Mitigating Cognitive Load and Situationally Induced Impairments through Closed-Loop Adaptive User Interfaces: A Scoping Review

IntroductionAs we adapt to evolving technology and implement digital interfaces across domains like mobile, automotive, and spatial computing, we subject users to an increased cognitive load and divided attention (Hwang et al., 2026). Mobile computing and dynamic, high demand environments frequently expose users to Situationally Induced Impairments and Disabilities (SIIDs), where environmental, application-specific, or human factors temporarily impair a user’s interaction capabilities (Chen et al., 2026). Operating in these complex or high-demand environments, the rapid influx of information and distraction creates an overwhelming cognitive burden, which can degrade the user’s task performance, slow their response times, and increase the risk of operational errors (Mahmud et al., 2026). Traditional static user interfaces struggle to accommodate these shifting contextual demands (Kim et al., 2026). These rigid designs rely on a one-size-fits-all approach that fails to support varying user skills or changing environmental constraints and factors (Matteri et al., 2025). They rely heavily on manual adjustments, for example, manually zooming, or altering brightness, which place a continuous attention burden on users, interrupt their workflow, and result in cognitive overload (Chen et al., 2026). To address these limitations, self-adaptive systems have emerged that can independently change their configuration or behaviour at runtime in response to the operating environment (Bajelan & Khadangi, 2025). Closed-loop Adaptive User Interfaces (AUIs) specifically aim to reduce cognitive load by dynamically responding to real-time multimodal signals (Alrob & Aburub, 2025). These intelligent adaptive interfaces utilize diverse inputs such as user emotions, physiological states, and environmental context to trigger the necessary adjustments (Papakostas et al., 2025). Although multimodal interactions are growing rapidly, and have shown benefits, across domains like automotive, healthcare, and education, current implementation and research suffers from highly fragmented approaches and disjointed methodologies (Omar & Gomez, 2026). A scoping review approach is required to systematically map this fragmented landscape, synthesize the existing adaptive strategies, and identify the critical gaps in how these interfaces are designed and evaluated (Tricco et al., 2018). Objectives and Research QuestionsThe primary objective of this scoping review is to systematically map the existing literature on Adaptive User Interfaces designed to reduce cognitive load and SIIDs in neurotypical adults across various environments. To achieve this, our first research question investigates how these systems translate real-time multidimensional signals into specific UI adaptations. We categorize these mechanisms into short-term cosmetic adjustments and long-term structural reconfigurations (Hwang et al., 2026). We also examine dynamic modality switching (Mahmud et al., 2026) as a method to mitigate sensory overload. Our second research question evaluates the empirical methodologies and performance-based metrics currently utilised for system validation, charting the reliance on generalized psychometric tools like the System Usability Scale against the need for emerging, context-aware metrics (Deshmukh & Pfleging, 2025). At last, our third research question examines the ‘Adaptability-Stability trade-off’ to understand how autonomous adaptation impacts the predictability and spatial stability of the interface (Hwang et al., 2026). We see how systems balance automated reconfiguration with human agency by charting the integration of Explainable AI (XAI), and lightweight, reversible user controls (Kim et al., 2026). By synthesizing this evidence, this scoping review aims to provide UX designers, software engineers, and researchers with a consolidated framework to build more effective, trustworthy, and context-aware adaptive user interfaces.