Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2023

Abstract

Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design concept on the overall user experience. Our user study results show that each design has its own strengths and can improve player experiences in different ways such as decreasing perceived errors (Ambiguity), explaining the system (Transparency), and enabling users to control the rate of uncertainties (Controllability).

Keywords

computer vision, vision sensing, Human-AI Interaction

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

CHI '23: Proceedings of the the ACM CHI Conference on Human Factors in Computing Systems, Hamburg, April 23-28

First Page

1

Last Page

18

ISBN

9781450394215

Identifier

10.1145/3544548.3581270

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3544548.3581270

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