Neural Machine Translation for Cross-Language Robotics Programming Education
Аннотация
Robotics programming education is expanding globally, where students and instructors often engage with learning resources, tutorials, and programming exercises written in multiple languages. While this opens access to diverse knowledge sources, the lack of standardized cross-language instructional material hinders effective learning and collaboration in programming environments. Language barriers create inconsistencies in understanding algorithms, control logic, and domain-specific robotics terminology, particularly when nonnative learners work with technical code documentation or tutorials available only in English. To address this limitation, the paper propose a Neural Machine Translation for Robotics Education Framework (NMTRF), which leverages transformer-based neural machine translation models fine-tuned on domain-specific robotics programming corpora. The framework is designed to handle technical terminology, code-comment translations, and dual-mode educational materials, ensuring semantic accuracy and pedagogical clarity across languages. NMTRF integrates context-aware translation modules with preannotated robotics datasets, thereby delivering accurate translations for both textual instructions and embedded code explanations. Experiments conducted on multilingual robotics courseware and online repositories demonstrated that NMTRF achieved a BLEU score improvement of 14% over baseline NMT systems while reducing terminology mistranslations by 21%. Furthermore, student comprehension assessments indicated significant learning improvements for non-native participants exposed to translated material versus traditional bilingual reference sheets. These results confirm that AI-driven translation adapted to robotics contexts can eliminate cross-language barriers, enhance inclusivity, and support international collaboration in robotics programming education. The NMTRF framework is expected to serve as a foundation for building multilingual learning platforms and global robotics education ecosystems.