The efficacy of fully AI-generated lectures

Abstract

This thesis explores the efficacy and usability of fully AI-generated lectures. With the
recent advancements in Generative Artificial Intelligence (GenAI) technologies, particularly Large Language Models (LLMs) like ChatGPT, there has been a large shift in how
information can be easily accessed, generated, and utilized. This research focuses on
leveraging these advancements to create a tool that automatically generates complete
lectures, encompassing the entire process from structure outlining and scriptwriting to
slide creation and delivery via a digital avatar.
The motivation behind this study comes from the challenges faced in the educational
sector, including the time-consuming nature of lecture preparation and the potentially
static nature of reused lectures. By integrating LLMs and other GenAI technologies
such as image, video, and speech synthesis, the proposed solution aims to provide a
dynamic and adaptable teaching tool that may speed up the lecture creation process
and keeps content up-to-date.
To evaluate the usability and effectiveness of the tool, a user study was conducted
with 12 experienced educators from various fields and educational levels. The study
revealed that the prototype achieved a mean System Usability Scale (SUS) score of
80,42, indicating a good level of usability. It was found that the tool increased workflow
efficiency, with most participants agreeing that it made lecture creation faster and more
streamlined. Most participants said they would integrate this tool into their workflow,
but only a few believed it would improve the quality of their lectures.
Overall, this research demonstrates the practical applications of GenAI technologies
in an educational context. While the prototype shows promise in increasing educators’
productivity and streamlining the lecture creation process, it also highlights the need
for expert oversight to make sure the content is accurate and qualitative. Future work
may focus on addressing the identified limitations and further refining the tool to better
meet the needs of educators.

Date of Award	2024
Original language	English (American)
Supervisor	Andreas Stöckl (Supervisor)