Extending the UXR Point of View Pyramid: A Generative AI-Augmented Methodology for Human-Centred AI Systems

2026-05-29

AI Summary

This study addresses the limitations of existing user research Points of View (PoV) frameworks in tackling the challenges of explainability, fairness, and accountability posed by AI-driven financial systems, particularly within high-risk debt management contexts in the UK where methodological adaptation is lacking. To bridge this gap, the authors propose a human-centered AI-augmented PoV pyramid that integrates a structured prompt synthesis mechanism with a traceable AI Playbook card system. This approach embeds generative AI as a human-validated cognitive aid within the user research workflow. Operating under stringent ethical and regulatory constraints, the methodology enables responsible user experience research (UXR) practices for high-stakes financial AI applications—such as debt assessment, repayment planning, and financial stress forecasting—while reinforcing human-led strategic decision support.

Abstract

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.

Problem

• Human-Centred AI
• UXR Points of View
• AI interpretability
• financial debt management
• algorithmic accountability

Innovation

• Generative AI
• Human-Centred AI
• User Experience Research (UXR)
• AI-Augmented Methodology
• Responsible AI