Future-proofing Education: A Prototype for Simulating Oral Examinations Using Large Language Models
This addresses the problem of workload and accessibility in education for educators and students, but it is incremental as it builds on existing LLM technology for a specific application.
The study tackled automating oral examinations in higher education by developing a prototype using Large Language Models, which proved effective in simulating exams, providing personalized feedback, and reducing educators' workloads.
This study explores the impact of Large Language Models (LLMs) in higher education, focusing on an automated oral examination simulation using a prototype. The design considerations of the prototype are described, and the system is evaluated with a select group of educators and students. Technical and pedagogical observations are discussed. The prototype proved to be effective in simulating oral exams, providing personalized feedback, and streamlining educators' workloads. The promising results of the prototype show the potential for LLMs in democratizing education, inclusion of diverse student populations, and improvement of teaching quality and efficiency.