Authors: Balvir Singh Thakur, Chakshu Bhardwaj
Abstract: Artificial intelligence (AI) is frequently suggested as a means to reduce the participation thresholds that presently inhibit gamers with disabilities from playing digital games. However, evidence to support this assertion has been scattered across academic literature, open datasets, industry standards, and practitioner discourse and seldom consolidated. This review brings together and compares the four kinds of evidence, not to develop new theoretical concepts or to present new experimental data, but to emphasize the areas of agreement and disagreement in the literature. Following the PRISMA 2020 methodology, a repeatable search and filtering pipeline was developed, covering IEEE Xplore, the ACM Digital Library, Scopus, Web of Science, and Google Scholar, for the period 2018-February 2026, further enriched by a qualitative evidence synthesis of open accessibility resources: large-scale Steam review datasets, the Game Accessibility Guidelines (GaG), the IncluSet repository, and AbleGamers Accessible Player Experiences (APX), and a comparative analysis of academic research results against player signals and practitioner recommendations. Analysis of this corpus reveals recurrent themes, with advanced AI applications being largely limited to speech-to-text captioning, text-to-speech audio, computer vision for navigation and object recognition, and reinforcement learning for adaptive difficulty. Cutting-edge yet less-explored solutions involve large language models and other forms of generative AI. There is considerable agreement between literature, expressed user interest, and practitioner recommendations regarding captioning, data privacy in adaptive accessibility systems, and multiplayer game balance. Still more research on making games accessible to players with disabilities is focused on visual and hearing impairments than on motor or cognitive disabilities. Together, these point to opportunities for an integrated agenda that prioritizes disability-balanced training data, ecologically valid evaluation of gaming accessibility, and participatory AI design approaches centered on user privacy.
