Towards an interoperable adaptive tutoring agent for simulations and serious games
Computer simulations and digital game based learning systems have to adapt to the experience and knowledge level of the users to exploit their intrinsic motivation for interaction and learning. To facilitate the deployment of intelligent tutoring tools they must follow interoperability standards. The solution approach described in this paper is a distributed intelligent tutoring agent framework. It communicates with the game engines via the High Level Architecture (HLA) with payloads based on the eXperience API (xAPI) and the IEEE Learning Object Model (LOM). This framework adapts the connected simulations to the experience level of the users to keep them in the flow channel. Its application is image interpretation for reconnaissance. This paper describes the overall system architecture and the general adaptation design principles.