LLM-based conversational agents for dyslexia treatment: From chatbots to structured tutors
TL;DR
Foxy, a proactive LLM-driven Conversational Agent designed to assist Italian children aged 8–11 with dyslexia during morphological training, acts as a specialized tutor that provides scaffolding, limits topic drift, and reduces hallucinations via a verified lexical knowledge base.
Abstract
Developmental dyslexia involves persistent difficulties in word-level reading and decoding, requiring sustained linguistic practice that is difficult to maintain without supervision. Although Generative AI offers personalized support, standard Large Language Models (LLMs) often lack the pedagogical and therapeutic knowledge required for linguistic intervention. We present Foxy, a proactive LLM-driven Conversational Agent designed to assist Italian children aged 8–11 with dyslexia during morphological training. Through a modular prompt-orchestration framework and an event-driven architecture, Foxy acts as a specialized tutor that provides scaffolding, limits topic drift, and reduces hallucinations via a verified lexical knowledge base. The system was refined through expert-led co-design and evaluated in a pilot study with 18 educators, and therapists. Results indicate positive perceptions of usability, usefulness, and appropriateness, with recognition of Foxy’s motivational benefits. These findings suggest Foxy is a promising tool for dyslexia intervention, pending wider empirical validation.