Varied Human-Like Gestures for Social Robots: Investigating the Effects on Children’s Engagement and Language Learning

To investigate whether a humanoid robot’s use of gestures improves children’s learning of second language vocabulary, and if variation in gestures strengthens this effect, we conducted a field study where a total of 94 children (aged 4–6 years old) played a language learning game with a NAO robot. The robot either used no gestures at all, repeated the same gesture every time a target word was presented, or produced a different gesture for each occurrence of a target word. We found that, contrary to what the majority of existing research suggests, the robot’s use of gestures did not result in increased learning outcomes, compared to a robot that did not use gestures. However, engagement between child and robot was higher in both the repeated and varied gesture conditions, compared to the condition without gestures. An exploratory analysis showed that age played a role: the older children in the study learned more than the younger children when the robot used gestures. It is therefore important to carefully consider the design and application of robot gestures to support the learning process. The contribution of this work is twofold: it is a conceptual reproduction of a previous study, and we have taken first steps towards exploring the role of variation in gestures. The study was preregistered, and all materials are made publicly available. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; •Applied computing → Interactive learning environments. Acm Reference Format: Jan de Wit, Arold Brandse, Emiel Krahmer, and Paul Vogt. 2020. Varied Human-Like Gestures for Social Robots: Investigating the Effects on Children’s Engagement and Language Learning. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374815