  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.
