Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods. Our work constitutes a step towards automating the optimization of batch text data for readability and performance in augmented reality.
@article{arxiv.2402.18797,
title = {ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality},
author = {Guande Wu and Jing Qian and Sonia Castelo and Shaoyu Chen and Joao Rulff and Claudio Silva},
journal= {arXiv preprint arXiv:2402.18797},
year = {2024}
}