Refined metric interpretation in natural language for educational videogames using fuzzy logic

With digital gaming's increasing popularity, Educational Digital Games (EDG) are being more commonly used to complement children's early education. Controlled EDG provides educators a way to observe progress. Many existing applications fail to generate automatic data collection to provide reliable information for feedback on academic aspects needed. This paper describes the usefulness of MIDI-AM, a series of EDG, to link a dashboard, including informative outcomes about use and Playability. It explains how rules of fuzzy logic and Natural Language (NL) can provide consumable feedback. The research objective provides a new component in a longitudinal study to identify a more efficient process for developing and implementing a module to refine the dashboard metrics and outcomes of MIDI-AM EDG. The initial platform was redundant and created inconsistent results. Using Artificial Intelligence (AI) generates valuable information to refine the process of generating feedback reports using detailed data interpretations in NL.