Can AI Voice-Modding Enhance Clarity and Boost Performance in Introductory Statistics?

Music City Center 

This study aims to bridge the gap between research and practice by examining the impact of voice-modded lectures on student performance within the Introductory Statistics service unit, enrolling over a thousand students each session from various predominantly non-statistical disciplines. Previous research suggests that heavily accented speech can hinder learning outcomes, particularly for non-English speaking students. This research explores whether AI-based voice-modding technology can enhance lecture clarity, engagement, and academic performance. By comparing student perceptions and performance between original and voice-modded lectures, the study aims to provide practical insights into the technology's potential to improve accessibility and equity in higher education.

AI in Education

Large enrolment service unit Introductory Statistics

Student engagement

Voice-Modded Technology

Inclusive Learning Practices

Equity in Higher Education 

International Statistical Institute