GUEST PODCAST: EdTechnical Series 4 Episode 3
Authors
This season EdTech funder Libby Hills and AI researcher Owen Henkel continue to speak with leading researchers, practitioners and educators on the EdTechnical podcast series about the cutting edge of AI in education. They will break down complex AI concepts into non-technical insights to better understand what the research says and help educators sift the useful insights from the AI hype.
In this episode of EdTechnical, Libby and Owen speak with Panos Ipeirotis, Professor at NYU Stern School of Business, about his experiment using AI to run oral exams in university courses. As generative AI makes it easier for students to outsource written assignments, educators are asking whether traditional take-home assessments still measure real understanding.
Panos introduced AI-mediated oral assessments after noticing a mismatch between high-quality written submissions and weak classroom discussion. In the new system, students answer questions from a voice agent that probes their understanding of the material and their own work.
Panos tells Libby and Owen how the exams work, including an AI “council” of language models that evaluates transcripts and produces detailed feedback. What does this approach reveal about the future of assessment? Could AI make oral exams scalable in higher education, and even improve fairness and grading consistency?
Links
Panos Ipeirotis – NYU Stern Faculty Profile
NYU Professor Uses AI-Run Oral Exams to “Fight Fire with Fire”
Article: The case for oral assessment in the age of AI
Bio
Panos Ipeirotis is a Professor of Information, Operations and Management Sciences at NYU Stern School of Business. His research focuses on data science, AI, and human-AI collaboration. In addition to his academic work, he experiments with practical applications of AI in education, including new models of assessment that combine oral exams with AI-based evaluation.
Authors