Advances in Brain-Computer Interfaces and Neural Integration
Design and Evaluation of Adaptive User Interfaces for Workers with Special Accommodations: Task Allocation and Cognitive Load Reduction
Abstract
Sreedhar Srinivasan and Vachaspathy Sreedhar
This paper presents a research-driven analysis of Adaptive User Interfaces (AUIs) designed to optimize task allocation and reduce cognitive load for workers with temporary disabilities, including pregnant workers and those returning from medical leave (Family and Medical Leave Act - FMLA). Drawing on cognitive psychology principles, we explore how AUIs mitigate cognitive strain by leveraging selective attention, working memory, and cognitive load theory. These principles inform the design of user-centered features, including progressive disclosure, calculated fields, and forgiving error handling, tailored to meet the specific needs of users requiring accommodations.
The system’s adaptive logic integrates behavioral pattern recognition and reinforcement learning to personalize task management dynamically. Empirical evaluation through prototype testing, task performance metrics, and user feedback demonstrates significant improvements in task accuracy and reductions in cognitive load. Statistical analyses, including regression analysis and ANOVA, validate the system’s effectiveness. This research bridges theoretical insights from cognitive psychology with practical AUI applications, advancing our understanding of how intelligent systems can support users with temporary disabilities while complying with workplace accommodation laws such as the Pregnant Workers Fairness Act (PWFA). India