Traditional assessment methods collapse when students use generative AI to complete work without genuine engagement, creating an illusion of competence where they believe they're learning but aren't. This paper presents the conversational exam -- a scalable oral examination format that restores assessment validity by having students code live while explaining their reasoning. Drawing on human-computer interaction principles, we examined 58 students in small groups across just two days, demonstrating that oral exams can scale to typical class sizes. The format combines authentic practice (students work with documentation and supervised AI access) with inherent validity (real-time performance cannot be faked). We provide detailed implementation guidance to help instructors adapt this approach, offering a practical path forward when many educators feel paralyzed between banning AI entirely or accepting that valid assessment is impossible.

翻译：当学生利用生成式人工智能完成作业却未真正投入时，传统评估方法便会失效，这制造了一种能力假象——学生自以为在学习实则不然。本文提出对话式考试，这是一种可扩展的口试形式，通过让学生在实时编码过程中解释其推理思路，从而恢复评估的有效性。基于人机交互原理，我们在短短两天内对58名学生进行了小组测试，证明口试能够扩展至常规班级规模。该形式融合了真实性实践（学生可查阅文档并在受监督下使用AI）与内在有效性（实时表现无法造假）。我们提供了详细的实施指南，以帮助教师采用此方法，为许多教育工作者在“完全禁用AI”与“认定有效评估已无可能”的两难困境中，提供了一条切实可行的前进路径。