Designing Adaptive Digital Nudging Systems with LLM-Driven Reasoning
Tiziano Santilli, Mina Alipour, Mahyar Tourchi Moghaddam
TLDR
An LLM-driven architecture for adaptive digital nudging systems integrates behavioral science with ethical compliance, validated by architects and users.
Key contributions
- Proposes an LLM-driven architecture for adaptive digital nudging, integrating behavioral science and ethics.
- Synthesizes 68 nudging strategies, 11 quality attributes, and 3 user profiling dimensions into requirements.
- Validates feasibility with an LLM-powered energy sustainability PoC, showing positive user impact.
Why it matters
Digital nudging systems often lack clear architectural guidance for integrating behavioral science and ethics. This paper provides a much-needed framework, bridging the gap between theory and practical software design. It offers reusable patterns for creating effective yet ethically compliant adaptive systems.
Original Abstract
Digital nudging systems lack architectural guidance for translating behavioral science into software design. While research identifies nudge strategies and quality attributes, existing architectures fail to integrate multi-dimensional user modeling with ethical compliance as architectural concerns. We present an architecture that uses behavioral theory through explicit architectural decisions, treating ethics and fairness as structural guardrails rather than implementation details. A literature review synthesized 68 nudging strategies, 11 quality attributes, and 3 user profiling dimensions into architectural requirements. The architecture implements sequential processing layers with cross-cutting evaluation modules enforcing regulatory compliance. Validation with 13 software architects confirmed requirements satisfaction and domain transferability. An LLM-powered proof-of-concept in residential energy sustainability demonstrated feasibility through evaluation with 15 users, achieving high perceived intervention quality and measurable positive emotional impact. This work bridges behavioral science and software architecture by providing reusable patterns for adaptive systems that balance effectiveness with ethical constraints.
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