K-Qbot: Language Learning Chatbot Based on Reinforcement Learning

Abstract

The application of Reinforcement Learning (RL) as an emergent field of Machine Learning has shown positive results in interdisciplinary fields. Although research has proven its effectiveness in language education through various agents (e.g., chatbots, robots, talking avatars), its application in letter acquisition is relatively new. In light of the alphabet transition from Cyrillic to Latin for the Kazakh language, potential challenges might be associated with learning and memorizing the new alphabet. Specifically, students with extant alphabet knowledge might struggle in using the later-learnt new alphabet given no sufficient practice. In this paper, we present a chatbot based on Reinforcement Learning that is anticipated to assist university students in learning the Kazakh Latin alphabet during an interaction in a letter acquisition scenario. Thus, we attempt to identify whether the RL chatbot is efficient for this learning scenario through an online survey study involving pre-test, chatbot interaction, and post-test.