Multimodal Feedback in Educational Robots
How voice gender and body movements influence affective outcomes, attention, and cerebral activity in robot-assisted learning.
Chen Fang, Fu Guo, Xuegong Bao, and Xianfan Qu
Despite growing interest in multimodal communication in HRI, the design of multimodal feedback for educational robots remains underexplored. This study used a 2 x 3 within-subject design to test how voice gender and body movements affect users’ learning outcomes.
We evaluated the system from three complementary perspectives: affective outcomes, visual attention, and cerebral activity. The results showed significant effects of voice gender and body movements on affective outcomes and cerebral activation, as well as an interaction effect on cerebral activity.
The findings suggest that female-voice feedback paired with combined body movements can improve learning outcomes in low-demand feedback tasks. This work was accepted by Ergonomics.
