Exploring the Suitability of Conversational AI for Child-Robot Interaction

Current approaches in education, while aiming to be universally effective, often struggle to fully adapt to the unique needs and communication styles of individual children; this disparity can limit the children’s engagement and hinder their learning progress. Similarly, parents or guardians, despite their good intentions, may also be unable to provide consistent and personalized support to each child. In this work, we investigate the use of conversational systems for socially assistive robots (SARs) as a potential solution to this problem, as such systems have the potential to allow children to interact and learn at their own pace, in a way that aligns with their communication preferences. To ensure that the robot’s language is suitable for children, we present a system that leverages a combination of natural language processing (NLP) techniques, including dialog management, child-friendly language generation, and context-aware response adaptation; to achieve this, our system combines Rasa for dialog management, GPT-3.5 for language generation, and textstat for language complexity evaluation. We evaluate the suitability of the generated language for a young audience through two user studies with adult participants, one in which the conversational system was embodied in a robot and involved direct interaction between a human and a robot, and another where participants evaluated conversational transcripts from the first study. Our results suggest that the system has the potential to maintain engaging and safe conversations, and adapt its language to individual needs.