Bridging Language Gaps in Global Learning: The Role of Generative AI in Multilingual Education

Multilingual education stands to benefit immensely from recent advances in generative Artificial Intelligence (AI). This paper explores how generative AI – especially large language models (LLMs) and neural machine translation systems – can bridge language gaps in global learning. I analyze the capabilities of AI-based translation models (DeepL, Google Translate, ChatGPT, etc.) in educational contexts and evaluate their effectiveness in translating complex academic content while preserving context. I present experimental insights into LLM translation accuracy for scholarly text, case studies of AI-generated summaries and notes that aid second-language learners, and the integration of voice recognition and optical character recognition (OCR) with generative AI for real-time language support. Technical aspects of these AI systems are emphasized, and I compare models in terms of translation quality, latency, and usability. The paper concludes with pedagogical implications, discussing how educators and learners can leverage generative AI while addressing its limitations to achieve more inclusive, accessible education globally.