Teacher in the Loop: Customizing Educational Games Using Natural Language

Despite significant advances in educational technology and design methodologies, current educational games demonstrate a fundamental limitation: educators are unable to modify content after the games are deployed, limiting curriculum alignment and pedagogical customization. This paper introduces Imikathen-VR, a solution built upon a text-to-animation system, supporting K-1 and K-2 teachers to create minigames for their students to practice basic writing skills. Our implementation extends an existing animation pipeline by integrating a fine-tuned T5 model for sentence simplification, achieving 95% F1 BERT score and 76% ROUGE-L score in maintaining semantic and lexical fidelity. We improve visual reasoning by transforming the task of identifying missing visual details into a Masked Language Modeling problem. Preliminary results demonstrate the system's effectiveness in generating curriculum-aligned VR exercises, though comprehensive classroom testing remains pending. This work advances the integration of customizable VR technology in early education, providing teachers with enhanced control over educational content.