Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
This addresses a specific educational challenge for non-native English learners in computing, offering a novel approach with potential global impact, though it is incremental in applying existing AI methods to a new context.
The study tackled the problem of non-native English speakers facing barriers in learning programming by exploring multilingual prompting with generative AI, finding that students could successfully use their native languages to solve programming problems but encountered difficulties with programming terminology.
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.