用生成式AI增强用户体验研究，构建更透明的金融信贷AI系统设计框架
Extending the UXR Point of View Pyramid: A Generative AI-Augmented Methodology for Human-Centred AI Systems
- 将用户体验视角金字塔升级为AI增强方法论，融入生成式AI支持
- 提出可追溯的提示架构与行动卡系统，提升金融AI决策透明度
- 适合关注负责任AI、金融科技与人因设计的研究者与从业者
英国家庭债务上升与生活成本压力加剧了人工智能驱动的金融技术在信用评估、还款规划及债务支持服务中的作用。这些系统深刻影响关键财务决策，却运行于受监管约束、算法不透明且风险脆弱的复杂社会技术环境之中。用户体验研究（UXR）的视角（PoV）对于将多元研究证据转化为产品与治理策略至关重要。然而，现有UXR PoV框架未针对以可解释性、公平性与问责制为核心的AI中介型金融系统而设计。本文将UXR PoV金字塔扩展为面向英国金融服务业的人工智能增强方法论框架，包含：（1）人工智能增强的PoV金字塔；（2）用于综合与假设生成的结构化提示架构；（3）嵌入生成式AI的可追溯行动卡系统，同时保障人类验证与合规监督。生成式AI被定位为认知辅助而非分析权威，确保人工审核与监管意识。该框架聚焦债务管理技术，涵盖偿付能力评估、还款计划制定与财务压力预测系统，推动高风险金融AI环境中用户体验研究方法的演进，并为CHI社区内负责任的AI赋能用户体验实践作出贡献。
原文摘要 · Abstract (English)
Rising household debt and cost-of-living pressures in the United Kingdom have intensified the role of AI-driven financial technologies in mediating credit assessment, repayment structuring, and debt support services. These systems increasingly shape consequential financial decisions, yet they operate within complex socio-technical environments characterised by regulatory constraint, algorithmic opacity, and heightened vulnerability risk. User Experience Research (UXR) Points of View (PoVs) are critical in translating heterogeneous research evidence into strategic direction for product and governance decisions. However, the existing UXR PoV framework was not designed for AI-mediated financial systems where interpretability, fairness, and accountability are central. This paper extends the UXR PoV pyramid into an AI-augmented methodological framework for Human-Centred AI debt management technologies in the UK financial services context. We formalise (1) an AI-Augmented PoV Pyramid, (2) a structured prompt architecture for synthesis and hypothesis generation, and (3) an AI-enabled Playbook Card system that embeds Generative AI into UXR workflows while preserving traceability and ethical oversight. Generative AI is positioned not as an analytic authority, but as an epistemic support mechanism subject to human validation and regulatory awareness. By grounding the framework in debt management technologies, including affordability assessment, repayment planning, and financial stress prediction systems, this work advances UXR methodology for high-stakes financial AI environments and contributes to the evolution of responsible, AI-powered UXR practice within the CHI community.
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