Introduction: Digital accessibility involves designing digital systems and services to enable access for individuals, including those with disabilities, including visual, auditory, motor, or cognitive impairments. Artificial intelligence (AI) has the potential to enhance accessibility for people with disabilities and improve their overall quality of life.Methods: This systematic review, covering academic articles from 2018 to 2023, focuses on AI applications for digital accessibility. Initially, 3,706 articles were screened from five scholarly databases—ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and Springer.Results: The analysis narrowed down to 43 articles, presenting a classification framework based on applications, challenges, AI methodologies, and accessibility standards.Discussion: This research emphasizes the predominant focus on AI-driven digital accessibility for visual impairments, revealing a critical gap in addressing speech and hearing impairments, autism spectrum disorder, neurological disorders, and motor impairments. This highlights the need for a more balanced research distribution to ensure equitable support for all communities with disabilities. The study also pointed out a lack of adherence to accessibility standards in existing systems, stressing the urgency for a fundamental shift in designing solutions for people with disabilities. Overall, this research underscores the vital role of accessible AI in preventing exclusion and discrimination, urging a comprehensive approach to digital accessibility to cater to diverse disability needs.

The following article investigates the Metaverse and its potential to bolster digital accessibility for persons with disabilities. Through qualitative analysis, we examine responses from eleven experts in digital accessibility, Metaverse development, disability advocacy, and policy formulation. This exploration uncovers key insights into the Metaverse’s current state, its inherent principles, and the challenges and opportunities it presents in terms of accessibility. The findings reveal a mixed state of inclusivity within the Metaverse, highlighting significant advancements along with notable gaps, especially in integrating assistive technologies and ensuring interoperability across different virtual environments. This study emphasizes the Metaverse’s potential to revolutionize experiences for individuals with disabilities, provided that accessibility is embedded in its foundational design. Ethical and legal considerations, such as privacy, non-discrimination, and evolving legal frameworks, are identified as critical factors that shape an inclusive Metaverse. We propose a comprehensive framework that emphasizes technological adaptation and innovation, user-centric design, universal access, social and economic considerations, and global standards. This framework aims to guide future research and policy interventions to foster an inclusive digital environment in the Metaverse. This paper contributes to the emerging discourse on the Metaverse and digital accessibility, offering a nuanced understanding of its complexities and a roadmap for future exploration and development. This underscores the necessity of a multi-faceted approach that incorporates technological innovation, user-centered design, ethical considerations, legal compliance, and continuous research to create an inclusive and accessible Metaverse.

The use of automated accessibility testing tools remains a common practice for evaluating web accessibility. However, the results obtained from these tools may not always provide a comprehensive and complete view of a site's accessibility status. The main purpose of this study is to improve web accessibility by automatically remediating non-accessible ones using Large Language Models (LLM), particularly ChatGPT. The effectiveness of the used model in detecting and remediating accessibility issues to ensure compliance with the Web Content Accessibility Guidelines (WCAG 2.1) is also discussed. By using ChatGPT as a remediation tool, this study investigates the potential of LLM in improving web accessibility. In the case study, two websites that did not adhere to the WCAG 2.1 guidelines were selected as the primary experimental subjects for the study. These websites were assessed using the web accessibility evaluation tool, WAVE, to detect accessibility issues. The identified issues served then as the basis for remediation using ChatGPT. The effectiveness of the used advanced language model as a web accessibility remediation tool was evaluated by comparing its findings with those obtained from manual accessibility testing. The results of this comparison have significant implications for stakeholders involved in achieving WCAG compliance and contribute to the development of more accessible online platforms for individuals with disabilities.

Automatic Speech Recognition (ASR), also known as Speech-To-Text (STT) or computer speech recognition, has been an active field of research recently. This study aims to chart this field by performing a Systematic Literature Review (SLR) to give insight into the ASR studies proposed, especially for the Arabic language. The purpose is to highlight the trends of research about Arabic ASR and guide researchers with the most significant studies published over ten years from 2011 to 2021. This SLR attempts to tackle seven specific research questions related to the toolkits used for developing and evaluating Arabic ASR, the supported type of the Arabic language, the used feature extraction/classification techniques, the type of speech recognition, the performance of Arabic ASR, the existing gaps facing researchers, along with some future research. Across five databases, 38 studies met our defined inclusion criteria. Our results showed different open-source toolkits to support Arabic speech recognition. The most prominent ones were KALDI, HTK, then CMU Sphinx toolkits. A total of 89.47% of the retained studies cover modern standard Arabic, whereas 26.32% of them were dedicated to different dialects of Arabic. MFCC and HMM were presented as the most used feature extraction and classification techniques, respectively: 63% of the papers were based on MFCC and 21% were based on HMM. The review also shows that the performance of Arabic ASR systems depends mainly on different criteria related to the availability of resources, the techniques used for acoustic modeling, and the used datasets.

Sign language (SL) avatar systems aid communication between the hearing and deaf communities. Despite technological progress, there is a lack of a standardized avatar development framework. This paper offers a systematic review of SL avatar systems spanning from 1982 to 2022. Using PRISMA guidelines, we shortlisted 47 papers from an initial 1765, focusing on sign synthesis techniques, corpora, design strategies, and facial expression methods. We also discuss both objective and subjective evaluation methodologies. Our findings highlight key trends and suggest new research avenues for improving SL avatars.