Quick Fix: Validating Learning in the AI Era Through a Streamlined Interactive Oral Assessment Model

As generative AI becomes a routine study companion, written assessments no longer reliably show whether students have demonstrated the intended learning outcomes. Ultimately, educators need assessment formats that allow them to determine whether students have genuinely achieved the learning outcomes of their courses. This Quick Fix offers a practical model for validating learning through interactive oral assessment, a format that keeps individual thinking visible by requiring students to articulate ideas spontaneously, respond to prompts, and demonstrate understanding in real time. Drawing on a streamlined implementation in a postgraduate public health and criminal justice course, we show how interactive oral assessments can foreground real-time reasoning, professional communication, and responsiveness to prompts, capabilities that generative AI cannot easily perform on behalf of students. The model is grounded in five design principles: (1) authentic assessment design; (2) clarity of rubric design and qualitative marking approach; (3) predictable digital workflow; (4) intentional student preparation; and (5) structured facilitation. Together, these principles support a practical approach to validating student learning in the AI era.