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Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners

Authors
James Prather,
Brent N. Reeves,
Paul Denny,
Juho Leinonen,
Stephen MacNeil,
Andrew Luxton-Reilly,
Jo‹o Orvalho,
Amin Alipour,
Ali Alfageeh,
Thezyrie Amarouche,
Bailey Kimmel,
Jared Wright,
Musa Blake,
Gweneth Barbre
Date
Publisher
arXiv
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.
Study at a glance
  • Question: Can non-native English speaking students use AI in their native languages to generate code for programming problems?
  • Setting and data: Non-native English speaking students who speak Arabic, Chinese, and Portuguese.
  • Method: Students prompted generative AI in their native languages to generate code to solve programming problems.
Why it matters

Non-native English speakers face barriers learning programming because syntax and instruction are often in English, even when they know more than they can articulate. While generative AI supports multiple languages, prompting in students' native languages remains unexplored.

Key findings
  • Non-native English speaking students successfully used their native languages, including Arabic, Chinese, and Portuguese, to prompt generative AI and solve programming problems.
  • Students faced difficulty specifying programming terminology and concepts when prompting in their native languages.
  • Many non-native English learners know more about programming than they can articulate in English and face barriers asking for help in English-dominant classrooms.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

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