Talk · IEEE RO-MAN · August 28, 2023
QWriter System for Robot-Assisted Alphabet Acquisition
Aida Amirova, Nurziya Oralbayeva, Zhansaule Telisheva, Aida Zhanatkyzy, Aidar Shakerimov, Shamil Sarmonov
Abstract
The present study applies a novel Reinforcement Learning-based (RL) alphabet learning system named QWriter for the acquisition of the Kazakh Latin alphabet. We conducted a between-subject design experiment with 108 Kazakh children aged 6-8 years old in a public school and compared their learning rates across the two conditions: an RL-based QWriter robot and a human tutor (HT) as a baseline. The results show that children learned significantly more letters with the HT compared to the QWriter robot, showing that the RL-based robot is not effective for learning in the short term. Yet, we observe some interesting results by children’s age and gender. The results need further investigation comparing the QWriter with other robot baselines with different roles and across various learning tasks.
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