The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning. Very recent advances in artificial intelligence have resulted in code generation models that can produce source code from natural language problem descriptions -- with impressive accuracy in many cases. The wide availability of these models and their ease of use has raised concerns about potential impacts on many aspects of society, including the future of computing education. In this paper, we discuss the challenges and opportunities such models present to computing educators, with a focus on introductory programming classrooms. We summarize the results of two recent articles, the first evaluating the performance of code generation models on typical introductory-level programming problems, and the second exploring the quality and novelty of learning resources generated by these models. We consider likely impacts of such models upon pedagogical practice in the context of the most recent advances at the time of writing.

翻译：计算教育领域有着悠久的教学创新历史，旨在支持学生入门课程学习，并帮助教师促进学生的学习。人工智能的最新进展催生了代码生成模型，这些模型能够根据自然语言问题描述生成源代码——且在多数情况下具有令人瞩目的准确性。这些模型的广泛可及性和易用性引发了对其可能对社会诸多领域（包括计算教育的未来）产生影响的担忧。本文聚焦于编程入门课堂，探讨了此类模型给计算教育工作者带来的挑战与机遇。我们总结了两篇近期文章的研究成果：第一篇评估了代码生成模型在典型入门级编程问题上的表现，第二篇探讨了这些模型生成学习资源的质量与新颖性。结合撰写之际的最新进展，我们进一步思考了此类模型对教学实践可能产生的影响。