Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction are often in English, or more subtle, such as being afraid to ask for help in a classroom full of native English speakers. However, these barriers are frustrating because many NNES students know more about programming than they can articulate in English. Advances in generative AI (GenAI) have the potential to break down these barriers because state of the art models can support interactions in multiple languages. Moreover, recent work has shown that GenAI can be highly accurate at code generation and explanation. In this paper, we provide the first exploration of NNES students prompting in their native languages (Arabic, Chinese, and Portuguese) to generate code to solve programming problems. Our results show that students are able to successfully use their native language to solve programming problems, but not without some difficulty specifying programming terminology and concepts. We discuss the challenges they faced, the implications for practice in the short term, and how this might transform computing education globally in the long term.

翻译：非英语母语学习者在学习编程时面临多重障碍。这些障碍可能显而易见，例如编程语言的语法和指令通常以英语呈现；也可能更为隐晦，例如在全英语母语者的课堂中不敢提问求助。然而，这些障碍尤其令人沮丧，因为许多非英语母语学生对编程知识的实际掌握程度往往超过其英语表达能力。生成式人工智能的进展有望打破这些壁垒，因为最先进的模型已能支持多语言交互。此外，近期研究表明，生成式人工智能在代码生成与解释方面具有极高的准确性。本文首次探索了非英语母语学生使用母语（阿拉伯语、汉语和葡萄牙语）通过提示生成代码以解决编程问题的实践。研究结果表明，学生能够成功运用母语解决编程问题，但在准确表述编程术语与概念时仍存在一定困难。我们深入探讨了他们面临的挑战、短期内对教学实践的启示，以及长期而言这一模式可能对全球计算机教育产生的变革性影响。