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

2026-06-25

AI Summary

This work proposes a large language model (LLM)-based framework for adaptive health dashboards that overcomes the limitations of traditional rule-based systems or data-intensive approaches, which struggle to deliver real-time personalization from sparse and heterogeneous user interactions. By employing structured, hierarchical prompt engineering, the framework integrates three distinct behavioral signals—explicit preference feedback, spatial drag-and-drop rearrangements, and hover dwell duration—while decoupling temporal context modeling, behavior interpretation, preference constraints, and user profile synthesis. This design enables immediate fusion of multimodal inputs and supports high-level layout decisions. Evaluated in a scenario comprising 14 health metrics and 7 visualization components, the approach demonstrates significant improvements in both interface personalization and interpretability.

Abstract

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.

Problem

• adaptive user interfaces
• behavioral synthesis
• personalization
• health dashboards
• user interaction signals

Innovation

• LLM-mediated synthesis
• adaptive user interfaces
• behavioral signal integration
• prompt engineering
• health dashboard personalization