基于LLM驱动推理的自适应数字引导系统设计
Designing Adaptive Digital Nudging Systems with LLM-Driven Reasoning
AI总结：
本文提出一种将行为科学与软件设计结合的自适应数字引导系统架构，通过整合多维用户建模与伦理合规，验证了其在住宅能源可持续性中的可行性。
摘要：
数字引导系统缺乏将行为科学转化为软件设计的架构指导。尽管研究识别了引导策略和质量属性，现有架构未能整合多维用户建模与伦理合规作为架构关注点。本文提出一种架构，通过显式架构决策应用行为理论，将伦理和公平视为结构护栏而非实现细节。文献综述整合了68种引导策略、11种质量属性和3种用户建模维度为架构需求。架构实现顺序处理层与交叉评估模块，强制执行监管合规。与13位软件架构师的验证确认了需求满足和领域可转移性。一个基于LLM的证明概念在住宅能源可持续性中通过15名用户的评估，实现了高感知干预质量和可测量的积极情绪影响。本文通过提供可重用的模式，将行为科学与软件架构结合，为在有效性与伦理约束之间平衡的自适应系统提供支持。
英文摘要：
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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