An Oral Exam Module

Many educators are seeking to integrate AI-powered tools tailored for classroom instruction and learning. In this project, we embark on an exploration of AI-enhanced tools designed for classroom. The Oral Exam module leverages AI to foster enriched classroom learning through high quality learning assessments and feedback using generative AI platforms.

Motivated with self-directed learning and high quality learning assessments, Dr. Yaa and other PIs showed interests in oral exam modules, where students are assessed through verbal means. The conversation evolved into practicing presentations and even Ph.d dissertation defense. To address this, we introduced two distinct modes: the Presentation Mode and the Oral Exam Mode. While each mode has its unique features, the underlying AI components are consistent across both.

In this project, I was tasked with developing a comprehensive and interactive oral exam module. The process begins with providing essential instructions, such as grading rubrics and foundational knowledge. Following this, the Gen AI formulates questions based on the instructor’s input. Students can then choose to respond either verbally or in writing. Their answers are subsequently transcribed using the Whipser API. In the final step, the Gen AI assesses the student's performance and provides detailed feedback. Additionally, in presentation mode, a presentation is delivered and transcribed after the initial instruction but before the question formulation. The Gen AI then generates follow-up questions based on this presentation, helping students better prepare for their presentations.