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How generative language models can enhance interactive learning with social robots
Type
conference paper
Date Issued
2022-11
Author(s)
Abstract (De)
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.
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Publisher place
19th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2022)
Event Title
19th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2022)
Subject(s)
Division(s)
Eprints ID
268223