Bubbleu: Exploring augmented reality game design with uncertain AI-based interaction

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, Ambiguit...

Full description

Saved in:
Bibliographic Details
Main Authors: KIM, Minji, LEE, Kyungjin, BALAN, Rajesh Krishna, LEE, Youngki
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/8652
https://ink.library.smu.edu.sg/context/sis_research/article/9655/viewcontent/3544548.3581270.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary: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).