AI-Driven Socially Assistive Robots with Turkish Language Support for Smart Environments

Abstract

This study presents the development of a Turkish Natural Language Processing (NLP) module for Socially Assistive Robots (SARs), aiming to enhance their integration into public spaces and smart environments. The rising labor costs and limitations of human staffing in developed regions, including Turkey, underscore the need for localized language capabilities in SARs. Leveraging advanced machine learning techniques, the system fine-tunes a BERT-based model specifically for Turkish language understanding and response generation. Trained on a dataset of 61,293 question–answer pairs, the system achieved 95% voice recognition accuracy, 82% response accuracy, and a 92% speech comprehensibility rating among native Turkish speakers. It also reached a 72% F1-score and a 72% Exact Match (EM) score, confirming its ability to generate contextually appropriate responses. These results highlight the system’s effectiveness in enabling natural and intuitive interactions. The developed module demonstrates significant potential for deployment in public domains such as universities, hospitals, airports, and shopping centers, where it can assist users in navigating environments and accessing essential services. Furthermore, the research showcases educational benefits through the Temi OS platform, offering hands-on learning opportunities in robotics, AI, and natural language processing. Competitions based on SAR development further encourage innovation among students and researchers. In conclusion, this work illustrates the critical role of Turkish-language SARs in improving service accessibility and advancing AI education. Future efforts will focus on refining linguistic capabilities, expanding interactive features, and conducting long-term user engagement studies to maximize societal impact.