Attention-centered Generative User Interfaces for All

Attention is a variable and increasingly limited human ability, yet most interactive systems treat it as fixed or infer it implicitly through opaque signals. This PhD research investigates how attention-centered generative user interfaces (GenUIs) can be designed to support diverse attention abilities while preserving user trust and agency. Using reader modes as a design space, the dissertation integrates participatory design, empirical evaluation, and generative prototyping to move beyond content filtering toward user-controlled, ability-based interface generation. Across three research projects, it (1) elicits user expectations and ethical considerations for (attentive) GenUIs through Design Fiction, (2) empirically identifies the limits of contemporary reader modes on comprehension and cognitive load, followed by designs of attention-adaptable generative alternatives, and (3) examines how ability profiles shape perceptions of agency and epistemic trust in GenUIs. The research contributes design knowledge, empirical insights, and inclusive methods for attention-adaptive generative systems in HCI.