Towards a Usage-Based Pedagogy for Second Language Learning with Robots

Abstract

Recent advances in generative AI and social robotics have opened new possibilities for robot-assisted language learning, yet integrating these technologies in pedagogically sound ways remains challenging. This paper matches theories of language learning to the design of autonomous robot tutors. Usage-based language learning, learning in context, Self-Determination Theory and Dual Coding Theory lend themselves to being operationalised for Robot-Assisted Language Learning. We present a proof-of-concept shared story-building system, in which a learner co-creates a story with a robot tutor. The system leverages large language models for dynamic content generation, automatic speech recognition for learner input, and image generation to provide multimodal scaffolding. By embedding vocabulary, adapting to learner input, and avoiding explicit corrections, the system aligns with usage-based and interactionist theories of language acquisition. We discuss the technological enablers and barriers - such as large language model adaptability and automatic speech recognition limitations - and propose directions for future work. This work contributes to the growing field of AI-powered social robots in education, demonstrating how theory-driven design can enhance engagement and learning outcomes.