PicLingo: A GenAI-Based System for Language-Disabled Children

Children with language disorders often face challenges in understanding sentences and communicating effectively with others. While previous studies have utilized static digital games and automated feedback to support vocabulary and spelling, there remains a significant gap in leveraging generative models to provide dynamic, personalized visual reinforcement for verbal tasks. This study presents a new approach to support language development through PicLingo, a GenAI-powered system developed to assist both children and their mentors. A comparative experimental methodology is used to evaluate multiple generative models using MS COCO samples and standard metrics, including Inception Score (IS), Fréchet Inception Distance (FID), and human evaluation. PicLingo’s primary feature is a text-to-image generation (TTI) task that generates illustrative images from textual descriptions. Additionally, the system includes an interactive game that uses speech recognition technology to encourage active verbal participation. This approach aims to enhance language development and overall communication skills. The experimental results demonstrate that the proposed Stable Diffusion-based architecture significantly outperforms baseline models in generating high-quality, semantically accurate images, suggesting PicLingo as a promising, interactive tool for enhancing verbal communication and tracking linguistic progress in children with language disorders.