Text Entry for XR Trove (TEXT): Collecting and Analyzing Techniques for Text Input in XR

2025-03-14

AI Summary

This study addresses the lack of a unified evaluation framework for Text Entry Techniques (TETs) in Extended Reality (XR) and the insufficient understanding of how interactive attributes affect user performance. We construct the first standardized knowledge base covering 176 TETs, systematically coding interactive attributes—including visual feedback, input modality, and spatial mapping—conducting quantitative meta-analysis, and building a relational database. Based on this, we propose an “interactive attributes–performance” analytical framework and develop TEXT, an interactive online tool (HTML/JS), enabling multidimensional search and visual comparison. Our analysis reveals, for the first time, that visual feedback significantly improves input speed and reduces error rates; moreover, spatial mapping strategies and modality combinations exhibit synergistic effects on performance. These findings provide empirical design guidelines for TET development and advance the usability of text entry in XR environments.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Text entry for extended reality (XR) is far from perfect, and a variety of text entry techniques (TETs) have been proposed to fit various contexts of use. However, comparing between TETs remains challenging due to the lack of a consolidated collection of techniques, and limited understanding of how interaction attributes of a technique (e.g., presence of visual feedback) impact user performance. To address these gaps, this paper examines the current landscape of XR TETs by creating a database of 176 different techniques. We analyze this database to highlight trends in the design of these techniques, the metrics used to evaluate them, and how various interaction attributes impact these metrics. We discuss implications for future techniques and present TEXT: Text Entry for XR Trove, an interactive online tool to navigate our database.

Problem

• Lack of consolidated collection of XR text entry techniques.
• Limited understanding of interaction attributes' impact on performance.
• Need for analysis of trends and metrics in XR text entry.

Innovation

• Created database of 176 XR text entry techniques
• Analyzed interaction attributes and user performance
• Developed TEXT: interactive online tool for navigation