Work-in-Progress: Facilitating Automated Feedback of Online Video Conferencing Through Generative Artificial Intelligence

To enhance teaching practices and lecture strategies, online lecturers need reliable and opportunity feedback. Post-observation feedback provides opportunities for reflection, dialogue, development, and helping lecturers to improve their teaching practices. However, this poses a significant challenge in online higher education due to traditional observation procedures requiring previously trained observers, rendering them expensive and time-consuming. In addition, the observation process has human biases such as those introduced in annotated data by trained observers. Automated solutions powered by Artificial Intelligence offer a viable alternative providing consistency and affordability, delivering actionable feedback to lecturers, thereby helping to improve their pedagogical techniques and supporting student success. Hence, analyzing the video conferencing of the lecturers in online courses would help provide feedback to instructors and insights about teaching practices to institutional leadership. Therefore, this Work in Progress describes a proof of concept on a speech processing pipeline for providing post-observation feedback using Generative Artificial Intelligence to support the reflection process about online higher education teachers' practices.