Abstract
This works addresses the lately initiated Cyrillic-to-Latin alphabet shift in Kazakhstan that may bring challenges for early literacy development and acquisition; both public and scientific communities agree on possible resistance to acquiring and using a new alphabet. To support the acquisition of the new Kazakh Latin alphabet and its handwriting, this study proposes a reinforcement-learning (RL) system named QWriter. It comprises a humanoid robot NAO, a tablet with a stylus, and an RL agent that learns from a child's mistakes and progresses to maximize alphabet learning in the shortest amount of time by altering the order of practice words in response to a child's mistakes. We conducted a five-sessions experiment using a between-subject design with 69 Kazakh children ages 7 to 10 and compared their learning performance with a human tutor to assess the effectiveness of the QWriter system. Overall results show no significant differences in learning gains between the two conditions. Our study foregrounds the promising potential of the RL-based social robot in teaching foundational letter acquisition and writing over time.