Generative artificial intelligence poses new challenges around assessment, increasingly driving introductory programming educators to employ invigilated exams. But exams do not afford more authentic programming experiences that involve planning, implementing, and debugging programs with computer interaction. In this experience report, we describe code interviews: a more authentic assessment method for take-home programming assignments. Through action research, we experimented with varying the number and type of questions as well as whether interviews were conducted individually or with groups of students. To scale the program, we converted most of our weekly teaching assistant (TA) sections to conduct code interviews on 5 major weekly take-home programming assignments. By triangulating data from 5 sources, we identified 4 themes. Code interviews (1) pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights; (2) enabled peer learning, reducing stress in some ways but increasing stress in other ways; (3) scaled with TA-led sections, replacing familiar practice with an unfamiliar assessment; (4) focused on student contributions, limiting opportunities for TAs to give guidance and feedback. We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.

翻译：生成式人工智能给评估带来了新的挑战，日益促使入门编程教育者采用监考考试。但考试无法提供涉及规划、实现以及与计算机交互进行程序调试的更真实的编程体验。在本经验报告中，我们描述了代码面试：一种针对带回家编程作业的更真实评估方法。通过行动研究，我们尝试了改变问题的数量和类型，以及面试是单独进行还是与学生小组进行。为了扩大项目规模，我们将大部分每周助教（TA）辅导课转换为对5个主要每周带回家编程作业进行代码面试。通过三角验证来自5个来源的数据，我们确定了4个主题。代码面试（1）促使学生讨论他们的工作，激发了更细致但有时重复的见解；（2）促进了同伴学习，在某些方面减轻了压力，但在其他方面增加了压力；（3）通过助教主导的辅导课实现了规模化，用不熟悉的评估取代了熟悉的练习；（4）侧重于学生的贡献，限制了助教提供指导和反馈的机会。最后，我们讨论了关于代码面试设计的不同决策，这些决策对学生体验、学术诚信和教学工作量具有影响。