Leveraging Oral Assessments to Enhance Learning and Integrity in Teaching Data Analytics

Abstract

The rapid advancement of generative AI tools, like ChatGPT, has significantly impacted academic integrity in higher education. This paper explores the integration of oral assessments with traditional project evaluation in data analytics courses to address these challenges. While oral assessments cannot completely prevent cheating, they enable examiners to probe students' understanding more deeply. We present an overview of our assessment design and processes rather than detailed student results. We compare the implementation of oral assessments in a fully online professional course and a face-to-face undergraduate course. Moreover, we compare results before and after oral assessment training and explore AI’s role in efficiently generating individualised questions. Our findings demonstrate that oral assessments reduce academic dishonesty, enhance comprehension, and increase assessment rigour.