Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret source code and natural language instructions. These emergent capabilities have sparked urgent questions in the computing education community around how educators should adapt their pedagogy to address the challenges and to leverage the opportunities presented by this new technology. In this working group report, we undertake a comprehensive exploration of LLMs in the context of computing education and make five significant contributions. First, we provide a detailed review of the literature on LLMs in computing education and synthesise findings from 71 primary articles. Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts. Third, to understand how pedagogy is already changing, we offer insights collected from in-depth interviews with 22 computing educators from five continents who have already adapted their curricula and assessments. Fourth, we use the ACM Code of Ethics to frame a discussion of ethical issues raised by the use of large language models in computing education, and we provide concrete advice for policy makers, educators, and students. Finally, we benchmark the performance of LLMs on various computing education datasets, and highlight the extent to which the capabilities of current models are rapidly improving. Our aim is that this report will serve as a focal point for both researchers and practitioners who are exploring, adapting, using, and evaluating LLMs and LLM-based tools in computing classrooms.

翻译：近年来人工智能（AI）的突破性进展正从根本上重塑计算领域，大型语言模型（LLM）现已能够高效生成和解读源代码及自然语言指令。这些新兴能力在计算教育界引发了紧迫问题：教育工作者应如何调整教学法以应对挑战、把握这项新技术带来的机遇？本工作组报告对LLM在计算教育中的运用展开了全面探索，并作出五项重要贡献。首先，我们系统梳理了计算教育中LLM相关文献，综合分析了71篇核心论文的研究发现。其次，我们报告了来自20个国家计算专业学生及教师的调查结果，揭示了当前对LLM及其在计算教育中应用的主流态度。第三，为理解教学法已发生的变革，我们提供了来自五大洲22位已调整课程与评估体系的计算机教育者的深度访谈见解。第四，我们运用ACM道德准则框架，探讨了在计算教育中使用LLM引发的伦理议题，并为政策制定者、教育工作者和学生提供具体建议。最后，我们基于多个计算教育数据集对LLM性能进行基准测试，揭示了当前模型能力快速提升的程度。本报告旨在为在计算课堂中探索、适配、使用及评估LLM及LLM工具的研究者与实践者提供核心参考。