RM-CILS: A Social Robot-Assisted System for Multilingual Teaching and Cross-Cultural Communication Using Adaptive NLP and Cultural Sensitivity Modeling

Abstract

With education and communication becoming increasingly global, the need for systems that support multilingual and cross-cultural interaction is more critical than ever. Traditional robot-assisted learning platforms often fail to accommodate multiple languages, cultural diversity, or emotional and ethical dimensions of communication, limiting their effectiveness in international contexts. To address these gaps, this paper proposes the Robotic Multilingual and Cross-cultural Interactive Learning System (RM-CILS). This adaptable framework integrates social robots with natural language processing and culturally sensitive behavioral modes. RM-CILS is designed with modular capabilities for language identification and recognition, culturally aware interaction, and real-time multimodal feedback, ensuring active learner engagement and meaningful intercultural communication. The system allows students to personalize language preferences and cultural norms, thereby creating a more inclusive and relatable environment. Evaluation results demonstrate significant improvements over conventional robot-assisted systems, language coverage, a cultural adaptability index, personalization, student engagement, and learning outcome impact. By addressing learners’ mental, emotional, and ethical needs, RM-CILS establishes itself as a highly effective solution for international classrooms. It not only enhances language learning and intercultural competence but also fosters motivation, social rapport, and collaboration, making education more engaging, inclusive, and globally relevant.