Second language acquisition (SLA) stands as a pivotal domain in fostering cross-cultural communication and expanding social networks. In this paper, we propose an innovative study on SLA that leverages sound sensors and transducers. The interactions between speaking and listening play a crucial role in the process of SLA. However, the integration of real-time interaction practices within SLA classrooms poses challenges because of constraints on educator availability and instruction efficacy. To address this issue, in this study, we evaluate the accessibility and effectiveness of a large language model (LLM)-based chatbot as a supplementary tool in facilitating one-on-one real-time interaction practices online. The research focuses on learners engaged in the acquisition of Chinese as a second or foreign language. Our findings underscore the need for enhancements in the design and functionality of the LLM-based chatbot specifically tailored to the objectives of SLA. Additionally, insights garnered from the analysis contribute to discussions surrounding the integration of technology, such as Copilot, as adjunctive aids in language education, particularly within the framework of SLA. In our future research endeavors, we will delve into a comprehensive analysis to identify factors influencing the availability and effectiveness of the chatbot. In this research, we advance technological solutions in language education to enhance interaction and improve SLA outcomes.