Machine Learning for Adaptive Accessible User Interfaces: Overview and Applications

This paper presents a systematic literature review on the use of machine learning (ML) for developing adaptive accessible user interfaces (AUI) with emphasis on applications in emerging technologies such as augmented and virtual reality (AR/VR). The review, conducted according to the PRISMA 2020 methodology, included 57 studies published between 2018 and 2025. Among them we identified 24 papers explicitly describing ML-based adaptive interface solutions. Supervised learning was dominant (83% of studies) with only isolated cases of reinforcement, generative AI, and fuzzy–NLP hybrid paradigms. The analysis of all 57 papers included in review revealed that adaptive interfaces dominate current research (65%), while intelligent or hybrid systems remain less explored. Mobile platforms were the most prevalent implementation environment (25%), followed by web-based (19%) and multi-platform systems (11%), with immersive (VR/XR) and IoT contexts still emerging. Among 43 studies addressing accessibility, the most were focused on visual impairments (33%), followed by cognitive and learning disorders (25%). The results of this review can inform the creation of accessibility guidelines in emerging AR and VR applications and support the development of inclusive solutions that benefit people with disabilities, older adults, and the general population. The main contribution of this paper lies in identifying existing gaps in the integration of accessibility and Universal Design principles into ML-based adaptive systems and in proposing a new AUI model that enables user-approved, time-delayed adaptations through machine learning, balancing autonomy, personalization, and user control.