ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality

Abstract

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.

Conditionally accepted by CHI '24

References in corpus (5)

• Towards a Critical Race Methodology in Algorithmic Fairness
• The Fallacy of AI Functionality
• Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
• Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
• Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis