利用大语言模型驱动推理设计自适应数字助推系统 / Designing Adaptive Digital Nudging Systems with LLM-Driven Reasoning
1️⃣ 一句话总结
这篇论文提出了一种新的软件架构,它利用大语言模型将行为科学理论转化为可执行的系统设计,在确保伦理合规的前提下,构建能自适应调整用户干预策略的数字助推系统,并通过能源可持续性案例验证了其可行性和积极效果。
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.
利用大语言模型驱动推理设计自适应数字助推系统 / Designing Adaptive Digital Nudging Systems with LLM-Driven Reasoning
这篇论文提出了一种新的软件架构,它利用大语言模型将行为科学理论转化为可执行的系统设计,在确保伦理合规的前提下,构建能自适应调整用户干预策略的数字助推系统,并通过能源可持续性案例验证了其可行性和积极效果。
源自 arXiv: 2604.11206