TalBot: Developing an LLM-Based Robot-Assisted Learning Application for Children with Language Vulnerabilities

Children with language vulnerabilities need extra support in their language development, and game-based learning is often used as a part of the interventions. Speech language therapists, educators and parents form a team around the child, working together to support language learning. However, resources such as time, guidance and competence are often scarce, leaving room for alternative solutions, like robot-assisted language learning (RALL). We introduce TalBot, a large language model (LLM)-powered robot application, aiming to lead and play the language game Alias with a small group of children with language vulnerabilities. Our application is designed to lead the game, give adaptive responses, manage turn-taking and engage the players by providing emotionally congruent verbal and non-verbal responses. By constraining the context and using an LLM, we believe that the effectiveness of automatic speech recognition (ASR) and management of turn-taking can be improved. In general, we suggest LLMs enable robots to better support children with language vulnerabilities - and seem especially suited to an area such as this where variable input is of importance. We hope other researchers will also further explore the use of LLMs in RALL, especially applications designed for children with language vulnerabilities.