discuss how the use of dynamic pathing and sequencing in simulation design can allow for players to assess their initial knowledge and skill level prior to and after game play through pre and post-testing. Also, in this paper we will review a conceptual design of an After-Action Review that can show traceability of user performance in an adaptive simulation system and provide remedial training recommendations to the player.