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.

This paper analyzes and evaluates different types of interactions in educational software that uses three-dimensional (3D) interactions and visualization technologies, with a focus on accessibility in such environments. To determine the accessibility parameters based on 3D user interactions, a two-phase pilot study was conducted. In the first phase, the GeometryGame research instrument was tested, in which objective metrics were collected. In the second phase, subjective metrics were collected in the form of participants’ opinions on certain aspects of accessibility and interactions implemented in the GeometryGame. The most important interaction parameters were evaluated, including the size of the objects, the presence of distractions, the intuitiveness of the user interface, and the size and distance of the interaction elements from the virtual hand. The results provide insight into user preferences and highlight the importance of customizing the user interface to ensure effective and accessible 3D interactions. Based on objective measurements and subjective user feedback, recommendations were developed to improve the accessibility of educational software that supports 3D interactions; to increase usability, reliability, and user comfort; and to enhance future research in this area.