mlsanityData to 7 Oct 2026

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

arXiv 2412.12800 ↗First posted 17 Dec 2024cs.CY cs.AI cs.HCcited by 2

Not on the map. Why?

Abstract (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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 69 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

28 of 69 references.

References · 28

  1. 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 124
  2. 2024LLaMA Beyond English: An Empirical Study on Language Capability Transfercited by 94
  3. 2020Low-resource Languages: A Review of Past Work and Future Challengescited by 90
  4. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  5. 2022Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookcited by 84
  6. 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 81
  7. 2022Using Large Language Models to Enhance Programming Error Messagescited by 67
  8. 2023Comparing Code Explanations Created by Students and Large Language Modelscited by 63
  9. 2023Computing Education in the Era of Generative AIcited by 50
  10. 2024The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmerscited by 46
  11. 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 44
  12. 2023Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Coursescited by 38
16 more
  1. 2023A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Educationcited by 34
  2. 2023Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Casescited by 32
  3. 2023Prompt Problems: A New Programming Exercise for the Generative AI Eracited by 31
  4. 2024Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Modelscited by 31
  5. 2023Exploring the Potential of Large Language Models to Generate Formative Programming Feedbackcited by 28
  6. 2024The Effects of Generative AI on Computing Students' Help-Seeking Preferencescited by 22
  7. 2024Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judgecited by 19
  8. 2024Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settingscited by 14
  9. 2024Evaluating Contextually Personalized Programming Exercises Created with Generative AIcited by 13
  10. 2023Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Modelscited by 11
  11. 2024Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skillscited by 11
  12. 2023More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problemscited by 10
  13. 2024Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Coursescited by 8
  14. 2024"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Modelscited by 5
  15. 2024Not the Silver Bullet: LLM-enhanced Programming Error Messages are Ineffective in Practicecited by 2
  16. 2024An Eye for an AI: Evaluating GPT-4o's Visual Perception Skills and Geometric Reasoning Skills Using Computer Graphics Questionscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.