Continuous Behavioral Synthesis for Adaptive Health Dashboards: An LLM-Mediated Architecture Integrating Explicit Preference, Spatial Reorganization, and Attention Allocation Signals EICS027

The engineering of adaptive user interfaces has traditionally relied on either rule-based systems encoding designer intuitions about user needs or machine learning approaches requiring substantial historical data before achieving effective personalization. We present a technical architecture that leverages Large Language Models as behavioral synthesis engines to enable immediate adaptation from sparse, heterogeneous user signals. Our system integrates three distinct behavioral channels, i) explicit micro-feedback on individual interface elements, ii) spatial priority inferred from manual widget reorganization through drag-and-drop interaction, iii) and attentional investment measured through dwell time during hover events, within a structured prompt engineering framework that continuously regenerates dashboard layouts while maintaining explanatory coherence. The architecture addresses the technical challenge of translating low-level interaction patterns into high-level design decisions through a layered prompt construction methodology that separates temporal context determination, behavioral signal extraction, explicit preference enforcement, and user profile synthesis. The approach combines manually specified behavioral interpretations and temporal heuristics with LLM-mediated synthesis, enabling the reconciliation of multiple simultaneous signals that would be difficult to encode through explicit rules alone.

We demonstrate the system through an instantiation in the personal health monitoring domain, including an analytical evaluation of adaptation behavior across multiple scenarios and a working implementation managing fourteen distinct health metrics across seven widget visualization modalities. The evaluation compares profile-driven initialization, multi-signal behavioral adaptation, and presents the resulting interfaces through representative post-adaptation screenshots. The analytical evaluation shows that the system preserves explicit user constraints, keeps user-prioritized metrics in prominent positions, expands widgets that receive sustained attention. The technical contribution comprises the multi-modal behavioral aggregation strategy, the structured LLM prompt engineering approach for maintaining design consistency across regeneration cycles, and the explainability generation mechanism that exposes adaptation rationale to end users. Our work provides a reproducible engineering approach for building LLM-powered adaptive interfaces that can be generalized beyond health dashboards to any domain requiring continuous interface personalization from heterogeneous user behavior. A current limitation is that, while the system can infer and act on behavioral signals, it does not yet incorporate mechanisms to independently verify whether adaptations improve user experience without additional feedback signals.