AI-POWERED COGNITIVE LOAD ANALYZER FOR DIGITAL INTERFACES

– Digital interfaces often become difficult to use due to visual clutter , poor layout organization, excessive text, and confusing color usage, which increase the mental effort required for interaction. this project proposes an ai-assisted cognitive load analyzer that evaluates digital interface screenshots to estimate the cognitive load imposed on users. grounded in cognitive load theory and human–computer interaction (hci) principles, the system measures visual attributes such as element density, text distribution, color variance, and layout fragmentation. these features are extracted programmatically using image processing techniques and analyzed through a rule-based heuristic model to compute a cognitive load score.an ai interpretation layer converts the computed metrics into clear, human-readable explanations that identify the factors contributing to mental strain. unlike traditional usability evaluation methods that rely on surveys, expert reviews, or user testing, this system provides an automated and objective analysis without requiring human participation. the framework is domain- independent and applicable to websites, dashboards, and application interfaces. the results help designers and developers identify design elements that cause attention overload and decision fatigue, enabling improvements in usability. this project bridges cognitive psychology, interface design, and artificial intelligence to support human- centered evaluation of digital interfaces.