Paper Type
Short
Paper Number
PACIS2025-1595
Description
Defensive driving education has traditionally relied on human instructors, but AI-driven systems present a scalable alternative. This study offers a novel perspective by examining how inherent biases in AI-generated feedback may have constructive applications. While prior research has focused on mitigating AI bias, this study explores how variations in AI voice acoustics, specifically harmonics-to-noise ratio and intonation variation, may influence user perceptions and engagement. By assessing the generalizability of non-human agents, this research seeks to highlight fundamental human cognitive limitations in processing AI feedback. The study aims to contribute to the understanding of how AI instructors are perceived and how their effectiveness can be enhanced in safety-critical domains such as driving education. Additionally, this research examines how stereotypes shape interactions with AI, offering insights into the broader implications of human-AI communication and the design of more effective AI-driven training systems.
Recommended Citation
Lunina, Yulia; Choi, Ben; and Pee, Loo Geok, "The Intricate Effects of AI Voice Acoustics: Occupational Stereotypes in AI Driving" (2025). PACIS 2025 Proceedings. 2.
https://aisel.aisnet.org/pacis2025/is_education/is_education/2
The Intricate Effects of AI Voice Acoustics: Occupational Stereotypes in AI Driving
Defensive driving education has traditionally relied on human instructors, but AI-driven systems present a scalable alternative. This study offers a novel perspective by examining how inherent biases in AI-generated feedback may have constructive applications. While prior research has focused on mitigating AI bias, this study explores how variations in AI voice acoustics, specifically harmonics-to-noise ratio and intonation variation, may influence user perceptions and engagement. By assessing the generalizability of non-human agents, this research seeks to highlight fundamental human cognitive limitations in processing AI feedback. The study aims to contribute to the understanding of how AI instructors are perceived and how their effectiveness can be enhanced in safety-critical domains such as driving education. Additionally, this research examines how stereotypes shape interactions with AI, offering insights into the broader implications of human-AI communication and the design of more effective AI-driven training systems.
Comments
e-Learning