HOW GENERATIVE LANGUAGE MODELS CAN ENHANCE INTERACTIVE LEARNING WITH SOCIAL ROBOTS

Abstract

The use of social robots in education is a growing area of research and the potential future applications are various. However, the conversational models behind current social robots and chatbot systems often rely on rule-based and retrieval-based methods. This limits the social robot to predefined responses and topics, thus hindering it from fluent communication and interaction. Generative language models such as GPT-3 could be beneficial in this context, e.g. for an improved conversation and open-ended question answering. This article presents an approach to utilizing generative language models to enhance interactive learning with educational social robots. The proposed model combines the technological possibilities of generative language models with the educational tasks of a social robot in the role of a tutor and learning partner. The implementation of the model in practice is illustrated by means of a use case consisting of different learning scenarios. The social robot generates explanations, questions, corrections, and answers based on the pre-trained GPT-3 model. By exploring the potential of generative language models for interactive learning with social robots on different levels of abstraction, the paper also aims to contribute to an understanding of the future relevance and possibilities that generative language models bring into education and educational technologies in general.