A Bridge for Seamless Connection: Exploring AI-driven UIs for Increasing Accessibility on Social AR/VR Platforms

Ramtin Ranjpour

School of Journalism and Communication at the University of Oregon

Abstract

Artificial intelligence (AI) and immersive media, also known as extended reality (XR), are seemingly destined to play a central role in society as did the Internet and social media once before. Yet one may wonder: Who benefits from these technologies? Only “typical” users? Or are users with cognitive challenges also part of the conversation for both ethical reasons and business goals? This study explores the relationship between AI-driven personalization and perceived accessibility while controlling for digital literacy levels among users with cognitive challenges on social XR platforms, with a particular focus on adaptive UIs. It also covers how users with cognitive challenges accept AI assistive tools and how usable they find them.

This research utilizes the technology acceptance model and cognitive load theory along with a survey to provide practical insights to inform developers, companies, researchers, policymakers, and others to make social XR spaces more accessible to people with diverse cognitive challenges. The analytical and cross-sectional survey will be administered to adult social XR users with cognitive load. The objective is to collect data based on the already lived experiences of diverse users with AI assistive technologies in social XR spaces. To measure the variables, the survey will use previously used and reliable scales such as the digital literacy scale by Bayrakcı & Narmanlıoğlu (2021) and the system usability scale by Lewis (2018).

In conclusion, this study follows in the footsteps of previous research in AR/VR accessibility that focused on various forms of cognitive disabilities as it explores interventions for communication skills, attention, and reduced cognitive load. This research aims to identify the best practices for designing AI-driven XR experiences that support users with situational cognitive load to improve usability and accessibility at scale. It will contribute to theory by extending the technology acceptance model and cognitive load theory, and to practice by guiding developers in creating more inclusive and user-friendly XR platforms.

Keywords

AI, Accessibility, XR, Interfaces, Acceptance