An Eye-Tracking-Driven Evaluation Framework for Age-Friendly Smart Home Interface

Smart home mobile applications are a primary digital channel through which older adults manage home devices and access daily services. Existing evaluation approaches do not adequately capture the cognitive burden experienced by older users, because dimensional weights are typically assigned through expert judgment rather than derived from target-user data. This study proposes a framework integrating eye-tracking-derived cognitive load with WCAG 2.2 criteria. Evaluation dimensions are defined based on the MOLD-US aging barrier classification, and nine indicators are selected according to compliance level, quantifiability, and relevance to cognitive aging. Cognitive load data from 35 older adults (aged 60–75) were used to calibrate dimensional priorities. Accessibility-related tasks produced significantly higher cognitive load than visual and operational tasks (Cohen’s dz = 0.855), and the ordering held across three Cognitive Load Index aggregation schemes. A hybrid scoring mechanism combining a multimodal large language model with rule-based scripts was implemented for scalable evaluation. Validation on six high-fidelity prototypes showed strong agreement with expert ratings (Spearman’s ρ = 0.71–0.93) and on the same scoring task, the framework required about 1/14 of the time taken by an expert panel. By calibrating dimensional weights with eye-tracking data from older adults instead of expert judgment alone, the framework integrates WCAG compliance scoring with group-specific priorities, positioned as a design-stage screening tool prior to deployment testing.